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When we use the tidyverse, we use R expressions in mainly three places: filter(), mutate(), and summarize().

library(dplyr, warn.conflicts = FALSE)

filter(mtcars, am + gear > carb)
mutate(mtcars, x = (qsec - mean(qsec) / sd(qsec)))
mtcars |> 
  group_by(cyl) |> 
  summarize(x = mean(qsec) / sd(qsec))

This is very convenient but creates a challenge for tidypolars. Indeed, while it is possible to pass R functions directly to a Polars Data/LazyFrame, it is strongly discouraged to do so because it doesn’t take advantage of Polars optimizations.

Indeed, Polars comes with dozens of built-in functions for maths (median, var, arccos, …), string manipulation (len_chars, starts, …), and date-time (hour, quarter, ordinal_day, …). All of these functions are optimized internally and are ran in parallel under the hood, which will not be the case if we pass R functions.

However, using these Polars expressions would imply that we need to learn these new functions and this new syntax. To avoid doing that, tidypolars will automatically translate R expressions into Polars ones. Basically, you can keep writing R expressions in most situations, and they will automatically be translated to Polars syntax.

However, there are some situations where this might not work, so this vignette explains the process and the limitations.

How does tidypolars translate R expressions into Polars expressions?

When tidypolars receives an expression, it runs a function translate() several times until all components are translated to their Polars equivalent. There are four possible components: single values, column names, external objects, and functions.

Single values, column names, and external objects

If you pass a single value, like x = 1 or x = "a", it is wrapped into pl$lit(). This is also the case for external objects with the difference that these need to be wrapped in {{ }} and are evaluated before being wrapped into pl$lit().

Column names, like x = mpg, are wrapped into pl$col().

x = "a"               ->  x = pl$lit("a")
x = {{ some_value }}  ->  x = pl$lit(*value*)
x = mpg               ->  x = pl$col("mpg")

Functions

Functions are split into two categories: built-in functions (i.e functions provided by base R or by other packages), and user-defined functions (UDF) that are written by the user (you).

Built-in functions

In the first case, tidypolars checks the function name and whether it has already been translated internally. For example, if we call the R function mean(x, trim = 2), then it looks for a translation of mean(). You can see the list of supported R functions at the bottom of this vignette. Note that most of essential base R functions are supported, as well as many functions from dplyr or from stringr for example.

Now that tidypolars knows that a translation of mean() exists, it parses the arguments in the call to translate them to the Polars syntax: internally, x is converted to pl$col("x") if there is a column "x" in the data. Sometimes, additional arguments do not have their equivalent in Polars. This is the case for the argument trim here. In this case, tidypolars ignores this argument and warns the user:

library(tidypolars)
library(polars)

