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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: statisticsglob...
Attribution: The data used in this video is taken from here www.kaggle.com...
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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