purrr: A functional programming toolkit for R

Complete and consistent set of tools for working with functions and vectors

Lists, vectors, and data.frames (or tibbles)

c(char = "hello", num = 1)

##    char     num 
## "hello"     "1"

lists can contain any object

list(char = "hello", num = 1, fun = mean)

## $char
## [1] "hello"
## 
## $num
## [1] 1
## 
## $fun
## function (x, ...) 
## UseMethod("mean")
## <bytecode: 0x7fb922834d08>
## <environment: namespace:base>

Your Turn 1

measurements <- list(
  blood_glucose = rnorm(10, mean = 140, sd = 10), 
  age = rnorm(5, mean = 40, sd = 5), 
  heartrate = rnorm(20, mean = 80, sd = 15)
)

There are two ways to subset lists: dollar signs and brackets. Try to subset blood_glucose from measurements using these approaches. Are they different? What about measurements[["blood_glucose"]]?

Your Turn 1

measurements["blood_glucose"]

## $blood_glucose
##  [1] 127.9293 142.7743 150.8444 116.5430 144.2912 145.0606 134.2526 134.5337 134.3555 131.0996

measurements$blood_glucose

##  [1] 127.9293 142.7743 150.8444 116.5430 144.2912 145.0606 134.2526 134.5337 134.3555 131.0996

measurements[["blood_glucose"]]

##  [1] 127.9293 142.7743 150.8444 116.5430 144.2912 145.0606 134.2526 134.5337 134.3555 131.0996

data frames are lists

x <- list(char = "hello", num = 1)
as.data.frame(x)

##    char num
## 1 hello   1

data frames are lists

library(gapminder)
head(gapminder$pop)

## [1]  8425333  9240934 10267083 11537966 13079460 14880372

data frames are lists

gapminder[1:6, "pop"]

data frames are lists

gapminder[1:6, "pop"]

## # A tibble: 6 x 1
##        pop
##      <int>
## 1  8425333
## 2  9240934
## 3 10267083
## 4 11537966
## 5 13079460
## 6 14880372

data frames are lists

head(gapminder[["pop"]])

## [1]  8425333  9240934 10267083 11537966 13079460 14880372

vectorized functions don't work on lists

sum(rnorm(10))

vectorized functions don't work on lists

sum(rnorm(10))

## [1] -3.831574

vectorized functions don't work on lists

sum(rnorm(10))

## [1] -3.831574

sum(list(x = rnorm(10), y = rnorm(10), z = rnorm(10)))

vectorized functions don't work on lists

sum(rnorm(10))

## [1] -3.831574

sum(list(x = rnorm(10), y = rnorm(10), z = rnorm(10)))

## Error in sum(list(x = rnorm(10), y = rnorm(10), z = rnorm(10))): invalid 'type' (list) of argument

map(.x, .f)

.x: a vector, list, or data frame

.f: a function

Returns a list

Using map()

library(purrr)
x_list <- list(x = rnorm(10), y = rnorm(10), z = rnorm(10))
map(x_list, mean)

Using map()

library(purrr)
x_list <- list(x = rnorm(10), y = rnorm(10), z = rnorm(10))
map(x_list, mean)

Using map()

library(purrr)
x_list <- list(x = rnorm(10), y = rnorm(10), z = rnorm(10))
map(x_list, mean)

Using map()

library(purrr)
x_list <- list(x = rnorm(10), y = rnorm(10), z = rnorm(10))
map(x_list, mean)

## $x
## [1] -0.6097971
## 
## $y
## [1] -0.2788647
## 
## $z
## [1] 0.6165922

Your Turn 2

Read the code in the first chunk and predict what will happen

Run the code in the first chunk. What does it return?

list(
  sum_blood_glucose = sum(measurements$blood_glucose),
  sum_age = sum(measurements$age),
  sum_heartrate = sum(measurements$heartrate)
)

Now, use map() to create the same output.

