--- title: "RMarkdown Admissions_Survey2021" output: html_document --- ```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE) library(ggplot2) library(usethis) library(devtools) library(likert) library(RColorBrewer) library("readxl") library(haven) # Define colour palettes for plots below coul3 <- brewer.pal(3, "RdYlBu") # Using RdYlBu range to generate 3 colour palette: https://colorbrewer2.org/#type=diverging&scheme=RdYlBu&n=5 TSR_data <- read.csv("./data/TSR data complete.csv") subject_data <- read.csv("./data/Subject data.csv") admissions_data <- read_excel("./data/TSR_data_numbers.xlsx", sheet = "Raw data - completes") # Set up local workspace, as needed: if (dir.exists("data") == FALSE) { dir.create("data") } # These paths are excluded from github as it is best practice for end-user to generate their own if (dir.exists("figures") == FALSE) { dir.create("figures") } if (dir.exists("derivedData") == FALSE) { dir.create("derivedData") } # Refactor data q2_labels <- c("15 or under" = 1, "16" = 2, "17" = 3, "18" = 4, "19" = 5, "20" = 6, "21 or over" = 7, "Prefer not to say" = 8) admissions_data$Q2 <- labelled(admissions_data$Q2, q2_labels, label = "How old are you?") admissions_data$Q3 <- labelled(admissions_data$Q3, c("Year 11/S4/Year 12(NI)" = 1, "Year 12/S5/Year 13(NI)" = 2, "Year 13/S6/Year 14(NI)" = 3, "I am currently on a gap year" = 4, "I am currently on an undergraduate/HE college course" = 5, "I am in full-time employment" = 6, "I am unemployed" = 7, "Other" = 8, "Prefer not to say" = 9), label = "Which of the following best describes your MOST RECENT year of study?") admissions_data$Q4 <- labelled(admissions_data$Q4, c("Yes, definitely" = 1, "Yes, probably" = 2, "I haven’t ruled it out" = 3), label = "Are you considering or planning to go to university in the future?") common_labels <- c( "Strongly agree" = 1, "Agree" = 2, "Neither/Nor" = 3, "Disagree" = 4, "Strongly disagree" = 5, "Prefer not to say" = 0 ) admissions_data <- admissions_data %>% mutate_at(vars(starts_with("Q5_")), ~ labelled(., common_labels, label = "I have a good understanding of what this subject involves")) admissions_data <- admissions_data %>% mutate_at(vars(starts_with("Q6_")), ~ labelled(., common_labels, label = "I would be interested in studying this subject at University")) common_labels2 <- c( "Good employability prospects" = 1, NULL = 2, NULL = 3, NULL = 4, "Poor employability prospects" = 5, "Prefer not to say" = 0 ) admissions_data <- admissions_data %>% mutate_at(vars(starts_with("Q7_")), ~ labelled(., common_labels2, label = "This subject has… employability prospects")) admissions_data$Q8 <- labelled(admissions_data$Q8, c("Theology is a subject for religious people" = 1, NULL = 2, NULL = 3, NULL = 4, "Theology is a subject for religious and non-religious people" = 5, "Prefer not to say" = 0), label = "Thinking about Theology, please select an option on the scale from 1 to 5 which best represents your opinion") admissions_data$Q9 <- labelled(admissions_data$Q9, c("Religion is a subject for religious people" = 1, NULL = 2, NULL = 3, NULL = 4, "Religion is a subject for religious and non-religious people" = 5, "Prefer not to say" = 0), label = "Thinking about Religion, please select an option on the scale from 1 to 5 which best represents your opinion") common_labels3 <- c( "Psychology" = 1, "Arts" = 2, "Sociology" = 3, "Politics" = 4, "History" = 5, "Philosophy" = 6, "Ethics" = 7, "Archaeology" = 8, "Textual studies" = 9, "Literature" = 10, "Law" = 11, "Economics" = 12, "Science" = 13, "Prefer not to say" = 0) admissions_data <- admissions_data %>% mutate_at(vars(starts_with("Q10_")), ~ labelled(., common_labels3, label = "I think that a theology degree would include...")) admissions_data <- admissions_data %>% mutate_at(vars(starts_with("Q11_")), ~ labelled(., common_labels3, label = "I think that a religious studies degree would include...")) common_labels4 <- c( "Politics" = 1, "History" = 2, "Ethics" = 3, "Theology" = 4, "Religion" = 5, "Law" = 6, "Economics" = 7, "Maths" = 8, "Logic" = 9, "Prefer not to say" = 0) admissions_data <- admissions_data %>% mutate_at(vars(starts_with("Q12_")), ~ labelled(., common_labels4, label = "I think that a philosophy degree would include...")) common_labels5 <- c("Yes" = 1, "No" = 2, "Prefer not to say" = 0) admissions_data <- admissions_data %>% mutate_at(vars(starts_with("Q13")), ~ labelled(., common_labels5, label = "Are you currently studying A level Religious Studies, or intending to?")) admissions_data <- admissions_data %>% mutate_at(vars(starts_with("Q14")), ~ labelled(., common_labels5, label = "Are you studying or did you previously study GCSE Religious Studies?")) admissions_data$Q16 <- labelled(admissions_data$Q16, c("Male"=1, "Female"=2, "I identify my gender in another way"=3, "Prefer not to say"=4), label = "I identify my gender as…") admissions_data$Q17 <- labelled(admissions_data$Q17, c("Arab"=1, "Indian"=2, "Pakistani"=3, "Bangladeshi"=4, "Chinese"=5, "Any other Asian background"=6, "Black - African"=7, "Black - Caribbean"=8, "Any other Black background"=9, "Mixed - White and Black Caribbean"=10, "Mixed - White and Black African"=11, "Mixed - White and Black Asian"=12, "Any other Mixed/Multiple Ethnic background"=13, "White - British"=14, "White - Irish"=15, "Gypsy or Irish Traveller"=16, "Any other White background"=17, "Other Ethnic group"=18, "Prefer not to say OR Other"=0), label = "What is your ethnic group?") admissions_data$Q18 <- labelled(admissions_data$Q18, c("Agnostic"=1, "Atheist"=2, "Baha'i"=3, "Buddhist"=4, "Christian"=5, "Confucian"=6, "Jain"=7, "Jewish"=8, "Hindu"=9, "Indigenous Traditional Religious"=10, "Muslim"=11, "Pagan"=12, "Shinto"=13, "Sikh"=14, "Spiritual but not religious"=15, "Zoroastrian"=16, "No religion"=17, "Prefer not to say OR Other"=0), label = "What is your religion?") # Create bins: # For Q5 - understanding admissions_data <- admissions_data %>% mutate( Q5_Theology = ifelse(Q5_Theology == 0, NA, Q5_Theology), understanding_theology_bin = case_when( Q5_Theology < 3 ~ "high", Q5_Theology > 3 ~ "low", Q5_Theology == 3 ~ "neutral", TRUE ~ NA ) %>% factor(levels = c("low", "neutral", "high")) ) # For Q5 - understanding admissions_data <- admissions_data %>% mutate( Q5_Religious_Studies = ifelse(Q5_Religious_Studies == 0, NA, Q5_Religious_Studies), understanding_religion_bin = case_when( Q5_Religious_Studies < 3 ~ "high", Q5_Religious_Studies > 3 ~ "low", Q5_Religious_Studies == 3 ~ "neutral", TRUE ~ NA ) %>% factor(levels = c("low", "neutral", "high")) ) # For Q6 - interest admissions_data <- admissions_data %>% mutate( Q6_Theology = ifelse(Q6_Theology == 0, NA, Q6_Theology), interest_theology_bin = case_when( Q6_Theology < 3 ~ "high", Q6_Theology > 3 ~ "low", Q6_Theology == 3 ~ "neutral", TRUE ~ NA ) %>% factor(levels = c("low", "neutral", "high")) ) # For Q6 - interest admissions_data <- admissions_data %>% mutate( Q6_Religious_Studies = ifelse(Q6_Religious_Studies == 0, NA, Q6_Religious_Studies), interest_religion_bin = case_when( Q6_Religious_Studies < 3 ~ "high", Q6_Religious_Studies > 3 ~ "low", Q6_Religious_Studies == 3 ~ "neutral", TRUE ~ NA ) %>% factor(levels = c("low", "neutral", "high")) ) # For Q7 - employability prospects admissions_data <- admissions_data %>% mutate( Q7_Theology = ifelse(Q7_Theology == 0, NA, Q7_Theology), employability_optimism_theology_bin = case_when( Q7_Theology < 3 ~ "high", Q7_Theology > 3 ~ "low", Q7_Theology == 3 ~ "neutral", TRUE ~ NA ) %>% factor(levels = c("low", "neutral", "high")) ) # For Q7 - employability prospects admissions_data <- admissions_data %>% mutate( Q7_Religious_Studies = ifelse(Q7_Religious_Studies == 0, NA, Q7_Religious_Studies), employability_optimism_religion_bin = case_when( Q7_Religious_Studies < 3 ~ "high", Q7_Religious_Studies > 3 ~ "low", Q7_Religious_Studies == 3 ~ "neutral", TRUE ~ NA ) %>% factor(levels = c("low", "neutral", "high")) ) # For Q8 - employability prospects admissions_data <- admissions_data %>% mutate( Q8 = ifelse(Q8 == 6, NA, Q8), theology_for_bin = case_when( Q8 < 3 ~ "religious", Q8 > 3 ~ "all_people", Q8 == 3 ~ "neutral", TRUE ~ NA ) %>% factor(levels = c("all_people", "neutral", "religious")) ) admissions_data <- admissions_data %>% mutate( Q9 = ifelse(Q9 == 6, NA, Q9), religion_for_bin = case_when( Q9 < 3 ~ "religious", Q9 > 3 ~ "all_people", Q9 == 3 ~ "neutral", TRUE ~ NA ) %>% factor(levels = c("all_people", "neutral", "religious")) ) # Q17 non-white / white ethnicity bins admissions_data <- admissions_data %>% mutate( Q17 = ifelse(Q17 == 0, NA, Q17), ethnicity_bin = case_when( Q17 > 13 | Q17 < 18 ~ "white", TRUE ~ "non-white" ) %>% factor(levels = c("white", "non-white")) ) # Q18 non-religious / institutioal bins admissions_data <- admissions_data %>% mutate( Q18 = ifelse(Q18 == 0, NA, Q18), religion_bin = case_when( Q18 %in% c(1, 2, 5, 17) ~ "non-religious", TRUE ~ "religious" ) %>% factor(levels = c("non-religious", "religious")) ) ``` ```{r correlations} Q5_data <- select(admissions_data, Q5_Philosophy:Q5_Business) cor(admissions_data$Q5_Philosophy, admissions_data$Q6_Philosophy) ``` # Basic demographic summary visualisations: ```{r demographic_summaries} # 1. Calculate for age of respondent: age <- factor(admissions_data$Q2), 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(admissions_data$Q2), col=coul3, cex = 0.8) # JK note: adding in a bar chart here, as according to the interwebs it's (apparently?) more accurate to visualise than bar charts ggplot(admissions_data, aes(factor(Q2))) + geom_bar() + geom_text(stat = "count", aes(label = after_stat(count)), vjust = -0.5) + labs(title = "Respondent Age Distribution", x = "Age", y = "") + scale_x_discrete(labels = labels(q2_labels)) # save to png file for reports ggsave("figures/TSR_data_summaries_age.png") ``` ```{r} # 2. Calculate for year of study: TSR_data_summaries_yos <- factor(TSR_data$MOSTRECENTyearofstudy, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9), labels = c("Year 11/S4/Year 12(NI)", "Year 12/S5/Year 13(NI)", "Year 13/S6/Year 14(NI)", "I am currently on a gap year", "I am currently on an undergraduate/HE college course", "I am in full-time employment", "I am unemployed", "Other", "Prefer not to say")) data_summaries_yos <- ggplot(TSR_data, aes(TSR_data_summaries_yos)) + geom_bar() data_summaries_yos + labs(title = "Respondent Most Recent Year of Study", x = "", y = "") # save to png file for reports ggsave("figures/TSR_data_summaries_yos.png") ``` ```{r} # 3. Gender identity: TSR_data_summaries_gender <- factor(TSR_data$Gender, levels = c(1, 2, 3, 4), labels = c("Male", "Female", "I identify my gender in another way", "Prefer not to say")) # JK note: using stringr here to wrap axis titles TSR_data_summaries_gender <- str_wrap(TSR_data_summaries_gender, width = 10) data_summaries_gender <- ggplot(TSR_data, aes(TSR_data_summaries_gender)) + geom_bar() data_summaries_gender + labs(title = "Respondent gender self-identification", x = "", y = "") # save to png file for reports ggsave("figures/TSR_data_summaries_gender.png") # 4. Ethnic self-identification: TSR_data_summaries_ethnicity <- factor(TSR_data$Ethnicity, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 ,18, 19), labels = c("Arab", "Indian", "Pakistani", "Bangladeshi", "Chinese", "Any other Asian background", "Black - African", "Black - Caribbean", "Any other Black background", "Mixed - White and Black Caribbean", "Mixed - White and Black African", "Mixed - White and Black Asian", "Any other Mixed/Multiple Ethnic background", "White - British", "White - Irish", "Gypsy or Irish Traveller", "Any other White background", "Other Ethnic group", "Prefer not to say")) TSR_data_summaries_ethnicity <- str_wrap(TSR_data_summaries_ethnicity, width = 30) data_summaries_ethnicity <- ggplot(TSR_data, aes(TSR_data_summaries_ethnicity)) + geom_bar() + xlab(NULL) + coord_flip() data_summaries_ethnicity + labs(title = "Respondent ethnic self-identification", x = "", y = "") # save to png file for reports ggsave("figures/TSR_data_summaries_ethnicity.png") # 5. Religion TSR_data_summaries_religion <- factor(TSR_data$ReligiousAffliation, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19), labels = c("Agnostic", "Atheist", "Baha'i", "Buddhist", "Christian", "Confucian", "Jain", "Jewish", "Hindu", "Indigenous Traditional Religious", "Muslim", "Pagan", "Shinto", "Sikh", "Spiritual but not religious", "Zoroastrian", "No religion", "Prefer not to say", "Other")) # Adding a subset for charting out composition of group which specifically marked positive sentiments wrt/ theology or religious studies TSR_data_theology_positive <- filter(TSR_data, InterestedinstudyingTheology >= 4 & InterestedinstudyingTheology != 0) TSR_data_theology_negative <- filter(TSR_data, InterestedinstudyingTheology <= 2 & InterestedinstudyingTheology != 0) TSR_data_rs_positive <- filter(TSR_data, InterestedinstudyingReligiousStudies >= 4 & InterestedinstudyingReligiousStudies != 0) TSR_data_rs_negative <- filter(TSR_data, InterestedinstudyingReligiousStudies <= 2 & InterestedinstudyingReligiousStudies != 0) TSR_data_theology_positive_summaries_religion <- factor(TSR_data_theology_positive$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_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$Ethnicity, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 ,18, 19), labels = c("Arab", "Indian", "Pakistani", "Bangladeshi", "Chinese", "Any other Asian background", "Black - African", "Black - Caribbean", "Any other Black background", "Mixed - White and Black Caribbean", "Mixed - White and Black African", "Mixed - White and Black Asian", "Any other Mixed/Multiple Ethnic background", "White - British", "White - Irish", "Gypsy or Irish Traveller", "Any other White background", "Other Ethnic group", "Prefer not to say")) TSR_data_rs_positive_summaries_ethnicity <- str_wrap(TSR_data_rs_positive_summaries_ethnicity, width = 30) TSR_data_theology_positive_summaries_gender <- factor(TSR_data_theology_positive$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) # Calculate graphs 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") # Additional graphs for theology/rs positive sentiment cohorts # Theology - religious identification # JK note: need to add percentages to each line, as per https://stackoverflow.com/questions/52373049/display-percentage-on-ggplot-bar-chart-in-r 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") # RS 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") # Theology positive - gender 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 = "") # save to png file for reports ggsave("figures/TSR_data_summaries_gender2.png") # Religion positive - gender 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") # Theology positive - ethnicity 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") # Religion positive - ethnicity 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") ``` # Visualisations of LIKERT responses (RH): - For questions Q6 (subject interest) / Q5 (subject knowledge) / Q7 employability prospects: - visualisation as summaries for all subjects LIKERT data as stacked bar chart (colours for bar segments from cool to warm) ```{r Visualization by Subject} ## Files have been reverse scored - Higher score now indicates more agreement #Q5 Subject Knowledge/Understanding subject_data$Subject <- factor(subject_data$Subject, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), labels = c("Philosophy", "Sociology", "Psychology", "History", "Ethics", "Theology", "Religious Studies", "Politics", "English", "Math", "Computer Science", "Business")) understanding_mean <- aggregate(Understanding ~ Subject, data = subject_data, mean) understanding_bar <- ggplot(understanding_mean, aes(x = Subject, y = Understanding)) + stat_summary(fun = "mean", geom = "bar") + coord_flip() understanding_bar + labs(title = "I have a good understanding of what this subject involves?", x = "", y = "") # save to png file for reports ggsave("figures/TSR_data_subject_understanding.png") #Q6 Subject Interest interest_mean <- aggregate(Interest ~ Subject, data = subject_data, mean) interest_bar <- ggplot(interest_mean, aes(x = Subject, y = Interest)) + stat_summary(fun = "mean", geom = "bar") + coord_flip() interest_bar + labs(title = "I would be interested in studying this subject at University?", x = "", y = "") # save to png file for reports ggsave("figures/TSR_data_subject_interest.png") #Q7 Employability Prospects employability_mean <- aggregate(Employability ~ Subject, data = subject_data, mean) employability_bar <- ggplot(employability_mean, aes(x = Subject, y = Employability)) + stat_summary(fun = "mean", geom = "bar") + coord_flip() employability_bar + labs(title = "please rate ... employability prospects", x = "", y = "") # save to png file for reports ggsave("figures/TSR_data_subject_employability.png") ``` ```{r} ### Likert Stacked Bar Chart ### # Take selection of data re: understanding Understanding_data <- TSR_data[, 6:17] # Convert each column to factors Understanding_data$GoodunderstandingofPhilosophy = factor(Understanding_data$GoodunderstandingofPhilosophy, ordered = TRUE, levels = c("1", "2", "3", "4", "5") ) Understanding_data$GoodunderstandingofSociology = factor(Understanding_data$GoodunderstandingofSociology, ordered = TRUE, levels = c("1", "2", "3", "4", "5") ) Understanding_data$GoodunderstandingofPsychology = factor(Understanding_data$GoodunderstandingofPsychology, ordered = TRUE, levels = c("1", "2", "3", "4", "5") ) Understanding_data$GoodunderstandingofHistory = factor(Understanding_data$GoodunderstandingofHistory, ordered = TRUE, levels = c("1", "2", "3", "4", "5") ) Understanding_data$GoodunderstandingofEthics = factor(Understanding_data$GoodunderstandingofEthics, ordered = TRUE, levels = c("1", "2", "3", "4", "5") ) Understanding_data$GoodunderstandingofTheology = factor(Understanding_data$GoodunderstandingofTheology, ordered = TRUE, levels = c("1", "2", "3", "4", "5") ) Understanding_data$GoodunderstandingofReligiousStudies = factor(Understanding_data$GoodunderstandingofReligiousStudies, ordered = TRUE, levels = c("1", "2", "3", "4", "5") ) Understanding_data$GoodunderstandingofPolitics = factor(Understanding_data$GoodunderstandingofPolitics, ordered = TRUE, levels = c("1", "2", "3", "4", "5") ) Understanding_data$GoodunderstandingofEnglish = factor(Understanding_data$GoodunderstandingofEnglish, ordered = TRUE, levels = c("1", "2", "3", "4", "5") ) Understanding_data$GoodunderstandingofMath = factor(Understanding_data$GoodunderstandingofMath, ordered = TRUE, levels = c("1", "2", "3", "4", "5") ) Understanding_data$GoodunderstandingofComputerScience = factor(Understanding_data$GoodunderstandingofComputerScience, ordered = TRUE, levels = c("1", "2", "3", "4", "5") ) Understanding_data$GoodunderstandingofBusiness = factor(Understanding_data$GoodunderstandingofBusiness, ordered = TRUE, levels = c("1", "2", "3", "4", "5") ) names(Understanding_data) <- c("Philosophy", "Sociology", "Psychology", "History", "Ethics", "Theology", "Religious Studies", "Politics", "English", "Math", "Computer Science", "Business") str(Understanding_data) levels(Understanding_data) summary(Understanding_data) likert_test_understand <- likert(Understanding_data) Understanding_data <- Q5_data likert_test_understand <- likert(Q5_data) plot(likert_test_understand) # save to png file for reports ggsave("figures/understanding_likert.png") ``` ```{r} Interest_data <- TSR_data[, 18:29] # Convert each column to factors Interest_data$InterestinstudyingPhilosophy = factor(Interest_data$InterestinstudyingPhilosophy, ordered = TRUE, levels = c("1", "2", "3", "4", "5") ) Interest_data$InterestinstudyingSociaology = factor(Interest_data$InterestinstudyingSociaology, ordered = TRUE, levels = c("1", "2", "3", "4", "5") ) Interest_data$InterestedinStudyingPsychology = factor(Interest_data$InterestedinStudyingPsychology, ordered = TRUE, levels = c("1", "2", "3", "4", "5") ) Interest_data$InterestedinstudyingHistory = factor(Interest_data$InterestedinstudyingHistory, ordered = TRUE, levels = c("1", "2", "3", "4", "5") ) Interest_data$InterestedinstudyingEthics = factor(Interest_data$InterestedinstudyingEthics, ordered = TRUE, levels = c("1", "2", "3", "4", "5") ) Interest_data$InterestedinstudyingTheology = factor(Interest_data$InterestedinstudyingTheology, ordered = TRUE, levels = c("1", "2", "3", "4", "5") ) Interest_data$InterestedinstudyingReligiousStudies = factor(Interest_data$InterestedinstudyingReligiousStudies, ordered = TRUE, levels = c("1", "2", "3", "4", "5") ) Interest_data$InterestedinstudyingPolitics = factor(Interest_data$InterestedinstudyingPolitics, ordered = TRUE, levels = c("1", "2", "3", "4", "5") ) Interest_data$InterestedinstudyingEnglish = factor(Interest_data$InterestedinstudyingEnglish, ordered = TRUE, levels = c("1", "2", "3", "4", "5") ) Interest_data$InterestedinstudyingMath = factor(Interest_data$InterestedinstudyingMath, ordered = TRUE, levels = c("1", "2", "3", "4", "5") ) Interest_data$InterestedinstudyingComputerScience = factor(Interest_data$InterestedinstudyingComputerScience, ordered = TRUE, levels = c("1", "2", "3", "4", "5") ) Interest_data$InterestedinstudyingBusiness = factor(Interest_data$InterestedinstudyingBusiness, ordered = TRUE, levels = c("1", "2", "3", "4", "5") ) names(Interest_data) <- c("Philosophy", "Sociology", "Psychology", "History", "Ethics", "Theology", "Religious Studies", "Politics", "English", "Math", "Computer Science", "Business") likert_test_interest <- likert(Interest_data) plot(likert_test_interest) # save to png file for reports ggsave("figures/interest_likert.png") Employability_data <- TSR_data[, 30:41] # Convert each column to factors Employability_data$Philosophyemployability = factor(Employability_data$Philosophyemployability, ordered = TRUE, levels = c("1", "2", "3", "4", "5") ) Employability_data$Sociologyemployability = factor(Employability_data$Sociologyemployability, ordered = TRUE, levels = c("1", "2", "3", "4", "5") ) Employability_data$PsychologyEmployability = factor(Employability_data$PsychologyEmployability, ordered = TRUE, levels = c("1", "2", "3", "4", "5") ) Employability_data$Historyemployability = factor(Employability_data$Historyemployability, ordered = TRUE, levels = c("1", "2", "3", "4", "5") ) Employability_data$Ethicsemployability = factor(Employability_data$Ethicsemployability, ordered = TRUE, levels = c("1", "2", "3", "4", "5") ) Employability_data$Theologyemployability = factor(Employability_data$Theologyemployability, ordered = TRUE, levels = c("1", "2", "3", "4", "5") ) Employability_data$ReligiousStudiesemployability = factor(Employability_data$ReligiousStudiesemployability, ordered = TRUE, levels = c("1", "2", "3", "4", "5") ) Employability_data$Politicsemployability = factor(Employability_data$Politicsemployability, ordered = TRUE, levels = c("1", "2", "3", "4", "5") ) Employability_data$Englishemployability = factor(Employability_data$Englishemployability, ordered = TRUE, levels = c("1", "2", "3", "4", "5") ) Employability_data$Mathemployability = factor(Employability_data$Mathemployability, ordered = TRUE, levels = c("1", "2", "3", "4", "5") ) Employability_data$ComputerScienceemployability = factor(Employability_data$ComputerScienceemployability, ordered = TRUE, levels = c("1", "2", "3", "4", "5") ) Employability_data$Businessemployability = factor(Employability_data$Businessemployability, ordered = TRUE, levels = c("1", "2", "3", "4", "5") ) names(Employability_data) <- c("Philosophy", "Sociology", "Psychology", "History", "Ethics", "Theology", "Religious Studies", "Politics", "English", "Math", "Computer Science", "Business") likert_test_employability <- likert(Employability_data) plot(likert_test_employability) # save to png file for reports ggsave("figures/employability_likert.png") ``` - separate visualisation of summary data as pie chart only for 4 key subjects: Philosophy, Ethics, Theology, Religious Studies, but with data represented as aggregated "Positive" / "Negative" responses ```{r Visualization for 4 Key Subjects} ## Subset by "Positive" / "Negative" # jk note: I've filled in the other fields just for fun keysubjects_data <- subject_data[subject_data$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$Interest >=4, "Positive", "Negative") keysubjects_data <- cbind(keysubjects_data, recode_interest) keysubjects_data$recode_interest <- factor(keysubjects_data$recode_interest) interest_table <- table(keysubjects_data$recode_interest, keysubjects_data$Subject) # export table to csv write.csv(interest_table, "derivedData/interest_table.csv", row.names=TRUE) ``` - subsetted visualisations of responses with separate subsetting by response to Q8-9, Q18, Q17, Q16 - For question Q8 + Q9 (for religious people) - visualisation summary of responses - show subsetted visualisations of responses by response to, Q18, Q17, Q16, Q13, Q14 - For responses to Q10-12 (what subjects are involved in...): - represent answer counts as descending bar chart for each Q - subset answers by Q6 (positive / negative) and Q5 (positive / negative) # Correlation testing: - For Q6 (subject interest) / Q5 (subject knowledge) / Q7 employability prospects, test for nature / strength of correlation with responses to: - Q8-9 responses - Q18 responses - Q17 ```{r Q6 Correlations - Subject Interest} #Q8-9 (8 - Theology as subject for religious people; 9 - Religion as study for religious people) # This would be suitable for correlation #Q17 (Ethnicity) # This would be categorical, so ANOVA #Q18 (Religion) # This would also be categorical, so ANOVA ``` ```{r Q5 Correlations - Subject Knowledge} #Q8-9 (8 - Theology as subject for religious