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Data Cleaning with R

Tidyverse Refresher

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

Transcript

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Your Turn

Solution

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####         Useful dplyr functions - your turn         ####
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# Load the mammal sleep data bundled with ggplot2
# Select "name" and "conservation" columns and those that include the string 'sleep' in their name
# Create a new column that contains the values of 'sleep_total' multiplied by 3
# Filter the data to remove domesticated mammals
# Use pipes to chain the operations


# load packages -----------------------------------------------------------
library(dplyr)
library(ggplot2)

# load data ---------------------------------------------------------------
data("msleep")

# transform data ----------------------------------------------------------
# Select "name" and "conservation" columns and those that include the string 'sleep' in their name
# explore variable names
names(msleep)
# Select columns
msleep %>% select(name, conservation, sleep_total, sleep_rem, sleep_cycle)

# Create a new column that contains the values of 'sleep_total' multiplied by 3
msleep %>%
  select(name, conservation, sleep_total, sleep_rem, sleep_cycle) %>%
  mutate(sleep3 = sleep_total * 3)

# Filter the data to remove domesticated mammals
msleep %>%
  select(name, conservation, sleep_total, sleep_rem, sleep_cycle) %>%
  mutate(sleep3 = sleep_total * 3) %>%
  filter(conservation != "domesticated")

Create a new R script file. Within it, do the following:

Load the mammal sleep data bundled with ggplot2
Select “name” and “conservation” columns and those that include the string ‘sleep’ in their name
Create a new column that contains the values of ‘sleep_total’ multiplied by 3
Filter the data to remove domesticated mammals

Use pipes to chain the operations!

Learn More

If you're interested in learning more about the Tidyverse approach, see this article on R for the Rest of Us website. This lesson from Adam Sullivan provides an overview of data chaining. And the University of Toronto Coders Club has a nice overview of how the pipe works.

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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