Iteration

Introduction

In this chapter, you’ll learn tools for iteration, repeatedly performing the same action on different objects. Iteration in R generally tends to look rather different from other programming languages because so much of it is implicit and we get it for free. For example, if you want to double a numeric vector x in R, you can just write 2 * x. In most other languages, you’d need to explicitly double each element of x using some sort of for loop.

This book has already given you a small but powerful number of tools that perform the same action for multiple “things”:

Now it’s time to learn some more general tools, often called functional programming tools because they are built around functions that take other functions as inputs. Learning functional programming can easily veer into the abstract, but in this chapter we’ll keep things concrete by focusing on three common tasks: modifying multiple columns, reading multiple files, and saving multiple objects.

Prerequisites

This chapter relies on features only found in purrr 1.0.0 and dplyr 1.1.0, which are still in development. If you want to live life on the edge you can get the dev version with devtools::install_github(c("tidyverse/purrr", "tidyverse/dplyr")).

In this chapter, we’ll focus on tools provided by dplyr and purrr, both core members of the tidyverse. You’ve seen dplyr before, but purrr is new. We’re going to use just a couple of purrr functions from in this chapter, but it’s a great package to explore as you improve your programming skills.

library(tidyverse)

Modifying multiple columns

Imagine you have this simple tibble and you want to count the number of observations and compute the median of every column.

df <- tibble(
  a = rnorm(10),
  b = rnorm(10),
  c = rnorm(10),
  d = rnorm(10)
)

You could do it with copy-and-paste:

df |> summarise(
  n = n(),
  a = median(a),
  b = median(b),
  c = median(c),
  d = median(d),
)
#> # A tibble: 1 × 5
#>       n      a      b       c     d
#>   <int>  <dbl>  <dbl>   <dbl> <dbl>
#> 1    10 -0.246 -0.287 -0.0567 0.144

That breaks our rule of thumb to never copy and paste more than twice, and you can imagine that this will get very tedious if you have tens or even hundreds of columns. Instead you can use across():

df |> summarise(
  n = n(),
  across(a:d, median),
)
#> # A tibble: 1 × 5
#>       n      a      b       c     d
#>   <int>  <dbl>  <dbl>   <dbl> <dbl>
#> 1    10 -0.246 -0.287 -0.0567 0.144

across() has three particularly important arguments, which we’ll discuss in detail in the following sections. You’ll use the first two every time you use across(): the first argument, .cols, specifies which columns you want to iterate over, and the second argument, .fns, specifies what to do with each column. You can use the .names argument when you need additional control over the names of output columns, which is particularly important when you use across() with mutate(). We’ll also discuss two important variations, if_any() and if_all(), which work with filter().

Selecting columns with.cols

The first argument to across(), .cols, selects the columns to transform. This uses the same specifications as select(), #sec-select, so you can use functions like starts_with() and ends_with() to select columns based on their name.

There are two additional selection techniques that are particularly useful for across(): everything() and where(). everything() is straightforward: it selects every (non-grouping) column:

df <- tibble(
  grp = sample(2, 10, replace = TRUE),
  a = rnorm(10),
  b = rnorm(10),
  c = rnorm(10),
  d = rnorm(10)
)

df |> 
  group_by(grp) |> 
  summarise(across(everything(), median))
#> # A tibble: 2 × 5
#>     grp       a       b     c     d
#>   <int>   <dbl>   <dbl> <dbl> <dbl>
#> 1     1 -0.0935 -0.0163 0.363 0.364
#> 2     2  0.312  -0.0576 0.208 0.565

Note grouping columns (grp here) are not included in across(), because they’re automatically preserved by summarise().

where() allows you to select columns based on their type:

  • where(is.numeric) selects all numeric columns.
  • where(is.character) selects all string columns.
  • where(is.Date) selects all date columns.
  • where(is.POSIXct) selects all date-time columns.
  • where(is.logical) selects all logical columns.
df_types <- tibble(
  x1 = 1:3,
  x2 = runif(3),
  y1 = sample(letters, 3),
  y2 = c("banana", "apple", "egg")
)

df_types |> 
  summarise(across(where(is.numeric), mean))
#> # A tibble: 1 × 2
#>      x1    x2
#>   <dbl> <dbl>
#> 1     2 0.370

df_types |> 
  summarise(across(where(is.character), str_flatten))
#> # A tibble: 1 × 2
#>   y1    y2            
#>   <chr> <chr>         
#> 1 kjh   bananaappleegg

Just like other selectors, you can combine these with Boolean algebra. For example, !where(is.numeric) selects all non-numeric columns and starts_with("a") & where(is.logical) selects all logical columns whose name starts with “a”.

Calling a single function

The second argument to across() defines how each column will be transformed. In simple cases, as above, this will be a single existing function. This is a pretty special feature of R: we’re passing one function (median, mean, str_flatten, …) to another function (across). This is one of the features that makes R a function programming language.

It’s important to note that we’re passing this function to across(), so across() can call it, not calling it ourselves. That means the function name should never be followed by (). If you forget, you’ll get an error:

df |> 
  group_by(grp) |> 
  summarise(across(everything(), median()))
#> Error in vapply(.x, .f, .mold, ..., USE.NAMES = FALSE): values must be length 1,
#>  but FUN(X[[1]]) result is length 0

This error arises because you’re calling the function with no input, e.g.:

median()
#> Error in is.factor(x): argument "x" is missing, with no default

Calling multiple functions

In more complex cases, you might want to supply additional arguments or perform multiple transformations. Lets motivate this problem with a simple example: what happens if we have some missing values in our data? median() propagates those missing values, giving us a suboptimal output:

rnorm_na <- function(n, n_na, mean = 0, sd = 1) {
  sample(c(rnorm(n - n_na, mean = mean, sd = 1), rep(NA, n_na)))
}

df_miss <- tibble(
  a = rnorm_na(5, 1),
  b = rnorm_na(5, 1),
  c = rnorm_na(5, 2),
  d = rnorm(5)
)
df_miss |> 
  summarise(
    across(a:d, median),
    n = n()
  )
#> # A tibble: 1 × 5
#>       a     b     c     d     n
#>   <dbl> <dbl> <dbl> <dbl> <int>
#> 1    NA    NA    NA 0.704     5

