mirror of
https://github.com/kidwellj/re_connect_survey.git
synced 2024-11-01 07:52:21 +00:00
a5451ab618
I've done graphs with subsetting by the different questions (with the exception of "- Q1 (grade level) + Q35 (teaching role) + +Q5 (teaching proportion) Q2 (tenure) + and Q3 (subjects taught), + Q6/Q7 (management)" as I'm not sure how to do this yet. The graphs are still rough, and need to be prettied up but I've added labels to the axes. Let me know if these graphs so far are alright
715 lines
40 KiB
Plaintext
715 lines
40 KiB
Plaintext
---
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title: "Connect Project"
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output: html_document
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---
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```{r setup, include=FALSE}
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knitr::opts_chunk$set(echo = TRUE)
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# Load RColorBrewer
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# install.packages("RColorBrewer")
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library(RColorBrewer)
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# Define colour palettes for plots below
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coul3 <- brewer.pal(3, "RdYlBu") # Using RdYlBu range to generate 3 colour palette: https://colorbrewer2.org/#type=diverging&scheme=RdYlBu&n=5
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```
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Note that the `echo = FALSE` parameter was added to the code chunk to prevent printing of the R code that generated the plot.
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### To Do List
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## Upload Data
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```{r Data Upload}
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connect_data = read.csv("./data/connectDATA.csv")
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```
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## Summary of Data
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Data summary/visualisation with subsetting:
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- RH: display simple summary of data (bar/pie chart) to Q25/26, Q3
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```{r Frequencies}
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#Frequencies#
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Q25_frequencies <- table(connect_data$Q25)
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Q25_frequencies
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Q26_freq <- table(connect_data$Q26)
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Q26_freq
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Q3_freq <- table(connect_data$Q3)
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Q3_freq
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#test3 = as.factor(connect_data$Q3, levels = c(1, 2, 3, 4, 5), labels = c("Worldviews", "Religion", "Theology", "Ethics", "Philosophy"))
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```
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```{r Q25 bar/pie}
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pie(Q25_frequencies, labels = c("Maybe", "No", "Yes"))
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pie(Q25_frequencies, labels = c("Maybe", "No", "Yes"), col = coul3)
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```
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```{r Q26 bar/pie}
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Q26_data <- read.csv("./data/Q26_data.csv")
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Q26_freq_data <- data.frame(c("Other Priorities", "Lack Subject Knowledge", "Lack Confidence", "Current Syllabus", "Pupil Disinterest", "Department Head", "Available Work Schemes", "Unavailable Resources", "Uncertain of Pedagogical Approach"), c(table(Q26_data[,2]) [names(table(Q26_data[,2])) == "TRUE"],
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table(Q26_data[,3]) [names(table(Q26_data[,3])) == "TRUE"],
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table(Q26_data[,4]) [names(table(Q26_data[,4])) == "TRUE"],
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table(Q26_data[,5]) [names(table(Q26_data[,5])) == "TRUE"],
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table(Q26_data[,6]) [names(table(Q26_data[,6])) == "TRUE"],
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table(Q26_data[,7]) [names(table(Q26_data[,7])) == "TRUE"],
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table(Q26_data[,8]) [names(table(Q26_data[,8])) == "TRUE"],
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table(Q26_data[,9]) [names(table(Q26_data[,9])) == "TRUE"],
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table(Q26_data[,10]) [names(table(Q26_data[,10])) == "TRUE"]))
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head(Q26_freq_data)
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names(Q26_freq_data)[1] <- "Reasons"
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names(Q26_freq_data)[2] <- "Frequency"
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head(Q26_freq_data)
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pie(Q26_freq_data$Frequency, labels = c("Other Priorities", "Lack Subject Knowledge", "Lack Confidence", "Current Syllabus", "Pupil Disinterest", "Department Head", "Available Work Schemes", "Unavailable Resources", "Uncertain of Pedagogical Approach"))
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# Bar graph tidier
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```
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pie(Q26_freq)
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#very messy as a pie chart - split by type? Or is it important to see crossover
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Could potentially see crossover with crosstabs by type (since response is now binary variable T/F), maybe chi square; perhaps just descriptives
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```{r Q3 bar/pie}
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Q3_data <- read.csv("./data/Q3.csv")
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#head(Q3_data)
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#table(Q3_data [,3:7])
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#pie(table(Q3_data [,3:7]))
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Q3_data2 <- Q3_data[ ,3:7]
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#head(Q3_data2)
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#table(Q3_data2)
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#table(Q3_data2[,1])
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### want to take only the count of "True" (1) in each column. Then pie chart of the frequencies
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#Q3_data3 <- read.csv("~/Documents/Github/re_connect_survey/data/Q3 copydata.csv")
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#table(Q3_data3)
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#count(Q3_data3, 1)
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#table(Q3_data3) [names(table(Q3_data3)) == 1]
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#table(Q3_data3)
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table(Q3_data2[,1]) [names(table(Q3_data2[,1])) == "TRUE"]
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test2 <- data.frame(c("Worldviews", "Religion", "Theology", "Ethics", "Philosophy"), c(table(Q3_data2[,1]) [names(table(Q3_data2[,1])) == "TRUE"],
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table(Q3_data2[,2]) [names(table(Q3_data2[,2])) == "TRUE"],
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table(Q3_data2[,3]) [names(table(Q3_data2[,3])) == "TRUE"],
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table(Q3_data2[,4]) [names(table(Q3_data2[,4])) == "TRUE"],
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table(Q3_data2[,5]) [names(table(Q3_data2[,5])) == "TRUE"]))
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head(test2)
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names(test2)[1] <- "Subject"
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names(test2)[2] <- "Frequency"
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head(test2)
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pie(test2$Frequency, labels = c("Worldviews", "Religion", "Theology", "Ethics", "Philosophy"))
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# JK note on Q3: consider here whether to use alternative forms of visualiation to reflect the overlaps when respondents picked multiple categories in responses
