Functional programming
& purrr

Lecture 08

Dr. Colin Rundel

Functional Programming

Functions as objects

We have mentioned in passing that in R functions are treated as 1st class objects (like vectors), meaning they can be assigned names, stored in lists, etc.

f = function(x) {
  x*x
}

f(2)

[1] 4

g = f

g(2)

[1] 4

l = list(f = f, g = g)

l$f(3)

[1] 9

l[[2]](4)

[1] 16

l[1](3)

Error in eval(expr, envir, enclos): attempt to apply non-function

Functions as arguments

We can pass in functions as arguments to other functions,

do_calc = function(v, func) {
  func(v)
}

do_calc(1:3, sum)

[1] 6

do_calc(1:3, mean)

[1] 2

do_calc(1:3, sd)

[1] 1

Anonymous functions

These are short functions that are created without ever assigning a name,

function(x) {x+1}

function(x) {x+1}

(function(y) {y-1})(10)

[1] 9

this can be particularly helpful for implementing certain types of tasks,

integrate(function(x) x, 0, 1)

0.5 with absolute error < 5.6e-15

integrate(function(x) x^2-2*x+1, 0, 1)

0.3333333 with absolute error < 3.7e-15

Base R anonymous function (lambda) shorthand

Along with the base pipe (|>), R v4.1.0 introduced a shortcut for anonymous functions using \(),

(\(x) {1+x})(1:5)

[1] 2 3 4 5 6

(\(x) x^2)(10)

[1] 100

integrate(\(x) sin(x)^2, 0, 1)

0.2726756 with absolute error < 3e-15

Use of this with the base pipe helps avoid the need for _, e.g.

data.frame(x = runif(10), y = runif(10)) |>
  {\(d) lm(y~x, data = d)}()


Call:
lm(formula = y ~ x, data = d)

Coefficients:
(Intercept)            x  
     0.3817       0.2145

apply (base R)

Apply functions

The apply functions are a collection of tools for functional programming in base R, they are variations of the map function found in many other languages and apply a function over the elements of an input (vector).

??base::apply

## Help files with alias or concept or title matching ‘apply’ using fuzzy
## matching:
## 
## base::apply             Apply Functions Over Array Margins
## base::.subset           Internal Objects in Package 'base'
## base::by                Apply a Function to a Data Frame Split by Factors
## base::eapply            Apply a Function Over Values in an Environment
## base::lapply            Apply a Function over a List or Vector (Aliases: lapply, sapply, vapply)
## base::mapply            Apply a Function to Multiple List or Vector Arguments
## base::rapply            Recursively Apply a Function to a List
## base::tapply            Apply a Function Over a Ragged Array

lapply

Usage: lapply(X, FUN, ...)

lapply returns a list of the same length as X, each element of which is the result of applying FUN to the corresponding element of X.

lapply(1:8, sqrt) |> 
  str()

List of 8
 $ : num 1
 $ : num 1.41
 $ : num 1.73
 $ : num 2
 $ : num 2.24
 $ : num 2.45
 $ : num 2.65
 $ : num 2.83

lapply(1:8, function(x) (x+1)^2) |> 
  str()

List of 8
 $ : num 4
 $ : num 9
 $ : num 16
 $ : num 25
 $ : num 36
 $ : num 49
 $ : num 64
 $ : num 81

Argument matching

lapply(1:8, function(x, pow) x^pow, pow=3) |> 
  str()

List of 8
 $ : num 1
 $ : num 8
 $ : num 27
 $ : num 64
 $ : num 125
 $ : num 216
 $ : num 343
 $ : num 512

lapply(1:8, function(x, pow) x^pow, x=2) |> 
  str()

List of 8
 $ : num 2
 $ : num 4
 $ : num 8
 $ : num 16
 $ : num 32
 $ : num 64
 $ : num 128
 $ : num 256

sapply

Usage: sapply(X, FUN, ..., simplify = TRUE, USE.NAMES = TRUE)

sapply is a user-friendly version and wrapper of lapply, it is a simplifying version of lapply. Whenever possible it will return a vector, matrix, or an array.

