diff --git a/Ex09DataFinal.csv b/Ex09DataFinal.csv
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+++ b/Ex09DataFinal.csv
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+Name,Age,Net Worth (millions)
+Billie Eilish ,20,30
+Yungblud,25,4
+Kevin Abstract,26,1.5
+Kehlani,27,12
+Phoebe Bridgers,28,5
+Joji,30,8
+Jessie Reyez,31,1.4
+Rihanna,34,1400
+Kendrick Lamar,35,75
+Drake,36,250
\ No newline at end of file
diff --git a/Exercise09Data - Sheet1.csv b/Exercise09Data - Sheet1.csv
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+++ b/Exercise09Data - Sheet1.csv	
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+Name,Age,Net Worth (millions)
+Billie Eilish ,20,30
+Yungblud,25,4
+Kevin Abstract,26,1.5
+Kehlani,27,12
+Phoebe Bridgers,28,5
+Joji,30,8
+Jessie Reyez,31,1.4
+Rihanna,34,1400
+Kendrick Lamar,35,75
+Drake,36,250
\ No newline at end of file
diff --git a/Exercise09Final.R b/Exercise09Final.R
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+++ b/Exercise09Final.R
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+#Exercise09
+
+#Part1
+#set directory
+setwd("/Users/romanfresquez/Tutorials/Exercise09")
+#create variable with data on the net worth of singers as their age increases
+artist.worth<-read.csv("Ex09DataFinal.csv")
+
+#load the plotting packages
+library(ggplot2)
+library(cowplot)
+
+#plot age against net worth and add a linear trend line
+ggplot(data=artist.worth,
+       aes(x=Age,y=Net.Worth..millions.))+
+  geom_point()+
+  stat_smooth(method="lm")+
+  theme_classic()
+
+#Part 2
+#create variable with data from data.txt
+location.data<-read.csv("data.txt")
+#find out the column names by pulling the first 10 lines
+location.data[c(1:10),]
+
+#create bar graph of data points
+ggplot(data=location.data, aes(x=region,y=observations))+
+  stat_summary(fun.y=mean,
+               geom="bar")
+
+#following is for checking that my averages on the bar graph were correct
+#create variables for each region data points
+north<-location.data[location.data$region=="north",]
+east<-location.data[location.data$region=="east",]
+south<-location.data[location.data$region=="south",]
+west<-location.data[location.data$region=="west",]
+#check the averages with the bar graph 
+north.mean<-mean(north$observations)
+east.mean<-mean(east$observations)
+south.mean<-mean(south$observations)
+west.mean<-mean(west$observations)
+
+#scatter plot for observartions
+ggplot(data=location.data, aes(x=region,y=observations))+
+  geom_jitter()+
+  theme_classic()
+
+