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 and Jupyter Notebook should be already installed.

Table of Contents

• 1  Run R on Jupyter Notebook
  • 1.1  Install R kernel for Jupyter Notebook
  • 1.2  Kernel Available on Jupyter Notebook
  • 1.3  Launch Jupyter Notebook with R
  • 1.4  Install R Packages and Load in Jupyter Notebook
  • 1.5  Add Table of Content on Jupyter Notebook
• 2  Orientation to R
• 3  Data Structures
  • 3.1  Vectors (single dimension)
  • 3.2  Matrix (two dimension)
  • 3.3  Math: element-wise Operation
  • 3.4  Matrix multiplication
  • 3.5  Array (n dimensional )
• 4  Data Types
  • 4.1  Lists and Data Frames
  • 4.2  Lists
  • 4.3  Data Frame
• 5  Visualizing Data
  • 5.1  Scatterplot
  • 5.2  Aesthetics
  • 5.3  Facetting
  • 5.4  Geoms
  • 5.5  Position Adjustments
    • 5.5.1  Histogram
    • 5.5.2  Bar Chart
  • 5.6  Visualizing Distributions
  • 5.7  Adding Zoom
  • 5.8  Visalizing Big Data
• 6  Saving Graphs

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) :

In [ ]:

Orientation to R¶

In [1]:

print("Hello World", quote=FALSE)

[1] Hello World

In [2]:

a<- 1  # a=1
b<- 2  # b=2

In [3]:

a+b

3

In [4]:

round(3.1415)

3

In [5]:

factorial(3)  # 3!=3*2*1

6

In [6]:

sqrt(14)  # square root

3.74165738677394

In [7]:

factorial (round(2.0015)+1)

6

In [8]:

log10(1000)+log10(100)

5

In [9]:

pi

3.14159265358979

Data Structures¶

In [10]:

# Load WorldPhones data set
WorldPhones  # Number of phone numbers

A matrix: 7 × 7 of type dbl

	N.Amer	Europe	Asia	S.Amer	Oceania	Africa	Mid.Amer
1951	45939	21574	2876	1815	1646	89	555
1956	60423	29990	4708	2568	2366	1411	733
1957	64721	32510	5230	2695	2526	1546	773
1958	68484	35218	6662	2845	2691	1663	836
1959	71799	37598	6856	3000	2868	1769	911
1960	76036	40341	8220	3145	3054	1905	1008
1961	79831	43173	9053	3338	3224	2005	1076

Vectors (single dimension)¶

In [11]:

vec <- c(1,2,3,10,100) # c create vector
vec

1. 1
2. 2
3. 3
4. 10
5. 100

Matrix (two dimension)¶

In [12]:

mat <- matrix (c(1,2,3,4,5,6),nrow=2)
mat

A matrix: 2 × 3 of type dbl

1	3	5
2	4	6

Math: element-wise Operation¶

In [13]:

vec+4

1. 5
2. 6
3. 7
4. 14
5. 104

In [14]:

vec*4

1. 4
2. 8
3. 12
4. 40
5. 400

In [15]:

vec*vec

1. 1
2. 4
3. 9
4. 100
5. 10000

Matrix multiplication¶

In [16]:

vec %*% vec # inner multipication

A matrix: 1 × 1 of type dbl

10114

In [17]:

vec%o% vec  # outer multipication

A matrix: 5 × 5 of type dbl

1	2	3	10	100
2	4	6	20	200
3	6	9	30	300
10	20	30	100	1000
100	200	300	1000	10000

In [18]:

# Transpose matrix by t
mat

A matrix: 2 × 3 of type dbl

1	3	5
2	4	6

In [19]:

t(mat)

A matrix: 3 × 2 of type dbl

1	2
3	4
5	6

Array (n dimensional )¶

In [20]:

array(c(1,2,3,4,5,6), dim=c(2,2,3))

1. 1
2. 2
3. 3
4. 4
5. 5
6. 6
7. 1
8. 2
9. 3
10. 4
11. 5
12. 6

Data Types¶

In [21]:

# Numeric
class(0.00001) # class function gives type of data

'numeric'

In [22]:

# Character
class('hello')

'character'

In [23]:

nchar('hello')

