What's the R Equivalent of SQL's Like 'Description%' Statement

What's the R equivalent of SQL's LIKE 'description%' statement?

The R analog to SQL's LIKE is just R's ordinary indexing syntax.

The 'LIKE' operator selects data rows from a table by matching string values in a specified column against a user-supplied pattern

> # create a data frame having a character column
> clrs = c("blue", "black", "brown", "beige", "berry", "bronze", "blue-green", "blueberry")
> dfx = data.frame(Velocity=sample(100, 8), Colors=clrs)
> dfx
            Velocity    Colors
        1       90       blue
        2       94      black
        3       71      brown
        4       36      beige
        5       75      berry
        6        2     bronze
        7       89    blue-green
        8       93    blueberry

> # create a pattern to use (the same as you would do when using the LIKE operator)
> ptn = '^be.*?'  # gets beige and berry but not blueberry
> # execute a pattern-matching function on your data to create an index vector
> ndx = grep(ptn, dfx$Colors, perl=T)
> # use this index vector to extract the rows you want from the data frome:
> selected_rows = dfx[ndx,]
> selected_rows
   Velocity Colors
     4       36  beige
     5       75  berry 

In SQL, that would be:

SELECT * FROM dfx WHERE Colors LIKE ptn3

Equivalent of SQL LIKE operator in R

you can use grepl() as in this answer

subset(a, grepl("1", a$filename))

Or if you're coming from an SQL background, you might want to look into sqldf

What is the R equivalent of SQL's LIKE '%searched_word%' ?

Given

schools <- data.frame(rank = 1:20, 
                 name = rep(c("X Public School", "Y Private School"), 10))

try this:

subset(schools, grepl("Public School", name))

or this:

schools[ grep("Public School", schools$name), ]

or this:

library(sqldf)
sqldf("SELECT * FROM schools WHERE name LIKE '%Public School%'")

or this:

library(data.table)
data.table(schools)[ grep("Public School", name) ]

or this:

library(dplyr)
schools %>% filter(grepl("Public School", name))

What is the equivalent of SQL's IN keyword in R?

shortlisted_colors <- subset(colors, color %in% c('Red', 'Blue', 'Green'))

Case When LIKE equivalent in R

Actually not sure if I got the question right but if you mean you want to test if "Alpha" is in the column Letter-Test, then this works:

    > df <- data.frame("Letter-Test" = c("Alpha - Test", "Beta- Test", "Zeta-Test", "Alpha-Two", "Beta-Two"),
+                  stringsAsFactors = FALSE)
> 
> ifelse(test = grepl("Alpha", df$Letter.Test), yes = "Alpha", no = df$Letter.Test)
[1] "Alpha"      "Beta- Test" "Zeta-Test"  "Alpha"      "Beta-Two"  

Test takes TRUE and FALSE, grepl returns TRUE if the word was found inside the column Letter.Test.

Or you can put the results directly into a new column in the data frame:

> df$AplhaTest <- ifelse(test = grepl("Alpha", df$Letter.Test), yes = "Alpha", no = df$Letter.Test)
> df
   Letter.Test  AplhaTest
1 Alpha - Test      Alpha
2   Beta- Test Beta- Test
3    Zeta-Test  Zeta-Test
4    Alpha-Two      Alpha
5     Beta-Two   Beta-Two

Equals(=) vs. LIKE

Different Operators

LIKE and = are different operators. Most answers here focus on the wildcard support, which is not the only difference between these operators!

= is a comparison operator that operates on numbers and strings. When comparing strings, the comparison operator compares whole strings.

LIKE is a string operator that compares character by character.

To complicate matters, both operators use a collation which can have important effects on the result of the comparison.

Motivating Example

Let us first identify an example where these operators produce obviously different results. Allow me to quote from the MySQL manual:

Per the SQL standard, LIKE performs matching on a per-character basis, thus it can produce results different from the = comparison operator:

mysql> SELECT 'ä' LIKE 'ae' COLLATE latin1_german2_ci;
+-----------------------------------------+
| 'ä' LIKE 'ae' COLLATE latin1_german2_ci |
+-----------------------------------------+
|                                       0 |
+-----------------------------------------+
mysql> SELECT 'ä' = 'ae' COLLATE latin1_german2_ci;
+--------------------------------------+
| 'ä' = 'ae' COLLATE latin1_german2_ci |
+--------------------------------------+
|                                    1 |
+--------------------------------------+

Please note that this page of the MySQL manual is called String Comparison Functions, and = is not discussed, which implies that = is not strictly a string comparison function.

