tidyfast | efficient alternatives to tidyr functions | Grid library
kandi X-RAY | tidyfast Summary
kandi X-RAY | tidyfast Summary
The goal of tidyfast is to provide fast and efficient alternatives to some tidyr (and a few dplyr) functions using data.table under the hood. Each have the prefix of dt_ to allow for autocomplete in IDEs such as RStudio. These should compliment some of the current functionality in dtplyr (but notably does not use the lazy_dt() framework of dtplyr). This package imports data.table and Rcpp (no other dependencies). These are, in essence, translations from a more tidyverse grammar to data.table. Most functions herein are in places where, in my opinion, the data.table syntax is not obvious or clear. As such, these functions can translate a simple function call into the fast, efficient, and concise syntax of data.table.
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QUESTION
I have a dataset with 40
sensors with odd names (e.g. A_B_Loc_1
). I need to convert these data to long format to plot them. I need to split the names so I know the sensor name (e.g. from A_B_Loc_1, name=AB
) and the sensor location (e.g. from A_B_Loc_1, location=1
).
ANSWER
Answered 2021-Dec-08 at 14:16We experimented with a few approaches to splitting columns by regular expressions. separate
was very slow, but the fastest seems to be stringr::str_split(..., simplify=TRUE)
to make new columns (for a tibble):
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