Functional Programming with purrr
Simplifying Iterations and lists with purrr
The power of iteration
purrr allows us to simplify iteration, either with vectors or lists without having to deal with for loops. Iteration allows us to do same thing again and again with different inputs. This means we don’t have to write repetitive lines of code for different inputs.Iteration saves time, reduces line of code and reduces typos.For loops are powerful but often the place where typos which are tough to identify pop-up and this prevents you from moving on in your workflow.
# Initialize list
all_files <- list()
# For loop to read files into a list
for(i in seq_along(files)){
all_files[[i]] <- read_csv(file = files[[i]])
}
# Output size of list object
length(all_files)purrr makes iteration easy. It wraps a for loop into a single function map(object, function)(object can be list or vector) which reduces the number of lines of code we need and allows us to focus on important pieces of what we are trying to accomplish. Instead of worrying about indices, brackets or curly braces we can just put in the pieces and get what we need.
# Use map to iterate
all_files_purrr <- map(files, read_csv)
# Output size of list object
length(all_files_purrr)
## Bird Counts Example using For loop
bird_sum <- list()
for(i in seq_along(bird_counts)){
bird_sum[[i]] <- sum(bird_counts[[i]])
}
## Using Map
bird_sum <- map(bird_counts, sum)# Check the class type of the first element
class(list_of_df[[1]])
# Change each element from a character to a number
for(i in seq_along(list_of_df)){
list_of_df[[i]] <- as.numeric(list_of_df[[i]])
}
# Change each character element to a number using map
list_of_df <- map(list_of_df, as.numeric)
# Check the class type of the first element again
class(list_of_df[[1]])
# Print out the list
list_of_dfSub-setting Lists
Lists require different grammar compared to dataframes or vectors. Lists can store different data types, they can hold a wide variety of things. The contents of a list don’t have to be the same data type, and as long as you know how it’s organized, you can grab out what you need by sub-setting.
Both named and unnamed lists can be subset using double square brackets [[ ]] OR listname[[ index ]]
If a list is named, you can also use $ for subsetting. The syntax list$elementname pulls out the named element from the list. Like any other kind of object in R, you can use the str() to determine the structure of the list.
lo <- list()
lo[["data"]] <- data.frame(bird = c("robin", "sparrow", "jay"), weight = c(76, 14, 100), wing_length = c(100, 35, 130))
lo[["model"]] <- lm(weight ~ wing_length,data = lo[["data"]])
lo[["plot"]] <- ggplot(data = lo[["model"]], aes(x = weight, y = wing_length)) + geom_point()
# Create a dataframe to place the results in
df_rows <- data.frame(names = names(survey_data), rows = NA)
# Loop over survey_data to determine how many rows are in each element
for(i in 1:length(survey_data)){
df_rows[i,'rows'] <- nrow(survey_data[[i]])
}
map(survey_data, ~nrow(.x))You can also subset within list elements using bracket notation like this: ListName$ElementName[VectorNumber]. If a list element is a dataframe, you can pull out a column like this: ListName$ElementName$ColumnName or ListName[[1]][,1].
# Load repurrrsive package, to get access to the wesanderson dataset
library(repurrrsive)
# Load wesanderson dataset
data(wesanderson)
data(sw_films)
# Get structure of first element in wesanderson
str(wesanderson[1])## List of 1
## $ GrandBudapest: chr [1:4] "#F1BB7B" "#FD6467" "#5B1A18" "#D67236"## chr [1:4] "#F1BB7B" "#FD6467" "#5B1A18" "#D67236"## [1] "#5B1A18"## [1] "#D67236"## $title
## [1] "A New Hope"
##
## $episode_id
## [1] 4
##
## $opening_crawl
## [1] "It is a period of civil war.\r\nRebel spaceships, striking\r\nfrom a hidden base, have won\r\ntheir first victory against\r\nthe evil Galactic Empire.\r\n\r\nDuring the battle, Rebel\r\nspies managed to steal secret\r\nplans to the Empire's\r\nultimate weapon, the DEATH\r\nSTAR, an armored space\r\nstation with enough power\r\nto destroy an entire planet.\r\n\r\nPursued by the Empire's\r\nsinister agents, Princess\r\nLeia races home aboard her\r\nstarship, custodian of the\r\nstolen plans that can save her\r\npeople and restore\r\nfreedom to the galaxy...."
##
## $director
## [1] "George Lucas"
##
## $producer
## [1] "Gary Kurtz, Rick McCallum"
##
## $release_date
## [1] "1977-05-25"
##
## $characters
## [1] "http://swapi.co/api/people/1/" "http://swapi.co/api/people/2/"
## [3] "http://swapi.co/api/people/3/" "http://swapi.co/api/people/4/"
## [5] "http://swapi.co/api/people/5/" "http://swapi.co/api/people/6/"
## [7] "http://swapi.co/api/people/7/" "http://swapi.co/api/people/8/"
## [9] "http://swapi.co/api/people/9/" "http://swapi.co/api/people/10/"
## [11] "http://swapi.co/api/people/12/" "http://swapi.co/api/people/13/"
## [13] "http://swapi.co/api/people/14/" "http://swapi.co/api/people/15/"
## [15] "http://swapi.co/api/people/16/" "http://swapi.co/api/people/18/"
## [17] "http://swapi.co/api/people/19/" "http://swapi.co/api/people/81/"
##
## $planets
## [1] "http://swapi.co/api/planets/2/" "http://swapi.co/api/planets/3/"
## [3] "http://swapi.co/api/planets/1/"
##
## $starships
## [1] "http://swapi.co/api/starships/2/" "http://swapi.co/api/starships/3/"
## [3] "http://swapi.co/api/starships/5/" "http://swapi.co/api/starships/9/"
## [5] "http://swapi.co/api/starships/10/" "http://swapi.co/api/starships/11/"
## [7] "http://swapi.co/api/starships/12/" "http://swapi.co/api/starships/13/"
##
## $vehicles
## [1] "http://swapi.co/api/vehicles/4/" "http://swapi.co/api/vehicles/6/"
## [3] "http://swapi.co/api/vehicles/7/" "http://swapi.co/api/vehicles/8/"
##
## $species
## [1] "http://swapi.co/api/species/5/" "http://swapi.co/api/species/3/"
## [3] "http://swapi.co/api/species/2/" "http://swapi.co/api/species/1/"
## [5] "http://swapi.co/api/species/4/"
##
## $created
## [1] "2014-12-10T14:23:31.880000Z"
##
## $edited
## [1] "2015-04-11T09:46:52.774897Z"
##
## $url
## [1] "http://swapi.co/api/films/1/"## [1] "A New Hope"In the previous example we saw that map outputs a list. Sometimes this helps but if we look at the kinds of problem we solve everyday we may want a different kind of output like a vector.
