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https://github.com/kidwellj/re_connect_survey.git
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And look, more graphs
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
This commit is contained in:
rehughes07
parent
8b0670f432
commit
a5451ab618
2 changed files with 314 additions and 123 deletions
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@ -149,52 +149,59 @@ 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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#testplot <- ggplot(summaries_data, aes(x = Q8_recode, y = Q22_avg)) + geom_bar(stat = "identity")
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#testplot
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Q22_by_Q8visualization <- ggplot(summaries_data, aes(x = Q8_recode, y = Q22_avg)) + stat_summary(fun = "mean", geom = "bar")
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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")
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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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# Not sure what all the options are?
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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")
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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")
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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")
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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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###OR###
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Q22_by_Q21visualization2 <- ggplot(summaries_data, aes(x = Q21, y = Q22_avg)) + stat_summary(fun = "mean", geom = "bar")
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Q22_by_Q21visualization2
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#Q4 (teacher's degree subject) - write-in...figure out how to sort
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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")
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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()
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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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@ -205,88 +212,269 @@ summaries_data$Q8_recode <- factor(summaries_data$Q8_recode, levels = c(1, 2, 3,
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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")
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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")
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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")
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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")
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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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# Not sure what all the options are?
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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")
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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")
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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")
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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")
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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")
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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")
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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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###OR###
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#Q22_by_Q21visualization2 <- ggplot(summaries_data, aes(x = Q21, y = Q22_avg)) + stat_summary(fun = "mean", geom = "bar")
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#Q22_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")
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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")
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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()
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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()
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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")
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Q24_byQ9visualization3
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Q24_byQ9visualization4 <- ggplot(summaries_data, aes(x = Climate.crisis.and.or.diversity, fill = Q9_recode)) + geom_bar(position = "stack") + labs(fill = "School Faculty Size")
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Q24_byQ9visualization4
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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))
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annotate_figure(Q24_by_Q9_allvisualization, top = text_grob("Units of Work Covered Within RE by School Size"))
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Q24_by_Q9_allvisualization
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#Q10 (school location)
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Q24_byQ10visualization1 <- ggplot(summaries_data, aes(x = Understanding.of.nature, fill = Q10)) + geom_bar(position = "stack") + labs(fill = "School Location")
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Q24_byQ10visualization1
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Q24_byQ10visualization2 <- ggplot(summaries_data, aes(x = Relationship.between.spiritual.and.matiral.worlds, fill = Q10)) + geom_bar(position = "stack") + labs(fill = "School Location")
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Q24_byQ10visualization2
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Q24_byQ10visualization3 <- ggplot(summaries_data, aes(x = Human.beings..responsibility.towards.the.earth, fill = Q10)) + geom_bar(position = "stack") + labs(fill = "School Location")
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Q24_byQ10visualization3
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Q24_byQ10visualization4 <- ggplot(summaries_data, aes(x = Climate.crisis.and.or.diversity, fill = Q10)) + geom_bar(position = "stack") + labs(fill = "School Location")
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Q24_byQ10visualization4
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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))
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annotate_figure(Q24_by_Q10_allvisualization, top = text_grob("Units of Work Covered Within RE by School Location"))
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Q24_by_Q10_allvisualization
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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"))
|
||||
|
||||
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
|
||||
# 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
|
||||
# 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")
|
||||
|
|
@ -336,6 +524,9 @@ Q22_by_Q18visualization
|
|||
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"))
|
||||
|
||||
|
||||
```
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue