The purrr package is a convenient tool for manipulating lists with functions. This assignment is a demonstration of how to use the functions in the purrr package.
Load the purrr library and the dplyr library as an assistant. We will use the college_all_ages data set from the fivethirtyeight package.
library(purrr)
library(fivethirtyeight)First, let look at the first few rows of the college_all_ages data set
head(college_all_ages)## major_code major
## 1 1100 General Agriculture
## 2 1101 Agriculture Production And Management
## 3 1102 Agricultural Economics
## 4 1103 Animal Sciences
## 5 1104 Food Science
## 6 1105 Plant Science And Agronomy
## major_category total employed
## 1 Agriculture & Natural Resources 128148 90245
## 2 Agriculture & Natural Resources 95326 76865
## 3 Agriculture & Natural Resources 33955 26321
## 4 Agriculture & Natural Resources 103549 81177
## 5 Agriculture & Natural Resources 24280 17281
## 6 Agriculture & Natural Resources 79409 63043
## employed_fulltime_yearround unemployed unemployment_rate p25th median
## 1 74078 2423 0.02614711 34000 50000
## 2 64240 2266 0.02863606 36000 54000
## 3 22810 821 0.03024832 40000 63000
## 4 64937 3619 0.04267890 30000 46000
## 5 12722 894 0.04918845 38500 62000
## 6 51077 2070 0.03179089 35000 50000
## p75th
## 1 80000
## 2 80000
## 3 98000
## 4 72000
## 5 90000
## 6 75000Extract the unemployment_rate column as a list
major_unemp <- college_all_ages$unemployment_rateThe set_names function is used to assign a name to each of the element in a list.
In this example, I assign the names by their corresponding majors
major_unemp <- set_names(major_unemp,college_all_ages$major)
head(major_unemp)## General Agriculture
## 0.02614711
## Agriculture Production And Management
## 0.02863606
## Agricultural Economics
## 0.03024832
## Animal Sciences
## 0.04267890
## Food Science
## 0.04918845
## Plant Science And Agronomy
## 0.03179089Section 1 - select particular elements from a list:
1.pluck() —- select an element by name or index.
For example: We can select the first element by
pluck(major_unemp, "General Agriculture")## [1] 0.02614711# or
pluck(major_unemp, 1)## [1] 0.026147112.keep() —- select all elements that pass a logicl test.
For example: We can select all elements whose value is greater than 0.05
res <- keep(major_unemp, function(x) x > 0.05)
head(res)## Soil Science Environmental Science
## 0.05086705 0.05128983
## Natural Resources Management Architecture
## 0.05434128 0.08599113
## Area Ethnic And Civilization Studies Communications
## 0.06793896 0.064360313.discard() —- select all elements that DO NOT pass a logicl test.
For example: We can select all elements whose value is NOT greater than 0.05
res <- discard(major_unemp, function(x) x > 0.05)
head(res)## General Agriculture
## 0.02614711
## Agriculture Production And Management
## 0.02863606
## Agricultural Economics
## 0.03024832
## Animal Sciences
## 0.04267890
## Food Science
## 0.04918845
## Plant Science And Agronomy
## 0.031790894.head_while() —- select all elements from the beginning of a list up to and exclude the element that does not pass a logical test.
For example: we can select all elements whose value is greater than 0.02 from the beginning of a list
res <- head_while(major_unemp, function(x) x > 0.02)
head(res)## General Agriculture
## 0.02614711
## Agriculture Production And Management
## 0.02863606
## Agricultural Economics
## 0.03024832
## Animal Sciences
## 0.04267890
## Food Science
## 0.04918845
## Plant Science And Agronomy
## 0.031790895.tail_while() —- select all elements from the end of a list up to and exclude the element that does not pass a logical test.
For example: we can select all elements whose value is greater than 0.05 from the end of a list
res <- tail_while(major_unemp, function(x) x > 0.05)
head(res)## Miscellaneous Business & Medical Administration
## 0.05267856
## History
## 0.06585101
## United States History
## 0.07349961Section 2 - logical operations:
1.every() —- Check if all elements in a list pass a logical test
For example: we can check if all elements are greater than 0
every(major_unemp,function(x) x > 0)## [1] FALSE2.some() —- Check if some elements in a list pass a logical test
For example: we can check if some elements are 0
some(major_unemp,function(x) x == 0)## [1] TRUE3.some() —- Check if a value is an element of a list
For example: we can check if 0 is in the list or not
has_element(major_unemp,0)## [1] TRUE4.detect() —- Find the first element that passes a logical test
For example: we can find the first element that is greater than 0.08
detect(major_unemp,function(x) x > 0.08)## [1] 0.085991135.detect_index() —- Find the index of the first element that passes a logical test
For example: we can find index of the first element that is greater than 0.08
detect_index(major_unemp,function(x) x > 0.08)## [1] 12Section 3 - applying function conditionally:
1.modify() —- Apply a function to each of the elements in a list
For example: multiply all values in the list by 100
res <- modify(major_unemp, function(x) x * 100)
head(res)## General Agriculture
## 2.614711
## Agriculture Production And Management
## 2.863606
## Agricultural Economics
## 3.024832
## Animal Sciences
## 4.267890
## Food Science
## 4.918845
## Plant Science And Agronomy
## 3.1790892.modify_at() —- Apply a function to an element by name or index
For exmaple: multiply the first element by 100
res <- modify_at(major_unemp,1, function(x) x * 100)
head(res)## General Agriculture
