Filter rows based on text patterns using R

In this article, we will explore various methods to filter rows based on text patterns by using the R Programming Language.

How do we filter the rows based on text patterns?

R language offers various methods to filter the rows based on text patterns in various data sets. By using these methods provided by R, it is possible to filter the rows efficiently. Some of the methods to filter the rows based on text patterns are:

By using the subset() function

This method is used to filter the rows based on the text patterns efficiently. In the below example, we created a data frame and filtered the rows based on the text patterns.

R

# Original DataFrame
data <- data.frame(
  Name = c("John", "Alice", "Bob", "Emily", "David"),
  Age = c(25, 30, 35, 28, 40),
  City = c("New York", "Los Angeles", "Chicago", "San Francisco", "Boston")
)
print("original data")
print(data)

# Filter rows where City contains "New"
filtered_data <- subset(data, grepl("New", City))

print("Filtered Data:")
print(filtered_data)

Output:

[1] "original data"
     Name   Age          City
1    John     25        New York
2   Alice     30       Los Angeles
3   Bob      35         Chicago
4  Emily    28      San Francisco
5  David   40        Boston

[1] "Filtered Data:"
    Name    Age     City
1   John       25     New York

In the below example, we created a data frame and filtered the rows based on the text patterns.

R

# Original DataFrame
data <- data.frame(
  Name = c("Mahesh","suresh","ramesh","kamesh","lokesh"),
   id_no= c(1125, 3034, 5635, 2768, 4075),
    college_name= c("abc", "xyz", "raghu", "aditya", "vishnu")
)
print("original data")
print(data)
# Filter rows where City contains "New"
filtered_data <- subset(data, grepl("ad", college_name))

print("Filtered Data:")
print(filtered_data)

Output:

[1] "original data"
     Name      id_no     college_name
1  Mahesh     1125           abc
2  suresh       3034          xyz
3  ramesh     5635         raghu
4  kamesh    2768         aditya
5  lokesh       4075        vishnu

[1] "Filtered Data:"
     Name     id_no     college_name
4  kamesh    2768         aditya

By using the grep() function

This method is used to filter the rows based on the text patterns efficiently. In the below example, we created a data frame and filtered the rows based on the text patterns.

R

# Original DataFrame
data <- data.frame(
  Name = c("John", "Alice", "Bob", "Emily", "David"),
  Age = c(25, 30, 35, 28, 40),
  City = c("New York", "Los Angeles", "Chicago", "San Francisco", "Boston")
)

print("Original data")
print(data)

# Filter rows where City contains "Los"
filtered_indices <- grep(4, data$Age)
filtered_data <- data[filtered_indices, ]

# Output
print("Filtered Data:")
print(filtered_data)

Output:

[1] "Original data"
      Name    Age          City
1     John       25       New York
2    Alice       30      Los Angeles
3    Bob        35       Chicago
4   Emily      28       San Francisco
5   David     40        Boston

[1] "Filtered Data:"
    Name    Age    City
5  David   40    Boston

In the below example, we created a data frame and filtered the rows based on the text patterns.

R

Name = c("jai","price","gaurav","sohit","arun")
Age = c(27,34,65,77,89)
Address = c("delhi", "kanpur", "allhabad", "raipur", "kerla")
Qualification = c("Msc", "Phd", "Mca", "B.tech", "Degree")
# Original DataFrame
data <- data.frame(Name, Age, Address, Qualification)

print("Original data")
print(data)

# Filter rows where City contains "Los"
filtered_indices <- grep("e", data$Address)
filtered_data <- data[filtered_indices, ]

print("Filtered Data:")
print(filtered_data)

Output:

[1] "Original data"
    Name    Age     Address    Qualification
1    jai           27       delhi               Msc
2   price      34      kanpur             Phd
3  gaurav   65    allhabad           Mca
4   sohit       77      raipur              B.tech
5   arun       89       kerla              Degree

[1] "Filtered Data:"
      Name     Age    Address     Qualification
1      jai          27         delhi             Msc
5     arun      89         kerla          Degree

Conclusion

In conclusion, we learned about how to filter the rows based on text patterns in R. R language offers versatile tools while performing the filter concepts.