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Vectorised Pattern Matching with Keyboard Shortcut

Source: R/like.R
like.Rd

Convenient wrapper around grepl() to match a pattern: x %like% pattern. It always returns a logical vector and is always case-insensitive (use x %like_case% pattern for case-sensitive matching). Also, pattern can be as long as x to compare items of each index in both vectors, or they both can have the same length to iterate over all cases.

Usage

like(x, pattern, ignore.case = TRUE)

x %like% pattern

x %unlike% pattern

x %like_case% pattern

x %unlike_case% pattern

Source

Idea from the like function from the data.table package, although altered as explained in Details.

Arguments

x

a character vector where matches are sought, or an object which can be coerced by as.character() to a character vector.

pattern

a character vector containing regular expressions (or a character string for fixed = TRUE) to be matched in the given character vector. Coerced by as.character() to a character string if possible.

ignore.case

if FALSE, the pattern matching is case sensitive and if TRUE, case is ignored during matching.

Value

A logical vector

Details

These like() and %like%/%unlike% functions:

  • Are case-insensitive (use %like_case%/%unlike_case% for case-sensitive matching)

  • Support multiple patterns

  • Check if pattern is a valid regular expression and sets fixed = TRUE if not, to greatly improve speed (vectorised over pattern)

  • Always use compatibility with Perl unless fixed = TRUE, to greatly improve speed

Using RStudio? The %like%/%unlike% functions can also be directly inserted in your code from the Addins menu and can have its own keyboard shortcut like Shift+Ctrl+L or Shift+Cmd+L (see menu Tools > Modify Keyboard Shortcuts...). If you keep pressing your shortcut, the inserted text will be iterated over %like% -> %unlike% -> %like_case% -> %unlike_case%.

See also

grepl()

Examples

# data.table has a more limited version of %like%, so:
detach("package:data.table", unload = TRUE)

a <- "This is a test"
b <- "TEST"
a %like% b
#> [1] TRUE
b %like% a
#> [1] FALSE

# also supports multiple patterns
a <- c("Test case", "Something different", "Yet another thing")
b <- c("case", "diff", "yet")
a %like% b
#> [1] TRUE TRUE TRUE
a %unlike% b
#> [1] FALSE FALSE FALSE

a[1] %like% b
#> [1]  TRUE FALSE FALSE
a %like% b[1]
#> [1]  TRUE FALSE FALSE

# \donttest{
# get isolates whose name start with 'Entero' (case-insensitive)
example_isolates[which(mo_name() %like% "^entero"), ]
#> ℹ Using column 'mo' as input for mo_name()
#> # A tibble: 106 × 46
#>    date       patient   age gender ward    mo            PEN   OXA   FLC   AMX  
#>    <date>     <chr>   <dbl> <chr>  <chr>   <mo>          <sir> <sir> <sir> <sir>
#>  1 2002-02-21 4FC193     69 M      Clinic… B_ENTRC_FACM  NA    NA    NA    NA   
#>  2 2002-04-08 130252     78 M      ICU     B_ENTRC_FCLS  NA    NA    NA    NA   
#>  3 2002-06-23 798871     82 M      Clinic… B_ENTRC_FCLS  NA    NA    NA    NA   
#>  4 2002-06-23 798871     82 M      Clinic… B_ENTRC_FCLS  NA    NA    NA    NA   
#>  5 2003-04-20 6BC362     62 M      ICU     B_ENTRC       NA    NA    NA    NA   
#>  6 2003-04-21 6BC362     62 M      ICU     B_ENTRC       NA    NA    NA    NA   
#>  7 2003-08-13 F35553     52 M      ICU     B_ENTRBC_CLOC R     NA    NA    R    
#>  8 2003-08-13 F35553     52 M      ICU     B_ENTRC_FCLS  NA    NA    NA    NA   
#>  9 2003-09-05 F35553     52 M      ICU     B_ENTRC       NA    NA    NA    NA   
#> 10 2003-09-05 F35553     52 M      ICU     B_ENTRBC_CLOC R     NA    NA    R    
#> # … with 96 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>,
#> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>,
#> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>,
#> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>,
#> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>,
#> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>,
#> #   COL <sir>, MUP <sir>, RIF <sir>

if (require("dplyr")) {
  example_isolates %>%
    filter(mo_name() %like% "^ent")
}
#> ℹ Using column 'mo' as input for mo_name()
#> # A tibble: 106 × 46
#>    date       patient   age gender ward    mo            PEN   OXA   FLC   AMX  
#>    <date>     <chr>   <dbl> <chr>  <chr>   <mo>          <sir> <sir> <sir> <sir>
#>  1 2002-02-21 4FC193     69 M      Clinic… B_ENTRC_FACM  NA    NA    NA    NA   
#>  2 2002-04-08 130252     78 M      ICU     B_ENTRC_FCLS  NA    NA    NA    NA   
#>  3 2002-06-23 798871     82 M      Clinic… B_ENTRC_FCLS  NA    NA    NA    NA   
#>  4 2002-06-23 798871     82 M      Clinic… B_ENTRC_FCLS  NA    NA    NA    NA   
#>  5 2003-04-20 6BC362     62 M      ICU     B_ENTRC       NA    NA    NA    NA   
#>  6 2003-04-21 6BC362     62 M      ICU     B_ENTRC       NA    NA    NA    NA   
#>  7 2003-08-13 F35553     52 M      ICU     B_ENTRBC_CLOC R     NA    NA    R    
#>  8 2003-08-13 F35553     52 M      ICU     B_ENTRC_FCLS  NA    NA    NA    NA   
#>  9 2003-09-05 F35553     52 M      ICU     B_ENTRC       NA    NA    NA    NA   
#> 10 2003-09-05 F35553     52 M      ICU     B_ENTRBC_CLOC R     NA    NA    R    
#> # … with 96 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>,
#> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>,
#> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>,
#> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>,
#> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>,
#> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>,
#> #   COL <sir>, MUP <sir>, RIF <sir>
# }