---
title: "Connect Project"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
# Load RColorBrewer
# install.packages("RColorBrewer")
library(RColorBrewer)

# Define colour palettes for plots below
coul3 <- brewer.pal(3, "RdYlBu") # Using RdYlBu range to generate 3 colour palette: https://colorbrewer2.org/#type=diverging&scheme=RdYlBu&n=5

```

Note that the `echo = FALSE` parameter was added to the code chunk to prevent printing of the R code that generated the plot.

### To Do List

## Upload Data
```{r Data Upload}
connect_data = read.csv("./data/connectDATA.csv")
```

## Summary of Data
Data summary/visualisation with subsetting:
  - RH: display simple summary of data (bar/pie chart) to Q25/26, Q3
  
```{r Frequencies}  
#Frequencies#
  
Q25_frequencies <- table(connect_data$Q25)
Q25_frequencies
Q26_freq <- table(connect_data$Q26)
Q26_freq
Q3_freq <- table(connect_data$Q3)
Q3_freq

#test3 = as.factor(connect_data$Q3, levels = c(1, 2, 3, 4, 5), labels = c("Worldviews", "Religion", "Theology", "Ethics", "Philosophy"))
```

```{r Q25 bar/pie}
pie(Q25_frequencies, labels = c("Maybe", "No", "Yes"))
pie(Q25_frequencies, labels = c("Maybe", "No", "Yes"), col = coul3)

```

```{r Q26 bar/pie}

Q26_data <- read.csv("./data/Q26_data.csv")

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"],
                table(Q26_data[,3]) [names(table(Q26_data[,3])) == "TRUE"],
                table(Q26_data[,4]) [names(table(Q26_data[,4])) == "TRUE"],
                table(Q26_data[,5]) [names(table(Q26_data[,5])) == "TRUE"],
                table(Q26_data[,6]) [names(table(Q26_data[,6])) == "TRUE"],
                table(Q26_data[,7]) [names(table(Q26_data[,7])) == "TRUE"],
                table(Q26_data[,8]) [names(table(Q26_data[,8])) == "TRUE"],
                table(Q26_data[,9]) [names(table(Q26_data[,9])) == "TRUE"],
                table(Q26_data[,10]) [names(table(Q26_data[,10])) == "TRUE"]))

head(Q26_freq_data)
names(Q26_freq_data)[1] <- "Reasons"
names(Q26_freq_data)[2] <- "Frequency"
head(Q26_freq_data)
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"))

# Bar graph tidier


```

pie(Q26_freq) 
  #very messy as a pie chart - split by type? Or is it important to see crossover
  
  Could potentially see crossover with crosstabs by type (since response is now binary variable T/F), maybe chi square; perhaps just descriptives
  

```{r Q3 bar/pie}

Q3_data <- read.csv("./data/Q3.csv")

#head(Q3_data)
#table(Q3_data [,3:7])
#pie(table(Q3_data [,3:7]))

Q3_data2 <- Q3_data[ ,3:7]
#head(Q3_data2)
#table(Q3_data2)
#table(Q3_data2[,1])

### want to take only the count of "True" (1) in each column. Then pie chart of the frequencies

#Q3_data3 <- read.csv("~/Documents/Github/re_connect_survey/data/Q3 copydata.csv")

#table(Q3_data3)
  #count(Q3_data3, 1)
#table(Q3_data3) [names(table(Q3_data3)) == 1]
#table(Q3_data3)

table(Q3_data2[,1]) [names(table(Q3_data2[,1])) == "TRUE"]

test2 <- data.frame(c("Worldviews", "Religion", "Theology", "Ethics", "Philosophy"), c(table(Q3_data2[,1]) [names(table(Q3_data2[,1])) == "TRUE"],
                table(Q3_data2[,2]) [names(table(Q3_data2[,2])) == "TRUE"],
                table(Q3_data2[,3]) [names(table(Q3_data2[,3])) == "TRUE"],
                table(Q3_data2[,4]) [names(table(Q3_data2[,4])) == "TRUE"],
                table(Q3_data2[,5]) [names(table(Q3_data2[,5])) == "TRUE"]))
head(test2)
names(test2)[1] <- "Subject"
names(test2)[2] <- "Frequency"
head(test2)
pie(test2$Frequency, labels = c("Worldviews", "Religion", "Theology", "Ethics", "Philosophy"))

