What is R?

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R provides a wide variety of statistical (linear and nonlinear modelling, time-series analysis, classification, clustering etc) and graphical techniques, and is highly extensible.
The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity.

For more details on authoring R presentations

https://support.rstudio.com/hc/en-us/articles/200486468.

OPEN SOURCE

https://github.com/tidyverse/ggplot2/wiki/Why-use-ggplot2

BOOK DOWNLOAD: “R GRAPH COOKBOOK” http://www.12968.com/01database/%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90%E5%B8%88%E7%9B%B8%E5%85%B3/%E6%96%87%E5%AD%97%E8%B5%84%E6%96%99/R%E8%AF%AD%E8%A8%80/%E8%8B%B1%E6%96%87%E4%B9%A6%E7%B1%8D/R_Graph_Cookbook.pdf

Step 1 R Base install

https://cran.r-project.org/bin/windows/base/

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Step 2 Download and Ticket IT will help and install Rstudios Desktop

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https://www.rstudio.com/products/rstudio/download/

Step 3 Self-study R

Book “The Art of R Programming” http://diytranscriptomics.com/Reading/files/The%20Art%20of%20R%20Programming.pdf
Write in Console Studio hit enter hay vào script nhấn Ctrl+Enter trên window để chay từng dòng code (hướng dẫn bên dưới)

install.packages("swirl")
library("swirl")
# Làm theo hướng dẫn mở swirl()

library("swirl")

## Warning: package 'swirl' was built under R version 3.4.3

## 
## | Hi! Type swirl() when you are ready to begin.

Step 3: Các bước tiến hành trên Rstudios

Giao diện new-talk

Vào File > New > tạo R script new-talk

Write in Script or Console Studio
Install package để mở library()

install.packages("swirl")
library("swirl")
# Làm theo hướng dẫn mở swirl()

library("swirl")

Step 4: Làm quen với việc lưu trữ, viết script và bỏ file vào thư mục

new-talk

getwd()
setwd("G:/Team Drives/RStudios Learning Sharing/Num1")

VD 1

library(ggplot2)

## Warning: package 'ggplot2' was built under R version 3.4.3

library(scales)

## Warning: package 'scales' was built under R version 3.4.3

library(reshape2)

## Warning: package 'reshape2' was built under R version 3.4.3

data(tips)

dat <- data.frame(
  time = factor(c("Lunch","Dinner"), levels=c("Lunch","Dinner")),
  total_bill = c(14.89, 17.23)
)
# No1: The idea is that you can piece together various parts using the grammar for other visualization types. Whereas the single function call to barplot() is specialized to one thing.
ggplot(data=dat, aes(x=time, y=total_bill, fill=time)) +
  geom_bar(colour="black", fill="#DD8888", width=.8, stat="identity") +
  guides(fill=FALSE) +
  xlab("Time of day") + ylab("Total bill") +
  ggtitle("Average bill for 2 people")

par(las=1)
barplot(dat$total_bill,
        names.arg=dat$time,
        col="#AFC0CB",
        border=FALSE,
        main="Average Bill for Two-Person Meal")

VD 2

# No2: Again, notice the component approach for ggplot2 with calls to geom_point() and geom_line(). In contrast, I use a call to plot() to make a line chart and then points() to add the circles at the end of the line.
ggplot(data=dat, aes(x=time, y=total_bill, group=1)) +
  geom_line(colour="red", linetype="dashed", size=1.5) +
  geom_point(colour="red", size=4, shape=21, fill="white")

plot(c(1,2), dat$total_bill, type="l", xlab="time", ylab="",
    lty=2, lwd=3, col="red")
points(c(1,2), dat$total_bill, pch=21, col="red", cex=2,
    bg="white", lwd=3)

VD 3 ========================================================

# No3: legend: mutiple variance

dat1 <- data.frame(
  sex = factor(c("Female","Female","Male","Male")),
  time = factor(c("Lunch","Dinner","Lunch","Dinner"), levels=c("Lunch","Dinner")),
  total_bill = c(13.53, 16.81, 16.24, 17.42)
)

ggplot(data=dat1, aes(x=time, y=total_bill, fill=sex)) +
  geom_bar(stat="identity", position=position_dodge(),
      colour="black") +
  scale_fill_manual(values=c("#999999", "#E69F00"))

dat1mat <- matrix( dat1$total_bill,
                   nrow = 2,
                   byrow=TRUE,
                   dimnames = list(c("Female", "Male"), c("Lunch", "Dinner"))
                   )

mf_col <- c("#3CC3BD", "#FD8210")
barplot(dat1mat, beside = TRUE, border=NA, col=mf_col)
legend("topleft", row.names(dat1mat), pch=15, col=mf_col)

