diff --git a/.RData b/.RData
index 07b203d..e90e4e1 100644
Binary files a/.RData and b/.RData differ
diff --git a/.Rhistory b/.Rhistory
index 16b713b..1f75692 100644
--- a/.Rhistory
+++ b/.Rhistory
@@ -1,458 +1,8 @@
-age_pie <- pie(table(TSR_data$Age), col=coul3)
-TSR_data$MOST.RECENT.year.of.study <- factor(TSR_data$MOST.RECENT.year.of.study, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9), labels = c("Year 11/S4/Year 12(NI)", "Year 12/S5/Year 13(NI)", "Year 13/S6/Year 14(NI)", "I am currently on a gap year", "I am currently on an undergraduate/HE college course", "I am in full-time employment", "I am unemployed", "Other", "Prefer not to say"))
-TSR_data$Age <- age_pie <- pie(table(factor(TSR_data$Age, levels = c(1, 2, 3, 4, 5, 6, 7, 8), labels = c("15 or under", "16", "17", "18", "19", "20", "21 or over", "Prefer not to say"))), col=coul3)
-TSR_data$Age <- age_pie <- pie(table(factor(TSR_data$Age, levels = c(1, 2, 3, 4, 5, 6, 7, 8), labels = c("15 or under", "16", "17", "18", "19", "20", "21 or over", "Prefer not to say")), col=coul3)
-pie(table(factor(TSR_data$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"))), col=coul3)
-pie(table(factor(TSR_data$Age, levels = c(1, 2, 3, 4, 5, 6, 7, 8), labels = c("15 or under", "16", "17", "18", "19", "20", "21 or over", "Prefer not to say"))), col=coul3))
-age_pie <- pie(table(TSR_data$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")), col=coul3)
-age_pie <- pie(table(TSR_data$Age, levels = c(1, 2, 3, 4, 5, 6, 7, 8), labels = c("15 or under", "16", "17", "18", "19", "20", "21 or over", "Prefer not to say")), col=coul3))
-TSR_data <- read.csv("./data/TSR data complete.csv")
-TSR_data_summaries$Age <- factor(TSR_data$Age, levels = c(1, 2, 3, 4, 5, 6, 7, 8), labels = c("15 or under", "16", "17", "18", "19", "20", "21 or over", "Prefer not to say"))
-TSR_data_summaries <- factor(TSR_data$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$Age), col=coul3)
-age_pie <- pie(table(TSR_data_summaries), col=coul3)
-# JK note: To keep things tidy, I've shifted the code here so that this isn't overwriting the dataset, but is instead operating off a separate df (same below)
-TSR_data_summaries_age <- factor(TSR_data$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)
-age_pie <- pie(table(TSR_data_summaries_age), col=coul3, cex = 0.8)
-Ethnicity_bar <- ggplot(TSR_data, aes(Ethnicity)) + geom_bar() + coord_flip()
-Ethnicity_bar
-ggplot(TSR_data, aes(TSR_data_summaries_age)) + geom_bar()
-# JK note: adding in a bar chart here, as according to the interwebs it's (apparently?) more accurate to visualise than bar charts
-ggplot(TSR_data, aes(TSR_data_summaries_age)) + geom_bar() +coord_flip()
-# JK note: adding in a bar chart here, as according to the interwebs it's (apparently?) more accurate to visualise than bar charts
-ggplot(TSR_data, aes(TSR_data_summaries_age, fill=match)) + geom_bar() +coord_flip()
-# JK note: adding in a bar chart here, as according to the interwebs it's (apparently?) more accurate to visualise than bar charts
-ggplot(TSR_data, aes(TSR_data_summaries_age, fill=coul3)) + geom_bar() +coord_flip()
-# JK note: adding in a bar chart here, as according to the interwebs it's (apparently?) more accurate to visualise than bar charts
-ggplot(TSR_data, aes(TSR_data_summaries_age)) + geom_bar()
-# JK note: adding in a bar chart here, as according to the interwebs it's (apparently?) more accurate to visualise than bar charts
-ggplot(TSR_data, aes(TSR_data_summaries_age)) + geom_bar()
-# save to png file for reports
-ggsave("TSR_data_summaries_age.png")
-# save to png file for reports
-ggsave("figures/TSR_data_summaries_age.png")
-# JK note: adding in a bar chart here, as according to the interwebs it's (apparently?) more accurate to visualise than bar charts
-ggplot(TSR_data, aes(TSR_data_summaries_age)) + geom_bar() + title("Age of respondents")
-# 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$Age, levels = c(1, 2, 3, 4, 5, 6, 7, 8), labels = c("15 or under", "16", "17", "18", "19", "20", "21 or over", "Prefer not to say"))
-data_summaries_age labs(colour = "TSR_data_summaries_age")
-data_summaries_age labs(title = "Respondent Age")
-# JK note: adding in a bar chart here, as according to the interwebs it's (apparently?) more accurate to visualise than bar charts
-data_summaries_age <- ggplot(TSR_data, aes(TSR_data_summaries_age)) + geom_bar() + title("Age of respondents")
-# JK note: adding in a bar chart here, as according to the interwebs it's (apparently?) more accurate to visualise than bar charts
-data_summaries_age <- ggplot(TSR_data, aes(TSR_data_summaries_age)) + geom_bar()
-data_summaries_age labs(title = "Respondent Age")
-data_summaries_age + labs(title = "Respondent Age")
-data_summaries_age + labs(title = "Respondent Age Distribution", x = "Age")
-data_summaries_age + labs(title = "Respondent Age Distribution", x = "Age", y = "")
-# save to png file for reports
-ggsave("figures/TSR_data_summaries_age.png")
-TSR_data_summaries_most_recent_year_of_study <- factor(TSR_data$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_summaries_most_recent_year_of_study))
-Year_study_pie <- pie(table(TSR_data_summaries_most_recent_year_of_study$MOST.RECENT.year.of.study))
-TSR_data_summaries_most_recent_year_of_study <- factor(TSR_data$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"))
-data_summaries_yos <- factor(TSR_data$MOST.RECENT.year.of.study, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9), labels = c("Year 11/S4/Year 12(NI)", "Year 12/S5/Year 13(NI)", "Year 13/S6/Year 14(NI)", "I am currently on a gap year", "I am currently on an undergraduate/HE college course", "I am in full-time employment", "I am unemployed", "Other", "Prefer not to say"))
-Year_study_pie <- pie(table(data_summaries_yos$MOST.RECENT.year.of.study))
-Year_study_pie <- pie(table(data_summaries_yos))
-TSR_data <- read.csv("./data/TSR data complete.csv")
-data_summaries_yos <- factor(TSR_data$MOST.RECENT.year.of.study, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9), labels = c("Year 11/S4/Year 12(NI)", "Year 12/S5/Year 13(NI)", "Year 13/S6/Year 14(NI)", "I am currently on a gap year", "I am currently on an undergraduate/HE college course", "I am in full-time employment", "I am unemployed", "Other", "Prefer not to say"))
-Year_study_pie <- pie(table(data_summaries_yos))
-pie(table(data_summaries_yos))
-TSR_data_summaries_yos <- factor(TSR_data$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"))
-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 = "Age", y = "")
-data_summaries_yos <- ggplot(TSR_data_summaries_yos, aes(TSR_data_summaries_yos)) + geom_bar()
-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"))
-TSR_data <- read.csv("./data/TSR data complete.csv")
-View(TSR_data)
-View(TSR_data)
-TSR_data_summaries_yos <- factor(TSR_data$MOSTRECENTyearofstudy, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9), labels = c("Year 11/S4/Year 12(NI)", "Year 12/S5/Year 13(NI)", "Year 13/S6/Year 14(NI)", "I am currently on a gap year", "I am currently on an undergraduate/HE college course", "I am in full-time employment", "I am unemployed", "Other", "Prefer not to say"))
-data_summaries_yos <- ggplot(TSR_data_summaries_yos, aes(TSR_data_summaries_yos)) + geom_bar()
-data_summaries_yos <- ggplot(TSR_data, aes(TSR_data_summaries_yos)) + geom_bar()
-data_summaries_yos + labs(title = "Respondent Most Recent Year of Study", x = "Age", y = "")
-data_summaries_yos + labs(title = "Respondent Most Recent Year of Study", x = "", y = "")
-# save to png file for reports
-ggsave("figures/TSR_data_summaries_age.png")
-# save to png file for reports
-ggsave("figures/TSR_data_summaries_yos.png")
-# JK note: adding in a bar chart here, as according to the interwebs it's (apparently?) more accurate to visualise than bar charts
-data_summaries_age <- ggplot(TSR_data, aes(TSR_data_summaries_age)) + geom_bar()
-data_summaries_age + labs(title = "Respondent Age Distribution", x = "Age", y = "")
-# save to png file for reports
-ggsave("figures/TSR_data_summaries_age.png")
-TSR_data_summaries_gender <- factor(TSR_data$Gender, levels = c(1, 2, 3, 4), labels = c("Male", "Female", "I identify my gender in another way", "Prefer not to say"))
-data_summaries_gender + labs(title = "Respondent gender self-identification", x = "", y = "")
-TSR_data_summaries_gender <- factor(TSR_data$Gender, levels = c(1, 2, 3, 4), labels = c("Male", "Female", "I identify my gender in another way", "Prefer not to say"))
-data_summaries_gender <- ggplot(TSR_data, aes(TSR_data_summaries_gender)) + geom_bar()
-data_summaries_gender + labs(title = "Respondent gender self-identification", x = "", y = "")
-# save to png file for reports
-ggsave("figures/TSR_data_summaries_gender.png")
-install.packages("stringr")          # Install stringr package, used for wrapping label text in plots
-install.packages("stringr")
-library("stringr")                   # Load stringr
-data_summaries_gender + labs(title = "Respondent gender self-identification", x = "", y = "") + str_wrap(x, width = 10))
-data_summaries_gender + labs(title = "Respondent gender self-identification", x = "", y = "") str_wrap(x, width = 10))
-data_summaries_gender + labs(title = "Respondent gender self-identification", x = "", y = "") + str_wrap(x, width = 10)
-data_summaries_gender <- ggplot(TSR_data, aes(TSR_data_summaries_gender)) + geom_bar()
-library(ggplot2)
-library(devtools)
-library(devtools)
-library(usethis)
-library(devtools)
-library(likert)
-# Load RColorBrewer
-# install.packages("RColorBrewer")
-library(RColorBrewer)
-install.packages("stringr")          # Install stringr package, used for wrapping label text in plots
-library("stringr")                   # Load stringr package, used for wrapping label text in plots
-# Define colour palettes for plots below
-coul3 <- brewer.pal(3, "RdYlBu") # Using RdYlBu range to generate 3 colour palette: https://colorbrewer2.org/#type=diverging&scheme=RdYlBu&n=5
-TSR_data <- read.csv("./data/TSR data complete.csv")
-subject_data <- read.csv("./data/Subject data.csv")
-knitr::opts_chunk$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")
-# 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$Age, levels = c(1, 2, 3, 4, 5, 6, 7, 8), labels = c("15 or under", "16", "17", "18", "19", "20", "21 or over", "Prefer not to say"))
-# JK note: adding in a bar chart here, as according to the interwebs it's (apparently?) more accurate to visualise than bar charts
