Data Pivoting with tidyr
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Reshaping data from long to wide format, or wide to long format, is a common task in data science. Until recently, the best functions for performing this task in R were the gather and spread functions from the tidyr package. However, these functions had limitations, such as only being able to reshape one variable at a time, that required creative workarounds. The newest version of tidyr introduces the pivot_longer() and pivot_wider() functions that perform the same tasks, but that also handle a wider variety of use cases. Additionally, the function and argument names have been changed to be more intuitive. The purpose of this blog post is to help make the transition from gather() and spread() to the new pivoting functions.
It is commonly said that data scientists spend 80% of their time data cleaning and only 20% actually analyzing the data. Every dataset is messy in its own way, and it can take a while to get the data into a format that your analysis tools can work with. The package tidyr provides tools to help you get your input data into a standardized tidy dataframe.
Some of the tasks that tidyr can help with include:
- pivoting: changing the representation of a rectangular dataset (e.g. reshaping from long to wide format)
- rectangling: turning nested lists into tibbles
- nesting: dataframe where a column is a list of data-frames
- separating/combining columns: splitting a single character vector into multiple, or combining multiple into one
- missing values: tools for handling missing values and converting between implicit and explicit missing values
In this post we will focus on pivoting. In particular, tidyr’s change in syntax from the gather() and spread() functions to pivot_longer() and pivot_wider().
tidyr syntax changes
The most popular functions from tidyr are those used to pivot a rectangular dataset to a longer or wider format, gather() and spread(). However, with the release of tidyr version 1.0.0 (09/11/19), pivot_longer() and pivot_wider() have been released to replace them.
A high-level comparison of the old and new syntax:
- Pivot to a wider format
spread(data, key, value)key– Values of thekeycolumn will become column namesvalue– Cell values will be taken from thevaluecolumn
pivot_wider(data, names_from, values_from)names_from– Values of thenames_fromcolumn will become column namesvalues_from– Cell values will be taken from thevalues_fromcolumn
- Pivot to a longer format
gather(data, key, value, ...)key– Name of column to be created which contains the column names of gathered columns as valuesvalue– Name of column to be created with the data stored in cell values of gathered columns...– Columns to pivot to longer format
pivot_longer(data, cols, names_to, values_to)cols– Columns to pivot to longer formatnames_to– Name of column to be created which contains the column names of gathered columns as valuesvalues_to– Name of column to be created with the data stored in cell values of gathered columns
The usage of the functions remains the same, but the function and argument names have been changed to be more intuitive.
Example Data
As an example, we will look at how to use tidyr to change between three representations of the gapminder::gapminder dataset.
We first load in the packages that we’ll use and create two additional representations of the data. Don’t worry about understanding this code for now.
library(tidyverse)
library(gapminder)
gapminder_long <- gapminder %>%
pivot_longer(
lifeExp:gdpPercap,
names_to = "measure",
values_to = "value"
)
gapminder_wide <- gapminder %>%
pivot_wider(
names_from = year,
values_from = c(lifeExp, pop, gdpPercap)
) %>%
select(
country, continent, ends_with("52"), ends_with("57"),
ends_with("62"), ends_with("67"), ends_with("72"),
ends_with("77"), ends_with("82"), ends_with("87"),
ends_with("92"), ends_with("97"), ends_with("02"),
ends_with("07")
)
We now have three representations of the same dataset, gapminder, gapminder_long, and gapminder_wide.
gapminder ## # A tibble: 1,704 x 6 ## country continent year lifeExp pop gdpPercap ## <fct> <fct> <int> <dbl> <int> <dbl> ## 1 Afghanistan Asia 1952 28.8 8425333 779. ## 2 Afghanistan Asia 1957 30.3 9240934 821. ## 3 Afghanistan Asia 1962 32.0 10267083 853. ## 4 Afghanistan Asia 1967 34.0 11537966 836. ## 5 Afghanistan Asia 1972 36.1 13079460 740. ## 6 Afghanistan Asia 1977 38.4 14880372 786. ## 7 Afghanistan Asia 1982 39.9 12881816 978. ## 8 Afghanistan Asia 1987 40.8 13867957 852. ## 9 Afghanistan Asia 1992 41.7 16317921 649. ## 10 Afghanistan Asia 1997 41.8 22227415 635. ## # ... with 1,694 more rows
gapminder has one row for each pair of country and year, and one column for each measure (lifeExp, pop, gdpPercap).
gapminder_long ## # A tibble: 5,112 x 5 ## country continent year measure value ## <fct> <fct> <int> <chr> <dbl> ## 1 Afghanistan Asia 1952 lifeExp 28.8 ## 2 Afghanistan Asia 1952 pop 8425333 ## 3 Afghanistan Asia 1952 gdpPercap 779. ## 4 Afghanistan Asia 1957 lifeExp 30.3 ## 5 Afghanistan Asia 1957 pop 9240934 ## 6 Afghanistan Asia 1957 gdpPercap 821. ## 7 Afghanistan Asia 1962 lifeExp 32.0 ## 8 Afghanistan Asia 1962 pop 10267083 ## 9 Afghanistan Asia 1962 gdpPercap 853. ## 10 Afghanistan Asia 1967 lifeExp 34.0 ## # ... with 5,102 more rows
We can notice that the three measure columns from before have been combined into two columns: measure and value. Also, the data now has three rows for each pair of country and year. This is considered to be in a longer format, because columns were collapsed and the information is stored as additional rows.
