Graphics in R

Library: ggplot2

Data structures

Most of the data in R are saved in a dataframe format or tibble format . This will help your data analysis and also make easy the use of many libraries in R.

library(readr)# read cvs
library(readxl) #read xls
library(dplyr)
library(knitr)      # web widget
library(tidyverse)  # data manipulation
library(data.table) # fast file reading
library(kableExtra) # nice table html formating 
library(gridExtra)  # arranging ggplot in grid
library(caTools)    # split 
library(plotrix)
library(MASS)

Import data: Data used in this material are downloaded from this link: https://www.kaggle.com/sonujha090/bank-marketing

data_bank <- read.csv("~/ALDA DOC/ALDA 2020/Tirana bank-desktop/Tirana bank-trajnimi qershor Alda/Tirana Bank-R materials/Data _bank dataset/bank-full.csv", sep=";")

#ggplot library

Libraries helpfull to import and read the data.

library(readxl)
library(httr)

Applied to bank_data

d.age= ggplot (data_bank) 
g1 = d.age + geom_histogram(aes(x=age),color="black", fill="white", binwidth = 5) +
  ggtitle('Age histogram (red line=mean)') +
  ylab('Frequence') +
  xlab('Age') +
  geom_vline(aes(xintercept = mean(age), color = "red")) +
  scale_x_continuous(breaks = seq(0,100,5)) +
  geom_text(x=45, y=7500, label="Mean",color="red",size=6)+
  theme(legend.position = "none")
g1

Box Plot

g2 = d.age + geom_boxplot(aes(x='Clients', y=age),color="red",fill="yellow")+ ggtitle('Box-Plot age') + ylab('age')
g2

Grouping by a categorical variable (example “y” variable in data_bank)

g3 <- ggplot(data_bank, aes(x=age, fill=marital)) + 
  geom_histogram(binwidth = 2, alpha=0.7) +
  facet_grid(cols = vars(y)) +
  expand_limits(x=c(0,100)) +
  scale_x_continuous(breaks = seq(0,100,10)) +
  ggtitle("Age and civil status")+
  ylab('Frequence') +
  xlab('Age')
g3

g3 <- ggplot(data_bank, aes(x=age, fill=marital)) + 
  geom_histogram(binwidth = 2, alpha=0.7) +
  facet_grid(cols = vars(education)) +
  expand_limits(x=c(0,100)) +
  scale_x_continuous(breaks = seq(0,100,10)) +
  ggtitle("Age and civil status")+
  ylab('Frequence') +
  xlab('Age')
g3

Grouping by “y” variable (which has two cahtegories “yes” and “no”)

m.1 <- data_bank %>% group_by(y) %>% summarize(grp.mean=mean(age))# average age for each group

`summarise()` ungrouping output (override with `.groups` argument)

View(m.1)# 
g4<-ggplot (data_bank, aes(x=age)) + 
  geom_histogram(color = "blue", fill = "orange", binwidth = 5) +
  facet_grid(cols=vars(y)) + 
  ggtitle('Age distribution') + ylab('Frequence') + xlab('Age') +
  scale_x_continuous(breaks = seq(0,100,5)) +
  geom_vline(data=m.1, aes(xintercept=grp.mean), color="red",linetype="dashed")
g4

Having higher education level is seen to contribute to higher subscription of term deposit. Most clients who subscribe are from ‘secondary’ and ‘tertiary’ education levels. Tertiary educated clients have higher rate of subscription (15%) from total clients called.

g6<-ggplot(data = data_bank, aes(x=education, fill=y)) +
  geom_bar() +
  ggtitle("Education vs Y") +
  xlab(" Education level") +
  guides(fill=guide_legend(title="Y"))
g6

Clients that subscribe to term deposits have lower loan balances?

m.2 <- data_bank %>% group_by(y) %>% summarize(grp.mean2=mean(balance))

`summarise()` ungrouping output (override with `.groups` argument)

g5<-ggplot (data_bank, aes(x=balance)) + 
  geom_histogram(color = "blue", fill = "blue") +
  stat_bin(bins=3)+
  facet_grid(cols=vars(y)) + 
  ggtitle('Balance histogram') + ylab('Frequency') + xlab('Balance') +
  geom_vline(data=m.2, aes(xintercept=grp.mean2), color="red",linetype="dashed")
g5

