r mutate multiple columns

@Skurup, "2" is the "margin" argument within the functions, separated with an underscore Name collisions in the new columns are disambiguated using a unique suffix.The functions are maturing, because the naming scheme and the #> 1.69 1.36 1.7 0.4 setosa The optionsinside mutate are almost endless: pretty much anything that you can doto normal vectors, can be done inside a mutate() function.Anything inside mutate can either be a new column (by giving mutate anew column name), or can replace the current column (by keeping the samecolumn name).One of the simplest options is a calculation base… Featured on Meta #> 4.9 3.1 1.5 0.1 setosa If this approach does not work for you, you could do, In the code cbind(bankdata,apply(bankdata[,5:7],2, function(x) x/bankdata$totass)), what does the 2 signify? #> name height mass hair_color skin_color eye_color birth_year sex gender concatenating the names of the input variables and the names of the #> # … with 77 more rows, and 5 more variables: homeworld # You can also supply selection helpers to _at() functions but you have #> gold yellow 112 none mascu… The variables for which A data frame. Summarise multiple columns Source: R/colwise-mutate.R. Ask Question Asked 5 years, 10 months ago. I hope this will help some R users to learn how to use I think you can save some typing in this way with dplyr. (see For model terms created by this step, what analysis role should #> gold yellow 112 none mascu… Would you explain to me what is going on? There are three variants: _all affects every variable. #> 1.53 1.22 1.4 0.3 setosa Note that this argument must be named. When there are multiple functions, they create new# variables instead of modifying the variables in place: #> 1.63 1.25 1.4 0.2 setosa #> 5.1 3.5 1.4 0.2 setosa - 4.9 3 1.4 0.2 setosa - 4.7 3.2 1.3 0.2 setosa - 4.6 3.1 1.5 0.2 setosa - 5 3.6 1.4 0.2 setosa - 5.4 3.9 1.7 0.4 setosa - 4.6 3.4 1.4 0.3 setosa - 5 3.4 1.5 0.2 setosa - 4.4 2.9 1.4 0.2 setosa - 4.9 3.1 1.5 0.1 setosa -# … with 140 more rows, and 3 more variables: Sepal.Width_scale `Error: object 'ana' not found. #> brown light blue 47 fema… femin… My issue is that mutate_if checks for conditions on the specific columns themselves, and mutate_at seems to limit all references to just those same specific columns. #>

#> 4.6 3.1 1.5 0.2 setosa #> 1.53 1.13 1.5 0.2 setosa #> 4.9 3.1 1.5 0.1 setosa 1.59 #> 1.53 1.22 1.4 0.3 setosa #> 4 3 1 0 setosa The names of the new columns are derived from the names of the

#> 0.2 3.4 0.667 0.2 setosa #> 1.59 1.13 1.5 0.1 setosa I'm sure there is a simple way to create a new column with mutate aggregating many columns at once. #> 0.204 3 0.714 0.2 setosa #> auburn, w… fair blue-gray 57 male mascu… How can I mutate multiple variables using dplyr? Here we divide all the numeric columns by 100:# mutate_if() is particularly useful for transforming variables from# Multiple transformations ----------------------------------------# If you want to apply multiple transformations, pass a list of# functions. #> 4 3 1 0 setosa Adding New Variables in R. The following functions from the dplyr library can be used to add new variables to a data frame: mutate() – adds new variables to a data frame while preserving existing variables transmute() – adds new variables to a data frame and drops existing variables

When there are multiple functions, they create new# variables instead of modifying the variables in place:# When there's only one function in the list, it modifies existing# variables in place. The mutate() function is a function for creating new variables. #> white, bl… red 33 none mascu… The downside is you overwrite cash, bond, and loans.If you prefer your expected outcome, I think some typing is necessary. #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species _at affects variables selected with a character vector or vars() The names of the new columns are derived from the names of the #> 0.213 3.2 0.769 0.2 setosa r, dplyr. #> brown light brown 19 fema… femin… #> 1.61 1.28 1.4 0.2 setosa #> 5 3.4 1.5 0.2 setosa 3.8.3 separate() a column into multiple columns; 3.9 arrange() rows. #> # … with 77 more rows, and 5 more variables: homeworld # You can pass additional arguments to the function: #> brown light blue 47 fema… femin… #> 0.217 3.4 0.714 0.3 setosa Like all of the dplyr functions, it is designed to do one thing. #> 0.196 3.5 0.714 0.2 setosa

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