-data_summaries_age <- ggplot(TSR_data, aes(TSR_data_summaries_age)) + geom_bar()
-data_summaries_age + labs(title = "Respondent Age Distribution", x = "Age", y = "")
-TSR_data_summaries_yos <- factor(TSR_data$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")
-TSR_data_summaries_gender <- factor(TSR_data$Gender, levels = c(1, 2, 3, 4), labels = c("Male", "Female", "I identify my gender in another way", "Prefer not to say"))
-data_summaries_gender <- ggplot(TSR_data, aes(TSR_data_summaries_gender)) + geom_bar()
-data_summaries_gender + labs(title = "Respondent gender self-identification", x = "", y = "") + str_wrap(x, width = 10)
-TSR_data_summaries_gender <- str_wrap(TSR_data_summaries_gender, width = 10)
-data_summaries_gender <- ggplot(TSR_data, aes(TSR_data_summaries_gender)) + geom_bar()
-data_summaries_gender + labs(title = "Respondent gender self-identification", x = "", y = "") + str_wrap(x, width = 10)
-data_summaries_gender + labs(title = "Respondent gender self-identification", x = "", y = "")
-# save to png file for reports
-ggsave("figures/TSR_data_summaries_gender.png")
-TSR_data_summaries_ethnicity$ethnicity <- factor(TSR_data$Ethnicity, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 ,18, 19), labels = c("Arab", "Asian/Asian British - Indian", "Asian/Asian British - Pakistani", "Asian/Asian British - Bangladeshi", "Asian/Asian British - Chinese", "Asian/Asian British - Any other Asian background", "Black/Black British - African", "Black/Black British - Caribbean", "Black/Black British - Any other Black background", "Mixed/Multiple Ethnic Groups - White and Black Caribbean", "Mixed/Multiple Ethnic Groups - White and Black African", "Mixed/Multiple Ethnic Groups - White and Black Asian", "Mixed/Multiple Ethnic Groups - Any other Mixed/Multiple Ethnic background", "White - English/Welsh/Scottish/Northern Irish/British", "White - Irish", "White - Gypsy or Irish Traveller", "White - Any other White background", "Other Ethnic group, please describe", "Prefer not to say"))
-TSR_data_summaries_ethnicity <- factor(TSR_data$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", "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 <- ggplot(TSR_data, aes(TSR_data_summaries_ethnicity)) + geom_bar() + coord_flip()
-Ethnicity_bar
-data_summaries_ethnicity <- ggplot(TSR_data, aes(TSR_data_summaries_ethnicity)) + geom_bar() + coord_flip()
-data_summaries_ethnicity
-TSR_data_summaries_ethnicity <- str_wrap(TSR_data_summaries_ethnicity, width = 20)
-data_summaries_ethnicity <- ggplot(TSR_data, aes(TSR_data_summaries_ethnicity)) + geom_bar() + coord_flip()
-data_summaries_ethnicity
-data_summaries_ethnicity <- ggplot(TSR_data, aes(TSR_data_summaries_ethnicity)) + geom_bar() + coord_flip() + xlab(NULL) + ylab(yaxis_label)
-data_summaries_ethnicity <- ggplot(TSR_data, aes(TSR_data_summaries_ethnicity)) + geom_bar() + coord_flip() + xlab(NULL)
-data_summaries_ethnicity
-# save to png file for reports
-ggsave("figures/TSR_data_summaries_ethnicity.png")
-data_summaries_ethnicity <- ggplot(TSR_data, aes(TSR_data_summaries_ethnicity)) + geom_col()) + coord_flip() + xlab(NULL)
-data_summaries_ethnicity <- ggplot(TSR_data, aes(TSR_data_summaries_ethnicity)) + geom_col() + coord_flip() + xlab(NULL)
-data_summaries_ethnicity
-data_summaries_ethnicity <- ggplot(TSR_data, aes(TSR_data_summaries_ethnicity)) + geom_bar() + scale_x_discrete(labels = wrap) + xlab(NULL) + coord_flip()
-data_summaries_ethnicity <- ggplot(TSR_data, aes(TSR_data_summaries_ethnicity)) + geom_bar() +  xlab(NULL) + coord_flip()
-data_summaries_ethnicity
-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
-TSR_data_summaries_ethnicity <- factor(TSR_data$Ethnicity, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 ,18, 19), labels = c("Arab", "Indian", "Pakistani", "Bangladeshi", "Chinese", "Any other Asian background", "African", "Caribbean", "Any other Black background", "White and Black Caribbean", "White and Black African", "White and Black Asian", "Any other Mixed/Multiple Ethnic background", "White - British", "White - Irish", "Gypsy or Irish Traveller", "Any other White background", "Other Ethnic group", "Prefer not to say"))
-TSR_data_summaries_ethnicity <- str_wrap(TSR_data_summaries_ethnicity, width = 30)
-data_summaries_ethnicity <- ggplot(TSR_data, aes(TSR_data_summaries_ethnicity)) + geom_bar() +  xlab(NULL) + coord_flip()
-data_summaries_ethnicity
-TSR_data_summaries_ethnicity <- factor(TSR_data$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
-# save to png file for reports
-ggsave("figures/TSR_data_summaries_ethnicity.png")
-TSR_data_summaries_religion <- factor(TSR_data$Religious.Affliation, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19), labels = c("Agnostic", "Atheist", "Baha'i", "Buddhist", "Christian", "Confucian", "Jain", "Jewish", "Hindu", "Indigenous Traditional Religious", "Muslim", "Pagan", "Shinto", "Sikh", "Spiritual but not religious", "Zoroastrian", "No religion", "Prefer not to say", "Other"))
-Religious_affiliation_bar <- ggplot(TSR_data, aes(Religious.Affliation)) + geom_bar() + coord_flip()
-Religious_affiliation_bar
-Religious_affiliation_bar <- ggplot(TSR_data, aes(TSR_data_summaries_religion)) + geom_bar() + coord_flip()
-Religious_affiliation_bar
-TSR_data_summaries_religion <- factor(TSR_data$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(TSR_data_summaries_religion)) + geom_bar() + coord_flip()
-Religious_affiliation_bar
-Religious_affiliation_bar <- ggplot(TSR_data_summaries_religion, aes(TSR_data_summaries_religion)) + geom_bar() + coord_flip()
-Religious_affiliation_bar
-View(Religious_affiliation_bar)
-TSR_data_summaries_religion <- factor(TSR_data$ReligiousAffliation, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19), labels = c("Agnostic", "Atheist", "Baha'i", "Buddhist", "Christian", "Confucian", "Jain", "Jewish", "Hindu", "Indigenous Traditional Religious", "Muslim", "Pagan", "Shinto", "Sikh", "Spiritual but not religious", "Zoroastrian", "No religion", "Prefer not to say", "Other"))
-Religious_affiliation_bar <- ggplot(TSR_data, aes(Religious.Affliation)) + geom_bar() + coord_flip()
-Religious_affiliation_bar
-Religious_affiliation_bar <- ggplot(TSR_data, aes(TSR_data_summaries_religion)) + geom_bar() + coord_flip()
-Religious_affiliation_bar
-data_summaries_ethnicity + labs(title = "Respondent ethnic self-identification", x = "", y = "")
-data_summaries_ethnicity
-# save to png file for reports
-ggsave("figures/TSR_data_summaries_ethnicity.png")
-TSR_data_summaries_ethnicity <- str_wrap(TSR_data_summaries_ethnicity, width = 30)
-data_summaries_ethnicity <- ggplot(TSR_data, aes(TSR_data_summaries_ethnicity)) + geom_bar() +  xlab(NULL) + coord_flip()
-data_summaries_ethnicity + labs(title = "Respondent ethnic self-identification", x = "", y = "")
-data_summaries_ethnicity
-# save to png file for reports
-ggsave("figures/TSR_data_summaries_ethnicity.png")
-data_summaries_ethnicity + labs(title = "Respondent ethnic self-identification", x = "", y = "")
-# save to png file for reports
-ggsave("figures/TSR_data_summaries_ethnicity.png")
-Religious_affiliation_bar <- ggplot(TSR_data, aes(TSR_data_summaries_religion)) + geom_bar() + coord_flip()
-Religious_affiliation_bar + labs(title = "Respondent religious self-identification", x = "", y = "")
-# save to png file for reports
-ggsave("figures/TSR_data_summaries_religion.png")
-#Q5 Subject Knowledge/Understanding
-subject_data$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
-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")
-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")
-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 from 1 to 5 where 1 represents 'Good employability prospects’ and 5 represents ‘Poor employability prospects'", x = "", y = "")
-# save to png file for reports
-ggsave("figures/TSR_data_subject_employability.png")
-employability_bar + labs(title = "please rate ... employability prospects", x = "", y = "")
-# save to png file for reports
-ggsave("figures/TSR_data_subject_employability.png")
-#likert_test <- likert(subject_data)
-Understanding_data <- TSR_data[, 6:17]
-likert_test_understand <- likert(Understanding_data)
-#likert_test <- likert(subject_data)
-likert(TSR_data[, 6:17])
-#likert_test <- likert(subject_data)
-likert(TSR_data[, 6])
-#likert_test <- likert(subject_data)
-likert(items=TSR_data[, 6], drop=FALSE)
-#likert_test <- likert(subject_data)
-likert(items=TSR_data[, 6])
-#likert_test <- likert(subject_data)
-Understanding_data <- TSR_data[, 6:17]
-View(Understanding_data)
-likert_test_understand <- likert(Understanding_data$GoodunderstandingofTheology)
-likert_test_understand <- likert(Understanding_data)
-understanding_data <- TSR_data[, 6]
-understanding_data <- TSR_data[, 6]
-l24g <- likert(understanding_data)
-TSR_data <- read.csv("./data/TSR data complete.csv")
-understanding_data <- TSR_data[, 6]
-likert_test_understand <- likert(understanding_data)
-as.data.frame(TSR_data)
-#likert_test <- likert(subject_data)
-understanding_data <- as.data.frame(TSR_data)
-View(understanding_mean)
-View(understanding_mean)
-View(understanding_data)
-View(understanding_data)
-likert_test_understand <- likert(understanding_data)
-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)
-View(keysubjects_data)
-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)
-View(keysubjects_data)
-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)
-write.csv(table(keysubjects_data$recode_interest, keysubjects_data$Subject), "derivedData/interest_table.csv", row.names=TRUE)
-interest_table <- table(keysubjects_data$recode_interest, keysubjects_data$Subject)
-write.csv(interest_table, "derivedData/interest_table.csv", row.names=TRUE)
-derivedData
-write.csv(interest_table, "derivedData/interest_table.csv", row.names=TRUE)
-# Set up local workspace:
-if (dir.exists("data") == FALSE) {
-dir.create("data")
-}
-if (dir.exists("figures") == FALSE) {
-dir.create("figures")
-}
-if (dir.exists("derivedData") == FALSE) {
-dir.create("derivedData")
-}
-# export table to csv
-write.csv(interest_table, "derivedData/interest_table.csv", row.names=TRUE)
-### Philosophy ###
-Philos_subset_Low <- TSR_data[TSR_data$GoodunderstandingofPhilosophy < 3 & TSR_data$GoodunderstandingofPhilosophy != 0, ]
-Philos_subset_High <- TSR_data[TSR_data$GoodunderstandingofPhilosophy > 3, ]
-# Low interest cohort
-Philos_subset_Low <- TSR_data[TSR_data$GoodunderstandingofPhilosophy < 3 & TSR_data$GoodunderstandingofPhilosophy != 0, ]
