In this example, we’ll rename latitude and longitude to lat and long respectively: ufos % rename(lat = latitude, long = longitude )Īnd calling colnames() to confirm: colnames(ufos) “datetime” “city” “state” “country” “shape” “conds.” “.” “ments” “date.posted” “lat” “long”Īnd there we have it! As you can see, it’s super easy to rename columns with dplyr. We simply pass multiple the columns as a comma separated list. What about if we wanted to rename more than one column in a single statement? Well this is easily done too. To change the column name with dplyr, we can specify the following: ufos % rename(ments = comments)įrom this example, we can note that the syntax of rename is as follows rename(new variable name = existing variable name)Īnd that’s all there is to it! We can confirm that our change has been made by re-running colnames: colnames(ufos) “datetime” “city” “state” “country” “shape” “conds.” “.” “ments” “date.posted” “latitude” “longitude” In this instance, let’s change the “comments” column to “ments”: We might want to add more clarity around the “comments” column, perhaps specifying that these aren’t metadata comments from the analyst, but an actual part of the dataset.

Using colnames() we can take a look at the existing column names: colnames(ufos) “datetime” “city” “state” “country” “shape” “conds.” “.” “comments” “date.posted” “latitude” “longitude” We’ll take a look at it now with the UFOs dataset from Kaggle.

While there are numerous ways to rename columns within R, I’ve found that dplyr’s approach is arguably one of the most intuitive. This is particularly handy if you’re sharing your work with others, or indeed if you’re in an environment where multiple people are working on the same data, meaning that clarity is key. This can be handy if you want to join two dataframes on a key, and it’s easier to just rename the column than specifying further in the join.Īlternatively, from a data munging perspective, sometimes you can have unhelpful column names like x1, x2, x3, so cleaning these up makes your dataframes and work more legible. With dplyr, it’s super easy to rename columns within your dataframe. with 83 more rows, and 3 more variables: films, vehicles, #> # starships iris %>% select ( ! c ( Sepal.Length, Petal.Length ) ) #> # A tibble: 150 x 3 #> Sepal.Width Petal.Width Species #> #> 1 3.5 0.2 setosa #> 2 3 0.2 setosa #> 3 3.2 0.2 setosa #> 4 3.1 0.2 setosa #> #. Starwars %>% select ( ! ( name : mass ) ) #> # A tibble: 87 x 11 #> hair_color skin_color eye_color birth_year sex gender homeworld species #> #> 1 blond fair blue 19 male masculine Tatooine Human #> 2 gold yellow 112 none masculine Tatooine Droid #> 3 white, blue red 33 none masculine Naboo Droid #> 4 none white yellow 41.9 male masculine Tatooine Human #> #. Where(): Applies a function to all variables and selects those This helper selects variables with a function: The basic syntax for doing so is as follows: data > rename(newname1 oldname1, newname2 oldname2.

Names must be present, otherwise an out-of-bounds error isĪny_of(): Same as all_of(), except that no error is thrown Another way to rename columns in R is by using the rename() function in the dplyr package. These helpers select variables from a character vector:Īll_of(): Matches variable names in a character vector. Num_range(): Matches a numerical range like x01, x02, x03. These helpers select variables by matching patterns in their names:

Last_col(): Select last variable, possibly with an offset. In addition, you can use selection helpers. & and | for selecting the intersection or the union of two ! for taking the complement of a set of variables. : for selecting a range of consecutive variables. Tidyverse selections implement a dialect of R where operators make

Rename column r