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trs_admissions_survey2021
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fixed likert stacked bars

This commit is contained in:
Jeremy Kidwell 2021-12-13 10:31:37 +00:00
parent eb62ab07e5
commit 38da21b4b0
2 changed files with 191 additions and 18 deletions
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19
TODO.taskpaper
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@ -1,12 +1,12 @@
# Prep work (JK):
- JK: aggregation of free-text answers: Q17, Q18 so that they can be used for subsetting
- JK: aggregation of free-text answers: Q17, Q18 so that they can be used for subsetting @done
# Basic summary visualisations (RH):
- Q2 (respondent age)
- Q3 (year of study)
- Q16 (gender identity)
- Q17 (ethnic self-id)
- Q18 (religion)
- Q2 (respondent age) @done
- Q3 (year of study) @done
- Q16 (gender identity) @done
- Q17 (ethnic self-id) @done
- Q18 (religion) @done
# Visualisations of LIKERT responses (RH):
- For questions Q6 (subject interest) / Q5 (subject knowledge) / Q7 employability prospects:
@ -27,6 +27,13 @@
- Q17
- Q18
# Dataset corrections:
- typo in Interest_data$InterestinstudyingSociaology
- adapt Interest_data$InterestedinStudyingPsychology to be consistent and others
# Visualisation formatting:
- Add colour to bar charts with colorbrewer()
For later:
- JK: calcuate baseline comparator using UCAS application data for Q5/6, stack with UOB application data for key subjects + 2-3 other samples

190
tsr_survey_analysis.Rmd
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@ -16,7 +16,7 @@ library(RColorBrewer)
library("stringr") # Load stringr package, used for wrapping label text in plots
library(dplyr) # Used for filtering below
library(scales) # Used for adding percentages to bar charts
library(kable) # Used for generating markdown tables
# library(kable) # Used for generating markdown tables
# Define colour palettes for plots below
coul3 <- brewer.pal(3, "RdYlBu") # Using RdYlBu range to generate 3 colour palette: https://colorbrewer2.org/#type=diverging&scheme=RdYlBu&n=5
@ -25,9 +25,13 @@ TSR_data <- read.csv("./data/TSR data complete.csv")
subject_data <- read.csv("./data/Subject data.csv")
# Set up local workspace, as needed:
if (dir.exists("data") == FALSE) {
dir.create("data")
}
# These paths are excluded from github as it is best practice for end-user to generate their own
if (dir.exists("figures") == FALSE) {
dir.create("figures")
}
@ -109,9 +113,9 @@ TSR_data_rs_positive_summaries_ethnicity <- factor(TSR_data_rs_positive$Ethnicit
TSR_data_rs_positive_summaries_ethnicity <- str_wrap(TSR_data_rs_positive_summaries_ethnicity, width = 30)
TSR_data__theology_positive_summaries_gender <- factor(TSR_data_theology_positive$Gender, levels = c(1, 2, 3, 4), labels = c("Male", "Female", "I identify my gender in another way", "Prefer not to say"))
TSR_data_theology_positive_summaries_gender <- factor(TSR_data_theology_positive$Gender, levels = c(1, 2, 3, 4), labels = c("Male", "Female", "I identify my gender in another way", "Prefer not to say"))
# JK note: using stringr here to wrap axis titles
TSR_data__theology_positive_summaries_gender <- str_wrap(TSR_data__theology_positive_summaries_gender, width = 10)
TSR_data_theology_positive_summaries_gender <- str_wrap(TSR_data_theology_positive_summaries_gender, width = 10)
TSR_data_rs_positive_summaries_gender <- factor(TSR_data_rs_positive$Gender, levels = c(1, 2, 3, 4), labels = c("Male", "Female", "I identify my gender in another way", "Prefer not to say"))
# JK note: using stringr here to wrap axis titles
@ -180,14 +184,9 @@ ggsave("figures/TSR_data_summaries_ethnicity3.png")
- visualisation as summaries for all subjects LIKERT data as stacked bar chart (colours for bar segments from cool to warm)
```{r Visualization by Subject}
