setwd("C:/Users/Lenovo/OneDrive/Documents/Data Visualisation")
install.packages("tidyverse")
library(tidyverse)
install.packages("broom")
##dplyr
install.packages("ggplot2")
geom
library(ggplot2)
##ggplot2 for graphs
## data :- elements and the data present in the loaded file
## Aesthetics includes x- axis, y-axis, width, color
##geometrics : geoms --> specify what kind of graph to be plotted
##facets : subset of the data
## statistics: intermediate, descriptive, smoothing, Binning
##Co-Ordinates: the space between data and dsiplay using cartesian, fixed, polar, limits
## Themes: non-data link
##Template
## ggplot(data=<DATA>)+ <GEOM_FUNCTION>(mapping = aes(<MAPPING>))
## ggplot(data=<DATA>), mapping = aes(<mapping>)) + <GEOM_FUNCTION>()
##ggplot(<DATa>, aes(<MAPPING> + <GEOM_FUNCTION>)
## ggplot(<DATA> + <GEOM_FUNCTION>)(aes(<mapppings>))
library(dplyr)
summary(mpg)
glimpse(mpg)
View(mpg)
ggplot(data=mpg)+ geom_bar(aes(x=displ, color=class))
ggplot(data=mpg)+geom_boxplot()
## first 6 observations are given by head
tail(mpg)
## last 6 observations are given by tail
ggplot(data = mpg)
ggplot(mpg, aes(x= class, y=displ))
ggplot(mpg, aes(x= class, y=displ)) + geom_point()
ggplot(mpg, aes(x= cty,y=displ)) + geom_point()
ggplot(mpg) + geom_histogram(aes(x=manufacturer), y="year")
ggplot(mpg) + geom_point(aes(x= displ, y =hwy,))
#segregating on the basis of class
ggplot(mpg) + geom_point(aes(x= displ, y =hwy, color=class))
# segregating on the basis of size
ggplot(mpg) + geom_point(aes(x= displ, y =hwy, size=class))
#segregating based on shape
ggplot(mpg) + geom_point(aes(x= displ, y= hwy, shape=class))
#segregating based on shades
ggplot(mpg) + geom_point(aes(x= displ, y= hwy, alpha=class))
ggplot(mpg) + geom_point(aes(x= displ, y =hwy, color='tomato', shape=6, alpha=0.1))
##Adding Title
ggplot(mpg) + geom_point(aes(x= displ, y =hwy, color=class))+
ggtitle("car performance", subtitle= "MPG") +
xlab("Displacement") +
ylab("Highway Milage") +
theme_grey() +
theme(axis.text.x= element_text(face = 'bold.italic',
color = 'darkgreen',
size = 10, angle = 180),
axis.text.y= element_text(face = 'bold',
color = 'blue',
size = 10, angle = 90))
#facets for suplots-- different geometrical graphs for each of the diagram
ggplot(mpg) + geom_point(aes(x= displ, y =hwy)) + facet_wrap(~class, nrow=5)+
ggtitle("car performance", subtitle= "MPG") +
xlab("Displacement") +
ylab("Highway Milage") +
theme_grey() +
theme(axis.text.x= element_text(face = 'bold.italic',
color = 'darkgreen',
size = 10, angle = 180),
axis.text.y= element_text(face = 'bold',
color = 'blue',
size = 10, angle = 90))
install.packages("ggplot2")
library(ggplot2)
summary(diamonds)
diamonds
print(diamonds)
View(diamonds)
##1. Bar Plot
ggplot(diamonds)+ geom_bar(aes(x= cut, color=cut))
##2.Percentage Bar Diagram
ggplot(diamonds)+ geom_bar(aes(x=cut, fill = clarity), alpha = 1 , position = "fill")
ggplot(diamonds)+ geom_bar(aes(x=cut, fill = clarity), alpha = 1/5, position = "fill")
##3. Grouped Bar Diagram
ggplot(diamonds)+ geom_bar(aes(x = cut, fill = clarity), alpha = 9/10, position = "dodge")
##4. Horizontal Bar Diagram
ggplot(diamonds)+ coord_flip()+ geom_bar(aes(x = cut, fill = clarity), alpha = 1, position = "dodge")
##5. Boxplot
ggplot(diamonds) + geom_boxplot(aes(x=cut, y=price, fill="pink"))
##6. Titles & Labels
ggplot(diamonds, aes(x=cut, y=price, fill="pink")) + geom_boxplot() + ggtitle("Diamonds", subtitle= "Quality") +
xlab("Type of Cut") +
ylab("Price") +
theme_light() + theme(axis.text.x= element_text(face = 'italic',
color = 'black',
size = 8, angle = 0),
axis.text.y= element_text(face = 'italic',
color = 'black',
size = 8, angle = 90))+
theme(plot.title = element_text(face='bold.italic',
size= 10,
color = 'darkblue'))
install.packages("ggplot2")
library(ggplot2)
summary(diamonds)
diamonds
print(diamonds)
View(diamonds)
jpeg(file= "boxplot1.jpg")
