This lab is due on Monday, September 22 at 11:59pm. To be considered on time, the following must be done by the due date:
.pdf file submitted on GradescopeThe main goal is to learn data processsing using tidyverse and introduce you to version control using Github. Most of these labs is adopted from Nick Hagerty course materials.
By the end of the lab, you will learn:
A whole “universe” of functions within R
Advantages:
The two most important properties of tidy data are:
[Image is from “R for Data Science” by Hadley Wickham & Garrett Grolemund, used under CC BY-NC-ND 3.0]
Tidy data is easier to work with, because you have a consistent way of referring to variables and observations. It then becomes easy to manipulate, visualize, and model.
Both of these data sets display information on heart rate observed in individuals across 3 different time periods:
name time1 time2 time3
1 Wilbur 67 56 70
2 Petunia 80 90 67
3 Gregory 64 50 101 name time heartrate
1 Wilbur 1 67
2 Petunia 1 80
3 Gregory 1 64
4 Wilbur 2 56
5 Petunia 2 90
6 Gregory 2 50
7 Wilbur 3 70
8 Petunia 3 67
9 Gregory 3 10Which dataframe is in tidy format?
Wide data:
Long data:
Tidy data is more likely to be long.
“Tidy datasets are all alike but every messy dataset is messy in its own way.”
– Hadley Wickham
We need to install and load a couple of packages. Run these preliminaries:
# load and install package if necessary
if (!require("pacman")) install.packages("pacman")
pacman::p_load(
tidyverse,
nycflights13
)We see that we have actually loaded a number of packages (which could also be loaded individually): ggplot2, tibble, dplyr, etc. - We can also see information about the package versions and some namespace conflicts.
The tidyverse actually comes with a lot more packages than those that are just loaded automatically.
tidyverse_packages() [1] "broom" "conflicted" "cli" "dbplyr"
[5] "dplyr" "dtplyr" "forcats" "ggplot2"
[9] "googledrive" "googlesheets4" "haven" "hms"
[13] "httr" "jsonlite" "lubridate" "magrittr"
[17] "modelr" "pillar" "purrr" "ragg"
[21] "readr" "readxl" "reprex" "rlang"
[25] "rstudioapi" "rvest" "stringr" "tibble"
[29] "tidyr" "xml2" "tidyverse" All of these are super useful
This labs will focus on two that are automatically loaded: dplyr and tidyr.
Pipes take the output of one function and feed it into the first argument of the next (which you then skip).
dataframe |> filter(condition) is equivalent to filter(dataframe, condition).
Note: |> on these slides is generated by the two characters | >, without the space.
Older version of the pipe: %>% * From the magrittr package loaded with the tidyverse * Works identically to |> in most situations.
Keyboard shortcut: Ctl/Cmd + Shift + M
|> the defaultPipes can dramatically improve the experience of reading and writing code. Compare:
## These next two lines of code do exactly the same thing.
mpg |> filter(manufacturer=="audi") |>
group_by(model) |>
summarize(hwy_mean = mean(hwy))# A tibble: 3 × 2
model hwy_mean
<chr> <dbl>
1 a4 28.3
2 a4 quattro 25.8
3 a6 quattro 24 summarize(group_by(filter(mpg, manufacturer=="audi"), model), hwy_mean = mean(hwy))# A tibble: 3 × 2
model hwy_mean
<chr> <dbl>
1 a4 28.3
2 a4 quattro 25.8
3 a6 quattro 24 The first line reads from left to right, exactly how you think about the operations.
The second line totally inverts this logical order (the final operation comes first!)
Best practice is to put each function on its own line and indent. Look how much more readable this is:
mpg |>
filter(manufacturer == "audi") |>
group_by(model) |>
summarize(hwy_mean = mean(hwy))# A tibble: 3 × 2
model hwy_mean
<chr> <dbl>
1 a4 28.3
2 a4 quattro 25.8
3 a6 quattro 24 Vertical space costs nothing and makes for much more readable/writable code than cramming things horizontally.
