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added a few more charts for subgroups
This commit is contained in:
Jeremy Kidwell
parent
e4b27b3308
commit
57e574d953
166
.Rhistory
166
.Rhistory
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@ -1,86 +1,3 @@
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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$InterestedinstudyingReligiousStudies), na.rm = TRUE)
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### Philosophy ###
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Philos_subset_Low <- TSR_data[TSR_data$GoodunderstandingofPhilosophy < 3 & TSR_data$GoodunderstandingofPhilosophy != 0, ]
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Philos_subset_High <- TSR_data[TSR_data$GoodunderstandingofPhilosophy > 3, ]
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## Theology Knowledge
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#Low knowledge in Philosophy
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mean(as.numeric(Philos_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
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#High knowledge in Philosophy
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mean(as.numeric(Philos_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
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## Religious Studies Knowledge
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#Low knowledge in Philosophy
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mean(as.numeric(Philos_subset_Low$GoodunderstandingofReligiousStudies), na.rm = TRUE)
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#High knowledge in Philosophy
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mean(as.numeric(Philos_subset_High$GoodunderstandingofReligiousStudies), na.rm = TRUE)
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## Religious Studies Knowledge
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#Low knowledge in Philosophy
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mean(as.numeric(Philos_subset_Low$GoodunderstandingofReligiousStudies), na.rm = TRUE)
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#High knowledge in Philosophy
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mean(as.numeric(Philos_subset_High$GoodunderstandingofReligiousStudies), na.rm = TRUE)
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```{r Sociology}
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### Sociology ###
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Soc_subset_Low <- TSR_data[TSR_data$GoodunderstandingofSociology < 3 & TSR_data$GoodunderstandingofSociology != 0, ]
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Soc_subset_High <- TSR_data[TSR_data$GoodunderstandingofSociology > 3, ]
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## Theology knowledge
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#Low knowledge in Sociology
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mean(as.numeric(Soc_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
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#High knowledge in Sociology
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mean(as.numeric(Soc_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
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## Religious Studies knowledge
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#Low knowledge in Sociology
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mean(as.numeric(Soc_subset_Low$GoodunderstandingofReligiousStudies), na.rm = TRUE)
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#High knowledge in Sociology
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mean(as.numeric(Soc_subset_High$GoodunderstandingofReligiousStudies), na.rm = TRUE)
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## Religious Studies knowledge
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#Low knowledge in Sociology
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mean(as.numeric(Soc_subset_Low$GoodunderstandingofReligiousStudies), na.rm = TRUE)
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#High knowledge in Sociology
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mean(as.numeric(Soc_subset_High$GoodunderstandingofReligiousStudies), na.rm = TRUE)
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```{r Psychology}
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### Psychology ###
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Psych_subset_Low <- TSR_data[TSR_data$GoodunderstandingofPsychology < 3 & TSR_data$GoodunderstandingofPsychology != 0, ]
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Psych_subset_High <- TSR_data[TSR_data$GoodunderstandingofPsychology > 3, ]
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## Theology knowledge
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#Low knowledge in Psychology
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mean(as.numeric(Psych_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
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#High knowledge in Psychology
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mean(as.numeric(Psych_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
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## Religious Studies knowledge
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#Low knowledge in Psychology
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mean(as.numeric(Psych_subset_Low$GoodunderstandingofReligiousStudies), na.rm = TRUE)
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#High knowledge in Psychology
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mean(as.numeric(Psych_subset_High$GoodunderstandingofReligiousStudies), na.rm = TRUE)
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### History ###
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Hist_subset_Low <- TSR_data[TSR_data$GoodunderstandingofHistory < 3 & TSR_data$GoodunderstandingofHistory != 0, ]
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Hist_subset_High <- TSR_data[TSR_data$GoodunderstandingofHistory > 3, ]
