From fda1c444cf47f745ba0df3cf7cdda798de4ace66 Mon Sep 17 00:00:00 2001 From: amykwinter Date: Sun, 14 Jul 2024 09:53:45 -0400 Subject: [PATCH] update modules 06-09 --- .../execute-results/html.json | 18 + .../site_libs/revealjs/dist/theme/quarto.css | 7 +- docs/modules/Module06-DataSubset.html | 3218 +++++++++++++++++ docs/modules/images/View.png | Bin 0 -> 48370 bytes docs/modules/images/ViewTab.png | Bin 0 -> 226651 bytes docs/search.json | 203 ++ .../site_libs/quarto-html/quarto-html.min.css | 2 +- .../quarto-syntax-highlighting.css | 2 - docs/site_libs/revealjs/dist/theme/quarto.css | 7 +- modules/Module06-DataSubset.qmd | 145 +- .../Module07-VarCreationClassesSummaries.qmd | 4 +- modules/Module09-DataAnalysis.qmd | 4 +- modules/data/serodata.xlsx | Bin 33462 -> 33464 bytes 13 files changed, 3543 insertions(+), 67 deletions(-) create mode 100644 _freeze/modules/Module06-DataSubset/execute-results/html.json create mode 100644 docs/modules/Module06-DataSubset.html create mode 100644 docs/modules/images/View.png create mode 100644 docs/modules/images/ViewTab.png diff --git a/_freeze/modules/Module06-DataSubset/execute-results/html.json b/_freeze/modules/Module06-DataSubset/execute-results/html.json new file mode 100644 index 0000000..9376191 --- /dev/null +++ b/_freeze/modules/Module06-DataSubset/execute-results/html.json @@ -0,0 +1,18 @@ +{ + "hash": "a55663183334bb6cd6f8411f5a7fd0e8", + "result": { + "markdown": "---\ntitle: \"Module 6: Get to Know Your Data and Subsetting\"\nformat: \n revealjs:\n scrollable: true\n smaller: true\n toc: false\n---\n\n\n## Learning Objectives\n\nAfter module 6, you should be able to...\n\n- Use basic functions to get to know you data\n- Use three indexing approaches\n- Rely on indexing to extract part of an object (e.g., subset data) and to replace parts of an object (e.g., rename variables / columns)\n- Describe what logical operators are and how to use them\n- Use on the `subset()` function to subset data\n\n\n## Getting to know our data\n\nThe `dim()`, `nrow()`, and `ncol()` functions are good options to check the dimensions of your data before moving forward. \n\nLet's first read in the data from the previous module.\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndf <- read.csv(file = \"data/serodata.csv\") #relative path\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\ndim(df) # rows, columns\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 651 5\n```\n:::\n\n```{.r .cell-code}\nnrow(df) # number of rows\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 651\n```\n:::\n\n```{.r .cell-code}\nncol(df) # number of columns\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 5\n```\n:::\n:::\n\n\n## Quick summary of data\n\nThe `colnames()`, `str()` and `summary()`functions from Base R are great functions to assess the data type and some summary statistics. \n\n\n::: {.cell}\n\n```{.r .cell-code}\ncolnames(df)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] \"observation_id\" \"IgG_concentration\" \"age\" \n[4] \"gender\" \"slum\" \n```\n:::\n\n```{.r .cell-code}\nstr(df)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n'data.frame':\t651 obs. of 5 variables:\n $ observation_id : int 5772 8095 9784 9338 6369 6885 6252 8913 7332 6941 ...\n $ IgG_concentration: num 0.318 3.437 0.3 143.236 0.448 ...\n $ age : int 2 4 4 4 1 4 4 NA 4 2 ...\n $ gender : chr \"Female\" \"Female\" \"Male\" \"Male\" ...\n $ slum : chr \"Non slum\" \"Non slum\" \"Non slum\" \"Non slum\" ...\n```\n:::\n\n```{.r .cell-code}\nsummary(df)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n observation_id IgG_concentration age gender \n Min. :5006 Min. : 0.0054 Min. : 1.000 Length:651 \n 1st Qu.:6306 1st Qu.: 0.3000 1st Qu.: 3.000 Class :character \n Median :7495 Median : 1.6658 Median : 6.000 Mode :character \n Mean :7492 Mean : 87.3683 Mean : 6.606 \n 3rd Qu.:8749 3rd Qu.:141.4405 3rd Qu.:10.000 \n Max. :9982 Max. :916.4179 Max. :15.000 \n NA's :10 NA's :9 \n slum \n Length:651 \n Class :character \n Mode :character \n \n \n \n \n```\n:::\n:::\n\n\nNote, if you have a very large dataset with 15+ variables, `summary()` is not so efficient. \n\n## Description of data\n\nThis is data based on a simulated pathogen X IgG antibody serological survey. The rows represent individuals. Variables include IgG concentrations in IU/mL, age in years, gender, and residence based on slum characterization. We will use this dataset for lectures throughout the Workshop.\n\n## View the data as a whole dataframe\n\nThe `View()` function, one of the few Base R functions with a capital letter can be used to open a new tab in the Console and view the data as you would in excel.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nView(df)\n```\n:::\n\n::: {.cell}\n::: {.cell-output-display}\n![](images/ViewTab.png){width=100%}\n:::\n:::\n\n\n## View the data as a whole dataframe\n\nYou can also open a new tab of the data by clicking on the data icon beside the object in the Environment window.\n\n\n::: {.cell}\n::: {.cell-output-display}\n![](images/View.png){width=90%}\n:::\n:::\n\n\n## Indexing\n\nR contains several constructs which allow access to individual elements or subsets through indexing operations. Indexing can be used both to extract part of an object and to replace parts of an object (or to add parts). There are three basic indexing syntax: `[ ]`, `[[ ]]` and `$`.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nx[i] #if x is a vector\nx[i, j] #if x is a matrix/data frame\nx[[i]] #if x is a list\nx$a #if x is a data frame or list\nx$\"a\" #if x is a data frame or list\n```\n:::\n\n\n## Vectors and multi-dimensional objects\n\nTo index a vector, `vector[i]` select the ith element. To index a multi-dimensional objects such as a matrix, `matrix[i, j]` selects the element in row i and column j, where as in a three dimensional `array[k, i, i, j]` selects the element in matrix k, row i, and column j. \n\nLet's practice by first creating the same objects as we did in Module 1.\n\n::: {.cell}\n\n```{.r .cell-code}\nnumber.object <- 3\ncharacter.object <- \"blue\"\nvector.object1 <- c(2,3,4,5)\nvector.object2 <- c(\"blue\", \"red\", \"yellow\")\nmatrix.object <- matrix(data=vector.object1, nrow=2, ncol=2, byrow=TRUE)\n```\n:::\n\n\nHere is a reminder of what these objects look like.\n\n::: {.cell}\n\n```{.r .cell-code}\nvector.object1\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 2 3 4 5\n```\n:::\n\n```{.r .cell-code}\nmatrix.object\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n [,1] [,2]\n[1,] 2 3\n[2,] 4 5\n```\n:::\n:::\n\n\nFinally, let's use indexing to pull our elements of the objects. \n\n::: {.cell}\n\n```{.r .cell-code}\nvector.object1[2] #pulling the second element\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 3\n```\n:::\n\n```{.r .cell-code}\nmatrix.object[1,2] #pulling the element in row 1 column 2\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 3\n```\n:::\n:::\n\n\n\n## List objects\n\nFor lists, one generally uses `list[[p]]` to select any single element p.\n\nLet's practice by creating the same list as we did in Module 1.\n\n::: {.cell}\n\n```{.r .cell-code}\nlist.object <- list(number.object, vector.object2, matrix.object)\nlist.object\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[[1]]\n[1] 3\n\n[[2]]\n[1] \"blue\" \"red\" \"yellow\"\n\n[[3]]\n [,1] [,2]\n[1,] 2 3\n[2,] 4 5\n```\n:::\n:::\n\n\nNow we use indexing to pull out the 3rd element in the list.\n\n::: {.cell}\n\n```{.r .cell-code}\nlist.object[[3]]\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n [,1] [,2]\n[1,] 2 3\n[2,] 4 5\n```\n:::\n:::\n\n\n## $ for indexing\n\n`$` allows only a literal character string or a symbol as the index.\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndf$IgG_concentration\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n [1] 3.176895e-01 3.436823e+00 3.000000e-01 1.432363e+02 4.476534e-01\n [6] 2.527076e-02 6.101083e-01 3.000000e-01 2.916968e+00 1.649819e+00\n [11] 4.574007e+00 1.583904e+02 NA 1.065068e+02 1.113870e+02\n [16] 4.144893e+01 3.000000e-01 2.527076e-01 8.159247e+01 1.825342e+02\n [21] 4.244656e+01 1.193493e+02 3.000000e-01 3.000000e-01 9.025271e-01\n [26] 3.501805e-01 3.000000e-01 1.227437e+00 1.702055e+02 3.000000e-01\n [31] 4.801444e-01 2.527076e-02 3.000000e-01 5.776173e-02 4.801444e-01\n [36] 3.826715e-01 3.000000e-01 4.048558e+02 3.000000e-01 5.451264e-01\n [41] 3.000000e-01 5.590753e+01 2.202166e-01 1.709760e+02 1.227437e+00\n [46] 4.567527e+02 4.838480e+01 1.227437e-01 1.877256e-01 3.000000e-01\n [51] 3.501805e-01 3.339350e+00 3.000000e-01 5.451264e-01 NA\n [56] 2.104693e+00 NA 3.826715e-01 3.926366e+01 1.129964e+00\n [61] 3.501805e+00 7.542808e+01 4.800475e+01 1.000000e+00 4.068884e+01\n [66] 3.000000e-01 4.377672e+01 1.193493e+02 6.977740e+01 1.373288e+02\n [71] 1.642979e+02 NA 1.542808e+02 6.033058e-01 2.809917e-01\n [76] 1.966942e+00 2.041322e+00 2.115702e+00 4.663043e+02 3.000000e-01\n [81] 1.500796e+02 1.543790e+02 2.561983e-01 1.596338e+02 1.732484e+02\n [86] 4.641304e+02 3.736364e+01 1.572452e+02 3.000000e-01 3.000000e-01\n [91] 8.264463e-02 6.776859e-01 7.272727e-01 2.066116e-01 1.966942e+00\n [96] 3.000000e-01 3.000000e-01 2.809917e-01 8.016529e-01 1.818182e-01\n[101] 1.818182e-01 8.264463e-02 3.422727e+01 8.743506e+00 3.000000e-01\n[106] 1.641720e+02 4.049587e-01 1.001592e+02 4.489130e+02 1.101911e+02\n[111] 4.440909e+01 1.288217e+02 2.840909e+01 1.003981e+02 8.512397e-01\n[116] 1.322314e-01 1.297521e+00 1.570248e-01 1.966942e+00 1.536624e+02\n[121] 3.000000e-01 3.000000e-01 1.074380e+00 1.099174e+00 3.057851e-01\n[126] 3.000000e-01 5.785124e-02 4.391304e+02 6.130435e+02 1.074380e-01\n[131] 7.125796e+01 4.222727e+01 1.620223e+02 3.750000e+01 1.534236e+02\n[136] 6.239130e+02 5.521739e+02 5.785124e-02 6.547945e-01 8.767123e-02\n[141] 3.000000e-01 2.849315e+00 3.835616e-02 2.849315e-01 4.649315e+00\n[146] 1.369863e-01 3.589041e-01 1.049315e+00 4.668998e+01 1.473510e+02\n[151] 4.589744e+01 2.109589e-01 1.741722e+02 2.496503e+01 1.850993e+02\n[156] 1.863014e-01 1.863014e-01 4.589744e+01 1.942881e+02 5.079646e+02\n[161] 8.767123e-01 2.750685e+00 1.503311e+02 3.000000e-01 3.095890e-01\n[166] 3.000000e-01 6.371681e+02 6.054795e-01 1.955298e+02 1.786424e+02\n[171] 1.120861e+02 1.331954e+02 2.159292e+02 5.628319e+02 1.900662e+02\n[176] 6.547945e-01 1.665753e+00 1.739238e+02 9.991722e+01 9.321192e+01\n[181] 8.767123e-02 NA 6.794521e-01 5.808219e-01 1.369863e-01\n[186] 2.060274e+00 1.610099e+02 4.082192e-01 8.273973e-01 4.601770e+02\n[191] 1.389073e+02 3.867133e+01 9.260274e-01 5.918874e+01 1.870861e+02\n[196] 4.328767e-01 6.301370e-02 3.000000e-01 1.548013e+02 5.819536e+01\n[201] 1.724338e+02 1.932401e+01 2.164420e+00 9.757412e-01 1.509434e-01\n[206] 1.509434e-01 7.766571e+01 4.319563e+01 1.752022e-01 3.094775e+01\n[211] 1.266846e-01 2.919806e+01 9.545455e+00 2.735115e+01 1.314841e+02\n[216] 3.643985e+01 1.498559e+02 9.363636e+00 2.479784e-01 5.390836e-02\n[221] 8.787062e-01 1.994609e-01 3.000000e-01 3.000000e-01 5.390836e-03\n[226] 4.177898e-01 3.000000e-01 2.479784e-01 2.964960e-02 2.964960e-01\n[231] 5.148248e+00 1.994609e-01 3.000000e-01 1.779539e+02 3.290210e+02\n[236] 3.000000e-01 1.809798e+02 4.905660e-01 1.266846e-01 1.543948e+02\n[241] 1.379683e+02 6.153846e+02 1.474784e+02 3.000000e-01 1.024259e+00\n[246] 4.444056e+02 3.000000e-01 2.504043e+00 3.000000e-01 3.000000e-01\n[251] 7.816712e-02 3.000000e-01 5.390836e-02 1.494236e+02 5.972622e+01\n[256] 6.361186e-01 1.837896e+02 1.320809e+02 1.571906e-01 1.520231e+02\n[261] 3.000000e-01 3.000000e-01 1.823699e+02 3.000000e-01 2.173913e+00\n[266] 2.142202e+01 3.000000e-01 3.408027e+00 4.155963e+01 9.698997e-02\n[271] 1.238532e+01 9.528926e+00 1.916185e+02 1.060201e+00 3.679104e+02\n[276] 4.288991e+01 9.971098e+01 3.000000e-01 1.208092e+02 3.000000e-01\n[281] 6.688963e-03 2.505017e+00 1.481605e+00 3.000000e-01 5.183946e-01\n[286] 3.000000e-01 1.872910e-01 3.678930e-01 3.000000e-01 4.529851e+02\n[291] 3.169725e+01 3.000000e-01 4.922018e+01 2.548507e+02 1.661850e+02\n[296] 9.164179e+02 3.678930e-01 1.236994e+02 6.705202e+01 3.834862e+01\n[301] 1.963211e+00 3.000000e-01 2.474916e-01 3.000000e-01 2.173913e-01\n[306] 8.193980e-01 2.444816e+00 3.000000e-01 1.571906e-01 1.849711e+02\n[311] 6.119403e+02 3.000000e-01 4.280936e-01 9.698997e-02 3.678930e-02\n[316] 4.832090e+02 1.390173e+02 3.000000e-01 6.555970e+02 1.526012e+02\n[321] 3.000000e-01 7.222222e-01 7.724426e+01 3.000000e-01 6.111111e-01\n[326] 1.555556e+00 3.055556e-01 1.500000e+00 1.470772e+02 1.694444e+00\n[331] 3.138298e+02 1.414405e+02 1.990605e+02 4.212766e+02 3.000000e-01\n[336] 3.000000e-01 6.478723e+02 3.000000e-01 2.222222e+00 3.000000e-01\n[341] 2.055556e+00 2.777778e-02 8.333333e-02 1.032359e+02 1.611111e+00\n[346] 8.333333e-02 2.333333e+00 5.755319e+02 1.686848e+02 1.111111e-01\n[351] 3.000000e-01 8.372340e+02 3.000000e-01 3.784504e+01 3.819149e+02\n[356] 5.555556e-02 3.000000e+02 1.855950e+02 1.944444e-01 3.000000e-01\n[361] 5.555556e-02 1.138889e+00 4.254237e+01 3.000000e-01 3.000000e-01\n[366] 3.000000e-01 3.000000e-01 3.138298e+02 1.235908e+02 4.159574e+02\n[371] 3.009685e+01 1.567850e+02 1.367432e+02 3.731235e+01 9.164927e+01\n[376] 2.936170e+02 8.820459e+01 1.035491e+02 7.379958e+01 3.000000e-01\n[381] 1.718750e+02 2.128527e+00 1.253918e+00 2.382445e-01 4.639498e-01\n[386] 1.253918e-01 1.253918e-01 3.000000e-01 1.000000e+00 1.570043e+02\n[391] 4.344086e+02 2.184953e+00 1.507837e+00 3.228840e-01 4.588024e+01\n[396] 1.660560e+02 3.000000e-01 3.043011e+02 2.612903e+02 1.621767e+02\n[401] 3.228840e-01 4.639498e-01 2.495298e+00 3.257053e+00 3.793103e-01\n[406] NA 6.896552e-02 3.000000e-01 1.423197e+00 3.000000e-01\n[411] 3.000000e-01 1.786638e+02 3.279570e+02 NA 1.903017e+02\n[416] 1.654095e+02 4.639498e-01 1.815733e+02 1.366771e+00 1.536050e-01\n[421] 1.306587e+01 2.129032e+02 1.925647e+02 3.000000e-01 1.028213e+00\n[426] 3.793103e-01 8.025078e-01 4.860215e+02 3.000000e-01 2.100313e-01\n[431] 2.767665e+01 1.592476e+00 9.717868e-02 1.028213e+00 3.793103e-01\n[436] 1.292026e+02 4.425150e+01 3.193548e+02 1.860991e+02 6.614420e-01\n[441] 5.203762e-01 1.330819e+02 1.673491e+02 3.000000e-01 1.117457e+02\n[446] 3.045509e+01 3.000000e-01 8.280255e-02 3.000000e-01 1.200637e+00\n[451] 1.687898e-01 7.367273e+02 8.280255e-02 5.127389e-01 1.974522e-01\n[456] 7.993631e-01 3.000000e-01 3.298182e+02 9.736842e+01 3.000000e-01\n[461] 3.000000e-01 4.214545e+02 3.000000e-01 2.578182e+02 2.261147e-01\n[466] 3.000000e-01 1.883901e+02 9.458204e+01 3.000000e-01 3.000000e-01\n[471] 7.707006e-01 5.032727e+02 1.544586e+00 1.431115e+02 3.000000e-01\n[476] 1.458599e+00 1.247678e+02 NA 4.334545e+02 3.000000e-01\n[481] 6.156364e+02 9.574303e+01 1.928019e+02 1.888545e+02 1.598297e+02\n[486] 5.127389e-01 1.171053e+02 NA 2.547771e-02 1.707430e+02\n[491] 3.000000e-01 1.869969e+02 4.731481e+01 1.988390e+02 3.000000e-01\n[496] 8.808050e+01 2.003185e+00 3.000000e-01 3.509259e+01 9.365325e+01\n[501] 3.000000e-01 3.736111e+01 1.674923e+02 8.808050e+01 1.656347e+02\n[506] 3.722222e+01 6.756364e+02 3.000000e-01 1.698142e+02 1.628483e+02\n[511] 5.985130e-01 1.903346e+00 3.000000e-01 3.000000e-01 8.996283e-01\n[516] 3.977695e-01 3.000000e-01 3.000000e-01 3.000000e-01 3.000000e-01\n[521] 7.446809e+02 6.095745e+02 1.427445e+02 3.000000e-01 2.973978e-02\n[526] 3.977695e-01 4.095745e+02 4.595745e+02 3.000000e-01 1.976341e+02\n[531] 3.776596e+02 1.777603e+02 4.312268e-01 6.765957e+02 7.978723e+02\n[536] 9.665427e-02 1.879338e+02 4.358670e+01 3.000000e-01 3.000000e-01\n[541] 2.638955e+01 3.180523e+01 1.746845e+02 1.876972e+02 1.044164e+02\n[546] 1.202681e+02 1.630915e+02 1.276025e+02 8.880126e+01 3.563830e+02\n[551] 2.212766e+02 1.969121e+01 3.755319e+02 1.214511e+02 1.034700e+02\n[556] 3.000000e-01 3.643123e-01 6.319703e-02 3.000000e-01 3.000000e-01\n[561] 3.000000e-01 3.000000e-01 3.000000e-01 3.000000e-01 3.000000e-01\n[566] 3.000000e-01 1.664038e+02 2.946809e+02 4.391924e+01 1.874606e+02\n[571] 1.143533e+02 1.600158e+02 1.635688e-01 8.809148e+01 1.337539e+02\n[576] 1.985804e+02 1.578864e+02 3.000000e-01 3.000000e-01 1.953642e-01\n[581] 1.119205e+00 2.523636e+02 3.000000e-01 4.844371e+00 3.000000e-01\n[586] 1.492553e+02 1.993617e+02 2.847682e-01 3.145695e-01 3.000000e-01\n[591] 3.406429e+01 6.595745e+01 3.000000e-01 2.174545e+02 NA\n[596] 5.957447e+01 7.236364e+02 3.000000e-01 3.000000e-01 3.000000e-01\n[601] 2.676364e+02 1.891489e+02 3.036364e+02 3.000000e-01 3.000000e-01\n[606] 3.000000e-01 3.000000e-01 3.000000e-01 1.447020e+00 2.130909e+02\n[611] 1.357616e-01 3.000000e-01 3.000000e-01 5.534545e+02 1.891489e+02\n[616] 7.202128e+01 3.250287e+01 1.655629e-02 3.123636e+02 3.000000e-01\n[621] 7.138298e+01 3.000000e-01 6.946809e+01 4.012629e+01 1.629787e+02\n[626] 1.508511e+02 1.655629e-02 3.000000e-01 4.635762e-02 3.000000e-01\n[631] 3.000000e-01 3.000000e-01 1.942553e+02 3.690909e+02 3.000000e-01\n[636] 3.000000e-01 2.847682e+00 1.435106e+02 3.000000e-01 4.752009e+01\n[641] 2.621125e+01 1.055319e+02 3.000000e-01 1.149007e+00 2.927273e+02\n[646] 3.000000e-01 3.000000e-01 4.839265e+01 3.000000e-01 3.000000e-01\n[651] 2.251656e-01\n```\n:::\n:::\n\n\nNote, if you have spaces in your variable name, you will need to use back ticks `variable name` after the `$`. This is a good reason to not create variables / column names with spaces.\n\n## $ for indexing with lists\n\nList elements can be named\n\n::: {.cell}\n\n```{.r .cell-code}\nlist.object.named <- list(\n emory = number.object,\n uga = vector.object2,\n gsu = matrix.object\n)\nlist.object.named\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n$emory\n[1] 3\n\n$uga\n[1] \"blue\" \"red\" \"yellow\"\n\n$gsu\n [,1] [,2]\n[1,] 2 3\n[2,] 4 5\n```\n:::\n:::\n\n\nIf list elements are named, than you can reference data from list using `$` or using double square brackets, `[[ ]]`\n\n::: {.cell}\n\n```{.r .cell-code}\nlist.object.named$uga \n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] \"blue\" \"red\" \"yellow\"\n```\n:::\n\n```{.r .cell-code}\nlist.object.named[[\"uga\"]] \n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] \"blue\" \"red\" \"yellow\"\n```\n:::\n:::\n\n\n\n## Using indexing to rename columns\n\nAs mentioned above, indexing can be used both to extract part of an object and to replace parts of an object (or to add parts).