It would be nice if we could pass along na.rm = TRUE to median() to remove these missing values. To do so, instead of calling median() directly, we need to create a new function that calls median() with the desired arguments:

df_miss |> 
  summarise(
    across(a:d, function(x) median(x, na.rm = TRUE)),
    n = n()
  )
#> # A tibble: 1 × 5
#>       a      b      c     d     n
#>   <dbl>  <dbl>  <dbl> <dbl> <int>
#> 1 0.429 -0.721 -0.796 0.704     5

This is a little verbose, so R comes with a handy shortcut: for this sort of throw away, or anonymousAnonymous, because we never explicitly gave it a name with <-. Another term programmers use for this is “lambda function”., function you can replace function with \In older code you might see syntax that looks like ~ .x + 1. This is another way to write anonymous functions but it only works inside tidyverse functions and always uses the variable name .x. We now recommend the base syntax, \(x) x + 1.:

df_miss |> 
  summarise(
    across(a:d, \(x) median(x, na.rm = TRUE)),
    n = n()
  )

In either case, across() effectively expands to the following code:

df_miss |> 
  summarise(
    a = median(a, na.rm = TRUE),
    b = median(b, na.rm = TRUE),
    c = median(c, na.rm = TRUE),
    d = median(d, na.rm = TRUE),
    n = n()
  )

When we remove the missing values from the median(), it would be nice to know just how many values we were removing. We can find that out by supplying two functions to across(): one to compute the median and the other to count the missing values. You supply multiple functions by using a named list to .fns:

df_miss |> 
  summarise(
    across(a:d, list(
      median = \(x) median(x, na.rm = TRUE),
      n_miss = \(x) sum(is.na(x))
    )),
    n = n()
  )
#> # A tibble: 1 × 9
#>   a_median a_n_miss b_median b_n_miss c_median c_n_miss d_med…¹ d_n_m…²     n
#>      <dbl>    <int>    <dbl>    <int>    <dbl>    <int>   <dbl>   <int> <int>
#> 1    0.429        1   -0.721        1   -0.796        2   0.704       0     5
#> # … with abbreviated variable names ¹​d_median, ²​d_n_miss

If you look carefully, you might intuit that the columns are named using using a glue specification (#sec-glue) like {.col}_{.fn} where .col is the name of the original column and .fn is the name of the function. That’s not a coincidence! As you’ll learn in the next section, you can use .names argument to supply your own glue spec.

Column names

The result of across() is named according to the specification provided in the .names argument. We could specify our own if we wanted the name of the function to come firstYou can’t currently change the order of the columns, but you could reorder them after the fact using relocate() or similar.:

df_miss |> 
  summarise(
    across(
      a:d,
      list(
        median = \(x) median(x, na.rm = TRUE),
        n_miss = \(x) sum(is.na(x))
      ),
      .names = "{.fn}_{.col}"
    ),
    n = n(),
  )
#> # A tibble: 1 × 9
#>   median_a n_miss_a median_b n_miss_b median_c n_miss_c media…¹ n_mis…²     n
#>      <dbl>    <int>    <dbl>    <int>    <dbl>    <int>   <dbl>   <int> <int>
#> 1    0.429        1   -0.721        1   -0.796        2   0.704       0     5
#> # … with abbreviated variable names ¹​median_d, ²​n_miss_d

The .names argument is particularly important when you use across() with mutate(). By default the output of across() is given the same names as the inputs. This means that across() inside of mutate() will replace existing columns. For example, here we use coalesce() to replace NAs with 0:

df_miss |> 
  mutate(
    across(a:d, \(x) coalesce(x, 0))
  )
#> # A tibble: 5 × 4
#>        a      b      c      d
#>    <dbl>  <dbl>  <dbl>  <dbl>
#> 1  0     -0.463  0      2.13 
#> 2 -0.382 -0.980  0      0.704
#> 3  0.434  0     -1.06   0.715
#> 4  1.06   1.21  -0.796 -1.09 
#> 5  0.424 -1.28  -0.785  0.402

If you’d like to instead create new columns, you can use the .names argument to give the output new names:

df_miss |> 
  mutate(
    across(a:d, \(x) abs(x), .names = "{.col}_abs")
  )
#> # A tibble: 5 × 8
#>        a      b      c      d  a_abs  b_abs  c_abs d_abs
#>    <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl> <dbl>
#> 1 NA     -0.463 NA      2.13  NA      0.463 NA     2.13 
#> 2 -0.382 -0.980 NA      0.704  0.382  0.980 NA     0.704
#> 3  0.434 NA     -1.06   0.715  0.434 NA      1.06  0.715
#> 4  1.06   1.21  -0.796 -1.09   1.06   1.21   0.796 1.09 
#> 5  0.424 -1.28  -0.785  0.402  0.424  1.28   0.785 0.402

