tidyr Package in R Programming

Packages in the R language are a collection of R functions, compiled code, and sample data. They are stored under a directory called “library” in the R environment. By default, R installs a set of packages during installation.  One of the most important packages in R is the tidyr package. The sole purpose of the tidyr package is to simplify the process of creating tidy data. Tidy data describes a standard way of storing data that is used wherever possible throughout the tidyverse. If you once make sure that your data is tidy, you’ll spend less time punching with the tools and more time working on your analysis.

Installation 

To use a package in R programming one must have to install the package first. This task can be done using the command install.packages(“packagename”). To install the whole tidyverse package type this:

install.packages("tidyverse")

installing tidyverse

Alternatively, to install just tidyr package type this:

install.packages("tidyr")

To install the development version from GitHub type this:



# install.packages("devtools")
devtools::install_github("tidyverse/tidyr")

Important Verb Functions in tidyr Package

The Dataset:

Before going to the important verb function let’s prepare the data set first. Define a dataset tidy_dataframe that contains data about the frequency of people in a particular group.

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# load the tidyr package
library(tidyr)
  
n = 10
# creating a data frame
tidy_dataframe = data.frame(
                      S.No = c(1:n), 
                    Group.1 = c(23, 345, 76, 212, 88, 
                                199, 72, 35, 90, 265),
                    Group.2 = c(117, 89, 66, 334, 90, 
                               101, 178, 233, 45, 200),
                    Group.3 = c(29, 101, 239, 289, 176,
                                320, 89, 109, 199, 56))
  
# print the elements of the data frame
tidy_dataframe

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Output:

    S.No Group.1 Group.2 Group.3
1     1      23     117      29
2     2     345      89     101
3     3      76      66     239
4     4     212     334     289
5     5      88      90     176
6     6     199     101     320
7     7      72     178      89
8     8      35     233     109
9     9      90      45     199
10   10     265     200      56

tidyr package provides various important functions that can be used for Data Cleaning. Those are:

  • gather() function: It takes multiple columns and gathers them into key-value pairs. Basically it makes “wide” data longer. The gather() function will take multiple columns and collapse them into key-value pairs, duplicating all other columns as needed.

Syntax:        

gather(data, key = “key”, value = “value”, …, na.rm = FALSE, convert = FALSE, factor_key = FALSE) 

Parameter



Description

data the data frame.
key, value

the names of new key and value columns, 

as strings or as symbols.

…….

the selection of columns. If left empty, all variables are selected. 

You can supply bare variable names, select all variables between

 x and z with x:z, exclude y with -y.

na.rm if set TRUE, it will remove rows from output where the value column is NA.
convert

is set TRUE, it will automatically run type.convert() on the key column. 

This is useful if the column types are actually numeric,

 integer, or logical.

factor_key

if FALSE, the default, the key values will be stored as a character vector.



 If TRUE, will be stored as a factor, which preserves

the original ordering of the columns.

Example: 

Now for a better understanding, we will make our data long with gather() function.

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# using gather() function on tidy_dataframe
long <- tidy_dataframe %>% 
            gather(Group, Frequency,
                   Group.1:Group.3)
  
# print the data frame in a long format
long

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 Output:

    S.No  Group   Frequency
1     1 Group.1        23
2     2 Group.1       345
3     3 Group.1        76
4     4 Group.1       212
5     5 Group.1        88
6     6 Group.1       199
7     7 Group.1        72
8     8 Group.1        35
9     9 Group.1        90
10   10 Group.1       265
11    1 Group.2       117
12    2 Group.2        89
13    3 Group.2        66
14    4 Group.2       334
15    5 Group.2        90
16    6 Group.2       101
17    7 Group.2       178
18    8 Group.2       233
19    9 Group.2        45
20   10 Group.2       200
21    1 Group.3        29
22    2 Group.3       101
23    3 Group.3       239
24    4 Group.3       289
25    5 Group.3       176
26    6 Group.3       320
27    7 Group.3        89
28    8 Group.3       109
29    9 Group.3       199
30   10 Group.3        56
  • separate() function: It converts longer data to a wider format. The separate() function turns a single character column into multiple columns.

