diff --git a/Exercise09.R b/Exercise09.R
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+++ b/Exercise09.R
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+library('ggplot2')
+
+##task 1
+data_1 <- read.table("data_for_task_1.txt",header = TRUE,sep = "\t")
+ggplot(data = data_1,
+       aes(x = x, y = y)) +
+  geom_point() +
+  stat_smooth(method="loess")
+
+##task 2
+#barplot of the means of the four populations
+data_2 <- read.table("data.txt",header = TRUE,sep = ",")
+ggplot(data_2, aes(x = region, y = observations)) +
+      stat_summary(fun.y = mean,
+               geom = "bar")
+#scatter plot of all of the observations
+ggplot(data = data_2,
+       aes(x = region, y = observations)) +
+       geom_jitter()
+#Do the bar and scatter plots tell you different stories? Why?
+#Yes, they do. The bar plot shows the mean of each group, which helps us to 
+#know about the overall value and central tendency of them; the scatter plot 
+#shows the distribution of data inside the group.
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diff --git a/data_for_task_1.txt b/data_for_task_1.txt
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index 0000000..e5ea5a0
--- /dev/null
+++ b/data_for_task_1.txt
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+x	y
+0.0944615	1.820397
+1.5610722	2.236232
+2.8582951	4.511865
+4.2315074	4.110922
+5.9662839	7.328110
+6.4333511	7.801370
+7.6254131	8.534576
+7.8351134	8.613648
+9.3212257	11.250261
+9.4099321	10.831325
+11.5185412	13.283047
+12.6260518	12.919002
+13.3778587	14.740210
+13.9052150	14.424189
+15.0510354	17.099260
+16.1385003	17.204369
+17.4947985	17.985392
+18.8766295	19.063242
+18.4800961	20.776075
+19.6601686	21.306753
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