With dplyr, it’s super easy to rename columns within your dataframe. View source: R/rename.R. Select and renaming select() and rename() are now significantly more flexible thanks to enhancements to the tidyselect package. Since the column “Distance” has an index of 16, assign the new column name “distance” to the element of the names vector selected using the index.That’s it for now on dplyr rename(). We have updated the syntax for selecting with a function. #> Sepal.Length Sepal.Width PETAL.LENGTH PETAL.WIDTH Species

You can optionally choose which columns to apply the transformation to:For a long time, people have asked an easy way to change the order of columns in data frame. #> 5 3.6 1.4 0.2 setosa It’s always been possible to perform some transformations with If you want to move columns to a different position use Together these three functions form a family of functions for working with columns:It’s interesting to think about how these compare to their row-based equivalents:

#> 5 3.6 1.4 0.2 setosa Where you would use The rest of this post has been updated accordingly.Here’s a few examples of how you might use these techniques in with some toy data:They differ only in what happens when variables are not present in the data frame.
Rename the column name in R using Dplyr.

#> 4.9 3 1.4 0.2 setosa Hence, it is better to use dplyr rename instead of dplyr select to modify column names.This is similar to the code for renaming single column that we had seen above, except that we use pairs of new and old column names now.Let’s see the code for dplyr rename multiple columns in action.Imagine that you want to rename 100’s of columns at once.Using dplyr rename() is not a good option in that scenario.This is where the three variants of dplyr rename() – namely , rename_all(), rename_if(), rename_at() comes in handy.Use rename_at() to rename multiple columns at once. In this example, we’ll rename latitude and longitude to lat and long respectively:And there we have it! Description. Any help is appreciated.
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See Column names are changed; column order is preserved.The following methods are currently available in loaded packages: Select (and optionally rename) variables in a data frame, using a concise mini-language that makes it easy to refer to variables based on their name (e.g. In this post, we will learn about dplyr rename function.dplyr rename is used to modify dataframe column names or  tibble column names. #> 4.7 3.2 1.3 0.2 setosa #> 5 3.4 1.5 0.2 setosa As R user you will agree: To rename column names is one of the most often applied data manipulations in R.However, depending on your specific data situation, a different R syntax might be needed. Looks like ‘assignee.login’ is the column that holds the assignee name information so I want to keep only this column. In this instance, let’s change the “comments” column to “spotter.comments”:To change the column name with dplyr, we can specify the following:From this example, we can note that the syntax of rename is as followsAnd that’s all there is to it! #> 4.6 3.4 1.4 0.3 setosa

#> 4.4 2.9 1.4 0.2 setosa #> 4.9 3 1.4 0.2 setosa

#> 4.9 3.1 1.5 0.1 setosa dbplyr ( The first step before using rename() is to know what are the existing column names.This is done using colnames().Lets use dplyr rename  to modify column names in a dataframe or a tibble. #> 4.9 3 1.4 0.2 setosa #> 4.9 3.1 1.5 0.1 setosa #> 4.6 3.1 1.5 0.2 setosa

For example, FlightNum is changed to FlightNumber !Use rename_all() to change the names of dataframe columns without any logical condition.For example, consider that you would like to change column names, irrespective of it being  a numeric or not , and if they contain Num in the column name, you want to modify it to Number.Post this operation, you can see that FlightNumber got changed to FlightNumberber and TailNum changed to TailNumber.Along with dplyr rename() , you can also rename columns of a dataframe using a logical vector or an index.Let us now modify the column name “Month” of hflights to “month” using logical vector.Another approach to rename columns of a dataframe is by using the appropriate index on the names vector.Let us now modify the column name “Distance” to “distance”. #> 4.4 2.9 1.4 0.2 setosa #>