Pivoting data frames just got easier thanks to `pivot_wide()` and `pivot_long()`

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There’s a lot going on in the development version of {tidyr}. New functions for pivoting data
frames, pivot_wide() and pivot_long() are coming, and will replace the current functions,
spread() and gather(). spread() and gather() will remain in the package though:

If you want to try out these new functions, you need to install the development version of {tidyr}:

devtools::install_github("tidyverse/tidyr")

and you can read the vignette here.
Because these functions are still being developed, some more changes might be introduced, but I guess
that the main functionality will not change much.

Let’s play around with these functions and the mtcars data set. First let’s load the packages and
the data:

library(tidyverse)
data(mtcars)

First, let’s create a wide dataset, by spreading the levels of the “am” column to two new columns:

mtcars_wide1 <- mtcars %>% 
    pivot_wide(names_from = "am", values_from = "mpg") 

mtcars_wide1 %>% 
    select(`0`, `1`, everything())
## # A tibble: 32 x 11
##      `0`   `1`   cyl  disp    hp  drat    wt  qsec    vs  gear  carb
##              
##  1  NA    21       6  160    110  3.9   2.62  16.5     0     4     4
##  2  NA    21       6  160    110  3.9   2.88  17.0     0     4     4
##  3  NA    22.8     4  108     93  3.85  2.32  18.6     1     4     1
##  4  21.4  NA       6  258    110  3.08  3.22  19.4     1     3     1
##  5  18.7  NA       8  360    175  3.15  3.44  17.0     0     3     2
##  6  18.1  NA       6  225    105  2.76  3.46  20.2     1     3     1
##  7  14.3  NA       8  360    245  3.21  3.57  15.8     0     3     4
##  8  24.4  NA       4  147.    62  3.69  3.19  20       1     4     2
##  9  22.8  NA       4  141.    95  3.92  3.15  22.9     1     4     2
## 10  19.2  NA       6  168.   123  3.92  3.44  18.3     1     4     4
## # … with 22 more rows

pivot_wide()’s arguments are quite explicit: names_from = is where you specify the column that
will be spread across the data frame, meaning, the levels of this column will become new columns.
values_from = is where you specify the column that will fill in the values of the new columns.

“0” and “1” are the new columns (“am” had two levels, 0 and 1), which contain the miles per
gallon for manual and automatic cars respectively. Let’s also take a look at the data frame itself:

mtcars_wide1 %>% 
    select(`0`, `1`, everything())
## # A tibble: 32 x 11
##      `0`   `1`   cyl  disp    hp  drat    wt  qsec    vs  gear  carb
##              
##  1  NA    21       6  160    110  3.9   2.62  16.5     0     4     4
##  2  NA    21       6  160    110  3.9   2.88  17.0     0     4     4
##  3  NA    22.8     4  108     93  3.85  2.32  18.6     1     4     1
##  4  21.4  NA       6  258    110  3.08  3.22  19.4     1     3     1
##  5  18.7  NA       8  360    175  3.15  3.44  17.0     0     3     2
##  6  18.1  NA       6  225    105  2.76  3.46  20.2     1     3     1
##  7  14.3  NA       8  360    245  3.21  3.57  15.8     0     3     4
##  8  24.4  NA       4  147.    62  3.69  3.19  20       1     4     2
##  9  22.8  NA       4  141.    95  3.92  3.15  22.9     1     4     2
## 10  19.2  NA       6  168.   123  3.92  3.44  18.3     1     4     4
## # … with 22 more rows

