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Data Cleaning with R

31 steps

Welcome to Data Cleaning with R
What is Data Cleaning?
Course Logistics and Materials
Data Organization
Data Organization Best Practices
Tidy Data
Grouping and Indicator Variables
NA and Empty Values
Data Sharing Best Practices
Restructuring Data
Tidyverse Refresher
Working with Columns with across()
Pivoting Data
coalesce() and fill()
Regular Expressions
What are Regular Expressions?
Understanding and Testing Regular Expressions
Literal Characters and Metacharacters
Metacharacters: Quantifiers
Metacharacters: Alternation, Special Sequences, and Escapes
Combining Metacharacters
Regex in R
Regular Expressions and Data Cleaning, Part 1
Regular Expressions and Data Cleaning, Part 2
Common Issues
Common Issues in Data Cleaning
Unusable Variable Names
Whitespace
Letter Case
Missing, Implicit, or Misplaced Grouping Variables
Compound Values
Duplicated Values
Broken Values
Empty Rows and Columns
Parsing Numbers
Putting Everything Together

Data Cleaning with R

Whitespace

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This lesson is called Whitespace, part of the Data Cleaning with R course. This lesson is called Whitespace, part of the Data Cleaning with R course.

Transcript

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Your Turn

Solution

## %######################################################%##
#                                                          #
####               Whitespace - your turn               ####
#                                                          #
## %######################################################%##

# - Import the Marine Protected Areas data (MPAS-your.csv) from the previous lesson
# - check the Country variable for leading or trailing whitespace
# - Remove it if necessary.

# load packages -----------------------------------------------------------
library(readr)
library(stringr)
library(dplyr)

# import data -------------------------------------------------------------
MPAs <- read_csv("data/MPAS-your.csv", trim_ws = FALSE)


# check for whitespace ----------------------------------------------------
MPAs %>%
  mutate(
    leading_ws = str_detect(Country, "^ "),
    trailing_ws = str_detect(Country, " $")
  ) %>%
  select(Country, leading_ws, trailing_ws)

# remove whitespace -------------------------------------------------------
MPAs <- MPAs %>% mutate(Country = str_squish(Country))

# check if whitespace was removed
MPAs %>%
  mutate(
    leading_ws = str_detect(Country, "^ "),
    trailing_ws = str_detect(Country, " $")
  ) %>%
  select(Country, leading_ws, trailing_ws)

In the Marine Protected Areas dataset from the previous lesson (MPAS-your.csv), check the Country variable for leading or trailing whitespace and remove it if necessary.

Learn More

This book by Lingyun Zhang has a chapter on dealing with white space as does this blog post from School of Data.

Have any questions? Put them below and we will help you out!

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Data Cleaning with R

Welcome to Data Cleaning with R

Data Organization

Restructuring Data

Regular Expressions

Common Issues

Data Cleaning with R

Whitespace

This lesson is locked

Transcript

Your Turn

Solution

Learn More

Have any questions? Put them below and we will help you out!