Analyzing Mammalian Sleep Data: Problem Set 1 Guide

Problem Set 1: Manipulation of mammalian sleep data Your name and student ID Today’s date Instructions • Solutions will be released by Friday, January 19th. • This semester, problem sets are for practice only and will not be turned in for marks. Helpful hints: • Every function you need to use was taught during lecture! So you may need to revisit the lecture code to help you along by opening the relevant files on Datahub. Alternatively, you may wish to view the code in the condensed PDFs posted on the course website. Good luck! • Knit your file early and often to minimize knitting errors! If you copy and paste code for the slides, you are bound to get an error that is hard to diagnose. Typing out the code is the way to smooth knitting! We recommend knitting your file each time after you write a few sentences/add a new code chunk, so you can detect the source of knitting errors more easily. This will save you and the GSIs from frustration! • It is good practice to not allow your code to run off the page. To avoid this, have a look at your knitted PDF and ensure all the code fits in the file. If it doesn’t look right, go back to your .Rmd file and add spaces (new lines) using the return or enter key so that the code runs onto the next line. 1

Begin by knitting this document by pushing the “Knit” button above. As you fill in code and text in the document, you can re-knit (push the button again) and see how the document changes. It is important to re-knit often, because if there is any error in your code, the file will not generate a PDF, so our advice is to knit early and often! Using dplyr to investigate sleep times in mammals The data file sleep.csv contains the sleep times and weights for a set of mammals. Hit the green arrow icon in the line below to execute the lines of code in the code chunk, or execute them line by line by placing your cursor on the first line and hitting cmd + enter on Mac or ctrl + enter on PC. library (dplyr) ## ## Attaching package: ' dplyr ' ## The following object is masked from ' package:testthat ' : ## ## matches ## The following objects are masked from ' package:stats ' : ## ## filter, lag ## The following objects are masked from ' package:base ' : ## ## intersect, setdiff, setequal, union library (readr) ## ## Attaching package: ' readr ' ## The following objects are masked from ' package:testthat ' : ## ## edition_get, local_edition sleep <- read_csv ( "data/sleep.csv" ) ## Rows: 83 Columns: 11 ## -- Column specification -------------------------------------------------------- ## Delimiter: "," ## chr (5): name, genus, vore, order, conservation ## dbl (6): sleep_total, sleep_rem, sleep_cycle, awake, brainwt, bodywt ## ## i Use ` spec() ` to retrieve the full column specification for this data. ## i Specify the column types or set ` show_col_types = FALSE ` to quiet this message. • The library command loads the library dplyr into memory. • The readr library contains functions to read in the dataset. • The dplyr library contains functions we will use to manipulate data. Notice that an object called sleep appears in the Environment tab under “Data”. 2

1. [2 points] Use four useful functions discussed in lecture to examine the sleep dataset: # Text inside a code chunk that begins with "#" is called a comment. # We sometimes use comments to explain code to you in plain English. # Write your four functions below these comments, replacing the placeholder # text "<<<<YOUR CODE HERE>>>>". Remember, code does *not* begin with a "#" dim (sleep) ## [1] 83 11 head (sleep) ## # A tibble: 6 x 11 ## name genus vore order conservation sleep_total sleep_rem sleep_cycle awake ## <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> ## 1 Cheetah Acin~ carni Carn~ lc 12.1 NA NA 11.9 ## 2 Owl mo~ Aotus omni Prim~ <NA> 17 1.8 NA 7 ## 3 Mounta~ Aplo~ herbi Rode~ nt 14.4 2.4 NA 9.6 ## 4 Greate~ Blar~ omni Sori~ lc 14.9 2.3 0.133 9.1 ## 5 Cow Bos herbi Arti~ domesticated 4 0.7 0.667 20 ## 6 Three-~ Brad~ herbi Pilo~ <NA> 14.4 2.2 0.767 9.6 ## # i 2 more variables: brainwt <dbl>, bodywt <dbl> str (sleep) ## spc_tbl_ [83 x 11] (S3: spec_tbl_df/tbl_df/tbl/data.frame) ## $ name : chr [1:83] "Cheetah" "Owl monkey" "Mountain beaver" "Greater short-tailed shrew" ... ## $ genus : chr [1:83] "Acinonyx" "Aotus" "Aplodontia" "Blarina" ... ## $ vore : chr [1:83] "carni" "omni" "herbi" "omni" ... ## $ order : chr [1:83] "Carnivora" "Primates" "Rodentia" "Soricomorpha" ... ## $ conservation: chr [1:83] "lc" NA "nt" "lc" ... ## $ sleep_total : num [1:83] 12.1 17 14.4 14.9 4 14.4 8.7 7 10.1 3 ... ## $ sleep_rem : num [1:83] NA 1.8 2.4 2.3 0.7 2.2 1.4 NA 2.9 NA ... ## $ sleep_cycle : num [1:83] NA NA NA 0.133 0.667 ... ## $ awake : num [1:83] 11.9 7 9.6 9.1 20 9.6 15.3 17 13.9 21 ... ## $ brainwt : num [1:83] NA 0.0155 NA 0.00029 0.423 NA NA NA 0.07 0.0982 ... ## $ bodywt : num [1:83] 50 0.48 1.35 0.019 600 ... ## - attr(*, "spec")= ## .. cols( ## .. name = col_character(), ## .. genus = col_character(), ## .. vore = col_character(), ## .. order = col_character(), ## .. conservation = col_character(), ## .. sleep_total = col_double(), ## .. sleep_rem = col_double(), ## .. sleep_cycle = col_double(), ## .. awake = col_double(), ## .. brainwt = col_double(), ## .. bodywt = col_double() ## .. ) ## - attr(*, "problems")=<externalptr> names (sleep) ## [1] "name" "genus" "vore" "order" "conservation" 3

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## [6] "sleep_total" "sleep_rem" "sleep_cycle" "awake" "brainwt" ## [11] "bodywt" # Then, assign p1 to a vector of your function names, in alphabetical order. # For example, assigning p0 to a vector of fruits looks like this: # p0 <- c("apple", "banana", "orange") p1 <- c ( "dim" , "head" , "names" , "str" ) p1 ## [1] "dim" "head" "names" "str" . = ottr :: check ( "tests/p1.R" ) ## ## All tests passed! 4

Description of the variables found in the sleep dataset: 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 Rapid eye movement (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 2. [2 points] Write code to select a set of columns. Specifically select the awake , brainwt , and bodywt columns. Assign this smaller dataset to a dataframe called sleep_small . sleep_small <- select (sleep, awake, brainwt, bodywt) sleep_small ## # A tibble: 83 x 3 ## awake brainwt bodywt ## <dbl> <dbl> <dbl> ## 1 11.9 NA 50 ## 2 7 0.0155 0.48 ## 3 9.6 NA 1.35 ## 4 9.1 0.00029 0.019 ## 5 20 0.423 600 ## 6 9.6 NA 3.85 ## 7 15.3 NA 20.5 ## 8 17 NA 0.045 ## 9 13.9 0.07 14 ## 10 21 0.0982 14.8 ## # i 73 more rows . = ottr :: check ( "tests/p2.R" ) ## ## All tests passed! 5

3. [1 point] To select a range of columns by name, use the ‘:’ (colon) operator. Redo the selection for question 1, but use the colon operator. Assign this to sleep_small_colon . Note that this returns the same data frame as the previous problem, but is not recommended in practice because it depends on the ordering of the columns and isn’t explicit in the columns that are selected, whereas selecting columns by name offers much higher readability for someone else looking at your code later on. sleep_small_colon <- sleep %>% select (awake : bodywt) sleep_small_colon ## # A tibble: 83 x 3 ## awake brainwt bodywt ## <dbl> <dbl> <dbl> ## 1 11.9 NA 50 ## 2 7 0.0155 0.48 ## 3 9.6 NA 1.35 ## 4 9.1 0.00029 0.019 ## 5 20 0.423 600 ## 6 9.6 NA 3.85 ## 7 15.3 NA 20.5 ## 8 17 NA 0.045 ## 9 13.9 0.07 14 ## 10 21 0.0982 14.8 ## # i 73 more rows . = ottr :: check ( "tests/p3.R" ) ## ## All tests passed! 6

