{"id":33,"date":"2026-03-03T06:48:58","date_gmt":"2026-03-03T06:48:58","guid":{"rendered":"https:\/\/gigz.pk\/r\/?post_type=lesson&p=33"},"modified":"2026-04-01T10:54:33","modified_gmt":"2026-04-01T10:54:33","slug":"filtering-selecting-and-arranging-data","status":"publish","type":"lesson","link":"https:\/\/gigz.pk\/r\/lesson\/filtering-selecting-and-arranging-data\/","title":{"rendered":"Filtering, Selecting, and Arranging Data"},"content":{"rendered":"\n

Efficient data analysis in R requires knowing how to filter rows, select specific columns, and arrange data in a meaningful order. The dplyr<\/code> package makes these operations intuitive and fast.<\/p>\n\n\n\n

1. Filtering Data with filter()<\/strong><\/h1>\n\n\n\n

The filter()<\/code> function is used to extract rows that meet specific conditions.<\/p>\n\n\n\n

library(dplyr)data <- data.frame(
  Name = c(\"Alice\", \"Bob\", \"Charlie\", \"David\"),
  Age = c(25, 30, 28, 22),
  Score = c(90, 85, 88, 95)
)# Filter rows where Age is greater than 25
filter(data, Age > 25)# Filter rows where Score is at least 90
filter(data, Score >= 90)<\/pre>\n\n\n\n
You can also combine multiple conditions using &<\/code> (AND) and |<\/code> (OR):<\/p>\n\n\n\n
# Age > 25 AND Score >= 88
filter(data, Age > 25 & Score >= 88)<\/pre>\n\n\n\n
2. Selecting Columns with select()<\/strong><\/h1>\n\n\n\n
The select()<\/code> function is used to pick specific columns from a dataset.<\/p>\n\n\n\n
# Select Name and Score columns
select(data, Name, Score)# Exclude a column
select(data, -Age)<\/pre>\n\n\n\n
You can also select columns using helpers like starts_with()<\/code>, ends_with()<\/code>, or contains()<\/code>:<\/p>\n\n\n\n
# Select columns that start with \"S\"
select(data, starts_with(\"S\"))<\/pre>\n\n\n\n
3. Arranging Data with arrange()<\/strong><\/h1>\n\n\n\n
The arrange()<\/code> function is used to reorder rows based on one or more columns.<\/p>\n\n\n\n
# Arrange by Age ascending
arrange(data, Age)# Arrange by Score descending
arrange(data, desc(Score))# Arrange by multiple columns
arrange(data, Age, desc(Score))<\/pre>\n\n\n\n
4. Combining Filtering, Selecting, and Arranging<\/strong><\/h1>\n\n\n\n
Using the pipe operator %>%<\/code> from dplyr<\/code>, you can chain operations together:<\/p>\n\n\n\n
data %>%
  filter(Age > 25) %>%
  select(Name, Score) %>%
  arrange(desc(Score))<\/pre>\n\n\n\n
This filters rows where Age is greater than 25, selects only Name and Score columns, and sorts the results by Score in descending order.<\/p>\n\n\n\n
5. Advantages of Using These Functions<\/strong><\/h1>\n\n\n\n