Merge remote-tracking branch 'origin/main'

modules/Module01-Intro.qmd

@@ -61,17 +61,21 @@ knitr::include_graphics("images/both.png")
 
 ## Working with R in RStudio - 2 major panes:
 
-1) The **Source/Editor**: xxamy
+1. The **Source/Editor**:
+
+</br> 
 
 - "Analysis" Script
+
 - Static copy of what you did (reproducibility)
+
 - Top by default
-
-2)  The **R Console**: "interprets" whatever you type:
 
-    -   Calculator
-    -   Try things out interactively, then add to your editor
-    -   Bottom by default
+2. The **R Console**: "interprets" whatever you type:
+
+- Calculator
+- Try things out interactively, then add to your editor
+- Bottom by default
 
 ## Source / Editor
 
@@ -178,7 +182,7 @@ You can do basic arithmetic in R, which I surprisingly use all the time.
 
 To execute or run a line of code (i.e., command), you just put your cursor on the command and then:
 
-  1. Press Run (which you will find at the top of your window)
+  1. Press Run (which you will find at the top of your Console pane)
 
   OR
 
@@ -276,13 +280,13 @@ I tend to use:
 number.object <- 3
 character.object <- "blue"
 vector.object1 <- c(2,3,4,5)
-vector.object2 <- c("blue", "red", "yellow")
+vector.object2 <- paste0(c("b", "t", "u"), c(8,4,2))
 matrix.object <- matrix(data=vector.object1, nrow=2, ncol=2, byrow=TRUE)
 ```
 
-Note, `c()` and `matrix()` are functions, which we will talk more about in module 2.
+Note, `c()`, `paste0()`, and `matrix()` are functions, which we will talk more about in module 2.
 
-## Object names
+## Object names  - Good coding
 
 - In general, any object name can be typed into R.
 - However, only some are considered "valid". If you use a non-valid object name,
@@ -295,7 +299,7 @@ you will have to enclose it in backticks \``like this`\` for R to recognize it.
 `next`, `break`, `TRUE`, `FALSE`, `NULL`, `Inf`, `NaN`, `NA`, `NA_integer_`,
 `NA_real_`, `NA_Complex_`, `_NA_Character`, `...`, `..1`, `..2`, `..3`, and so on.
 
-## Object names
+## Object names  - Good coding
 
 | Valid        | Invalid |
 |--------------|---------|
@@ -305,6 +309,11 @@ you will have to enclose it in backticks \``like this`\` for R to recognize it.
 | `measles_data` | `.9data`  |
 | `.calc`        | `xX~mŷ_δätą~Xx` |
 
+
+## Object assingment - Good coding
+
+`=` and `<-` can both be used for assignment, but `<-` is better coding practice, because sometimes `=` doesn't work and we want to distinguish between the logical operator `==`. We will talk about this more, later.
+
 ## Mini Exercise
 
 Try creating one or two of these objects in your R script
@@ -313,13 +322,13 @@ Try creating one or two of these objects in your R script
 number.object <- 3
 character.object <- "blue"
 vector.object1 <- c(2,3,4,5)
-vector.object2 <- c("blue", "red", "yellow")
+vector.object2 <- paste0(c("b", "t", "u"), c(8,4,2))
 matrix.object <- matrix(data=vector.object1, nrow=2, ncol=2, byrow=TRUE)
 ```
 
 ## Objects 
 
-Note, you can find these objects now in the Global Environment.
+Note, you can find these objects now in your Environment pane.
 
 ```{r, out.width = "90%", echo = FALSE}
 knitr::include_graphics("images/global_env.png")
@@ -336,13 +345,6 @@ character.object
 matrix.object
 ```
 
-
-# Object names and assingment - Good coding
-
-xxzane
-
-`=` and `<-` can both be used for assignment, but `<-` is better coding practice, because sometimes `=` doesn't work and we want to distinguish between the logical operator `==`. We will talk about this more, later.
-
 ## Lists
 
 List is a special data class, that can hold vectors, strings, matrices, models, list of other lists.

modules/Module02-Functions.qmd

@@ -76,20 +76,15 @@ log(10)
 - what arguments a function includes? 
 - how to specify the arguments?
 