Filtering

across() is a great match for summarise() and mutate() but it’s more awkward to use with filter(), because you usually combine multiple conditions with either | or &. It’s clear that across() can help to create multiple logical columns, but then what? So dplyr provides two variants of across() called if_any() and if_all():

df_miss |> filter(is.na(a) | is.na(b) | is.na(c) | is.na(d))
#> # A tibble: 3 × 4
#>        a      b     c     d
#>    <dbl>  <dbl> <dbl> <dbl>
#> 1 NA     -0.463 NA    2.13 
#> 2 -0.382 -0.980 NA    0.704
#> 3  0.434 NA     -1.06 0.715
# same as:
df_miss |> filter(if_any(a:d, is.na))
#> # A tibble: 3 × 4
#>        a      b     c     d
#>    <dbl>  <dbl> <dbl> <dbl>
#> 1 NA     -0.463 NA    2.13 
#> 2 -0.382 -0.980 NA    0.704
#> 3  0.434 NA     -1.06 0.715

df_miss |> filter(is.na(a) & is.na(b) & is.na(c) & is.na(d))
#> # A tibble: 0 × 4
#> # … with 4 variables: a <dbl>, b <dbl>, c <dbl>, d <dbl>
# same as:
df_miss |> filter(if_all(a:d, is.na))
#> # A tibble: 0 × 4
#> # … with 4 variables: a <dbl>, b <dbl>, c <dbl>, d <dbl>

across() in functions

across() is particularly useful to program with because it allows you to operate on multiple columns. For example, Jacob Scott uses this little helper which wraps a bunch of lubridate function to expand all date columns into year, month, and day columns:

library(lubridate)
#> Loading required package: timechange
#> 
#> Attaching package: 'lubridate'
#> The following objects are masked from 'package:base':
#> 
#>     date, intersect, setdiff, union

expand_dates <- function(df) {
  df |> 
    mutate(
      across(where(is.Date), list(year = year, month = month, day = mday))
    )
}

df_date <- tibble(
  name = c("Amy", "Bob"),
  date = ymd(c("2009-08-03", "2010-01-16"))
)

df_date |> 
  expand_dates()
#> # A tibble: 2 × 5
#>   name  date       date_year date_month date_day
#>   <chr> <date>         <dbl>      <dbl>    <int>
#> 1 Amy   2009-08-03      2009          8        3
#> 2 Bob   2010-01-16      2010          1       16

across() also makes it easy to supply multiple columns in a single argument because the first argument uses tidy-select; you just need to remember to embrace that argument, as we discussed in #sec-embracing. For example, this function will compute the means of numeric columns by default. But by supplying the second argument you can choose to summarize just selected columns:

summarise_means <- function(df, summary_vars = where(is.numeric)) {
  df |> 
    summarise(
      across({{ summary_vars }}, \(x) mean(x, na.rm = TRUE)),
      n = n()
    )
}
diamonds |> 
  group_by(clarity) |> 
  summarise_means()
#> # A tibble: 8 × 9
#>   clarity carat depth table price     x     y     z     n
#>   <ord>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 I1      1.28   62.7  58.3 3924.  6.76  6.71  4.21   741
#> 2 SI2     1.08   61.8  57.9 5063.  6.40  6.40  3.95  9194
#> 3 SI1     0.850  61.9  57.7 3996.  5.89  5.89  3.64 13065
#> 4 VS2     0.764  61.7  57.4 3925.  5.66  5.66  3.49 12258
#> 5 VS1     0.727  61.7  57.3 3839.  5.57  5.58  3.44  8171
#> 6 VVS2    0.596  61.7  57.0 3284.  5.22  5.23  3.22  5066
#> # … with 2 more rows

diamonds |> 
  group_by(clarity) |> 
  summarise_means(c(carat, x:z))
#> # A tibble: 8 × 6
#>   clarity carat     x     y     z     n
#>   <ord>   <dbl> <dbl> <dbl> <dbl> <int>
#> 1 I1      1.28   6.76  6.71  4.21   741
#> 2 SI2     1.08   6.40  6.40  3.95  9194
#> 3 SI1     0.850  5.89  5.89  3.64 13065
#> 4 VS2     0.764  5.66  5.66  3.49 12258
#> 5 VS1     0.727  5.57  5.58  3.44  8171
#> 6 VVS2    0.596  5.22  5.23  3.22  5066
#> # … with 2 more rows

Vspivot_longer()

Before we go on, it’s worth pointing out an interesting connection between across() and pivot_longer() (#sec-pivoting). In many cases, you perform the same calculations by first pivoting the data and then performing the operations by group rather than by column. For example, take this multi-function summary:

df |> 
  summarise(across(a:d, list(median = median, mean = mean)))
#> # A tibble: 1 × 8
#>   a_median a_mean b_median b_mean c_median c_mean d_median d_mean
#>      <dbl>  <dbl>    <dbl>  <dbl>    <dbl>  <dbl>    <dbl>  <dbl>
#> 1   0.0380  0.205  -0.0163 0.0910    0.260 0.0716    0.540  0.508

We could compute the same values by pivoting longer and then summarizing:

long <- df |> 
  pivot_longer(a:d) |> 
  group_by(name) |> 
  summarise(
    median = median(value),
    mean = mean(value)
  )
long
#> # A tibble: 4 × 3
#>   name   median   mean
#>   <chr>   <dbl>  <dbl>
#> 1 a      0.0380 0.205 
#> 2 b     -0.0163 0.0910
#> 3 c      0.260  0.0716
#> 4 d      0.540  0.508

And if you wanted the same structure as across() you could pivot again:

long |> 
  pivot_wider(
    names_from = name,
    values_from = c(median, mean),
    names_vary = "slowest",
    names_glue = "{name}_{.value}"
  )
#> # A tibble: 1 × 8
#>   a_median a_mean b_median b_mean c_median c_mean d_median d_mean
#>      <dbl>  <dbl>    <dbl>  <dbl>    <dbl>  <dbl>    <dbl>  <dbl>
#> 1   0.0380  0.205  -0.0163 0.0910    0.260 0.0716    0.540  0.508

This is a useful technique to know about because sometimes you’ll hit a problem that’s not currently possible to solve with across(): when you have groups of columns that you want to compute with simultaneously. For example, imagine that our data frame contains both values and weights and we want to compute a weighted mean:

df_paired <- tibble(
  a_val = rnorm(10),
  a_wts = runif(10),
  b_val = rnorm(10),
  b_wts = runif(10),
  c_val = rnorm(10),
  c_wts = runif(10),
  d_val = rnorm(10),
  d_wts = runif(10)
)