\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncolnames(df) # just prints\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] \"observation_id\" \"IgG_concentration\" \"age\" \n[4] \"gender\" \"slum\" \n```\n:::\n\n```{.r .cell-code}\ncolnames(df)[1:2] <- c(\"IgG_concentration_mIU/mL\", \"age_year\") # reassigns\ncolnames(df)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] \"IgG_concentration_mIU/mL\" \"age_year\" \n[3] \"age\" \"gender\" \n[5] \"slum\" \n```\n:::\n\n```{.r .cell-code}\ncolnames(df)[1:2] <- c(\"IgG_concentration\", \"age\") #reset\n```\n:::\n\n\n## Using indexing to subset by columns\n\nWe can also subset a data frames and matrices (2-dimensional objects) using the bracket `[ row , column ]`. We can subset by columns and pull the `x` column using the index of the column or the column name. \n\nFor example, here I am pulling the 3nd column, which has the variable name `age`\n\n::: {.cell}\n\n```{.r .cell-code}\ndf[ , \"age\"] #same as df[ , 3]\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n [1] 3.176895e-01 3.436823e+00 3.000000e-01 1.432363e+02 4.476534e-01\n [6] 2.527076e-02 6.101083e-01 3.000000e-01 2.916968e+00 1.649819e+00\n [11] 4.574007e+00 1.583904e+02 NA 1.065068e+02 1.113870e+02\n [16] 4.144893e+01 3.000000e-01 2.527076e-01 8.159247e+01 1.825342e+02\n [21] 4.244656e+01 1.193493e+02 3.000000e-01 3.000000e-01 9.025271e-01\n [26] 3.501805e-01 3.000000e-01 1.227437e+00 1.702055e+02 3.000000e-01\n [31] 4.801444e-01 2.527076e-02 3.000000e-01 5.776173e-02 4.801444e-01\n [36] 3.826715e-01 3.000000e-01 4.048558e+02 3.000000e-01 5.451264e-01\n [41] 3.000000e-01 5.590753e+01 2.202166e-01 1.709760e+02 1.227437e+00\n [46] 4.567527e+02 4.838480e+01 1.227437e-01 1.877256e-01 3.000000e-01\n [51] 3.501805e-01 3.339350e+00 3.000000e-01 5.451264e-01 NA\n [56] 2.104693e+00 NA 3.826715e-01 3.926366e+01 1.129964e+00\n [61] 3.501805e+00 7.542808e+01 4.800475e+01 1.000000e+00 4.068884e+01\n [66] 3.000000e-01 4.377672e+01 1.193493e+02 6.977740e+01 1.373288e+02\n [71] 1.642979e+02 NA 1.542808e+02 6.033058e-01 2.809917e-01\n [76] 1.966942e+00 2.041322e+00 2.115702e+00 4.663043e+02 3.000000e-01\n [81] 1.500796e+02 1.543790e+02 2.561983e-01 1.596338e+02 1.732484e+02\n [86] 4.641304e+02 3.736364e+01 1.572452e+02 3.000000e-01 3.000000e-01\n [91] 8.264463e-02 6.776859e-01 7.272727e-01 2.066116e-01 1.966942e+00\n [96] 3.000000e-01 3.000000e-01 2.809917e-01 8.016529e-01 1.818182e-01\n[101] 1.818182e-01 8.264463e-02 3.422727e+01 8.743506e+00 3.000000e-01\n[106] 1.641720e+02 4.049587e-01 1.001592e+02 4.489130e+02 1.101911e+02\n[111] 4.440909e+01 1.288217e+02 2.840909e+01 1.003981e+02 8.512397e-01\n[116] 1.322314e-01 1.297521e+00 1.570248e-01 1.966942e+00 1.536624e+02\n[121] 3.000000e-01 3.000000e-01 1.074380e+00 1.099174e+00 3.057851e-01\n[126] 3.000000e-01 5.785124e-02 4.391304e+02 6.130435e+02 1.074380e-01\n[131] 7.125796e+01 4.222727e+01 1.620223e+02 3.750000e+01 1.534236e+02\n[136] 6.239130e+02 5.521739e+02 5.785124e-02 6.547945e-01 8.767123e-02\n[141] 3.000000e-01 2.849315e+00 3.835616e-02 2.849315e-01 4.649315e+00\n[146] 1.369863e-01 3.589041e-01 1.049315e+00 4.668998e+01 1.473510e+02\n[151] 4.589744e+01 2.109589e-01 1.741722e+02 2.496503e+01 1.850993e+02\n[156] 1.863014e-01 1.863014e-01 4.589744e+01 1.942881e+02 5.079646e+02\n[161] 8.767123e-01 2.750685e+00 1.503311e+02 3.000000e-01 3.095890e-01\n[166] 3.000000e-01 6.371681e+02 6.054795e-01 1.955298e+02 1.786424e+02\n[171] 1.120861e+02 1.331954e+02 2.159292e+02 5.628319e+02 1.900662e+02\n[176] 6.547945e-01 1.665753e+00 1.739238e+02 9.991722e+01 9.321192e+01\n[181] 8.767123e-02 NA 6.794521e-01 5.808219e-01 1.369863e-01\n[186] 2.060274e+00 1.610099e+02 4.082192e-01 8.273973e-01 4.601770e+02\n[191] 1.389073e+02 3.867133e+01 9.260274e-01 5.918874e+01 1.870861e+02\n[196] 4.328767e-01 6.301370e-02 3.000000e-01 1.548013e+02 5.819536e+01\n[201] 1.724338e+02 1.932401e+01 2.164420e+00 9.757412e-01 1.509434e-01\n[206] 1.509434e-01 7.766571e+01 4.319563e+01 1.752022e-01 3.094775e+01\n[211] 1.266846e-01 2.919806e+01 9.545455e+00 2.735115e+01 1.314841e+02\n[216] 3.643985e+01 1.498559e+02 9.363636e+00 2.479784e-01 5.390836e-02\n[221] 8.787062e-01 1.994609e-01 3.000000e-01 3.000000e-01 5.390836e-03\n[226] 4.177898e-01 3.000000e-01 2.479784e-01 2.964960e-02 2.964960e-01\n[231] 5.148248e+00 1.994609e-01 3.000000e-01 1.779539e+02 3.290210e+02\n[236] 3.000000e-01 1.809798e+02 4.905660e-01 1.266846e-01 1.543948e+02\n[241] 1.379683e+02 6.153846e+02 1.474784e+02 3.000000e-01 1.024259e+00\n[246] 4.444056e+02 3.000000e-01 2.504043e+00 3.000000e-01 3.000000e-01\n[251] 7.816712e-02 3.000000e-01 5.390836e-02 1.494236e+02 5.972622e+01\n[256] 6.361186e-01 1.837896e+02 1.320809e+02 1.571906e-01 1.520231e+02\n[261] 3.000000e-01 3.000000e-01 1.823699e+02 3.000000e-01 2.173913e+00\n[266] 2.142202e+01 3.000000e-01 3.408027e+00 4.155963e+01 9.698997e-02\n[271] 1.238532e+01 9.528926e+00 1.916185e+02 1.060201e+00 3.679104e+02\n[276] 4.288991e+01 9.971098e+01 3.000000e-01 1.208092e+02 3.000000e-01\n[281] 6.688963e-03 2.505017e+00 1.481605e+00 3.000000e-01 5.183946e-01\n[286] 3.000000e-01 1.872910e-01 3.678930e-01 3.000000e-01 4.529851e+02\n[291] 3.169725e+01 3.000000e-01 4.922018e+01 2.548507e+02 1.661850e+02\n[296] 9.164179e+02 3.678930e-01 1.236994e+02 6.705202e+01 3.834862e+01\n[301] 1.963211e+00 3.000000e-01 2.474916e-01 3.000000e-01 2.173913e-01\n[306] 8.193980e-01 2.444816e+00 3.000000e-01 1.571906e-01 1.849711e+02\n[311] 6.119403e+02 3.000000e-01 4.280936e-01 9.698997e-02 3.678930e-02\n[316] 4.832090e+02 1.390173e+02 3.000000e-01 6.555970e+02 1.526012e+02\n[321] 3.000000e-01 7.222222e-01 7.724426e+01 3.000000e-01 6.111111e-01\n[326] 1.555556e+00 3.055556e-01 1.500000e+00 1.470772e+02 1.694444e+00\n[331] 3.138298e+02 1.414405e+02 1.990605e+02 4.212766e+02 3.000000e-01\n[336] 3.000000e-01 6.478723e+02 3.000000e-01 2.222222e+00 3.000000e-01\n[341] 2.055556e+00 2.777778e-02 8.333333e-02 1.032359e+02 1.611111e+00\n[346] 8.333333e-02 2.333333e+00 5.755319e+02 1.686848e+02 1.111111e-01\n[351] 3.000000e-01 8.372340e+02 3.000000e-01 3.784504e+01 3.819149e+02\n[356] 5.555556e-02 3.000000e+02 1.855950e+02 1.944444e-01 3.000000e-01\n[361] 5.555556e-02 1.138889e+00 4.254237e+01 3.000000e-01 3.000000e-01\n[366] 3.000000e-01 3.000000e-01 3.138298e+02 1.235908e+02 4.159574e+02\n[371] 3.009685e+01 1.567850e+02 1.367432e+02 3.731235e+01 9.164927e+01\n[376] 2.936170e+02 8.820459e+01 1.035491e+02 7.379958e+01 3.000000e-01\n[381] 1.718750e+02 2.128527e+00 1.253918e+00 2.382445e-01 4.639498e-01\n[386] 1.253918e-01 1.253918e-01 3.000000e-01 1.000000e+00 1.570043e+02\n[391] 4.344086e+02 2.184953e+00 1.507837e+00 3.228840e-01 4.588024e+01\n[396] 1.660560e+02 3.000000e-01 3.043011e+02 2.612903e+02 1.621767e+02\n[401] 3.228840e-01 4.639498e-01 2.495298e+00 3.257053e+00 3.793103e-01\n[406] NA 6.896552e-02 3.000000e-01 1.423197e+00 3.000000e-01\n[411] 3.000000e-01 1.786638e+02 3.279570e+02 NA 1.903017e+02\n[416] 1.654095e+02 4.639498e-01 1.815733e+02 1.366771e+00 1.536050e-01\n[421] 1.306587e+01 2.129032e+02 1.925647e+02 3.000000e-01 1.028213e+00\n[426] 3.793103e-01 8.025078e-01 4.860215e+02 3.000000e-01 2.100313e-01\n[431] 2.767665e+01 1.592476e+00 9.717868e-02 1.028213e+00 3.793103e-01\n[436] 1.292026e+02 4.425150e+01 3.193548e+02 1.860991e+02 6.614420e-01\n[441] 5.203762e-01 1.330819e+02 1.673491e+02 3.000000e-01 1.117457e+02\n[446] 3.045509e+01 3.000000e-01 8.280255e-02 3.000000e-01 1.200637e+00\n[451] 1.687898e-01 7.367273e+02 8.280255e-02 5.127389e-01 1.974522e-01\n[456] 7.993631e-01 3.000000e-01 3.298182e+02 9.736842e+01 3.000000e-01\n[461] 3.000000e-01 4.214545e+02 3.000000e-01 2.578182e+02 2.261147e-01\n[466] 3.000000e-01 1.883901e+02 9.458204e+01 3.000000e-01 3.000000e-01\n[471] 7.707006e-01 5.032727e+02 1.544586e+00 1.431115e+02 3.000000e-01\n[476] 1.458599e+00 1.247678e+02 NA 4.334545e+02 3.000000e-01\n[481] 6.156364e+02 9.574303e+01 1.928019e+02 1.888545e+02 1.598297e+02\n[486] 5.127389e-01 1.171053e+02 NA 2.547771e-02 1.707430e+02\n[491] 3.000000e-01 1.869969e+02 4.731481e+01 1.988390e+02 3.000000e-01\n[496] 8.808050e+01 2.003185e+00 3.000000e-01 3.509259e+01 9.365325e+01\n[501] 3.000000e-01 3.736111e+01 1.674923e+02 8.808050e+01 1.656347e+02\n[506] 3.722222e+01 6.756364e+02 3.000000e-01 1.698142e+02 1.628483e+02\n[511] 5.985130e-01 1.903346e+00 3.000000e-01 3.000000e-01 8.996283e-01\n[516] 3.977695e-01 3.000000e-01 3.000000e-01 3.000000e-01 3.000000e-01\n[521] 7.446809e+02 6.095745e+02 1.427445e+02 3.000000e-01 2.973978e-02\n[526] 3.977695e-01 4.095745e+02 4.595745e+02 3.000000e-01 1.976341e+02\n[531] 3.776596e+02 1.777603e+02 4.312268e-01 6.765957e+02 7.978723e+02\n[536] 9.665427e-02 1.879338e+02 4.358670e+01 3.000000e-01 3.000000e-01\n[541] 2.638955e+01 3.180523e+01 1.746845e+02 1.876972e+02 1.044164e+02\n[546] 1.202681e+02 1.630915e+02 1.276025e+02 8.880126e+01 3.563830e+02\n[551] 2.212766e+02 1.969121e+01 3.755319e+02 1.214511e+02 1.034700e+02\n[556] 3.000000e-01 3.643123e-01 6.319703e-02 3.000000e-01 3.000000e-01\n[561] 3.000000e-01 3.000000e-01 3.000000e-01 3.000000e-01 3.000000e-01\n[566] 3.000000e-01 1.664038e+02 2.946809e+02 4.391924e+01 1.874606e+02\n[571] 1.143533e+02 1.600158e+02 1.635688e-01 8.809148e+01 1.337539e+02\n[576] 1.985804e+02 1.578864e+02 3.000000e-01 3.000000e-01 1.953642e-01\n[581] 1.119205e+00 2.523636e+02 3.000000e-01 4.844371e+00 3.000000e-01\n[586] 1.492553e+02 1.993617e+02 2.847682e-01 3.145695e-01 3.000000e-01\n[591] 3.406429e+01 6.595745e+01 3.000000e-01 2.174545e+02 NA\n[596] 5.957447e+01 7.236364e+02 3.000000e-01 3.000000e-01 3.000000e-01\n[601] 2.676364e+02 1.891489e+02 3.036364e+02 3.000000e-01 3.000000e-01\n[606] 3.000000e-01 3.000000e-01 3.000000e-01 1.447020e+00 2.130909e+02\n[611] 1.357616e-01 3.000000e-01 3.000000e-01 5.534545e+02 1.891489e+02\n[616] 7.202128e+01 3.250287e+01 1.655629e-02 3.123636e+02 3.000000e-01\n[621] 7.138298e+01 3.000000e-01 6.946809e+01 4.012629e+01 1.629787e+02\n[626] 1.508511e+02 1.655629e-02 3.000000e-01 4.635762e-02 3.000000e-01\n[631] 3.000000e-01 3.000000e-01 1.942553e+02 3.690909e+02 3.000000e-01\n[636] 3.000000e-01 2.847682e+00 1.435106e+02 3.000000e-01 4.752009e+01\n[641] 2.621125e+01 1.055319e+02 3.000000e-01 1.149007e+00 2.927273e+02\n[646] 3.000000e-01 3.000000e-01 4.839265e+01 3.000000e-01 3.000000e-01\n[651] 2.251656e-01\n```\n:::\n:::\n\nWe can select multiple columns using multiple column names:\n\n::: {.cell}\n\n```{.r .cell-code}\ndf[, c(\"age\", \"gender\")] #same as df[ , c(3,4)]\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n age gender\n1 3.176895e-01 Female\n2 3.436823e+00 Female\n3 3.000000e-01 Male\n4 1.432363e+02 Male\n5 4.476534e-01 Male\n6 2.527076e-02 Male\n7 6.101083e-01 Female\n8 3.000000e-01 Female\n9 2.916968e+00 Male\n10 1.649819e+00 Male\n11 4.574007e+00 Male\n12 1.583904e+02 Female\n13 NA Male\n14 1.065068e+02 Male\n15 1.113870e+02 Male\n16 4.144893e+01 Male\n17 3.000000e-01 Male\n18 2.527076e-01 Female\n19 8.159247e+01 Female\n20 1.825342e+02 Male\n21 4.244656e+01 Male\n22 1.193493e+02 Female\n23 3.000000e-01 Male\n24 3.000000e-01 Female\n25 9.025271e-01 Female\n26 3.501805e-01 Male\n27 3.000000e-01 Male\n28 1.227437e+00 Female\n29 1.702055e+02 Female\n30 3.000000e-01 Female\n31 4.801444e-01 Male\n32 2.527076e-02 Male\n33 3.000000e-01 Female\n34 5.776173e-02 Male\n35 4.801444e-01 Female\n36 3.826715e-01 Female\n37 3.000000e-01 Male\n38 4.048558e+02 Male\n39 3.000000e-01 Male\n40 5.451264e-01 Male\n41 3.000000e-01 Female\n42 5.590753e+01 Male\n43 2.202166e-01 Female\n44 1.709760e+02 Male\n45 1.227437e+00 Male\n46 4.567527e+02 Male\n47 4.838480e+01 Male\n48 1.227437e-01 Female\n49 1.877256e-01 Female\n50 3.000000e-01 Female\n51 3.501805e-01 Male\n52 3.339350e+00 Male\n53 3.000000e-01 Female\n54 5.451264e-01 Female\n55 NA Male\n56 2.104693e+00 Male\n57 NA Male\n58 3.826715e-01 Female\n59 3.926366e+01 Female\n60 1.129964e+00 Male\n61 3.501805e+00 Female\n62 7.542808e+01 Female\n63 4.800475e+01 Female\n64 1.000000e+00 Male\n65 4.068884e+01 Male\n66 3.000000e-01 Female\n67 4.377672e+01 Female\n68 1.193493e+02 Male\n69 6.977740e+01 Male\n70 1.373288e+02 Female\n71 1.642979e+02 Male\n72 NA Female\n73 1.542808e+02 Male\n74 6.033058e-01 Male\n75 2.809917e-01 Male\n76 1.966942e+00 Male\n77 2.041322e+00 Male\n78 2.115702e+00 Female\n79 4.663043e+02 Male\n80 3.000000e-01 Male\n81 1.500796e+02 Male\n82 1.543790e+02 Female\n83 2.561983e-01 Female\n84 1.596338e+02 Male\n85 1.732484e+02 Female\n86 4.641304e+02 Female\n87 3.736364e+01 Male\n88 1.572452e+02 Female\n89 3.000000e-01 Male\n90 3.000000e-01 Male\n91 8.264463e-02 Male\n92 6.776859e-01 Female\n93 7.272727e-01 Male\n94 2.066116e-01 Female\n95 1.966942e+00 Male\n96 3.000000e-01 Male\n97 3.000000e-01 Male\n98 2.809917e-01 Female\n99 8.016529e-01 Female\n100 1.818182e-01 Female\n101 1.818182e-01 Male\n102 8.264463e-02 Female\n103 3.422727e+01 Female\n104 8.743506e+00 Male\n105 3.000000e-01 Male\n106 1.641720e+02 Female\n107 4.049587e-01 Male\n108 1.001592e+02 Male\n109 4.489130e+02 Female\n110 1.101911e+02 Female\n111 4.440909e+01 Male\n112 1.288217e+02 Female\n113 2.840909e+01 Male\n114 1.003981e+02 Female\n115 8.512397e-01 Female\n116 1.322314e-01 Male\n117 1.297521e+00 Female\n118 1.570248e-01 Male\n119 1.966942e+00 Female\n120 1.536624e+02 Male\n121 3.000000e-01 Female\n122 3.000000e-01 Female\n123 1.074380e+00 Male\n124 1.099174e+00 Female\n125 3.057851e-01 Female\n126 3.000000e-01 Female\n127 5.785124e-02 Female\n128 4.391304e+02 Female\n129 6.130435e+02 Female\n130 1.074380e-01 Male\n131 7.125796e+01 Male\n132 4.222727e+01 Male\n133 1.620223e+02 Female\n134 3.750000e+01 Female\n135 1.534236e+02 Female\n136 6.239130e+02 Female\n137 5.521739e+02 Male\n138 5.785124e-02 Female\n139 6.547945e-01 Female\n140 8.767123e-02 Female\n141 3.000000e-01 Male\n142 2.849315e+00 Female\n143 3.835616e-02 Male\n144 2.849315e-01 Male\n145 4.649315e+00 Male\n146 1.369863e-01 Female\n147 3.589041e-01 Male\n148 1.049315e+00 Male\n149 4.668998e+01 Female\n150 1.473510e+02 Female\n151 4.589744e+01 Male\n152 2.109589e-01 Male\n153 1.741722e+02 Female\n154 2.496503e+01 Female\n155 1.850993e+02 Male\n156 1.863014e-01 Male\n157 1.863014e-01 Male\n158 4.589744e+01 Female\n159 1.942881e+02 Female\n160 5.079646e+02 Female\n161 8.767123e-01 Male\n162 2.750685e+00 Male\n163 1.503311e+02 Female\n164 3.000000e-01 Male\n165 3.095890e-01 Male\n166 3.000000e-01 Male\n167 6.371681e+02 Female\n168 6.054795e-01 Female\n169 1.955298e+02 Female\n170 1.786424e+02 Male\n171 1.120861e+02 Female\n172 1.331954e+02 Male\n173 2.159292e+02 Male\n174 5.628319e+02 Male\n175 1.900662e+02 Female\n176 6.547945e-01 Male\n177 1.665753e+00 Male\n178 1.739238e+02 Male\n179 9.991722e+01 Male\n180 9.321192e+01 Male\n181 8.767123e-02 Female\n182 NA Male\n183 6.794521e-01 Female\n184 5.808219e-01 Male\n185 1.369863e-01 Female\n186 2.060274e+00 Female\n187 1.610099e+02 Male\n188 4.082192e-01 Female\n189 8.273973e-01 Male\n190 4.601770e+02 Female\n191 1.389073e+02 Female\n192 3.867133e+01 Female\n193 9.260274e-01 Female\n194 5.918874e+01 Female\n195 1.870861e+02 Female\n196 4.328767e-01 Male\n197 6.301370e-02 Male\n198 3.000000e-01 Female\n199 1.548013e+02 Male\n200 5.819536e+01 Female\n201 1.724338e+02 Female\n202 1.932401e+01 Female\n203 2.164420e+00 Female\n204 9.757412e-01 Female\n205 1.509434e-01 Male\n206 1.509434e-01 Female\n207 7.766571e+01 Male\n208 4.319563e+01 Female\n209 1.752022e-01 Male\n210 3.094775e+01 Female\n211 1.266846e-01 Male\n212 2.919806e+01 Male\n213 9.545455e+00 Female\n214 2.735115e+01 Female\n215 1.314841e+02 Female\n216 3.643985e+01 Male\n217 1.498559e+02 Female\n218 9.363636e+00 Female\n219 2.479784e-01 Male\n220 5.390836e-02 Female\n221 8.787062e-01 Female\n222 1.994609e-01 Male\n223 3.000000e-01 Female\n224 3.000000e-01 Male\n225 5.390836e-03 Female\n226 4.177898e-01 Female\n227 3.000000e-01 Female\n228 2.479784e-01 Male\n229 2.964960e-02 Male\n230 2.964960e-01 Male\n231 5.148248e+00 Female\n232 1.994609e-01 Male\n233 3.000000e-01 Male\n234 1.779539e+02 Male\n235 3.290210e+02 Female\n236 3.000000e-01 Male\n237 1.809798e+02 Female\n238 4.905660e-01 Male\n239 1.266846e-01 Male\n240 1.543948e+02 Female\n241 1.379683e+02 Female\n242 6.153846e+02 Male\n243 1.474784e+02 Male\n244 3.000000e-01 Female\n245 1.024259e+00 Male\n246 4.444056e+02 Female\n247 3.000000e-01 Male\n248 2.504043e+00 Female\n249 3.000000e-01 Female\n250 3.000000e-01 Female\n251 7.816712e-02 Female\n252 3.000000e-01 Female\n253 5.390836e-02 Male\n254 1.494236e+02 Female\n255 5.972622e+01 Male\n256 6.361186e-01 Female\n257 1.837896e+02 Female\n258 1.320809e+02 Female\n259 1.571906e-01 Male\n260 1.520231e+02 Male\n261 3.000000e-01 Female\n262 3.000000e-01 Female\n263 1.823699e+02 Male\n264 3.000000e-01 Male\n265 2.173913e+00 Male\n266 2.142202e+01 Male\n267 3.000000e-01 Female\n268 3.408027e+00 Male\n269 4.155963e+01 Male\n270 9.698997e-02 Male\n271 1.238532e+01 Female\n272 9.528926e+00 Male\n273 1.916185e+02 Female\n274 1.060201e+00 Male\n275 3.679104e+02 Female\n276 4.288991e+01 Male\n277 9.971098e+01 Male\n278 3.000000e-01 Male\n279 1.208092e+02 Male\n280 3.000000e-01 Male\n281 6.688963e-03 Female\n282 2.505017e+00 Female\n283 1.481605e+00 Male\n284 3.000000e-01 Female\n285 5.183946e-01 Female\n286 3.000000e-01 Female\n287 1.872910e-01 Male\n288 3.678930e-01 Female\n289 3.000000e-01 Male\n290 4.529851e+02 Female\n291 3.169725e+01 Female\n292 3.000000e-01 Male\n293 4.922018e+01 Male\n294 2.548507e+02 Male\n295 1.661850e+02 Male\n296 9.164179e+02 Male\n297 3.678930e-01 Female\n298 1.236994e+02 Male\n299 6.705202e+01 Male\n300 3.834862e+01 Male\n301 1.963211e+00 Female\n302 3.000000e-01 Male\n303 2.474916e-01 Male\n304 3.000000e-01 Female\n305 2.173913e-01 Male\n306 8.193980e-01 Male\n307 2.444816e+00 Female\n308 3.000000e-01 Male\n309 1.571906e-01 Female\n310 1.849711e+02 Male\n311 6.119403e+02 Female\n312 3.000000e-01 Female\n313 4.280936e-01 Female\n314 9.698997e-02 Male\n315 3.678930e-02 Female\n316 4.832090e+02 Male\n317 1.390173e+02 Female\n318 3.000000e-01 Male\n319 6.555970e+02 Female\n320 1.526012e+02 Female\n321 3.000000e-01 Female\n322 7.222222e-01 Male\n323 7.724426e+01 Male\n324 3.000000e-01 Male\n325 6.111111e-01 Female\n326 1.555556e+00 Female\n327 3.055556e-01 Male\n328 1.500000e+00 Male\n329 1.470772e+02 Male\n330 1.694444e+00 Female\n331 3.138298e+02 Female\n332 1.414405e+02 Female\n333 1.990605e+02 Female\n334 4.212766e+02 Male\n335 3.000000e-01 Male\n336 3.000000e-01 Male\n337 6.478723e+02 Male\n338 3.000000e-01 Male\n339 2.222222e+00 Female\n340 3.000000e-01 Male\n341 2.055556e+00 Male\n342 2.777778e-02 Female\n343 8.333333e-02 Male\n344 1.032359e+02 Female\n345 1.611111e+00 Female\n346 8.333333e-02 Female\n347 2.333333e+00 Female\n348 5.755319e+02 Male\n349 1.686848e+02 Female\n350 1.111111e-01 Male\n351 3.000000e-01 Male\n352 8.372340e+02 Female\n353 3.000000e-01 Male\n354 3.784504e+01 Male\n355 3.819149e+02 Male\n356 5.555556e-02 Female\n357 3.000000e+02 Female\n358 1.855950e+02 Male\n359 1.944444e-01 Female\n360 3.000000e-01 Male\n361 5.555556e-02 Female\n362 1.138889e+00 Male\n363 4.254237e+01 Female\n364 3.000000e-01 Male\n365 3.000000e-01 Male\n366 3.000000e-01 Female\n367 3.000000e-01 Female\n368 3.138298e+02 Female\n369 1.235908e+02 Male\n370 4.159574e+02 Male\n371 3.009685e+01 Female\n372 1.567850e+02 Female\n373 1.367432e+02 Female\n374 3.731235e+01 Female\n375 9.164927e+01 Male\n376 2.936170e+02 Female\n377 8.820459e+01 Female\n378 1.035491e+02 Male\n379 7.379958e+01 Female\n380 3.000000e-01 Male\n381 1.718750e+02 Male\n382 2.128527e+00 Male\n383 1.253918e+00 Female\n384 2.382445e-01 Male\n385 4.639498e-01 Female\n386 1.253918e-01 Male\n387 1.253918e-01 Male\n388 3.000000e-01 Female\n389 1.000000e+00 Male\n390 1.570043e+02 Male\n391 4.344086e+02 Female\n392 2.184953e+00 Male\n393 1.507837e+00 Female\n394 3.228840e-01 Female\n395 4.588024e+01 Male\n396 1.660560e+02 Male\n397 3.000000e-01 Male\n398 3.043011e+02 Male\n399 2.612903e+02 Female\n400 1.621767e+02 Male\n401 3.228840e-01 Male\n402 4.639498e-01 Female\n403 2.495298e+00 Female\n404 3.257053e+00 Female\n405 3.793103e-01 Female\n406 NA Male\n407 6.896552e-02 Female\n408 3.000000e-01 Male\n409 1.423197e+00 Female\n410 3.000000e-01 Female\n411 3.000000e-01 Female\n412 1.786638e+02 Male\n413 3.279570e+02 Male\n414 NA Female\n415 1.903017e+02 Male\n416 1.654095e+02 Female\n417 4.639498e-01 Female\n418 1.815733e+02 Male\n419 1.366771e+00 Male\n420 1.536050e-01 Female\n421 1.306587e+01 Male\n422 2.129032e+02 Female\n423 1.925647e+02 Male\n424 3.000000e-01 Female\n425 1.028213e+00 Female\n426 3.793103e-01 Female\n427 8.025078e-01 Female\n428 4.860215e+02 Female\n429 3.000000e-01 Female\n430 2.100313e-01 Male\n431 2.767665e+01 Female\n432 1.592476e+00 Male\n433 9.717868e-02 Female\n434 1.028213e+00 Female\n435 3.793103e-01 Male\n436 1.292026e+02 Male\n437 4.425150e+01 Female\n438 3.193548e+02 Female\n439 1.860991e+02 Female\n440 6.614420e-01 Female\n441 5.203762e-01 Male\n442 1.330819e+02 Male\n443 1.673491e+02 Female\n444 3.000000e-01 Male\n445 1.117457e+02 Male\n446 3.045509e+01 Female\n447 3.000000e-01 Male\n448 8.280255e-02 Female\n449 3.000000e-01 Female\n450 1.200637e+00 Female\n451 1.687898e-01 Male\n452 7.367273e+02 Female\n453 8.280255e-02 Male\n454 5.127389e-01 Male\n455 1.974522e-01 Male\n456 7.993631e-01 Female\n457 3.000000e-01 Male\n458 3.298182e+02 Male\n459 9.736842e+01 Female\n460 3.000000e-01 Female\n461 3.000000e-01 Female\n462 4.214545e+02 Female\n463 3.000000e-01 Male\n464 2.578182e+02 Female\n465 2.261147e-01 Male\n466 3.000000e-01 Female\n467 1.883901e+02 Male\n468 9.458204e+01 Female\n469 