All together, this multi-line line of code is called a pipeline.
There are five key dplyr verbs that you need to learn.
filter: Filter (i.e. subset) rows based on their values.
arrange: Arrange (i.e. reorder) rows based on their values.
select: Select (i.e. subset) columns by their names:
mutate: Create new columns.
summarize: Collapse multiple rows into a single summary value.
Let’s practice these functions together using the starwars data frame that comes pre-packaged with dplyr.
We can chain multiple filter commands with the pipe (|>), or just separate them within a single filter command using commas.
starwars |>
filter(
species == "Human",
height >= 190
) # A tibble: 4 × 14
name height mass hair_color skin_color eye_color birth_year sex gender
<chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
1 Darth Va… 202 136 none white yellow 41.9 male mascu…
2 Qui-Gon … 193 89 brown fair blue 92 male mascu…
3 Dooku 193 80 white fair brown 102 male mascu…
4 Bail Pre… 191 NA black tan brown 67 male mascu…
# ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
# vehicles <list>, starships <list>Regular expressions work well too.
starwars |>
filter(str_detect(name, "Skywalker"))# A tibble: 3 × 14
name height mass hair_color skin_color eye_color birth_year sex gender
<chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
1 Luke Sky… 172 77 blond fair blue 19 male mascu…
2 Anakin S… 188 84 blond fair blue 41.9 male mascu…
3 Shmi Sky… 163 NA black fair brown 72 fema… femin…
# ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
# vehicles <list>, starships <list>A very common filter use case is identifying (or removing) missing data cases.
starwars |>
filter(is.na(height))# A tibble: 6 × 14
name height mass hair_color skin_color eye_color birth_year sex gender
<chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
1 Arvel Cr… NA NA brown fair brown NA male mascu…
2 Finn NA NA black dark dark NA male mascu…
3 Rey NA NA brown light hazel NA fema… femin…
4 Poe Dame… NA NA brown light brown NA male mascu…
5 BB8 NA NA none none black NA none mascu…
6 Captain … NA NA none none unknown NA fema… femin…
# ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
# vehicles <list>, starships <list>To remove missing observations, simply use negation: filter(!is.na(height)).
Try this yourself.
clean_starwars<-starwars |>
filter(is.na(height))
clean_starwars# A tibble: 6 × 14
name height mass hair_color skin_color eye_color birth_year sex gender
<chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
1 Arvel Cr… NA NA brown fair brown NA male mascu…
2 Finn NA NA black dark dark NA male mascu…
3 Rey NA NA brown light hazel NA fema… femin…
4 Poe Dame… NA NA brown light brown NA male mascu…
5 BB8 NA NA none none black NA none mascu…
6 Captain … NA NA none none unknown NA fema… femin…
# ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
# vehicles <list>, starships <list>
arrange sorts your data frame by a particular column (numerically, or alphabetically)