There’s currently no way to do this with across()Maybe there will be one day, but currently we don’t see how., but it’s relatively straightforward with pivot_longer():

df_long <- df_paired |> 
  pivot_longer(
    everything(), 
    names_to = c("group", ".value"), 
    names_sep = "_"
  )
df_long
#> # A tibble: 40 × 3
#>   group    val   wts
#>   <chr>  <dbl> <dbl>
#> 1 a      0.404 0.678
#> 2 b      1.74  0.650
#> 3 c     -0.921 0.261
#> 4 d     -0.953 0.327
#> 5 a      2.04  0.665
#> 6 b     -1.64  0.815
#> # … with 34 more rows

df_long |> 
  group_by(group) |> 
  summarise(mean = weighted.mean(val, wts))
#> # A tibble: 4 × 2
#>   group    mean
#>   <chr>   <dbl>
#> 1 a      0.109 
#> 2 b      0.585 
#> 3 c     -0.746 
#> 4 d     -0.0142

If needed, you could pivot_wider() this back to the original form.

Exercises

  1. Compute the number of unique values in each column of palmerpenguins::penguins.

  2. Compute the mean of every column in mtcars.

  3. Group diamonds by cut, clarity, and color then count the number of observations and the mean of each numeric column.

  4. What happens if you use a list of functions, but don’t name them? How is the output named?

  5. It is possible to use across() inside filter() where it’s equivalent to if_all(). Can you explain why?

  6. Adjust expand_dates() to automatically remove the date columns after they’ve been expanded. Do you need to embrace any arguments?

  7. Explain what each step of the pipeline in this function does. What special feature of where() are we taking advantage of?

    show_missing <- function(df, group_vars, summary_vars = everything()) {
  df |> 
    group_by(pick({{ group_vars }})) |> 
    summarise(
      across({{ summary_vars }}, \(x) sum(is.na(x))),
      .groups = "drop"
    ) |>
    select(where(\(x) any(x > 0)))
}
nycflights13::flights |> show_missing(c(year, month, day))

Reading multiple files

In the previous section, you learned how to use dplyr::across() to repeat a transformation on multiple columns. In this section, you’ll learn how to use purrr::map() to do something to every file in a directory. Let’s start with a little motivation: imagine you have a directory full of excel spreadsheetsIf you instead had a directory of csv files with the same format, you can use the technique from #sec-readr-directory. you want to read. You could do it with copy and paste:

data2019 <- readxl::read_excel("data/y2019.xlsx")
data2020 <- readxl::read_excel("data/y2020.xlsx")
data2021 <- readxl::read_excel("data/y2021.xlsx")
data2022 <- readxl::read_excel("data/y2022.xlsx")

And then use dplyr::bind_rows() to combine them all together:

data <- bind_rows(data2019, data2020, data2021, data2022)

You can imagine that this would get tedious quickly, especially if you had hundreds of files, not just four. The following sections show you how to automate this sort of task. There are three basic steps: use list.files() to list all the files in a directory, then use purrr::map() to read each of them into a list, then use purrr::list_rbind() to combine them into a single data frame. We’ll then discuss how you can handle situations of increasing heterogeneity, where you can’t do exactly the same thing to every file.

Listing files in a directory

As the name suggests, list.files() lists the files in a directory. TO CONSIDER: why not use it via the more obvious name list.files()? You’ll almost always use three arguments:

  • The first argument, path, is the directory to look in.

  • pattern is a regular expression used to filter the file names. The most common pattern is something like [.]xlsx$ or [.]csv$ to find all files with a specified extension.

  • full.names determines whether or not the directory name should be included in the output. You almost always want this to be TRUE.

To make our motivating example concrete, this book contains a folder with 12 excel spreadsheets containing data from the gapminder package. Each file contains one year’s worth of data for 142 countries. We can list them all with the appropriate call to list.files():

paths <- list.files("data/gapminder", pattern = "[.]xlsx$", full.names = TRUE)
paths
#>  [1] "data/gapminder/1952.xlsx" "data/gapminder/1957.xlsx"
#>  [3] "data/gapminder/1962.xlsx" "data/gapminder/1967.xlsx"
#>  [5] "data/gapminder/1972.xlsx" "data/gapminder/1977.xlsx"
#>  [7] "data/gapminder/1982.xlsx" "data/gapminder/1987.xlsx"
#>  [9] "data/gapminder/1992.xlsx" "data/gapminder/1997.xlsx"
#> [11] "data/gapminder/2002.xlsx" "data/gapminder/2007.xlsx"

Lists

Now that we have these 12 paths, we could call read_excel() 12 times to get 12 data frames:

gapminder_1952 <- readxl::read_excel("data/gapminder/1952.xlsx")
gapminder_1957 <- readxl::read_excel("data/gapminder/1957.xlsx")
gapminder_1962 <- readxl::read_excel("data/gapminder/1962.xlsx")
 ...,
gapminder_2007 <- readxl::read_excel("data/gapminder/2007.xlsx")

But putting each sheet into its own variable is going to make it hard to work with them a few steps down the road. Instead, they’ll be easier to work with if we put them into a single object. A list is the perfect tool for this job:

files <- list(
  readxl::read_excel("data/gapminder/1952.xlsx"),
  readxl::read_excel("data/gapminder/1957.xlsx"),
  readxl::read_excel("data/gapminder/1962.xlsx"),
  ...,
  readxl::read_excel("data/gapminder/2007.xlsx")
)

