From bd94b3b11865282b782a5faec025ec0c445a2ef4 Mon Sep 17 00:00:00 2001
From: Bethany Oceguera <bocegue2@nd.edu>
Date: Fri, 18 Nov 2022 10:12:02 -0500
Subject: [PATCH] Completed Exercise 09

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 Ex09.R | 65 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 65 insertions(+)
 create mode 100644 Ex09.R

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+#Exercise 09
+
+#Set the working directory and load packages
+setwd("/Users/bethoceguera/Documents/PhD Year 1/First Semester/Introduction to Biocomputing/Exercises/Exercise09")
+library(ggplot2) #To load ggplot package
+library(cowplot) #To load cowplot package
+
+# 1. Write a script that loads this text file and produces a scatter plot of those two variables that inlcudes a trendline
+stats <- read.csv("local-authority-statistics-june-2022-quarter.csv")
+
+localAuthority <- ggplot(data=stats,aes(x=Period,y=Data_value,))+
+  geom_point()+
+  xlab("Year")+ylab("Dollars")+
+  ggtitle("Local Authority Financial Statistics")+
+  theme_classic()+
+  theme(plot.title = element_text(hjust = 0.5))+
+  geom_smooth(method="lm")
+
+localAuthority
+  
+# 2. Write a script that generates two figures that summarize the data 
+
+# First, show a barplot of the means of the four populations
+data <- read.csv("data.txt") #Load Data
+northData <- data[data$region=="north",] #North region data
+eastData <- data[data$region=="east",] #East region data
+southData <- data[data$region=="south",] #South region data
+westData <- data[data$region=="west",] #West region data
+
+northMean <- mean(northData$observations) #Mean of North Data
+eastMean <- mean(eastData$observations) #Mean of East Data
+southMean <- mean(southData$observations) #Mean of South Data
+westMean <- mean(westData$observations) #Mean of West Data
+
+SummTable <- data.frame(Region=c("North","East","South","West"),
+  Mean=c(northMean,southMean,eastMean,westMean)) #Summary Table
+
+barplot <- ggplot(data=SummTable,aes(x=Region,y=Mean))+
+  geom_bar(stat = "identity")+
+  theme_bw()+theme_classic()+ 
+  xlab("Region")+ylab("Means")             #Variable of barplot of means
+
+barplot #Shows the result
+
+#Second, show a scatter plot of all the observations 
+scatterplot <- ggplot(data=data,aes(x=region,y=observations))+
+  geom_jitter()+
+  theme_bw()+theme_classic()+
+  xlab("Region")+ylab("Observations")      #Variable of scatter plot of all observations
+
+scatterplot #Shows the result
+
+#Plot both together
+fig <- plot_grid(barplot,scatterplot,labels=c("a","b"),rel_widths=c(1,1),ncol=2,nrow=1)
+fig #Shows the resultant 2 graphs adjacent to each other 
+
+#Do the bar and scatter plots tell you different stories? Why?
+#   The bar and scatter plots do tell different stories.
+#   The bar chart is rather limited in that it only tells the mean values of each region
+#     while not showing distribution of values.
+#   Conversely, the scatter plot does not account for mean values but does shows the significant
+#     distribution that exists among the values. Although knowing the mean may be helpful,
+#     the bar graph doesn't showcase that observation values can go as high as 30+ or as low as 1-. 
+
+##DONE