I'm struggling with ways to efficiently turn labelled variables into factors. The dataset I'm working with is available from here: [https://www.dropbox.com/s/jhp780hd0ii3dnj/out.sav?dl=0][1]. It was an spss data file, which I like to use because of what my colleagues use.
When I read in the data, you can see that every single factor from the file is turned into a "labelled" class.
#load libraries
library(haven)
library(tidy)
library(dplyr)
#Import
test<-read_sav(path='~/your/path/name/out.sav')
#Structure
str(test)
#Find Class
sapply(test, class)
The first problem that I have is that ggplot2 doesn't know how to apply a scale to a labelled class.
#
td<-ford %>%
select(income, stress) %>%
group_by(income, stress)%>%
filter(is.na(stress)==FALSE)%>%
filter(is.na(income)==FALSE)%>%
summarize(Freq=n())%>%
mutate(Percent=(Freq/sum(Freq))*100)
#Draw plot
ggplot(td, aes(x=income, y=Percent, group=stress))+
#barplot
geom_bar(aes(fill=stress), stat='identity')
That can be solved quite nicely by wrapping the categorical variable 'income' in as_factor()
#Draw plot
ggplot(td, aes(x=as_ford(income), y=Percent, group=stress))+
#barplot
geom_bar(aes(fill=stress), stat='identity')
That works of rone variable, however, If I'm doing exploratory research , I may be doing a lot of plots with a lot of labelled variables. That strikes me as quite a lot of extra typing.
This problem is magnified with the problem of that when you gather a lot of variables to plot several crosstabs, you lose the value labels.
##Visualizations
test<-ford %>%
#The first two variables are the grouping, variables for a series of cross tabs
select(ford, stress,resp_gender, immigrant2, education, property, commute, cars, religion) %>%
#Some renamings
rename(gender=resp_gender, educ=education, immigrant=immigrant2, relig=religion)%>%
#Melt all variables other than ford and stress
gather(variable, category, -ford, -stress)%>%
#Group by all variables
group_by(variable, category, ford, stress) %>%
#filter out missings
filter(is.na(stress)==FALSE&is.na(ford)==FALSE)%>%
#filter out missings
filter(is.na(value)==FALSE)%>%
#summarize
summarize(freq=n())
#Show plots
ggplot(test, aes(x=as_factor(value), y=freq, group=as_factor(ford)))+geom_bar(stat='identity',position='dodge', aes(fill=as_factor(ford)))+facet_grid(~category, scales='free')
So, now all of the value labels for the variables that were melted have disappeared. So, the only way that I can see to prevent this is to individually use as_factor() to turn each labelled variable to a factor with the value labels as the factor levels. But, again, that is a lot of typing.
I guess my question is how to most efficiently to deal with the labelled class, turning them into factors, specifically as regards to ggplot2.
It's been a while, and the answers are already there in the comments, but I'll post an answer using dplyr anyways.
library(haven)
# Load Stata file and look at it
nlsw88 <- read_dta('http://www.stata-press.com/data/r15/nlsw88.dta')
head(nlsw88)
We see that there are some labelled variables. If we only want to convert specific variables, we can use mutate_at from dplyr.
# Convert specific variables to factor
nlsw88 %>%
mutate_at(
vars('race'),
funs(as_factor(.))
) %>%
head()
Along Gregor's and aosmith's comments we can also convert all labelled variables using the mutate_if function, testing for the labelled class. This will save you a lot of extra typing.
# Convert all labelled variables to factor
nlsw88 %>%
mutate_if(
is.labelled,
funs(as_factor(.))
) %>%
head()
This can be used to create bar plots similar to what you described (although this particular plot might not make much sense):
nlsw88 %>%
select(race, married, collgrad, union) %>%
mutate_if(
is.labelled,
funs(as_factor(.))
) %>%
gather(variable, category, -c(race, married)) %>%
group_by(race, married, variable, category) %>%
summarise(freq = n()) %>%
filter(!is.na(category)) %>%
ggplot(aes(x = category, y = freq)) +
geom_bar(stat = 'identity', aes(fill=race)) +
facet_grid(~married)