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require(devtools)
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library(ggplot2)
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require(ggplot2)
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require(usethis)
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require(devtools)
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require(likert)
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TSR_data <- read.csv("./data/TSR data complete.csv")
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subject_data <- read.csv("./data/Subject data.csv")
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TSR_data$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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# Load RColorBrewer
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# install.packages("RColorBrewer")
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library(RColorBrewer)
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# Define colour palettes for plots below
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coul3 <- brewer.pal(3, "RdYlBu") # Using RdYlBu range to generate 3 colour palette: https://colorbrewer2.org/#type=diverging&scheme=RdYlBu&n=5
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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$Age), col=coul3)
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TSR_data$Age <- factor(TSR_data$Age, levels = c(1, 2, 3, 4, 5, 6, 7, 8), labels = c("15 or under", "16", "17", "18", "19", "20", "21 or over", "Prefer not to say"))
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View(TSR_data)
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TSR_data <- read.csv("./data/TSR data complete.csv")
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View(TSR_data)
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TSR_data$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), col=coul3)
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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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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)
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@ -510,3 +427,86 @@ library(scales) # Used for adding percentages to bar charts
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Religious_affiliation_bar3 <- ggplot(TSR_data_rs_positive, aes(TSR_data_rs_positive_summaries_religion, label=scales::percent(pct))) + geom_bar() + coord_flip()
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Religious_affiliation_bar3 + labs(title = "Respondent religious self-identification, positive sentiment towards rs", x = "", y = "")
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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()
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knitr::opts_chunk$set(echo = TRUE)
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library(ggplot2)
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library(devtools)
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library(usethis)
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library(devtools)
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library(likert)
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# Load RColorBrewer
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# install.packages("RColorBrewer")
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library(RColorBrewer)
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library("stringr") # Load stringr package, used for wrapping label text in plots
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library(dplyr) # Used for filtering below
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library(scales) # Used for adding percentages to bar charts
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# Define colour palettes for plots below
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coul3 <- brewer.pal(3, "RdYlBu") # Using RdYlBu range to generate 3 colour palette: https://colorbrewer2.org/#type=diverging&scheme=RdYlBu&n=5
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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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# Set up local workspace (as needed):
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if (dir.exists("data") == FALSE) {
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dir.create("data")
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}
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if (dir.exists("figures") == FALSE) {
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dir.create("figures")
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}
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if (dir.exists("derivedData") == FALSE) {
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dir.create("derivedData")
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}
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# Set up local workspace, as needed:
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if (dir.exists("data") == FALSE) {
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dir.create("data")
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}
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if (dir.exists("figures") == FALSE) {
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dir.create("figures")
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}
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if (dir.exists("derivedData") == FALSE) {
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dir.create("derivedData")
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}
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TSR_data_theology_positive_summaries_ethnicity <- factor(TSR_data_theology_positive$Ethnicity, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 ,18, 19), labels = c("Arab", "Indian", "Pakistani", "Bangladeshi", "Chinese", "Any other Asian background", "Black - African", "Black - Caribbean", "Any other Black background", "Mixed - White and Black Caribbean", "Mixed - White and Black African", "Mixed - White and Black Asian", "Any other Mixed/Multiple Ethnic background", "White - British", "White - Irish", "Gypsy or Irish Traveller", "Any other White background", "Other Ethnic group", "Prefer not to say"))
