How to Import, Manipulate & Visualize Data Using the tidyverse in R | readr, dplyr & ggplot2 Package

This video demonstrates how to import, manipulate, and visualize data using the tidyverse in the R programming language. The video is part of a teaser series for the Statistics Globe online course on "Data Manipulation in R Using dplyr & the tidyverse". More info: https://statisticsglobe.com/online-co...

Attribution: The data used in this video is taken from here https://www.kaggle.com/datasets/divya...

R code of this video:

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

my_path ＜- "D:/Dropbox/Jock/Data Sets/" # Specify directory path

tib_dest ＜- read_csv(str_c(my_path, # Import CSV file
"Most_Visited_Destination_in_2018_and_2019.csv"))
tib_dest # Print tibble

tib_dest %＞% # Class of data set
class()

tib_dest %＞% # Show entire data set
View()

tib_dest_new ＜- tib_dest %＞% # Rename column
rename(T2019 = `International tourist arrivals (2019)`)
tib_dest_new # Print updated tibble

tib_dest_new2 ＜- tib_dest_new %＞% # Remove certain columns
select(- ...1, - `International tourist arrivals (2018)`)
tib_dest_new2 # Print updated tibble

tib_dest_new3 ＜- tib_dest_new2 %＞% # Replace values
mutate(across(everything(), ~ replace(., . == "–", NA)),
T2019 = as.numeric(str_replace(T2019, " million", "")) * 1e6)
tib_dest_new3 # Print updated tibble

tib_dest_new4 ＜- tib_dest_new3 %＞% # Remove NA rows
na.omit()
tib_dest_new4 # Print updated tibble

tib_dest_new5 ＜- tib_dest_new4 %＞% # Remove duplicate row
filter(Destination != "Egypt" | Region == "Africa")
tib_dest_new5 # Print updated tibble

my_ggp ＜- tib_dest_new5 %＞% # Create ggplot2 plot
mutate(Destination = reorder(Destination, - T2019)) %＞%
ggplot(aes(x = Destination,
y = T2019,
fill = Region)) +
geom_col() +
theme(axis.text.x = element_text(angle = 90,
hjust = 1,
vjust = 0.5))
my_ggp # Draw ggplot2 plot

tib_dest %＞% # Do all at once
rename(T2019 = `International tourist arrivals (2019)`) %＞%
select(- ...1, - `International tourist arrivals (2018)`) %＞%
mutate(across(everything(), ~ replace(., . == "–", NA)),
T2019 = as.numeric(str_replace(T2019, " million", "")) * 1e6) %＞%
na.omit() %＞%
filter(Destination != "Egypt" | Region == "Africa") %＞%
mutate(Destination = reorder(Destination, - T2019)) %＞%
ggplot(aes(x = Destination,
y = T2019,
fill = Region)) +
geom_col() +
theme(axis.text.x = element_text(angle = 90,
hjust = 1,
vjust = 0.25))

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