Try to change stat_bin(bins=30)

g7<-ggplot(data = data_bank, aes(x=marital, fill=y)) +
  geom_bar() +
  ggtitle("Civil status vs Deposit") +
  xlab(" Civil status") +
  ylab("Freq")+
  guides(fill=guide_legend(title="Deposit"))
g7

data_bank %>% 
  ggplot(aes(age, duration)) +
  geom_point(color="blue") +
  facet_grid(cols = vars(y)) +
  scale_x_continuous(breaks = seq(0,100,10)) +
  ylab("Age")+
  xlab("Duration")+
  ggtitle("Scatterplot")

data_bank %>% filter(campaign < 63) %>% 
  ggplot(aes(campaign, duration)) +
  geom_point(color="red") +
  facet_grid(cols = vars(y)) +
  ylab("Duration")+
  xlab("Campain")+
  ggtitle("Scatterplot")

We will use the COVID-19 is stored with automatic updates every day at ECDC site.

Reference : https://rpubs.com/eraldagjika/601456

Some libraries are:

library(ggplot2)
library(gganimate)
library(quantmod)
library(directlabels)
library(hrbrthemes)
library(viridis)
library(dplyr)
library(dbplyr)
library(gifski)

Import the data and save it as: data<-

lista<-c("Romania","Albania","Italy", "France","United_States_of_America","Germany", "United_Kingdom")
europa<-filter(data,data$countriesAndTerritories %in% lista)
p2.1<-ggplot(europa,aes(europa$dateRep,europa$cases,group=europa$countriesAndTerritories, color=factor(europa$countriesAndTerritories))) +
  geom_line() +
  geom_point() +
  ylab("Cases") +
  xlab("Data")+
  scale_color_brewer(palette="Dark2") +
  ggtitle("New cases (31 Dec 2019-13 Dec 2020)") +
  geom_dl(aes(label =europa$countriesAndTerritories), method = list(dl.trans(x = x - 1.2), "last.points"))+
  theme(legend.position="top")
p2.1

lista<-c("Romania","Albania","Kosovo")
europa<-filter(data,data$countriesAndTerritories %in% lista)
europa

p2.1<-ggplot(europa,aes(europa$dateRep,europa$cases,group=europa$countriesAndTerritories, color=factor(europa$countriesAndTerritories))) +
  geom_line() +
  geom_point() +
  ylab("Cases") +
  xlab("Data")+
  scale_color_brewer(palette="Dark2") +
  ggtitle("New cases (31 Dec 2019-13 Dec 2020)") +
  geom_dl(aes(label =europa$countriesAndTerritories), method = list(dl.trans(x = x - 1.2), "last.points"))+
  theme(legend.position="top")
p2.1

p2.1+facet_wrap(~europa$countriesAndTerritories)
p2.1+transition_reveal(europa$day)

Use of `europa$dateRep` is discouraged. Use `dateRep` instead.Use of `europa$cases` is discouraged. Use `cases` instead.Use of `europa$countriesAndTerritories` is discouraged. Use `countriesAndTerritories` instead.Use of `europa$countriesAndTerritories` is discouraged. Use `countriesAndTerritories` instead.Use of `europa$dateRep` is discouraged. Use `dateRep` instead.Use of `europa$cases` is discouraged. Use `cases` instead.Use of `europa$countriesAndTerritories` is discouraged. Use `countriesAndTerritories` instead.Use of `europa$countriesAndTerritories` is discouraged. Use `countriesAndTerritories` instead.Use of `europa$countriesAndTerritories` is discouraged. Use `countriesAndTerritories` instead.Use of `europa$dateRep` is discouraged. Use `dateRep` instead.Use of `europa$cases` is discouraged. Use `cases` instead.Use of `europa$countriesAndTerritories` is discouraged. Use `countriesAndTerritories` instead.Use of `europa$countriesAndTerritories` is discouraged. Use `countriesAndTerritories` instead.

