Reproducible Data Analysis
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One example of these problems is shown every time we load tidyverse:
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You will see a new button in RStudio: 
Reading data from fileCountryNamePhonenumber
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Descriptive Statistics
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{Copyright Allison
Horst}
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Or even specify the exact intervals we want, here intervals from 0 to
6500 gram in intervals of 250 gram:
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The histogram provides us with a visual indication of both range, the
variation of the values, and an idea about where the data is
located.
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Histograms
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Table One
Tidy Data
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The normal distribution
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The area under the curve is 1,
equivalent to 100%.
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Testing for normality
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Our histogram does not really look like the theoretical curve. The fact
that mean and median are almost identical was not a sufficient criterium
for normalcy.
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How is the data distributed?
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Linear regression
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They are relatively close to normal.
Multiple Linear Regression
Logistic regression
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Central Limit Theorem
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This is definitely not normally distributed.
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Nicer barcharts
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It is not strictly necessary to remove the label of the x-axis, but it
is superfluous in this case.
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This facilitates the reading of the graph - it becomes very easy to see
that the most frequent species of penguin is Adelie penguins.
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We also changed the scaling of the title of the plot. The size of that
is now 10% larger than the base size. We can do that by specifying a
specific size, but here we have done it using the rel()
function which changes the size relative to the base font size in the
plot.
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We control what is happening on the x-scale by using the family of
scale_x functions. Because it is a continuous scale, more
specifically scale_x_continuous().
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First we change the default theme of the plot from
theme_grey to theme_minimal, which gets rid of
the grey background. In the additional theme() function we
remove the gridlines, both major and minor gridlines, on the y-axis, by
setting them to the speciel plot element
element_blank()
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Power Calculations
k-means
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No. Even though there might actually be clusters in the data, the
algorithm is not necessarily able to find them. Consider this data:
There is obviously a cluster centered around (0,0). And another cluster
more or lesss evenly spread around it.
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But not the ones we want.
ANOVA
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That looks reasonable.
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Cohens Kappa
R on Ucloud
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In it we find a listing of what is in our drive: 
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Sometimes we close a window, or nagivate away from it. Where can we
find it again? In the navigationbar to the left in Ucloud we find this
icon. 
It provides us with a
list or running jobs (yes, we can have more than one). Click on the job,
and we get back to the job, where we can extend time or open the
interface again.
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The easy way is to do it through RStudio. Select the folder and/or
files you want to save, click on the little cogwheel saying “More” and
chose “Export”. RStudio will now zip the files, and ask you where to
save them. 
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A deeper dive into pipes
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Place a tickmark in the box next to
“Use native pipe operator, |>”.
Setup for GIS
Setup for Git
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Begin by introducing yourself to Git. Go to the terminal 
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Click “Generate token”, and copy the token: 
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Practice makes perfect
Statistical tests
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When install.packages fail
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allow us to chose one or more of the repositories that R knows about:
