You will need Joey’s singer R package for this exercise. And to install that, you’ll need to install devtools. Running this code in your console should do the trick:
install.packages("devtools")
devtools::install_github("JoeyBernhardt/singer")Load required packages:
singerThe package singer comes with two smallish data frames about songs. Let’s take a look at them (after minor modifications by renaming and shuffling):
(time <- as_tibble(songs) %>%
rename(song = title))## # A tibble: 22 x 3
## song artist_name year
## <chr> <chr> <int>
## 1 Corduroy Pearl Jam 1994
## 2 Grievance Pearl Jam 2000
## 3 Stupidmop Pearl Jam 1994
## 4 Present Tense Pearl Jam 1996
## 5 MFC Pearl Jam 1998
## 6 Lukin Pearl Jam 1996
## 7 It's Lulu The Boo Radleys 1995
## 8 Sparrow The Boo Radleys 1992
## 9 Martin_ Doom! It's Seven O'Clock The Boo Radleys 1995
## 10 Leaves And Sand The Boo Radleys 1993
## # … with 12 more rows(album <- as_tibble(locations) %>%
select(title, everything()) %>%
rename(album = release,
song = title))## # A tibble: 14 x 4
## song artist_name city album
## <chr> <chr> <chr> <chr>
## 1 Grievance Pearl Jam Seattle, WA Binaural
## 2 Stupidmop Pearl Jam Seattle, WA Vitalogy
## 3 Present Tense Pearl Jam Seattle, WA No Code
## 4 MFC Pearl Jam Seattle, WA Live On Two Legs
## 5 Lukin Pearl Jam Seattle, WA Seattle Washingto…
## 6 Stuck On Amber The Boo Radl… Liverpool,… Wake Up!
## 7 It's Lulu The Boo Radl… Liverpool,… Best Of
## 8 Sparrow The Boo Radl… Liverpool,… Everything's Alri…
## 9 High as Monkeys The Boo Radl… Liverpool,… Kingsize
## 10 Butterfly McQueen The Boo Radl… Liverpool,… Giant Steps
## 11 My One and Only Love Carly Simon New York, … Moonlight Serenade
## 12 It Was So Easy (LP Versio… Carly Simon New York, … No Secrets
## 13 I've Got A Crush On You Carly Simon New York, … Clouds In My Coff…
## 14 "Manha De Carnaval (Theme … Carly Simon New York, … Into Whitetime. But, which of those songs do we know its corresponding album?# semi_join filters time tibble to include only those that have a song and artist name in album
time %>%
semi_join(album, by = c("song", "artist_name"))## # A tibble: 13 x 3
## song artist_name year
## <chr> <chr> <int>
## 1 Grievance Pearl Jam 2000
## 2 Stupidmop Pearl Jam 1994
## 3 Present Tense Pearl Jam 1996
## 4 MFC Pearl Jam 1998
## 5 Lukin Pearl Jam 1996
## 6 It's Lulu The Boo Radleys 1995
## 7 Sparrow The Boo Radleys 1992
## 8 High as Monkeys The Boo Radleys 1998
## 9 Butterfly McQueen The Boo Radleys 1993
## 10 My One and Only Love Carly Simon 2005
## 11 It Was So Easy (LP Version) Carly Simon 1972
## 12 I've Got A Crush On You Carly Simon 1994
## 13 "Manha De Carnaval (Theme from \"Black Orpheus\")" Carly Simon 2007# inner_join combines the two tibbles, only keeping those present in both (drop all outliers in either tibble) but retains columns of both tibbles
time %>%
inner_join(album, by = c("song", "artist_name"))## # A tibble: 13 x 5
## song artist_name year city album
## <chr> <chr> <int> <chr> <chr>
## 1 Grievance Pearl Jam 2000 Seattle, … Binaural
## 2 Stupidmop Pearl Jam 1994 Seattle, … Vitalogy
## 3 Present Tense Pearl Jam 1996 Seattle, … No Code
## 4 MFC Pearl Jam 1998 Seattle, … Live On Two Legs
## 5 Lukin Pearl Jam 1996 Seattle, … Seattle Washing…
## 6 It's Lulu The Boo Radl… 1995 Liverpool… Best Of
## 7 Sparrow The Boo Radl… 1992 Liverpool… Everything's Al…
## 8 High as Monkeys The Boo Radl… 1998 Liverpool… Kingsize
## 9 Butterfly McQueen The Boo Radl… 1993 Liverpool… Giant Steps
## 10 My One and Only Love Carly Simon 2005 New York,… Moonlight Seren…
## 11 It Was So Easy (LP Ver… Carly Simon 1972 New York,… No Secrets
## 12 I've Got A Crush On You Carly Simon 1994 New York,… Clouds In My Co…
## 13 "Manha De Carnaval (The… Carly Simon 2007 New York,… Into Whitetime tibble, being sure to preserve rows even if album info is not readily available.# left_join joins tibble time with album, adding album tibble columns to time, using NA if information is not available.
