#####################################################################################
##
##  Intro STATS : Basic Graphics in R
##  JDRS
##
#####################################################################################


#Get the Crimean War Data

crimea <- read.csv("http://reuningscherer.net/stat10x/data/crimean war.csv")

#reformat data for this particular data
levels=levels(crimea$Death_Cause)
crimea2=cbind(crimea[crimea$Death_Cause==levels[1],3],crimea[crimea$Death_Cause==levels[2],3],crimea[crimea$Death_Cause==levels[3],3])
rownames(crimea2)=crimea[1:24,1]
colnames(crimea2)=levels

#see new data
crimea2

###################################
#Bar Char for overall cause of death
barplot(apply(crimea2,2,sum),names.arg=levels, main="Cause of Death", col='blue')

#barchart by month and cause of death
par(mar=c(8,3,2,1))
barplot(t(crimea2),xlab="", col=c("green", "red", "blue"), 
        legend=colnames(crimea2), beside=TRUE, las=2)
mtext("Month/Year", side=1, line=6)


#Pie Chart
pie(apply(crimea2,2,sum), labels=levels, main="Pie Chart of Death Cause")

par(mar=c(5,4,4,2)+.1)

#####################################
# Pie Chart with Percentages
slices <- apply(crimea2,2,sum)
pct <- round(slices/sum(slices)*100)
lbls <- paste(levels, pct) # add percents to labels 
lbls <- paste(lbls,"%",sep="") # ad % to labels 
pie(slices,labels = lbls, col=rainbow(length(lbls)),
    main="Pie Chart of Death Cause") 


#3D Pie Chart
library(plotrix)
pie3D(slices,labels=lbls,explode=0.05,
      main="Pie Chart of Death Cause ")


#for a polar graph, see  https://rdrr.io/cran/HistData/man/Nightingale.html  


#####################################
# Histogram for World Bank Fertility Data

#Get WB Data

WB <- read.csv("http://reuningscherer.net/stat10x/data/WorldBank2013.csv")

#see variables available
names(WB)

#Make a histrogram of fertility rates
hist(WB$Fertility, col="blue", main="Histogram of Fertility Rates", xlab="Avg Births Per Woman Over Lifetime", breaks=10)



#####################################
# DotPlots for World Bank Data
#  Called a STRIPCHART in R


#Life Expectancy
stripchart(round(WB$LifeExp), method="stack", offset=.5, at=.15, pch=19, cex=1.3, col="blue", main="Dot Plot of Fertility Rates", xlab="Years")

#GNI per capita
stripchart(round(WB$GNI/1000), method="stack", offset=.5, at=.15, pch=19, cex=1.3, col="blue", main="Dot Plot of GNI Per Capita Rates", xlab="GNI (Thousands of Dollar Equivalents)")

#histogram for comparison
hist(WB$GNI/1000, col="blue", main="Histogram of GNI per Capita", xlab="GNI per Capita (Thousands of Dollars)", breaks=10)


########################################
#   Boxplots

#Boxplot of Fertility Rates

boxplot(WB$Fertility, col="yellow", lwd=2, main="Boxplot of Fertility Rates", ylab="Avg Number of Children per Woman")

#Horizontal Boxplot
boxplot(WB$Fertility, col="yellow", lwd=2, main="Boxplot of Fertility Rates", xlab="Avg Number of Children per Woman", horizontal=T)

#Boxplot for Roundup Data

#get data
roundup <- read.csv("http://reuningscherer.net/stat10x/data/roundup.csv")

#just radishes
radish=roundup[1:15,]

#Boxplot for radish by concentration
boxplot(radish$Dry.Weight~radish$Concentration, col="green", lwd=2, main="Radish Dry Weight by Concentration", ylab="Dry Weight", xlab="Concentration")