diff --git a/exercise9script.R b/exercise9script.R
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+# ggplot2 and cowplot load
+library(ggplot2)
+library(cowplot)
+
+# set up directory
+setwd("/Users/7907blueyes/Desktop/Biocomputing/R/Exercise09")
+
+# QUESTION ONE  
+# load in data
+predationrisk <- read.csv("predationrisk.csv", header = TRUE, stringsAsFactors = FALSE)
+# scatter plot with trend line
+ggplot(predationrisk, aes(x = Group.Size, y = Predation.Risk)) + geom_point() +
+  xlab("Group Size") + ylab("Predation Risk") + stat_smooth(method = "lm")
+
+# QUESTION TWO
+# load in data (separated by commas)
+data <- read.csv("data.txt", header = TRUE, stringsAsFactors = FALSE)
+# calculating mean of averages
+avgnorth <- mean(data[data$region == "north",2])
+avgsouth <- mean(data[data$region == "south",2])
+avgwest <- mean(data[data$region == "west",2])
+avgeast <- mean(data[data$region == "east",2])
+# saving averages in data frame
+avgdata <- data.frame(region = c("north", "south", "east", "west"), average = c(avgnorth, avgsouth, avgeast, avgwest))
+# bar plot
+ggplot(avgdata, aes(x = region, y = average)) + geom_bar(stat = "identity") + theme_bw()
+# scatter plot with observations
+ggplot(data, aes(x = region, y = observations)) + geom_jitter()
+
+# Do the bar and scatter plots tell you different stories? Yes, the two graphs tell different 
+# stories about the data. In the bar graph, the region averages are incredibly similar leading 
+# one to believe that the observations are fairly close. In the scatter plots, it is clear that 
+# the observations are concentrated at different points and that the regions are not altogether 
+# that similar. 
+
+                                                                 
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diff --git a/predationrisk.csv b/predationrisk.csv
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+++ b/predationrisk.csv
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+Group Size,Predation Risk
+1,0.7
+2,0.35
+3,0.24
+4,0.23
+5,0.2
+6,0.17
+7,0.16
+8,0.14
+9,0.13
+10,0.1
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