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Analyzing a lot of datasets can be tedious. In my work, I often have to compute descriptive statistics, or plot some graphs for some variables for a lot of datasets. The variables in question have the same name accross the datasets but are measured for different years. As an example, imagine you have this situation:
data2000 <- mtcars data2001 <- mtcars
For the sake of argument, imagine that data2000 is data from a survey conducted in the year 2000 and data2001 is the same survey but conducted in the year 2001. For illustration purposes, I use the mtcars dataset, but I could have used any other example. In these sort of situations, the variables are named the same in both datasets. Now if I want to check the summary statistics of a variable, I might do it by running:
summary(data2000$cyl) summary(data2001$cyl)
but this can get quite tedious, especially if instead of only having two years of data, you have 20 years. Another possibility is to merge both datasets and then check the summary statistics of the variable of interest. But this might require a lot of preprocessing, and sometimes you really just want to do a quick check, or some dirty graphs. So you might be tempted to write a loop, which would require to put these two datasets in some kind of structure, such as a list:
list_data <- list("data2000" = data2000, "data2001" = data2001)
for (i in 1:2){
print(summary(list_data[[i]]$cyl))
}
## Min. 1st Qu. Median Mean 3rd Qu. Max.
4.000 4.000 6.000 6.188 8.000 8.000
Min. 1st Qu. Median Mean 3rd Qu. Max.
4.000 4.000 6.000 6.188 8.000 8.000
But this also might get tedious, especially if you want to do this for a lot of different variables, and want to use different functions than summary().
Another, simpler way of doing this, is to use purrr::map() or lapply(). But there is a catch though: how do we specify the column we want to work on? Let’s try some things out:
library(purrr) map(list_data, summary(cyl)) Error in summary(cyl) : object ‘cyl’ not found
Maybe this will work:
map(list_data, summary, cyl)
## $data2000
mpg cyl disp hp
Min. :10.40 Min. :4.000 Min. : 71.1 Min. : 52.0
1st Qu.:15.43 1st Qu.:4.000 1st Qu.:120.8 1st Qu.: 96.5
Median :19.20 Median :6.000 Median :196.3 Median :123.0
Mean :20.09 Mean :6.188 Mean :230.7 Mean :146.7
3rd Qu.:22.80 3rd Qu.:8.000 3rd Qu.:326.0 3rd Qu.:180.0
Max. :33.90 Max. :8.000 Max. :472.0 Max. :335.0
drat wt qsec vs
Min. :2.760 Min. :1.513 Min. :14.50 Min. :0.0000
1st Qu.:3.080 1st Qu.:2.581 1st Qu.:16.89 1st Qu.:0.0000
Median :3.695 Median :3.325 Median :17.71 Median :0.0000
Mean :3.597 Mean :3.217 Mean :17.85 Mean :0.4375
3rd Qu.:3.920 3rd Qu.:3.610 3rd Qu.:18.90 3rd Qu.:1.0000
Max. :4.930 Max. :5.424 Max. :22.90 Max. :1.0000
am gear carb
Min. :0.0000 Min. :3.000 Min. :1.000
1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:2.000
Median :0.0000 Median :4.000 Median :2.000
Mean :0.4062 Mean :3.688 Mean :2.812
3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:4.000
