Chapter 24 More dplyr
You have learned the big 7 verbs in dplyr, including
- filteR
- seleCt
- mutate
- group_by
- summarize
- arrange
- count
You can review with this interactive tutorial:
click here
But there is more!!
24.0.1 Rename
when you don’t need a full mutate, just better names.
This often comes up after you have used clean_names to improve the formatting of variable names, but you end up with long, long names.
First run the code chunk below to see what you have in the data frame
Then pipe tbl into a rename function, which has the format
rename(new_name = old_name)
and rename the sbp as sbp_basel
name <- c("Bob", "Carla", "Dave")
the_value_of_sbp_at_baseline <- c(120, 134, 96)
tbl <- data.frame(name, the_value_of_sbp_at_baseline)
tbl %>%
rename(sbp_base = the_value_of_sbp_at_baseline)## name sbp_base
## 1 Bob 120
## 2 Carla 134
## 3 Dave 9624.0.2 Re-arrange your variables/columns
If you don’t like the order of your variables, you can use select to reorder them. use the format: select(var1, var2, var3:var4, everything())
note that everything() puts all the other variables in at the end, in their previous order. This is helpful for pulling interesting variables to the front of the line.
Run the chunk below, then pipe the tbl into select(pt_id, everything())
firstname <- c("Bob", "Carla", "Dave", "Elena")
pt_id <- c(001, 002, 003, 004)
lastname <- c("Edwards", "Frankel", "Genghis", "Harrison")
tbl <- data.frame(firstname, pt_id, lastname)
tbl %>%
select(pt_id, everything())## pt_id firstname lastname
## 1 1 Bob Edwards
## 2 2 Carla Frankel
## 3 3 Dave Genghis
## 4 4 Elena Harrison24.0.3 Find distinct rows
If you want to list only the distinct, unique observations, you can use the distinct function.
Run the code chunk below to see the replicates by visit_num
Then pipe tbl into distinct(var1, var2) - the variables you care about
In this case, use firstname, lastname to find the distinct patients
firstname <- rep(c("Bob", "Carla", "Dave", "Elena"),3)
pt_id <- rep(c(001, 002, 003, 004),3)
lastname <- rep(c("Edwards", "Frankel", "Genghis", "Harrison"),3)
visit_num <- c(rep(1,4), rep(2, 4), rep(3,4))
tbl <- data.frame(pt_id, firstname, lastname, visit_num)
tbl %>%
distinct(firstname, lastname) ->
unique_pts24.0.4 Select a group of rows with slice()
Sometimes you just want a few contiguous rows, like with head(), or tail(). but you can also pick which rows with slice()
This is a simple command with
slice(start_row:end_row) to select the rows you want
Run the chunk below to find out how many rows are in the starwars dataset.
Then look at head and tail functions
Then pipe starwars into a slice function.