Now suppose that we want to spread the values of “am” times “cyl”, and filling the data with the
values of “mpg”:

mtcars_wide2 <- mtcars %>% 
    pivot_wide(names_from = c("am", "cyl"), values_from = "mpg") 

mtcars_wide2 %>% 
    select(matches("^0|1"), everything())
## # A tibble: 32 x 14
##    `1_6` `1_4` `0_6` `0_8` `0_4` `1_8`  disp    hp  drat    wt  qsec    vs
##               
##  1    21  NA    NA    NA    NA      NA  160    110  3.9   2.62  16.5     0
##  2    21  NA    NA    NA    NA      NA  160    110  3.9   2.88  17.0     0
##  3    NA  22.8  NA    NA    NA      NA  108     93  3.85  2.32  18.6     1
##  4    NA  NA    21.4  NA    NA      NA  258    110  3.08  3.22  19.4     1
##  5    NA  NA    NA    18.7  NA      NA  360    175  3.15  3.44  17.0     0
##  6    NA  NA    18.1  NA    NA      NA  225    105  2.76  3.46  20.2     1
##  7    NA  NA    NA    14.3  NA      NA  360    245  3.21  3.57  15.8     0
##  8    NA  NA    NA    NA    24.4    NA  147.    62  3.69  3.19  20       1
##  9    NA  NA    NA    NA    22.8    NA  141.    95  3.92  3.15  22.9     1
## 10    NA  NA    19.2  NA    NA      NA  168.   123  3.92  3.44  18.3     1
## # … with 22 more rows, and 2 more variables: gear , carb 

As you can see, this is easily achieved by simply providing more columns to names_from =.

Finally, it is also possible to use an optional data set which contains the specifications of the
new columns:

mtcars_spec <- mtcars %>% 
    expand(am, cyl, .value = "mpg") %>%
    unite(".name", am, cyl, remove = FALSE)

mtcars_spec
## # A tibble: 6 x 4
##   .name    am   cyl .value
##       
## 1 0_4       0     4 mpg   
## 2 0_6       0     6 mpg   
## 3 0_8       0     8 mpg   
## 4 1_4       1     4 mpg   
## 5 1_6       1     6 mpg   
## 6 1_8       1     8 mpg

This optional data set defines how the columns “0_4”, “0_6” etc are constructed, and also the
value that shall be used to fill in the values. “am” and “cyl” will be used to create the “.name”
and the “mpg” column will be used for the “.value”:

mtcars %>% 
    pivot_wide(spec = mtcars_spec) %>% 
    select(matches("^0|1"), everything())
## # A tibble: 32 x 14
##    `0_4` `0_6` `0_8` `1_4` `1_6` `1_8`  disp    hp  drat    wt  qsec    vs
##               
##  1  NA    NA    NA    NA      21    NA  160    110  3.9   2.62  16.5     0
##  2  NA    NA    NA    NA      21    NA  160    110  3.9   2.88  17.0     0
##  3  NA    NA    NA    22.8    NA    NA  108     93  3.85  2.32  18.6     1
##  4  NA    21.4  NA    NA      NA    NA  258    110  3.08  3.22  19.4     1
##  5  NA    NA    18.7  NA      NA    NA  360    175  3.15  3.44  17.0     0
##  6  NA    18.1  NA    NA      NA    NA  225    105  2.76  3.46  20.2     1
##  7  NA    NA    14.3  NA      NA    NA  360    245  3.21  3.57  15.8     0
##  8  24.4  NA    NA    NA      NA    NA  147.    62  3.69  3.19  20       1
##  9  22.8  NA    NA    NA      NA    NA  141.    95  3.92  3.15  22.9     1
## 10  NA    19.2  NA    NA      NA    NA  168.   123  3.92  3.44  18.3     1
## # … with 22 more rows, and 2 more variables: gear , carb 

Using a spec is especially useful if you need to make new levels that are not in the data.
For instance, suppose that there are actually 10-cylinder cars too, but they do not appear in our
sample. We would like to make the fact that they’re missing explicit:

mtcars_spec2 <- mtcars %>% 
    expand(am, "cyl" = c(cyl, 10), .value = "mpg") %>%
    unite(".name", am, cyl, remove = FALSE)