sapply(1:8, sqrt)

[1] 1.000000 1.414214 1.732051 2.000000 2.236068 2.449490 2.645751 2.828427

sapply(1:8, function(x) (x+1)^2)

[1]  4  9 16 25 36 49 64 81

sapply(1:8, function(x) c(x, x^2, x^3))

     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
[1,]    1    2    3    4    5    6    7    8
[2,]    1    4    9   16   25   36   49   64
[3,]    1    8   27   64  125  216  343  512

Legnth mismatch?

sapply(1:6, seq) |> str()

List of 6
 $ : int 1
 $ : int [1:2] 1 2
 $ : int [1:3] 1 2 3
 $ : int [1:4] 1 2 3 4
 $ : int [1:5] 1 2 3 4 5
 $ : int [1:6] 1 2 3 4 5 6

lapply(1:6, seq) |> str()

List of 6
 $ : int 1
 $ : int [1:2] 1 2
 $ : int [1:3] 1 2 3
 $ : int [1:4] 1 2 3 4
 $ : int [1:5] 1 2 3 4 5
 $ : int [1:6] 1 2 3 4 5 6

Type mismatch?

l = list(a = 1:3, b = 4:6, c = 7:9, d = list(10, 11, "A"))

sapply(l, function(x) x[1]) |> str()

List of 4
 $ a: int 1
 $ b: int 4
 $ c: int 7
 $ d: num 10

sapply(l, function(x) x[[1]]) |> str()

 Named num [1:4] 1 4 7 10
 - attr(*, "names")= chr [1:4] "a" "b" "c" "d"

sapply(l, function(x) x[[3]]) |> str()

 Named chr [1:4] "3" "6" "9" "A"
 - attr(*, "names")= chr [1:4] "a" "b" "c" "d"

`*`apply and data frames

We can use these functions with data frames, the key is to remember that a data frame is just a fancy list.

df = data.frame(
  a = 1:6, 
  b = letters[1:6], 
  c = c(TRUE,FALSE)
)

lapply(df, class) |> str()

List of 3
 $ a: chr "integer"
 $ b: chr "character"
 $ c: chr "logical"

sapply(df, class)

          a           b           c 
  "integer" "character"   "logical"

A more useful example

Some sources of data (e.g. some US government agencies) will encode missing values with -999, if want to replace these with NAs lapply is not a bad choice.

d = tibble::tribble(
  ~patient_id, ~age,  ~bp,  ~o2,
            1,   32,  110,   97,
            2,   27,  100,   95,
            3,   56,  125, -999,
            4,   19, -999, -999,
            5,   65, -999,   99
)

fix_missing = function(x) {
  x[x == -999] = NA
  x
}
lapply(d, fix_missing)

$patient_id
[1] 1 2 3 4 5

$age
[1] 32 27 56 19 65

$bp
[1] 110 100 125  NA  NA

$o2
[1] 97 95 NA NA 99

lapply(d, fix_missing) |>
  as_tibble()

# A tibble: 5 × 4
  patient_id   age    bp    o2
       <dbl> <dbl> <dbl> <dbl>
1          1    32   110    97
2          2    27   100    95
3          3    56   125    NA
4          4    19    NA    NA
5          5    65    NA    99

dplyr alternative

dplyr is also a viable option here using the across() helper,

d |>
  mutate(
    across(
      bp:o2, 
      fix_missing
    )
  )

# A tibble: 5 × 4
  patient_id   age    bp    o2
       <dbl> <dbl> <dbl> <dbl>
1          1    32   110    97
2          2    27   100    95
3          3    56   125    NA
4          4    19    NA    NA
5          5    65    NA    99

d |>
  mutate(
    across(
      where(is.numeric), 
      fix_missing
    )
  )

# A tibble: 5 × 4
  patient_id   age    bp    o2
       <dbl> <dbl> <dbl> <dbl>
1          1    32   110    97
2          2    27   100    95
3          3    56   125    NA
4          4    19    NA    NA
5          5    65    NA    99

other less common apply functions

apply() - applies a function over the rows or columns of a data frame, matrix or array
vapply() - is similar to sapply, but has a enforced return type and size
mapply() - like sapply but will iterate over multiple vectors at the same time.
rapply() - a recursive version of lapply, behavior depends largely on the how argument
eapply() - apply a function over an environment.

