- First, you need to tell ggplot what dataset to use. This is done using the ggplot(df).
- Generic function for plotting of R objects. For more details about the graphical parameter arguments, see par. For simple scatter plots, plot.default will be used. However, there are plot methods for many R objects, including function s, data.frame s, density objects, etc. Use methods (plot) and the documentation for these.
- Plots With Rl In Kalamna Nagpur
- Plot With Rankin Bass Movie
- Box And Whisker Plots With Repeated Numbers
- Color Lines In Plot R
- Radar Plots With Different Ranges
- Plots With R
- 3d Plots With R
This is the first post of a series that will look at how to create graphics in R using the plot function from the base package. There are of course other packages to make cool graphs in R (like ggplot2 or lattice), but so far plot always gave me satisfaction. In this post we will. R has a number of functions which are designed to draw in the plot region. 3.1.1 Adding Points To A Plot The function pointscan be used to add a set of points to a plot. The simplest form of a call to pointshas the form points(x, y) where xand ycontain the x and y coordinates of the point to be plotted. Although subplot accepts an arbitrary number of plot objects, passing a list of plots can save typing and redundant code when dealing with a large number of plots. For example, you can share the x-axis by utlising shareX, set axis ID, and and specify the number of of rows with nrows.
This is the first post of a series that will look at how to create graphics in R using the plot function from the base package. There are of course other packages to make cool graphs in R (like ggplot2 or lattice), but so far plot always gave me satisfaction.
In this post we will see how to add information in basic scatterplots, how to draw a legend and finally how to add regression lines.
Data simulation
Adding colors
First plot adding colors for the different treatments, one way to do this is to pass a vector of colors to the col argument in the plot function.
Here is the plot:
Change plotting symbols
We can also create a vector of plotting symbols to represent data from the two different sites, the different plotting symbols available can be seen here.
Here is the plot:
Add a legend to the graph
Now we should add a legend to the graph:
Here is the plot:
The first argument to legend is basically its position in the graph, then comes the text of the legend. Optionally one may also specify the colors, plotting symbols etc … of the legend symbol. Have a look at ?legend for more options.
We can also add a legend outside of the graph by setting xpd=TRUE and by specifying the x and y coordinates of the legend.
Here is the plot:
Add regression lines
The last thing we might want to add are regression lines
Here is the plot:
There is a whole bunch of function to draw elements within the plotting area, a few examples are: points, lines, rect, text. They are handy in many situations and are very similar of use.
That’s it for this basic post, next times we’ll see how to control axis labels and tick marks.
Plots With Rl In Kalamna Nagpur
Simple Scatterplot
There are many ways to create a scatterplot in R. The basic function is plot(x, y), where x and y are numeric vectors denoting the (x,y) points to plot.
# Simple Scatterplot
attach(mtcars)
plot(wt, mpg, main='Scatterplot Example',
xlab='Car Weight ', ylab='Miles Per Gallon ', pch=19)
click to view
(To practice making a simple scatterplot, try this interactive example from DataCamp.)
# Add fit lines
abline(lm(mpg~wt), col='red') # regression line (y~x)
lines(lowess(wt,mpg), col='blue') # lowess line (x,y)
click to view
The scatterplot( ) function in the car package offers many enhanced features, including fit lines, marginal box plots, conditioning on a factor, and interactive point identification. Each of these features is optional.
# Enhanced Scatterplot of MPG vs. Weight
# by Number of Car Cylinders
library(car)
scatterplot(mpg ~ wt cyl, data=mtcars,
xlab='Weight of Car', ylab='Miles Per Gallon',
main='Enhanced Scatter Plot',
labels=row.names(mtcars))
click to view
Scatterplot Matrices
There are at least 4 useful functions for creating scatterplot matrices. Analysts must love scatterplot matrices!
# Basic Scatterplot Matrix
pairs(~mpg+disp+drat+wt,data=mtcars,
main='Simple Scatterplot Matrix')
click to view
The lattice package provides options to condition the scatterplot matrix on a factor.
