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Last week I showed how to read a lot of datasets at once with R, and this week I’ll continue from there and show a very simple function that uses this list of read datasets and merges them all together.

First we’ll use read_list() to read all the datasets at once (for more details read last week’s post):

library("readr")
library("tibble")

data_files <- list.files(pattern = ".csv")

print(data_files)
## [1] "data_1.csv" "data_2.csv" "data_3.csv"

glimpse(list_of_data_sets)
## List of 3
##  $data_1:Classes 'tbl_df', 'tbl' and 'data.frame': 19 obs. of 3 variables: ## ..$ col1: chr [1:19] "0,018930679" "0,8748013128" "0,1025635934" "0,6246140983" ...
##   ..$col2: chr [1:19] "0,0377725807" "0,5959457638" "0,4429121533" "0,558387159" ... ## ..$ col3: chr [1:19] "0,6241767189" "0,031324594" "0,2238059868" "0,2773350732" ...
##  $data_2:Classes 'tbl_df', 'tbl' and 'data.frame': 19 obs. of 3 variables: ## ..$ col1: chr [1:19] "0,9098418493" "0,1127788509" "0,5818891392" "0,1011773532" ...
##   ..$col2: chr [1:19] "0,7455905887" "0,4015039612" "0,6625796605" "0,029955339" ... ## ..$ col3: chr [1:19] "0,327232932" "0,2784035673" "0,8092386735" "0,1216045306" ...
##  $data_3:Classes 'tbl_df', 'tbl' and 'data.frame': 19 obs. of 3 variables: ## ..$ col1: chr [1:19] "0,9236124896" "0,6303271761" "0,6413583054" "0,5573887416" ...
##   ..$col2: chr [1:19] "0,2114708388" "0,6984538266" "0,0469865249" "0,9271510226" ... ## ..$ col3: chr [1:19] "0,4941919971" "0,7391538511" "0,3876723797" "0,2815014394" ...

You see that all these datasets have the same column names. We can now merge them using this simple function:

multi_join <- function(list_of_loaded_data, join_func, ...){

require("dplyr")

output <- Reduce(function(x, y) {join_func(x, y, ...)}, list_of_loaded_data)

return(output)
}

This function uses Reduce(). Reduce() is a very important function that can be found in all functional programming languages. What does Reduce() do? Let’s take a look at the following example:

Reduce(+, c(1, 2, 3, 4, 5))
## [1] 15

Reduce() has several arguments, but you need to specify at least two: a function, here + and a list, here c(1, 2, 3, 4, 5). The next code block shows what Reduce() basically does:

0 + c(1, 2, 3, 4, 5)
0 + 1 + c(2, 3, 4, 5)
0 + 1 + 2 + c(3, 4, 5)
0 + 1 + 2 + 3 + c(4, 5)
0 + 1 + 2 + 3 + 4 + c(5)
0 + 1 + 2 + 3 + 4 + 5

0 had to be added as in “init”. You can also specify this “init” to Reduce():

Reduce(+, c(1, 2, 3, 4, 5), init = 20)
## [1] 35

So what multi_join() does, is the same operation as in the example above, but where the function is a user supplied join or merge function, and the list of datasets is the one read with read_list().

Let’s see what happens when we use multi_join() on our list:

merged_data <- multi_join(list_of_data_sets, full_join)
class(merged_data)
## [1] "tbl_df"     "tbl"        "data.frame"
glimpse(merged_data)
## Observations: 57
## Variables: 3
## $col1 "0,018930679", "0,8748013128", "0,1025635934", "0,6246140... ##$ col2  "0,0377725807", "0,5959457638", "0,4429121533", "0,558387...
## \$ col3  "0,6241767189", "0,031324594", "0,2238059868", "0,2773350...

You should make sure that all the data frames have the same column names but you can also join data frames with different column names if you give the argument by to the join function. This is possible thanks to ... that allows you to pass further argument to join_func().

This function was inspired by the one found on the blog Coffee and Econometrics in the Morning.