5

In [24]:

paste ('Hi','There')

'Hi There'

In [25]:

# Logical
class(TRUE)

'logical'

In [26]:

class(T)

'logical'

In [27]:

# factor or categorical data

In [28]:

fac <- factor(c('a','b','c'))
fac

1. a
2. b
3. c

Levels:

1. 'a'
2. 'b'
3. 'c'

In [29]:

class (fac)

'factor'

Lists and Data Frames¶

Lists¶

A list is a one dimensional group of R objects. Create lists with list:

In [30]:

lst <- list(1,'R', TRUE)
lst

1. 1
2. 'R'
3. TRUE

In [31]:

class(lst)

'list'

In [32]:

list(c(1,2),TRUE,c('a','b','c'))

1. 1. 1
   2. 2
2. TRUE
3. 1. 'a'
   2. 'b'
   3. 'c'

Data Frame¶

In [33]:

# Each column can be a different data type
df <- data.frame(c(1,2,3),c('R','S','T'),c(TRUE,FALSE,TRUE))
df

A data.frame: 3 × 3

c.1..2..3.	c..R....S....T..	c.TRUE..FALSE..TRUE.
<dbl>	<chr>	<lgl>
1	R	TRUE
2	S	FALSE
3	T	TRUE

In [34]:

class(df)

'data.frame'

In [35]:

nvec <- c(one=1, two=2, three=3)
nvec

one

1

two

2

three

3

In [36]:

nvec+1

one

2

two

3

three

4

In [37]:

ndf <- data.frame(numbers=c(1,2,3),letters=c('R','S','T'),logic=c(TRUE,FALSE,TRUE))
ndf

A data.frame: 3 × 3

numbers	letters	logic
<dbl>	<chr>	<lgl>
1	R	TRUE
2	S	FALSE
3	T	TRUE

In [38]:

names(ndf)

1. 'numbers'
2. 'letters'
3. 'logic'

In [39]:

names(nvec) <- c('uno','dos','tres')
nvec

uno

1

dos

2

tres

3

Visualizing Data¶

Scatterplot¶

In [40]:

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

In [41]:

library('ggplot2')
diamonds[1:5,] # diamonds data set comes with ggplot2 package

A tibble: 5 × 10

carat	cut	color	clarity	depth	table	price	x	y	z
<dbl>	<ord>	<ord>	<ord>	<dbl>	<dbl>	<int>	<dbl>	<dbl>	<dbl>
0.23	Ideal	E	SI2	61.5	55	326	3.95	3.98	2.43
0.21	Premium	E	SI1	59.8	61	326	3.89	3.84	2.31
0.23	Good	E	VS1	56.9	65	327	4.05	4.07	2.31
0.29	Premium	I	VS2	62.4	58	334	4.20	4.23	2.63
0.31	Good	J	SI2	63.3	58	335	4.34	4.35	2.75

In [42]:

qplot(x,y,data=diamonds,color=price)

Warning message:
"`qplot()` was deprecated in ggplot2 3.4.0."

In [43]:

?mpg

In [44]:

View(mpg)

