Row-wise operations

dplyr, and R in general, are particularly well suited to performing operations over columns, and performing operations over rows is much harder. In this vignette, you’ll learn dplyr’s approach centred around the row-wise data frame created by rowwise().

These types of problems are often easily solved with a for loop, but it’s nice to have a solution that fits naturally into a pipeline.

Creating

Row-wise operations require a special type of grouping where each group consists of a single row. You create this with rowwise():

df <- tibble(x = 1:2, y = 3:4, z = 5:6)
df %>% rowwise()
#> # A tibble: 2 x 3
#> # Rowwise: 
#>       x     y     z
#>   <int> <int> <int>
#> 1     1     3     5
#> 2     2     4     6

Like group_by(), rowwise() doesn’t really do anything itself; it just changes how the other verbs work. For example, compare the results of mutate() in the following code:

df %>% mutate(m = mean(c(x, y, z)))
#> # A tibble: 2 x 4
#>       x     y     z     m
#>   <int> <int> <int> <dbl>
#> 1     1     3     5   3.5
#> 2     2     4     6   3.5
df %>% rowwise() %>% mutate(m = mean(c(x, y, z)))
#> # A tibble: 2 x 4
#> # Rowwise: 
#>       x     y     z     m
#>   <int> <int> <int> <dbl>
#> 1     1     3     5     3
#> 2     2     4     6     4

If you use mutate() with a regular data frame, it computes the mean of x, y, and z across all rows. If you apply it to a row-wise data frame, it computes the mean for each row.

You can optionally supply “identifier” variables in your call to rowwise(). These variables are preserved when you call summarise(), so they behave somewhat similarly to the grouping variables passed to group_by():

df <- tibble(name = c("Mara", "Hadley"), x = 1:2, y = 3:4, z = 5:6)

df %>% 
  rowwise() %>% 
  summarise(m = mean(c(x, y, z)))
#> `summarise()` ungrouping output (override with `.groups` argument)
#> # A tibble: 2 x 1
#>       m
#>   <dbl>
#> 1     3
#> 2     4

df %>% 
  rowwise(name) %>% 
  summarise(m = mean(c(x, y, z)))
#> `summarise()` regrouping output by 'name' (override with `.groups` argument)
#> # A tibble: 2 x 2
#> # Groups:   name [2]
#>   name       m
#>   <chr>  <dbl>
#> 1 Mara       3
#> 2 Hadley     4

rowwise() is just a special form of grouping, so if you want to remove it from a data frame, just call ungroup().

Per row summary statistics

dplyr::summarise() makes it really easy to summarise values across rows within one column. When combined with rowwise() it also makes it easy to summarise values across columns within one row. To see how, we’ll start by making a little dataset:

df <- tibble(id = 1:6, w = 10:15, x = 20:25, y = 30:35, z = 40:45)
df
#> # A tibble: 6 x 5
#>      id     w     x     y     z
#>   <int> <int> <int> <int> <int>
#> 1     1    10    20    30    40
#> 2     2    11    21    31    41
#> 3     3    12    22    32    42
#> 4     4    13    23    33    43
#> # … with 2 more rows

Let’s say we want compute the sum of w, x, y, and z for each row. We start by making a row-wise data frame:

rf <- df %>% rowwise(id)

We can then use mutate() to add a new column to each row, or summarise() to return just that one summary:

rf %>% mutate(total = sum(c(w, x, y, z)))
#> # A tibble: 6 x 6
#> # Rowwise:  id
#>      id     w     x     y     z total
#>   <int> <int> <int> <int> <int> <int>
#> 1     1    10    20    30    40   100
#> 2     2    11    21    31    41   104
#> 3     3    12    22    32    42   108
#> 4     4    13    23    33    43   112
#> # … with 2 more rows
rf %>% summarise(total = sum(c(w, x, y, z)))
#> `summarise()` regrouping output by 'id' (override with `.groups` argument)
#> # A tibble: 6 x 2
#> # Groups:   id [6]
#>      id total
#>   <int> <int>
#> 1     1   100
#> 2     2   104
#> 3     3   108
#> 4     4   112
#> # … with 2 more rows