Your Turn 2

map(measurements, sum)

## $blood_glucose
## [1] 1361.684
## 
## $age
## [1] 193.8606
## 
## $heartrate
## [1] 1509.304

using map() with data frames

library(dplyr)
gapminder %>% 
  select(where(is.numeric)) %>%
  map(sd)

using map() with data frames

library(dplyr)
gapminder %>%
  select(where(is.numeric)) %>%
  map(sd)

using map() with data frames

library(dplyr)
gapminder %>% 
  select(where(is.numeric)) %>%
  map(sd)

using map() with data frames

library(dplyr)
gapminder %>% 
  select(where(is.numeric)) %>%
  map(sd)

## $year
## [1] 17.26533
## 
## $lifeExp
## [1] 12.91711
## 
## $pop
## [1] 106157897
## 
## $gdpPercap
## [1] 9857.455

Your Turn 3

Pass diabetes to map() and map using class(). What are these results telling you?

Your Turn 3

head(
  map(diabetes, class),
  3
)

## $id
## [1] "numeric"
## 
## $chol
## [1] "numeric"
## 
## $stab.glu
## [1] "numeric"

Review: writing functions

x <- x^2
x <- scale(x)
x <- max(x)

Review: writing functions

x <- x^2
x <- scale(x)
x <- max(x)
y <- x^2
y <- scale(y)
y <- max(y)
z <- z^2
z <- scale(x)
z <- max(z)

Review: writing functions

x <- x^2
x <- scale(x)
x <- max(x)
y <- x^2
y <- scale(y)
y <- max(y)
z <- z^2
z <- scale(x)
z <- max(z)

Review: writing functions

x <- x^3
x <- scale(x)
x <- max(x)
y <- x^2
y <- scale(y)
y <- max(y)
z <- z^2
z <- scale(x)
z <- max(z)

Review: writing functions

.f <- function(x) {
  x <- x^3
  x <- scale(x)
  max(x)
}
.f(x)
.f(y)
.f(z)

Your Turn 4

Write a function that returns the mean and standard deviation of a numeric vector.

Give the function a name

Find the mean and SD of x

Map your function to measurements

Your Turn 4

mean_sd <- function(x) {
  x_mean <- mean(x)
  x_sd <- sd(x)
  tibble(mean = x_mean, sd = x_sd)
}
map(measurements, mean_sd)

Your Turn 4

## $blood_glucose
## # A tibble: 1 x 2
##    mean    sd
##   <dbl> <dbl>
## 1  136.  9.96
## 
## $age
## # A tibble: 1 x 2
##    mean    sd
##   <dbl> <dbl>
## 1  38.8  3.91
## 
## $heartrate
## # A tibble: 1 x 2
##    mean    sd
##   <dbl> <dbl>
## 1  75.5  13.8

map(gapminder, ~length(unique(.x)))

map(gapminder, ~length(unique(.x)))

## $country
## [1] 142
## 
## $continent
## [1] 5
## 
## $year
## [1] 12
## 
## $lifeExp
## [1] 1626
## 
## $pop
## [1] 1704
## 
## $gdpPercap
## [1] 1704

Returning types

Returning types

map_int(gapminder, ~length(unique(.x)))

Returning types

map_int(gapminder, ~length(unique(.x)))

##   country continent      year   lifeExp       pop gdpPercap 
##       142         5        12      1626      1704      1704

Your Turn 5

Do the same as #3 above but return a vector instead of a list.

Your Turn 5

map_chr(diabetes, class)

##          id        chol    stab.glu         hdl       ratio       glyhb    location         age      gender      height      weight       frame       bp.1s       bp.1d       bp.2s 
##   "numeric"   "numeric"   "numeric"   "numeric"   "numeric"   "numeric" "character"   "numeric" "character"   "numeric"   "numeric" "character"   "numeric"   "numeric"   "numeric" 
##       bp.2d       waist         hip    time.ppn 
##   "numeric"   "numeric"   "numeric"   "numeric"

Your Turn 6

Check diabetes for any missing data.

