From dd1940aca7ed1ad8cad53f2e0145d6ded161e782 Mon Sep 17 00:00:00 2001 From: rehughes07 <58590776+rehughes07@users.noreply.github.com> Date: Tue, 7 Dec 2021 14:45:05 +0000 Subject: [PATCH] Visualization of Likert Responses Creation of .csv file with only the complete participants' responses to the likert questions of (1)understanding/knowledge, (2)interest, and (3)employability prospects rating by the 12 subjects. Each bar graph represents the mean answer to these likert questions. I didn't reverse score the answers, so right now the bar graphs show the following: higher scores indicate less of each rating (interest, knowledge, employability). The bar graphs visualize these scores overall across the entire respondent cohort. --- To-do list Markdown.Rmd | 35 +- data/Making subject data.xlsx | Bin 0 -> 329763 bytes data/Subject data.csv | 11197 ++++++++++++++++++++++++++++++++ 3 files changed, 11228 insertions(+), 4 deletions(-) create mode 100644 data/Making subject data.xlsx create mode 100644 data/Subject data.csv diff --git a/To-do list Markdown.Rmd b/To-do list Markdown.Rmd index 9affa34..c281d6d 100644 --- a/To-do list Markdown.Rmd +++ b/To-do list Markdown.Rmd @@ -55,16 +55,18 @@ gender_pie <- pie(table(TSR_data$Gender)) ``` - Q17 (ethnic self-id) ```{r ethnic self-id} - +library(ggplot2) TSR_data$Ethnicity <- factor(TSR_data$Ethnicity, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 ,18, 19), labels = c("Arab", "Asian/Asian British - Indian", "Asian/Asian British - Pakistani", "Asian/Asian British - Bangladeshi", "Asian/Asian British - Chinese", "Asian/Asian British - Any other Asian background", "Black/Black British - African", "Black/Black British - Caribbean", "Black/Black British - Any other Black background", "Mixed/Multiple Ethnic Groups - White and Black Caribbean", "Mixed/Multiple Ethnic Groups - White and Black African", "Mixed/Multiple Ethnic Groups - White and Black Asian", "Mixed/Multiple Ethnic Groups - Any other Mixed/Multiple Ethnic background", "White - English/Welsh/Scottish/Northern Irish/British", "White - Irish", "White - Gypsy or Irish Traveller", "White - Any other White background", "Other Ethnic group, please describe", "Prefer not to say")) -#Ethnicity_bar <- ggplot(TSR_data, aes(x = Ethnicity) + geom_bar(position = "stack")) +Ethnicity_bar <- ggplot(TSR_data, aes(Ethnicity)) + geom_bar() + coord_flip() +Ethnicity_bar ``` - Q18 (religion) ```{r religion} TSR_data$Religious.Affliation <- factor(TSR_data$Religious.Affliation, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19), labels = c("Agnostic", "Atheist", "Baha'i", "Buddhist", "Christian", "Confucian", "Jain", "Jewish", "Hindu", "Indigenous Traditional Religious", "Muslim", "Pagan", "Shinto", "Sikh", "Spiritual but not religious", "Zoroastrian", "No religion", "Prefer not to say", "Other")) -#Religious_affiliation_bar <- ggplot(TSR_data, aes(x = Religious.Affliation) + geom_bar(position = "stack")) +Religious_affiliation_bar <- ggplot(TSR_data, aes(Religious.Affliation)) + geom_bar() + coord_flip() +Religious_affiliation_bar ``` @@ -74,13 +76,38 @@ TSR_data$Religious.Affliation <- factor(TSR_data$Religious.Affliation, levels = ```{r Visualization by Subject} ### Each Subject is a different column so will need to figure out how to code the columns together into one graph + # Higher score indicates less agreement...need to reverse score + +## 1=5, 2=4, 3=3, 4=2, 5=1, 6=0 --- Not done yet. See what they look like without reverse scoring + +# The way the code is now - the below will help you visualize overall across the entire respondent cohort what the understanding, interest, and view of employability are by subject + +subject_data <- read.csv("./data/Subject data.csv") + +#Q5 Subject Knowledge/Understanding +subject_data$Subject <- factor(subject_data$Subject, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), labels = c("Philosophy", "Sociology", "Psychology", "History", "Ethics", "Theology", "Religious Studies", "Politics", "English", "Math", "Computer Science", "Business")) + +understanding_mean <- aggregate(Understanding ~ Subject, data = subject_data, mean) + + +#understanding_bar <- ggplot(subject_data, aes(x = Subject, y = aggregate(Understanding, by = Subject, FUN = mean))) + geom_bar() + labs(x = "Subject") + labs(y = "Understanding") +#understanding_bar + +understanding_bar <- ggplot(understanding_mean, aes(x = Subject, y = Understanding)) + stat_summary(fun = "mean", geom = "bar") + coord_flip() +understanding_bar #Q6 Subject Interest +interest_mean <- aggregate(Interest ~ Subject, data = subject_data, mean) -#Q5 Subject Knowledge +interest_bar <- ggplot(interest_mean, aes(x = Subject, y = Interest)) + stat_summary(fun = "mean", geom = "bar") + coord_flip() +interest_bar #Q7 Employability Prospects +employability_mean <- aggregate(Employability ~ Subject, data = subject_data, mean) + +employability_bar <- ggplot(employability_mean, aes(x = Subject, y = Employability)) + stat_summary(fun = "mean", geom = "bar") + coord_flip() +employability_bar ``` - separate visualisation of summary data as pie chart only for 4 key subjects: Philosophy, Ethics, Theology, Religious Studies, but with data represented as aggregated "Positive" / "Negative" responses diff --git a/data/Making subject data.xlsx b/data/Making subject data.xlsx new file mode 100644 index 0000000000000000000000000000000000000000..96246f3e537c6dc54adc676009fd2363ddd6e2f7 GIT binary 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