Of course, if you have a lot of variables, it’s going to be tedious to type in every variable name. Instead, you can use c_across() which uses tidy selection syntax so you can to succinctly select many variables:

rf %>% mutate(total = sum(c_across(w:z)))
#> # A tibble: 6 x 6
#> # Rowwise:  id
#>      id     w     x     y     z total
#>   <int> <int> <int> <int> <int> <int>
#> 1     1    10    20    30    40   100
#> 2     2    11    21    31    41   104
#> 3     3    12    22    32    42   108
#> 4     4    13    23    33    43   112
#> # … with 2 more rows
rf %>% mutate(total = sum(c_across(where(is.numeric))))
#> # A tibble: 6 x 6
#> # Rowwise:  id
#>      id     w     x     y     z total
#>   <int> <int> <int> <int> <int> <int>
#> 1     1    10    20    30    40   100
#> 2     2    11    21    31    41   104
#> 3     3    12    22    32    42   108
#> 4     4    13    23    33    43   112
#> # … with 2 more rows

You could combine this with column-wise operations (see vignette("colwise") for more details) to compute the proportion of the total for each column:

rf %>% 
  mutate(total = sum(c_across(w:z))) %>% 
  ungroup() %>% 
  mutate(across(w:z, ~ . / total))
#> # A tibble: 6 x 6
#>      id     w     x     y     z total
#>   <int> <dbl> <dbl> <dbl> <dbl> <int>
#> 1     1 0.1   0.2   0.3   0.4     100
#> 2     2 0.106 0.202 0.298 0.394   104
#> 3     3 0.111 0.204 0.296 0.389   108
#> 4     4 0.116 0.205 0.295 0.384   112
#> # … with 2 more rows

Row-wise summary functions

The rowwise() approach will work for any summary function. But if you need greater speed, it’s worth looking for a built-in row-wise variant of your summary function. These are more efficient because they operate on the data frame as whole; they don’t split it into rows, compute the summary, and then join the results back together again.

df %>% mutate(total = rowSums(across(where(is.numeric))))
#> # A tibble: 6 x 6
#>      id     w     x     y     z total
#>   <int> <int> <int> <int> <int> <dbl>
#> 1     1    10    20    30    40   101
#> 2     2    11    21    31    41   106
#> 3     3    12    22    32    42   111
#> 4     4    13    23    33    43   116
#> # … with 2 more rows
df %>% mutate(mean = rowMeans(across(where(is.numeric))))
#> # A tibble: 6 x 6
#>      id     w     x     y     z  mean
#>   <int> <int> <int> <int> <int> <dbl>
#> 1     1    10    20    30    40  20.2
#> 2     2    11    21    31    41  21.2
#> 3     3    12    22    32    42  22.2
#> 4     4    13    23    33    43  23.2
#> # … with 2 more rows

NB: I use df (not rf) and across() (not c_across()) here because rowMeans() and rowSums() take a multi-row data frame as input.

List-columns

rowwise() operations are a natural pairing when you have list-columns. They allow you to avoid explicit loops and/or functions from the apply() or purrr::map() families.

Motivation

Imagine you have this data frame, and you want to count the lengths of each element:

df <- tibble(
  x = list(1, 2:3, 4:6)
)

You might try calling length():

df %>% mutate(l = length(x))
#> # A tibble: 3 x 2
#>   x             l
#>   <list>    <int>
#> 1 <dbl [1]>     3
#> 2 <int [2]>     3
#> 3 <int [3]>     3

But that returns the length of the column, not the length of the individual values. If you’re an R documentation aficionado, you might know there’s already a base R function just for this purpose:

df %>% mutate(l = lengths(x))
#> # A tibble: 3 x 2
#>   x             l
#>   <list>    <int>
#> 1 <dbl [1]>     1
#> 2 <int [2]>     2
#> 3 <int [3]>     3

Or if you’re an experienced R programmer, you might know how to apply a function to each element of a list using sapply(), vapply(), or one of the purrr map() functions:

df %>% mutate(l = sapply(x, length))
#> # A tibble: 3 x 2
#>   x             l
#>   <list>    <int>
#> 1 <dbl [1]>     1
#> 2 <int [2]>     2
#> 3 <int [3]>     3
df %>% mutate(l = purrr::map_int(x, length))
#> # A tibble: 3 x 2
#>   x             l
#>   <list>    <int>
#> 1 <dbl [1]>     1
#> 2 <int [2]>     2
#> 3 <int [3]>     3

But wouldn’t it be nice if you could just write length(x) and dplyr would figure out that you wanted to compute the length of the element inside of x? Since you’re here, you might already be guessing at the answer: this is just another application of the row-wise pattern.

df %>% 
  rowwise() %>% 
  mutate(l = length(x))
#> # A tibble: 3 x 2
#> # Rowwise: 
#>   x             l
#>   <list>    <int>
#> 1 <dbl [1]>     1
#> 2 <int [2]>     2
#> 3 <int [3]>     3

Subsetting

Before we continue on, I wanted to briefly mention the magic that makes this work. This isn’t something you’ll generally need to think about (it’ll just work), but it’s useful to know about when something goes wrong.

