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Data Cleaning with R

Broken Values

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

Transcript

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Your Turn

Solution

## %######################################################%##
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####             Broken values - your turn              ####
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# Load the raw Age of Empires units dataset from csv (aoe_raw.csv)
# Identify the broken values in both the 'Type' and 'Name' columns and unbreak them
# Clean up any separator-related issues arising from the 'unbreaking'

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

# load data ---------------------------------------------------------------
aoe_raw <- read_csv("data/aoe_raw.csv")

# unbreak -----------------------------------------------------------------
aoe_ub <- aoe_raw %>%
  unbreak_vals("^\\(", ogcol = Name, newcol = Name_ub) %>% # trailing half of broken values
  unbreak_rows("-$", ogcol = Type) # leading half

# cleanup -----------------------------------------------------------------
aoe_ub <- aoe_ub %>% mutate(Type = str_replace(
  string = Type,
  pattern = "- ",
  replacement = " "
))

Load the messy Age of Empires units dataset from csv (aoe_raw.csv)

Identify the broken values in both the ‘Type’ and ‘Name’ columns and unbreak them

Clean up any separator-related issues arising from the ‘unbreaking’

Learn More

To learn more about merging rows, Luis has written a helpful blog post. And to learn more about unbreaking values, check out this blog post Luis has written on the topic.

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

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Course Content

32 Lessons

What is Data Cleaning?

Course Logistics and Materials

Data Organization Best Practices

Grouping and Indicator Variables

NA and Empty Values

Data Sharing Best Practices

Tidyverse Refresher

Working with Columns with across()

coalesce() and fill()

What are Regular Expressions?

Understanding and Testing Regular Expressions

Literal Characters and Metacharacters

Metacharacters: Quantifiers

Metacharacters: Alternation, Special Sequences, and Escapes

Combining Metacharacters

Regular Expressions and Data Cleaning, Part 1

Regular Expressions and Data Cleaning, Part 2

Common Issues in Data Cleaning

Unusable Variable Names

Missing, Implicit, or Misplaced Grouping Variables

Compound Values

Duplicated Values

Empty Rows and Columns

Parsing Numbers

Putting Everything Together

Wrapping Up Data Cleaning with R