New Win-Vector Package replyr: for easier dplyr

Using dplyr with a specific data frame, where all the columns are known, is an effective and pleasant way to execute declarative (SQL-like) operations on dataframes and dataframe-like objects in R. It also has the advantage of working not only on local data, but also on dplyr-supported remote data stores, like SQL databases or Spark.

However, once we know longer know the column names, the pleasure quickly fades. The currently recommended way to handle dplyr‘s non-standard evaluation is via the lazyeval package. This is not pretty. I never want to write anything like the following, ever again.

# target is a moving target, so to speak
target = "column_I_want"


# return all the rows where target column is NA
dframe %>%
  filter_(interp(~, ) 

This example is fairly simple, but the more complex the dplyr expression, and the more columns involved, the more unwieldy the lazyeval solution becomes.

The difficulty of parameterizing dplyr expressions is part of the motivation for Win-Vector’s new package, replyr. I’ve just posted an article to the Win-Vector blog, on the function replyr::let, which lets us parametrize dplyr expressions without lazyeval.

target = "column_I_want"


# return all the rows where target column is NA
let(alias = list(col=target),
    expr = dframe %>% filter(

The dplyr expression is no more complicated than the equivalent expression when the columns are known, and using multiple columns only involves additional entries in the alias mapping.

Read the article here.

replyr is a new package, and it is still going through growing pains as we figure out the best ways to implement desired functionality. We welcome suggestions for new functions, and more efficient or more general ways to implement the functionality that we supply.

About nzumel
I dance. I'm a data scientist. I'm a dancing data scientist. In my spare time, I like to read folklore (and research about folklore), ghost stories, random cognitive science papers, and to sometimes blog about it all.

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