Map functions

Basic functions for looping over objects and returning a value (of a specific type) - replacement for lapply/sapply/vapply.

map() - returns a list, equivalent to lapply()
map_lgl() - returns a logical vector.
map_int() - returns a integer vector.
map_dbl() - returns a double vector.
map_chr() - returns a character vector.
walk() - returns nothing, used for side effects

Type Consistency

R is a weakly / dynamically typed language which means there is no syntactic way to define a function which enforces argument or return types. This flexibility can be useful at times, but often it makes it hard to reason about your code and requires more verbose code to handle edge cases.

x = list(rnorm(1e3), rnorm(1e3), rnorm(1e3))

map_dbl(x, mean)

[1]  0.02809283  0.04633194 -0.03583281

map_chr(x, mean)

[1] "0.028093"  "0.046332"  "-0.035833"

map_int(x, mean)

Error in `map_int()`:
ℹ In index: 1.
Caused by error:
! Can't coerce from a number to an integer.

map(x, mean) |> str()

List of 3
 $ : num 0.0281
 $ : num 0.0463
 $ : num -0.0358

lapply(x, mean) |> str()

List of 3
 $ : num 0.0281
 $ : num 0.0463
 $ : num -0.0358

Working with Data Frames

purrr offers the functions map_dfr and map_dfc (which were superseded as of v1.0.0) - these allow for the construction of a data frame by row or by column respectively.

d = tibble::tribble(
  ~patient_id, ~age,  ~bp,  ~o2,
            1,   32,  110,   97,
            2,   27,  100,   95,
            3,   56,  125, -999,
            4,   19, -999, -999,
            5,   65, -999,   99
)

fix_missing = function(x) {
  x[x == -999] = NA
  x
}

purrr::map_dfc(d, fix_missing)

# A tibble: 5 × 4
  patient_id   age    bp    o2
       <dbl> <dbl> <dbl> <dbl>
1          1    32   110    97
2          2    27   100    95
3          3    56   125    NA
4          4    19    NA    NA
5          5    65    NA    99

purrr::map(d, fix_missing) |> 
  bind_cols()

# A tibble: 5 × 4
  patient_id   age    bp    o2
       <dbl> <dbl> <dbl> <dbl>
1          1    32   110    97
2          2    27   100    95
3          3    56   125    NA
4          4    19    NA    NA
5          5    65    NA    99

Building by row

map(sw_people, function(x) x[1:5]) |> bind_rows()

# A tibble: 87 × 5
   name               height mass  hair_color    skin_color 
   <chr>              <chr>  <chr> <chr>         <chr>      
 1 Luke Skywalker     172    77    blond         fair       
 2 C-3PO              167    75    n/a           gold       
 3 R2-D2              96     32    n/a           white, blue
 4 Darth Vader        202    136   none          white      
 5 Leia Organa        150    49    brown         light      
 6 Owen Lars          178    120   brown, grey   light      
 7 Beru Whitesun lars 165    75    brown         light      
 8 R5-D4              97     32    n/a           white, red 
 9 Biggs Darklighter  183    84    black         light      
10 Obi-Wan Kenobi     182    77    auburn, white fair       
# ℹ 77 more rows

map(sw_people, function(x) x) |> bind_rows()

Error in `vctrs::data_frame()`:
! Can't recycle `name` (size 5) to match `vehicles` (size 2).

purrr style anonymous functions

purrr lets us write anonymous functions using one sided formulas where the argument is given by . or .x for map and related functions.

map_dbl(1:5, function(x) x/(x+1))

[1] 0.5000000 0.6666667 0.7500000 0.8000000 0.8333333

map_dbl(1:5, ~ ./(.+1))

[1] 0.5000000 0.6666667 0.7500000 0.8000000 0.8333333

map_dbl(1:5, ~ .x/(.x+1))

[1] 0.5000000 0.6666667 0.7500000 0.8000000 0.8333333

Generally, the latter option is preferred to avoid confusion with magrittr.