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TSR_data_theology_positive_summaries_ethnicity <- str_wrap(TSR_data_summaries_ethnicity, width = 30)
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TSR_data_rs_positive_summaries_ethnicity <- factor(TSR_data_rs_positive$Ethnicity, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 ,18, 19), labels = c("Arab", "Indian", "Pakistani", "Bangladeshi", "Chinese", "Any other Asian background", "Black - African", "Black - Caribbean", "Any other Black background", "Mixed - White and Black Caribbean", "Mixed - White and Black African", "Mixed - White and Black Asian", "Any other Mixed/Multiple Ethnic background", "White - British", "White - Irish", "Gypsy or Irish Traveller", "Any other White background", "Other Ethnic group", "Prefer not to say"))
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TSR_data_rs_positive_summaries_ethnicity <- str_wrap(TSR_data_summaries_ethnicity, width = 30)
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TSR_data__theology_positive_summaries_gender <- factor(TSR_data_theology_positive$Gender, levels = c(1, 2, 3, 4), labels = c("Male", "Female", "I identify my gender in another way", "Prefer not to say"))
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# JK note: using stringr here to wrap axis titles
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TSR_data__theology_positive_summaries_gender <- str_wrap(TSR_data__theology_positive_summaries_gender, width = 10)
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TSR_data_rs_positive_summaries_gender <- factor(TSR_data_rs_positive$Gender, levels = c(1, 2, 3, 4), labels = c("Male", "Female", "I identify my gender in another way", "Prefer not to say"))
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# JK note: using stringr here to wrap axis titles
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TSR_data_rs_positive_summaries_gender <- str_wrap(TSR_data_rs_positive_summaries_gender, width = 10)
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gender_bar2 <- ggplot(TSR_data_theology_positive, aes(TSR_data__theology_positive_summaries_gender)) + geom_bar()
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gender_bar2 + labs(title = "Respondent gender self-identification, theology positive", x = "", y = "")
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gender_bar2 + labs(title = "Respondent gender self-identification, theology positive", x = "", y = "")
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# save to png file for reports
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ggsave("figures/TSR_data_summaries_gender2.png")
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gender_bar3 <- ggplot(TSR_data_rs_positive, aes(TSR_data_rs_positive_summaries_gender)) + geom_bar()
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gender_bar3 + labs(title = "Respondent gender self-identification, rs positive", x = "", y = "")
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# save to png file for reports
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ggsave("figures/TSR_data_summaries_gender3.png")
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data_summaries_theology_positive_ethnicity <- ggplot(TSR_data_theology_positive, aes(TSR_data_theology_positive_summaries_ethnicity)) + geom_bar() + xlab(NULL) + coord_flip()
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data_summaries_theology_positive_ethnicity + labs(title = "Respondent ethnic self-identification, theology positive", x = "", y = "")
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data_summaries_theology_positive_ethnicity + labs(title = "Respondent ethnic self-identification, theology positive", x = "", y = "")
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data_summaries_theology_positive_ethnicity <- ggplot(TSR_data_theology_positive, aes(TSR_data_theology_positive_summaries_ethnicity)) + geom_bar() + xlab(NULL) + coord_flip()
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data_summaries_theology_positive_ethnicity + labs(title = "Respondent ethnic self-identification, theology positive", x = "", y = "")
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data_summaries_theology_positive_ethnicity + labs(title = "Respondent ethnic self-identification", x = "", y = "")
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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"))
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TSR_data_theology_positive_summaries_ethnicity <- factor(TSR_data_theology_positive$Ethnicity, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 ,18, 19), labels = c("Arab", "Indian", "Pakistani", "Bangladeshi", "Chinese", "Any other Asian background", "Black - African", "Black - Caribbean", "Any other Black background", "Mixed - White and Black Caribbean", "Mixed - White and Black African", "Mixed - White and Black Asian", "Any other Mixed/Multiple Ethnic background", "White - British", "White - Irish", "Gypsy or Irish Traveller", "Any other White background", "Other Ethnic group", "Prefer not to say"))
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TSR_data_theology_positive_summaries_ethnicity <- str_wrap(TSR_data_theology_positive_summaries_ethnicity, width = 30)
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TSR_data_rs_positive_summaries_ethnicity <- factor(TSR_data_rs_positive$Ethnicity, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 ,18, 19), labels = c("Arab", "Indian", "Pakistani", "Bangladeshi", "Chinese", "Any other Asian background", "Black - African", "Black - Caribbean", "Any other Black background", "Mixed - White and Black Caribbean", "Mixed - White and Black African", "Mixed - White and Black Asian", "Any other Mixed/Multiple Ethnic background", "White - British", "White - Irish", "Gypsy or Irish Traveller", "Any other White background", "Other Ethnic group", "Prefer not to say"))