A tibble: 234 × 11

manufacturer	model	displ	year	cyl	trans	drv	cty	hwy	fl	class
<chr>	<chr>	<dbl>	<int>	<int>	<chr>	<chr>	<int>	<int>	<chr>	<chr>
audi	a4	1.8	1999	4	auto(l5)	f	18	29	p	compact
audi	a4	1.8	1999	4	manual(m5)	f	21	29	p	compact
audi	a4	2.0	2008	4	manual(m6)	f	20	31	p	compact
audi	a4	2.0	2008	4	auto(av)	f	21	30	p	compact
audi	a4	2.8	1999	6	auto(l5)	f	16	26	p	compact
audi	a4	2.8	1999	6	manual(m5)	f	18	26	p	compact
audi	a4	3.1	2008	6	auto(av)	f	18	27	p	compact
audi	a4 quattro	1.8	1999	4	manual(m5)	4	18	26	p	compact
audi	a4 quattro	1.8	1999	4	auto(l5)	4	16	25	p	compact
audi	a4 quattro	2.0	2008	4	manual(m6)	4	20	28	p	compact
audi	a4 quattro	2.0	2008	4	auto(s6)	4	19	27	p	compact
audi	a4 quattro	2.8	1999	6	auto(l5)	4	15	25	p	compact
audi	a4 quattro	2.8	1999	6	manual(m5)	4	17	25	p	compact
audi	a4 quattro	3.1	2008	6	auto(s6)	4	17	25	p	compact
audi	a4 quattro	3.1	2008	6	manual(m6)	4	15	25	p	compact
audi	a6 quattro	2.8	1999	6	auto(l5)	4	15	24	p	midsize
audi	a6 quattro	3.1	2008	6	auto(s6)	4	17	25	p	midsize
audi	a6 quattro	4.2	2008	8	auto(s6)	4	16	23	p	midsize
chevrolet	c1500 suburban 2wd	5.3	2008	8	auto(l4)	r	14	20	r	suv
chevrolet	c1500 suburban 2wd	5.3	2008	8	auto(l4)	r	11	15	e	suv
chevrolet	c1500 suburban 2wd	5.3	2008	8	auto(l4)	r	14	20	r	suv
chevrolet	c1500 suburban 2wd	5.7	1999	8	auto(l4)	r	13	17	r	suv
chevrolet	c1500 suburban 2wd	6.0	2008	8	auto(l4)	r	12	17	r	suv
chevrolet	corvette	5.7	1999	8	manual(m6)	r	16	26	p	2seater
chevrolet	corvette	5.7	1999	8	auto(l4)	r	15	23	p	2seater
chevrolet	corvette	6.2	2008	8	manual(m6)	r	16	26	p	2seater
chevrolet	corvette	6.2	2008	8	auto(s6)	r	15	25	p	2seater
chevrolet	corvette	7.0	2008	8	manual(m6)	r	15	24	p	2seater
chevrolet	k1500 tahoe 4wd	5.3	2008	8	auto(l4)	4	14	19	r	suv
chevrolet	k1500 tahoe 4wd	5.3	2008	8	auto(l4)	4	11	14	e	suv
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
toyota	toyota tacoma 4wd	3.4	1999	6	auto(l4)	4	15	19	r	pickup
toyota	toyota tacoma 4wd	4.0	2008	6	manual(m6)	4	15	18	r	pickup
toyota	toyota tacoma 4wd	4.0	2008	6	auto(l5)	4	16	20	r	pickup
volkswagen	gti	2.0	1999	4	manual(m5)	f	21	29	r	compact
volkswagen	gti	2.0	1999	4	auto(l4)	f	19	26	r	compact
volkswagen	gti	2.0	2008	4	manual(m6)	f	21	29	p	compact
volkswagen	gti	2.0	2008	4	auto(s6)	f	22	29	p	compact
volkswagen	gti	2.8	1999	6	manual(m5)	f	17	24	r	compact
volkswagen	jetta	1.9	1999	4	manual(m5)	f	33	44	d	compact
volkswagen	jetta	2.0	1999	4	manual(m5)	f	21	29	r	compact
volkswagen	jetta	2.0	1999	4	auto(l4)	f	19	26	r	compact
volkswagen	jetta	2.0	2008	4	auto(s6)	f	22	29	p	compact
volkswagen	jetta	2.0	2008	4	manual(m6)	f	21	29	p	compact
volkswagen	jetta	2.5	2008	5	auto(s6)	f	21	29	r	compact
volkswagen	jetta	2.5	2008	5	manual(m5)	f	21	29	r	compact
volkswagen	jetta	2.8	1999	6	auto(l4)	f	16	23	r	compact
volkswagen	jetta	2.8	1999	6	manual(m5)	f	17	24	r	compact
volkswagen	new beetle	1.9	1999	4	manual(m5)	f	35	44	d	subcompact
volkswagen	new beetle	1.9	1999	4	auto(l4)	f	29	41	d	subcompact
volkswagen	new beetle	2.0	1999	4	manual(m5)	f	21	29	r	subcompact
volkswagen	new beetle	2.0	1999	4	auto(l4)	f	19	26	r	subcompact
volkswagen	new beetle	2.5	2008	5	manual(m5)	f	20	28	r	subcompact
volkswagen	new beetle	2.5	2008	5	auto(s6)	f	20	29	r	subcompact
volkswagen	passat	1.8	1999	4	manual(m5)	f	21	29	p	midsize
volkswagen	passat	1.8	1999	4	auto(l5)	f	18	29	p	midsize
volkswagen	passat	2.0	2008	4	auto(s6)	f	19	28	p	midsize
volkswagen	passat	2.0	2008	4	manual(m6)	f	21	29	p	midsize
volkswagen	passat	2.8	1999	6	auto(l5)	f	16	26	p	midsize
volkswagen	passat	2.8	1999	6	manual(m5)	f	18	26	p	midsize
volkswagen	passat	3.6	2008	6	auto(s6)	f	17	26	p	midsize