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4. [1 point] Select all of the columns except for the vore variable from the original sleep dataset. Assign this to sleep_no_vore . sleep_no_vore <- sleep %>% select ( - vore) sleep_no_vore ## # A tibble: 83 x 10 ## name genus order conservation sleep_total sleep_rem sleep_cycle awake ## <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> ## 1 Cheetah Acin~ Carn~ lc 12.1 NA NA 11.9 ## 2 Owl monkey Aotus Prim~ <NA> 17 1.8 NA 7 ## 3 Mountain be~ Aplo~ Rode~ nt 14.4 2.4 NA 9.6 ## 4 Greater sho~ Blar~ Sori~ lc 14.9 2.3 0.133 9.1 ## 5 Cow Bos Arti~ domesticated 4 0.7 0.667 20 ## 6 Three-toed ~ Brad~ Pilo~ <NA> 14.4 2.2 0.767 9.6 ## 7 Northern fu~ Call~ Carn~ vu 8.7 1.4 0.383 15.3 ## 8 Vesper mouse Calo~ Rode~ <NA> 7 NA NA 17 ## 9 Dog Canis Carn~ domesticated 10.1 2.9 0.333 13.9 ## 10 Roe deer Capr~ Arti~ lc 3 NA NA 21 ## # i 73 more rows ## # i 2 more variables: brainwt <dbl>, bodywt <dbl> . = ottr :: check ( "tests/p4.R" ) ## ## All tests passed! 7

5. [1 point] Run the following chunk of code. select (sleep, starts_with ( "sl" )) ## # A tibble: 83 x 3 ## sleep_total sleep_rem sleep_cycle ## <dbl> <dbl> <dbl> ## 1 12.1 NA NA ## 2 17 1.8 NA ## 3 14.4 2.4 NA ## 4 14.9 2.3 0.133 ## 5 4 0.7 0.667 ## 6 14.4 2.2 0.767 ## 7 8.7 1.4 0.383 ## 8 7 NA NA ## 9 10.1 2.9 0.333 ## 10 3 NA NA ## # i 73 more rows What does it return? Copy your choice and assign it to p5 . # p5 <- "returns the number of columns that start with sl" # p5 <- "returns all columns that start with sl" # p5 <- "returns all rows that start with sl" # p5 <- "returns all animals whose names start with sl"" p5 <- "returns all columns that start with sl" p5 ## [1] "returns all columns that start with sl" . = ottr :: check ( "tests/p5.R" ) ## ## All tests passed! 8

6. [1 point] Rewrite the chunk of code that selects columns starting with “sl” (in question 5) using the pipe operator. Assign this to sleep_sl . sleep_sl <- sleep %>% select ( starts_with ( "sl" )) sleep_sl ## # A tibble: 83 x 3 ## sleep_total sleep_rem sleep_cycle ## <dbl> <dbl> <dbl> ## 1 12.1 NA NA ## 2 17 1.8 NA ## 3 14.4 2.4 NA ## 4 14.9 2.3 0.133 ## 5 4 0.7 0.667 ## 6 14.4 2.2 0.767 ## 7 8.7 1.4 0.383 ## 8 7 NA NA ## 9 10.1 2.9 0.333 ## 10 3 NA NA ## # i 73 more rows . = ottr :: check ( "tests/p6.R" ) ## ## All tests passed! 9

