Notes on the tidyr package

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This is a compilation of notes containing examples on the use of tidyr package in R.

To begin with, let us load the package

library(tidyr)

What is a tidy data set?

A data set is called tidy when:

• each column represents a variable
• each row represent an observation

Reshaping data using tidyr

The tidyr package provides four functions to help you change the layout of your data set.

1. gather(): which collapse columns into rows
2. spread(): which spreads rows into columns
3. separate(): splits a single column into multiple
4. unite(): unite multiple columns into one

Example data sets

We’ll use the R built-in USArrests data sets. We start by subsetting a small data set, which will be used in the next sections as an example data set:

my_data<-USArrests[c(1,10,20,30),]
my_data

Row names are states, so let us use the function cbind() to add a column named “state” in the data. This will made the data tidy and the analysis easier

my_data<-cbind(state=rownames(my_data),my_data)

gather()

The function gather() collapses multiple columns into key-value pairs. It produces a “long” data format from a “wide” one. It’s an alternative of melt() function [in reshape2 package].

1. Simplified format
   • data : A data frame
   • key, value: Names of key and value columns to create an output
2. Examples of Usage:
   • Gather all columns except column state

my_data2<-gather(my_data, key="arrest_attribute", value="arrest_estimate",-state)

my_data2

¹.

Gather only Murder and Assault columns

new_data<-my_data[,(1:3)]
new_data<-gather(new_data, key="arrest_attribute", value="arrest_estimate",-state)

new_data

spread()

The function spread() does the reverse of gather. It takes down two columns( key and value) and spreads into multiple columns. It produces a wide data format from a “long” one. It’s an alternative of the function cast () [in reshape2 package].

1. Simplified format.

• data: A data frame
• key: The name of column whose values will be used as column headings
• value: The names of the column whose values will populate the cells.

2. Examples of usage Spread “my_data2” to turn back to original data:

my_data3<-spread(my_data2, key="arrest_attribute", value="arrest_estimate")

my_data3

unite()

The function unite() takes multiple columns and paste them together into one.

1. Simplified format

• data: A data frame:
• col: The new name of the of column to add
• sep: Separator to use between values

2. Examples of usage The R code below uses the dataset “my_data” and unites the column murder and assault.

my_data4<-unite(my_data,col="Murder_Assault", Murder, Assault, sep="_")

my_data4

separate()

The function separate () is the reverse of unite (). It takes values inside a single character column and separates them into multiple columns.

1. Simplified format.

• data: A data frame
• col: column names
• into: character vector specifying the names of new variables to be created.
• sep: separator between columns:
  • If character, is interpreted as a regular expression.
  • if numeric, interpreted as positions to split at. Positive values starts at 1 at the far left of the string; nagative value start at -1 at the far right of the string.

2. Examples of Usage

Separate the column “Murder_Assault” into two columns.

separate(my_data4, col="Murder_Assault", into=c("Murder", "Assault"), sep="_")

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1. Note that all column names has been collapsed into one single column except for the “state” column↩