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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

Regular Expressions and Data Cleaning, Part 2

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This lesson is called Regular Expressions and Data Cleaning, Part 2, part of the Data Cleaning with R course. This lesson is called Regular Expressions and Data Cleaning, Part 2, part of the Data Cleaning with R course.

Transcript

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Your Turn

Solution

## %######################################################%##
#                                                          #
####        Regex for data cleaning - your turn         ####
#                                                          #
## %######################################################%##

# 1. Download CRAN package descriptions
# 2. Select Package name, author, description, and all variables that end in 'ports'
# 3. Filter rows for packages with names that:
#    - end in plot
#    - contain Bayes
#    - contain digits
#    - are all UPPER CASE


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

# download package details ------------------------------------------------
p_db <- tools::CRAN_package_db()

# select variables of interest -------------------------------------------
p_db <- p_db %>% select(Package, Author, Description, matches("ports$"))

# filter rows for package names ------------------------------------------

# end in plot
p_db %>%
  filter(str_detect(Package, "plot$")) %>%
  pull(Package)

# contains Bayes
p_db %>%
  filter(str_detect(Package, "Bayes")) %>%
  pull(Package)

# contain digits
p_db %>%
  filter(str_detect(Package, "[0-9]")) %>%
  pull(Package)

# UPPER CASE pkg name
p_db %>%
  filter(str_detect(Package, "^[A-Z]+$")) %>%
  pull(Package)

Download CRAN package descriptions using the tools package
Select package name, author, description, and all variables that end in ‘ports’
Filter rows for packages with names that:

end in plot
contain Bayes
contain digits
are all UPPER CASE

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Data Cleaning with R

Welcome to Data Cleaning with R

Data Organization

Restructuring Data