-# High interest cohort
-Philos_subset_High <- TSR_data[TSR_data$GoodunderstandingofPhilosophy > 3, ]
-# Low understanding of philosophy cohort
-Philos_subset_Low <- TSR_data[TSR_data$GoodunderstandingofPhilosophy < 3 & TSR_data$GoodunderstandingofPhilosophy != 0, ]
-# High understanding of philosophy cohort
-Philos_subset_High <- TSR_data[TSR_data$GoodunderstandingofPhilosophy > 3, ]
-subject_data$Subject
-keysubjects_data <- subject_data[subject_data$Subject == "Philosophy" | subject_data$Subject == "Ethics" | subject_data$Subject == "Theology" | subject_data$Subject == "Religious Studies" | subject_data$Subject == "Sociology", ]
-recode_interest <- ifelse(2 <= keysubjects_data$Interest & keysubjects_data$Interest >=4, "Positive", "Negative")
-keysubjects_data <- cbind(keysubjects_data, recode_interest)
-keysubjects_data$recode_interest <- factor(keysubjects_data$recode_interest)
-interest_table <- table(keysubjects_data$recode_interest, keysubjects_data$Subject)
-# export table to csv
-write.csv(interest_table, "derivedData/interest_table.csv", row.names=TRUE)
-keysubjects_data <- subject_data[subject_data$Subject == "Philosophy" | subject_data$Subject == "Ethics" | subject_data$Subject == "Theology" | subject_data$Subject == "Religious Studies" | subject_data$Subject == "Sociology" | subject_data$Subject == "Psychology" | subject_data$Subject == "History" | subject_data$Subject == "Politics" | subject_data$Subject == "English" | subject_data$Subject == "Math" | subject_data$Subject == "Computer Science" | subject_data$Subject == "Business", ]
-recode_interest <- ifelse(2 <= keysubjects_data$Interest & keysubjects_data$Interest >=4, "Positive", "Negative")
-keysubjects_data <- cbind(keysubjects_data, recode_interest)
-keysubjects_data$recode_interest <- factor(keysubjects_data$recode_interest)
-interest_table <- table(keysubjects_data$recode_interest, keysubjects_data$Subject)
-# export table to csv
-write.csv(interest_table, "derivedData/interest_table.csv", row.names=TRUE)
-keysubjects_data$Interest
-prop.table(keysubjects_data$Interest)
-keysubjects_data
-View(subject_data)
-TSR_data_theology_positive <- TSR_data$InterestedinstudyingTheology >=4
-TSR_data_theology_positive
-TSR_data_theology_positive <- TSR_data$InterestedinstudyingTheology[] >=4
-library(dplyr)
-TSR_data_theology_positive %>% filter(TSR_data, InterestedinstudyingTheology >= 4)
-TSR_data$InterestedinstudyingTheology
-TSR_data$InterestedinstudyingTheology >4
-TSR_data$InterestedinstudyingTheology >3
-count(TSR_data$InterestedinstudyingTheology >3)
-length(TSR_data$InterestedinstudyingTheology >3)
-# TSR_data_theology_positive %>%
-filter(TSR_data, InterestedinstudyingTheology >= 4)
-length(TSR_data$InterestedinstudyingTheology >3)
-# TSR_data_theology_positive %>%
-filter(TSR_data, InterestedinstudyingTheology >= 4)
-filter(TSR_data, InterestedinstudyingTheology == 4)
-filter(TSR_data, InterestedinstudyingTheology >= 4)
-TSR_data_theology_positive %>% filter(TSR_data, InterestedinstudyingTheology >= 4)
-TSR_data_theology_positive <- filter(TSR_data, InterestedinstudyingTheology >= 4)
-View(TSR_data_theology_positive)
-TSR_data_theology_positive <- filter(TSR_data, InterestedinstudyingTheology < 3)
-TSR_data_theology_positive <- filter(TSR_data, InterestedinstudyingTheology < 3)
-TSR_data_theology_positive <- filter(TSR_data, InterestedinstudyingTheology > 3)
-TSR_data_theology_positive <- filter(TSR_data, InterestedinstudyingTheology > 2)
-TSR_data_theology_positive <- filter(TSR_data, InterestedinstudyingTheology > 2)
-TSR_data_theology_positive <- filter(TSR_data, InterestedinstudyingTheology < 3)
-TSR_data_theology_positive <- filter(TSR_data, InterestedinstudyingTheology < 2)
-TSR_data_theology_positive <- filter(TSR_data, InterestedinstudyingTheology < 1)
-TSR_data_theology_positive <- filter(TSR_data, InterestedinstudyingTheology >= 4)
-TSR_data_theology_negative <- filter(TSR_data, InterestedinstudyingTheology <= 2)
-TSR_data_theology_positive <- filter(TSR_data, InterestedinstudyingTheology >= 4 & InterestedinstudyingTheology != 0)
-TSR_data_theology_negative <- filter(TSR_data, InterestedinstudyingTheology <= 2 & InterestedinstudyingTheology != 0)
-TSR_data_rs_positive <- filter(TSR_data, InterestedinstudyingReligion >= 4 & InterestedinstudyingReligion != 0)
-TSR_data_rs_positive <- filter(TSR_data, InterestedinstudyingReligiousStudies >= 4 & InterestedinstudyingReligiousStudies != 0)
-TSR_data_rs_negative <- filter(TSR_data, InterestedinstudyingReligiousStudies <= 2 & InterestedinstudyingReligiousStudies != 0)
-knitr::opts_chunk$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
-library(dplyr)    # Used for filtering below
-# 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")
-# Set up local workspace:
-if (dir.exists("data") == FALSE) {
-dir.create("data")
-}
-if (dir.exists("figures") == FALSE) {
-dir.create("figures")
-}
-if (dir.exists("derivedData") == FALSE) {
-dir.create("derivedData")
-}
-TSR_data_theology_positive_summaries_religion <- factor(TSR_data_theology_positive$ReligiousAffliation, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19), labels = c("Agnostic", "Atheist", "Baha'i", "Buddhist", "Christian", "Confucian", "Jain", "Jewish", "Hindu", "Indigenous Traditional Religious", "Muslim", "Pagan", "Shinto", "Sikh", "Spiritual but not religious", "Zoroastrian", "No religion", "Prefer not to say", "Other"))
-TSR_data_theology_positive_summaries_religion <- factor(TSR_data_theology_positive$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"))
-TSR_data_theology_negative_summaries_religion <- factor(TSR_data_theology_negative$ReligiousAffliation, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19), labels = c("Agnostic", "Atheist", "Baha'i", "Buddhist", "Christian", "Confucian", "Jain", "Jewish", "Hindu", "Indigenous Traditional Religious", "Muslim", "Pagan", "Shinto", "Sikh", "Spiritual but not religious", "Zoroastrian", "No religion", "Prefer not to say", "Other"))
-TSR_data_rs_positive_summaries_religion <- factor(TSR_data_rs_positive$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"))
-TSR_data_rs_positive_summaries_religion <- factor(TSR_data_rs_negative$ReligiousAffliation, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19), labels = c("Agnostic", "Atheist", "Baha'i", "Buddhist", "Christian", "Confucian", "Jain", "Jewish", "Hindu", "Indigenous Traditional Religious", "Muslim", "Pagan", "Shinto", "Sikh", "Spiritual but not religious", "Zoroastrian", "No religion", "Prefer not to say", "Other"))
-Religious_affiliation_bar2 <- ggplot(TSR_data, aes(TSR_data_theology_positive_summaries_religion)) + geom_bar() + coord_flip()
-Religious_affiliation_bar2 + labs(title = "Respondent religious self-identification", x = "", y = "")
-Religious_affiliation_bar2 <- ggplot(TSR_data_theology_positive, aes(TSR_data_theology_positive_summaries_religion)) + geom_bar() + coord_flip()
-Religious_affiliation_bar2 + labs(title = "Respondent religious self-identification", x = "", y = "")
-# save to png file for reports
-ggsave("figures/TSR_data_summaries_religion_theologypositive.png")
-TSR_data_rs_positive_summaries_religion <- factor(TSR_data_rs_positive$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"))
-TSR_data_rs_negative_summaries_religion <- factor(TSR_data_rs_negative$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_bar3 <- ggplot(TSR_data_rs_positive, aes(TSR_data_rs_positive_summaries_religion)) + geom_bar() + coord_flip()
-Religious_affiliation_bar3 + labs(title = "Respondent religious self-identification", x = "", y = "")
-# save to png file for reports
-ggsave("figures/TSR_data_summaries_religion_theologypositive.png")
-Religious_affiliation_bar2 <- ggplot(TSR_data_theology_positive, aes(TSR_data_theology_positive_summaries_religion)) + geom_bar() + coord_flip()
-Religious_affiliation_bar2 + labs(title = "Respondent religious self-identification (positive sentiment towards "theology")", x = "", y = "")
-# save to png file for reports
-ggsave("figures/TSR_data_summaries_religion_theologypositive.png")
-Religious_affiliation_bar2 + labs(title = "Respondent religious self-identification - positive sentiment towards theology"), x = "", y = "")
-"
-Religious_affiliation_bar2 + labs(title = "Respondent religious self-identification - positive sentiment towards theology", x = "", y = "")
-Religious_affiliation_bar2 + labs(title = "Respondent religious self-identification, positive sentiment towards theology", x = "", y = "")
-Religious_affiliation_bar2 <- ggplot(TSR_data_theology_positive, aes(TSR_data_theology_positive_summaries_religion)) + geom_bar() + coord_flip()
-Religious_affiliation_bar2 + labs(title = "Respondent religious self-identification, positive sentiment towards theology", x = "", y = "")
-# save to png file for reports
-ggsave("figures/TSR_data_summaries_religion_theologypositive.png")
-Religious_affiliation_bar3 <- ggplot(TSR_data_rs_positive, aes(TSR_data_rs_positive_summaries_religion)) + geom_bar() + coord_flip()
-Religious_affiliation_bar3 + labs(title = "Respondent religious self-identification, positive sentiment towards rs", x = "", y = "")
-# save to png file for reports
-ggsave("figures/TSR_data_summaries_religion_rspositive.png")
-library(scales) # Used for adding percentages to bar charts
-Religious_affiliation_bar3 <- ggplot(TSR_data_rs_positive, aes(TSR_data_rs_positive_summaries_religion, label=scales::percent(pct))) + geom_bar() + coord_flip()
-Religious_affiliation_bar3 + labs(title = "Respondent religious self-identification, positive sentiment towards rs", x = "", y = "")
-Religious_affiliation_bar3 <- ggplot(TSR_data_rs_positive, aes(TSR_data_rs_positive_summaries_religion) + geom_bar() + geom_text(label=scales::percent(pct))) + coord_flip()
-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
-library(dplyr)        # Used for filtering below
 library(scales)       # Used for adding percentages to bar charts
 # 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")
-# Set up local workspace (as needed):
-if (dir.exists("data") == FALSE) {
-dir.create("data")
-}
-if (dir.exists("figures") == FALSE) {
-dir.create("figures")
-}
-if (dir.exists("derivedData") == FALSE) {
-dir.create("derivedData")
-}
 # Set up local workspace, as needed:
 if (dir.exists("data") == FALSE) {
 dir.create("data")
@@ -463,50 +13,500 @@ dir.create("figures")
 if (dir.exists("derivedData") == FALSE) {
 dir.create("derivedData")
 }
