# strsplit function in R

If you’re a programmer, you’ll often come across situations where you have to handle multiple strings. Concatenating and splitting them is a common task. In such cases, you can make use of the strsplit() function in R . In a previous article, we explored the paste() function for string concatenation. Now, let’s delve into how we can achieve string vector splitting using strsplit().

strsplit() is a remarkable function in R that divides a input string vector into smaller sub-strings. Now, we will explore the functionality of this function and the various methods available to split strings in R utilizing strsplit().

## Syntax of the Strsplit() function

Strsplit() is a function in the R language that is utilized for dividing strings into smaller substrings using specified split arguments.

```
strsplit(x,split,fixed=T)
```

What is the location?

- X = input data file, vector or a stings.
- Split = Splits the strings into reuired formats.
- Fixed = Matches the split or uses the regular expression.

## Employ strsplit() function in R for its implementation.

In this portion, we will examine a basic illustration that demonstrates the utilization of the strsplit() function. In this scenario, strsplit() will divide the provided input into a collection of strings or values.

Let’s observe its functioning.

```
df<-("R is the statistical analysis language")
strsplit(df, split = " ")
```

Can you provide me with a single paraphrase for the term “Output” using natural language?

```
"R" "is" "the" "statistical" "analysis" "language"
```

We have accomplished it! This approach enables us to effortlessly separate the strings within the data. One of the finest applications of the strsplit() function occurs in generating word clouds. For this purpose, we reuire a multitude of word strings to plot the most freuently used or repeated word. Conseuently, we utilize this function to extract the strings from the data, which provides us with a list of strings.

### 1. Employing the strsplit() function by specifying the delimiter.

In general, a delimiter is a basic symbol, character, or value that is used to separate words or text within data. In this section, we will explore the utilization of different symbols as delimiters.

```
df<-"get%better%every%day"
strsplit(df,split = '%')
```

I only need one alternative for the paraphrase:

Result =

```
"get" "better" "every" "day"
```

In this scenario, we have the % as a delimiter in the input text. Our objective is to extract the text without the delimiter and convert it into a list of strings. The task has been accomplished using the strsplit() function. It effectively eliminated the delimiter and provided the desired strings in a list format.

### The strsplit() function utilizes a delimiter based on Regular Expression.

In this part, we will explore how to divide text using regular expressions. Intriguing, isn’t it? Let’s proceed.

```
df<-"all16i5need6is4a9long8vacation"
strsplit(df,split = "[0-9]+")
```

Provide only one possible option for paraphrasing the given phrase natively:

“Result” or “Final result”

```
"all" "i" "need" "is" "a" "long" "vacation"
```

In this instance, our given input consists of numbers ranging from 0 to 9. Therefore, we utilized the regular expression [0-9]+ to divide the data by eliminating the numbers. The strsplit() function will present a collection of strings as the resulting output like the above example.

### 3. Break down every character contained in the input string.

So far, we have encountered different ways to split a given string. Now, what if we to split every single character in the string? In that case, we can utilize the strsplit() function with a distinct split argument to retrieve each individual character.

Let’s witness how it operates.

```
df<-"You can type () in Rstudio to uit R"
strsplit(df,split="")
```

Provide one paraphrase of the following sentence, using natural language:

Output = Result or outcome.

```
"Y" "o" "u" " " "c" "a" "n" " " "t" "y" "p" "e" " " "" "(" ")" " " "i"
"n" " " "R" "s" "t" "u" "d" "i" "o" " " "t" "o" " " "" "u" "i" "t" " "
"R"
```

### 4. Dividing the dates by utilizing the strsplit() function in the R language.

Another great use for the strsplit() function is dividing dates. This particular application is both impressive and valuable to pursue. Now, let’s delve into how this process unfolds.

```
test_dates<-c("24-07-2020","25-07-2020","26-07-2020","27-07-2020","28-07-2020")
test_mat<-strsplit(test_dates,split = "-")
test_mat
```

Give me only one option to paraphrase the following sentence natively:

The end result is produced.

```
"24" "07" "2020"
"25" "07" "2020"
"26" "07" "2020"
"27" "07" "2020"
"28" "07" "2020"
```

Are you able to witness an appealing outcome? With the use of this function, we possess the capability to generate multiple divisions from the input strings or data. Furthermore, it is possible to transform the dates into a matrix layout.

```
matrix(unlist(test_mat),ncol=3,byrow=T)
```

Generate one alternative option for the following phrase:

“The result”

```
[,1] [,2] [,3]
[1,] "24" "07" "2020"
[2,] "25" "07" "2020"
[3,] "26" "07" "2020"
[4,] "27" "07" "2020"
[5,] "28" "07" "2020"
```

The above mentioned results depict the creation of a matrix from the divided data, as organizing the data is crucial for subseuent processing. Simply splitting the text is meaningless unless it is structured or arranged in a dependable manner, similar to the example mentioned above.

## In conclusion

Now, as we come to the end of the article, I hope you now have a clearer grasp on how the strsplit() function in R works and its various applications. This function is highly utilized and renowned for dividing strings. That’s all for now, but stay tuned for more functions in the future.

Further research: R documentations

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