3.000000e-01 Female\n470 3.000000e-01 Male\n471 7.707006e-01 Female\n472 5.032727e+02 Male\n473 1.544586e+00 Female\n474 1.431115e+02 Female\n475 3.000000e-01 Male\n476 1.458599e+00 Male\n477 1.247678e+02 Female\n478 NA Female\n479 4.334545e+02 Male\n480 3.000000e-01 Female\n481 6.156364e+02 Female\n482 9.574303e+01 Male\n483 1.928019e+02 Male\n484 1.888545e+02 Male\n485 1.598297e+02 Female\n486 5.127389e-01 Male\n487 1.171053e+02 Female\n488 NA Male\n489 2.547771e-02 Female\n490 1.707430e+02 Female\n491 3.000000e-01 Male\n492 1.869969e+02 Male\n493 4.731481e+01 Male\n494 1.988390e+02 Female\n495 3.000000e-01 Male\n496 8.808050e+01 Male\n497 2.003185e+00 Female\n498 3.000000e-01 Male\n499 3.509259e+01 Female\n500 9.365325e+01 Female\n501 3.000000e-01 Male\n502 3.736111e+01 Female\n503 1.674923e+02 Female\n504 8.808050e+01 Male\n505 1.656347e+02 Female\n506 3.722222e+01 Female\n507 6.756364e+02 Female\n508 3.000000e-01 Male\n509 1.698142e+02 Male\n510 1.628483e+02 Female\n511 5.985130e-01 Male\n512 1.903346e+00 Female\n513 3.000000e-01 Male\n514 3.000000e-01 Male\n515 8.996283e-01 Male\n516 3.977695e-01 Female\n517 3.000000e-01 Male\n518 3.000000e-01 Male\n519 3.000000e-01 Male\n520 3.000000e-01 Female\n521 7.446809e+02 Male\n522 6.095745e+02 Female\n523 1.427445e+02 Male\n524 3.000000e-01 Female\n525 2.973978e-02 Male\n526 3.977695e-01 Female\n527 4.095745e+02 Female\n528 4.595745e+02 Male\n529 3.000000e-01 Female\n530 1.976341e+02 Female\n531 3.776596e+02 Female\n532 1.777603e+02 Female\n533 4.312268e-01 Male\n534 6.765957e+02 Female\n535 7.978723e+02 Male\n536 9.665427e-02 Male\n537 1.879338e+02 Male\n538 4.358670e+01 Female\n539 3.000000e-01 Female\n540 3.000000e-01 Male\n541 2.638955e+01 Male\n542 3.180523e+01 Female\n543 1.746845e+02 Male\n544 1.876972e+02 Male\n545 1.044164e+02 Male\n546 1.202681e+02 Male\n547 1.630915e+02 Female\n548 1.276025e+02 Female\n549 8.880126e+01 Male\n550 3.563830e+02 Male\n551 2.212766e+02 Male\n552 1.969121e+01 Female\n553 3.755319e+02 Female\n554 1.214511e+02 Male\n555 1.034700e+02 Female\n556 3.000000e-01 Female\n557 3.643123e-01 Female\n558 6.319703e-02 Female\n559 3.000000e-01 Male\n560 3.000000e-01 Male\n561 3.000000e-01 Female\n562 3.000000e-01 Female\n563 3.000000e-01 Male\n564 3.000000e-01 Male\n565 3.000000e-01 Female\n566 3.000000e-01 Male\n567 1.664038e+02 Female\n568 2.946809e+02 Female\n569 4.391924e+01 Male\n570 1.874606e+02 Female\n571 1.143533e+02 Male\n572 1.600158e+02 Male\n573 1.635688e-01 Male\n574 8.809148e+01 Female\n575 1.337539e+02 Male\n576 1.985804e+02 Male\n577 1.578864e+02 Female\n578 3.000000e-01 Female\n579 3.000000e-01 Male\n580 1.953642e-01 Female\n581 1.119205e+00 Male\n582 2.523636e+02 Male\n583 3.000000e-01 Male\n584 4.844371e+00 Female\n585 3.000000e-01 Male\n586 1.492553e+02 Female\n587 1.993617e+02 Male\n588 2.847682e-01 Female\n589 3.145695e-01 Female\n590 3.000000e-01 Male\n591 3.406429e+01 Female\n592 6.595745e+01 Male\n593 3.000000e-01 Male\n594 2.174545e+02 Male\n595 NA Female\n596 5.957447e+01 Female\n597 7.236364e+02 Female\n598 3.000000e-01 Male\n599 3.000000e-01 Female\n600 3.000000e-01 Male\n601 2.676364e+02 Male\n602 1.891489e+02 Male\n603 3.036364e+02 Female\n604 3.000000e-01 Female\n605 3.000000e-01 Male\n606 3.000000e-01 Male\n607 3.000000e-01 Female\n608 3.000000e-01 Male\n609 1.447020e+00 Male\n610 2.130909e+02 Female\n611 1.357616e-01 Female\n612 3.000000e-01 Female\n613 3.000000e-01 Female\n614 5.534545e+02 Female\n615 1.891489e+02 Female\n616 7.202128e+01 Female\n617 3.250287e+01 Male\n618 1.655629e-02 Male\n619 3.123636e+02 Male\n620 3.000000e-01 Male\n621 7.138298e+01 Male\n622 3.000000e-01 Female\n623 6.946809e+01 Female\n624 4.012629e+01 Male\n625 1.629787e+02 Female\n626 1.508511e+02 Female\n627 1.655629e-02 Male\n628 3.000000e-01 Male\n629 4.635762e-02 Male\n630 3.000000e-01 Female\n631 3.000000e-01 Female\n632 3.000000e-01 Male\n633 1.942553e+02 Male\n634 3.690909e+02 Male\n635 3.000000e-01 Female\n636 3.000000e-01 Female\n637 2.847682e+00 Male\n638 1.435106e+02 Female\n639 3.000000e-01 Male\n640 4.752009e+01 Female\n641 2.621125e+01 Female\n642 1.055319e+02 Female\n643 3.000000e-01 Female\n644 1.149007e+00 Male\n645 2.927273e+02 Female\n646 3.000000e-01 Female\n647 3.000000e-01 Female\n648 4.839265e+01 Male\n649 3.000000e-01 Male\n650 3.000000e-01 Female\n651 2.251656e-01 Female\n```\n:::\n:::\n\nWe can remove select columns using indexing as well, OR by simply changing the column to `NULL`\n\n::: {.cell}\n\n```{.r .cell-code}\ndf[, -5] #remove column 5, \"slum\" variable\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n IgG_concentration age age.1 gender\n1 5772 3.176895e-01 2 Female\n2 8095 3.436823e+00 4 Female\n3 9784 3.000000e-01 4 Male\n4 9338 1.432363e+02 4 Male\n5 6369 4.476534e-01 1 Male\n6 6885 2.527076e-02 4 Male\n7 6252 6.101083e-01 4 Female\n8 8913 3.000000e-01 NA Female\n9 7332 2.916968e+00 4 Male\n10 6941 1.649819e+00 2 Male\n11 5104 4.574007e+00 3 Male\n12 9078 1.583904e+02 15 Female\n13 9960 NA 8 Male\n14 9651 1.065068e+02 12 Male\n15 9229 1.113870e+02 15 Male\n16 5210 4.144893e+01 9 Male\n17 5105 3.000000e-01 8 Male\n18 7607 2.527076e-01 7 Female\n19 7582 8.159247e+01 11 Female\n20 8179 1.825342e+02 10 Male\n21 5660 4.244656e+01 8 Male\n22 6696 1.193493e+02 11 Female\n23 7842 3.000000e-01 2 Male\n24 6578 3.000000e-01 2 Female\n25 9619 9.025271e-01 3 Female\n26 9838 3.501805e-01 5 Male\n27 6935 3.000000e-01 1 Male\n28 5885 1.227437e+00 3 Female\n29 9657 1.702055e+02 5 Female\n30 9146 3.000000e-01 5 Female\n31 7056 4.801444e-01 3 Male\n32 9144 2.527076e-02 1 Male\n33 8696 3.000000e-01 4 Female\n34 7042 5.776173e-02 3 Male\n35 5278 4.801444e-01 2 Female\n36 6541 3.826715e-01 11 Female\n37 6070 3.000000e-01 7 Male\n38 5490 4.048558e+02 8 Male\n39 6527 3.000000e-01 6 Male\n40 5389 5.451264e-01 6 Male\n41 9003 3.000000e-01 11 Female\n42 6682 5.590753e+01 10 Male\n43 7844 2.202166e-01 6 Female\n44 8257 1.709760e+02 12 Male\n45 7767 1.227437e+00 11 Male\n46 8391 4.567527e+02 10 Male\n47 8317 4.838480e+01 11 Male\n48 7397 1.227437e-01 13 Female\n49 8495 1.877256e-01 3 Female\n50 8093 3.000000e-01 4 Female\n51 7375 3.501805e-01 3 Male\n52 5255 3.339350e+00 1 Male\n53 8445 3.000000e-01 2 Female\n54 8959 5.451264e-01 2 Female\n55 8400 NA 4 Male\n56 7420 2.104693e+00 2 Male\n57 5206 NA 2 Male\n58 7431 3.826715e-01 3 Female\n59 7230 3.926366e+01 3 Female\n60 8208 1.129964e+00 4 Male\n61 8538 3.501805e+00 1 Female\n62 6125 7.542808e+01 13 Female\n63 5767 4.800475e+01 13 Female\n64 5487 1.000000e+00 6 Male\n65 5539 4.068884e+01 13 Male\n66 5759 3.000000e-01 5 Female\n67 6845 4.377672e+01 13 Female\n68 7170 1.193493e+02 14 Male\n69 6588 6.977740e+01 13 Male\n70 7939 1.373288e+02 8 Female\n71 5006 1.642979e+02 7 Male\n72 9180 NA 6 Female\n73 9638 1.542808e+02 13 Male\n74 7781 6.033058e-01 3 Male\n75 6932 2.809917e-01 4 Male\n76 8120 1.966942e+00 2 Male\n77 9292 2.041322e+00 NA Male\n78 9228 2.115702e+00 5 Female\n79 8185 4.663043e+02 3 Male\n80 6797 3.000000e-01 3 Male\n81 5970 1.500796e+02 14 Male\n82 7219 1.543790e+02 11 Female\n83 6870 2.561983e-01 7 Female\n84 7653 1.596338e+02 7 Male\n85 8824 1.732484e+02 11 Female\n86 8311 4.641304e+02 9 Female\n87 9458 3.736364e+01 14 Male\n88 8275 1.572452e+02 13 Female\n89 6786 3.000000e-01 1 Male\n90 6595 3.000000e-01 1 Male\n91 5264 8.264463e-02 4 Male\n92 9188 6.776859e-01 1 Female\n93 6611 7.272727e-01 2 Male\n94 6840 2.066116e-01 3 Female\n95 5663 1.966942e+00 2 Male\n96 9611 3.000000e-01 1 Male\n97 7717 3.000000e-01 2 Male\n98 8374 2.809917e-01 2 Female\n99 5134 8.016529e-01 4 Female\n100 8122 1.818182e-01 5 Female\n101 6192 1.818182e-01 5 Male\n102 9668 8.264463e-02 6 Female\n103 9577 3.422727e+01 14 Female\n104 6403 8.743506e+00 14 Male\n105 9464 3.000000e-01 10 Male\n106 8157 1.641720e+02 6 Female\n107 9451 4.049587e-01 6 Male\n108 6615 1.001592e+02 8 Male\n109 9074 4.489130e+02 6 Female\n110 7479 1.101911e+02 12 Female\n111 8946 4.440909e+01 12 Male\n112 5296 1.288217e+02 14 Female\n113 6238 2.840909e+01 15 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4.649315e+00 3 Male\n146 8792 1.369863e-01 4 Female\n147 6721 3.589041e-01 3 Male\n148 9042 1.049315e+00 3 Male\n149 7407 4.668998e+01 13 Female\n150 7229 1.473510e+02 7 Female\n151 7532 4.589744e+01 10 Male\n152 6516 2.109589e-01 6 Male\n153 7941 1.741722e+02 10 Female\n154 8124 2.496503e+01 12 Female\n155 7869 1.850993e+02 10 Male\n156 5647 1.863014e-01 10 Male\n157 9120 1.863014e-01 13 Male\n158 6608 4.589744e+01 13 Female\n159 8635 1.942881e+02 5 Female\n160 9341 5.079646e+02 3 Female\n161 9982 8.767123e-01 4 Male\n162 6976 2.750685e+00 1 Male\n163 6008 1.503311e+02 3 Female\n164 5432 3.000000e-01 4 Male\n165 5749 3.095890e-01 4 Male\n166 6428 3.000000e-01 1 Male\n167 5947 6.371681e+02 5 Female\n168 6027 6.054795e-01 6 Female\n169 5064 1.955298e+02 14 Female\n170 5861 1.786424e+02 6 Male\n171 6702 1.120861e+02 13 Female\n172 7851 1.331954e+02 9 Male\n173 8310 2.159292e+02 11 Male\n174 5897 5.628319e+02 10 Male\n175 9249 1.900662e+02 5 Female\n176 9163 6.547945e-01 14 Male\n177 6550 1.665753e+00 7 Male\n178 5859 1.739238e+02 10 Male\n179 5607 9.991722e+01 6 Male\n180 8746 9.321192e+01 5 Male\n181 5274 8.767123e-02 3 Female\n182 9412 NA 4 Male\n183 5691 6.794521e-01 2 Female\n184 9016 5.808219e-01 3 Male\n185 9128 1.369863e-01 3 Female\n186 8539 2.060274e+00 2 Female\n187 5703 1.610099e+02 3 Male\n188 9573 4.082192e-01 5 Female\n189 5852 8.273973e-01 2 Male\n190 5971 4.601770e+02 3 Female\n191 7015 1.389073e+02 14 Female\n192 8221 3.867133e+01 9 Female\n193 6752 9.260274e-01 14 Female\n194 7436 5.918874e+01 9 Female\n195 6869 1.870861e+02 8 Female\n196 8947 4.328767e-01 7 Male\n197 7360 6.301370e-02 13 Male\n198 7494 3.000000e-01 8 Female\n199 8243 1.548013e+02 6 Male\n200 6176 5.819536e+01 12 Female\n201 6818 1.724338e+02 14 Female\n202 8083 1.932401e+01 15 Female\n203 6711 2.164420e+00 2 Female\n204 8890 9.757412e-01 4 Female\n205 5576 1.509434e-01 3 Male\n206 8396 1.509434e-01 3 Female\n207 5986 7.766571e+01 3 Male\n208 9758 4.319563e+01 4 Female\n209 5444 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1.621767e+02 10 Male\n401 9444 3.228840e-01 2 Male\n402 6964 4.639498e-01 4 Female\n403 6420 2.495298e+00 3 Female\n404 9189 3.257053e+00 2 Female\n405 9368 3.793103e-01 1 Female\n406 6360 NA 3 Male\n407 8196 6.896552e-02 3 Female\n408 8297 3.000000e-01 4 Male\n409 6674 1.423197e+00 5 Female\n410 5269 3.000000e-01 5 Female\n411 6599 3.000000e-01 1 Female\n412 7713 1.786638e+02 11 Male\n413 8644 3.279570e+02 6 Male\n414 9680 NA 14 Female\n415 6305 1.903017e+02 8 Male\n416 8493 1.654095e+02 8 Female\n417 5297 4.639498e-01 9 Female\n418 7723 1.815733e+02 7 Male\n419 7510 1.366771e+00 6 Male\n420 5102 1.536050e-01 12 Female\n421 7816 1.306587e+01 8 Male\n422 5143 2.129032e+02 11 Female\n423 7414 1.925647e+02 14 Male\n424 5127 3.000000e-01 3 Female\n425 5830 1.028213e+00 1 Female\n426 8929 3.793103e-01 5 Female\n427 7993 8.025078e-01 2 Female\n428 8092 4.860215e+02 3 Female\n429 9750 3.000000e-01 4 Female\n430 6660 2.100313e-01 2 Male\n431 8054 2.767665e+01 3 Female\n432 6086 1.592476e+00 4 Male\n433 6878 9.717868e-02 1 Female\n434 8125 1.028213e+00 7 Female\n435 9500 3.793103e-01 10 Male\n436 8105 1.292026e+02 11 Male\n437 9593 4.425150e+01 7 Female\n438 5202 3.193548e+02 10 Female\n439 7207 1.860991e+02 14 Female\n440 5518 6.614420e-01 7 Female\n441 9820 5.203762e-01 11 Male\n442 6958 1.330819e+02 12 Male\n443 9445 1.673491e+02 10 Female\n444 8774 3.000000e-01 6 Male\n445 9614 1.117457e+02 13 Male\n446 9810 3.045509e+01 8 Female\n447 7271 3.000000e-01 2 Male\n448 8031 8.280255e-02 3 Female\n449 7232 3.000000e-01 1 Female\n450 7452 1.200637e+00 2 Female\n451 5921 1.687898e-01 NA Male\n452 8136 7.367273e+02 NA Female\n453 6605 8.280255e-02 4 Male\n454 5125 5.127389e-01 4 Male\n455 5911 1.974522e-01 1 Male\n456 9644 7.993631e-01 2 Female\n457 5760 3.000000e-01 2 Male\n458 7055 3.298182e+02 12 Male\n459 9064 9.736842e+01 12 Female\n460 6925 3.000000e-01 8 Female\n461 7757 3.000000e-01 14 Female\n462 8527 4.214545e+02 13 Female\n463 8521 3.000000e-01 6 Male\n464 6260 2.578182e+02 11 Female\n465 9578 2.261147e-01 11 Male\n466 9570 3.000000e-01 10 Female\n467 6246 1.883901e+02 12 Male\n468 9622 9.458204e+01 14 Female\n469 7661 3.000000e-01 11 Female\n470 9374 3.000000e-01 1 Male\n471 8446 7.707006e-01 2 Female\n472 8332 5.032727e+02 3 Male\n473 8008 1.544586e+00 3 Female\n474 9365 1.431115e+02 5 Female\n475 9819 3.000000e-01 3 Male\n476 5173 1.458599e+00 1 Male\n477 6722 1.247678e+02 4 Female\n478 7668 NA 4 Female\n479 8980 4.334545e+02 4 Male\n480 5204 3.000000e-01 2 Female\n481 6412 6.156364e+02 5 Female\n482 6404 9.574303e+01 7 Male\n483 5693 1.928019e+02 8 Male\n484 8100 1.888545e+02 10 Male\n485 9760 1.598297e+02 6 Female\n486 6377 5.127389e-01 7 Male\n487 6012 1.171053e+02 10 Female\n488 6224 NA 6 Male\n489 6561 2.547771e-02 6 Female\n490 8475 1.707430e+02 15 Female\n491 6629 3.000000e-01 5 Male\n492 7200 1.869969e+02 3 Male\n493 9453 4.731481e+01 5 Male\n494 6449 1.988390e+02 3 Female\n495 9452 3.000000e-01 5 Male\n496 7162 8.808050e+01 5 Male\n497 8962 2.003185e+00 1 Female\n498 7328 3.000000e-01 1 Male\n499 9097 3.509259e+01 7 Female\n500 9131 9.365325e+01 14 Female\n501 7280 3.000000e-01 9 Male\n502 5783 3.736111e+01 10 Female\n503 9895 1.674923e+02 10 Female\n504 7986 8.808050e+01 11 Male\n505 7146 1.656347e+02 11 Female\n506 8671 3.722222e+01 12 Female\n507 5273 6.756364e+02 11 Female\n508 5063 3.000000e-01 12 Male\n509 6729 1.698142e+02 12 Male\n510 9085 1.628483e+02 10 Female\n511 9929 5.985130e-01 1 Male\n512 8479 1.903346e+00 2 Female\n513 7395 3.000000e-01 4 Male\n514 6374 3.000000e-01 2 Male\n515 7878 8.996283e-01 3 Male\n516 9603 3.977695e-01 3 Female\n517 7994 3.000000e-01 2 Male\n518 5277 3.000000e-01 4 Male\n519 5054 3.000000e-01 3 Male\n520 5440 3.000000e-01 1 Female\n521 6551 7.446809e+02 4 Male\n522 5281 6.095745e+02 12 Female\n523 7145 1.427445e+02 6 Male\n524 5275 3.000000e-01 7 Female\n525 9542 2.973978e-02 7 Male\n526 9371 3.977695e-01 13 Female\n527 5598 4.095745e+02 8 Female\n528 7148 4.595745e+02 7 Male\n529 5624 3.000000e-01 8 Female\n530 6998 1.976341e+02 8 Female\n531 9286 3.776596e+02 11 Female\n532 7589 1.777603e+02 14 Female\n533 7095 4.312268e-01 3 Male\n534 5455 6.765957e+02 2 Female\n535 6257 7.978723e+02 2 Male\n536 8627 9.665427e-02 3 Male\n537 9786 1.879338e+02 2 Male\n538 8176 4.358670e+01 2 Female\n539 9198 3.000000e-01 3 Female\n540 6586 3.000000e-01 2 Male\n541 8850 2.638955e+01 5 Male\n542 9560 3.180523e+01 10 Female\n543 7144 1.746845e+02 14 Male\n544 8230 1.876972e+02 9 Male\n545 7559 1.044164e+02 6 Male\n546 5312 1.202681e+02 7 Male\n547 6560 1.630915e+02 14 Female\n548 6091 1.276025e+02 7 Female\n549 5578 8.880126e+01 7 Male\n550 5837 3.563830e+02 9 Male\n551 8347 2.212766e+02 14 Male\n552 6453 1.969121e+01 10 Female\n553 5758 3.755319e+02 13 Female\n554 5569 1.214511e+02 5 Male\n555 8766 1.034700e+02 4 Female\n556 8002 3.000000e-01 4 Female\n557 7839 3.643123e-01 5 Female\n558 5434 6.319703e-02 4 Female\n559 7636 3.000000e-01 4 Male\n560 6164 3.000000e-01 4 Male\n561 9243 3.000000e-01 3 Female\n562 5872 3.000000e-01 1 Female\n563 8079 3.000000e-01 4 Male\n564 9762 3.000000e-01 1 Male\n565 9476 3.000000e-01 1 Female\n566 8345 3.000000e-01 7 Male\n567 8128 1.664038e+02 13 Female\n568 7956 2.946809e+02 10 Female\n569 8677 4.391924e+01 14 Male\n570 5881 1.874606e+02 12 Female\n571 7498 1.143533e+02 14 Male\n572 8134 1.600158e+02 8 Male\n573 7748 1.635688e-01 7 Male\n574 7990 8.809148e+01 11 Female\n575 6184 1.337539e+02 8 Male\n576 6339 1.985804e+02 12 Male\n577 5113 1.578864e+02 9 Female\n578 9449 3.000000e-01 5 Female\n579 8110 3.000000e-01 4 Male\n580 9307 1.953642e-01 3 Female\n581 5555 1.119205e+00 2 Male\n582 9152 2.523636e+02 2 Male\n583 7969 3.000000e-01 3 Male\n584 6116 4.844371e+00 4 Female\n585 8294 3.000000e-01 4 Male\n586 8938 1.492553e+02 4 Female\n587 9539 1.993617e+02 5 Male\n588 9470 2.847682e-01 3 Female\n589 6677 3.145695e-01 6 Female\n590 8752 3.000000e-01 3 Male\n591 5574 3.406429e+01 11 Female\n592 5989 6.595745e+01 11 Male\n593 9813 3.000000e-01 7 Male\n594 6150 2.174545e+02 8 Male\n595 5730 NA 6 Female\n596 8038 5.957447e+01 10 Female\n597 5964 7.236364e+02 8 Female\n598 9043 3.000000e-01 8 Male\n599 5095 3.000000e-01 9 Female\n600 8922 3.000000e-01 8 Male\n601 5469 2.676364e+02 13 Male\n602 6726 1.891489e+02 11 Male\n603 7495 3.036364e+02 8 Female\n604 8159 3.000000e-01 2 Female\n605 6709 3.000000e-01 4 Male\n606 5855 3.000000e-01 2 Male\n607 6058 3.000000e-01 2 Female\n608 7292 3.000000e-01 4 Male\n609 6437 1.447020e+00 2 Male\n610 9326 2.130909e+02 4 Female\n611 8222 1.357616e-01 2 Female\n612 6789 3.000000e-01 4 Female\n613 6348 3.000000e-01 1 Female\n614 5958 5.534545e+02 4 Female\n615 9211 1.891489e+02 12 Female\n616 9450 7.202128e+01 7 Female\n617 6540 3.250287e+01 11 Male\n618 8796 1.655629e-02 6 Male\n619 7971 3.123636e+02 8 Male\n620 7549 3.000000e-01 14 Male\n621 9799 7.138298e+01 11 Male\n622 7013 3.000000e-01 7 Female\n623 5599 6.946809e+01 14 Female\n624 8601 4.012629e+01 6 Male\n625 7383 1.629787e+02 13 Female\n626 6656 1.508511e+02 13 Female\n627 5641 1.655629e-02 3 Male\n628 6222 3.000000e-01 1 Male\n629 7674 4.635762e-02 3 Male\n630 5293 3.000000e-01 1 Female\n631 6715 3.000000e-01 1 Female\n632 7057 3.000000e-01 2 Male\n633 7072 1.942553e+02 4 Male\n634 6380 3.690909e+02 4 Male\n635 6762 3.000000e-01 2 Female\n636 5799 3.000000e-01 4 Female\n637 6681 2.847682e+00 5 Male\n638 8755 1.435106e+02 3 Female\n639 6896 3.000000e-01 3 Male\n640 5945 4.752009e+01 6 Female\n641 5035 2.621125e+01 11 Female\n642 6776 1.055319e+02 9 Female\n643 7863 3.000000e-01 7 Female\n644 9836 1.149007e+00 8 Male\n645 7860 2.927273e+02 NA Female\n646 5248 3.000000e-01 8 Female\n647 5677 3.000000e-01 14 Female\n648 9576 4.839265e+01 10 Male\n649 5824 3.000000e-01 10 Male\n650 9184 3.000000e-01 11 Female\n651 5397 2.251656e-01 13 Female\n```\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\ndf$slum <- NULL # this is the same as above\n```\n:::\n\nWe can also grab the `age` column using the `$` operator. \n\n::: {.cell}\n\n```{.r .cell-code}\ndf$age\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n [1] 3.176895e-01 3.436823e+00 3.000000e-01 1.432363e+02 4.476534e-01\n [6] 2.527076e-02 6.101083e-01 3.000000e-01 2.916968e+00 1.649819e+00\n [11] 4.574007e+00 1.583904e+02 NA 1.065068e+02 1.113870e+02\n [16] 4.144893e+01 3.000000e-01 2.527076e-01 8.159247e+01 1.825342e+02\n [21] 4.244656e+01 1.193493e+02 3.000000e-01 3.000000e-01 9.025271e-01\n [26] 3.501805e-01 3.000000e-01 1.227437e+00 1.702055e+02 3.000000e-01\n [31] 4.801444e-01 2.527076e-02 3.000000e-01 5.776173e-02 4.801444e-01\n [36] 3.826715e-01 3.000000e-01 4.048558e+02 3.000000e-01 5.451264e-01\n [41] 3.000000e-01 5.590753e+01 2.202166e-01 1.709760e+02 1.227437e+00\n [46] 4.567527e+02 4.838480e+01 1.227437e-01 1.877256e-01 3.000000e-01\n [51] 3.501805e-01 3.339350e+00 3.000000e-01 5.451264e-01 NA\n [56] 2.104693e+00 NA 3.826715e-01 3.926366e+01 1.129964e+00\n [61] 3.501805e+00 7.542808e+01 4.800475e+01 1.000000e+00 4.068884e+01\n [66] 3.000000e-01 4.377672e+01 1.193493e+02 6.977740e+01 1.373288e+02\n [71] 1.642979e+02 NA 1.542808e+02 6.033058e-01 2.809917e-01\n [76] 1.966942e+00 2.041322e+00 2.115702e+00 4.663043e+02 3.000000e-01\n [81] 1.500796e+02 1.543790e+02 2.561983e-01 1.596338e+02 1.732484e+02\n [86] 4.641304e+02 3.736364e+01 1.572452e+02 3.000000e-01 3.000000e-01\n [91] 8.264463e-02 6.776859e-01 7.272727e-01 2.066116e-01 1.966942e+00\n [96] 3.000000e-01 3.000000e-01 2.809917e-01 8.016529e-01 1.818182e-01\n[101] 1.818182e-01 8.264463e-02 3.422727e+01 8.743506e+00 3.000000e-01\n[106] 1.641720e+02 4.049587e-01 1.001592e+02 4.489130e+02 1.101911e+02\n[111] 4.440909e+01 1.288217e+02 2.840909e+01 1.003981e+02 8.512397e-01\n[116] 1.322314e-01 1.297521e+00 1.570248e-01 1.966942e+00 1.536624e+02\n[121] 3.000000e-01 3.000000e-01 1.074380e+00 1.099174e+00 