Multiargument anonymous functions

Functions with the map2 prefix work the same as the map prefixed functions but they iterate over two objects instead of one. Arguments for an anonymous function are given by .x and .y (or ..1 and ..2) respectively.

map2_dbl(1:5, 1:5, function(x,y) x / (y+1))

[1] 0.5000000 0.6666667 0.7500000 0.8000000 0.8333333

map2_dbl(1:5, 1:5, ~ .x/(.y+1))

[1] 0.5000000 0.6666667 0.7500000 0.8000000 0.8333333

map2_dbl(1:5, 1:5, ~ ..1/(..2+1))

[1] 0.5000000 0.6666667 0.7500000 0.8000000 0.8333333

map2_chr(LETTERS[1:5], letters[1:5], paste0)

[1] "Aa" "Bb" "Cc" "Dd" "Ee"

Lookups

Very often we want to extract only certain values by name or position from a list, purrr provides a shorthand for this operation - instead of a function you can provide either a character or numeric vector, those values will be used to sequentially subset the elements being iterated.

purrr::map_chr(sw_people, "name") |> head()

[1] "Luke Skywalker" "C-3PO"          "R2-D2"          "Darth Vader"   
[5] "Leia Organa"    "Owen Lars"

purrr::map_chr(sw_people, 1) |> head()

[1] "Luke Skywalker" "C-3PO"          "R2-D2"          "Darth Vader"   
[5] "Leia Organa"    "Owen Lars"

purrr::map_chr(sw_people, list("films", 1)) |> head(n=10)

 [1] "http://swapi.co/api/films/6/" "http://swapi.co/api/films/5/"
 [3] "http://swapi.co/api/films/5/" "http://swapi.co/api/films/6/"
 [5] "http://swapi.co/api/films/6/" "http://swapi.co/api/films/5/"
 [7] "http://swapi.co/api/films/5/" "http://swapi.co/api/films/1/"
 [9] "http://swapi.co/api/films/1/" "http://swapi.co/api/films/5/"

Length coercion?

purrr::map_chr(sw_people, list("starships", 1))

Error in `purrr::map_chr()`:
ℹ In index: 2.
Caused by error:
! Result must be length 1, not 0.

sw_people[[2]]$name

[1] "C-3PO"

sw_people[[2]]$starships

NULL

purrr::map_chr(sw_people, list("starships", 1), .default = NA) |> head()

[1] "http://swapi.co/api/starships/12/" NA                                 
[3] NA                                  "http://swapi.co/api/starships/13/"
[5] NA                                  NA

purrr::map(sw_people, list("starships", 1)) |> head() |> str()

List of 6
 $ : chr "http://swapi.co/api/starships/12/"
 $ : NULL
 $ : NULL
 $ : chr "http://swapi.co/api/starships/13/"
 $ : NULL
 $ : NULL

list columns

(chars = tibble(
  name = purrr::map_chr(
    sw_people, "name"
  ),
  starships = purrr::map(
    sw_people, "starships"
  )
))

# A tibble: 87 × 2
   name               starships
   <chr>              <list>   
 1 Luke Skywalker     <chr [2]>
 2 C-3PO              <NULL>   
 3 R2-D2              <NULL>   
 4 Darth Vader        <chr [1]>
 5 Leia Organa        <NULL>   
 6 Owen Lars          <NULL>   
 7 Beru Whitesun lars <NULL>   
 8 R5-D4              <NULL>   
 9 Biggs Darklighter  <chr [1]>
10 Obi-Wan Kenobi     <chr [5]>
# ℹ 77 more rows

chars |>
  mutate(
    n_starships = map_int(
      starships, length
    )
  )