### Each Subject is a different column so will need to figure out how to code the columns together into one graph
# Higher score indicates less agreement...need to reverse score
## 1=5, 2=4, 3=3, 4=2, 5=1, 6=0 --- Not done yet. See what they look like without reverse scoring
## Files have been reverse scored - Higher score now indicates more agreement
#Q5 Subject Knowledge/Understanding
subject_data$Subject <- factor(subject_data$Subject, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), labels = c("Philosophy", "Sociology", "Psychology", "History", "Ethics", "Theology", "Religious Studies", "Politics", "English", "Math", "Computer Science", "Business"))
@ -216,17 +215,184 @@ employability_bar + labs(title = "please rate ... employability prospects", x =
# save to png file for reports
ggsave("figures/TSR_data_subject_employability.png")
### Categorical Graph ###
### Likert Stacked Bar Chart ###
#devtools::install_github('jbryer/likert')
#likert_test <- likert(subject_data)
# Take selection of data re: understanding
Understanding_data <- TSR_data[, 6:17]
# Convert each column to factors
Understanding_data$GoodunderstandingofPhilosophy = factor(Understanding_data$GoodunderstandingofPhilosophy,
ordered = TRUE,
levels = c("1", "2", "3", "4", "5")
)
Understanding_data$GoodunderstandingofSociology = factor(Understanding_data$GoodunderstandingofSociology,
ordered = TRUE,
levels = c("1", "2", "3", "4", "5")
)
Understanding_data$GoodunderstandingofPsychology = factor(Understanding_data$GoodunderstandingofPsychology,
ordered = TRUE,
levels = c("1", "2", "3", "4", "5")
)
Understanding_data$GoodunderstandingofHistory = factor(Understanding_data$GoodunderstandingofHistory,
ordered = TRUE,
levels = c("1", "2", "3", "4", "5")
)
Understanding_data$GoodunderstandingofEthics = factor(Understanding_data$GoodunderstandingofEthics,
ordered = TRUE,
levels = c("1", "2", "3", "4", "5")
)
Understanding_data$GoodunderstandingofTheology = factor(Understanding_data$GoodunderstandingofTheology,
ordered = TRUE,
levels = c("1", "2", "3", "4", "5")
)
Understanding_data$GoodunderstandingofReligiousStudies = factor(Understanding_data$GoodunderstandingofReligiousStudies,
ordered = TRUE,
levels = c("1", "2", "3", "4", "5")
)
Understanding_data$GoodunderstandingofPolitics = factor(Understanding_data$GoodunderstandingofPolitics,
ordered = TRUE,
levels = c("1", "2", "3", "4", "5")
)
Understanding_data$GoodunderstandingofEnglish = factor(Understanding_data$GoodunderstandingofEnglish,
ordered = TRUE,
levels = c("1", "2", "3", "4", "5")
)
Understanding_data$GoodunderstandingofMath = factor(Understanding_data$GoodunderstandingofMath,
ordered = TRUE,
levels = c("1", "2", "3", "4", "5")
)
Understanding_data$GoodunderstandingofComputerScience = factor(Understanding_data$GoodunderstandingofComputerScience,
ordered = TRUE,
levels = c("1", "2", "3", "4", "5")
)
Understanding_data$GoodunderstandingofBusiness = factor(Understanding_data$GoodunderstandingofBusiness,
ordered = TRUE,
levels = c("1", "2", "3", "4", "5")
)
names(Understanding_data) <- c("Philosophy", "Sociology", "Psychology", "History", "Ethics", "Theology", "Religious Studies", "Politics", "English", "Math", "Computer Science", "Business")
str(Understanding_data)
levels(Understanding_data)
summary(Understanding_data)
likert_test_understand <- likert(Understanding_data)
plot(likert_test_understand)
# save to png file for reports
ggsave("figures/understanding_likert.png")
Interest_data <- TSR_data[, 18:29]
# Convert each column to factors
Interest_data$InterestinstudyingPhilosophy = factor(Interest_data$InterestinstudyingPhilosophy,
ordered = TRUE,
levels = c("1", "2", "3", "4", "5")
)
Interest_data$InterestinstudyingSociaology = factor(Interest_data$InterestinstudyingSociaology,
ordered = TRUE,
levels = c("1", "2", "3", "4", "5")
)
Interest_data$InterestedinStudyingPsychology = factor(Interest_data$InterestedinStudyingPsychology,
ordered = TRUE,
levels = c("1", "2", "3", "4", "5")