gapminder_wide ## # A tibble: 142 x 38 ## country continent lifeExp_1952 pop_1952 gdpPercap_1952 lifeExp_1957 ## <fct> <fct> <dbl> <int> <dbl> <dbl> ## 1 Afghan~ Asia 28.8 8425333 779. 30.3 ## 2 Albania Europe 55.2 1282697 1601. 59.3 ## 3 Algeria Africa 43.1 9279525 2449. 45.7 ## 4 Angola Africa 30.0 4232095 3521. 32.0 ## 5 Argent~ Americas 62.5 17876956 5911. 64.4 ## 6 Austra~ Oceania 69.1 8691212 10040. 70.3 ## 7 Austria Europe 66.8 6927772 6137. 67.5 ## 8 Bahrain Asia 50.9 120447 9867. 53.8 ## 9 Bangla~ Asia 37.5 46886859 684. 39.3 ## 10 Belgium Europe 68 8730405 8343. 69.2 ## # ... with 132 more rows, and 32 more variables: pop_1957 <int>, ## # gdpPercap_1957 <dbl>, lifeExp_1962 <dbl>, pop_1962 <int>, ## # gdpPercap_1962 <dbl>, lifeExp_1967 <dbl>, pop_1967 <int>, ## # gdpPercap_1967 <dbl>, lifeExp_1972 <dbl>, pop_1972 <int>, ## # gdpPercap_1972 <dbl>, lifeExp_1977 <dbl>, pop_1977 <int>, ## # gdpPercap_1977 <dbl>, lifeExp_1982 <dbl>, pop_1982 <int>, ## # gdpPercap_1982 <dbl>, lifeExp_1987 <dbl>, pop_1987 <int>, ## # gdpPercap_1987 <dbl>, lifeExp_1992 <dbl>, pop_1992 <int>, ## # gdpPercap_1992 <dbl>, lifeExp_1997 <dbl>, pop_1997 <int>, ## # gdpPercap_1997 <dbl>, lifeExp_2002 <dbl>, pop_2002 <int>, ## # gdpPercap_2002 <dbl>, lifeExp_2007 <dbl>, pop_2007 <int>, ## # gdpPercap_2007 <dbl>
In gapminder_wide the year variable has been spread into multiple columns. There is now only one row per country, but a column for each pair of measure and year. This is considered to be a wider representation, because information that was being stored as rows are now additional columns.
pivot_wider() example
Suppose we start with gapminder_long, but we need the data to be formatted like gapminder.
gapminder_long ## # A tibble: 5,112 x 5 ## country continent year measure value ## <fct> <fct> <int> <chr> <dbl> ## 1 Afghanistan Asia 1952 lifeExp 28.8 ## 2 Afghanistan Asia 1952 pop 8425333 ## 3 Afghanistan Asia 1952 gdpPercap 779. ## 4 Afghanistan Asia 1957 lifeExp 30.3 ## 5 Afghanistan Asia 1957 pop 9240934 ## 6 Afghanistan Asia 1957 gdpPercap 821. ## 7 Afghanistan Asia 1962 lifeExp 32.0 ## 8 Afghanistan Asia 1962 pop 10267083 ## 9 Afghanistan Asia 1962 gdpPercap 853. ## 10 Afghanistan Asia 1967 lifeExp 34.0 ## # ... with 5,102 more rows gapminder ## # A tibble: 1,704 x 6 ## country continent year lifeExp pop gdpPercap ## <fct> <fct> <int> <dbl> <int> <dbl> ## 1 Afghanistan Asia 1952 28.8 8425333 779. ## 2 Afghanistan Asia 1957 30.3 9240934 821. ## 3 Afghanistan Asia 1962 32.0 10267083 853. ## 4 Afghanistan Asia 1967 34.0 11537966 836. ## 5 Afghanistan Asia 1972 36.1 13079460 740. ## 6 Afghanistan Asia 1977 38.4 14880372 786. ## 7 Afghanistan Asia 1982 39.9 12881816 978. ## 8 Afghanistan Asia 1987 40.8 13867957 852. ## 9 Afghanistan Asia 1992 41.7 16317921 649. ## 10 Afghanistan Asia 1997 41.8 22227415 635. ## # ... with 1,694 more rows
We’d like there to be columns for lifeExp, pop, and gdpPercap.
We need to:
- pivot the dataset to a wider format (
pivot_wider()) - names of the new columns come from the
measurecolumn (names_from = measure) - values for the new columns come from the
valuecolumn (values_from = value)
gapminder_long %>%
pivot_wider(
names_from = measure,
values_from = value
)
## # A tibble: 1,704 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <dbl> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
## 7 Afghanistan Asia 1982 39.9 12881816 978.
## 8 Afghanistan Asia 1987 40.8 13867957 852.
## 9 Afghanistan Asia 1992 41.7 16317921 649.
## 10 Afghanistan Asia 1997 41.8 22227415 635.
## # ... with 1,694 more rows
With spread(), the syntax is the same, but the arguments are named key and value.
gapminder_long %>%
spread(
key = measure,
value = value
)
## # A tibble: 1,704 x 6
## country continent year gdpPercap lifeExp pop
## <fct> <fct> <int> <dbl> <dbl> <dbl>
## 1 Afghanistan Asia 1952 779. 28.8 8425333
## 2 Afghanistan Asia 1957 821. 30.3 9240934
## 3 Afghanistan Asia 1962 853. 32.0 10267083
## 4 Afghanistan Asia 1967 836. 34.0 11537966
## 5 Afghanistan Asia 1972 740. 36.1 13079460
## 6 Afghanistan Asia 1977 786. 38.4 14880372
## 7 Afghanistan Asia 1982 978. 39.9 12881816
## 8 Afghanistan Asia 1987 852. 40.8 13867957
## 9 Afghanistan Asia 1992 649. 41.7 16317921
## 10 Afghanistan Asia 1997 635. 41.8 22227415
## # ... with 1,694 more rows
pivot_longer() example
For this example, we will format gapminder_wide so that there is a row for every country and year pair.
