diff --git a/Assignment10Maron.R b/Assignment10Maron.R
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+#Tutorial 12 Assignment 
+setwd("/Users/erinmaron/Desktop/r-novice-inflammation/BioComp/ClassWork/Biocomp_tutorial12")
+
+#Problem 1
+#Uses Energy vs. Time Data for a football player doing a flip, data collected from a Physics project 
+library(ggplot2)
+data<-read.table(file="Energy.txt", header=TRUE, sep="\t")
+
+ggplot(data=data, aes(x=Time, y=Energy))+
+  geom_point() +
+  ylab("Energy(J)")+
+  xlab("Time(s)")+
+  theme_classic()+
+  stat_smooth(method = "lm")
+
+#Problem 2 
+obsv<-read.table(file="data.txt", header=TRUE, sep=",")
+#Barplot using means of 4 populations 
+ggplot(data=obsv, aes(x=region))+
+  geom_bar()+
+  theme_classic()
+
+#Scatterplot of all observations (jitter)
+ggplot(data=obsv, aes(x=region, y=observations))+
+  geom_jitter()+
+  theme_classic()
+
+  #The two plots do tell different stories, as the scatterplot shows the distribution of the population 
+#as well as the different groupings of data. It allows for a visualization of the distribution by region.
+#The bar plot does not show much information besides the total number of observations within a region.
+#There is no way to diffrentiate from the regions, it is only shown that they all have the same total count. 
\ No newline at end of file
diff --git a/Energy.txt b/Energy.txt
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+++ b/Energy.txt
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+Time	Energy
+0.03	2804.56318
+0.07	4785.40265
+0.1	12797.61902
+0.13	6785.5305
+0.17	4045.48011
+0.2	3464.37218
+0.23	3850.94958
+0.27	1585.90583
+0.3	1826.28253
+0.33	4062.90954
+0.37	2261.33812
+0.4	1428.47739
+0.43	1721.58945
+0.47	2248.39566
+0.5	1404.22996
+0.53	1160.67113
+0.57	1064.6325
+0.6	1063.47221
+0.63	1030.9257
+0.67	915.12021
+0.7	1027.92638
+0.73	681.57503
+0.77	546.68208
+0.8	909.1173
\ No newline at end of file