# JK note on Q3: consider here whether to use alternative forms of visualiation to reflect the overlaps when respondents picked multiple categories in responses

```

xtabs(Frequency ~ Subject, test2)

pie(Q3_freq)
  #also not optimal as pie...perhaps bar

  #sum(Q3_data2)
  
Q3_1factor = as.factor(Q3_data2$Religion)
table(Q3_1factor)
   #count(Q3_1factor, "TRUE")

  #test = replace(Q3_1factor, "TRUE", 1)
  #test
  #Q3_1factor

  - RH: display summaries of responses to key questions for Q22 (syllabus evaluation), Q23, Q24, Q25, Q26, Q27, with subsetting by:
    - Q8 (school type)
    - Q9 (school size)
    - Q10 (school location)
    - Q1 (grade level) + Q35 (teaching role) + +Q5 (teaching proportion) Q2 (tenure) + and Q3 (subjects taught), + Q6/Q7 (management)
    - Q12-14 (school's official religion) / Q15-16 (school's informal religion)
    - Q21 (respondent personal religious background)
    - Q4 (teacher's degree subject)
    - Q18 (respondent gender)
    - Q19 (respondent ethnic self-desc)
    
```{r Plots Q22}
library(ggplot2)
summaries_data <- read.csv("./data/visualization data.csv")

# Q22
   #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"))

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")
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") + labs(x = "School Faculty Size") + labs(y = "Syllabus Allows Exploration of Relationship Between Environment and Religion/Worldview")
Q22_by_Q9visualization

  #Q10 (school location)
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")
Q22_by_Q10visualization

  #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") + labs(x = "School Official Relgious Affiliation") + labs(y = "Syllabus Allows Exploration of Relationship Between Environment and Religion/Worldview")
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") + labs(x = "School Unofficial Religious Affiliation") + labs(y = "Syllabus Allows Exploration of Relationship Between Environment and Religion/Worldview")
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") + labs(x = "Personal Religious Background") + labs(y = "Syllabus Allows Exploration of Relationship Between Environment and Religion/Worldview")
Q22_by_Q21visualization


  #Q4 (teacher's degree subject) - write-in...figure out how to sort -- recode to sort similar subjects
Q22_by_Q4visualization <- ggplot(summaries_data, aes(x = Q4, y = Q22_avg)) + stat_summary(fun = "mean", geom = "bar") + coord_flip()
Q22_by_Q4visualization

  #Q18 (respondent gender)
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")
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") + coord_flip() + labs(x = "Ethnicity") + labs(y = "Syllabus Allows Exploration of Relationship Between Environment and Religion/Worldview")
Q22_by_Q19visualization


install.packages("ggpubr")
#library(ggplot2)

library(ggpubr)

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))

test_plot_together
```

```{r Plots Q23}
# Q23
   #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"))
 
 #split??

#Q23_by_Q8visualization <- ggplot(summaries_data, aes(x = Q8_recode, y = cbind(Q23_1, Q23_2))) + stat_summary(fun = "mean", geom = "bar")
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")
Q23_by_Q8visualization1

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")
Q23_by_Q8visualization2

  #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"))

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")
Q23_by_Q9visualization1

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")
Q23_by_Q9visualization2

  #Q10 (school location)
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")
Q23_by_Q10visualization1

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")
Q23_by_Q10visualization2

  #Q12-14 (school's official religion) 
summaries_data$Q12 <- factor(summaries_data$Q12, levels = c("No", "Yes"), labels = c("No", "Yes"))

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")
Q23_by_Q12visualization1

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")
Q23_by_Q12visualization2

  #Q15-16 (school's informal religion)
summaries_data$Q15 <- factor(summaries_data$Q15, levels = c("No", "Yes"), labels = c("No", "Yes"))

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")
Q23_by_Q15visualization1

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")
Q23_by_Q15visualization2

  #Q21 (respondent personal religious background)
summaries_data$Q21_binaryrecode <- factor(summaries_data$Q21_binaryrecode, levels = c(1, 2), labels = c("No", "Yes"))

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")
Q23_by_Q21visualization1

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")
Q23_by_Q21visualization2

  #Q4 (teacher's degree subject) - write-in...figure out how to sort

  #Q18 (respondent gender)
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")
Q23_by_Q18visualization1

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")
Q23_by_Q18visualization2

  #Q19 (respondent ethnic self-desc)
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")
Q23_by_Q19visualization1

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")
Q23_by_Q19visualization2

```

```{r Plots Q24}
# Q24
  #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"))

Q24_byQ8visualization1 <- ggplot(summaries_data, aes(x = Understanding.of.nature, fill = Q8_recode)) + geom_bar(position = "stack") + labs(fill = "School Type")
Q24_byQ8visualization1

Q24_byQ8visualization2 <- ggplot(summaries_data, aes(x = Relationship.between.spiritual.and.matiral.worlds, fill = Q8_recode)) + geom_bar(position = "stack") + labs(fill = "School Type")
Q24_byQ8visualization2