map_dbl() - returns a vector of numbers
map_lgl() - returns a logical vector
map_chr() - returns a character vector
map_int() - returns a integer vector
# Determine row number
map_dbl(survey_data, ~nrow(.x))
# Determine if elements have 14 rows
map_lgl(survey_data, ~nrow(.x)==14)
# Map over species_names list
map_chr(species_names, ~.x)
# Create a dataframe called survey_rows
survey_rows <- data.frame(names = names(survey_data),
rows = NA)
# Map over survey_data to determine row number in each element
survey_rows$rows <- map_dbl(survey_data, ~nrow(.x))
# Print out the survey_rows dataframe
survey_rowsmap(list, ~function(.x)) gives the same result as map(list, function). To specify how the list is used in the function, use the argument .x to denote where the list element goes inside the function. When you want to use .x to show where the element goes in the function, you need to put a ~ in front of the function in the second argument of map()
## $GrandBudapest
## [1] 4
##
## $Moonrise1
## [1] 4
##
## $Royal1
## [1] 4
##
## $Moonrise2
## [1] 4
##
## $Cavalcanti
## [1] 5
##
## $Royal2
## [1] 5
##
## $GrandBudapest2
## [1] 4
##
## $Moonrise3
## [1] 5
##
## $Chevalier
## [1] 4
##
## $Zissou
## [1] 5
##
## $FantasticFox
## [1] 5
##
## $Darjeeling
## [1] 5
##
## $Rushmore
## [1] 5
##
## $BottleRocket
## [1] 7
##
## $Darjeeling2
## [1] 5## $GrandBudapest
## [1] 4
##
## $Moonrise1
## [1] 4
##
## $Royal1
## [1] 4
##
## $Moonrise2
## [1] 4
##
## $Cavalcanti
## [1] 5
##
## $Royal2
## [1] 5
##
## $GrandBudapest2
## [1] 4
##
## $Moonrise3
## [1] 5
##
## $Chevalier
## [1] 4
##
## $Zissou
## [1] 5
##
## $FantasticFox
## [1] 5
##
## $Darjeeling
## [1] 5
##
## $Rushmore
## [1] 5
##
## $BottleRocket
## [1] 7
##
## $Darjeeling2
## [1] 5# Create a numcolors column and fill with length of each wesanderson element
data.frame(numcolors = map_dbl(wesanderson, ~length(.x)))## numcolors
## GrandBudapest 4
## Moonrise1 4
## Royal1 4
## Moonrise2 4
## Cavalcanti 5
## Royal2 5
## GrandBudapest2 4
## Moonrise3 5
## Chevalier 4
## Zissou 5
## FantasticFox 5
## Darjeeling 5
## Rushmore 5
## BottleRocket 7
## Darjeeling2 5More Complex Iterations
Working with Unnamed lists
Since purrr is a part of set of packages called as tidyverse it works well with pipes %>%. Pipes take the output from the function in front of it and makes it the the input for the next function. Pipes are powerful because they prevent us from needing intermediate objects. If a list doesn’t have name we can set them which is helpful for indexing, sub-setting and generally working with a list. Especially one that might grow or change over time where numeric indexing ultimately won’t be useful.
names(survey_data)
survey_data %>% names()
# Pipes within Map
map(waterfowl_data, ~.x %>% sum() %>% log())## List of 7
## $ :List of 14
## ..$ title : chr "A New Hope"
## ..$ episode_id : int 4
## ..$ opening_crawl: chr "It is a period of civil war.\r\nRebel spaceships, striking\r\nfrom a hidden base, have won\r\ntheir first victo"| __truncated__
## ..$ director : chr "George Lucas"
## ..$ producer : chr "Gary Kurtz, Rick McCallum"
## ..$ release_date : chr "1977-05-25"
## ..$ characters : chr [1:18] "http://swapi.co/api/people/1/" "http://swapi.co/api/people/2/" "http://swapi.co/api/people/3/" "http://swapi.co/api/people/4/" ...
## ..$ planets : chr [1:3] "http://swapi.co/api/planets/2/" "http://swapi.co/api/planets/3/" "http://swapi.co/api/planets/1/"
## ..$ starships : chr [1:8] "http://swapi.co/api/starships/2/" "http://swapi.co/api/starships/3/" "http://swapi.co/api/starships/5/" "http://swapi.co/api/starships/9/" ...
## ..$ vehicles : chr [1:4] "http://swapi.co/api/vehicles/4/" "http://swapi.co/api/vehicles/6/" "http://swapi.co/api/vehicles/7/" "http://swapi.co/api/vehicles/8/"
## ..$ species : chr [1:5] "http://swapi.co/api/species/5/" "http://swapi.co/api/species/3/" "http://swapi.co/api/species/2/" "http://swapi.co/api/species/1/" ...
## ..$ created : chr "2014-12-10T14:23:31.880000Z"
## ..$ edited : chr "2015-04-11T09:46:52.774897Z"
## ..$ url : chr "http://swapi.co/api/films/1/"
## $ :List of 14
## ..$ title : chr "Attack of the Clones"
## ..$ episode_id : int 2
## ..$ opening_crawl: chr "There is unrest in the Galactic\r\nSenate. Several thousand solar\r\nsystems have declared their\r\nintentions "| __truncated__
## ..$ director : chr "George Lucas"
## ..$ producer : chr "Rick McCallum"
## ..$ release_date : chr "2002-05-16"
## ..$ characters : chr [1:40] "http://swapi.co/api/people/2/" "http://swapi.co/api/people/3/" "http://swapi.co/api/people/6/" "http://swapi.co/api/people/7/" ...
## ..$ planets : chr [1:5] "http://swapi.co/api/planets/8/" "http://swapi.co/api/planets/9/" "http://swapi.co/api/planets/10/" "http://swapi.co/api/planets/11/" ...
## ..$ starships : chr [1:9] "http://swapi.co/api/starships/21/" "http://swapi.co/api/starships/39/" "http://swapi.co/api/starships/43/" "http://swapi.co/api/starships/47/" ...
## ..$ vehicles : chr [1:11] "http://swapi.co/api/vehicles/4/" "http://swapi.co/api/vehicles/44/" "http://swapi.co/api/vehicles/45/" "http://swapi.co/api/vehicles/46/" ...
## ..$ species : chr [1:14] "http://swapi.co/api/species/32/" "http://swapi.co/api/species/33/" "http://swapi.co/api/species/2/" "http://swapi.co/api/species/35/" ...
## ..$ created : chr "2014-12-20T10:57:57.886000Z"
## ..$ edited : chr "2015-04-11T09:45:01.623982Z"
## ..$ url : chr "http://swapi.co/api/films/5/"
## $ :List of 14
## ..$ title : chr "The Phantom Menace"
## ..$ episode_id : int 1
## ..$ opening_crawl: chr "Turmoil has engulfed the\r\nGalactic Republic. The taxation\r\nof trade routes to outlying star\r\nsystems is i"| __truncated__
## ..$ director : chr "George Lucas"
## ..$ producer : chr "Rick McCallum"
## ..$ release_date : chr "1999-05-19"
## ..$ characters : chr [1:34] "http://swapi.co/api/people/2/" "http://swapi.co/api/people/3/" "http://swapi.co/api/people/10/" "http://swapi.co/api/people/11/" ...
## ..$ planets : chr [1:3] "http://swapi.co/api/planets/8/" "http://swapi.co/api/planets/9/" "http://swapi.co/api/planets/1/"
## ..$ starships : chr [1:5] "http://swapi.co/api/starships/40/" "http://swapi.co/api/starships/41/" "http://swapi.co/api/starships/31/" "http://swapi.co/api/starships/32/" ...
## ..$ vehicles : chr [1:7] "http://swapi.co/api/vehicles/33/" "http://swapi.co/api/vehicles/34/" "http://swapi.co/api/vehicles/35/" "http://swapi.co/api/vehicles/36/" ...
