diff --git a/Exercise10.R b/Exercise10.R
new file mode 100644
index 0000000..04a7134
--- /dev/null
+++ b/Exercise10.R
@@ -0,0 +1,69 @@
+setwd("~/OneDrive - Johns Hopkins/Documents/Notre Dame/Semester 1/Introduction to Biocomputing/Biocomp_tutorial12/")
+
+# load packages in
+library(ggplot2)
+library(cowplot)
+
+### Problem 1 ###
+
+# load in data
+drug.el <- read.table(file = "drug-elimination.txt", header = TRUE, sep = '\t',stringsAsFactors = FALSE)
+
+# scatter plot of data
+fig1 <- ggplot(drug.el, aes(x = Time, y = Drug.Concentration)) +
+  stat_smooth(method="loess") +
+  geom_point() +
+  theme_classic() +
+  xlab("Time (hr)") +
+  ylab("[Drug] (mg/dL)") +
+  ggtitle("Elimination of Drug over Time")
+
+# alternative scatter plot of data with linear trend
+fig2 <- ggplot(drug.el, aes(x = Time, y = ln.Drug.Concentration.)) +
+  stat_smooth(method="lm") +
+  geom_point() +
+  theme_classic() +
+  xlab("Time (hr)") +
+  ylab("ln([Drug])") +
+  ggtitle("Elimination of Drug over Time (Linearized)")
+
+plot_grid(fig1, fig2, 
+          labels = c("a", "b"),
+          rel_widths = c(1, 0.85),
+          ncol = 2,
+          nrow = 1)
+
+### Problem 2
+
+# load in data
+data <- read.table(file = "data.txt", header = TRUE, sep = ',',stringsAsFactors = FALSE)
+
+# bar plot of mean of data
+fig3 <- ggplot(data, aes(x = region, y = observations)) +
+  stat_summary(fun.y = mean, geom ="bar") +
+  theme_classic() +
+  theme(axis.text.x = element_text(angle=65, vjust=0.6)) +
+  xlab("Region") +
+  ylab("Observation")
+
+# scatter plot of data
+fig4 <- ggplot(data, aes(x = region, y = observations)) +
+  geom_jitter() +
+  theme_classic() +
+  theme(axis.text.x = element_text(angle=65, vjust=0.6)) +
+  xlab("Region") +
+  ylab("Observation")
+ 
+plot_grid(fig3, fig4, 
+          labels = c("a", "b"),
+          rel_widths = c(1, 0.85),
+          ncol = 2,
+          nrow = 1)
+
+
+# Based on the plots that were displayed, they do not tell the same story. While the bar plot shows that the mean
+# value of the four populations is very similar, that does not necessarily mean the data that makes up each of these
+# populations is spread out in a similar manner. This is evident in the scatter plot which shows that while the east 
+# and west regions are quite spread out, this is not the case for the north on south regions which have pockets of 
+# of data concentrated around a particular region. 
+
diff --git a/drug-elimination.txt b/drug-elimination.txt
new file mode 100644
index 0000000..b0e7932
--- /dev/null
+++ b/drug-elimination.txt
@@ -0,0 +1,8 @@
+Time 	Drug Concentration	ln(Drug Concentration)
+1	3	1.098612289
+3	1.695	0.527682741
+5	1.05	0.048790164
+7	0.645	-0.438504962
+10	0.3	-1.203972804
+18	0.0375	-3.283414346
+24	0.00803	-4.824570751