3.057851e-01\n[126] 3.000000e-01 5.785124e-02 4.391304e+02 6.130435e+02 1.074380e-01\n[131] 7.125796e+01 4.222727e+01 1.620223e+02 3.750000e+01 1.534236e+02\n[136] 6.239130e+02 5.521739e+02 5.785124e-02 6.547945e-01 8.767123e-02\n[141] 3.000000e-01 2.849315e+00 3.835616e-02 2.849315e-01 4.649315e+00\n[146] 1.369863e-01 3.589041e-01 1.049315e+00 4.668998e+01 1.473510e+02\n[151] 4.589744e+01 2.109589e-01 1.741722e+02 2.496503e+01 1.850993e+02\n[156] 1.863014e-01 1.863014e-01 4.589744e+01 1.942881e+02 5.079646e+02\n[161] 8.767123e-01 2.750685e+00 1.503311e+02 3.000000e-01 3.095890e-01\n[166] 3.000000e-01 6.371681e+02 6.054795e-01 1.955298e+02 1.786424e+02\n[171] 1.120861e+02 1.331954e+02 2.159292e+02 5.628319e+02 1.900662e+02\n[176] 6.547945e-01 1.665753e+00 1.739238e+02 9.991722e+01 9.321192e+01\n[181] 8.767123e-02 NA 6.794521e-01 5.808219e-01 1.369863e-01\n[186] 2.060274e+00 1.610099e+02 4.082192e-01 8.273973e-01 4.601770e+02\n[191] 1.389073e+02 3.867133e+01 9.260274e-01 5.918874e+01 1.870861e+02\n[196] 4.328767e-01 6.301370e-02 3.000000e-01 1.548013e+02 5.819536e+01\n[201] 1.724338e+02 1.932401e+01 2.164420e+00 9.757412e-01 1.509434e-01\n[206] 1.509434e-01 7.766571e+01 4.319563e+01 1.752022e-01 3.094775e+01\n[211] 1.266846e-01 2.919806e+01 9.545455e+00 2.735115e+01 1.314841e+02\n[216] 3.643985e+01 1.498559e+02 9.363636e+00 2.479784e-01 5.390836e-02\n[221] 8.787062e-01 1.994609e-01 3.000000e-01 3.000000e-01 5.390836e-03\n[226] 4.177898e-01 3.000000e-01 2.479784e-01 2.964960e-02 2.964960e-01\n[231] 5.148248e+00 1.994609e-01 3.000000e-01 1.779539e+02 3.290210e+02\n[236] 3.000000e-01 1.809798e+02 4.905660e-01 1.266846e-01 1.543948e+02\n[241] 1.379683e+02 6.153846e+02 1.474784e+02 3.000000e-01 1.024259e+00\n[246] 4.444056e+02 3.000000e-01 2.504043e+00 3.000000e-01 3.000000e-01\n[251] 7.816712e-02 3.000000e-01 5.390836e-02 1.494236e+02 5.972622e+01\n[256] 6.361186e-01 1.837896e+02 1.320809e+02 1.571906e-01 1.520231e+02\n[261] 3.000000e-01 3.000000e-01 1.823699e+02 3.000000e-01 2.173913e+00\n[266] 2.142202e+01 3.000000e-01 3.408027e+00 4.155963e+01 9.698997e-02\n[271] 1.238532e+01 9.528926e+00 1.916185e+02 1.060201e+00 3.679104e+02\n[276] 4.288991e+01 9.971098e+01 3.000000e-01 1.208092e+02 3.000000e-01\n[281] 6.688963e-03 2.505017e+00 1.481605e+00 3.000000e-01 5.183946e-01\n[286] 3.000000e-01 1.872910e-01 3.678930e-01 3.000000e-01 4.529851e+02\n[291] 3.169725e+01 3.000000e-01 4.922018e+01 2.548507e+02 1.661850e+02\n[296] 9.164179e+02 3.678930e-01 1.236994e+02 6.705202e+01 3.834862e+01\n[301] 1.963211e+00 3.000000e-01 2.474916e-01 3.000000e-01 2.173913e-01\n[306] 8.193980e-01 2.444816e+00 3.000000e-01 1.571906e-01 1.849711e+02\n[311] 6.119403e+02 3.000000e-01 4.280936e-01 9.698997e-02 3.678930e-02\n[316] 4.832090e+02 1.390173e+02 3.000000e-01 6.555970e+02 1.526012e+02\n[321] 3.000000e-01 7.222222e-01 7.724426e+01 3.000000e-01 6.111111e-01\n[326] 1.555556e+00 3.055556e-01 1.500000e+00 1.470772e+02 1.694444e+00\n[331] 3.138298e+02 1.414405e+02 1.990605e+02 4.212766e+02 3.000000e-01\n[336] 3.000000e-01 6.478723e+02 3.000000e-01 2.222222e+00 3.000000e-01\n[341] 2.055556e+00 2.777778e-02 8.333333e-02 1.032359e+02 1.611111e+00\n[346] 8.333333e-02 2.333333e+00 5.755319e+02 1.686848e+02 1.111111e-01\n[351] 3.000000e-01 8.372340e+02 3.000000e-01 3.784504e+01 3.819149e+02\n[356] 5.555556e-02 3.000000e+02 1.855950e+02 1.944444e-01 3.000000e-01\n[361] 5.555556e-02 1.138889e+00 4.254237e+01 3.000000e-01 3.000000e-01\n[366] 3.000000e-01 3.000000e-01 3.138298e+02 1.235908e+02 4.159574e+02\n[371] 3.009685e+01 1.567850e+02 1.367432e+02 3.731235e+01 9.164927e+01\n[376] 2.936170e+02 8.820459e+01 1.035491e+02 7.379958e+01 3.000000e-01\n[381] 1.718750e+02 2.128527e+00 1.253918e+00 2.382445e-01 4.639498e-01\n[386] 1.253918e-01 1.253918e-01 3.000000e-01 1.000000e+00 1.570043e+02\n[391] 4.344086e+02 2.184953e+00 1.507837e+00 3.228840e-01 4.588024e+01\n[396] 1.660560e+02 3.000000e-01 3.043011e+02 2.612903e+02 1.621767e+02\n[401] 3.228840e-01 4.639498e-01 2.495298e+00 3.257053e+00 3.793103e-01\n[406] NA 6.896552e-02 3.000000e-01 1.423197e+00 3.000000e-01\n[411] 3.000000e-01 1.786638e+02 3.279570e+02 NA 1.903017e+02\n[416] 1.654095e+02 4.639498e-01 1.815733e+02 1.366771e+00 1.536050e-01\n[421] 1.306587e+01 2.129032e+02 1.925647e+02 3.000000e-01 1.028213e+00\n[426] 3.793103e-01 8.025078e-01 4.860215e+02 3.000000e-01 2.100313e-01\n[431] 2.767665e+01 1.592476e+00 9.717868e-02 1.028213e+00 3.793103e-01\n[436] 1.292026e+02 4.425150e+01 3.193548e+02 1.860991e+02 6.614420e-01\n[441] 5.203762e-01 1.330819e+02 1.673491e+02 3.000000e-01 1.117457e+02\n[446] 3.045509e+01 3.000000e-01 8.280255e-02 3.000000e-01 1.200637e+00\n[451] 1.687898e-01 7.367273e+02 8.280255e-02 5.127389e-01 1.974522e-01\n[456] 7.993631e-01 3.000000e-01 3.298182e+02 9.736842e+01 3.000000e-01\n[461] 3.000000e-01 4.214545e+02 3.000000e-01 2.578182e+02 2.261147e-01\n[466] 3.000000e-01 1.883901e+02 9.458204e+01 3.000000e-01 3.000000e-01\n[471] 7.707006e-01 5.032727e+02 1.544586e+00 1.431115e+02 3.000000e-01\n[476] 1.458599e+00 1.247678e+02 NA 4.334545e+02 3.000000e-01\n[481] 6.156364e+02 9.574303e+01 1.928019e+02 1.888545e+02 1.598297e+02\n[486] 5.127389e-01 1.171053e+02 NA 2.547771e-02 1.707430e+02\n[491] 3.000000e-01 1.869969e+02 4.731481e+01 1.988390e+02 3.000000e-01\n[496] 8.808050e+01 2.003185e+00 3.000000e-01 3.509259e+01 9.365325e+01\n[501] 3.000000e-01 3.736111e+01 1.674923e+02 8.808050e+01 1.656347e+02\n[506] 3.722222e+01 6.756364e+02 3.000000e-01 1.698142e+02 1.628483e+02\n[511] 5.985130e-01 1.903346e+00 3.000000e-01 3.000000e-01 8.996283e-01\n[516] 3.977695e-01 3.000000e-01 3.000000e-01 3.000000e-01 3.000000e-01\n[521] 7.446809e+02 6.095745e+02 1.427445e+02 3.000000e-01 2.973978e-02\n[526] 3.977695e-01 4.095745e+02 4.595745e+02 3.000000e-01 1.976341e+02\n[531] 3.776596e+02 1.777603e+02 4.312268e-01 6.765957e+02 7.978723e+02\n[536] 9.665427e-02 1.879338e+02 4.358670e+01 3.000000e-01 3.000000e-01\n[541] 2.638955e+01 3.180523e+01 1.746845e+02 1.876972e+02 1.044164e+02\n[546] 1.202681e+02 1.630915e+02 1.276025e+02 8.880126e+01 3.563830e+02\n[551] 2.212766e+02 1.969121e+01 3.755319e+02 1.214511e+02 1.034700e+02\n[556] 3.000000e-01 3.643123e-01 6.319703e-02 3.000000e-01 3.000000e-01\n[561] 3.000000e-01 3.000000e-01 3.000000e-01 3.000000e-01 3.000000e-01\n[566] 3.000000e-01 1.664038e+02 2.946809e+02 4.391924e+01 1.874606e+02\n[571] 1.143533e+02 1.600158e+02 1.635688e-01 8.809148e+01 1.337539e+02\n[576] 1.985804e+02 1.578864e+02 3.000000e-01 3.000000e-01 1.953642e-01\n[581] 1.119205e+00 2.523636e+02 3.000000e-01 4.844371e+00 3.000000e-01\n[586] 1.492553e+02 1.993617e+02 2.847682e-01 3.145695e-01 3.000000e-01\n[591] 3.406429e+01 6.595745e+01 3.000000e-01 2.174545e+02 NA\n[596] 5.957447e+01 7.236364e+02 3.000000e-01 3.000000e-01 3.000000e-01\n[601] 2.676364e+02 1.891489e+02 3.036364e+02 3.000000e-01 3.000000e-01\n[606] 3.000000e-01 3.000000e-01 3.000000e-01 1.447020e+00 2.130909e+02\n[611] 1.357616e-01 3.000000e-01 3.000000e-01 5.534545e+02 1.891489e+02\n[616] 7.202128e+01 3.250287e+01 1.655629e-02 3.123636e+02 3.000000e-01\n[621] 7.138298e+01 3.000000e-01 6.946809e+01 4.012629e+01 1.629787e+02\n[626] 1.508511e+02 1.655629e-02 3.000000e-01 4.635762e-02 3.000000e-01\n[631] 3.000000e-01 3.000000e-01 1.942553e+02 3.690909e+02 3.000000e-01\n[636] 3.000000e-01 2.847682e+00 1.435106e+02 3.000000e-01 4.752009e+01\n[641] 2.621125e+01 1.055319e+02 3.000000e-01 1.149007e+00 2.927273e+02\n[646] 3.000000e-01 3.000000e-01 4.839265e+01 3.000000e-01 3.000000e-01\n[651] 2.251656e-01\n```\n:::\n:::\n\n\n\n## Using indexing to subset by rows\n\nWe can use indexing to also subset by rows. For example, here we pull the 100th observation/row.\n\n::: {.cell}\n\n```{.r .cell-code}\ndf[100,] \n```\n\n::: {.cell-output .cell-output-stdout}\n```\n IgG_concentration age age gender slum\n100 8122 0.1818182 5 Female Non slum\n```\n:::\n:::\n\nAnd, here we pull the `age` of the 100th observation/row.\n\n::: {.cell}\n\n```{.r .cell-code}\ndf[100,\"age\"] \n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 0.1818182\n```\n:::\n:::\n\n \n\n## Logical operators\n\nLogical operators can be evaluated on object(s) in order to return a binary response of TRUE/FALSE\n\noperator | operator option |description\n-----|-----|-----:\n`<`|%l%|less than\n`<=`|%le%|less than or equal to\n`>`|%g%|greater than\n`>=`|%ge%|greater than or equal to\n`==`||equal to\n`!=`|not equal to\n`x&y`||x and y\n`x|y`||x or y\n`%in%`||match\n`%!in%`||do not match\n\n\n## Logical operators examples\n\nLet's practice. First, here is a reminder of what the number.object contains.\n\n::: {.cell}\n\n```{.r .cell-code}\nnumber.object\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 3\n```\n:::\n:::\n\n\nNow, we will use logical operators to evaluate the object.\n\n::: {.cell}\n\n```{.r .cell-code}\nnumber.object<4\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] TRUE\n```\n:::\n\n```{.r .cell-code}\nnumber.object>=3\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] TRUE\n```\n:::\n\n```{.r .cell-code}\nnumber.object!=5\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] TRUE\n```\n:::\n\n```{.r .cell-code}\nnumber.object %in% c(6,7,2)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] FALSE\n```\n:::\n:::\n\n\n\n## Using indexing and logical operators to rename columns\n\n1. We can assign the column names from data frame `df` to an object `cn`, then we can modify `cn` directly using indexing and logical operators, finally we reassign the column names, `cn`, back to the data frame `df`:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncn <- colnames(df)\ncn\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] \"IgG_concentration\" \"age\" \"age\" \n[4] \"gender\" \"slum\" \n```\n:::\n\n```{.r .cell-code}\ncn[cn==\"IgG_concentration\"] <-\"IgG_concentration_mIU\" #rename cn to \"IgG_concentration_mIU\" when cn is \"IgG_concentration\"\ncolnames(df) <- cn\n```\n:::\n\n\nNote, I am resetting the column name back to the original name for the sake of the rest of the module.\n\n::: {.cell}\n\n```{.r .cell-code}\ncolnames(df)[colnames(df)==\"IgG_concentration_mIU\"] <- \"IgG_concentration\" #reset\n```\n:::\n\n\n\n## Using indexing and logical operators to subset data\n\n\nIn this example, we subset by rows and pull only observations with an age of less than or equal to 10 and then saved the subset data to `df_lt10`. Note that the logical operators `df$age<=10` is before the comma because I want to subset by rows (the first dimension).\n\n::: {.cell}\n\n```{.r .cell-code}\ndf_lte10 <- df[df$age<=10, ]\n```\n:::\n\nIn this example, we subset by rows and pull only observations with an age of less than or equal to 5 OR greater than 10.\n\n::: {.cell}\n\n```{.r .cell-code}\ndf_lte5_gt10 <- df[df$age<=5 | df$age>10, ]\n```\n:::\n\nLets check that my subsets worked using the `summary()` function. \n\n::: {.cell}\n\n```{.r .cell-code}\nsummary(df_lte10$age)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n Min. 1st Qu. Median Mean 3rd Qu. Max. NA's \n0.005391 0.300000 0.300000 0.724742 0.640788 9.545455 10 \n```\n:::\n\n```{.r .cell-code}\nsummary(df_lte5_gt10$age)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n Min. 1st Qu. Median Mean 3rd Qu. Max. NA's \n 0.0054 0.3000 1.6018 87.9886 142.8362 916.4179 10 \n```\n:::\n:::\n\n\n\n## Missing values \n\nMissing data need to be carefully described and dealt with in data analysis. Understanding the different types of missing data and how you can identify them, is the first step to data cleaning.\n\nTypes of \"missing\" values:\n\n- `NA` - general missing data\n- `NaN` - stands for \"**N**ot **a** **N**umber\", happens when you do\n 0/0.\n- `Inf` and `-Inf` - Infinity, happens when you divide a positive\n number (or negative number) by 0.\n- blank space - sometimes when data is read it, there is a blank space left\n\n## Logical operators to help identify and missing data\n\noperator | operator option |description\n-----|-----|-----:\n`is.na`||is NAN or NA\n`is.nan`||is NAN\n`!is.na`||is not NAN or NA\n`!is.nan`||is not NAN\n`is.infinite`||is infinite\n`any`||are any TRUE\n`which`||which are TRUE\n\n## More logical operators examples\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntest <- c(0,NA, -1)/0\ntest\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] NaN NA -Inf\n```\n:::\n\n```{.r .cell-code}\nis.na(test)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] TRUE TRUE FALSE\n```\n:::\n\n```{.r .cell-code}\nis.nan(test)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] TRUE FALSE FALSE\n```\n:::\n\n```{.r .cell-code}\nis.infinite(test)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] FALSE FALSE TRUE\n```\n:::\n:::\n\n\n## More logical operators examples\n\n`any(is.na(x))` means do we have any `NA`'s in the object `x`?\n\n\n::: {.cell}\n\n```{.r .cell-code}\nany(is.na(df$IgG_concentration)) # are there any NAs - YES/TRUE\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] FALSE\n```\n:::\n\n```{.r .cell-code}\nany(is.na(df$slum)) # are there any NAs- NO/FALSE\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] FALSE\n```\n:::\n:::\n\n\n`which(is.na(x))` means which of the elements in object `x` are `NA`'s?\n\n\n::: {.cell}\n\n```{.r .cell-code}\nwhich(is.na(df$IgG_concentration)) \n```\n\n::: {.cell-output .cell-output-stdout}\n```\ninteger(0)\n```\n:::\n\n```{.r .cell-code}\nwhich(is.na(df$slum)) \n```\n\n::: {.cell-output .cell-output-stdout}\n```\ninteger(0)\n```\n:::\n:::\n\n\n## `subset()` function\n\nThe Base R `subset()` function is a slightly easier way to select variables and observations.\n\n\n::: {.cell}\n\n```{.r .cell-code}\n?subset\n```\n:::\n\n\n```\nRegistered S3 method overwritten by 'printr':\n method from \n knit_print.data.frame rmarkdown\n```\n\nSubsetting Vectors, Matrices and Data Frames\n\nDescription:\n\n Return subsets of vectors, matrices or data frames which meet\n conditions.\n\nUsage:\n\n subset(x, ...)\n \n ## Default S3 method:\n subset(x, subset, ...)\n \n ## S3 method for class 'matrix'\n subset(x, subset, select, drop = FALSE, ...)\n \n ## S3 method for class 'data.frame'\n subset(x, subset, select, drop = FALSE, ...)\n \nArguments:\n\n x: object to be subsetted.\n\n subset: logical expression indicating elements or rows to keep:\n missing values are taken as false.\n\n select: expression, indicating columns to select from a data frame.\n\n drop: passed on to '[' indexing operator.\n\n ...: further arguments to be passed to or from other methods.\n\nDetails:\n\n This is a generic function, with methods supplied for matrices,\n data frames and vectors (including lists). Packages and users can\n add further methods.\n\n For ordinary vectors, the result is simply 'x[subset &\n !is.na(subset)]'.\n\n For data frames, the 'subset' argument works on the rows. Note\n that 'subset' will be evaluated in the data frame, so columns can\n be referred to (by name) as variables in the expression (see the\n examples).\n\n The 'select' argument exists only for the methods for data frames\n and matrices. It works by first replacing column names in the\n selection expression with the corresponding column numbers in the\n data frame and then using the resulting integer vector to index\n the columns. This allows the use of the standard indexing\n conventions so that for example ranges of columns can be specified\n easily, or single columns can be dropped (see the examples).\n\n The 'drop' argument is passed on to the indexing method for\n matrices and data frames: note that the default for matrices is\n different from that for indexing.\n\n Factors may have empty levels after subsetting; unused levels are\n not automatically removed. See 'droplevels' for a way to drop all\n unused levels from a data frame.\n\nValue:\n\n An object similar to 'x' contain just the selected elements (for a\n vector), rows and columns (for a matrix or data frame), and so on.\n\nWarning:\n\n This is a convenience function intended for use interactively.\n For programming it is better to use the standard subsetting\n functions like '[', and in particular the non-standard evaluation\n of argument 'subset' can have unanticipated consequences.\n\nAuthor(s):\n\n Peter Dalgaard and Brian Ripley\n\nSee Also:\n\n '[', 'transform' 'droplevels'\n\nExamples:\n\n subset(airquality, Temp > 80, select = c(Ozone, Temp))\n subset(airquality, Day == 1, select = -Temp)\n subset(airquality, select = Ozone:Wind)\n \n with(airquality, subset(Ozone, Temp > 80))\n \n ## sometimes requiring a logical 'subset' argument is a nuisance\n nm <- rownames(state.x77)\n start_with_M <- nm %in% grep(\"^M\", nm, value = TRUE)\n subset(state.x77, start_with_M, Illiteracy:Murder)\n # but in recent versions of R this can simply be\n subset(state.x77, grepl(\"^M\", nm), Illiteracy:Murder)\n\n\n## Subsetting use the `subset()` function\n\nHere are a few examples using the `subset()` function\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndf_lte10_v2 <- subset(df, df$age<=10, select=c(IgG_concentration, age))\ndf_lt5_f <- subset(df, df$age<=5 & gender==\"Female\", select=c(IgG_concentration, slum))\n```\n:::\n\n\n## `subset()` function vs logical operators\n\n`subset()` automatically removes NAs, which is a different behavior from doing logical operations on NAs.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsummary(df_lte10$age)\n```\n\n::: {.cell-output-display}\n| Min.| 1st Qu.| Median| Mean| 3rd Qu.| Max.| NA's|\n|---------:|-------:|------:|---------:|---------:|--------:|----:|\n| 0.0053908| 0.3| 0.3| 0.7247421| 0.6407876| 9.545454| 10|\n:::\n\n```{.r .cell-code}\nsummary(df_lte10_v2$age)\n```\n\n::: {.cell-output-display}\n| Min.| 1st Qu.| Median| Mean| 3rd Qu.| Max.|\n|---------:|-------:|------:|---------:|---------:|--------:|\n| 0.0053908| 0.3| 0.3| 0.7247421| 0.6407876| 9.545454|\n:::\n:::\n\n\nWe can also see this by looking at the number or rows in each dataset.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnrow(df_lte10)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 370\n```\n:::\n\n```{.r .cell-code}\nnrow(df_lte10_v2)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 360\n```\n:::\n:::\n\n\n\n\n## Summary\n\n- `colnames()`, `str()` and `summary()`functions from Base R are great functions to assess the data type and some summary statistics\n- There are three basic indexing syntax: `[ ]`, `[[ ]]` and `$`\n- Indexing can be used to extract part of an object (e.g., subset data) and to replace parts of an object (e.g., rename variables / columns)\n- Logical operators can be evaluated on object(s) in order to return a binary response of TRUE/FALSE, and are useful for decision rules for indexing\n- There are 5 “types” of missing values, the most common being “NA”\n- Logical operators meant to determine missing values are very helpful for data cleaning\n- The Base R `subset()` function is a slightly easier way to select variables and observations.\n\n## Acknowledgements\n\nThese are the materials we looked through, modified, or extracted to complete this module's lecture.\n\n- [\"Introduction to R for Public Health Researchers\" Johns Hopkins University](https://jhudatascience.org/intro_to_r/)\n- [\"Indexing\" CRAN Project](https://cran.r-project.org/doc/manuals/R-lang.html#Indexing)\n- [\"Logical operators\" CRAN Project](https://cran.r-project.org/web/packages/extraoperators/vignettes/logicals-vignette.html)\n\n", + "supporting": [], + "filters": [ + "rmarkdown/pagebreak.lua" + ], + "includes": { + "include-after-body": [ + "\n\n\n" + ] + }, + "engineDependencies": {}, + "preserve": {}, + "postProcess": true + } +} \ No newline at end of file diff --git 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/dev/null +++ b/docs/modules/Module06-DataSubset.html @@ -0,0 +1,3218 @@ + + + + + + + + + + + + + + + SISMID Module NUMBER Materials (2025) - Module 6: Get to Know Your Data and Subsetting + + + + + + + + + + + + + + + +
+
+ +
+