# A tibble: 87 × 3
   name               starships n_starships
   <chr>              <list>          <int>
 1 Luke Skywalker     <chr [2]>           2
 2 C-3PO              <NULL>              0
 3 R2-D2              <NULL>              0
 4 Darth Vader        <chr [1]>           1
 5 Leia Organa        <NULL>              0
 6 Owen Lars          <NULL>              0
 7 Beru Whitesun lars <NULL>              0
 8 R5-D4              <NULL>              0
 9 Biggs Darklighter  <chr [1]>           1
10 Obi-Wan Kenobi     <chr [5]>           5
# ℹ 77 more rows

Example

List columns and approximating pi

Example

discog - purrr vs tidyr

Complex heirarchical data

Often we may encounter complex data structures where our goal is not to rectangle every value (which may not even be possible) but rather to rectangle a small subset of the data.

str(repurrrsive::discog, max.level = 3)

List of 155
 $ :List of 5
  ..$ instance_id      : int 354823933
  ..$ date_added       : chr "2019-02-16T17:48:59-08:00"
  ..$ basic_information:List of 11
  .. ..$ labels      :List of 1
  .. ..$ year        : int 2015
  .. ..$ master_url  : NULL
  .. ..$ artists     :List of 1
  .. ..$ id          : int 7496378
  .. ..$ thumb       : chr "https://img.discogs.com/vEVegHrMNTsP6xG_K6OuFXz4h_U=/fit-in/150x150/filters:strip_icc():format(jpeg):mode_rgb()"| __truncated__
  .. ..$ title       : chr "Demo"
  .. ..$ formats     :List of 1
  .. ..$ cover_image : chr "https://img.discogs.com/EmbMh7vsElksjRgoXLFSuY1sjRQ=/fit-in/500x499/filters:strip_icc():format(jpeg):mode_rgb()"| __truncated__
  .. ..$ resource_url: chr "https://api.discogs.com/releases/7496378"
  .. ..$ master_id   : int 0
  ..$ id               : int 7496378
  ..$ rating           : int 0
 $ :List of 5
  ..$ instance_id      : int 354092601
  ..$ date_added       : chr "2019-02-13T14:13:11-08:00"
  ..$ basic_information:List of 11
  .. ..$ labels      :List of 1
  .. ..$ year        : int 2013
  .. ..$ master_url  : chr "https://api.discogs.com/masters/553057"
  .. ..$ artists     :List of 1
  .. ..$ id          : int 4490852
  .. ..$ thumb       : chr "https://img.discogs.com/nRZbcvVNygyNFKg5IjUd5YesF_c=/fit-in/150x150/filters:strip_icc():format(jpeg):mode_rgb()"| __truncated__
  .. ..$ title       : chr "Observant Com El Mon Es Destrueix"
  .. ..$ formats     :List of 1
  .. ..$ cover_image : chr "https://img.discogs.com/eBbQ9jaiHKFeSXLwBEbxq6nl8Wo=/fit-in/600x600/filters:strip_icc():format(jpeg):mode_rgb()"| __truncated__
  .. ..$ resource_url: chr "https://api.discogs.com/releases/4490852"
  .. ..$ master_id   : int 553057
  ..$ id               : int 4490852
  ..$ rating           : int 0
 $ :List of 5
  ..$ instance_id      : int 354091476
  ..$ date_added       : chr "2019-02-13T14:07:23-08:00"
  ..$ basic_information:List of 11
  .. ..$ labels      :List of 1
  .. ..$ year        : int 2017
  .. ..$ master_url  : chr "https://api.discogs.com/masters/1109943"
  .. ..$ artists     :List of 1
  .. ..$ id          : int 9827276
  .. ..$ thumb       : chr "https://img.discogs.com/x6GUri3hXAcfzF2wz5jQloomOoY=/fit-in/150x150/filters:strip_icc():format(jpeg):mode_rgb()"| __truncated__
