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-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$Gender)) +#High interest in Sociology +hSocTheo <- mean(as.numeric(Soc_subset_Low$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) +### 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$GoodunderstandingofReligiousStudies), na.rm = TRUE) +## Religious Studies Knowledge +#Low knowledge in Philosophy +mean(as.numeric(Philos_subset_Low$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) +## 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$GoodunderstandingofReligiousStudies), na.rm = TRUE) +### History ### +Hist_subset_Low <- TSR_data[TSR_data$GoodunderstandingofHistory < 3 & TSR_data$GoodunderstandingofHistory != 0, ] +Hist_subset_High <- TSR_data[TSR_data$GoodunderstandingofHistory > 3, ] +require(devtools) 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")) +require(ggplot2) +require(usethis) +require(devtools) +require(likert) +TSR_data <- read.csv("./data/TSR data complete.csv") +subject_data <- read.csv("./data/Subject data.csv") +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$Age)) +# Load RColorBrewer +# install.packages("RColorBrewer") +library(RColorBrewer) +# Define colour palettes for plots below +coul3 <- brewer.pal(3, "RdYlBu") # Using RdYlBu range to generate 3 colour palette: https://colorbrewer2.org/#type=diverging&scheme=RdYlBu&n=5 +TSR_data$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), col=coul3) +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")) +View(TSR_data) +TSR_data <- read.csv("./data/TSR data complete.csv") +View(TSR_data) +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$Age), col=coul3) +TSR_data$MOST.RECENT.year.of.study <- factor(TSR_data$MOST.RECENT.year.of.study, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9), labels = c("Year 11/S4/Year 12(NI)", "Year 12/S5/Year 13(NI)", "Year 13/S6/Year 14(NI)", "I am currently on a gap year", "I am currently on an undergraduate/HE college course", "I am in full-time employment", "I am unemployed", "Other", "Prefer not to say")) +TSR_data$Age <- age_pie <- pie(table(factor(TSR_data$Age, levels = c(1, 2, 3, 4, 5, 6, 7, 8), labels = c("15 or under", "16", "17", "18", "19", "20", "21 or over", "Prefer not to say"))), col=coul3) +TSR_data$Age <- age_pie <- pie(table(factor(TSR_data$Age, levels = c(1, 2, 3, 4, 5, 6, 7, 8), labels = c("15 or under", "16", "17", "18", "19", "20", "21 or over", "Prefer not to say")), col=coul3) +pie(table(factor(TSR_data$Age, levels = c(1, 2, 3, 4, 5, 6, 7, 8), labels = c("15 or under", "16", "17", "18", "19", "20", "21 or over", "Prefer not to say"))), col=coul3) +pie(table(factor(TSR_data$Age, levels = c(1, 2, 3, 4, 5, 6, 7, 8), labels = c("15 or under", "16", "17", "18", "19", "20", "21 or over", "Prefer not to say"))), col=coul3)) +age_pie <- pie(table(TSR_data$Age, levels = c(1, 2, 3, 4, 5, 6, 7, 8), labels = c("15 or under", "16", "17", "18", "19", "20", "21 or over", "Prefer not to say")), col=coul3) +age_pie <- pie(table(TSR_data$Age, levels = c(1, 2, 3, 4, 5, 6, 7, 8), labels = c("15 or under", "16", "17", "18", "19", "20", "21 or over", "Prefer not to say")), col=coul3)) +TSR_data <- read.csv("./data/TSR data complete.csv") +TSR_data_summaries$Age <- factor(TSR_data$Age, levels = c(1, 2, 3, 4, 5, 6, 7, 8), labels = c("15 or under", "16", "17", "18", "19", "20", "21 or over", "Prefer not to say")) +TSR_data_summaries <- factor(TSR_data$Age, levels = c(1, 2, 3, 4, 5, 6, 7, 8), labels = c("15 or under", "16", "17", "18", "19", "20", "21 or over", "Prefer not to say")) +age_pie <- pie(table(TSR_data_summaries$Age), col=coul3) +age_pie <- pie(table(TSR_data_summaries), col=coul3) +# JK note: To keep things tidy, I've shifted the code here so that this isn't overwriting the dataset, but is instead operating off a separate df (same below) +TSR_data_summaries_age <- factor(TSR_data$Age, levels = c(1, 2, 3, 4, 5, 6, 7, 8), labels = c("15 or under", "16", "17", "18", "19", "20", "21 or over", "Prefer not to say")) +age_pie <- pie(table(TSR_data_summaries_age), col=coul3) +age_pie <- pie(table(TSR_data_summaries_age), col=coul3, cex = 0.8) Ethnicity_bar <- ggplot(TSR_data, aes(Ethnicity)) + geom_bar() + coord_flip() Ethnicity_bar -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")) +ggplot(TSR_data, aes(TSR_data_summaries_age)) + geom_bar() +# JK note: adding in a bar chart here, as according to the interwebs it's (apparently?) more accurate to visualise than bar charts +ggplot(TSR_data, aes(TSR_data_summaries_age)) + geom_bar() +coord_flip() +# JK note: adding in a bar chart here, as according to the interwebs it's (apparently?) more accurate to visualise than bar charts +ggplot(TSR_data, aes(TSR_data_summaries_age, fill=match)) + geom_bar() +coord_flip() +# JK note: adding in a bar chart here, as according to the interwebs it's (apparently?) more accurate to visualise than bar charts +ggplot(TSR_data, aes(TSR_data_summaries_age, fill=coul3)) + geom_bar() +coord_flip() +# JK note: adding in a bar chart here, as according to the interwebs it's (apparently?) more accurate to visualise than bar charts +ggplot(TSR_data, aes(TSR_data_summaries_age)) + geom_bar() +# JK note: adding in a bar chart here, as according to the interwebs it's (apparently?) more accurate to visualise than bar charts +ggplot(TSR_data, aes(TSR_data_summaries_age)) + geom_bar() +# save to png file for reports +ggsave("TSR_data_summaries_age.png") +# save to png file for reports +ggsave("figures/TSR_data_summaries_age.png") +# JK note: adding in a bar chart here, as according to the interwebs it's (apparently?) more accurate to visualise than bar charts +ggplot(TSR_data, aes(TSR_data_summaries_age)) + geom_bar() + title("Age of respondents") +# JK note: To keep things tidy, I've shifted the code here so that this isn't overwriting the dataset, but is instead operating off a separate df (same below) +TSR_data_summaries_age <- factor(TSR_data$Age, levels = c(1, 2, 3, 4, 5, 6, 7, 8), labels = c("15 or under", "16", "17", "18", "19", "20", "21 or over", "Prefer not to say")) +data_summaries_age labs(colour = "TSR_data_summaries_age") +data_summaries_age labs(title = "Respondent Age") +# JK note: adding in a bar chart here, as according to the interwebs it's (apparently?) more accurate to visualise than bar charts +data_summaries_age <- ggplot(TSR_data, aes(TSR_data_summaries_age)) + geom_bar() + title("Age of respondents") +# JK note: adding in a bar chart here, as according to the interwebs it's (apparently?) more accurate to visualise than bar charts +data_summaries_age <- ggplot(TSR_data, aes(TSR_data_summaries_age)) + geom_bar() +data_summaries_age labs(title = "Respondent Age") +data_summaries_age + labs(title = "Respondent Age") +data_summaries_age + labs(title = "Respondent Age Distribution", x = "Age") +data_summaries_age + labs(title = "Respondent Age Distribution", x = "Age", y = "") +# save to png file for reports +ggsave("figures/TSR_data_summaries_age.png") +TSR_data_summaries_most_recent_year_of_study <- factor(TSR_data$MOST.RECENT.year.of.study, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9), labels = c("Year 11/S4/Year 12(NI)", "Year 12/S5/Year 13(NI)", "Year 13/S6/Year 14(NI)", "I am currently on a gap year", "I am currently on an undergraduate/HE college course", "I am in full-time employment", "I am unemployed", "Other", "Prefer not to say")) +Year_study_pie <- pie(table(TSR_data_summaries_most_recent_year_of_study)) +Year_study_pie <- pie(table(TSR_data_summaries_most_recent_year_of_study$MOST.RECENT.year.of.study)) +TSR_data_summaries_most_recent_year_of_study <- factor(TSR_data$MOST.RECENT.year.of.study, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9), labels = c("Year 11/S4/Year 12(NI)", "Year 12/S5/Year 13(NI)", "Year 13/S6/Year 14(NI)", "I am currently on a gap year", "I am currently on an undergraduate/HE college course", "I am in full-time employment", "I am unemployed", "Other", "Prefer not to say")) +data_summaries_yos <- factor(TSR_data$MOST.RECENT.year.of.study, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9), labels = c("Year 11/S4/Year 12(NI)", "Year 12/S5/Year 13(NI)", "Year 13/S6/Year 14(NI)", "I am currently on a gap year", "I am currently on an undergraduate/HE college course", "I am in full-time employment", "I am unemployed", "Other", "Prefer not to say")) +Year_study_pie <- pie(table(data_summaries_yos$MOST.RECENT.year.of.study)) +Year_study_pie <- pie(table(data_summaries_yos)) +TSR_data <- read.csv("./data/TSR data complete.csv") +data_summaries_yos <- factor(TSR_data$MOST.RECENT.year.of.study, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9), labels = c("Year 11/S4/Year 12(NI)", "Year 12/S5/Year 13(NI)", "Year 13/S6/Year 14(NI)", "I am currently on a gap year", "I am currently on an undergraduate/HE college course", "I am in full-time employment", "I am unemployed", "Other", "Prefer not to say")) +Year_study_pie <- pie(table(data_summaries_yos)) +pie(table(data_summaries_yos)) +TSR_data_summaries_yos <- factor(TSR_data$MOST.RECENT.year.of.study, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9), labels = c("Year 11/S4/Year 12(NI)", "Year 12/S5/Year 13(NI)", "Year 13/S6/Year 14(NI)", "I am currently on a gap year", "I am currently on an undergraduate/HE college course", "I am in full-time employment", "I am unemployed", "Other", "Prefer not to say")) +data_summaries_yos <- ggplot(TSR_data, aes(TSR_data_summaries_yos)) + geom_bar() +data_summaries_yos + labs(title = "Respondent Most Recent Year of Study", x = "Age", y = "") +data_summaries_yos <- ggplot(TSR_data_summaries_yos, aes(TSR_data_summaries_yos)) + geom_bar() +TSR_data$MOST.RECENT.year.of.study <- factor(TSR_data$MOST.RECENT.year.of.study, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9), labels = c("Year 11/S4/Year 12(NI)", "Year 12/S5/Year 13(NI)", "Year 13/S6/Year 14(NI)", "I am currently on a gap year", "I am currently on an undergraduate/HE college course", "I am in full-time employment", "I am unemployed", "Other", "Prefer not to say")) +TSR_data <- read.csv("./data/TSR data complete.csv") +View(TSR_data) +View(TSR_data) +TSR_data_summaries_yos <- factor(TSR_data$MOSTRECENTyearofstudy, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9), labels = c("Year 11/S4/Year 12(NI)", "Year 12/S5/Year 13(NI)", "Year 13/S6/Year 14(NI)", "I am currently on a gap year", "I am currently on an