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+---
+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)
+
+```