Guide to using GenForward data in R
GenForward data files are provided in the .dta file format, which is a format developed for the Stata software. Reading these files in R can be done easily using a number of packages available on CRAN including haven and readstata13.
For a step-by-step guide on how to read GenForward data files in R, see below or this PDF.
GenForward data files are provided in the Stata file format, with file extension .dta. There are multiple packages available to read Stata files. We will use the haven package.
Before you use the package for the first time, you will need to install it. You can do so using the code install.packages(“haven”).
Let’s load the package.
library(haven)
The function to read Stata files in the package is read_dta(). Let’s load an example file:
survey <- read_dta("genforward_2022-07_unembargoed.dta")
Let’s take a look at the imported data frame:
survey # A tibble: 4,201 × 204 PANEL_TYPE GENFRACE WEIGHT_TN Q1A Q1 Q2 Q3A Q3 <dbl+lbl> <dbl+lb> <dbl> <dbl+l> <dbl+lb> <dbl+lb> <dbl+lb> <dbl+lb> 1 21 [Non-proba… 2 [Whit… 1.05 1 [App… 1 [Ver… 2 [Som… 1 [Ver… 24 [Fre… 2 21 [Non-proba… 2 [Whit… 2.76 5 [Dis… 2 [Som… 4 [Ver… 1 [Ver… 11 [Eco… 3 21 [Non-proba… 2 [Whit… 3.27 1 [App… 1 [Ver… 1 [Ver… 1 [Ver… 26 [Too… 4 21 [Non-proba… 2 [Whit… 1.41 2 [App… 4 [Ver… 1 [Ver… 4 [Ver… 9 [Inc… 5 21 [Non-proba… 2 [Whit… 2.82 5 [Dis… 2 [Som… 4 [Ver… 1 [Ver… 11 [Eco… 6 21 [Non-proba… 2 [Whit… 0.354 1 [App… 3 [Som… 1 [Ver… 2 [Som… 5 [Gun… 7 21 [Non-proba… 2 [Whit… 3.18 5 [Dis… 1 [Ver… 4 [Ver… 1 [Ver… 21 [Ter… 8 21 [Non-proba… 2 [Whit… 2.98 5 [Dis… 98 [SKI… 98 [SKI… 3 [Som… 18 [Cri… 9 21 [Non-proba… 2 [Whit… 0.946 1 [App… 1 [Ver… 1 [Ver… 1 [Ver… 5 [Gun… 10 21 [Non-proba… 2 [Whit… 0.821 1 [App… 4 [Ver… 1 [Ver… 77 [Don… 1 [The… # ℹ 4,191 more rows # ℹ 196 more variables: Q4 <dbl+lbl>, Q5 <dbl+lbl>, Q6 <dbl+lbl>, Q7 <dbl+lbl>, # Q8 <dbl+lbl>, Q9 <dbl+lbl>, Q12 <dbl+lbl>, Q13 <dbl+lbl>, Q14 <dbl+lbl>, # Q15 <dbl+lbl>, Q16 <dbl+lbl>, Q17 <dbl+lbl>, Q18 <dbl+lbl>, Q19 <dbl+lbl>, # Q20 <dbl+lbl>, Q21 <dbl+lbl>, Q22 <dbl+lbl>, Q23 <dbl+lbl>, Q24 <dbl+lbl>, # Q25 <dbl+lbl>, Q25_OE <chr>, Q26 <dbl+lbl>, Q27 <dbl+lbl>, Q28_1 <dbl+lbl>, # Q28_2 <dbl+lbl>, Q28_3 <dbl+lbl>, Q28_4 <dbl+lbl>, Q28_5 <dbl+lbl>, …
You’ll notice that read_dta() imports non-numeric variables in a special class called labelled variables. This class stores the variable and value labels (the lbl part of dbl+lbl). To learn more about this format and some of its uses, read the documentation for read_dta() and labelled().
