“R, at its heart, is a functional programming (FP) language. This means that it provides many tools for the creation and manipulation of functions.” - Hadley Wickham
Don’t need to write for-loops! - check this video.
Functional Programming
Iterative work
Allows you to flexibly iterate functions to multiple elements of a data object!
Useful when you want to apply a function to:
* lots of columns in a tibble
* multiple tibbles
* multiple data files
* or perform fancy functions with vectors (or tibble columns)
base R apply functions
Works really simply for all columns.
sapply(mtcars, FUN = round)
## mpg cyl disp hp drat wt qsec vs am gear carb ## [1,] 21 6 160 110 4 3 16 0 1 4 4 ## [2,] 21 6 160 110 4 3 17 0 1 4 4 ## [3,] 23 4 108 93 4 2 19 1 1 4 1 ## [4,] 21 6 258 110 3 3 19 1 0 3 1 ## [5,] 19 8 360 175 3 3 17 0 0 3 2 ## [6,] 18 6 225 105 3 3 20 1 0 3 1 ## [7,] 14 8 360 245 3 4 16 0 0 3 4 ## [8,] 24 4 147 62 4 3 20 1 0 4 2 ## [9,] 23 4 141 95 4 3 23 1 0 4 2 ## [10,] 19 6 168 123 4 3 18 1 0 4 4 ## [11,] 18 6 168 123 4 3 19 1 0 4 4 ## [12,] 16 8 276 180 3 4 17 0 0 3 3 ## [13,] 17 8 276 180 3 4 18 0 0 3 3 ## [14,] 15 8 276 180 3 4 18 0 0 3 3 ## [15,] 10 8 472 205 3 5 18 0 0 3 4 ## [16,] 10 8 460 215 3 5 18 0 0 3 4 ## [17,] 15 8 440 230 3 5 17 0 0 3 4 ## [18,] 32 4 79 66 4 2 19 1 1 4 1 ## [19,] 30 4 76 52 5 2 19 1 1 4 2 ## [20,] 34 4 71 65 4 2 20 1 1 4 1 ## [21,] 22 4 120 97 4 2 20 1 0 3 1 ## [22,] 16 8 318 150 3 4 17 0 0 3 2 ## [23,] 15 8 304 150 3 3 17 0 0 3 2 ## [24,] 13 8 350 245 4 4 15 0 0 3 4 ## [25,] 19 8 400 175 3 4 17 0 0 3 2 ## [26,] 27 4 79 66 4 2 19 1 1 4 1 ## [27,] 26 4 120 91 4 2 17 0 1 5 2 ## [28,] 30 4 95 113 4 2 17 1 1 5 2 ## [29,] 16 8 351 264 4 3 14 0 1 5 4 ## [30,] 20 6 145 175 4 3 16 0 1 5 6 ## [31,] 15 8 301 335 4 4 15 0 1 5 8 ## [32,] 21 4 121 109 4 3 19 1 1 4 2
Base R apply functions specific columns
Harder to use for specific columns.
