tidyr Package in R Programming
Last Updated :
06 Aug, 2020
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")
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.
R
library(tidyr)
n = 10
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))
tidy_dataframe
|
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.
R
long <- tidy_dataframe %>%
gather(Group, Frequency,
Group.1:Group.3)
long
|
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().
R
library(tidyr)
long <- tidy_dataframe %>%
gather(Group, Frequency,
Group.1:Group.3)
separate_data <- long %>%
separate(Group, c("Allotment",
"Number"))
separate_data
|
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 “.”.
R
library(tidyr)
long <- tidy_dataframe %>%
gather(Group, Frequency,
Group.1:Group.3)
separate_data <- long %>%
separate(Group, c("Allotment",
"Number"))
unite_data <- separate_data %>%
unite(Group, Allotment,
Number, sep = ".")
unite_data
|
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.
R
library(tidyr)
long <- tidy_dataframe %>%
gather(Group, Frequency,
Group.1:Group.3)
separate_data <- long %>%
separate(Group, c("Allotment",
"Number"))
unite_data <- separate_data %>%
unite(Group, Allotment,
Number, sep = ".")
back_to_wide <- unite_data %>%
spread(Group, Frequency)
back_to_wide
|
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.
R
library(tidyr)
df <- tidy_dataframe
df %>% nest(data = c(Group.1))
|
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.
R
library(tidyr)
df <- iris
names(iris)
head(df %>% nest(data = c(Species)))
head(df %>% unnest(Species,.drop = NA,
.preserve = NULL))
|
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:
R
df <- data.frame(Month = 1:6,
Year = c(2000, rep(NA, 5)))
df
df %>% fill(Year)
|
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:
R
library(tidyr)
num_vec <- c(1, 7, 9, 14, 19, 20)
full_seq(num_vector, 1)
|
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:
R
library(tidyr)
df <- tibble(S.No = c(1:10),
Name = c('John', 'Smith', 'Peter',
'Luke', 'King', rep(NA, 5)))
df
df %>% drop_na(Name)
|
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:
R
library(tidyr)
df <- data.frame(S.No = c(1:10),
Name = c('John', 'Smith',
'Peter', 'Luke',
'King', rep(NA, 5)))
df
df %>% replace_na(list(Name = 'Henry'))
|
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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Encapsulation in R Programming
In R programming, OOPs provides classes and objects as its key tools to reduce and manage the complexity of the program. R is a functional language that uses concepts of OOPs. We can think of class like a sketch of a car. It contains all the details about the model_name, model_no, engine, etc. Based
4 min read
Polymorphism in R Programming
R language implements parametric polymorphism, which means that methods in R refer to functions, not classes. Parametric polymorphism primarily lets us define a generic method or function for types of objects we haven’t yet defined and may never do. This means that one can use the same name for seve
6 min read
R - Inheritance
Inheritance is one of the concept in object oriented programming by which new classes can derived from existing or base classes helping in re-usability of code. Derived classes can be the same as a base class or can have extended features which creates a hierarchical structure of classes in the prog
3 min read
Abstraction in R Programming
People who've been using the R language for any period of time have likely grown to be conversant in passing features as arguments to other functions. However, people are a whole lot much less probably to go back functions from their personal custom code. This is simply too horrific because doing so
5 min read
Looping over Objects in R Programming
One of the biggest issues with the “for†loop is its memory consumption and its slowness in executing a repetitive task. When it comes to dealing with a large data set and iterating over it, a for loop is not advised. In this article we will discuss How to loop over a list in R Programming Language
4 min read
S3 class in R Programming
All things in the R language are considered objects. Objects have attributes and the most common attribute related to an object is class. The command class is used to define a class of an object or learn about the classes of an object. Class is a vector and this property allows two things:  Objects
8 min read
Explicit Coercion in R Programming
Coercing of an object from one type of class to another is known as explicit coercion. It is achieved through some functions which are similar to the base functions. But they differ from base functions as they are not generic and hence do not call S3 class methods for conversion. Difference between
3 min read