Module 12 – Binding Rows & Columns

Module 12 delves into the art of data set restructuring in R, focusing on adding new cases (rows) and variables (columns) using the tidyverse and dplyr packages. In this module’s video lecture, you’ll learn how to combine different data sets effectively using the bind_rows() and bind_cols() functions. To solidify your understanding, the exercises providing practical application of these methods are included.

Video Lecture

Exercises

Create a tibble sleep_this_week containing the hours of sleep you got each night during the last 7 days (feel free to make up numbers, in case you don’t remember).
Create another tibble sleep_last_week containing the hours of sleep you got each night the 7 days before.
Add a new column week_no containing the character string “week_1” repeatedly to the tibble sleep_last_week using bind_cols(). Do the same for sleep_this_week, but add the character string “week_2”.
Use bind_rows() to combine sleep_this_week and sleep_last_week into a single tibble called combined_sleep_data.
Export combined_sleep_data to a new CSV file called sleep-data.csv.

The solutions to these exercises can be found at the bottom of this page.

R Code of This Lecture

# install.packages("tidyverse")                   # Install tidyverse packages
library("tidyverse")                              # Load tidyverse packages
 
vec_days <- c("Monday", "Tuesday",                # Create vector of weekdays
              "Wednesday", "Thursday",
              "Friday", "Saturday", "Sunday")
vec_days                                          # Print vector of weekdays
 
team_coffee_Cansu <- tibble(day = vec_days,       # Create tibble for Cansu
                            cups = c(1, 0, 2, 1, 3, 0, 0))
team_coffee_Cansu                                 # Print tibble
 
team_coffee_Ifeanyi <- tibble(day = vec_days,     # Create tibble for Ifeanyi
                              cups = 2)
team_coffee_Ifeanyi                               # Print tibble
 
team_coffee_Joachim <- tibble(day = vec_days,     # Create tibble for Joachim
                              cups = c(4, 3, 2, 3, 4, 1, 1))
team_coffee_Joachim                               # Print tibble
 
team_coffee_Cansu <- bind_cols(team_coffee_Cansu, # Bind columns
                               member = c("Cansu", "Cansu", "Cansu",
                                          "Cansu", "Cansu", "Cansu", "Cansu"))
team_coffee_Cansu                                 # Print updated tibble
 
team_coffee_Ifeanyi <- bind_cols(team_coffee_Ifeanyi, # Bind columns
                                 member = "Ifeanyi")
team_coffee_Ifeanyi                               # Print updated tibble
 
team_coffee_Joachim <- bind_cols(team_coffee_Joachim, # Bind columns
                                 member = "Joachim")
team_coffee_Joachim                               # Print updated tibble
 
team_coffee <- bind_rows(team_coffee_Cansu,       # Bind rows
                         team_coffee_Ifeanyi,
                         team_coffee_Joachim)
team_coffee %>%                                   # Print new tibble 
  print(n = nrow(team_coffee))
 
team_coffee <- team_coffee %>%                    # Reorder columns
  select(member, day, cups)
team_coffee %>%                                   # Print new tibble 
  print(n = nrow(team_coffee))
 
my_path <- "D:/Dropbox/Jock/Data Sets/dplyr Course/"  # Specify directory path
 
team_coffee %>%                                   # Export CSV file
  write_csv(str_c(my_path, "Team-Coffee-Data.csv"))

Exercise Solutions

Below, you can find our solutions for the exercises of this module. Before beginning the exercises, we will install and load the tidyverse packages. The tidyverse enables us to use the dplyr functions.

install.packages("tidyverse")		                                         # Install tidyverse packages
 
library(tidyverse)		                                                 # Load tidyverse packages

With the tidyverse packages loaded, we can now proceed to the solutions of the exercises.

Exercise 1: Create a tibble sleep_this_week containing the hours of sleep you got each night during the last 7 days (feel free to make up numbers, in case you don’t remember).

sleep_this_week <- tibble(days = c("Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"), # Create tibble
                          sleep_hours = c(5,8,4,3,7,6,5))		
 
sleep_this_week		                                                         # Print to console

In the solution above, we used the tibble() function to create a tibble sleep_this_week containing the columns days and sleep_hours. The days column contains the days of the week, while the sleep_hours column contains the hours slept each day.

Exercise 2: Create another tibble sleep_last_week containing the hours of sleep you got each night the 7 days before.

sleep_last_week <- tibble(days = c("Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"), # Create tibble
                          sleep_hours = c(7,4,6,5,3,8,2))
 
sleep_last_week		                                                         # Print to console

In the above solution, we used the tibble() function to create another tibble sleep_last_week containing the columns days and sleep_hours just like in the last solution.

Exercise 3: Add a new column week_no containing the character string “week_1” repeatedly to the tibble sleep_last_week using bind_cols(). Do the same for sleep_this_week, but add the character string “week_2”.

sleep_last_week <- bind_cols(sleep_last_week,                                    # Create new column
                             week_no = "week_1")		 
 
sleep_last_week		                                                         # Print to console
 
sleep_this_week <- bind_cols(sleep_this_week,                                    # Create new column
                             week_no = "week_2")		
 
sleep_this_week		                                                         # Print to console

Here we used the bind_cols() function to create new columns in both tibbles. In the tibble sleep_last_week, we created the new column week_no containing the character string week_1. In the tibble sleep_this_week, we created the new column week_no containing the character string week_2. In both examples, the character string of the new columns is repeated as many times as the number of rows in the tibbles.

Exercise 4: Use bind_rows() to combine sleep_this_week and sleep_last_week into a single tibble called combined_sleep_data.

combined_sleep_data <- bind_rows(sleep_this_week,                                # Bind tibbles rows
                                 sleep_last_week)		                                       
 
combined_sleep_data		                                                 # Print to console

In the solution above, we used the bind_rows() function to combine sleep_this_week and sleep_last_week together as one tibble combined_sleep_data.

Exercise 5: Export combined_sleep_data to a new CSV file called sleep-data.csv.

my_path <- "your project working directory path"	                         # Specify directory path
 
combined_sleep_data %>%                                                          # Export CSV file

write_csv(str_c(my_path, “sleep-Data.csv”))

Here we exported the combined_sleep_data tibble to our project working directory using a combination of write_csv() and str_c() functions where we specified the project working directory path. Please ensure that your working directory path ends with “/”.

Solutions to these exercises were created in collaboration with Ifeanyi Idiaye and Cansu Kebabci. Thanks to them for their contribution!

Further Resources

You can access the course overview page, timetable, and table of contents by clicking here.