## ..$ species : chr [1:20] "http://swapi.co/api/species/1/" "http://swapi.co/api/species/2/" "http://swapi.co/api/species/6/" "http://swapi.co/api/species/11/" ...
## ..$ created : chr "2014-12-19T16:52:55.740000Z"
## ..$ edited : chr "2015-04-11T09:45:18.689301Z"
## ..$ url : chr "http://swapi.co/api/films/4/"
## $ :List of 14
## ..$ title : chr "Revenge of the Sith"
## ..$ episode_id : int 3
## ..$ opening_crawl: chr "War! The Republic is crumbling\r\nunder attacks by the ruthless\r\nSith Lord, Count Dooku.\r\nThere are heroes "| __truncated__
## ..$ director : chr "George Lucas"
## ..$ producer : chr "Rick McCallum"
## ..$ release_date : chr "2005-05-19"
## ..$ characters : chr [1:34] "http://swapi.co/api/people/1/" "http://swapi.co/api/people/2/" "http://swapi.co/api/people/3/" "http://swapi.co/api/people/4/" ...
## ..$ planets : chr [1:13] "http://swapi.co/api/planets/2/" "http://swapi.co/api/planets/5/" "http://swapi.co/api/planets/8/" "http://swapi.co/api/planets/9/" ...
## ..$ starships : chr [1:12] "http://swapi.co/api/starships/48/" "http://swapi.co/api/starships/59/" "http://swapi.co/api/starships/61/" "http://swapi.co/api/starships/32/" ...
## ..$ vehicles : chr [1:13] "http://swapi.co/api/vehicles/33/" "http://swapi.co/api/vehicles/50/" "http://swapi.co/api/vehicles/60/" "http://swapi.co/api/vehicles/62/" ...
## ..$ species : chr [1:20] "http://swapi.co/api/species/19/" "http://swapi.co/api/species/33/" "http://swapi.co/api/species/2/" "http://swapi.co/api/species/3/" ...
## ..$ created : chr "2014-12-20T18:49:38.403000Z"
## ..$ edited : chr "2015-04-11T09:45:44.862122Z"
## ..$ url : chr "http://swapi.co/api/films/6/"
## $ :List of 14
## ..$ title : chr "Return of the Jedi"
## ..$ episode_id : int 6
## ..$ opening_crawl: chr "Luke Skywalker has returned to\r\nhis home planet of Tatooine in\r\nan attempt to rescue his\r\nfriend Han Solo"| __truncated__
## ..$ director : chr "Richard Marquand"
## ..$ producer : chr "Howard G. Kazanjian, George Lucas, Rick McCallum"
## ..$ release_date : chr "1983-05-25"
## ..$ characters : chr [1:20] "http://swapi.co/api/people/1/" "http://swapi.co/api/people/2/" "http://swapi.co/api/people/3/" "http://swapi.co/api/people/4/" ...
## ..$ planets : chr [1:5] "http://swapi.co/api/planets/5/" "http://swapi.co/api/planets/7/" "http://swapi.co/api/planets/8/" "http://swapi.co/api/planets/9/" ...
## ..$ starships : chr [1:12] "http://swapi.co/api/starships/10/" "http://swapi.co/api/starships/11/" "http://swapi.co/api/starships/12/" "http://swapi.co/api/starships/15/" ...
## ..$ vehicles : chr [1:8] "http://swapi.co/api/vehicles/8/" "http://swapi.co/api/vehicles/16/" "http://swapi.co/api/vehicles/18/" "http://swapi.co/api/vehicles/19/" ...
## ..$ species : chr [1:9] "http://swapi.co/api/species/5/" "http://swapi.co/api/species/6/" "http://swapi.co/api/species/8/" "http://swapi.co/api/species/9/" ...
## ..$ created : chr "2014-12-18T10:39:33.255000Z"
## ..$ edited : chr "2015-04-11T09:46:05.220365Z"
## ..$ url : chr "http://swapi.co/api/films/3/"
## $ :List of 14
## ..$ title : chr "The Empire Strikes Back"
## ..$ episode_id : int 5
## ..$ opening_crawl: chr "It is a dark time for the\r\nRebellion. Although the Death\r\nStar has been destroyed,\r\nImperial troops have "| __truncated__
## ..$ director : chr "Irvin Kershner"
## ..$ producer : chr "Gary Kutz, Rick McCallum"
## ..$ release_date : chr "1980-05-17"
## ..$ characters : chr [1:16] "http://swapi.co/api/people/1/" "http://swapi.co/api/people/2/" "http://swapi.co/api/people/3/" "http://swapi.co/api/people/4/" ...
## ..$ planets : chr [1:4] "http://swapi.co/api/planets/4/" "http://swapi.co/api/planets/5/" "http://swapi.co/api/planets/6/" "http://swapi.co/api/planets/27/"
## ..$ starships : chr [1:9] "http://swapi.co/api/starships/10/" "http://swapi.co/api/starships/11/" "http://swapi.co/api/starships/12/" "http://swapi.co/api/starships/15/" ...
## ..$ vehicles : chr [1:6] "http://swapi.co/api/vehicles/8/" "http://swapi.co/api/vehicles/14/" "http://swapi.co/api/vehicles/16/" "http://swapi.co/api/vehicles/18/" ...
## ..$ species : chr [1:5] "http://swapi.co/api/species/6/" "http://swapi.co/api/species/7/" "http://swapi.co/api/species/3/" "http://swapi.co/api/species/2/" ...
## ..$ created : chr "2014-12-12T11:26:24.656000Z"
## ..$ edited : chr "2015-04-11T09:46:31.433607Z"
## ..$ url : chr "http://swapi.co/api/films/2/"
## $ :List of 13
## ..$ title : chr "The Force Awakens"
## ..$ episode_id : int 7
## ..$ opening_crawl: chr "Luke Skywalker has vanished.\r\nIn his absence, the sinister\r\nFIRST ORDER has risen from\r\nthe ashes of the "| __truncated__
## ..$ director : chr "J. J. Abrams"
## ..$ producer : chr "Kathleen Kennedy, J. J. Abrams, Bryan Burk"
## ..$ release_date : chr "2015-12-11"
## ..$ characters : chr [1:11] "http://swapi.co/api/people/1/" "http://swapi.co/api/people/3/" "http://swapi.co/api/people/5/" "http://swapi.co/api/people/13/" ...
## ..$ planets : chr "http://swapi.co/api/planets/61/"
## ..$ starships : chr [1:2] "http://swapi.co/api/starships/77/" "http://swapi.co/api/starships/10/"
## ..$ species : chr [1:3] "http://swapi.co/api/species/3/" "http://swapi.co/api/species/2/" "http://swapi.co/api/species/1/"
## ..$ created : chr "2015-04-17T06:51:30.504780Z"
## ..$ edited : chr "2015-12-17T14:31:47.617768Z"
## ..$ url : chr "http://swapi.co/api/films/7/"## [1] "A New Hope" "Attack of the Clones"
## [3] "The Phantom Menace" "Revenge of the Sith"
## [5] "Return of the Jedi" "The Empire Strikes Back"
## [7] "The Force Awakens"# Create a list of values from 1 through 10
numlist <- list(1:10)
# Iterate over the numlist
map(numlist, ~.x %>% sqrt() %>% sin())## [[1]]
## [1] 0.84147098 0.98776595 0.98702664 0.90929743 0.78674913 0.63815764
## [7] 0.47577184 0.30807174 0.14112001 -0.02068353More with Map
Different tasks that can be done with map like simulating data, running linear models and outputting different classes of information.
