Pivoting your tables with Tidyr: Part II

Converting “long” to “wide” format


Vishal Katti


August 29, 2022

This post demonstrates how to use pivot_wider() to convert your long data to wide data. This is part 2 of the Pivoting your tables with Tidyr series.


We discussed the advantages of using the long format during data analysis, most users feel that the wide format is more readable by human. This is why most reports tend to have the data arranged in the wide format.

The wide format has at least one column which acts as a primary key i.e. it is unique and each value appears only once. It can also have multiple column whose unique combination acts as a primary key i.e. each combination appears only once.

Read more about wide vs. long formats here.

While the long format is preferred and is desirable for data and plotting operations using R, Python or other data processing programming languages, the wide format is more human-readable. The {tidyr} R package has functions that allow you to transform your tabular data between the two formats.

In this post, we will see how to convert a long dataframe to wide format using the pivot_wider() function from {tidyr} package.

The long one

Consider the following data table. It has been created from the famous Gapminder dataset. This table shows the average life expectancy in each continent for 2 years. While some of you may say that Gapminder data contains records for a lot more number of years, here we consider just the latest 2 years for ease of explanation and visual purposes. We have added an extra id column for teaching purpose.

Figure 1: Continent-wise Average Life-expectancy over last 2 years, in Long format

my_data is in the long format as we have continent names and year in their own column and average life expectancy values for each unique combination of year and continent. If we want to compare life expectancy across years for each continent, we need to have the life expectancy values for each continent side-by-side for easier viewing i.e. we need to convert to the wide format. To convert this tibble to the wide format, we need to push the year values into the headers and the average_life_expectancy values under the corresponding year column.

The wide one

The wide format of this table would ideally have only continent and columns having each unique value in the year column as a header. In this case, the wide one would look something like the table below.

Figure 2: Same as Figure 1 but in Wide format

The wide format has unique values of the column that are not pushed into headers. In this case, the continent column becomes unique for each row.

Let’s recreate the above transformation in R. First, we create the my_data table.

my_data <- data.frame(
  id = 1:10,
  year = c(2002L, 2002L, 2002L, 2002L, 2002L, 2007L, 2007L, 2007L, 2007L, 2007L),
  continent = c("Africa", "Americas", "Asia", "Europe", "Oceania", "Africa", 
                "Americas", "Asia", "Europe", "Oceania"),
  average_life_expectancy = c(53.33, 72.42, 69.23, 76.7, 79.74, 54.81, 73.61, 70.73, 77.65, 80.72)

id year continent average_life_expectancy
1 2002 Africa 53.33
2 2002 Americas 72.42
3 2002 Asia 69.23
4 2002 Europe 76.70
5 2002 Oceania 79.74
6 2007 Africa 54.81
7 2007 Americas 73.61
8 2007 Asia 70.73
9 2007 Europe 77.65
10 2007 Oceania 80.72

To convert this table into wide format, we use the pivot_wider() function from {tidyr} R package. Let us see how to use this function.


Use formals to view all the formal arguments of a function and their default values. formals returns a named list.

library(tidyr, quietly = TRUE, warn.conflicts = FALSE)





[1] ""

[1] "_"



[1] "fastest"


[1] "check_unique"






The result of formals(pivot_wider) tells us that the minimum information needed to use this function is to provide values to the data,names_from and values_from arguments as all other arguments have default values and hence, are optional.

Using only the minimum arguments with pivot_wider(), we get a wide formatted tibble but with missing data!

wide_minimal <- pivot_wider(
                        data        = my_data,
                        names_from  = year,
                        values_from = average_life_expectancy

id continent 2002 2007
1 Africa 53.33 NA
2 Americas 72.42 NA
3 Asia 69.23 NA
4 Europe 76.70 NA
5 Oceania 79.74 NA
6 Africa NA 54.81
7 Americas NA 73.61
8 Asia NA 70.73
9 Europe NA 77.65
10 Oceania NA 80.72

So why did NAs appear in the result?

pivot_wider() creates unique combinations of all columns not included in names_from or values_from argument. Therefore, if your dataframe/tibble had a primary key prior to the transformation, the primary key of your transformed “wide” dataframe is your old primary key + unique combinations of all columns not included in names_from or values_from argument. We do have id column as a primary key in the original tibble. This gives an unusable output with NAs for each combination.

To specify which column/s to be made unique, pass their name to the id_cols argument. Here we pass the continent column to the id_cols argument.

my_data_longer <- pivot_wider(
                        data        = my_data,
                        id_cols     = continent, 
                        names_from  = year,
                        values_from = average_life_expectancy

continent 2002 2007
Africa 53.33 54.81
Americas 72.42 73.61
Asia 69.23 70.73
Europe 76.70 77.65
Oceania 79.74 80.72

If you are a visual person like me and wish to see this transformation with explanations, check out this GIF I made using good ol’ PowerPoint.

{tidyr} pivot_wider() explained


pivot_wider() is the successor for the great spread() function and has many advantages over the latter. This function has many other arguments that allow some truly great transformations. Mastering this function (and its long counterpart) is a great skill upgrade while massaging your data to make it “tidy”.

Happy Spreading!




BibTeX citation:
  author = {Vishal Katti},
  editor = {},
  title = {Pivoting Your Tables with {Tidyr:} {Part} {II}},
  date = {2022-08-29},
  url = {https://vishalkatti.com/posts/tidyr-pivot-wider},
  langid = {en},
  abstract = {This post demonstrates how to use `pivot\_wider()` to
    convert your long data to wide data. This is part 2 of the Pivoting
    your tables with Tidyr series.}
For attribution, please cite this work as:
Vishal Katti. 2022. “Pivoting Your Tables with Tidyr: Part II.” August 29, 2022. https://vishalkatti.com/posts/tidyr-pivot-wider.