VD 4 ========================================================

#No4: default color
ggplot(data=dat1, aes(x=time, y=total_bill, fill=sex)) +
  geom_bar(colour="black", stat="identity",
           position=position_dodge(),
           size=.3) +                       
  scale_fill_hue(name="Sex of payer") +     
  xlab("Time of day") + ylab("Total bill") +
  ggtitle("Average bill for 2 people") +    
  theme_bw() +
  coord_cartesian(ylim = c(12,18))

par(cex=1.2, cex.axis=1.1)
barplot(dat1mat, beside = TRUE, border=NA, col=mf_col,
    main="Average Bill for Two People", yaxt="n")
axis(2, at=axTicks(2), labels=sprintf("$%s", axTicks(2)),
    las=1, cex.axis=0.8)
grid(NA, NULL, lwd=1, lty=1, col="#ffffff")
abline(0, 0)
text(1.5, dat1mat["Female", "Lunch"], "Female", pos=3)
text(2.5, dat1mat["Male", "Lunch"], "Male", pos=3)
text(1.5, dat1mat["Female", "Lunch"]+0.7, "SEX OF PAYER",
    pos=3, cex=0.75)

VD 5 ========================================================

# tuong tu
ggplot(data=dat1, aes(x=time, y=total_bill, group=sex,
    shape=sex, colour=sex)) +
  geom_line(aes(linetype=sex), size=1) +    
  geom_point(size=3, fill="white") +        
  expand_limits(y=0) +                      
  scale_colour_hue(name="Sex of payer", l=30) +
  scale_shape_manual(name="Sex of payer", values=c(22,21)) +
  scale_linetype_discrete(name="Sex of payer") +
  xlab("Time of day") + ylab("Total bill") +
  ggtitle("Average bill for 2 people") +    
  theme_bw() +
  theme(legend.position=c(.7, .4))

par(cex=1.2, cex.axis=1.1)
matplot(dat1mat, type="b", lty=1, pch=19, col=mf_col,
    cex=1.5, lwd=3, las=1, bty="n", xaxt="n",
    xlim=c(0.7, 2.2), ylim=c(12,18), ylab="",
    main="Average Bill for Two People", yaxt="n")
axis(2, at=axTicks(2), labels=sprintf("$%s", axTicks(2)),
    las=1, cex.axis=0.8, col=NA, line = -0.5)
grid(NA, NULL, lty=3, lwd=1, col="#000000")
abline(v=c(1,2), lty=3, lwd=1, col="#000000")
mtext("Lunch", side=1, at=1)
mtext("Dinner", side=1, at=2)
text(1.5, 17.3, "Male", srt=8, font=3)
text(1.5, 15.1, "Female", srt=33, font=3)

Facet_grid with GGPLOT2
========================================================

#Facet grid
data(tips)
sp <- ggplot(tips,aes(x=total_bill, y=tip/total_bill))
sp + geom_point()

sp + facet_grid(. ~ sex) + geom_point()

With Base ========================================================

par(mfrow=c(1,2))
sexes <- unique(tips$sex)
for (i in 1:length(sexes)) {
  currdata <- tips[tips$sex == sexes[i],]
  plot(currdata$total_bill, currdata$tip/currdata$total_bill,
    main=sexes[i], ylim=c(0,0.7))
}

Facet_grid with GGPLOT2
========================================================

#Facet grid
sp <- ggplot(tips, aes(x=total_bill, y=tip/total_bill))
sp    + geom_point(shape =1)

sp + facet_grid(sex ~ day)+ geom_point(shape =1)

Facet_grid with GGPLOT2
========================================================

#Facet grid
sp <- ggplot(tips, aes(x=total_bill, y=tip/total_bill))
sp    + geom_point(shape =1)

sp + facet_grid(sex ~ day)+ geom_point(shape =1)

Facet_grid With Base ========================================================

par(mfrow=c(2,4))
days <- c("Thur", "Fri", "Sat", "Sun")
sexes <- unique(tips$sex)
for (i in 1:length(sexes)) {
  for (j in 1:length(days)) {
    currdata <- tips[tips$day == days[j] & tips$sex == sexes[i],]
    plot(currdata$total_bill, currdata$tip/currdata$total_bill,
        main=paste(days[j], sexes[i], sep=", "), ylim=c(0,0.7), las=1)
  }
}

Facet_grid With Base

par(mfrow=c(2,4))
days <- c("Thur", "Fri", "Sat", "Sun")
sexes <- unique(tips$sex)
for (i in 1:length(sexes)) {
  for (j in 1:length(days)) {
    currdata <- tips[tips$day == days[j] & tips$sex == sexes[i],]
    plot(currdata$total_bill, currdata$tip/currdata$total_bill,
        main=paste(days[j], sexes[i], sep=", "), ylim=c(0,0.7), las=1)
  }
}

Down Audience 6.csv, và Audience v2 6.csv từ Team drive vào getwd():

https://drive.google.com/drive/folders/0AAGr9J_23BVCUk9PVA

Other example A

library(Rcmdr)