# list_of_df contains list of means that we want to use generate/simulate datasets
list_of_df <- map(list_of_means, ~data.frame(a = rnorm(mean = .x, n = 200, sd = (5/2))))
# education_data is a list of two datasets, we applied the linear model
# formula on both datasets one-by-one and then summarize the model
models <- education_data %>% map(~ lm(income ~ education_level, data=.x)) %>% map(summary)
map_chr(livingthings, ~.x[["species"]])
map_lgl(livingthings, ~.x[["species"]]=="Purple Flowers")
map_dbl(bird_measurements, ~.x[["weight"]])
map_int(bird_measurements, ~.x[["wing length"]])Till now we have seen map returning vectors only which is one-dimensional data. Oftentimes we want a dataframe which is two-dimensional. Dataframes have great utility and are a required input for many R functions.
bird_measurements %>%
map_df(~ data_frame(weight=.x[["weight"]], wing_length = .x[["wing length"]]))# List of sites north, east, and west
sites <- list("north", "east", "west")
# Create a list of dataframes, each with a years, a, and b column
list_of_df <- map(sites,
~data.frame(sites = .x,
a = rnorm(mean = 5, n = 200, sd = (5/2)),
b = rnorm(mean = 200, n = 200, sd = 15)))
list_of_df## [[1]]
## sites a b
## 1 north 3.97616671 202.0769
## 2 north 4.05091960 218.9514
## 3 north 8.03937680 199.5588
## 4 north 5.27338680 214.7802
## 5 north 6.20274797 194.2441
## 6 north 9.01864184 218.4840
## 7 north 8.29413255 205.7070
## 8 north 6.68484718 217.0811
## 9 north 4.13365936 165.5650
## 10 north 6.12254945 194.7986
## 11 north 6.39987168 204.2222
## 12 north 4.37116540 181.1408
## 13 north 5.17217057 172.8057
## 14 north 2.86700101 238.0278
## 15 north 2.33341864 181.8626
## 16 north 8.40593730 210.8515
## 17 north 5.52441136 233.7396
## 18 north 3.06844729 209.8683
## 19 north 8.63489095 165.5810
## 20 north 6.02508649 187.5543
## 21 north 4.99572979 204.1705
## 22 north 7.14289354 188.3075
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##
## [[2]]
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##
## [[3]]
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## 189 west 5.8472477 190.3797
## 190 west 8.5042420 194.3233
## 191 west 3.5285498 215.7122
## 192 west 3.0990339 166.0223
## 193 west 10.5414437 196.0819
## 194 west 6.0740939 200.4535
## 195 west 7.2395845 178.2125
## 196 west 8.5479798 219.8218
## 197 west 5.6850631 198.8678
## 198 west 3.8120197 202.5325
## 199 west 1.8474705 180.1095
## 200 west 4.6744996 191.9134# Map over the models to look at the relationship of a vs b
list_of_df %>%
map(~ lm(a ~ b, data = .)) %>%
map(summary)## [[1]]
##
## Call:
## lm(formula = a ~ b, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.5585 -1.6848 -0.0443 1.7994 6.6383
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.616317 2.253431 2.492 0.0135 *
## b -0.003055 0.011156 -0.274 0.7845
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.491 on 198 degrees of freedom
## Multiple R-squared: 0.0003787, Adjusted R-squared: -0.00467
## F-statistic: 0.075 on 1 and 198 DF, p-value: 0.7845
##
##
## [[2]]
##
## Call:
## lm(formula = a ~ b, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.1219 -1.5046 0.1634 1.8899 6.2464
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.92893 2.23737 3.991 9.27e-05 ***
## b -0.02087 0.01126 -1.853 0.0653 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.558 on 198 degrees of freedom
## Multiple R-squared: 0.01705, Adjusted R-squared: 0.01209
## F-statistic: 3.435 on 1 and 198 DF, p-value: 0.06532
##
##
## [[3]]
##
## Call:
## lm(formula = a ~ b, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.4562 -1.4734 0.1834 1.5866 9.3445
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.101665 2.373359 1.728 0.0855 .
## b 0.003486 0.011906 0.293 0.7700
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.43 on 198 degrees of freedom
## Multiple R-squared: 0.0004329, Adjusted R-squared: -0.004615
## F-statistic: 0.08574 on 1 and 198 DF, p-value: 0.77# Pull out the director element of sw_films in a list and character vector
map(sw_films, ~.x[["director"]])## $`A New Hope`
## [1] "George Lucas"
##
## $`Attack of the Clones`
## [1] "George Lucas"
##
## $`The Phantom Menace`
## [1] "George Lucas"
##
## $`Revenge of the Sith`
## [1] "George Lucas"
##
## $`Return of the Jedi`
## [1] "Richard Marquand"
##
## $`The Empire Strikes Back`
## [1] "Irvin Kershner"
##
## $`The Force Awakens`
## [1] "J. J. Abrams"## A New Hope Attack of the Clones The Phantom Menace
## "George Lucas" "George Lucas" "George Lucas"
## Revenge of the Sith Return of the Jedi The Empire Strikes Back
## "George Lucas" "Richard Marquand" "Irvin Kershner"
## The Force Awakens
## "J. J. Abrams"# Compare outputs when checking if director is George Lucas
map(sw_films, ~.x[["director"]] == "George Lucas")## $`A New Hope`
## [1] TRUE
##
## $`Attack of the Clones`
## [1] TRUE
##
## $`The Phantom Menace`
## [1] TRUE
##
## $`Revenge of the Sith`
## [1] TRUE
##
## $`Return of the Jedi`
## [1] FALSE
##
## $`The Empire Strikes Back`
## [1] FALSE
##
## $`The Force Awakens`
## [1] FALSE## A New Hope Attack of the Clones The Phantom Menace
## TRUE TRUE TRUE
## Revenge of the Sith Return of the Jedi The Empire Strikes Back
## TRUE FALSE FALSE
## The Force Awakens
## FALSE## $`A New Hope`
## [1] 4
##
## $`Attack of the Clones`
## [1] 2
##
## $`The Phantom Menace`
## [1] 1
##
## $`Revenge of the Sith`
## [1] 3
##
## $`Return of the Jedi`
## [1] 6
##
## $`The Empire Strikes Back`
## [1] 5
##
## $`The Force Awakens`
## [1] 7## A New Hope Attack of the Clones The Phantom Menace
## 4 2 1
## Revenge of the Sith Return of the Jedi The Empire Strikes Back
## 3 6 5
## The Force Awakens
## 7## A New Hope Attack of the Clones The Phantom Menace
## 4 2 1
## Revenge of the Sith Return of the Jedi The Empire Strikes Back
## 3 6 5
## The Force Awakens
## 7map2() and pmap()
Oftentimes we need to use multiple datasets stored as lists to answer questions. In this case we need to use map2 function to pull out information from each list and bring it together. If you have three or more lists you can use pmap. There is difference in how we use pmap compared to map or map2. The first difference is how we input lists. In pmap we need to create a list of lists we want to use as input. The second argument to pmap is a custom function argument. Inside the custom function is the names of the elements from our input list as the arguments. This allows us to use name of those lists as arguments instead of using .x or .y.
