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.

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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

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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.

• Show me your script and output to receive your grade and be dismissed. If you aren’t able to complete the assignment in class, you can upload the script to the Lab 02 Script assignment.

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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