## Warning: package 'Rcmdr' was built under R version 3.4.3

## Loading required package: splines

## Loading required package: RcmdrMisc

## Warning: package 'RcmdrMisc' was built under R version 3.4.3

## Loading required package: car

## Warning: package 'car' was built under R version 3.4.3

## Loading required package: sandwich

## Warning: package 'sandwich' was built under R version 3.4.3

## Loading required package: effects

## Warning: package 'effects' was built under R version 3.4.3

## Loading required package: carData

## 
## Attaching package: 'carData'

## The following objects are masked from 'package:car':
## 
##     Guyer, UN, Vocab

## lattice theme set by effectsTheme()
## See ?effectsTheme for details.

## The Commander GUI is launched only in interactive sessions

library(scales)
library(GGally)

## Warning: package 'GGally' was built under R version 3.4.3

library(ggplot2)
library(reshape2)
library(magrittr)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following object is masked from 'package:GGally':
## 
##     nasa

## The following object is masked from 'package:car':
## 
##     recode

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(varhandle)
library(car)
library(plyr)

## -------------------------------------------------------------------------

## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)

## -------------------------------------------------------------------------

## 
## Attaching package: 'plyr'

## The following objects are masked from 'package:dplyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize

library(lubridate)

## Warning: package 'lubridate' was built under R version 3.4.3

## 
## Attaching package: 'lubridate'

## The following object is masked from 'package:plyr':
## 
##     here

## The following object is masked from 'package:base':
## 
##     date

library(rgl)

## Warning: package 'rgl' was built under R version 3.4.3

Audience <- read.csv("Audience 6.csv")
Audience2 <- read.csv("Audience v2 6.csv")

Audience$Rev30<- gsub(",", "", as.character(factor(Audience$Rev30)))
Audience$Rev30 <- as.numeric(Audience$Rev30)

Audience$Rev90<- gsub(",", "", as.character(factor(Audience$Rev90)))
Audience$Rev90 <- as.numeric(Audience$Rev90)

Audience$RR1 <- gsub(",", ".", as.character(factor(Audience$RR1)))
Audience$RR1 <- gsub("%", "", as.character(factor(Audience$RR1)))
Audience$RR1 <- as.numeric(Audience$RR1)

Audience$RR7 <- gsub(",", ".", as.character(factor(Audience$RR7)))
Audience$RR7 <- gsub("%", "", as.character(factor(Audience$RR7)))
Audience$RR7 <- as.numeric(Audience$RR7)


Audience$Imp<- gsub(",", "", as.character(factor(Audience$Imp)))
Audience$Imp <- as.numeric(Audience$Imp)

Audience$Click<- gsub(",", "", as.character(factor(Audience$Click)))
Audience$Click <- as.numeric(Audience$Click)

Audience$Install..GMT.7.<- gsub(",", "", as.character(factor(Audience$Install..GMT.7.)))
Audience$Install..GMT.7. <- as.numeric(Audience$Install..GMT.7.)

Audience$NRU<- gsub(",", "", as.character(factor(Audience$NRU)))
Audience$NRU <- as.numeric(Audience$NRU)

Audience$NRU0<- gsub(",", "", as.character(factor(Audience$NRU0)))
Audience$NRU0 <- as.numeric(Audience$NRU0)


arm <- Audience

arm$CTR <- NULL
arm$CR <- NULL
arm$CPC <- NULL
arm$NRU0.Install <- NULL
arm$CR7 <- NULL
arm$CR30 <- NULL
arm$Rev7.CostVND <- NULL
arm$Rev30.CostVND <- NULL
arm$Rev90.CostVND <- NULL
arm$CTR <- NULL

#
arm$RR1 <- NULL
arm$RR7 <- NULL
arm$Rev7 <- NULL
arm$Rev30 <- NULL
arm$Rev90 <- NULL
arm$Cost... <- NULL

arm$ï..Date <- as.Date(arm$ï..Date)
arm$ï..Date <- as.Date(arm$ï..Date, format="%m/%d/%Y")

At6 <- subset(arm,arm$ï..Date >='2017-06-01' & arm$ï..Date<= '2017-06-30')
At7 <- subset(arm,arm$ï..Date >='2017-07-01' & arm$ï..Date<= '2017-07-31')
At8 <- subset(arm,arm$ï..Date >='2017-08-01' & arm$ï..Date<= '2017-08-30')

#Bao quat
arm$ï..Date <- NULL

arm1 <- ggpairs(arm,
                upper = list(continuous = wrap("density", alpha = 0.5), combo = "box"),
                lower = list(continuous = wrap("points", alpha = 0.3,    size=0.1), 
                             combo = wrap("dot", alpha = 0.4, size=0.2)),cardinality_threshold = NULL)
arm1

B

AudienceIns7 <- ggplot(At7) + 
  aes(x= Install..GMT.7., y= Audience) + 
  geom_point(aes(color = Device.Os),alpha = 0.4,position = "jitter")+
  ggtitle("Tháng 7")

AudienceIns7

AudienceIns8 <- ggplot(At8) + 
  aes(x= Install..GMT.7., y= Audience) + 
  geom_point(aes(color = Device.Os),alpha = 0.4,position = "jitter")+
  ggtitle("Tháng 8")
AudienceIns8

