# strict v0.0.0.9000

## Make R Just a Little Stricter

This packages tweaks the operation of base R code to make things a little stricter.

# strict

The goal of strict to make R behave a little more strictly, making base functions more likely to throw an error rather than returning potentially ambiguous results.

library(strict) forces you to confront potential problems now, instead of in the future. This has both pros and cons: often you can most easily fix a potential ambiguity when you're working on the code (rather than in six months time when you've forgotten how it works), but it also forces you to resolve ambiguities that might never occur with your code/data.

## Installation

# install.packages("devtools")


## Features

library(strict) affects code in the current script/session only (i.e. it doesn't affect code in others packages).

• An alternative conflict resolution mechanism. Instead of warning about conflicts on package load and letting the last loaded package win, strict throws an error when you access ambiguous functions:

library(strict)
library(plyr)
library(Hmisc)

is.discrete
#> Error: [strict]
#> Multiple definitions found for is.discrete.
#>  * Hmisc::is.discrete
#>  * plyr::is.discrete


(Thanks to @krlmlr for this neat idea!)

• Shims for functions with "risky" arguments, i.e. arguments that either rely on global options (like stringsAsFactors) or have computed defaults that 90% evaluate to one thing (like drop). strict forces you to supply values for these arguments.

library(strict)
mtcars[, 1]
#> Error: [strict]
#> Please explicitly specify drop when selecting a single column
#> Please see ?strict_drop for more details

data.frame(x = "a")
#> Error: [strict]
#> Please supply a value for stringsAsFactors argument.
#> Please see ?strict_arg for more details

• Automatically sets options to warn when partial matching occurs.

library(strict)

df <- data.frame(xyz = 1)
df$x #> Warning in $.data.frame(df, x): Partial match of 'x' to 'xyz' in data
#> frame
#> [1] 1

• T and F generate errors, forcing you to use TRUE and FALSE.

library(strict)
T
#> Error: [strict]
#> Please use TRUE, not T

• sapply() throws an error suggesting that you use the type-safe vapply() instead. apply() throws an error if you use it with a data frame.

library(strict)
sapply(1:10, sum)
#> Error: [strict]
#> Please use vapply() instead of sapply().
#> Please see ?strict_sapply for more details

• : will throw an error instead of creating a decreasing sequence that terminates in 0.

library(strict)

x <- numeric()
1:length(x)
#> Error: [strict]
#> Tried to create descending sequence 1:0. Do you want to seq_along() instead?
#>
#> Please see ?shim_colon for more details

• diag() and sample() throw an error if given scalar x. This avoids an otherwise unpleasant surprise.

library(strict)

sample(5:3)
#> [1] 5 4 3
sample(5:4)
#> [1] 5 4
lax(sample(5:5))
#> [1] 3 2 1 4 5

sample(5:5)
#> Error: [strict]
#> sample() has surprising behaviour when x is a scalar.
#> Use sample.int() instead.
#> Please see ?strict_sample for more details


Once strict is loaded, you can continue to run code in a lax manner using lax().

## Functions in strict

 Name Description strict_apply Strict version of apply() strict_arg Strict arguments is_strict Is code run in a strict environment? lax Be lax in an otherwise strict environment strict_deactivate Manually activate and deactive strict mode strict_sapply Strict version of sapply() shim_colon Strict version of : strict_sample Strict behaviour for functions with special scalar behaviour No Results!