mtcars_spec2
## # A tibble: 8 x 4
##   .name    am   cyl .value
##       
## 1 0_4       0     4 mpg   
## 2 0_6       0     6 mpg   
## 3 0_8       0     8 mpg   
## 4 0_10      0    10 mpg   
## 5 1_4       1     4 mpg   
## 6 1_6       1     6 mpg   
## 7 1_8       1     8 mpg   
## 8 1_10      1    10 mpg
mtcars %>% 
    pivot_wide(spec = mtcars_spec2) %>% 
    select(matches("^0|1"), everything())
## # A tibble: 32 x 16
##    `0_4` `0_6` `0_8` `0_10` `1_4` `1_6` `1_8` `1_10`  disp    hp  drat
##                
##  1  NA    NA    NA       NA  NA      21    NA     NA  160    110  3.9 
##  2  NA    NA    NA       NA  NA      21    NA     NA  160    110  3.9 
##  3  NA    NA    NA       NA  22.8    NA    NA     NA  108     93  3.85
##  4  NA    21.4  NA       NA  NA      NA    NA     NA  258    110  3.08
##  5  NA    NA    18.7     NA  NA      NA    NA     NA  360    175  3.15
##  6  NA    18.1  NA       NA  NA      NA    NA     NA  225    105  2.76
##  7  NA    NA    14.3     NA  NA      NA    NA     NA  360    245  3.21
##  8  24.4  NA    NA       NA  NA      NA    NA     NA  147.    62  3.69
##  9  22.8  NA    NA       NA  NA      NA    NA     NA  141.    95  3.92
## 10  NA    19.2  NA       NA  NA      NA    NA     NA  168.   123  3.92
## # … with 22 more rows, and 5 more variables: wt , qsec ,
## #   vs , gear , carb 

As you can see, we now have two more columns have been added, and they are full of NA’s.

Now, let’s try to go from wide to long data sets, using pivot_long():

mtcars_wide1 %>% 
  pivot_long(cols = c(`1`, `0`), names_to = "am", values_to = "mpg") %>% 
  select(am, mpg, everything())
## # A tibble: 64 x 11
##    am      mpg   cyl  disp    hp  drat    wt  qsec    vs  gear  carb
##              
##  1 1      21       6   160   110  3.9   2.62  16.5     0     4     4
##  2 0      NA       6   160   110  3.9   2.62  16.5     0     4     4
##  3 1      21       6   160   110  3.9   2.88  17.0     0     4     4
##  4 0      NA       6   160   110  3.9   2.88  17.0     0     4     4
##  5 1      22.8     4   108    93  3.85  2.32  18.6     1     4     1
##  6 0      NA       4   108    93  3.85  2.32  18.6     1     4     1
##  7 1      NA       6   258   110  3.08  3.22  19.4     1     3     1
##  8 0      21.4     6   258   110  3.08  3.22  19.4     1     3     1
##  9 1      NA       8   360   175  3.15  3.44  17.0     0     3     2
## 10 0      18.7     8   360   175  3.15  3.44  17.0     0     3     2
## # … with 54 more rows

The arguments of pivot_long() are quite explicit too, and similar to the ones in pivot_wide().
cols = is where the user specifies the columns that need to be pivoted. names_to = is where
the user can specify the name of the new columns, whose levels will be exactly the ones specified
to cols =. values_to = is where the user specifies the column name of the new column that
will contain the values.

It is also possible to specify the columns that should not be transformed, by using -:

mtcars_wide1 %>% 
  pivot_long(cols = -matches("^[[:alpha:]]"), names_to = "am", values_to = "mpg") %>% 
  select(am, mpg, everything())
## # A tibble: 64 x 11
##    am      mpg   cyl  disp    hp  drat    wt  qsec    vs  gear  carb
##              
##  1 1      21       6   160   110  3.9   2.62  16.5     0     4     4
##  2 0      NA       6   160   110  3.9   2.62  16.5     0     4     4
##  3 1      21       6   160   110  3.9   2.88  17.0     0     4     4
##  4 0      NA       6   160   110  3.9   2.88  17.0     0     4     4
##  5 1      22.8     4   108    93  3.85  2.32  18.6     1     4     1
##  6 0      NA       4   108    93  3.85  2.32  18.6     1     4     1
##  7 1      NA       6   258   110  3.08  3.22  19.4     1     3     1
##  8 0      21.4     6   258   110  3.08  3.22  19.4     1     3     1
##  9 1      NA       8   360   175  3.15  3.44  17.0     0     3     2
## 10 0      18.7     8   360   175  3.15  3.44  17.0     0     3     2
## # … with 54 more rows

