r studio update column based on another column

keep = 'none' ) #> # A tibble: 1 × 1 #> z #> #> 1 3 # Grouping - # The mutate operation may yield different results on grouped # tibbles because the expressions are computed within groups. keep = 'unused' ) #> # A tibble: 1 × 3 #> a b z #> #> 1 a b 3 df %>% mutate (z = x + y.

keep = 'all' ) # the default #> # A tibble: 1 × 5 #> x y a b z #> #> 1 1 2 a b 3 df %>% mutate (z = x + y. after = x ) #> # A tibble: 1 × 3 #> x z y #> #> 1 1 3 2 # By default, mutate() keeps all columns from the input data. df % mutate (z = x + y ) #> # A tibble: 1 × 3 #> x y z #> #> 1 1 2 3 df %>% mutate (z = x + y. starwars %>% select ( name, homeworld, species ) %>% mutate ( across ( ! name, as.factor ) ) #> # A tibble: 87 × 3 #> name homeworld species #> #> 1 Luke Skywalker Tatooine Human #> 2 C-3PO Tatooine Droid #> 3 R2-D2 Naboo Droid #> 4 Darth Vader Tatooine Human #> 5 Leia Organa Alderaan Human #> 6 Owen Lars Tatooine Human #> 7 Beru Whitesun lars Tatooine Human #> 8 R5-D4 Tatooine Droid #> 9 Biggs Darklighter Tatooine Human #> 10 Obi-Wan Kenobi Stewjon Human #> # ℹ 77 more rows # see more in ?across # Window functions are useful for grouped mutates: starwars %>% select ( name, mass, homeworld ) %>% group_by ( homeworld ) %>% mutate (rank = min_rank ( desc ( mass ) ) ) #> # A tibble: 87 × 4 #> # Groups: homeworld #> name mass homeworld rank #> #> 1 Luke Skywalker 77 Tatooine 5 #> 2 C-3PO 75 Tatooine 6 #> 3 R2-D2 32 Naboo 6 #> 4 Darth Vader 136 Tatooine 1 #> 5 Leia Organa 49 Alderaan 2 #> 6 Owen Lars 120 Tatooine 2 #> 7 Beru Whitesun lars 75 Tatooine 6 #> 8 R5-D4 32 Tatooine 8 #> 9 Biggs Darklighter 84 Tatooine 3 #> 10 Obi-Wan Kenobi 77 Stewjon 1 #> # ℹ 77 more rows # see `vignette('window-functions')` for more details # By default, new columns are placed on the far right. starwars %>% select ( name, height, mass, homeworld ) %>% mutate ( mass = NULL, height = height * 0.0328084 # convert to feet ) #> # A tibble: 87 × 3 #> name height homeworld #> #> 1 Luke Skywalker 5.64 Tatooine #> 2 C-3PO 5.48 Tatooine #> 3 R2-D2 3.15 Naboo #> 4 Darth Vader 6.63 Tatooine #> 5 Leia Organa 4.92 Alderaan #> 6 Owen Lars 5.84 Tatooine #> 7 Beru Whitesun lars 5.41 Tatooine #> 8 R5-D4 3.18 Tatooine #> 9 Biggs Darklighter 6.00 Tatooine #> 10 Obi-Wan Kenobi 5.97 Stewjon #> # ℹ 77 more rows # Use across() with mutate() to apply a transformation # to multiple columns in a tibble. # Newly created variables are available immediately starwars %>% select ( name, mass ) %>% mutate ( mass2 = mass * 2, mass2_squared = mass2 * mass2 ) #> # A tibble: 87 × 4 #> name mass mass2 mass2_squared #> #> 1 Luke Skywalker 6 #> 2 C-3PO 0 #> 3 R2-D2 32 64 4096 #> 4 Darth Vader 14 #> 5 Leia Organa 49 98 9604 #> 6 Owen Lars 10 #> 7 Beru Whitesun lars 0 #> 8 R5-D4 32 64 4096 #> 9 Biggs Darklighter 4 #> 10 Obi-Wan Kenobi 6 #> # ℹ 77 more rows # As well as adding new variables, you can use mutate() to # remove variables and modify existing variables.

Should appear (the default is to add to the right hand side). 'none' doesn't retain any extra columns from. This is useful if you generate new columns, but no longer need 'unused' retains only the columns not used in. This is useful for checking your work, as it displays inputs Forĭetails and examples, see ?dplyr_by.keepĬontrol which columns from. Group by for just this operation, functioning as an alternative to group_by(). The name gives the name of the column in the output.Ī vector of length 1, which will be recycled to the correct length.Ī vector the same length as the current group (or the whole data frameĪ data frame or tibble, to create multiple columns in the output.

R studio update column based on another column