  .. ..$ title       : chr "I"
  .. ..$ formats     :List of 1
  .. ..$ cover_image : chr "https://img.discogs.com/7aJPlo2phtFL-T2Kt6MTBc0uftY=/fit-in/600x600/filters:strip_icc():format(jpeg):mode_rgb()"| __truncated__
  .. ..$ resource_url: chr "https://api.discogs.com/releases/9827276"
  .. ..$ master_id   : int 1109943
  ..$ id               : int 9827276
  ..$ rating           : int 0
 $ :List of 5
  ..$ instance_id      : int 351244906
  ..$ date_added       : chr "2019-02-02T11:39:58-08:00"
  ..$ basic_information:List of 11
  .. ..$ labels      :List of 3
  .. ..$ year        : int 2017
  .. ..$ master_url  : chr "https://api.discogs.com/masters/1128934"
  .. ..$ artists     :List of 1
  .. ..$ id          : int 9769203
  .. ..$ thumb       : chr "https://img.discogs.com/eHz44JyqS_3lVHwmB7SiYsmaFgw=/fit-in/150x150/filters:strip_icc():format(jpeg):mode_rgb()"| __truncated__
  .. ..$ title       : chr "Oído Absoluto"
  .. ..$ formats     :List of 1
  .. ..$ cover_image : chr "https://img.discogs.com/gS7kZaQLa4PaOtLizmpdKOClSo8=/fit-in/600x600/filters:strip_icc():format(jpeg):mode_rgb()"| __truncated__
  .. ..$ resource_url: chr "https://api.discogs.com/releases/9769203"
  .. ..$ master_id   : int 1128934
  ..$ id               : int 9769203
  ..$ rating           : int 0
 $ :List of 5
  ..$ instance_id      : int 351244801
  ..$ date_added       : chr "2019-02-02T11:39:37-08:00"
  ..$ basic_information:List of 11
  .. ..$ labels      :List of 1
  .. ..$ year        : int 2015
  .. ..$ master_url  : chr "https://api.discogs.com/masters/857592"
  .. ..$ artists     :List of 1
  .. ..$ id          : int 7237138
  .. ..$ thumb       : chr "https://img.discogs.com/dqDk2TN2jkMZKFya71998oTRGcI=/fit-in/150x150/filters:strip_icc():format(jpeg):mode_rgb()"| __truncated__
  .. ..$ title       : chr "A Cat's Cause, No Dogs Problem"
  .. ..$ formats     :List of 1
  .. ..$ cover_image : chr "https://img.discogs.com/qXfi2e4YuS90_abWt0Tt-hsCHSQ=/fit-in/600x600/filters:strip_icc():format(jpeg):mode_rgb()"| __truncated__
  .. ..$ resource_url: chr "https://api.discogs.com/releases/7237138"
  .. ..$ master_id   : int 857592
  ..$ id               : int 7237138
  ..$ rating           : int 0
 $ :List of 5
  ..$ instance_id      : int 351052065
  ..$ date_added       : chr "2019-02-01T20:40:53-08:00"
  ..$ basic_information:List of 11
  .. ..$ labels      :List of 1
  .. ..$ year        : int 2019
  .. ..$ master_url  : chr "https://api.discogs.com/masters/1498137"
  .. ..$ artists     :List of 1
  .. ..$ id          : int 13117042
  .. ..$ thumb       : chr "https://img.discogs.com/ahomwZjjZ5-l730LYIsJp3BikX4=/fit-in/150x150/filters:strip_icc():format(jpeg):mode_rgb()"| __truncated__
  .. ..$ title       : chr "Tashme"
  .. ..$ formats     :List of 1
  .. ..$ cover_image : chr "https://img.discogs.com/LRDyHX3PR-m6gBt67pJ7JbdFB6M=/fit-in/600x600/filters:strip_icc():format(jpeg):mode_rgb()"| __truncated__
  .. ..$ resource_url: chr "https://api.discogs.com/releases/13117042"
  .. ..$ master_id   : int 1498137
  ..$ id               : int 13117042
  ..$ rating           : int 0
