mirror of
https://github.com/kidwellj/re_connect_survey.git
synced 2024-11-01 07:52:21 +00:00
168 lines
5.4 KiB
Plaintext
168 lines
5.4 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"), 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$Frequency, labels = c("Worldviews", "Religion", "Theology", "Ethics", "Philosophy"))
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# JK note on Q3: consider here whether to use alternative forms of visualiation to reflect the overlaps when respondents picked multiple categories in responses
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```
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pie(Q3_freq)
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#also not optimal as pie...perhaps bar
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#sum(Q3_data2)
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Q3_1factor = as.factor(Q3_data2$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}
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# Q22
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# Q23
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# Q24
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# Q25
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# Q26
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# Q27
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```
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## Correlation testing:
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- RH: test for correlation between "social issue" box ticked on Q20 and responses to Q22, Q23, Q27
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- Make Q20 a factor with 14 levels
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- Collapse 2 Q22 columns into one mean for analyses
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- Analyse 1 way anova Q20 (14 levels) by Q22; Q23[1-2]; Q27[1-7]
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```{r Correlation 1}
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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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