Basic R for Graphics"

Scott Karr

HW4 School Expenditures by State

11.29.2015

Packages: ggplot2, data.tables

This data frame contains the following columns:

education Per-capita education expenditures, dollars.

income Per-capita income, dollars.

young Proportion under 18, per 1000.

urban Proportion urban, per 1000.

Setup and Load data

  1. Load Anscombe education expenditure data by state.
## Load Data Frame from website
require(data.table)
## Loading required package: data.table
require(ggplot2)
## Loading required package: ggplot2
AnscombeUrl <- "http://vincentarelbundock.github.io/Rdatasets/csv/car/Anscombe.csv"
df_Anscombe <- read.table(file = AnscombeUrl, header = TRUE, sep = ",")
dt_Anscombe <- data.table(df_Anscombe)

Generate Descriptive Statistics

  1. Generate summary level descriptive statistics:
head(df_Anscombe)
##    X education income young urban
## 1 ME       189   2824 350.7   508
## 2 NH       169   3259 345.9   564
## 3 VT       230   3072 348.5   322
## 4 MA       168   3835 335.3   846
## 5 RI       180   3549 327.1   871
## 6 CT       193   4256 341.0   774
str(df_Anscombe)
## 'data.frame':    51 obs. of  5 variables:
##  $ X        : Factor w/ 51 levels "AK","AL","AR",..: 22 31 47 20 40 7 35 32 39 36 ...
##  $ education: int  189 169 230 168 180 193 261 214 201 172 ...
##  $ income   : int  2824 3259 3072 3835 3549 4256 4151 3954 3419 3509 ...
##  $ young    : num  351 346 348 335 327 ...
##  $ urban    : int  508 564 322 846 871 774 856 889 715 753 ...

Prepare Dataset for Graphics

  1. Rename columns as needed
## Rename "state" column
##setnames(df_Anscombe, old=c(""), new=c("state"))
colnames(df_Anscombe)[c(1)] <- c("state")
  1. Histogram showing count of states by increasing per-capita-spending by state
ggplot(data = df_Anscombe) + geom_histogram(aes(x = education))
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.

  1. Plot per-capita-spending to per-capita income grouped by state
g <- ggplot(df_Anscombe, aes(x = education, y = income)) + geom_point()
g <- g + geom_point(aes(color = state)) + facet_wrap(~state)
g