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tidied code, added graphs and labels
This commit is contained in:
Jeremy Kidwell
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knitr::opts_chunk$set(echo = TRUE)
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TSR_data <- read.csv("./data/TSR data complete.csv")
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```{r respondent age}
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TSR_data$Age <- factor(TSR_data$Age, levels = c(1, 2, 3, 4, 5, 6, 7, 8), labels = c("15 or under", "16", "17", "18", "19", "20", "21 or over", "Prefer not to say"))
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```{r respondent_age}
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age_pie <- pie(table(TSR_data$Age))
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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"))
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Year_study_pie <- pie(table(TSR_data$MOST.RECENT.year.of.study))
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TSR_data$Gender <- factor(TSR_data$Gender, levels = c(1, 2, 3, 4), labels = c("Male", "Female", "I identify my gender in another way", "Prefer not to say"))
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gender_pie <- pie(table(TSR_data$Gender))
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library(ggplot2)
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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"))
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Ethnicity_bar <- ggplot(TSR_data, aes(Ethnicity)) + geom_bar() + coord_flip()
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Ethnicity_bar
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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"))
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Religious_affiliation_bar <- ggplot(TSR_data, aes(Religious.Affliation)) + geom_bar() + coord_flip()
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Religious_affiliation_bar
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subject_data <- read.csv("./data/Subject data.csv")
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#Q5 Subject Knowledge/Understanding
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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"))
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understanding_mean <- aggregate(Understanding ~ Subject, data = subject_data, mean)
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understanding_bar <- ggplot(understanding_mean, aes(x = Subject, y = Understanding)) + stat_summary(fun = "mean", geom = "bar") + coord_flip()
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understanding_bar
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#Q6 Subject Interest
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interest_mean <- aggregate(Interest ~ Subject, data = subject_data, mean)
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interest_bar <- ggplot(interest_mean, aes(x = Subject, y = Interest)) + stat_summary(fun = "mean", geom = "bar") + coord_flip()
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interest_bar
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employability_mean <- aggregate(Employability ~ Subject, data = subject_data, mean)
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employability_bar <- ggplot(employability_mean, aes(x = Subject, y = Employability)) + stat_summary(fun = "mean", geom = "bar") + coord_flip()
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employability_bar
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keysubjects_data <- subject_data[subject_data$Subject == "Philosophy" | subject_data$Subject == "Ethics" | subject_data$Subject == "Theology" | subject_data$Subject == "Religious Studies", ]
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recode_interest <- ifelse(1 <= keysubjects_data$Interest & keysubjects_data$Interest >=3, "Positive", "Negative")
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keysubjects_data <- cbind(keysubjects_data, recode_interest)
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keysubjects_data$recode_interest <- factor(keysubjects_data$recode_interest)
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table(keysubjects_data$recode_interest, keysubjects_data$Subject)
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understanding_bar
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#Q6 Subject Interest
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interest_mean <- aggregate(Interest ~ Subject, data = subject_data, mean)
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interest_bar <- ggplot(interest_mean, aes(x = Subject, y = Interest)) + stat_summary(fun = "mean", geom = "bar") + coord_flip()
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interest_bar
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@ -1,770 +0,0 @@
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---
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title: "RMarkdown Admissions_Survey2021"
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output: html_document
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---
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```{r setup, include=FALSE}
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knitr::opts_chunk$set(echo = TRUE)
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library(ggplot2)
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```
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# Upload Data
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```{r Upload Data}
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TSR_data <- read.csv("./data/TSR data complete.csv")
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subject_data <- read.csv("./data/Subject data.csv")
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```
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# Basic summary visualisations (RH):
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- Q2 (respondent age)
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```{r respondent age}
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TSR_data$Age <- factor(TSR_data$Age, levels = c(1, 2, 3, 4, 5, 6, 7, 8), labels = c("15 or under", "16", "17", "18", "19", "20", "21 or over", "Prefer not to say"))
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age_pie <- pie(table(TSR_data$Age))
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```
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- Q3 (year of study)
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```{r year of study}
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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"))
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Year_study_pie <- pie(table(TSR_data$MOST.RECENT.year.of.study))
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```
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- Q16 (gender identity)
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```{r gender identity}
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TSR_data$Gender <- factor(TSR_data$Gender, levels = c(1, 2, 3, 4), labels = c("Male", "Female", "I identify my gender in another way", "Prefer not to say"))
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gender_pie <- pie(table(TSR_data$Gender))
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```
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- Q17 (ethnic self-id)
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```{r ethnic self-id}
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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"))
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Ethnicity_bar <- ggplot(TSR_data, aes(Ethnicity)) + geom_bar() + coord_flip()
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Ethnicity_bar
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```
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- Q18 (religion)
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```{r religion}
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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"))
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Religious_affiliation_bar <- ggplot(TSR_data, aes(Religious.Affliation)) + geom_bar() + coord_flip()
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Religious_affiliation_bar
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```
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# Visualisations of LIKERT responses (RH):
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- For questions Q6 (subject interest) / Q5 (subject knowledge) / Q7 employability prospects:
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- visualisation as summaries for all subjects LIKERT data as stacked bar chart (colours for bar segments from cool to warm)
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```{r Visualization by Subject}
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### Each Subject is a different column so will need to figure out how to code the columns together into one graph
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# Higher score indicates less agreement...need to reverse score
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## 1=5, 2=4, 3=3, 4=2, 5=1, 6=0 --- Not done yet. See what they look like without reverse scoring
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## Files have been reverse scored - Higher score now indicates more agreement
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#Q5 Subject Knowledge/Understanding
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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"))
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understanding_mean <- aggregate(Understanding ~ Subject, data = subject_data, mean)
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understanding_bar <- ggplot(understanding_mean, aes(x = Subject, y = Understanding)) + stat_summary(fun = "mean", geom = "bar") + coord_flip()
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understanding_bar
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#Q6 Subject Interest
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interest_mean <- aggregate(Interest ~ Subject, data = subject_data, mean)
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interest_bar <- ggplot(interest_mean, aes(x = Subject, y = Interest)) + stat_summary(fun = "mean", geom = "bar") + coord_flip()
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interest_bar
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#Q7 Employability Prospects
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employability_mean <- aggregate(Employability ~ Subject, data = subject_data, mean)
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employability_bar <- ggplot(employability_mean, aes(x = Subject, y = Employability)) + stat_summary(fun = "mean", geom = "bar") + coord_flip()
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employability_bar
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### Categorical Graph ###
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install.packages("devtools")
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install.packages("usethis")
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library(devtools)
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require(likert)
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#devtools::install_github('jbryer/likert')
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#likert_test <- likert(subject_data)
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Understanding_data <- TSR_data[, 6:17]
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likert_test_understand <- likert(Understanding_data)
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Interest_data <- TSR_data[, 18:29]
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Employability_data <- TSR_data[, 30:41]
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```
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- 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
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```{r Visualization for 4 Key Subjects}
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## Subset by "Positive" / "Negative"
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keysubjects_data <- subject_data[subject_data$Subject == "Philosophy" | subject_data$Subject == "Ethics" | subject_data$Subject == "Theology" | subject_data$Subject == "Religious Studies", ]
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recode_interest <- ifelse(3 <= keysubjects_data$Interest & keysubjects_data$Interest >=5, "Positive", "Negative")
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keysubjects_data <- cbind(keysubjects_data, recode_interest)
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keysubjects_data$recode_interest <- factor(keysubjects_data$recode_interest)
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table(keysubjects_data$recode_interest, keysubjects_data$Subject)
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```
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- subsetted visualisations of responses with separate subsetting by response to Q8-9, Q18, Q17, Q16
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- For question Q8 + Q9 (for religious people)
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- visualisation summary of responses
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- show subsetted visualisations of responses by response to, Q18, Q17, Q16, Q13, Q14
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- For responses to Q10-12 (what subjects are involved in...):
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- represent answer counts as descending bar chart for each Q
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- subset answers by Q6 (positive / negative) and Q5 (positive / negative)
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# Correlation testing:
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- For Q6 (subject interest) / Q5 (subject knowledge) / Q7 employability prospects, test for nature / strength of correlation with responses to:
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- Q8-9 responses
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- Q18 responses
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- Q17
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```{r Q6 Correlations - Subject Interest}
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#Q8-9 (8 - Theology as subject for religious people; 9 - Religion as study for religious people)
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# This would be suitable for correlation
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#Q17 (Ethnicity)
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# This would be categorical, so ANOVA
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#Q18 (Religion)
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# This would also be categorical, so ANOVA
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```
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```{r Q5 Correlations - Subject Knowledge}
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#Q8-9 (8 - Theology as subject for religious people; 9 - Religion as study for religious people)
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# This would be suitable for correlation
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#Q17 (Ethnicity)
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# This would be categorical, so ANOVA
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#Q18 (Religion)
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# This would also be categorical, so ANOVA
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```
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```{r Q7 Correlations - Employability}
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#Q8-9 (8 - Theology as subject for religious people; 9 - Religion as study for religious people)
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# This would be suitable for correlation
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#Q17 (Ethnicity)
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# This would be categorical, so ANOVA
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#Q18 (Religion)
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# This would also be categorical, so ANOVA
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```
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```{r testing fun}
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#testcor_data <- subject_data[subject_data$Subject == "Psychology" | subject_data$Subject == "Theology", ]
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#t.test(Interest ~ Subject, data = testcor_data)
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# testsubset <- TSR_data[TSR_data$Interested.in.Studying.Psychology < 3 & TSR_data$Interested.in.Studying.Psychology != 0, ]
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# as.numeric(testsubset$Interested.in.studying.Theology)
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# mean(testsubset$Interested.in.studying.Theology, na.rm = TRUE)
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#
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# mean(as.numeric(testsubset$Interested.in.studying.Theology), na.rm = TRUE)
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#
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# as.numeric(testsubset$Interested.in.studying.Religious.Studies)