people; 9 - Religion as study for religious people) # This would be suitable for correlation #Q17 (Ethnicity) # This would be categorical, so ANOVA #Q18 (Religion) # This would also be categorical, so ANOVA ``` ```{r Q7 Correlations - Employability} #Q8-9 (8 - Theology as subject for religious people; 9 - Religion as study for religious people) # This would be suitable for correlation #Q17 (Ethnicity) # This would be categorical, so ANOVA #Q18 (Religion) # This would also be categorical, so ANOVA ``` ```{r testing fun} #testcor_data <- subject_data[subject_data$Subject == "Psychology" | subject_data$Subject == "Theology", ] #t.test(Interest ~ Subject, data = testcor_data) # testsubset <- TSR_data[TSR_data$Interested.in.Studying.Psychology < 3 & TSR_data$Interested.in.Studying.Psychology != 0, ] # as.numeric(testsubset$Interested.in.studying.Theology) # mean(testsubset$Interested.in.studying.Theology, na.rm = TRUE) # # mean(as.numeric(testsubset$Interested.in.studying.Theology), na.rm = TRUE) # # as.numeric(testsubset$Interested.in.studying.Religious.Studies) # mean(testsubset$Interested.in.studying.Religious.Studies, na.rm = TRUE) # # testsubset2 <- TSR_data[TSR_data$Interested.in.Studying.Psychology > 3, ] # mean(as.numeric(testsubset2$Interested.in.studying.Theology), na.rm = TRUE) # mean(as.numeric(testsubset2$Interested.in.studying.Religious.Studies), na.rm = TRUE) ``` ## Mean Interest in Theology and Religious Studies by High/Low Subject Interest ```{r Philosophy} # Base mean: mean(as.numeric(TSR_data$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$InterestinstudyingPhilosophy != 0, ] Philos_subset_High <- TSR_data[TSR_data$InterestinstudyingPhilosophy > 3, ] ## Theology Interest #Low interest in Philosophy mean(as.numeric(Philos_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) #High interest in Philosophy mean(as.numeric(Philos_subset_High$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) ``` ```{r Sociology} ### Sociology ### Soc_subset_Low <- TSR_data[TSR_data$InterestinstudyingSociaology < 3 & TSR_data$InterestinstudyingSociaology != 0, ] Soc_subset_High <- TSR_data[TSR_data$InterestinstudyingSociaology > 3, ] ## Theology Interest #Low interest in Sociology mean(as.numeric(Soc_subset_Low$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$InterestedinstudyingReligiousStudies), na.rm = TRUE) ``` ```{r Psychology} ### Psychology ### Psych_subset_Low <- TSR_data[TSR_data$InterestedinStudyingPsychology < 3 & TSR_data$InterestedinStudyingPsychology != 0, ] Psych_subset_High <- TSR_data[TSR_data$InterestedinStudyingPsychology > 3, ] ## Theology Interest #Low interest in Psychology mean(as.numeric(Psych_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) #High interest in Psychology mean(as.numeric(Psych_subset_High$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) ``` ```{r History} ### History ### Hist_subset_Low <- TSR_data[TSR_data$InterestedinstudyingHistory < 3 & TSR_data$InterestedinstudyingHistory != 0, ] Hist_subset_High <- TSR_data[TSR_data$InterestedinstudyingHistory > 3, ] ## Theology Interest #Low interest in History mean(as.numeric(Hist_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) #High interest in History mean(as.numeric(Hist_subset_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$InterestedinstudyingReligiousStudies), na.rm = TRUE) ``` ```{r Ethics} ### Ethics ### Ethics_subset_Low <- TSR_data[TSR_data$InterestedinstudyingEthics < 3 & TSR_data$InterestedinstudyingEthics != 0, ] Ethics_subset_High <- TSR_data[TSR_data$InterestedinstudyingEthics > 3, ] ## Theology Interest #Low interest in Ethics mean(as.numeric(Ethics_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) #High interest in Ethics mean(as.numeric(Ethics_subset_High$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) ``` ```{r Politics} ### Politics ### Polit_subset_Low <- TSR_data[TSR_data$InterestedinstudyingPolitics < 3 & TSR_data$InterestedinstudyingPolitics != 0, ] Polit_subset_High <- TSR_data[TSR_data$InterestedinstudyingPolitics > 3, ] ## Theology Interest #Low interest in Politics mean(as.numeric(Polit_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) #High interest in Politics mean(as.numeric(Polit_subset_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$InterestedinstudyingReligiousStudies), na.rm = TRUE) ``` ```{r English} ### English ### Eng_subset_Low <- TSR_data[TSR_data$InterestedinstudyingEnglish < 3 & TSR_data$InterestedinstudyingEnglish != 0, ] Eng_subset_High <- TSR_data[TSR_data$InterestedinstudyingEnglish > 3, ] ## Theology Interest #Low interest in English mean(as.numeric(Eng_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) #High interest in English mean(as.numeric(Eng_subset_High$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) ``` ```{r Math} ### 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$InterestedinstudyingReligiousStudies), na.rm = TRUE) ``` ```{r Computer Science} ### Computer Science ### CompSci_subset_Low <- TSR_data[TSR_data$InterestedinstudyingComputerScience < 3 & TSR_data$InterestedinstudyingComputerScience != 0, ] CompSci_subset_High <- TSR_data[TSR_data$InterestedinstudyingComputerScience > 3, ] ## Theology Interest #Low interest in Computer Science mean(as.numeric(CompSci_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) #High interest in Computer Science mean(as.numeric(CompSci_subset_High$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) ``` ```{r Business} ### Business ### Busi_subset_Low <- TSR_data[TSR_data$InterestedinstudyingBusiness < 3 & TSR_data$InterestedinstudyingBusiness != 0, ] Busi_subset_High <- TSR_data[TSR_data$InterestedinstudyingBusiness > 3, ] ## Theology Interest #Low interest in Business mean(as.numeric(Busi_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) #High interest in Business mean(as.numeric(Busi_subset_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$InterestedinstudyingReligiousStudies), na.rm = TRUE) ``` ## Mean Knowledge in Theology and Religious Studies by High/Low Subject Knowledge ```{r Philosophy} # Base calculations mean(as.numeric(TSR_data$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$GoodunderstandingofMath), na.rm = TRUE) ### Philosophy ### # Low understanding of philosophy cohort Philos_subset_Low <- TSR_data[TSR_data$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) ``` ```{r Sociology} ### Sociology ### Soc_subset_Low <- TSR_data[TSR_data$GoodunderstandingofSociology < 3 & TSR_data$GoodunderstandingofSociology != 0, ] Soc_subset_High <- TSR_data[TSR_data$GoodunderstandingofSociology > 3, ] ## Theology knowledge #Low knowledge in Sociology mean(as.numeric(Soc_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) #High knowledge in Sociology mean(as.numeric(Soc_subset_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$GoodunderstandingofReligiousStudies), na.rm = TRUE) ``` ```{r Psychology} ### Psychology ### Psych_subset_Low <- TSR_data[TSR_data$GoodunderstandingofPsychology < 3 & TSR_data$GoodunderstandingofPsychology != 0, ] Psych_subset_High <- TSR_data[TSR_data$GoodunderstandingofPsychology > 3, ] ## Theology knowledge #Low knowledge in Psychology mean(as.numeric(Psych_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) #High knowledge in Psychology mean(as.numeric(Psych_subset_High$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) ``` ```{r History} ### History ### Hist_subset_Low <- TSR_data[TSR_data$GoodunderstandingofHistory < 3 & TSR_data$GoodunderstandingofHistory != 0, ] Hist_subset_High <- TSR_data[TSR_data$GoodunderstandingofHistory > 3, ] ## Theology knowledge #Low knowledge in History mean(as.numeric(Hist_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) #High knowledge in History mean(as.numeric(Hist_subset_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$GoodunderstandingofReligiousStudies), na.rm = TRUE) ``` ```{r Ethics} ### Ethics ### Ethics_subset_Low <- TSR_data[TSR_data$GoodunderstandingofEthics < 3 & TSR_data$GoodunderstandingofEthics != 0, ] Ethics_subset_High <- TSR_data[TSR_data$GoodunderstandingofEthics > 3, ] ## Theology knowledge #Low knowledge in Ethics mean(as.numeric(Ethics_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) #High knowledge in Ethics mean(as.numeric(Ethics_subset_High$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) ``` ```{r Politics} ### Politics ### Polit_subset_Low <- TSR_data[TSR_data$GoodunderstandingofPolitics < 3 & TSR_data$GoodunderstandingofPolitics != 0, ] Polit_subset_High <- TSR_data[TSR_data$GoodunderstandingofPolitics > 3, ] ## Theology knowledge #Low knowledge in Politics mean(as.numeric(Polit_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) #High knowledge in Politics mean(as.numeric(Polit_subset_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$GoodunderstandingofReligiousStudies), na.rm = TRUE) ``` ```{r English} ### English ### Eng_subset_Low <- TSR_data[TSR_data$GoodunderstandingofEnglish < 3 & TSR_data$GoodunderstandingofEnglish != 0, ] Eng_subset_High <- TSR_data[TSR_data$GoodunderstandingofEnglish > 3, ] ## Theology knowledge #Low knowledge in English mean(as.numeric(Eng_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) #High knowledge in English mean(as.numeric(Eng_subset_High$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) ``` ```{r Math} ### Math ### Math_subset_Low <- TSR_data[TSR_data$GoodunderstandingofMath < 3 & TSR_data$GoodunderstandingofMath != 0, ] Math_subset_High <- TSR_data[TSR_data$GoodunderstandingofMath > 3, ] ## Theology knowledge #Low knowledge in Math mean(as.numeric(Math_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) #High knowledge in Math mean(as.numeric(Math_subset_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$GoodunderstandingofReligiousStudies), na.rm = TRUE) ``` ```{r Computer Science} ### Computer Science ### CompSci_subset_Low <- TSR_data[TSR_data$GoodunderstandingofComputerScience < 3 & TSR_data$GoodunderstandingofComputerScience != 0, ] CompSci_subset_High <- TSR_data[TSR_data$GoodunderstandingofComputerScience > 3, ] ## Theology knowledge #Low knowledge in Computer Science mean(as.numeric(CompSci_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) #High knowledge in Computer Science mean(as.numeric(CompSci_subset_High$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) ``` ```{r