simdata <- map2(list_of_means, list_of_sd, ~data.frame(a = rnorm(mean=.x, n=200, sd=.y)
,b = rnorm(mean=200, n=200, sd=15)))
simdata <- pmap(inputs_list,
function(means, sd, samplesize)
data.frame(a = rnorm(mean=means, n=samplesize, sd=sd)))# List of 1, 2 and 3
means <- list(1, 2, 3)
# Create sites list
sites <- list("north", "west", "east")
# Map over two arguments: sites and means
list_of_files_map2 <- map2(sites, means, ~data.frame(sites = .x,
a = rnorm(mean = .y, n = 200, sd = (5/2))))
head(list_of_files_map2)## [[1]]
## sites a
## 1 north 1.201042075
## 2 north 1.924388004
## 3 north -1.259059559
## 4 north -0.623257471
## 5 north -2.357483619
## 6 north 0.756781574
## 7 north 4.387023953
## 8 north 4.945243426
## 9 north -0.024052202
## 10 north -0.606934011
## 11 north -0.945287083
## 12 north -0.645948642
## 13 north 2.609348741
## 14 north 0.360216380
## 15 north -2.865828044
## 16 north 1.057092026
## 17 north 3.036849468
## 18 north -1.964446954
## 19 north 2.609268199
## 20 north -1.312604107
## 21 north 0.049118669
## 22 north 5.682607943
## 23 north 2.008908889
## 24 north 2.847976574
## 25 north -1.886712670
## 26 north -0.342737324
## 27 north -0.510206056
## 28 north 1.839170877
## 29 north -2.126780561
## 30 north 0.718868001
## 31 north 0.174577909
## 32 north -2.150583809
## 33 north -3.982026598
## 34 north 4.335070491
## 35 north 6.604466102
## 36 north 1.646700270
## 37 north 1.757934995
## 38 north 2.404339775
## 39 north 4.982363131
## 40 north 4.534428249
## 41 north 3.415205552
## 42 north 1.869097190
## 43 north 1.198577761
## 44 north 2.768035611
## 45 north 2.748229467
## 46 north 1.962682724
## 47 north 2.411908295
## 48 north 3.460844985
## 49 north 5.099069367
## 50 north -0.254522023
## 51 north 1.673113441
## 52 north 2.333146958
## 53 north 4.440476618
## 54 north 4.116855053
## 55 north 0.700575547
## 56 north -0.971285575
## 57 north 3.103890703
## 58 north 0.002309598
## 59 north 6.291612911
## 60 north 3.519609097
## 61 north 2.057330542
## 62 north 5.257586252
## 63 north 0.222928618
## 64 north 2.118909814
## 65 north 3.209799423
## 66 north 3.400224680
## 67 north 4.989770973
## 68 north -1.361963653
## 69 north -0.827626105
## 70 north 2.126956289
## 71 north 3.027725142
## 72 north -1.055050102
## 73 north -0.912500745
## 74 north 0.791751437
## 75 north 4.348360569
## 76 north 1.171643937
## 77 north 0.277352912
## 78 north -3.055037169
## 79 north -0.882422561
## 80 north 1.146662981
## 81 north -2.610340666
## 82 north -0.710289834
## 83 north 4.386325533
## 84 north 0.755271857
## 85 north 1.706355101
## 86 north 2.130451389
## 87 north 0.155466440
## 88 north -0.612707861
## 89 north -1.362188691
## 90 north -3.248551843
## 91 north -0.550968485
## 92 north -0.513081543
## 93 north 0.331851667
## 94 north -2.860530785
## 95 north 0.827152870
## 96 north 0.633122756
## 97 north 1.451769440
## 98 north 1.531869736
## 99 north 3.458081330
## 100 north 2.951857970
## 101 north 0.036149258
## 102 north -2.993048960
## 103 north 0.874443248
## 104 north 1.009216260
## 105 north 2.450200013
## 106 north -0.149207216
## 107 north 1.222752870
## 108 north 0.410020582
## 109 north -1.697795193
## 110 north 3.843121310
## 111 north 0.480065097
## 112 north -0.033400560
## 113 north -4.124338200
## 114 north 3.448968575
## 115 north 4.548266370
## 116 north 0.869154847
## 117 north 3.052173350
## 118 north -3.190211752
## 119 north 0.813342466
## 120 north 5.378860305
## 121 north -1.990712721
## 122 north 2.218611435
## 123 north 4.810096309
## 124 north 1.456130523
## 125 north 1.200539258
## 126 north 1.335253455
## 127 north 1.601113128
## 128 north -2.673021381
## 129 north 2.553445336
## 130 north 0.402321483
## 131 north 6.705150419
## 132 north -3.394877400
## 133 north 2.939954279
## 134 north 0.306031686
## 135 north 1.110914441
## 136 north -0.229485041
## 137 north -0.514215391
## 138 north 0.220893002
## 139 north 0.654229875
## 140 north -0.653514756
## 141 north 2.747670449
## 142 north 3.530782716
## 143 north -0.445349660
## 144 north -3.007507365
## 145 north -0.834961381
## 146 north 4.570180133
## 147 north 4.635285509
## 148 north -3.365924204
## 149 north 1.932145936
## 150 north -3.367283511
## 151 north 1.005167456
## 152 north 1.109710292
## 153 north 3.889483260
## 154 north 1.364599702
## 155 north 3.518296550
## 156 north 3.113472263
## 157 north -0.480624185
## 158 north 1.893208057
## 159 north 2.308442797
## 160 north -0.922853828
## 161 north 4.003537326
## 162 north 0.557615089
## 163 north 1.400244777
## 164 north 1.637128611
## 165 north 5.804685489
## 166 north 5.779034729
## 167 north -2.637267998
## 168 north 1.106257193
## 169 north 6.615700757
## 170 north 4.869709813
## 171 north 0.173439847
## 172 north -2.264095178
## 173 north 1.531409946
## 174 north -4.410560350
## 175 north 0.756212423
## 176 north 4.663108116
## 177 north 3.172222058
## 178 north 1.570867901
## 179 north -0.785074267
## 180 north -1.831257156
## 181 north -1.846336823
## 182 north 0.601868177
## 183 north -3.751329803
## 184 north 2.655289363
## 185 north 4.267055387
## 186 north -2.124821062
## 187 north 1.033435591
## 188 north -0.544497931
## 189 north 1.676904904
## 190 north 3.211668872
## 191 north -0.953436019
## 192 north -1.062234517
## 193 north 1.571507133
## 194 north -0.854086324
## 195 north 1.953977473
## 196 north -0.665321717
## 197 north 5.306634726
## 198 north 3.816056689
## 199 north -1.910010926
## 200 north -1.680030280
##
## [[2]]
## sites a
## 1 west 2.53013294
## 2 west 7.31481447
## 3 west -0.22935737
## 4 west 2.20915439
## 5 west 0.61943402
## 6 west 1.23355286
## 7 west 0.20316611
## 8 west 0.95760308
## 9 west -0.50368855
## 10 west 1.57688661
## 11 west 1.99734675
## 12 west 1.91053974
## 13 west 7.66316221
## 14 west 3.46536030
## 15 west 4.45288893
## 16 west 0.61806449
## 17 west 4.07173403
## 18 west 