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```
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xtabs(Frequency ~ Subject, test2)
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pie(Q3_freq)
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#also not optimal as pie...perhaps bar
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#sum(Q3_data2)
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Q3_1factor = as.factor(Q3_data2$Religion)
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table(Q3_1factor)
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#count(Q3_1factor, "TRUE")
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#test = replace(Q3_1factor, "TRUE", 1)
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#test
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#Q3_1factor
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- RH: display summaries of responses to key questions for Q22 (syllabus evaluation), Q23, Q24, Q25, Q26, Q27, with subsetting by:
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- Q8 (school type)
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- Q9 (school size)
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- Q10 (school location)
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- Q1 (grade level) + Q35 (teaching role) + +Q5 (teaching proportion) Q2 (tenure) + and Q3 (subjects taught), + Q6/Q7 (management)
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- Q12-14 (school's official religion) / Q15-16 (school's informal religion)
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- Q21 (respondent personal religious background)
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- Q4 (teacher's degree subject)
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- Q18 (respondent gender)
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- Q19 (respondent ethnic self-desc)
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```{r Plots Q22}
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library(ggplot2)
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summaries_data <- read.csv("./data/visualization data.csv")
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# Q22
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#Q8 (school type)
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summaries_data$Q8_recode <- factor(summaries_data$Q8_recode, levels = c(1, 2, 3, 4), labels = c("local authority", "academy", "free school", "independent school"))
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Q22_by_Q8visualization <- ggplot(summaries_data, aes(x = Q8_recode, y = Q22_avg)) + stat_summary(fun = "mean", geom = "bar") + labs(x = "School Type") + labs(y = "Syllabus Allows Exploration of Relationship Between Environment and Religion/Worldview")
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Q22_by_Q8visualization
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#Q9 (school size)
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summaries_data$Q9_recode <- factor(summaries_data$Q9_recode, levels = c(1, 2, 3), labels = c("1-9", "10-25", "25-100"))
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Q22_by_Q9visualization <- ggplot(summaries_data, aes(x = Q9_recode, y = Q22_avg)) + stat_summary(fun = "mean", geom = "bar") + labs(x = "School Faculty Size") + labs(y = "Syllabus Allows Exploration of Relationship Between Environment and Religion/Worldview")
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Q22_by_Q9visualization
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#Q10 (school location)
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Q22_by_Q10visualization <- ggplot(summaries_data, aes(x = Q10, y = Q22_avg)) + stat_summary(fun = "mean", geom = "bar") + coord_flip() + labs(x = "School Location") + labs(y = "Syllabus Allows Exploration of Relationship Between Environment and Religion/Worldview")
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Q22_by_Q10visualization
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#Q12-14 (school's official religion)
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summaries_data$Q12 <- factor(summaries_data$Q12, levels = c("No", "Yes"), labels = c("No", "Yes"))
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Q22_by_Q12visualization <- ggplot(summaries_data, aes(x = Q12, y = Q22_avg)) + stat_summary(fun = "mean", geom = "bar") + labs(x = "School Official Relgious Affiliation") + labs(y = "Syllabus Allows Exploration of Relationship Between Environment and Religion/Worldview")
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Q22_by_Q12visualization
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#Q15-16 (school's informal religion)
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summaries_data$Q15 <- factor(summaries_data$Q15, levels = c("No", "Yes"), labels = c("No", "Yes"))
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Q22_by_Q15visualization <- ggplot(summaries_data, aes(x = Q15, y = Q22_avg)) + stat_summary(fun = "mean", geom = "bar") + labs(x = "School Unofficial Religious Affiliation") + labs(y = "Syllabus Allows Exploration of Relationship Between Environment and Religion/Worldview")
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Q22_by_Q15visualization
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#Q21 (respondent personal religious background)
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summaries_data$Q21_binaryrecode <- factor(summaries_data$Q21_binaryrecode, levels = c(1, 2), labels = c("No", "Yes"))
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Q22_by_Q21visualization <- ggplot(summaries_data, aes(x = Q21_binaryrecode, y = Q22_avg)) + stat_summary(fun = "mean", geom = "bar") + labs(x = "Personal Religious Background") + labs(y = "Syllabus Allows Exploration of Relationship Between Environment and Religion/Worldview")
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Q22_by_Q21visualization
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#Q4 (teacher's degree subject) - write-in...figure out how to sort -- recode to sort similar subjects
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Q22_by_Q4visualization <- ggplot(summaries_data, aes(x = Q4, y = Q22_avg)) + stat_summary(fun = "mean", geom = "bar") + coord_flip()
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Q22_by_Q4visualization
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#Q18 (respondent gender)
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Q22_by_Q18visualization <- ggplot(summaries_data, aes(x = Q18, y = Q22_avg)) + stat_summary(fun = "mean", geom = "bar") + labs(x = "Gender") + labs(y = "Syllabus Allows Exploration of Relationship Between Environment and Religion/Worldview")
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Q22_by_Q18visualization
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#Q19 (respondent ethnic self-desc)
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Q22_by_Q19visualization <- ggplot(summaries_data, aes(x = Q19, y = Q22_avg)) + stat_summary(fun = "mean", geom = "bar") + coord_flip() + labs(x = "Ethnicity") + labs(y = "Syllabus Allows Exploration of Relationship Between Environment and Religion/Worldview")
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Q22_by_Q19visualization
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install.packages("ggpubr")
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#library(ggplot2)
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library(ggpubr)
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test_plot_together <- ggarrange(Q22_by_Q4visualization, Q22_by_Q8visualization, Q22_by_Q9visualization, Q22_by_Q10visualization, Q22_by_Q12visualization, Q22_by_Q15visualization, Q22_by_Q18visualization, Q22_by_Q19visualization, Q22_by_Q21visualization, labels = c("Teacher's Degree Subject", "School Type", "School Size", "School Location", "School Formal Religious Affiliation", "School Informal Religious Affiliation", "Respondent Gender", "Respondent Ethnicity", "Respondent Personal Religious Background", ncol = 3, nrow = 3))
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test_plot_together
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```
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```{r Plots Q23}
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# Q23
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#Q8 (school type)
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summaries_data$Q8_recode <- factor(summaries_data$Q8_recode, levels = c(1, 2, 3, 4), labels = c("local authority", "academy", "free school", "independent school"))
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#split??