)
Interest_data$InterestedinstudyingHistory = factor(Interest_data$InterestedinstudyingHistory,
ordered = TRUE,
levels = c("1", "2", "3", "4", "5")
)
Interest_data$InterestedinstudyingEthics = factor(Interest_data$InterestedinstudyingEthics,
ordered = TRUE,
levels = c("1", "2", "3", "4", "5")
)
Interest_data$InterestedinstudyingTheology = factor(Interest_data$InterestedinstudyingTheology,
ordered = TRUE,
levels = c("1", "2", "3", "4", "5")
)
Interest_data$InterestedinstudyingReligiousStudies = factor(Interest_data$InterestedinstudyingReligiousStudies,
ordered = TRUE,
levels = c("1", "2", "3", "4", "5")
)
Interest_data$InterestedinstudyingPolitics = factor(Interest_data$InterestedinstudyingPolitics,
ordered = TRUE,
levels = c("1", "2", "3", "4", "5")
)
Interest_data$InterestedinstudyingEnglish = factor(Interest_data$InterestedinstudyingEnglish,
ordered = TRUE,
levels = c("1", "2", "3", "4", "5")
)
Interest_data$InterestedinstudyingMath = factor(Interest_data$InterestedinstudyingMath,
ordered = TRUE,
levels = c("1", "2", "3", "4", "5")
)
Interest_data$InterestedinstudyingComputerScience = factor(Interest_data$InterestedinstudyingComputerScience,
ordered = TRUE,
levels = c("1", "2", "3", "4", "5")
)
Interest_data$InterestedinstudyingBusiness = factor(Interest_data$InterestedinstudyingBusiness,
ordered = TRUE,
levels = c("1", "2", "3", "4", "5")
)
names(Interest_data) <- c("Philosophy", "Sociology", "Psychology", "History", "Ethics", "Theology", "Religious Studies", "Politics", "English", "Math", "Computer Science", "Business")
likert_test_interest <- likert(Interest_data)
plot(likert_test_interest)
# save to png file for reports
ggsave("figures/interest_likert.png")
Employability_data <- TSR_data[, 30:41]
# Convert each column to factors
Employability_data$Philosophyemployability = factor(Employability_data$Philosophyemployability,
ordered = TRUE,
levels = c("1", "2", "3", "4", "5")
)
Employability_data$Sociologyemployability = factor(Employability_data$Sociologyemployability,
ordered = TRUE,
levels = c("1", "2", "3", "4", "5")
)
Employability_data$PsychologyEmployability = factor(Employability_data$PsychologyEmployability,
ordered = TRUE,
levels = c("1", "2", "3", "4", "5")
)
Employability_data$Historyemployability = factor(Employability_data$Historyemployability,
ordered = TRUE,
levels = c("1", "2", "3", "4", "5")
)
Employability_data$Ethicsemployability = factor(Employability_data$Ethicsemployability,
ordered = TRUE,
levels = c("1", "2", "3", "4", "5")
)
Employability_data$Theologyemployability = factor(Employability_data$Theologyemployability,
ordered = TRUE,
levels = c("1", "2", "3", "4", "5")
)
Employability_data$ReligiousStudiesemployability = factor(Employability_data$ReligiousStudiesemployability,
ordered = TRUE,
levels = c("1", "2", "3", "4", "5")
)
Employability_data$Politicsemployability = factor(Employability_data$Politicsemployability,
ordered = TRUE,
levels = c("1", "2", "3", "4", "5")
)
Employability_data$Englishemployability = factor(Employability_data$Englishemployability,
ordered = TRUE,
levels = c("1", "2", "3", "4", "5")
)
Employability_data$Mathemployability = factor(Employability_data$Mathemployability,
ordered = TRUE,
levels = c("1", "2", "3", "4", "5")
)
Employability_data$ComputerScienceemployability = factor(Employability_data$ComputerScienceemployability,
ordered = TRUE,
levels = c("1", "2", "3", "4", "5")
)
Employability_data$Businessemployability = factor(Employability_data$Businessemployability,
ordered = TRUE,
levels = c("1", "2", "3", "4", "5")
)
names(Employability_data) <- c("Philosophy", "Sociology", "Psychology", "History", "Ethics", "Theology", "Religious Studies", "Politics", "English", "Math", "Computer Science", "Business")
likert_test_employability <- likert(Employability_data)
plot(likert_test_employability)
# save to png file for reports
ggsave("figures/employability_likert.png")
```