‘SciViews::R’ - Tidy Functions

Philippe Grosjean

2025-08-29

The {svTidy} package provides a set of functions to manipulate data frames in a tidy way (like {dplyr} and {tidyr}), but faster and by evaluating its arguments in a standard way, or by mean of formulas for data-masking and tidyselect.

How to convert tidyverse code?

You have only a few rules to remember to convert Tidyverse code using {dplyr} and/or {tidyr} into {svTidy} code:

• append ’_’ at the end of the function name (ex.: select() -> select_()), and make sure that {svTidy} is loaded higher in the search path than {dplyr} and {tidyr}.

• either:

  • Convert the arguments into standard evaluation -SE- (name of variables between quotes and df$var instead of var for a column named “var” in a data frame df), or
  • Use formulas for non-standard evaluation -NSE-: use a tilde ~ in front of your NSE code and do not quote variable names. You can keep ~varinstead of df$var.

• Use “fast” collapse functions instead of base equivalent (for instance, fmean()instead of mean()). In fact, you can continue to use base function, but you will not benefit from the speed increase of the fast functions, especially if your code involves grouped data.

• The ’_’ function automatically ungroup the data at the enc, on the contrary to their Tidyverse equivalent [note: not true for all functions for now, check your results].

• You benefit from referential transparency in SE mode: if x <- 'var', you can use x instead of 'var' everywhere. You do not need to “embrace” the argument, like this {{ x }} (only required in Tidyverse functions). Idem for formulas: write x <- ~var, and you can use x everywhere instead of ~var.

• To rename variables, you replace the (ugly) Tidyverse syntax {{varname}} := expr by a two-sided formula: varname ~ expr.

• If a function accepts both a data frame or a vector as first argument (e.g., replace_na_(), you must write v = vector if you provide a vector, to mark your intention to use it with something else than a data frame.

• The ’_’ functions are “data-dot”. It means they inject . as first argument (usually .data= if no data frame is provided.

• You cannot mix SE code and NSE code through formulas. Either use SE code for all arguments, or formulas only.

• Formulas are converted into expressions that are evaluated in the environment where the first provided formula was created. If you need an evaluation in a different environment, you can use retarget(formula) to change its environment.