Module 6: Get to Know Your Data and Subsetting

+ +
+
+
+Amy Winter +
+
+
+
+Zane Billings +
+
+
+ +
+
+

Learning Objectives

+

After module 6, you should be able to…

+
    +
  • Use basic functions to get to know you data
    • +
  • Use three indexing approaches
    • +
  • Rely on indexing to extract part of an object (e.g., subset data) and to replace parts of an object (e.g., rename variables / columns)
    • +
  • Describe what logical operators are and how to use them
    • +
  • Use on the subset() function to subset data
    • +
+
+
+

Getting to know our data

+

The dim(), nrow(), and ncol() functions are good options to check the dimensions of your data before moving forward.

+

Let’s first read in the data from the previous module.

+
+
df <- read.csv(file = "data/serodata.csv") #relative path
+
+
+
dim(df) # rows, columns
+
+
[1] 651   5
+
+
nrow(df) # number of rows
+
+
[1] 651
+
+
ncol(df) # number of columns
+
+
[1] 5
+
+
+
+
+

Quick summary of data

+

The colnames(), str() and summary()functions from Base R are great functions to assess the data type and some summary statistics.

+
+
colnames(df)
+
+
[1] "observation_id"    "IgG_concentration" "age"              
+[4] "gender"            "slum"
+
+
str(df)
+
+
'data.frame':   651 obs. of  5 variables:
+ $ observation_id   : int  5772 8095 9784 9338 6369 6885 6252 8913 7332 6941 ...
+ $ IgG_concentration: num  0.318 3.437 0.3 143.236 0.448 ...
+ $ age              : int  2 4 4 4 1 4 4 NA 4 2 ...
+ $ gender           : chr  "Female" "Female" "Male" "Male" ...
+ $ slum             : chr  "Non slum" "Non slum" "Non slum" "Non slum" ...
+
+
summary(df)
+
+
 observation_id IgG_concentration       age            gender         
+ Min.   :5006   Min.   :  0.0054   Min.   : 1.000   Length:651        
+ 1st Qu.:6306   1st Qu.:  0.3000   1st Qu.: 3.000   Class :character  
+ Median :7495   Median :  1.6658   Median : 6.000   Mode  :character  
+ Mean   :7492   Mean   : 87.3683   Mean   : 6.606                     
+ 3rd Qu.:8749   3rd Qu.:141.4405   3rd Qu.:10.000                     
+ Max.   :9982   Max.   :916.4179   Max.   :15.000                     
+                NA's   :10         NA's   :9                          
+     slum          
+ Length:651        
+ Class :character  
+ Mode  :character  
+                   
+                   
+                   
+
+
+
+

Note, if you have a very large dataset with 15+ variables, summary() is not so efficient.

+
+
+

Description of data

+

This is data based on a simulated pathogen X IgG antibody serological survey. The rows represent individuals. Variables include IgG concentrations in IU/mL, age in years, gender, and residence based on slum characterization. We will use this dataset for lectures throughout the Workshop.

+
+
+

View the data as a whole dataframe

+

The View() function, one of the few Base R functions with a capital letter can be used to open a new tab in the Console and view the data as you would in excel.

+
+
View(df)
+
+ +
+
+

View the data as a whole dataframe

+

You can also open a new tab of the data by clicking on the data icon beside the object in the Environment window.

+ +
+
+

Indexing

+

R contains several constructs which allow access to individual elements or subsets through indexing operations. Indexing can be used both to extract part of an object and to replace parts of an object (or to add parts). There are three basic indexing syntax: [ ], [[ ]] and $.

+
+
x[i] #if x is a vector
+x[i, j] #if x is a matrix/data frame
+x[[i]] #if x is a list
+x$a #if x is a data frame or list
+x$"a" #if x is a data frame or list
+
+
+
+

Vectors and multi-dimensional objects

+

To index a vector, vector[i] select the ith element. To index a multi-dimensional objects such as a matrix, matrix[i, j] selects the element in row i and column j, where as in a three dimensional array[k, i, i, j] selects the element in matrix k, row i, and column j.

+

Let’s practice by first creating the same objects as we did in Module 1.

+
+
number.object <- 3
+character.object <- "blue"
+vector.object1 <- c(2,3,4,5)
+vector.object2 <- c("blue", "red", "yellow")
+matrix.object <- matrix(data=vector.object1, nrow=2, ncol=2, byrow=TRUE)
+
+

Here is a reminder of what these objects look like.

+
+
vector.object1
+
+
[1] 2 3 4 5
+
+
matrix.object
+
+
     [,1] [,2]
+[1,]    2    3
+[2,]    4    5
+
+
+

Finally, let’s use indexing to pull our elements of the objects.

+
+
vector.object1[2] #pulling the second element
+
+
[1] 3
+
+
matrix.object[1,2] #pulling the element in row 1 column 2
+
+
[1] 3
+
+
+
+
+

List objects

+

For lists, one generally uses list[[p]] to select any single element p.

+

Let’s practice by creating the same list as we did in Module 1.

+
+
list.object <- list(number.object, vector.object2, matrix.object)
+list.object
+
+
[[1]]
+[1] 3
+
+[[2]]
+[1] "blue"   "red"    "yellow"
+
+[[3]]
+     [,1] [,2]
+[1,]    2    3
+[2,]    4    5
+
+
+

Now we use indexing to pull out the 3rd element in the list.

+
+
list.object[[3]]
+
+
     [,1] [,2]
+[1,]    2    3
+[2,]    4    5
+
+
+
+
+

$ for indexing

+

$ allows only a literal character string or a symbol as the index.