Using the ~.f(.x) shorthand, check each column for any missing values using is.na() and any()

Return a logical vector. Are any columns missing data? What happens if you don't include any()? Why?

Try counting the number of missing, returning an integer vector

Your Turn 6

map_lgl(diabetes, ~any(is.na(.x)))

##       id     chol stab.glu      hdl    ratio    glyhb location      age   gender   height   weight    frame    bp.1s    bp.1d    bp.2s    bp.2d    waist      hip time.ppn 
##    FALSE     TRUE    FALSE     TRUE     TRUE     TRUE    FALSE    FALSE    FALSE     TRUE     TRUE     TRUE     TRUE     TRUE     TRUE     TRUE     TRUE     TRUE     TRUE

Your Turn 6

map_int(diabetes, ~sum(is.na(.x)))

##       id     chol stab.glu      hdl    ratio    glyhb location      age   gender   height   weight    frame    bp.1s    bp.1d    bp.2s    bp.2d    waist      hip time.ppn 
##        0        1        0        1        1       13        0        0        0        5        1       12        5        5      262      262        2        2        3

Your Turn 7

Turn diabetes into a list split by location using the split() function. Check its length.

Fill in the model_lm function to model chol (the outcome) with ratio and pass the .data argument to lm()

map model_lm to diabetes_list so that it returns a data frame (by row).

Your Turn 7

diabetes_list <- split(diabetes, diabetes$location)
length(diabetes_list)
model_lm <- function(.data) {
  mdl <- lm(chol ~ ratio, data = .data)
  # get model statistics
  broom::glance(mdl)
}
map(diabetes_list, model_lm)

Your Turn 7

## [1] 2

## $Buckingham
## # A tibble: 1 x 12
##   r.squared adj.r.squared sigma statistic  p.value    df
##       <dbl>         <dbl> <dbl>     <dbl>    <dbl> <dbl>
## 1     0.252         0.248  38.8      66.4 4.11e-14     1
## # … with 6 more variables: logLik <dbl>, AIC <dbl>,
## #   BIC <dbl>, deviance <dbl>, df.residual <int>,
## #   nobs <int>
## 
## $Louisa
## # A tibble: 1 x 12
##   r.squared adj.r.squared sigma statistic  p.value    df
##       <dbl>         <dbl> <dbl>     <dbl>    <dbl> <dbl>
## 1     0.204         0.201  39.4      51.7 1.26e-11     1
## # … with 6 more variables: logLik <dbl>, AIC <dbl>,
## #   BIC <dbl>, deviance <dbl>, df.residual <int>,
## #   nobs <int>

map2(.x, .y, .f)

.x, .y: a vector, list, or data frame

.f: a function

Returns a list

map2()

means <- c(-3, 4, 2, 2.3)
sds <- c(.3, 4, 2, 1)
map2_dbl(means, sds, rnorm, n = 1)

map2()

means <- c(-3, 4, 2, 2.3)
sds <- c(.3, 4, 2, 1)
map2_dbl(means, sds, rnorm, n = 1)

map2()

means <- c(-3, 4, 2, 2.3)
sds <- c(.3, 4, 2, 1)
map2_dbl(means, sds, rnorm, n = 1)

## [1] -2.997932  2.178125  1.266952  2.948287

Your Turn 8

Split the gapminder dataset into a list by country

Create a list of models using map(). For the first argument, pass gapminder_countries. For the second, use the ~.f() notation to write a model with lm(). Use lifeExp on the left hand side of the formula and year on the second. Pass .x to the data argument.

Use map2() to take the models list and the data set list and map them to predict(). Since we're not adding new arguments, you don't need to use ~.f().

