I have read in a Stata (dta) file into R and a snippet of the data looks like this:
short
# A tibble: 200 x 5
q4_1 q4_2 q4_3 q4_4 treatment_cur
<dbl+lbl> <dbl+lbl> <dbl+lbl> <dbl+lbl> <chr>
1 NA(z) NA(z) NA(z) NA(z) Control
2 NA(z) NA(z) NA(z) NA(z) Control
3 1 [1.Yes] 0 [0.No] 0 [0.No] 1 [1.Yes] Treatment
4 0 [0.No] 0 [0.No] 1 [1.Yes] 0 [0.No] Control
5 0 [0.No] 0 [0.No] 0 [0.No] 1 [1.Yes] Control
6 NA(z) NA(z) NA(z) NA(z) Control
7 1 [1.Yes] 1 [1.Yes] 1 [1.Yes] 1 [1.Yes] Control
8 NA(z) NA(z) NA(z) NA(z) Treatment
9 NA(z) NA(z) NA(z) NA(z) Control
10 0 [0.No] 0 [0.No] 1 [1.Yes] 0 [0.No] Control
The format of the variables is such:
str(short)
tibble [200 x 5] (S3: tbl_df/tbl/data.frame)
$ q4_1 : dbl+lbl [1:200] NA(z), NA(z), 1, 0, 0, NA(z), 1, NA(z), NA(z), 0, NA(z), 1, NA(z), 1, NA(z), 1, ...
..@ label : chr "q4_1r.Do you have any of ...assignments? Bilingual/ELL"
..@ format.stata: chr "%15.0g"
..@ labels : Named num [1:2] 0 1
.. ..- attr(*, "names")= chr [1:2] "0.No" "1.Yes"
$ q4_2 : dbl+lbl [1:200] NA(z), NA(z), 0, 0, 0, NA(z), 1, NA(z), NA(z), 0, NA(z), 0, NA(z), 0, NA(z), 0, ...
..@ label : chr "q4_2r.Do you have any of ...assignments? Sp Ed (self-c)"
..@ format.stata: chr "%34.0g"
..@ labels : Named num [1:2] 0 1
.. ..- attr(*, "names")= chr [1:2] "0.No" "1.Yes"
$ q4_3 : dbl+lbl [1:200] NA(z), NA(z), 0, 1, 0, NA(z), 1, NA(z), NA(z), 1, NA(z), 1, NA(z), 1, NA(z), 0, ...
..@ label : chr "q4_3r.Do you have any of ...assignments? Sp Ed (incl.)"
..@ format.stata: chr "%72.0g"
..@ labels : Named num [1:2] 0 1
.. ..- attr(*, "names")= chr [1:2] "0.No" "1.Yes"
$ q4_4 : dbl+lbl [1:200] NA(z), NA(z), 1, 0, 1, NA(z), 1, NA(z), NA(z), 0, NA(z), 1, NA(z), 0, NA(z), 0, ...
..@ label : chr "q4_4r.Do you have any of ...assignments? Gifted/Talented"
..@ format.stata: chr "%17.0g"
..@ labels : Named num [1:2] 0 1
.. ..- attr(*, "names")= chr [1:2] "0.No" "1.Yes"
$ treatment_cur: chr [1:200] "Control" "Control" "Treatment" "Control" ...
..- attr(*, "label")= chr "treatment_cur.treatment_cur"
..- attr(*, "format.stata")= chr "%9s"
This is the class of each variable:
> class(short$q4_1)
[1] "haven_labelled" "vctrs_vctr" "double"
I need to create descriptive tabulations of the data using tbl_summary from library(gtsummary)--which is a really cool package to create quick and customizable summary stats of the data.
The cool thing about my data is that each value already has a label associated with it. For example in q4_2, 0 is "No" and 1 is "Yes". So that when I feed the data into tbl_summary, instead of this showing up in the freq count:
q4_1 n
1 7
0 8
This can show up instead This is what I want:
"q4_1r.Do you have any of ...assignments? Bilingual/ELL"
n
No 7
Yes 8
This code is not working because tbl_summary only accepts certain formats.
tbl_summary(short)
Column(s) ‘q4_1’, ‘q4_2’, ‘q4_3’, and ‘q4_4’ omitted from output.
Accepted classes are ‘character’, ‘factor’, ‘numeric’, ‘logical’, ‘integer’, or ‘difftime’.
If I convert these variables into characters, they lose their value labels, and I only see the following, because converting it to a character makes the variable lose its label attributes.
q4_1 n
1 7
0 8
Are there any idea's for how I can work around this? I can't find an inbuilt R file that has this type of var format to make this more reproducible.
In the the case of the haven labelled class, it was never meant to be a class that was used in analysis or data exploration. Rather, it was created as an in-between when importing data from other languages where the data types don't have a one-to-one relationship with R. This is from a tidyverse blog post about the haven labelled class of variables. (https://haven.tidyverse.org/articles/semantics.html)
The goal of haven is not to provide a labelled vector that you can use everywhere in your analysis. The goal is to provide an intermediate data structure that you can convert into a regular R data frame.
To use tbl_summary() you'll first want to apply the as_factor() function on your imported data frame, e.g. haven::as_factor(short). This will convert your data frame to base R types, and retain the Stata value labels as factors.
FYI, we are making tbl_summary() compatible with all types, and in the next release of the package the as_factor() step will not be required. You can follow the progress of the implementation here: https://github.com/ddsjoberg/gtsummary/pull/603