Final Answers for Exercise Nine

Exercise09ans.R

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+#Problem 1
+#Find data on any two variable of interest and plot
+
+soccer<-read.csv("soccer - Sheet1 (2).csv",header=TRUE)
+ggplot(soccer, aes(x=Number.of.Matches, y=Avg..Game.Score)) 
++xlab("Number of Matches (Mexico)")+ylab("Mexico's Avg Score")+
+  geom_point()+geom_smooth(method=lm)+theme_classic()
+
+#Problem 2
+# Creating a scatter plot of region vs number of observations
+#make sure to be on the correct wd--> Exercise09
+
+dat<-read.table("data.txt",header=TRUE,stringsAsFactors = FALSE, sep=",")
+ggplot(dat, aes(x=region,y=observations))+ geom_jitter(alpha=0.3)
++xlab("Region")+ylab("Observations")
+
+#creating a bar plot
+#still using the same data as before
+dat<-read.table("data.txt",header=TRUE,stringsAsFactors = FALSE, sep=",")
+ggplot(dat, aes(x=region,y=observations))+ stat_summary(geom="bar",fun="mean")+
+  xlab("Region")+ylab("Mean Observation")+theme_classic()
+
+#The scatter plot and bar do tell me different stories. The scatter plot shows
+#the number of observations for each region. East and West both have observations
+#that ranges from 0-30 and some outliers. While North is specific to 10-20 and 
+#South is from 4-6 and 18-30. In contrast, the bar graph shows a mean of obser-
+#-vations for each region. They all appear to be around 15. As such, there 
+# is perhaps some variable that causes makes some observations more spread out
+#vs specific in different regions. 
+
+
+

Exercise09answers.R

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+#Problem 1
+#Find data on any two variable of interest and plot
+
+soccer<-read.csv("soccer - Sheet1 (2).csv",header=TRUE)
+ggplot(soccer, aes(x=Number.of.Matches, y=Avg..Game.Score)) 
++xlab("Number of Matches (Mexico)")+ylab("Mexico's Avg Score")+
+  geom_point()+geom_smooth(method=lm)+theme_classic()
+
+#Problem 2
+# Creating a bar plot