adds exercise 1 draft

README.md

@@ -4,7 +4,10 @@
 
 # SISMID `Introduction to R` Materials (2024)
 
-## About This Project
+This is the GitHub hosting the code and resources for the SISMID 2024
+Intro to R Module.
+
+## About This Course
 
 ### Module Summary
 

README.qmd

@@ -21,7 +21,10 @@ box::use(
 
 # SISMID `Introduction to R` Materials (2024)
 
-## About This Project
+This is the GitHub hosting the code and resources for the SISMID 2024 Intro
+to R Module.
+
+## About This Course
 
 ### Module Summary
 This module introduces the R statistical environment, assuming no prior

exercises/Exercise01-Soln.R

@@ -0,0 +1,119 @@
+################################################################################
+# SISMID Intro to R 2024
+# Exercise 01 Solution
+# Zane Billings and Amy Winter
+################################################################################
+
+# Before you start the exercise, make sure you create an R Project to hold all
+# of your materials for this class! Your R Project should contain (at least)
+# a Code directory and a Data directory. You could also add, for example, a
+# Results and a Notes directory to save various files.
+
+# Also, make sure you have your R project for this course OPEN in RStudio.
+# If you look in the top right corner, you should see a cube with the name of
+# your R project next to it. If it says "New Project" then your R Project is
+# NOT open and your working directory will be wrong!
+
+# QUESTION ZERO: print the working directory and make sure it makes your
+# project working directory!
+
+#### Question 1 ####
+# Part A: create a numeric vector with the values 1, 2, 5, 1, 7, and 8. Name
+# the vector q1_vec.
+q1_vec <- c(1, 2, 5, 1, 7, 8)
+
+# Part B: check the type of the vector and make sure it is numeric.
+class(q1_vec)
+
+# Part C: convert the type of the vector to integer, and check the type after.
+# Save the integer vector to a variable named q1_vec_int.
+q1_vec_int <- as.integer(q1_vec)
+class(q1_vec_int)
+
+# Part D: get the mean and standard deviation of q1_vec.
+mean(q1_vec)
+sd(q1_vec)
+
+# Part E: add a new element, 2.443, to q1_vec_int. Then check the class of
+# that object again. Can you explain what happened?
+q1_vec_int <- c(q1_vec_int, 2.443)
+class(q1_vec_int)
+
+# Because we added a numeric value that is not an integer, the entire vector
+# became numeric because vectors can only contain one type of element.
+
+#### Question 2 ####
+# Part A: create a character vector with six elements. Name it q2_vec.
+q2_vec <- c("A", "B", "C", "Bears", "Beets", "Battlestar Galactica")
+
+# Part B: verify the class and length of q2_vec using functions.
+class(q2_vec)
+length(q2_vec)
+
+# Part C: check whether the length of q2_vec is the same as the length of q1_vec
+# using functions.
+length(q1_vec) == length(q2_vec)
+
+# Part D: if you concentate q1_vec and q2_vec, what type will the output be?
+# Do it, save the result with whatever variable name you like, and check.
+# Can you explain the answer?
+q1q2_vec <- c(q1_vec, q2_vec)
+class(q1q2_vec)
+
+# Part E: What happens if you try to take the mean of q2_vec? Can you explain
+# the result?
+mean(q2_vec)
+
+# The result is NA and we get a warning message telling us that the argument
+# is not numeric or logical. This is because it doesn't make sense to take the
+# mean of a bunch of words.
+
+#### Question 3 ####
+# Part A: create a data frame named my_df. The first column of my_df should
+# be q1_vec, and the second column of my_df should be q2_vec.
+my_df <- data.frame(q1_vec, q2_vec)
+
+# Part B: call the summary() and str() functions on my_df. Make sure you
+# can explain the output that you see!
+summary(my_df)
+str(my_df)
+
+# Part C: save my_df as an Rds file named "my_df.Rds".
+saveRDS(my_df, "my_df.Rds")
+
+# Part D: read in the file my_df.Rds as an object called my_df2. Check whether
+# it is identical() to my_df.
+my_df2 <- readRDS("my_df.Rds")
+identical(my_df, my_df2)
+
+# Part E: save q1_vec, q2_vec, and my_df in a .Rdata file named
+# "exercise01.Rdata"
+save(q1_vec, q2_vec, my_df, file = "exercise01.Rdata")
+
+#### Question 4 ####
+# For this question, we want to load a data set that our friend give us in
+# Stata format. You should download the file "Diphtheria_IgG_Serology.sas7bdat"
+# to your data directory.
+
+# Part A: use google to find a package that can allow you to read a SAS
+# (sas7bdat) file into R. Install and load that package.
+install.packages("haven")
+library(haven)
+
+# Part B: read in the data set and use R functions to show the name and type
+# of each variable, the first 6 rows of the data, and a summary of each variable.
+diph <- haven::read_sas(here::here("data", "Diphtheria_IgG_Serology.sas7bdat"))
+str(diph)
+head(diph)
+summary(diph)
+
+# Part C: How many people got vaccinated in the data set? What is the overall
+# prevalence of diphtheria in the dataset?
+sum(diph$DP_vacc)
+mean(diph$DP_infection)
+
+# Part D: Make a 2x2 table of infection vs. vaccination. Make sure the rows
+# and columns have understandable labels.
+table(diph$DP_vacc, diph$DP_infection, dnn = list("Vaccination", "Infection"))
+
+#### Congratulations! You finished this exercise!! ####