-TSR_data_theology_positive_summaries_ethnicity <- factor(TSR_data_theology_positive$Ethnicity, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 ,18, 19), labels = c("Arab", "Indian", "Pakistani", "Bangladeshi", "Chinese", "Any other Asian background", "Black - African", "Black - Caribbean", "Any other Black background", "Mixed - White and Black Caribbean", "Mixed - White and Black African", "Mixed - White and Black Asian", "Any other Mixed/Multiple Ethnic background", "White - British", "White - Irish", "Gypsy or Irish Traveller", "Any other White background", "Other Ethnic group", "Prefer not to say"))
-TSR_data_theology_positive_summaries_ethnicity <- str_wrap(TSR_data_summaries_ethnicity, width = 30)
-TSR_data_rs_positive_summaries_ethnicity <- factor(TSR_data_rs_positive$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_rs_positive_summaries_ethnicity <- str_wrap(TSR_data_summaries_ethnicity, width = 30)
-TSR_data__theology_positive_summaries_gender <- factor(TSR_data_theology_positive$Gender, levels = c(1, 2, 3, 4), labels = c("Male", "Female", "I identify my gender in another way", "Prefer not to say"))
-# JK note: using stringr here to wrap axis titles
-TSR_data__theology_positive_summaries_gender <- str_wrap(TSR_data__theology_positive_summaries_gender, width = 10)
-TSR_data_rs_positive_summaries_gender <- factor(TSR_data_rs_positive$Gender, levels = c(1, 2, 3, 4), labels = c("Male", "Female", "I identify my gender in another way", "Prefer not to say"))
-# JK note: using stringr here to wrap axis titles
-TSR_data_rs_positive_summaries_gender <- str_wrap(TSR_data_rs_positive_summaries_gender, width = 10)
-gender_bar2 <- ggplot(TSR_data_theology_positive, aes(TSR_data__theology_positive_summaries_gender)) + geom_bar()
-gender_bar2 + labs(title = "Respondent gender self-identification, theology positive", x = "", y = "")
-gender_bar2 + labs(title = "Respondent gender self-identification, theology positive", x = "", y = "")
-# save to png file for reports
-ggsave("figures/TSR_data_summaries_gender2.png")
-gender_bar3 <- ggplot(TSR_data_rs_positive, aes(TSR_data_rs_positive_summaries_gender)) + geom_bar()
-gender_bar3 + labs(title = "Respondent gender self-identification, rs positive", x = "", y = "")
-# save to png file for reports
-ggsave("figures/TSR_data_summaries_gender3.png")
-data_summaries_theology_positive_ethnicity <- ggplot(TSR_data_theology_positive, aes(TSR_data_theology_positive_summaries_ethnicity)) + geom_bar() +  xlab(NULL) + coord_flip()
-data_summaries_theology_positive_ethnicity + labs(title = "Respondent ethnic self-identification, theology positive", x = "", y = "")
-data_summaries_theology_positive_ethnicity + labs(title = "Respondent ethnic self-identification, theology positive", x = "", y = "")
-data_summaries_theology_positive_ethnicity <- ggplot(TSR_data_theology_positive, aes(TSR_data_theology_positive_summaries_ethnicity)) + geom_bar() +  xlab(NULL) + coord_flip()
-data_summaries_theology_positive_ethnicity + labs(title = "Respondent ethnic self-identification, theology positive", x = "", y = "")
-data_summaries_theology_positive_ethnicity + labs(title = "Respondent ethnic self-identification", x = "", y = "")
-TSR_data_rs_negative_summaries_religion <- factor(TSR_data_rs_negative$ReligiousAffliation, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19), labels = c("Agnostic", "Atheist", "Baha'i", "Buddhist", "Christian", "Confucian", "Jain", "Jewish", "Hindu", "Indigenous Traditional Religious", "Muslim", "Pagan", "Shinto", "Sikh", "Spiritual but not religious", "Zoroastrian", "No religion", "Prefer not to say", "Other"))
-TSR_data_theology_positive_summaries_ethnicity <- factor(TSR_data_theology_positive$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_theology_positive_summaries_ethnicity <- str_wrap(TSR_data_theology_positive_summaries_ethnicity, width = 30)
-TSR_data_rs_positive_summaries_ethnicity <- factor(TSR_data_rs_positive$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_rs_positive_summaries_ethnicity <- str_wrap(TSR_data_rs_positive_summaries_ethnicity, width = 30)
-TSR_data_rs_positive_summaries_ethnicity <- str_wrap(TSR_data_rs_positive_summaries_ethnicity, width = 30)
-data_summaries_rs_positive_ethnicity <- ggplot(TSR_data_rs_positive, aes(TSR_data_rs_positive_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_ethnicity3.png")
-data_summaries_theology_positive_ethnicity <- ggplot(TSR_data_theology_positive, aes(TSR_data_theology_positive_summaries_ethnicity)) + geom_bar() +  xlab(NULL) + coord_flip()
-data_summaries_theology_positive_ethnicity + labs(title = "Respondent ethnic self-identification", x = "", y = "")
-# save to png file for reports
-ggsave("figures/TSR_data_summaries_ethnicity2.png")
-data_summaries_theology_positive_ethnicity <- ggplot(TSR_data_theology_positive, aes(TSR_data_theology_positive_summaries_ethnicity)) + geom_bar() +  xlab(NULL) + coord_flip()
-data_summaries_theology_positive_ethnicity + labs(title = "Respondent ethnic self-identification - theology positive", x = "", y = "")
-# save to png file for reports
-ggsave("figures/TSR_data_summaries_ethnicity2.png")
-data_summaries_rs_positive_ethnicity <- ggplot(TSR_data_rs_positive, aes(TSR_data_rs_positive_summaries_ethnicity)) + geom_bar() +  xlab(NULL) + coord_flip()
-data_summaries_ethnicity + labs(title = "Respondent ethnic self-identification - religion positive", x = "", y = "")
-# save to png file for reports
-ggsave("figures/TSR_data_summaries_ethnicity3.png")
+Philos_subset_High <- TSR_data[TSR_data$InterestinstudyingPhilosophy > 3, ]
+### Philosophy ###
+Philos_subset_Low <- TSR_data[TSR_data$InterestinstudyingPhilosophy < 3 & TSR_data$InterestinstudyingPhilosophy != 0, ]
+## 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)
+mean(as.numeric(TSR_data$InterestedinstudyingTheology))
+mean(as.numeric(TSR_data$InterestedinstudyingTheology), na.rm = TRUE)
+mean(as.numeric(TSR_data$InterestedinstudyingReligiousStudies), na.rm = TRUE)
+library(kable)        # Used for generating markdown tables
+install.packages("kable")
+## Theology Interest
+#Low interest in Philosophy
+overall_mean_theology_interest <- mean(as.numeric(Philos_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
+#High interest in Philosophy
+overall_mean_rs_interest <- mean(as.numeric(Philos_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
+overall_mean_theology_interest <- mean(as.numeric(TSR_data$InterestedinstudyingTheology), na.rm = TRUE)
+overall_mean_rs_interest <- mean(as.numeric(TSR_data$InterestedinstudyingReligiousStudies), na.rm = TRUE)
+### Philosophy ###
+Philos_subset_Low <- TSR_data[TSR_data$InterestinstudyingPhilosophy < 3 & TSR_data$InterestinstudyingPhilosophy != 0, ]
+Philos_subset_High <- TSR_data[TSR_data$InterestinstudyingPhilosophy > 3, ]
+mean(as.numeric(TSR_data$InterestedinstudyingTheology), na.rm = TRUE)
+mean(as.numeric(TSR_data$InterestedinstudyingReligiousStudies), na.rm = TRUE)
+mean(as.numeric(TSR_data$InterestinstudyingPhilosophy), na.rm = TRUE)
+mean(as.numeric(TSR_data$InterestedinStudyingPsychology), na.rm = TRUE)
+### Philosophy ###
+Philos_subset_Low <- TSR_data[TSR_data$InterestinstudyingPhilosophy < 3 & TSR_data$InterestinstudyingPhilosophy != 0, ]
+Philos_subset_High <- TSR_data[TSR_data$InterestinstudyingPhilosophy > 3, ]
+## Theology Interest
+#Low interest in Philosophy
+philosophy_interest_low_theology_interest_mean <- (as.numeric(Philos_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
+## 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)
+#High interest in Philosophy
+mean(as.numeric(Philos_subset_High$InterestedinstudyingTheology), na.rm = TRUE)
+## Religious Studies Interest
+#Low interest in Philosophy
+mean(as.numeric(Philos_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE)
+#High interest in Philosophy
+mean(as.numeric(Philos_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE)
+### Sociology ###
+Soc_subset_Low <- TSR_data[TSR_data$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)
+## Theology Interest
+#Low interest in Sociology
+lSocTheo <- mean(as.numeric(Soc_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
+## Theology Interest
+#Low interest in Sociology
+mean(as.numeric(Soc_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
+#High interest in Sociology
+mean(as.numeric(Soc_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
+## Theology Interest
+#Low interest in Sociology
+mean(as.numeric(Soc_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
+#High interest in Sociology
+mean(as.numeric(Soc_subset_High$InterestedinstudyingTheology), na.rm = TRUE)
+## Religious Studies Interest
+#Low interest in Sociology
+mean(as.numeric(Soc_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE)
+#High interest in Sociology
+mean(as.numeric(Soc_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE)
+### Psychology ###
+Psych_subset_Low <- TSR_data[TSR_data$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)
+#High interest in Psychology
+mean(as.numeric(Psych_subset_High$InterestedinstudyingTheology), na.rm = TRUE)
+## Religious Studies Interest
+#Low interest in Psychology
+mean(as.numeric(Psych_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE)
+#High interest in Psychology
+mean(as.numeric(Psych_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE)
+### History ###
+Hist_subset_Low <- TSR_data[TSR_data$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$InterestedinstudyingTheology), na.rm = TRUE)
+#High interest in History
+mean(as.numeric(Hist_subset_High$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)
+### 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_High$InterestedinstudyingTheology), na.rm = TRUE)
+## Religious Studies Interest
+#Low interest in Ethics
+mean(as.numeric(Ethics_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE)
+#High interest in Ethics
+mean(as.numeric(Ethics_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE)
+### Politics ###
+Polit_subset_Low <- TSR_data[TSR_data$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$InterestedinstudyingTheology), na.rm = TRUE)
+#High interest in Politics
+mean(as.numeric(Polit_subset_High$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)
+### 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_High$InterestedinstudyingTheology), na.rm = TRUE)
+## Religious Studies Interest
+#Low interest in English
+mean(as.numeric(Eng_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE)
+#High interest in English