starwars |>
arrange(birth_year)# A tibble: 87 × 14
name height mass hair_color skin_color eye_color birth_year sex gender
<chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
1 Wicket … 88 20 brown brown brown 8 male mascu…
2 IG-88 200 140 none metal red 15 none mascu…
3 Luke Sk… 172 77 blond fair blue 19 male mascu…
4 Leia Or… 150 49 brown light brown 19 fema… femin…
5 Wedge A… 170 77 brown fair hazel 21 male mascu…
6 Plo Koon 188 80 none orange black 22 male mascu…
7 Biggs D… 183 84 black light brown 24 male mascu…
8 Han Solo 180 80 brown fair brown 29 male mascu…
9 Lando C… 177 79 black dark brown 31 male mascu…
10 Boba Fe… 183 78.2 black fair brown 31.5 male mascu…
# ℹ 77 more rows
# ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
# vehicles <list>, starships <list>We can also arrange items in descending order using arrange(desc()).
starwars |>
arrange(desc(birth_year))# A tibble: 87 × 14
name height mass hair_color skin_color eye_color birth_year sex gender
<chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
1 Yoda 66 17 white green brown 896 male mascu…
2 Jabba D… 175 1358 <NA> green-tan… orange 600 herm… mascu…
3 Chewbac… 228 112 brown unknown blue 200 male mascu…
4 C-3PO 167 75 <NA> gold yellow 112 none mascu…
5 Dooku 193 80 white fair brown 102 male mascu…
6 Qui-Gon… 193 89 brown fair blue 92 male mascu…
7 Ki-Adi-… 198 82 white pale yellow 92 male mascu…
8 Finis V… 170 NA blond fair blue 91 male mascu…
9 Palpati… 170 75 grey pale yellow 82 male mascu…
10 Cliegg … 183 NA brown fair blue 82 male mascu…
# ℹ 77 more rows
# ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
# vehicles <list>, starships <list>
Use commas to select multiple columns out of a data frame. (You can also use “first:last” for consecutive columns). Deselect a column with “-”.
starwars |>
select(name:skin_color, species, -height)# A tibble: 87 × 5
name mass hair_color skin_color species
<chr> <dbl> <chr> <chr> <chr>
1 Luke Skywalker 77 blond fair Human
2 C-3PO 75 <NA> gold Droid
3 R2-D2 32 <NA> white, blue Droid
4 Darth Vader 136 none white Human
5 Leia Organa 49 brown light Human
6 Owen Lars 120 brown, grey light Human
7 Beru Whitesun Lars 75 brown light Human
8 R5-D4 32 <NA> white, red Droid
9 Biggs Darklighter 84 black light Human
10 Obi-Wan Kenobi 77 auburn, white fair Human
# ℹ 77 more rowsYou can also rename some (or all) of your selected variables in place.
starwars |>
select(alias=name, planet=homeworld) # A tibble: 87 × 2
alias planet
<chr> <chr>
1 Luke Skywalker Tatooine
2 C-3PO Tatooine
3 R2-D2 Naboo
4 Darth Vader Tatooine
5 Leia Organa Alderaan
6 Owen Lars Tatooine
7 Beru Whitesun Lars Tatooine
8 R5-D4 Tatooine
9 Biggs Darklighter Tatooine
10 Obi-Wan Kenobi Stewjon
# ℹ 77 more rowsIf you just want to rename columns without subsetting them, you can use rename. Try this!
starwars_rename<-starwars |>
rename(alias=name)
names(starwars_rename) [1] "alias" "height" "mass" "hair_color" "skin_color"
[6] "eye_color" "birth_year" "sex" "gender" "homeworld"
[11] "species" "films" "vehicles" "starships" The select(contains(PATTERN)) option provides a nice shortcut in relevant cases.
starwars |>
select(name, contains("color"))# A tibble: 87 × 4
name hair_color skin_color eye_color
<chr> <chr> <chr> <chr>
1 Luke Skywalker blond fair blue
2 C-3PO <NA> gold yellow
3 R2-D2 <NA> white, blue red
4 Darth Vader none white yellow
5 Leia Organa brown light brown
6 Owen Lars brown, grey light blue
7 Beru Whitesun Lars brown light blue
8 R5-D4 <NA> white, red red
9 Biggs Darklighter black light brown
10 Obi-Wan Kenobi auburn, white fair blue-gray
# ℹ 77 more rowsSome other selection helpers: starts_with(), ends_with(), all_of(c("name1", "name2")), matches().
You can create new columns from scratch, or (more commonly) as transformations of existing columns.