C

arm1 <- Audience 

arm1$ï..Date <- as.Date(arm1$ï..Date)
arm1$ï..Date <- as.Date(arm1$ï..Date, format="%m/%d/%Y")

AT6 <- subset(arm1,arm1$ï..Date >='2017-06-01' & arm1$ï..Date<= '2017-06-30')
AT7 <- subset(arm1,arm1$ï..Date >='2017-07-01' & arm1$ï..Date<= '2017-07-31')
AT8 <- subset(arm1,arm1$ï..Date >='2017-08-01' & arm1$ï..Date<= '2017-08-30')

rrrr8 <- ggplot(AT8) + 
  aes(x= RR1, y= RR7) + 
  geom_point(aes(color = Audience, shape = Source),alpha = 0.4,position = "jitter")+
  theme(axis.text.x  = element_text(angle = 60,hjust = 1))+
  coord_cartesian(xlim = c(0,100),ylim = c(0,80))+
  ggtitle("Tháng 8")
rrrr8

rrrr8 <- ggplot(AT8) + 
  aes(x= RR1, y= RR7) + 
  geom_point(aes(color = Audience, shape = Source),alpha = 0.4,position = "jitter")+
  theme(axis.text.x  = element_text(angle = 60,hjust = 1))+
  coord_cartesian(xlim =c(25,75) ,ylim = c(10,30))+
  ggtitle("Tháng 8")
rrrr8

D

rrrr7 <- ggplot(AT7) + 
  aes(x= RR1, y= RR7) + 
  geom_point(aes(color = Audience),alpha = 0.4,position = "jitter")+
  theme(axis.text.x  = element_text(angle = 60,hjust = 1))+
  coord_cartesian(xlim =c(0,100),ylim = c(0,80))+
  ggtitle("Tháng 7")
rrrr7

rrrr7 <- ggplot(AT7) + 
  aes(x= RR1, y= RR7) + 
  geom_point(aes(color = Audience),alpha = 0.4,position = "jitter")+
  theme(axis.text.x  = element_text(angle = 60,hjust = 1))+
  coord_cartesian(xlim = c(25,75) ,ylim = c(10,30))+
  ggtitle("Tháng 7")
rrrr7

HISTOGRAM

data=data.frame(value=rnorm(10000))
# Uniform color
ggplot(data, aes(x=value)) + 
    geom_histogram(binwidth = 0.2, color="white", fill=rgb(0.2,0.7,0.1,0.4) )

# Proportional color
ggplot(data, aes(x=value)) + 
    geom_histogram(binwidth = 0.2, aes(fill = ..count..) )

COLOR

# create a dataset
specie=c(rep("sorgho" , 3) , rep("poacee" , 3) , rep("banana" , 3) , rep("triticum" , 3) )
condition=rep(c("normal" , "stress" , "Nitrogen") , 4)
value=abs(rnorm(12 , 0 , 15))

data=data.frame(specie,condition,value)
# color with RcolorBrewer
ggplot(data, aes(fill=condition, y=value, x=specie)) +
    geom_bar( stat="identity", position="fill") +    
    scale_fill_brewer(palette = "Set1")

# Faceting
ggplot(data, aes(y=value, x=specie, color=specie, fill=specie)) +
    geom_bar( stat="identity") +    
    facet_wrap(~condition)

TEXT LABEL ========================================================

# The mtcars dataset is proposed in R
data=head(mtcars, 30)
 
# 1/ add text with geom_text, use nudge to nudge the text
ggplot(data, aes(x=wt, y=mpg)) +
  geom_point() + 
  geom_text(label=rownames(data), nudge_x = 0.25, nudge_y = 0.25, check_overlap = T)

# 2/ to improve readability, use geom_label
ggplot(data, aes(x=wt, y=mpg)) +
  geom_point() + 
  geom_label(label=rownames(data), nudge_x = 0.25, nudge_y = 0.2)

# 3/ custom geom_label like any other geom.
ggplot(data, aes(x=wt, y=mpg, fill=cyl)) +
  geom_label(label=rownames(data), color="white", size=5)

BOXPLOT

# The mtcars dataset is proposed in R
data(mtcars)
# geom_boxplot proposes several arguments to custom appearance
ggplot(mpg, aes(x=reorder(class, hwy), y=hwy, fill=class)) + 
    geom_boxplot() +
    xlab("class") +
    theme(legend.position="none")

# Data 
names=c(rep("A", 80) , rep("B", 50) , rep("C", 70))
value=c( sample(2:5, 80 , replace=T) , sample(4:10, 50 , replace=T), 
       sample(1:7, 70 , replace=T) )
data=data.frame(names,value)
 
#Graph
qplot( x=names , y=value , data=data , geom=c("boxplot","jitter") , fill=names)

CONNECTION MAP ========================================================

library(tidyverse)

