You should see a plot appear if setup is successful.

• Wildly popular package for statistical graphics: over 1.75 million downloads from CRAN in 2015 (~ 6,200 times per day)
• Developed by Hadley Wickham (An ISU Alumni)
• Designed to adhere to good graphical practices
• Supports a wide variety plot types
• Constructs plots using the concept of layers
• http://ggplot2.org/book/ or Hadley's book ggplot2: Elegant Graphics for Data Analysis for reference material

The qplot() function is the basic workhorse of ggplot2

• Produces all plot types available with ggplot2
• Allows for plotting options within the function statement
• Creates an object that can be saved
• Plot layers can be added to modify plot complexity

The qplot() function has a basic syntax:

qplot(variables, plot type, dataset, options)

• variables: list of variables used for the plot
• plot type: specified with a geom = statement
• dataset: specified with a data = statement
• options: there are so, so many options!

We will explore the diamonds data set (preloaded along with ggplot2) using qplot for basic plotting.

The data set was scraped from a diamond exchange company data base by Hadley. It contains the prices and attributes of over 50,000 diamonds

What does the data look like?

Lets look at the top few rows of the diamond data frame to find out!

head(diamonds)

##   carat       cut color clarity depth table price    x    y    z
## 1  0.23     Ideal     E     SI2  61.5    55   326 3.95 3.98 2.43
## 2  0.21   Premium     E     SI1  59.8    61   326 3.89 3.84 2.31
## 3  0.23      Good     E     VS1  56.9    65   327 4.05 4.07 2.31
## 4  0.29   Premium     I     VS2  62.4    58   334 4.20 4.23 2.63
## 5  0.31      Good     J     SI2  63.3    58   335 4.34 4.35 2.75
## 6  0.24 Very Good     J    VVS2  62.8    57   336 3.94 3.96 2.48

Basic scatter plot of diamond price vs carat weight

qplot(carat, price, geom = "point", data = diamonds)

Scatter plot of diamond price vs carat weight showing versitility of options in qplot

qplot(carat, log(price), geom = "point", data = diamonds, 
    alpha = I(0.2), colour = color, 
    main = "Log price by carat weight, grouped by color") + 
    xlab("Carat Weight") + ylab("Log Price")

All of the your turns for this section will use the tips data set:

tips <- read.csv("http://heike.github.io/rwrks/summerschool/data/tips.csv")

1. Use qplot to build a scatterplot of variables tips and total bill
2. Use options within qplot to color points by smokers
3. Clean up axis labels and add main plot title

Basic histogram of price

qplot(price, geom = "histogram", data = diamonds)

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Histogram of price, binwidth is set to $50

qplot(price, geom = "histogram",  binwidth = 50, data = diamonds)

The gap in prices at around $2000 is due to the scraping procedure.

Price histograms faceted by clarity

qplot(price, geom = "histogram", data = diamonds, binwidth = 100, facets = .~clarity)

1. Create a new variable in tips data frame rate = tip / total bill
2. Use qplot to create a histogram of rate
3. Change the bin width on that histogram to 0.05
4. Facet this histogram by size of the group

Side by side boxplot of diamond prices within clarity groupings

qplot(clarity, log(price), geom = "boxplot", data = diamonds)

Why does price decrease as the quality of the diamonds increases?

Side by side boxplot of log prices within clarity groupings with jittered values overlay

qplot(clarity, log(price), geom = "boxplot", data = diamonds, 
    main = "Boxplots of log Diamond Prices Grouped by Clarity") +
    geom_jitter(alpha = I(.025))

There are two groups of prices … maybe related to size?

Side by side boxplot of log prices within clarity groupings

qplot(clarity, log(price)/carat, geom = "boxplot", data = diamonds)

1. Make side by side boxplots of tipping rate for males and females
2. Overlay jittered points for observed values onto this boxplot

To investigate bar plots we will switch over to the Titanic data set:

titanic <- as.data.frame(Titanic)

Data includes passenger characteristics and survival outcomes for those aboard the RMS Titanic's ill fated maiden voyage

Basic bar plot of survival outcomes

qplot(Survived, geom = "bar", data = titanic, weight = Freq)

Bar plot faceted by gender and class

qplot(Survived, geom = "bar", data = titanic, weight = Freq, 
      facets = Sex~Class)

1. Use the tips data to make a barplot for counts of smoking and non smoking customers
2. Facet using day of week and time of day to view how smoking status changes for different meal times