gapminder_wide ## # A tibble: 142 x 38 ## country continent lifeExp_1952 pop_1952 gdpPercap_1952 lifeExp_1957 ## <fct> <fct> <dbl> <int> <dbl> <dbl> ## 1 Afghan~ Asia 28.8 8425333 779. 30.3 ## 2 Albania Europe 55.2 1282697 1601. 59.3 ## 3 Algeria Africa 43.1 9279525 2449. 45.7 ## 4 Angola Africa 30.0 4232095 3521. 32.0 ## 5 Argent~ Americas 62.5 17876956 5911. 64.4 ## 6 Austra~ Oceania 69.1 8691212 10040. 70.3 ## 7 Austria Europe 66.8 6927772 6137. 67.5 ## 8 Bahrain Asia 50.9 120447 9867. 53.8 ## 9 Bangla~ Asia 37.5 46886859 684. 39.3 ## 10 Belgium Europe 68 8730405 8343. 69.2 ## # ... with 132 more rows, and 32 more variables: pop_1957 <int>, ## # gdpPercap_1957 <dbl>, lifeExp_1962 <dbl>, pop_1962 <int>, ## # gdpPercap_1962 <dbl>, lifeExp_1967 <dbl>, pop_1967 <int>, ## # gdpPercap_1967 <dbl>, lifeExp_1972 <dbl>, pop_1972 <int>, ## # gdpPercap_1972 <dbl>, lifeExp_1977 <dbl>, pop_1977 <int>, ## # gdpPercap_1977 <dbl>, lifeExp_1982 <dbl>, pop_1982 <int>, ## # gdpPercap_1982 <dbl>, lifeExp_1987 <dbl>, pop_1987 <int>, ## # gdpPercap_1987 <dbl>, lifeExp_1992 <dbl>, pop_1992 <int>, ## # gdpPercap_1992 <dbl>, lifeExp_1997 <dbl>, pop_1997 <int>, ## # gdpPercap_1997 <dbl>, lifeExp_2002 <dbl>, pop_2002 <int>, ## # gdpPercap_2002 <dbl>, lifeExp_2007 <dbl>, pop_2007 <int>, ## # gdpPercap_2007 <dbl>
We want columns 3-38 to become year, lifeExp, pop, and gdpPercap.
We’ll come back to this problem, but for now let’s look at a simplified version with only the gdpPercap columns.
gapminder_wide_gdp <- gapminder_wide %>%
select(country, continent, starts_with("gdp"))
gapminder_wide_gdp
## # A tibble: 142 x 14
## country continent gdpPercap_1952 gdpPercap_1957 gdpPercap_1962
## <fct> <fct> <dbl> <dbl> <dbl>
## 1 Afghan~ Asia 779. 821. 853.
## 2 Albania Europe 1601. 1942. 2313.
## 3 Algeria Africa 2449. 3014. 2551.
## 4 Angola Africa 3521. 3828. 4269.
## 5 Argent~ Americas 5911. 6857. 7133.
## 6 Austra~ Oceania 10040. 10950. 12217.
## 7 Austria Europe 6137. 8843. 10751.
## 8 Bahrain Asia 9867. 11636. 12753.
## 9 Bangla~ Asia 684. 662. 686.
## 10 Belgium Europe 8343. 9715. 10991.
## # ... with 132 more rows, and 9 more variables: gdpPercap_1967 <dbl>,
## # gdpPercap_1972 <dbl>, gdpPercap_1977 <dbl>, gdpPercap_1982 <dbl>,
## # gdpPercap_1987 <dbl>, gdpPercap_1992 <dbl>, gdpPercap_1997 <dbl>,
## # gdpPercap_2002 <dbl>, gdpPercap_2007 <dbl>
We want columns 3-14 to become two columns: year and gdpPercap. To do this we pivot the data to a longer format (pivot_longer()).
gapminder_wide_gdp %>%
pivot_longer(
gdpPercap_1952:gdpPercap_2007
)
## # A tibble: 1,704 x 4
## country continent name value
## <fct> <fct> <chr> <dbl>
## 1 Afghanistan Asia gdpPercap_1952 779.
## 2 Afghanistan Asia gdpPercap_1957 821.
## 3 Afghanistan Asia gdpPercap_1962 853.
## 4 Afghanistan Asia gdpPercap_1967 836.
## 5 Afghanistan Asia gdpPercap_1972 740.
## 6 Afghanistan Asia gdpPercap_1977 786.
## 7 Afghanistan Asia gdpPercap_1982 978.
## 8 Afghanistan Asia gdpPercap_1987 852.
## 9 Afghanistan Asia gdpPercap_1992 649.
## 10 Afghanistan Asia gdpPercap_1997 635.
## # ... with 1,694 more rows
gapminder_wide_gdp %>%
gather(
gdpPercap_1952:gdpPercap_2007
)
## Must supply a symbol or a string as argument
This intuitive syntax doesn’t work for gather(). We have to remember to first pass names of new columns to key and value.
gapminder_wide_gdp %>%
gather(
"key",
"value",
gdpPercap_1952:gdpPercap_2007
)
## # A tibble: 1,704 x 4
## country continent key value
## <fct> <fct> <chr> <dbl>
## 1 Afghanistan Asia gdpPercap_1952 779.
## 2 Albania Europe gdpPercap_1952 1601.
## 3 Algeria Africa gdpPercap_1952 2449.
## 4 Angola Africa gdpPercap_1952 3521.
## 5 Argentina Americas gdpPercap_1952 5911.
## 6 Australia Oceania gdpPercap_1952 10040.
## 7 Austria Europe gdpPercap_1952 6137.
## 8 Bahrain Asia gdpPercap_1952 9867.
## 9 Bangladesh Asia gdpPercap_1952 684.
## 10 Belgium Europe gdpPercap_1952 8343.
## # ... with 1,694 more rows
- Column names should go to a
yearvariable (names_to = year) - Cell values should go to a
gdpPercapvariable (values_to = gdpPercap)
gapminder_wide_gdp %>%
pivot_longer(
gdpPercap_1952:gdpPercap_2007,
names_to = "year",
values_to = "gdpPercap"
)
## # A tibble: 1,704 x 4
## country continent year gdpPercap
## <fct> <fct> <chr> <dbl>
## 1 Afghanistan Asia gdpPercap_1952 779.
## 2 Afghanistan Asia gdpPercap_1957 821.
## 3 Afghanistan Asia gdpPercap_1962 853.
## 4 Afghanistan Asia gdpPercap_1967 836.
## 5 Afghanistan Asia gdpPercap_1972 740.
## 6 Afghanistan Asia gdpPercap_1977 786.
## 7 Afghanistan Asia gdpPercap_1982 978.
## 8 Afghanistan Asia gdpPercap_1987 852.
## 9 Afghanistan Asia gdpPercap_1992 649.
## 10 Afghanistan Asia gdpPercap_1997 635.
## # ... with 1,694 more rows
gapminder_wide_gdp %>%
gather(
key = "year",
value = "gdpPercap",
gdpPercap_1952:gdpPercap_2007
)
## # A tibble: 1,704 x 4
## country continent year gdpPercap
## <fct> <fct> <chr> <dbl>
## 1 Afghanistan Asia gdpPercap_1952 779.
## 2 Albania Europe gdpPercap_1952 1601.
## 3 Algeria Africa gdpPercap_1952 2449.
## 4 Angola Africa gdpPercap_1952 3521.
## 5 Argentina Americas gdpPercap_1952 5911.
## 6 Australia Oceania gdpPercap_1952 10040.
## 7 Austria Europe gdpPercap_1952 6137.
## 8 Bahrain Asia gdpPercap_1952 9867.
## 9 Bangladesh Asia gdpPercap_1952 684.
## 10 Belgium Europe gdpPercap_1952 8343.
## # ... with 1,694 more rows
The year column needs some cleaning, but this is the structure that we were looking for.
New Features
Aside from the minor syntax changes, the new pivoting functions have additional features that its predecessors do not.
pivot_wider():names_fromandvalues_fromcan be multiple columns rather than onenames_sep: when there are multiplenames_fromorvalues_fromcolumns,names_sepwill be used to join values together to form column names
names_prefix: append a string to the beginning of every variable name
pivot_longer():names_tocan be a character vector, creating multiple columns (requiresnames_sepornames_pattern)names_sep: numeric vector (specifying positions to break on), or a single string (specifying a regular expression to split on) (separate())names_pattern: regular expression containing matching groups (specified by()) (extract())
names_prefix: remove matching text from the beginning of every variable namenames_ptypesandvalues_ptypesallows you to specify the column types of the newly created name and value columns
pivot_wider() new features
names_prefix
gapminder_gdp <- gapminder %>% select(country, continent, year, gdpPercap) gapminder_gdp ## # A tibble: 1,704 x 4 ## country continent year gdpPercap ## <fct> <fct> <int> <dbl> ## 1 Afghanistan Asia 1952 779. ## 2 Afghanistan Asia 1957 821. ## 3 Afghanistan Asia 1962 853. ## 4 Afghanistan Asia 1967 836. ## 5 Afghanistan Asia 1972 740. ## 6 Afghanistan Asia 1977 786. ## 7 Afghanistan Asia 1982 978. ## 8 Afghanistan Asia 1987 852. ## 9 Afghanistan Asia 1992 649. ## 10 Afghanistan Asia 1997 635. ## # ... with 1,694 more rows
Suppose we want this data in a wide format, with only one row per country. We can do this by pivoting such that there is a column for each year.
gapminder_gdp %>%
pivot_wider(
names_from = year,
values_from = gdpPercap
)
## # A tibble: 142 x 14
## country continent `1952` `1957` `1962` `1967` `1972` `1977` `1982`
## <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Afghan~ Asia 779. 821. 853. 836. 740. 786. 978.
## 2 Albania Europe 1601. 1942. 2313. 2760. 3313. 3533. 3631.
## 3 Algeria Africa 2449. 3014. 2551. 3247. 4183. 4910. 5745.
## 4 Angola Africa 3521. 3828. 4269. 5523. 5473. 3009. 2757.
## 5 Argent~ Americas 5911. 6857. 7133. 8053. 9443. 10079. 8998.
## 6 Austra~ Oceania 10040. 10950. 12217. 14526. 16789. 18334. 19477.
## 7 Austria Europe 6137. 8843. 10751. 12835. 16662. 19749. 21597.
## 8 Bahrain Asia 9867. 11636. 12753. 14805. 18269. 19340. 19211.
## 9 Bangla~ Asia 684. 662. 686. 721. 630. 660. 677.
## 10 Belgium Europe 8343. 9715. 10991. 13149. 16672. 19118. 20980.
## # ... with 132 more rows, and 5 more variables: `1987` <dbl>,
## # `1992` <dbl>, `1997` <dbl>, `2002` <dbl>, `2007` <dbl>
These column names are not syntactically valid, because names are not supposed to start with a number.
names_prefix allows us to easily add a string to the start of each created name.
gapminder_gdp %>%
pivot_wider(
names_from = year,
names_prefix = "year_",
values_from = gdpPercap
)
## # A tibble: 142 x 14
## country continent year_1952 year_1957 year_1962 year_1967 year_1972
## <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Afghan~ Asia 779. 821. 853. 836. 740.
## 2 Albania Europe 1601. 1942. 2313. 2760. 3313.
## 3 Algeria Africa 2449. 3014. 2551. 3247. 4183.
## 4 Angola Africa 3521. 3828. 4269. 5523. 5473.
## 5 Argent~ Americas 5911. 6857. 7133. 8053. 9443.
## 6 Austra~ Oceania 10040. 10950. 12217. 14526. 16789.
## 7 Austria Europe 6137. 8843. 10751. 12835. 16662.
## 8 Bahrain Asia 9867. 11636. 12753. 14805. 18269.
## 9 Bangla~ Asia 684. 662. 686. 721. 630.
## 10 Belgium Europe 8343. 9715. 10991. 13149. 16672.
## # ... with 132 more rows, and 7 more variables: year_1977 <dbl>,
## # year_1982 <dbl>, year_1987 <dbl>, year_1992 <dbl>, year_1997 <dbl>,
## # year_2002 <dbl>, year_2007 <dbl>
Multiple values_from columns
Suppose that we have gapminder and we need there to be one row per country like gapminder_wide.
