re_connect_survey/Connect Project R Markdown.Rmd

---
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"), 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

# Q23

# Q24

# Q25

# Q26

# Q27
```

## 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]