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Assignment 4: Data Wrangling and Unsupervised Learning

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CptS 483-04: Introduction to Data Science
Assignment 4: Data Wrangling and Unsupervised Learning

For this assignment you will be using the dplyr package to manipulate and clean up a dataset.
The dataset is called msleep (mammals sleep), and is available on the course webpage (at
https://scads.eecs.wsu.edu/wp-content/uploads/2017/10/msleep_ggplot2.csv). The dataset
contains the sleeptimes and weights for a set of mammals. It has 83 rows and 11 variables. Here
is a description of the varaibles:

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CptS 483-04: Introduction to Data Science
Assignment 4: Data Wrangling and Unsupervised Learning

For this assignment you will be using the dplyr package to manipulate and clean up a dataset.
The dataset is called msleep (mammals sleep), and is available on the course webpage (at
https://scads.eecs.wsu.edu/wp-content/uploads/2017/10/msleep_ggplot2.csv). The dataset
contains the sleeptimes and weights for a set of mammals. It has 83 rows and 11 variables. Here
is a description of the varaibles:
column name Description
name common name
genus taxonomic rank
vore carnivore, omnivore or herbivore?
order taxonomic rank
conservation the conservation status of the mammal
sleep_total total amount of sleep, in hours
sleep_rem rem sleep, in hours
sleep_cycle length of sleep cycle, in hours
awake amount of time spent awake, in hours
brainwt brain weight in kilograms
bodywt body weight in kilograms
Load the data into R, and check the first few rows for abnormalities. You will likely notice
several.
Below are the tasks to perform. You are encouraged to use R Markdown to generate your report
(in PDF).
a) Use select() to print the head of the columns with a title including “sleep”.
b) Use filter() to count the number of animals which weigh over 50 kilograms and sleep more
than 6 hours a day.
c) Use piping (%%), select() and arrange() to print the name, order, sleep time and bodyweight
of the animals with the top 6 sleep times, in order of sleep time.
d) Use mutate to add two new columns to the dataframe; wt_ratio with the ratio of brain size to
body weight, rem_ratio with the ratio of rem sleep to sleep time. If you think they might be
useful, feel free to extract more features than these, and describe what they are.
e) Use group_by() and summarize() to display the average, min and max sleep times for each
order. Remember to use ungroup() when you are done.
f) Make a copy of your dataframe, and use group_by() and mutate() to impute the missing brain
weights as the average wt_ratio for that animal’s order times the animal’s weight. Make a
second copy of your dataframe, but this time use group_by() and mutate() to impute missing
brain weights with the average brain weight for that animal’s order. What assumptions do
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these data filling methods make? Which is a better way to impute the data, or do you see a
better way, and why? You may impute or remove other variables as you find appropriate.
Explain your decisions.
g) Generate a complete linkage clustering of the msleep data using Euclidian distances.
Generate a dendogram of your clustering. What does it tell you about the sleep data? Which
animals are outliers? Describe how feature selection could change this clustering.
h) Cut the clustering so that there are 4-8 clusters, depending on your dendogram. Print a table
that shows the number of animals from each order that appear in each cluster. How do the
sleep patterns of rodents, primates and carnivores compare?
i) Perform PCA on the data and print a biplot of the result.
j) Perform PCA, using standard deviation scaling and print a biplot for the data. How does
scaling affect the PCA results? In your opinion, should the variables be scaled before the
inter-observation dissimilarities are computed? Provide a justification for your answer.