gapminder ## # A tibble: 1,704 x 6 ## country continent year lifeExp pop gdpPercap ## <fct> <fct> <int> <dbl> <int> <dbl> ## 1 Afghanistan Asia 1952 28.8 8425333 779. ## 2 Afghanistan Asia 1957 30.3 9240934 821. ## 3 Afghanistan Asia 1962 32.0 10267083 853. ## 4 Afghanistan Asia 1967 34.0 11537966 836. ## 5 Afghanistan Asia 1972 36.1 13079460 740. ## 6 Afghanistan Asia 1977 38.4 14880372 786. ## 7 Afghanistan Asia 1982 39.9 12881816 978. ## 8 Afghanistan Asia 1987 40.8 13867957 852. ## 9 Afghanistan Asia 1992 41.7 16317921 649. ## 10 Afghanistan Asia 1997 41.8 22227415 635. ## # ... with 1,694 more rows gapminder_wide ## # A tibble: 142 x 38 ## country continent lifeExp_1952 pop_1952 gdpPercap_1952 lifeExp_1957 ## <fct> <fct> <dbl> <int> <dbl> <dbl> ## 1 Afghan~ Asia 28.8 8425333 779. 30.3 ## 2 Albania Europe 55.2 1282697 1601. 59.3 ## 3 Algeria Africa 43.1 9279525 2449. 45.7 ## 4 Angola Africa 30.0 4232095 3521. 32.0 ## 5 Argent~ Americas 62.5 17876956 5911. 64.4 ## 6 Austra~ Oceania 69.1 8691212 10040. 70.3 ## 7 Austria Europe 66.8 6927772 6137. 67.5 ## 8 Bahrain Asia 50.9 120447 9867. 53.8 ## 9 Bangla~ Asia 37.5 46886859 684. 39.3 ## 10 Belgium Europe 68 8730405 8343. 69.2 ## # ... with 132 more rows, and 32 more variables: pop_1957 <int>, ## # gdpPercap_1957 <dbl>, lifeExp_1962 <dbl>, pop_1962 <int>, ## # gdpPercap_1962 <dbl>, lifeExp_1967 <dbl>, pop_1967 <int>, ## # gdpPercap_1967 <dbl>, lifeExp_1972 <dbl>, pop_1972 <int>, ## # gdpPercap_1972 <dbl>, lifeExp_1977 <dbl>, pop_1977 <int>, ## # gdpPercap_1977 <dbl>, lifeExp_1982 <dbl>, pop_1982 <int>, ## # gdpPercap_1982 <dbl>, lifeExp_1987 <dbl>, pop_1987 <int>, ## # gdpPercap_1987 <dbl>, lifeExp_1992 <dbl>, pop_1992 <int>, ## # gdpPercap_1992 <dbl>, lifeExp_1997 <dbl>, pop_1997 <int>, ## # gdpPercap_1997 <dbl>, lifeExp_2002 <dbl>, pop_2002 <int>, ## # gdpPercap_2002 <dbl>, lifeExp_2007 <dbl>, pop_2007 <int>, ## # gdpPercap_2007 <dbl>
With spread() it isn’t possible to pivot multiple value columns based on a single key. The hack was to first use gather() and unite() to create a single value column to spread.
gapminder
## # A tibble: 1,704 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
## 7 Afghanistan Asia 1982 39.9 12881816 978.
## 8 Afghanistan Asia 1987 40.8 13867957 852.
## 9 Afghanistan Asia 1992 41.7 16317921 649.
## 10 Afghanistan Asia 1997 41.8 22227415 635.
## # ... with 1,694 more rows
gapminder %>%
gather(
key = "key",
value = "value",
lifeExp:gdpPercap
)
## # A tibble: 5,112 x 5
## country continent year key value
## <fct> <fct> <int> <chr> <dbl>
## 1 Afghanistan Asia 1952 lifeExp 28.8
## 2 Afghanistan Asia 1957 lifeExp 30.3
## 3 Afghanistan Asia 1962 lifeExp 32.0
## 4 Afghanistan Asia 1967 lifeExp 34.0
## 5 Afghanistan Asia 1972 lifeExp 36.1
## 6 Afghanistan Asia 1977 lifeExp 38.4
## 7 Afghanistan Asia 1982 lifeExp 39.9
## 8 Afghanistan Asia 1987 lifeExp 40.8
## 9 Afghanistan Asia 1992 lifeExp 41.7
## 10 Afghanistan Asia 1997 lifeExp 41.8
## # ... with 5,102 more rows
gapminder %>%
gather(
key = "key",
value = "value",
lifeExp:gdpPercap
) %>%
unite(temp, key, year)
## # A tibble: 5,112 x 4
## country continent temp value
## <fct> <fct> <chr> <dbl>
## 1 Afghanistan Asia lifeExp_1952 28.8
## 2 Afghanistan Asia lifeExp_1957 30.3
## 3 Afghanistan Asia lifeExp_1962 32.0
## 4 Afghanistan Asia lifeExp_1967 34.0
## 5 Afghanistan Asia lifeExp_1972 36.1
## 6 Afghanistan Asia lifeExp_1977 38.4
## 7 Afghanistan Asia lifeExp_1982 39.9
## 8 Afghanistan Asia lifeExp_1987 40.8
## 9 Afghanistan Asia lifeExp_1992 41.7
## 10 Afghanistan Asia lifeExp_1997 41.8
## # ... with 5,102 more rows
gapminder %>%
gather(
key = "key",
value = "value",
lifeExp:gdpPercap
) %>%
unite(temp, key, year) %>%
spread(
key = temp,
value = value
)
