Using R for Social Science Research: From Data Import to Publication-Ready Tables

Introduction

R is a free, open-source programming language increasingly used in social science research. Unlike SPSS's point-and-click interface, R requires writing code—but offers greater flexibility and transparency. This guide covers the essentials for social scientists new to R.

Getting Started

Installation

1. Download R from r-project.org
2. Download RStudio (a user-friendly interface for R) from posit.co

RStudio makes R much more accessible than the base R interface.

Basic R Interface

Console: Where you type commands and see output

Script: Where you write and save code (best practice)

Environment: Shows variables and data you've loaded

Files/Plots: Shows your working directory, plots you've created

Basic R Concepts

Variables and Assignment

age <- 25  # Store the value 25 in a variable called "age"
print(age)  # Display the value

The <- operator assigns values to variables.

Data Types

# Numeric
income <- 50000

# Character (text)
name <- "Sarah"

# Logical (TRUE/FALSE)
employed <- TRUE

# Vector (collection of values)
test_scores <- c(85, 92, 78, 88)

Functions

Functions are commands that do something. Basic structure:

function_name(argument1, argument2)

Example:

mean(c(85, 92, 78))  # Calculate mean of these three values
sd(test_scores)  # Calculate standard deviation

Loading and Exploring Data

Importing Data

# From CSV file
data <- read.csv("mydata.csv")

# From Excel (requires readxl package)
library(readxl)
data <- read_excel("mydata.xlsx")

Exploring Your Data

# View first few rows
head(data)

# Get basic information
str(data)  # Structure
summary(data)  # Summary statistics

# Dimensions
dim(data)  # Number of rows and columns

Descriptive Statistics

Basic Calculations

mean(data$variable)  # Average
median(data$variable)  # Middle value
sd(data$variable)  # Standard deviation

Creating Frequency Tables

table(data$gender)  # Count of each category

# More detailed
library(dplyr)
data %>%
  group_by(gender) %>%
  summarize(n = n(),
            mean_age = mean(age),
            sd_age = sd(age))

Cleaning and Preparing Data

Handling Missing Values

# Check for missing values
is.na(data$age)  # Returns TRUE/FALSE

# Remove missing values
data_clean <- na.omit(data)

# Or keep only complete cases for specific variables
data_clean <- data[complete.cases(data$age, data$income), ]

Recoding Variables

# Recode gender (1=Male, 2=Female)
data$gender <- recode(data$gender, 
                      '1' = 'Male', 
                      '2' = 'Female')

Creating New Variables

# Create age groups
data$age_group <- cut(data$age, 
                      breaks = c(0, 25, 35, 50, 100),
                      labels = c("18-25", "26-35", "36-50", "50+"))

Statistical Tests

Descriptive Statistics Table

library(psych)
describe(data[c("age", "income", "education")])

Correlation Analysis

# Pearson correlation
cor.test(data$anxiety, data$test_score)

# Correlation matrix
cor(data[c("anxiety", "depression", "stress")])

T-Test

# Compare two groups
t.test(test_score ~ gender, data = data)

Interpretation: Look for p-value < .05 for statistical significance.

ANOVA (3+ groups)

# Compare across teaching methods
aov_model <- aov(test_score ~ teaching_method, data = data)
summary(aov_model)

Chi-Square Test (categorical)

# Test association between two categorical variables
chisq.test(data$gender, data$major)

Creating Publication-Ready Tables

Using {gt} Package

library(gt)

# Summary table
summary_table <- data %>%
  group_by(condition) %>%
  summarize(
    N = n(),
    Mean_Score = mean(test_score),
    SD_Score = sd(test_score),
    Mean_Age = mean(age)
  ) %>%
  gt() %>%
  fmt_number(columns = c(Mean_Score, SD_Score, Mean_Age),
             decimals = 2)

# Display in document
summary_table

Using {stargazer} Package

library(stargazer)

# Create regression table
stargazer(model1, model2, 
          type = "text",
          summary = TRUE)

Data Visualization

Basic Plots with ggplot2

library(ggplot2)

# Scatter plot
ggplot(data, aes(x = anxiety, y = test_score)) +
  geom_point() +
  geom_smooth(method = "lm") +
  labs(title = "Anxiety and Test Performance",
       x = "Anxiety Level",
       y = "Test Score")

# Boxplot comparing groups
ggplot(data, aes(x = teaching_method, y = test_score)) +
  geom_boxplot() +
  theme_minimal()

# Histogram
ggplot(data, aes(x = age)) +
  geom_histogram(binwidth = 5) +
  facet_wrap(~gender)

Regression Analysis

Simple Linear Regression

# Model: predict test_score from anxiety
model <- lm(test_score ~ anxiety, data = data)
summary(model)

Output shows:

• Coefficients (how much test_score changes per unit of anxiety)
• R-squared (how much variance is explained)
• P-values (statistical significance)

Multiple Regression

# Multiple predictors
model <- lm(test_score ~ anxiety + age + gender, data = data)
summary(model)

Reporting Results

# Extract key statistics for reporting
coefficients(model)
confint(model)  # Confidence intervals

Common Mistakes

1. Not checking assumptions – Different tests have different assumptions (normality, homogeneity of variance, etc.)
2. Ignoring missing data – Missing values can bias results
3. Not visualizing data – A plot often reveals patterns numbers hide
4. Confusing <- and = – Use <- for assignment in R
5. Not saving your code – Always work in scripts, not just the console

Resources

• Free R Tutorials: DataCamp, Coursera, YouTube
• R Documentation: Type ?function_name in R console
• R Communities: Stack Overflow, Reddit's r/rstats
• Useful Books: "R for Data Science" by Hadley Wickham & Garrett Grolemund

Conclusion

R has a learning curve, but offers powerful tools for social science research. Start with simple descriptive statistics, build to more complex analyses, and don't hesitate to ask for help. The R community is remarkably supportive.

The investment in learning R pays off through greater transparency, reproducibility, and flexibility in your research.

Need help with R analysis? EssayBoffin's data science tutors can guide you from basics to advanced statistical modeling. Get R programming support