Q24_byQ8visualization3 <- ggplot(summaries_data, aes(x = Human.beings..responsibility.towards.the.earth, fill = Q8_recode)) + geom_bar(position = "stack") + labs(fill = "School Type")
Q24_byQ8visualization3

Q24_byQ8visualization4 <- ggplot(summaries_data, aes(x = Climate.crisis.and.or.diversity, fill = Q8_recode)) + geom_bar(position = "stack") + labs(fill = "School Type")
Q24_byQ8visualization4

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))
annotate_figure(Q24_by_Q8_allvisualization, top = text_grob("Units of Work Covered Within RE by School Type"))
Q24_by_Q8_allvisualization

  #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"))

Q24_byQ9visualization1 <- ggplot(summaries_data, aes(x = Understanding.of.nature, fill = Q9_recode)) + geom_bar(position = "stack") + labs(fill = "School Faculty Size")
Q24_byQ9visualization1

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")
Q24_byQ9visualization2

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)
env <- aggregate(cbind(Q23_1, Q23_2) ~ environment, data = social_issues_data, FUN = mean)


#SORT OUT MEANS FOR THIS -- interesting pattern viewed with means

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)
summary(Q_27test)
  #No significant difference in responses to Q27 based on what they considered   important
```
  - RH: test for correlation between responses to religion questions: Q12-14, Q15-16 and Q21 and responses to Q22, Q23, Q27,  [Q24, Q25, Q30]
  
``` {r Analyses based on religious affiliation}
religion_affiliation_data <- read.csv("./data/Religious affiliation data.csv")
head(religion_affiliation_data)

## Q12-14, with Q22, Q23, Q27
  # 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)
religion_affiliation_data$Q12 <- factor(religion_affiliation_data$Q12, levels = c("No", "Yes"), labels = c("No", "Yes"))

  ## Q22
formal_affiliation_test_Q22 <- t.test(Q22_average ~ Q12, data = religion_affiliation_data, paired = FALSE)
formal_affiliation_test_Q22

  ## Q23
formal_affiliation_test_Q23 <- manova(cbind(Q23_1, Q23_2) ~ Q12, data = religion_affiliation_data)
summary(formal_affiliation_test_Q23)

  ## Q27
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)
summary(formal_affiliation_test_Q27)
  
# 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
  # first subset the data
Q13_data <- religion_affiliation_data[religion_affiliation_data$Q12 == "Yes", ]
head(Q13_data)

  # then analyze based on specific one
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"))

# Test with only included levels
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"))
# No change with this one...still nonsignificant difference

  # Q22
hist(Q13_data$Q22_average)
specific_affiliation_test <- aov(Q22_average ~ Q13_recode, data = Q13_data)
typeof(Q13_data$Q13_recode)
summary(specific_affiliation_test)

  # Q23
specific_affiliation_test_Q23 <- manova(cbind(Q23_1, Q23_2) ~ Q13_recode, data = Q13_data)
summary(specific_affiliation_test_Q23)

  # Q27
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)
summary(specific_affiliation_test_Q27)

## Q15-16 with Q22, Q23, Q27
  # 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

religion_affiliation_data$Q15 <- factor(religion_affiliation_data$Q15, levels = c("No", "Yes"), labels = c("No", "Yes"))

  ## Q22
informal_affiliation_test_Q22 <- t.test(Q22_average ~ Q15, data = religion_affiliation_data, paired = FALSE)
informal_affiliation_test_Q22

  ## Q23
informal_affiliation_test_Q23 <- manova(cbind(Q23_1, Q23_2) ~ Q15, data = religion_affiliation_data)
summary(informal_affiliation_test_Q23)

  ## Q27
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)
summary(informal_affiliation_test_Q27)

```

```{r Analyses based on personal religious affiliation}
## Q21 with Q22, Q23, Q27
  # 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

personal_religious_affiliation_data <- read.csv("./data/Personal religious affiliation data.csv")

head(personal_religious_affiliation_data)

personal_religious_affiliation_data$Q21_binaryrecode <- factor(personal_religious_affiliation_data$Q21_binaryrecode, levels = c(1, 2), labels = c("none", "answered"))

  ## Q22
personal_religious_affiliation_test_Q22 <- t.test(Q22_avg ~ Q21_binaryrecode, data = personal_religious_affiliation_data, paired = FALSE)
personal_religious_affiliation_test_Q22

  ## Q23
personal_religious_affiliation_test_Q23 <- manova(cbind(Q23_1, Q23_2) ~ Q21_binaryrecode, data = personal_religious_affiliation_data)
summary(personal_religious_affiliation_test_Q23)

  ## Q27
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)
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)
```