R - Data Frames

R - Modern Data Frames with the Tidyverse

A data frame is R’s primary structure for storing tabular data. Think of it as a spreadsheet or a database table, where columns represent variables and rows represent observations. Columns can contain different data types (e.g., numbers, text, dates).

The modern R ecosystem, particularly the tidyverse, has introduced the tibble, a next-generation data frame. Tibbles are more user-friendly: they print better, avoid changing variable names, and never convert strings to factors by default. For all new data analysis projects, using tibbles is highly recommended.

Creating a Modern Data Frame (Tibble)

We’ll use the tibble() function from the tibble package (part of the tidyverse).

# First, ensure the tidyverse is installed and loaded
# install.packages("tidyverse")
library(tibble)

# Create a tibble
emp_data <- tibble(
  emp_id = 1:5,
  emp_name = c("Rick", "Dan", "Michelle", "Ryan", "Gary"),
  salary = c(623.3, 515.2, 611.0, 729.0, 843.25),
  start_date = as.Date(c("2012-01-01", "2013-09-23", "2014-11-15", "2014-05-11", "2015-03-27"))
)

# Print the tibble. Notice the clean output!
print(emp_data)

Executing this code produces a neatly formatted output:

# A tibble: 5 × 4
  emp_id emp_name salary start_date
   <int> <chr>     <dbl> <date>
1      1 Rick       623. 2012-01-01
2      2 Dan        515. 2013-09-23
3      3 Michelle   611  2014-11-15
4      4 Ryan       729  2014-05-11
5      5 Gary       843. 2015-03-27

Inspecting the Structure of a Data Frame

The str() (structure) and summary() functions from base R still work perfectly. However, the tidyverse provides glimpse(), which offers a compact and highly readable summary.

library(dplyr) # glimpse is in the dplyr package

# Get the structure using glimpse()
glimpse(emp_data)

The output of glimpse() is designed for easy reading:

Rows: 5
Columns: 4
$ emp_id     <int> 1, 2, 3, 4, 5
$ emp_name   <chr> "Rick", "Dan", "Michelle", "Ryan", "Gary"
$ salary     <dbl> 623.30, 515.20, 611.00, 729.00, 843.25
$ start_date <date> 2012-01-01, 2013-09-23, 2014-11-15, 2014-05-11, 2015-03-27

Manipulating Data with dplyr

The dplyr package provides a powerful and intuitive set of verbs for data manipulation. We often chain these verbs together using the pipe operator |> (or %>%). The pipe takes the result of the expression on its left and passes it as the first argument to the function on its right.

Selecting Columns with select()

# Load dplyr
library(dplyr)

# Select specific columns
name_and_salary <- emp_data |>
  select(emp_name, salary)

print(name_and_salary)

Filtering Rows with filter()

# Find employees with a salary over 700
high_earners <- emp_data |>
  filter(salary > 700)

print(high_earners)

Adding/Modifying Columns with mutate()

# Add a new department column and a calculated bonus column
emp_data_expanded <- emp_data |>
  mutate(
    dept = c("IT", "Operations", "IT", "HR", "Finance"),
    bonus = salary * 0.10
  )

print(emp_data_expanded)

Adding Rows with bind_rows()

# Create a new tibble with new employee data
new_hires <- tibble(
  emp_id = 6:7,
  emp_name = c("Rasmi", "Pranab"),
  salary = c(578.0, 722.5),
  start_date = as.Date(c("2023-05-21", "2023-07-30")),
  dept = c("IT", "Operations")
)

# Combine the original and new data
# bind_rows is safer than rbind()
all_employees <- bind_rows(emp_data_expanded, new_hires)

print(all_employees)

Putting It All Together: A dplyr Workflow

The true power of dplyr comes from chaining operations to perform a complex query in a readable way. Let’s find the names of employees in the IT department who started after 2013.

it_employees_post_2013 <- all_employees |>
  filter(dept == "IT", start_date > as.Date("2013-12-31")) |>
  select(emp_name, salary)

print(it_employees_post_2013)

This readable chain of commands produces the final, filtered result:

# A tibble: 2 × 2
  emp_name salary
  <chr>     <dbl>
1 Michelle    611
2 Rasmi       578

Further Learning

This is just the beginning! The next steps in mastering dplyr involve learning to group and summarize your data:

• group_by(): Groups the data by one or more variables, so that subsequent operations are performed on each group separately.
• summarise(): Collapses each group into a single row, calculating summary statistics like mean, sum, or count.
• arrange(): Sorts the rows of your data by one or more columns.