1.64808457
## 19 west 0.09864664
## 20 west 4.84666871
## 21 west 4.34335403
## 22 west 3.54886727
## 23 west 3.09252986
## 24 west 4.16032098
## 25 west 2.65558297
## 26 west 4.76816815
## 27 west 3.18616301
## 28 west -1.87333681
## 29 west 2.93388660
## 30 west 3.95077741
## 31 west 4.74264946
## 32 west 1.15871508
## 33 west 1.15997115
## 34 west 2.63072470
## 35 west 2.42248550
## 36 west 2.62748005
## 37 west 2.98546001
## 38 west 5.76164611
## 39 west 3.37366623
## 40 west 0.10982056
## 41 west 4.26289112
## 42 west 1.31219265
## 43 west 0.49374456
## 44 west 5.35326175
## 45 west 0.96187258
## 46 west 1.18298461
## 47 west 5.10098469
## 48 west 4.82693111
## 49 west 6.14429835
## 50 west 1.22999807
## 51 west 3.37218938
## 52 west 6.98722585
## 53 west 0.99691521
## 54 west 4.12877849
## 55 west 1.03135911
## 56 west 0.86339196
## 57 west 0.53211469
## 58 west 2.62476841
## 59 west -0.22409637
## 60 west 1.16909925
## 61 west 3.52255982
## 62 west -1.94083115
## 63 west 0.94329926
## 64 west 2.51317290
## 65 west 7.33756588
## 66 west 2.43371622
## 67 west 2.19658571
## 68 west 0.34327074
## 69 west 0.47535093
## 70 west -1.68672366
## 71 west -1.33782154
## 72 west 5.80539270
## 73 west 5.62598674
## 74 west 2.21809832
## 75 west 1.21427922
## 76 west 0.80872523
## 77 west -0.48621678
## 78 west 2.65831525
## 79 west 3.77714528
## 80 west 2.09677667
## 81 west 2.47548582
## 82 west 3.08412712
## 83 west 1.26709673
## 84 west 0.55582500
## 85 west 0.56118208
## 86 west -0.52099318
## 87 west 1.23166187
## 88 west 2.30955699
## 89 west 7.20976008
## 90 west 3.14867592
## 91 west 2.61292547
## 92 west -0.14750892
## 93 west 2.34457430
## 94 west 1.91441191
## 95 west 1.53314095
## 96 west 1.38353233
## 97 west 0.36420783
## 98 west 3.86044324
## 99 west 3.17790720
## 100 west -1.32272922
## 101 west 4.82838234
## 102 west 4.93736755
## 103 west 5.65819398
## 104 west -3.00386728
## 105 west 1.06815443
## 106 west -0.18035300
## 107 west -0.20140237
## 108 west -1.94130130
## 109 west 6.57575182
## 110 west 0.28248838
## 111 west 1.55152051
## 112 west 4.29979008
## 113 west 3.55284910
## 114 west -1.30288175
## 115 west 9.72167287
## 116 west -3.99496499
## 117 west 3.22456895
## 118 west 2.64239155
## 119 west -0.00113236
## 120 west 0.73569655
## 121 west -2.90186223
## 122 west 0.67477064
## 123 west -0.24542862
## 124 west 8.06559684
## 125 west 5.59031496
## 126 west 5.64992644
## 127 west 2.52150080
## 128 west 4.27668557
## 129 west -0.22113368
## 130 west 3.21691657
## 131 west 5.27835892
## 132 west -5.94852669
## 133 west 0.50729528
## 134 west 6.21269364
## 135 west 2.45194841
## 136 west 6.81851037
## 137 west 4.37162329
## 138 west 1.08544893
## 139 west -0.92408539
## 140 west -1.73566526
## 141 west -0.20353871
## 142 west 4.09194235
## 143 west 2.20103652
## 144 west 3.90381791
## 145 west 1.26776388
## 146 west 1.32675681
## 147 west 2.98980297
## 148 west 5.49323410
## 149 west 2.36405481
## 150 west 1.19512478
## 151 west 3.05424601
## 152 west 0.49705636
## 153 west 2.34170286
## 154 west 4.25699303
## 155 west 5.01375694
## 156 west 1.04724958
## 157 west -0.33298458
## 158 west 2.51921200
## 159 west 1.85559477
## 160 west -0.68096405
## 161 west 5.97715093
## 162 west 2.36474030
## 163 west 0.85791068
## 164 west 3.12286986
## 165 west 1.79208136
## 166 west 5.24882711
## 167 west 2.93612778
## 168 west 2.43499948
## 169 west 1.79781476
## 170 west 2.92001862
## 171 west 6.00920801
## 172 west 3.75227181
## 173 west 4.21294227
## 174 west 1.06837219
## 175 west 1.55560267
## 176 west 2.95158243
## 177 west 2.31104818
## 178 west 0.63934949
## 179 west 2.42516589
## 180 west 3.28591726
## 181 west 2.00760567
## 182 west 4.77191260
## 183 west 2.90666015
## 184 west 1.43640396
## 185 west -0.18606765
## 186 west 2.03426725
## 187 west 3.84400578
## 188 west 1.36327074
## 189 west -0.43189009
## 190 west 4.09937041
## 191 west 1.53605354
## 192 west 1.74469667
## 193 west 1.67792490
## 194 west -2.10861140
## 195 west 3.55156929
## 196 west 0.26932483
## 197 west 3.31673125
## 198 west 4.99518135
## 199 west -1.64565110
## 200 west 3.21781023
##
## [[3]]
## sites a
## 1 east 1.20501747
## 2 east -0.13082587
## 3 east 3.57228633
## 4 east 1.30970449
## 5 east 5.43703568
## 6 east 5.17765415
## 7 east 1.21774710
## 8 east 1.91804190
## 9 east 2.36518882
## 10 east 3.97289676
## 11 east 3.11571877
## 12 east 3.39947248
## 13 east 0.82778063
## 14 east 3.83505236
## 15 east -1.57514789
## 16 east 4.88165340
## 17 east 3.81781117
## 18 east 3.14187379
## 19 east 1.23726941
## 20 east 2.30080088
## 21 east 2.42017488
## 22 east 5.75445830
## 23 east 3.81862643
## 24 east 4.95558399
## 25 east 2.35119900
## 26 east 14.29937817
## 27 east 1.48695463
## 28 east 0.03814956
## 29 east 5.06254299
## 30 east 2.94588283
## 31 east -1.87008503
## 32 east 2.88699921
## 33 east 1.64596057
## 34 east 3.21729582
## 35 east 0.06221238
## 36 east -0.16950214
## 37 east -2.20744037
## 38 east 1.80860465
## 39 east 3.77797119
## 40 east -0.54164113
## 41 east 4.83847827
## 42 east -1.52457804
## 43 east 4.72929223
## 44 east 5.31767226
## 45 east 3.22545987
## 46 east 5.66608995
## 47 east 2.54195108
## 48 east -1.11647077
## 49 east -1.11771621
## 50 east 4.17976702
## 51 east -2.21556762
## 52 east 0.66896572
## 53 east -2.84155927
## 54 east 1.56570279
## 55 east 3.72242392
## 56 east -0.47010462
## 57 east -0.69411338
## 58 east 2.51136097
## 59 east 4.03796393
## 60 east 9.08801169
## 61 east 2.57847878
## 62 east -0.06851133
## 63 east 4.87633342
## 64 east 3.58242481
## 65 east 2.17318440
## 66 east 6.07221051
## 67 east 2.97248098
## 68 east 4.60444285
## 69 east 1.17339736
## 70 east 1.10077866
## 71 east 2.17662987
## 72 east 4.81936294
## 73 east 4.88474206
## 74 east 2.56959495
## 75 east 7.74482139
## 76 east 2.67725121
## 77 east -0.97433692
## 78 east 3.80334422
## 79 east 