Returning multiple values from functions

Introduction to Writing Functions in R

Richie Cotton

Data Evangelist at DataCamp

Getting the session information

R.version.string

"R version 3.5.3 (2019-03-11)"

Sys.info()[c("sysname", "release")]

sysname                       release 
"Linux" "4.14.106-79.86.amzn1.x86_64"

loadedNamespaces()

 [1] "Rcpp"       "grDevices"  "crayon"    
 [4] "dplyr"      "assertthat" "R6"        
 [7] "magrittr"   "datasets"   "pillar"    
[10] "rlang"      "utils"      "praise"    
[13] "rstudioapi" "graphics"   "base"      
[16] "tools"      "glue"       "purrr"     
[19] "yaml"       "compiler"   "pkgconfig" 
[22] "stats"      "tidyselect" "methods"   
[25] "tibble"

Defining session()

session <- function() {
  r_version <- R.version.string,
  operating_system <- Sys.info()[c("sysname", "release")],
  loaded_pkgs <- loadedNamespaces()
  # ???
}

Defining session()

session <- function() {
  list(
    r_version = R.version.string,
    operating_system = Sys.info()[c("sysname", "release")],
    loaded_pkgs = loadedNamespaces()
  )
}

Calling session()

session()

$r_version
[1] "R version 3.5.3 (2019-03-11)"

$operating_system
sysname                       release 
"Linux" "4.14.106-79.86.amzn1.x86_64"

$loaded_pkgs
 [1] "Rcpp"       "grDevices"  "crayon"    
 [4] "dplyr"      "assertthat" "R6"        
 [7] "magrittr"   "datasets"   "pillar"    
[10] "rlang"      "utils"      "praise"    
[13] "rstudioapi" "graphics"   "base"      
[16] "tools"      "glue"       "purrr"     
[19] "yaml"       "compiler"   "pkgconfig" 
[22] "stats"      "tidyselect" "methods"   
[25] "tibble"

Multi-assignment

library(zeallot)
c(vrsn, os, pkgs) %<-% session()

vrsn

"R version 3.5.3 (2019-03-11)"

os

sysname                       release 
"Linux" "4.14.106-79.86.amzn1.x86_64"

Attributes

month_no <- setNames(1:12, month.abb)
month_no

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 
  1   2   3   4   5   6   7   8   9  10  11  12

attributes(month_no)

$names
 [1] "Jan" "Feb" "Mar" "Apr" "May" "Jun" "Jul"
 [8] "Aug" "Sep" "Oct" "Nov" "Dec"

attr(month_no, "names")

 [1] "Jan" "Feb" "Mar" "Apr" "May" "Jun" "Jul"
 [8] "Aug" "Sep" "Oct" "Nov" "Dec"

attr(month_no, "names") <- month.name
month_no

  January  February     March     April       May 
        1         2         3         4         5 
     June      July    August September   October 
        6         7         8         9        10 
 November  December 
       11        12

Attributes of a data frame

orange_trees

# A tibble: 35 x 3
   Tree    age circumference
   <ord> <dbl>         <dbl>
 1 1       118            30
 2 1       484            58
 3 1       664            87
 4 1      1004           115
 5 1      1231           120
 6 1      1372           142
 7 1      1582           145
 8 2       118            33
 9 2       484            69
10 2       664           111
# … with 25 more rows

attributes(orange_trees)

$names
[1] "Tree"          "age"          
[3] "circumference"

$row.names
 [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15
[16] 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
[31] 31 32 33 34 35

$class
[1] "tbl_df"     "tbl"        "data.frame"

¹ data(Orange, package = "datasets")

Attributes added by group_by()

library(dplyr)
orange_trees %>% 
  group_by(Tree) %>%
  attributes()

$names
[1] "Tree"          "age"           "circumference"

$row.names
 [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18
[19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35

$class
[1] "grouped_df" "tbl_df"     "tbl"        "data.frame"

$groups
# A tibble: 5 x 2
  Tree  .rows    
  <ord> <list>   
1 3     <int [7]>
2 1     <int [7]>
3 5     <int [7]>
4 2     <int [7]>
5 4     <int [7]>

When to use each technique

If you need the result to have a particular type, add additional return values as attributes.
Otherwise, collect all return values into a list.

broom

Model objects are converted into 3 data frames.

function	level	example
`glance()`	model	degrees of freedom
`tidy()`	coefficient	p-values
`augment()`	observation	residuals

Let's practice!

Introduction to Writing Functions in R