What is the use of spread function in tidyr package?

Recipe Objective

In Data Science, we need to organise, clean, explore the data before we can create any machine learning models for best accuracy. Organising of data can be done in many ways which helps us to analyse the data well. There is a concept of Tidy Data introduced by Hadley Wickman in his paper in 2014 where every row is an observation, every column is a variable and every cell contains values in a dataframe. ​

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tidyr package is used to achieve tidy data and contains the following important functions:

1. Gather
2. Spread
3. Separate
4. Unite

In this recipe, we will demonstrate what is the use of spread() function in tidyr package. ​

Step 1: Loading required package and a dataset

Dataset description: It is the basic data about the customers going to the supermarket mall. The variable that we interested in is Annual.Income which is in 1000s , Spending Score and Age ​

# Data manipulation package library(tidyr) # ggplot for data visualisation library(ggplot2) # reading a dataset customer_seg = read.csv('R_215_Mall_Customers.csv') glimpse(customer_seg)

Rows: 200
Columns: 5
$ CustomerID              1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 1...
$ Gender                  Male, Male, Female, Female, Female, Female, ...
$ Age                     19, 21, 20, 23, 31, 22, 35, 23, 64, 30, 67, ...
$ Annual.Income..k..      15, 15, 16, 16, 17, 17, 18, 18, 19, 19, 19, ...
$ Spending.Score..1.100.  39, 81, 6, 77, 40, 76, 6, 94, 3, 72, 14, 99,...

Step 2: Using spread() function

Spread function is used to widen the dataset by spreading a key-value pair across multiple columns. In this example, we will spread the unique values in "Gender" variable in multiple columns with giving values of "Age" in each cell. ​

Syntax: spread(data = , key = , value =) ​

where: ​

1. data = dataframe
2. key = column name which needs to be expanded based on it's unique values
3. value = column name whose value is inserted into each newly formed columns

new_dataframe = spread(data = customer_seg, key = Gender, value = Age) new_dataframe

Rows: 200
Columns: 5
$ CustomerID              1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 1...
$ Annual.Income..k..      15, 15, 16, 16, 17, 17, 18, 18, 19, 19, 19, ...
$ Spending.Score..1.100.  39, 81, 6, 77, 40, 76, 6, 94, 3, 72, 14, 99,...
$ Female                  NA, NA, 20, 23, 31, 22, 35, 23, NA, 30, NA, ...
$ Male                    19, 21, NA, NA, NA, NA, NA, NA, 64, NA, 67, ...