undergraduate/HE college course", "I am in full-time employment", "I am unemployed", "Other", "Prefer not to say")) +data_summaries_yos <- ggplot(TSR_data_summaries_yos, aes(TSR_data_summaries_yos)) + geom_bar() +data_summaries_yos <- ggplot(TSR_data, aes(TSR_data_summaries_yos)) + geom_bar() +data_summaries_yos + labs(title = "Respondent Most Recent Year of Study", x = "Age", y = "") +data_summaries_yos + labs(title = "Respondent Most Recent Year of Study", x = "", y = "") +# save to png file for reports +ggsave("figures/TSR_data_summaries_age.png") +# save to png file for reports +ggsave("figures/TSR_data_summaries_yos.png") +# JK note: adding in a bar chart here, as according to the interwebs it's (apparently?) more accurate to visualise than bar charts +data_summaries_age <- ggplot(TSR_data, aes(TSR_data_summaries_age)) + geom_bar() +data_summaries_age + labs(title = "Respondent Age Distribution", x = "Age", y = "") +# save to png file for reports +ggsave("figures/TSR_data_summaries_age.png") +TSR_data_summaries_gender <- factor(TSR_data$Gender, levels = c(1, 2, 3, 4), labels = c("Male", "Female", "I identify my gender in another way", "Prefer not to say")) +data_summaries_gender + labs(title = "Respondent gender self-identification", x = "", y = "") +TSR_data_summaries_gender <- factor(TSR_data$Gender, levels = c(1, 2, 3, 4), labels = c("Male", "Female", "I identify my gender in another way", "Prefer not to say")) +data_summaries_gender <- ggplot(TSR_data, aes(TSR_data_summaries_gender)) + geom_bar() +data_summaries_gender + labs(title = "Respondent gender self-identification", x = "", y = "") +# save to png file for reports +ggsave("figures/TSR_data_summaries_gender.png") +install.packages("stringr") # Install stringr package, used for wrapping label text in plots +install.packages("stringr") +library("stringr") # Load stringr +data_summaries_gender + labs(title = "Respondent gender self-identification", x = "", y = "") + str_wrap(x, width = 10)) +data_summaries_gender + labs(title = "Respondent gender self-identification", x = "", y = "") str_wrap(x, width = 10)) +data_summaries_gender + labs(title = "Respondent gender self-identification", x = "", y = "") + str_wrap(x, width = 10) +data_summaries_gender <- ggplot(TSR_data, aes(TSR_data_summaries_gender)) + geom_bar() +library(ggplot2) +library(devtools) +library(devtools) +library(usethis) +library(devtools) +library(likert) +# Load RColorBrewer +# install.packages("RColorBrewer") +library(RColorBrewer) +install.packages("stringr") # Install stringr package, used for wrapping label text in plots +library("stringr") # Load stringr package, used for wrapping label text in plots +# Define colour palettes for plots below +coul3 <- brewer.pal(3, "RdYlBu") # Using RdYlBu range to generate 3 colour palette: https://colorbrewer2.org/#type=diverging&scheme=RdYlBu&n=5 +TSR_data <- read.csv("./data/TSR data complete.csv") +subject_data <- read.csv("./data/Subject data.csv") +knitr::opts_chunk$set(echo = TRUE) +library(ggplot2) +library(devtools) +library(usethis) +library(devtools) +library(likert) +# Load RColorBrewer +# install.packages("RColorBrewer") +library(RColorBrewer) +library("stringr") # Load stringr package, used for wrapping label text in plots +# Define colour palettes for plots below +coul3 <- brewer.pal(3, "RdYlBu") # Using RdYlBu range to generate 3 colour palette: https://colorbrewer2.org/#type=diverging&scheme=RdYlBu&n=5 +TSR_data <- read.csv("./data/TSR data complete.csv") +subject_data <- read.csv("./data/Subject data.csv") +# JK note: To keep things tidy, I've shifted the code here so that this isn't overwriting the dataset, but is instead operating off a separate df (same below) +TSR_data_summaries_age <- factor(TSR_data$Age, levels = c(1, 2, 3, 4, 5, 6, 7, 8), labels = c("15 or under", "16", "17", "18", "19", "20", "21 or over", "Prefer not to say")) +# JK note: adding in a bar chart here, as according to the interwebs it's (apparently?) more accurate to visualise than bar charts +data_summaries_age <- ggplot(TSR_data, aes(TSR_data_summaries_age)) + geom_bar() +data_summaries_age + labs(title = "Respondent Age Distribution", x = "Age", y = "") +TSR_data_summaries_yos <- factor(TSR_data$MOSTRECENTyearofstudy, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9), labels = c("Year 11/S4/Year 12(NI)", "Year 12/S5/Year 13(NI)", "Year 13/S6/Year 14(NI)", "I am currently on a gap year", "I am currently on an undergraduate/HE college course", "I am in full-time employment", "I am unemployed", "Other", "Prefer not to say")) +data_summaries_yos <- ggplot(TSR_data, aes(TSR_data_summaries_yos)) + geom_bar() +data_summaries_yos + labs(title = "Respondent Most Recent Year of Study", x = "", y = "") +# save to png file for reports +ggsave("figures/TSR_data_summaries_yos.png") +TSR_data_summaries_gender <- factor(TSR_data$Gender, levels = c(1, 2, 3, 4), labels = c("Male", "Female", "I identify my gender in another way", "Prefer not to say")) +data_summaries_gender <- ggplot(TSR_data, aes(TSR_data_summaries_gender)) + geom_bar() +data_summaries_gender + labs(title = "Respondent gender self-identification", x = "", y = "") + str_wrap(x, width = 10) +TSR_data_summaries_gender <- str_wrap(TSR_data_summaries_gender, width = 10) +data_summaries_gender <- ggplot(TSR_data, aes(TSR_data_summaries_gender)) + geom_bar() +data_summaries_gender + labs(title = "Respondent gender self-identification", x = "", y = "") + str_wrap(x, width = 10) +data_summaries_gender + labs(title = "Respondent gender self-identification", x = "", y = "") +# save to png file for reports +ggsave("figures/TSR_data_summaries_gender.png") +TSR_data_summaries_ethnicity$ethnicity <- factor(TSR_data$Ethnicity, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 ,18, 19), labels = c("Arab", "Asian/Asian British - Indian", "Asian/Asian British - Pakistani", "Asian/Asian British - Bangladeshi", "Asian/Asian British - Chinese", "Asian/Asian British - Any other Asian background", "Black/Black British - African", "Black/Black British - Caribbean", "Black/Black British - Any other Black background", "Mixed/Multiple Ethnic Groups - White and Black Caribbean", "Mixed/Multiple Ethnic Groups - White and Black African", "Mixed/Multiple Ethnic Groups - White and Black Asian", "Mixed/Multiple Ethnic Groups - Any other Mixed/Multiple Ethnic background", "White - English/Welsh/Scottish/Northern Irish/British", "White - Irish", "White - Gypsy or Irish Traveller", "White - Any other White background", "Other Ethnic group, please describe", "Prefer not to say")) +TSR_data_summaries_ethnicity <- factor(TSR_data$Ethnicity, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 ,18, 19), labels = c("Arab", "Asian/Asian British - Indian", "Asian/Asian British - Pakistani", "Asian/Asian British - Bangladeshi", "Asian/Asian British - Chinese", "Asian/Asian British - Any other Asian background", "Black/Black British - African", "Black/Black British - Caribbean", "Black/Black British - Any other Black background", "Mixed/Multiple Ethnic Groups - White and Black Caribbean", "Mixed/Multiple Ethnic Groups - White and Black African", "Mixed/Multiple Ethnic Groups - White and Black Asian", "Mixed/Multiple Ethnic Groups - Any other Mixed/Multiple Ethnic background", "White - English/Welsh/Scottish/Northern Irish/British", "White - Irish", "White - Gypsy or Irish Traveller", "White - Any other White background", "Other Ethnic group, please describe", "Prefer not to say")) +Ethnicity_bar <- ggplot(TSR_data, aes(Ethnicity)) + geom_bar() + coord_flip() +Ethnicity_bar <- ggplot(TSR_data, aes(TSR_data_summaries_ethnicity)) + geom_bar() + coord_flip() +Ethnicity_bar +data_summaries_ethnicity <- ggplot(TSR_data, aes(TSR_data_summaries_ethnicity)) + geom_bar() + coord_flip() +data_summaries_ethnicity +TSR_data_summaries_ethnicity <- str_wrap(TSR_data_summaries_ethnicity, width = 20) +data_summaries_ethnicity <- ggplot(TSR_data, aes(TSR_data_summaries_ethnicity)) + geom_bar() + coord_flip() +data_summaries_ethnicity +data_summaries_ethnicity <- ggplot(TSR_data, aes(TSR_data_summaries_ethnicity)) + geom_bar() + coord_flip() + xlab(NULL) + ylab(yaxis_label) +data_summaries_ethnicity <- ggplot(TSR_data, aes(TSR_data_summaries_ethnicity)) + geom_bar() + coord_flip() + xlab(NULL) +data_summaries_ethnicity +# save to png file for reports +ggsave("figures/TSR_data_summaries_ethnicity.png") +data_summaries_ethnicity <- ggplot(TSR_data, aes(TSR_data_summaries_ethnicity)) + geom_col()) + coord_flip() + xlab(NULL) +data_summaries_ethnicity <- ggplot(TSR_data, aes(TSR_data_summaries_ethnicity)) + geom_col() + coord_flip() + xlab(NULL) +data_summaries_ethnicity +data_summaries_ethnicity <- ggplot(TSR_data, aes(TSR_data_summaries_ethnicity)) + geom_bar() + scale_x_discrete(labels = wrap) + xlab(NULL) + coord_flip() +data_summaries_ethnicity <- ggplot(TSR_data, aes(TSR_data_summaries_ethnicity)) + geom_bar() + xlab(NULL) + coord_flip() +data_summaries_ethnicity +TSR_data_summaries_ethnicity <- str_wrap(TSR_data_summaries_ethnicity, width = 30) +data_summaries_ethnicity <- ggplot(TSR_data, aes(TSR_data_summaries_ethnicity)) + geom_bar() + xlab(NULL) + coord_flip() +data_summaries_ethnicity +TSR_data_summaries_ethnicity <- factor(TSR_data$Ethnicity, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 ,18, 19), labels = c("Arab", "Indian", "Pakistani", "Bangladeshi", "Chinese", "Any other Asian background", "African", "Caribbean", "Any other Black background", "White and Black Caribbean", "White and Black African", "White and Black Asian", "Any other Mixed/Multiple Ethnic background", "White - British", "White - Irish", "Gypsy or Irish Traveller", "Any other White background", "Other Ethnic group", "Prefer not to say")) +TSR_data_summaries_ethnicity <- str_wrap(TSR_data_summaries_ethnicity, width = 30) +data_summaries_ethnicity <- ggplot(TSR_data, aes(TSR_data_summaries_ethnicity)) + geom_bar() + xlab(NULL) + coord_flip() +data_summaries_ethnicity +TSR_data_summaries_ethnicity <- factor(TSR_data$Ethnicity, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 ,18, 19), labels = c("Arab", "Indian", "Pakistani", "Bangladeshi", "Chinese", "Any other Asian background", "Black - African", "Black - Caribbean", "Any other Black background", "Mixed - White and Black Caribbean", "Mixed - White and Black African", "Mixed - White and Black Asian", "Any other Mixed/Multiple Ethnic background", "White - British", "White - Irish", "Gypsy or Irish Traveller", "Any other White background", "Other Ethnic group", "Prefer not to say")) +TSR_data_summaries_ethnicity <- str_wrap(TSR_data_summaries_ethnicity, width = 30) +data_summaries_ethnicity <- ggplot(TSR_data, aes(TSR_data_summaries_ethnicity)) + geom_bar() + xlab(NULL) + coord_flip() +data_summaries_ethnicity +# save to png file for reports +ggsave("figures/TSR_data_summaries_ethnicity.png") +TSR_data_summaries_religion <- factor(TSR_data$Religious.Affliation, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19), labels = c("Agnostic", "Atheist", "Baha'i", "Buddhist", "Christian", "Confucian", "Jain", "Jewish", "Hindu", "Indigenous Traditional Religious", "Muslim", "Pagan", "Shinto", "Sikh", "Spiritual but not religious", "Zoroastrian", "No religion", "Prefer not to say", "Other")) Religious_affiliation_bar <- ggplot(TSR_data, aes(Religious.Affliation)) + geom_bar() + coord_flip() Religious_affiliation_bar -subject_data <- read.csv("./data/Subject data.csv") +Religious_affiliation_bar <- ggplot(TSR_data, aes(TSR_data_summaries_religion)) + geom_bar() + coord_flip() +Religious_affiliation_bar +TSR_data_summaries_religion <- factor(TSR_data$Religious.Affliation, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19), labels = c("Agnostic", "Atheist", "Baha'i", "Buddhist", "Christian", "Confucian", "Jain", "Jewish", "Hindu", "Indigenous Traditional Religious", "Muslim", "Pagan", "Shinto", "Sikh", "Spiritual but not religious", "Zoroastrian", "No religion", "Prefer not to say", "Other")) +Religious_affiliation_bar <- ggplot(TSR_data, aes(TSR_data_summaries_religion)) + geom_bar() + coord_flip() +Religious_affiliation_bar +Religious_affiliation_bar <- ggplot(TSR_data_summaries_religion, aes(TSR_data_summaries_religion)) + geom_bar() + coord_flip() +Religious_affiliation_bar +View(Religious_affiliation_bar) +TSR_data_summaries_religion <- factor(TSR_data$ReligiousAffliation, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19), labels = c("Agnostic", "Atheist", "Baha'i", "Buddhist", "Christian", "Confucian", "Jain", "Jewish", "Hindu", "Indigenous Traditional Religious", "Muslim", "Pagan", "Shinto", "Sikh", "Spiritual but not religious", "Zoroastrian", "No religion", "Prefer not to say", "Other")) +Religious_affiliation_bar <- ggplot(TSR_data, aes(Religious.Affliation)) + geom_bar() + coord_flip() +Religious_affiliation_bar +Religious_affiliation_bar <- ggplot(TSR_data, aes(TSR_data_summaries_religion)) + geom_bar() + coord_flip() +Religious_affiliation_bar +data_summaries_ethnicity + labs(title = "Respondent ethnic self-identification", x = "", y = "") +data_summaries_ethnicity +# save to png file for reports +ggsave("figures/TSR_data_summaries_ethnicity.png") +TSR_data_summaries_ethnicity <- str_wrap(TSR_data_summaries_ethnicity, width = 30) +data_summaries_ethnicity <- ggplot(TSR_data, aes(TSR_data_summaries_ethnicity)) + geom_bar() + xlab(NULL) + coord_flip() +data_summaries_ethnicity + labs(title = "Respondent ethnic self-identification", x = "", y = "") +data_summaries_ethnicity +# save to png file for reports +ggsave("figures/TSR_data_summaries_ethnicity.png") +data_summaries_ethnicity + labs(title = "Respondent ethnic self-identification", x = "", y = "") +# save to png file for reports +ggsave("figures/TSR_data_summaries_ethnicity.png") +Religious_affiliation_bar <- ggplot(TSR_data, aes(TSR_data_summaries_religion)) + geom_bar() + coord_flip() +Religious_affiliation_bar + labs(title = "Respondent religious self-identification", x = "", y = "") +# save to png file for reports +ggsave("figures/TSR_data_summaries_religion.png") #Q5 Subject Knowledge/Understanding subject_data$Subject <- factor(subject_data$Subject, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), labels = c("Philosophy", "Sociology", "Psychology", "History", "Ethics", "Theology", "Religious Studies", "Politics", "English", "Math", "Computer Science", "Business")) understanding_mean <- aggregate(Understanding ~ Subject, data = subject_data, mean) understanding_bar <- ggplot(understanding_mean, aes(x = Subject, y = Understanding)) + stat_summary(fun = "mean", geom = "bar") + coord_flip() understanding_bar +understanding_bar + labs(title = "I have a good understanding of what this subject involves?", x = "", y = "") +# save to png file for reports +ggsave("figures/TSR_data_subject_understanding.png") +understanding_mean <- aggregate(Understanding ~ Subject, data = subject_data, mean) +understanding_bar <- ggplot(understanding_mean, aes(x = Subject, y = Understanding)) + stat_summary(fun = "mean", geom = "bar") + coord_flip() +understanding_bar + labs(title = "I have a good understanding of what this subject involves?", x = "", y = "") +# save to png file for reports +ggsave("figures/TSR_data_subject_understanding.png") #Q6 Subject Interest interest_mean <- aggregate(Interest ~ Subject, data = subject_data, mean) interest_bar <- ggplot(interest_mean, aes(x = Subject, y = Interest)) + stat_summary(fun = "mean", geom = "bar") + coord_flip() -interest_bar +interest_bar + labs(title = "I would be interested in studying this subject at University?", x = "", y = "") +# save to png file for reports +ggsave("figures/TSR_data_subject_interest.png") employability_mean <- aggregate(Employability ~ Subject, data = subject_data, mean) employability_bar <- ggplot(employability_mean, aes(x = Subject, y = Employability)) + stat_summary(fun = "mean", geom = "bar") + coord_flip() -employability_bar +employability_bar + labs(title = "please rate from 1 to 5 where 1 represents 'Good employability prospects’ and 5 represents ‘Poor employability prospects'", x = "", y = "") +# save to png file for reports +ggsave("figures/TSR_data_subject_employability.png") +employability_bar + labs(title = "please rate ... employability prospects", x = "", y = "") +# save to png file for reports +ggsave("figures/TSR_data_subject_employability.png") +#likert_test <- likert(subject_data) +Understanding_data <- TSR_data[, 6:17] +likert_test_understand <- likert(Understanding_data) +#likert_test <- likert(subject_data) +likert(TSR_data[, 6:17]) +#likert_test <- likert(subject_data) +likert(TSR_data[, 6]) +#likert_test <- likert(subject_data) +likert(items=TSR_data[, 6], drop=FALSE) +#likert_test <- likert(subject_data) +likert(items=TSR_data[, 6]) +#likert_test <- likert(subject_data) +Understanding_data <- TSR_data[, 6:17] +View(Understanding_data) +likert_test_understand <- likert(Understanding_data$GoodunderstandingofTheology) +likert_test_understand <- likert(Understanding_data) +understanding_data <- TSR_data[, 6] +understanding_data <- TSR_data[, 6] +l24g <- likert(understanding_data) +TSR_data <- read.csv("./data/TSR data complete.csv") +understanding_data <- TSR_data[, 6] +likert_test_understand <- likert(understanding_data) +as.data.frame(TSR_data) +#likert_test <- likert(subject_data) +understanding_data <- as.data.frame(TSR_data) +View(understanding_mean) +View(understanding_mean) +View(understanding_data) +View(understanding_data) +likert_test_understand <- likert(understanding_data) keysubjects_data <- subject_data[subject_data$Subject == "Philosophy" | subject_data$Subject == "Ethics" | subject_data$Subject == "Theology" | subject_data$Subject == "Religious Studies", ] -recode_interest <- ifelse(1 <= keysubjects_data$Interest & keysubjects_data$Interest >=3, "Positive", "Negative") +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) -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 +View(keysubjects_data) +recode_interest <- ifelse(2 <= keysubjects_data$Interest & keysubjects_data$Interest >=4, "Positive", "Negative") +keysubjects_data <- cbind(keysubjects_data, recode_interest) +keysubjects_data$recode_interest <- factor(keysubjects_data$recode_interest) +table(keysubjects_data$recode_interest, keysubjects_data$Subject) +View(keysubjects_data) +keysubjects_data <- subject_data[subject_data$Subject == "Philosophy" | subject_data$Subject == "Ethics" | subject_data$Subject == "Theology" | subject_data$Subject == "Religious Studies", ] +recode_interest <- ifelse(2 <= keysubjects_data$Interest & keysubjects_data$Interest >=4, "Positive", "Negative") +keysubjects_data <- cbind(keysubjects_data, recode_interest) +keysubjects_data$recode_interest <- factor(keysubjects_data$recode_interest) +table(keysubjects_data$recode_interest, keysubjects_data$Subject) +write.csv(table(keysubjects_data$recode_interest, keysubjects_data$Subject), "derivedData/interest_table.csv", row.names=TRUE) +interest_table <- table(keysubjects_data$recode_interest, keysubjects_data$Subject) +write.csv(interest_table, "derivedData/interest_table.csv", row.names=TRUE) +derivedData +write.csv(interest_table, "derivedData/interest_table.csv", row.names=TRUE) +# Set up local workspace: +if (dir.exists("data") == FALSE) { +dir.create("data") +} +if (dir.exists("figures") == FALSE) { +dir.create("figures") +} +if (dir.exists("derivedData") == FALSE) { +dir.create("derivedData") +} +# export table to csv +write.csv(interest_table, "derivedData/interest_table.csv", row.names=TRUE) +### Philosophy ### +Philos_subset_Low <- TSR_data[TSR_data$GoodunderstandingofPhilosophy < 3 & TSR_data$GoodunderstandingofPhilosophy != 0, ] +Philos_subset_High <- TSR_data[TSR_data$GoodunderstandingofPhilosophy > 3, ] +# Low interest cohort +Philos_subset_Low <- TSR_data[TSR_data$GoodunderstandingofPhilosophy < 3 & TSR_data$GoodunderstandingofPhilosophy != 0, ] +# High interest cohort +Philos_subset_High <- TSR_data[TSR_data$GoodunderstandingofPhilosophy > 3, ] +# Low understanding of philosophy cohort +Philos_subset_Low <- TSR_data[TSR_data$GoodunderstandingofPhilosophy < 3 & TSR_data$GoodunderstandingofPhilosophy != 0, ] +# High understanding of philosophy cohort +Philos_subset_High <- TSR_data[TSR_data$GoodunderstandingofPhilosophy > 3, ] +subject_data$Subject +keysubjects_data <- subject_data[subject_data$Subject == "Philosophy" | subject_data$Subject == "Ethics" | subject_data$Subject == "Theology" | subject_data$Subject == "Religious Studies" | subject_data$Subject == "Sociology", ] +recode_interest <- ifelse(2 <= keysubjects_data$Interest & keysubjects_data$Interest >=4, "Positive", "Negative") +keysubjects_data <- cbind(keysubjects_data, recode_interest) +keysubjects_data$recode_interest <- factor(keysubjects_data$recode_interest) +interest_table <- table(keysubjects_data$recode_interest, keysubjects_data$Subject) +# export