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Data Visualization with ggplot
1. Understanding the ggplot syntax
The syntax for constructing ggplots could be puzzling if you are a beginner or work primarily
with base graphics. The main difference is that, unlike base graphics, ggplot works with
dataframes and not individual vectors. All the data needed to make the plot is typically be
contained within the dataframe supplied to the ggplot() itself or can be supplied to respective
geoms. More on that later.
The second noticeable feature is that you can keep enhancing the plot by adding more layers (and
themes) to an existing plot created using the ggplot() function.
Let's initialize a basic ggplot based on the midwest dataset.
# Setup options (scipen= 999 ) # turn off scientific notation like 1e+ library (ggplot2) data ("midwest", package = "ggplot2") _# load the data
midwest <- read.csv("http://goo.gl/G1K41K") # alt source
Init Ggplot_
ggplot (midwest, aes (x=area, y=poptotal)) # area and poptotal are columns in 'midw est'
A blank ggplot is drawn. Even though the x and y are specified, there are no points or lines in it.
This is because, ggplot doesn't assume that you meant a scatterplot or a line chart to be drawn. I
have only told ggplot what dataset to use and what columns should be used for X and Y axis. I
haven't explicitly asked it to draw any points.
Also note that aes() function is used to specify the X and Y axes. That's because, any information
that is part of the source dataframe has to be specified inside the aes() function.
2. How to Make a Simple Scatterplot
Let's make a scatterplot on top of the blank ggplot by adding points using a geom layer called
geom_point.
library (ggplot2) ggplot (midwest, aes (x=area, y=poptotal)) + geom_point ()
We got a basic scatterplot, where each point represents a county. However, it lacks some basic
components such as the plot title, meaningful axis labels etc. Moreover most of the points are
concentrated on the bottom portion of the plot, which is not so nice. You will see how to rectify
these in upcoming steps.
Like geom_point(), there are many such geom layers which we will see in a subsequent part in
this tutorial series. For now, let's just add a smoothing layer using geom_smooth(method='lm').
Since the method is set as lm (short for linear model), it draws the line of best fit.
Warning: Removed 5 rows containing non-finite values (stat_smooth).
Warning: Removed 5 rows containing missing values (geom_point).
# g + xlim(0, 0.1) + ylim(0, 1000000) # deletes points
In this case, the chart was not built from scratch but rather was built on top of g. This is because,
the previous plot was stored as g, a ggplot object, which when called will reproduce the original
plot. Using ggplot, you can add more layers, themes and other settings on top of this plot.
Did you notice that the line of best fit became more horizontal compared to the original plot? This
is because, when using xlim() and ylim(), the points outside the specified range are deleted and
will not be considered while drawing the line of best fit (using geom_smooth(method='lm')). This
feature might come in handy when you wish to know how the line of best fit would change when
some extreme values (or outliers) are removed.
Method 2: Zooming In
The other method is to change the X and Y axis limits by zooming in to the region of interest
without deleting the points. This is done using coord_cartesian().
Let's store this plot as g1.
library (ggplot2) g <- ggplot (midwest, aes (x=area, y=poptotal)) + geom_point () + geom_smooth (method= "lm") _# set se=FALSE to turnoff confidence bands
Zoom in without deleting the points outside the limits.
As a result, the line of best fit is the same as the original plot._
g1 <- g + coord_cartesian (xlim= c ( 0 ,0.1), ylim= c ( 0 , 1000000 )) # zooms in plot (g1)
Since all points were considered, the line of best fit did not change.
4. How to Change the Title and Axis Labels
I have stored this as g1. Let's add the plot title and labels for X and Y axis. This can be done in one
go using the labs() function with title, x and y arguments. Another option is to use the ggtitle(),
xlab() and ylab().
library (ggplot2) g <- ggplot (midwest, aes (x=area, y=poptotal)) + geom_point () + geom_smooth (method= "lm") # set se=FALSE to turnoff confidence bands g1 <- g + coord_cartesian (xlim= c ( 0 ,0.1), ylim= c ( 0 , 1000000 )) _# zooms in
Add Title and Labels_
g1 + labs (title="Area Vs Population", subtitle="From midwest dataset", y="Populati on", x="Area", caption="Midwest Demographics")
Excellent! So here is the full function call.
# Full Plot call library (ggplot2) ggplot (midwest, aes (x=area, y=poptotal)) + geom_point () + geom_smooth (method="lm") + coord_cartesian (xlim= c ( 0 ,0.1), ylim= c ( 0 , 1000000 )) + labs (title="Area Vs Population", subtitle="From midwest dataset", y="Population" , x="Area", caption="Midwest Demographics")
5. How to Change the Color and Size of Points
How to Change the Color and Size To Static? We can change the aesthetics of a geom layer by
modifying the respective geoms. Let's change the color of the points and the line to a static value.
library (ggplot2) ggplot (midwest, aes (x=area, y=poptotal)) + geom_point (col="steelblue", size= 3 ) + # Set static color and size for points geom_smooth (method="lm", col="firebrick") + # change the color of line coord_cartesian (xlim= c ( 0 , 0.1), ylim= c ( 0 , 1000000 )) + labs (title="Area Vs Population", subtitle="From midwest dataset", y="Population" , x="Area", caption="Midwest Demographics")
Also, You can change the
color palette entirely.
gg + scale_colour_brewer (palette = "Set1") # change color palette
6. How to Change the X Axis Texts and Ticks Location How to Change the X and Y Axis Text and its Location?
Alright, now let's see how to change the X and Y axis text and its location. This involves two
aspects: breaks and labels.
