Lab 2

Introduction to R

In this lab you will be introduced to R, a programming language designed for statistical analysis.

Why learn R?

In this lab, you will:

• Sign up for an account on posit.cloud and learn how to use RStudio Cloud.
• Load data contained in a .csv file into R.
• Conduct basic data exploration tasks in R.

---

Instructions

Follow along as I show the class how to conduct today’s lab.

If you followed along correctly, you should end up with the following script. The script shows you how to:

• Read data from a CSV file into R and store it in a dataframe.
• Display the variables inside a dataframe and their data types.
• Tabulate and show summary statistics for variables in a dataframe.
• Create new variables based on existing variables.

rm(list=ls())   # Clear the workspace

# Read IPUMS_ACS2019_CA_1.csv and store it in df
df <- read.csv("IPUMS_ACS2019_CA_1.csv")

# Show the "structure" of the data
str(df)

# Tabulate SEX, MARST, and RACHSING
table(df$SEX)
table(df$MARST)
table(df$RACHSING)

# Show summary statistics for AGE and INCWAGE
summary(df$AGE)
summary(df$INCWAGE)

# Create a boolean variable called EMPLOYED 
# that is TRUE when the person is employed
# and FALSE otherwise
df$EMPLOYED <- (df$EMPSTAT==1)

# Create a boolean variable called WORKING_AGE
# that is TRUE when the person's AGE is 
# between 25 and 65 and FALSE otherwise
df$WORKING_AGE <- (df$AGE>=25) & (df$AGE<=65)

# Create a boolean variable called WORKING_AGE_EMPLOYED
# that is TRUE when the person's AGE is
# between 25 and 65 and the person is employed,
# and FALSE otherwise
df$WORKING_AGE_EMPLOYED <- (df$EMPLOYED==TRUE) & (df$WORKING_AGE==TRUE)

# Create a variable called LOG_INCWAGE that is
# equal to the log of INCWAGE
df$LOG_INCWAGE <- log(df$INCWAGE)

# Create a variable called BIRTH_YEAR that is 
# equal to YEAR minus AGE
df$BIRTH_YEAR <- df$YEAR - df$AGE

# Show structure of data again
str(df)

# Tabulate EMPLOYED, WORKING_AGE, and WORKING_ADULT
table(df$EMPLOYED)
table(df$WORKING_AGE)
table(df$WORKING_AGE_EMPLOYED)

# Show summary statistics of LOG_INCWAGE
summary(df$LOG_INCWAGE)

If you missed something during lecture, or if you need a refresher, you may find the following docs helpful:

• Vignettes:
  • RStudio basics
  • Editing scripts
  • Anatomy of an R command
  • Check your data!
  • Creating variables
• Functions:
  • rm(list=ls())
  • read.csv
  • str
  • table
  • summary
• Glossary:
  • dataframe
  • variable
  • data type
  • logical operator

---

Assignment

• Create a new script that accomplishes the following tasks:
  • Read IPUMS_ACS2019_CA_1.csv and store it in a dataframe called df.
  • Create a boolean variable called UNEMPLOYED_WORKING_AGE_MALE that is TRUE if the person is:
    • Unemployed (but in the labor force)
    • Between the ages of 25 and 65
    • Male
  • Create a boolean variable called NLF_WORKING_AGE_MALE that is TRUE if the person is:
    • Not in the labor force
    • Between the ages of 25 and 65
    • Male
  • Show the structure of the data after having created the above two variables
  • Tabulate UNEMPLOYED_WORKING_AGE_MALE and NLF_WORKING_AGE_MALE

  Hint: You’ll need to look up the codes for EMPSTAT and SEX in IPUMS.

• Upload the script to the Lab 02 Script assignment.

---

Takeaways

• You can use RStudio Cloud.
• You can do the following basic tasks in R:
  • Read a CSV file into a dataframe
  • View and browse a dataframe
  • Show the structure of a dataframe
  • Identify the datatypes of variables inside a dataframe
  • Tabulate and summarize variables inside a dataframe
  • Create new variables in a dataframe
  • Use logical operators to create new boolean variables
• You understand the concept of data types