+
+
df$IgG_concentration
+
+
  [1] 3.176895e-01 3.436823e+00 3.000000e-01 1.432363e+02 4.476534e-01
+  [6] 2.527076e-02 6.101083e-01 3.000000e-01 2.916968e+00 1.649819e+00
+ [11] 4.574007e+00 1.583904e+02           NA 1.065068e+02 1.113870e+02
+ [16] 4.144893e+01 3.000000e-01 2.527076e-01 8.159247e+01 1.825342e+02
+ [21] 4.244656e+01 1.193493e+02 3.000000e-01 3.000000e-01 9.025271e-01
+ [26] 3.501805e-01 3.000000e-01 1.227437e+00 1.702055e+02 3.000000e-01
+ [31] 4.801444e-01 2.527076e-02 3.000000e-01 5.776173e-02 4.801444e-01
+ [36] 3.826715e-01 3.000000e-01 4.048558e+02 3.000000e-01 5.451264e-01
+ [41] 3.000000e-01 5.590753e+01 2.202166e-01 1.709760e+02 1.227437e+00
+ [46] 4.567527e+02 4.838480e+01 1.227437e-01 1.877256e-01 3.000000e-01
+ [51] 3.501805e-01 3.339350e+00 3.000000e-01 5.451264e-01           NA
+ [56] 2.104693e+00           NA 3.826715e-01 3.926366e+01 1.129964e+00
+ [61] 3.501805e+00 7.542808e+01 4.800475e+01 1.000000e+00 4.068884e+01
+ [66] 3.000000e-01 4.377672e+01 1.193493e+02 6.977740e+01 1.373288e+02
+ [71] 1.642979e+02           NA 1.542808e+02 6.033058e-01 2.809917e-01
+ [76] 1.966942e+00 2.041322e+00 2.115702e+00 4.663043e+02 3.000000e-01
+ [81] 1.500796e+02 1.543790e+02 2.561983e-01 1.596338e+02 1.732484e+02
+ [86] 4.641304e+02 3.736364e+01 1.572452e+02 3.000000e-01 3.000000e-01
+ [91] 8.264463e-02 6.776859e-01 7.272727e-01 2.066116e-01 1.966942e+00
+ [96] 3.000000e-01 3.000000e-01 2.809917e-01 8.016529e-01 1.818182e-01
+[101] 1.818182e-01 8.264463e-02 3.422727e+01 8.743506e+00 3.000000e-01
+[106] 1.641720e+02 4.049587e-01 1.001592e+02 4.489130e+02 1.101911e+02
+[111] 4.440909e+01 1.288217e+02 2.840909e+01 1.003981e+02 8.512397e-01
+[116] 1.322314e-01 1.297521e+00 1.570248e-01 1.966942e+00 1.536624e+02
+[121] 3.000000e-01 3.000000e-01 1.074380e+00 1.099174e+00 3.057851e-01
+[126] 3.000000e-01 5.785124e-02 4.391304e+02 6.130435e+02 1.074380e-01
+[131] 7.125796e+01 4.222727e+01 1.620223e+02 3.750000e+01 1.534236e+02
+[136] 6.239130e+02 5.521739e+02 5.785124e-02 6.547945e-01 8.767123e-02
+[141] 3.000000e-01 2.849315e+00 3.835616e-02 2.849315e-01 4.649315e+00
+[146] 1.369863e-01 3.589041e-01 1.049315e+00 4.668998e+01 1.473510e+02
+[151] 4.589744e+01 2.109589e-01 1.741722e+02 2.496503e+01 1.850993e+02
+[156] 1.863014e-01 1.863014e-01 4.589744e+01 1.942881e+02 5.079646e+02
+[161] 8.767123e-01 2.750685e+00 1.503311e+02 3.000000e-01 3.095890e-01
+[166] 3.000000e-01 6.371681e+02 6.054795e-01 1.955298e+02 1.786424e+02
+[171] 1.120861e+02 1.331954e+02 2.159292e+02 5.628319e+02 1.900662e+02
+[176] 6.547945e-01 1.665753e+00 1.739238e+02 9.991722e+01 9.321192e+01
+[181] 8.767123e-02           NA 6.794521e-01 5.808219e-01 1.369863e-01
+[186] 2.060274e+00 1.610099e+02 4.082192e-01 8.273973e-01 4.601770e+02
+[191] 1.389073e+02 3.867133e+01 9.260274e-01 5.918874e+01 1.870861e+02
+[196] 4.328767e-01 6.301370e-02 3.000000e-01 1.548013e+02 5.819536e+01
+[201] 1.724338e+02 1.932401e+01 2.164420e+00 9.757412e-01 1.509434e-01
+[206] 1.509434e-01 7.766571e+01 4.319563e+01 1.752022e-01 3.094775e+01
+[211] 1.266846e-01 2.919806e+01 9.545455e+00 2.735115e+01 1.314841e+02
+[216] 3.643985e+01 1.498559e+02 9.363636e+00 2.479784e-01 5.390836e-02
+[221] 8.787062e-01 1.994609e-01 3.000000e-01 3.000000e-01 5.390836e-03
+[226] 4.177898e-01 3.000000e-01 2.479784e-01 2.964960e-02 2.964960e-01
+[231] 5.148248e+00 1.994609e-01 3.000000e-01 1.779539e+02 3.290210e+02
+[236] 3.000000e-01 1.809798e+02 4.905660e-01 1.266846e-01 1.543948e+02
+[241] 1.379683e+02 6.153846e+02 1.474784e+02 3.000000e-01 1.024259e+00
+[246] 4.444056e+02 3.000000e-01 2.504043e+00 3.000000e-01 3.000000e-01
+[251] 7.816712e-02 3.000000e-01 5.390836e-02 1.494236e+02 5.972622e+01
+[256] 6.361186e-01 1.837896e+02 1.320809e+02 1.571906e-01 1.520231e+02
+[261] 3.000000e-01 3.000000e-01 1.823699e+02 3.000000e-01 2.173913e+00
+[266] 2.142202e+01 3.000000e-01 3.408027e+00 4.155963e+01 9.698997e-02
+[271] 1.238532e+01 9.528926e+00 1.916185e+02 1.060201e+00 3.679104e+02
+[276] 4.288991e+01 9.971098e+01 3.000000e-01 1.208092e+02 3.000000e-01
+[281] 6.688963e-03 2.505017e+00 1.481605e+00 3.000000e-01 5.183946e-01
+[286] 3.000000e-01 1.872910e-01 3.678930e-01 3.000000e-01 4.529851e+02
+[291] 3.169725e+01 3.000000e-01 4.922018e+01 2.548507e+02 1.661850e+02
+[296] 9.164179e+02 3.678930e-01 1.236994e+02 6.705202e+01 3.834862e+01
+[301] 1.963211e+00 3.000000e-01 2.474916e-01 3.000000e-01 2.173913e-01
+[306] 8.193980e-01 2.444816e+00 3.000000e-01 1.571906e-01 1.849711e+02
+[311] 6.119403e+02 3.000000e-01 4.280936e-01 9.698997e-02 3.678930e-02
+[316] 4.832090e+02 1.390173e+02 3.000000e-01 6.555970e+02 1.526012e+02
+[321] 3.000000e-01 7.222222e-01 7.724426e+01 3.000000e-01 6.111111e-01
+[326] 1.555556e+00 3.055556e-01 1.500000e+00 1.470772e+02 1.694444e+00
+[331] 3.138298e+02 1.414405e+02 1.990605e+02 4.212766e+02 3.000000e-01
+[336] 3.000000e-01 6.478723e+02 3.000000e-01 2.222222e+00 3.000000e-01
+[341] 2.055556e+00 2.777778e-02 8.333333e-02 1.032359e+02 1.611111e+00
+[346] 8.333333e-02 2.333333e+00 5.755319e+02 1.686848e+02 1.111111e-01
+[351] 3.000000e-01 8.372340e+02 3.000000e-01 3.784504e+01 3.819149e+02
+[356] 5.555556e-02 3.000000e+02 1.855950e+02 1.944444e-01 3.000000e-01
+[361] 5.555556e-02 1.138889e+00 4.254237e+01 3.000000e-01 3.000000e-01
+[366] 3.000000e-01 3.000000e-01 3.138298e+02 1.235908e+02 4.159574e+02
+[371] 3.009685e+01 1.567850e+02 1.367432e+02 3.731235e+01 9.164927e+01
+[376] 2.936170e+02 8.820459e+01 1.035491e+02 7.379958e+01 3.000000e-01
+[381] 1.718750e+02 2.128527e+00 1.253918e+00 2.382445e-01 4.639498e-01
+[386] 1.253918e-01 1.253918e-01 3.000000e-01 1.000000e+00 1.570043e+02
+[391] 4.344086e+02 2.184953e+00 1.507837e+00 3.228840e-01 4.588024e+01
+[396] 1.660560e+02 3.000000e-01 3.043011e+02 2.612903e+02 1.621767e+02
+[401] 3.228840e-01 4.639498e-01 2.495298e+00 3.257053e+00 3.793103e-01
+[406]           NA 6.896552e-02 3.000000e-01 1.423197e+00 3.000000e-01
+[411] 3.000000e-01 1.786638e+02 3.279570e+02           NA 1.903017e+02
+[416] 1.654095e+02 4.639498e-01 1.815733e+02 1.366771e+00 1.536050e-01
+[421] 1.306587e+01 2.129032e+02 1.925647e+02 3.000000e-01 1.028213e+00
+[426] 3.793103e-01 8.025078e-01 4.860215e+02 3.000000e-01 2.100313e-01
+[431] 2.767665e+01 1.592476e+00 9.717868e-02 1.028213e+00 3.793103e-01
+[436] 1.292026e+02 4.425150e+01 3.193548e+02 1.860991e+02 6.614420e-01
+[441] 5.203762e-01 1.330819e+02 1.673491e+02 3.000000e-01 1.117457e+02
+[446] 3.045509e+01 3.000000e-01 8.280255e-02 3.000000e-01 1.200637e+00
+[451] 1.687898e-01 7.367273e+02 8.280255e-02 5.127389e-01 1.974522e-01
+[456] 7.993631e-01 3.000000e-01 3.298182e+02 9.736842e+01 3.000000e-01
+[461] 3.000000e-01 4.214545e+02 3.000000e-01 2.578182e+02 2.261147e-01
+[466] 3.000000e-01 1.883901e+02 9.458204e+01 3.000000e-01 3.000000e-01
+[471] 7.707006e-01 5.032727e+02 1.544586e+00 1.431115e+02 3.000000e-01
+[476] 1.458599e+00 1.247678e+02           NA 4.334545e+02 3.000000e-01
+[481] 6.156364e+02 9.574303e+01 1.928019e+02 1.888545e+02 1.598297e+02
+[486] 5.127389e-01 1.171053e+02           NA 2.547771e-02 1.707430e+02
+[491] 3.000000e-01 1.869969e+02 4.731481e+01 1.988390e+02 3.000000e-01
+[496] 8.808050e+01 2.003185e+00 3.000000e-01 3.509259e+01 9.365325e+01
+[501] 3.000000e-01 3.736111e+01 1.674923e+02 8.808050e+01 1.656347e+02
+[506] 3.722222e+01 6.756364e+02 3.000000e-01 1.698142e+02 1.628483e+02
+[511] 5.985130e-01 1.903346e+00 3.000000e-01 3.000000e-01 8.996283e-01
+[516] 3.977695e-01 3.000000e-01 3.000000e-01 3.000000e-01 3.000000e-01
+[521] 7.446809e+02 6.095745e+02 1.427445e+02 3.000000e-01 2.973978e-02
+[526] 3.977695e-01 4.095745e+02 4.595745e+02 3.000000e-01 1.976341e+02
+[531] 3.776596e+02 1.777603e+02 4.312268e-01 6.765957e+02 7.978723e+02
+[536] 9.665427e-02 1.879338e+02 4.358670e+01 3.000000e-01 3.000000e-01
+[541] 2.638955e+01 3.180523e+01 1.746845e+02 1.876972e+02 1.044164e+02
+[546] 1.202681e+02 1.630915e+02 1.276025e+02 8.880126e+01 3.563830e+02
+[551] 2.212766e+02 1.969121e+01 3.755319e+02 1.214511e+02 1.034700e+02
+[556] 3.000000e-01 3.643123e-01 6.319703e-02 3.000000e-01 3.000000e-01
+[561] 3.000000e-01 3.000000e-01 3.000000e-01 3.000000e-01 3.000000e-01
+[566] 3.000000e-01 1.664038e+02 2.946809e+02 4.391924e+01 1.874606e+02
+[571] 1.143533e+02 1.600158e+02 1.635688e-01 8.809148e+01 1.337539e+02
+[576] 1.985804e+02 1.578864e+02 3.000000e-01 3.000000e-01 1.953642e-01
+[581] 1.119205e+00 2.523636e+02 3.000000e-01 4.844371e+00 3.000000e-01
+[586] 1.492553e+02 1.993617e+02 2.847682e-01 3.145695e-01 3.000000e-01
+[591] 3.406429e+01 6.595745e+01 3.000000e-01 2.174545e+02           NA
+[596] 5.957447e+01 7.236364e+02 3.000000e-01 3.000000e-01 3.000000e-01
+[601] 2.676364e+02 1.891489e+02 3.036364e+02 3.000000e-01 3.000000e-01
+[606] 3.000000e-01 3.000000e-01 3.000000e-01 1.447020e+00 2.130909e+02
+[611] 1.357616e-01 3.000000e-01 3.000000e-01 5.534545e+02 1.891489e+02
+[616] 7.202128e+01 3.250287e+01 1.655629e-02 3.123636e+02 3.000000e-01
+[621] 7.138298e+01 3.000000e-01 6.946809e+01 4.012629e+01 1.629787e+02
+[626] 1.508511e+02 1.655629e-02 3.000000e-01 4.635762e-02 3.000000e-01
+[631] 3.000000e-01 3.000000e-01 1.942553e+02 3.690909e+02 3.000000e-01
+[636] 3.000000e-01 2.847682e+00 1.435106e+02 3.000000e-01 4.752009e+01
+[641] 2.621125e+01 1.055319e+02 3.000000e-01 1.149007e+00 2.927273e+02
+[646] 3.000000e-01 3.000000e-01 4.839265e+01 3.000000e-01 3.000000e-01
+[651] 2.251656e-01
+
+
+

Note, if you have spaces in your variable name, you will need to use back ticks variable name after the $. This is a good reason to not create variables / column names with spaces.

+
+
+

$ for indexing with lists

+

List elements can be named

+
+
list.object.named <- list(
+  emory = number.object,
+  uga = vector.object2,
+  gsu = matrix.object
+)
+list.object.named
+
+
$emory
+[1] 3
+
+$uga
+[1] "blue"   "red"    "yellow"
+
+$gsu
+     [,1] [,2]
+[1,]    2    3
+[2,]    4    5
+
+
+

If list elements are named, than you can reference data from list using $ or using double square brackets, [[ ]]

+
+
list.object.named$uga
+
+
[1] "blue"   "red"    "yellow"
+
+
list.object.named[["uga"]]
+
+
[1] "blue"   "red"    "yellow"
+
+
+
+
+

Using indexing to rename columns

+

As mentioned above, indexing can be used both to extract part of an object and to replace parts of an object (or to add parts).

+
+
colnames(df) # just prints
+
+
[1] "observation_id"    "IgG_concentration" "age"              
+[4] "gender"            "slum"
+
+
colnames(df)[1:2] <- c("IgG_concentration_mIU/mL", "age_year") # reassigns
+colnames(df)
+
+
[1] "IgG_concentration_mIU/mL" "age_year"                
+[3] "age"                      "gender"                  
+[5] "slum"
+
+
colnames(df)[1:2] <- c("IgG_concentration", "age") #reset
+
+
+
+

Using indexing to subset by columns

+

We can also subset a data frames and matrices (2-dimensional objects) using the bracket [ row , column ]. We can subset by columns and pull the x column using the index of the column or the column name.

+

For example, here I am pulling the 3nd column, which has the variable name age

+
+
df[ , "age"] #same as df[ , 3]
+
+
  [1] 3.176895e-01 3.436823e+00 3.000000e-01 1.432363e+02 4.476534e-01
+  [6] 2.527076e-02 6.101083e-01 3.000000e-01 2.916968e+00 1.649819e+00
+ [11] 4.574007e+00 1.583904e+02           NA 1.065068e+02 1.113870e+02
+ [16] 4.144893e+01 3.000000e-01 2.527076e-01 8.159247e+01 1.825342e+02
+ [21] 4.244656e+01 1.193493e+02 3.000000e-01 3.000000e-01 9.025271e-01
+ [26] 3.501805e-01 3.000000e-01 1.227437e+00 1.702055e+02 3.000000e-01
+ [31] 4.801444e-01 2.527076e-02 3.000000e-01 5.776173e-02 4.801444e-01
+ [36] 3.826715e-01 3.000000e-01 4.048558e+02 3.000000e-01 5.451264e-01
+ [41] 3.000000e-01 5.590753e+01 2.202166e-01 1.709760e+02 1.227437e+00
+ [46] 4.567527e+02 4.838480e+01 1.227437e-01 1.877256e-01 3.000000e-01
+ [51] 3.501805e-01 3.339350e+00 3.000000e-01 5.451264e-01           NA
+ [56] 2.104693e+00           NA 3.826715e-01 3.926366e+01 1.129964e+00
+ [61] 3.501805e+00 7.542808e+01 4.800475e+01 1.000000e+00 4.068884e+01
+ [66] 3.000000e-01 4.377672e+01 1.193493e+02 6.977740e+01 1.373288e+02
+ [71] 1.642979e+02           NA 1.542808e+02 6.033058e-01 2.809917e-01
+ [76] 1.966942e+00 2.041322e+00 2.115702e+00 4.663043e+02 3.000000e-01
+ [81] 1.500796e+02 1.543790e+02 2.561983e-01 1.596338e+02 1.732484e+02
+ [86] 4.641304e+02 3.736364e+01 1.572452e+02 3.000000e-01 3.000000e-01
+ [91] 8.264463e-02 6.776859e-01 7.272727e-01 2.066116e-01 1.966942e+00
+ [96] 3.000000e-01 3.000000e-01 2.809917e-01 8.016529e-01 1.818182e-01
+[101] 1.818182e-01 8.264463e-02 3.422727e+01 8.743506e+00 3.000000e-01
+[106] 1.641720e+02 4.049587e-01 1.001592e+02 4.489130e+02 1.101911e+02
+[111] 4.440909e+01 1.288217e+02 2.840909e+01 1.003981e+02 8.512397e-01
+[116] 1.322314e-01 1.297521e+00 1.570248e-01 1.966942e+00 1.536624e+02
+[121] 3.000000e-01 3.000000e-01 1.074380e+00 1.099174e+00 3.057851e-01
+[126] 3.000000e-01 5.785124e-02 4.391304e+02 6.130435e+02 1.074380e-01
+[131] 7.125796e+01 4.222727e+01 1.620223e+02 3.750000e+01 1.534236e+02
+[136] 6.239130e+02 5.521739e+02 5.785124e-02 6.547945e-01 8.767123e-02
+[141] 3.000000e-01 2.849315e+00 3.835616e-02 2.849315e-01 4.649315e+00
+[146] 1.369863e-01 3.589041e-01 1.049315e+00 4.668998e+01 1.473510e+02
+[151] 4.589744e+01 2.109589e-01 1.741722e+02 2.496503e+01 1.850993e+02
+[156] 1.863014e-01 1.863014e-01 4.589744e+01 1.942881e+02 5.079646e+02
+[161] 8.767123e-01 2.750685e+00 1.503311e+02 3.000000e-01 3.095890e-01
+[166] 3.000000e-01 6.371681e+02 6.054795e-01 1.955298e+02 1.786424e+02
+[171] 1.120861e+02 1.331954e+02 2.159292e+02 5.628319e+02 1.900662e+02
+[176] 6.547945e-01 1.665753e+00 1.739238e+02 9.991722e+01 9.321192e+01
+[181] 8.767123e-02           NA 6.794521e-01 5.808219e-01 1.369863e-01
+[186] 2.060274e+00 1.610099e+02 4.082192e-01 8.273973e-01 4.601770e+02
+[191] 1.389073e+02 3.867133e+01 9.260274e-01 5.918874e+01 1.870861e+02
+[196] 4.328767e-01 6.301370e-02 3.000000e-01 1.548013e+02 5.819536e+01
+[201] 1.724338e+02 1.932401e+01 2.164420e+00 9.757412e-01 1.509434e-01
+[206] 1.509434e-01 7.766571e+01 4.319563e+01 1.752022e-01 3.094775e+01
+[211] 1.266846e-01 2.919806e+01 9.545455e+00 2.735115e+01 1.314841e+02
+[216] 3.643985e+01 1.498559e+02 9.363636e+00 2.479784e-01 5.390836e-02
+[221] 8.787062e-01 1.994609e-01 3.000000e-01 3.000000e-01 5.390836e-03
+[226] 4.177898e-01 3.000000e-01 2.479784e-01 2.964960e-02 2.964960e-01
+[231] 5.148248e+00 1.994609e-01 3.000000e-01 1.779539e+02 3.290210e+02
+[236] 3.000000e-01 1.809798e+02 4.905660e-01 1.266846e-01 1.543948e+02
+[241] 1.379683e+02 6.153846e+02 1.474784e+02 3.000000e-01 1.024259e+00
+[246] 4.444056e+02 3.000000e-01 2.504043e+00 3.000000e-01 3.000000e-01
+[251] 7.816712e-02 3.000000e-01 5.390836e-02 1.494236e+02 5.972622e+01
+[256] 6.361186e-01 1.837896e+02 1.320809e+02 1.571906e-01 1.520231e+02
+[261] 3.000000e-01 3.000000e-01 1.823699e+02 3.000000e-01 2.173913e+00
+[266] 2.142202e+01 3.000000e-01 3.408027e+00 4.155963e+01 9.698997e-02
+[271] 1.238532e+01 9.528926e+00 1.916185e+02 1.060201e+00 3.679104e+02
+[276] 4.288991e+01 9.971098e+01 3.000000e-01 1.208092e+02 3.000000e-01
+[281] 6.688963e-03 2.505017e+00 1.481605e+00 3.000000e-01 5.183946e-01
+[286] 3.000000e-01 1.872910e-01 3.678930e-01 3.000000e-01 4.529851e+02
+[291] 3.169725e+01 3.000000e-01 4.922018e+01 2.548507e+02 1.661850e+02
+[296] 9.164179e+02 3.678930e-01 1.236994e+02 6.705202e+01 3.834862e+01
+[301] 1.963211e+00 3.000000e-01 2.474916e-01 3.000000e-01 2.173913e-01
+[306] 8.193980e-01 2.444816e+00 3.000000e-01 1.571906e-01 1.849711e+02
+[311] 6.119403e+02 3.000000e-01 4.280936e-01 9.698997e-02 3.678930e-02
+[316] 4.832090e+02 1.390173e+02 3.000000e-01 6.555970e+02 1.526012e+02
+[321] 3.000000e-01 7.222222e-01 7.724426e+01 3.000000e-01 6.111111e-01
+[326] 1.555556e+00 3.055556e-01 1.500000e+00 1.470772e+02 1.694444e+00
+[331] 3.138298e+02 1.414405e+02 1.990605e+02 4.212766e+02 3.000000e-01
+[336] 3.000000e-01 6.478723e+02 3.000000e-01 2.222222e+00 3.000000e-01
+[341] 2.055556e+00 2.777778e-02 8.333333e-02 1.032359e+02 1.611111e+00
+[346] 8.333333e-02 2.333333e+00 5.755319e+02 1.686848e+02 1.111111e-01
+[351] 3.000000e-01 8.372340e+02 3.000000e-01 3.784504e+01 3.819149e+02
+[356] 5.555556e-02 3.000000e+02 1.855950e+02 1.944444e-01 3.000000e-01
+[361] 5.555556e-02 1.138889e+00 4.254237e+01 3.000000e-01 3.000000e-01
+[366] 3.000000e-01 3.000000e-01 3.138298e+02 1.235908e+02 4.159574e+02
+[371] 3.009685e+01 1.567850e+02 1.367432e+02 3.731235e+01 9.164927e+01
+[376] 2.936170e+02 8.820459e+01 1.035491e+02 7.379958e+01 3.000000e-01
+[381] 1.718750e+02 2.128527e+00 1.253918e+00 2.382445e-01 4.639498e-01
+[386] 1.253918e-01 1.253918e-01 3.000000e-01 1.000000e+00 1.570043e+02
+[391] 4.344086e+02 2.184953e+00 1.507837e+00 3.228840e-01 4.588024e+01
+[396] 1.660560e+02 3.000000e-01 3.043011e+02 2.612903e+02 1.621767e+02
+[401] 3.228840e-01 4.639498e-01 2.495298e+00 3.257053e+00 3.793103e-01
+[406]           NA 6.896552e-02 3.000000e-01 1.423197e+00 3.000000e-01
+[411] 3.000000e-01 1.786638e+02 3.279570e+02           NA 1.903017e+02
+[416] 1.654095e+02 4.639498e-01 1.815733e+02 1.366771e+00 1.536050e-01
+[421] 1.306587e+01 2.129032e+02 1.925647e+02 3.000000e-01 1.028213e+00
+[426] 3.793103e-01 8.025078e-01 4.860215e+02 3.000000e-01 2.100313e-01
+[431] 2.767665e+01 1.592476e+00 9.717868e-02 1.028213e+00 3.793103e-01
+[436] 1.292026e+02 4.425150e+01 3.193548e+02 1.860991e+02 6.614420e-01
+[441] 5.203762e-01 1.330819e+02 1.673491e+02 3.000000e-01 1.117457e+02
+[446] 3.045509e+01 3.000000e-01 8.280255e-02 3.000000e-01 1.200637e+00
+[451] 1.687898e-01 7.367273e+02 8.280255e-02 5.127389e-01 1.974522e-01
+[456] 7.993631e-01 3.000000e-01 3.298182e+02 9.736842e+01 3.000000e-01
+[461] 3.000000e-01 4.214545e+02 3.000000e-01 2.578182e+02 2.261147e-01
+[466] 3.000000e-01 1.883901e+02 9.458204e+01 3.000000e-01 3.000000e-01
+[471] 7.707006e-01 5.032727e+02 1.544586e+00 1.431115e+02 3.000000e-01
+[476] 1.458599e+00 1.247678e+02           NA 4.334545e+02 3.000000e-01
+[481] 6.156364e+02 9.574303e+01 1.928019e+02 1.888545e+02 1.598297e+02
+[486] 5.127389e-01 1.171053e+02           NA 2.547771e-02 1.707430e+02
+[491] 3.000000e-01 1.869969e+02 4.731481e+01 1.988390e+02 3.000000e-01
+[496] 8.808050e+01 2.003185e+00 3.000000e-01 3.509259e+01 9.365325e+01
+[501] 3.000000e-01 3.736111e+01 1.674923e+02 8.808050e+01 1.656347e+02
+[506] 3.722222e+01 6.756364e+02 3.000000e-01 1.698142e+02 1.628483e+02
+[511] 5.985130e-01 1.903346e+00 3.000000e-01 3.000000e-01 8.996283e-01
+[516] 3.977695e-01 3.000000e-01 3.000000e-01 3.000000e-01 3.000000e-01
+[521] 7.446809e+02 6.095745e+02 1.427445e+02 3.000000e-01 2.973978e-02
+[526] 3.977695e-01 4.095745e+02 4.595745e+02 3.000000e-01 1.976341e+02
+[531] 3.776596e+02 1.777603e+02 4.312268e-01 6.765957e+02 7.978723e+02
+[536] 9.665427e-02 1.879338e+02 4.358670e+01 3.000000e-01 3.000000e-01
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+[646] 3.000000e-01 3.000000e-01 4.839265e+01 3.000000e-01 3.000000e-01
+[651] 2.251656e-01
+
+
+

We can select multiple columns using multiple column names:

+
+
df[, c("age", "gender")] #same as df[ , c(3,4)]
+
+
             age gender
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+2   3.436823e+00 Female
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+
+
+

We can remove select columns using indexing as well, OR by simply changing the column to NULL