Here the columns that should not be modified are all those that start with a letter, hence the “1
regular expression. It is also possible to remove all the NA’s from the data frame, with na.rm =.

mtcars_wide1 %>% 
  pivot_long(cols = c(`1`, `0`), names_to = "am", values_to = "mpg", na.rm = TRUE) %>% 
  select(am, mpg, everything())
## # A tibble: 32 x 11
##    am      mpg   cyl  disp    hp  drat    wt  qsec    vs  gear  carb
##              
##  1 1      21       6  160    110  3.9   2.62  16.5     0     4     4
##  2 1      21       6  160    110  3.9   2.88  17.0     0     4     4
##  3 1      22.8     4  108     93  3.85  2.32  18.6     1     4     1
##  4 0      21.4     6  258    110  3.08  3.22  19.4     1     3     1
##  5 0      18.7     8  360    175  3.15  3.44  17.0     0     3     2
##  6 0      18.1     6  225    105  2.76  3.46  20.2     1     3     1
##  7 0      14.3     8  360    245  3.21  3.57  15.8     0     3     4
##  8 0      24.4     4  147.    62  3.69  3.19  20       1     4     2
##  9 0      22.8     4  141.    95  3.92  3.15  22.9     1     4     2
## 10 0      19.2     6  168.   123  3.92  3.44  18.3     1     4     4
## # … with 22 more rows

We can also pivot data frames where the names of the columns are made of two or more variables,
for example in our mtcars_wide2 data frame:

mtcars_wide2 %>% 
    select(matches("^0|1"), everything())
## # A tibble: 32 x 14
##    `1_6` `1_4` `0_6` `0_8` `0_4` `1_8`  disp    hp  drat    wt  qsec    vs
##               
##  1    21  NA    NA    NA    NA      NA  160    110  3.9   2.62  16.5     0
##  2    21  NA    NA    NA    NA      NA  160    110  3.9   2.88  17.0     0
##  3    NA  22.8  NA    NA    NA      NA  108     93  3.85  2.32  18.6     1
##  4    NA  NA    21.4  NA    NA      NA  258    110  3.08  3.22  19.4     1
##  5    NA  NA    NA    18.7  NA      NA  360    175  3.15  3.44  17.0     0
##  6    NA  NA    18.1  NA    NA      NA  225    105  2.76  3.46  20.2     1
##  7    NA  NA    NA    14.3  NA      NA  360    245  3.21  3.57  15.8     0
##  8    NA  NA    NA    NA    24.4    NA  147.    62  3.69  3.19  20       1
##  9    NA  NA    NA    NA    22.8    NA  141.    95  3.92  3.15  22.9     1
## 10    NA  NA    19.2  NA    NA      NA  168.   123  3.92  3.44  18.3     1
## # … with 22 more rows, and 2 more variables: gear , carb 

All the columns that start with either “0” or “1” must be pivoted:

mtcars_wide2 %>% 
  pivot_long(cols = matches("0|1"), names_to = "am_cyl", values_to = "mpg", na.rm = TRUE) %>% 
  select(am_cyl, everything())
## # A tibble: 32 x 10
##    am_cyl  disp    hp  drat    wt  qsec    vs  gear  carb   mpg
##              
##  1 1_6     160    110  3.9   2.62  16.5     0     4     4  21  
##  2 1_6     160    110  3.9   2.88  17.0     0     4     4  21  
##  3 1_4     108     93  3.85  2.32  18.6     1     4     1  22.8
##  4 0_6     258    110  3.08  3.22  19.4     1     3     1  21.4
##  5 0_8     360    175  3.15  3.44  17.0     0     3     2  18.7
##  6 0_6     225    105  2.76  3.46  20.2     1     3     1  18.1
##  7 0_8     360    245  3.21  3.57  15.8     0     3     4  14.3
##  8 0_4     147.    62  3.69  3.19  20       1     4     2  24.4
##  9 0_4     141.    95  3.92  3.15  22.9     1     4     2  22.8
## 10 0_6     168.   123  3.92  3.44  18.3     1     4     4  19.2
## # … with 22 more rows