## # A tibble: 142 x 38
## country continent gdpPercap_1952 gdpPercap_1957 gdpPercap_1962
## <fct> <fct> <dbl> <dbl> <dbl>
## 1 Afghan~ Asia 779. 821. 853.
## 2 Albania Europe 1601. 1942. 2313.
## 3 Algeria Africa 2449. 3014. 2551.
## 4 Angola Africa 3521. 3828. 4269.
## 5 Argent~ Americas 5911. 6857. 7133.
## 6 Austra~ Oceania 10040. 10950. 12217.
## 7 Austria Europe 6137. 8843. 10751.
## 8 Bahrain Asia 9867. 11636. 12753.
## 9 Bangla~ Asia 684. 662. 686.
## 10 Belgium Europe 8343. 9715. 10991.
## # ... with 132 more rows, and 33 more variables: gdpPercap_1967 <dbl>,
## # gdpPercap_1972 <dbl>, gdpPercap_1977 <dbl>, gdpPercap_1982 <dbl>,
## # gdpPercap_1987 <dbl>, gdpPercap_1992 <dbl>, gdpPercap_1997 <dbl>,
## # gdpPercap_2002 <dbl>, gdpPercap_2007 <dbl>, lifeExp_1952 <dbl>,
## # lifeExp_1957 <dbl>, lifeExp_1962 <dbl>, lifeExp_1967 <dbl>,
## # lifeExp_1972 <dbl>, lifeExp_1977 <dbl>, lifeExp_1982 <dbl>,
## # lifeExp_1987 <dbl>, lifeExp_1992 <dbl>, lifeExp_1997 <dbl>,
## # lifeExp_2002 <dbl>, lifeExp_2007 <dbl>, pop_1952 <dbl>,
## # pop_1957 <dbl>, pop_1962 <dbl>, pop_1967 <dbl>, pop_1972 <dbl>,
## # pop_1977 <dbl>, pop_1982 <dbl>, pop_1987 <dbl>, pop_1992 <dbl>,
## # pop_1997 <dbl>, pop_2002 <dbl>, pop_2007 <dbl>
Now multiple value columns can be added to the values_from argument.
gapminder %>%
pivot_wider(
names_from = year,
values_from = c(lifeExp, pop, gdpPercap)
)
## # A tibble: 142 x 38
## country continent lifeExp_1952 lifeExp_1957 lifeExp_1962 lifeExp_1967
## <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 Afghan~ Asia 28.8 30.3 32.0 34.0
## 2 Albania Europe 55.2 59.3 64.8 66.2
## 3 Algeria Africa 43.1 45.7 48.3 51.4
## 4 Angola Africa 30.0 32.0 34 36.0
## 5 Argent~ Americas 62.5 64.4 65.1 65.6
## 6 Austra~ Oceania 69.1 70.3 70.9 71.1
## 7 Austria Europe 66.8 67.5 69.5 70.1
## 8 Bahrain Asia 50.9 53.8 56.9 59.9
## 9 Bangla~ Asia 37.5 39.3 41.2 43.5
## 10 Belgium Europe 68 69.2 70.2 70.9
## # ... with 132 more rows, and 32 more variables: lifeExp_1972 <dbl>,
## # lifeExp_1977 <dbl>, lifeExp_1982 <dbl>, lifeExp_1987 <dbl>,
## # lifeExp_1992 <dbl>, lifeExp_1997 <dbl>, lifeExp_2002 <dbl>,
## # lifeExp_2007 <dbl>, pop_1952 <int>, pop_1957 <int>, pop_1962 <int>,
## # pop_1967 <int>, pop_1972 <int>, pop_1977 <int>, pop_1982 <int>,
## # pop_1987 <int>, pop_1992 <int>, pop_1997 <int>, pop_2002 <int>,
## # pop_2007 <int>, gdpPercap_1952 <dbl>, gdpPercap_1957 <dbl>,
## # gdpPercap_1962 <dbl>, gdpPercap_1967 <dbl>, gdpPercap_1972 <dbl>,
## # gdpPercap_1977 <dbl>, gdpPercap_1982 <dbl>, gdpPercap_1987 <dbl>,
## # gdpPercap_1992 <dbl>, gdpPercap_1997 <dbl>, gdpPercap_2002 <dbl>,
## # gdpPercap_2007 <dbl>
Multiple names_from columns
Now suppose that our starting dataset is gapminder_long and we want one row per country.
gapminder_long ## # A tibble: 5,112 x 5 ## country continent year measure value ## <fct> <fct> <int> <chr> <dbl> ## 1 Afghanistan Asia 1952 lifeExp 28.8 ## 2 Afghanistan Asia 1952 pop 8425333 ## 3 Afghanistan Asia 1952 gdpPercap 779. ## 4 Afghanistan Asia 1957 lifeExp 30.3 ## 5 Afghanistan Asia 1957 pop 9240934 ## 6 Afghanistan Asia 1957 gdpPercap 821. ## 7 Afghanistan Asia 1962 lifeExp 32.0 ## 8 Afghanistan Asia 1962 pop 10267083 ## 9 Afghanistan Asia 1962 gdpPercap 853. ## 10 Afghanistan Asia 1967 lifeExp 34.0 ## # ... with 5,102 more rows
In this situation, we want both the values of measure and year to make up the new column names. Rather than having to combine them first, we can pass both into the names_from argument.
gapminder_long %>%
pivot_wider(
names_from = c(measure, year),
values_from = value
)
## # A tibble: 142 x 38
## country continent lifeExp_1952 pop_1952 gdpPercap_1952 lifeExp_1957
## <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 Afghan~ Asia 28.8 8425333 779. 30.3
## 2 Albania Europe 55.2 1282697 1601. 59.3
## 3 Algeria Africa 43.1 9279525 2449. 45.7
## 4 Angola Africa 30.0 4232095 3521. 32.0
## 5 Argent~ Americas 62.5 17876956 5911. 64.4
## 6 Austra~ Oceania 69.1 8691212 10040. 70.3
## 7 Austria Europe 66.8 6927772 6137. 67.5
## 8 Bahrain Asia 50.9 120447 9867. 53.8
## 9 Bangla~ Asia 37.5 46886859 684. 39.3
## 10 Belgium Europe 68 8730405 8343. 69.2
## # ... with 132 more rows, and 32 more variables: pop_1957 <dbl>,
## # gdpPercap_1957 <dbl>, lifeExp_1962 <dbl>, pop_1962 <dbl>,
## # gdpPercap_1962 <dbl>, lifeExp_1967 <dbl>, pop_1967 <dbl>,
## # gdpPercap_1967 <dbl>, lifeExp_1972 <dbl>, pop_1972 <dbl>,
## # gdpPercap_1972 <dbl>, lifeExp_1977 <dbl>, pop_1977 <dbl>,
## # gdpPercap_1977 <dbl>, lifeExp_1982 <dbl>, pop_1982 <dbl>,
## # gdpPercap_1982 <dbl>, lifeExp_1987 <dbl>, pop_1987 <dbl>,
## # gdpPercap_1987 <dbl>, lifeExp_1992 <dbl>, pop_1992 <dbl>,
## # gdpPercap_1992 <dbl>, lifeExp_1997 <dbl>, pop_1997 <dbl>,
## # gdpPercap_1997 <dbl>, lifeExp_2002 <dbl>, pop_2002 <dbl>,
## # gdpPercap_2002 <dbl>, lifeExp_2007 <dbl>, pop_2007 <dbl>,
## # gdpPercap_2007 <dbl>
pivot_longer() new features
names_prefix and names_ptypes
Earlier when pivoting gapminder_wide_gdp we noticed that it would require additional cleaning to extract the year out of the original column names.