   Syntax:

   separate(data, col, into, sep = ” “, remove = TRUE, convert = FALSE)

Parameter

Description



data A data frame.
col Column name or position.
into

Names of new variables to create as character vector. 

Use NA to omit the variable in the output.

sep The separator between the columns.
remove If set TRUE, it will remove input column from the output data frame.
convert If TRUE, will run type.convert() with as.is = TRUE on new columns.

Example: 

We can say that the long datasets created using gather() is appropriate for use, but we can break down Group variable even further using separate()

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# import tidyr package
library(tidyr)
long <- tidy_dataframe %>%
            gather(Group, Frequency,
                   Group.1:Group.3)
  
# use separate() function to make data wider
separate_data <- long %>% 
            separate(Group, c("Allotment"
                              "Number"))
  
# print the wider format
separate_data

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Output:

   S.No Allotment Number Frequency
1     1     Group      1        23
2     2     Group      1       345
3     3     Group      1        76
4     4     Group      1       212
5     5     Group      1        88
6     6     Group      1       199
7     7     Group      1        72
8     8     Group      1        35
9     9     Group      1        90
10   10     Group      1       265
11    1     Group      2       117
12    2     Group      2        89
13    3     Group      2        66
14    4     Group      2       334
15    5     Group      2        90
16    6     Group      2       101
17    7     Group      2       178
18    8     Group      2       233
19    9     Group      2        45
20   10     Group      2       200
21    1     Group      3        29
22    2     Group      3       101
23    3     Group      3       239
24    4     Group      3       289
25    5     Group      3       176
26    6     Group      3       320
27    7     Group      3        89
28    8     Group      3       109
29    9     Group      3       199
30   10     Group      3        56
  • unite() function: It merges two columns into one column. The unite() function is a convenience function to paste together multiple variable values into one. In essence, it combines two variables of a single observation into one variable.

Syntax: 

unite(data, col, …, sep = “_”, remove = TRUE)

Parameter

Description

data A data frame.
col The name of the new column.
…. A selection of desired columns. If empty, all variables are selected. 
sep A separator to use between values.
remove If TRUE, remove input columns from output data frame.

Example:



Unite is the compliment of separate. To undo separate(), we can use unite(), which merges two variables into one. Here we will merge two columns Group and Number with a separator “.”

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# import tidyr package 
library(tidyr)
  
long <- tidy_dataframe %>%
            gather(Group, Frequency, 
                   Group.1:Group.3)
  
# use separate() function to make data wider
separate_data <- long %>% 
            separate(Group, c("Allotment",
                              "Number"))
  
# use unite() function to glue 
# Allotment and Number columns
unite_data <- separate_data %>%
            unite(Group, Allotment, 
                  Number, sep = ".")
  
# print the new data frame
unite_data

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Output:

   S.No   Group Frequency
1     1 Group.1        23
2     2 Group.1       345
3     3 Group.1        76
4     4 Group.1       212
5     5 Group.1        88
6     6 Group.1       199
7     7 Group.1        72
8     8 Group.1        35
9     9 Group.1        90
10   10 Group.1       265
11    1 Group.2       117
12    2 Group.2        89
13    3 Group.2        66
14    4 Group.2       334
15    5 Group.2        90
16    6 Group.2       101
17    7 Group.2       178
18    8 Group.2       233
19    9 Group.2        45
20   10 Group.2       200
21    1 Group.3        29
22    2 Group.3       101
23    3 Group.3       239
24    4 Group.3       289
25    5 Group.3       176
26    6 Group.3       320
27    7 Group.3        89
28    8 Group.3       109
29    9 Group.3       199
30   10 Group.3        56
  • spread() function: It helps in reshaping a longer format to a wider format. The spread() function spreads a key-value pair across multiple columns.

Syntax:

spread(data, key, value, fill = NA, convert = FALSE)

Parameter

Description

data A data frame.
key Column names or positions.
value Column names or positions.
fill If set, missing values will be replaced with this value. 
convert If TRUE, type.convert() with asis = TRUE will be run on each of the new columns.

Example:

We can transform the data from long back to wide with the spread() function. 

