Histograms

library('devtools')
library('ggplot2')
load_all('~/lehmansociology')

## Loading lehmansociology

In this template we are going to produce a lot of variations of histograms and some other visualizations of a single interval variable.. For your analyses you should pick the one that produces the best information and communicates it most effectively.

For this analysis we will use the poverty.counties data. You can see the raw data by clicking on the dataset name in the Environment window.

For the example we will use PCTPOVALL_2013 which we will refer to as poverty.counties$PCTPOVALL_2013. It is very important that you spell the name correctly including capitalization.

There are many options in making graphs with R. This template will just show a few very simple graphs for one variable, but you should explore these either in the help files or by searching the internet.

Introduction

<— This is where you should introduce your analysis —>

Graphs

Simple Histogram

hist(poverty.counties$PCTPOVALL_2013)

hist(poverty.counties$PCTPOVALL_2013, breaks=c(0,5,10,15,20,25,30,35,40,50,60),
     main="Distribution of Poverty Rate by Country, 2013", xlab="Percent in Poverty" )

ggplot(poverty.counties, aes(x=poverty.counties$PCTPOVALL_2013)) + geom_histogram(binwidth=.5)

 ggplot(poverty.counties, aes(x=poverty.counties$PCTPOVALL_2013)) + geom_histogram(binwidth=.5, aes(y = ..density..))  +
    geom_density(color="blue")

## Warning: Removed 1 rows containing non-finite values (stat_density).

## Stem and leaf. These work better with less data so use states.
stem(poverty.states$PCTPOVALL_2013, 1)

## 
##   The decimal point is at the |
## 
##    8 | 0
##   10 | 1279224679
##   12 | 366999579
##   14 | 0016768889
##   16 | 0157801588
##   18 | 45688904
##   20 | 04
##   22 | 9

## Cumulative 
 ggplot(poverty.counties, aes(x=poverty.counties$PCTPOVALL_2013)) + 
          geom_histogram(binwidth=2, aes(y=cumsum(..count..))) 

## These will often be better when you are comparing two or more groups. You can
## do that by changing the options.
## Boxplot
boxplot(poverty.counties$PCTPOVALL_2013, horizontal=TRUE, col="blue")

ggplot(poverty.counties, aes(factor(1),
    poverty.counties$PCTPOVALL_2013))+
  geom_boxplot(aes(color=factor(1))) +
  labs(x = " ", y = "Percent", title = "Percent Poverty for Counties in 2013") +
   theme(legend.position = "none")

## Warning: Removed 1 rows containing non-finite values (stat_boxplot).

# Jitter
ggplot(poverty.counties, aes(factor(1),
    poverty.counties$PCTPOVALL_2013)) + 
  labs(x = " ", y = "Percent", title = "Percent Poverty for Counties in 2013") +
  geom_jitter(alpha=I(1/4), aes(color=factor(1))) +
  theme(legend.position = "none")

## Warning: Removed 1 rows containing missing values (geom_point).

Description in words