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map is a function that takes a vector (or list) and another function for which each item from the input vector (or list) is evaluated against.

map <- function(.x, .f, ...)

map returns a list - but you can use the map_ variants if you want to return a specific type of data as a vector:

• map_chr for a character vector
• map_dbl for a numeric vector
• map_lgl for a logical (TRUE/FALSE) vector
• map_int for an integer vector
• map_raw for a raw rector
• map_dfr if your function returns dataframes and you want to bind by rows (rbind)
• map_dfc if your function returns dataframes and you want to bind by columns (cbind)

Let's say we have a function

fn <- function(x, a) { x^2 + a }

We can use map to iterate over the numbers from 1 to 5 and return the results as a list.

map(1:5, fn, 3)

## [[1]]
## [1] 4
## 
## [[2]]
## [1] 7
## 
## [[3]]
## [1] 12
## 
## [[4]]
## [1] 19
## 
## [[5]]
## [1] 28

Let's generate a table with random binomially distributed values:

df <- tibble(i = 1:20) %>%
  mutate(N = rpois(max(i), 100)) %>%
  mutate(p = map_dbl(N, rbinom,
                     n = 1, prob = 0.75))
knitr::kable(head(df, 5), format="html")

pmap

If you have more than 2 items you need to map over, then you want pmap:

Your input must be a list, and each item in the list must contain the same number of items.

list(x = 1:3, y = 4:6, z = 7:9) %>%
  pmap_dbl(function(x, y, z) x^y+z)

## [1]   8  40 738

If you pass in a dataframe then it will run a function for each row.

The functions arguments need to match the name of the items in the list/columns in the dataframe rather than by position.

Composing multiple functions together

You can chain multiple map's together, for example:

1:5 %>% map_dbl(~.x^2) %>% map_dbl(~.x+3)

## [1]  4  7 12 19 28

the compose function allows us to combine functions together.

map_dbl(1:5, compose(~.x^2, ~.x+3,
                     .dir="forward"))

## [1]  4  7 12 19 28

by default, compose works backwards, like the mathematical "dot" composition, you can change this by setting .dir="forward".

Below we take the ae_attendances data and sum to get just the monthly performance values. We use the BinomCI function from the DescTools package to calculate the percentage of attendances that "breached" the 4 hour target, along with the 95% confidence intervals.

We wouldn't normally be able to call this function inside a tidy pipe because it's expecting a single value for the x and n arguments. Using map2 we run the function once for each pair of values. We compose with as_tibble to convert the results into a table - this helps us in the next step to unnest this column.