Max. :1.0000 Max. :5.000 Max. :8.000
data2001
mpg cyl disp hp
Min. :10.40 Min. :4.000 Min. : 71.1 Min. : 52.0
1st Qu.:15.43 1st Qu.:4.000 1st Qu.:120.8 1st Qu.: 96.5
Median :19.20 Median :6.000 Median :196.3 Median :123.0
Mean :20.09 Mean :6.188 Mean :230.7 Mean :146.7
3rd Qu.:22.80 3rd Qu.:8.000 3rd Qu.:326.0 3rd Qu.:180.0
Max. :33.90 Max. :8.000 Max. :472.0 Max. :335.0
drat wt qsec vs
Min. :2.760 Min. :1.513 Min. :14.50 Min. :0.0000
1st Qu.:3.080 1st Qu.:2.581 1st Qu.:16.89 1st Qu.:0.0000
Median :3.695 Median :3.325 Median :17.71 Median :0.0000
Mean :3.597 Mean :3.217 Mean :17.85 Mean :0.4375
3rd Qu.:3.920 3rd Qu.:3.610 3rd Qu.:18.90 3rd Qu.:1.0000
Max. :4.930 Max. :5.424 Max. :22.90 Max. :1.0000
am gear carb
Min. :0.0000 Min. :3.000 Min. :1.000
1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:2.000
Median :0.0000 Median :4.000 Median :2.000
Mean :0.4062 Mean :3.688 Mean :2.812
3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:4.000
Max. :1.0000 Max. :5.000 Max. :8.000
Not quite! You get the summary statistics of every variable, cyl simply gets ignored. This might be ok in our small toy example, but if you have dozens of datasets with hundreds of variables, the output becomes unreadable. The solution is to use an anonymous functions:
map(list_data, (function(x) summary(x$cyl))) ## $data2000 Min. 1st Qu. Median Mean 3rd Qu. Max. 4.000 4.000 6.000 6.188 8.000 8.000 $data2001 Min. 1st Qu. Median Mean 3rd Qu. Max. 4.000 4.000 6.000 6.188 8.000 8.000
This is, in my opinion, much more readable than a loop, and the output of this is another list, so it’s easy to save it:
summary_cyl <- map(list_data, (function(x) summary(x$cyl))) str(summary_cyl) ## List of 2 $ data2000:Classes ‘summaryDefault’, ‘table’ Named num [1:6] 4 4 6 6.19 8 … .. ..- attr(, "names")= chr [1:6] "Min." "1st Qu." "Median" "Mean" … $ data2001:Classes ‘summaryDefault’, ‘table’ Named num [1:6] 4 4 6 6.19 8 … .. ..- attr(, "names")= chr [1:6] "Min." "1st Qu." "Median" "Mean" …
With the loop, you would need to “allocate” an empty list that you would fill at each iteration.
So this is already nice, but wouldn’t it be nicer to simply have to type:
summary(list_data$cyl)
and have the summary of variable cyl for each dataset in the list? Well it is possible with the following function I wrote to make my life easier:
to_map <- function(func){
function(list, column, …){
if(missing(column)){
res <- purrr::map(list, (function(x) func(x, …)))
} else {
res <- purrr::map(list, (function(x) func(x[column], …)))
}
res
}
}
By following this chapter of Hadley Wickham’s book, Advanced R, I was able to write this function. What does it do? It basically generalizes a function to work on a list of datasets instead of just on a dataset. So for example, in the case of summary():
summarymap <- to_map(summary)
summarymap(list_data, "cyl")
$data2000
cyl
Min. :4.000
1st Qu.:4.000
Median :6.000
Mean :6.188
3rd Qu.:8.000
Max. :8.000
$data2001
cyl
Min. :4.000
1st Qu.:4.000
Median :6.000
Mean :6.188
3rd Qu.:8.000
Max. :8.000