exercises/Exercise01.R

@@ -0,0 +1,100 @@
+################################################################################
+# SISMID Intro to R 2024
+# Exercise 01 Solution
+# Zane Billings and Amy Winter
+################################################################################
+
+# Before you start the exercise, make sure you create an R Project to hold all
+# of your materials for this class! Your R Project should contain (at least)
+# a Code directory and a Data directory. You could also add, for example, a
+# Results and a Notes directory to save various files.
+
+# Also, make sure you have your R project for this course OPEN in RStudio.
+# If you look in the top right corner, you should see a cube with the name of
+# your R project next to it. If it says "New Project" then your R Project is
+# NOT open and your working directory will be wrong!
+
+# QUESTION ZERO: print the working directory and make sure it makes your
+# project working directory!
+
+#### Question 1 ####
+# Part A: create a numeric vector with the values 1, 2, 5, 1, 7, and 8. Name
+# the vector q1_vec.
+
+
+# Part B: check the type of the vector and make sure it is numeric.
+
+
+# Part C: convert the type of the vector to integer, and check the type after.
+# Save the integer vector to a variable named q1_vec_int.
+
+
+# Part D: get the mean and standard deviation of q1_vec.
+
+
+# Part E: add a new element, 2.443, to q1_vec_int. Then check the class of
+# that object again. Can you explain what happened?
+
+
+#### Question 2 ####
+# Part A: create a character vector with six elements. Name it q2_vec.
+
+
+# Part B: verify the class and length of q2_vec using functions.
+
+
+# Part C: check whether the length of q2_vec is the same as the length of q1_vec
+# using functions.
+
+
+# Part D: if you concentate q1_vec and q2_vec, what type will the output be?
+# Do it, save the result with whatever variable name you like, and check.
+# Can you explain the answer?
+
+
+# Part E: What happens if you try to take the mean of q2_vec? Can you explain
+# the result?
+
+
+#### Question 3 ####
+# Part A: create a data frame named my_df. The first column of my_df should
+# be q1_vec, and the second column of my_df should be q2_vec.
+
+
+# Part B: call the summary() and str() functions on my_df. Make sure you
+# can explain the output that you see!
+
+
+# Part C: save my_df as an Rds file named "my_df.Rds".
+
+
+# Part D: read in the file my_df.Rds as an object called my_df2. Check whether
+# it is identical() to my_df.
+
+
+# Part E: save q1_vec, q2_vec, and my_df in a .Rdata file named
+# "exercise01.Rdata"
+
+
+#### Question 4 ####
+# For this question, we want to load a data set that our friend give us in
+# Stata format. You should download the file "Diphtheria_IgG_Serology.sas7bdat"
+# to your data directory.
+
+# Part A: use google to find a package that can allow you to read a SAS
+# (sas7bdat) file into R. Install and load that package.
+
+
+# Part B: read in the data set and use R functions to show the name and type
+# of each variable, the first 6 rows of the data, and a summary of each variable.
+
+
+# Part C: How many people got vaccinated in the data set? What is the overall
+# prevalence of diphtheria in the dataset?
+
+
+# Part D: Make a 2x2 table of infection vs. vaccination. Make sure the rows
+# and columns have understandable labels.
+
+
+#### Congratulations! You finished this exercise!! ####