diff --git a/Exercise9.R b/Exercise9.R
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--- /dev/null
+++ b/Exercise9.R
@@ -0,0 +1,37 @@
+# Nathan Kroeze
+# Exercise 9
+# 11/18/2022
+
+library(tidyverse)
+
+# Q1: Enter those data into a text file or Excel and then
+# save a text file, and write a script that loads this text file and produces a scatter plot of those two variables
+# that includes a trend line.
+
+setwd("C:/Users/Natha/Desktop/Biocomputing/Exercise09/")
+researchData <- read.csv("researchData.csv")
+researchData <- researchData[,-1] #remove count column
+
+ggplot(researchData, aes(d13C,d15N)) + 
+  geom_jitter() + geom_smooth(method = "lm") + 
+  labs(title = "Research Isotope Data")
+ 
+# Q2: Write a script that generates two figures that sumamrize the data. First, show
+# a barplot of the means of the four populations. Second, show a scatter plot of all of the observations.
+
+dataEx <- read.table("data.txt",header = TRUE, sep = ",")
+
+ggplot(data = dataEx, aes(x=region,y=observations)) + 
+  stat_summary(geom = "bar",fun = "mean") +
+  xlab("Region") + ylab("Means of Populations") + theme_bw() 
+
+ggplot(data = dataEx, aes(x=region,y=observations)) + 
+  geom_jitter() +
+  xlab("Region") + ylab("Observations") + theme_bw() 
+
+# Answers:
+# The plots tell different stories because the bar plot demonstrates that the average population for 
+# each region is roughly the same, however the scatter plot shows that the variance in these data are not
+# consistent across regions. For example, the western region has a wide variance of observations but
+# the northern region has a narrow densely populated band. This difference is lost in the bar plot and
+# would have been missed if not plotted separately.
diff --git a/researchData.txt b/researchData.txt
new file mode 100644
index 0000000..f780f66
--- /dev/null
+++ b/researchData.txt
@@ -0,0 +1,242 @@
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