0.38452829
## 80 east 0.02403884
## 81 east 4.65278900
## 82 east 2.76169172
## 83 east 4.48904079
## 84 east 2.34026575
## 85 east 0.65184813
## 86 east 3.62037477
## 87 east 3.93546360
## 88 east 4.76539313
## 89 east 2.90077682
## 90 east 1.97365977
## 91 east -1.74734854
## 92 east 6.49015367
## 93 east -3.16520115
## 94 east 3.61365848
## 95 east -0.04409108
## 96 east 4.51719536
## 97 east 0.13326370
## 98 east 8.01099832
## 99 east 1.32283274
## 100 east 2.16588980
## 101 east 1.85057842
## 102 east -4.07187238
## 103 east 7.32369315
## 104 east -0.12090439
## 105 east 3.98638881
## 106 east 10.34292913
## 107 east 0.16390101
## 108 east 0.12683027
## 109 east 2.99750129
## 110 east 1.61959920
## 111 east 2.02293490
## 112 east 1.57722891
## 113 east 1.49520917
## 114 east 0.92701974
## 115 east -0.74460820
## 116 east 4.25862026
## 117 east 1.60324271
## 118 east 4.32095526
## 119 east 7.25739955
## 120 east 4.61552165
## 121 east 2.30058066
## 122 east 5.30624097
## 123 east 1.25255139
## 124 east 4.47988432
## 125 east 7.52858622
## 126 east 1.40932343
## 127 east 3.59940410
## 128 east 3.01727898
## 129 east 6.39015988
## 130 east 1.67064848
## 131 east 4.06642632
## 132 east 4.97471477
## 133 east 5.85506719
## 134 east 4.01123864
## 135 east 2.31465085
## 136 east 4.21950373
## 137 east 4.76073934
## 138 east 2.67732703
## 139 east 4.17010248
## 140 east 1.72450767
## 141 east 2.66562367
## 142 east 5.65957024
## 143 east 1.29202958
## 144 east 4.02208223
## 145 east 5.17042417
## 146 east 3.67199714
## 147 east 2.87933926
## 148 east -1.78416651
## 149 east 1.58403610
## 150 east 2.97040202
## 151 east 4.24488634
## 152 east 4.95937112
## 153 east 5.94545288
## 154 east 4.42323493
## 155 east 5.84288767
## 156 east 0.72989615
## 157 east 3.29435597
## 158 east 3.25511794
## 159 east 6.54213592
## 160 east 1.69979886
## 161 east 1.29567267
## 162 east 5.28213552
## 163 east 1.85190336
## 164 east 4.71702022
## 165 east 3.45389829
## 166 east 7.15422419
## 167 east 5.37220000
## 168 east 2.86582652
## 169 east 2.36296709
## 170 east 0.72512967
## 171 east 1.64374758
## 172 east -1.89614073
## 173 east 5.73224545
## 174 east -1.19114823
## 175 east 4.38098391
## 176 east 4.81763990
## 177 east 7.29629553
## 178 east 6.34064943
## 179 east 1.80891079
## 180 east 0.67173408
## 181 east 2.85263854
## 182 east 5.06744911
## 183 east 7.36452347
## 184 east 5.83753462
## 185 east -0.37490838
## 186 east 6.98783095
## 187 east -0.71327543
## 188 east 2.28163707
## 189 east 1.52908125
## 190 east 1.02355557
## 191 east 1.33645355
## 192 east 1.17449515
## 193 east 1.65642903
## 194 east 6.88881606
## 195 east 5.37370832
## 196 east -1.63738985
## 197 east 2.02411320
## 198 east 0.47893964
## 199 east 5.26574657
## 200 east 3.10569240# Create a master list, a list of lists
pmapinputs <- list(sites = sites, means = means, sigma = sigma,
means2 = means2, sigma2 = sigma2)
# Map over the master list
list_of_files_pmap <- pmap(pmapinputs,
function(sites, means, means2, sigma, sigma2)
data.frame(sites = sites,
a = rnorm(mean = means, n = 200, sd = sigma),
b = rnorm(mean = means2, n = 200, sd = sigma2)))
head(list_of_files_pmap)Troubleshooting Lists with purrr
How to purrr safely()
When you try to use map over a list and one of the elements has a wrong data type then the map() function will not work.
In the below example we are using another function safely() inside of map to identify where in the list an issue is coming. By using safely argument otherwise we can tell safely how to deal with input that it does not expect. Supplying NA_real_ to the otherwise argument outputs NA when an unexpected input comes from the list element and then map will continue to the next element without breaking.
Using safely with map produces more complex output than just using map on its own. Each output list has a result element and an error element so that you can see where the errors are occurring and what they are.
Using safely our main purpose is to find what the errors are and which elements in the list caused them. Grouping together error messages can also make this easier. This can be done using transpose() function at the end of our workflow.
a <- list("unknown", 10) %>% map(safely(function(x) x * 10, otherwise = NA_real_))
# Using transpose
a <- list("unknown", 10) %>% map(safely(function(x) x * 10, otherwise = NA_real_)) %>% transpose()# Map safely over log
a <- list(-10, 1, 10, 0) %>%
map(safely(log, otherwise = NA_real_)) %>%
# Transpose the result
transpose()## Warning in .f(...): NaNs produced## $result
## $result[[1]]
## [1] NaN
##
## $result[[2]]
## [1] 0
##
## $result[[3]]
## [1] 2.302585
##
## $result[[4]]
## [1] -Inf
##
##
## $error
## $error[[1]]
## NULL
##
## $error[[2]]
## NULL
##
## $error[[3]]
## NULL
##
## $error[[4]]
## NULL## [[1]]
## [1] NaN
##
## [[2]]
## [1] 0
##
## [[3]]
## [1] 2.302585
##
## [[4]]
## [1] -Inf## [[1]]
## NULL
##
## [[2]]
## NULL
##
## [[3]]
## NULL
##
## [[4]]
## NULL# Load sw_people data
data(sw_people)
# Map over sw_people and pull out the height element
height_cm <- map(sw_people, "height") %>%
map(function(x){
ifelse(x == "unknown",NA,
as.numeric(x))
})# Map over sw_people and pull out the height element
# Set quiet = FALSE so that errors are printed.
height_ft <- map(sw_people , "height") %>%
map(safely(function(x){
x * 0.0328084
}, quiet = FALSE)) %>%
transpose()
# Print your list, the result element, and the error element
height_ft
height_ft[["result"]]
height_ft[["error"]]Possibly()
safely helps us in pointing where the issue is and what the issue is with messy data, possibly() helps us get past it and get on with our day. It is a good idea to diagnose your issue with safely and then replace it with possibly once the issue is resolved so that you can get the output you want and keep working. possibly will just return the results and not the error elements.
