¿Cómo calcular y agregar nuevas variables en R?

How to compute and add new variables in R?

This tutorial describes how to compute and add new variables to a data frame in R. You will learn the following R functions from the dplyr R package: mutate (): compute and add new variables into a data table. It preserves existing variables. transmute (): compute new columns but drop existing variables.

The previous output of the RStudio console shows the structure of our exemplifying data: It’s a data frame containing three numeric columns. Each of the columns has a non-neglectable amount of NA values. When inspecting the missing data structure of a data frame, the first step should always be to count the missing values in each variable.

transmute (): compute new columns but drop existing variables. We’ll also present three variants of mutate () and transmute () to modify multiple columns at once: mutate_all () / transmute_all (): apply a function to every columns in the data frame.

To illustrate how to calculate VIF for a regression model in R, we will use the built-in dataset mtcars: First, we’ll fit a regression model using mpg as the response variable and disp, hp, wt, and drat as the predictor variables: We can see from the output that the R-squared value for the model is 0.8376.

Table 1: The Iris Data Matrix. As you can see based on Table 1, the Iris Flower data contains four numeric columns as well as the grouping factor column Species Next, I’ll show you how to calculate the average for each of these groups. Keep on reading! The first example shows how to calculate the mean per group with the aggregate function.

table is a package is used for working with tabular data in R. It provides the efficient data.table object which is a much improved version of the default data.frame . It is super fast and has intuitive and terse syntax.

It is aimed at beginners, and if you’re not yet familiar with the basic syntax of the R language we recommend you to first have a look at this introductory R tutorial. Conceptually, a loop is a way to repeat a sequence of instructions under certain conditions. They allow you to automate parts of your code that are in need of repetition.

A wide array of operators and functions are available here. (To practice working with variables in R, try the first chapter of this free interactive course .) In order to recode data, you will probably use one or more of R’s control structures. You can rename variables programmatically or interactively. # is that you need to enter all of them!

Although the row names of the data frames new.df and baskets.df differ, R will ignore this and just use the row names of the first data frame in the cbind () function, as you can see from the output of the following code: When using a data frame or a matrix with column names, R will use those as the names of the variables.

mutate () 1 data: the new data frame to assign the new variables to 2 new_variable: the name of the new variable 3 existing_variable: the existing variable in the data frame that you wish to perform some operation on to create the new variable

Many functions in R expect data to be in a long format rather than a wide format. Programs like SPSS, however, often use wide-formatted data. There are two sets of methods that are explained below: gather () and spread () from the tidyr package.

Multiple statements can be performed, but as above they must be inside {} (curly brackets). Unlike if and if-else, ifelse works with vectors. Thus it can be applied to a column of data within a data object. which (Condition) returns row number (s) from a data object meeting Condition.

As you can see, the field ‘Job Description’ holds the EntryID, that I would like to append to the end of my weblink URL housed in the ‘Job Description’ link. ? The result I’m looking for is: http://serverurl/weblink8/Browse.aspx?dbid=0&startid= DOC_ID but want it to appear as a link with ‘Job Description’ as the verbiage.

You shouldn’t put the hyperlink inside the R code block. xxx is the Markdown syntax. If you put it after {r}, knitr/rmarkdown will treat it as part of the R code and, of course, R will produce an error message.

The mutate_all () function modifies all of the variables in a data frame at once, allowing you to perform a specific function on all of the variables by using the funs ()function. The following code illustrates how to divide all of the columns in a data frame by 10 using mutate_all ():

It is not uncommon to wish to run an analysis in R in which one analysis step is repeated with a different variable each time. Often, the easiest way to list these variable names is as strings.

There are two other forms: Loop over the elements: for (x in xs). This is most useful if you only care about side-effects, like plotting or saving a file, because it’s difficult to save the output efficiently. Loop over the names: for (nm in names (xs)). This gives you name, which you can use to access the value with x [ [nm]].

The first contains categories such as “First”, “Second”, “Third”, etc. The second has numbers which represent the number of times I saw “First”. How would I do this in R? The fastest way in base R is rowsum. – Michael M Jan 4 ’19 at 18:58 In the example above, multiple dimensions can be specified in the list.

I want newvar to take the value 1 if factor1>=5 and factor2<19 and (factor3=”b” or factor3=”c”) and factor4 is different from missing and newvar is equal to missing Ideally I want to specify different conditions, so some observations will be value 1, 2, 3 and 4 in the variable newvar dependent on the values of several other variables.

For recoding variable I used the function ifelse (), but you can use other functions as well. Merging datasets means to combine different datasets into one. If datasets are in different locations, first you need to import in R as we explained previously. You can merge columns, by adding new variables; or you can merge rows, by adding observations.

A series of commands are needed to create a categorical variable that takes on more than two categories. For example, to create an agecat variable that takes on the values 1, 2, 3, or 4 for those under 20, between 20 and 39, between 40 and 59, and over 60, respectively:

Creating ordered factor variables We can create ordered factor variables by using the function ordered. This function has the same arguments as the factor function. Let’s create an ordered factor variable called ses.order based on the variable ses created in the above example. 3. Adding and dropping levels in factor variables

…and then we can apply our previously created standard error function as follows: As you can see, the standard error of the mean of our example vector is 1.911298. You don’t want to create a function yourself?