From 29dba6fed57c9de9177a9b77821e5c722844bf6f Mon Sep 17 00:00:00 2001 From: Jeremy Kidwell Date: Sun, 12 Dec 2021 22:41:32 +0000 Subject: [PATCH] tidied code, added graphs and labels --- .Rhistory | 40 +++ To-do list Markdown.Rmd | 770 ---------------------------------------- 2 files changed, 40 insertions(+), 770 deletions(-) delete mode 100644 To-do list Markdown.Rmd diff --git a/.Rhistory b/.Rhistory index e69de29..04b71af 100644 --- a/.Rhistory +++ b/.Rhistory @@ -0,0 +1,40 @@ +knitr::opts_chunk$set(echo = TRUE) +TSR_data <- read.csv("./data/TSR data complete.csv") +```{r respondent age} +TSR_data$Age <- factor(TSR_data$Age, levels = c(1, 2, 3, 4, 5, 6, 7, 8), labels = c("15 or under", "16", "17", "18", "19", "20", "21 or over", "Prefer not to say")) +```{r respondent_age} +age_pie <- pie(table(TSR_data$Age)) +TSR_data$MOST.RECENT.year.of.study <- factor(TSR_data$MOST.RECENT.year.of.study, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9), labels = c("Year 11/S4/Year 12(NI)", "Year 12/S5/Year 13(NI)", "Year 13/S6/Year 14(NI)", "I am currently on a gap year", "I am currently on an undergraduate/HE college course", "I am in full-time employment", "I am unemployed", "Other", "Prefer not to say")) +Year_study_pie <- pie(table(TSR_data$MOST.RECENT.year.of.study)) +TSR_data$Gender <- factor(TSR_data$Gender, levels = c(1, 2, 3, 4), labels = c("Male", "Female", "I identify my gender in another way", "Prefer not to say")) +gender_pie <- pie(table(TSR_data$Gender)) +library(ggplot2) +TSR_data$Ethnicity <- factor(TSR_data$Ethnicity, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 ,18, 19), labels = c("Arab", "Asian/Asian British - Indian", "Asian/Asian British - Pakistani", "Asian/Asian British - Bangladeshi", "Asian/Asian British - Chinese", "Asian/Asian British - Any other Asian background", "Black/Black British - African", "Black/Black British - Caribbean", "Black/Black British - Any other Black background", "Mixed/Multiple Ethnic Groups - White and Black Caribbean", "Mixed/Multiple Ethnic Groups - White and Black African", "Mixed/Multiple Ethnic Groups - White and Black Asian", "Mixed/Multiple Ethnic Groups - Any other Mixed/Multiple Ethnic background", "White - English/Welsh/Scottish/Northern Irish/British", "White - Irish", "White - Gypsy or Irish Traveller", "White - Any other White background", "Other Ethnic group, please describe", "Prefer not to say")) +Ethnicity_bar <- ggplot(TSR_data, aes(Ethnicity)) + geom_bar() + coord_flip() +Ethnicity_bar +TSR_data$Religious.Affliation <- factor(TSR_data$Religious.Affliation, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19), labels = c("Agnostic", "Atheist", "Baha'i", "Buddhist", "Christian", "Confucian", "Jain", "Jewish", "Hindu", "Indigenous Traditional Religious", "Muslim", "Pagan", "Shinto", "Sikh", "Spiritual but not religious", "Zoroastrian", "No religion", "Prefer not to say", "Other")) +Religious_affiliation_bar <- ggplot(TSR_data, aes(Religious.Affliation)) + geom_bar() + coord_flip() +Religious_affiliation_bar +subject_data <- read.csv("./data/Subject data.csv") +#Q5 Subject Knowledge/Understanding +subject_data$Subject <- factor(subject_data$Subject, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), labels = c("Philosophy", "Sociology", "Psychology", "History", "Ethics", "Theology", "Religious Studies", "Politics", "English", "Math", "Computer Science", "Business")) +understanding_mean <- aggregate(Understanding ~ Subject, data = subject_data, mean) +understanding_bar <- ggplot(understanding_mean, aes(x = Subject, y = Understanding)) + stat_summary(fun = "mean", geom = "bar") + coord_flip() +understanding_bar +#Q6 Subject Interest +interest_mean <- aggregate(Interest ~ Subject, data = subject_data, mean) +interest_bar <- ggplot(interest_mean, aes(x = Subject, y = Interest)) + stat_summary(fun = "mean", geom = "bar") + coord_flip() +interest_bar +employability_mean <- aggregate(Employability ~ Subject, data = subject_data, mean) +employability_bar <- ggplot(employability_mean, aes(x = Subject, y = Employability)) + stat_summary(fun = "mean", geom = "bar") + coord_flip() +employability_bar +keysubjects_data <- subject_data[subject_data$Subject == "Philosophy" | subject_data$Subject == "Ethics" | subject_data$Subject == "Theology" | subject_data$Subject == "Religious Studies", ] +recode_interest <- ifelse(1 <= keysubjects_data$Interest & keysubjects_data$Interest >=3, "Positive", "Negative") +keysubjects_data <- cbind(keysubjects_data, recode_interest) +keysubjects_data$recode_interest <- factor(keysubjects_data$recode_interest) +table(keysubjects_data$recode_interest, keysubjects_data$Subject) +understanding_bar +#Q6 Subject Interest +interest_mean <- aggregate(Interest ~ Subject, data = subject_data, mean) +interest_bar <- ggplot(interest_mean, aes(x = Subject, y = Interest)) + stat_summary(fun = "mean", geom = "bar") + coord_flip() +interest_bar diff --git a/To-do list Markdown.Rmd b/To-do list Markdown.Rmd deleted file mode 100644 index 531841d..0000000 --- a/To-do list Markdown.Rmd +++ /dev/null @@ -1,770 +0,0 @@ ---- -title: "RMarkdown Admissions_Survey2021" -output: html_document ---- - -```{r setup, include=FALSE} -knitr::opts_chunk$set(echo = TRUE) -library(ggplot2) -``` - -# Upload Data -```{r Upload Data} -TSR_data <- read.csv("./data/TSR data complete.csv") -subject_data <- read.csv("./data/Subject data.csv") -``` - -# Basic summary visualisations (RH): -- Q2 (respondent age) -```{r respondent age} -TSR_data$Age <- factor(TSR_data$Age, levels = c(1, 2, 3, 4, 5, 6, 7, 8), labels = c("15 or under", "16", "17", "18", "19", "20", "21 or over", "Prefer not to say")) -age_pie <- pie(table(TSR_data$Age)) - -``` -- Q3 (year of study) -```{r year of study} -TSR_data$MOST.RECENT.year.of.study <- factor(TSR_data$MOST.RECENT.year.of.study, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9), labels = c("Year 11/S4/Year 12(NI)", "Year 12/S5/Year 13(NI)", "Year 13/S6/Year 14(NI)", "I am currently on a gap year", "I am currently on an undergraduate/HE college course", "I am in full-time employment", "I am unemployed", "Other", "Prefer not to say")) - -Year_study_pie <- pie(table(TSR_data$MOST.RECENT.year.of.study)) - -``` -- Q16 (gender identity) -```{r gender identity} - -TSR_data$Gender <- factor(TSR_data$Gender, levels = c(1, 2, 3, 4), labels = c("Male", "Female", "I identify my gender in another way", "Prefer not to say")) - -gender_pie <- pie(table(TSR_data$Gender)) -``` -- Q17 (ethnic self-id) -```{r ethnic self-id} -TSR_data$Ethnicity <- factor(TSR_data$Ethnicity, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 ,18, 19), labels = c("Arab", "Asian/Asian British - Indian", "Asian/Asian British - Pakistani", "Asian/Asian British - Bangladeshi", "Asian/Asian British - Chinese", "Asian/Asian British - Any other Asian background", "Black/Black British - African", "Black/Black British - Caribbean", "Black/Black British - Any other Black background", "Mixed/Multiple Ethnic Groups - White and Black Caribbean", "Mixed/Multiple Ethnic Groups - White and Black African", "Mixed/Multiple Ethnic Groups - White and Black Asian", "Mixed/Multiple Ethnic Groups - Any other Mixed/Multiple Ethnic background", "White - English/Welsh/Scottish/Northern Irish/British", "White - Irish", "White - Gypsy or Irish Traveller", "White - Any other White background", "Other Ethnic group, please describe", "Prefer not to say")) - -Ethnicity_bar <- ggplot(TSR_data, aes(Ethnicity)) + geom_bar() + coord_flip() -Ethnicity_bar -``` -- Q18 (religion) -```{r religion} -TSR_data$Religious.Affliation <- factor(TSR_data$Religious.Affliation, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19), labels = c("Agnostic", "Atheist", "Baha'i", "Buddhist", "Christian", "Confucian", "Jain", "Jewish", "Hindu", "Indigenous Traditional Religious", "Muslim", "Pagan", "Shinto", "Sikh", "Spiritual but not religious", "Zoroastrian", "No religion", "Prefer not to say", "Other")) - -Religious_affiliation_bar <- ggplot(TSR_data, aes(Religious.Affliation)) + geom_bar() + coord_flip() -Religious_affiliation_bar - -``` - -# Visualisations of LIKERT responses (RH): -- For questions Q6 (subject interest) / Q5 (subject knowledge) / Q7 employability prospects: - - visualisation as summaries for all subjects LIKERT data as stacked bar chart (colours for bar segments from cool to warm) - -```{r Visualization by Subject} -### Each Subject is a different column so will need to figure out how to code the columns together into one graph - # Higher score indicates less agreement...need to reverse score - -## 1=5, 2=4, 3=3, 4=2, 5=1, 6=0 --- Not done yet. See what they look like without reverse scoring - -## Files have been reverse scored - Higher score now indicates more agreement - - -#Q5 Subject Knowledge/Understanding -subject_data$Subject <- factor(subject_data$Subject, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), labels = c("Philosophy", "Sociology", "Psychology", "History", "Ethics", "Theology", "Religious Studies", "Politics", "English", "Math", "Computer Science", "Business")) - -understanding_mean <- aggregate(Understanding ~ Subject, data = subject_data, mean) - -understanding_bar <- ggplot(understanding_mean, aes(x = Subject, y = Understanding)) + stat_summary(fun = "mean", geom = "bar") + coord_flip() -understanding_bar - -#Q6 Subject Interest -interest_mean <- aggregate(Interest ~ Subject, data = subject_data, mean) - -interest_bar <- ggplot(interest_mean, aes(x = Subject, y = Interest)) + stat_summary(fun = "mean", geom = "bar") + coord_flip() -interest_bar - -#Q7 Employability Prospects - -employability_mean <- aggregate(Employability ~ Subject, data = subject_data, mean) - -employability_bar <- ggplot(employability_mean, aes(x = Subject, y = Employability)) + stat_summary(fun = "mean", geom = "bar") + coord_flip() -employability_bar - - -### Categorical Graph ### -install.packages("devtools") -install.packages("usethis") -library(devtools) -require(likert) - -#devtools::install_github('jbryer/likert') - -#likert_test <- likert(subject_data) -Understanding_data <- TSR_data[, 6:17] -likert_test_understand <- likert(Understanding_data) - -Interest_data <- TSR_data[, 18:29] - -Employability_data <- TSR_data[, 30:41] - -``` - - - separate visualisation of summary data as pie chart only for 4 key subjects: Philosophy, Ethics, Theology, Religious Studies, but with data represented as aggregated "Positive" / "Negative" responses - -```{r Visualization for 4 Key Subjects} -## Subset by "Positive" / "Negative" - -keysubjects_data <- subject_data[subject_data$Subject == "Philosophy" | subject_data$Subject == "Ethics" | subject_data$Subject == "Theology" | subject_data$Subject == "Religious Studies", ] - -recode_interest <- ifelse(3 <= keysubjects_data$Interest & keysubjects_data$Interest >=5, "Positive", "Negative") - -keysubjects_data <- cbind(keysubjects_data, recode_interest) -keysubjects_data$recode_interest <- factor(keysubjects_data$recode_interest) - -table(keysubjects_data$recode_interest, keysubjects_data$Subject) - -``` - - subsetted visualisations of responses with separate subsetting by response to Q8-9, Q18, Q17, Q16 -- For question Q8 + Q9 (for religious people) - - visualisation summary of responses - - show subsetted visualisations of responses by response to, Q18, Q17, Q16, Q13, Q14 -- For responses to Q10-12 (what subjects are involved in...): - - represent answer counts as descending bar chart for each Q - - subset answers by Q6 (positive / negative) and Q5 (positive / negative) - -# Correlation testing: -- For Q6 (subject interest) / Q5 (subject knowledge) / Q7 employability prospects, test for nature / strength of correlation with responses to: - - Q8-9 responses - - Q18 responses - - Q17 - - -```{r Q6 Correlations - Subject Interest} -#Q8-9 (8 - Theology as subject for religious people; 9 - Religion as study for religious people) - # This would be suitable for correlation - -#Q17 (Ethnicity) - # This would be categorical, so ANOVA - -#Q18 (Religion) - # This would also be categorical, so ANOVA - -``` - -```{r Q5 Correlations - Subject Knowledge} -#Q8-9 (8 - Theology as subject for religious people; 9 - Religion as study for religious people) - # This would be suitable for correlation - -#Q17 (Ethnicity) - # This would be categorical, so ANOVA - -#Q18 (Religion) - # This would also be categorical, so ANOVA -``` - -```{r Q7 Correlations - Employability} -#Q8-9 (8 - Theology as subject for religious people; 9 - Religion as study for religious people) - # This would be suitable for correlation - -#Q17 (Ethnicity) - # This would be categorical, so ANOVA - -#Q18 (Religion) - # This would also be categorical, so ANOVA -``` - - -```{r testing fun} -#testcor_data <- subject_data[subject_data$Subject == "Psychology" | subject_data$Subject == "Theology", ] - -#t.test(Interest ~ Subject, data = testcor_data) - -# testsubset <- TSR_data[TSR_data$Interested.in.Studying.Psychology < 3 & TSR_data$Interested.in.Studying.Psychology != 0, ] -# as.numeric(testsubset$Interested.in.studying.Theology) -# mean(testsubset$Interested.in.studying.Theology, na.rm = TRUE) -# -# mean(as.numeric(testsubset$Interested.in.studying.Theology), na.rm = TRUE) -# -# as.numeric(testsubset$Interested.in.studying.Religious.Studies) -# mean(testsubset$Interested.in.studying.Religious.Studies, na.rm = TRUE) -# -# testsubset2 <- TSR_data[TSR_data$Interested.in.Studying.Psychology > 3, ] -# mean(as.numeric(testsubset2$Interested.in.studying.Theology), na.rm = TRUE) -# mean(as.numeric(testsubset2$Interested.in.studying.Religious.Studies), na.rm = TRUE) -``` - -## Mean Interest in Theology and Religious Studies by High/Low Subject Interest - -```{r Philosophy} -### Philosophy ### -Philos_subset_Low <- TSR_data[TSR_data$InterestinstudyingPhilosophy < 3 & TSR_data$InterestinstudyingPhilosophy != 0, ] - -Philos_subset_High <- TSR_data[TSR_data$InterestinstudyingPhilosophy > 3, ] - -## Theology Interest -#Low interest in Philosophy -mean(as.numeric(Philos_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) -#High interest in Philosophy -mean(as.numeric(Philos_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) - -## Religious Studies Interest -#Low interest in Philosophy -mean(as.numeric(Philos_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE) -#High interest in Philosophy -mean(as.numeric(Philos_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE) -``` - -```{r Sociology} -### Sociology ### -Soc_subset_Low <- TSR_data[TSR_data$InterestinstudyingSociaology < 3 & TSR_data$InterestinstudyingSociaology != 0, ] - -Soc_subset_High <- TSR_data[TSR_data$InterestinstudyingSociaology > 3, ] - -## Theology Interest -#Low interest in Sociology -lSocTheo <- mean(as.numeric(Soc_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) -#High interest in Sociology -hSocTheo <- mean(as.numeric(Soc_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) - -## Religious Studies Interest -#Low interest in Sociology -mean(as.numeric(Soc_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE) -#High interest in Sociology -mean(as.numeric(Soc_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE) -``` - -```{r Psychology} -### Psychology ### -Psych_subset_Low <- TSR_data[TSR_data$InterestedinStudyingPsychology < 3 & TSR_data$InterestedinStudyingPsychology != 0, ] - -Psych_subset_High <- TSR_data[TSR_data$InterestedinStudyingPsychology > 3, ] - -## Theology Interest -#Low interest in Psychology -mean(as.numeric(Psych_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) -#High interest in Psychology -mean(as.numeric(Psych_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) - -## Religious Studies Interest -#Low interest in Psychology -mean(as.numeric(Psych_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE) -#High interest in Psychology -mean(as.numeric(Psych_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE) -``` - -```{r History} -### History ### -Hist_subset_Low <- TSR_data[TSR_data$InterestedinstudyingHistory < 3 & TSR_data$InterestedinstudyingHistory != 0, ] - -Hist_subset_High <- TSR_data[TSR_data$InterestedinstudyingHistory > 3, ] - -## Theology Interest -#Low interest in History -mean(as.numeric(Hist_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) -#High interest in History -mean(as.numeric(Hist_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) - -## Religious Studies Interest -#Low interest in History -mean(as.numeric(Hist_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE) -#High interest in History -mean(as.numeric(Hist_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE) -``` - -```{r Ethics} -### Ethics ### -Ethics_subset_Low <- TSR_data[TSR_data$InterestedinstudyingEthics < 3 & TSR_data$InterestedinstudyingEthics != 0, ] - -Ethics_subset_High <- TSR_data[TSR_data$InterestedinstudyingEthics > 3, ] - -## Theology Interest -#Low interest in Ethics -mean(as.numeric(Ethics_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) -#High interest in Ethics -mean(as.numeric(Ethics_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) - -## Religious Studies Interest -#Low interest in Ethics -mean(as.numeric(Ethics_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE) -#High interest in Ethics -mean(as.numeric(Ethics_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE) -``` - -```{r Politics} -### Politics ### -Polit_subset_Low <- TSR_data[TSR_data$InterestedinstudyingPolitics < 3 & TSR_data$InterestedinstudyingPolitics != 0, ] - -Polit_subset_High <- TSR_data[TSR_data$InterestedinstudyingPolitics > 3, ] - -## Theology Interest -#Low interest in Politics -mean(as.numeric(Polit_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) -#High interest in Politics -mean(as.numeric(Polit_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) - -## Religious Studies Interest -#Low interest in Politics -mean(as.numeric(Polit_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE) -#High interest in Politics -mean(as.numeric(Polit_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE) -``` - -```{r English} -### English ### -Eng_subset_Low <- TSR_data[TSR_data$InterestedinstudyingEnglish < 3 & TSR_data$InterestedinstudyingEnglish != 0, ] - -Eng_subset_High <- TSR_data[TSR_data$InterestedinstudyingEnglish > 3, ] - -## Theology Interest -#Low interest in English -mean(as.numeric(Eng_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) -#High interest in English -mean(as.numeric(Eng_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) - -## Religious Studies Interest -#Low interest in English -mean(as.numeric(Eng_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE) -#High interest in English -mean(as.numeric(Eng_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE) -``` - -```{r Math} -### Math ### -Math_subset_Low <- TSR_data[TSR_data$InterestedinstudyingMath < 3 & TSR_data$InterestedinstudyingMathy != 0, ] - -Math_subset_High <- TSR_data[TSR_data$InterestedinstudyingMath > 3, ] - -## Theology Interest -#Low interest in Math -mean(as.numeric(Math_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) -#High interest in Math -mean(as.numeric(Math_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) - -## Religious Studies Interest -#Low interest in Math -mean(as.numeric(Math_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE) -#High interest in Math -mean(as.numeric(Math_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE) -``` - -```{r Computer Science} -### Computer Science ### -CompSci_subset_Low <- TSR_data[TSR_data$InterestedinstudyingComputerScience < 3 & TSR_data$InterestedinstudyingComputerScience != 0, ] - -CompSci_subset_High <- TSR_data[TSR_data$InterestedinstudyingComputerScience > 3, ] - -## Theology Interest -#Low interest in Computer Science -mean(as.numeric(CompSci_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) -#High interest in Computer Science -mean(as.numeric(CompSci_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) - -## Religious Studies Interest -#Low interest in Computer Science -mean(as.numeric(CompSci_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE) -#High interest in Computer Science -mean(as.numeric(CompSci_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE) -``` - -```{r Business} -### Business ### -Busi_subset_Low <- TSR_data[TSR_data$InterestedinstudyingBusiness < 3 & TSR_data$InterestedinstudyingBusiness != 0, ] - -Busi_subset_High <- TSR_data[TSR_data$InterestedinstudyingBusiness > 3, ] - -## Theology Interest -#Low interest in Business -mean(as.numeric(Busi_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) -#High interest in Business -mean(as.numeric(Busi_subset_Low$InterestedinstudyingTheology), na.rm = TRUE) - -## Religious Studies Interest -#Low interest in Business -mean(as.numeric(Busi_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE) -#High interest in Business -mean(as.numeric(Busi_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE) - -``` - - -## Mean Knowledge in Theology and Religious Studies by High/Low Subject Knowledge - -```{r Philosophy} -### Philosophy ### -Philos_subset_Low <- TSR_data[TSR_data$GoodunderstandingofPhilosophy < 3 & TSR_data$GoodunderstandingofPhilosophy != 0, ] - -Philos_subset_High <- TSR_data[TSR_data$GoodunderstandingofPhilosophy > 3, ] - -## Theology Knowledge -#Low knowledge in Philosophy -mean(as.numeric(Philos_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) -#High knowledge in Philosophy -mean(as.numeric(Philos_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) - -## Religious Studies Knowledge -#Low knowledge in Philosophy -mean(as.numeric(Philos_subset_Low$GoodunderstandingofReligiousStudies), na.rm = TRUE) -#High knowledge in Philosophy -mean(as.numeric(Philos_subset_High$GoodunderstandingofReligiousStudies), na.rm = TRUE) -``` - -```{r Sociology} -### Sociology ### -Soc_subset_Low <- TSR_data[TSR_data$GoodunderstandingofSociology < 3 & TSR_data$GoodunderstandingofSociology != 0, ] - -Soc_subset_High <- TSR_data[TSR_data$GoodunderstandingofSociology > 3, ] - -## Theology knowledge -#Low knowledge in Sociology -mean(as.numeric(Soc_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) -#High knowledge in Sociology -mean(as.numeric(Soc_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) - -## Religious Studies knowledge -#Low knowledge in Sociology -mean(as.numeric(Soc_subset_Low$GoodunderstandingofReligiousStudies), na.rm = TRUE) -#High knowledge in Sociology -mean(as.numeric(Soc_subset_High$GoodunderstandingofReligiousStudies), na.rm = TRUE) -``` - -```{r Psychology} -### Psychology ### -Psych_subset_Low <- TSR_data[TSR_data$GoodunderstandingofPsychology < 3 & TSR_data$GoodunderstandingofPsychology != 0, ] - -Psych_subset_High <- TSR_data[TSR_data$GoodunderstandingofPsychology > 3, ] - -## Theology knowledge -#Low knowledge in Psychology -mean(as.numeric(Psych_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) -#High knowledge in Psychology -mean(as.numeric(Psych_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) - -## Religious Studies knowledge -#Low knowledge in Psychology -mean(as.numeric(Psych_subset_Low$GoodunderstandingofReligiousStudies), na.rm = TRUE) -#High knowledge in Psychology -mean(as.numeric(Psych_subset_High$GoodunderstandingofReligiousStudies), na.rm = TRUE) -``` - -```{r History} -### History ### -Hist_subset_Low <- TSR_data[TSR_data$GoodunderstandingofHistory < 3 & TSR_data$GoodunderstandingofHistory != 0, ] - -Hist_subset_High <- TSR_data[TSR_data$GoodunderstandingofHistory > 3, ] - -## Theology knowledge -#Low knowledge in History -mean(as.numeric(Hist_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) -#High knowledge in History -mean(as.numeric(Hist_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) - -## Religious Studies knowledge -#Low knowledge in History -mean(as.numeric(Hist_subset_Low$GoodunderstandingofReligiousStudies), na.rm = TRUE) -#High knowledge in History -mean(as.numeric(Hist_subset_High$GoodunderstandingofReligiousStudies), na.rm = TRUE) -``` - -```{r Ethics} -### Ethics ### -Ethics_subset_Low <- TSR_data[TSR_data$GoodunderstandingofEthics < 3 & TSR_data$GoodunderstandingofEthics != 0, ] - -Ethics_subset_High <- TSR_data[TSR_data$GoodunderstandingofEthics > 3, ] - -## Theology knowledge -#Low knowledge in Ethics -mean(as.numeric(Ethics_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) -#High knowledge in Ethics -mean(as.numeric(Ethics_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) - -## Religious Studies knowledge -#Low knowledge in Ethics -mean(as.numeric(Ethics_subset_Low$GoodunderstandingofReligiousStudies), na.rm = TRUE) -#High knowledge in Ethics -mean(as.numeric(Ethics_subset_High$GoodunderstandingofReligiousStudies), na.rm = TRUE) -``` - -```{r Politics} -### Politics ### -Polit_subset_Low <- TSR_data[TSR_data$GoodunderstandingofPolitics < 3 & TSR_data$GoodunderstandingofPolitics != 0, ] - -Polit_subset_High <- TSR_data[TSR_data$GoodunderstandingofPolitics > 3, ] - -## Theology knowledge -#Low knowledge in Politics -mean(as.numeric(Polit_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) -#High knowledge in Politics -mean(as.numeric(Polit_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) - -## Religious Studies knowledge -#Low knowledge in Politics -mean(as.numeric(Polit_subset_Low$GoodunderstandingofReligiousStudies), na.rm = TRUE) -#High knowledge in Politics -mean(as.numeric(Polit_subset_High$GoodunderstandingofReligiousStudies), na.rm = TRUE) -``` - -```{r English} -### English ### -Eng_subset_Low <- TSR_data[TSR_data$GoodunderstandingofEnglish < 3 & TSR_data$GoodunderstandingofEnglish != 0, ] - -Eng_subset_High <- TSR_data[TSR_data$GoodunderstandingofEnglish > 3, ] - -## Theology knowledge -#Low knowledge in English -mean(as.numeric(Eng_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) -#High knowledge in English -mean(as.numeric(Eng_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) - -## Religious Studies knowledge -#Low knowledge in English -mean(as.numeric(Eng_subset_Low$GoodunderstandingofReligiousStudies), na.rm = TRUE) -#High knowledge in English -mean(as.numeric(Eng_subset_High$GoodunderstandingofReligiousStudies), na.rm = TRUE) -``` - -```{r Math} -### Math ### -Math_subset_Low <- TSR_data[TSR_data$GoodunderstandingofMath < 3 & TSR_data$GoodunderstandingofMath != 0, ] - -Math_subset_High <- TSR_data[TSR_data$GoodunderstandingofMath > 3, ] - -## Theology knowledge -#Low knowledge in Math -mean(as.numeric(Math_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) -#High knowledge in Math -mean(as.numeric(Math_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) - -## Religious Studies knowledge -#Low knowledge in Math -mean(as.numeric(Math_subset_Low$GoodunderstandingofReligiousStudies), na.rm = TRUE) -#High knowledge in Math -mean(as.numeric(Math_subset_High$GoodunderstandingofReligiousStudies), na.rm = TRUE) -``` - -```{r Computer Science} -### Computer Science ### -CompSci_subset_Low <- TSR_data[TSR_data$GoodunderstandingofComputerScience < 3 & TSR_data$GoodunderstandingofComputerScience != 0, ] - -CompSci_subset_High <- TSR_data[TSR_data$GoodunderstandingofComputerScience > 3, ] - -## Theology knowledge -#Low knowledge in Computer Science -mean(as.numeric(CompSci_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) -#High knowledge in Computer Science -mean(as.numeric(CompSci_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) - -## Religious Studies knowledge -#Low knowledge in Computer Science -mean(as.numeric(CompSci_subset_Low$GoodunderstandingofReligiousStudies), na.rm = TRUE) -#High knowledge in Computer Science -mean(as.numeric(CompSci_subset_High$GoodunderstandingofReligiousStudies), na.rm = TRUE) -``` - -```{r Business} -### Business ### -Busi_subset_Low <- TSR_data[TSR_data$GoodunderstandingofBusiness < 3 & TSR_data$GoodunderstandingofBusiness != 0, ] - -Busi_subset_High <- TSR_data[TSR_data$GoodunderstandingofBusiness > 3, ] - -## Theology knowledge -#Low knowledge in Business -mean(as.numeric(Busi_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) -#High knowledge in Business -mean(as.numeric(Busi_subset_Low$GoodunderstandingofTheology), na.rm = TRUE) - -## Religious Studies knowledge -#Low knowledge in Business -mean(as.numeric(Busi_subset_Low$GoodunderstandingofReligiousStudies), na.rm = TRUE) -#High knowledge in Business -mean(as.numeric(Busi_subset_High$GoodunderstandingofReligiousStudies), na.rm = TRUE) - -``` - - -## Mean Employability in Theology and Religious Studies by High/Low Subject Employability - -```{r Philosophy} -### Philosophy ### -Philos_subset_Low <- TSR_data[TSR_data$Philosophyemployability < 3 & TSR_data$Philosophyemployability != 0, ] - -Philos_subset_High <- TSR_data[TSR_data$Philosophyemployability > 3, ] - -## Theology employability -#Low employability in Philosophy -mean(as.numeric(Philos_subset_Low$Theologyemployability), na.rm = TRUE) -#High employability in Philosophy -mean(as.numeric(Philos_subset_Low$Theologyemployability), na.rm = TRUE) - -## Religious Studies employability -#Low employability in Philosophy -mean(as.numeric(Philos_subset_Low$ReligiousStudiesemployability), na.rm = TRUE) -#High employability in Philosophy -mean(as.numeric(Philos_subset_High$ReligiousStudiesemployability), na.rm = TRUE) -``` - -```{r Sociology} -### Sociology ### -Soc_subset_Low <- TSR_data[TSR_data$Sociologyemployability < 3 & TSR_data$Sociologyemployability != 0, ] - -Soc_subset_High <- TSR_data[TSR_data$Sociologyemployability > 3, ] - -## Theology employability -#Low employability in Sociology -mean(as.numeric(Soc_subset_Low$Theologyemployability), na.rm = TRUE) -#High employability in Sociology -mean(as.numeric(Soc_subset_Low$Theologyemployability), na.rm = TRUE) - -## Religious Studies employability -#Low employability in Sociology -mean(as.numeric(Soc_subset_Low$ReligiousStudiesemployability), na.rm = TRUE) -#High employability in Sociology -mean(as.numeric(Soc_subset_High$ReligiousStudiesemployability), na.rm = TRUE) -``` - -```{r Psychology} -### Psychology ### -Psych_subset_Low <- TSR_data[TSR_data$PsychologyEmployability < 3 & TSR_data$PsychologyEmployability != 0, ] - -Psych_subset_High <- TSR_data[TSR_data$PsychologyEmployability > 3, ] - -## Theology employability -#Low employability in Psychology -mean(as.numeric(Psych_subset_Low$Theologyemployability), na.rm = TRUE) -#High employability in Psychology -mean(as.numeric(Psych_subset_Low$Theologyemployability), na.rm = TRUE) - -## Religious Studies employability -#Low employability in Psychology -mean(as.numeric(Psych_subset_Low$ReligiousStudiesemployability), na.rm = TRUE) -#High employability in Psychology -mean(as.numeric(Psych_subset_High$ReligiousStudiesemployability), na.rm = TRUE) -``` - -```{r History} -### History ### -Hist_subset_Low <- TSR_data[TSR_data$Historyemployability < 3 & TSR_data$Historyemployability != 0, ] - -Hist_subset_High <- TSR_data[TSR_data$Historyemployability > 3, ] - -## Theology employability -#Low employability in History -mean(as.numeric(Hist_subset_Low$Theologyemployability), na.rm = TRUE) -#High employability in History -mean(as.numeric(Hist_subset_Low$Theologyemployability), na.rm = TRUE) - -## Religious Studies employability -#Low employability in History -mean(as.numeric(Hist_subset_Low$ReligiousStudiesemployability), na.rm = TRUE) -#High employability in History -mean(as.numeric(Hist_subset_High$ReligiousStudiesemployability), na.rm = TRUE) -``` - -```{r Ethics} -### Ethics ### -Ethics_subset_Low <- TSR_data[TSR_data$Ethicsemployability < 3 & TSR_data$Ethicsemployability != 0, ] - -Ethics_subset_High <- TSR_data[TSR_data$Ethicsemployability > 3, ] - -## Theology employability -#Low employability in Ethics -mean(as.numeric(Ethics_subset_Low$Theologyemployability), na.rm = TRUE) -#High employability in Ethics -mean(as.numeric(Ethics_subset_Low$Theologyemployability), na.rm = TRUE) - -## Religious Studies employability -#Low employability in Ethics -mean(as.numeric(Ethics_subset_Low$ReligiousStudiesemployability), na.rm = TRUE) -#High employability in Ethics -mean(as.numeric(Ethics_subset_High$ReligiousStudiesemployability), na.rm = TRUE) -``` - -```{r Politics} -### Politics ### -Polit_subset_Low <- TSR_data[TSR_data$Politicsemployability < 3 & TSR_data$Politicsemployability != 0, ] - -Polit_subset_High <- TSR_data[TSR_data$Politicsemployability > 3, ] - -## Theology employability -#Low employability in Politics -mean(as.numeric(Polit_subset_Low$Theologyemployability), na.rm = TRUE) -#High employability in Politics -mean(as.numeric(Polit_subset_Low$Theologyemployability), na.rm = TRUE) - -## Religious Studies employability -#Low employability in Politics -mean(as.numeric(Polit_subset_Low$ReligiousStudiesemployability), na.rm = TRUE) -#High employability in Politics -mean(as.numeric(Polit_subset_High$ReligiousStudiesemployability), na.rm = TRUE) -``` - -```{r English} -### English ### -Eng_subset_Low <- TSR_data[TSR_data$Englishemployability < 3 & TSR_data$Englishemployability != 0, ] - -Eng_subset_High <- TSR_data[TSR_data$Englishemployability > 3, ] - -## Theology employability -#Low employability in English -mean(as.numeric(Eng_subset_Low$Theologyemployability), na.rm = TRUE) -#High employability in English -mean(as.numeric(Eng_subset_Low$Theologyemployability), na.rm = TRUE) - -## Religious Studies employability -#Low employability in English -mean(as.numeric(Eng_subset_Low$ReligiousStudiesemployability), na.rm = TRUE) -#High employability in English -mean(as.numeric(Eng_subset_High$ReligiousStudiesemployability), na.rm = TRUE) -``` - -```{r Math} -### Math ### -Math_subset_Low <- TSR_data[TSR_data$Mathemployability < 3 & TSR_data$Mathemployability != 0, ] - -Math_subset_High <- TSR_data[TSR_data$Mathemployability > 3, ] - -## Theology employability -#Low employability in Math -mean(as.numeric(Math_subset_Low$Theologyemployability), na.rm = TRUE) -#High employability in Math -mean(as.numeric(Math_subset_Low$Theologyemployability), na.rm = TRUE) - -## Religious Studies employability -#Low employability in Math -mean(as.numeric(Math_subset_Low$ReligiousStudiesemployability), na.rm = TRUE) -#High employability in Math -mean(as.numeric(Math_subset_High$ReligiousStudiesemployability), na.rm = TRUE) -``` - -```{r Computer Science} -### Computer Science ### -CompSci_subset_Low <- TSR_data[TSR_data$ComputerScienceemployability < 3 & TSR_data$ComputerScienceemployability != 0, ] - -CompSci_subset_High <- TSR_data[TSR_data$ComputerScienceemployability > 3, ] - -## Theology employability -#Low employability in Computer Science -mean(as.numeric(CompSci_subset_Low$Theologyemployability), na.rm = TRUE) -#High employability in Computer Science -mean(as.numeric(CompSci_subset_Low$Theologyemployability), na.rm = TRUE) - -## Religious Studies employability -#Low employability in Computer Science -mean(as.numeric(CompSci_subset_Low$ReligiousStudiesemployability), na.rm = TRUE) -#High employability in Computer Science -mean(as.numeric(CompSci_subset_High$ReligiousStudiesemployability), na.rm = TRUE) -``` - -```{r Business} -### Business ### -Busi_subset_Low <- TSR_data[TSR_data$Businessemployability < 3 & TSR_data$Businessemployability != 0, ] - -Busi_subset_High <- TSR_data[TSR_data$Businessemployability > 3, ] - -## Theology employability -#Low employability in Business -mean(as.numeric(Busi_subset_Low$Theologyemployability), na.rm = TRUE) -#High employability in Business -mean(as.numeric(Busi_subset_Low$Theologyemployability), na.rm = TRUE) - -## Religious Studies employability -#Low employability in Business -mean(as.numeric(Busi_subset_Low$ReligiousStudiesemployability), na.rm = TRUE) -#High employability in Business -mean(as.numeric(Busi_subset_High$ReligiousStudiesemployability), na.rm = TRUE) - -```