mtcars |> 
  as_polars_df() |> 
  mutate(x = mean(mpg, trim = 2))
#> Warning: 
#> Package tidypolars doesn't know how to use some arguments of `mean()`.
#> The following argument(s) will be ignored: `trim`.
#> shape: (32, 12)
#> ┌──────┬─────┬───────┬───────┬───┬─────┬──────┬──────┬───────────┐
#> │ mpg  ┆ cyl ┆ disp  ┆ hp    ┆ … ┆ am  ┆ gear ┆ carb ┆ x         │
#> │ ---  ┆ --- ┆ ---   ┆ ---   ┆   ┆ --- ┆ ---  ┆ ---  ┆ ---       │
#> │ f64  ┆ f64 ┆ f64   ┆ f64   ┆   ┆ f64 ┆ f64  ┆ f64  ┆ f64       │
#> ╞══════╪═════╪═══════╪═══════╪═══╪═════╪══════╪══════╪═══════════╡
#> │ 21.0 ┆ 6.0 ┆ 160.0 ┆ 110.0 ┆ … ┆ 1.0 ┆ 4.0  ┆ 4.0  ┆ 20.090625 │
#> │ 21.0 ┆ 6.0 ┆ 160.0 ┆ 110.0 ┆ … ┆ 1.0 ┆ 4.0  ┆ 4.0  ┆ 20.090625 │
#> │ 22.8 ┆ 4.0 ┆ 108.0 ┆ 93.0  ┆ … ┆ 1.0 ┆ 4.0  ┆ 1.0  ┆ 20.090625 │
#> │ 21.4 ┆ 6.0 ┆ 258.0 ┆ 110.0 ┆ … ┆ 0.0 ┆ 3.0  ┆ 1.0  ┆ 20.090625 │
#> │ 18.7 ┆ 8.0 ┆ 360.0 ┆ 175.0 ┆ … ┆ 0.0 ┆ 3.0  ┆ 2.0  ┆ 20.090625 │
#> │ …    ┆ …   ┆ …     ┆ …     ┆ … ┆ …   ┆ …    ┆ …    ┆ …         │
#> │ 30.4 ┆ 4.0 ┆ 95.1  ┆ 113.0 ┆ … ┆ 1.0 ┆ 5.0  ┆ 2.0  ┆ 20.090625 │
#> │ 15.8 ┆ 8.0 ┆ 351.0 ┆ 264.0 ┆ … ┆ 1.0 ┆ 5.0  ┆ 4.0  ┆ 20.090625 │
#> │ 19.7 ┆ 6.0 ┆ 145.0 ┆ 175.0 ┆ … ┆ 1.0 ┆ 5.0  ┆ 6.0  ┆ 20.090625 │
#> │ 15.0 ┆ 8.0 ┆ 301.0 ┆ 335.0 ┆ … ┆ 1.0 ┆ 5.0  ┆ 8.0  ┆ 20.090625 │
#> │ 21.4 ┆ 4.0 ┆ 121.0 ┆ 109.0 ┆ … ┆ 1.0 ┆ 4.0  ┆ 2.0  ┆ 20.090625 │
#> └──────┴─────┴───────┴───────┴───┴─────┴──────┴──────┴───────────┘

User-defined functions

User-defined functions (UDF) are more challenging. Indeed, it is technically possible to inspect the code inside a UDF, but rewriting it to match Polars syntax would be extremely complicated. In this situation, you will have to rewrite your custom function using Polars syntax so that it returns a Polars expression. For example, we could make a function to standardize a column like this:

pl_standardize <- function(x) {
  (x - x$mean()) / x$std()
}

Remember that the column name used as x will end up wrapped into pl$col(), so to check that your function returns a Polars expression, you have to provide a pl$col() call:

pl_standardize(pl$col("mpg"))
#> polars Expr: [([(col("mpg")) - (col("mpg").mean())]) // (col("mpg").std())]

This function correctly returns a Polars expression, so we can now use it like any other function:

mtcars |> 
  as_polars_df() |> 
  mutate(x = pl_standardize(mpg))
#> shape: (32, 12)
#> ┌──────┬─────┬───────┬───────┬───┬─────┬──────┬──────┬───────────┐
#> │ mpg  ┆ cyl ┆ disp  ┆ hp    ┆ … ┆ am  ┆ gear ┆ carb ┆ x         │
#> │ ---  ┆ --- ┆ ---   ┆ ---   ┆   ┆ --- ┆ ---  ┆ ---  ┆ ---       │
#> │ f64  ┆ f64 ┆ f64   ┆ f64   ┆   ┆ f64 ┆ f64  ┆ f64  ┆ f64       │
#> ╞══════╪═════╪═══════╪═══════╪═══╪═════╪══════╪══════╪═══════════╡
#> │ 21.0 ┆ 6.0 ┆ 160.0 ┆ 110.0 ┆ … ┆ 1.0 ┆ 4.0  ┆ 4.0  ┆ 0.150885  │
#> │ 21.0 ┆ 6.0 ┆ 160.0 ┆ 110.0 ┆ … ┆ 1.0 ┆ 4.0  ┆ 4.0  ┆ 0.150885  │
#> │ 22.8 ┆ 4.0 ┆ 108.0 ┆ 93.0  ┆ … ┆ 1.0 ┆ 4.0  ┆ 1.0  ┆ 0.449543  │
#> │ 21.4 ┆ 6.0 ┆ 258.0 ┆ 110.0 ┆ … ┆ 0.0 ┆ 3.0  ┆ 1.0  ┆ 0.217253  │
#> │ 18.7 ┆ 8.0 ┆ 360.0 ┆ 175.0 ┆ … ┆ 0.0 ┆ 3.0  ┆ 2.0  ┆ -0.230735 │
#> │ …    ┆ …   ┆ …     ┆ …     ┆ … ┆ …   ┆ …    ┆ …    ┆ …         │
#> │ 30.4 ┆ 4.0 ┆ 95.1  ┆ 113.0 ┆ … ┆ 1.0 ┆ 5.0  ┆ 2.0  ┆ 1.710547  │
#> │ 15.8 ┆ 8.0 ┆ 351.0 ┆ 264.0 ┆ … ┆ 1.0 ┆ 5.0  ┆ 4.0  ┆ -0.711907 │
#> │ 19.7 ┆ 6.0 ┆ 145.0 ┆ 175.0 ┆ … ┆ 1.0 ┆ 5.0  ┆ 6.0  ┆ -0.064813 │
#> │ 15.0 ┆ 8.0 ┆ 301.0 ┆ 335.0 ┆ … ┆ 1.0 ┆ 5.0  ┆ 8.0  ┆ -0.844644 │
#> │ 21.4 ┆ 4.0 ┆ 121.0 ┆ 109.0 ┆ … ┆ 1.0 ┆ 4.0  ┆ 2.0  ┆ 0.217253  │
#> └──────┴─────┴───────┴───────┴───┴─────┴──────┴──────┴───────────┘

Special case: across()

across() is a very useful function that applies a function (or a list of functions) to a selection of columns. It accepts built-in functions, UDFs, and anonymous functions.

mtcars |> 
  as_polars_df() |> 
  mutate(
    across(
      .cols = contains("a"),
      list(mean = mean, stand = pl_standardize, ~ sd(.x))
    )
  )
#> shape: (32, 23)
#> ┌──────┬─────┬───────┬───────┬───┬──────────┬───────────┬────────────┬────────┐
#> │ mpg  ┆ cyl ┆ disp  ┆ hp    ┆ … ┆ gear_3   ┆ carb_mean ┆ carb_stand ┆ carb_3 │
#> │ ---  ┆ --- ┆ ---   ┆ ---   ┆   ┆ ---      ┆ ---       ┆ ---        ┆ ---    │
#> │ f64  ┆ f64 ┆ f64   ┆ f64   ┆   ┆ f64      ┆ f64       ┆ f64        ┆ f64    │
#> ╞══════╪═════╪═══════╪═══════╪═══╪══════════╪═══════════╪════════════╪════════╡
#> │ 21.0 ┆ 6.0 ┆ 160.0 ┆ 110.0 ┆ … ┆ 0.737804 ┆ 2.8125    ┆ 0.735203   ┆ 1.6152 │
#> │ 21.0 ┆ 6.0 ┆ 160.0 ┆ 110.0 ┆ … ┆ 0.737804 ┆ 2.8125    ┆ 0.735203   ┆ 1.6152 │
#> │ 22.8 ┆ 4.0 ┆ 108.0 ┆ 93.0  ┆ … ┆ 0.737804 ┆ 2.8125    ┆ -1.122152  ┆ 1.6152 │
#> │ 21.4 ┆ 6.0 ┆ 258.0 ┆ 110.0 ┆ … ┆ 0.737804 ┆ 2.8125    ┆ -1.122152  ┆ 1.6152 │
#> │ 18.7 ┆ 8.0 ┆ 360.0 ┆ 175.0 ┆ … ┆ 0.737804 ┆ 2.8125    ┆ -0.503034  ┆ 1.6152 │
#> │ …    ┆ …   ┆ …     ┆ …     ┆ … ┆ …        ┆ …         ┆ …          ┆ …      │
#> │ 30.4 ┆ 4.0 ┆ 95.1  ┆ 113.0 ┆ … ┆ 0.737804 ┆ 2.8125    ┆ -0.503034  ┆ 1.6152 │
#> │ 15.8 ┆ 8.0 ┆ 351.0 ┆ 264.0 ┆ … ┆ 0.737804 ┆ 2.8125    ┆ 0.735203   ┆ 1.6152 │
#> │ 19.7 ┆ 6.0 ┆ 145.0 ┆ 175.0 ┆ … ┆ 0.737804 ┆ 2.8125    ┆ 1.97344    ┆ 1.6152 │
#> │ 15.0 ┆ 8.0 ┆ 301.0 ┆ 335.0 ┆ … ┆ 0.737804 ┆ 2.8125    ┆ 3.211677   ┆ 1.6152 │
#> │ 21.4 ┆ 4.0 ┆ 121.0 ┆ 109.0 ┆ … ┆ 0.737804 ┆ 2.8125    ┆ -0.503034  ┆ 1.6152 │
#> └──────┴─────┴───────┴───────┴───┴──────────┴───────────┴────────────┴────────┘