There’s an important difference between a grouped data frame where each group happens to have one row, and a row-wise data frame where every group always has one row. Take these two data frames:

df <- tibble(g = 1:2, y = list(1:3, "a"))
gf <- df %>% group_by(g)
rf <- df %>% rowwise(g)

If we compute some properties of y, you’ll notice the results look different:

gf %>% mutate(type = typeof(y), length = length(y))
#> # A tibble: 2 x 4
#> # Groups:   g [2]
#>       g y         type  length
#>   <int> <list>    <chr>  <int>
#> 1     1 <int [3]> list       1
#> 2     2 <chr [1]> list       1
rf %>% mutate(type = typeof(y), length = length(y))
#> # A tibble: 2 x 4
#> # Rowwise:  g
#>       g y         type      length
#>   <int> <list>    <chr>      <int>
#> 1     1 <int [3]> integer        3
#> 2     2 <chr [1]> character      1

They key difference is that when mutate() slices up the columns to pass to length(y) the grouped mutate uses [ and the row-wise mutate uses [[. The following code gives a flavour of the differences if you used a for loop:

# grouped
out1 <- integer(2)
for (i in 1:2) {
  out1[[i]] <- length(df$y[i])
}
out1
#> [1] 1 1

# rowwise
out2 <- integer(2)
for (i in 1:2) {
  out2[[i]] <- length(df$y[[i]])
}
out2
#> [1] 3 1

Note that this magic only applies when you’re referring to existing columns, not when you’re creating new rows. This is potentially confusing, but we’re fairly confident it’s the least worst solution, particularly given the hint in the error message.

gf %>% mutate(y2 = y)
#> # A tibble: 2 x 3
#> # Groups:   g [2]
#>       g y         y2       
#>   <int> <list>    <list>   
#> 1     1 <int [3]> <int [3]>
#> 2     2 <chr [1]> <chr [1]>
rf %>% mutate(y2 = y)
#> Error: Problem with `mutate()` input `y2`.
#> x Input `y2` can't be recycled to size 1.
#> ℹ Input `y2` is `y`.
#> ℹ Input `y2` must be size 1, not 3.
#> ℹ Did you mean: `y2 = list(y)` ?
#> ℹ The error occurred in row 1.
rf %>% mutate(y2 = list(y))
#> # A tibble: 2 x 3
#> # Rowwise:  g
#>       g y         y2       
#>   <int> <list>    <list>   
#> 1     1 <int [3]> <int [3]>
#> 2     2 <chr [1]> <chr [1]>

Modelling

rowwise() data frames allow you to solve a variety of modelling problems in what I think is a particularly elegant way. We’ll start by creating a nested data frame:

by_cyl <- mtcars %>% nest_by(cyl)
#> `summarise()` ungrouping output (override with `.groups` argument)
by_cyl
#> # A tibble: 3 x 2
#> # Rowwise:  cyl
#>     cyl data              
#>   <dbl> <list>            
#> 1     4 <tibble [11 × 12]>
#> 2     6 <tibble [7 × 12]> 
#> 3     8 <tibble [14 × 12]>

This is a little different to the usual group_by() output: we have visibly changed the structure of the data. Now we have three rows (one for each group), and we have a list-col, data, that stores the data for that group. Also note that the output is rowwise(); this is important because it’s going to make working with that list of data frames much easier.

Once we have one data frame per row, it’s straightforward to make one model per row:

mods <- by_cyl %>% mutate(mod = list(lm(mpg ~ wt, data = data)))
mods
#> # A tibble: 3 x 3
#> # Rowwise:  cyl
#>     cyl data               mod   
#>   <dbl> <list>             <list>
#> 1     4 <tibble [11 × 12]> <lm>  
#> 2     6 <tibble [7 × 12]>  <lm>  
#> 3     8 <tibble [14 × 12]> <lm>

And supplement that with one set of predictions per row:

mods <- mods %>% mutate(pred = list(predict(mod, data)))
mods
#> # A tibble: 3 x 4
#> # Rowwise:  cyl
#>     cyl data               mod    pred      
#>   <dbl> <list>             <list> <list>    
#> 1     4 <tibble [11 × 12]> <lm>   <dbl [11]>
#> 2     6 <tibble [7 × 12]>  <lm>   <dbl [7]> 
#> 3     8 <tibble [14 × 12]> <lm>   <dbl [14]>