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# import tidyr package 
library(tidyr)
  
long <- tidy_dataframe %>% 
            gather(Group, Frequency,
                   Group.1:Group.3)
  
# use separate() function to make data wider
separate_data <- long %>% 
            separate(Group, c("Allotment"
                              "Number"))
  
# use unite() function to glue
# Allotment and Number columns
unite_data <- separate_data %>% 
            unite(Group, Allotment,
                  Number, sep = ".")
  
# use unite() function to make data wider
back_to_wide <- unite_data %>% 
            spread(Group, Frequency)
  
# print the new data frame
back_to_wide

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Output:



   S.No Group.1 Group.2 Group.3
1     1      23     117      29
2     2     345      89     101
3     3      76      66     239
4     4     212     334     289
5     5      88      90     176
6     6     199     101     320
7     7      72     178      89
8     8      35     233     109
9     9      90      45     199
10   10     265     200      56
  • nest() function: It creates a list of data frames containing all the nested variables. Nesting is implicitly a summarizing operation. This is useful in conjunction with other summaries that work with whole datasets, most notably models.

Syntax: nest(data, …, .key = “data”)

Parameter

Description

data A data frame.
…. A selection of columns. If empty, all variables are selected.
.key The name of the new column, as a string or symbol.

Example: Let’s try to nest Group.2 column from the tidy_dataframe we created in the data set.

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# import tidyr package
library(tidyr)
  
df <- tidy_dataframe
  
# nest column Group.1 in 
# tidy_dataframe using nest()
df %>% nest(data = c(Group.1))

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Output:

# A tibble: 10 x 4
    S.No Group.1 Group.3 data            
   <int>   <dbl>   <dbl> <list>          
 1     1      23      29 <tibble [1 x 1]>
 2     2     345     101 <tibble [1 x 1]>
 3     3      76     239 <tibble [1 x 1]>
 4     4     212     289 <tibble [1 x 1]>
 5     5      88     176 <tibble [1 x 1]>
 6     6     199     320 <tibble [1 x 1]>
 7     7      72      89 <tibble [1 x 1]>
 8     8      35     109 <tibble [1 x 1]>
 9     9      90     199 <tibble [1 x 1]>
10    10     265      56 <tibble [1 x 1]>
  • unnest() function: It basically reverses the nest operation. It makes each element of the list its own row. It can handle list columns that contain atomic vectors, lists, or data frames (but not a mixture of the different types).

Syntax:

unnest(data, …, .drop = NA, .id = NULL, .sep = NULL, .preserve = NULL)

Parameter

Description

data A data frame
…. Specification of columns to unnest.  If omitted, defaults to all list-columns.
.drop

Should additional list columns be dropped? By default,

it will drop them if unnesting 



the specified columns requires the rows to be duplicated.

.id Data frame identifier.
.sep

If non-NULL, the names of unnested data frame columns 

will combine the name of the original list-col with

the names from nested data frame, separated by .sep.

.preserve

List-columns to preserve in the output. These will be

 duplicated in the same way as atomic vectors.

Example:

We will try to nest and unnest Species column in the iris dataframe in the tidyr package.

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# import the tidyr package
library(tidyr)
  
df <- iris
names(iris)
  
# nesting the species column in 
# the df data frame using nest()
head(df %>% nest(data = c(Species)))  # Output (i)
  
# unnesting the species column 
# in the df data frame using unnest()
head(df %>% unnest(Species,.drop = NA,
                   .preserve = NULL)) # Output (ii)

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Output (i): 



# A tibble: 6 x 5
  Sepal.Length Sepal.Width Petal.Length Petal.Width data            
         <dbl>       <dbl>        <dbl>       <dbl> <list>          
1          5.1         3.5          1.4         0.2 <tibble [1 x 1]>
2          4.9         3            1.4         0.2 <tibble [1 x 1]>
3          4.7         3.2          1.3         0.2 <tibble [1 x 1]>
4          4.6         3.1          1.5         0.2 <tibble [1 x 1]>
5          5           3.6          1.4         0.2 <tibble [1 x 1]>
6          5.4         3.9          1.7         0.4 <tibble [1 x 1]>

Output (ii):

# A tibble: 6 x 5
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
         <dbl>       <dbl>        <dbl>       <dbl> <fct>  
1          5.1         3.5          1.4         0.2 setosa 
2          4.9         3            1.4         0.2 setosa 
3          4.7         3.2          1.3         0.2 setosa 
4          4.6         3.1          1.5         0.2 setosa 
5          5           3.6          1.4         0.2 setosa 
6          5.4         3.9          1.7         0.4 setosa 
  • fill() function: Used to fill in the missing values in selected columns using the previous entry. This is useful in the common output format where values are not repeated, they’re recorded each time they change. Missing values are replaced in atomic vectors; NULL is replaced in the list.

