Subset multiple columns in R or unix.
Note that when you quit R (by typing q()), it asks if you want to save the workspace image, if you specify yes (y), it writes out two files to the current directory, called .RData and .Rhistory.The former contains the contents of the saved session (i.e. all the R objects in memory) and the latter is a list of all the command history (it's just a simple text file you can look at).
This article continues the examples started in our data frame tutorial.We’re using the ChickWeight data frame example which is included in the standard R distribution.
Subsetting Datasets in R. Subsetting datasets is a crucial skill for any data professional. Learn and practice subsetting data in this quick interactive tutorial! Whether you're comparing how different demographics respond to marketing campaigns, zooming in on a specific time frame, or pulling information about a select few products from the inventory, subsetting datasets enables you to.
R lets you assign descriptive names to the rows and columns of a matrix. It is useful for subsetting and printing the matrix. It is useful for subsetting and printing the matrix. You can do this by assigning two element list containing row and column names to the dimnames attribute.
It works by first replacing column names in the selection expression with the corresponding column numbers in the data frame and then using the resulting integer vector to index the columns. This allows the use of the standard indexing conventions so that for example ranges of columns can be specified easily, or single columns can be dropped (see the examples).
The subset function with a logical statement will let you subset the data frame by observations. In the following example the write.50 data frame contains only the observations for which the values of the variable write is greater than 50. Note that one convenient feature of the subset function, is R assumes variable names are within the data frame being subset, so there is no need to tell R.
Data manipulation in R. Antoine Soetewey 2019-12-24 18 minute read R; Basics; Introduction; Dataset; Subset a dataset. First or last observations; Random sample of observations; Based on row or column numbers; Based on variable names; Based on one or multiple criterion; Create a new variable. Transform a continuous variable into a categorical variable; Sum and mean in rows; Sum and mean in.