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