+mean(as.numeric(Eng_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE)
+### Math ###
+Math_subset_Low <- TSR_data[TSR_data$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$InterestedinstudyingTheology), na.rm = TRUE)
+#High interest in Math
+mean(as.numeric(Math_subset_High$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)
+## Theology Interest
+#Low interest in Math
+mean(as.numeric(Math_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
+### Math ###
+Math_subset_Low <- TSR_data[TSR_data$InterestedinstudyingMath < 3 & TSR_data$InterestedinstudyingMath != 0, ]
+Math_subset_High <- TSR_data[TSR_data$InterestedinstudyingMath > 3, ]
+## Theology Interest
+#Low interest in Math
+mean(as.numeric(Math_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
+#High interest in Math
+mean(as.numeric(Math_subset_High$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)
+### 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$InterestedinstudyingTheology), na.rm = TRUE)
+#High interest in Computer Science
+mean(as.numeric(CompSci_subset_High$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)
+### 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_High$InterestedinstudyingTheology), na.rm = TRUE)
+## Religious Studies Interest
+#Low interest in Business
+mean(as.numeric(Busi_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE)
+#High interest in Business
+mean(as.numeric(Busi_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE)
+mean(as.numeric(TSR_data$GoodunderstandingofTheology), na.rm = TRUE)
+mean(as.numeric(TSR_data$GoodunderstandingofReligiousStudies), na.rm = TRUE)
+mean(as.numeric(TSR_data$GoodunderstandingofPhilosophy), na.rm = TRUE)
+mean(as.numeric(TSR_data$GoodunderstandingofPsychology), na.rm = TRUE)
+mean(as.numeric(TSR_data$GoodunderstandingofEnglish), na.rm = TRUE)
+mean(as.numeric(TSR_data$GoodunderstandingofMath), na.rm = TRUE)
+# Low understanding of philosophy cohort
+Philos_subset_Low <- TSR_data[TSR_data$GoodunderstandingofPhilosophy < 3 & TSR_data$GoodunderstandingofPhilosophy != 0, ]
+# High understanding of philosophy cohort
+Philos_subset_High <- TSR_data[TSR_data$GoodunderstandingofPhilosophy > 3, ]
+## Theology Knowledge
+#Low knowledge in Philosophy
+mean(as.numeric(Philos_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
+#High knowledge in Philosophy
+mean(as.numeric(Philos_subset_High$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)
+### 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_High$GoodunderstandingofTheology), na.rm = TRUE)
+## Religious Studies knowledge
+#Low knowledge in Sociology
+mean(as.numeric(Soc_subset_Low$GoodunderstandingofReligiousStudies), na.rm = TRUE)
+#High knowledge in Sociology
+mean(as.numeric(Soc_subset_High$GoodunderstandingofReligiousStudies), na.rm = TRUE)
+### Psychology ###
+Psych_subset_Low <- TSR_data[TSR_data$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$GoodunderstandingofTheology), na.rm = TRUE)
+#High knowledge in Psychology
+mean(as.numeric(Psych_subset_High$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)
+### 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_High$GoodunderstandingofTheology), na.rm = TRUE)
+## Religious Studies knowledge
+#Low knowledge in History
+mean(as.numeric(Hist_subset_Low$GoodunderstandingofReligiousStudies), na.rm = TRUE)
+#High knowledge in History
+mean(as.numeric(Hist_subset_High$GoodunderstandingofReligiousStudies), na.rm = TRUE)
+### Ethics ###
+Ethics_subset_Low <- TSR_data[TSR_data$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$GoodunderstandingofTheology), na.rm = TRUE)
+#High knowledge in Ethics
+mean(as.numeric(Ethics_subset_High$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)
+### 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_High$GoodunderstandingofTheology), na.rm = TRUE)
+## Religious Studies knowledge
+#Low knowledge in Politics
+mean(as.numeric(Polit_subset_Low$GoodunderstandingofReligiousStudies), na.rm = TRUE)
+#High knowledge in Politics
+mean(as.numeric(Polit_subset_High$GoodunderstandingofReligiousStudies), na.rm = TRUE)
+### English ###
+Eng_subset_Low <- TSR_data[TSR_data$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)
+#High knowledge in English
+mean(as.numeric(Eng_subset_High$GoodunderstandingofTheology), na.rm = TRUE)
+## Religious Studies knowledge
+#Low knowledge in English
+mean(as.numeric(Eng_subset_Low$GoodunderstandingofReligiousStudies), na.rm = TRUE)
+#High knowledge in English
+mean(as.numeric(Eng_subset_High$GoodunderstandingofReligiousStudies), na.rm = TRUE)
+### Math ###
+Math_subset_Low <- TSR_data[TSR_data$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$GoodunderstandingofTheology), na.rm = TRUE)
+#High knowledge in Math
+mean(as.numeric(Math_subset_High$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)
+### 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_High$GoodunderstandingofTheology), na.rm = TRUE)
+## Religious Studies knowledge
+#Low knowledge in Computer Science
+mean(as.numeric(CompSci_subset_Low$GoodunderstandingofReligiousStudies), na.rm = TRUE)
+#High knowledge in Computer Science
+mean(as.numeric(CompSci_subset_High$GoodunderstandingofReligiousStudies), na.rm = TRUE)
+### Business ###
+Busi_subset_Low <- TSR_data[TSR_data$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$GoodunderstandingofTheology), na.rm = TRUE)
+#High knowledge in Business
+mean(as.numeric(Busi_subset_High$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)
+### 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_High$Theologyemployability), na.rm = TRUE)
+## Religious Studies employability
+#Low employability in Philosophy
+mean(as.numeric(Philos_subset_Low$ReligiousStudiesemployability), na.rm = TRUE)
+#High employability in Philosophy
+mean(as.numeric(Philos_subset_High$ReligiousStudiesemployability), na.rm = TRUE)
+mean(as.numeric(TSR_data$Theologyemployability), na.rm = TRUE)
+mean(as.numeric(TSR_data$ReligiousStudiesemployability), na.rm = TRUE)
+mean(as.numeric(TSR_data$Philosophyemployability), na.rm = TRUE)
+mean(as.numeric(TSR_data$PsychologyEmployability), na.rm = TRUE)
+mean(as.numeric(TSR_data$Englishemployability), na.rm = TRUE)
+mean(as.numeric(TSR_data$Mathemployability), na.rm = TRUE)
+mean(as.numeric(TSR_data$Businessemployability), na.rm = TRUE)
+mean(as.numeric(TSR_data$Theologyemployability), na.rm = TRUE)
+mean(as.numeric(TSR_data$ReligiousStudiesemployability), na.rm = TRUE)
+mean(as.numeric(TSR_data$Philosophyemployability), na.rm = TRUE)
+mean(as.numeric(TSR_data$PsychologyEmployability), na.rm = TRUE)
+mean(as.numeric(TSR_data$Englishemployability), na.rm = TRUE)
+mean(as.numeric(TSR_data$Mathemployability), na.rm = TRUE)
+mean(as.numeric(TSR_data$Businessemployability), na.rm = TRUE)
+mean(as.numeric(TSR_data$ComputerScienceemployability), na.rm = TRUE)
+### 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_High$Theologyemployability), na.rm = TRUE)
+## Religious Studies employability
+#Low employability in Philosophy
+mean(as.numeric(Philos_subset_Low$ReligiousStudiesemployability), na.rm = TRUE)
+#High employability in Philosophy
+mean(as.numeric(Philos_subset_High$ReligiousStudiesemployability), na.rm = TRUE)
+### Sociology ###
+Soc_subset_Low <- TSR_data[TSR_data$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$Theologyemployability), na.rm = TRUE)
+#High employability in Sociology
+mean(as.numeric(Soc_subset_High$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)
+### 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_High$Theologyemployability), na.rm = TRUE)
+## Religious Studies employability
+#Low employability in Psychology
+mean(as.numeric(Psych_subset_Low$ReligiousStudiesemployability), na.rm = TRUE)
+#High employability in Psychology
+mean(as.numeric(Psych_subset_High$ReligiousStudiesemployability), na.rm = TRUE)
+### History ###
+Hist_subset_Low <- TSR_data[TSR_data$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$Theologyemployability), na.rm = TRUE)
+#High employability in History
+mean(as.numeric(Hist_subset_High$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)
+### 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_High$Theologyemployability), na.rm = TRUE)
+## Religious Studies employability
+#Low employability in Ethics
+mean(as.numeric(Ethics_subset_Low$ReligiousStudiesemployability), na.rm = TRUE)
+#High employability in Ethics
+mean(as.numeric(Ethics_subset_High$ReligiousStudiesemployability), na.rm = TRUE)
+### Politics ###
+Polit_subset_Low <- TSR_data[TSR_data$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$Theologyemployability), na.rm = TRUE)
+#High employability in Politics
+mean(as.numeric(Polit_subset_High$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)
+### 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_High$Theologyemployability), na.rm = TRUE)
+## Religious Studies employability
+#Low employability in English
+mean(as.numeric(Eng_subset_Low$ReligiousStudiesemployability), na.rm = TRUE)
+#High employability in English
+mean(as.numeric(Eng_subset_High$ReligiousStudiesemployability), na.rm = TRUE)
+### Math ###
+Math_subset_Low <- TSR_data[TSR_data$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$Theologyemployability), na.rm = TRUE)
+#High employability in Math
+mean(as.numeric(Math_subset_High$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)
+### 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_High$Theologyemployability), na.rm = TRUE)
+## Religious Studies employability
+#Low employability in Computer Science
+mean(as.numeric(CompSci_subset_Low$ReligiousStudiesemployability), na.rm = TRUE)
+#High employability in Computer Science
+mean(as.numeric(CompSci_subset_High$ReligiousStudiesemployability), na.rm = TRUE)
+### Business ###
+Busi_subset_Low <- TSR_data[TSR_data$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$Theologyemployability), na.rm = TRUE)
+#High employability in Business
+mean(as.numeric(Busi_subset_High$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)
diff --git a/tsr_survey_analysis.Rmd b/tsr_survey_analysis.Rmd
index 2d17f0f..820f1fb 100644
--- a/tsr_survey_analysis.Rmd
+++ b/tsr_survey_analysis.Rmd
@@ -16,6 +16,7 @@ library(RColorBrewer)
 library("stringr")    # Load stringr package, used for wrapping label text in plots
 library(dplyr)        # Used for filtering below
 library(scales)       # Used for adding percentages to bar charts
+library(kable)        # Used for generating markdown tables
 
