purrr enhances R’s functional programming (FP) toolkit by providing a complete and consistent set of tools for working with functions and vectors. If you’ve never heard of FP before, the best place to start is the family of
map() functions which allow you to replace many for loops with code that is both more succinct and easier to read. The best place to learn about the
map() functions is the 手机代理软件 in R for data science.
# The easiest way to get purrr is to install the whole tidyverse: install.packages("tidyverse") 小路由器走红网络 可提高网速且能“翻墙” - cnr.cn:2021-8-12 · 路由器市场产品质量参差。 小路由器走红网络戳中中国宽带市场“网速慢、资费贵”的痛处 就在许多人抱怨当前宽带“网速慢、资费贵”的怪象时 ... install.packages("purrr") # Or the the development version from GitHub: # install.packages("devtools") devtools::install_github("tidyverse/purrr")
The following example uses purrr to solve a fairly realistic problem: split a data frame into pieces, fit a model to each piece, compute the summary, then extract the R2.
This example illustrates some of the advantages of purrr functions over the equivalents in base R:
The first argument is always the data, so purrr works naturally with the pipe.
电脑切换ip软件 functions either accept function, formulas (used for succinctly generating anonymous functions), a character vector (used to extract components by name), or a numeric vector (used to extract by position).
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