To convert labelled variables into the more familiar factor variables, we can use the as_factor() function. You can use this function on a single variable or the whole data frame.
as_factor(survey) # A tibble: 4,201 × 204 PANEL_TYPE GENFRACE WEIGHT_TN Q1A Q1 Q2 Q3A Q3 Q4 Q5 Q6 <fct> <fct> <dbl> <fct> <fct> <fct> <fct> <fct> <fct> <fct> <fct> 1 Non-proba… White, … 1.05 Appr… Very… Some… "Ver… Free… Off … Very… Neit… 2 Non-proba… White, … 2.76 Disa… Some… Very… "Ver… Econ… Off … Very… Some… 3 Non-proba… White, … 3.27 Appr… Very… Very… "Ver… Too … Off … Very… Very… 4 Non-proba… White, … 1.41 Appr… Very… Very… "Ver… Inco… Off … Neit… Some… 5 Non-proba… White, … 2.82 Disa… Some… Very… "Ver… Econ… Off … Very… Very… 6 Non-proba… White, … 0.354 Appr… Some… Very… "Som… Gun … Gene… Some… Very… 7 Non-proba… White, … 3.18 Disa… Very… Very… "Ver… Terr… Off … Some… Neit… 8 Non-proba… White, … 2.98 Disa… SKIP… SKIP… "Som… Crime Off … Neit… Neit… 9 Non-proba… White, … 0.946 Appr… Very… Very… "Ver… Gun … Off … Very… Very… 10 Non-proba… White, … 0.821 Appr… Very… Very… "Don… The … Gene… Very… Very… # ℹ 4,191 more rows # ℹ 193 more variables: Q7 <fct>, Q8 <fct>, Q9 <fct>, Q12 <fct>, Q13 <fct>, # Q14 <fct>, Q15 <fct>, Q16 <fct>, Q17 <fct>, Q18 <fct>, Q19 <fct>, # Q20 <fct>, Q21 <fct>, Q22 <fct>, Q23 <fct>, Q24 <fct>, Q25 <fct>, # Q25_OE <chr>, Q26 <fct>, Q27 <fct>, Q28_1 <fct>, Q28_2 <fct>, Q28_3 <fct>, # Q28_4 <fct>, Q28_5 <fct>, Q28_DK <fct>, Q28_SKP <fct>, Q28_REF <fct>, # Q29 <fct>, RND_01 <fct>, INS1_Q30 <fct>, INS2_Q30 <fct>, Q30 <fct>, …
Alternatively, you can convert all these labelled variables to numeric format using the zap_labels() function.
zap_labels(survey) # A tibble: 4,201 × 204 PANEL_TYPE GENFRACE WEIGHT_TN Q1A Q1 Q2 Q3A Q3 Q4 Q5 Q6 <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> 1 21 2 1.05 1 1 2 1 24 1 1 3 2 21 2 2.76 5 2 4 1 11 1 5 4 3 21 2 3.27 1 1 1 1 26 1 1 1 4 21 2 1.41 2 4 1 4 9 1 3 4 5 21 2 2.82 5 2 4 1 11 1 5 5 6 21 2 0.354 1 3 1 2 5 2 2 1 7 21 2 3.18 5 1 4 1 21 1 4 3 8 21 2 2.98 5 98 98 3 18 1 3 3 9 21 2 0.946 1 1 1 1 5 1 1 1 10 21 2 0.821 1 4 1 77 1 2 5 1 # ℹ 4,191 more rows # ℹ 193 more variables: Q7 <dbl>, Q8 <dbl>, Q9 <dbl>, Q12 <dbl>, Q13 <dbl>, # Q14 <dbl>, Q15 <dbl>, Q16 <dbl>, Q17 <dbl>, Q18 <dbl>, Q19 <dbl>, # Q20 <dbl>, Q21 <dbl>, Q22 <dbl>, Q23 <dbl>, Q24 <dbl>, Q25 <dbl>, # Q25_OE <chr>, Q26 <dbl>, Q27 <dbl>, Q28_1 <dbl>, Q28_2 <dbl>, Q28_3 <dbl>, # Q28_4 <dbl>, Q28_5 <dbl>, Q28_DK <dbl>, Q28_SKP <dbl>, Q28_REF <dbl>, # Q29 <dbl>, RND_01 <dbl>, INS1_Q30 <dbl>, INS2_Q30 <dbl>, Q30 <dbl>, …
For full variable and value labels, make sure to consult the codebook file provided alongside each GenForward dataset.