 # 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
@@ -234,6 +235,8 @@ Employability_data <- TSR_data[, 30:41]
 ```{r Visualization for 4 Key Subjects}
 ## Subset by "Positive" / "Negative"
 
+# jk note: I've filled in the other fields just for fun
+
 keysubjects_data <- subject_data[subject_data$Subject == "Philosophy" | subject_data$Subject == "Ethics" | subject_data$Subject == "Theology" | subject_data$Subject == "Religious Studies" | subject_data$Subject == "Sociology" | subject_data$Subject == "Psychology" | subject_data$Subject == "History" | subject_data$Subject == "Politics" | subject_data$Subject == "English" | subject_data$Subject == "Math" | subject_data$Subject == "Computer Science" | subject_data$Subject == "Business", ]
 
 recode_interest <- ifelse(2 <= keysubjects_data$Interest & keysubjects_data$Interest >=4, "Positive", "Negative")
@@ -320,6 +323,13 @@ write.csv(interest_table, "derivedData/interest_table.csv", row.names=TRUE)
 ## Mean Interest in Theology and Religious Studies by High/Low Subject Interest
 
 ```{r Philosophy}
+# Base mean:
+
+mean(as.numeric(TSR_data$InterestedinstudyingTheology), na.rm = TRUE)
+mean(as.numeric(TSR_data$InterestedinstudyingReligiousStudies), na.rm = TRUE)
+mean(as.numeric(TSR_data$InterestinstudyingPhilosophy), na.rm = TRUE)
+mean(as.numeric(TSR_data$InterestedinStudyingPsychology), na.rm = TRUE)
+
 ### Philosophy ###
 Philos_subset_Low <- TSR_data[TSR_data$InterestinstudyingPhilosophy < 3 & TSR_data$InterestinstudyingPhilosophy != 0, ]
 