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7. [1 point] Filter the original sleep dataset to include rows with mammals that sleep a total of more than 16 hours. Assign this to sleep_over16 . sleep_over16 <- sleep %>% filter (sleep_total > 16 ) sleep_over16 ## # A tibble: 8 x 11 ## name genus vore order conservation sleep_total sleep_rem sleep_cycle awake ## <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> ## 1 Owl mo~ Aotus omni Prim~ <NA> 17 1.8 NA 7 ## 2 Long-n~ Dasy~ carni Cing~ lc 17.4 3.1 0.383 6.6 ## 3 North ~ Dide~ omni Dide~ lc 18 4.9 0.333 6 ## 4 Big br~ Epte~ inse~ Chir~ lc 19.7 3.9 0.117 4.3 ## 5 Thick-~ Lutr~ carni Dide~ lc 19.4 6.6 NA 4.6 ## 6 Little~ Myot~ inse~ Chir~ <NA> 19.9 2 0.2 4.1 ## 7 Giant ~ Prio~ inse~ Cing~ en 18.1 6.1 NA 5.9 ## 8 Arctic~ Sper~ herbi Rode~ lc 16.6 NA NA 7.4 ## # i 2 more variables: brainwt <dbl>, bodywt <dbl> . = ottr :: check ( "tests/p7.R" ) ## ## All tests passed! 10

8. [2 points] Filter the rows to include mammals that sleep a total of more than 16 hours and have a body weight of greater than 1 kilogram. Assign this to sleep_mammals . sleep_mammals <- sleep %>% filter (sleep_total > 16 & bodywt > 1 ) sleep_mammals ## # A tibble: 3 x 11 ## name genus vore order conservation sleep_total sleep_rem sleep_cycle awake ## <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> ## 1 Long-n~ Dasy~ carni Cing~ lc 17.4 3.1 0.383 6.6 ## 2 North ~ Dide~ omni Dide~ lc 18 4.9 0.333 6 ## 3 Giant ~ Prio~ inse~ Cing~ en 18.1 6.1 NA 5.9 ## # i 2 more variables: brainwt <dbl>, bodywt <dbl> . = ottr :: check ( "tests/p8.R" ) ## ## All tests passed! 11

9. [1 point] Suppose you are specifically interested in the sleep times of horses and giraffes. Use the original sleep dataset and assign sleep_hg to a dataframe that only includes horses and giraffes. sleep_hg <- sleep %>% filter (name %in% c ( "Horse" , "Giraffe" )) sleep_hg ## # A tibble: 2 x 11 ## name genus vore order conservation sleep_total sleep_rem sleep_cycle awake ## <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> ## 1 Horse Equus herbi Peri~ domesticated 2.9 0.6 1 21.1 ## 2 Giraffe Gira~ herbi Arti~ cd 1.9 0.4 NA 22.1 ## # i 2 more variables: brainwt <dbl>, bodywt <dbl> . = ottr :: check ( "tests/p9.R" ) ## ## All tests passed! 12

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10. [1 point] Order the original dataset from shortest sleep time to longest sleep time. Assign this to sleep_time . sleep_time <- sleep %>% arrange (sleep_total) sleep_time ## # A tibble: 83 x 11 ## name genus vore order conservation sleep_total sleep_rem sleep_cycle awake ## <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> ## 1 Giraf~ Gira~ herbi Arti~ cd 1.9 0.4 NA 22.1 ## 2 Pilot~ Glob~ carni Ceta~ cd 2.7 0.1 NA 21.4 ## 3 Horse Equus herbi Peri~ domesticated 2.9 0.6 1 21.1 ## 4 Roe d~ Capr~ herbi Arti~ lc 3 NA NA 21 ## 5 Donkey Equus herbi Peri~ domesticated 3.1 0.4 NA 20.9 ## 6 Afric~ Loxo~ herbi Prob~ vu 3.3 NA NA 20.7 ## 7 Caspi~ Phoca carni Carn~ vu 3.5 0.4 NA 20.5 ## 8 Sheep Ovis herbi Arti~ domesticated 3.8 0.6 NA 20.2 ## 9 Asian~ Elep~ herbi Prob~ en 3.9 NA NA 20.1 ## 10 Cow Bos herbi Arti~ domesticated 4 0.7 0.667 20 ## # i 73 more rows ## # i 2 more variables: brainwt <dbl>, bodywt <dbl> . = ottr :: check ( "tests/p10.R" ) ## ## All tests passed! 13