Now that you have these data frames in a list, how do you get one out? You can use files[[i]] to extract the i-th element:

files[[3]]
#> # A tibble: 142 × 5
#>   country     continent lifeExp      pop gdpPercap
#>   <chr>       <chr>       <dbl>    <dbl>     <dbl>
#> 1 Afghanistan Asia         32.0 10267083      853.
#> 2 Albania     Europe       64.8  1728137     2313.
#> 3 Algeria     Africa       48.3 11000948     2551.
#> 4 Angola      Africa       34    4826015     4269.
#> 5 Argentina   Americas     65.1 21283783     7133.
#> 6 Australia   Oceania      70.9 10794968    12217.
#> # … with 136 more rows

We’ll come back to [[ in more detail in #sec-subset-one.

purrr::map() and list_rbind()

The code to collect those data frames in a list “by hand” is basically just as tedious to type as code that reads the files one-by-one. Happily, we can use purrr::map() to make even better use of our paths vector. map() is similar toacross(), but instead of doing something to each column in a data frame, it does something to each element of a vector.map(x, f) is shorthand for:

list(
  f(x[[1]]),
  f(x[[2]]),
  ...,
  f(x[[n]])
)

So we can use map() get a list of 12 data frames:

files <- map(paths, readxl::read_excel)
length(files)
#> [1] 12

files[[1]]
#> # A tibble: 142 × 5
#>   country     continent lifeExp      pop gdpPercap
#>   <chr>       <chr>       <dbl>    <dbl>     <dbl>
#> 1 Afghanistan Asia         28.8  8425333      779.
#> 2 Albania     Europe       55.2  1282697     1601.
#> 3 Algeria     Africa       43.1  9279525     2449.
#> 4 Angola      Africa       30.0  4232095     3521.
#> 5 Argentina   Americas     62.5 17876956     5911.
#> 6 Australia   Oceania      69.1  8691212    10040.
#> # … with 136 more rows

(This is another data structure that doesn’t display particularly compactly with str() so you might want to load into RStudio and inspect it with View()).

Now we can use purrr::list_rbind() to combine that list of data frames into a single data frame:

list_rbind(files)
#> # A tibble: 1,704 × 5
#>   country     continent lifeExp      pop gdpPercap
#>   <chr>       <chr>       <dbl>    <dbl>     <dbl>
#> 1 Afghanistan Asia         28.8  8425333      779.
#> 2 Albania     Europe       55.2  1282697     1601.
#> 3 Algeria     Africa       43.1  9279525     2449.
#> 4 Angola      Africa       30.0  4232095     3521.
#> 5 Argentina   Americas     62.5 17876956     5911.
#> 6 Australia   Oceania      69.1  8691212    10040.
#> # … with 1,698 more rows

Or we could do both steps at once in pipeline:

paths |> 
  map(readxl::read_excel) |> 
  list_rbind()

What if we want to pass in extra arguments to read_excel()? We use the same technique that we used with across(). For example, it’s often useful to peak at the first few row of the data with n_max = 1:

paths |> 
  map(\(path) readxl::read_excel(path, n_max = 1)) |> 
  list_rbind()
#> # A tibble: 12 × 5
#>   country     continent lifeExp      pop gdpPercap
#>   <chr>       <chr>       <dbl>    <dbl>     <dbl>
#> 1 Afghanistan Asia         28.8  8425333      779.
#> 2 Afghanistan Asia         30.3  9240934      821.
#> 3 Afghanistan Asia         32.0 10267083      853.
#> 4 Afghanistan Asia         34.0 11537966      836.
#> 5 Afghanistan Asia         36.1 13079460      740.
#> 6 Afghanistan Asia         38.4 14880372      786.
#> # … with 6 more rows

This makes it clear that something is missing: there’s no year column because that value is recorded in the path, not the individual files. We’ll tackle that problem next.

Data in the path

Sometimes the name of the file is itself data. In this example, the file name contains the year, which is not otherwise recorded in the individual files. To get that column into the final data frame, we need to do two things.

First, we name the vector of paths. The easiest way to do this is with the set_names() function, which can take a function. Here we use basename() to extract just the file name from the full path:

paths |> set_names(basename) 
#>                  1952.xlsx                  1957.xlsx 
#> "data/gapminder/1952.xlsx" "data/gapminder/1957.xlsx" 
#>                  1962.xlsx                  1967.xlsx 
#> "data/gapminder/1962.xlsx" "data/gapminder/1967.xlsx" 
#>                  1972.xlsx                  1977.xlsx 
#> "data/gapminder/1972.xlsx" "data/gapminder/1977.xlsx" 
#>                  1982.xlsx                  1987.xlsx 
#> "data/gapminder/1982.xlsx" "data/gapminder/1987.xlsx" 
#>                  1992.xlsx                  1997.xlsx 
#> "data/gapminder/1992.xlsx" "data/gapminder/1997.xlsx" 
#>                  2002.xlsx                  2007.xlsx 
#> "data/gapminder/2002.xlsx" "data/gapminder/2007.xlsx"

Those names are automatically carried along by all the map functions, so the list of data frames will have those same names:

files <- paths |> 
  set_names(basename) |> 
  map(readxl::read_excel)

That makes this call to map() shorthand for:

files <- list(
  "1952.xlsx" = readxl::read_excel("data/gapminder/1952.xlsx"),
  "1957.xlsx" = readxl::read_excel("data/gapminder/1957.xlsx"),
  "1962.xlsx" = readxl::read_excel("data/gapminder/1962.xlsx"),
  ...,
  "2007.xlsx" = readxl::read_excel("data/gapminder/2007.xlsx")
)

You can also use [[ to extract elements by name:

files[["1962.xlsx"]]
#> # A tibble: 142 × 5
#>   country     continent lifeExp      pop gdpPercap
#>   <chr>       <chr>       <dbl>    <dbl>     <dbl>
#> 1 Afghanistan Asia         32.0 10267083      853.
#> 2 Albania     Europe       64.8  1728137     2313.
#> 3 Algeria     Africa       48.3 11000948     2551.
#> 4 Angola      Africa       34    4826015     4269.
#> 5 Argentina   Americas     65.1 21283783     7133.
#> 6 Australia   Oceania      70.9 10794968    12217.
#> # … with 136 more rows