-## Seeking help for using functions
+## Seeking help for using functions (*)
 
 The best way of finding out this information is to use the `?` followed by the name of the function. Doing this will open up the help manual in the bottom RStudio Help panel. It provides a description of the function, usage, arguments, details, and examples. Lets look at the help file for the function `round()`
 
-```{r echo=TRUE, eval=FALSE}
-?log
+```{r, out.width = "70%", echo = FALSE}
+knitr::include_graphics("images/help_file.png")
 ```
 
 
-```{r, echo = FALSE, results = "asis"}
-library(printr)
-?log
-```
-
 ## How to specify arguments
 
 1.  Arguments are separated with a comma
@@ -178,11 +173,30 @@ library(printr)
 ?c
 ```
 
+## Functions from Module 1
+
+The `paste0()` function concatenate/combines vectors after converting to character.
+
+```{r echo=TRUE, eval=FALSE}
+vector.object2 <- paste0(c("b", "t", "u"), c(8,4,2))
+vector.object2
+?paste0
+```
+
+```{r echo=FALSE}
+library(printr)
+?paste0
+```
+
+
+
 ## Functions from Module 1
 
 The `matrix()` function creates a matrix from the given set of values.
 
 ```{r echo=TRUE, eval=FALSE}
+matrix.object <- matrix(data=vector.object1, nrow=2, ncol=2, byrow=TRUE)
+matrix.object
 ?matrix
 ```
 

modules/Module09-DataAnalysis.qmd

@@ -262,9 +262,13 @@ summary(fit2)
 
 ## Summary
 
--	Use `cor()` or `cor.test()` to calculate correlation between two numeric vectors.
-- `t.test()` tests the mean compared to null or difference in means between two groups
--		... xxamy more
+- the `aggregate()` function can be used to conduct analyses across groups (i.e., categorical variables in the data(
+- the `table()` function can generate frequency tables for 2 plus variables, but to get percentage tables, the `prop.table()` is useful
+- the `chisq.test()` function tests independence of factor variables 
+- the `cor()` or `cor.test()` functions can be used to calculate correlation between two numeric vectors
+- the `t.test()` functions conducts one and two sample (paired or unpaired) t-tests
+- the function `glm()` fits generalized linear modules to data and returns a fit object that can be read with the `summary()` function
+- changing the `family` argument in the `glm()` function allows you to fit models with different link functions
 
 ## Acknowledgements
 

modules/Module10-DataVisualization.qmd

@@ -127,7 +127,7 @@ We will only look at four types of plots today:
 - `barplot()` displays barplot
 
 
-## `histogram()` Help File
+## `hist()` Help File
 
 ```{r echo=TRUE, eval=FALSE}
 ?hist
@@ -138,7 +138,7 @@ library(printr)
 ?hist
 ```
 
-## `histogram()` example
+## `hist()` example
 
 Reminder function signature
 ```
@@ -231,6 +231,7 @@ points(
 ```
 
 * The `lines()` function works similarly for connected lines.
+* Note that the `points()` or `lines()` functions must be called with a `plot()`-style function
 * We will show how we could draw a `legend()` in a future section.
 
 
@@ -435,10 +436,17 @@ axis(2, at = c(0.2, 0.4, 0.6, 0.8,1))
 legend("topright", fill=c("darkblue","red"),  legend = c("seronegative", "seropositive"))
 ```
 
+## Base R plots vs the Tidyverse ggplot2 package
+
+It is good to know both b/c they each have their strengths
 
 ## Summary
 
--		
+- the Base R 'graphics' package has a ton of graphics options that allow for ultimate flexibility
+- Base R plots typically include setting plot options (`par()`), mapping data to the plot (e.g., `plot()`, `barplot()`, `points()`, `lines()`), and creating a legend (`legend()`). 
+- the functions `points()` or `lines()` add additional points or additional lines to an existing plot, but must be called with a `plot()`-style function
+- in Base R plotting the legend is not automatically generated, so be careful when creating it
+
 
 ## Acknowledgements