11. [1 point] Now order the original dataset from longest to shortest sleep time. Assign this to sleep_rev . sleep_rev <- sleep %>% arrange ( - sleep_total) sleep_rev ## # A tibble: 83 x 11 ## name genus vore order conservation sleep_total sleep_rem sleep_cycle awake ## <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> ## 1 Littl~ Myot~ inse~ Chir~ <NA> 19.9 2 0.2 4.1 ## 2 Big b~ Epte~ inse~ Chir~ lc 19.7 3.9 0.117 4.3 ## 3 Thick~ Lutr~ carni Dide~ lc 19.4 6.6 NA 4.6 ## 4 Giant~ Prio~ inse~ Cing~ en 18.1 6.1 NA 5.9 ## 5 North~ Dide~ omni Dide~ lc 18 4.9 0.333 6 ## 6 Long-~ Dasy~ carni Cing~ lc 17.4 3.1 0.383 6.6 ## 7 Owl m~ Aotus omni Prim~ <NA> 17 1.8 NA 7 ## 8 Arcti~ Sper~ herbi Rode~ lc 16.6 NA NA 7.4 ## 9 Golde~ Sper~ herbi Rode~ lc 15.9 3 NA 8.1 ## 10 Tiger Pant~ carni Carn~ en 15.8 NA NA 8.2 ## # i 73 more rows ## # i 2 more variables: brainwt <dbl>, bodywt <dbl> . = ottr :: check ( "tests/p11.R" ) ## ## All tests passed! 14

12. [2 points] Suppose you are interested in the order of sleep time (longest to shortest), but according to whether the animal is a carnivore, herbivore, or omnivore. Write the code that orders sleep time according to the animal’s type of -vore . Call this sleep_time_rev . sleep_time_rev <- sleep %>% arrange (vore, - sleep_total) sleep_time_rev ## # A tibble: 83 x 11 ## name genus vore order conservation sleep_total sleep_rem sleep_cycle awake ## <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> ## 1 Thick~ Lutr~ carni Dide~ lc 19.4 6.6 NA 4.6 ## 2 Long-~ Dasy~ carni Cing~ lc 17.4 3.1 0.383 6.6 ## 3 Tiger Pant~ carni Carn~ en 15.8 NA NA 8.2 ## 4 North~ Onyc~ carni Rode~ lc 14.5 NA NA 9.5 ## 5 Lion Pant~ carni Carn~ vu 13.5 NA NA 10.5 ## 6 Domes~ Felis carni Carn~ domesticated 12.5 3.2 0.417 11.5 ## 7 Arcti~ Vulp~ carni Carn~ <NA> 12.5 NA NA 11.5 ## 8 Cheet~ Acin~ carni Carn~ lc 12.1 NA NA 11.9 ## 9 Slow ~ Nyct~ carni Prim~ <NA> 11 NA NA 13 ## 10 Jaguar Pant~ carni Carn~ nt 10.4 NA NA 13.6 ## # i 73 more rows ## # i 2 more variables: brainwt <dbl>, bodywt <dbl> . = ottr :: check ( "tests/p12.R" ) ## ## All tests passed! 15

