Bubble Charts: An in-class challenge

A bubble chart is a scatterplot where the size of the bubble is scaled by some value. Bubble charts were made small f famous by the late Hans Rosling in one of the most watched Ted talks ever.

Your challenge in class today: Make a bubble chart out of the dataset of majors by race and sex. I'm interested in gender differences by majors.

I'll help you set up the data.



In [1]:

    
library(dplyr)
library(ggplot2)
library(reshape2)









    



Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union



In [2]:

    
enrollment <- read.csv("../../Data/collegeenrollment.csv")



In [3]:

    
head(enrollment)









    





College Degree MajorCode MajorName RaceGender Race Gender Count Total

	College of Agri Sci and Natl Resources B1BC                                  BIOC                                  Biochemistry                          NonResidentAlienMale                  NonResidentAlien                      Male                                  3                                      97                                   
	College of Agri Sci and Natl Resources B1AS                                  ASCI                                  Animal Science                        NonResidentAlienMale                  NonResidentAlien                      Male                                  0                                     338                                   
	College of Agri Sci and Natl Resources B1FW                                  FWL                                   Fisheries and Wildlife                NonResidentAlienMale                  NonResidentAlien                      Male                                  0                                     191                                   
	College of Agri Sci and Natl Resources B1AP                                  APSC                                  Applied Science                       NonResidentAlienMale                  NonResidentAlien                      Male                                  1                                      71                                   
	College of Agri Sci and Natl Resources B1HO                                  HORT                                  Horticulture                          NonResidentAlienMale                  NonResidentAlien                      Male                                  1                                      52                                   
	College of Agri Sci and Natl Resources B1ED                                  AEDU                                  Agricultural Education                NonResidentAlienMale                  NonResidentAlien                      Male                                  0                                     103

Note we have data that is long -- data by race and gender. We just want it by gender. So we need to do some grouping together and later we need to make this wider. So we have to first group by College, Major and Gender and add them up.



In [4]:

    
majors <- enrollment %>% 
    group_by(College, MajorName, Gender) %>%
    summarize(
       Total=sum(Count)
    )

Now we need to make that long data wide, so Male and Female are on the same line.



In [5]:

    
majors_bubble <- dcast(majors, College + MajorName ~ Gender)









    



Using Total as value column: use value.var to override.



In [6]:

    
head(majors_bubble)









    





College MajorName Female Male

	College of Agri Sci and Natl Resources Agribusiness                          66                                    175                                   
	College of Agri Sci and Natl Resources     Agricultural & Env Sci Comm            27                                           5
	College of Agri Sci and Natl Resources Agricultural Economics                23                                    109                                   
	College of Agri Sci and Natl Resources Agricultural Education                80                                     23                                   
	College of Agri Sci and Natl Resources Agricultural Journalism                2                                      0                                   
	College of Agri Sci and Natl Resources Agronomy                              24                                    167

We now have enough to do a scatterplot, but what are we lacking for a bubble chart? Some kind of weighting. So let's create a couple.



In [7]:

    
bubble <- majors_bubble %>% 
    mutate(
       Total = Male+Female,
       Difference = abs(Male-Female)
    )

The abs() bits there mean give me the absolute value -- so everything is above zero, regardless of which is larger.

So let's try a plot:



In [9]:

    
ggplot(bubble, aes(x = Male, y = Female, size=Difference)) +
geom_point(alpha=0.4, color='red') +
scale_size_continuous(range=c(.1, 20)) +
scale_colour_continuous(guide = FALSE) +
geom_text(data=bubble, aes(label=MajorName, size=10), check_overlap=TRUE)

Your challenge: Make this better. Here's some guidance on things you can change. Here's more. We'll talk at the end of class.



In [ ]:

College	Degree	MajorCode	MajorName	RaceGender	Race	Gender	Count	Total
College of Agri Sci and Natl Resources	B1BC	BIOC	Biochemistry	NonResidentAlienMale	NonResidentAlien	Male	3	97
College of Agri Sci and Natl Resources	B1AS	ASCI	Animal Science	NonResidentAlienMale	NonResidentAlien	Male	0	338
College of Agri Sci and Natl Resources	B1FW	FWL	Fisheries and Wildlife	NonResidentAlienMale	NonResidentAlien	Male	0	191
College of Agri Sci and Natl Resources	B1AP	APSC	Applied Science	NonResidentAlienMale	NonResidentAlien	Male	1	71
College of Agri Sci and Natl Resources	B1HO	HORT	Horticulture	NonResidentAlienMale	NonResidentAlien	Male	1	52
College of Agri Sci and Natl Resources	B1ED	AEDU	Agricultural Education	NonResidentAlienMale	NonResidentAlien	Male	0	103

College	MajorName	Female	Male
College of Agri Sci and Natl Resources	Agribusiness	66	175
College of Agri Sci and Natl Resources	Agricultural & Env Sci Comm	27	5
College of Agri Sci and Natl Resources	Agricultural Economics	23	109
College of Agri Sci and Natl Resources	Agricultural Education	80	23
College of Agri Sci and Natl Resources	Agricultural Journalism	2	0
College of Agri Sci and Natl Resources	Agronomy	24	167