rounded_mtcars <- mtcars %>% select(disp, drat, wt, qsec) %>% sapply(FUN = round) bind_cols(rounded_mtcars, select(mtcars, -c(disp, drat, wt, qsec)))
## disp drat wt qsec mpg cyl hp vs am gear carb ## Mazda RX4 160 4 3 16 21.0 6 110 0 1 4 4 ## Mazda RX4 Wag 160 4 3 17 21.0 6 110 0 1 4 4 ## Datsun 710 108 4 2 19 22.8 4 93 1 1 4 1 ## Hornet 4 Drive 258 3 3 19 21.4 6 110 1 0 3 1 ## Hornet Sportabout 360 3 3 17 18.7 8 175 0 0 3 2 ## Valiant 225 3 3 20 18.1 6 105 1 0 3 1 ## Duster 360 360 3 4 16 14.3 8 245 0 0 3 4 ## Merc 240D 147 4 3 20 24.4 4 62 1 0 4 2 ## Merc 230 141 4 3 23 22.8 4 95 1 0 4 2 ## Merc 280 168 4 3 18 19.2 6 123 1 0 4 4 ## Merc 280C 168 4 3 19 17.8 6 123 1 0 4 4 ## Merc 450SE 276 3 4 17 16.4 8 180 0 0 3 3 ## Merc 450SL 276 3 4 18 17.3 8 180 0 0 3 3 ## Merc 450SLC 276 3 4 18 15.2 8 180 0 0 3 3 ## Cadillac Fleetwood 472 3 5 18 10.4 8 205 0 0 3 4 ## Lincoln Continental 460 3 5 18 10.4 8 215 0 0 3 4 ## Chrysler Imperial 440 3 5 17 14.7 8 230 0 0 3 4 ## Fiat 128 79 4 2 19 32.4 4 66 1 1 4 1 ## Honda Civic 76 5 2 19 30.4 4 52 1 1 4 2 ## Toyota Corolla 71 4 2 20 33.9 4 65 1 1 4 1 ## Toyota Corona 120 4 2 20 21.5 4 97 1 0 3 1 ## Dodge Challenger 318 3 4 17 15.5 8 150 0 0 3 2 ## AMC Javelin 304 3 3 17 15.2 8 150 0 0 3 2 ## Camaro Z28 350 4 4 15 13.3 8 245 0 0 3 4 ## Pontiac Firebird 400 3 4 17 19.2 8 175 0 0 3 2 ## Fiat X1-9 79 4 2 19 27.3 4 66 1 1 4 1 ## Porsche 914-2 120 4 2 17 26.0 4 91 0 1 5 2 ## Lotus Europa 95 4 2 17 30.4 4 113 1 1 5 2 ## Ford Pantera L 351 4 3 14 15.8 8 264 0 1 5 4 ## Ferrari Dino 145 4 3 16 19.7 6 175 0 1 5 6 ## Maserati Bora 301 4 4 15 15.0 8 335 0 1 5 8 ## Volvo 142E 121 4 3 19 21.4 4 109 1 1 4 2
Working across multiple columns
Say we wanted to round multiple columns of the mtcars data. We could do so one column at a time, or we could use the across function from the dplyr package. Needs to be used within other dplyr functions such as mutate.
mutate(across(which_columns, which function or operation))
head(mtcars, 2)
## mpg cyl disp hp drat wt qsec vs am gear carb ## Mazda RX4 21 6 160 110 3.9 2.620 16.46 0 1 4 4 ## Mazda RX4 Wag 21 6 160 110 3.9 2.875 17.02 0 1 4 4
mtcars %>% mutate(across(.cols = c(disp, drat, wt, qsec), round)) %>% head(2)
## mpg cyl disp hp drat wt qsec vs am gear carb ## Mazda RX4 21 6 160 110 4 3 16 0 1 4 4 ## Mazda RX4 Wag 21 6 160 110 4 3 17 0 1 4 4
Special tilda use
If you see ~ .x or . this means we are passing x to a function.
See purrr-shortcuts See here for info about the difference between them.
For example - this is not necessary but you could use it here:
mtcars %>% mutate(across(.cols = c(disp, drat, wt, qsec), ~ round(.x))) %>% head(2)
## mpg cyl disp hp drat wt qsec vs am gear carb ## Mazda RX4 21 6 160 110 4 3 16 0 1 4 4 ## Mazda RX4 Wag 21 6 160 110 4 3 17 0 1 4 4
mtcars %>% mutate(across(.cols = c(disp, drat, wt, qsec), ~ round(.))) %>% head(2)
## mpg cyl disp hp drat wt qsec vs am gear carb ## Mazda RX4 21 6 160 110 4 3 16 0 1 4 4 ## Mazda RX4 Wag 21 6 160 110 4 3 17 0 1 4 4
Using across with arguments
If you wish to also pass arguments to the function that you are applying to the various columns, then you need to use the ~ and .x (or .) as a place holder for what you the values you will be passing into the function.
mtcars %>% mutate(across(.cols = c(disp, drat, wt, qsec), ~ round(.x, digits = 1))) %>% head(n = 2)
## mpg cyl disp hp drat wt qsec vs am gear carb ## Mazda RX4 21 6 160 110 3.9 2.6 16.5 0 1 4 4 ## Mazda RX4 Wag 21 6 160 110 3.9 2.9 17.0 0 1 4 4
mtcars %>% mutate(across(.cols = c(disp, drat, wt, qsec), ~ round(., digits = 1))) %>% head(n = 2)
## mpg cyl disp hp drat wt qsec vs am gear carb ## Mazda RX4 21 6 160 110 3.9 2.6 16.5 0 1 4 4 ## Mazda RX4 Wag 21 6 160 110 3.9 2.9 17.0 0 1 4 4
Previously we filtered for patterns or conditions..
Dilemma: Seems a bit repetitive!
mtcars %>%
filter(cyl > 3 & cyl < 8,
gear > 3 & gear < 8,
carb > 3 & carb < 8)
## mpg cyl disp hp drat wt qsec vs am gear carb ## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 ## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 ## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 ## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 ## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Now we can filter multiple columns!
if_all(): helps us filter on multiple similar conditions simultaneously!
mtcars %>% filter(if_all(c(cyl, gear, carb), ~.x > 3 & .x < 8))
## mpg cyl disp hp drat wt qsec vs am gear carb ## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 ## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 ## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 ## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 ## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Writing functions
Why write your own functions?
- Cut down on repetitive code (easier to fix things!)
- Organize code into manageable chunks
- Avoid running code unintentionally
Functions in R
To create a function we can use the function function and specify what input the function will take and what it will do to it.
my_function <- function(x){x + 1}
my_function
## function(x){x + 1}
## <environment: 0x1040561c8>
my_data <- c(2,3,4) my_function(x = my_data)
## [1] 3 4 5
my_function(my_data)
## [1] 3 4 5
shortcut for functions
Alternatively we can use \(x). See this link about function shortcuts.
my_function <- \(x){x + 1}
my_function
## \(x){x + 1}
## <environment: 0x1040561c8>
my_function(x = my_data)
## [1] 3 4 5
purrr is also a super helpful package!
“Designed to make your functions purrr.”
dplyr is designed for data frames purrr is designed for vectors
The purrr package can be very helpful!
purrr main functions
map and map_* and modify
- applies function to each element of an vector or object (map returns a list, modify returns the same object type) - map_df will output a dataframe
map2 and map2*
- applies function to each element of two vectors or objects
pmap and pmap_* - applies function to each element of 3+ vector or objects (requires a list for input)
the _* options specify the type of data output
Using map()
[source]
Using map_df() amd modify()
my_tibble <- tibble(values = c(1.2, 2.3, 3.5, 4.6)) map_df(my_tibble, round)
## # A tibble: 4 × 1 ## values ## <dbl> ## 1 1 ## 2 2 ## 3 4 ## 4 5
modify(my_tibble, round)
## # A tibble: 4 × 1 ## values ## <dbl> ## 1 1 ## 2 2 ## 3 4 ## 4 5
It’s a bit simpler than across…
But across keeps rownames.