@@ -329,7 +339,7 @@ Philos_subset_High <- TSR_data[TSR_data$InterestinstudyingPhilosophy > 3, ]
 #Low interest in Philosophy
 mean(as.numeric(Philos_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
 #High interest in Philosophy
-mean(as.numeric(Philos_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
+mean(as.numeric(Philos_subset_High$InterestedinstudyingTheology), na.rm = TRUE)
 
 ## Religious Studies Interest
 #Low interest in Philosophy
@@ -346,9 +356,9 @@ Soc_subset_High <- TSR_data[TSR_data$InterestinstudyingSociaology > 3, ]
 
 ## Theology Interest
 #Low interest in Sociology
-lSocTheo <- mean(as.numeric(Soc_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
+mean(as.numeric(Soc_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
 #High interest in Sociology
-hSocTheo <- mean(as.numeric(Soc_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
+mean(as.numeric(Soc_subset_High$InterestedinstudyingTheology), na.rm = TRUE)
 
 ## Religious Studies Interest
 #Low interest in Sociology
@@ -367,7 +377,7 @@ Psych_subset_High <- TSR_data[TSR_data$InterestedinStudyingPsychology > 3, ]
 #Low interest in Psychology
 mean(as.numeric(Psych_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
 #High interest in Psychology
-mean(as.numeric(Psych_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
+mean(as.numeric(Psych_subset_High$InterestedinstudyingTheology), na.rm = TRUE)
 
 ## Religious Studies Interest
 #Low interest in Psychology
@@ -386,7 +396,7 @@ Hist_subset_High <- TSR_data[TSR_data$InterestedinstudyingHistory > 3, ]
 #Low interest in History
 mean(as.numeric(Hist_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
 #High interest in History
-mean(as.numeric(Hist_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
+mean(as.numeric(Hist_subset_High$InterestedinstudyingTheology), na.rm = TRUE)
 
 ## Religious Studies Interest
 #Low interest in History
@@ -405,7 +415,7 @@ Ethics_subset_High <- TSR_data[TSR_data$InterestedinstudyingEthics > 3, ]
 #Low interest in Ethics
 mean(as.numeric(Ethics_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
 #High interest in Ethics
-mean(as.numeric(Ethics_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
+mean(as.numeric(Ethics_subset_High$InterestedinstudyingTheology), na.rm = TRUE)
 
 ## Religious Studies Interest
 #Low interest in Ethics
@@ -424,7 +434,7 @@ Polit_subset_High <- TSR_data[TSR_data$InterestedinstudyingPolitics > 3, ]
 #Low interest in Politics
 mean(as.numeric(Polit_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
 #High interest in Politics
-mean(as.numeric(Polit_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
+mean(as.numeric(Polit_subset_High$InterestedinstudyingTheology), na.rm = TRUE)
 
 ## Religious Studies Interest
 #Low interest in Politics
@@ -443,7 +453,7 @@ Eng_subset_High <- TSR_data[TSR_data$InterestedinstudyingEnglish > 3, ]
 #Low interest in English
 mean(as.numeric(Eng_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
 #High interest in English
-mean(as.numeric(Eng_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
+mean(as.numeric(Eng_subset_High$InterestedinstudyingTheology), na.rm = TRUE)
 
 ## Religious Studies Interest
 #Low interest in English
@@ -454,7 +464,7 @@ mean(as.numeric(Eng_subset_High$InterestedinstudyingReligiousStudies), na.rm = T
 
 ```{r Math}
 ### Math ###
-Math_subset_Low <- TSR_data[TSR_data$InterestedinstudyingMath < 3 & TSR_data$InterestedinstudyingMathy != 0, ]
+Math_subset_Low <- TSR_data[TSR_data$InterestedinstudyingMath < 3 & TSR_data$InterestedinstudyingMath != 0, ]
 
 Math_subset_High <- TSR_data[TSR_data$InterestedinstudyingMath > 3, ]
 
@@ -462,7 +472,7 @@ Math_subset_High <- TSR_data[TSR_data$InterestedinstudyingMath > 3, ]
 #Low interest in Math
 mean(as.numeric(Math_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
 #High interest in Math
-mean(as.numeric(Math_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
+mean(as.numeric(Math_subset_High$InterestedinstudyingTheology), na.rm = TRUE)
 
 ## Religious Studies Interest
 #Low interest in Math
@@ -481,7 +491,7 @@ CompSci_subset_High <- TSR_data[TSR_data$InterestedinstudyingComputerScience > 3
 #Low interest in Computer Science
 mean(as.numeric(CompSci_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
 #High interest in Computer Science
-mean(as.numeric(CompSci_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
+mean(as.numeric(CompSci_subset_High$InterestedinstudyingTheology), na.rm = TRUE)
 
 ## Religious Studies Interest
 #Low interest in Computer Science
@@ -500,7 +510,7 @@ Busi_subset_High <- TSR_data[TSR_data$InterestedinstudyingBusiness > 3, ]
 #Low interest in Business
 mean(as.numeric(Busi_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
 #High interest in Business
-mean(as.numeric(Busi_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
+mean(as.numeric(Busi_subset_High$InterestedinstudyingTheology), na.rm = TRUE)
 
 ## Religious Studies Interest
 #Low interest in Business
@@ -514,6 +524,15 @@ mean(as.numeric(Busi_subset_High$InterestedinstudyingReligiousStudies), na.rm =
 ## Mean Knowledge in Theology and Religious Studies by High/Low Subject Knowledge
 
 ```{r Philosophy}
+# Base calculations
+
+mean(as.numeric(TSR_data$GoodunderstandingofTheology), na.rm = TRUE)
+mean(as.numeric(TSR_data$GoodunderstandingofReligiousStudies), na.rm = TRUE)
+mean(as.numeric(TSR_data$GoodunderstandingofPhilosophy), na.rm = TRUE)
+mean(as.numeric(TSR_data$GoodunderstandingofPsychology), na.rm = TRUE)
+mean(as.numeric(TSR_data$GoodunderstandingofEnglish), na.rm = TRUE)
+mean(as.numeric(TSR_data$GoodunderstandingofMath), na.rm = TRUE)
+
 ### Philosophy ###
 
 # Low understanding of philosophy cohort
@@ -526,7 +545,7 @@ Philos_subset_High <- TSR_data[TSR_data$GoodunderstandingofPhilosophy > 3, ]
 #Low knowledge in Philosophy
 mean(as.numeric(Philos_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
 #High knowledge in Philosophy
-mean(as.numeric(Philos_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
+mean(as.numeric(Philos_subset_High$GoodunderstandingofTheology), na.rm = TRUE)
 
 ## Religious Studies Knowledge
 #Low knowledge in Philosophy
@@ -545,7 +564,7 @@ Soc_subset_High <- TSR_data[TSR_data$GoodunderstandingofSociology > 3, ]
 #Low knowledge in Sociology
 mean(as.numeric(Soc_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
 #High knowledge in Sociology
-mean(as.numeric(Soc_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
+mean(as.numeric(Soc_subset_High$GoodunderstandingofTheology), na.rm = TRUE)
 
 ## Religious Studies knowledge
 #Low knowledge in Sociology
@@ -564,7 +583,7 @@ Psych_subset_High <- TSR_data[TSR_data$GoodunderstandingofPsychology > 3, ]
 #Low knowledge in Psychology
 mean(as.numeric(Psych_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
 #High knowledge in Psychology
-mean(as.numeric(Psych_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
+mean(as.numeric(Psych_subset_High$GoodunderstandingofTheology), na.rm = TRUE)
 
 ## Religious Studies knowledge
 #Low knowledge in Psychology
@@ -583,7 +602,7 @@ Hist_subset_High <- TSR_data[TSR_data$GoodunderstandingofHistory > 3, ]
 #Low knowledge in History
 mean(as.numeric(Hist_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
 #High knowledge in History
-mean(as.numeric(Hist_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
+mean(as.numeric(Hist_subset_High$GoodunderstandingofTheology), na.rm = TRUE)
 
