361 lines
823 KiB
HTML
361 lines
823 KiB
HTML
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<!DOCTYPE html>
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<html xmlns="http://www.w3.org/1999/xhtml">
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<head>
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<meta charset="utf-8" />
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<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
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<meta name="generator" content="pandoc" />
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<meta name="author" content="Jonathan Herrewijnen" />
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<meta name="date" content="2018-09-17" />
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<title>Case Study Sleep</title>
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<h1 class="title toc-ignore">Case Study Sleep</h1>
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<h4 class="author"><em>Jonathan Herrewijnen</em></h4>
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<h4 class="date"><em>September 17, 2018</em></h4>
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<div id="r-markdown" class="section level2">
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<h2>R Markdown</h2>
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<p>This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see <a href="http://rmarkdown.rstudio.com" class="uri">http://rmarkdown.rstudio.com</a>.</p>
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<p>When you click the <strong>Knit</strong> button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:</p>
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<pre class="r"><code>summary(cars)</code></pre>
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<pre><code>## speed dist
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## Min. : 4.0 Min. : 2.00
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## 1st Qu.:12.0 1st Qu.: 26.00
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## Median :15.0 Median : 36.00
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## Mean :15.4 Mean : 42.98
|
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## 3rd Qu.:19.0 3rd Qu.: 56.00
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## Max. :25.0 Max. :120.00</code></pre>
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</div>
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<div id="including-plots" class="section level2">
|
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<h2>Including Plots</h2>
|
||
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<p>You can also embed plots, for example:</p>
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|
|
<p><img src="data:image/png;base64,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
|
||
|
|
<pre><code>## extra group ID
|
||
|
|
## 1 0.7 1 1
|
||
|
|
## 2 -1.6 1 2
|
||
|
|
## 3 -0.2 1 3
|
||
|
|
## 4 -1.2 1 4
|
||
|
|
## 5 -0.1 1 5
|
||
|
|
## 6 3.4 1 6</code></pre>
|
||
|
|
<pre><code>## extra group ID
|
||
|
|
## 1 0.7 1 1
|
||
|
|
## 2 -1.6 1 2
|
||
|
|
## 3 -0.2 1 3
|
||
|
|
## 4 -1.2 1 4
|
||
|
|
## 5 -0.1 1 5
|
||
|
|
## 6 3.4 1 6
|
||
|
|
## 7 3.7 1 7
|
||
|
|
## 8 0.8 1 8
|
||
|
|
## 9 0.0 1 9
|
||
|
|
## 10 2.0 1 10
|
||
|
|
## 11 1.9 2 1
|
||
|
|
## 12 0.8 2 2
|
||
|
|
## 13 1.1 2 3
|
||
|
|
## 14 0.1 2 4
|
||
|
|
## 15 -0.1 2 5
|
||
|
|
## 16 4.4 2 6
|
||
|
|
## 17 5.5 2 7
|
||
|
|
## 18 1.6 2 8
|
||
|
|
## 19 4.6 2 9
|
||
|
|
## 20 3.4 2 10</code></pre>
|
||
|
|
<p>Note that the <code>echo = FALSE</code> parameter was added to the code chunk to prevent printing of the R code that generated the plot.</p>
|
||
|
|
<p>We found two groups in our data, but we are not sure whether the second group is our control group or a second medicine. This is not clarified clearly in the data description. So we are assuming that the second group is the control group, but we are not certain.</p>
|
||
|
|
<p>//OPDRACHTEN - EXERCISES Question 1) Describe the dataset by explaining the variables. If any what are the replicates and/or controls?</p>
|
||
|
|
<pre class="r"><code>summary(sleep)</code></pre>
|
||
|
|
<pre><code>## extra group ID
|
||
|
|
## Min. :-1.600 1:10 1 :2
|
||
|
|
## 1st Qu.:-0.025 2:10 2 :2
|
||
|
|
## Median : 0.950 3 :2
|
||
|
|
## Mean : 1.540 4 :2
|
||
|
|
## 3rd Qu.: 3.400 5 :2
|
||
|
|
## Max. : 5.500 6 :2
|
||
