Writing a Plot Function

Set-up Work

We’ll use the following packages:

library(dplyr)
library(ggplot2)

Here is our data:

Dataset <- readxl::read_excel("Dataset.xlsx")

Make Factors:

Dataset$Participant <- factor(Dataset$Participant)
Dataset$Colleges <- factor(Dataset$Colleges)
Dataset$Department <- factor(Dataset$Department)
Dataset$Class <- factor(Dataset$Class)
Dataset$Major <- factor(Dataset$Major)
Dataset$Number <- factor(Dataset$Number)
Dataset$Level <- factor(Dataset$Level)
Dataset$Textbook <- factor(Dataset$Textbook)
Dataset$BookChapter <- factor(Dataset$BookChapter)
Dataset$JournalArticle <- factor(Dataset$JournalArticle)
Dataset$Condition <- factor(Dataset$Condition)
Dataset$Question <- factor(Dataset$Question)
Dataset$Reading <- factor(Dataset$Reading)
Dataset$ReadingTypes <- factor(Dataset$ReadingTypes)
Dataset$Behavior <- factor(Dataset$Behavior)

Dataset$Question <- relevel(Dataset$Question, "Useful")
Dataset$Reading <- relevel(Dataset$Reading, "Applying")
Dataset$Reading <- relevel(Dataset$Reading, "Synthesizing")
Dataset$Reading <- relevel(Dataset$Reading, "Elaborating")
Dataset$Reading <- relevel(Dataset$Reading, "Summarizing")
Dataset$Reading <- relevel(Dataset$Reading, "Reviewing")
Dataset$Reading <- relevel(Dataset$Reading, "Previewing")
Dataset$ReadingTypes <- relevel(Dataset$ReadingTypes, "SynthesizingApplying")
Dataset$ReadingTypes <- relevel(Dataset$ReadingTypes, "SummarizingElaborating")
Dataset$ReadingTypes <- relevel(Dataset$ReadingTypes, "PreviewingReviewing")

Dataset$College <- factor(Dataset$College)
Dataset$College <- relevel(Dataset$College, "JusticeSafety")
Dataset$College <- relevel(Dataset$College, "Education")
Dataset$College <- relevel(Dataset$College, "CLASS")
Dataset$College <- relevel(Dataset$College, "HealthSciences")
Dataset$College <- relevel(Dataset$College, "Science")
Dataset$College <- relevel(Dataset$College, "BusinessTechnology")

HSW: Here’s an easier way to re-order the levels of a factor:

levels(Dataset$Reading) <-
    c(
      "Useful",
      "Applying",
      "Synthesizing",
      "Summarizing",
      "Reviewing",
      "Previewing"
    )

A Bar Plot

Useful <- filter(Dataset, Question == "Useful")

Code for the bar plot:

level <- 0.95
summaryData <-
  Useful %>% 
  group_by(Reading, College) %>% 
  summarize(
    meanRating = mean(Rating),
    lower = mean(Rating) - qnorm(1 - level / 2) * sd(Rating),
    upper = mean(Rating) + qnorm(1 - level / 2) * sd(Rating)
  )
summaryData %>% 
  ggplot(aes(x = Reading, y = meanRating, fill = Reading)) +
  geom_bar(stat = "identity") +
  geom_errorbar(aes(ymin = lower, ymax = upper), width = 0.5) +
  labs(y = "Rating", x = "") +
  facet_wrap(vars(College), nrow =3) +
  theme(axis.text.x=element_blank())

A Bar Plot Function

The following is based on a tutorial:

Programming with dplyr

(It’s also available as a Vignette in the dplyr package.)

makePlot <- function(data, grouper, response, facetter, level = 0.95) {
  grouper <- enquo(grouper)
  response <- enquo(response)
  facetter <- enquo(facetter)
  response_name <- quo_name(response)
  level <- 0.95
  summaryData <-
    data %>% 
    group_by(!! grouper, !! facetter) %>% 
  summarize(
    meanResponse = mean(!! response),
    lower = mean(!! response) - qnorm(1 - level / 2) * sd(!! response),
    upper = mean(!! response) + qnorm(1 - level / 2) * sd(!! response)
    )
  
summaryData %>%
  ggplot(aes(x = !! grouper, y = meanResponse, fill = !! grouper)) +
  geom_bar(stat = "identity") +
  geom_errorbar(aes(ymin = lower, ymax = upper), width = 0.5) +
  labs(y = response_name, x = "") +
  facet_wrap(vars(!! facetter), nrow = 3) +
  theme(axis.text.x=element_blank())
}

Let’s try it:

makePlot(data = Useful, grouper = Reading, response = Rating, facetter = College)