From 0da6527adf2b2076ef116f632806c06d95119142 Mon Sep 17 00:00:00 2001 From: "Jeremy Kidwell (Theology and Religion)" Date: Tue, 20 Feb 2024 14:01:35 +0000 Subject: [PATCH] major rewriting --- ...and RS Survey Raw Data 5 July 2021 V2.xlsx | Bin 747009 -> 0 bytes ...gy and RS Survey Raw Data 5 July 2021.xlsx | Bin 798734 -> 0 bytes tsr_survey_analysis.Rmd | 231 +++++++++++++++++- 3 files changed, 219 insertions(+), 12 deletions(-) delete mode 100644 data/TSR Insight Birmingham Theology and RS Survey Raw Data 5 July 2021 V2.xlsx delete mode 100644 data/TSR Insight Birmingham Theology and RS Survey Raw Data 5 July 2021.xlsx diff --git a/data/TSR Insight Birmingham Theology and RS Survey Raw Data 5 July 2021 V2.xlsx b/data/TSR Insight Birmingham Theology and RS Survey Raw Data 5 July 2021 V2.xlsx deleted file mode 100644 index f81c3daf248395133f022c2282f18288d7c51a81..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 747009 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mutate_at(vars(starts_with("Q5_")), ~ labelled(., common_labels, label = "I have a good understanding of what this subject involves")) + +admissions_data <- admissions_data %>% + mutate_at(vars(starts_with("Q6_")), ~ labelled(., common_labels, label = "I would be interested in studying this subject at University")) + +common_labels2 <- c( + "Good employability prospects" = 1, + NULL = 2, + NULL = 3, + NULL = 4, + "Poor employability prospects" = 5, + "Prefer not to say" = 0 +) + +admissions_data <- admissions_data %>% + mutate_at(vars(starts_with("Q7_")), ~ labelled(., common_labels2, label = "This subject has… employability prospects")) + +admissions_data$Q8 <- labelled(admissions_data$Q8, c("Theology is a subject for religious people" = 1, NULL = 2, NULL = 3, NULL = 4, "Theology is a subject for religious and non-religious people" = 5, "Prefer not to say" = 0), label = "Thinking about Theology, please select an option on the scale from 1 to 5 which best represents your opinion") + +admissions_data$Q9 <- labelled(admissions_data$Q9, c("Religion is a subject for religious people" = 1, NULL = 2, NULL = 3, NULL = 4, "Religion is a subject for religious and non-religious people" = 5, "Prefer not to say" = 0), label = "Thinking about Religion, please select an option on the scale from 1 to 5 which best represents your opinion") + +common_labels3 <- c( + "Psychology" = 1, "Arts" = 2, "Sociology" = 3, "Politics" = 4, "History" = 5, "Philosophy" = 6, "Ethics" = 7, "Archaeology" = 8, "Textual studies" = 9, "Literature" = 10, "Law" = 11, "Economics" = 12, "Science" = 13, "Prefer not to say" = 0) + +admissions_data <- admissions_data %>% + mutate_at(vars(starts_with("Q10_")), ~ labelled(., common_labels3, label = "I think that a theology degree would include...")) + +admissions_data <- admissions_data %>% + mutate_at(vars(starts_with("Q11_")), ~ labelled(., common_labels3, label = "I think that a religious studies degree would include...")) + +common_labels4 <- c( + "Politics" = 1, "History" = 2, "Ethics" = 3, "Theology" = 4, "Religion" = 5, "Law" = 6, "Economics" = 7, "Maths" = 8, "Logic" = 9, "Prefer not to say" = 0) + +admissions_data <- admissions_data %>% + mutate_at(vars(starts_with("Q12_")), ~ labelled(., common_labels4, label = "I think that a philosophy degree would include...")) + +common_labels5 <- c("Yes" = 1, "No" = 2, "Prefer not to say" = 0) + +admissions_data <- admissions_data %>% + mutate_at(vars(starts_with("Q13")), ~ labelled(., common_labels5, label = "Are you currently studying A level Religious Studies, or intending to?")) + +admissions_data <- admissions_data %>% + mutate_at(vars(starts_with("Q14")), ~ labelled(., common_labels5, label = "Are you studying or did you previously study GCSE Religious Studies?")) + +admissions_data$Q16 <- labelled(admissions_data$Q16, c("Male"=1, "Female"=2, "I identify my gender in another way"=3, "Prefer not to say"=4), label = "I identify my gender as…") + +admissions_data$Q17 <- labelled(admissions_data$Q17, c("Arab"=1, "Indian"=2, "Pakistani"=3, "Bangladeshi"=4, "Chinese"=5, "Any other Asian background"=6, "Black - African"=7, "Black - Caribbean"=8, "Any other Black background"=9, "Mixed - White and Black Caribbean"=10, "Mixed - White and Black African"=11, "Mixed - White and Black Asian"=12, "Any other Mixed/Multiple Ethnic background"=13, "White - British"=14, "White - Irish"=15, "Gypsy or Irish Traveller"=16, "Any other White background"=17, "Other Ethnic group"=18, "Prefer not to say OR Other"=0), label = "What is your ethnic group?") + +admissions_data$Q18 <- labelled(admissions_data$Q18, c("Agnostic"=1, "Atheist"=2, "Baha'i"=3, "Buddhist"=4, "Christian"=5, "Confucian"=6, "Jain"=7, "Jewish"=8, "Hindu"=9, "Indigenous Traditional Religious"=10, "Muslim"=11, "Pagan"=12, "Shinto"=13, "Sikh"=14, "Spiritual but not religious"=15, "Zoroastrian"=16, "No religion"=17, "Prefer not to say OR Other"=0), label = "What is your religion?") + +# Create bins: + +# For Q5 - understanding + +admissions_data <- admissions_data %>% + mutate( + Q5_Theology = ifelse(Q5_Theology == 0, NA, Q5_Theology), + understanding_theology_bin = case_when( + Q5_Theology < 3 ~ "high", + Q5_Theology > 3 ~ "low", + Q5_Theology == 3 ~ "neutral", + TRUE ~ NA + ) %>% factor(levels = c("low", "neutral", "high")) + ) +# For Q5 - understanding + +admissions_data <- admissions_data %>% + mutate( + Q5_Religious_Studies = ifelse(Q5_Religious_Studies == 0, NA, Q5_Religious_Studies), + understanding_religion_bin = case_when( + Q5_Religious_Studies < 3 ~ "high", + Q5_Religious_Studies > 3 ~ "low", + Q5_Religious_Studies == 3 ~ "neutral", + TRUE ~ NA + ) %>% factor(levels = c("low", "neutral", "high")) + ) +# For Q6 - interest + +admissions_data <- admissions_data %>% + mutate( + Q6_Theology = ifelse(Q6_Theology == 0, NA, Q6_Theology), + interest_theology_bin = case_when( + Q6_Theology < 3 ~ "high", + Q6_Theology > 3 ~ "low", + Q6_Theology == 3 ~ "neutral", + TRUE ~ NA + ) %>% factor(levels = c("low", "neutral", "high")) + ) + +# For Q6 - interest + +admissions_data <- admissions_data %>% + mutate( + Q6_Religious_Studies = ifelse(Q6_Religious_Studies == 0, NA, Q6_Religious_Studies), + interest_religion_bin = case_when( + Q6_Religious_Studies < 3 ~ "high", + Q6_Religious_Studies > 3 ~ "low", + Q6_Religious_Studies == 3 ~ "neutral", + TRUE ~ NA + ) %>% factor(levels = c("low", "neutral", "high")) + ) + +# For Q7 - employability prospects + +admissions_data <- admissions_data %>% + mutate( + Q7_Theology = ifelse(Q7_Theology == 0, NA, Q7_Theology), + employability_optimism_theology_bin = case_when( + Q7_Theology < 3 ~ "high", + Q7_Theology > 3 ~ "low", + Q7_Theology == 3 ~ "neutral", + TRUE ~ NA + ) %>% factor(levels = c("low", "neutral", "high")) + ) + +# For Q7 - employability prospects + +admissions_data <- admissions_data %>% + mutate( + Q7_Religious_Studies = ifelse(Q7_Religious_Studies == 0, NA, Q7_Religious_Studies), + employability_optimism_religion_bin = case_when( + Q7_Religious_Studies < 3 ~ "high", + Q7_Religious_Studies > 3 ~ "low", + Q7_Religious_Studies == 3 ~ "neutral", + TRUE ~ NA + ) %>% factor(levels = c("low", "neutral", "high")) + ) + +# For Q8 - employability prospects + +admissions_data <- admissions_data %>% + mutate( + Q8 = ifelse(Q8 == 