gapminder_wide_gdp
## # A tibble: 142 x 14
## country continent gdpPercap_1952 gdpPercap_1957 gdpPercap_1962
## <fct> <fct> <dbl> <dbl> <dbl>
## 1 Afghan~ Asia 779. 821. 853.
## 2 Albania Europe 1601. 1942. 2313.
## 3 Algeria Africa 2449. 3014. 2551.
## 4 Angola Africa 3521. 3828. 4269.
## 5 Argent~ Americas 5911. 6857. 7133.
## 6 Austra~ Oceania 10040. 10950. 12217.
## 7 Austria Europe 6137. 8843. 10751.
## 8 Bahrain Asia 9867. 11636. 12753.
## 9 Bangla~ Asia 684. 662. 686.
## 10 Belgium Europe 8343. 9715. 10991.
## # ... with 132 more rows, and 9 more variables: gdpPercap_1967 <dbl>,
## # gdpPercap_1972 <dbl>, gdpPercap_1977 <dbl>, gdpPercap_1982 <dbl>,
## # gdpPercap_1987 <dbl>, gdpPercap_1992 <dbl>, gdpPercap_1997 <dbl>,
## # gdpPercap_2002 <dbl>, gdpPercap_2007 <dbl>
gapminder_wide_gdp %>%
pivot_longer(
gdpPercap_1952:gdpPercap_2007,
names_to = "year",
values_to = "gdpPercap"
)
## # A tibble: 1,704 x 4
## country continent year gdpPercap
## <fct> <fct> <chr> <dbl>
## 1 Afghanistan Asia gdpPercap_1952 779.
## 2 Afghanistan Asia gdpPercap_1957 821.
## 3 Afghanistan Asia gdpPercap_1962 853.
## 4 Afghanistan Asia gdpPercap_1967 836.
## 5 Afghanistan Asia gdpPercap_1972 740.
## 6 Afghanistan Asia gdpPercap_1977 786.
## 7 Afghanistan Asia gdpPercap_1982 978.
## 8 Afghanistan Asia gdpPercap_1987 852.
## 9 Afghanistan Asia gdpPercap_1992 649.
## 10 Afghanistan Asia gdpPercap_1997 635.
## # ... with 1,694 more rows
The argument names_prefix allows us to remove the prefix from the column names.
gapminder_wide_gdp %>%
pivot_longer(
gdpPercap_1952:gdpPercap_2007,
names_to = "year",
names_prefix = "gdpPercap_",
values_to = "gdpPercap"
)
## # A tibble: 1,704 x 4
## country continent year gdpPercap
## <fct> <fct> <chr> <dbl>
## 1 Afghanistan Asia 1952 779.
## 2 Afghanistan Asia 1957 821.
## 3 Afghanistan Asia 1962 853.
## 4 Afghanistan Asia 1967 836.
## 5 Afghanistan Asia 1972 740.
## 6 Afghanistan Asia 1977 786.
## 7 Afghanistan Asia 1982 978.
## 8 Afghanistan Asia 1987 852.
## 9 Afghanistan Asia 1992 649.
## 10 Afghanistan Asia 1997 635.
## # ... with 1,694 more rows
Additionally, year shouldn’t be a character vector, it makes more sense as an integer. We can set the type using names_ptypes.
gapminder_wide_gdp %>%
pivot_longer(
gdpPercap_1952:gdpPercap_2007,
names_to = "year",
names_prefix = "gdpPercap_",
names_ptypes = list(year = integer()),
values_to = "gdpPercap"
)
## # A tibble: 1,704 x 4
## country continent year gdpPercap
## <fct> <fct> <int> <dbl>
## 1 Afghanistan Asia 1952 779.
## 2 Afghanistan Asia 1957 821.
## 3 Afghanistan Asia 1962 853.
## 4 Afghanistan Asia 1967 836.
## 5 Afghanistan Asia 1972 740.
## 6 Afghanistan Asia 1977 786.
## 7 Afghanistan Asia 1982 978.
## 8 Afghanistan Asia 1987 852.
## 9 Afghanistan Asia 1992 649.
## 10 Afghanistan Asia 1997 635.
## # ... with 1,694 more rows
Multiple names_to columns
As promised, let’s revisit gapminder_wide. In a prior section we tidied a simplified version of this, now let’s try to do the whole thing.
gapminder_wide ## # A tibble: 142 x 38 ## country continent lifeExp_1952 pop_1952 gdpPercap_1952 lifeExp_1957 ## <fct> <fct> <dbl> <int> <dbl> <dbl> ## 1 Afghan~ Asia 28.8 8425333 779. 30.3 ## 2 Albania Europe 55.2 1282697 1601. 59.3 ## 3 Algeria Africa 43.1 9279525 2449. 45.7 ## 4 Angola Africa 30.0 4232095 3521. 32.0 ## 5 Argent~ Americas 62.5 17876956 5911. 64.4 ## 6 Austra~ Oceania 69.1 8691212 10040. 70.3 ## 7 Austria Europe 66.8 6927772 6137. 67.5 ## 8 Bahrain Asia 50.9 120447 9867. 53.8 ## 9 Bangla~ Asia 37.5 46886859 684. 39.3 ## 10 Belgium Europe 68 8730405 8343. 69.2 ## # ... with 132 more rows, and 32 more variables: pop_1957 <int>, ## # gdpPercap_1957 <dbl>, lifeExp_1962 <dbl>, pop_1962 <int>, ## # gdpPercap_1962 <dbl>, lifeExp_1967 <dbl>, pop_1967 <int>, ## # gdpPercap_1967 <dbl>, lifeExp_1972 <dbl>, pop_1972 <int>, ## # gdpPercap_1972 <dbl>, lifeExp_1977 <dbl>, pop_1977 <int>, ## # gdpPercap_1977 <dbl>, lifeExp_1982 <dbl>, pop_1982 <int>, ## # gdpPercap_1982 <dbl>, lifeExp_1987 <dbl>, pop_1987 <int>, ## # gdpPercap_1987 <dbl>, lifeExp_1992 <dbl>, pop_1992 <int>, ## # gdpPercap_1992 <dbl>, lifeExp_1997 <dbl>, pop_1997 <int>, ## # gdpPercap_1997 <dbl>, lifeExp_2002 <dbl>, pop_2002 <int>, ## # gdpPercap_2002 <dbl>, lifeExp_2007 <dbl>, pop_2007 <int>, ## # gdpPercap_2007 <dbl>
The final goal is to have columns country, continent, year, lifeExp, pop, and gdpPercap. We can’t do this all in one step, so let’s first just gather all of the value columns.
