From 985a64cd6ac0b6cae5333bc090c49e32c94df80e Mon Sep 17 00:00:00 2001 From: Jeremy Kidwell Date: Sun, 12 Dec 2021 22:43:56 +0000 Subject: [PATCH] tidied code, added graphs and labels and tweaked key subjects ranking --- tsr_survey_analysis.Rmd | 799 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 799 insertions(+) create mode 100644 tsr_survey_analysis.Rmd diff --git a/tsr_survey_analysis.Rmd b/tsr_survey_analysis.Rmd new file mode 100644 index 0000000..24d9063 --- /dev/null +++ b/tsr_survey_analysis.Rmd @@ -0,0 +1,799 @@ +--- +title: "RMarkdown Admissions_Survey2021" +output: html_document +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(echo = TRUE) +library(ggplot2) +library(devtools) +library(usethis) +library(devtools) +library(likert) +# Load RColorBrewer +# install.packages("RColorBrewer") +library(RColorBrewer) +library("stringr") # Load stringr package, used for wrapping label text in plots + +# Define colour palettes for plots below +coul3 <- brewer.pal(3, "RdYlBu") # Using RdYlBu range to generate 3 colour palette: https://colorbrewer2.org/#type=diverging&scheme=RdYlBu&n=5 + +TSR_data <- read.csv("./data/TSR data complete.csv") +subject_data <- read.csv("./data/Subject data.csv") +``` + +# Basic demographic summary visualisations: +```{r demographic_summaries} + +# 1. Calculate for age of respondent: + +# JK note: To keep things tidy, I've shifted the code here so that this isn't overwriting the dataset, but is instead operating off a separate df (same below) +TSR_data_summaries_age <- factor(TSR_data$Age, levels = c(1, 2, 3, 4, 5, 6, 7, 8), labels = c("15 or under", "16", "17", "18", "19", "20", "21 or over", "Prefer not to say")) +age_pie <- pie(table(TSR_data_summaries_age), col=coul3, cex = 0.8) +# JK note: adding in a bar chart here, as according to the interwebs it's (apparently?) more accurate to visualise than bar charts +data_summaries_age <- ggplot(TSR_data, aes(TSR_data_summaries_age)) + geom_bar() +data_summaries_age + labs(title = "Respondent Age Distribution", x = "Age", y = "") +# save to png file for reports +ggsave("figures/TSR_data_summaries_age.png") + +# 2. Calculate for year of study: + +TSR_data_summaries_yos <- factor(TSR_data$MOSTRECENTyearofstudy, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9), labels = c("Year 11/S4/Year 12(NI)", "Year 12/S5/Year 13(NI)", "Year 13/S6/Year 14(NI)", "I am currently on a gap year", "I am currently on an undergraduate/HE college course", "I am in full-time employment", "I am unemployed", "Other", "Prefer not to say")) + +data_summaries_yos <- ggplot(TSR_data, aes(TSR_data_summaries_yos)) + geom_bar() +data_summaries_yos + labs(title = "Respondent Most Recent Year of Study", x = "", y = "") +# save to png file for reports +ggsave("figures/TSR_data_summaries_yos.png") + +# 3. Gender identity: + +TSR_data_summaries_gender <- factor(TSR_data$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_summaries_gender <- str_wrap(TSR_data_summaries_gender, width = 10) + +data_summaries_gender <- ggplot(TSR_data, aes(TSR_data_summaries_gender)) + geom_bar() +data_summaries_gender + labs(title = "Respondent gender self-identification", x = "", y = "") +# save to png file for reports +ggsave("figures/TSR_data_summaries_gender.png") + +# 4. Ethnic self-identification: + +TSR_data_summaries_ethnicity <- factor(TSR_data$Ethnicity, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 ,18, 19), labels = c("Arab", "Indian", "Pakistani", "Bangladeshi", "Chinese", "Any other Asian background", "Black - African", "Black - Caribbean", "Any other Black background", "Mixed - White and Black Caribbean", "Mixed - White and Black African", "Mixed - White and Black Asian", "Any other Mixed/Multiple Ethnic background", "White - British", "White - Irish", "Gypsy or Irish Traveller", "Any other White background", "Other Ethnic group", "Prefer not to say")) +TSR_data_summaries_ethnicity <- str_wrap(TSR_data_summaries_ethnicity, width = 30) + +data_summaries_ethnicity <- ggplot(TSR_data, aes(TSR_data_summaries_ethnicity)) + geom_bar() + xlab(NULL) + coord_flip() +data_summaries_ethnicity + labs(title = "Respondent ethnic self-identification", x = "", y = "") + +# save to png file for reports +ggsave("figures/TSR_data_summaries_ethnicity.png") + +# 5. Religion + +TSR_data_summaries_religion <- factor(TSR_data$ReligiousAffliation, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19), labels = c("Agnostic", "Atheist", "Baha'i", "Buddhist", "Christian", "Confucian", "Jain", "Jewish", "Hindu", "Indigenous Traditional Religious", "Muslim", "Pagan", "Shinto", "Sikh", "Spiritual but not religious", "Zoroastrian", "No religion", "Prefer not to say", "Other")) + +Religious_affiliation_bar <- ggplot(TSR_data, aes(TSR_data_summaries_religion)) + geom_bar() + coord_flip() +Religious_affiliation_bar + labs(title = "Respondent religious self-identification", x = "", y = "") +# save to png file for reports +ggsave("figures/TSR_data_summaries_religion.png") + +``` + +# Visualisations of LIKERT responses (RH): +- For questions Q6 (subject interest) / Q5 (subject knowledge) / Q7 employability prospects: + - 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")) + +understanding_mean <- aggregate(Understanding ~ Subject, data = subject_data, mean) + +understanding_bar <- ggplot(understanding_mean, aes(x = Subject, y = Understanding)) + stat_summary(fun = "mean", geom = "bar") + coord_flip() +understanding_bar + labs(title = "I have a good understanding of what this subject involves?", x = "", y = "") +# save to png file for reports +ggsave("figures/TSR_data_subject_understanding.png") + + +#Q6 Subject Interest +interest_mean <- aggregate(Interest ~ Subject, data = subject_data, mean) + +interest_bar <- ggplot(interest_mean, aes(x = Subject, y = Interest)) + stat_summary(fun = "mean", geom = "bar") + coord_flip() +interest_bar + labs(title = "I would be interested in studying this subject at University?", x = "", y = "") +# save to png file for reports +ggsave("figures/TSR_data_subject_interest.png") + +#Q7 Employability Prospects + +employability_mean <- aggregate(Employability ~ Subject, data = subject_data, mean) + +employability_bar <- ggplot(employability_mean, aes(x = Subject, y = Employability)) + stat_summary(fun = "mean", geom = "bar") + coord_flip() +employability_bar + labs(title = "please rate ... employability prospects", x = "", y = "") +# save to png file for reports +ggsave("figures/TSR_data_subject_employability.png") + +### Categorical Graph ### + +#devtools::install_github('jbryer/likert') + +#likert_test <- likert(subject_data) +Understanding_data <- TSR_data[, 6:17] +likert_test_understand <- likert(Understanding_data) + +Interest_data <- TSR_data[, 18:29] + +Employability_data <- TSR_data[, 30:41] + +``` + + - separate visualisation of summary data as pie chart only for 4 key subjects: Philosophy, Ethics, Theology, Religious Studies, but with data represented as aggregated "Positive" / "Negative" responses + +```{r Visualization for 4 Key Subjects} +## Subset by "Positive" / "Negative" + +keysubjects_data <- subject_data[subject_data$Subject == "Philosophy" | subject_data$Subject == "Ethics" | subject_data$Subject == "Theology" | subject_data$Subject == "Religious Studies", ] + +recode_interest <- ifelse(2 <= keysubjects_data$Interest & keysubjects_data$Interest >=4, "Positive", "Negative") + +keysubjects_data <- cbind(keysubjects_data, recode_interest) +keysubjects_data$recode_interest <- factor(keysubjects_data$recode_interest) + +table(keysubjects_data$recode_interest, keysubjects_data$Subject) + +``` + - subsetted visualisations of responses with separate subsetting by response to Q8-9, Q18, Q17, Q16 +- For question Q8 + Q9 (for religious people) + - visualisation summary of responses + - show subsetted visualisations of responses by response to, Q18, Q17, Q16, Q13, Q14 +- For responses to Q10-12 (what subjects are involved in...): + - represent answer counts as descending bar chart for each Q + - subset answers by Q6 (positive / negative) and Q5 (positive / negative) + +# Correlation testing: +- For Q6 (subject interest) / Q5 (subject knowledge) / Q7 employability prospects, test for nature / strength of correlation with responses to: + - Q8-9 responses + - Q18 responses + - Q17 + + +```{r Q6 Correlations - Subject Interest} +#Q8-9 (8 - Theology as subject for religious people; 9 - Religion as study for religious people) + # This would be suitable for correlation + +#Q17 (Ethnicity) + # This would be categorical, so ANOVA + +#Q18 (Religion) + # This would also be categorical, so ANOVA + +``` + +```{r Q5 Correlations - Subject Knowledge} +#Q8-9 (8 - Theology as subject for religious people; 9 - Religion as study for religious people) + # This would be suitable for correlation + +#Q17 (Ethnicity) + # This would be categorical, so ANOVA + +#Q18 (Religion) + # This would also be categorical, so ANOVA +``` + +```{r Q7 Correlations - Employability} +#Q8-9 (8 - Theology as subject for religious people; 9 - Religion as study for religious people) + # This would be suitable for correlation + +#Q17 (Ethnicity) + # This would be categorical, so ANOVA + +#Q18 (Religion) + # This would also be categorical, so ANOVA +``` + + +```{r testing fun} +#testcor_data <- subject_data[subject_data$Subject == "Psychology" | subject_data$Subject == "Theology", ] + +#t.test(Interest ~ Subject, data = testcor_data) + +# testsubset <- TSR_data[TSR_data$Interested.in.Studying.Psychology < 3 & TSR_data$Interested.in.Studying.Psychology != 0, ] +# as.numeric(testsubset$Interested.in.studying.Theology) +# mean(testsubset$Interested.in.studying.Theology, na.rm = TRUE) +# +# mean(as.numeric(testsubset$Interested.in.studying.Theology), na.rm = TRUE) +# +# as.numeric(testsubset$Interested.in.studying.Religious.Studies) +# mean(testsubset$Interested.in.studying.Religious.Studies, na.rm = TRUE) +# +# testsubset2 <- TSR_data[TSR_data$Interested.in.Studying.Psychology > 3, ] +# mean(as.numeric(testsubset2$Interested.in.studying.Theology), na.rm = TRUE) +# mean(as.numeric(testsubset2$Interested.in.studying.Religious.Studies), na.rm = TRUE) +``` + +## Mean Interest in Theology and Religious Studies by High/Low Subject Interest + +```{r Philosophy} +### Philosophy ### +Philos_subset_Low <- TSR_data[TSR_data$InterestinstudyingPhilosophy < 3 & TSR_data$InterestinstudyingPhilosophy != 0, ] + +Philos_subset_High <- TSR_data[TSR_data$InterestinstudyingPhilosophy > 3, ] + +## Theology Interest +#Low interest in Philosophy +mean(as.numeric(Philos_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) +#High interest in Philosophy +mean(as.numeric(Philos_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) + +## Religious Studies Interest +#Low interest in Philosophy +mean(as.numeric(Philos_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE) +#High interest in Philosophy +mean(as.numeric(Philos_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE) +``` + +```{r Sociology} +### Sociology ### +Soc_subset_Low <- TSR_data[TSR_data$InterestinstudyingSociaology < 3 & TSR_data$InterestinstudyingSociaology != 0, ] + +Soc_subset_High <- TSR_data[TSR_data$InterestinstudyingSociaology > 3, ] + +## Theology Interest +#Low interest in Sociology +lSocTheo <- mean(as.numeric(Soc_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) +#High interest in Sociology +hSocTheo <- mean(as.numeric(Soc_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) + +## Religious Studies Interest +#Low interest in Sociology +mean(as.numeric(Soc_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE) +#High interest in Sociology +mean(as.numeric(Soc_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE) +``` + +```{r Psychology} +### Psychology ### +Psych_subset_Low <- TSR_data[TSR_data$InterestedinStudyingPsychology < 3 & TSR_data$InterestedinStudyingPsychology != 0, ] + +Psych_subset_High <- TSR_data[TSR_data$InterestedinStudyingPsychology > 3, ] + +## Theology Interest +#Low interest in Psychology +mean(as.numeric(Psych_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) +#High interest in Psychology +mean(as.numeric(Psych_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) + +## Religious Studies Interest +#Low interest in Psychology +mean(as.numeric(Psych_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE) +#High interest in Psychology +mean(as.numeric(Psych_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE) +``` + +```{r History} +### History ### +Hist_subset_Low <- TSR_data[TSR_data$InterestedinstudyingHistory < 3 & TSR_data$InterestedinstudyingHistory != 0, ] + +Hist_subset_High <- TSR_data[TSR_data$InterestedinstudyingHistory > 3, ] + +## Theology Interest +#Low interest in History +mean(as.numeric(Hist_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) +#High interest in History +mean(as.numeric(Hist_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) + +## Religious Studies Interest +#Low interest in History +mean(as.numeric(Hist_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE) +#High interest in History +mean(as.numeric(Hist_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE) +``` + +```{r Ethics} +### Ethics ### +Ethics_subset_Low <- TSR_data[TSR_data$InterestedinstudyingEthics < 3 & TSR_data$InterestedinstudyingEthics != 0, ] + +Ethics_subset_High <- TSR_data[TSR_data$InterestedinstudyingEthics > 3, ] + +## Theology Interest +#Low interest in Ethics +mean(as.numeric(Ethics_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) +#High interest in Ethics +mean(as.numeric(Ethics_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) + +## Religious Studies Interest +#Low interest in Ethics +mean(as.numeric(Ethics_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE) +#High interest in Ethics +mean(as.numeric(Ethics_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE) +``` + +```{r Politics} +### Politics ### +Polit_subset_Low <- TSR_data[TSR_data$InterestedinstudyingPolitics < 3 & TSR_data$InterestedinstudyingPolitics != 0, ] + +Polit_subset_High <- TSR_data[TSR_data$InterestedinstudyingPolitics > 3, ] + +## Theology Interest +#Low interest in Politics +mean(as.numeric(Polit_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) +#High interest in Politics +mean(as.numeric(Polit_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) + +## Religious Studies Interest +#Low interest in Politics +mean(as.numeric(Polit_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE) +#High interest in Politics +mean(as.numeric(Polit_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE) +``` + +```{r English} +### English ### +Eng_subset_Low <- TSR_data[TSR_data$InterestedinstudyingEnglish < 3 & TSR_data$InterestedinstudyingEnglish != 0, ] + +Eng_subset_High <- TSR_data[TSR_data$InterestedinstudyingEnglish > 3, ] + +## Theology Interest +#Low interest in English +mean(as.numeric(Eng_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) +#High interest in English +mean(as.numeric(Eng_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) + +## Religious Studies Interest +#Low interest in English +mean(as.numeric(Eng_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE) +#High interest in English +mean(as.numeric(Eng_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE) +``` + +```{r Math} +### Math ### +Math_subset_Low <- TSR_data[TSR_data$InterestedinstudyingMath < 3 & TSR_data$InterestedinstudyingMathy != 0, ] + +Math_subset_High <- TSR_data[TSR_data$InterestedinstudyingMath > 3, ] + +## Theology Interest +#Low interest in Math +mean(as.numeric(Math_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) +#High interest in Math +mean(as.numeric(Math_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) + +## Religious Studies Interest +#Low interest in Math +mean(as.numeric(Math_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE) +#High interest in Math +mean(as.numeric(Math_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE) +``` + +```{r Computer Science} +### Computer Science ### +CompSci_subset_Low <- TSR_data[TSR_data$InterestedinstudyingComputerScience < 3 & TSR_data$InterestedinstudyingComputerScience != 