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#Q23_by_Q8visualization <- ggplot(summaries_data, aes(x = Q8_recode, y = cbind(Q23_1, Q23_2))) + stat_summary(fun = "mean", geom = "bar")
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Q23_by_Q8visualization1 <- ggplot(summaries_data, aes(x = Q8_recode, y = Q23_1)) + stat_summary(fun = "mean", geom = "bar") + labs(x = "School Type") + labs(y = "Environment is a Prominent Syllabus Theme")
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Q23_by_Q8visualization1
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Q23_by_Q8visualization2 <- ggplot(summaries_data, aes(x = Q8_recode, y = Q23_2)) + stat_summary(fun = "mean", geom = "bar") + labs(x = "School Type") + labs(y = "Environment Should be a Prominent Syllabus Theme")
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Q23_by_Q8visualization2
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#Q9 (school size)
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summaries_data$Q9_recode <- factor(summaries_data$Q9_recode, levels = c(1, 2, 3), labels = c("1-9", "10-25", "25-100"))
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Q23_by_Q9visualization1 <- ggplot(summaries_data, aes(x = Q9_recode, y = Q23_1)) + stat_summary(fun = "mean", geom = "bar") + labs(x = "School Faculty Size") + labs(y = "Environment is a Prominent Syllabus Theme")
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Q23_by_Q9visualization1
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Q23_by_Q9visualization2 <- ggplot(summaries_data, aes(x = Q9_recode, y = Q23_2)) + stat_summary(fun = "mean", geom = "bar") + labs(x = "School Faculty Size") + labs(y = "Environment Should be a Prominent Syllabus Theme")
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Q23_by_Q9visualization2
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#Q10 (school location)
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Q23_by_Q10visualization1 <- ggplot(summaries_data, aes(x = Q10, y = Q23_1)) + stat_summary(fun = "mean", geom = "bar") + coord_flip() + labs(x = "School Location") + labs(y = "Environment is a Prominent Syllabus Theme")
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Q23_by_Q10visualization1
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Q23_by_Q10visualization2 <- ggplot(summaries_data, aes(x = Q10, y = Q23_2)) + stat_summary(fun = "mean", geom = "bar") + coord_flip() + labs(x = "School Location") + labs(y = "Environment Should be a Prominent Syllabus Theme")
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Q23_by_Q10visualization2
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#Q12-14 (school's official religion)
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summaries_data$Q12 <- factor(summaries_data$Q12, levels = c("No", "Yes"), labels = c("No", "Yes"))
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Q23_by_Q12visualization1 <- ggplot(summaries_data, aes(x = Q12, y = Q23_1)) + stat_summary(fun = "mean", geom = "bar") + labs(x = "School Official Religious Affiliation") + labs(y = "Environment is a Prominent Syllabus Theme")
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Q23_by_Q12visualization1
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Q23_by_Q12visualization2 <- ggplot(summaries_data, aes(x = Q12, y = Q23_2)) + stat_summary(fun = "mean", geom = "bar") + labs(x = "School Official Religious Affiliation") + labs(y = "Environment Should be a Prominent Syllabus Theme")
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Q23_by_Q12visualization2
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#Q15-16 (school's informal religion)
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summaries_data$Q15 <- factor(summaries_data$Q15, levels = c("No", "Yes"), labels = c("No", "Yes"))
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Q23_by_Q15visualization1 <- ggplot(summaries_data, aes(x = Q15, y = Q23_1)) + stat_summary(fun = "mean", geom = "bar") + labs(x = "School Unofficial Religious Affiliation") + labs(y = "Environment is a Prominent Syllabus Theme")
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Q23_by_Q15visualization1
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Q23_by_Q15visualization2 <- ggplot(summaries_data, aes(x = Q15, y = Q23_2)) + stat_summary(fun = "mean", geom = "bar") + labs(x = "School Unofficial Religious Affiliation") + labs(y = "Environment Should be a Prominent Syllabus Theme")
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Q23_by_Q15visualization2
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#Q21 (respondent personal religious background)
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summaries_data$Q21_binaryrecode <- factor(summaries_data$Q21_binaryrecode, levels = c(1, 2), labels = c("No", "Yes"))
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Q23_by_Q21visualization1 <- ggplot(summaries_data, aes(x = Q21_binaryrecode, y = Q23_1)) + stat_summary(fun = "mean", geom = "bar") + labs(x = "Personal Religious Background") + labs(y = "Environment is a Prominent Syllabus Theme")
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Q23_by_Q21visualization1
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Q23_by_Q21visualization2 <- ggplot(summaries_data, aes(x = Q21_binaryrecode, y = Q23_2)) + stat_summary(fun = "mean", geom = "bar") + labs(x = "Personal Religious Background") + labs(y = "Environment Should be a Prominent Syllabus Theme")
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Q23_by_Q21visualization2
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#Q4 (teacher's degree subject) - write-in...figure out how to sort
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#Q18 (respondent gender)
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Q23_by_Q18visualization1 <- ggplot(summaries_data, aes(x = Q18, y = Q23_1)) + stat_summary(fun = "mean", geom = "bar") + labs(x = "Gender") + labs(y = "Environment is a Prominent Syllabus Theme")
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Q23_by_Q18visualization1
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Q23_by_Q18visualization2 <- ggplot(summaries_data, aes(x = Q18, y = Q23_2)) + stat_summary(fun = "mean", geom = "bar") + labs(x = "Gender") + labs(y = "Environment Should be a Prominent Syllabus Theme")
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Q23_by_Q18visualization2