6, NA, Q8), + theology_for_bin = case_when( + Q8 < 3 ~ "religious", + Q8 > 3 ~ "all_people", + Q8 == 3 ~ "neutral", + TRUE ~ NA + ) %>% factor(levels = c("all_people", "neutral", "religious")) + ) + +admissions_data <- admissions_data %>% + mutate( + Q9 = ifelse(Q9 == 6, NA, Q9), + religion_for_bin = case_when( + Q9 < 3 ~ "religious", + Q9 > 3 ~ "all_people", + Q9 == 3 ~ "neutral", + TRUE ~ NA + ) %>% factor(levels = c("all_people", "neutral", "religious")) + ) + +# Q17 non-white / white ethnicity bins +admissions_data <- admissions_data %>% + mutate( + Q17 = ifelse(Q17 == 0, NA, Q17), + ethnicity_bin = case_when( + Q17 > 13 | Q17 < 18 ~ "white", + TRUE ~ "non-white" + ) %>% factor(levels = c("white", "non-white")) + ) + +# Q18 non-religious / institutioal bins +admissions_data <- admissions_data %>% + mutate( + Q18 = ifelse(Q18 == 0, NA, Q18), + religion_bin = case_when( + Q18 %in% c(1, 2, 5, 17) ~ "non-religious", + TRUE ~ "religious" + ) %>% factor(levels = c("non-religious", "religious")) + ) + +``` + +```{r correlations} +Q5_data <- select(admissions_data, Q5_Philosophy:Q5_Business) +cor(admissions_data$Q5_Philosophy, admissions_data$Q6_Philosophy) + ``` # Basic demographic summary visualisations: @@ -45,15 +238,21 @@ if (dir.exists("derivedData") == FALSE) { # 1. Calculate for age of respondent: -# 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$Age, levels = c(1, 2, 3, 4, 5, 6, 7, 8), labels = c("15 or under", "16", "17", "18", "19", "20", "21 or over", "Prefer not to say")) -age_pie <- pie(table(TSR_data_summaries_age), col=coul3, cex = 0.8) +age <- factor(admissions_data$Q2), 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(admissions_data$Q2), col=coul3, cex = 0.8) # JK note: adding in a bar chart here, as according to the interwebs it's (apparently?) more accurate to visualise than bar charts -data_summaries_age <- ggplot(TSR_data, aes(TSR_data_summaries_age)) + geom_bar() -data_summaries_age + labs(title = "Respondent Age Distribution", x = "Age", y = "") + +ggplot(admissions_data, aes(factor(Q2))) + + geom_bar() + + geom_text(stat = "count", aes(label = after_stat(count)), vjust = -0.5) + + labs(title = "Respondent Age Distribution", x = "Age", y = "") + + scale_x_discrete(labels = labels(q2_labels)) + # save to png file for reports ggsave("figures/TSR_data_summaries_age.png") +``` +```{r} # 2. Calculate for year of study: TSR_data_summaries_yos <- factor(TSR_data$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")) @@ -62,7 +261,9 @@ 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") +``` +```{r} # 3. Gender identity: TSR_data_summaries_gender <- factor(TSR_data$Gender, levels = c(1, 2, 3, 4), labels = c("Male", "Female", "I identify my gender in another way", "Prefer not to say")) @@ -214,7 +415,9 @@ employability_bar <- ggplot(employability_mean, aes(x = Subject, y = Employabili employability_bar + labs(title = "please rate ... employability prospects", x = "", y = "") # save to png file for reports ggsave("figures/TSR_data_subject_employability.png") +``` +```{r} ### Likert Stacked Bar Chart ### # Take selection of data re: understanding @@ -274,10 +477,14 @@ str(Understanding_data) levels(Understanding_data) summary(Understanding_data) likert_test_understand <- likert(Understanding_data) +Understanding_data <- Q5_data +likert_test_understand <- likert(Q5_data) plot(likert_test_understand) # save to png file for reports ggsave("figures/understanding_likert.png") +``` +```{r} Interest_data <- TSR_data[, 18:29] # Convert each column to factors Interest_data$InterestinstudyingPhilosophy = factor(Interest_data$InterestinstudyingPhilosophy,