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Pie Charts and More Data Finagling

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Figuring how to put frequencies of multiple response data into a clean format, then creating rough code for a simple pie chart (to be made pretty later).

Finished this for the 3 questions needing pie charts.
This commit is contained in:
rehughes07 2021-10-25 16:13:22 +01:00
parent 0b15014181
commit dd66f2f923
4 changed files with 246 additions and 65 deletions
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65
Connect Project R Markdown.Rmd
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@ -31,7 +31,7 @@ Note that the `echo = FALSE` parameter was added to the code chunk to prevent pr
## Upload Data
```{r Data Upload}
connect_data = read.csv("connectDATA.csv")
connect_data = read.csv("~/Documents/Github/re_connect_survey/data/connectDATA.csv")
```
## Summary of Data
@ -60,6 +60,25 @@ pie(Q25_frequencies, labels = c("Maybe", "No", "Yes"))
# rough draft of piechart
```{r Q26 bar/pie}
Q26_data <- read.csv("~/Documents/Github/re_connect_survey/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$Frequency, labels = c("Other Priorities", "Lack Subject Knowledge", "Lack Confidence", "Current Syllabus", "Pupil Disinterest", "Department Head", "Available Work Schemes", "Unavailable Resources", "Uncertain of Pedagogical Approach"))
```
pie(Q26_freq)
@ -67,7 +86,39 @@ pie(Q26_freq)
```{r Q3 bar/pie}
Q3_data <- read.csv("Q3.csv")
Q3_data <- read.csv("~/Documents/Github/re_connect_survey/data/Q3.csv")
#head(Q3_data)
#table(Q3_data [,3:7])
#pie(table(Q3_data [,3:7]))
Q3_data2 <- Q3_data[,3:7]
#head(Q3_data2)
#table(Q3_data2)
#table(Q3_data2[,1])
### want to take only the count of "True" (1) in each column. Then pie chart of the frequencies
#Q3_data3 <- read.csv("~/Documents/Github/re_connect_survey/data/Q3 copydata.csv")
#table(Q3_data3)
#count(Q3_data3, 1)
#table(Q3_data3) [names(table(Q3_data3)) == 1]
#table(Q3_data3)
table(Q3_data2[,1]) [names(table(Q3_data2[,1])) == "TRUE"]
test2 <- data.frame(c("Worldviews", "Religion", "Theology", "Ethics", "Philosophy"), c(table(Q3_data2[,1]) [names(table(Q3_data2[,1])) == "TRUE"],
table(Q3_data2[,2]) [names(table(Q3_data2[,2])) == "TRUE"],
table(Q3_data2[,3]) [names(table(Q3_data2[,3])) == "TRUE"],
table(Q3_data2[,4]) [names(table(Q3_data2[,4])) == "TRUE"],
table(Q3_data2[,5]) [names(table(Q3_data2[,5])) == "TRUE"]))
head(test2)
names(test2)[1] <- "Subject"
names(test2)[2] <- "Frequency"
head(test2)
pie(test2$Frequency, labels = c("Worldviews", "Religion", "Theology", "Ethics", "Philosophy"))
```
@ -75,7 +126,15 @@ Q3_data <- read.csv("Q3.csv")
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)
@ -113,4 +172,4 @@ pie(Q3_freq)
```{r Correlation 1}
```
- RH: test for correlation between responses to religion questions: Q12-14, Q15-16 and Q21 and responses to Q22, Q23, Q24, Q25, Q27, Q30
- RH: test for correlation between responses to religion questions: Q12-14, Q15-16 and Q21 and responses to Q22, Q23, Q27, [Q24, Q25, Q30]