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#Q19 (respondent ethnic self-desc)
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Q23_by_Q19visualization1 <- ggplot(summaries_data, aes(x = Q19, y = Q23_1)) + stat_summary(fun = "mean", geom = "bar") + coord_flip() + labs(x = "Ethnicity") + labs(y = "Environment is a Prominent Syllabus Theme")
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Q23_by_Q19visualization1
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Q23_by_Q19visualization2 <- ggplot(summaries_data, aes(x = Q19, y = Q23_2)) + stat_summary(fun = "mean", geom = "bar") + coord_flip() + labs(x = "Ethnicity") + labs(y = "Environment Should be a Prominent Syllabus Theme")
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Q23_by_Q19visualization2
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```
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```{r Plots Q24}
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# Q24
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#Q8(school type)
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summaries_data$Q8_recode <- factor(summaries_data$Q8_recode, levels = c(1, 2, 3, 4), labels = c("local authority", "academy", "free school", "independent school"))
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Q24_byQ8visualization1 <- ggplot(summaries_data, aes(x = Understanding.of.nature, fill = Q8_recode)) + geom_bar(position = "stack") + labs(fill = "School Type")
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Q24_byQ8visualization1
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Q24_byQ8visualization2 <- ggplot(summaries_data, aes(x = Relationship.between.spiritual.and.matiral.worlds, fill = Q8_recode)) + geom_bar(position = "stack") + labs(fill = "School Type")
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Q24_byQ8visualization2
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Q24_byQ8visualization3 <- ggplot(summaries_data, aes(x = Human.beings..responsibility.towards.the.earth, fill = Q8_recode)) + geom_bar(position = "stack") + labs(fill = "School Type")
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Q24_byQ8visualization3
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Q24_byQ8visualization4 <- ggplot(summaries_data, aes(x = Climate.crisis.and.or.diversity, fill = Q8_recode)) + geom_bar(position = "stack") + labs(fill = "School Type")
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Q24_byQ8visualization4
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Q24_by_Q8_allvisualization <- ggarrange(Q24_byQ8visualization1, Q24_byQ8visualization2, Q24_byQ8visualization3, Q24_byQ8visualization4, labels = c("Understanding of Nature", "Relationship Between Spiritual and Material Worlds", "Human Beings' Responsibility Towards the Earth", "Climate Crisis and/or Diversity", ncol = 2, nrow = 2))
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annotate_figure(Q24_by_Q8_allvisualization, top = text_grob("Units of Work Covered Within RE by School Type"))
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Q24_by_Q8_allvisualization
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#Q9 (school size)
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summaries_data$Q9_recode <- factor(summaries_data$Q9_recode, levels = c(1, 2, 3), labels = c("1-9", "10-25", "25-100"))
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Q24_byQ9visualization1 <- ggplot(summaries_data, aes(x = Understanding.of.nature, fill = Q9_recode)) + geom_bar(position = "stack") + labs(fill = "School Faculty Size")
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Q24_byQ9visualization1
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Q24_byQ9visualization2 <- ggplot(summaries_data, aes(x = Relationship.between.spiritual.and.matiral.worlds, fill = Q9_recode)) + geom_bar(position = "stack") + labs(fill = "School Faculty Size")
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Q24_byQ9visualization2
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Q24_byQ9visualization3 <- ggplot(summaries_data, aes(x = Human.beings..responsibility.towards.the.earth, fill = Q9_recode)) + geom_bar(position = "stack") + labs(fill = "School Faculty Size")
|
|
Q24_byQ9visualization3
|
|
|
|
Q24_byQ9visualization4 <- ggplot(summaries_data, aes(x = Climate.crisis.and.or.diversity, fill = Q9_recode)) + geom_bar(position = "stack") + labs(fill = "School Faculty Size")
|
|
Q24_byQ9visualization4
|
|
|
|
Q24_by_Q9_allvisualization <- ggarrange(Q24_byQ9visualization1, Q24_byQ9visualization2, Q24_byQ9visualization3, Q24_byQ9visualization4, labels = c("Understanding of Nature", "Relationship Between Spiritual and Material Worlds", "Human Beings' Responsibility Towards the Earth", "Climate Crisis and/or Diversity", ncol = 2, nrow = 2))
|
|
annotate_figure(Q24_by_Q9_allvisualization, top = text_grob("Units of Work Covered Within RE by School Size"))
|
|
Q24_by_Q9_allvisualization
|
|
|
|
|
|
#Q10 (school location)
|
|
Q24_byQ10visualization1 <- ggplot(summaries_data, aes(x = Understanding.of.nature, fill = Q10)) + geom_bar(position = "stack") + labs(fill = "School Location")
|
|
Q24_byQ10visualization1
|
|
|
|
Q24_byQ10visualization2 <- ggplot(summaries_data, aes(x = Relationship.between.spiritual.and.matiral.worlds, fill = Q10)) + geom_bar(position = "stack") + labs(fill = "School Location")
|
|
Q24_byQ10visualization2
|
|
|
|
Q24_byQ10visualization3 <- ggplot(summaries_data, aes(x = Human.beings..responsibility.towards.the.earth, fill = Q10)) + geom_bar(position = "stack") + labs(fill = "School Location")
|
|
Q24_byQ10visualization3
|
|
|
|
Q24_byQ10visualization4 <- ggplot(summaries_data, aes(x = Climate.crisis.and.or.diversity, fill = Q10)) + geom_bar(position = "stack") + labs(fill = "School Location")
|
|
Q24_byQ10visualization4
|
|
|
|
Q24_by_Q10_allvisualization <- ggarrange(Q24_byQ10visualization1, Q24_byQ10visualization2, Q24_byQ10visualization3, Q24_byQ10visualization4, labels = c("Understanding of Nature", "Relationship Between Spiritual and Material Worlds", "Human Beings' Responsibility Towards the Earth", "Climate Crisis and/or Diversity", ncol = 2, nrow = 2))
|
|
annotate_figure(Q24_by_Q10_allvisualization, top = text_grob("Units of Work Covered Within RE by School Location"))
|
|
Q24_by_Q10_allvisualization
|
|
|
|
#Q12-14 (school's official religion)
|
|
summaries_data$Q12 <- factor(summaries_data$Q12, levels = c("No", "Yes"), labels = c("No", "Yes"))