|
|
## (Other):8</code></pre>
|
||
|
|
<pre class="r"><code>sleep</code></pre>
|
||
|
|
<pre><code>## extra group ID
|
||
|
|
## 1 0.7 1 1
|
||
|
|
## 2 -1.6 1 2
|
||
|
|
## 3 -0.2 1 3
|
||
|
|
## 4 -1.2 1 4
|
||
|
|
## 5 -0.1 1 5
|
||
|
|
## 6 3.4 1 6
|
||
|
|
## 7 3.7 1 7
|
||
|
|
## 8 0.8 1 8
|
||
|
|
## 9 0.0 1 9
|
||
|
|
## 10 2.0 1 10
|
||
|
|
## 11 1.9 2 1
|
||
|
|
## 12 0.8 2 2
|
||
|
|
## 13 1.1 2 3
|
||
|
|
## 14 0.1 2 4
|
||
|
|
## 15 -0.1 2 5
|
||
|
|
## 16 4.4 2 6
|
||
|
|
## 17 5.5 2 7
|
||
|
|
## 18 1.6 2 8
|
||
|
|
## 19 4.6 2 9
|
||
|
|
## 20 3.4 2 10</code></pre>
|
||
|
|
<pre class="r"><code>?sleep</code></pre>
|
||
|
|
<pre><code>## starting httpd help server ... done</code></pre>
|
||
|
|
<p>See the code above, the table “sleep” contains 3 variables, namely: extra, group, ID. -“extra” stands for the extra hours of sleep a student who took a certain drug -“group” stands for the drug a student was given, with 2 different types of drugs -“ID” stands for the patient, one ID per patient/student</p>
|
||
|
|
<p>20 observations and 3 variables are contained within the table. Second group is the control group.</p>
|
||
|
|
<p>Question 2) Describe the performed experiment/ analysis. What was of interest/ what was the research question?</p>
|
||
|
|
<p>Students were given a drug, two seperate groups were created, in which the first group got a sleep prolongation drug, and second group was used as control group. The researchers were interested in the amount of prolonged sleep in hours.</p>
|
||
|
|
<p>Using ?sleep.</p>
|
||
|
|
<p>Question 3) Summarize the data statistically.</p>
|
||
|
|
<p>See the summary and t.test below in the next code snippet box</p>
|
||
|
|
<p>Question 4) Are there any clear outliers or missing values? If so, which ones?</p>
|
||
|
|
<p>The boxplot is not showing any outliers, so no outliers are visible.</p>
|
||
|
|
<pre class="r"><code>require(stats)
|
||
|
|
|
||
|
|
#QUESTION THREE
|
||
|
|
summary(sleep)</code></pre>
|
||
|
|
<pre><code>## extra group ID
|
||
|
|
## Min. :-1.600 1:10 1 :2
|
||
|
|
## 1st Qu.:-0.025 2:10 2 :2
|
||
|
|
## Median : 0.950 3 :2
|
||
|
|
## Mean : 1.540 4 :2
|
||
|
|
## 3rd Qu.: 3.400 5 :2
|
||
|
|
## Max. : 5.500 6 :2
|
||
|
|
## (Other):8</code></pre>
|
||
|
|
<pre class="r"><code>#Simple t.test
|
||
|
|
with(sleep, t.test(extra[group == 1], extra[group == 2], paired = TRUE))</code></pre>
|
||
|
|
<pre><code>##
|
||
|
|
## Paired t-test
|
||
|
|
##
|
||
|
|
## data: extra[group == 1] and extra[group == 2]
|
||
|
|
## t = -4.0621, df = 9, p-value = 0.002833
|
||
|
|
## alternative hypothesis: true difference in means is not equal to 0
|
||
|
|
## 95 percent confidence interval:
|
||
|
|
## -2.4598858 -0.7001142
|
||
|
|
## sample estimates:
|
||
|
|
## mean of the differences
|
||
|
|
## -1.58</code></pre>
|
||
|
|
<pre class="r"><code>?sleep
|
||
|
|
?boxplot
|
||
|
|
|
||
|
|
#QUESTION FOUR
|
||
|
|
#Individual plots
|
||
|
|
boxplot(with(sleep, extra[group==1]))
|
||
|
|
boxplot(with(sleep, extra[group==2]), add=TRUE)</code></pre>
|
||
|
|
<p><img src="data:image/png;base64,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
|
||
|
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<pre class="r"><code>boxplot(with(sleep, extra[group==1], extra[group==2]), paired=TRUE)</code></pre>
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<p><img src="data:image/png;base64,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
|
||
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<pre class="r"><code>#plot(with(sleep, extra[group==2]), add = TRUE)
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boxplot(sleep$extra ~ sleep$group, xlab="Groups", ylab="Sleep prolongation (h)")
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points(sleep$extra ~ sleep$group, pch = 1)</code></pre>
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<p><img src="data:image/png;base64,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
|
||
|
|
<p>Question 5) Summarize the data graphically using only 1 figure. Choose a figure that summarizes the data adequately. Use the graphical package ggplot2. Explain your choice.</p>
|
||
|
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<p>We have two groups and only one variable. So a boxplot is a logical choice for comparing both datasets.</p>
|
||
|
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<pre class="r"><code>library(ggplot2)
|
||
|
|
|
||
|