Similarly, UDFs and anonymous functions will error if they don’t return a Polars expression:

mtcars |> 
  as_polars_df() |> 
  mutate(
    across(
      .cols = contains("a"),
      .fns = list(
        mean = mean,  
        function(x) {
           (x - mean(x)) / sd(x)
        },
        ~ sd(.x)
      )
    )
  )
#> Error in `mutate()`:
#> ! Could not evaluate an anonymous function in `across()`.
#>  Are you sure the anonymous function returns a Polars expression?

List of base R and tidyverse functions supported by tidypolars

Package Function Notes
base abs
base acos
base acosh
base all
base any
base asin
base asinh
base atan
base atanh
base ceiling
base cos
base cosh
base cummin
base cumsum
base diff
base exp
base floor
base grepl
base ifelse
base ISOdatetime
base length
base log
base log10
base max
base mean
base min
base nchar
base paste0
base paste
base rank
base rev
base round
base sin
base sinh
base sort
base sqrt
base strptime
base tan
base tanh
base tolower
base toupper
base unique
base which.min
base which.max
dplyr between
dplyr case_match
dplyr case_when
dplyr coalesce
dplyr consecutive_id
dplyr dense_rank
dplyr first
dplyr group_keys
dplyr group_vars
dplyr if_else
dplyr lag
dplyr last
dplyr min_rank
dplyr n
dplyr nth
dplyr n_distinct
dplyr row_number Doesn’t work when x is missing.
lubridate ddays
lubridate dhours
lubridate dmilliseconds
lubridate dminutes
lubridate dseconds
lubridate dweeks
lubridate make_date
lubridate make_datetime In lubridate::make_datetime(), when there is an overflow (for example hours = 25), then it is automatically converted to the higher unit (for example 1 day and 1h). In Polars, this returns NA.
lubridate wday Requires week_start == 7. If label = TRUE, it returns a string variable and not a factor as in lubridate.
stats median
stats lag
stats sd
stats var
stringr regex
stringr str_count
stringr str_dup
stringr str_ends
stringr str_extract
stringr str_extract_all
stringr str_length
stringr str_pad
stringr str_remove
stringr str_remove_all
stringr str_replace
stringr str_replace_all
stringr str_split
stringr str_split_i
stringr str_squish
stringr str_starts
stringr str_sub
stringr str_trim
stringr str_to_lower
stringr str_to_title
stringr str_to_upper
stringr str_trunc
stringr word
tidyr replace_na
tools toTitleCase