ISQ Plot

Python

main · Streamlit

Dependencies

streamlit
pandas
plotly-express

To install, simply run this code snippet in your terminal:

pip install -r requirements.txt

Running locally

To run the web app, simply run this code snippet in your terminal:

streamlit run main.py

Deployment

Open a PR and once merged, it'll update in the cloud!

R

Dependencies

Figures are drawn using ggplot2.

library(ggplot2)
library(ggplot2bdc)
library(ggalt)
library(scales)
library(plyr)
library(dplyr)

Data

This example uses data from Computer Science 1 from 2012-2018, available at data/COP2220.csv. The code can be adapted to use data from any source so long as all the required columns are satisfied. Check out ISQool to learn how to generate CSVs for other UNF courses.

fileName <- "COP2220"
read_data <- function(fileName) {
  read.csv(paste0("data/", fileName, ".csv")) %>%
    select(course, term, instructor, rating, average_gpa) %>%
    na.omit()
}
df <- read_data(fileName)

course	term	instructor	rating	average_gpa
COP2220	Spring 2018	Jethwani	3.38	2.91
COP2220	Spring 2018	Jethwani	3.71	2.11
COP2220	Spring 2018	Liu	3.14	2.59
COP2220	Spring 2018	Jethwani	2.86	3.15
COP2220	Spring 2018	Liu	3.16	2.92
COP2220	Spring 2018	Monsorno	2.46	2.37

Based on the file name, we can determine if the file represents one course and various professors, like above, or one professor and various courses. We’re plotting the feature that varies (as the other will be constant).

add_feature <- function(df, fileName) {
  isProfessor <- startsWith(fileName, "N") && nchar(fileName) == 9
  if(isProfessor) {
    df$feature = df$course
    comment(df$feature) <- "Course"
  } else {
    df$feature = df$instructor
    comment(df$feature) <- "Instructor"
  }
  df
}
df <- add_feature(df, fileName)

Scatter Plot

ggplot(df, aes(x = rating, y = average_gpa, color = feature)) +
geom_encircle(aes(fill = feature), s_shape = 0.7, expand = 0.02, spread = 0.015, alpha = 0.1) +
geom_text(aes(label = feature), nudge_y = -0.035, size = 3) +
geom_point() +
guides(size = guide_legend(override.aes = list(linetype = 0))) +
labs(
  title = fileName,
  subtitle = paste0("Course evaluation results from ", nrow(df), " classes"),
  caption = "Source: UNF ISQ Departmental Data Summary",
  color = comment(df$feature),
  fill = comment(df$feature),
  x = "Average Student Rating",
  y = "Average GPA"
) +
theme_bdc_grey(grid.x = TRUE, grid.y = TRUE) +
theme(legend.position = "right", legend.direction = "vertical", legend.title.align = 0)

Focused Scatter Plot

If a plot contains a lot of data (400+ points), it would be nice to differentiate only those points which we care about. We can highlight a specific professor we’re interested in by reducing our feature to just two levels.

highlight <- "Berry"
df$feature <- ifelse(df$feature == highlight, highlight, "Other")

Let’s gray out the other data points and use blue as our accent.

palette <- c("#003886", "#CCD7E7")
names(palette) <- c(highlight, "Other")

We’ll then adjust the geom_text to hide superfluous labels and move the geom_points behind the text.

ggplot(df, aes(rating, average_gpa, color = feature)) +
  geom_encircle(aes(fill = feature), s_shape = 0.7, expand = 0.02, spread = 0.015, alpha = 0.1) +
  geom_point() +
  geom_text(aes(label = ifelse(feature == highlight, highlight, "")), nudge_y = -0.035, size = 3) +
  scale_color_manual(values = palette) +
  scale_fill_manual(values = palette) +
  labs(
    title = fileName,
    subtitle = paste0("Course evaluation results from ", nrow(df), " classes"),
    caption = "Source: UNF ISQ Departmental Data Summary",
    color = comment(df$feature),
    fill = comment(df$feature),
    x = "Average Student Rating",
    y = "Average GPA"
  ) +
  theme_bdc_grey(grid.x = TRUE, grid.y = TRUE) +
  theme(legend.position = "right", legend.direction = "vertical", legend.title.align = 0)

Saving to an image

# Add a plot margin to make it look pretty
last_plot() + theme(plot.margin = margin(2, 2, 2, 2, "cm"))

# Save as a 15 x 8 inch image
ggsave(paste0(fileName, ".png"), width = 14, height = 8, dpi = 100)