table to csv +write.csv(interest_table, "derivedData/interest_table.csv", row.names=TRUE) +keysubjects_data <- subject_data[subject_data$Subject == "Philosophy" | subject_data$Subject == "Ethics" | subject_data$Subject == "Theology" | subject_data$Subject == "Religious Studies" | subject_data$Subject == "Sociology" | subject_data$Subject == "Psychology" | subject_data$Subject == "History" | subject_data$Subject == "Politics" | subject_data$Subject == "English" | subject_data$Subject == "Math" | subject_data$Subject == "Computer Science" | subject_data$Subject == "Business", ] +recode_interest <- ifelse(2 <= keysubjects_data$Interest & keysubjects_data$Interest >=4, "Positive", "Negative") +keysubjects_data <- cbind(keysubjects_data, recode_interest) +keysubjects_data$recode_interest <- factor(keysubjects_data$recode_interest) +interest_table <- table(keysubjects_data$recode_interest, keysubjects_data$Subject) +# export table to csv +write.csv(interest_table, "derivedData/interest_table.csv", row.names=TRUE) +keysubjects_data$Interest +prop.table(keysubjects_data$Interest) +keysubjects_data +View(subject_data) +TSR_data_theology_positive <- TSR_data$InterestedinstudyingTheology >=4 +TSR_data_theology_positive +TSR_data_theology_positive <- TSR_data$InterestedinstudyingTheology[] >=4 +library(dplyr) +TSR_data_theology_positive %>% filter(TSR_data, InterestedinstudyingTheology >= 4) +TSR_data$InterestedinstudyingTheology +TSR_data$InterestedinstudyingTheology >4 +TSR_data$InterestedinstudyingTheology >3 +count(TSR_data$InterestedinstudyingTheology >3) +length(TSR_data$InterestedinstudyingTheology >3) +# TSR_data_theology_positive %>% +filter(TSR_data, InterestedinstudyingTheology >= 4) +length(TSR_data$InterestedinstudyingTheology >3) +# TSR_data_theology_positive %>% +filter(TSR_data, InterestedinstudyingTheology >= 4) +filter(TSR_data, InterestedinstudyingTheology == 4) +filter(TSR_data, InterestedinstudyingTheology >= 4) +TSR_data_theology_positive %>% filter(TSR_data, InterestedinstudyingTheology >= 4) +TSR_data_theology_positive <- filter(TSR_data, InterestedinstudyingTheology >= 4) +View(TSR_data_theology_positive) +TSR_data_theology_positive <- filter(TSR_data, InterestedinstudyingTheology < 3) +TSR_data_theology_positive <- filter(TSR_data, InterestedinstudyingTheology < 3) +TSR_data_theology_positive <- filter(TSR_data, InterestedinstudyingTheology > 3) +TSR_data_theology_positive <- filter(TSR_data, InterestedinstudyingTheology > 2) +TSR_data_theology_positive <- filter(TSR_data, InterestedinstudyingTheology > 2) +TSR_data_theology_positive <- filter(TSR_data, InterestedinstudyingTheology < 3) +TSR_data_theology_positive <- filter(TSR_data, InterestedinstudyingTheology < 2) +TSR_data_theology_positive <- filter(TSR_data, InterestedinstudyingTheology < 1) +TSR_data_theology_positive <- filter(TSR_data, InterestedinstudyingTheology >= 4) +TSR_data_theology_negative <- filter(TSR_data, InterestedinstudyingTheology <= 2) +TSR_data_theology_positive <- filter(TSR_data, InterestedinstudyingTheology >= 4 & InterestedinstudyingTheology != 0) +TSR_data_theology_negative <- filter(TSR_data, InterestedinstudyingTheology <= 2 & InterestedinstudyingTheology != 0) +TSR_data_rs_positive <- filter(TSR_data, InterestedinstudyingReligion >= 4 & InterestedinstudyingReligion != 0) +TSR_data_rs_positive <- filter(TSR_data, InterestedinstudyingReligiousStudies >= 4 & InterestedinstudyingReligiousStudies != 0) +TSR_data_rs_negative <- filter(TSR_data, InterestedinstudyingReligiousStudies <= 2 & InterestedinstudyingReligiousStudies != 0) +knitr::opts_chunk$set(echo = TRUE) +library(ggplot2) +library(devtools) +library(usethis) +library(devtools) +library(likert) +# Load RColorBrewer +# install.packages("RColorBrewer") +library(RColorBrewer) +library("stringr") # Load stringr package, used for wrapping label text in plots +library(dplyr) # Used for filtering below +# Define colour palettes for plots below +coul3 <- brewer.pal(3, "RdYlBu") # Using RdYlBu range to generate 3 colour palette: https://colorbrewer2.org/#type=diverging&scheme=RdYlBu&n=5 +TSR_data <- read.csv("./data/TSR data complete.csv") +subject_data <- read.csv("./data/Subject data.csv") +# Set up local workspace: +if (dir.exists("data") == FALSE) { +dir.create("data") +} +if (dir.exists("figures") == FALSE) { +dir.create("figures") +} +if (dir.exists("derivedData") == FALSE) { +dir.create("derivedData") +} +TSR_data_theology_positive_summaries_religion <- factor(TSR_data_theology_positive$ReligiousAffliation, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19), labels = c("Agnostic", "Atheist", "Baha'i", "Buddhist", "Christian", "Confucian", "Jain", "Jewish", "Hindu", "Indigenous Traditional Religious", "Muslim", "Pagan", "Shinto", "Sikh", "Spiritual but not religious", "Zoroastrian", "No religion", "Prefer not to say", "Other")) +TSR_data_theology_positive_summaries_religion <- factor(TSR_data_theology_positive$ReligiousAffliation, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19), labels = c("Agnostic", "Atheist", "Baha'i", "Buddhist", "Christian", "Confucian", "Jain", "Jewish", "Hindu", "Indigenous Traditional Religious", "Muslim", "Pagan", "Shinto", "Sikh", "Spiritual