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TSR_data_rs_positive_summaries_ethnicity <- str_wrap(TSR_data_rs_positive_summaries_ethnicity, width = 30)
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TSR_data_rs_positive_summaries_ethnicity <- str_wrap(TSR_data_rs_positive_summaries_ethnicity, width = 30)
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data_summaries_rs_positive_ethnicity <- ggplot(TSR_data_rs_positive, aes(TSR_data_rs_positive_summaries_ethnicity)) + geom_bar() + xlab(NULL) + coord_flip()
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data_summaries_ethnicity + labs(title = "Respondent ethnic self-identification", x = "", y = "")
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# save to png file for reports
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ggsave("figures/TSR_data_summaries_ethnicity3.png")
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data_summaries_theology_positive_ethnicity <- ggplot(TSR_data_theology_positive, aes(TSR_data_theology_positive_summaries_ethnicity)) + geom_bar() + xlab(NULL) + coord_flip()
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data_summaries_theology_positive_ethnicity + labs(title = "Respondent ethnic self-identification", x = "", y = "")
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# save to png file for reports
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ggsave("figures/TSR_data_summaries_ethnicity2.png")
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data_summaries_theology_positive_ethnicity <- ggplot(TSR_data_theology_positive, aes(TSR_data_theology_positive_summaries_ethnicity)) + geom_bar() + xlab(NULL) + coord_flip()
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data_summaries_theology_positive_ethnicity + labs(title = "Respondent ethnic self-identification - theology positive", x = "", y = "")
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# save to png file for reports
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ggsave("figures/TSR_data_summaries_ethnicity2.png")
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data_summaries_rs_positive_ethnicity <- ggplot(TSR_data_rs_positive, aes(TSR_data_rs_positive_summaries_ethnicity)) + geom_bar() + xlab(NULL) + coord_flip()
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data_summaries_ethnicity + labs(title = "Respondent ethnic self-identification - religion positive", x = "", y = "")
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# save to png file for reports
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ggsave("figures/TSR_data_summaries_ethnicity3.png")
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@ -13,9 +13,9 @@ library(likert)
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# Load RColorBrewer
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# install.packages("RColorBrewer")
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library(RColorBrewer)
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library("stringr") # Load stringr package, used for wrapping label text in plots
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library(dplyr) # Used for filtering below
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library(scales) # Used for adding percentages to bar charts
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library("stringr") # Load stringr package, used for wrapping label text in plots
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library(dplyr) # Used for filtering below
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library(scales) # Used for adding percentages to bar charts
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# Define colour palettes for plots below
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coul3 <- brewer.pal(3, "RdYlBu") # Using RdYlBu range to generate 3 colour palette: https://colorbrewer2.org/#type=diverging&scheme=RdYlBu&n=5
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@ -23,7 +23,7 @@ coul3 <- brewer.pal(3, "RdYlBu") # Using RdYlBu range to generate 3 colour palet
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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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# Set up local workspace:
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# Set up local workspace, as needed:
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if (dir.exists("data") == FALSE) {
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dir.create("data")
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}
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@ -100,6 +100,23 @@ TSR_data_rs_positive_summaries_religion <- factor(TSR_data_rs_positive$Religious
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TSR_data_rs_negative_summaries_religion <- factor(TSR_data_rs_negative$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"))
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TSR_data_theology_positive_summaries_ethnicity <- factor(TSR_data_theology_positive$Ethnicity, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 ,18, 19), labels = c("Arab", "Indian", "Pakistani", "Bangladeshi", "Chinese", "Any other Asian background", "Black - African", "Black - Caribbean", "Any other Black background", "Mixed - White and Black Caribbean", "Mixed - White and Black African", "Mixed - White and Black Asian", "Any other Mixed/Multiple Ethnic background", "White - British", "White - Irish", "Gypsy or Irish Traveller", "Any other White background", "Other Ethnic group", "Prefer not to say"))
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TSR_data_theology_positive_summaries_ethnicity <- str_wrap(TSR_data_theology_positive_summaries_ethnicity, width = 30)