starwars |>
select(name, birth_year) |>
mutate(dog_years = birth_year * 7) |>
mutate(comment = paste0(name, " is ", dog_years, " in dog years."))# A tibble: 87 × 4
name birth_year dog_years comment
<chr> <dbl> <dbl> <chr>
1 Luke Skywalker 19 133 Luke Skywalker is 133 in dog years.
2 C-3PO 112 784 C-3PO is 784 in dog years.
3 R2-D2 33 231 R2-D2 is 231 in dog years.
4 Darth Vader 41.9 293. Darth Vader is 293.3 in dog years.
5 Leia Organa 19 133 Leia Organa is 133 in dog years.
6 Owen Lars 52 364 Owen Lars is 364 in dog years.
7 Beru Whitesun Lars 47 329 Beru Whitesun Lars is 329 in dog yea…
8 R5-D4 NA NA R5-D4 is NA in dog years.
9 Biggs Darklighter 24 168 Biggs Darklighter is 168 in dog year…
10 Obi-Wan Kenobi 57 399 Obi-Wan Kenobi is 399 in dog years.
# ℹ 77 more rowsNote: mutate is order aware. So you can chain multiple mutates in a single call.
starwars |>
select(name, birth_year) |>
mutate(
dog_years = birth_year * 7, # Separate with a comma
comment = paste0(name, " is ", dog_years, " in dog years.")
)# A tibble: 87 × 4
name birth_year dog_years comment
<chr> <dbl> <dbl> <chr>
1 Luke Skywalker 19 133 Luke Skywalker is 133 in dog years.
2 C-3PO 112 784 C-3PO is 784 in dog years.
3 R2-D2 33 231 R2-D2 is 231 in dog years.
4 Darth Vader 41.9 293. Darth Vader is 293.3 in dog years.
5 Leia Organa 19 133 Leia Organa is 133 in dog years.
6 Owen Lars 52 364 Owen Lars is 364 in dog years.
7 Beru Whitesun Lars 47 329 Beru Whitesun Lars is 329 in dog yea…
8 R5-D4 NA NA R5-D4 is NA in dog years.
9 Biggs Darklighter 24 168 Biggs Darklighter is 168 in dog year…
10 Obi-Wan Kenobi 57 399 Obi-Wan Kenobi is 399 in dog years.
# ℹ 77 more rowsBoolean, logical and conditional operators all work well with mutate too.
starwars |>
select(name, height) |>
filter(name %in% c("Luke Skywalker", "Anakin Skywalker")) |>
mutate(tall1 = height > 180) |>
mutate(tall2 = if_else(height > 180, "Tall", "Short"))# A tibble: 2 × 4
name height tall1 tall2
<chr> <int> <lgl> <chr>
1 Luke Skywalker 172 FALSE Short
2 Anakin Skywalker 188 TRUE Tall Lastly, combining mutate with across allows you to easily perform the same operation on a subset of variables.
starwars |>
select(name:eye_color) |>
mutate(across(where(is.character), toupper))# A tibble: 87 × 6
name height mass hair_color skin_color eye_color
<chr> <int> <dbl> <chr> <chr> <chr>
1 LUKE SKYWALKER 172 77 BLOND FAIR BLUE
2 C-3PO 167 75 <NA> GOLD YELLOW
3 R2-D2 96 32 <NA> WHITE, BLUE RED
4 DARTH VADER 202 136 NONE WHITE YELLOW
5 LEIA ORGANA 150 49 BROWN LIGHT BROWN
6 OWEN LARS 178 120 BROWN, GREY LIGHT BLUE
7 BERU WHITESUN LARS 165 75 BROWN LIGHT BLUE
8 R5-D4 97 32 <NA> WHITE, RED RED
9 BIGGS DARKLIGHTER 183 84 BLACK LIGHT BROWN
10 OBI-WAN KENOBI 182 77 AUBURN, WHITE FAIR BLUE-GRAY
# ℹ 77 more rows
Particularly useful in combination with the group_by command.