+
+
df[, -5] #remove column 5, "slum" variable
+
+
    IgG_concentration          age age.1 gender
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+388              9224 3.000000e-01     2 Female
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+390              7176 1.570043e+02    11   Male
+391              6102 4.344086e+02    10 Female
+392              7817 2.184953e+00     6   Male
+393              9719 1.507837e+00    12 Female
+394              9740 3.228840e-01    10 Female
+395              9528 4.588024e+01     8   Male
+396              7142 1.660560e+02     8   Male
+397              5689 3.000000e-01    13   Male
+398              5439 3.043011e+02    10   Male
+399              6718 2.612903e+02    13 Female
+400              6569 1.621767e+02    10   Male
+401              9444 3.228840e-01     2   Male
+402              6964 4.639498e-01     4 Female
+403              6420 2.495298e+00     3 Female
+404              9189 3.257053e+00     2 Female
+405              9368 3.793103e-01     1 Female
+406              6360           NA     3   Male
+407              8196 6.896552e-02     3 Female
+408              8297 3.000000e-01     4   Male
+409              6674 1.423197e+00     5 Female
+410              5269 3.000000e-01     5 Female
+411              6599 3.000000e-01     1 Female
+412              7713 1.786638e+02    11   Male
+413              8644 3.279570e+02     6   Male
+414              9680           NA    14 Female
+415              6305 1.903017e+02     8   Male
+416              8493 1.654095e+02     8 Female
+417              5297 4.639498e-01     9 Female
+418              7723 1.815733e+02     7   Male
+419              7510 1.366771e+00     6   Male
+420              5102 1.536050e-01    12 Female
+421              7816 1.306587e+01     8   Male
+422              5143 2.129032e+02    11 Female
+423              7414 1.925647e+02    14   Male
+424              5127 3.000000e-01     3 Female
+425              5830 1.028213e+00     1 Female
+426              8929 3.793103e-01     5 Female
+427              7993 8.025078e-01     2 Female
+428              8092 4.860215e+02     3 Female
+429              9750 3.000000e-01     4 Female
+430              6660 2.100313e-01     2   Male
+431              8054 2.767665e+01     3 Female
+432              6086 1.592476e+00     4   Male
+433              6878 9.717868e-02     1 Female
+434              8125 1.028213e+00     7 Female
+435              9500 3.793103e-01    10   Male
+436              8105 1.292026e+02    11   Male
+437              9593 4.425150e+01     7 Female
+438              5202 3.193548e+02    10 Female
+439              7207 1.860991e+02    14 Female
+440              5518 6.614420e-01     7 Female
+441              9820 5.203762e-01    11   Male
+442              6958 1.330819e+02    12   Male
+443              9445 1.673491e+02    10 Female
+444              8774 3.000000e-01     6   Male
+445              9614 1.117457e+02    13   Male
+446              9810 3.045509e+01     8 Female
+447              7271 3.000000e-01     2   Male
+448              8031 8.280255e-02     3 Female
+449              7232 3.000000e-01     1 Female
+450              7452 1.200637e+00     2 Female
+451              5921 1.687898e-01    NA   Male
+452              8136 7.367273e+02    NA Female
+453              6605 8.280255e-02     4   Male
+454              5125 5.127389e-01     4   Male
+455              5911 1.974522e-01     1   Male
+456              9644 7.993631e-01     2 Female
+457              5760 3.000000e-01     2   Male
+458              7055 3.298182e+02    12   Male
+459              9064 9.736842e+01    12 Female
+460              6925 3.000000e-01     8 Female
+461              7757 3.000000e-01    14 Female
+462              8527 4.214545e+02    13 Female
+463              8521 3.000000e-01     6   Male
+464              6260 2.578182e+02    11 Female
+465              9578 2.261147e-01    11   Male
+466              9570 3.000000e-01    10 Female
+467              6246 1.883901e+02    12   Male
+468              9622 9.458204e+01    14 Female
+469              7661 3.000000e-01    11 Female
+470              9374 3.000000e-01     1   Male
+471              8446 7.707006e-01     2 Female
+472              8332 5.032727e+02     3   Male
+473              8008 1.544586e+00     3 Female
+474              9365 1.431115e+02     5 Female
+475              9819 3.000000e-01     3   Male
+476              5173 1.458599e+00     1   Male
+477              6722 1.247678e+02     4 Female
+478              7668           NA     4 Female
+479              8980 4.334545e+02     4   Male
+480              5204 3.000000e-01     2 Female
+481              6412 6.156364e+02     5 Female
+482              6404 9.574303e+01     7   Male
+483              5693 1.928019e+02     8   Male
+484              8100 1.888545e+02    10   Male
+485              9760 1.598297e+02     6 Female
+486              6377 5.127389e-01     7   Male
+487              6012 1.171053e+02    10 Female
+488              6224           NA     6   Male
+489              6561 2.547771e-02     6 Female
+490              8475 1.707430e+02    15 Female
+491              6629 3.000000e-01     5   Male
+492              7200 1.869969e+02     3   Male
+493              9453 4.731481e+01     5   Male
+494              6449 1.988390e+02     3 Female
+495              9452 3.000000e-01     5   Male
+496              7162 8.808050e+01     5   Male
+497              8962 2.003185e+00     1 Female
+498              7328 3.000000e-01     1   Male
+499              9097 3.509259e+01     7 Female
+500              9131 9.365325e+01    14 Female
+501              7280 3.000000e-01     9   Male
+502              5783 3.736111e+01    10 Female
+503              9895 1.674923e+02    10 Female
+504              7986 8.808050e+01    11   Male
+505              7146 1.656347e+02    11 Female
+506              8671 3.722222e+01    12 Female
+507              5273 6.756364e+02    11 Female
+508              5063 3.000000e-01    12   Male
+509              6729 1.698142e+02    12   Male
+510              9085 1.628483e+02    10 Female
+511              9929 5.985130e-01     1   Male
+512              8479 1.903346e+00     2 Female
+513              7395 3.000000e-01     4   Male
+514              6374 3.000000e-01     2   Male
+515              7878 8.996283e-01     3   Male
+516              9603 3.977695e-01     3 Female
+517              7994 3.000000e-01     2   Male
+518              5277 3.000000e-01     4   Male
+519              5054 3.000000e-01     3   Male
+520              5440 3.000000e-01     1 Female
+521              6551 7.446809e+02     4   Male
+522              5281 6.095745e+02    12 Female
+523              7145 1.427445e+02     6   Male
+524              5275 3.000000e-01     7 Female
+525              9542 2.973978e-02     7   Male
+526              9371 3.977695e-01    13 Female
+527              5598 4.095745e+02     8 Female
+528              7148 4.595745e+02     7   Male
+529              5624 3.000000e-01     8 Female
+530              6998 1.976341e+02     8 Female
+531              9286 3.776596e+02    11 Female
+532              7589 1.777603e+02    14 Female
+533              7095 4.312268e-01     3   Male
+534              5455 6.765957e+02     2 Female
+535              6257 7.978723e+02     2   Male
+536              8627 9.665427e-02     3   Male
+537              9786 1.879338e+02     2   Male
+538              8176 4.358670e+01     2 Female
+539              9198 3.000000e-01     3 Female
+540              6586 3.000000e-01     2   Male
+541              8850 2.638955e+01     5   Male
+542              9560 3.180523e+01    10 Female
+543              7144 1.746845e+02    14   Male
+544              8230 1.876972e+02     9   Male
+545              7559 1.044164e+02     6   Male
+546              5312 1.202681e+02     7   Male
+547              6560 1.630915e+02    14 Female
+548              6091 1.276025e+02     7 Female
+549              5578 8.880126e+01     7   Male
+550              5837 3.563830e+02     9   Male
+551              8347 2.212766e+02    14   Male
+552              6453 1.969121e+01    10 Female
+553              5758 3.755319e+02    13 Female
+554              5569 1.214511e+02     5   Male
+555              8766 1.034700e+02     4 Female
+556              8002 3.000000e-01     4 Female
+557              7839 3.643123e-01     5 Female
+558              5434 6.319703e-02     4 Female
+559              7636 3.000000e-01     4   Male
+560              6164 3.000000e-01     4   Male
+561              9243 3.000000e-01     3 Female
+562              5872 3.000000e-01     1 Female
+563              8079 3.000000e-01     4   Male
+564              9762 3.000000e-01     1   Male
+565              9476 3.000000e-01     1 Female
+566              8345 3.000000e-01     7   Male
+567              8128 1.664038e+02    13 Female
+568              7956 2.946809e+02    10 Female
+569              8677 4.391924e+01    14   Male
+570              5881 1.874606e+02    12 Female
+571              7498 1.143533e+02    14   Male
+572              8134 1.600158e+02     8   Male
+573              7748 1.635688e-01     7   Male
+574              7990 8.809148e+01    11 Female
+575              6184 1.337539e+02     8   Male
+576              6339 1.985804e+02    12   Male
+577              5113 1.578864e+02     9 Female
+578              9449 3.000000e-01     5 Female
+579              8110 3.000000e-01     4   Male
+580              9307 1.953642e-01     3 Female
+581              5555 1.119205e+00     2   Male
+582              9152 2.523636e+02     2   Male
+583              7969 3.000000e-01     3   Male
+584              6116 4.844371e+00     4 Female
+585              8294 3.000000e-01     4   Male
+586              8938 1.492553e+02     4 Female
+587              9539 1.993617e+02     5   Male
+588              9470 2.847682e-01     3 Female
+589              6677 3.145695e-01     6 Female
+590              8752 3.000000e-01     3   Male
+591              5574 3.406429e+01    11 Female
+592              5989 6.595745e+01    11   Male
+593              9813 3.000000e-01     7   Male
+594              6150 2.174545e+02     8   Male
+595              5730           NA     6 Female
+596              8038 5.957447e+01    10 Female
+597              5964 7.236364e+02     8 Female
+598              9043 3.000000e-01     8   Male
+599              5095 3.000000e-01     9 Female
+600              8922 3.000000e-01     8   Male
+601              5469 2.676364e+02    13   Male
+602              6726 1.891489e+02    11   Male
+603              7495 3.036364e+02     8 Female
+604              8159 3.000000e-01     2 Female
+605              6709 3.000000e-01     4   Male
+606              5855 3.000000e-01     2   Male
+607              6058 3.000000e-01     2 Female
+608              7292 3.000000e-01     4   Male
+609              6437 1.447020e+00     2   Male
+610              9326 2.130909e+02     4 Female
+611              8222 1.357616e-01     2 Female
+612              6789 3.000000e-01     4 Female
+613              6348 3.000000e-01     1 Female
+614              5958 5.534545e+02     4 Female
+615              9211 1.891489e+02    12 Female
+616              9450 7.202128e+01     7 Female
+617              6540 3.250287e+01    11   Male
+618              8796 1.655629e-02     6   Male
+619              7971 3.123636e+02     8   Male
+620              7549 3.000000e-01    14   Male
+621              9799 7.138298e+01    11   Male
+622              7013 3.000000e-01     7 Female
+623              5599 6.946809e+01    14 Female
+624              8601 4.012629e+01     6   Male
+625              7383 1.629787e+02    13 Female
+626              6656 1.508511e+02    13 Female
+627              5641 1.655629e-02     3   Male
+628              6222 3.000000e-01     1   Male
+629              7674 4.635762e-02     3   Male
+630              5293 3.000000e-01     1 Female
+631              6715 3.000000e-01     1 Female
+632              7057 3.000000e-01     2   Male
+633              7072 1.942553e+02     4   Male
+634              6380 3.690909e+02     4   Male
+635              6762 3.000000e-01     2 Female
+636              5799 3.000000e-01     4 Female
+637              6681 2.847682e+00     5   Male
+638              8755 1.435106e+02     3 Female
+639              6896 3.000000e-01     3   Male
+640              5945 4.752009e+01     6 Female
+641              5035 2.621125e+01    11 Female
+642              6776 1.055319e+02     9 Female
+643              7863 3.000000e-01     7 Female
+644              9836 1.149007e+00     8   Male
+645              7860 2.927273e+02    NA Female
+646              5248 3.000000e-01     8 Female
+647              5677 3.000000e-01    14 Female
+648              9576 4.839265e+01    10   Male
+649              5824 3.000000e-01    10   Male
+650              9184 3.000000e-01    11 Female
+651              5397 2.251656e-01    13 Female
+
+
+
+
df$slum <- NULL # this is the same as above
+
+

We can also grab the age column using the $ operator.

+
+
df$age
+
+
  [1] 3.176895e-01 3.436823e+00 3.000000e-01 1.432363e+02 4.476534e-01
+  [6] 2.527076e-02 6.101083e-01 3.000000e-01 2.916968e+00 1.649819e+00
+ [11] 4.574007e+00 1.583904e+02           NA 1.065068e+02 1.113870e+02
+ [16] 4.144893e+01 3.000000e-01 2.527076e-01 8.159247e+01 1.825342e+02
+ [21] 4.244656e+01 1.193493e+02 3.000000e-01 3.000000e-01 9.025271e-01
+ [26] 3.501805e-01 3.000000e-01 1.227437e+00 1.702055e+02 3.000000e-01
+ [31] 4.801444e-01 2.527076e-02 3.000000e-01 5.776173e-02 4.801444e-01
+ [36] 3.826715e-01 3.000000e-01 4.048558e+02 3.000000e-01 5.451264e-01
+ [41] 3.000000e-01 5.590753e+01 2.202166e-01 1.709760e+02 1.227437e+00
+ [46] 4.567527e+02 4.838480e+01 1.227437e-01 1.877256e-01 3.000000e-01
+ [51] 3.501805e-01 3.339350e+00 3.000000e-01 5.451264e-01           NA
+ [56] 2.104693e+00           NA 3.826715e-01 3.926366e+01 1.129964e+00
+ [61] 3.501805e+00 7.542808e+01 4.800475e+01 1.000000e+00 4.068884e+01
+ [66] 3.000000e-01 4.377672e+01 1.193493e+02 6.977740e+01 1.373288e+02
+ [71] 1.642979e+02           NA 1.542808e+02 6.033058e-01 2.809917e-01
+ [76] 1.966942e+00 2.041322e+00 2.115702e+00 4.663043e+02 3.000000e-01
+ [81] 1.500796e+02 1.543790e+02 2.561983e-01 1.596338e+02 1.732484e+02
+ [86] 4.641304e+02 3.736364e+01 1.572452e+02 3.000000e-01 3.000000e-01
+ [91] 8.264463e-02 6.776859e-01 7.272727e-01 2.066116e-01 1.966942e+00
+ [96] 3.000000e-01 3.000000e-01 2.809917e-01 8.016529e-01 1.818182e-01
+[101] 1.818182e-01 8.264463e-02 3.422727e+01 8.743506e+00 3.000000e-01
+[106] 1.641720e+02 4.049587e-01 1.001592e+02 4.489130e+02 1.101911e+02
+[111] 4.440909e+01 1.288217e+02 2.840909e+01 1.003981e+02 8.512397e-01
+[116] 1.322314e-01 1.297521e+00 1.570248e-01 1.966942e+00 1.536624e+02
+[121] 3.000000e-01 3.000000e-01 1.074380e+00 1.099174e+00 3.057851e-01
+[126] 3.000000e-01 5.785124e-02 4.391304e+02 6.130435e+02 1.074380e-01
+[131] 7.125796e+01 4.222727e+01 1.620223e+02 3.750000e+01 1.534236e+02
+[136] 6.239130e+02 5.521739e+02 5.785124e-02 6.547945e-01 8.767123e-02
+[141] 3.000000e-01 2.849315e+00 3.835616e-02 2.849315e-01 4.649315e+00
+[146] 1.369863e-01 3.589041e-01 1.049315e+00 4.668998e+01 1.473510e+02
+[151] 4.589744e+01 2.109589e-01 1.741722e+02 2.496503e+01 1.850993e+02
+[156] 1.863014e-01 1.863014e-01 4.589744e+01 1.942881e+02 5.079646e+02
+[161] 8.767123e-01 2.750685e+00 1.503311e+02 3.000000e-01 3.095890e-01
+[166] 3.000000e-01 6.371681e+02 6.054795e-01 1.955298e+02 1.786424e+02
+[171] 1.120861e+02 1.331954e+02 2.159292e+02 5.628319e+02 1.900662e+02
+[176] 6.547945e-01 1.665753e+00 1.739238e+02 9.991722e+01 9.321192e+01
+[181] 8.767123e-02           NA 6.794521e-01 5.808219e-01 1.369863e-01
+[186] 2.060274e+00 1.610099e+02 4.082192e-01 8.273973e-01 4.601770e+02
+[191] 1.389073e+02 3.867133e+01 9.260274e-01 5.918874e+01 1.870861e+02
+[196] 4.328767e-01 6.301370e-02 3.000000e-01 1.548013e+02 5.819536e+01
+[201] 1.724338e+02 1.932401e+01 2.164420e+00 9.757412e-01 1.509434e-01
+[206] 1.509434e-01 7.766571e+01 4.319563e+01 1.752022e-01 3.094775e+01
+[211] 1.266846e-01 2.919806e+01 9.545455e+00 2.735115e+01 1.314841e+02
+[216] 3.643985e+01 1.498559e+02 9.363636e+00 2.479784e-01 5.390836e-02
+[221] 8.787062e-01 1.994609e-01 3.000000e-01 3.000000e-01 5.390836e-03
+[226] 4.177898e-01 3.000000e-01 2.479784e-01 2.964960e-02 2.964960e-01
+[231] 5.148248e+00 1.994609e-01 3.000000e-01 1.779539e+02 3.290210e+02
+[236] 3.000000e-01 1.809798e+02 4.905660e-01 1.266846e-01 1.543948e+02
+[241] 1.379683e+02 6.153846e+02 1.474784e+02 3.000000e-01 1.024259e+00
+[246] 4.444056e+02 3.000000e-01 2.504043e+00 3.000000e-01 3.000000e-01
+[251] 7.816712e-02 3.000000e-01 5.390836e-02 1.494236e+02 5.972622e+01
+[256] 6.361186e-01 1.837896e+02 1.320809e+02 1.571906e-01 1.520231e+02
+[261] 3.000000e-01 3.000000e-01 1.823699e+02 3.000000e-01 2.173913e+00
+[266] 2.142202e+01 3.000000e-01 3.408027e+00 4.155963e+01 9.698997e-02
+[271] 1.238532e+01 9.528926e+00 1.916185e+02 1.060201e+00 3.679104e+02
+[276] 4.288991e+01 9.971098e+01 3.000000e-01 1.208092e+02 3.000000e-01
+[281] 6.688963e-03 2.505017e+00 1.481605e+00 3.000000e-01 5.183946e-01
+[286] 3.000000e-01 1.872910e-01 3.678930e-01 3.000000e-01 4.529851e+02
+[291] 3.169725e+01 3.000000e-01 4.922018e+01 2.548507e+02 1.661850e+02
+[296] 9.164179e+02 3.678930e-01 1.236994e+02 6.705202e+01 3.834862e+01
+[301] 1.963211e+00 3.000000e-01 2.474916e-01 3.000000e-01 2.173913e-01
+[306] 8.193980e-01 2.444816e+00 3.000000e-01 1.571906e-01 1.849711e+02
+[311] 6.119403e+02 3.000000e-01 4.280936e-01 9.698997e-02 3.678930e-02
+[316] 4.832090e+02 1.390173e+02 3.000000e-01 6.555970e+02 1.526012e+02
+[321] 3.000000e-01 7.222222e-01 7.724426e+01 3.000000e-01 6.111111e-01
+[326] 1.555556e+00 3.055556e-01 1.500000e+00 1.470772e+02 1.694444e+00
+[331] 3.138298e+02 1.414405e+02 1.990605e+02 4.212766e+02 3.000000e-01
+[336] 3.000000e-01 6.478723e+02 3.000000e-01 2.222222e+00 3.000000e-01
+[341] 2.055556e+00 2.777778e-02 8.333333e-02 1.032359e+02 1.611111e+00
+[346] 8.333333e-02 2.333333e+00 5.755319e+02 1.686848e+02 1.111111e-01
+[351] 3.000000e-01 8.372340e+02 3.000000e-01 3.784504e+01 3.819149e+02
+[356] 5.555556e-02 3.000000e+02 1.855950e+02 1.944444e-01 3.000000e-01
+[361] 5.555556e-02 1.138889e+00 4.254237e+01 3.000000e-01 3.000000e-01
+[366] 3.000000e-01 3.000000e-01 3.138298e+02 1.235908e+02 4.159574e+02
+[371] 3.009685e+01 1.567850e+02 1.367432e+02 3.731235e+01 9.164927e+01
+[376] 2.936170e+02 8.820459e+01 1.035491e+02 7.379958e+01 3.000000e-01
+[381] 1.718750e+02 2.128527e+00 1.253918e+00 2.382445e-01 4.639498e-01
+[386] 1.253918e-01 1.253918e-01 3.000000e-01 1.000000e+00 1.570043e+02
+[391] 4.344086e+02 2.184953e+00 1.507837e+00 3.228840e-01 4.588024e+01
+[396] 1.660560e+02 3.000000e-01 3.043011e+02 2.612903e+02 1.621767e+02
+[401] 3.228840e-01 4.639498e-01 2.495298e+00 3.257053e+00 3.793103e-01
+[406]           NA 6.896552e-02 3.000000e-01 1.423197e+00 3.000000e-01
+[411] 3.000000e-01 1.786638e+02 3.279570e+02           NA 1.903017e+02
+[416] 1.654095e+02 4.639498e-01 1.815733e+02 1.366771e+00 1.536050e-01
+[421] 1.306587e+01 2.129032e+02 1.925647e+02 3.000000e-01 1.028213e+00
+[426] 3.793103e-01 8.025078e-01 4.860215e+02 3.000000e-01 2.100313e-01
+[431] 2.767665e+01 1.592476e+00 9.717868e-02 1.028213e+00 3.793103e-01
+[436] 1.292026e+02 4.425150e+01 3.193548e+02 1.860991e+02 6.614420e-01
+[441] 5.203762e-01 1.330819e+02 1.673491e+02 3.000000e-01 1.117457e+02
+[446] 3.045509e+01 3.000000e-01 8.280255e-02 3.000000e-01 1.200637e+00
+[451] 1.687898e-01 7.367273e+02 8.280255e-02 5.127389e-01 1.974522e-01
+[456] 7.993631e-01 3.000000e-01 3.298182e+02 9.736842e+01 3.000000e-01
+[461] 3.000000e-01 4.214545e+02 3.000000e-01 2.578182e+02 2.261147e-01
+[466] 3.000000e-01 1.883901e+02 9.458204e+01 3.000000e-01 3.000000e-01
+[471] 7.707006e-01 5.032727e+02 1.544586e+00 1.431115e+02 3.000000e-01
+[476] 1.458599e+00 1.247678e+02           NA 4.334545e+02 3.000000e-01
+[481] 6.156364e+02 9.574303e+01 1.928019e+02 1.888545e+02 1.598297e+02
+[486] 5.127389e-01 1.171053e+02           NA 2.547771e-02 1.707430e+02
+[491] 3.000000e-01 1.869969e+02 4.731481e+01 1.988390e+02 3.000000e-01
+[496] 8.808050e+01 2.003185e+00 3.000000e-01 3.509259e+01 9.365325e+01
+[501] 3.000000e-01 3.736111e+01 1.674923e+02 8.808050e+01 1.656347e+02
+[506] 3.722222e+01 6.756364e+02 3.000000e-01 1.698142e+02 1.628483e+02
+[511] 5.985130e-01 1.903346e+00 3.000000e-01 3.000000e-01 8.996283e-01
+[516] 3.977695e-01 3.000000e-01 3.000000e-01 3.000000e-01 3.000000e-01
+[521] 7.446809e+02 6.095745e+02 1.427445e+02 3.000000e-01 2.973978e-02
+[526] 3.977695e-01 4.095745e+02 4.595745e+02 3.000000e-01 1.976341e+02
+[531] 3.776596e+02 1.777603e+02 4.312268e-01 6.765957e+02 7.978723e+02
+[536] 9.665427e-02 1.879338e+02 4.358670e+01 3.000000e-01 3.000000e-01
+[541] 2.638955e+01 3.180523e+01 1.746845e+02 1.876972e+02 1.044164e+02
+[546] 1.202681e+02 1.630915e+02 1.276025e+02 8.880126e+01 3.563830e+02
+[551] 2.212766e+02 1.969121e+01 3.755319e+02 1.214511e+02 1.034700e+02
+[556] 3.000000e-01 3.643123e-01 6.319703e-02 3.000000e-01 3.000000e-01
+[561] 3.000000e-01 3.000000e-01 3.000000e-01 3.000000e-01 3.000000e-01
+[566] 3.000000e-01 1.664038e+02 2.946809e+02 4.391924e+01 1.874606e+02
+[571] 1.143533e+02 1.600158e+02 1.635688e-01 8.809148e+01 1.337539e+02
+[576] 1.985804e+02 1.578864e+02 3.000000e-01 3.000000e-01 1.953642e-01
+[581] 1.119205e+00 2.523636e+02 3.000000e-01 4.844371e+00 3.000000e-01
+[586] 1.492553e+02 1.993617e+02 2.847682e-01 3.145695e-01 3.000000e-01
+[591] 3.406429e+01 6.595745e+01 3.000000e-01 2.174545e+02           NA
+[596] 5.957447e+01 7.236364e+02 3.000000e-01 3.000000e-01 3.000000e-01
+[601] 2.676364e+02 1.891489e+02 3.036364e+02 3.000000e-01 3.000000e-01
+[606] 3.000000e-01 3.000000e-01 3.000000e-01 1.447020e+00 2.130909e+02
+[611] 1.357616e-01 3.000000e-01 3.000000e-01 5.534545e+02 1.891489e+02
+[616] 7.202128e+01 3.250287e+01 1.655629e-02 3.123636e+02 3.000000e-01
+[621] 7.138298e+01 3.000000e-01 6.946809e+01 4.012629e+01 1.629787e+02
+[626] 1.508511e+02 1.655629e-02 3.000000e-01 4.635762e-02 3.000000e-01
+[631] 3.000000e-01 3.000000e-01 1.942553e+02 3.690909e+02 3.000000e-01
+[636] 3.000000e-01 2.847682e+00 1.435106e+02 3.000000e-01 4.752009e+01
+[641] 2.621125e+01 1.055319e+02 3.000000e-01 1.149007e+00 2.927273e+02
+[646] 3.000000e-01 3.000000e-01 4.839265e+01 3.000000e-01 3.000000e-01
+[651] 2.251656e-01
+
+
+
+
+

Using indexing to subset by rows

+

We can use indexing to also subset by rows. For example, here we pull the 100th observation/row.