Then we use the names_to argument to list_rbind() to tell it to save the names into a new column called year then use readr::parse_number() to extract the number from the string.

paths |> 
  set_names(basename) |> 
  map(readxl::read_excel) |> 
  list_rbind(names_to = "year") |> 
  mutate(year = parse_number(year))
#> # A tibble: 1,704 × 6
#>    year country     continent lifeExp      pop gdpPercap
#>   <dbl> <chr>       <chr>       <dbl>    <dbl>     <dbl>
#> 1  1952 Afghanistan Asia         28.8  8425333      779.
#> 2  1952 Albania     Europe       55.2  1282697     1601.
#> 3  1952 Algeria     Africa       43.1  9279525     2449.
#> 4  1952 Angola      Africa       30.0  4232095     3521.
#> 5  1952 Argentina   Americas     62.5 17876956     5911.
#> 6  1952 Australia   Oceania      69.1  8691212    10040.
#> # … with 1,698 more rows

In more complicated cases, there might be other variables stored in the directory name, or maybe the file name contains multiple bits of data. In that case, use set_names() (without any arguments) to record the full path, and then use tidyr::separate_wider_delim() and friends to turn them into useful columns.

# NOTE: this chapter also depends on dev tidyr (in addition to dev purrr and dev dplyr)
paths |> 
  set_names() |> 
  map(readxl::read_excel) |> 
  list_rbind(names_to = "year") |> 
  separate_wider_delim(year, delim = "/", names = c(NA, "dir", "file")) |> 
  separate_wider_delim(file, delim = ".", names = c("file", "ext"))
#> # A tibble: 1,704 × 8
#>   dir       file  ext   country     continent lifeExp      pop gdpPercap
#>   <chr>     <chr> <chr> <chr>       <chr>       <dbl>    <dbl>     <dbl>
#> 1 gapminder 1952  xlsx  Afghanistan Asia         28.8  8425333      779.
#> 2 gapminder 1952  xlsx  Albania     Europe       55.2  1282697     1601.
#> 3 gapminder 1952  xlsx  Algeria     Africa       43.1  9279525     2449.
#> 4 gapminder 1952  xlsx  Angola      Africa       30.0  4232095     3521.
#> 5 gapminder 1952  xlsx  Argentina   Americas     62.5 17876956     5911.
#> 6 gapminder 1952  xlsx  Australia   Oceania      69.1  8691212    10040.
#> # … with 1,698 more rows

Save your work

Now that you’ve done all this hard work to get to a nice tidy data frame, it’s a great time to save your work:

gapminder <- paths |> 
  set_names(basename) |> 
  map(readxl::read_excel) |> 
  list_rbind(names_to = "year") |> 
  mutate(year = parse_number(year))

write_csv(gapminder, "gapminder.csv")

Now when you come back to this problem in the future, you can read in a single csv file.

If you’re working in a project, we’d suggest calling the file that does this sort of data prep work something like 0-cleanup.R. The 0 in the file name suggests that this should be run before anything else.

If your input data files change over time, you might consider learning a tool like targets to set up your data cleaning code to automatically re-run whenever one of the input files is modified.

Many simple iterations

Here we’ve just loaded the data directly from disk, and were lucky enough to get a tidy dataset. In most cases, you’ll need to do some additional tidying, and you have two basic basic options: you can do one round of iteration with a complex function, or do a multiple rounds of iteration with simple functions. In our experience most folks reach first for one complex iteration, but you’re often better by doing multiple simple iterations.

For example, imagine that you want to read in a bunch of files, filter out missing values, pivot, and then combine. One way to approach the problem is write a function that takes a file and does all those steps then call map() once:

process_file <- function(path) {
  df <- read_csv(path)
  
  df |> 
    filter(!is.na(id)) |> 
    mutate(id = tolower(id)) |> 
    pivot_longer(jan:dec, names_to = "month")
}

paths |> 
  map(process_file) |> 
  list_rbind()

Alternatively, you could perform each step of process_file() to every file:

paths |> 
  map(read_csv) |> 
  map(\(df) df |> filter(!is.na(id))) |> 
  map(\(df) df |> mutate(id = tolower(id))) |> 
  map(\(df) df |> pivot_longer(jan:dec, names_to = "month")) |> 
  list_rbind()

We recommend this approach because it stops you getting fixated on getting the first file right because moving on to the rest. By considering all of the data when doing tidying and cleaning, you’re more likely to think holistically and end up with a higher quality result.

In this particular example, there’s another optimization you could make, by binding all the data frames together earlier. Then you can rely on regular dplyr behavior:

paths |> 
  map(read_csv) |> 
  list_rbind() |> 
  filter(!is.na(id)) |> 
  mutate(id = tolower(id)) |> 
  pivot_longer(jan:dec, names_to = "month")

Heterogeneous data

Unfortunately sometimes it’s not possible to go from map() straight to list_rbind() because the data frames are so heterogeneous that list_rbind() either fails or yields a data frame that’s not very useful. In that case, it’s still useful to start by loading all of the files:

files <- paths |> 
  map(readxl::read_excel)

Then a very useful strategy is to capture the structure of the data frames to data so that you can explore it using your data science skills. One way to do so is with this handy df_types function that returns a tibble with one row for each column:

df_types <- function(df) {
  tibble(
    col_name = names(df), 
    col_type = map_chr(df, vctrs::vec_ptype_full),
    n_miss = map_int(df, \(x) sum(is.na(x)))
  )
}

df_types(starwars)
#> # A tibble: 14 × 3
#>   col_name   col_type  n_miss
#>   <chr>      <chr>      <int>
#> 1 name       character      0
#> 2 height     integer        6
#> 3 mass       double        28
#> 4 hair_color character      5
#> 5 skin_color character      0
#> 6 eye_color  character      0
#> # … with 8 more rows
df_types(nycflights13::flights)
#> # A tibble: 19 × 3
#>   col_name       col_type n_miss
#>   <chr>          <chr>     <int>
#> 1 year           integer       0
#> 2 month          integer       0
#> 3 day            integer       0
#> 4 dep_time       integer    8255
#> 5 sched_dep_time integer       0
#> 6 dep_delay      double     8255
#> # … with 13 more rows