0, ] + +CompSci_subset_High <- TSR_data[TSR_data$InterestedinstudyingComputerScience > 3, ] + +## Theology Interest +#Low interest in Computer Science +mean(as.numeric(CompSci_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) +#High interest in Computer Science +mean(as.numeric(CompSci_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) + +## Religious Studies Interest +#Low interest in Computer Science +mean(as.numeric(CompSci_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE) +#High interest in Computer Science +mean(as.numeric(CompSci_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE) +``` + +```{r Business} +### Business ### +Busi_subset_Low <- TSR_data[TSR_data$InterestedinstudyingBusiness < 3 & TSR_data$InterestedinstudyingBusiness != 0, ] + +Busi_subset_High <- TSR_data[TSR_data$InterestedinstudyingBusiness > 3, ] + +## Theology Interest +#Low interest in Business +mean(as.numeric(Busi_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) +#High interest in Business +mean(as.numeric(Busi_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) + +## Religious Studies Interest +#Low interest in Business +mean(as.numeric(Busi_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE) +#High interest in Business +mean(as.numeric(Busi_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE) + +``` + + +## Mean Knowledge in Theology and Religious Studies by High/Low Subject Knowledge + +```{r Philosophy} +### Philosophy ### +Philos_subset_Low <- TSR_data[TSR_data$GoodunderstandingofPhilosophy < 3 & TSR_data$GoodunderstandingofPhilosophy != 0, ] + +Philos_subset_High <- TSR_data[TSR_data$GoodunderstandingofPhilosophy > 3, ] + +## Theology Knowledge +#Low knowledge in Philosophy +mean(as.numeric(Philos_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) +#High knowledge in Philosophy +mean(as.numeric(Philos_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) + +## Religious Studies Knowledge +#Low knowledge in Philosophy +mean(as.numeric(Philos_subset_Low$GoodunderstandingofReligiousStudies), na.rm = TRUE) +#High knowledge in Philosophy +mean(as.numeric(Philos_subset_High$GoodunderstandingofReligiousStudies), na.rm = TRUE) +``` + +```{r Sociology} +### Sociology ### +Soc_subset_Low <- TSR_data[TSR_data$GoodunderstandingofSociology < 3 & TSR_data$GoodunderstandingofSociology != 0, ] + +Soc_subset_High <- TSR_data[TSR_data$GoodunderstandingofSociology > 3, ] + +## Theology knowledge +#Low knowledge in Sociology +mean(as.numeric(Soc_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) +#High knowledge in Sociology +mean(as.numeric(Soc_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) + +## Religious Studies knowledge +#Low knowledge in Sociology +mean(as.numeric(Soc_subset_Low$GoodunderstandingofReligiousStudies), na.rm = TRUE) +#High knowledge in Sociology +mean(as.numeric(Soc_subset_High$GoodunderstandingofReligiousStudies), na.rm = TRUE) +``` + +```{r Psychology} +### Psychology ### +Psych_subset_Low <- TSR_data[TSR_data$GoodunderstandingofPsychology < 3 & TSR_data$GoodunderstandingofPsychology != 0, ] + +Psych_subset_High <- TSR_data[TSR_data$GoodunderstandingofPsychology > 3, ] + +## Theology knowledge +#Low knowledge in Psychology +mean(as.numeric(Psych_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) +#High knowledge in Psychology +mean(as.numeric(Psych_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) + +## Religious Studies knowledge +#Low knowledge in Psychology +mean(as.numeric(Psych_subset_Low$GoodunderstandingofReligiousStudies), na.rm = TRUE) +#High knowledge in Psychology +mean(as.numeric(Psych_subset_High$GoodunderstandingofReligiousStudies), na.rm = TRUE) +``` + +```{r History} +### History ### +Hist_subset_Low <- TSR_data[TSR_data$GoodunderstandingofHistory < 3 & TSR_data$GoodunderstandingofHistory != 0, ] + +Hist_subset_High <- TSR_data[TSR_data$GoodunderstandingofHistory > 3, ] + +## Theology knowledge +#Low knowledge in History +mean(as.numeric(Hist_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) +#High knowledge