Now, there is one new column, “am_cyl” which must still be transformed by separating “am_cyl” into two new columns:

mtcars_wide2 %>% 
  pivot_long(cols = matches("0|1"), names_to = "am_cyl", values_to = "mpg", na.rm = TRUE) %>% 
  separate(am_cyl, into = c("am", "cyl"), sep = "_") %>% 
  select(am, cyl, everything())
## # A tibble: 32 x 11
##    am    cyl    disp    hp  drat    wt  qsec    vs  gear  carb   mpg
##              
##  1 1     6      160    110  3.9   2.62  16.5     0     4     4  21  
##  2 1     6      160    110  3.9   2.88  17.0     0     4     4  21  
##  3 1     4      108     93  3.85  2.32  18.6     1     4     1  22.8
##  4 0     6      258    110  3.08  3.22  19.4     1     3     1  21.4
##  5 0     8      360    175  3.15  3.44  17.0     0     3     2  18.7
##  6 0     6      225    105  2.76  3.46  20.2     1     3     1  18.1
##  7 0     8      360    245  3.21  3.57  15.8     0     3     4  14.3
##  8 0     4      147.    62  3.69  3.19  20       1     4     2  24.4
##  9 0     4      141.    95  3.92  3.15  22.9     1     4     2  22.8
## 10 0     6      168.   123  3.92  3.44  18.3     1     4     4  19.2
## # … with 22 more rows

It is also possible to achieve this using a data frame with the specification of what you need:

mtcars_spec_long <- mtcars_wide2 %>% 
  pivot_long_spec(matches("0|1"), values_to = "mpg") %>% 
  separate(name, c("am", "cyl"), sep = "_")

mtcars_spec_long
## # A tibble: 6 x 4
##   .name .value am    cyl  
##       
## 1 1_6   mpg    1     6    
## 2 1_4   mpg    1     4    
## 3 0_6   mpg    0     6    
## 4 0_8   mpg    0     8    
## 5 0_4   mpg    0     4    
## 6 1_8   mpg    1     8

Providing this spec to pivot_long() solves the issue:

mtcars_wide2 %>% 
  pivot_long(spec = mtcars_spec_long, na.rm = TRUE) %>% 
  select(am, cyl, everything())
## # A tibble: 32 x 11
##    am    cyl    disp    hp  drat    wt  qsec    vs  gear  carb   mpg
##              
##  1 1     6      160    110  3.9   2.62  16.5     0     4     4  21  
##  2 1     6      160    110  3.9   2.88  17.0     0     4     4  21  
##  3 1     4      108     93  3.85  2.32  18.6     1     4     1  22.8
##  4 0     6      258    110  3.08  3.22  19.4     1     3     1  21.4
##  5 0     8      360    175  3.15  3.44  17.0     0     3     2  18.7
##  6 0     6      225    105  2.76  3.46  20.2     1     3     1  18.1
##  7 0     8      360    245  3.21  3.57  15.8     0     3     4  14.3
##  8 0     4      147.    62  3.69  3.19  20       1     4     2  24.4
##  9 0     4      141.    95  3.92  3.15  22.9     1     4     2  22.8
## 10 0     6      168.   123  3.92  3.44  18.3     1     4     4  19.2
## # … with 22 more rows

Stay tuned to Hadley Wickham’s twitter as there will definitely
be announcements soon!

Hope you enjoyed! If you found this blog post useful, you might want to follow
me on twitter for blog post updates and
buy me an espresso or paypal.me.

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