Practice exploring college education (data)
library(tidyverse)The Department of Education collects annual statistics on colleges and universities in the United States. I have included a subset of this data from 2013 in the rcfss library from GitHub. To install the package, run the command devtools::install_github("uc-cfss/rcfss") in the console.
If you don’t already have the
devtoolslibrary installed, you will get an error. Go back and install this first usinginstall.packages("devtools"), then rundevtools::install_github("uc-cfss/rcfss").
library(rcfss)
data("scorecard")
str(scorecard)## Classes 'tbl_df', 'tbl' and 'data.frame': 1849 obs. of 12 variables:
## $ unitid : int 450234 448479 456427 459596 459851 482477 482547 482592 482617 482662 ...
## $ name : chr "ITT Technical Institute-Wichita" "ITT Technical Institute-Swartz Creek" "ITT Technical Institute-Concord" "ITT Technical Institute-Tallahassee" ...
## $ state : chr "KS" "MI" "CA" "FL" ...
## $ type : chr "Private, for-profit" "Private, for-profit" "Private, for-profit" "Private, for-profit" ...
## $ cost : int 28306 26994 26353 28894 23928 25625 24265 NA 20983 21999 ...
## $ admrate : num 81.3 98.3 89.3 58.4 68.8 ...
## $ satavg : num NA NA NA NA NA NA NA NA NA NA ...
## $ avgfacsal: num 45054 52857 NA 47196 55089 ...
## $ pctpell : num 0.803 0.773 0.704 0.778 0.61 ...
## $ comprate : num 0.6 0.336 NA NA NA ...
## $ firstgen : num 0.506 0.506 0.506 0.506 0.517 ...
## $ debt : num 13000 13000 13000 13000 9500 ...Type ?scorecard in the console to open up the help file for this data set. This includes the documentation for all the variables. Use your knowledge of dplyr and ggplot2 functions to answer the following questions.
Which type of college has the highest average SAT score?
NOTE: This time, use a graph to visualize your answer, not a table.
Click for the solution
We could use a boxplot to visualize the distribution of SAT scores.
ggplot(scorecard, aes(type, satavg)) +
geom_boxplot()## Warning: Removed 471 rows containing non-finite values (stat_boxplot).
According to this graph, private, nonprofit schools have the highest average SAT score, followed by public and then private, for-profit schools. But this doesn’t reveal the entire picture. What happens if we plot a histogram or frequency polygon?
ggplot(scorecard, aes(satavg)) +
geom_histogram() +
facet_wrap(~ type)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 471 rows containing non-finite values (stat_bin).
ggplot(scorecard, aes(satavg, color = type)) +
geom_freqpoly()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 471 rows containing non-finite values (stat_bin).
Now we can see the averages for each college type are based on widely varying sample sizes.
ggplot(scorecard, aes(type)) +
geom_bar()
What is the relationship between college attendance cost and faculty salaries? How does this relationship differ across types of colleges?
Click for the solution
# geom_point
ggplot(scorecard, aes(cost, avgfacsal)) +
geom_point() +
geom_smooth()## `geom_smooth()` using method = 'gam'## Warning: Removed 42 rows containing non-finite values (stat_smooth).## Warning: Removed 42 rows containing missing values (geom_point).
# geom_point with alpha transparency to reveal dense clusters
ggplot(scorecard, aes(cost, avgfacsal)) +
geom_point(alpha = .2) +
geom_smooth()## `geom_smooth()` using method = 'gam'## Warning: Removed 42 rows containing non-finite values (stat_smooth).
## Warning: Removed 42 rows containing missing values (geom_point).
# geom_hex
ggplot(scorecard, aes(cost, avgfacsal)) +
geom_hex() +
geom_smooth()## Warning: Removed 42 rows containing non-finite values (stat_binhex).## `geom_smooth()` using method = 'gam'## Warning: Removed 42 rows containing non-finite values (stat_smooth).
# geom_point with smoothing lines for each type
ggplot(scorecard, aes(cost, avgfacsal, color = type)) +
geom_point(alpha = .2) +
geom_smooth()## `geom_smooth()` using method = 'gam'## Warning: Removed 42 rows containing non-finite values (stat_smooth).
## Warning: Removed 42 rows containing missing values (geom_point).
# geom_point with facets for each type
ggplot(scorecard, aes(cost, avgfacsal, color = type)) +
geom_point(alpha = .2) +
geom_smooth() +
facet_grid(. ~ type)## `geom_smooth()` using method = 'gam'## Warning: Removed 42 rows containing non-finite values (stat_smooth).
## Warning: Removed 42 rows containing missing values (geom_point).
How are a college’s Pell Grant recipients related to the average student’s education debt?
Click for the solution
Two continuous variables suggest a scatterplot would be appropriate.
ggplot(scorecard, aes(pctpell, debt)) +
geom_point()## Warning: Removed 75 rows containing missing values (geom_point).
Hmm. There seem to be a lot of data points. It isn’t really clear if there is a trend. What if we jitter the data points?
ggplot(scorecard, aes(pctpell, debt)) +
geom_jitter()## Warning: Removed 75 rows containing missing values (geom_point).
Meh, didn’t really do much. What if we make our data points semi-transparent using the alpha aesthetic?
ggplot(scorecard, aes(pctpell, debt)) +
geom_point(alpha = .2)## Warning: Removed 75 rows containing missing values (geom_point).
Now we’re getting somewhere. I’m beginning to see some dense clusters in the middle. Maybe a hexagon binning plot would help
ggplot(scorecard, aes(pctpell, debt)) +
geom_hex()## Warning: Removed 75 rows containing non-finite values (stat_binhex).
This is getting better. It looks like there might be a downward trend; that is, as the percentage of Pell grant recipients increases, average student debt decreases. Let’s confirm this by going back to the scatterplot and overlaying a smoothing line.
ggplot(scorecard, aes(pctpell, debt)) +
geom_point(alpha = .2) +
geom_smooth()## `geom_smooth()` using method = 'gam'## Warning: Removed 75 rows containing non-finite values (stat_smooth).## Warning: Removed 75 rows containing missing values (geom_point).
This confirms our initial evidence - there is an apparent negative relationship. Notice how I iterated through several different plots before I created one that provided the most informative visualization. You will not create the perfect graph on your first attempt. Trial and error is necessary in this exploratory stage. Be prepared to revise your code again and again.
Session Info
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## yaml 2.1.18 2018-03-08 CRAN (R 3.4.4)This work is licensed under the CC BY-NC 4.0 Creative Commons License.