So now everytime I want to have summary statistics for a variable, I just need to use summarymap():
summarymap(list_data, "mpg")
## $data2000
mpg
Min. :10.40
1st Qu.:15.43
Median :19.20
Mean :20.09
3rd Qu.:22.80
Max. :33.90
$data2001
mpg
Min. :10.40
1st Qu.:15.43
Median :19.20
Mean :20.09
3rd Qu.:22.80
Max. :33.90
If I want the summary statistics for every variable, I simply omit the column name:
summarymap(list_data)
$data2000
mpg cyl disp hp
Min. :10.40 Min. :4.000 Min. : 71.1 Min. : 52.0
1st Qu.:15.43 1st Qu.:4.000 1st Qu.:120.8 1st Qu.: 96.5
Median :19.20 Median :6.000 Median :196.3 Median :123.0
Mean :20.09 Mean :6.188 Mean :230.7 Mean :146.7
3rd Qu.:22.80 3rd Qu.:8.000 3rd Qu.:326.0 3rd Qu.:180.0
Max. :33.90 Max. :8.000 Max. :472.0 Max. :335.0
drat wt qsec vs
Min. :2.760 Min. :1.513 Min. :14.50 Min. :0.0000
1st Qu.:3.080 1st Qu.:2.581 1st Qu.:16.89 1st Qu.:0.0000
Median :3.695 Median :3.325 Median :17.71 Median :0.0000
Mean :3.597 Mean :3.217 Mean :17.85 Mean :0.4375
3rd Qu.:3.920 3rd Qu.:3.610 3rd Qu.:18.90 3rd Qu.:1.0000
Max. :4.930 Max. :5.424 Max. :22.90 Max. :1.0000
am gear carb
Min. :0.0000 Min. :3.000 Min. :1.000
1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:2.000
Median :0.0000 Median :4.000 Median :2.000
Mean :0.4062 Mean :3.688 Mean :2.812
3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:4.000
Max. :1.0000 Max. :5.000 Max. :8.000
$data2001
mpg cyl disp hp
Min. :10.40 Min. :4.000 Min. : 71.1 Min. : 52.0
1st Qu.:15.43 1st Qu.:4.000 1st Qu.:120.8 1st Qu.: 96.5
Median :19.20 Median :6.000 Median :196.3 Median :123.0
Mean :20.09 Mean :6.188 Mean :230.7 Mean :146.7
3rd Qu.:22.80 3rd Qu.:8.000 3rd Qu.:326.0 3rd Qu.:180.0
Max. :33.90 Max. :8.000 Max. :472.0 Max. :335.0
drat wt qsec vs
Min. :2.760 Min. :1.513 Min. :14.50 Min. :0.0000
1st Qu.:3.080 1st Qu.:2.581 1st Qu.:16.89 1st Qu.:0.0000
Median :3.695 Median :3.325 Median :17.71 Median :0.0000
Mean :3.597 Mean :3.217 Mean :17.85 Mean :0.4375
3rd Qu.:3.920 3rd Qu.:3.610 3rd Qu.:18.90 3rd Qu.:1.0000
Max. :4.930 Max. :5.424 Max. :22.90 Max. :1.0000
am gear carb
Min. :0.0000 Min. :3.000 Min. :1.000
1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:2.000
Median :0.0000 Median :4.000 Median :2.000
Mean :0.4062 Mean :3.688 Mean :2.812
3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:4.000
Max. :1.0000 Max. :5.000 Max. :8.000
I can use any function:
tablemap <- to_map(table) tablemap(list_data, "cyl") ## $data2000 4 6 8 11 7 14 $data2001 4 6 8 11 7 14 tablemap(list_data, "mpg") ## $data2000 10.4 13.3 14.3 14.7 15 15.2 15.5 15.8 16.4 17.3 17.8 18.1 18.7 19.2 19.7 2 1 1 1 1 2 1 1 1 1 1 1 1 2 1 21 21.4 21.5 22.8 24.4 26 27.3 30.4 32.4 33.9 2 2 1 2 1 1 1 2 1 1 $data2001 10.4 13.3 14.3 14.7 15 15.2 15.5 15.8 16.4 17.3 17.8 18.1 18.7 19.2 19.7 2 1 1 1 1 2 1 1 1 1 1 1 1 2 1 21 21.4 21.5 22.8 24.4 26 27.3 30.4 32.4 33.9 2 2 1 2 1 1 1 2 1 1
I hope you will find this little function useful, and as usual, for any comments just drop me an email by clicking the red enveloppe in the top right corner or tweet me.