time %>%
left_join(album, by = c("song", "artist_name"))## # A tibble: 22 x 5
## song artist_name year city album
## <chr> <chr> <int> <chr> <chr>
## 1 Corduroy Pearl Jam 1994 <NA> <NA>
## 2 Grievance Pearl Jam 2000 Seattle, WA Binaural
## 3 Stupidmop Pearl Jam 1994 Seattle, WA Vitalogy
## 4 Present Tense Pearl Jam 1996 Seattle, WA No Code
## 5 MFC Pearl Jam 1998 Seattle, WA Live On Two Legs
## 6 Lukin Pearl Jam 1996 Seattle, WA Seattle Washington…
## 7 It's Lulu The Boo Radle… 1995 Liverpool,… Best Of
## 8 Sparrow The Boo Radle… 1992 Liverpool,… Everything's Alrig…
## 9 Martin_ Doom! It's… The Boo Radle… 1995 <NA> <NA>
## 10 Leaves And Sand The Boo Radle… 1993 <NA> <NA>
## # … with 12 more rowstime %>%
semi_join(album, by = "song")## # A tibble: 13 x 3
## song artist_name year
## <chr> <chr> <int>
## 1 Grievance Pearl Jam 2000
## 2 Stupidmop Pearl Jam 1994
## 3 Present Tense Pearl Jam 1996
## 4 MFC Pearl Jam 1998
## 5 Lukin Pearl Jam 1996
## 6 It's Lulu The Boo Radleys 1995
## 7 Sparrow The Boo Radleys 1992
## 8 High as Monkeys The Boo Radleys 1998
## 9 Butterfly McQueen The Boo Radleys 1993
## 10 My One and Only Love Carly Simon 2005
## 11 It Was So Easy (LP Version) Carly Simon 1972
## 12 I've Got A Crush On You Carly Simon 1994
## 13 "Manha De Carnaval (Theme from \"Black Orpheus\")" Carly Simon 2007time, but not in album?# anti_join filters time and drops all time rows that are in album
time %>%
anti_join(album, by = "artist_name")## # A tibble: 5 x 3
## song artist_name year
## <chr> <chr> <int>
## 1 Mine Again Mariah Carey 2005
## 2 Don't Forget About Us Mariah Carey 2005
## 3 Babydoll Mariah Carey 1997
## 4 Don't Forget About Us Mariah Carey 2005
## 5 Vision Of Love Mariah Carey 1990# joins both tibbles and adds NA wherever needed
time %>%
full_join(album, by = c("song", "artist_name"))## # A tibble: 23 x 5
## song artist_name year city album
## <chr> <chr> <int> <chr> <chr>
## 1 Corduroy Pearl Jam 1994 <NA> <NA>
## 2 Grievance Pearl Jam 2000 Seattle, WA Binaural
## 3 Stupidmop Pearl Jam 1994 Seattle, WA Vitalogy
## 4 Present Tense Pearl Jam 1996 Seattle, WA No Code
## 5 MFC Pearl Jam 1998 Seattle, WA Live On Two Legs
## 6 Lukin Pearl Jam 1996 Seattle, WA Seattle Washington…
## 7 It's Lulu The Boo Radle… 1995 Liverpool,… Best Of
## 8 Sparrow The Boo Radle… 1992 Liverpool,… Everything's Alrig…
## 9 Martin_ Doom! It's… The Boo Radle… 1995 <NA> <NA>
## 10 Leaves And Sand The Boo Radle… 1993 <NA> <NA>
## # … with 13 more rowsLoad in the three Lord of the Rings tibbles that we saw last time:
fell <- read_csv("https://raw.githubusercontent.com/jennybc/lotr-tidy/master/data/The_Fellowship_Of_The_Ring.csv")## Parsed with column specification:
## cols(
## Film = col_character(),
## Race = col_character(),
## Female = col_double(),
## Male = col_double()
## )ttow <- read_csv("https://raw.githubusercontent.com/jennybc/lotr-tidy/master/data/The_Two_Towers.csv")## Parsed with column specification:
## cols(
## Film = col_character(),
## Race = col_character(),
## Female = col_double(),
## Male = col_double()
## )retk <- read_csv("https://raw.githubusercontent.com/jennybc/lotr-tidy/master/data/The_Return_Of_The_King.csv")## Parsed with column specification:
## cols(
## Film = col_character(),
## Race = col_character(),
## Female = col_double(),
## Male = col_double()
## )bind_rows(fell, ttow, retk)## # A tibble: 9 x 4
## Film Race Female Male
## <chr> <chr> <dbl> <dbl>
## 1 The Fellowship Of The Ring Elf 1229 971
## 2 The Fellowship Of The Ring Hobbit 14 3644
## 3 The Fellowship Of The Ring Man 0 1995
## 4 The Two Towers Elf 331 513
## 5 The Two Towers Hobbit 0 2463
## 6 The Two Towers Man 401 3589
## 7 The Return Of The King Elf 183 510
## 8 The Return Of The King Hobbit 2 2673
## 9 The Return Of The King Man 268 2459fell), but not in any of the other ones?fell %>%
anti_join(ttow, by = "Race") %>%
anti_join(retk, by = "Race")## # A tibble: 0 x 4
## # … with 4 variables: Film <chr>, Race <chr>, Female <dbl>, Male <dbl>Let’s use three set functions: intersect, union and setdiff. We’ll work with two toy tibbles named y and z, similar to Data Wrangling Cheatsheet
(y <- tibble(x1 = LETTERS[1:3], x2 = 1:3))## # A tibble: 3 x 2
## x1 x2
## <chr> <int>
## 1 A 1
## 2 B 2
## 3 C 3(z <- tibble(x1 = c("B", "C", "D"), x2 = 2:4))## # A tibble: 3 x 2
## x1 x2
## <chr> <int>
## 1 B 2
## 2 C 3
## 3 D 4y and z# intersect gets rows that occur (identically) in both tibbles
intersect(y, z)## # A tibble: 2 x 2
## x1 x2
## <chr> <int>
## 1 B 2
## 2 C 3y on Day 1, and z in Day 2. Make a data set to reflect that.# union returns unique observations in both tibbles
# since all are unique, all are retained
union(
mutate(y, day = "Day 1"),
mutate(z, day = "Day 2")
)## # A tibble: 6 x 3
## x1 x2 day
## <chr> <int> <chr>
## 1 A 1 Day 1
## 2 B 2 Day 1
## 3 C 3 Day 1
## 4 B 2 Day 2
## 5 C 3 Day 2
## 6 D 4 Day 2# bind_rows binds rows together (stacks tibbles on top of each other)
bind_rows(
mutate(y, day = "Day 1"),
mutate(z, day = "Day 2")
)## # A tibble: 6 x 3
## x1 x2 day
## <chr> <int> <chr>
## 1 A 1 Day 1
## 2 B 2 Day 1
## 3 C 3 Day 1
## 4 B 2 Day 2
## 5 C 3 Day 2
## 6 D 4 Day 2z are bad! Remove those rows from y.# both ways work!
# setdiff returns observations found in y that are not in z
setdiff(y,z)## # A tibble: 1 x 2
## x1 x2
## <chr> <int>
## 1 A 1# anti_join filters y by excluding y values that are in tibble z
anti_join(y,z)## Joining, by = c("x1", "x2")## # A tibble: 1 x 2
## x1 x2
## <chr> <int>
## 1 A 1