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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

Empty Rows and Columns

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This lesson is called Empty Rows and Columns, part of the Data Cleaning with R course. This lesson is called Empty Rows and Columns, part of the Data Cleaning with R course.

Transcript

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Your Turn

Solution

## %######################################################%##
#                                                          #
####         Empty rows and columns - your turn         ####
#                                                          #
## %######################################################%##


# Import the Marine Protected Areas dataset (MPAS-your.csv)
# Identify the empty rows and columns, and create a new object with only the empty rows and columns
#  Remove the empty rows and columns

# load packages -----------------------------------------------------------
library(readr)
library(tibble)
library(dplyr)
library(naniar)
library(janitor)
library(unheadr)

# import data ---------------------------------------------------------------
MPAs <- read_csv("data/MPAS-your.csv")

# identify empty rows and columns -----------------------------------------
# empty row ids
empty_rows <- MPAs %>%
  rowid_to_column() %>%
  filter(across(c(-rowid, -`Rank (by extent)`), is.na)) %>%
  pull(rowid)
# empty column names
empty_cols <- MPAs %>%
  select(where(all_na)) %>%
  names()

# object with only empty rows and columns
MPAs %>%
  rowid_to_column() %>%
  select(rowid, empty_cols) %>%
  slice(empty_rows)


# remove empty ------------------------------------------------------------
MPAs %>% remove_empty(which = c("rows", "cols")) # still too many empty rows

MPAs %>%
  remove_empty(which = c("rows", "cols")) %>%
  mash_colnames(1) %>%
  filter(!across(-`Rank (by extent)`, is.na)) # negate the row selection

Import the Marine Protected Areas dataset (MPAS-your.csv)

Identify the empty rows and columns

Remove the empty rows and columns

Learn More

If you want to do even more with missing data, check out the naniar package.

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

Empty Rows and Columns

This lesson is locked

Transcript

Your Turn

Solution

Learn More

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