## 2.61471060
## Agriculture Production And Management
## 0.02863606
## Agricultural Economics
## 0.03024832
## Animal Sciences
## 0.04267890
## Food Science
## 0.04918845
## Plant Science And Agronomy
## 0.031790893.modify_if() —- Apply a function to elements that pass a logical test
For exmaple: multiply the elements that are greater than 0.05 by 100
res <- modify_if(major_unemp, function(x) x > 0.05, function(x) x *100)
tail(res)## International Business
## 7.13537080
## Hospitality Management
## 5.14469830
## Management Information Systems And Statistics
## 0.04397714
## Miscellaneous Business & Medical Administration
## 5.26785610
## History
## 6.58510060
## United States History
## 7.34996100Section 4 - lists combine:
1.append() —- Append another list to the end of a list
For example: Append the 3rd and 4th elements to the 1st and 2nd elements as a list
append(major_unemp[1:2], major_unemp[3:4])## General Agriculture
## 0.02614711
## Agriculture Production And Management
## 0.02863606
## Agricultural Economics
## 0.03024832
## Animal Sciences
## 0.042678902.prepend() —- Add another list to the beginning of a list
For example: Add the 3rd and 4th elements to the beginning of the 1st and 2nd elements as a list
prepend(major_unemp[1:2], major_unemp[3:4])## Agricultural Economics
## 0.03024832
## Animal Sciences
## 0.04267890
## General Agriculture
## 0.02614711
## Agriculture Production And Management
## 0.028636063.splice(): combine all lists and other elements into a list
For example: Combine the 1st and 2nd elements as a list, the 3rd and 4th elements as a list and a new element “0.123” into one list
splice(major_unemp[1:2], major_unemp[3:4], "new value" = "0.123")## [[1]]
## General Agriculture
## 0.02614711
## Agriculture Production And Management
## 0.02863606
##
## [[2]]
## Agricultural Economics Animal Sciences
## 0.03024832 0.04267890
##
## $`new value`
## [1] "0.123"Other functions:
cross2() —- Using elements from 2 lists, create a list of all possible combinations with 1 element for the first list and another element from the second list
For example: create all combinations by using the first 2 values from the major column and the first 2 values from the unemployment rate column
cross2(college_all_ages$major[1:2],college_all_ages$unemployment_rate[1:2])## [[1]]
## [[1]][[1]]
## [1] "General Agriculture"
##
## [[1]][[2]]
## [1] 0.02614711
##
##
## [[2]]
## [[2]][[1]]
## [1] "Agriculture Production And Management"
##
## [[2]][[2]]
## [1] 0.02614711
##
##
## [[3]]
## [[3]][[1]]
## [1] "General Agriculture"
##
## [[3]][[2]]
## [1] 0.02863606
##
##
## [[4]]
## [[4]][[1]]
## [1] "Agriculture Production And Management"
##
## [[4]][[2]]
## [1] 0.02863606array_tree() —- Turn an array into a list
class(major_unemp)## [1] "numeric"res <- array_tree(major_unemp)
class(res)## [1] "list"map2() —- apply a function to each pair of elements from 2 lists and return the results as a list
For example: we combine 1 element from the major_category list and a corresponding element from the unemployment_rate list into a list. The result will be a list of 2-element lists.
major_unemp_cate <- map2(college_all_ages$major_category, college_all_ages$unemployment_rate, function(x,y) list(x,y))
major_unemp_cate <- set_names(major_unemp_cate,college_all_ages$major)
head(major_unemp_cate)## $`General Agriculture`
## $`General Agriculture`[[1]]
## [1] "Agriculture & Natural Resources"
##
## $`General Agriculture`[[2]]
## [1] 0.02614711
##
##
## $`Agriculture Production And Management`
## $`Agriculture Production And Management`[[1]]
## [1] "Agriculture & Natural Resources"
##
## $`Agriculture Production And Management`[[2]]
## [1] 0.02863606
##
##
## $`Agricultural Economics`
## $`Agricultural Economics`[[1]]
## [1] "Agriculture & Natural Resources"
##
## $`Agricultural Economics`[[2]]
## [1] 0.03024832
##
##
## $`Animal Sciences`
## $`Animal Sciences`[[1]]
## [1] "Agriculture & Natural Resources"
##
## $`Animal Sciences`[[2]]
## [1] 0.0426789
##
##
## $`Food Science`
## $`Food Science`[[1]]
## [1] "Agriculture & Natural Resources"
##
## $`Food Science`[[2]]
## [1] 0.04918845
##
##
## $`Plant Science And Agronomy`
## $`Plant Science And Agronomy`[[1]]
## [1] "Agriculture & Natural Resources"
##
## $`Plant Science And Agronomy`[[2]]
## [1] 0.03179089invoke_map() —- given a list of functions and a list of elements of the same length, apply each of the function to a corresponding element. For example: we can multiply the first element by 100 and multiply the second element by -1
res <- invoke_map(list(function(x) x * 100, function(y) y * -1), major_unemp[1:2])
head(res)## [[1]]
## [1] 2.614711
##
## [[2]]
## [1] -0.02863606flatten() —- remove a level of indexes from a list
For example, we can flatten the just created list of 2-element lists into a single list
length(major_unemp_cate)## [1] 173res <-flatten(major_unemp_cate)
length(res)## [1] 346transpose() —- Transposes the index order in a multi-level list
For example, we can Transposes just created the list of 2-element lists
length(major_unemp_cate)## [1] 173res <- transpose(major_unemp_cate)
length(res)## [1] 2reduce() —- apply a functin recursively to each element of a list
For example: we can add up all elements in a list recursively
reduce(major_unemp, sum)## [1] 9.922493accumulate() —- the same as reduce(), but also return all intermediate results as a list
res <- accumulate(major_unemp, sum)
tail(res)## International Business
## 9.635040
## Hospitality Management
## 9.686487
## Management Information Systems And Statistics
## 9.730464
## Miscellaneous Business & Medical Administration
## 9.783143
## History
## 9.848994
## United States History
## 9.922493