Introduction
R is an interpreted programming language for statistical computing and graphics supported by the R Foundation. It is widely used among statisticians and data miners for developing statistical software and data analysis. This page presents how to run R in Jupyter Notebook for better documentation, reproducibility and sharing the results. Python, Jupyter Notebook and R should be already installed.
Run R on Jupyter Notebook¶
The distributions of R packages (Linux, Mac OS X, and Windows) are available at the Comprehensive R Archive Network. Download R for Windows, macOS or Linux and then install it on your machine.
Install R kernel for Jupyter Notebook¶
Jupyter Notebook has kernels which are processes that run interactive code in a particular programming language and return output to the user. IRkernel is an R kernel for Jupyter Notebook.
IRkernel packages can be installed by running to the following command in an R console:
install.packages('IRkernel')
Kernel Available on Jupyter Notebook¶
Then, you will have to make Jupyter see the newly installed R kernel by installing a kernel spec. To install system-wide, set user to False in the installspec command:
IRkernel::installspec(user = FALSE)
Launch Jupyter Notebook with R¶
Now, the Jupyter Notebook supports both Python 3 and R programming languages. Type jupyter notebook in the anaconda console and press enter to pop up empty workspace. Now create a new R notebook by clicking on the New button and selecting R.
Install R Packages and Load in Jupyter Notebook¶
R packages should be installed in R console. For example, for installing “ggplot2“ type:
install.packages("ggplot2")
Then you can load the package in Jupyter Notebook and plot and embedded image as below:
Add Table of Content on Jupyter Notebook¶
First, install jupyter_nbextensions_configurator by typing this command in the anaconda console and press enter:
pip install jupyter_nbextensions_configurator
Then, type jupyter notebook in the anaconda console
Finally, click on Table of contents icon to generate table of contents
Table of Contents can be generated on first cell. First click on Contents on top left figure below (Step 1) and then tick Add notebook TOC cell (Step 2) :
Orientation to R¶
print("Hello World", quote=FALSE)
a<- 1 # a=1
b<- 2 # b=2
a+b
round(3.1415)
factorial(3) # 3!=3*2*1
sqrt(14) # square root
factorial (round(2.0015)+1)
log10(1000)+log10(100)
pi
Data Structures¶
# Load WorldPhones data set
WorldPhones # Number of phone numbers
Vectors (single dimension)¶
vec <- c(1,2,3,10,100) # c create vector
vec
Matrix (two dimension)¶
mat <- matrix (c(1,2,3,4,5,6),nrow=2)
mat
Math: element-wise Operation¶
vec+4
vec*4
vec*vec
Matrix multiplication¶
vec %*% vec # inner multipication
vec%o% vec # outer multipication
# Transpose matrix by t
mat
t(mat)
Array (n dimensional )¶
array(c(1,2,3,4,5,6), dim=c(2,2,3))
Data Types¶
# Numeric
class(0.00001) # class function gives type of data
# Character
class('hello')
nchar('hello')
paste ('Hi','There')
# Logical
class(TRUE)
class(T)
# factor or categorical data
fac <- factor(c('a','b','c'))
fac
class (fac)
Lists and Data Frames¶
Lists¶
A list is a one dimensional group of R objects. Create lists with list:
lst <- list(1,'R', TRUE)
lst
class(lst)
list(c(1,2),TRUE,c('a','b','c'))
Data Frame¶
# Each column can be a different data type
df <- data.frame(c(1,2,3),c('R','S','T'),c(TRUE,FALSE,TRUE))
df
class(df)
nvec <- c(one=1, two=2, three=3)
nvec
nvec+1
ndf <- data.frame(numbers=c(1,2,3),letters=c('R','S','T'),logic=c(TRUE,FALSE,TRUE))
ndf
names(ndf)
names(nvec) <- c('uno','dos','tres')
nvec
Visualizing Data¶
Scatterplot¶
#first install ggplot2
#install.packages("ggplot2")
library('ggplot2')
diamonds[1:5,] # diamonds data set comes with ggplot2 package
qplot(x,y,data=diamonds,color=price)
?mpg
View(mpg)
qplot(displ,hwy,data=mpg)
Aesthetics¶
qplot(displ,hwy,data=mpg,color=class)
qplot(displ,hwy,data=mpg,shape=class)
Facetting¶
Smaller plots that display different subsets of the data. Also useful for exploring conditional relationships. Useful for large data.
qplot(displ,hwy,data=mpg)+facet_grid(. ~ cyl)
qplot(displ,hwy,data=mpg)+facet_grid(drv ~ .)
qplot(displ,hwy,data=mpg)+facet_grid(drv ~ cyl)
qplot(displ,hwy,data=mpg)+facet_grid( ~ class)
Geoms¶
qplot(displ,hwy,data=mpg,geom=c("point","smooth"))
qplot(class,hwy,data=mpg,geom='boxplot')
qplot(reorder(class,hwy),hwy,data=mpg,geom='boxplot') # Reorder is based on mean
Position Adjustments¶
Histogram¶
qplot(x,data=diamonds)
Bar Chart¶
qplot(cut,data=diamonds,geom='bar',fill=cut)
qplot(color,data=diamonds,geom='bar',fill=cut)
Visualizing Distributions¶
qplot(carat,data=diamonds,binwidth=0.1)
Adding Zoom¶
zoom <- coord_cartesian(xlim=c(50,70))
qplot(depth,data=diamonds,binwidth=0.2)+zoom
qplot(depth,data=diamonds,binwidth=0.2,fill=cut)+zoom
qplot(depth,data=diamonds,geom="freqpoly", color=cut)+zoom
qplot(depth,data=diamonds,geom="density", color=cut)+zoom
Visalizing Big Data¶
# install.packages('hexbin')
qplot(carat,price,data=diamonds,geom='hex')
qplot(carat,price,data=diamonds,geom='density2d')
qplot(carat,price,data=diamonds,geom=c('point','density2d'))
qplot(carat,price,data=diamonds,geom='smooth')
qplot(carat,price,data=diamonds,geom='smooth',color=cut)
qplot(carat,price,data=diamonds,geom='smooth',group=cut)
qplot(carat,price,data=diamonds,geom='smooth',color=cut,se=FALSE)
qplot(carat,price,data=diamonds,geom='smooth',color=cut,method=lm)
qplot(carat,price,data=diamonds,color='blue')
qplot(carat,price,data=diamonds,color=I('blue'))
qplot(carat,price,data=diamonds,size=I(0.5), alpha=I(0.1))
Saving Graphs¶
# get working directory
#getwd()
# Specify size in inches
ggsave("my-plot.pdf", width=6, height=6)