Now we have shooting deaths, which is quite a bit more. In fact, the vast majority of homicides in Baltimore are shooting deaths. Another possible way to do this is to grep on the cause of death field, which seems to have the format Cause: shooting.
We can grep on this literally and get. Notice that we seem to be undercounting again. We can handle this variation by using a character class in our regular expression. One thing you have to be careful of when processing text data is not not grep things out of context. For example, suppose we just grep -ed on the expression [Ss]hooting. Notice that we see to pick up 2 extra homicides this way. We can figure out which ones they are by comparing the results of the two expressions.
Now we just need to identify which are the entries that the vectors i and j do not have in common. Here we can see that the index vector j has two entries that are not in i : entries , We can take a look at these entries directly to see what makes them different. Sometimes we want to identify elements of a character vector that match a pattern, but instead of returning their indices we want the actual values that satisfy the match.
This gives us the indices into the state. The function grepl works much like grep except that it differs in its return value. Here, we can see that grepl returns a logical vector that can be used to subset the original state. Both the grep and the grepl functions have some limitations.
Now that we know the meta characters, let us look at some examples. In the first example, we want to detect package names separated by a dot. If you look at the output, it includes names of even those package names which are not separated by dot. Why is this happening? A dot is special character in regular expressions. It is also known as wildcard character i. Feel free to play around with other special characters mentioned in the table but ensure that you use a different data set.
Quantifiers are very powerful and we need to be careful while using them. They always act on items to the immediate left and are used to specify the number of times a pattern must appear or be matched.
The below table shows the different quantifiers and their description:. Keep in mind that it will match only 1 character and if you want to match more than 1 character, you need to specify as many dots. Let us look at a few examples. In the below example, we are looking for package names that include the following pattern:. The OR operator is useful when you want to match one amongst the given options. For example, let us say we are looking for package names that begin with g and is followed by either another g or l.
The square brackets [] can be used in place of as shown in the below example where we are looking for package names that begin with the letter d and is followed by either e or p or a. Let us use it to find package names that include a digit. In the next few examples, we will not use R package names data, instead we will use dummy data of Invoice IDs and see if they conform to certain rules such as:.
Let us use it to remove invoice ids that include only numbers and no letters. As you can see below, thre are 3 invoice ids that did not conform to the rules and have been removed. Only those invoice ids that have both letter and numbers have been returned. Let us use it to detect invoice ids that include any white space space or tab. Let us use it to remove any invoice ids which are blank or missing.
As you can see below, two invoice ids which were blank have been removed. If you observe carefully, it does not remove any invoice ids which have a white space character present, it only removes those which are completely blank i. Let us use it to remove those invoice ids which include only symbols or special characters.
Again, you can see that it does not remove those ids which include both word characters and symbols as it will match any string that includes word characters. It includes everything that is not a word character. Let us use it to detect invoice ids that include any non-word character.
As you can see only 4 ids do not include non-word characters. They match at a position called word boundary. Now, what is a word boundary? The following 3 positions qualify as word boundaries:. In the first 2 cases, the character must be a word character whereas in the last case, one should be a word character and another non-word character. Sounds confusing? It will be clear once we go through a few examples. Let us say we are looking for package names beginning with the string stat.
If you observe the output, you can find package names that do not end with the string stat. We will use some small examples to introduce regular expression syntax and what these metacharacters mean. There are some special characters in R that cannot be directly coded in a string.
For example, apostrophes. Apostrophes can be used in R to define strings as well as quotation marks. There are other characters in R that require escaping, and this rule applies to all string functions in R, including regular expressions:. Character classes allows to — surprise!
They are sometimes interchangeable. This form ignores spaces and newlines, and anything everything after. This is a useful way of describing complex regular expressions:. And this tells R to look for an explicit. Special characters Escapes also allow you to specify individual characters that are otherwise hard to type.
Matching multiple characters There are a number of patterns that match more than one character. Alternation is the alternation operator, which will pick between one or more possible matches. Anchors By default, regular expressions will match any part of a string. Repetition You can control how many times a pattern matches with the repetition operators:? Comments There are two ways to include comments in a regular expression.
0コメント