Plotly for College Analysis

By Jamel Thomas

Community colleges for many is a stepping stone to a larger dream of four year university. However, many of the risk factors that prevent students from succeeding in school are highly prevelant in the student body of 2-year institutions.

In this post, we explore open college data. This dataset includes the name of the college, the aggregated number of people who passed a particular subject by transfer level and race. The data itself contains information on race, and college name. It also contains information on the pass rate/number of students who passed by subject.

Let’s read our data and take a look:

There is a wonderful package called DT that creates interactive plots in HTML. It even comes with a search feature. Check it out.

#Load Packages:
require(DT)

## Loading required package: DT

require(plotly)

## Loading required package: plotly

## Loading required package: ggplot2

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

school <- read.table(url, header = T)

ggplot

The ggplot package is essentially the bread and butter of beautiful and easy to use plots. We will start with data = school (duh), and x = ethnicity, y = sum. The “sum”" here is just the total number of students who passed given course.

g <- ggplot(data = school,
            aes(x = ethnicity, y = sum, fill = group)) + geom_boxplot() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) #Vertical Axis
g

Interactivity

In order to make this plot interactive, we have to do something really challenging. Are you ready? Here we go…

Type the following line of code.

ggplotly(g)

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

That’s it! We can now do all sorts of amazing plots with minimal code! Let’s look as some more stuff.

g <- ggplot(data = school,
            aes(x = ethnicity, y = success, fill = ethnicity)) +
  geom_bar(stat = "identity")  +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) #Vertical Axis
ggplotly(g)

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

Make sure you clean your data!

#Refactor
school$group <- factor(school$group,
                       levels = c("One Level Below Transfer",
                                  "Two Levels Below Transfer",
                                  "Three Levels Below Transfer",
                                  "Four Levels Below Transfer"))

g <- ggplot(data = school,
            aes(x = students, y = sum, color = ethnicity)) +
  geom_point() + facet_wrap( ~ group, ncol=2)
ggplotly(g)

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

g <- ggplot(data = school,
            aes(x = subject, y = sum, color = ethnicity)) +
  geom_boxplot() + facet_wrap( ~ ethnicity, ncol=2)
ggplotly(g)

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

g <- ggplot(data = school,
            aes(x = group, y = sum, fill = subject)) +
  geom_boxplot() + facet_wrap( ~ subject, ncol=2)+
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) #Vertical Axis
ggplotly(g)

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

Plotly for College Analysis

ggplot

Interactivity

In God we trust. All others must bring data.