You could then summarise the model in a variety of ways:

mods %>% summarise(rmse = sqrt(mean((pred - data$mpg) ^ 2)))
#> `summarise()` regrouping output by 'cyl' (override with `.groups` argument)
#> # A tibble: 3 x 2
#> # Groups:   cyl [3]
#>     cyl  rmse
#>   <dbl> <dbl>
#> 1     4 3.01 
#> 2     6 0.985
#> 3     8 1.87
mods %>% summarise(rsq = summary(mod)$r.squared)
#> `summarise()` regrouping output by 'cyl' (override with `.groups` argument)
#> # A tibble: 3 x 2
#> # Groups:   cyl [3]
#>     cyl   rsq
#>   <dbl> <dbl>
#> 1     4 0.509
#> 2     6 0.465
#> 3     8 0.423
mods %>% summarise(broom::glance(mod))
#> `summarise()` regrouping output by 'cyl' (override with `.groups` argument)
#> # A tibble: 3 x 13
#> # Groups:   cyl [3]
#>     cyl r.squared adj.r.squared sigma statistic p.value    df logLik   AIC   BIC
#>   <dbl>     <dbl>         <dbl> <dbl>     <dbl>   <dbl> <dbl>  <dbl> <dbl> <dbl>
#> 1     4     0.509         0.454  3.33      9.32  0.0137     1 -27.7   61.5  62.7
#> 2     6     0.465         0.357  1.17      4.34  0.0918     1  -9.83  25.7  25.5
#> 3     8     0.423         0.375  2.02      8.80  0.0118     1 -28.7   63.3  65.2
#> # … with 3 more variables: deviance <dbl>, df.residual <int>, nobs <int>

Or easily access the parameters of each model:

mods %>% summarise(broom::tidy(mod))
#> `summarise()` regrouping output by 'cyl' (override with `.groups` argument)
#> # A tibble: 6 x 6
#> # Groups:   cyl [3]
#>     cyl term        estimate std.error statistic    p.value
#>   <dbl> <chr>          <dbl>     <dbl>     <dbl>      <dbl>
#> 1     4 (Intercept)    39.6       4.35      9.10 0.00000777
#> 2     4 wt             -5.65      1.85     -3.05 0.0137    
#> 3     6 (Intercept)    28.4       4.18      6.79 0.00105   
#> 4     6 wt             -2.78      1.33     -2.08 0.0918    
#> # … with 2 more rows

Repeated function calls

rowwise() doesn’t just work with functions that return a length-1 vector (aka summary functions); it can work with any function if the result is a list. This means that rowwise() and mutate() provide an elegant way to call a function many times with varying arguments, storing the outputs alongside the inputs.

Simulations

I think this is a particularly elegant way to perform simulations, because it lets you store simulated values along with the parameters that generated them. For example, imagine you have the following data frame that describes the properties of 3 samples from the uniform distribution:

df <- tribble(
  ~ n, ~ min, ~ max,
    1,     0,     1,
    2,    10,   100,
    3,   100,  1000,
)

You can supply these parameters to runif() by using rowwise() and mutate():

df %>% 
  rowwise() %>% 
  mutate(data = list(runif(n, min, max)))
#> # A tibble: 3 x 4
#> # Rowwise: 
#>       n   min   max data     
#>   <dbl> <dbl> <dbl> <list>   
#> 1     1     0     1 <dbl [1]>
#> 2     2    10   100 <dbl [2]>
#> 3     3   100  1000 <dbl [3]>

Note the use of list() here - runif() returns multiple values and a mutate() expression has to return something of length 1. list() means that we’ll get a list column where each row is a list containing multiple values. If you forget to use list(), dplyr will give you a hint:

df %>% 
  rowwise() %>% 
  mutate(data = runif(n, min, max))
#> Error: Problem with `mutate()` input `data`.
#> x Input `data` can't be recycled to size 1.
#> ℹ Input `data` is `runif(n, min, max)`.
#> ℹ Input `data` must be size 1, not 2.
#> ℹ Did you mean: `data = list(runif(n, min, max))` ?
#> ℹ The error occurred in row 2.

Multiple combinations

What if you want to call a function for every combination of inputs? You can use expand.grid() (or tidyr::expand_grid()) to generate the data frame and then repeat the same pattern as above:

df <- expand.grid(mean = c(-1, 0, 1), sd = c(1, 10, 100))

df %>% 
  rowwise() %>% 
  mutate(data = list(rnorm(10, mean, sd)))
#> # A tibble: 9 x 3
#> # Rowwise: 
#>    mean    sd data      
#>   <dbl> <dbl> <list>    
#> 1    -1     1 <dbl [10]>
#> 2     0     1 <dbl [10]>
#> 3     1     1 <dbl [10]>
#> 4    -1    10 <dbl [10]>
#> # … with 5 more rows

Varying functions

In more complicated problems, you might also want to vary the function being called. This tends to be a bit more of an awkward fit with this approach because the columns in the input tibble will be less regular. But it’s still possible, and it’s a natural place to use do.call():

df <- tribble(
   ~rng,     ~params,
   "runif",  list(n = 10), 
   "rnorm",  list(n = 20),
   "rpois",  list(n = 10, lambda = 5),
) %>%
  rowwise()

df %>% 
  mutate(data = list(do.call(rng, params)))
#> # A tibble: 3 x 3
#> # Rowwise: 
#>   rng   params           data      
#>   <chr> <list>           <list>    
#> 1 runif <named list [1]> <dbl [10]>
#> 2 rnorm <named list [1]> <dbl [20]>
#> 3 rpois <named list [2]> <int [10]>

Previously

`rowwise()`

rowwise() was also questioning for quite some time, partly because I didn’t appreciate how many people needed the native ability to compute summaries across multiple variables for each row. As an alternative, we recommended performing row-wise operations with the purrr map() functions. However, this was challenging because you needed to pick a map function based on the number of arguments that were varying and the type of result, which required quite some knowledge of purrr functions.

I was also resistant to rowwise() because I felt like automatically switching between [ to [[ was too magical in the same way that automatically list()-ing results made do() too magical. I’ve now persuaded myself that the row-wise magic is good magic partly because most people find the distinction between [ and [[ mystifying and rowwise() means that you don’t need to think about it.

Since rowwise() clearly is useful it is not longer questioning, and we expect it to be around for the long term.

`do()`

We’ve questioned the need for do() for quite some time, because it never felt very similar to the other dplyr verbs. It had two main modes of operation:

Without argument names: you could call functions that input and output data frames using . to refer to the “current” group. For example, the following code gets the first row of each group:

mtcars %>% 
  group_by(cyl) %>% 
  do(head(., 1))
#> # A tibble: 3 x 13
#> # Groups:   cyl [3]
#>     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb  cyl2  cyl4
#>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1  22.8     4   108    93  3.85  2.32  18.6     1     1     4     1     8    16
#> 2  21       6   160   110  3.9   2.62  16.5     0     1     4     4    12    24
#> 3  18.7     8   360   175  3.15  3.44  17.0     0     0     3     2    16    32

This has been superseded cur_data() plus the more permissive summarise() which can now create multiple columns and multiple rows.

mtcars %>% 
  group_by(cyl) %>% 
  summarise(head(cur_data(), 1))
#> `summarise()` ungrouping output (override with `.groups` argument)
#> # A tibble: 3 x 13
#>     cyl   mpg  disp    hp  drat    wt  qsec    vs    am  gear  carb  cyl2  cyl4
#>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1     4  22.8   108    93  3.85  2.32  18.6     1     1     4     1     8    16
#> 2     6  21     160   110  3.9   2.62  16.5     0     1     4     4    12    24
#> 3     8  18.7   360   175  3.15  3.44  17.0     0     0     3     2    16    32

With arguments: it worked like mutate() but automatically wrapped every element in a list:

mtcars %>% 
  group_by(cyl) %>% 
  do(nrows = nrow(.))
#> # A tibble: 3 x 2
#> # Rowwise: 
#>     cyl nrows    
#>   <dbl> <list>   
#> 1     4 <int [1]>
#> 2     6 <int [1]>
#> 3     8 <int [1]>

I now believe that behaviour is both too magical and not very useful, and it can be replaced by summarise() and cur_data() .

mtcars %>% 
  group_by(cyl) %>% 
  summarise(nrows = nrow(cur_data()))
#> `summarise()` ungrouping output (override with `.groups` argument)
#> # A tibble: 3 x 2
#>     cyl nrows
#>   <dbl> <int>
#> 1     4    11
#> 2     6     7
#> 3     8    14

If needed (unlike here), you can wrap the results in a list yourself.

The addition of cur_data()/across() and the increased scope of summarise() means that do() is no longer needed, so it is now superseded.