 ## Religious Studies knowledge
 #Low knowledge in History
@@ -602,7 +621,7 @@ Ethics_subset_High <- TSR_data[TSR_data$GoodunderstandingofEthics > 3, ]
 #Low knowledge in Ethics
 mean(as.numeric(Ethics_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
 #High knowledge in Ethics
-mean(as.numeric(Ethics_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
+mean(as.numeric(Ethics_subset_High$GoodunderstandingofTheology), na.rm = TRUE)
 
 ## Religious Studies knowledge
 #Low knowledge in Ethics
@@ -621,7 +640,7 @@ Polit_subset_High <- TSR_data[TSR_data$GoodunderstandingofPolitics > 3, ]
 #Low knowledge in Politics
 mean(as.numeric(Polit_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
 #High knowledge in Politics
-mean(as.numeric(Polit_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
+mean(as.numeric(Polit_subset_High$GoodunderstandingofTheology), na.rm = TRUE)
 
 ## Religious Studies knowledge
 #Low knowledge in Politics
@@ -640,7 +659,7 @@ Eng_subset_High <- TSR_data[TSR_data$GoodunderstandingofEnglish > 3, ]
 #Low knowledge in English
 mean(as.numeric(Eng_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
 #High knowledge in English
-mean(as.numeric(Eng_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
+mean(as.numeric(Eng_subset_High$GoodunderstandingofTheology), na.rm = TRUE)
 
 ## Religious Studies knowledge
 #Low knowledge in English
@@ -659,7 +678,7 @@ Math_subset_High <- TSR_data[TSR_data$GoodunderstandingofMath > 3, ]
 #Low knowledge in Math
 mean(as.numeric(Math_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
 #High knowledge in Math
-mean(as.numeric(Math_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
+mean(as.numeric(Math_subset_High$GoodunderstandingofTheology), na.rm = TRUE)
 
 ## Religious Studies knowledge
 #Low knowledge in Math
@@ -678,7 +697,7 @@ CompSci_subset_High <- TSR_data[TSR_data$GoodunderstandingofComputerScience > 3,
 #Low knowledge in Computer Science
 mean(as.numeric(CompSci_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
 #High knowledge in Computer Science
-mean(as.numeric(CompSci_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
+mean(as.numeric(CompSci_subset_High$GoodunderstandingofTheology), na.rm = TRUE)
 
 ## Religious Studies knowledge
 #Low knowledge in Computer Science
@@ -697,7 +716,7 @@ Busi_subset_High <- TSR_data[TSR_data$GoodunderstandingofBusiness > 3, ]
 #Low knowledge in Business
 mean(as.numeric(Busi_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
 #High knowledge in Business
-mean(as.numeric(Busi_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
+mean(as.numeric(Busi_subset_High$GoodunderstandingofTheology), na.rm = TRUE)
 
 ## Religious Studies knowledge
 #Low knowledge in Business
@@ -711,6 +730,17 @@ mean(as.numeric(Busi_subset_High$GoodunderstandingofReligiousStudies), na.rm = T
 ## Mean Employability in Theology and Religious Studies by High/Low Subject Employability
 
 ```{r Philosophy}
+# Base Calculations
+
+mean(as.numeric(TSR_data$Theologyemployability), na.rm = TRUE)
+mean(as.numeric(TSR_data$ReligiousStudiesemployability), na.rm = TRUE)
+mean(as.numeric(TSR_data$Philosophyemployability), na.rm = TRUE)
+mean(as.numeric(TSR_data$PsychologyEmployability), na.rm = TRUE)
+mean(as.numeric(TSR_data$Englishemployability), na.rm = TRUE)
+mean(as.numeric(TSR_data$Mathemployability), na.rm = TRUE)
+mean(as.numeric(TSR_data$Businessemployability), na.rm = TRUE)
+mean(as.numeric(TSR_data$ComputerScienceemployability), na.rm = TRUE)
+
 ### Philosophy ###
 Philos_subset_Low <- TSR_data[TSR_data$Philosophyemployability < 3 & TSR_data$Philosophyemployability != 0, ]
 
@@ -720,7 +750,7 @@ Philos_subset_High <- TSR_data[TSR_data$Philosophyemployability > 3, ]
 #Low employability in Philosophy
 mean(as.numeric(Philos_subset_Low$Theologyemployability), na.rm = TRUE)
 #High employability in Philosophy
-mean(as.numeric(Philos_subset_Low$Theologyemployability), na.rm = TRUE)
+mean(as.numeric(Philos_subset_High$Theologyemployability), na.rm = TRUE)
 
 ## Religious Studies employability
 #Low employability in Philosophy
@@ -739,7 +769,7 @@ Soc_subset_High <- TSR_data[TSR_data$Sociologyemployability > 3, ]
 #Low employability in Sociology
 mean(as.numeric(Soc_subset_Low$Theologyemployability), na.rm = TRUE)
 #High employability in Sociology
-mean(as.numeric(Soc_subset_Low$Theologyemployability), na.rm = TRUE)
+mean(as.numeric(Soc_subset_High$Theologyemployability), na.rm = TRUE)
 
 ## Religious Studies employability
 #Low employability in Sociology
@@ -758,7 +788,7 @@ Psych_subset_High <- TSR_data[TSR_data$PsychologyEmployability > 3, ]
 #Low employability in Psychology
 mean(as.numeric(Psych_subset_Low$Theologyemployability), na.rm = TRUE)
 #High employability in Psychology
-mean(as.numeric(Psych_subset_Low$Theologyemployability), na.rm = TRUE)
+mean(as.numeric(Psych_subset_High$Theologyemployability), na.rm = TRUE)
 
 ## Religious Studies employability
 #Low employability in Psychology
@@ -777,7 +807,7 @@ Hist_subset_High <- TSR_data[TSR_data$Historyemployability > 3, ]
 #Low employability in History
 mean(as.numeric(Hist_subset_Low$Theologyemployability), na.rm = TRUE)
 #High employability in History
-mean(as.numeric(Hist_subset_Low$Theologyemployability), na.rm = TRUE)
+mean(as.numeric(Hist_subset_High$Theologyemployability), na.rm = TRUE)
 
 ## Religious Studies employability
 #Low employability in History
@@ -796,7 +826,7 @@ Ethics_subset_High <- TSR_data[TSR_data$Ethicsemployability > 3, ]
 #Low employability in Ethics
 mean(as.numeric(Ethics_subset_Low$Theologyemployability), na.rm = TRUE)
 #High employability in Ethics
-mean(as.numeric(Ethics_subset_Low$Theologyemployability), na.rm = TRUE)
+mean(as.numeric(Ethics_subset_High$Theologyemployability), na.rm = TRUE)
 
 ## Religious Studies employability
 #Low employability in Ethics
@@ -815,7 +845,7 @@ Polit_subset_High <- TSR_data[TSR_data$Politicsemployability > 3, ]
 #Low employability in Politics
 mean(as.numeric(Polit_subset_Low$Theologyemployability), na.rm = TRUE)
 #High employability in Politics
-mean(as.numeric(Polit_subset_Low$Theologyemployability), na.rm = TRUE)
+mean(as.numeric(Polit_subset_High$Theologyemployability), na.rm = TRUE)
 
 ## Religious Studies employability
 #Low employability in Politics
@@ -834,7 +864,7 @@ Eng_subset_High <- TSR_data[TSR_data$Englishemployability > 3, ]
 #Low employability in English
 mean(as.numeric(Eng_subset_Low$Theologyemployability), na.rm = TRUE)
 #High employability in English
-mean(as.numeric(Eng_subset_Low$Theologyemployability), na.rm = TRUE)
+mean(as.numeric(Eng_subset_High$Theologyemployability), na.rm = TRUE)
 
 ## Religious Studies employability
 #Low employability in English
@@ -853,7 +883,7 @@ Math_subset_High <- TSR_data[TSR_data$Mathemployability > 3, ]
 #Low employability in Math
 mean(as.numeric(Math_subset_Low$Theologyemployability), na.rm = TRUE)
 #High employability in Math
-mean(as.numeric(Math_subset_Low$Theologyemployability), na.rm = TRUE)
+mean(as.numeric(Math_subset_High$Theologyemployability), na.rm = TRUE)
 
 ## Religious Studies employability
 #Low employability in Math
@@ -872,7 +902,7 @@ CompSci_subset_High <- TSR_data[TSR_data$ComputerScienceemployability > 3, ]
 #Low employability in Computer Science
 mean(as.numeric(CompSci_subset_Low$Theologyemployability), na.rm = TRUE)
 #High employability in Computer Science
-mean(as.numeric(CompSci_subset_Low$Theologyemployability), na.rm = TRUE)
+mean(as.numeric(CompSci_subset_High$Theologyemployability), na.rm = TRUE)
 
 ## Religious Studies employability
 #Low employability in Computer Science
@@ -891,7 +921,7 @@ Busi_subset_High <- TSR_data[TSR_data$Businessemployability > 3, ]
 #Low employability in Business
 mean(as.numeric(Busi_subset_Low$Theologyemployability), na.rm = TRUE)
 #High employability in Business
-mean(as.numeric(Busi_subset_Low$Theologyemployability), na.rm = TRUE)
+mean(as.numeric(Busi_subset_High$Theologyemployability), na.rm = TRUE)
 
 ## Religious Studies employability
 #Low employability in Business