R Programming Essentials: Data Manipulation & Visualization

Posted on Aug 28, 2025 in Economics

R Data Handling Essentials

Importing Data

Importing CSV Files

• Use read.csv() to import a Comma Separated Values file:

  df <- read.csv("filename.csv")

Importing Stata .dta Files

• Requires the haven package:

  library(haven)
  df <- read_dta("stata_file.dta")

Managing Your Environment

Setting and Checking the Working Directory

• Set: setwd("your/path/here")

• Get: getwd()

Data Frame Basics

Accessing a Column

• Access a column using the $ operator: df$column_name

Calculating Column Mean

• Calculate the mean of a numeric column: mean(df$column)

Creating a Frequency Table

• Generate a frequency table for a column: table(df$column)

Subsetting Rows by Condition

• Filter rows based on a condition:

  • Base R: df[df$state != "OR", ]

  • subset() function: subset(df, state != "OR")

Subsetting for Specific Values (e.g., CA or AZ)

• Select rows where a column matches specific values:

  • Base R: df[df$state == "CA" | df$state == "AZ", ]

  • subset() function: subset(df, state == "CA" | state == "AZ")

Subsetting with Functions (e.g., Mean Wage in CA)

• Apply a function to a subset of data:

  • Example: Calculate the mean wage for California: mean(df$wage[df$state == "CA"])

Excluding a Value

• Remove rows where a column equals a specific value:

  • Base R: df[df$col != "value", ]

  • subset() function: subset(df, col != "value")

Including Specific Values

• Keep rows where a column matches one of several specific values:

  • Base R: df[df$col == "A" | df$col == "B", ]

  • subset() function: subset(df, col == "A" | col == "B")

Viewing Dataset Portions

• First 6 rows: head(df)

• Last 6 rows: tail(df)

• Dimensions (rows, columns): dim(df)

• Summary statistics: summary(df)

• Structure: str(df)

Vector Operations

Creating and Assigning Vectors

• Create a numeric vector: x <- c(1, 2, 3) → [1] 1 2 3

Element-wise Vector Addition

• Add two vectors element by element:

  a <- c(1, 2, 3)
  b <- c(4, 5, 6)
  a + b

  → [1] 5 7 9

Logical Comparisons and Vectors

• Perform logical comparisons on vectors, returning a logical vector:

  vec <- c(1, 10, 11, 19)
  vec > 4

  → FALSE TRUE TRUE TRUE

R Logical Operators

• Combine logical conditions: (5 != 6 | 4 > 5) → TRUE

Tidyverse Data Manipulation

Loading the Tidyverse Package

• Load the tidyverse package for data manipulation and visualization:

  library(tidyverse)

  (Needed for: %>%, mutate(), group_by(), summarize())

Creating New Columns with mutate()

• Add or modify columns in a data frame:

  df <- df %>% mutate(new_col = ...)

Sorting Data

• Sort data by column(s):

  • Ascending: df %>% arrange(col)

  • Descending: df %>% arrange(desc(col))

Filtering Rows

• Filter rows based on a condition using filter():

  df %>% filter(condition)

Grouping and Summarizing Data

• Group data by a variable and calculate summary statistics:

  df %>% group_by(group_var) %>% summarize(mean_val = mean(num_var))

Grouping by Multiple Variables

• Group by multiple columns for more granular summaries:

  group_by(col1, col2) %>% summarize(...)

  → Output = 1 row per unique (col1, col2) combination

ggplot2 Data Visualization

Creating Plots with Titles

• Generate a scatter plot with a custom title:

  ggplot(df) +
    geom_point(aes(x = gdp, y = life_expectancy)) +
    ggtitle("My Plot Title")

Setting X and Y Axes in Time Series Plots

• Define aesthetics for time series plots: aes(x = year, y = averageSAT)

Common ggplot2 Plot Types

Essential R Utilities & Statistics

Checking Unique Values

• Find all unique values in a column: unique(df$column)

Installing R Packages

• Install new packages from CRAN: install.packages("packagename")

Linear Models in Base R

• Fit a linear regression model: lm(y ~ x, data = df)

Understanding the Intercept in Regression

• The intercept represents the approximate y-value when x = 0.

• It’s where the regression line crosses the y-axis.

Slope Interpretation Example

• If Z = 2X, the slope of Y ~ Z is ½ × the slope of Y ~ X.

Interpreting a Flat Regression Line

• A flat regression line (angle < 45°) indicates a slope less than 1.

Understanding the Interquartile Range (IQR)

• IQR = Q3 – Q1

• A wide box in a box plot indicates a large IQR.

• A narrow box indicates a small IQR.

Solving for Loops with Variable Updates

1. Find the starting value: sum <- 10

2. Check the loop iteration: for (i in 1:5)

3. Understand the update rule: sum <- sum + i

4. Add loop values: 1 + 2 + 3 + 4 + 5 = 15

5. Final result: 10 + 15 = 25

Converting Stata Dates to R Calendar Dates

• Stata’s date origin is January 1, 1960.

• Conversion example: as.Date(20819, origin = "1960-01-01")