ggplot(diamonds) + geom_boxplot(aes(x=cut, y=price, fill="pink"))
dev.off()
library(readxl)
prices <- read_excel("C:/Users/Lenovo/OneDrive/Documents/R Class Practice/Gold_silver_prices.xlsx")
View(prices)
##getwd() <-- to get the working directory
## to load excel files use function --> read_excel("__")
## the bracket should contain the path to the file
## name it and use view function
library(janitor)
prices <- prices %>% clean_names(case="lower_camel")
prices <- prices %>% clean_names(case="snake")
## clean_names(data, case = c(snake, lowercamel, upper camel)) --> used to clean names, headings, they were capital and different before, now they all will be small cap)
##Line Graph
library(ggplot2)
line_plot <- ggplot(prices, mapping= aes(x=year ,y=gold)) + geom_line()
show(line_plot)
ggplot(prices) + geom_line(aes(x=year ,y=gold))+ geom_line(aes(x=year, y=silver))
install.packages("reshape2")
library(reshape2)
## reshape function changes the data from wide to long
data_long <- melt(prices, id.vars = "year")
head(data_long)
View(data_long)
## melt function will change the data format from wide to long form of data
ggplot(data_long, aes(x=year, y=value, color=variable)) + geom_line()
nifty_50 <- read.csv("C:/Users/Lenovo/OneDrive/Documents/R Class Practice/nifty_50.csv")
View(nifty_50)
#treemap
install.packages("treemap")
library(treemap)
treemap(nifty_50, index=c("Company","Industry"), vSize= "Weightage",
type="index", palette = "Spectral", title= "Nifty 50 Index Weightage",
fontsize.title=12)
## R is viewing the weighatge as a string variable due to the % sign
## so to remove it we use the as.numeric function to remove the % sign
nifty_50$Weightage <- as.numeric(sub("%","", nifty_50$Weightage))
treemap(nifty_50, index=c("Company","Industry"), vSize= "Weightage",
type="index", palette = "Spectral", title= "Nifty 50 Index Weightage",
fontsize.title=12)
RColorBrewer::display.brewer.all()
library(ggplot2)
nifty_50 <- read.csv("C:/Users/Lenovo/OneDrive/Documents/R Class Practice/nifty_50.csv")
View(nifty_50)
data(nifty_50)
attach(nifty_50)
##1.a Pie Chart
nifty_50$Weightage <- as.numeric(sub("%","", nifty_50$Weightage))
ggplot(nifty_50,aes(x="", y=Weightage, fill=Industry)) +
geom_bar(stat ="identity", width = 1)+
coord_polar("y", start = 0)
theme_void()+
theme(legend.position = "bottom")
##2. Bubble Chart
nifty_50$Weightage <- as.numeric(sub("%","", nifty_50$Weightage))
ggplot(nifty_50, aes(Industry, Company, size=Weightage, color="rainbow")) + geom_point(alpha = 0.7)
+ scale_size(range = sizeRange)
##3.long to wide data form
#first convert to long
install.packages("reshape2")
library(reshape2)
prices <- read_excel("C:/Users/Lenovo/OneDrive/Documents/R Class Practice/Gold_silver_prices.xlsx")
View(prices)
library(janitor)
prices <- prices %>% clean_names(case="lower_camel")
data_long <- melt(prices, id.vars="year")
#now convert to wide
data_wide <- dcast(data_long,year ~ variable)
setwd("C:/Users/Lenovo/OneDrive/Documents/Financial Economics (BASE)")
install.packages("tidyverse")
library(tidyverse)
library(quantmod)
tickers = c('SBIN.NS','RELIANCE.NS','TCS.NS','HDFCBANK.NS','ITC.NS')
getSymbols(tickers)
data<-do.call(merge,lapply(tickers, function(x) Ad(get(x))))
mydata<- fortify.zoo(do.call(merge,lapply(tickers, function(x) Ad(get(x)))))
View(mydata)
which(is.na(mydata))
mydata<-na.omit(mydata)
install.packages("writexl")
library(writexl)
write_xlsx(mydata, "stock_price.xlsx")
View(mydata)
install.packages("ggplot2")
install.packages("tidyverse")
library(tidyverse)
library(ggplot2)
library(dplyr)
library(readxl)
H_FI <- read_xlsx("C:/Users/Lenovo/Downloads/hfi.xlsx")
View(H_FI)
attach(H_FI)
plot1 <- ggplot(data = H_FI, aes(x= Month, y = CPI_Core))+
geom_segment(aes(x =Month, xend= Month, y=CPI_Core, yend = CPI_Core), color= "blue", size=3)+ geom_point(color="red",size=5)
plot1
install.packages("gganimate")
