Next, here’s code to add a column called PythonUser to the data.table: dt1 I checked with data.table creator Matt Dowle, who said the advice to use it inside the brackets is because some extra performance optimization happens there.
The function documentation says it’s meant to be used inside data.table brackets, but actually you can use it in any of your code, not just with data.tables. I, well, like %like%. It’s a nice streamlined way to check for pattern matching. If you know SQL, you’ll recognize that “like” syntax. This is the simpler code to create a TRUE/FALSE vector that checks if each string in LanguageWorkedWith contains Python: ifelse(LanguageWorkedWith %like% "Python", TRUE, FALSE) Most have multiple languages separated by a semicolon.Īs is often the case, it’s easier to search for Python than R, since you can’t just search for "R" in the string (Ruby and Rust also contain a capital R) the way you can search for "Python". Several rows of the LanguagesWorkedWith column of Stack Overflow developer survey data.Įach answer is a single character string. The LanguageWorkedWith column has information about languages used, and a few rows of that data look like this: Sharon Machlis Next, I’d like add columns to see if each respondent uses R, if they use Python, if they use both, or if they use neither. If you find the tidyverse conventional multi-line approach more readable, this data.table code also works: mydt Add columns to a data.table For example: dt1 % count(Hobbyist, OpenSourcer) %>% order(Hobbyist, -n)
You can select data.table columns the typical base R way, with a conventional vector of quoted column names. But quoted is useful if you’re using data.table inside your own functions, or if you want to pass in a vector you created somewhere else in your code.
Unquoted is often more convenient (that’s usually the tidyverse way). One of the things I like about data.table is that it’s easy to select columns either quoted or unquoted. Since I’m selecting columns, that code goes in the “j” spot, which means the brackets need a comma first to leave the “i” spot empty: mydt Select data.table columns And the “by” section is new to data.table. However, note also that you can do a lot more inside data.table brackets than a base R data frame. So i is for operations you’d do on rows (choosing rows based on row numbers or conditions) j is what you’d do with columns (select columns or create new columns from calculations). The data.table package introduction says to read this as “take dt, subset or reorder rows using i, calculate j, grouped by by.” Keep in mind that i and j are similar to base R’s bracket ordering: rows first, columns second.
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