# Take the log of each element in the list
a <- list(-10, 1, 10, 0) %>%
map(possibly(function(x){
log(x)
},otherwise = NA_real_))## Warning in log(x): NaNs produced# Create a piped workflow that returns double vectors
height_cm %>%
map_dbl(possibly(function(x){
# Convert centimeters to feet
x * 0.0328084
}, otherwise = NA_real_)) ## [1] 5.643045 5.479003 3.149606 6.627297 4.921260 5.839895 5.413386 3.182415
## [9] 6.003937 5.971129 6.167979 5.905512 7.480315 5.905512 5.675853 5.741470
## [17] 5.577428 5.905512 2.165354 5.577428 6.003937 6.561680 6.233596 5.807087
## [25] 5.741470 5.905512 4.921260 NA 2.887139 5.249344 6.332021 6.266404
## [33] 5.577428 6.430446 7.349082 6.758530 6.003937 4.494751 3.674541 6.003937
## [41] 5.347769 5.741470 5.905512 5.839895 3.083990 4.002625 5.347769 6.167979
## [49] 6.496063 6.430446 5.610236 6.036746 6.167979 8.661418 6.167979 6.430446
## [57] 6.069554 5.150919 6.003937 6.003937 5.577428 5.446194 5.413386 6.332021
## [65] 6.266404 6.003937 5.511811 6.496063 7.513124 6.988189 5.479003 2.591864
## [73] 3.149606 6.332021 6.266404 5.839895 7.086614 7.677166 6.167979 5.839895
## [81] 6.758530 NA NA NA NA NA 5.413386walk() in the park
walk() helps make purrr cleaner and helps us create outputs that are more human readable.Regular lists output is bracketed elements and isn’t very easy to read. walk outputs same information as normal lists do but it is much more compact and easier to search through. It works like map and needs both .x and .f argument. Many times you want to plot multiple graphs that are part of list, this is the scenario where you can use walk to plot the graphs without printing the list elements.
## [[1]]
## [1] -10
##
## [[2]]
## [1] 1
##
## [[3]]
## [1] 10## [1] -10
## [1] 1
## [1] 10# Load the gap_split data
data(gap_split)
# Map over the first 10 elements of gap_split
plots <- map2(gap_split[1:10],
names(gap_split[1:10]),
~ ggplot(.x, aes(year, lifeExp)) +
geom_line() +
labs(title = .y))
# Object name, then function name
walk(plots, print)
Problem solving with purrr
Using purrr in your workflow
Setting Names using set_names function for unnamed lists.
#Setting Names
sw_films <- sw_films %>%
set_names(map_chr(sw_films,"title"))
# Sort by episode Id and set name
map_chr(sw_films, ~.x[["episode_id"]]) %>%
set_names(map_chr(sw_films,"title")) %>%
sort()## The Phantom Menace Attack of the Clones Revenge of the Sith
## "1" "2" "3"
## A New Hope The Empire Strikes Back Return of the Jedi
## "4" "5" "6"
## The Force Awakens
## "7"## NULL## [[1]]
## [1] "Gábor Csárdi"
##
## [[2]]
## [1] "Jennifer (Jenny) Bryan"
##
## [[3]]
## [1] "Jeff L."
##
## [[4]]
## [1] "Julia Silge"
##
## [[5]]
## [1] "Thomas J. Leeper"
##
## [[6]]
## [1] "Maëlle Salmon"# Name gh_users with the names of the users
gh_users_named <- gh_users %>%
set_names(map_chr(gh_users, "name"))
# Check gh_repos structure
# str(gh_repos)
# Name gh_repos with the names of the repo owner
gh_repos_named <- gh_repos %>%
map_chr(~ .[[1]]$owner$login) %>%
set_names(gh_repos, .)# Determine who joined github first
map_chr(gh_users, ~.x[["created_at"]]) %>%
set_names(map_chr(gh_users,"name")) %>%
sort()## Jennifer (Jenny) Bryan Gábor Csárdi Jeff L.
## "2011-02-03T22:37:41Z" "2011-03-09T17:29:25Z" "2012-03-24T18:16:43Z"
## Thomas J. Leeper Maëlle Salmon Julia Silge
## "2013-02-07T21:07:00Z" "2014-08-05T08:10:04Z" "2015-05-19T02:51:23Z"## [1] TRUE TRUE TRUE TRUE TRUE TRUE# Determine who has the most public repositories
map_int(gh_users, ~.x[["public_repos"]]) %>%
set_names(map_chr(gh_users, "name")) %>%
sort()## Julia Silge Maëlle Salmon Gábor Csárdi
## 26 31 52
## Jeff L. Thomas J. Leeper Jennifer (Jenny) Bryan
## 67 99 168Even more complex problems
Sometimes the problems you are trying to solve using lists are complex(ex. the variable name we are interested in is buried/nested inside multiple lists).
To extract data from a list that is inside a list we will use nested map function.
# Summary Statistics
# select_if will return only columns that match the criteria we put inside the function
# In the below example we are returning the columns that are numeric.
bird_measurements %>%
map_df(~ data_frame(
weight = .x[["weight"]],
wing_length = .x[["wing length"]],
taxa = "bird")) %>%
select_if(is.numeric) %>%
summary(.x)# Map over gh_repos to generate numeric output
map(gh_repos,
~map_dbl(.x,
~.x[["size"]])) %>%
# Grab the largest element
map(~max(.x))## [[1]]
## [1] 39461
##
## [[2]]
## [1] 96325
##
## [[3]]
## [1] 374812
##
## [[4]]
## [1] 24070
##
## [[5]]
## [1] 558176
##
## [[6]]
## [1] 76455Graphs in purrr
ggplot requires dataframe as input. Then add layers using + sign. geom_*() shows graph type. Since ggplot requires data frame as input but our data is often stored as list we can use map_df to transform the data from a list into a data frame we need.
We can pipe that dataframe directly into ggplot code without the need of the intermediate objects.
birddf <- bird_measurements %>%
map_df(~ data_frame(
wing_length = .x[["wing length"]],
weight = .x[["weight"]])) %>%
ggplot(aes(x = weight,
y = wing_length))+
geom_point()# Scatter plot of public repos and followers
ggplot(data = gh_users_df,
aes(x = public_repos, y = followers))+
geom_point()
# Histogram of followers
gh_users_df %>%
ggplot(aes(x = followers))+
geom_histogram()# Create a dataframe with four columns
map_df(gh_users, `[`,
c("login", "name", "followers", "public_repos")) %>%
# Plot followers by public_repos
ggplot(.,
aes(x = followers, y = public_repos)) +
# Create scatter plots
geom_point()
# Turn data into correct dataframe format
film_by_character <- tibble(filmtitle = map_chr(sw_films, "title")) %>%
mutate(filmtitle, characters = map(sw_films, "characters")) %>%
unnest()## Warning: `cols` is now required.
## Please use `cols = c(characters)`# Pull out elements from sw_people
sw_characters <- map_df(sw_people, `[`, c("height","mass","name","url"))
# Join the two new objects
character_data <- inner_join(film_by_character, sw_characters, by = c("characters" = "url")) %>%
# Make sure the columns are numbers
mutate(height = as.numeric(height), mass = as.numeric(mass))## Warning: NAs introduced by coercion## Warning: NAs introduced by coercion# Plot the heights, faceted by film title
ggplot(character_data, aes(x = height)) +
geom_histogram(stat = "count") +
facet_wrap(~ filmtitle)## Warning: Ignoring unknown parameters: binwidth, bins, pad## Warning: Removed 6 rows containing non-finite values (stat_count).
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