Your Turn 8

gapminder_countries <- split(gapminder, gapminder$country)
models <- map(gapminder_countries, ~ lm(lifeExp ~ year, data = .x))
preds <- map2(models, gapminder_countries, predict)
head(preds, 3)

Your Turn 8

gapminder_countries <- split(gapminder, gapminder$country)
models <- map(gapminder_countries, ~ lm(lifeExp ~ year, data = .x))
preds <- map2(models, gapminder_countries, predict)
head(preds, 3)

Your Turn 8

gapminder_countries <- split(gapminder, gapminder$country)
models <- map(gapminder_countries, ~ lm(lifeExp ~ year, data = .x))
preds <- map2(models, gapminder_countries, predict)
head(preds, 3)

Your Turn 8

## $Afghanistan
##        1        2        3        4        5        6 
## 29.90729 31.28394 32.66058 34.03722 35.41387 36.79051 
## 
## $Albania
##        1        2        3        4        5        6 
## 59.22913 60.90254 62.57596 64.24938 65.92279 67.59621 
## 
## $Algeria
##        1        2        3        4        5        6 
## 43.37497 46.22137 49.06777 51.91417 54.76057 57.60697

Other mapping functions

pmap() and friends: take n lists or data frame with argument names

Other mapping functions

pmap() and friends: take n lists or data frame with argument names

walk() and friends: for side effects like plotting; returns input invisibly

Other mapping functions

pmap() and friends: take n lists or data frame with argument names

walk() and friends: for side effects like plotting; returns input invisibly

imap() and friends: includes counter i

Other mapping functions

pmap() and friends: take n lists or data frame with argument names

walk() and friends: for side effects like plotting; returns input invisibly

imap() and friends: includes counter i

map_if(), map_at(): Apply only to certain elements

Your turn 9

Create a new directory using the fs package. Call it "figures".

Write a function to plot a line plot of a given variable in gapminder over time, faceted by continent. Then, save the plot (how do you save a ggplot?). For the file name, paste together the folder, name of the variable, and extension so it follows the pattern "folder/variable_name.png"

Create a character vector that has the three variables we'll plot: "lifeExp", "pop", and "gdpPercap".

Use walk() to save a plot for each of the variables

Your turn 9

fs::dir_create("figures")
ggsave_gapminder <- function(variable) {
  #  we're using `aes_string()` so we don't need the curly-curly syntax
  p <- ggplot(
    gapminder, 
    aes_string(x = "year", y = variable, color = "country")
  ) + 
    geom_line() + 
    scale_color_manual(values = country_colors) + 
    facet_wrap(vars(continent.)) + 
    theme(legend.position = "none")
  ggsave(
    filename = paste0("figures/", variable, ".png"), 
    plot = p, 
    dpi = 320
  )
}

Your turn 9

vars <- c("lifeExp", "pop", "gdpPercap")
walk(vars, ggsave_gapminder)

Base R

Loops vs functional programming

x <- rnorm(10)
y <- map(x, mean)

x <- rnorm(10)
y <- vector("list", length(x))
for (i in seq_along(x)) {
  y[[i]] <- mean(x[[i]])
}

Loops vs functional programming

x <- rnorm(10)
y <- map(x, mean)

x <- rnorm(10) 
y <- vector("list", length(x))
for (i in seq_along(x)) {
  y[[i]] <- mean(x[[i]]) 
}

Loops vs functional programming

x <- rnorm(10)
y <- map(x, mean)

x <- rnorm(10)
y <- vector("list", length(x)) 
for (i in seq_along(x)) {
  y[[i]] <- mean(x[[i]]) 
}

Loops vs functional programming

x <- rnorm(10)
y <- map(x, mean)

x <- rnorm(10)
y <- vector("list", length(x)) 
for (i in seq_along(x)) { 
  y[[i]] <- mean(x[[i]])
}

Working with lists and nested data

Working with lists and nested data

Adverbs: Modify function behavior