Lecture 9: Working with Data Frames, Lists, and Formulas in R

Author

Dr. Logan Kelly

Published

September 4, 2024

Overview

  • In this lecture, we’ll cover:
    • How to load and save data in R using data frames.
    • Working with lists to manage complex data.
    • How to use formulas for modeling and statistical analysis.

1. Loading and Saving Data

  • What is a data frame?
    • A data frame is a table-like structure where each column can hold different types of data (numeric, character, etc.), making it ideal for storing datasets.
  • Loading data into a data frame:
    • You can load external data, such as a CSV file, into a data frame using the read.csv() function.
  • Example: Loading a CSV file:
data <- read.csv("data.csv")
  • Saving data from a data frame:
    • You can save a data frame to a CSV file using the write.csv() function.
  • Example: Saving a data frame to a CSV file:
write.csv(data, "output.csv")
  • Viewing the structure of a data frame:
    • The str() function shows the structure of the data frame, including the types of variables and the first few rows of data.
  • Example: Viewing the structure of a data frame:
str(data)
function (..., list = character(), package = NULL, lib.loc = NULL, verbose = getOption("verbose"), 
    envir = .GlobalEnv, overwrite = TRUE)

2. Working with Lists

  • What is a list in R?
    • A list is a flexible data structure that can store objects of different types, including vectors, data frames, and other lists.
  • How to create a list:
    • Use the list() function to create a list that holds various elements.
  • Example: Creating a list with different data types:
my_list <- list(
  name = "Alice",
  age = 25,
  salary = 50000,
  skills = c("R", "Python", "SQL")
)
  • Accessing elements in a list:
    • Use double square brackets [[ ]] or the $ operator to access elements in a list.
  • Example: Accessing the name element:
my_list$name
[1] "Alice"
  • Example: Accessing the skills vector:
my_list$skills
[1] "R"      "Python" "SQL"
  • Modifying elements in a list:
    • You can modify existing elements by assigning new values.
  • Example: Changing the salary element:
my_list$salary <- 60000

3. Using Formulas in R

  • What is a formula?
    • A formula in R is used to express relationships between variables, especially in statistical modeling.
    • The general syntax for a formula is y ~ x, where y is the dependent variable and x is the independent variable.
  • Example: Creating a simple formula:
# A formula for predicting mpg using cyl and disp
formula <- mpg ~ cyl + disp
  • Formulas in regression analysis:
    • Formulas are widely used in regression analysis to specify models.
  • Example: Using a formula in a linear regression:
model <- lm(mpg ~ cyl + disp, data = mtcars)
summary(model)  # View the summary of the regression model

Call:
lm(formula = mpg ~ cyl + disp, data = mtcars)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4213 -2.1722 -0.6362  1.1899  7.0516 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 34.66099    2.54700  13.609 4.02e-14 ***
cyl         -1.58728    0.71184  -2.230   0.0337 *  
disp        -0.02058    0.01026  -2.007   0.0542 .  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 3.055 on 29 degrees of freedom
Multiple R-squared:  0.7596,    Adjusted R-squared:  0.743 
F-statistic: 45.81 on 2 and 29 DF,  p-value: 1.058e-09
  • Explanation:
    • The formula mpg ~ cyl + disp tells R to model mpg (miles per gallon) as a function of cyl (cylinders) and disp (displacement).
    • The lm() function fits a linear model, and summary() provides the model’s details.

4. Combining Data Frames, Lists, and Formulas

  • Lists of data frames:
    • You can store multiple data frames in a list and use formulas to perform statistical analysis on each one.
  • Example: Creating a list of data frames and applying a formula:
df1 <- data.frame(name = c("Alice", "Bob"), age = c(25, 30))
df2 <- data.frame(name = c("Charlie", "David"), age = c(35, 40))
data_list <- list(df1, df2)

# Applying a simple operation to one of the data frames
mean_age_df1 <- mean(data_list[[1]]$age)
  • Using formulas with data in lists:
    • You can apply formulas to perform calculations or modeling on data stored in lists.
  • Example: Applying a formula to a data frame in a list:
model_df1 <- lm(age ~ name, data = data_list[[1]])

Key Takeaways

  • Data frames allow you to load, store, and manipulate tabular data in R.
  • Lists are flexible and can store a variety of data types, making them useful for managing complex datasets.
  • Formulas are essential for statistical analysis and are commonly used in models like regression.

Looking Forward

  • In the next lecture, we’ll dive into programming in R, focusing on writing scripts, using loops and conditional statements, and writing custom functions to solve more advanced problems.