mtcars %>% modify(format, digits = 1) %>% head(n = 2)
## mpg cyl disp hp drat wt qsec vs am gear carb ## 1 21 6 160 110 4 3 16 0 1 4 4 ## 2 21 6 160 110 4 3 17 0 1 4 4
mtcars %>% mutate(across(.cols = everything(), ~ format(.x, digits = 1))) %>% head(n = 2)
## mpg cyl disp hp drat wt qsec vs am gear carb ## Mazda RX4 21 6 160 110 4 3 16 0 1 4 4 ## Mazda RX4 Wag 21 6 160 110 4 3 17 0 1 4 4
purrr apply function to some columns like across
Using modify_if() (or map_if()), we can specify what columns to modify
head(as_tibble(iris), 3)
## # A tibble: 3 × 5 ## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## <dbl> <dbl> <dbl> <dbl> <fct> ## 1 5.1 3.5 1.4 0.2 setosa ## 2 4.9 3 1.4 0.2 setosa ## 3 4.7 3.2 1.3 0.2 setosa
as_tibble(iris) %>% modify_if(is.numeric, as.character) %>% head(3)
## # A tibble: 3 × 5 ## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## <chr> <chr> <chr> <chr> <fct> ## 1 5.1 3.5 1.4 0.2 setosa ## 2 4.9 3 1.4 0.2 setosa ## 3 4.7 3.2 1.3 0.2 setosa
Lists
What is a ‘list’?
- Lists are the most flexible/“generic” data class in R
- Can be created using list()
- Can hold vectors, strings, matrices, models, list of other lists, lists upon lists!
- Can reference data using $ (if the elements are named), or using [], or [[]]
mylist <- list(
letters = c("A", "b", "c"),
numbers = 1:3,
matrix(1:25, ncol = 5),
matrix(1:25, ncol = 5)
)
List Structure
head(mylist)
## $letters ## [1] "A" "b" "c" ## ## $numbers ## [1] 1 2 3 ## ## [[3]] ## [,1] [,2] [,3] [,4] [,5] ## [1,] 1 6 11 16 21 ## [2,] 2 7 12 17 22 ## [3,] 3 8 13 18 23 ## [4,] 4 9 14 19 24 ## [5,] 5 10 15 20 25 ## ## [[4]] ## [,1] [,2] [,3] [,4] [,5] ## [1,] 1 6 11 16 21 ## [2,] 2 7 12 17 22 ## [3,] 3 8 13 18 23 ## [4,] 4 9 14 19 24 ## [5,] 5 10 15 20 25
List referencing
mylist[1] # returns a list
## $letters ## [1] "A" "b" "c"
mylist["letters"] # returns a list
## $letters ## [1] "A" "b" "c"
List referencing
mylist[[1]] # returns the vector 'letters'
## [1] "A" "b" "c"
mylist$letters # returns vector
## [1] "A" "b" "c"
mylist[["letters"]] # returns the vector 'letters'
## [1] "A" "b" "c"
List referencing
You can also select multiple lists with the single brackets.
mylist[1:2] # returns a list
## $letters ## [1] "A" "b" "c" ## ## $numbers ## [1] 1 2 3
… Why lists?
List: split() a dataset
We can create a list by splitting up a dataframe. We will use mtcars.
head(mtcars)
## mpg cyl disp hp drat wt qsec vs am gear carb ## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 ## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 ## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 ## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 ## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 ## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
List: split() the dataset by cyl
mtcars_split <-mtcars %>% split(.$cyl) str(mtcars_split)