starwars |>
group_by(species) |>
summarize(mean_height = mean(height))# A tibble: 38 × 2
species mean_height
<chr> <dbl>
1 Aleena 79
2 Besalisk 198
3 Cerean 198
4 Chagrian 196
5 Clawdite 168
6 Droid NA
7 Dug 112
8 Ewok 88
9 Geonosian 183
10 Gungan 209.
# ℹ 28 more rowsNotice that some of these summarized values are missing. If we want to ignore missing values, use na.rm = T:
## Much better
starwars |>
group_by(species) |>
summarize(mean_height = mean(height, na.rm = T))# A tibble: 38 × 2
species mean_height
<chr> <dbl>
1 Aleena 79
2 Besalisk 198
3 Cerean 198
4 Chagrian 196
5 Clawdite 168
6 Droid 131.
7 Dug 112
8 Ewok 88
9 Geonosian 183
10 Gungan 209.
# ℹ 28 more rowsThe same across-based workflow that we saw with mutate a few slides back also works with summarize.
starwars |>
group_by(species) |>
summarize(across(where(is.numeric), mean))# A tibble: 38 × 4
species height mass birth_year
<chr> <dbl> <dbl> <dbl>
1 Aleena 79 15 NA
2 Besalisk 198 102 NA
3 Cerean 198 82 92
4 Chagrian 196 NA NA
5 Clawdite 168 55 NA
6 Droid NA NA NA
7 Dug 112 40 NA
8 Ewok 88 20 8
9 Geonosian 183 80 NA
10 Gungan 209. NA NA
# ℹ 28 more rowsThe same across-based workflow that we saw with mutate a few slides back also works with summarize. Though to add arguments, we have to use an anonymous function:
starwars |>
group_by(species) |>
summarize(across(where(is.numeric), ~ mean(.x, na.rm=T)))# A tibble: 38 × 4
species height mass birth_year
<chr> <dbl> <dbl> <dbl>
1 Aleena 79 15 NaN
2 Besalisk 198 102 NaN
3 Cerean 198 82 92
4 Chagrian 196 NaN NaN
5 Clawdite 168 55 NaN
6 Droid 131. 69.8 53.3
7 Dug 112 40 NaN
8 Ewok 88 20 8
9 Geonosian 183 80 NaN
10 Gungan 209. 74 52
# ℹ 28 more rowsungroup: For ungrouping after using group_by. - Use after doing your grouped summarize or mutate operation, or everything else you do will be super slow.
slice: Subset rows by position rather than filtering by values. - E.g. starwars |> slice(1:10)
pull: Extract a column from as a data frame as a vector or scalar. - E.g. starwars |> filter(sex=="female") |> pull(height)
distinct and count: List unique values, with or without their number of appearances. - E.g. starwars |> distinct(species), or starwars |> count(species) - count is equivalent to group_by and summarize with n():
starwars |> group_by(species) |> summarize(n = n())# A tibble: 38 × 2
species n
<chr> <int>
1 Aleena 1
2 Besalisk 1
3 Cerean 1
4 Chagrian 1
5 Clawdite 1
6 Droid 6
7 Dug 1
8 Ewok 1
9 Geonosian 1
10 Gungan 3
# ℹ 28 more rowsList the most common eye colors among female Star Wars characters in descending order of frequency.
As usual, there are multiple solutions.
starwars |>
filter(sex == "female") |>
count(eye_color) |>
arrange(desc(n))# A tibble: 6 × 2
eye_color n
<chr> <int>
1 blue 6
2 brown 4
3 black 2
4 hazel 2
5 unknown 1
6 yellow 1starwars |>
filter(sex == "female") |>
group_by(eye_color) |>
summarize(n = n()) |>
arrange(desc(n))# A tibble: 6 × 2
eye_color n
<chr> <int>
1 blue 6
2 brown 4
3 black 2
4 hazel 2
5 unknown 1
6 yellow 1Explain what each line in the codes do:
Answer:
So far we haven’t been saving the dataframes that result from our code in memory. Usually, we will want to use them for the next task. Create a new object each time you write a pipeline.
women = starwars |> filter(sex == "female")
brown_eyed_women = women |> filter(eye_color == "brown")Resist the temptation to use the same object name. This is called clobbering since it overwrites the previous version. It ruins your ability to easily go back to previous steps.
