So how could I present these results? One idea I had was a stacked bar chart, and it took a bit of data wrangling to do it. That is, the rankings were all in separate variables, but I want them all on the same chart. Basically, I needed to create a dataset with:
- 1 variable to represent the ranking given (1-5, or 6 that I called “Not Ranked”)
- 1 variable to represent the number of people giving that particular rank that particular factor
1 variable to represent the factor being ranked
What I ultimately did was run frequencies for the factor variables, turn those frequency tables into data frames, and merged them together with rbind. I then created chart with ggplot. Here’s some code for a simplified example, which only uses 6 factors and asks people to rank the top 3.
First, let’s read in our sample dataset – note that these data were generated only for this example and are not real data:
## Warning: package 'ggplot2' was built under R version 3.5.1
ranks <- read_csv("C:/Users/slocatelli/Desktop/sample_ranks.csv", col_names = TRUE)
This dataset contains 7 variables – 1 respondent ID and 6 variables with ranks on factors considered important in a job: salary, recognition from employer, paid time off, insurance benefits, flexible scheduling, and opportunity to learn. I want to run frequencies for these variables, and turn those frequency tables into a data frame I can use in ggplot2. I’m sure there are much cleaner ways to do this (and please share in the comments!), but here’s one not so pretty way:
salary <- as.data.frame(table(ranks$Salary))
salary$Name <- "Salary"
recognition <- as.data.frame(table(ranks$Recognition))
recognition$Name <- "Recognition by \nEmployer"
PTO <- as.data.frame(table(ranks$PTO))
PTO$Name <- "Paid Time Off"
insurance <- as.data.frame(table(ranks$Insurance))
insurance$Name <- "Insurance"
flexible <- as.data.frame(table(ranks$FlexibleHours))
flexible$Name <- "Flexible Schedule"
learn <- as.data.frame(table(ranks$OptoLearn))
learn$Name <- "Opportunity to \nLearn"
rank_chart <- rbind(salary, recognition, PTO, insurance, flexible, learn)
rank_chart$Var1 <- as.numeric(rank_chart$Var1)
With my not-so-pretty data wrangling, the chart itself is actually pretty easy:
ggplot(rank_chart, aes(fill = Var1, y = Freq, x = Name)) +
geom_bar(stat = "identity") +
labs(title = "Ranking of Factors Most Important in a Job") +
xlab("Job Factors") +
scale_fill_continuous(name = "Ranking",
breaks = c(1:4),
labels = c("1","2","3","Not Ranked")) +
Based on this chart, we can see the top factor is Salary. Insurance is slightly more important than paid time off, but these are definitely the top 2 and 3 factors. Recognition wasn’t ranked by most people, but those who did considered it their #2 factor; ditto for flexible scheduling at #3. Opportunity to learn didn’t make the top 3 for most respondents.