|
|
|
|
Q24_byQ12visualization1 <- ggplot(summaries_data, aes(x = Understanding.of.nature, fill = Q12)) + geom_bar(position = "stack") + labs(fill = "Official Religious Affliiation")
|
|
Q24_byQ12visualization1
|
|
|
|
Q24_byQ12visualization2 <- ggplot(summaries_data, aes(x = Relationship.between.spiritual.and.matiral.worlds, fill = Q12)) + geom_bar(position = "stack") + labs(fill = "Official Religious Affliiation")
|
|
Q24_byQ12visualization2
|
|
|
|
Q24_byQ12visualization3 <- ggplot(summaries_data, aes(x = Human.beings..responsibility.towards.the.earth, fill = Q12)) + geom_bar(position = "stack") + labs(fill = "Official Religious Affliiation")
|
|
Q24_byQ12visualization3
|
|
|
|
Q24_byQ12visualization4 <- ggplot(summaries_data, aes(x = Climate.crisis.and.or.diversity, fill = Q12)) + geom_bar(position = "stack") + labs(fill = "Official Religious Affliiation")
|
|
Q24_byQ12visualization4
|
|
|
|
Q24_by_Q12_allvisualization <- ggarrange(Q24_byQ12visualization1, Q24_byQ12visualization2, Q24_byQ12visualization3, Q24_byQ12visualization4, labels = c("Understanding of Nature", "Relationship Between Spiritual and Material Worlds", "Human Beings' Responsibility Towards the Earth", "Climate Crisis and/or Diversity", ncol = 2, nrow = 2))
|
|
annotate_figure(Q24_by_Q12_allvisualization, top = text_grob("Units of Work Covered Within RE by School Official Religious Affiliation Status"))
|
|
Q24_by_Q12_allvisualization
|
|
|
|
|
|
#Q15-16 (school's informal religion)
|
|
summaries_data$Q15 <- factor(summaries_data$Q15, levels = c("No", "Yes"), labels = c("No", "Yes"))
|
|
|
|
Q24_byQ15visualization1 <- ggplot(summaries_data, aes(x = Understanding.of.nature, fill = Q15)) + geom_bar(position = "stack") + labs(fill = "Unofficial Religious Affiliation")
|
|
Q24_byQ15visualization1
|
|
|
|
Q24_byQ15visualization2 <- ggplot(summaries_data, aes(x = Relationship.between.spiritual.and.matiral.worlds, fill = Q15)) + geom_bar(position = "stack") + labs(fill = "Unofficial Religious Affiliation")
|
|
Q24_byQ15visualization2
|
|
|
|
Q24_byQ15visualization3 <- ggplot(summaries_data, aes(x = Human.beings..responsibility.towards.the.earth, fill = Q15)) + geom_bar(position = "stack") + labs(fill = "Unofficial Religious Affiliation")
|
|
Q24_byQ15visualization3
|
|
|
|
Q24_byQ15visualization4 <- ggplot(summaries_data, aes(x = Climate.crisis.and.or.diversity, fill = Q15)) + geom_bar(position = "stack") + labs(fill = "Unofficial Religious Affiliation")
|
|
Q24_byQ15visualization4
|
|
|
|
Q24_by_Q15_allvisualization <- ggarrange(Q24_byQ15visualization1, Q24_byQ15visualization2, Q24_byQ15visualization3, Q24_byQ15visualization4, labels = c("Understanding of Nature", "Relationship Between Spiritual and Material Worlds", "Human Beings' Responsibility Towards the Earth", "Climate Crisis and/or Diversity", ncol = 2, nrow = 2))
|
|
annotate_figure(Q24_by_Q15_allvisualization, top = text_grob("Units of Work Covered Within RE by School Unofficial Religious Affiliation Status"))
|
|
Q24_by_Q15_allvisualization
|
|
|
|
#Q4 (teacher's degree subject) - write-in...figure out how to sort
|
|
|
|
#Q18 (respondent gender)
|
|
Q24_byQ18visualization1 <- ggplot(summaries_data, aes(x = Understanding.of.nature, fill = Q18)) + geom_bar(position = "stack") + labs(fill = "Gender")
|
|
Q24_byQ18visualization1
|
|
|
|
Q24_byQ18visualization2 <- ggplot(summaries_data, aes(x = Relationship.between.spiritual.and.matiral.worlds, fill = Q18)) + geom_bar(position = "stack") + labs(fill = "Gender")
|
|
Q24_byQ18visualization2
|
|
|
|
Q24_byQ18visualization3 <- ggplot(summaries_data, aes(x = Human.beings..responsibility.towards.the.earth, fill = Q18)) + geom_bar(position = "stack") + labs(fill = "Gender")
|
|
Q24_byQ18visualization3
|
|
|
|
Q24_byQ18visualization4 <- ggplot(summaries_data, aes(x = Climate.crisis.and.or.diversity, fill = Q18)) + geom_bar(position = "stack") + labs(fill = "Gender")
|
|
Q24_byQ18visualization4
|
|
|
|
Q24_by_Q18_allvisualization <- ggarrange(Q24_byQ18visualization1, Q24_byQ18visualization2, Q24_byQ18visualization3, Q24_byQ18visualization4, labels = c("Understanding of Nature", "Relationship Between Spiritual and Material Worlds", "Human Beings' Responsibility Towards the Earth", "Climate Crisis and/or Diversity", ncol = 2, nrow = 2))
|
|
annotate_figure(Q24_by_Q18_allvisualization, top = text_grob("Units of Work Covered Within RE by Respondent Gender"))
|
|
Q24_by_Q18_allvisualization
|
|
|
|
|
|
#Q19 (respondent ethnic self-desc)
|
|
|
|
Q24_byQ19visualization1 <- ggplot(summaries_data, aes(x = Understanding.of.nature, fill = Q19)) + geom_bar(position = "stack") + labs(fill = "Ethicity")
|
|
Q24_byQ19visualization1
|
|
|
|
Q24_byQ19visualization2 <- ggplot(summaries_data, aes(x = Relationship.between.spiritual.and.matiral.worlds, fill = Q19)) + geom_bar(position = "stack") + labs(fill = "Ethicity")
|
|
Q24_byQ19visualization2
|
|
|
|
Q24_byQ19visualization3 <- ggplot(summaries_data, aes(x = Human.beings..responsibility.towards.the.earth, fill = Q19)) + geom_bar(position = "stack") + labs(fill = "Ethicity")
|
|
Q24_byQ19visualization3
|
|
|
|
Q24_byQ19visualization4 <- ggplot(summaries_data, aes(x = Climate.crisis.and.or.diversity, fill = Q19)) + geom_bar(position = "stack") + labs(fill = "Ethicity")
|
|
Q24_byQ19visualization4
|
|
|
|
Q24_by_Q19_allvisualization <- ggarrange(Q24_byQ19visualization1, Q24_byQ19visualization2, Q24_byQ19visualization3, Q24_byQ19visualization4, labels = c("Understanding of Nature", "Relationship Between Spiritual and Material Worlds", "Human Beings' Responsibility Towards the Earth", "Climate Crisis and/or Diversity", ncol = 1, nrow = 4))
|
|
annotate_figure(Q24_by_Q19_allvisualization, top = text_grob("Units of Work Covered Within RE by Respondent Ethnic Self-Identity"))
|
|
Q24_by_Q19_allvisualization
|
|
|
|
|
|
```
|
|
|
|
```{r Plots Q25}
|
|
|
|
summaries_data <- read.csv("./data/visualization data.csv")
|
|
summaries_data$Q25 <- factor(summaries_data$Q25, levels = c("Yes", "Maybe", "No"), labels = c("Yes", "Maybe", "No"))
|
|
|
|
# Q25
|
|
#Q8 (school type)
|
|
summaries_data$Q8_recode <- factor(summaries_data$Q8_recode, levels = c(1, 2, 3, 4), labels = c("local authority", "academy", "free school", "independent school"))
|
|
|
|
Q25_by_Q8visualization <- ggplot(summaries_data, aes(x = Q8_recode, fill = Q25)) + geom_bar(position = "stack") + labs(fill = "Want to Explore Environment as a Theme") + labs(x = "School Type") + coord_flip()
|
|
Q25_by_Q8visualization
|
|
|
|
#Q9 (school size)
|
|
summaries_data$Q9_recode <- factor(summaries_data$Q9_recode, levels = c(1, 2, 3), labels = c("1-9", "10-25", "25-100"))
|
|
|
|
Q25_by_Q9visualization <- ggplot(summaries_data, aes(x = Q9_recode, fill = Q25)) + geom_bar(position = "stack") + labs(fill = "Want to Explore Environment as a Theme") + labs(x = "School Faculty Size")
|
|
Q25_by_Q9visualization
|
|
|
|
#Q10 (school location)
|
|
Q25_by_Q10visualization <- ggplot(summaries_data, aes(x = Q10, fill = Q25)) + geom_bar(position = "stack") + labs(fill = "Want to Explore Environment as a Theme") + labs(x = "School Location") + coord_flip()
|
|
Q25_by_Q10visualization
|
|
|
|
#Q12-14 (school's official religion)
|
|
summaries_data$Q12 <- factor(summaries_data$Q12, levels = c("No", "Yes"), labels = c("No", "Yes"))
|
|
|
|
Q25_by_Q12visualization <- ggplot(summaries_data, aes(x = Q12, fill = Q25)) + geom_bar(position = "stack") + labs(fill = "Want to Explore Environment as a Theme") + labs(x = "School Official Religious Affiliation Status")
|
|
Q25_by_Q12visualization
|
|
|
|
#Q15-16 (school's informal religion)
|
|
summaries_data$Q15 <- factor(summaries_data$Q15, levels = c("No", "Yes"), labels = c("No", "Yes"))
|
|
|
|
Q25_by_Q15visualization <- ggplot(summaries_data, aes(x = Q15, fill = Q25)) + geom_bar(position = "stack") + labs(fill = "Want to Explore Environment as a Theme") + labs(x = "School Unofficial Religious Affiliation Status")
|
|
Q25_by_Q15visualization
|
|
|
|
#Q21 (respondent personal religious background)
|
|
summaries_data$Q21_binaryrecode <- factor(summaries_data$Q21_binaryrecode, levels = c(1, 2), labels = c("No", "Yes"))
|
|
|
|
Q25_by_Q21visualization <- ggplot(summaries_data, aes(x = Q21_binaryrecode, fill = Q25)) + geom_bar(position = "stack") + labs(fill = "Want to Explore Environment as a Theme") + labs(x = "Respondent Personal Religious Affiliation")
|
|
Q25_by_Q21visualization
|
|
|
|
#Q4 (teacher's degree subject) - write-in...figure out how to sort
|
|
|
|
#Q18 (respondent gender)
|
|
Q25_by_Q18visualization <- ggplot(summaries_data, aes(x = Q18, fill = Q25)) + geom_bar(position = "stack") + labs(fill = "Want to Explore Environment as a Theme") + labs(x = "Gender")
|
|
Q25_by_Q18visualization
|
|
|
|
#Q19 (respondent ethnic self-desc)
|
|
Q25_by_Q19visualization <- ggplot(summaries_data, aes(x = Q19, fill = Q25)) + geom_bar(position = "stack") + coord_flip() + labs(fill = "Want to Explore Environment as a Theme") + labs(x = "Ethnicity")
|
|
Q25_by_Q19visualization
|
|
|
|
```
|
|
|
|
```{r Plots Q26}
|
|
# Q26 -- This one is more tricky since there are 9 tick box options as a multi-response. There would need to be 9x graphs per subsetting resulting in 9x9 graphs...81. I can do them, but I'm not sure if it would be the best way to do this. Similar to Q27, which would be 9x7 graphs the way I'm doing them now
|
|
```
|
|
|
|
```{r Plots Q27}
|
|
# Q27 -- May need 7 different graphs per... time consuming but not too difficult
|
|
#Q8 (school type)
|
|
summaries_data$Q8_recode <- factor(summaries_data$Q8_recode, levels = c(1, 2, 3, 4), labels = c("local authority", "academy", "free school", "independent school"))
|
|
#testplot <- ggplot(summaries_data, aes(x = Q8_recode, y = Q22_avg)) + geom_bar(stat = "identity")
|
|
#testplot
|
|
|
|
Q22_by_Q8visualization <- ggplot(summaries_data, aes(x = Q8_recode, y = Q22_avg)) + stat_summary(fun = "mean", geom = "bar")
|
|
Q22_by_Q8visualization
|
|
|
|
#Q9 (school size)
|
|
summaries_data$Q9_recode <- factor(summaries_data$Q9_recode, levels = c(1, 2, 3), labels = c("1-9", "10-25", "25-100"))
|
|
|
|
Q22_by_Q9visualization <- ggplot(summaries_data, aes(x = Q9_recode, y = Q22_avg)) + stat_summary(fun = "mean", geom = "bar")
|
|
Q22_by_Q9visualization
|
|
|
|
#Q10 (school location)
|
|
# Not sure what all the options are?
|
|
|
|
#Q12-14 (school's official religion)
|
|
summaries_data$Q12 <- factor(summaries_data$Q12, levels = c("No", "Yes"), labels = c("No", "Yes"))
|
|
|
|
Q22_by_Q12visualization <- ggplot(summaries_data, aes(x = Q12, y = Q22_avg)) + stat_summary(fun = "mean", geom = "bar")
|
|
Q22_by_Q12visualization
|
|
|
|
#Q15-16 (school's informal religion)
|
|
summaries_data$Q15 <- factor(summaries_data$Q15, levels = c("No", "Yes"), labels = c("No", "Yes"))
|
|
Q22_by_Q15visualization <- ggplot(summaries_data, aes(x = Q15, y = Q22_avg)) + stat_summary(fun = "mean", geom = "bar")
|
|
Q22_by_Q15visualization
|
|
|
|
#Q21 (respondent personal religious background)
|
|
summaries_data$Q21_binaryrecode <- factor(summaries_data$Q21_binaryrecode, levels = c(1, 2), labels = c("No", "Yes"))
|
|
|
|
Q22_by_Q21visualization <- ggplot(summaries_data, aes(x = Q21_binaryrecode, y = Q22_avg)) + stat_summary(fun = "mean", geom = "bar")
|
|
Q22_by_Q21visualization
|
|
|
|
###OR###
|
|
Q22_by_Q21visualization2 <- ggplot(summaries_data, aes(x = Q21, y = Q22_avg)) + stat_summary(fun = "mean", geom = "bar")
|
|
Q22_by_Q21visualization2
|
|
|
|
|
|
#Q4 (teacher's degree subject) - write-in...figure out how to sort
|
|
|
|
#Q18 (respondent gender)
|
|
Q22_by_Q18visualization <- ggplot(summaries_data, aes(x = Q18, y = Q22_avg)) + stat_summary(fun = "mean", geom = "bar")
|
|
Q22_by_Q18visualization
|
|
|
|
#Q19 (respondent ethnic self-desc)
|
|
Q22_by_Q19visualization <- ggplot(summaries_data, aes(x = Q19, y = Q22_avg)) + stat_summary(fun = "mean", geom = "bar")
|
|
Q22_by_Q19visualization
|
|
|
|
#Q21 (respondent personal religious background)
|
|
summaries_data$Q21_binaryrecode <- factor(summaries_data$Q21_binaryrecode, levels = c(1, 2), labels = c("No", "Yes"))
|
|
|
|
|
|
```
|
|
|
|
## Correlation testing:
|
|
- RH: test for correlation between "social issue" box ticked on Q20 and responses to Q22, Q23, Q27
|
|
|
|
- Make Q20 a factor with 14 levels
|
|
- Collapse 2 Q22 columns into one mean for analyses
|
|
- Analyse 1 way anova Q20 (14 levels) by Q22; Q23[1-2]; Q27[1-7]
|
|
- 1 way within subjects?? Though not all participants ticked every box... Would it then be best to separate them out and do 14 separate analyses with bonferroni correction due to the multiple tests? - could then be 14 different t tests based on whether they ticked each one as important or not... Many analyses but that may be the most straightforward way to go. Factorial mixed ANOVA? 14 predictors, each with 2 levels (yes/no)??