but not religious", "Zoroastrian", "No religion", "Prefer not to say", "Other")) +TSR_data_theology_negative_summaries_religion <- factor(TSR_data_theology_negative$ReligiousAffliation, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19), labels = c("Agnostic", "Atheist", "Baha'i", "Buddhist", "Christian", "Confucian", "Jain", "Jewish", "Hindu", "Indigenous Traditional Religious", "Muslim", "Pagan", "Shinto", "Sikh", "Spiritual but not religious", "Zoroastrian", "No religion", "Prefer not to say", "Other")) +TSR_data_rs_positive_summaries_religion <- factor(TSR_data_rs_positive$ReligiousAffliation, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19), labels = c("Agnostic", "Atheist", "Baha'i", "Buddhist", "Christian", "Confucian", "Jain", "Jewish", "Hindu", "Indigenous Traditional Religious", "Muslim", "Pagan", "Shinto", "Sikh", "Spiritual but not religious", "Zoroastrian", "No religion", "Prefer not to say", "Other")) +TSR_data_rs_positive_summaries_religion <- factor(TSR_data_rs_negative$ReligiousAffliation, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19), labels = c("Agnostic", "Atheist", "Baha'i", "Buddhist", "Christian", "Confucian", "Jain", "Jewish", "Hindu", "Indigenous Traditional Religious", "Muslim", "Pagan", "Shinto", "Sikh", "Spiritual but not religious", "Zoroastrian", "No religion", "Prefer not to say", "Other")) +Religious_affiliation_bar2 <- ggplot(TSR_data, aes(TSR_data_theology_positive_summaries_religion)) + geom_bar() + coord_flip() +Religious_affiliation_bar2 + labs(title = "Respondent religious self-identification", x = "", y = "") +Religious_affiliation_bar2 <- ggplot(TSR_data_theology_positive, aes(TSR_data_theology_positive_summaries_religion)) + geom_bar() + coord_flip() +Religious_affiliation_bar2 + labs(title = "Respondent religious self-identification", x = "", y = "") +# save to png file for reports +ggsave("figures/TSR_data_summaries_religion_theologypositive.png") +TSR_data_rs_positive_summaries_religion <- factor(TSR_data_rs_positive$ReligiousAffliation, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19), labels = c("Agnostic", "Atheist", "Baha'i", "Buddhist", "Christian", "Confucian", "Jain", "Jewish", "Hindu", "Indigenous Traditional Religious", "Muslim", "Pagan", "Shinto", "Sikh", "Spiritual but not religious", "Zoroastrian", "No religion", "Prefer not to say", "Other")) +TSR_data_rs_negative_summaries_religion <- factor(TSR_data_rs_negative$ReligiousAffliation, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19), labels = c("Agnostic", "Atheist", "Baha'i", "Buddhist", "Christian", "Confucian", "Jain", "Jewish", "Hindu", "Indigenous Traditional Religious", "Muslim", "Pagan", "Shinto", "Sikh", "Spiritual but not religious", "Zoroastrian", "No religion", "Prefer not to say", "Other")) +Religious_affiliation_bar3 <- ggplot(TSR_data_rs_positive, aes(TSR_data_rs_positive_summaries_religion)) + geom_bar() + coord_flip() +Religious_affiliation_bar3 + labs(title = "Respondent religious self-identification", x = "", y = "") +# save to png file for reports +ggsave("figures/TSR_data_summaries_religion_theologypositive.png") +Religious_affiliation_bar2 <- ggplot(TSR_data_theology_positive, aes(TSR_data_theology_positive_summaries_religion)) + geom_bar() + coord_flip() +Religious_affiliation_bar2 + labs(title = "Respondent religious self-identification (positive sentiment towards "theology")", x = "", y = "") +# save to png file for reports +ggsave("figures/TSR_data_summaries_religion_theologypositive.png") +Religious_affiliation_bar2 + labs(title = "Respondent religious self-identification - positive sentiment towards theology"), x = "", y = "") +" +Religious_affiliation_bar2 + labs(title = "Respondent religious self-identification - positive sentiment towards theology", x = "", y = "") +Religious_affiliation_bar2 + labs(title = "Respondent religious self-identification, positive sentiment towards theology", x = "", y = "") +Religious_affiliation_bar2 <- ggplot(TSR_data_theology_positive, aes(TSR_data_theology_positive_summaries_religion)) + geom_bar() + coord_flip() +Religious_affiliation_bar2 + labs(title = "Respondent religious self-identification, positive sentiment towards theology", x = "", y = "") +# save to png file for reports +ggsave("figures/TSR_data_summaries_religion_theologypositive.png") +Religious_affiliation_bar3 <- ggplot(TSR_data_rs_positive, aes(TSR_data_rs_positive_summaries_religion)) + geom_bar() + coord_flip() +Religious_affiliation_bar3 + labs(title = "Respondent religious self-identification, positive sentiment towards rs", x = "", y = "") +# save to png file for reports +ggsave("figures/TSR_data_summaries_religion_rspositive.png") +library(scales) # Used for adding percentages to bar charts +Religious_affiliation_bar3 <- ggplot(TSR_data_rs_positive, aes(TSR_data_rs_positive_summaries_religion, label=scales::percent(pct))) + geom_bar() + coord_flip() +Religious_affiliation_bar3 + labs(title = "Respondent religious self-identification, positive sentiment towards rs", x = "", y = "") +Religious_affiliation_bar3 <- ggplot(TSR_data_rs_positive, aes(TSR_data_rs_positive_summaries_religion) + geom_bar() + geom_text(label=scales::percent(pct))) + coord_flip()