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Western Balkan Countries

lista.11<-c("Albania", "Kosovo", "Bosnia_and_Herzegovina","Montenegro","Serbia", "North_Macedonia")
europa.11<-filter(data,data$countriesAndTerritories %in% lista.11)
europa.11

p2.11<-ggplot(europa.11,aes(europa.11$dateRep, europa.11$cases, group=europa.11$countriesAndTerritories, color=factor(europa.11$countriesAndTerritories)) 
) +
  geom_line() +
  geom_point() +
  ylab("New Cases") +
  xlab("Time")+
  ylim(0,1000)+
  scale_color_brewer(palette="Dark2") +
  ggtitle("New Cases Western Balkan (31 Dec 2019-13 Dec 2020)") +
  geom_dl(aes(label =europa.11$countriesAndTerritories), method = list(dl.trans(x = x - 1.2), "last.points"))+
  theme(legend.position="top")
p2.11

p2.11+facet_wrap(~europa.11$countriesAndTerritories)

lista.11<-c("Albania", "Kosovo", "Bosnia_and_Herzegovina","Montenegro","Serbia", "North_Macedonia")
europa.11<-filter(data,data$countriesAndTerritories %in% lista.11)
europa.11

p2.11<-ggplot(europa.11,aes(europa.11$dateRep, europa.11$deaths, group=europa.11$countriesAndTerritories, color=factor(europa.11$countriesAndTerritories))) +
  geom_line() +
  geom_point() +
  ylab("Deaths") +
  xlab("Time")+
  ylim(0,50)+
  scale_color_brewer(palette="Dark2") +
  ggtitle("Deaths Western Balkan (31 Dec 2019-13 Dec 2020)") +
  geom_dl(aes(label =europa.11$countriesAndTerritories), method = list(dl.trans(x = x - 1.2), "last.points"))+
  theme(legend.position="top")
p2.11

p2.11+facet_wrap(~europa.11$countriesAndTerritories)

Dynamic graph showing the evolution of new cases

p2.11 + transition_reveal(europa.11$day) 

Use of `europa.11$dateRep` is discouraged. Use `dateRep` instead.Use of `europa.11$deaths` is discouraged. Use `deaths` instead.Use of `europa.11$countriesAndTerritories` is discouraged. Use `countriesAndTerritories` instead.Use of `europa.11$countriesAndTerritories` is discouraged. Use `countriesAndTerritories` instead.Use of `europa.11$dateRep` is discouraged. Use `dateRep` instead.Use of `europa.11$deaths` is discouraged. Use `deaths` instead.Use of `europa.11$countriesAndTerritories` is discouraged. Use `countriesAndTerritories` instead.Use of `europa.11$countriesAndTerritories` is discouraged. Use `countriesAndTerritories` instead.Use of `europa.11$countriesAndTerritories` is discouraged. Use `countriesAndTerritories` instead.Use of `europa.11$dateRep` is discouraged. Use `dateRep` instead.Use of `europa.11$deaths` is discouraged. Use `deaths` instead.Use of `europa.11$countriesAndTerritories` is discouraged. Use `countriesAndTerritories` instead.Use of `europa.11$countriesAndTerritories` is discouraged. Use `countriesAndTerritories` instead.

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Reference

https://daviddalpiaz.github.io/appliedstats/multiple-linear-regression.html

https://www.r-graph-gallery.com/ggplot2-package.html

https://cran.r-project.org/web/packages/Rcmdr/Rcmdr.pdf

https://www.tutorialspoint.com/r/r_pie_charts.htm

https://www.tutorialspoint.com/r/r_chi_square_tests.htm

https://www.tutorialspoint.com/r/r_analysis_of_covariance.htm

https://www.tutorialspoint.com/r/r_logistic_regression.htm

https://gregor-mathes.netlify.app/2021/01/01/rethinking-chapter-4/

https://www.r-bloggers.com/2013/02/collinearity-and-stepwise-vif-selection/

https://www.statology.org/variance-inflation-factor-r/

https://www.r-graph-gallery.com/index.html

Eralda Gjika

14 January 2021