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% patternSource
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 byas.character()to a character string if possible.- ignore.case
if
FALSE, the pattern matching is case sensitive and ifTRUE, 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
patternis a valid regular expression and setsfixed = TRUEif not, to greatly improve speed (vectorised overpattern)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%.
Examples
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
# get isolates whose name start with 'Ent' or 'ent'
example_isolates[which(mo_name(example_isolates$mo) %like% "^ent"), ]
#> # A tibble: 106 × 49
#> date hospi…¹ ward_…² ward_…³ ward_…⁴ age gender patie…⁵ mo
#> <date> <fct> <lgl> <lgl> <lgl> <dbl> <chr> <chr> <mo>
#> 1 2002-02-21 C FALSE TRUE FALSE 69 M 4FC193 B_ENTRC_FACM
#> 2 2002-04-08 A TRUE TRUE FALSE 78 M 130252 B_ENTRC_FCLS
#> 3 2002-06-23 D FALSE TRUE FALSE 82 M 798871 B_ENTRC_FCLS
#> 4 2002-06-23 D FALSE TRUE FALSE 82 M 798871 B_ENTRC_FCLS
#> 5 2003-04-20 D TRUE TRUE FALSE 62 M 6BC362 B_ENTRC
#> 6 2003-04-21 D TRUE TRUE FALSE 62 M 6BC362 B_ENTRC
#> 7 2003-08-13 B TRUE FALSE FALSE 52 M F35553 B_ENTRBC_CLOC
#> 8 2003-08-13 B TRUE FALSE FALSE 52 M F35553 B_ENTRC_FCLS
#> 9 2003-09-05 B TRUE FALSE FALSE 52 M F35553 B_ENTRC
#> 10 2003-09-05 B TRUE FALSE FALSE 52 M F35553 B_ENTRBC_CLOC
#> # … with 96 more rows, 40 more variables: PEN <rsi>, OXA <rsi>, FLC <rsi>,
#> # AMX <rsi>, AMC <rsi>, AMP <rsi>, TZP <rsi>, CZO <rsi>, FEP <rsi>,
#> # CXM <rsi>, FOX <rsi>, CTX <rsi>, CAZ <rsi>, CRO <rsi>, GEN <rsi>,
#> # TOB <rsi>, AMK <rsi>, KAN <rsi>, TMP <rsi>, SXT <rsi>, NIT <rsi>,
#> # FOS <rsi>, LNZ <rsi>, CIP <rsi>, MFX <rsi>, VAN <rsi>, TEC <rsi>,
#> # TCY <rsi>, TGC <rsi>, DOX <rsi>, ERY <rsi>, CLI <rsi>, AZM <rsi>,
#> # IPM <rsi>, MEM <rsi>, MTR <rsi>, CHL <rsi>, COL <rsi>, MUP <rsi>, …
# \donttest{
# faster way, since mo_name() is context-aware:
example_isolates[which(mo_name() %like% "^ent"), ]
#> ℹ Using column 'mo' as input for `mo_name()`
#> # A tibble: 106 × 49
#> date hospi…¹ ward_…² ward_…³ ward_…⁴ age gender patie…⁵ mo
#> <date> <fct> <lgl> <lgl> <lgl> <dbl> <chr> <chr> <mo>
#> 1 2002-02-21 C FALSE TRUE FALSE 69 M 4FC193 B_ENTRC_FACM
#> 2 2002-04-08 A TRUE TRUE FALSE 78 M 130252 B_ENTRC_FCLS