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ggplot(sleep, aes(x=group, y=extra)) + geom_boxplot()</code></pre>
|
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|
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<p><img src="data:image/png;base64,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
|
||
|
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<p>Question 6) Explore the data, include check for distribution/ normality range of variables mention number and type of variables and observations</p>
|
||
|
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<pre class="r"><code>#Checking for distribution using a QQplot
|
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|
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par(mfrow=c(2, 1)) #plots 2 graphs
|
||
|
|
with(sleep, hist(extra[group==1]))
|
||
|
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with(sleep, hist(extra[group==2]))</code></pre>
|
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|
|
<p><img src="data:image/png;base64,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
|
||
|
|
<p>No distribution seems to be visible (if so, then a bell curve would be expected)</p>
|
||
|
|
<p>Question 7) Calculate the mean sleep prolongation of the two drugs using the aggregate function on the sleep data frame.</p>
|
||
|
|
<pre class="r"><code>#Calculate mean sleep
|
||
|
|
aggregate(formula = extra~group, data = sleep, FUN = mean)</code></pre>
|
||
|
|
<pre><code>## group extra
|
||
|
|
## 1 1 0.75
|
||
|
|
## 2 2 2.33</code></pre>
|
||
|
|
<p>See function above, results are 0.75 hours and 2.33 hours extra sleep for drug one and two respectively</p>
|
||
|
|
<p>Question 8) Calculate the difference in sleep prolongation based on drug type for each patient. Store this new dataset in a new data frame including patient number. Do this by writing a function with as input argument the sleep data frame and as output the new data frame.</p>
|
||
|
|
<pre class="r"><code>my_dataframe <- data.frame(with(sleep, extra[group==1]-extra[group==2]))
|
||
|
|
my_dataframe</code></pre>
|
||
|
|
<pre><code>## with.sleep..extra.group....1....extra.group....2..
|
||
|
|
## 1 -1.2
|
||
|
|
## 2 -2.4
|
||
|
|
## 3 -1.3
|
||
|
|
## 4 -1.3
|
||
|
|
## 5 0.0
|
||
|
|
## 6 -1.0
|
||
|
|
## 7 -1.8
|
||
|
|
## 8 -0.8
|
||
|
|
## 9 -4.6
|
||
|
|
## 10 -1.4</code></pre>
|
||
|
|
<p>See above.</p>
|
||
|
|
<p>Question 9) Is the sleep prolongation difference between the drugs (more) normaly distributed?</p>
|
||
|
|
<pre class="r"><code>shapiro.test(sleep$extra)</code></pre>
|
||
|
|
<pre><code>##
|
||
|
|
## Shapiro-Wilk normality test
|
||
|
|
##
|
||
|
|
## data: sleep$extra
|
||
|
|
## W = 0.94607, p-value = 0.3114</code></pre>
|
||
|
|
<pre class="r"><code>hist(sleep$extra)</code></pre>
|
||
|
|
<p><img src="data:image/png;base64,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
|
||
|
|
<p>So the p-value here is 0.3114, meaning that it differs signifcantly from a normal distribution. We can not assume that the data is normaly distributed.</p>
|
||
|
|
<p>Question 10) Was there a significant difference in sleep prolongation between the drugs? Mention the effect size, is this of a relevant magnitude? Explain why.</p>
|
||
|
|
<p>Also explain why you chose this specific test.</p>
|
||
|
|
<pre class="r"><code>ThisData <- data.frame(with(sleep, mean(extra[group==1])), with(sleep, mean(extra[group==2])))
|
||
|
|
ThisData</code></pre>
|
||
|
|
<pre><code>## with.sleep..mean.extra.group....1...
|
||
|
|
## 1 0.75
|
||
|
|
## with.sleep..mean.extra.group....2...
|
||
|
|
## 1 2.33</code></pre>
|
||
|
|
<pre class="r"><code>with(sleep, t.test(extra[group == 1], extra[group == 2], paired = TRUE))</code></pre>
|
||
|
|
<pre><code>##
|
||
|
|
## Paired t-test
|
||
|
|
##
|
||
|
|
## data: extra[group == 1] and extra[group == 2]
|
||
|
|
## t = -4.0621, df = 9, p-value = 0.002833
|
||
|
|
## alternative hypothesis: true difference in means is not equal to 0
|
||
|
|
## 95 percent confidence interval:
|
||
|
|
## -2.4598858 -0.7001142
|
||
|
|
## sample estimates:
|
||
|
|
## mean of the differences
|
||
|
|
## -1.58</code></pre>
|
||
|
|
<p>The mean between both drugs is very different, 0.75 to 2.33, implying a difference.</p>
|
||
|
|
<p>The t.test shows a difference of -1.58 between the test with a reliability of 0.002833.</p>
|
||
|
|
</div>
|
||
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