过滤和选择数据

The Tidyverse is a collection of R packages designed for data science that share a common philosophy, grammar, and data structures. The core package for data manipulation in this suite is called dplyr.

dplyr introduces a set of functions ("verbs") that make data frame manipulation highly intuitive.

The Pipe Operator (%>% or |>)

The Tidyverse philosophy centers on chaining operations together. The pipe operator %>% (or the native pipe |> introduced in R 4.1+) takes the result of one expression and passes it as the first argument to the next function. This avoids nested function calls and prevents cluttering your workspace with temporary variables.

# Without pipe:
filter(select(df, name, age), age > 20)

# With pipe:
df %>%
  select(name, age) %>%
  filter(age > 20)

dplyr Verbs for Filtering and Selecting

The three fundamental verbs for extracting data from a data frame are:

1. select()

Selects specific columns from a data frame. You can list the column names to keep or use the - prefix to exclude columns.

# Select the 'name' and 'salary' columns
select(df, name, salary)

# Remove the 'address' column
select(df, -address)

2. filter()

Filters rows based on one or more logical conditions.

# Filter rows where age is greater than 30
filter(df, age > 30)

# Filter using multiple conditions (logical AND)
filter(df, age > 30, department == "HR")

3. arrange()

Sorts rows based on the values of one or more columns. The sorting order is ascending by default. To sort in descending order, wrap the column name in desc().

# Sort by age (ascending)
arrange(df, age)

# Sort by salary (descending)
arrange(df, desc(salary))

Try it yourself

Exercise 1: Select columns