# DON'T do this
starwars = starwars |> filter(sex == "female")By keeping multiple copies of very similar dataframes, will you waste your computer’s memory? Usually, no – R is smart and stores only the changes between objects.
pivot_longer: Pivot wide data into long format.
pivot_wider: Pivot long data into wide format.
separate: Separate (i.e. split) one column into multiple columns.
unite: Unite (i.e. combine) multiple columns into one.
Which of pivot_longer vs pivot_wider produces “tidy” data?
stocks = data.frame( ## Could use "tibble" instead of "data.frame" if you prefer
time = as.Date('2009-01-01') + 0:1,
X = rnorm(2, 10, 1),
Y = rnorm(2, 10, 2),
Z = rnorm(2, 10, 5)
)
stocks time X Y Z
1 2009-01-01 10.66492 6.701938 13.461354
2 2009-01-02 10.09950 11.513279 5.339974tidy_stocks = stocks |>
pivot_longer(cols=X:Z, names_to="stock", values_to="price")
tidy_stocks# A tibble: 6 × 3
time stock price
<date> <chr> <dbl>
1 2009-01-01 X 10.7
2 2009-01-01 Y 6.70
3 2009-01-01 Z 13.5
4 2009-01-02 X 10.1
5 2009-01-02 Y 11.5
6 2009-01-02 Z 5.34Now we can use pivot_wider to go back to the original dataframe:
tidy_stocks |> pivot_wider(names_from=stock, values_from=price)# A tibble: 2 × 4
time X Y Z
<date> <dbl> <dbl> <dbl>
1 2009-01-01 10.7 6.70 13.5
2 2009-01-02 10.1 11.5 5.34Or, we can put it into a new (“transposed”) format, in which the observations are stocks and the columns are dates:
tidy_stocks |> pivot_wider(names_from=time, values_from=price)# A tibble: 3 × 3
stock `2009-01-01` `2009-01-02`
<chr> <dbl> <dbl>
1 X 10.7 10.1
2 Y 6.70 11.5
3 Z 13.5 5.34separate helps when you have more than one value in a single column:
economists = data.frame(name = c("Adam_Smith", "Paul_Samuelson", "Milton_Friedman"))
economists name
1 Adam_Smith
2 Paul_Samuelson
3 Milton_Friedmaneconomists |> separate(name, c("first_name", "last_name")) first_name last_name
1 Adam Smith
2 Paul Samuelson
3 Milton Friedman–
This command is pretty smart. But to avoid ambiguity, you can also specify the separation character with the sep argument:
economists |> separate(name, c("first_name", "last_name"), sep = "_") first_name last_name
1 Adam Smith
2 Paul Samuelson
3 Milton FriedmanRelated is separate_rows, for splitting cells with multiple values into multiple rows:
jobs = data.frame(
name = c("Joe", "Jill"),
occupation = c("President", "First Lady, Professor, Grandmother")
)
jobs name occupation
1 Joe President
2 Jill First Lady, Professor, Grandmother# Now split out Jill's various occupations into different rows
jobs |> separate_rows(occupation)# A tibble: 5 × 2
name occupation
<chr> <chr>
1 Joe President
2 Jill First
3 Jill Lady
4 Jill Professor
5 Jill GrandmotherRelated is separate_rows, for splitting cells with multiple values into multiple rows:
jobs = data.frame(
name = c("Joe", "Jill"),
occupation = c("President", "First Lady, Professor, Grandmother")
)
jobs name occupation
1 Joe President
2 Jill First Lady, Professor, Grandmother# Now split out Jill's various occupations into different rows
jobs |> separate_rows(occupation, sep = ", ")# A tibble: 4 × 2
name occupation
<chr> <chr>
1 Joe President
2 Jill First Lady
3 Jill Professor
4 Jill Grandmothergdp = data.frame(
yr = rep(2016, times = 4),
mnth = rep(1, times = 4),
dy = 1:4,
gdp = rnorm(4, mean = 100, sd = 2)
)
gdp yr mnth dy gdp
1 2016 1 1 101.48266
2 2016 1 2 104.22571
3 2016 1 3 101.39903
4 2016 1 4 99.93644## Combine "yr", "mnth", and "dy" into one "date" column
gdp |> unite(date, c("yr", "mnth", "dy"), sep = "-") date gdp
1 2016-1-1 101.48266
2 2016-1-2 104.22571
3 2016-1-3 101.39903
4 2016-1-4 99.93644gdp_u <- gdp |> unite(date, c("yr", "mnth", "dy"), sep = "-")Note that unite will automatically create a character variable.
