Download all exercise files at http://www.github.com/summerofgeorge/rtraining/.

In this unit, we will be using the package ggplot2, part of the tidyverse, to create plots for exploring our data.

We will again use the hsbraw.csv from the UCLE IDRE group. Let's load the tidyverse and get to it.

This lesson is based on IDRE's training module at https://stats.idre.ucla.edu/stat/data/intro_r/intro_r_flat.html.

library(tidyverse)
d<-read_csv("C:/RFiles/hsbraw.csv" )

The basic specification for a ggplot2 plot is to specify which variables are mapped to which aspects of the graph (called aesthetics) and then to choose a shape (called a geom) to display on the graph.

For example, we can choose to map one variable to the x-axis, another variable to the y-axis, and to use geom_point() as the shape to plot, which produces a scatter plot.

Within the ggplot() function we specify (Note that the package is named ggplot2 while this function is called ggplot()):

• the dataset
• inside an aes() function, we then specify which variables are mapped to which aesthetics, which can include:
• x-axis and y-axis
• color, size, and shape of objects

Histograms bin continuous variables into intervals and count the frequency of observations in each interval.

For histograms and density plots, we map the variable of interest to x.

#use the bins= argument to control the # of intervals
ggplot(d,aes(x=write))+geom_histogram(bins=10)

To change the width of the bin in the histogram we can use bidwith in geom_histogram:

ggplot(d, aes(x=write))+geom_histogram(binwidth=10)

Density plots smooth out the shape of histograms.

ggplot(d, aes(x=write))+geom_density()

Boxplots show the median, lower and upper quartiles and outliers.

Unlike histograms and desntiy plots, we want to map the variable of interest to y instead of x. If we are making a single boxplot, we need an arbitrary value for x, just as a place holder.

#for the overall distribution of one variable, specify x=1 (or any other value)
ggplot(d, aes(x = 1, y = math)) + geom_boxplot()

Data exploration can help us identify suspicious looking values.

#for the overall distribution of one variable, specify x=1 (or any other value)
ggplot(d, aes(x = 1, y = science)) + geom_boxplot()

This value of -99 on science is probably a code for a missing value.

For categorical variables, summary statistics such as mean, median and variance cannot be calculated meaningfully.

Instead, we will use frequency tables to summarize the distribution of each category using the table function.

Use prop.table on the table provided by table to see frequencies stated in proportions.

#table() produces counts

table(d$female)
## 
## female   male 
##    109     91

#for proportions, use prop.table(table())

prop.table(table(d$female))
## 
## female   male 
##  0.545  0.455

Distributions of categorical variables are often depicted by bar graphs, which are easily made in ggplot2. By default, geom_bar() counts the number of observations for each value of the variable mapped to x.

ggplot(d, aes(x=prog))+geom_bar()

After inspecing distributions of variables individually, we proceed to explore relationships between variables.

In particular, we want to examine whether the values of one variable might be associated with another.

We will use different numerical and graphical methods for exploration depending on whether the variables are both continuous, both categorical, or one of each.

Correlations provide quick assesssments of whether two continuous variables are linearly related to one another.

The cor() function estimates correlations. If supplied with two vectors, cor will estimate a single correlation. If supplied a data frame with several variables, cor will estimate a correlation matrix.

cor(d$write, d$read)
## [1] 0.5967765

scores<-d[,c("read","write","math","science","socst")]
cor(scores)
##              read     write      math   science     socst
## read    1.0000000 0.5967765 0.6622801 0.1709428 0.1814928
## write   0.5967765 1.0000000 0.6174493 0.1289845 0.1504587
## math    0.6622801 0.6174493 1.0000000 0.2051668 0.1898648
## science 0.1709428 0.1289845 0.2051668 1.0000000 0.9361672
## socst   0.1814928 0.1504587 0.1898648 0.9361672 1.0000000

Scatter plots are an obvious choice to depict the relationship between two varialbes. We can also add a loess smoothing plot (geom_smooth()) that provides a best-fit curve to the data.

Note that the further layers are added with a +.

Here we examine the relationship between reading and writing test scores.

ggplot(d,aes(x=read, y=write))+
  geom_point()+
  geom_smooth()
## `geom_smooth()` using method = 'loess'

When exploring the relationship between a continuous and a categorical variable, we are often interested in whether the distribution of the continuous variable is the same between classes.

For example, we might wnt to know whether the means and variances of math test scores are the same between males and females.

For this, we will group them like in dplyr

by_female<-group_by(d,female)

Then we will use summarize to get the means and variances of math by gender:

summarize(by_female,mean(math),var(math))
## # A tibble: 2 x 3
##   female `mean(math)` `var(math)`
##   <chr>         <dbl>       <dbl>
## 1 female         52.4        83.7
## 2 male           52.9        93.4

To plot distribtuions of the continuous varialbes by gorups defined by the categorical variables, we will plot separate density plots of the continuous varialbes for each group of the categorical variable.

The grouping variable is commonly mapped to aesthetics that take on categories themselves, such as color or shape but can be mapped to x as well if it is numeric.

ggplot(d,aes(x=math,color=female))+
  geom_density()

Boxplots of math by female show the same similar-looking distributions.

ggplot(d,aes(x=female,y=math))

  geom_boxplot()
## geom_boxplot: outlier.colour = NULL, outlier.fill = NULL, outlier.shape = 19, outlier.size = 1.5, outlier.stroke = 0.5, outlier.alpha = NULL, notch = FALSE, notchwidth = 0.5, varwidth = FALSE, na.rm = FALSE
## stat_boxplot: na.rm = FALSE
## position_dodge

Two-way and multi-way frequency tables are used to explore the relationships between categorical variables.

We can use table and prop.table again. With prob.table use margin=1 for row proportions and margin=2 for column proportions. Omitting margin= will give proportions of the total.

Here, we check whether the proportions of observations that fall into each education program are about the same across socioeconomic statuses.

my2way<-table(d$prog,d$ses)

#counts in each crossing of prog and ses
my2way
##           
##            high low middle
##   academic   42  19     44
##   general     9  16     20
##   vocation    7  12     31

We can add a categorical variable to the bar graph of the other categorical variable to depict their relationship.

Here we map prog to fill, the color used to fill the bars of the bar graph.

ggplot(d,aes(x=ses,fill=prog)) +geom_bar()

The position argument in geom_bar() changes how the colors are sorted on the graph. We can specify that the color positions should stack (the default), dodge (side-by-side) or fill (uniform )

ggplot(d,aes(x=ses,fill=prog)) +geom_bar(position="dodge")