#> 3 2002-06-23 D FALSE TRUE FALSE 82 M 798871 B_ENTRC_FCLS
#> 4 2002-06-23 D FALSE TRUE FALSE 82 M 798871 B_ENTRC_FCLS
#> 5 2003-04-20 D TRUE TRUE FALSE 62 M 6BC362 B_ENTRC
#> 6 2003-04-21 D TRUE TRUE FALSE 62 M 6BC362 B_ENTRC
#> 7 2003-08-13 B TRUE FALSE FALSE 52 M F35553 B_ENTRBC_CLOC
#> 8 2003-08-13 B TRUE FALSE FALSE 52 M F35553 B_ENTRC_FCLS
#> 9 2003-09-05 B TRUE FALSE FALSE 52 M F35553 B_ENTRC
#> 10 2003-09-05 B TRUE FALSE FALSE 52 M F35553 B_ENTRBC_CLOC
#> # … with 96 more rows, 40 more variables: PEN <rsi>, OXA <rsi>, FLC <rsi>,
#> # AMX <rsi>, AMC <rsi>, AMP <rsi>, TZP <rsi>, CZO <rsi>, FEP <rsi>,
#> # CXM <rsi>, FOX <rsi>, CTX <rsi>, CAZ <rsi>, CRO <rsi>, GEN <rsi>,
#> # TOB <rsi>, AMK <rsi>, KAN <rsi>, TMP <rsi>, SXT <rsi>, NIT <rsi>,
#> # FOS <rsi>, LNZ <rsi>, CIP <rsi>, MFX <rsi>, VAN <rsi>, TEC <rsi>,
#> # TCY <rsi>, TGC <rsi>, DOX <rsi>, ERY <rsi>, CLI <rsi>, AZM <rsi>,
#> # IPM <rsi>, MEM <rsi>, MTR <rsi>, CHL <rsi>, COL <rsi>, MUP <rsi>, …
if (require("dplyr")) {
example_isolates %>%
filter(mo_name() %like% "^ent")
}
#> ℹ Using column 'mo' as input for `mo_name()`
#> # A tibble: 106 × 49
#> date hospi…¹ ward_…² ward_…³ ward_…⁴ age gender patie…⁵ mo
#> <date> <fct> <lgl> <lgl> <lgl> <dbl> <chr> <chr> <mo>
#> 1 2002-02-21 C FALSE TRUE FALSE 69 M 4FC193 B_ENTRC_FACM
#> 2 2002-04-08 A TRUE TRUE FALSE 78 M 130252 B_ENTRC_FCLS
#> 3 2002-06-23 D FALSE TRUE FALSE 82 M 798871 B_ENTRC_FCLS
#> 4 2002-06-23 D FALSE TRUE FALSE 82 M 798871 B_ENTRC_FCLS
#> 5 2003-04-20 D TRUE TRUE FALSE 62 M 6BC362 B_ENTRC
#> 6 2003-04-21 D TRUE TRUE FALSE 62 M 6BC362 B_ENTRC
#> 7 2003-08-13 B TRUE FALSE FALSE 52 M F35553 B_ENTRBC_CLOC
#> 8 2003-08-13 B TRUE FALSE FALSE 52 M F35553 B_ENTRC_FCLS
#> 9 2003-09-05 B TRUE FALSE FALSE 52 M F35553 B_ENTRC
#> 10 2003-09-05 B TRUE FALSE FALSE 52 M F35553 B_ENTRBC_CLOC
#> # … with 96 more rows, 40 more variables: PEN <rsi>, OXA <rsi>, FLC <rsi>,
#> # AMX <rsi>, AMC <rsi>, AMP <rsi>, TZP <rsi>, CZO <rsi>, FEP <rsi>,
#> # CXM <rsi>, FOX <rsi>, CTX <rsi>, CAZ <rsi>, CRO <rsi>, GEN <rsi>,
#> # TOB <rsi>, AMK <rsi>, KAN <rsi>, TMP <rsi>, SXT <rsi>, NIT <rsi>,
#> # FOS <rsi>, LNZ <rsi>, CIP <rsi>, MFX <rsi>, VAN <rsi>, TEC <rsi>,
#> # TCY <rsi>, TGC <rsi>, DOX <rsi>, ERY <rsi>, CLI <rsi>, AZM <rsi>,
#> # IPM <rsi>, MEM <rsi>, MTR <rsi>, CHL <rsi>, COL <rsi>, MUP <rsi>, …
# }