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TSR_data_rs_positive_summaries_ethnicity <- factor(TSR_data_rs_positive$Ethnicity, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 ,18, 19), labels = c("Arab", "Indian", "Pakistani", "Bangladeshi", "Chinese", "Any other Asian background", "Black - African", "Black - Caribbean", "Any other Black background", "Mixed - White and Black Caribbean", "Mixed - White and Black African", "Mixed - White and Black Asian", "Any other Mixed/Multiple Ethnic background", "White - British", "White - Irish", "Gypsy or Irish Traveller", "Any other White background", "Other Ethnic group", "Prefer not to say"))
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TSR_data_rs_positive_summaries_ethnicity <- str_wrap(TSR_data_rs_positive_summaries_ethnicity, width = 30)
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TSR_data__theology_positive_summaries_gender <- factor(TSR_data_theology_positive$Gender, levels = c(1, 2, 3, 4), labels = c("Male", "Female", "I identify my gender in another way", "Prefer not to say"))
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# JK note: using stringr here to wrap axis titles
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TSR_data__theology_positive_summaries_gender <- str_wrap(TSR_data__theology_positive_summaries_gender, width = 10)
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TSR_data_rs_positive_summaries_gender <- factor(TSR_data_rs_positive$Gender, levels = c(1, 2, 3, 4), labels = c("Male", "Female", "I identify my gender in another way", "Prefer not to say"))
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# JK note: using stringr here to wrap axis titles
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TSR_data_rs_positive_summaries_gender <- str_wrap(TSR_data_rs_positive_summaries_gender, width = 10)
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# Calculate graphs
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Religious_affiliation_bar <- ggplot(TSR_data, aes(TSR_data_summaries_religion)) + geom_bar() + coord_flip()
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@ -109,7 +126,7 @@ ggsave("figures/TSR_data_summaries_religion.png")
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# Additional graphs for theology/rs positive sentiment cohorts
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# Theology
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# Theology - religious identification
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# JK note: need to add percentages to each line, as per https://stackoverflow.com/questions/52373049/display-percentage-on-ggplot-bar-chart-in-r
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@ -125,14 +142,36 @@ Religious_affiliation_bar3 + labs(title = "Respondent religious self-identificat
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# save to png file for reports
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ggsave("figures/TSR_data_summaries_religion_rspositive.png")
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irisNew <- iris %>% group_by(Species) %>%
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summarize(count = n()) %>% # count records by species
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mutate(pct = count/sum(count)) # find percent of total
|
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# Theology positive - gender
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gender_bar2 <- ggplot(TSR_data_theology_positive, aes(TSR_data_theology_positive_summaries_gender)) + geom_bar()
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gender_bar2 + labs(title = "Respondent gender self-identification, theology positive", x = "", y = "")
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# save to png file for reports
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ggsave("figures/TSR_data_summaries_gender2.png")
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# Religion positive - gender
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gender_bar3 <- ggplot(TSR_data_rs_positive, aes(TSR_data_rs_positive_summaries_gender)) + geom_bar()
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gender_bar3 + labs(title = "Respondent gender self-identification, rs positive", x = "", y = "")
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# save to png file for reports
|
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ggsave("figures/TSR_data_summaries_gender3.png")
|
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|
||||
# Theology positive - ethnicity
|
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|
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data_summaries_theology_positive_ethnicity <- ggplot(TSR_data_theology_positive, aes(TSR_data_theology_positive_summaries_ethnicity)) + geom_bar() + xlab(NULL) + coord_flip()
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data_summaries_theology_positive_ethnicity + labs(title = "Respondent ethnic self-identification - theology positive", x = "", y = "")
|
||||
|
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# save to png file for reports
|
||||
ggsave("figures/TSR_data_summaries_ethnicity2.png")
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# Religion positive - ethnicity
|
||||
|
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data_summaries_rs_positive_ethnicity <- ggplot(TSR_data_rs_positive, aes(TSR_data_rs_positive_summaries_ethnicity)) + geom_bar() + xlab(NULL) + coord_flip()
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data_summaries_ethnicity + labs(title = "Respondent ethnic self-identification - religion positive", x = "", y = "")
|
||||
|
||||
# save to png file for reports
|
||||
ggsave("figures/TSR_data_summaries_ethnicity3.png")
|
||||
|
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ggplot(irisNew, aes(Species, pct, fill = Species)) +
|
||||
geom_bar(stat='identity') +
|
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geom_text(aes(label=scales::percent(pct)), position = position_stack(vjust = .5))+
|
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scale_y_continuous(labels = scales::percent)
|
||||
```
|
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|
||||
# Visualisations of LIKERT responses (RH):
|
||||
|
|
|
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Loading…
Reference in a new issue