diff --git a/.Rhistory b/.Rhistory
index e69de29..04b71af 100644
--- a/.Rhistory
+++ b/.Rhistory
@@ -0,0 +1,40 @@
+knitr::opts_chunk$set(echo = TRUE)
+TSR_data <- read.csv("./data/TSR data complete.csv")
+```{r respondent age}
+TSR_data$Age <- factor(TSR_data$Age, levels = c(1, 2, 3, 4, 5, 6, 7, 8), labels = c("15 or under", "16", "17", "18", "19", "20", "21 or over", "Prefer not to say"))
+```{r respondent_age}
+age_pie <- pie(table(TSR_data$Age))
+TSR_data$MOST.RECENT.year.of.study <- factor(TSR_data$MOST.RECENT.year.of.study, 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"))
+Year_study_pie <- pie(table(TSR_data$MOST.RECENT.year.of.study))
+TSR_data$Gender <- factor(TSR_data$Gender, levels = c(1, 2, 3, 4), labels = c("Male", "Female", "I identify my gender in another way", "Prefer not to say"))
+gender_pie <- pie(table(TSR_data$Gender))
+library(ggplot2)
+TSR_data$Ethnicity <- factor(TSR_data$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", "Asian/Asian British - Indian", "Asian/Asian British - Pakistani", "Asian/Asian British - Bangladeshi", "Asian/Asian British - Chinese", "Asian/Asian British - Any other Asian background", "Black/Black British - African", "Black/Black British - Caribbean", "Black/Black British - Any other Black background", "Mixed/Multiple Ethnic Groups - White and Black Caribbean", "Mixed/Multiple Ethnic Groups - White and Black African", "Mixed/Multiple Ethnic Groups - White and Black Asian", "Mixed/Multiple Ethnic Groups - Any other Mixed/Multiple Ethnic background", "White - English/Welsh/Scottish/Northern Irish/British", "White - Irish", "White - Gypsy or Irish Traveller", "White - Any other White background", "Other Ethnic group, please describe", "Prefer not to say"))
+Ethnicity_bar <- ggplot(TSR_data, aes(Ethnicity)) + geom_bar() + coord_flip()
+Ethnicity_bar
+TSR_data$Religious.Affliation <- factor(TSR_data$Religious.Affliation, 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(Religious.Affliation)) + geom_bar() + coord_flip()
+Religious_affiliation_bar
+subject_data <- read.csv("./data/Subject data.csv")
+#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
+#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
+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
+keysubjects_data <- subject_data[subject_data$Subject == "Philosophy" | subject_data$Subject == "Ethics" | subject_data$Subject == "Theology" | subject_data$Subject == "Religious Studies", ]
+recode_interest <- ifelse(1 <= keysubjects_data$Interest & keysubjects_data$Interest >=3, "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)
+understanding_bar
+#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
diff --git a/To-do list Markdown.Rmd b/To-do list Markdown.Rmd
deleted file mode 100644
index 531841d..0000000
--- a/To-do list Markdown.Rmd	
+++ /dev/null
@@ -1,770 +0,0 @@
----
-title: "RMarkdown Admissions_Survey2021"
-output: html_document
----
-
-```{r setup, include=FALSE}
-knitr::opts_chunk$set(echo = TRUE)
-library(ggplot2)
-```
-
-# Upload Data
-```{r Upload Data}
-TSR_data <- read.csv("./data/TSR data complete.csv")
-subject_data <- read.csv("./data/Subject data.csv")
-```
-
-# Basic summary visualisations (RH):
-- Q2 (respondent age)
-```{r respondent age}
-TSR_data$Age <- factor(TSR_data$Age, levels = c(1, 2, 3, 4, 5, 6, 7, 8), labels = c("15 or under", "16", "17", "18", "19", "20", "21 or over", "Prefer not to say"))
-age_pie <- pie(table(TSR_data$Age))
-
-```
-- Q3 (year of study)
-```{r year of study}
-TSR_data$MOST.RECENT.year.of.study <- factor(TSR_data$MOST.RECENT.year.of.study, 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"))
-
-Year_study_pie <- pie(table(TSR_data$MOST.RECENT.year.of.study))
-
-```
-- Q16 (gender identity)
-```{r gender identity}
-
-TSR_data$Gender <- factor(TSR_data$Gender, levels = c(1, 2, 3, 4), labels = c("Male", "Female", "I identify my gender in another way", "Prefer not to say"))
-
-gender_pie <- pie(table(TSR_data$Gender))
-```
-- Q17 (ethnic self-id)
-```{r ethnic self-id}
-TSR_data$Ethnicity <- factor(TSR_data$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", "Asian/Asian British - Indian", "Asian/Asian British - Pakistani", "Asian/Asian British - Bangladeshi", "Asian/Asian British - Chinese", "Asian/Asian British - Any other Asian background", "Black/Black British - African", "Black/Black British - Caribbean", "Black/Black British - Any other Black background", "Mixed/Multiple Ethnic Groups - White and Black Caribbean", "Mixed/Multiple Ethnic Groups - White and Black African", "Mixed/Multiple Ethnic Groups - White and Black Asian", "Mixed/Multiple Ethnic Groups - Any other Mixed/Multiple Ethnic background", "White - English/Welsh/Scottish/Northern Irish/British", "White - Irish", "White - Gypsy or Irish Traveller", "White - Any other White background", "Other Ethnic group, please describe", "Prefer not to say"))
-
-Ethnicity_bar <- ggplot(TSR_data, aes(Ethnicity)) + geom_bar() + coord_flip()
-Ethnicity_bar
-```
-- Q18 (religion)
-```{r religion}
-TSR_data$Religious.Affliation <- factor(TSR_data$Religious.Affliation, 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(Religious.Affliation)) + geom_bar() + coord_flip()
-Religious_affiliation_bar
-
-```
-
-# 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
-
-#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
-
-#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
-
-
-### Categorical Graph ###
-install.packages("devtools")
-install.packages("usethis")
-library(devtools)
-require(likert)
-
-#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(3 <= keysubjects_data$Interest & keysubjects_data$Interest >=5, "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)
-
-```