## List of 3 ## $ 4:'data.frame': 11 obs. of 11 variables: ## ..$ mpg : num [1:11] 22.8 24.4 22.8 32.4 30.4 33.9 21.5 27.3 26 30.4 ... ## ..$ cyl : num [1:11] 4 4 4 4 4 4 4 4 4 4 ... ## ..$ disp: num [1:11] 108 146.7 140.8 78.7 75.7 ... ## ..$ hp : num [1:11] 93 62 95 66 52 65 97 66 91 113 ... ## ..$ drat: num [1:11] 3.85 3.69 3.92 4.08 4.93 4.22 3.7 4.08 4.43 3.77 ... ## ..$ wt : num [1:11] 2.32 3.19 3.15 2.2 1.61 ... ## ..$ qsec: num [1:11] 18.6 20 22.9 19.5 18.5 ... ## ..$ vs : num [1:11] 1 1 1 1 1 1 1 1 0 1 ... ## ..$ am : num [1:11] 1 0 0 1 1 1 0 1 1 1 ... ## ..$ gear: num [1:11] 4 4 4 4 4 4 3 4 5 5 ... ## ..$ carb: num [1:11] 1 2 2 1 2 1 1 1 2 2 ... ## $ 6:'data.frame': 7 obs. of 11 variables: ## ..$ mpg : num [1:7] 21 21 21.4 18.1 19.2 17.8 19.7 ## ..$ cyl : num [1:7] 6 6 6 6 6 6 6 ## ..$ disp: num [1:7] 160 160 258 225 168 ... ## ..$ hp : num [1:7] 110 110 110 105 123 123 175 ## ..$ drat: num [1:7] 3.9 3.9 3.08 2.76 3.92 3.92 3.62 ## ..$ wt : num [1:7] 2.62 2.88 3.21 3.46 3.44 ... ## ..$ qsec: num [1:7] 16.5 17 19.4 20.2 18.3 ... ## ..$ vs : num [1:7] 0 0 1 1 1 1 0 ## ..$ am : num [1:7] 1 1 0 0 0 0 1 ## ..$ gear: num [1:7] 4 4 3 3 4 4 5 ## ..$ carb: num [1:7] 4 4 1 1 4 4 6 ## $ 8:'data.frame': 14 obs. of 11 variables: ## ..$ mpg : num [1:14] 18.7 14.3 16.4 17.3 15.2 10.4 10.4 14.7 15.5 15.2 ... ## ..$ cyl : num [1:14] 8 8 8 8 8 8 8 8 8 8 ... ## ..$ disp: num [1:14] 360 360 276 276 276 ... ## ..$ hp : num [1:14] 175 245 180 180 180 205 215 230 150 150 ... ## ..$ drat: num [1:14] 3.15 3.21 3.07 3.07 3.07 2.93 3 3.23 2.76 3.15 ... ## ..$ wt : num [1:14] 3.44 3.57 4.07 3.73 3.78 ... ## ..$ qsec: num [1:14] 17 15.8 17.4 17.6 18 ... ## ..$ vs : num [1:14] 0 0 0 0 0 0 0 0 0 0 ... ## ..$ am : num [1:14] 0 0 0 0 0 0 0 0 0 0 ... ## ..$ gear: num [1:14] 3 3 3 3 3 3 3 3 3 3 ... ## ..$ carb: num [1:14] 2 4 3 3 3 4 4 4 2 2 ...
List: model on each
mtcars %>%
split(.$cyl) %>% # creates split of data for each unique cyl value
map(~lm(mpg ~ wt, data = .)) %>% # apply linear model to each
map(summary) %>%
map_dbl("r.squared")
## 4 6 8 ## 0.5086326 0.4645102 0.4229655
Use lists to iterate through multiple files!
This comes up a lot in data cleaning and also when reading in multiple files!
library(here)
library(readr)
file_list <- list.files(here::here("data/iris/"), pattern = "*.csv")
file_list <- paste0(here::here("data/iris/"), file_list)
file_list
## [1] "/Users/avahoffman/Dropbox/JHSPH/Data-Wrangling_SISBID/data/iris/iris_q1.csv" ## [2] "/Users/avahoffman/Dropbox/JHSPH/Data-Wrangling_SISBID/data/iris/iris_q4.csv" ## [3] "/Users/avahoffman/Dropbox/JHSPH/Data-Wrangling_SISBID/data/iris/iris_q5.csv"
multifile_data <- file_list %>% map(read_csv)
Reading in multiple files -First item
multifile_data[[1]]
## # A tibble: 150 × 5 ## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## <dbl> <dbl> <dbl> <dbl> <chr> ## 1 5.1 3.5 1.4 0.2 setosa ## 2 4.9 3 1.4 0.2 setosa ## 3 4.7 3.2 1.3 0.2 setosa ## 4 4.6 3.1 1.5 0.2 setosa ## 5 5 3.6 1.4 0.2 setosa ## 6 5.4 3.9 1.7 0.4 setosa ## 7 4.6 3.4 1.4 0.3 setosa ## 8 5 3.4 1.5 0.2 setosa ## 9 4.4 2.9 1.4 0.2 setosa ## 10 4.9 3.1 1.5 0.1 setosa ## # ℹ 140 more rows
Reading in multiple files - 2nd item