## Warning: package 'tidyverse' was built under R version 3.4.3

## -- Attaching packages -------------------------------------- tidyverse 1.2.1 --

## v tibble  1.3.4     v purrr   0.2.4
## v tidyr   0.7.2     v stringr 1.2.0
## v readr   1.1.1     v forcats 0.2.0

## Warning: package 'tidyr' was built under R version 3.4.3

## Warning: package 'readr' was built under R version 3.4.3

## Warning: package 'stringr' was built under R version 3.4.3

## Warning: package 'forcats' was built under R version 3.4.3

## -- Conflicts ----------------------------------------- tidyverse_conflicts() --
## x plyr::arrange()          masks dplyr::arrange()
## x lubridate::as.difftime() masks base::as.difftime()
## x readr::col_factor()      masks scales::col_factor()
## x purrr::compact()         masks plyr::compact()
## x plyr::count()            masks dplyr::count()
## x lubridate::date()        masks base::date()
## x purrr::discard()         masks scales::discard()
## x tidyr::extract()         masks magrittr::extract()
## x plyr::failwith()         masks dplyr::failwith()
## x dplyr::filter()          masks stats::filter()
## x lubridate::here()        masks plyr::here()
## x plyr::id()               masks dplyr::id()
## x lubridate::intersect()   masks base::intersect()
## x dplyr::lag()             masks stats::lag()
## x plyr::mutate()           masks dplyr::mutate()
## x dplyr::recode()          masks car::recode()
## x plyr::rename()           masks dplyr::rename()
## x purrr::set_names()       masks magrittr::set_names()
## x lubridate::setdiff()     masks base::setdiff()
## x purrr::some()            masks car::some()
## x plyr::summarise()        masks dplyr::summarise()
## x plyr::summarize()        masks dplyr::summarize()
## x lubridate::union()       masks base::union()

library(maps)

## Warning: package 'maps' was built under R version 3.4.3

## 
## Attaching package: 'maps'

## The following object is masked from 'package:purrr':
## 
##     map

## The following object is masked from 'package:plyr':
## 
##     ozone

library(geosphere)

## Warning: package 'geosphere' was built under R version 3.4.3

library(ggplot2)
par(mar=c(0,0,0,0))
map('world',col="#f2f2f2", fill=TRUE, bg="white", lwd=0.05,mar=rep(0,4),border=0, ylim=c(-80,80) )

plot_my_connection=function( dep_lon, dep_lat, arr_lon, arr_lat, ...){
    inter <- gcIntermediate(c(dep_lon, dep_lat), c(arr_lon, arr_lat), n=50, addStartEnd=TRUE, breakAtDateLine=F)             
    inter=data.frame(inter)
    diff_of_lon=abs(dep_lon) + abs(arr_lon)
    if(diff_of_lon > 180){
        lines(subset(inter, lon>=0), ...)
        lines(subset(inter, lon<0), ...)
    }else{
        lines(inter, ...)
        }
    }
 

data=rbind(
    Buenos_aires=c(-58,-34),
    Paris=c(2,49),
    Melbourne=c(145,-38),
    Saint.Petersburg=c(30.32, 59.93),
    Abidjan=c(-4.03, 5.33),
    Montreal=c(-73.57, 45.52),
    Nairobi=c(36.82, -1.29),
    Salvador=c(-38.5, -12.97)
    )  %>% as.data.frame()
colnames(data)=c("long","lat")

all_pairs=cbind(t(combn(data$long, 2)), t(combn(data$lat, 2))) %>% as.data.frame()
colnames(all_pairs)=c("long1","long2","lat1","lat2")

# background map
par(mar=c(0,0,0,0))
map('world',col="#f2f2f2", fill=TRUE, bg="white", lwd=0.05,mar=rep(0,4),border=0, ylim=c(-80,80) )
 
# add every connections:
for(i in 1:nrow(all_pairs)){
    plot_my_connection(all_pairs$long1[i], all_pairs$lat1[i], all_pairs$long2[i], all_pairs$lat2[i], col="skyblue", lwd=1)
    }
 
# add points and names of cities
points(x=data$long, y=data$lat, col="slateblue", cex=2, pch=20)
text(rownames(data), x=data$long, y=data$lat,  col="slateblue", cex=1, pos=4)

DENSITY

library(rattle)

## Rattle: A free graphical interface for data science with R.
## Version 5.1.0 Copyright (c) 2006-2017 Togaware Pty Ltd.
## Type 'rattle()' to shake, rattle, and roll your data.

weatherAUS <- read.csv("weatherAUS.csv")

# To generate a density plot.
 
cities <- c("Canberra", "Darwin", "Melbourne", "Sydney")
ds <- subset(weatherAUS, Location %in% cities & ! is.na(Temp3pm))
p  <- ggplot(ds, aes(Temp3pm, colour=Location, fill=Location))
p  <- p + geom_density(alpha=0.55)
p

MARGINAL HISTOGRAM FOR GGPLOT2

library(ggExtra)