library(gganimate)
plot1+transition_time(Month)+
labs(title="Month:{frame_time}")+ shadow_mark(alpha=0.5,size=0.8)
plot1
library(dplyr)
install.packages('quantmod')
library(quantmod)
getSymbols("GAIL.NS",return.class = "data.frame",src = 'yahoo')
View(GAIL.NS)
getSymbols('GAIL.NS', return.class = 'data.frame',src = 'yahoo', from = '2017-04-02', to = '2018-03-12')
View(GAIL.NS)
chartSeries(GAIL.NS,type='line',theme=chartTheme('white'))
chartSeries(GAIL.NS,type='bar',theme=chartTheme('white'))
chartSeries(GAIL.NS,type='line',subset='2017-06', theme=chartTheme('white'))
gail <- GAIL.NS %>% mutate(Date = as.Date(row.names(.))) %>% select(Date, GAIL.NS.Adjusted) %>% rename(Adjuste_Close = GAIL.NS.Adjusted) %>% mutate(Company = 'GAIL')
View(gail)
##PIE CHART USING GGPLOT##
ggplot(nifty50,aes(x="",y=Weightage,fill=Industry))+
geom_bar()+
coord_polar("y",start=0)+
ggtitle("INDUSTRIAL WEIGHTAGE")+
theme(plot.title = element_text(face ='bold.italic',
size = 12,
colour = 'black'))+
geom_text(aes(x=1.5,label=Weightage),position = position_stack(vjust = 0))+
scale_fill_manual(values=c("#999999", "#E69F00", "#56B4E9","#111111","#E70E00","#76B4E8","#56E7B9","#45B7F8","#23B5E8","#555555","#666666","#333333","#444444"))
##BUBBLE PLOT##
install.packages("rainbow")
library(rainbow)
ggplot(nifty50,aes(x=Industry,y=Weightage,size=Weightage))+
geom_point(alpha=0.5,colour="53")+
xlab("INDUSTRY")+
theme(axis.title.x =element_text(face ='bold.italic',
size = 8,
colour = 'blue'))+
ylab("WEIGHTAGE")+
theme(axis.title.y = element_text(face = 'bold.italic',
size = 8,
colour = 'red'))
install.packages("ggplot2")
library(ggplot2)
summary(diamonds)
diamonds
print(diamonds)
View(diamonds)
jpeg(file= "boxplot1.jpg")
ggplot(diamonds) + geom_boxplot(aes(x=cut, y=price, fill="pink"))
dev.off()
library(readxl)
prices <- read_excel("C:/Users/Lenovo/OneDrive/Documents/R Class Practice/Gold_silver_prices.xlsx")
View(prices)
##getwd() <-- to get the working directory
## to load excel files use function --> read_excel("__")
## the bracket should contain the path to the file
## name it and use view function
library(janitor)
prices <- prices %>% clean_names(case="lower_camel")
prices <- prices %>% clean_names(case="snake")
## clean_names(data, case = c(snake, lowercamel, upper camel)) --> used to clean names, headings, they were capital and different before, now they all will be small cap)
##Line Graph
library(ggplot2)
line_plot <- ggplot(prices, mapping= aes(x=year ,y=gold)) + geom_line()
show(line_plot)
ggplot(prices) + geom_line(aes(x=year ,y=gold))+ geom_line(aes(x=year, y=silver))
install.packages("reshape2")
library(reshape2)
## reshape function changes the data from wide to long
data_long <- melt(prices, id.vars = "year")
head(data_long)
View(data_long)
## melt function will change the data format from wide to long form of data
ggplot(data_long, aes(x=year, y=value, color=variable)) + geom_line()
nifty_50 <- read.csv("C:/Users/Lenovo/OneDrive/Documents/R Class Practice/nifty_50.csv")
View(nifty_50)
#treemap
install.packages("treemap")
library(treemap)
treemap(nifty_50, index=c("Company","Industry"), vSize= "Weightage",
type="index", palette = "Spectral", title= "Nifty 50 Index Weightage",
fontsize.title=12)
## R is viewing the weighatge as a string variable due to the % sign
## so to remove it we use the as.numeric function to remove the % sign
nifty_50$Weightage <- as.numeric(sub("%","", nifty_50$Weightage))
treemap(nifty_50, index=c("Company","Industry"), vSize= "Weightage",
type="index", palette = "Spectral", title= "Nifty 50 Index Weightage",
fontsize.title=12)
RColorBrewer::display.brewer.all()
## Next Class create a pie chart using nifty 50 data with according to industries
## customization for treemaps`Preformatted text`
````Preformatted text`Ehm, what is your question?
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