From 23d0ffe55df84983d297ee04e9bf47428ed32549 Mon Sep 17 00:00:00 2001
From: Johanna1403 <johannaolesk99@gmail.com>
Date: Fri, 26 Nov 2021 16:18:55 -0500
Subject: [PATCH] Final code with the txt data file for the first task added.

---
 EstonianCoronaData.txt | 640 +++++++++++++++++++++++++++++++++++++++++
 Exercise10.R           |  59 ++++
 2 files changed, 699 insertions(+)
 create mode 100644 EstonianCoronaData.txt
 create mode 100644 Exercise10.R

diff --git a/EstonianCoronaData.txt b/EstonianCoronaData.txt
new file mode 100644
index 0000000..d2cb195
--- /dev/null
+++ b/EstonianCoronaData.txt
@@ -0,0 +1,640 @@
+"days","last14cases"
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diff --git a/Exercise10.R b/Exercise10.R
new file mode 100644
index 0000000..5874c24
--- /dev/null
+++ b/Exercise10.R
@@ -0,0 +1,59 @@
+# Exercise 10
+
+# task 1
+
+setwd("/Users/johi/Desktop/Courses/Biocomputing/Biocomp_tutorial12")
+
+# opening the initial data of Estonian corona statistics
+data <- read.table(file = "opendata_covid19_tests_total.csv", header = T, sep = ",", stringsAsFactors = FALSE)
+head(data)
+
+# creating a new dataframe that contains 2 columns (days from the first Covid positive test and TotalCasesLast14D - the sum of positive cases last 14 days)
+days = seq(1, dim(data)[1], 1)
+last14cases = data$TotalCasesLast14D
+
+coronaData = data.frame(days, last14cases)
+
+#writing a new txt file fir the new dataframe
+write.table(coronaData, file = "EstonianCoronaData.txt", sep = ",", col.names = TRUE)
+
+data <- read.table(file = "EstonianCoronaData.txt", header = T, sep = ",", stringsAsFactors = FALSE)
+
+# importing the ggplot library
+library(ggplot2)
+
+# plotting the data with trend line (polynomial trend line)
+ggplot(data = data,aes(x = days, y = last14cases)) +
+  geom_point() +
+  stat_smooth() +
+  xlab("Day from the start") +
+  ylab("Total cases last 14 days") +
+  theme_classic()
+
+
+# task 2
+
+data <- read.table(file = "data.txt", header = T, sep = ",", stringsAsFactors = FALSE)
+tail(data)
+
+# bar plot of the means of the four populations
+ggplot(data, aes(x = region, y = observations)) +
+  stat_summary(fun = mean, geom = "bar") +
+  xlab("Region") +
+  ylab("Observations") +
+  theme_classic()
+
+# scatterplot
+ggplot(data = data,aes(x = region, y = observations)) +
+  geom_point() +
+  geom_jitter()
+  xlab("Region") +
+  ylab("Observations") +
+  theme_classic()
+
+
+# Although they are spread very differently, the means are basically the same.
+# Therefore, there is no difference in the bar plot but when it comes to the scatterplot we can see the difference between the regional observations.
+# Scatterplot shows the variance while the bar plot doesn't.
+
+