R: ggplot using facets

In this example I am going to teach you to use facets to add another level to your ggplot data visualizations. In this example we will be working with the ChickWeight data set from R Datasets

First, let’s check out the data by running head()

head(ChickWeight)

facet1.jpg

We have 4 columns in our data, weight, Time, Chick, and Diet.

Now, let’s build our ggplot model. Below, we are setting our model to a variable “p1”. We then set ChickWeight as our data set and the weight column to our x axis. Remember, nothing will happen when running the line below.

p1 <- ggplot(data=ChickWeight, aes(x=weight))

Now lets graph our plot as a histogram – we will set our binwidth to 10 – if any of this confusing you, go back and check out my earlier lessons on ggplot and ggplot histograms

p1 + geom_histogram(binwidth = 10)

facet2.jpg

Let’s now add some color. I am going to set our “fill” to be Diet

p1 + geom_histogram(binwidth = 10, aes(fill=Diet))

facet3.jpg

Now, using facets, I am going to take it one step further and separate this into 4 histograms, one for each diet.

 p1 + geom_histogram(binwidth = 10, aes(fill=Diet)) + facet_grid(Diet~.)

facet4.jpg

The Code

#-- Look at data

head(ChickWeight)

#-- build ggplot model
p1 <- ggplot(data=ChickWeight, aes(x=weight))

#--plot ggplot as histogram
p1 + geom_histogram(binwidth = 10)

#-- set fill color to Diet
p1 + geom_histogram(binwidth = 10, aes(fill=Diet))

#-- create four histograms - one for each Diet
p1 + geom_histogram(binwidth = 10, aes(fill=Diet)) + facet_grid(Diet~.)

 

 

 

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R: Intro to ggplot()

ggplot() is a powerful graphing tool in R. While it is more complex to use than qplot(), its added complexity comes with advantages.

Don’t worry about the complexity, we are going to step into it slowly.

Let’s start by getting a data set. I am going to choose airquality from the R data sets package. You can find a list of data sets from that package here: Link

ggplotIntro

We are going to manipulate some of this data, so first let us set the dataframe to a new variable.

Next, take a look at the structure – str() – we have 6 variables and 153 rows. The first thing I notice is that Month is an int – I don’t want that. I would rather have Month be a factor      (a categorical variable)

ggplotIntro1.jpg

Use factor() to set Month to a factor. Now look at str() readout again. There are 5 levels (months) represented in our data set

ggplotIntro3

ggplot()

here is the basic syntax – ggplot(data, aes(x value, y value))+geom_[type of plot]

ggplot(data = airQ,  aes(x = Wind, y = Temp)) + geom_point()

ggplotIntro4

Now let’s add some color to the chart. Inside aes() – which stands for aesthetics – we are going to add color = Month

 ggplot(data = airQ, aes(x = Wind, y = Temp, color = Month)) + geom_point()

ggplotIntro5

Now add some size. Inside aes() add size = Ozone.

ggplot(data = airQ, aes(x = Wind, y = Temp, color = Month, size = Ozone)) + geom_point()

ggplotIntro6

The Code

library(ggplot2)

head(airquality)

#-- assign data to a new variable
airQ <- airquality

#-- look at structure
str(airquality)

#-- set month as a factor
airQ$Month <- factor(airQ$Month)
str(airQ)

#-- plot wind vs temp - scatter plot
 ggplot(data = airQ, aes(x = Wind, y = Temp)) + geom_point()
 
#-- plot add factor (Month) as a color
 ggplot(data = airQ, aes(x = Wind, y = Temp, color = Month)) + geom_point()
 
#-- set Ozone as a size element
ggplot(data = airQ, aes(x = Wind, y = Temp, color = Month, size = Ozone)) + geom_point()