 $ :List of 5
  ..$ instance_id      : int 350315345
  ..$ date_added       : chr "2019-01-29T15:48:37-08:00"
  ..$ basic_information:List of 11
  .. ..$ labels      :List of 1
  .. ..$ year        : int 2014
  .. ..$ master_url  : chr "https://api.discogs.com/masters/852880"
  .. ..$ artists     :List of 1
  .. ..$ id          : int 7113575
  .. ..$ thumb       : chr "https://img.discogs.com/Uo5drP3W-Fje5fPnS1te9J1hEcY=/fit-in/150x150/filters:strip_icc():format(jpeg):mode_rgb()"| __truncated__
  .. ..$ title       : chr "Demo"
  .. ..$ formats     :List of 1
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  .. ..$ title       : chr "Unbecoming"
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  .. ..$ title       : chr "Demo 2016"
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  .. ..$ labels      :List of 2
  .. ..$ year        : int 2014
  .. ..$ master_url  : chr "https://api.discogs.com/masters/709327"
  .. ..$ artists     :List of 1
  .. ..$ id          : int 6487988
  .. ..$ thumb       : chr "https://img.discogs.com/eR4rkFC_MG_ZnEpE4SqRmF6m-8U=/fit-in/150x150/filters:strip_icc():format(jpeg):mode_rgb()"| __truncated__
  .. ..$ title       : chr "Vexx"
  .. ..$ formats     :List of 1
  .. ..$ cover_image : chr "https://img.discogs.com/2HNTCn8IC1xhUNcCLqjLoNHTsxg=/fit-in/600x600/filters:strip_icc():format(jpeg):mode_rgb()"| __truncated__
  .. ..$ resource_url: chr "https://api.discogs.com/releases/6487988"
  .. ..$ master_id   : int 709327
  ..$ id               : int 6487988
  ..$ rating           : int 0
 $ :List of 5
  ..$ instance_id      : int 350227901
  ..$ date_added       : chr "2019-01-29T09:44:47-08:00"
  ..$ basic_information:List of 11
  .. ..$ labels      :List of 1
  .. ..$ year        : int 2014
  .. ..$ master_url  : chr "https://api.discogs.com/masters/687939"
  .. ..$ artists     :List of 3
  .. ..$ id          : int 5695822
  .. ..$ thumb       : chr "https://img.discogs.com/oIksRThmmBZt9Bf1AtGI25RqDj8=/fit-in/150x150/filters:strip_icc():format(jpeg):mode_rgb()"| __truncated__
  .. ..$ title       : chr "USA '13"
  .. ..$ formats     :List of 1
  .. ..$ cover_image : chr "https://img.discogs.com/u3NWWoeMwkLalbCoMVZt14JuBf4=/fit-in/600x600/filters:strip_icc():format(jpeg):mode_rgb()"| __truncated__
  .. ..$ resource_url: chr "https://api.discogs.com/releases/5695822"
  .. ..$ master_id   : int 687939
  ..$ id               : int 5695822
  ..$ rating           : int 0
  [list output truncated]

Functional programming& purrr

Functional Programming

Functions as objects

Functions as arguments

Anonymous functions

Base R anonymous function (lambda) shorthand

apply (base R)

Apply functions

lapply

Argument matching

sapply

Legnth mismatch?

Type mismatch?

*apply and data frames

A more useful example

dplyr alternative

other less common apply functions

Map functions

Type Consistency

Working with Data Frames

Building by row

purrr style anonymous functions

Multiargument anonymous functions

Lookups

Length coercion?

list columns

Example

Example

Complex heirarchical data

Functional programming
& purrr

`*`apply and data frames