Using readr and tidyr R tidyverse packages

Continuing our series of posts on the R language, to start coding we need to install the R application through the link https://cran.r-project.org/bin/windows/base/ (check your operating system). There is very good IDE called RStudio, but it’s paid, giving you a trial period. It can be downloaded from the link https://posit.co/download/rstudio-desktop/.

With R installed, let’s proceed with the demonstration of using two essential tidyverse packages called ‘ readr’ and ‘ tidyr’.

# We install the tidyverse package, but we could have installed only 'readr'

install.packages("tidyverse")

# We load the readr package

library(readr)

# We load the .csv file into a Data Frame variable (works like a table) using the read_csv2 function since our file uses semicolon as a separator. For files that use comma as separator, use the 'read_csv()' function

companies <- read_csv2("C:/.../COMPANIES.csv")

If you need to specify another delimiter for the file, just use another function called ‘read_delim()’ and inform the character that should be used as separator.

# We can visualize a sample of the newly loaded data using head() function

head(companies)

head() function result

# Let's load the tidyr package to clean and organize the data

library(tidyr)

# Through the 'separate' function we can split a column into one or more. In the case of the 'ADDRESS' column, we can split it by comma into other columns

companies <- companies %>%
 separate(ADDRESS, c("NUMBER", "STREET", "CITY", "STATE", "COUNTRY"), ",") 

The tidyr package has several other functions that can be explored. For more information, I suggest accessing the link https://livro.curso-r.com/7-3-tidyr.html

We can now visualize our data after the manipulations made:

# The 'View()' function generates a table view for the DataFrame

View(companies)

View() function result

All packages have a lot to explore, so feel free to navigate through https://livro.curso-r.com/ and try some others functions. In the next post about the R language, we will talk about the other two essential tidyverse packages called dplyr and ggplot2. Stay tuned!

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