You can then apply this function all of the files, and maybe do some pivoting to make it easy to see where there are differences. For example, this makes it easy to verify that the gapminder spreadsheets that we’ve been working with are all quite homogeneous:

files |> 
  map(df_types) |> 
  list_rbind(names_to = "file_name") |> 
  select(-n_miss) |> 
  pivot_wider(names_from = col_name, values_from = col_type)
#> # A tibble: 12 × 6
#>   file_name country   continent lifeExp pop    gdpPercap
#>   <chr>     <chr>     <chr>     <chr>   <chr>  <chr>    
#> 1 1952.xlsx character character double  double double   
#> 2 1957.xlsx character character double  double double   
#> 3 1962.xlsx character character double  double double   
#> 4 1967.xlsx character character double  double double   
#> 5 1972.xlsx character character double  double double   
#> 6 1977.xlsx character character double  double double   
#> # … with 6 more rows

If the files have heterogeneous formats you might need to do more processing before you can successfully merge them. Unfortunately we’re now going to leave you to figure that out on your own, but you might want to read about map_if() and map_at(). map_if() allows you to selectively modify elements of a list based on their values; map_at() allows you to selectively modify elements based on their names.

Handling failures

Sometimes the structure of your data might be sufficiently wild that you can’t even read all the files with a single command. And then you’ll encounter one of the downsides of map: it succeeds or fails as a whole. map() will either successfully read all of the files in a directory or fail with an error, reading zero files. This is annoying: why does one failure prevent you from accessing all the other successes?

Luckily, purrr comes with a helper to tackle this problem: possibly(). possibly() is what’s known as a function operator: it takes a function and returns a function with modified behavior. In particular, possibly() changes a function from erroring to returning a value that you specify:

files <- paths |> 
  map(possibly(\(path) readxl::read_excel(path), NULL))

data <- files |> list_rbind()

This works particularly well here because list_rbind(), like many tidyverse functions, automatically ignores NULLs.

Now you have all the data that can be read easily, and it’s time to tackle the hard part of figuring out why some files failed load and what do to about it. Start by getting the paths that failed:

failed <- map_vec(files, is.null)
paths[failed]
#> character(0)

Then call the import function again for each failure and figure out what went wrong.

Saving multiple outputs

In the last section, you learned about map(), which is useful for reading multiple files into a single object. In this section, we’ll now explore sort of the opposite problem: how can you take one or more R objects and save it to one or more files? We’ll explore this challenge using three examples:

  • Saving multiple data frames into one database.
  • Saving multiple data frames into multiple csv files.
  • Saving multiple plots to multiple .png files.

Writing to a database

Sometimes when working with many files at once, it’s not possible to fit all your data into memory at once, and you can’t do map(files, read_csv). One approach to deal with this problem is to load your into a database so you can access just the bits you need with dbplyr.

If you’re lucky, the database package you’re using will provide a handy function that takes a vector of paths and loads them all into the database. This is the case with duckdb’s duckdb_read_csv():

con <- DBI::dbConnect(duckdb::duckdb())
duckdb::duckdb_read_csv(con, "gapminder", paths)

This would work well here, but we don’t have csv files, instead we have excel spreadsheets. So we’re going to have to do it “by hand”. Learning to do it by hand will also help you when you have a bunch of csvs and the database that you’re working with doesn’t have one function that will load them all in.

We need to start by creating a table that will fill in with data. The easiest way to do this is by creating a template, a dummy data frame that contains all the columns we want, but only a sampling of the data. For the gapminder data, we can make that template by reading a single file and adding the year to it:

template <- readxl::read_excel(paths[[1]])
template$year <- 1952
template
#> # A tibble: 142 × 6
#>   country     continent lifeExp      pop gdpPercap  year
#>   <chr>       <chr>       <dbl>    <dbl>     <dbl> <dbl>
#> 1 Afghanistan Asia         28.8  8425333      779.  1952
#> 2 Albania     Europe       55.2  1282697     1601.  1952
#> 3 Algeria     Africa       43.1  9279525     2449.  1952
#> 4 Angola      Africa       30.0  4232095     3521.  1952
#> 5 Argentina   Americas     62.5 17876956     5911.  1952
#> 6 Australia   Oceania      69.1  8691212    10040.  1952
#> # … with 136 more rows

Now we can connect to the database, and use DBI::dbCreateTable() to turn our template into database table:

con <- DBI::dbConnect(duckdb::duckdb())
DBI::dbCreateTable(con, "gapminder", template)

dbCreateTable() doesn’t use the data in template, just the variable names and types. So if we inspect the gapminder table now you’ll see that it’s empty but it has the variables we need with the types we expect:

con |> tbl("gapminder")
#> # Source:   table<gapminder> [0 x 6]
#> # Database: DuckDB 0.5.1 [root@Darwin 22.1.0:R 4.2.1/:memory:]
#> # … with 6 variables: country <chr>, continent <chr>, lifeExp <dbl>,
#> #   pop <dbl>, gdpPercap <dbl>, year <dbl>

Next, we need a function that takes a single file path, reads it into R, and adds the result to the gapminder table. We can do that by combining read_excel() with DBI::dbAppendTable():

append_file <- function(path) {
  df <- readxl::read_excel(path)
  df$year <- parse_number(basename(path))
  
  DBI::dbAppendTable(con, "gapminder", df)
}

Now we need to call append_csv() once for each element of paths. That’s certainly possible with map():

paths |> map(append_file)