## Warning: package 'ggExtra' was built under R version 3.4.3

# The mtcars dataset is proposed in R
head(mtcars)

##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

# classic plot :
p=ggplot(mtcars, aes(x=wt, y=mpg, color=cyl, size=cyl)) +
      geom_point() +
      theme(legend.position="none")
 
# with marginal histogram
ggMarginal(p, type="histogram")
 
# marginal boxplot
ggMarginal(p, type="boxplot")

ANNOTATION

p=ggplot(mtcars, aes(x = wt, y = mpg)) + geom_point()
 
# Add text on a specific positions:
p + annotate("text", x = c(2,4.5), y = c(20,25), label = c("label 1", "label 2") , color="orange", size=5 , angle=45, fontface="bold")

SPYWEB ========================================================

# Library
library(fmsb)
 
# Create data: note in High school for Jonathan:
data=as.data.frame(matrix( sample( 2:20 , 10 , replace=T) , ncol=10))
colnames(data)=c("math" , "english" , "biology" , "music" , "R-coding", "data-viz" , "french" , "physic", "statistic", "sport" )
 
# To use the fmsb package, I have to add 2 lines to the dataframe: the max and min of each topic to show on the plot!
data=rbind(rep(20,10) , rep(0,10) , data)
 
# The default radar chart proposed by the library:
radarchart(data)

# Custom the radarChart !
radarchart( data  , axistype=1 ,
 
    #custom polygon
    pcol=rgb(0.2,0.5,0.5,0.9) , pfcol=rgb(0.2,0.5,0.5,0.5) , plwd=4 ,
 
    #custom the grid
    cglcol="grey", cglty=1, axislabcol="grey", caxislabels=seq(0,20,5), cglwd=0.8,
 
    #custom labels
    vlcex=0.8
    )

RADAR 1 ========================================================

# Library
library(fmsb)
 
# Create data: note in High school for several students
set.seed(99)
data=as.data.frame(matrix( sample( 0:20 , 15 , replace=F) , ncol=5))
colnames(data)=c("math" , "english" , "biology" , "music" , "R-coding" )
rownames(data)=paste("mister" , letters[1:3] , sep="-")
 
# To use the fmsb package, I have to add 2 lines to the dataframe: the max and min of each topic to show on the plot!
data=rbind(rep(20,5) , rep(0,5) , data)
 
 
 
 
 
#==================
# Plot 1: Default radar chart proposed by the library:
radarchart(data)

#==================
# Plot 2: Same plot with custom features
colors_border=c( rgb(0.2,0.5,0.5,0.9), rgb(0.8,0.2,0.5,0.9) , rgb(0.7,0.5,0.1,0.9) )
colors_in=c( rgb(0.2,0.5,0.5,0.4), rgb(0.8,0.2,0.5,0.4) , rgb(0.7,0.5,0.1,0.4) )
radarchart( data  , axistype=1 ,
    #custom polygon
    pcol=colors_border , pfcol=colors_in , plwd=4 , plty=1,
    #custom the grid
    cglcol="grey", cglty=1, axislabcol="grey", caxislabels=seq(0,20,5), cglwd=0.8,
    #custom labels
    vlcex=0.8
    )
legend(x=0.7, y=1, legend = rownames(data[-c(1,2),]), bty = "n", pch=20 , col=colors_in , text.col = "grey", cex=1.2, pt.cex=3)

RADAR 2 ========================================================

# Plot3: If you remove the 2 first lines, the function compute the max and min of each variable with the available data:
colors_border=c( rgb(0.2,0.5,0.5,0.9), rgb(0.8,0.2,0.5,0.9) , rgb(0.7,0.5,0.1,0.9) )
colors_in=c( rgb(0.2,0.5,0.5,0.4), rgb(0.8,0.2,0.5,0.4) , rgb(0.7,0.5,0.1,0.4) )
radarchart( data[-c(1,2),]  , axistype=0 , maxmin=F,
    #custom polygon
    pcol=colors_border , pfcol=colors_in , plwd=4 , plty=1,
    #custom the grid
    cglcol="grey", cglty=1, axislabcol="black", cglwd=0.8,
    #custom labels
    vlcex=0.8
    )
legend(x=0.7, y=1, legend = rownames(data[-c(1,2),]), bty = "n", pch=20 , col=colors_in , text.col = "grey", cex=1.2, pt.cex=3)

CIRCULAR BARPLOT

# make data
data=data.frame(group=c("A ","B ","C ","D ") , value=c(33,62,56,67) )

# Usual bar plot :
ggplot(data, aes(x = group, y = value ,fill = group )) + 
  geom_bar(width = 0.85, stat="identity")

# Circular one
ggplot(data, aes(x = group, y = value ,fill = group)) + 
  geom_bar(width = 0.85, stat="identity") +    
  
  # To use a polar plot and not a basic barplot
  coord_polar(theta = "y") +    
  
  #Remove useless labels of axis
  xlab("") + ylab("") +
  
  #Increase ylim to avoid having a complete circle
  ylim(c(0,75)) + 
  
  #Add group labels close to the bars :
  geom_text(data = data, hjust = 1, size = 3, aes(x = group, y = 0, label = group)) +
  