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13. [1 point] Create a new column called rem_proportion which is the ratio of rem sleep to total amount of sleep. Assign this new dataframe to sleep_ratio . sleep_ratio <- sleep %>% mutate ( rem_proportion = sleep_rem / sleep_total) sleep_ratio ## # A tibble: 83 x 12 ## name genus vore order conservation sleep_total sleep_rem sleep_cycle awake ## <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> ## 1 Cheet~ Acin~ carni Carn~ lc 12.1 NA NA 11.9 ## 2 Owl m~ Aotus omni Prim~ <NA> 17 1.8 NA 7 ## 3 Mount~ Aplo~ herbi Rode~ nt 14.4 2.4 NA 9.6 ## 4 Great~ Blar~ omni Sori~ lc 14.9 2.3 0.133 9.1 ## 5 Cow Bos herbi Arti~ domesticated 4 0.7 0.667 20 ## 6 Three~ Brad~ herbi Pilo~ <NA> 14.4 2.2 0.767 9.6 ## 7 North~ Call~ carni Carn~ vu 8.7 1.4 0.383 15.3 ## 8 Vespe~ Calo~ <NA> Rode~ <NA> 7 NA NA 17 ## 9 Dog Canis carni Carn~ domesticated 10.1 2.9 0.333 13.9 ## 10 Roe d~ Capr~ herbi Arti~ lc 3 NA NA 21 ## # i 73 more rows ## # i 3 more variables: brainwt <dbl>, bodywt <dbl>, rem_proportion <dbl> . = ottr :: check ( "tests/p13.R" ) ## ## All tests passed! 16

14. [1 point] Add another column to the sleep_ratio dataset called bodywt_grams which is the bodywt column in grams. Call this new dataframe sleep_bw . sleep_bw <- sleep %>% mutate ( rem_proportion = sleep_rem / sleep_total, bodywt_grams = bodywt * 1000 ) sleep_bw ## # A tibble: 83 x 13 ## name genus vore order conservation sleep_total sleep_rem sleep_cycle awake ## <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> ## 1 Cheet~ Acin~ carni Carn~ lc 12.1 NA NA 11.9 ## 2 Owl m~ Aotus omni Prim~ <NA> 17 1.8 NA 7 ## 3 Mount~ Aplo~ herbi Rode~ nt 14.4 2.4 NA 9.6 ## 4 Great~ Blar~ omni Sori~ lc 14.9 2.3 0.133 9.1 ## 5 Cow Bos herbi Arti~ domesticated 4 0.7 0.667 20 ## 6 Three~ Brad~ herbi Pilo~ <NA> 14.4 2.2 0.767 9.6 ## 7 North~ Call~ carni Carn~ vu 8.7 1.4 0.383 15.3 ## 8 Vespe~ Calo~ <NA> Rode~ <NA> 7 NA NA 17 ## 9 Dog Canis carni Carn~ domesticated 10.1 2.9 0.333 13.9 ## 10 Roe d~ Capr~ herbi Arti~ lc 3 NA NA 21 ## # i 73 more rows ## # i 4 more variables: brainwt <dbl>, bodywt <dbl>, rem_proportion <dbl>, ## # bodywt_grams <dbl> . = ottr :: check ( "tests/p14.R" ) ## ## All tests passed! 17

15. [1 point] Calculate the average sleep time across all the animals in the dataset using a dplyr function and label this value sleep_avg . Assign this one value to a dataframe called avg_sleep_time . avg_sleep_time <- sleep %>% summarize ( sleep_avg = mean (sleep_total)) avg_sleep_time ## # A tibble: 1 x 1 ## sleep_avg ## <dbl> ## 1 10.4 . = ottr :: check ( "tests/p15.R" ) ## ## All tests passed! 18

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16. [2 points] Calculate the average sleep time for each type of “-vore”. Hint: you’ll need to use two dplyr functions! Name the columns of the dataframe vore and sleep_avg and assign the dataframe to avg_by_vore . avg_by_vore <- sleep %>% group_by (vore) %>% summarize ( sleep_avg = mean (sleep_total)) avg_by_vore ## # A tibble: 5 x 2 ## vore sleep_avg ## <chr> <dbl> ## 1 carni 10.4 ## 2 herbi 9.51 ## 3 insecti 14.9 ## 4 omni 10.9 ## 5 <NA> 10.2 . = ottr :: check ( "tests/p16.R" ) ## ## All tests passed! END 19

hw01-sol

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