But we don’t care about the output of append_file(), so instead of map() it’s slightly nicer to use walk(). walk() does exactly the same thing as map() but throws the output away:

paths |> walk(append_file)

Now we can see if we have all the data in our table:

con |> 
  tbl("gapminder") |> 
  count(year)
#> # Source:   SQL [?? x 2]
#> # Database: DuckDB 0.5.1 [root@Darwin 22.1.0:R 4.2.1/:memory:]
#>    year     n
#>   <dbl> <dbl>
#> 1  1952   142
#> 2  1987   142
#> 3  1957   142
#> 4  1992   142
#> 5  1962   142
#> 6  1997   142
#> # … with more rows

Writing csv files

The same basic principle applies if we want to write multiple csv files, one for each group. Let’s imagine that we want to take the ggplot2::diamonds data and save one csv file for each clarity. First we need to make those individual datasets. There are many ways you could do that, but there’s one way we particularly like: group_nest().

by_clarity <- diamonds |> 
  group_nest(clarity)

by_clarity
#> # A tibble: 8 × 2
#>   clarity               data
#>   <ord>   <list<tibble[,9]>>
#> 1 I1               [741 × 9]
#> 2 SI2            [9,194 × 9]
#> 3 SI1           [13,065 × 9]
#> 4 VS2           [12,258 × 9]
#> 5 VS1            [8,171 × 9]
#> 6 VVS2           [5,066 × 9]
#> # … with 2 more rows

This gives us a new tibble with eight rows and two columns. clarity is our grouping variable and data is a list-column containing one tibble for each unique value of clarity:

by_clarity$data[[1]]
#> # A tibble: 741 × 9
#>   carat cut       color depth table price     x     y     z
#>   <dbl> <ord>     <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
#> 1  0.32 Premium   E      60.9    58   345  4.38  4.42  2.68
#> 2  1.17 Very Good J      60.2    61  2774  6.83  6.9   4.13
#> 3  1.01 Premium   F      61.8    60  2781  6.39  6.36  3.94
#> 4  1.01 Fair      E      64.5    58  2788  6.29  6.21  4.03
#> 5  0.96 Ideal     F      60.7    55  2801  6.37  6.41  3.88
#> 6  1.04 Premium   G      62.2    58  2801  6.46  6.41  4   
#> # … with 735 more rows

While we’re here, lets create a column that gives the name of output file, using mutate() and str_glue():

by_clarity <- by_clarity |> 
  mutate(path = str_glue("diamonds-{clarity}.csv"))

by_clarity
#> # A tibble: 8 × 3
#>   clarity               data path             
#>   <ord>   <list<tibble[,9]>> <glue>           
#> 1 I1               [741 × 9] diamonds-I1.csv  
#> 2 SI2            [9,194 × 9] diamonds-SI2.csv 
#> 3 SI1           [13,065 × 9] diamonds-SI1.csv 
#> 4 VS2           [12,258 × 9] diamonds-VS2.csv 
#> 5 VS1            [8,171 × 9] diamonds-VS1.csv 
#> 6 VVS2           [5,066 × 9] diamonds-VVS2.csv
#> # … with 2 more rows

So if we were going to save these data frames by hand, we might write something like:

write_csv(by_clarity$data[[1]], by_clarity$path[[1]])
write_csv(by_clarity$data[[2]], by_clarity$path[[2]])
write_csv(by_clarity$data[[3]], by_clarity$path[[3]])
...
write_csv(by_clarity$by_clarity[[8]], by_clarity$path[[8]])

This is a little different to our previous uses of map() because there are two arguments that are changing, not just one. That means we need a new function: map2(), which varies both the first and second arguments. And because we again don’t care about the output, we want walk2() rather than map2(). That gives us:

walk2(by_clarity$data, by_clarity$path, write_csv)

Saving plots

We can take the same basic approach to create many plots. Let’s first make a function that draws the plot we want:

carat_histogram <- function(df) {
  ggplot(df, aes(carat)) + geom_histogram(binwidth = 0.1)  
}

carat_histogram(by_clarity$data[[1]])

Now we can use map() to create a list of many plotsYou can print by_clarity$plot to get a crude animation — you’ll get one plot for each element of plots. NOTE: this didn’t happen for me. and their eventual file paths:

by_clarity <- by_clarity |> 
  mutate(
    plot = map(data, carat_histogram),
    path = str_glue("clarity-{clarity}.png")
  )

Then use walk2() with ggsave() to save each plot:

walk2(
  by_clarity$path,
  by_clarity$plot,
  \(path, plot) ggsave(path, plot, width = 6, height = 6)
)

This is shorthand for:

ggsave(by_clarity$path[[1]], by_clarity$plot[[1]], width = 6, height = 6)
ggsave(by_clarity$path[[2]], by_clarity$plot[[2]], width = 6, height = 6)
ggsave(by_clarity$path[[3]], by_clarity$plot[[3]], width = 6, height = 6)
...
ggsave(by_clarity$path[[8]], by_clarity$plot[[8]], width = 6, height = 6)

Summary

In this chapter you’ve seen how to use explicit iteration to solve three problems that come up frequently when doing data science: manipulating multiple columns, reading multiple files, and saving multiple outputs. But in general, iteration is a super power: if you know the right iteration technique, you can easily go from fixing one problem to fixing all the problems. Once you’ve mastered the techniques in this chapter, we highly recommend learning more by reading the Functionals chapter of Advanced R and consulting the purrr website.

If you know much about iteration in other languages you might be surprised that we didn’t discuss the for loop. That’s because R’s orientation towards data analysis changes how we iterate: in most cases you can rely on an existing idiom to do something to each columns or each group. And when you can’t, you can often use a functional programming tool like map() that does something to each element of a list. However, you will see for loops in wild-caught code, so you’ll learn about them in the next chapter where we’ll discuss some important base R tools.