Questionnaires in the social sciences often include rating items to measure the variability of peoples´ attitudes towards something. Respondents are given a statement and have to report how much they agree or disagree on a 5- or 7-point-scale. A set of rating-items like these can be combined to a likert-scale. It´s also common to build an index-value for the respondents, if the items meet certain criteria of quality. There´s still some controversy, if it´s adequate to use ranked (ordinal) data like likert-items to calculate means. Most researchers think it´s approriate if the scale has at least 5 points and the variable can be considered as an ordinal measure of a continuous attitude.
Anyway. Visualization of the data ist always a good starting point. For this purpose, there are a lot of R-Packages like the HH-Package with its Likert-Function, or the likert-package from Jason Bryer and last, but not least: The sjp.likert-Function from Daniel Lüdecke, which would be my favourite.
All these packages produce sophisticated and very appealing plots. Under its hood, the HH-package uses lattice and the likert and sjPlot package are build on ggplot2. I tried HH-package, but as a ggplot2-user i realized, it would take me too long to figure out the little details. The other two packages could do what i want, but they both need raw-data (SPSS-like) and can´t work with already aggregated data. Both also have distinct kinds of dealing with the „neutral“-category of the items.
Long story short, i decided to use ggplot2 directly instead of using packages build on ggplot2 that have developed a lot of complexity on their own.
This plot is a small example. If the code seems too messy to you, or you think the plot can be improved: i´m always interested in how to make things better, please leave a comment.
For example, one could criticize, that the x-axis isn´t meaningful, because of the neutral-category should not be splitted in negative/positive like this. So perhaps, the vertical line and the x-axis-labels should be removed. On the other hand, the HH-Plot likert-function does it the same way. It would be possible to add percentage-values inside the stacked bars, but i think that would be too much. I decided, to make a stacked-frequency table with the sjPlot-Package to complement my likert-plot.
And this is the code, i´ve written:
library("plyr") library("dplyr") library("ggplot2") # example data Variable<-c("1","1","1","1","1","2","2","2","2","2","3","3","3","3","3","4","4","4","4","4") level<-c(5,4,3,2,1,5,4,3,2,1,5,4,3,2,1,5,4,3,2,1) perc_w<-c(3.70,11.80,10.10,25.80,38.60,2.00,16.90,13.25,28.80,25.80,1.80,6.50,9.35,33.60,39.40,3.50,12.40,14.10,34.80,21.10) df<-data.frame(Variable,level,perc_w) df$perc_w<-as.numeric(df$perc_w) df$level<-as.factor(df$level) # item text items<-c("~ It´s not known, if climate change is real", "~ In my opinion, the risks of climate change are exaggerated by activists", "~ Climate change is not as dangerous as it is claimed", "~ I´m convinced that we can handle climate change") df$Variable<-as.character(df$Variable) df$Variable[df$Variable==1]<-items df$Variable[df$Variable==2]<-items df$Variable[df$Variable==3]<-items df$Variable[df$Variable==4]<-items df$Variable<-as.ordered(df$Variable) # calculate halves of the neutral category df.split <-df %>% filter(level==3) %>% mutate(perc_w=as.numeric(perc_w/2)) # replace old neutral-category df<-df %>% filter(!level==3) df<-full_join(df,df.split) %>% arrange(level) %>% arrange(desc(Variable)) #split dataframe df1<-df %>% filter(level == 3 | level== 2 | level==1) df2<-df %>% filter(level == 5 | level== 4 | level==3) %>% mutate(perc_w = perc_w *-1) # automatic line break df1$Variable <-str_wrap(df1$Variable, width = 41) df2$Variable <-str_wrap(df2$Variable, width = 41) # reorder factor "Variable" df1$Variable <- factor(df1$Variable, levels=rev(unique(df1$Variable))) df2$Variable <- factor(df2$Variable, levels=rev(unique(df2$Variable))) #Plot p<-ggplot() + geom_bar(data=df1, aes(x = Variable, y=perc_w, fill = level, order = -as.numeric(level)),position="stack", stat="identity") + geom_bar(data=df2, aes(x = Variable, y=perc_w, fill = level, order = as.numeric(level)),position="stack", stat="identity") + geom_hline(yintercept = 0, color =c("black"))+ theme_bw() + coord_flip() + guides(fill=guide_legend(title="",reverse=TRUE)) + scale_fill_brewer(palette="Blues", name="",labels=c("--","-","0","+","++")) + labs(title=expression(atop(bold("Attitudes towards climate change"), atop(italic("Some roughly translated items"),""))), y="percentages",x="") + theme(legend.position="top", axis.ticks = element_blank(), plot.title = element_text(size=25), axis.title.y=element_text(size=16), axis.text.y=element_text(size=13), axis.title.x=element_text(size=16), axis.text.x=element_text(size=13), legend.title=element_text(size=14), legend.text=element_text(size=12) ) p