How Does = Work?

The SQL Standard § 8.2 describes how = compares strings:

The comparison of two character strings is determined as follows:

a) If the length in characters of X is not equal to the length
in characters of Y, then the shorter string is effectively
replaced, for the purposes of comparison, with a copy of
itself that has been extended to the length of the longer
string by concatenation on the right of one or more pad
characters, where the pad character is chosen based on CS. If
CS has the NO PAD attribute, then the pad character is an
implementation-dependent character different from any
character in the character set of X and Y that collates less
than any string under CS. Otherwise, the pad character is a
<space>.

b) The result of the comparison of X and Y is given by the
collating sequence CS.

c) Depending on the collating sequence, two strings may
compare as equal even if they are of different lengths or
contain different sequences of characters. When the operations
MAX, MIN, DISTINCT, references to a grouping column, and the
UNION, EXCEPT, and INTERSECT operators refer to character
strings, the specific value selected by these operations from
a set of such equal values is implementation-dependent.

(Emphasis added.)

What does this mean? It means that when comparing strings, the = operator is just a thin wrapper around the current collation. A collation is a library that has various rules for comparing strings. Here is an example of a binary collation from MySQL:

static int my_strnncoll_binary(const CHARSET_INFO *cs __attribute__((unused)),
                               const uchar *s, size_t slen,
                               const uchar *t, size_t tlen,
                               my_bool t_is_prefix)
{
  size_t len= MY_MIN(slen,tlen);
  int cmp= memcmp(s,t,len);
  return cmp ? cmp : (int)((t_is_prefix ? len : slen) - tlen);
}

This particular collation happens to compare byte-by-byte (which is why it's called "binary" — it doesn't give any special meaning to strings). Other collations may provide more advanced comparisons.

For example, here is a UTF-8 collation that supports case-insensitive comparisons. The code is too long to paste here, but go to that link and read the body of my_strnncollsp_utf8mb4(). This collation can process multiple bytes at a time and it can apply various transforms (such as case insensitive comparison). The = operator is completely abstracted from the vagaries of the collation.

How Does LIKE Work?

The SQL Standard § 8.5 describes how LIKE compares strings:

The <predicate>

M LIKE P

is true if there exists a partitioning of M into substrings
such that:

i) A substring of M is a sequence of 0 or more contiguous
<character representation>s of M and each <character
representation> of M is part of exactly one substring.

ii) If the i-th substring specifier of P is an arbitrary
character specifier, the i-th substring of M is any single
<character representation>.

iii) If the i-th substring specifier of P is an arbitrary string
specifier, then the i-th substring of M is any sequence of
0 or more <character representation>s.

iv) If the i-th substring specifier of P is neither an
arbitrary character specifier nor an arbitrary string specifier,
then the i-th substring of M is equal to that substring
specifier according to the collating sequence of
the <like predicate>, without the appending of <space>
characters to M, and has the same length as that substring
specifier.

v) The number of substrings of M is equal to the number of
substring specifiers of P.

(Emphasis added.)

This is pretty wordy, so let's break it down. Items ii and iii refer to the wildcards _ and %, respectively. If P does not contain any wildcards, then only item iv applies. This is the case of interest posed by the OP.

In this case, it compares each "substring" (individual characters) in M against each substring in P using the current collation.

Conclusions

The bottom line is that when comparing strings, = compares the entire string while LIKE compares one character at a time. Both comparisons use the current collation. This difference leads to different results in some cases, as evidenced in the first example in this post.

Which one should you use? Nobody can tell you that — you need to use the one that's correct for your use case. Don't prematurely optimize by switching comparison operators.

SQL 'LIKE' query using '%' where the search criteria contains '%'

If you want a % symbol in search_criteria to be treated as a literal character rather than as a wildcard, escape it to [%]

... where name like '%' + replace(search_criteria, '%', '[%]') + '%'

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