In [45]:

qplot(displ,hwy,data=mpg)

Aesthetics¶

In [46]:

qplot(displ,hwy,data=mpg,color=class)

In [47]:

qplot(displ,hwy,data=mpg,shape=class)

Warning message:
"The shape palette can deal with a maximum of 6 discrete values because
more than 6 becomes difficult to discriminate; you have 7. Consider
specifying shapes manually if you must have them."
Warning message:
"Removed 62 rows containing missing values (`geom_point()`)."

Facetting¶

Smaller plots that display different subsets of the data. Also useful for exploring conditional relationships. Useful for large data.

In [48]:

qplot(displ,hwy,data=mpg)+facet_grid(. ~ cyl)

In [49]:

qplot(displ,hwy,data=mpg)+facet_grid(drv ~ .)

In [50]:

qplot(displ,hwy,data=mpg)+facet_grid(drv ~ cyl)

In [51]:

qplot(displ,hwy,data=mpg)+facet_grid( ~ class)

Geoms¶

In [52]:

qplot(displ,hwy,data=mpg,geom=c("point","smooth"))

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

In [53]:

qplot(class,hwy,data=mpg,geom='boxplot')

In [54]:

qplot(reorder(class,hwy),hwy,data=mpg,geom='boxplot') # Reorder is based on mean

Position Adjustments¶

Histogram¶

In [55]:

qplot(x,data=diamonds)

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Bar Chart¶

In [56]:

qplot(cut,data=diamonds,geom='bar',fill=cut)

In [57]:

qplot(color,data=diamonds,geom='bar',fill=cut)

Visualizing Distributions¶

In [58]:

qplot(carat,data=diamonds,binwidth=0.1)

Adding Zoom¶

In [59]:

zoom <- coord_cartesian(xlim=c(50,70))

In [60]:

qplot(depth,data=diamonds,binwidth=0.2)+zoom

In [61]:

qplot(depth,data=diamonds,binwidth=0.2,fill=cut)+zoom

In [62]:

qplot(depth,data=diamonds,geom="freqpoly", color=cut)+zoom

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

In [63]:

qplot(depth,data=diamonds,geom="density", color=cut)+zoom

Visalizing Big Data¶

In [64]:

# install.packages('hexbin')
qplot(carat,price,data=diamonds,geom='hex')

Warning message:
"Computation failed in `stat_binhex()`
Caused by error in `compute_group()`:
! The package `hexbin` is required for `stat_binhex()`"

In [65]:

qplot(carat,price,data=diamonds,geom='density2d')

In [66]:

qplot(carat,price,data=diamonds,geom=c('point','density2d'))

In [67]:

qplot(carat,price,data=diamonds,geom='smooth')

`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

In [68]:

qplot(carat,price,data=diamonds,geom='smooth',color=cut)

`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

In [69]:

qplot(carat,price,data=diamonds,geom='smooth',group=cut)

`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

In [70]:

qplot(carat,price,data=diamonds,geom='smooth',color=cut,se=FALSE)

`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

In [71]:

qplot(carat,price,data=diamonds,geom='smooth',color=cut,method=lm)

`geom_smooth()` using formula = 'y ~ x'

In [72]:

qplot(carat,price,data=diamonds,color='blue')

In [73]:

qplot(carat,price,data=diamonds,color=I('blue'))

In [74]:

qplot(carat,price,data=diamonds,size=I(0.5), alpha=I(0.1))

Saving Graphs¶

In [75]:

# get working directory
#getwd()

In [76]:

# Specify size in inches
ggsave("my-plot.pdf", width=6, height=6)

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