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# mean(testsubset$Interested.in.studying.Religious.Studies, na.rm = TRUE)
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#
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# testsubset2 <- TSR_data[TSR_data$Interested.in.Studying.Psychology > 3, ]
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# mean(as.numeric(testsubset2$Interested.in.studying.Theology), na.rm = TRUE)
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# mean(as.numeric(testsubset2$Interested.in.studying.Religious.Studies), na.rm = TRUE)
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```
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## Mean Interest in Theology and Religious Studies by High/Low Subject Interest
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```{r Philosophy}
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### Philosophy ###
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Philos_subset_Low <- TSR_data[TSR_data$InterestinstudyingPhilosophy < 3 & TSR_data$InterestinstudyingPhilosophy != 0, ]
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Philos_subset_High <- TSR_data[TSR_data$InterestinstudyingPhilosophy > 3, ]
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## Theology Interest
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#Low interest in Philosophy
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mean(as.numeric(Philos_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
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#High interest in Philosophy
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mean(as.numeric(Philos_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
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## Religious Studies Interest
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#Low interest in Philosophy
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mean(as.numeric(Philos_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE)
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#High interest in Philosophy
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mean(as.numeric(Philos_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE)
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```
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```{r Sociology}
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### Sociology ###
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Soc_subset_Low <- TSR_data[TSR_data$InterestinstudyingSociaology < 3 & TSR_data$InterestinstudyingSociaology != 0, ]
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Soc_subset_High <- TSR_data[TSR_data$InterestinstudyingSociaology > 3, ]
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## Theology Interest
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#Low interest in Sociology
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lSocTheo <- mean(as.numeric(Soc_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
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#High interest in Sociology
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hSocTheo <- mean(as.numeric(Soc_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
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## Religious Studies Interest
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#Low interest in Sociology
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mean(as.numeric(Soc_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE)
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#High interest in Sociology
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mean(as.numeric(Soc_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE)
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```
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```{r Psychology}
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### Psychology ###
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Psych_subset_Low <- TSR_data[TSR_data$InterestedinStudyingPsychology < 3 & TSR_data$InterestedinStudyingPsychology != 0, ]
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Psych_subset_High <- TSR_data[TSR_data$InterestedinStudyingPsychology > 3, ]
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## Theology Interest
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#Low interest in Psychology
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mean(as.numeric(Psych_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
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#High interest in Psychology
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mean(as.numeric(Psych_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
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## Religious Studies Interest
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#Low interest in Psychology
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mean(as.numeric(Psych_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE)
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#High interest in Psychology
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mean(as.numeric(Psych_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE)
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```
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```{r History}
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### History ###
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Hist_subset_Low <- TSR_data[TSR_data$InterestedinstudyingHistory < 3 & TSR_data$InterestedinstudyingHistory != 0, ]
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Hist_subset_High <- TSR_data[TSR_data$InterestedinstudyingHistory > 3, ]
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## Theology Interest
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#Low interest in History
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mean(as.numeric(Hist_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
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#High interest in History
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mean(as.numeric(Hist_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
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## Religious Studies Interest
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#Low interest in History
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mean(as.numeric(Hist_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE)
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#High interest in History
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mean(as.numeric(Hist_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE)
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```
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```{r Ethics}
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### Ethics ###
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Ethics_subset_Low <- TSR_data[TSR_data$InterestedinstudyingEthics < 3 & TSR_data$InterestedinstudyingEthics != 0, ]
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Ethics_subset_High <- TSR_data[TSR_data$InterestedinstudyingEthics > 3, ]
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## Theology Interest
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#Low interest in Ethics
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mean(as.numeric(Ethics_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
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#High interest in Ethics
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mean(as.numeric(Ethics_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
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## Religious Studies Interest
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#Low interest in Ethics
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mean(as.numeric(Ethics_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE)
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#High interest in Ethics
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mean(as.numeric(Ethics_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE)
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```
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```{r Politics}
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### Politics ###
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Polit_subset_Low <- TSR_data[TSR_data$InterestedinstudyingPolitics < 3 & TSR_data$InterestedinstudyingPolitics != 0, ]
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Polit_subset_High <- TSR_data[TSR_data$InterestedinstudyingPolitics > 3, ]
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## Theology Interest
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#Low interest in Politics
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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)
|
||||
|
||||
```
|
||||
Loading…
Reference in a new issue