Syntax: 

fill(data, …, .direction = c(“down”, “up”))

Parameter

Description

data A data frame.
…. A selection of columns. If empty, nothing happens.
direction Direction in which to fill missing values. Currently, either “down” (the default) or “up”

Example:

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# import the tidyr package
df <- data.frame(Month = 1:6, 
                 Year = c(2000, rep(NA, 5)))
  
# print the df data frame
df                   # Output (i)
  
# use fill() to fill missing values in 
# Year column in df data frame
df %>% fill(Year)    # Output (ii)

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Output (i):

  Month Year
1     1 2000
2     2   NA
3     3   NA
4     4   NA
5     5   NA
6     6   NA

Output (ii):

  Month Year
1     1 2000
2     2 2000
3     3 2000
4     4 2000
5     5 2000
6     6 2000
  • full_seq() function: It basically fills the missing values in a vector which should have been observed but weren’t. The vector should be numeric.

Syntax: full_seq(x, period, tol = 1e-06)

Parameter

Description



x A numeric vector.
period Gap between each observation.
tol Numerical tolerance for checking periodicity.

Example: 

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# import the tidyr package
library(tidyr)
  
# creating a numeric vector
num_vec <- c(1, 7, 9, 14, 19, 20)
  
# use full_seq() to fill missing
# values in num_vec
full_seq(num_vector, 1)

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Output:

[1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
  • drop_na() function: This function drops rows containing missing values.

Syntax: drop_na(data, …)

Parameter

Description

 data  A data frame.
 ….  A selection of columns. If empty, all variables are selected.

Example:

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# import tidyr package
library(tidyr)
  
# create a tibble df with missing values
df <- tibble(S.No = c(1:10),
             Name = c('John', 'Smith', 'Peter',
                      'Luke', 'King', rep(NA, 5)))
  
# print df tibble
df                    # Output (i)
  
# use drop_na() to drop columns 
# in df with missing values 
df %>% drop_na(Name)  # Output (ii)

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Output (i):

# A tibble: 10 x 2
    S.No Name 
   <int> <chr>
 1     1 John 
 2     2 Smith
 3     3 Peter
 4     4 Luke 
 5     5 King 
 6     6 <NA> 
 7     7 <NA> 
 8     8 <NA> 
 9     9 <NA> 
10    10 <NA> 

Output (ii):

# A tibble: 5 x 2
   S.No Name 
  <int> <chr>
1     1 John 
2     2 Smith
3     3 Peter
4     4 Luke 
5     5 King 
  • replace_na() function: It replaces missing values.

Syntax: replace_na(data, replace, …)



Parameter

Description

data A data frame.
replace

If data is a data frame, returns a data frame. If data is a vector, 

returns a vector of class determined by the union of data and replace.

Example:

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# import tidyr package
library(tidyr)
  
df <- data.frame(S.No = c(1:10),
                 Name = c('John', 'Smith'
                          'Peter', 'Luke',
                          'King', rep(NA, 5)))
  
df                                      # Output (i)
  
# use replace_na() to replace missing values or na
df %>% replace_na(list(Name = 'Henry')) # Output (ii)

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Output (i):

# A tibble: 10 x 2
     S.No Name 
   <int> <chr>
 1     1 John 
 2     2 Smith
 3     3 Peter
 4     4 Luke 
 5     5 King 
 6     6 <NA> 
 7     7 <NA> 
 8     8 <NA> 
 9     9 <NA> 
10    10 <NA> 

Output (ii):

    S.No  Name
1     1  John
2     2 Smith
3     3 Peter
4     4  Luke
5     5  King
6     6 Henry
7     7 Henry
8     8 Henry
9     9 Henry
10   10 Henry



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