multifile_data[[2]]
## # A tibble: 150 × 1 ## `Sepal.Length:Sepal.Width:Petal.Length:Petal.Width:Species` ## <chr> ## 1 5.1:3.5:1.4:0.2:setosa ## 2 4.9:3:1.4:0.2:setosa ## 3 4.7:3.2:1.3:0.2:setosa ## 4 4.6:3.1:1.5:0.2:setosa ## 5 5:3.6:1.4:0.2:setosa ## 6 5.4:3.9:1.7:0.4:setosa ## 7 4.6:3.4:1.4:0.3:setosa ## 8 5:3.4:1.5:0.2:setosa ## 9 4.4:2.9:1.4:0.2:setosa ## 10 4.9:3.1:1.5:0.1:setosa ## # ℹ 140 more rows
Reading in multiple files - 3rd item
multifile_data[[3]]
## # A tibble: 150 × 5 ## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## <dbl> <dbl> <dbl> <dbl> <chr> ## 1 -999 3.5 1.4 0.2 setosa ## 2 -999 3 1.4 0.2 setosa ## 3 -999 3.2 1.3 0.2 setosa ## 4 4.6 3.1 1.5 0.2 setosa ## 5 5 3.6 1.4 0.2 setosa ## 6 5.4 3.9 1.7 0.4 setosa ## 7 4.6 3.4 1.4 0.3 setosa ## 8 5 3.4 1.5 0.2 setosa ## 9 4.4 2.9 1.4 0.2 setosa ## 10 4.9 3.1 1.5 0.1 setosa ## # ℹ 140 more rows
Import with different delimiters
delimiters <- c(",", ":", ",") # delimiters for each file
# Write our own function to read files with specific delimiters:
read_with_delimiter <- function(file, delimiter) {
read_delim(file, delim = delimiter)
}
# Map over file_list and delimiters
multifile_data <- map2(file_list, delimiters, read_with_delimiter)
[source]
Reading in multiple files - 1st item
multifile_data[[1]]
## # A tibble: 150 × 5 ## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## <dbl> <dbl> <dbl> <dbl> <chr> ## 1 5.1 3.5 1.4 0.2 setosa ## 2 4.9 3 1.4 0.2 setosa ## 3 4.7 3.2 1.3 0.2 setosa ## 4 4.6 3.1 1.5 0.2 setosa ## 5 5 3.6 1.4 0.2 setosa ## 6 5.4 3.9 1.7 0.4 setosa ## 7 4.6 3.4 1.4 0.3 setosa ## 8 5 3.4 1.5 0.2 setosa ## 9 4.4 2.9 1.4 0.2 setosa ## 10 4.9 3.1 1.5 0.1 setosa ## # ℹ 140 more rows
Reading in multiple files - 2nd item
multifile_data[[2]]
## # A tibble: 150 × 5 ## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## <dbl> <dbl> <dbl> <dbl> <chr> ## 1 5.1 3.5 1.4 0.2 setosa ## 2 4.9 3 1.4 0.2 setosa ## 3 4.7 3.2 1.3 0.2 setosa ## 4 4.6 3.1 1.5 0.2 setosa ## 5 5 3.6 1.4 0.2 setosa ## 6 5.4 3.9 1.7 0.4 setosa ## 7 4.6 3.4 1.4 0.3 setosa ## 8 5 3.4 1.5 0.2 setosa ## 9 4.4 2.9 1.4 0.2 setosa ## 10 4.9 3.1 1.5 0.1 setosa ## # ℹ 140 more rows
Reading in multiple files - 3rd item
multifile_data[[3]]
## # A tibble: 150 × 5 ## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## <dbl> <dbl> <dbl> <dbl> <chr> ## 1 -999 3.5 1.4 0.2 setosa ## 2 -999 3 1.4 0.2 setosa ## 3 -999 3.2 1.3 0.2 setosa ## 4 4.6 3.1 1.5 0.2 setosa ## 5 5 3.6 1.4 0.2 setosa ## 6 5.4 3.9 1.7 0.4 setosa ## 7 4.6 3.4 1.4 0.3 setosa ## 8 5 3.4 1.5 0.2 setosa ## 9 4.4 2.9 1.4 0.2 setosa ## 10 4.9 3.1 1.5 0.1 setosa ## # ℹ 140 more rows
Reading in multiple files
The bind_rows() function can be great for combining data.