+
+
df[100,]
+
+
    IgG_concentration       age age gender     slum
+100              8122 0.1818182   5 Female Non slum
+
+
+

And, here we pull the age of the 100th observation/row.

+
+
df[100,"age"]
+
+
[1] 0.1818182
+
+
+
+
+

Logical operators

+

Logical operators can be evaluated on object(s) in order to return a binary response of TRUE/FALSE

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
operatoroperator optiondescription
<%l%less than
<=%le%less than or equal to
>%g%greater than
>=%ge%greater than or equal to
==equal to
!=not equal to
x&yx and y
x|yx or y
%in%match
%!in%do not match
+
+
+

Logical operators examples

+

Let’s practice. First, here is a reminder of what the number.object contains.

+
+
number.object
+
+
[1] 3
+
+
+

Now, we will use logical operators to evaluate the object.

+
+
number.object<4
+
+
[1] TRUE
+
+
number.object>=3
+
+
[1] TRUE
+
+
number.object!=5
+
+
[1] TRUE
+
+
number.object %in% c(6,7,2)
+
+
[1] FALSE
+
+
+
+
+

Using indexing and logical operators to rename columns

+
    +
  1. We can assign the column names from data frame df to an object cn, then we can modify cn directly using indexing and logical operators, finally we reassign the column names, cn, back to the data frame df:
    2. +
+
+
cn <- colnames(df)
+cn
+
+
[1] "IgG_concentration" "age"               "age"              
+[4] "gender"            "slum"
+
+
cn[cn=="IgG_concentration"] <-"IgG_concentration_mIU" #rename cn to "IgG_concentration_mIU" when cn is "IgG_concentration"
+colnames(df) <- cn
+
+

Note, I am resetting the column name back to the original name for the sake of the rest of the module.

+
+
colnames(df)[colnames(df)=="IgG_concentration_mIU"] <- "IgG_concentration" #reset
+
+
+
+

Using indexing and logical operators to subset data

+

In this example, we subset by rows and pull only observations with an age of less than or equal to 10 and then saved the subset data to df_lt10. Note that the logical operators df$age<=10 is before the comma because I want to subset by rows (the first dimension).

+
+
df_lte10 <- df[df$age<=10, ]
+
+

In this example, we subset by rows and pull only observations with an age of less than or equal to 5 OR greater than 10.

+
+
df_lte5_gt10 <- df[df$age<=5 | df$age>10, ]
+
+

Lets check that my subsets worked using the summary() function.

+
+
summary(df_lte10$age)
+
+
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
+0.005391 0.300000 0.300000 0.724742 0.640788 9.545455       10
+
+
summary(df_lte5_gt10$age)
+
+
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
+  0.0054   0.3000   1.6018  87.9886 142.8362 916.4179       10
+
+
+
+
+

Missing values

+

Missing data need to be carefully described and dealt with in data analysis. Understanding the different types of missing data and how you can identify them, is the first step to data cleaning.

+

Types of “missing” values:

+
    +
  • NA - general missing data
    • +
  • NaN - stands for “Not a Number”, happens when you do 0/0.
    • +
  • Inf and -Inf - Infinity, happens when you divide a positive number (or negative number) by 0.
    • +
  • blank space - sometimes when data is read it, there is a blank space left
    • +
+
+
+

Logical operators to help identify and missing data

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
operatoroperator optiondescription
is.nais NAN or NA
is.nanis NAN
!is.nais not NAN or NA
!is.nanis not NAN
is.infiniteis infinite
anyare any TRUE
whichwhich are TRUE
+
+
+

More logical operators examples

+
+
test <- c(0,NA, -1)/0
+test
+
+
[1]  NaN   NA -Inf
+
+
is.na(test)
+
+
[1]  TRUE  TRUE FALSE
+
+
is.nan(test)
+
+
[1]  TRUE FALSE FALSE
+
+
is.infinite(test)
+
+
[1] FALSE FALSE  TRUE
+
+
+
+
+

More logical operators examples

+

any(is.na(x)) means do we have any NA’s in the object x?

+
+
any(is.na(df$IgG_concentration)) # are there any NAs - YES/TRUE
+
+
[1] FALSE
+
+
any(is.na(df$slum)) # are there any NAs- NO/FALSE
+
+
[1] FALSE
+
+
+

which(is.na(x)) means which of the elements in object x are NA’s?

+
+
which(is.na(df$IgG_concentration))
+
+
integer(0)
+
+
which(is.na(df$slum))
+
+
integer(0)
+
+
+
+
+

subset() function

+

The Base R subset() function is a slightly easier way to select variables and observations.

+
+
?subset
+
+
Registered S3 method overwritten by 'printr':
+  method                from     
+  knit_print.data.frame rmarkdown
+

Subsetting Vectors, Matrices and Data Frames

+

Description:

+
 Return subsets of vectors, matrices or data frames which meet
+ conditions.
+

Usage:

+
 subset(x, ...)
+ 
+ ## Default S3 method:
+ subset(x, subset, ...)
+ 
+ ## S3 method for class 'matrix'
+ subset(x, subset, select, drop = FALSE, ...)
+ 
+ ## S3 method for class 'data.frame'
+ subset(x, subset, select, drop = FALSE, ...)
+
+

Arguments:

+
   x: object to be subsetted.
+

subset: logical expression indicating elements or rows to keep: missing values are taken as false.

+

select: expression, indicating columns to select from a data frame.

+
drop: passed on to '[' indexing operator.
+
+ ...: further arguments to be passed to or from other methods.
+

Details:

+
 This is a generic function, with methods supplied for matrices,
+ data frames and vectors (including lists).  Packages and users can
+ add further methods.
+
+ For ordinary vectors, the result is simply 'x[subset &
+ !is.na(subset)]'.
+
+ For data frames, the 'subset' argument works on the rows.  Note
+ that 'subset' will be evaluated in the data frame, so columns can
+ be referred to (by name) as variables in the expression (see the
+ examples).
+
+ The 'select' argument exists only for the methods for data frames
+ and matrices.  It works by first replacing column names in the
+ selection expression with the corresponding column numbers in the
+ data frame and then using the resulting integer vector to index
+ the columns.  This allows the use of the standard indexing
+ conventions so that for example ranges of columns can be specified
+ easily, or single columns can be dropped (see the examples).
+
+ The 'drop' argument is passed on to the indexing method for
+ matrices and data frames: note that the default for matrices is
+ different from that for indexing.
+
+ Factors may have empty levels after subsetting; unused levels are
+ not automatically removed.  See 'droplevels' for a way to drop all
+ unused levels from a data frame.
+

Value:

+
 An object similar to 'x' contain just the selected elements (for a
+ vector), rows and columns (for a matrix or data frame), and so on.
+

Warning:

+
 This is a convenience function intended for use interactively.
+ For programming it is better to use the standard subsetting
+ functions like '[', and in particular the non-standard evaluation
+ of argument 'subset' can have unanticipated consequences.
+

Author(s):

+
 Peter Dalgaard and Brian Ripley
+

See Also:

+
 '[', 'transform' 'droplevels'
+

Examples:

+
 subset(airquality, Temp > 80, select = c(Ozone, Temp))
+ subset(airquality, Day == 1, select = -Temp)
+ subset(airquality, select = Ozone:Wind)
+ 
+ with(airquality, subset(Ozone, Temp > 80))
+ 
+ ## sometimes requiring a logical 'subset' argument is a nuisance
+ nm <- rownames(state.x77)
+ start_with_M <- nm %in% grep("^M", nm, value = TRUE)
+ subset(state.x77, start_with_M, Illiteracy:Murder)
+ # but in recent versions of R this can simply be
+ subset(state.x77, grepl("^M", nm), Illiteracy:Murder)
+
+
+

Subsetting use the subset() function

+

Here are a few examples using the subset() function

+
+
df_lte10_v2 <- subset(df, df$age<=10, select=c(IgG_concentration, age))
+df_lt5_f <- subset(df, df$age<=5 & gender=="Female", select=c(IgG_concentration, slum))
+
+
+
+

subset() function vs logical operators

+

subset() automatically removes NAs, which is a different behavior from doing logical operations on NAs.

+
+
summary(df_lte10$age)
+
+ + + + + + + + + + + + + + + + + + + + + + + +
Min.1st Qu.MedianMean3rd Qu.Max.NA’s
0.00539080.30.30.72474210.64078769.54545410
+
+
summary(df_lte10_v2$age)
+
+ + + + + + + + + + + + + + + + + + + + + +
Min.1st Qu.MedianMean3rd Qu.Max.
0.00539080.30.30.72474210.64078769.545454
+
+
+

We can also see this by looking at the number or rows in each dataset.

+
+
nrow(df_lte10)
+
+
[1] 370
+
+
nrow(df_lte10_v2)
+
+
[1] 360
+
+
+
+
+

Summary

+
    +
  • colnames(), str() and summary()functions from Base R are great functions to assess the data type and some summary statistics
    • +
  • There are three basic indexing syntax: [ ], [[ ]] and $
    • +
  • Indexing can be used to extract part of an object (e.g., subset data) and to replace parts of an object (e.g., rename variables / columns)
    • +
  • Logical operators can be evaluated on object(s) in order to return a binary response of TRUE/FALSE, and are useful for decision rules for indexing
    • +
  • There are 5 “types” of missing values, the most common being “NA”
    • +
  • Logical operators meant to determine missing values are very helpful for data cleaning
    • +
  • The Base R subset() function is a slightly easier way to select variables and observations.
    • +
+
+
+

Acknowledgements

+

These are the materials we looked through, modified, or extracted to complete this module’s lecture.

+ + +
+ +
+
+
+
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a/modules/Module06-DataSubset.qmd +++ b/modules/Module06-DataSubset.qmd @@ -4,6 +4,7 @@ format: revealjs: scrollable: true smaller: true + toc: false --- ## Learning Objectives @@ -11,7 +12,7 @@ format: After module 6, you should be able to... - Use basic functions to get to know you data -- Use two approaches to indexing +- Use three indexing approaches - Rely on indexing to extract part of an object (e.g., subset data) and to replace parts of an object (e.g., rename variables / columns) - Describe what logical operators are and how to use them - Use on the `subset()` function to subset data @@ -19,7 +20,7 @@ After module 6, you should be able to... ## Getting to know our data -The `dim()` , `nrow()`, and `ncol()` functions are good options to check the dimensions of your data before moving forward. +The `dim()`, `nrow()`, and `ncol()` functions are good options to check the dimensions of your data before moving forward. Let's first read in the data from the previous module. @@ -47,13 +48,17 @@ Note, if you have a very large dataset with 15+ variables, `summary()` is not so ## Description of data -This is data based on a simulated pathogen X IgG serological survey. The rows represent individuals. Variables include IgG concentrations, age in years, gender, and residence based on slum characterization. We will use this dataset for lectures throughout the Workshop. +This is data based on a simulated pathogen X IgG antibody serological survey. The rows represent individuals. Variables include IgG concentrations in IU/mL, age in years, gender, and residence based on slum characterization. We will use this dataset for lectures throughout the Workshop. ## View the data as a whole dataframe -The `View()` function, one of the few Base R functions with a capital letter can be used to open a new tab in the Console and view the data as you would in excel, for example. +The `View()` function, one of the few Base R functions with a capital letter can be used to open a new tab in the Console and view the data as you would in excel. -```{r, out.width = "50%", echo = FALSE} +```{r echo=TRUE, eval=FALSE} +View(df) +``` + +```{r, out.width = "100%", echo = FALSE} knitr::include_graphics("images/ViewTab.png") ``` @@ -67,21 +72,22 @@ knitr::include_graphics("images/View.png") ## Indexing -R contains several constructs which allow access to individual elements or subsets through indexing operations. Indexing can be used both to extract part of an object and to replace parts of an object (or to add parts). +R contains several constructs which allow access to individual elements or subsets through indexing operations. Indexing can be used both to extract part of an object and to replace parts of an object (or to add parts). There are three basic indexing syntax: `[ ]`, `[[ ]]` and `$`. -``` -x[i] -x[i, j] -x[[i]] -x$a -x$"a" +```{r echo=TRUE, eval=FALSE} +x[i] #if x is a vector +x[i, j] #if x is a matrix/data frame +x[[i]] #if x is a list +x$a #if x is a data frame or list +x$"a" #if x is a data frame or list ``` ## Vectors and multi-dimensional objects To index a vector, `vector[i]` select the ith element. To index a multi-dimensional objects such as a matrix, `matrix[i, j]` selects the element in row i and column j, where as in a three dimensional `array[k, i, i, j]` selects the element in matrix k, row i, and column j. -```{r echo=F} +Let's practice by first creating the same objects as we did in Module 1. +```{r echo=T} number.object <- 3 character.object <- "blue" vector.object1 <- c(2,3,4,5) @@ -89,26 +95,30 @@ vector.object2 <- c("blue", "red", "yellow") matrix.object <- matrix(data=vector.object1, nrow=2, ncol=2, byrow=TRUE) ``` +Here is a reminder of what these objects look like. ```{r echo=T} vector.object1 matrix.object ``` +Finally, let's use indexing to pull our elements of the objects. ```{r echo=T} -vector.object1[2] -matrix.object[1,2] +vector.object1[2] #pulling the second element +matrix.object[1,2] #pulling the element in row 1 column 2 ``` ## List objects -For lists, one generally uses `list[[p]] to select any single element p. +For lists, one generally uses `list[[p]]` to select any single element p. -```{r} +Let's practice by creating the same list as we did in Module 1. +```{r echo=TRUE} list.object <- list(number.object, vector.object2, matrix.object) list.object ``` +Now we use indexing to pull out the 3rd element in the list. ```{r echo=T} list.object[[3]] ``` @@ -121,11 +131,12 @@ list.object[[3]] df$IgG_concentration ``` +Note, if you have spaces in your variable name, you will need to use back ticks `variable name` after the `$`. This is a good reason to not create variables / column names with spaces. + ## $ for indexing with lists List elements can be named - -```{r makeListv} +```{r echo=TRUE} list.object.named <- list( emory = number.object, uga = vector.object2, @@ -134,11 +145,10 @@ list.object.named <- list( list.object.named ``` -You can reference data from list using `$` (if elements are named) or using double square brackets, `[[ ]]` - -```{r} -list.object.named[["uga"]] +If list elements are named, than you can reference data from list using `$` or using double square brackets, `[[ ]]` +```{r echo=TRUE} list.object.named$uga +list.object.named[["uga"]] ``` @@ -153,34 +163,38 @@ colnames(df) colnames(df)[1:2] <- c("IgG_concentration", "age") #reset ``` -## Using indexing to subset data +## Using indexing to subset by columns -We can also subset a data frames and matrices (2-dimensional objects) using the bracket `[, ]`. - -We can subset by columns and pull the `x` column using the index of the column or the column name ("`age`") +We can also subset a data frames and matrices (2-dimensional objects) using the bracket `[ row , column ]`. We can subset by columns and pull the `x` column using the index of the column or the column name. +For example, here I am pulling the 3nd column, which has the variable name `age` ```{r echo=T} -df[, "age"] #same as df[, 2] +df[ , "age"] #same as df[ , 3] ``` We can select multiple columns using multiple column names: ```{r echo=T} -df[, c("age", "gender")] +df[, c("age", "gender")] #same as df[ , c(3,4)] ``` -We can remove select columns using column names as well: (xxzane - why - c("slum") not working) +We can remove select columns using indexing as well, OR by simply changing the column to `NULL` ```{r echo=T} -df[, -3] #remove column 3, "slum" variable -#Note df$slum <- NULL would also work +df[, -5] #remove column 5, "slum" variable +``` +```{r echo=TRUE, eval=FALSE} +df$slum <- NULL # this is the same as above ``` We can also grab the `age` column using the `$` operator. ```{r echo=T} df$age ``` -Or we can subset by rows and pull the 100th observation/row. + +## Using indexing to subset by rows + +We can use indexing to also subset by rows. For example, here we pull the 100th observation/row. ```{r echo=T} df[100,] ``` - or maybe the age of the 100th observation/row. +And, here we pull the `age` of the 100th observation/row. ```{r echo=T} df[100,"age"] ``` @@ -206,10 +220,12 @@ operator | operator option |description ## Logical operators examples +Let's practice. First, here is a reminder of what the number.object contains. ```{r echo=TRUE} number.object ``` +Now, we will use logical operators to evaluate the object. ```{r echo=TRUE} number.object<4 number.object>=3 @@ -220,23 +236,33 @@ number.object %in% c(6,7,2) ## Using indexing and logical operators to rename columns -We can assign the column names, change the ones we want, and then re-assign the column names: +1. We can assign the column names from data frame `df` to an object `cn`, then we can modify `cn` directly using indexing and logical operators, finally we reassign the column names, `cn`, back to the data frame `df`: -```{r} +```{r echo=TRUE} cn <- colnames(df) -cn[cn=="IgG_concentration"] <-"IgG_concentration_mIU" #rename cn to "IgG_concentration" when cn is "IgG_concentration_mIU" +cn +cn[cn=="IgG_concentration"] <-"IgG_concentration_mIU" #rename cn to "IgG_concentration_mIU" when cn is "IgG_concentration" colnames(df) <- cn +``` + +Note, I am resetting the column name back to the original name for the sake of the rest of the module. +```{r echo=TRUE} colnames(df)[colnames(df)=="IgG_concentration_mIU"] <- "IgG_concentration" #reset ``` + ## Using indexing and logical operators to subset data -Subset by rows and pull only observations with an age of less than or equal to 10. + +In this example, we subset by rows and pull only observations with an age of less than or equal to 10 and then saved the subset data to `df_lt10`. Note that the logical operators `df$age<=10` is before the comma because I want to subset by rows (the first dimension). ```{r echo=T} -df_lte10 <- df[df$age<=10,] -df_lte5_gt10 <- df[df$age<=5 | df$age>10,] +df_lte10 <- df[df$age<=10, ] +``` +In this example, we subset by rows and pull only observations with an age of less than or equal to 5 OR greater than 10. +```{r echo=TRUE} +df_lte5_gt10 <- df[df$age<=5 | df$age>10, ] ``` -Note that the logical operators `df$age<=10` and `df$age<=5 | df$age>10` are before the comma because I want to subset by rows. I saved the subset data to `df_lt10` and `df_lte5_gt10`. Lets check that my subsets worked using the `summary()` function. +Lets check that my subsets worked using the `summary()` function. ```{r echo=T} summary(df_lte10$age) summary(df_lte5_gt10$age) @@ -245,6 +271,8 @@ summary(df_lte5_gt10$age) ## Missing values +Missing data need to be carefully described and dealt with in data analysis. Understanding the different types of missing data and how you can identify them, is the first step to data cleaning. + Types of "missing" values: - `NA` - general missing data @@ -254,7 +282,7 @@ Types of "missing" values: number (or negative number) by 0. - blank space - sometimes when data is read it, there is a blank space left -## More Logical Operators +## Logical operators to help identify and missing data operator | operator option |description -----|-----|-----: @@ -264,10 +292,11 @@ operator | operator option |description `!is.nan`||is not NAN `is.infinite`||is infinite `any`||are any TRUE +`which`||which are TRUE ## More logical operators examples -```{r} +```{r echo=TRUE} test <- c(0,NA, -1)/0 test is.na(test) @@ -280,16 +309,24 @@ is.infinite(test) `any(is.na(x))` means do we have any `NA`'s in the object `x`? ```{r echo=TRUE} -A <- c(1, 2, 4, NA) -B <- c(1, 2, 3, 4) -any(is.na(A)) # are there any NAs - YES/TRUE -any(is.na(B)) # are there any NAs- NO/FALSE +any(is.na(df$IgG_concentration)) # are there any NAs - YES/TRUE +any(is.na(df$slum)) # are there any NAs- NO/FALSE ``` +`which(is.na(x))` means which of the elements in object `x` are `NA`'s? + +```{r echo=TRUE} +which(is.na(df$IgG_concentration)) +which(is.na(df$slum)) +``` ## `subset()` function -The Base R `subset()` function is a slighly easier way to select variables and observations. +The Base R `subset()` function is a slightly easier way to select variables and observations. + +```{r echo=TRUE, eval=FALSE} +?subset +``` ```{r, echo = FALSE, results = "asis"} library(printr) @@ -325,13 +362,19 @@ nrow(df_lte10_v2) ## Summary -- +- `colnames()`, `str()` and `summary()`functions from Base R are great functions to assess the data type and some summary statistics +- There are three basic indexing syntax: `[ ]`, `[[ ]]` and `$` +- Indexing can be used to extract part of an object (e.g., subset data) and to replace parts of an object (e.g., rename variables / columns) +- Logical operators can be evaluated on object(s) in order to return a binary response of TRUE/FALSE, and are useful for decision rules for indexing +- There are 5 “types” of missing values, the most common being “NA” +- Logical operators meant to determine missing values are very helpful for data cleaning +- The Base R `subset()` function is a slightly easier way to select variables and observations. ## Acknowledgements These are the materials we looked through, modified, or extracted to complete this module's lecture. - ["Introduction to R for Public Health Researchers" Johns Hopkins University](https://jhudatascience.org/intro_to_r/) -- [CRAN Project](https://cran.r-project.org/doc/manuals/R-lang.html#Indexing) -- [CRAN Project](https://cran.r-project.org/web/packages/extraoperators/vignettes/logicals-vignette.html) +- ["Indexing" CRAN Project](https://cran.r-project.org/doc/manuals/R-lang.html#Indexing) +- ["Logical operators" CRAN Project](https://cran.r-project.org/web/packages/extraoperators/vignettes/logicals-vignette.html) diff --git a/modules/Module07-VarCreationClassesSummaries.qmd b/modules/Module07-VarCreationClassesSummaries.qmd index 3dbf8ba..704d75b 100644 --- a/modules/Module07-VarCreationClassesSummaries.qmd +++ b/modules/Module07-VarCreationClassesSummaries.qmd @@ -31,12 +31,14 @@ library(printr) ## Adding new columns -You can add a new column, called `newcol` to `df`, using the `$` operator: +You can add a new column, called `log_IgG` to `df`, using the `$` operator: ```{r echo=TRUE} df$log_IgG <- log(df$IgG_concentration) head(df,3) ``` +Note, my use of the underscore in the variable name rather than a space. This is good coding practice and make calling variables much less prone to error. + ## Creating conditional variables One frequently-used tool is creating variables with conditions. A general function for creating new variables based on existing variables is the Base R `ifelse()` function, which "returns a value depending on whether the element of test is `TRUE` or `FALSE`." diff --git a/modules/Module09-DataAnalysis.qmd b/modules/Module09-DataAnalysis.qmd index c6fa1fb..616b464 100644 --- a/modules/Module09-DataAnalysis.qmd +++ b/modules/Module09-DataAnalysis.qmd @@ -33,7 +33,7 @@ df$age_group <- ifelse(df$age <= 5, "young", df$age_group <- factor(df$age_group, levels=c("young", "middle", "old")) ``` -Create `seropos` binary variable representing seropositivity if antibody concentrations are >10 mIUmL. +Create `seropos` binary variable representing seropositivity if antibody concentrations are >10 IU/mL. ```{r echo=TRUE} df$seropos <- ifelse(df$IgG_concentration<10, 0, ifelse(df$IgG_concentration>=10, 1, NA)) @@ -118,7 +118,7 @@ IgG_old <- df$IgG_concentration[df$age_group=="old"] t.test(IgG_young, IgG_old) ``` -The mean IgG concenration of young and old is 45.05 and 129.35 mIU/mL, respectively. We reject null hypothesis that the difference in the mean IgG concentration of young and old is 0 mIU/mL. +The mean IgG concenration of young and old is 45.05 and 129.35 IU/mL, respectively. 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