diff --git a/DailyMeetingPlot.R b/DailyMeetingPlot.R
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index 0000000..590ff84
--- /dev/null
+++ b/DailyMeetingPlot.R
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+#Generate a scatter plot showing the average time I spent in meetings (per day) during the month of November.
+
+data = read.table("MeetingData.csv", header = TRUE, sep = ",", stringsAsFactors = FALSE)
+data$Day <- factor(data$Day, levels = data$Day)
+library(ggplot2)
+ggplot(data, aes(x = Day, y = Hours)) +
+  geom_point() +
+  theme_classic() +
+  geom_smooth(method = "lm")
+
+ 
diff --git a/MeetingData.csv b/MeetingData.csv
new file mode 100644
index 0000000..11d86c7
--- /dev/null
+++ b/MeetingData.csv
@@ -0,0 +1 @@
+Day,Hours
Monday,4.325
Tuesday,4
Wednesday,3.5
Thursday,3.125
Friday,2.5
\ No newline at end of file
diff --git a/Problem 2.R b/Problem 2.R
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index 0000000..c966fd3
--- /dev/null
+++ b/Problem 2.R	
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+##Generate a barplot of the means of the four populations
+
+#Load the data file
+df <- read.table("data.txt", header = TRUE, sep=",", stringsAsFactors = TRUE)
+
+#Create the barplot
+ggplot(data = df, aes(x = region, y = observations))+
+  stat_summary(fun = mean, geom = "bar")+
+  theme_classic()
+
+##Generate a scatterplot of all of the observations
+
+ggplot(data = df, aes(x = region, y = observations)) +
+  geom_point() +
+  geom_jitter() +
+  theme_classic()
+
+#The barplot of the means, without error bars, makes the data appear as though 
+#the observations from the four regions are very similar. While the means of the 
+#regions are similar, the scatterplot shows that the clustering of the data is 
+#very different across the regions and the north region is the only region with 
+#observations clustered around the mean. Error bars on the barplot would show 
+#the data more accurately.