Getting Started with ggplot2 in R
Grammar
A grammar provides a foundation for understanding different types of
graphics. A grammar may also help us on what a well-formed or
correct graphic looks like, but there will still be many grammatically
correct but nonsensical graphics. This is easy to see by analogy to the
English language: good grammar is just the first step in creating a good
sentence.
Grammar of Graphics
A grammar of graphics is a tool that enables us to clearly
describe the components of a graphics. Such a grammar allows us to move
beyond named graphics (e.g., the “scatterplot”) and gain insights into
the deep structures that underlies the statistical graphics. ggplot2
.
proposes an alternative parameterization of the grammar, based around
the idea of building up a graphic from multiple layers of data.
Components of ggplot2
- Data and aesthetic mappings
- Geometric objects
- Scale
- Facet Specification
- Statistical Transformation
- Coordinate Syatem
Layered grammar of graphics
Together, the data, mappings, statistical transformations and geometric
objects form a layer. Plot may have different layers. Layers are
responsible for creating the objects that we expect on the plots.
How to use ggplot2 in R?
For this we need to have installed ggplot2 package in our IDE. Let us
use ggplot in R builtin data diamonds
.
library(ggplot2)
ggplot(diamonds, aes(carat,price)) + geom_point()
geom_point
is used for scatter plot. From above figure, we can see that whenever
diamond’s carat increases, prices also increases. We can not see
how the data distibuted for this, let's make some changes in our code.
ggplot(diamonds,aes(carat,price)) + geom_point() +
scale_x_continuous() + scale_y_continuous()
We can see better distribution of points than previous plot. We can clearly see
that carat and price variable are not linearly distributed. To make it
linearly distributable, lets make some changes in our code.
ggplot(diamonds, aes(carat,price)) + geom_point() +
stat_smooth(method = lm) + scale_x_log10() + scale_y_log10()
## geom_smooth()
using formula 'y ~ x'
From above graph, relationship between price and carat variables is
linear. If we try the code without stat_smooth(method= lm)
we can not
see linear line in graph. Where lm means linear model.
ggplot(diamonds, aes(carat,price)) + geom_point() +
scale_x_log10() + scale_y_log10()
Lets make histogram of diamonds
data
ggplot(diamonds, aes(price)) + geom_histogram()
## stat_bin()
using bins = 30
. Pick better value with binwidth
.
To build histogram, we use function geom_histogram()
. We should note that
histogram is made on one dimensional data. If we want to add title of
the plot we can do as,
ggplot(diamonds, aes(price)) + geom_histogram() + ggtitle("ggplot2 Histogram")
## stat_bin()
using bins = 30
. Pick better value with binwidth
.
Let us try some other ggplot2 features in R builtin data mtcars
ggplot(mpg, aes(x = displ, y = hwy)) +
geom_point()
The figure above shows scatterplot betweenhwy
anddispl
variables of mtcars
data from figure we can see as the values ofhwy
increases, values ofdispl
variable slightly decreases.
Let’s add geam_smooth()
: What will happen?
ggplot(mpg, aes(displ, hwy)) + geom_point() + geom_smooth()
## geom_smooth()
using method = 'loess' and formula 'y ~ x'
We see there is smooth line appearing on the middle of the data points.
Adding “wiggliness” in the smoothing plot
ggplot(mpg, aes(displ, hwy)) + geom_point() + geom_smooth(span = 0.2)
## geom_smooth()
using method = 'loess' and formula 'y ~ x'
What changes can we see in above graph and previous graph. Let us again
check by keeping span = 1
.
ggplot(mpg, aes(displ, hwy)) + geom_point()+ geom_smooth(span = 1)
## geom_smooth()
using method = 'loess' and formula 'y ~ x'
We can make sense that, by default ggplot kept value of span 1. If we set method= lm
we can find stright smooth line. Let us inside
geom_smooth()
try.
ggplot(mpg, aes(displ, hwy)) + geom_point() + geom_smooth(method = lm)
## geom_smooth()
using formula 'y ~ x'
Let Modify our code little,
ggplot(mpg, aes(displ, hwy)) + geom_point() + geom_smooth(method = lm, se= FALSE)
## geom_smooth()
using formula 'y ~ x'
By se= FALSE
we added a smooth line.
Fixed color
ggplot(mpg, aes(displ,hwy)) + geom_point(color = 'red')
Changing color by variable attributes
Lets change our color based on class.
ggplot(mpg, aes(displ, hwy, colour = class)) +
geom_point()
Here we gave colors according to variable's name.
Getting multiple scatterplot of attributes
We can get multiple scatterplot by using facet_wrap()
function.
ggplot(mpg, aes(displ, hwy)) + geom_point() +
facet_wrap(~class)
In above figure, we found distribution of various variables along with displ
and hwy
variables.
Histogram
ggplot(mpg, aes(hwy)) + geom_histogram()
## stat_bin()
using bins = 30
. Pick better value with binwidth
.
hwy
variable bins automatically.
Changing bin size of the histogram
ggplot(mpg, aes(hwy)) + geom_histogram(binwidth = 2.5)
Frequency polygon
A frequency polygon is a line graph of class
frequency plotted against class midpoint. It can be obtained by joining
the midpoints of the top of the rectangles in the histogram.
ggplot(mpg, aes(hwy)) + geom_freqpoly()
## stat_bin()
using bins = 30
. Pick better value with binwidth
.
Change Bin size of frequency Polygon
ggplot(mpg, aes(hwy)) + geom_freqpoly(binwidth= 1)
We can see the effect of binwidth from figure by comparing above figure
with previous one.
Histogram with faceting:
We have already discussed about what a facet does in scatter plot. Similarly, in
histogram it gives multiple subplots.
ggplot(mpg, aes(displ, fill = drv)) + geom_histogram(binwidth = 0.5) +
facet_wrap(~drv, ncol = 1)
Bar plot
ggplot(mpg, aes(manufacturer)) + geom_bar()
We can draw bar plot in geom_bar()
function. From bar plot we can see
dodge
and toyotao
has maximum frequency.
Let’s Use alpha inside geom_point()
ggplot(mpg, aes(cty, hwy)) + geom_point(alpha = 1 / 3)
Alpha refers to the opacity of a geom. Values of alpha range from 0 to
1, with lower values corresponding to more transparent colors.
Modifying the axes
ggplot(mpg, aes(cty, hwy)) +geom_point(alpha = 1 / 3) + xlab("city driving (mpg)") +
ylab("highway driving (mpg)")
ggplot(mpg, aes(cty, hwy)) + geom_point(alpha = 1 / 3) + xlab(NULL) +
ylab(NULL)
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