  #Remove useless legend, y axis ticks and y axis text
  theme(legend.position = "none" , axis.text.y = element_blank() , axis.ticks = element_blank())

STACK PLOT

# library
library(ggplot2)
 
# create a dataset
specie=c(rep("sorgho" , 3) , rep("poacee" , 3) , rep("banana" , 3) , rep("triticum" , 3) )
condition=rep(c("normal" , "stress" , "Nitrogen") , 4)
value=abs(rnorm(12 , 0 , 15))
data=data.frame(specie,condition,value)
 
# Grouped
ggplot(data, aes(fill=condition, y=value, x=specie)) +
    geom_bar(position="dodge", stat="identity")

# Stacked
ggplot(data, aes(fill=condition, y=value, x=specie)) +
    geom_bar( stat="identity")

# Stacked Percent
ggplot(data, aes(fill=condition, y=value, x=specie)) +
    geom_bar( stat="identity", position="fill")

TREEMAP

# library
library(treemap)

## Warning: package 'treemap' was built under R version 3.4.3

# Create data
group=c(rep("group-1",4),rep("group-2",2),rep("group-3",3))
subgroup=paste("subgroup" , c(1,2,3,4,1,2,1,2,3), sep="-")
value=c(13,5,22,12,11,7,3,1,23)
data=data.frame(group,subgroup,value)
 
# Custom labels:
treemap(data, index=c("group","subgroup"),     vSize="value", type="index",
 
    fontsize.labels=c(15,12),                # size of labels. Give the size per level of aggregation: size for group, size for subgroup, sub-subgroups...
    fontcolor.labels=c("white","orange"),    # Color of labels
    fontface.labels=c(2,1),                  # Font of labels: 1,2,3,4 for normal, bold, italic, bold-italic...
    bg.labels=c("transparent"),              # Background color of labels
    align.labels=list(
        c("center", "center"), 
        c("right", "bottom")
        ),                                   # Where to place labels in the rectangle?
    overlap.labels=0.5,                      # number between 0 and 1 that determines the tolerance of the overlap between labels. 0 means that labels of lower levels are not printed if higher level labels overlap, 1  means that labels are always printed. In-between values, for instance the default value .5, means that lower level labels are printed if other labels do not overlap with more than .5  times their area size.
    inflate.labels=F,                        # If true, labels are bigger when rectangle is bigger.
 
)

TREEMAP ========================================================

# Custom borders:
treemap(data, index=c("group","subgroup"), vSize="value", type="index",
 
    border.col=c("black","white"),             # Color of borders of groups, of subgroups, of subsubgroups ....
    border.lwds=c(7,2)                         # Width of colors
        
    )

TREEMAP ========================================================

# Custom borders:
treemap(data, index=c("group","subgroup"), vSize="value", 
 
    type="index",                            # How you color the treemap. type help(treemap) for more info
    palette = "Set1",                        # Select your color palette from the RColorBrewer presets or make your own.
    title="My Treemap",                      # Customize your title
    fontsize.title=12,                       # Size of the title
 
)

Slide With Plotly

Plotly’s R library is free and open source!
Get started by downloading the client and reading the primer.
You can set up Plotly to work in online or offline mode.
We also have a quick-reference cheatsheet (new!) to help you get started! https://github.com/plotly/plotly.py https://images.plot.ly/plotly-documentation/images/r_cheat_sheet.pdf

library(plotly)

## Warning: package 'plotly' was built under R version 3.4.3

## 
## Attaching package: 'plotly'

## The following objects are masked from 'package:plyr':
## 
##     arrange, mutate, rename, summarise

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

plotlyy<- ggplot(mpg, aes(x=reorder(class, hwy), y=hwy, fill=class)) + 
    geom_boxplot() +
    xlab("class") +
    theme(legend.position="none")
 ggplotly(plotlyy)

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

# Tất cả chart tĩnh vẽ từ ggplot sẽ trở thành chart động thông qua ggplotly, hãy chạm chuột thử phóng vào.

Advantages

It lets you create interactive visualizations built using D3.js without even having to know D3.js.
It provides compatibility with number of different languages/ tools like R, Python, MATLAB, Perl, Julia, Arduino.
Using plotly, interactive plots can easily be shared online with multiple people.
Plotly can also be used by people with no technical background for creating interactive plots by uploading the data and using plotly GUI. Plotly is compatible with ggplots in R and Python.
It allows to embed interactive plots in projects or websites using iframes or html.
The syntax for creating interactive plots using plotly is very simple as well.

Disadvantages

The plots made using plotly community version are always public and can be viewed by anyone.
For plotly community version, there is an upper limit on the API calls per day.
There are also limited number of color Palettes available in community version which acts as an upper bound on the coloring options.