Slice out rows 15:25 or 46:60
## [1] 87 14## # A tibble: 7 × 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 C-3PO 167 75 <NA> gold yellow 112 none mascu…
## 3 R2-D2 96 32 <NA> white, bl… red 33 none mascu…
## 4 Darth Va… 202 136 none white yellow 41.9 male mascu…
## 5 Leia Org… 150 49 brown light brown 19 fema… femin…
## 6 Owen Lars 178 120 brown, gr… light blue 52 male mascu…
## 7 Beru Whi… 165 75 brown light blue 47 fema… femin…
## # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
## # vehicles <list>, starships <list>## # A tibble: 10 × 14
## name height mass hair_color skin_color eye_color birth_year sex gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 Grievous 216 159 none brown, wh… green, y… NA male mascu…
## 2 Tarfful 234 136 brown brown blue NA male mascu…
## 3 Raymus … 188 79 brown light brown NA male mascu…
## 4 Sly Moo… 178 48 none pale white NA <NA> <NA>
## 5 Tion Me… 206 80 none grey black NA male mascu…
## 6 Finn NA NA black dark dark NA male mascu…
## 7 Rey NA NA brown light hazel NA fema… femin…
## 8 Poe Dam… NA NA brown light brown NA male mascu…
## 9 BB8 NA NA none none black NA none mascu…
## 10 Captain… NA NA none none unknown NA fema… femin…
## # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
## # vehicles <list>, starships <list>## # A tibble: 21 × 14
## name height mass hair_color skin_color eye_color birth_year sex gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 Greedo 173 74 <NA> green black 44 male mascu…
## 2 Jabba … 175 1358 <NA> green-tan… orange 600 herm… mascu…
## 3 Wedge … 170 77 brown fair hazel 21 male mascu…
## 4 Jek To… 180 110 brown fair blue NA <NA> <NA>
## 5 Yoda 66 17 white green brown 896 male mascu…
## 6 Palpat… 170 75 grey pale yellow 82 male mascu…
## 7 Boba F… 183 78.2 black fair brown 31.5 male mascu…
## 8 IG-88 200 140 none metal red 15 none mascu…
## 9 Bossk 190 113 none green red 53 male mascu…
## 10 Lando … 177 79 black dark brown 31 male mascu…
## # ℹ 11 more rows
## # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
## # vehicles <list>, starships <list>24.0.5 Randomly sample some rows with sample_n() or sample_frac()
Sometimes you want a smaller but representative sample of rows, which you can get with sample_n() or sample_frac()
sample_n needs a size (number of rows)
while sample_frac needs a size between 0 and 1.
You can also choose to sample with replacement (replace = TRUE)
Using the chunk below, try to
- sample 30 records from starwars (without replacement)
- take a 20% random sample (with replacement)
## # A tibble: 30 × 14
## name height mass hair_color skin_color eye_color birth_year sex gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 Cliegg … 183 NA brown fair blue 82 male mascu…
## 2 Mace Wi… 188 84 none dark brown 72 male mascu…
## 3 Yarael … 264 NA none white yellow NA male mascu…
## 4 Captain… NA NA none none unknown NA fema… femin…
## 5 Ayla Se… 178 55 none blue hazel 48 fema… femin…
## 6 Leia Or… 150 49 brown light brown 19 fema… femin…
## 7 Watto 137 NA black blue, grey yellow NA male mascu…
## 8 Leia Or… 150 49 brown light brown 19 fema… femin…
## 9 Ayla Se… 178 55 none blue hazel 48 fema… femin…
## 10 Darth V… 202 136 none white yellow 41.9 male mascu…
## # ℹ 20 more rows
## # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
## # vehicles <list>, starships <list>## # A tibble: 17 × 14
## name height mass hair_color skin_color eye_color birth_year sex gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 Nien Nu… 160 68 none grey black NA male mascu…
## 2 Ayla Se… 178 55 none blue hazel 48 fema… femin…
## 3 BB8 NA NA none none black NA none mascu…
## 4 Darth M… 175 80 none red yellow 54 male mascu…
## 5 Obi-Wan… 182 77 auburn, w… fair blue-gray 57 male mascu…
## 6 Jabba D… 175 1358 <NA> green-tan… orange 600 herm… mascu…
## 7 Luke Sk… 172 77 blond fair blue 19 male mascu…
## 8 Tion Me… 206 80 none grey black NA male mascu…
## 9 Biggs D… 183 84 black light brown 24 male mascu…
## 10 Eeth Ko… 171 NA black brown brown NA male mascu…
## 11 Zam Wes… 168 55 blonde fair, gre… yellow NA fema… femin…
## 12 Gasgano 122 NA none white, bl… black NA male mascu…
## 13 Biggs D… 183 84 black light brown 24 male mascu…
## 14 Bail Pr… 191 NA black tan brown 67 male mascu…
## 15 Taun We 213 NA none grey black NA fema… femin…
## 16 Shaak Ti 178 57 none red, blue… black NA fema… femin…
## 17 Sebulba 112 40 none grey, red orange NA male mascu…
## # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
## # vehicles <list>, starships <list>but you can also pick which rows with slice()