R introduction

Rstudio

RStudio is a free and open-source integrated development environment (IDE) for R, a programming language for statistical computing and graphics.
RStudio was founded by JJ Allaire, creator of the programming language ColdFusion. Hadley Wickham is the Chief Scientist at RStudio.

ggplot2

ggplot2 is a data visualization package for the statistical programming language R. Created by Hadley Wickham in 2005. It’s a function to build plots piece by piece.
The concept behind ggplot2 divides plot into three different fundamental parts: Plot = data + Aesthetics + Geometry.

The principal components of every plot can be defined as follow:

• data is a data frame
• Aesthetics is used to indicate x and y variables. It can also be used to control the color, the size or the shape of points, the height of bars, etc.
• Geometry defines the type of graphics (histogram, box plot, line plot, density plot, dot plot, ..)

There are two major functions in ggplot2 package:

qplot() stands for quick plot, which can be used to produce easily simple plots.
ggplot() function is more flexible and robust than qplot for building a plot piece by piece.

Install and load ggplot2 package:

#install.packages("ggplot2")
library(ggplot2)

plot type selection

One variable: Continuous

• geom_area(): Create an area plot
• geom_density(): Create a smooth density estimate
• geom_dotplot(): Dot plot
• geom_freqpoly(): Frequency polygon
• geom_histogram(): Histogram
• stat_ecdf(): Empirical Cumulative Density Function
• stat_qq(): quantile - quantile plot

One variable: Discrete

Two variables: Continuous X, Continuous Y

• geom_point(): Scatter plot
• geom_smooth(): Add regression line or smoothed conditional mean
• geom_quantile(): Add quantile lines from a quantile regression
• geom_rug(): Add marginal rug to scatter plots
• geom_jitter(): Jitter points to reduce overplotting
• geom_text(): Textual annotations

Two variables: Continuous bivariate distribution

• geom_bin2d(): Add heatmap of 2d bin counts
• geom_hex(): Add hexagon bining
• geom_density_2d(): Add contours from a 2d density estimate

Two variables: Continuous function

Two variables: Discrete X, Continuous Y

• geom_boxplot(): Box and whiskers plot
• geom_violin(): Violin plot
• geom_dotplot(): Dot plot
• geom_jitter(): Strip charts
• geom_line(): Line plot
• geom_bar(): Bar plot

Two variables: Discrete X, Discrete Y

Two variables: Visualizing error

• geom_crossbar(): Hollow bar with middle indicated by horizontal line
• geom_errorbar(): Error bars
• geom_errorbarh(): Horizontal error bars
• geom_linerange() and geom_pointrange(): An interval represented by a vertical line
• Combine geom_dotplot and error bars

Two variables: Maps

Three variables

Interesting R

Pure Love

n=50000;
r=0.7;r_e=(1-r*r)^.5;
X=rnorm(n);
Y=X*r+r_e*rnorm(n);
Y=ifelse(X>0,Y,-Y);
plot(X,Y,col="red", main = "Pure Love")

Love is colorful

n=50000;
r=0.7;r_e=(1-r*r)^.5;
X=rnorm(n);
Y=X*r+r_e*rnorm(n);
Y=ifelse(X>0,Y,-Y);
a<-sample(c(2,6,7,8),50000,T)
b<-sample(c(76,79,86,69),50000,T)
plot(X,Y,col=0, main = "Love is colorful")
text(X,Y,"lOVE",col=a)

Embarrassing

library(ggplot2)
f <- function(x) 1/(x^2-1)
x <- seq(-3,3, by=0.001)
y <- f(x)
d <- data.frame(x=x,y=y)

p <- ggplot()
p <- p+geom_rect(fill = "white",color="black",size=3,
                 aes(xmin=-3, xmax=3, ymin=-3,ymax=3, alpha=0.1))

p <- p + geom_line(data=d, aes(x,y), size=3)+ylim(-3,3)
p <- p + theme_bw() +
    theme(axis.text.x=element_blank(),
          axis.text.y=element_blank(),
          legend.position="none",
          panel.grid.minor=element_blank(),
          panel.grid.major=element_blank(),
          panel.background=element_blank(),
          axis.ticks=element_blank(),
          panel.border=element_blank())

p <- p+xlab("")+ylab("")
print(p)

So, R is quite interesting and attracting, why not learn it. Let’s go~

References

Be Awesome in ggplot2
R STHDA
Interesting R
Cookbook for R
ggplot2 for bignners
ggplot2 - Essentials
Guide to Create Beautiful Graphics in R (Book)