gapminder_wide %>%
pivot_longer(
lifeExp_1952:gdpPercap_2007
)
## # A tibble: 5,112 x 4
## country continent name value
## <fct> <fct> <chr> <dbl>
## 1 Afghanistan Asia lifeExp_1952 28.8
## 2 Afghanistan Asia pop_1952 8425333
## 3 Afghanistan Asia gdpPercap_1952 779.
## 4 Afghanistan Asia lifeExp_1957 30.3
## 5 Afghanistan Asia pop_1957 9240934
## 6 Afghanistan Asia gdpPercap_1957 821.
## 7 Afghanistan Asia lifeExp_1962 32.0
## 8 Afghanistan Asia pop_1962 10267083
## 9 Afghanistan Asia gdpPercap_1962 853.
## 10 Afghanistan Asia lifeExp_1967 34.0
## # ... with 5,102 more rows
The name column has two parts, the measure and the year. We can use tidyr::separate() to break it up.
gapminder_wide %>%
pivot_longer(
lifeExp_1952:gdpPercap_2007
) %>%
separate(
col = "name",
into = c("measure", "year"),
sep = "_"
)
## # A tibble: 5,112 x 5
## country continent measure year value
## <fct> <fct> <chr> <chr> <dbl>
## 1 Afghanistan Asia lifeExp 1952 28.8
## 2 Afghanistan Asia pop 1952 8425333
## 3 Afghanistan Asia gdpPercap 1952 779.
## 4 Afghanistan Asia lifeExp 1957 30.3
## 5 Afghanistan Asia pop 1957 9240934
## 6 Afghanistan Asia gdpPercap 1957 821.
## 7 Afghanistan Asia lifeExp 1962 32.0
## 8 Afghanistan Asia pop 1962 10267083
## 9 Afghanistan Asia gdpPercap 1962 853.
## 10 Afghanistan Asia lifeExp 1967 34.0
## # ... with 5,102 more rows
Rather than using separate(), we can specify multiple names_to columns in pivot_longer() along with the names_sep argument.
gapminder_wide %>%
pivot_longer(
lifeExp_1952:gdpPercap_2007,
names_to = c("measure", "year"),
names_sep = "_"
)
## # A tibble: 5,112 x 5
## country continent measure year value
## <fct> <fct> <chr> <chr> <dbl>
## 1 Afghanistan Asia lifeExp 1952 28.8
## 2 Afghanistan Asia pop 1952 8425333
## 3 Afghanistan Asia gdpPercap 1952 779.
## 4 Afghanistan Asia lifeExp 1957 30.3
## 5 Afghanistan Asia pop 1957 9240934
## 6 Afghanistan Asia gdpPercap 1957 821.
## 7 Afghanistan Asia lifeExp 1962 32.0
## 8 Afghanistan Asia pop 1962 10267083
## 9 Afghanistan Asia gdpPercap 1962 853.
## 10 Afghanistan Asia lifeExp 1967 34.0
## # ... with 5,102 more rows
names_pattern is a more flexible way to specify how to split up the names. It uses regex and will be necessary for more complex naming patterns.
In our previous example, we can get the same behavior by using the regex "(.+)_(.+)".
gapminder_wide %>%
pivot_longer(
lifeExp_1952:gdpPercap_2007,
names_to = c("measure", "year"),
names_pattern = "(.+)_(.+)"
)
## # A tibble: 5,112 x 5
## country continent measure year value
## <fct> <fct> <chr> <chr> <dbl>
## 1 Afghanistan Asia lifeExp 1952 28.8
## 2 Afghanistan Asia pop 1952 8425333
## 3 Afghanistan Asia gdpPercap 1952 779.
## 4 Afghanistan Asia lifeExp 1957 30.3
## 5 Afghanistan Asia pop 1957 9240934
## 6 Afghanistan Asia gdpPercap 1957 821.
## 7 Afghanistan Asia lifeExp 1962 32.0
## 8 Afghanistan Asia pop 1962 10267083
## 9 Afghanistan Asia gdpPercap 1962 853.
## 10 Afghanistan Asia lifeExp 1967 34.0
## # ... with 5,102 more rows
Now that we’ve broken up the column names, the final step is to use pivot_wider() to create columns for lifeExp, pop, and gdpPercap.
gapminder_wide %>%
pivot_longer(
lifeExp_1952:gdpPercap_2007,
names_to = c("measure", "year"),
names_sep = "_"
) %>%
pivot_wider(
names_from = measure,
values_from = value
)
## # A tibble: 1,704 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <chr> <dbl> <dbl> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
## 7 Afghanistan Asia 1982 39.9 12881816 978.
## 8 Afghanistan Asia 1987 40.8 13867957 852.
## 9 Afghanistan Asia 1992 41.7 16317921 649.
## 10 Afghanistan Asia 1997 41.8 22227415 635.
## # ... with 1,694 more rows
Conclusion
The new tidyr functions have intuitive syntax, are easy to use, and are more flexibile than the prior functions. Several of the new arguments and features are extremely useful, and will save lots of time on common tasks.
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