recall that modify keeps the same data type (here, a list). We want a data frame instead.
See https://www.opencasestudies.org/ocs-bp-vaping-case-study for more information!
all_files_data <- multifile_data %>% map_df(bind_rows, .id = "experiment") glimpse(all_files_data)
## Rows: 450 ## Columns: 6 ## $ experiment <chr> "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1… ## $ Sepal.Length <dbl> 5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.… ## $ Sepal.Width <dbl> 3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.… ## $ Petal.Length <dbl> 1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.… ## $ Petal.Width <dbl> 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.… ## $ Species <chr> "setosa", "setosa", "setosa", "setosa", "setosa", "setosa…
Summary
function(x){ }or\(x){ }denotes a function. You also commonly see~.xinsideacrossmap_dfandmodifyapply functions to each element of an object.mapreturns a list,modifyreturns the same object type.- The purrr package has other useful functional programming features
- lists can be great for storing iterative work.
https://sisbid.github.io/Data-Wrangling/14_Functional_Programming/lab/functional-program-lab.Rmd
Extra Slides
Using across with helpers to apply function to multiple columns
?tidyr_tidy_select
mtcars %>% mutate(across(.cols = disp:wt, round)) %>% head(2)
## mpg cyl disp hp drat wt qsec vs am gear carb ## Mazda RX4 21 6 160 110 4 3 16.46 0 1 4 4 ## Mazda RX4 Wag 21 6 160 110 4 3 17.02 0 1 4 4
mtcars %>% mutate(across(.cols = everything(), round))%>% head(2)
## mpg cyl disp hp drat wt qsec vs am gear carb ## Mazda RX4 21 6 160 110 4 3 16 0 1 4 4 ## Mazda RX4 Wag 21 6 160 110 4 3 17 0 1 4 4
Speed test! modify_if vs across
system.time(iris %>%
modify_if(is.factor, as.character))
## user system elapsed ## 0.000 0.000 0.001
system.time(iris %>%
mutate(across(.cols = where(is.factor), as.character)))
## user system elapsed ## 0.001 0.000 0.001
Alternatively we could have fixed the second file
Looks like it had a different delimiter First, separating by the :.
multifile_data[[2]] <-
separate(
multifile_data[[2]],
col = 1,
into = colnames(multifile_data[[1]]),
sep = ":"
)
## Warning: Expected 5 pieces. Missing pieces filled with `NA` in 150 rows [1, 2, ## 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].
head(multifile_data[[2]], 3)
## # A tibble: 3 × 5 ## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## <chr> <chr> <chr> <chr> <chr> ## 1 5.1 <NA> <NA> <NA> <NA> ## 2 4.9 <NA> <NA> <NA> <NA> ## 3 4.7 <NA> <NA> <NA> <NA>
Fixing the second file
Second, making sure values are numeric.
multifile_data[[2]] <- multifile_data[[2]] %>% mutate(across(!Species, as.numeric)) head(multifile_data[[2]], 3)
## # A tibble: 3 × 5 ## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## <dbl> <dbl> <dbl> <dbl> <chr> ## 1 5.1 NA NA NA <NA> ## 2 4.9 NA NA NA <NA> ## 3 4.7 NA NA NA <NA>
```