Business} ### Business ### Busi_subset_Low <- TSR_data[TSR_data$GoodunderstandingofBusiness < 3 & TSR_data$GoodunderstandingofBusiness != 0, ] Busi_subset_High <- TSR_data[TSR_data$GoodunderstandingofBusiness > 3, ] ## Theology knowledge #Low knowledge in Business mean(as.numeric(Busi_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) #High knowledge in Business mean(as.numeric(Busi_subset_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$GoodunderstandingofReligiousStudies), na.rm = TRUE) ``` ## Mean Employability in Theology and Religious Studies by High/Low Subject Employability ```{r Philosophy} # Base Calculations mean(as.numeric(TSR_data$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$Philosophyemployability != 0, ] Philos_subset_High <- TSR_data[TSR_data$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) ``` ```{r Sociology} ### Sociology ### Soc_subset_Low <- TSR_data[TSR_data$Sociologyemployability < 3 & TSR_data$Sociologyemployability != 0, ] Soc_subset_High <- TSR_data[TSR_data$Sociologyemployability > 3, ] ## Theology employability #Low employability in Sociology mean(as.numeric(Soc_subset_Low$Theologyemployability), na.rm = TRUE) #High employability in Sociology mean(as.numeric(Soc_subset_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$ReligiousStudiesemployability), na.rm = TRUE) ``` ```{r Psychology} ### Psychology ### Psych_subset_Low <- TSR_data[TSR_data$PsychologyEmployability < 3 & TSR_data$PsychologyEmployability != 0, ] Psych_subset_High <- TSR_data[TSR_data$PsychologyEmployability > 3, ] ## Theology employability #Low employability in Psychology mean(as.numeric(Psych_subset_Low$Theologyemployability), na.rm = TRUE) #High employability in Psychology mean(as.numeric(Psych_subset_High$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) ``` ```{r History} ### History ### Hist_subset_Low <- TSR_data[TSR_data$Historyemployability < 3 & TSR_data$Historyemployability != 0, ] Hist_subset_High <- TSR_data[TSR_data$Historyemployability > 3, ] ## Theology employability #Low employability in History mean(as.numeric(Hist_subset_Low$Theologyemployability), na.rm = TRUE) #High employability in History mean(as.numeric(Hist_subset_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$ReligiousStudiesemployability), na.rm = TRUE) ``` ```{r Ethics} ### Ethics ### Ethics_subset_Low <- TSR_data[TSR_data$Ethicsemployability < 3 & TSR_data$Ethicsemployability != 0, ] Ethics_subset_High <- TSR_data[TSR_data$Ethicsemployability > 3, ] ## Theology employability #Low employability in Ethics mean(as.numeric(Ethics_subset_Low$Theologyemployability), na.rm = TRUE) #High employability in Ethics mean(as.numeric(Ethics_subset_High$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) ``` ```{r Politics} ### Politics ### Polit_subset_Low <- TSR_data[TSR_data$Politicsemployability < 3 & TSR_data$Politicsemployability != 0, ] Polit_subset_High <- TSR_data[TSR_data$Politicsemployability > 3, ] ## Theology employability #Low employability in Politics mean(as.numeric(Polit_subset_Low$Theologyemployability), na.rm = TRUE) #High employability in Politics mean(as.numeric(Polit_subset_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$ReligiousStudiesemployability), na.rm = TRUE) ``` ```{r English} ### English ### Eng_subset_Low <- TSR_data[TSR_data$Englishemployability < 3 & TSR_data$Englishemployability != 0, ] Eng_subset_High <- TSR_data[TSR_data$Englishemployability > 3, ] ## Theology employability #Low employability in English mean(as.numeric(Eng_subset_Low$Theologyemployability), na.rm = TRUE) #High employability in English mean(as.numeric(Eng_subset_High$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) ``` ```{r Math} ### Math ### Math_subset_Low <- TSR_data[TSR_data$Mathemployability < 3 & TSR_data$Mathemployability != 0, ] Math_subset_High <- TSR_data[TSR_data$Mathemployability > 3, ] ## Theology employability #Low employability in Math mean(as.numeric(Math_subset_Low$Theologyemployability), na.rm = TRUE) #High employability in Math mean(as.numeric(Math_subset_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$ReligiousStudiesemployability), na.rm = TRUE) ``` ```{r Computer Science} ### Computer Science ### CompSci_subset_Low <- TSR_data[TSR_data$ComputerScienceemployability < 3 & TSR_data$ComputerScienceemployability != 0, ] CompSci_subset_High <- TSR_data[TSR_data$ComputerScienceemployability > 3, ] ## Theology employability #Low employability in Computer Science mean(as.numeric(CompSci_subset_Low$Theologyemployability), na.rm = TRUE) #High employability in Computer Science mean(as.numeric(CompSci_subset_High$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) ``` ```{r Business} ### Business ### Busi_subset_Low <- TSR_data[TSR_data$Businessemployability < 3 & TSR_data$Businessemployability != 0, ] Busi_subset_High <- TSR_data[TSR_data$Businessemployability > 3, ] ## Theology employability #Low employability in Business mean(as.numeric(Busi_subset_Low$Theologyemployability), na.rm = TRUE) #High employability in Business mean(as.numeric(Busi_subset_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) ```