Step 1: Set the breaks
The breaks should be of the same scale as the X axis variable. Note that I am using
scale_x_continuous because, the X axis variable is a continuous variable. Had it been a date
variable, scale_x_date could be used. Like scale_x_continuous() an equivalent
scale_y_continuous() is available for Y axis.
library (ggplot2) # Base plot gg <- ggplot (midwest, aes (x=area, y=poptotal)) + geom_point ( aes (col=state), size= 3 ) + # Set color to vary based on state categor ies. geom_smooth (method="lm", col="firebrick", size= 2 ) + coord_cartesian (xlim= c ( 0 , 0.1), ylim= c ( 0 , 1000000 )) + labs (title="Area Vs Population", subtitle="From midwest dataset", y="Population" , x="Area", caption="Midwest Demographics") # Change breaks gg + scale_x_continuous (breaks= seq ( 0 , 0.1, 0.01))
library (ggplot2) gg <- ggplot (midwest, aes (x=area, y=poptotal)) + geom_point ( aes (col=state), size= 3 ) + # Set color to vary based on state categor ies. geom_smooth (method="lm", col="firebrick", size= 2 ) + coord_cartesian (xlim= c ( 0 , 0.1), ylim= c ( 0 , 1000000 )) + labs (title="Area Vs Population", subtitle="From midwest dataset", y="Population" , x="Area", caption="Midwest Demographics") # Reverse X Axis Scale gg + scale_x_reverse ()
How to Customize the Entire Theme in One Shot using Pre-Built Themes?
Finally, instead of changing the theme components individually (which I discuss in detail in part
2), we can change the entire theme itself using pre-built themes. The help page ?theme_bw shows
all the available built-in themes.
This again is commonly done in couple of ways. Use the theme_set() to set the theme before
drawing the ggplot. Note that this setting will affect all future plots. Draw the ggplot and then add
the overall theme setting (eg. theme_bw())
library (ggplot2) # Base plot gg <- ggplot (midwest, aes (x=area, y=poptotal)) + geom_point ( aes (col=state), size= 3 ) + # Set color to vary based on state categor ies. geom_smooth (method="lm", col="firebrick", size= 2 ) + coord_cartesian (xlim= c ( 0 , 0.1), ylim= c ( 0 , 1000000 )) + labs (title="Area Vs Population", subtitle="From midwest dataset", y="Population" , x="Area", caption="Midwest Demographics") gg <- gg + scale_x_continuous (breaks= seq ( 0 , 0.1, 0.01)) # method 1: Using theme_set() theme_set ( theme_classic ()) _# not run gg
method 2: Adding theme Layer itself._
gg + theme_bw () + labs (subtitle="BW Theme")
Bubble plot
While scatterplot lets you compare the relationship between 2 continuous variables, bubble chart
serves well if you want to understand relationship within the underlying groups based on:
A Categorical variable (by changing the color) and Another continuous variable (by changing the
size of points). In simpler words, bubble charts are more suitable if you have 4-Dimensional data
where two of them are numeric (X and Y) and one other categorical (color) and another numeric
variable (size).
The bubble chart clearly distinguishes the range of displ between the manufacturers and how the
slope of lines-of-best-fit varies, providing a better visual comparison between the groups.
# load package and data library (ggplot2) data (mpg, package="ggplot2") # mpg <- read.csv("http://goo.gl/uEeRGu") mpg_select <- mpg[mpg$manufacturer %in% c ("audi", "ford", "honda", "hyundai"), ] # Scatterplot theme_set ( theme_bw ()) # pre-set the bw theme. g <- ggplot (mpg_select, aes (displ, cty)) + labs (subtitle="mpg: Displacement vs City Mileage", title="Bubble chart") g + geom_jitter ( aes (col=manufacturer, size=hwy)) + geom_smooth ( aes (col=manufacturer), method="lm", se=F)
Histogram
By default, if only one variable is supplied, the geom_bar() tries to calculate the count. In order
for it to behave like a bar chart, the stat=identity option has to be set and x and y values must be
provided.
Histogram on a continuous variable
Histogram on a continuous variable can be accomplished using either geom_bar() or
geom_histogram(). When using geom_histogram(), you can control the number of bars using the
bins option. Else, you can set the range covered by each bin using binwidth. The value of binwidth
is on the same scale as the continuous variable on which histogram is built. Since,
geom_histogram gives facility to control both number of bins as well as binwidth, it is the
preferred option to create histogram on continuous variables.
library (ggplot2) theme_set ( theme_classic ()) # Histogram on a Continuous (Numeric) Variable g <- ggplot (mpg, aes (displ)) + scale_fill_brewer (palette = "Spectral") g + geom_histogram ( aes (fill=class), binwidth =. 1 , col="black", size=. 1 ) + # change binwidth labs (title="Histogram with Auto Binning", subtitle="Engine Displacement across Vehicle Classes")
