diff --git a/Exercise09_script.R b/Exercise09_script.R
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+++ b/Exercise09_script.R
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+#Assumes working directory is set to Exercise09 directory
+
+library(ggplot2)
+library(cowplot)
+
+#1
+
+#loads txt file
+q1_data <- read.table("q1_data.txt", header=TRUE, sep="\t")
+
+#generates scatterplot with trendline
+ggplot(q1_data, aes(x = Height, y = Weight)) +
+  geom_point() +
+  xlab("Height (ft)") +
+  ylab("Weight (lbs)") +
+  stat_smooth(method="lm") +
+  theme_classic()
+
+#2
+
+#loads txt file
+q2_data <- read.table("data.txt", header=TRUE, sep=",")
+
+#generates barplot showing the mean observations for each region
+ggplot(q2_data, aes(x=region,y=observations, fill=region)) +
+  stat_summary(fun=mean, geom= "bar") +
+  xlab("Region") +
+  ylab("Average Observations") +
+  theme_classic()
+
+#generates scatterplot showing observations for each region
+ggplot(q2_data, aes(x=region,y=observations, color=region)) +
+  geom_jitter() +
+  xlab("Region") +
+  ylab("Observations") +
+  theme_classic()
+
+#The scatterplot and barplot do tell different stories. The barplot makes it seem like
+#the observations are the same in all regions. However, looking at the scatterplot,
+#we can see that the distribution of observations varies quite a bit between regions.
+#For example, in the east there is a wide range of observations. In the north, the range
+#of observations is small, yet the average is the same as the other regions. In the south,
+#there seems to be two clusters of groups, a high and low obervation cluster. The west
+#also showed a wide distribution of values.
diff --git a/q1_data.txt b/q1_data.txt
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--- /dev/null
+++ b/q1_data.txt
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+Height	Weight
+6.1	203
+4.8	122
+5.4	145
+5.2	135
+5.7	162
+5.8	164
+6.3	220
+5.5	151
+5.5	157
+5.2	123
+5.1	118
+6.2	199
+6.5	241
+5.6	157
+5.7	170
+4.8	111
+4.9	118
+5.2	130
+5.4	147
+5.5	153
+4.8	109
+4.9	117
+5	124
+5.1	125
+5.2	137
+5.3	135
+5.4	151
+5.5	158
+5.6	165
+5.7	169
+5.8	173
+5.9	183
+6	190
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