Art

library(gsubfn)

## Warning: package 'gsubfn' was built under R version 3.4.3

## Loading required package: proto

## Warning: package 'proto' was built under R version 3.4.3

library(stringr)
library(dplyr)
library(ggplot2)
 
#Plant 1
axiom="F"
rules=list("F"="FF-[-F+F+F]+[+F-F-F]")
angle=22.5
depth=4
 
#Plant 2
axiom="X"
rules=list("X"="F[+X][-X]FX", "F"="FF")
angle=25.7
depth=7
 
#Plant 3
axiom="X"
rules=list("X"="F[+X]F[-X]+X", "F"="FF")
angle=20
depth=7
 
#Plant 4
axiom="X"
rules=list("X"="F-[[X]+X]+F[+FX]-X", "F"="FF")
angle=22.5
depth=5

for (i in 1:depth) axiom=gsubfn(".", rules, axiom)
 
actions=str_extract_all(axiom, "\\d*\\+|\\d*\\-|F|L|R|\\[|\\]|\\|") %>% unlist
 
status=data.frame(x=numeric(0), y=numeric(0), alfa=numeric(0))
points=data.frame(x1 = 0, y1 = 0, x2 = NA, y2 = NA, alfa=90, depth=1)
 
 
for (action in actions) 
{
  if (action=="F")
  {
    x=points[1, "x1"]+cos(points[1, "alfa"]*(pi/180))
    y=points[1, "y1"]+sin(points[1, "alfa"]*(pi/180))
    points[1,"x2"]=x
    points[1,"y2"]=y
    data.frame(x1 = x, y1 = y, x2 = NA, y2 = NA, 
               alfa=points[1, "alfa"],
               depth=points[1,"depth"]) %>% rbind(points)->points
  }
  if (action %in% c("+", "-")){
    alfa=points[1, "alfa"]
    points[1, "alfa"]=eval(parse(text=paste0("alfa",action, angle)))
  }
  if(action=="["){ 
    data.frame(x=points[1, "x1"], y=points[1, "y1"], alfa=points[1, "alfa"]) %>% 
      rbind(status) -> status
    points[1, "depth"]=points[1, "depth"]+1
  }
   
  if(action=="]"){ 
    depth=points[1, "depth"]
    points[-1,]->points
    data.frame(x1=status[1, "x"], y1=status[1, "y"], x2=NA, y2=NA, 
               alfa=status[1, "alfa"],
               depth=depth-1) %>% 
      rbind(points) -> points
    status[-1,]->status
  }
}
 
ggplot() + 
  geom_segment(aes(x = x1, y = y1, xend = x2, yend = y2), 
               lineend = "round", 
               colour="white",
               data=na.omit(points)) + 
  coord_fixed(ratio = 1) +
  theme(legend.position="none",
        panel.background = element_rect(fill="black"),
        panel.grid=element_blank(),
        axis.ticks=element_blank(),
        axis.title=element_blank(),
        axis.text=element_blank())

#Plant 6
axiom="F"
rules=list("F"="F[+F]F[-F][F]")
angle=20
depth=5
 
 
for (i in 1:depth) axiom=gsubfn(".", rules, axiom)
 
actions=str_extract_all(axiom, "\\d*\\+|\\d*\\-|F|L|R|\\[|\\]|\\|") %>% unlist
 
status=data.frame(x=numeric(0), y=numeric(0), alfa=numeric(0))
points=data.frame(x1 = 0, y1 = 0, x2 = NA, y2 = NA, alfa=90, depth=1)
 
 
for (action in actions) 
{
  if (action=="F")
  {
    x=points[1, "x1"]+cos(points[1, "alfa"]*(pi/180))
    y=points[1, "y1"]+sin(points[1, "alfa"]*(pi/180))
    points[1,"x2"]=x
    points[1,"y2"]=y
    data.frame(x1 = x, y1 = y, x2 = NA, y2 = NA, 
               alfa=points[1, "alfa"],
               depth=points[1,"depth"]) %>% rbind(points)->points
  }
  if (action %in% c("+", "-")){
    alfa=points[1, "alfa"]
    points[1, "alfa"]=eval(parse(text=paste0("alfa",action, angle)))
  }
  if(action=="["){ 
    data.frame(x=points[1, "x1"], y=points[1, "y1"], alfa=points[1, "alfa"]) %>% 
      rbind(status) -> status
    points[1, "depth"]=points[1, "depth"]+1
  }
   
  if(action=="]"){ 
    depth=points[1, "depth"]
    points[-1,]->points
    data.frame(x1=status[1, "x"], y1=status[1, "y"], x2=NA, y2=NA, 
               alfa=status[1, "alfa"],
               depth=depth-1) %>% 
      rbind(points) -> points
    status[-1,]->status
  }
}
 
ggplot() + 
  geom_segment(aes(x = x1, y = y1, xend = x2, yend = y2), 
               lineend = "round", 
               colour="white",
               data=na.omit(points)) + 
  coord_fixed(ratio = 1) +
  theme(legend.position="none",
        panel.background = element_rect(fill="black"),
        panel.grid=element_blank(),
        axis.ticks=element_blank(),
        axis.title=element_blank(),
        axis.text=element_blank())