If you want to convert it to something else (e.g. date or numeric) then you will need to modify it using mutate. This example uses the lubridate package’s super helpful date conversion functions.
library(lubridate)
gdp_u |> mutate(date = ymd(date)) date gdp
1 2016-01-01 101.48266
2 2016-01-02 104.22571
3 2016-01-03 101.39903
4 2016-01-04 99.93644Using nycflights13, create a table of average arrival delay (in minutes) by day (in rows) and carrier (in columns).
Hint: Recall that you can tabulate summary statistics using group_by and summarize:
flights |>
group_by(carrier) |>
summarize(avg_late = mean(arr_delay, na.rm=T))# A tibble: 16 × 2
carrier avg_late
<chr> <dbl>
1 9E 7.38
2 AA 0.364
3 AS -9.93
4 B6 9.46
5 DL 1.64
6 EV 15.8
7 F9 21.9
8 FL 20.1
9 HA -6.92
10 MQ 10.8
11 OO 11.9
12 UA 3.56
13 US 2.13
14 VX 1.76
15 WN 9.65
16 YV 15.6 Solution:
delay_long = flights |>
group_by(carrier, day) |>
summarize(avg_late = mean(arr_delay, na.rm=T))
delay_wide = delay_long |>
pivot_wider(names_from=carrier, values_from=avg_late)
head(delay_wide, 4)# A tibble: 4 × 17
day `9E` AA AS B6 DL EV F9 FL HA MQ OO
<int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 7.21 -1.23 -5.96 11.9 0.866 21.3 21.7 22.7 -15.4 12.9 NA
2 2 7.35 -0.905 -13.7 9.90 3.05 18.0 7.71 20.9 -16.1 9.04 NaN
3 3 5.80 -3.09 -20.8 5.26 -0.204 15.3 18.0 19.6 -16.8 13.7 0.5
4 4 -2.11 -5.80 -22.3 -0.0939 -6.24 3.87 14.5 4.38 -15.2 3.82 -12
# ℹ 5 more variables: UA <dbl>, US <dbl>, VX <dbl>, WN <dbl>, YV <dbl>Explain what each line in the codes do:
Answer:
Go to github.com and sign up to create an account. Report your GitHub username via this form:
You will submit the PDF documents in to Gradescope as part of your final submission.
Remember – you must turn in a PDF file to the Gradescope page before the submission deadline for full credit.
Instructions to combine PDFs:
Preview (Mac): support.apple.com/guide/preview/combine-pdfs-prvw43696/mac
Adobe (Mac or PC): helpx.adobe.com/acrobat/using/merging-files-single-pdf.html
To submit your assignment:
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Click on the assignment, and you’ll be prompted to submit it.
Mark the pages associated with each exercise. All of the pages of your lab should be associated with at least one question (i.e., should be “checked”).
Select the first page of your .PDF submission to be associated with the “Workflow & formatting” section.
| Component | Points |
|---|---|
| Replicating Ex 1-10 | 85 |
| Challenge | 5 |
| Creating Github Account | 5 |
| Workflow & formatting | 5 |
The “Workflow & formatting” grade is to assess the reproducible workflow and document format.