|
|
- 14 predictors, within subjects, 2 levels (yes/no). DV as responses to questions. Q22 would be a factorial between subjects (only 1 option on IVs) ANOVA. Qs 23, 27 would be factorial between subjects MANOVA
|
|
|
|
|
|
```{r Analyses 1 - As Factor}
|
|
social_issues_data <- read.csv("./data/Q20_data.csv")
|
|
head(social_issues_data)
|
|
|
|
# All 14 as factors, with 2 levels: 1=YES, 2=NO
|
|
|
|
social_issues_data$brexit <- factor(social_issues_data$brexit, levels = c(1, 2), labels = c("Yes", "No"))
|
|
class(social_issues_data$brexit)
|
|
|
|
#social_issues_data[ ,4:5] <- factor(social_issues_data[ ,4:5], levels = c(1, 2), labels = c("Yes", "No"))
|
|
#Did not work; made 2 columns "NA" so am going through to make factors individually
|
|
|
|
### OR ###
|
|
|
|
#social_issues_data[ ,4:5] <- lapply(social_issues_data[ ,4:5], factor(social_issues_data[ ,4:5], levels = c(1, 2), labels = c("Yes", "No")))
|
|
|
|
social_issues_data$economy <- factor(social_issues_data$economy, levels = c(1, 2), labels = c("Yes", "No"))
|
|
|
|
social_issues_data$immigration <- factor(social_issues_data$immigration, levels = c(1, 2), labels = c("Yes", "No"))
|
|
|
|
social_issues_data$crime <- factor(social_issues_data$crime, levels = c(1, 2), labels = c("Yes", "No"))
|
|
|
|
social_issues_data$health <- factor(social_issues_data$health, levels = c(1, 2), labels = c("Yes", "No"))
|
|
|
|
social_issues_data$education <- factor(social_issues_data$education, levels = c(1, 2), labels = c("Yes", "No"))
|
|
|
|
social_issues_data$housing <- factor(social_issues_data$housing, levels = c(1, 2), labels = c("Yes", "No"))
|
|
|
|
social_issues_data$welfare <- factor(social_issues_data$welfare, levels = c(1, 2), labels = c("Yes", "No"))
|
|
|
|
social_issues_data$defence <- factor(social_issues_data$defence, levels = c(1, 2), labels = c("Yes", "No"))
|
|
|
|
social_issues_data$environment <- factor(social_issues_data$environment, levels = c(1, 2), labels = c("Yes", "No"))
|
|
|
|
social_issues_data$tax <- factor(social_issues_data$tax, levels = c(1, 2), labels = c("Yes", "No"))
|
|
|
|
social_issues_data$pensions <- factor(social_issues_data$pensions, levels = c(1, 2), labels = c("Yes", "No"))
|
|
|
|
social_issues_data$family.life <- factor(social_issues_data$family.life, levels = c(1, 2), labels = c("Yes", "No"))
|
|
|
|
social_issues_data$transport <- factor(social_issues_data$transport, levels = c(1, 2), labels = c("Yes", "No"))
|
|
|
|
```
|
|
|
|
``` {r Analyses 2 - ANOVA and MANOVA}
|
|
## Q22; Q23[1-2]; Q27[1-7]
|
|
#Q22_average
|
|
#Q23_1, Q23_2
|
|
#Q27_1 - Q27_7
|
|
#t.test to see if difference in one variable - Q22_average
|
|
hist(social_issues_data$Q22_average)
|
|
t.test(Q22_average~brexit, data = social_issues_data, paired = FALSE)
|
|
#no significant difference between scores on Q22, and whether they thought brexit was important
|
|
|
|
|
|
Q_22test <- aov(Q22_average ~ brexit + economy + immigration + crime + health + education + housing + welfare + defence + environment + tax + pensions + family.life + transport, data = social_issues_data)
|
|
summary(Q_22test)
|
|
#no significant different between scores on Q22 and their opinion on social issues
|
|
|
|
Q_23test <- manova(cbind(Q23_1, Q23_2) ~ brexit + economy + immigration + crime + health + education + housing + welfare + defence + environment + tax + pensions + family.life + transport, data = social_issues_data)
|
|
summary(Q_23test)
|
|
#significant difference between scores on Q23 with economy, health, and environment
|
|
|
|
econ <- aggregate(cbind(Q23_1, Q23_2) ~ economy, data = social_issues_data, FUN = mean)
|
|
health <- aggregate(cbind(Q23_1, Q23_2) ~ health, data = social_issues_data, FUN = mean)
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env <- aggregate(cbind(Q23_1, Q23_2) ~ environment, data = social_issues_data, FUN = mean)
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#SORT OUT MEANS FOR THIS -- interesting pattern viewed with means
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Q_27test <- manova(cbind(Q27_1, Q27_2, Q27_3, Q27_4, Q27_5, Q27_6, Q27_7) ~ brexit + economy + immigration + crime + health + education + housing + welfare + defence + environment + tax + pensions + family.life + transport, data = social_issues_data)
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summary(Q_27test)
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#No significant difference in responses to Q27 based on what they considered important
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```
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- RH: test for correlation between responses to religion questions: Q12-14, Q15-16 and Q21 and responses to Q22, Q23, Q27, [Q24, Q25, Q30]
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``` {r Analyses based on religious affiliation}
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religion_affiliation_data <- read.csv("./data/Religious affiliation data.csv")
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head(religion_affiliation_data)
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## Q12-14, with Q22, Q23, Q27
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# Q12 is binary, 1st test whether difference in answers based on whether the school has formal religious character or not (similar ANOVA/MANOVA as the questions above)
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religion_affiliation_data$Q12 <- factor(religion_affiliation_data$Q12, levels = c("No", "Yes"), labels = c("No", "Yes"))
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## Q22
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formal_affiliation_test_Q22 <- t.test(Q22_average ~ Q12, data = religion_affiliation_data, paired = FALSE)
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formal_affiliation_test_Q22
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## Q23
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formal_affiliation_test_Q23 <- manova(cbind(Q23_1, Q23_2) ~ Q12, data = religion_affiliation_data)
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summary(formal_affiliation_test_Q23)
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## Q27
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formal_affiliation_test_Q27 <- manova(cbind(Q27_1, Q27_2, Q27_3, Q27_4, Q27_5, Q27_6, Q27_7) ~ Q12, data = religion_affiliation_data)
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summary(formal_affiliation_test_Q27)
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# Then, if there is (or can anyway), explore only the "Yes" data, and see if there is a difference in answers based on the specific religious character -- Q13
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# first subset the data
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Q13_data <- religion_affiliation_data[religion_affiliation_data$Q12 == "Yes", ]
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head(Q13_data)