in History +mean(as.numeric(Hist_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) + +## Religious Studies knowledge +#Low knowledge in History +mean(as.numeric(Hist_subset_Low$GoodunderstandingofReligiousStudies), na.rm = TRUE) +#High knowledge in History +mean(as.numeric(Hist_subset_High$GoodunderstandingofReligiousStudies), na.rm = TRUE) +``` + +```{r Ethics} +### Ethics ### +Ethics_subset_Low <- TSR_data[TSR_data$GoodunderstandingofEthics < 3 & TSR_data$GoodunderstandingofEthics != 0, ] + +Ethics_subset_High <- TSR_data[TSR_data$GoodunderstandingofEthics > 3, ] + +## Theology knowledge +#Low knowledge in Ethics +mean(as.numeric(Ethics_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) +#High knowledge in Ethics +mean(as.numeric(Ethics_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) + +## Religious Studies knowledge +#Low knowledge in Ethics +mean(as.numeric(Ethics_subset_Low$GoodunderstandingofReligiousStudies), na.rm = TRUE) +#High knowledge in Ethics +mean(as.numeric(Ethics_subset_High$GoodunderstandingofReligiousStudies), na.rm = TRUE) +``` + +```{r Politics} +### Politics ### +Polit_subset_Low <- TSR_data[TSR_data$GoodunderstandingofPolitics < 3 & TSR_data$GoodunderstandingofPolitics != 0, ] + +Polit_subset_High <- TSR_data[TSR_data$GoodunderstandingofPolitics > 3, ] + +## Theology knowledge +#Low knowledge in Politics +mean(as.numeric(Polit_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) +#High knowledge in Politics +mean(as.numeric(Polit_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) + +## Religious Studies knowledge +#Low knowledge in Politics +mean(as.numeric(Polit_subset_Low$GoodunderstandingofReligiousStudies), na.rm = TRUE) +#High knowledge in Politics +mean(as.numeric(Polit_subset_High$GoodunderstandingofReligiousStudies), na.rm = TRUE) +``` + +```{r English} +### English ### +Eng_subset_Low <- TSR_data[TSR_data$GoodunderstandingofEnglish < 3 & TSR_data$GoodunderstandingofEnglish != 0, ] + +Eng_subset_High <- TSR_data[TSR_data$GoodunderstandingofEnglish > 3, ] + +## Theology knowledge +#Low knowledge in English +mean(as.numeric(Eng_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) +#High knowledge in English +mean(as.numeric(Eng_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) + +## Religious Studies knowledge +#Low knowledge in English +mean(as.numeric(Eng_subset_Low$GoodunderstandingofReligiousStudies), na.rm = TRUE) +#High knowledge in English +mean(as.numeric(Eng_subset_High$GoodunderstandingofReligiousStudies), na.rm = TRUE) +``` + +```{r Math} +### Math ### +Math_subset_Low <- TSR_data[TSR_data$GoodunderstandingofMath < 3 & TSR_data$GoodunderstandingofMath != 0, ] + +Math_subset_High <- TSR_data[TSR_data$GoodunderstandingofMath > 3, ] + +## Theology knowledge +#Low knowledge in Math +mean(as.numeric(Math_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) +#High knowledge in Math +mean(as.numeric(Math_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) + +## Religious Studies knowledge +#Low knowledge in Math +mean(as.numeric(Math_subset_Low$GoodunderstandingofReligiousStudies), na.rm = TRUE) +#High knowledge in Math +mean(as.numeric(Math_subset_High$GoodunderstandingofReligiousStudies), na.rm = TRUE) +``` + +```{r Computer Science} +### Computer Science ### +CompSci_subset_Low <- TSR_data[TSR_data$GoodunderstandingofComputerScience < 3 & TSR_data$GoodunderstandingofComputerScience != 0, ] + +CompSci_subset_High <- TSR_data[TSR_data$GoodunderstandingofComputerScience > 3, ] + +## Theology knowledge +#Low knowledge in Computer Science +mean(as.numeric(CompSci_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) +#High knowledge in Computer Science +mean(as.numeric(CompSci_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) + +## Religious Studies knowledge +#Low knowledge in Computer Science +mean(as.numeric(CompSci_subset_Low$GoodunderstandingofReligiousStudies), na.rm = TRUE) +#High knowledge in Computer Science +mean(as.numeric(CompSci_subset_High$GoodunderstandingofReligiousStudies), na.rm = TRUE) +``` + +```{r Business} +### Business ### +Busi_subset_Low <- TSR_data[TSR_data$GoodunderstandingofBusiness < 3 & TSR_data$GoodunderstandingofBusiness != 0, ] + +Busi_subset_High <- TSR_data[TSR_data$GoodunderstandingofBusiness > 3, ] + +## Theology knowledge +#Low knowledge in Business +mean(as.numeric(Busi_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) +#High knowledge in Business +mean(as.numeric(Busi_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) + +## Religious Studies knowledge +#Low knowledge in Business +mean(as.numeric(Busi_subset_Low$GoodunderstandingofReligiousStudies), na.rm = TRUE) +#High knowledge in Business +mean(as.numeric(Busi_subset_High$GoodunderstandingofReligiousStudies), na.rm = TRUE) + +``` + + +## Mean Employability in Theology and Religious Studies by High/Low Subject Employability + +```{r Philosophy} +### Philosophy ### +Philos_subset_Low <- TSR_data[TSR_data$Philosophyemployability < 3 & TSR_data$Philosophyemployability != 0, ] + +Philos_subset_High <- TSR_data[TSR_data$Philosophyemployability > 3, ] + +## Theology employability +#Low employability in Philosophy +mean(as.numeric(Philos_subset_Low$Theologyemployability), na.rm = TRUE) +#High employability in Philosophy +mean(as.numeric(Philos_subset_Low$Theologyemployability), na.rm = TRUE) + +## Religious Studies employability +#Low employability in Philosophy +mean(as.numeric(Philos_subset_Low$ReligiousStudiesemployability), na.rm = TRUE) +#High employability in Philosophy +mean(as.numeric(Philos_subset_High$ReligiousStudiesemployability), na.rm = TRUE) +``` + +```{r Sociology} +### Sociology ### +Soc_subset_Low <- TSR_data[TSR_data$Sociologyemployability < 3 & TSR_data$Sociologyemployability != 0, ] + +Soc_subset_High <- TSR_data[TSR_data$Sociologyemployability > 3, ] + +## Theology employability +#Low employability in Sociology +mean(as.numeric(Soc_subset_Low$Theologyemployability), na.rm = TRUE) +#High employability in Sociology +mean(as.numeric(Soc_subset_Low$Theologyemployability), na.rm = TRUE) + +## Religious Studies employability +#Low employability in Sociology +mean(as.numeric(Soc_subset_Low$ReligiousStudiesemployability), na.rm = TRUE) +#High employability in Sociology +mean(as.numeric(Soc_subset_High$ReligiousStudiesemployability), na.rm = TRUE) +``` + +```{r Psychology} +### Psychology ### +Psych_subset_Low <- TSR_data[TSR_data$PsychologyEmployability < 3 & TSR_data$PsychologyEmployability != 0, ] + +Psych_subset_High <- TSR_data[TSR_data$PsychologyEmployability > 3, ] + +## Theology employability +#Low employability in Psychology +mean(as.numeric(Psych_subset_Low$Theologyemployability), na.rm = TRUE) +#High employability in Psychology +mean(as.numeric(Psych_subset_Low$Theologyemployability), na.rm = TRUE) + +## Religious Studies employability +#Low employability in Psychology +mean(as.numeric(Psych_subset_Low$ReligiousStudiesemployability), na.rm = TRUE) +#High employability in Psychology +mean(as.numeric(Psych_subset_High$ReligiousStudiesemployability), na.rm = TRUE) +``` + +```{r History} +### History ### +Hist_subset_Low <- TSR_data[TSR_data$Historyemployability < 3 & TSR_data$Historyemployability != 0, ] + +Hist_subset_High <- TSR_data[TSR_data$Historyemployability > 3, ] + +## Theology employability +#Low employability in History +mean(as.numeric(Hist_subset_Low$Theologyemployability), na.rm = TRUE) +#High employability in History +mean(as.numeric(Hist_subset_Low$Theologyemployability), na.rm = TRUE) + +## Religious Studies employability +#Low employability in History +mean(as.numeric(Hist_subset_Low$ReligiousStudiesemployability), na.rm = TRUE) +#High employability in History +mean(as.numeric(Hist_subset_High$ReligiousStudiesemployability), na.rm = TRUE) +``` + +```{r Ethics} +### Ethics ### +Ethics_subset_Low <- TSR_data[TSR_data$Ethicsemployability < 3 & TSR_data$Ethicsemployability != 0, ] + +Ethics_subset_High <- TSR_data[TSR_data$Ethicsemployability > 3, ] + +## Theology employability +#Low employability in Ethics +mean(as.numeric(Ethics_subset_Low$Theologyemployability), na.rm = TRUE) +#High employability in Ethics +mean(as.numeric(Ethics_subset_Low$Theologyemployability), na.rm = TRUE) + +## Religious Studies employability +#Low employability in Ethics +mean(as.numeric(Ethics_subset_Low$ReligiousStudiesemployability), na.rm = TRUE) +#High employability in Ethics +mean(as.numeric(Ethics_subset_High$ReligiousStudiesemployability), na.rm = TRUE) +``` + +```{r Politics} +### Politics ### +Polit_subset_Low <- TSR_data[TSR_data$Politicsemployability < 3 & TSR_data$Politicsemployability != 0, ] + +Polit_subset_High <- TSR_data[TSR_data$Politicsemployability > 3, ] + +## Theology employability +#Low employability in Politics +mean(as.numeric(Polit_subset_Low$Theologyemployability), na.rm = TRUE) +#High employability in Politics +mean(as.numeric(Polit_subset_Low$Theologyemployability), na.rm = TRUE) + +## Religious Studies employability +#Low employability in Politics +mean(as.numeric(Polit_subset_Low$ReligiousStudiesemployability), na.rm = TRUE) +#High employability in Politics +mean(as.numeric(Polit_subset_High$ReligiousStudiesemployability), na.rm = TRUE) +``` + +```{r English} +### English ### +Eng_subset_Low <- TSR_data[TSR_data$Englishemployability < 3 & TSR_data$Englishemployability != 0, ] + +Eng_subset_High <- TSR_data[TSR_data$Englishemployability > 3, ] + +## Theology employability +#Low employability in English +mean(as.numeric(Eng_subset_Low$Theologyemployability), na.rm = TRUE) +#High employability in English +mean(as.numeric(Eng_subset_Low$Theologyemployability), na.rm = TRUE) + +## Religious Studies employability +#Low employability in English +mean(as.numeric(Eng_subset_Low$ReligiousStudiesemployability), na.rm = TRUE) +#High employability in English +mean(as.numeric(Eng_subset_High$ReligiousStudiesemployability), na.rm = TRUE) +``` + +```{r Math} +### Math ### +Math_subset_Low <- TSR_data[TSR_data$Mathemployability < 3 & TSR_data$Mathemployability != 0, ] + +Math_subset_High <- TSR_data[TSR_data$Mathemployability > 3, ] + +## Theology employability +#Low employability in Math +mean(as.numeric(Math_subset_Low$Theologyemployability), na.rm = TRUE) +#High employability in Math +mean(as.numeric(Math_subset_Low$Theologyemployability), na.rm = TRUE) + +## Religious Studies employability +#Low employability in Math +mean(as.numeric(Math_subset_Low$ReligiousStudiesemployability), na.rm = TRUE) +#High employability in Math +mean(as.numeric(Math_subset_High$ReligiousStudiesemployability), na.rm = TRUE) +``` + +```{r Computer Science} +### Computer Science ### +CompSci_subset_Low <- TSR_data[TSR_data$ComputerScienceemployability < 3 & TSR_data$ComputerScienceemployability != 0, ] + +CompSci_subset_High <- TSR_data[TSR_data$ComputerScienceemployability > 3, ] + +## Theology employability +#Low employability in Computer Science +mean(as.numeric(CompSci_subset_Low$Theologyemployability), na.rm = TRUE) +#High employability in Computer Science +mean(as.numeric(CompSci_subset_Low$Theologyemployability), na.rm = TRUE) + +## Religious Studies employability +#Low employability in Computer Science +mean(as.numeric(CompSci_subset_Low$ReligiousStudiesemployability), na.rm = TRUE) +#High employability in Computer Science +mean(as.numeric(CompSci_subset_High$ReligiousStudiesemployability), na.rm = TRUE) +``` + +```{r Business} +### Business ### +Busi_subset_Low <- TSR_data[TSR_data$Businessemployability < 3 & TSR_data$Businessemployability != 0, ] + +Busi_subset_High <- TSR_data[TSR_data$Businessemployability > 3, ] + +## Theology employability +#Low employability in Business +mean(as.numeric(Busi_subset_Low$Theologyemployability), na.rm = TRUE) +#High employability in Business +mean(as.numeric(Busi_subset_Low$Theologyemployability), na.rm = TRUE) + +## Religious Studies employability +#Low employability in Business +mean(as.numeric(Busi_subset_Low$ReligiousStudiesemployability), na.rm = TRUE) +#High employability in Business +mean(as.numeric(Busi_subset_High$ReligiousStudiesemployability), na.rm = TRUE) + +```