# Scatterplot Matrices from the lattice Package
library(lattice)
splom(mtcars[c(1,3,5,6)], groups=cyl, data=mtcars,
panel=panel.superpose,
key=list(title='Three Cylinder Options',
columns=3,
points=list(pch=super.sym$pch[1:3],
col=super.sym$col[1:3]),
text=list(c('4 Cylinder','6 Cylinder','8 Cylinder'))))
Plot With Rankin Bass Movie
click to view
The car package can condition the scatterplot matrix on a factor, and optionally include lowess and linear best fit lines, and boxplot, densities, or histograms in the principal diagonal, as well as rug plots in the margins of the cells.
# Scatterplot Matrices from the car Package
library(car)
scatterplot.matrix(~mpg+disp+drat+wt cyl, data=mtcars,
main='Three Cylinder Options')
click to view
The gclus package provides options to rearrange the variables so that those with higher correlations are closer to the principal diagonal. It can also color code the cells to reflect the size of the correlations.
# Scatterplot Matrices from the glus Package
library(gclus)
dta <- mtcars[c(1,3,5,6)] # get data
dta.r <- abs(cor(dta)) # get correlations
dta.col <- dmat.color(dta.r) # get colors
# reorder variables so those with highest correlation
# are closest to the diagonal
dta.o <- order.single(dta.r)
cpairs(dta, dta.o, panel.colors=dta.col, gap=.5,
main='Variables Ordered and Colored by Correlation' )
click to view
High Density Scatterplots
When there are many data points and significant overlap, scatterplots become less useful. There are several approaches that be used when this occurs. The hexbin(x, y) function in the hexbin package provides bivariate binning into hexagonal cells (it looks better than it sounds).
# High Density Scatterplot with Binning
library(hexbin)
x <- rnorm(1000)
y <- rnorm(1000)
bin<-hexbin(x, y, xbins=50)
plot(bin, main='Hexagonal Binning')
click to view
Another option for a scatterplot with significant point overlap is the sunflowerplot. See help(sunflowerplot) for details.
Finally, you can save the scatterplot in PDF format and use color transparency to allow points that overlap to show through (this idea comes from B.S. Everrit in HSAUR).
# High Density Scatterplot with Color Transparency
pdf('c:/scatterplot.pdf')
x <- rnorm(1000)
y <- rnorm(1000)
plot(x,y, main='PDF Scatterplot Example', col=rgb(0,100,0,50,maxColorValue=255), pch=16)
dev.off()
Box And Whisker Plots With Repeated Numbers
click to view
Note: You can use the col2rgb( ) function to get the rbg values for R colors. For example, col2rgb('darkgreen') yeilds r=0, g=100, b=0. Then add the alpha transparency level as the 4th number in the color vector. A value of zero means fully transparent. See help(rgb) for more information.
3D Scatterplots
You can create a 3D scatterplot with the scatterplot3d package. Use the function scatterplot3d(x,y,z).
# 3D Scatterplot
library(scatterplot3d)
attach(mtcars)
scatterplot3d(wt,disp,mpg, main='3D Scatterplot')
click to view
Color Lines In Plot R
# 3D Scatterplot with Coloring and Vertical Drop Lines
library(scatterplot3d)
attach(mtcars)
scatterplot3d(wt,disp,mpg, pch=16, highlight.3d=TRUE,
type='h', main='3D Scatterplot')
Radar Plots With Different Ranges
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# 3D Scatterplot with Coloring and Vertical Lines
# and Regression Plane
library(scatterplot3d)
attach(mtcars)
s3d <-scatterplot3d(wt,disp,mpg, pch=16, highlight.3d=TRUE,
type='h', main='3D Scatterplot')
fit <- lm(mpg ~ wt+disp)
s3d$plane3d(fit)
Plots With R
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Spinning 3D Scatterplots
You can also create an interactive 3D scatterplot using the plot3D(x, y, z) function in the rgl package. It creates a spinning 3D scatterplot that can be rotated with the mouse. The first three arguments are the x, y, and z numeric vectors representing points. col= and size= control the color and size of the points respectively.
# Spinning 3d Scatterplot
library(rgl)
plot3d(wt, disp, mpg, col='red', size=3)
3d Plots With R
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You can perform a similar function with the scatter3d(x, y, z) in the Rcmdrpackage.
# Another Spinning 3d Scatterplot
library(Rcmdr)
attach(mtcars)
scatter3d(wt, disp, mpg)
click to view
To Practice
Try the creating scatterplot exercises in this course on data visualization in R.