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# then analyze based on specific one
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Q13_data$Q13_recode <- factor(Q13_data$Q13_recode, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10), labels = c("Church of England", "Roman Catholic", "Methodist", "Other Christian", "Jewish", "Muslim", "Sikh", "Hindu", "Multi-Faith", "None of the above"))
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# Test with only included levels
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Q13_data$Q13_recode <- factor(Q13_data$Q13_recode, levels = c(1, 2, 4, 6), labels = c("Church of England", "Roman Catholic", "Other Christian", "Muslim"))
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# No change with this one...still nonsignificant difference
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# Q22
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hist(Q13_data$Q22_average)
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specific_affiliation_test <- aov(Q22_average ~ Q13_recode, data = Q13_data)
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typeof(Q13_data$Q13_recode)
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summary(specific_affiliation_test)
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# Q23
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specific_affiliation_test_Q23 <- manova(cbind(Q23_1, Q23_2) ~ Q13_recode, data = Q13_data)
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summary(specific_affiliation_test_Q23)
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# Q27
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specific_affiliation_test_Q27 <- manova(cbind(Q27_1, Q27_2, Q27_3, Q27_4, Q27_5, Q27_6, Q27_7) ~ Q13_recode, data = Q13_data)
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summary(specific_affiliation_test_Q27)
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## Q15-16 with Q22, Q23, Q27
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# Q15 is binary; 1st test whether difference in answers based on whether the school has an informal religious character or not. Q16 provides further detail and can be explored
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religion_affiliation_data$Q15 <- factor(religion_affiliation_data$Q15, levels = c("No", "Yes"), labels = c("No", "Yes"))
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## Q22
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informal_affiliation_test_Q22 <- t.test(Q22_average ~ Q15, data = religion_affiliation_data, paired = FALSE)
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informal_affiliation_test_Q22
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|
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## Q23
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informal_affiliation_test_Q23 <- manova(cbind(Q23_1, Q23_2) ~ Q15, data = religion_affiliation_data)
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summary(informal_affiliation_test_Q23)
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|
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## Q27
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informal_affiliation_test_Q27 <- manova(cbind(Q27_1, Q27_2, Q27_3, Q27_4, Q27_5, Q27_6, Q27_7) ~ Q15, data = religion_affiliation_data)
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summary(informal_affiliation_test_Q27)
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|
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```
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```{r Analyses based on personal religious affiliation}
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## Q21 with Q22, Q23, Q27
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# Q21 is personal religious affiliation. This may be more tricky as it is a free answer...but can code the type of religious affiliation and test that way? -- would be chi-square or some sort of non-para analysis due to the small number of respondents who answered this
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personal_religious_affiliation_data <- read.csv("./data/Personal religious affiliation data.csv")
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|
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head(personal_religious_affiliation_data)
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|
|
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personal_religious_affiliation_data$Q21_binaryrecode <- factor(personal_religious_affiliation_data$Q21_binaryrecode, levels = c(1, 2), labels = c("none", "answered"))
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|
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## Q22
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personal_religious_affiliation_test_Q22 <- t.test(Q22_avg ~ Q21_binaryrecode, data = personal_religious_affiliation_data, paired = FALSE)
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personal_religious_affiliation_test_Q22
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|
|
|
## Q23
|
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personal_religious_affiliation_test_Q23 <- manova(cbind(Q23_1, Q23_2) ~ Q21_binaryrecode, data = personal_religious_affiliation_data)
|
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summary(personal_religious_affiliation_test_Q23)
|
|
|
|
## Q27
|
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personal_religious_affiliation_test_Q27 <- manova(cbind(Q27_1, Q27_2, Q27_3, Q27_4, Q27_5, Q27_6, Q27_7) ~ Q21_binaryrecode, data = personal_religious_affiliation_data)
|
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summary(personal_religious_affiliation_test_Q27)
|
|
|
|
# Significant difference between answers to Q27 and whether participants indicated a personal religious affiliation -- with the small sample size it may be easier to visualize the differences here based on freeform answer
|
|
|
|
head(personal_religious_affiliation_data)
|
|
personal_religious_affiliation_means <- aggregate(cbind(Q27_1, Q27_2, Q27_3, Q27_4, Q27_5, Q27_6, Q27_7) ~ Q21_binaryrecode, data = personal_religious_affiliation_data, FUN = mean)
|
|
personal_religious_affiliation_means
|
|
|
|
## In viewing the means, it is likely the significant difference viewed in the above MANOVA is within Q27_7, with those who indicated having a personal religious affiliation reporting lower scores (M = 2.94) than those who did not answer or indicated they had no religious affiliation (M = 3.83). This makes sense as a higher score indicates they disagree that they know about "how they put their beliefs about the climate/biodiversity crisis into practice"
|
|
# Also a slight difference in Q27_3 with those indicating having a personal religious affiliation reporting slightly lower scores (M = 3.24) than those who did not answer to indicated they had no religious affiliation (M = 3.76)
|
|
```
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