R Data Frame

A data frame can be understood as the "table" we commonly refer to.

A data frame is a data structure in R language, which is a special two-dimensional list.

Each column in a data frame has a unique column name and all columns are of equal length. The data type within the same column must be consistent, but different columns can have different data types.

To create a data frame in R, the data.frame() function is used with the following syntax:

data.frame(..., row.names = NULL, check.rows = FALSE,
           check.names = TRUE, fix.empty.names = TRUE,
           stringsAsFactors = default.stringsAsFactors())

• ...: Column vectors, which can be of any type (character, numeric, logical), typically represented in the form of tag = value, or simply value.
• row.names: Row names, default is NULL, can be set to a single number, string, or a vector of strings and numbers.
• check.rows: Checks if the row names and lengths are consistent.
• check.names: Checks if the variable names in the data frame are valid.
• fix.empty.names: Determines whether unnamed parameters should be automatically named.
• stringsAsFactors: Boolean value, whether characters should be converted to factors. The default value is TRUE, but can be modified by setting the option (stringsAsFactors=FALSE).

Below is an example of creating a simple data frame containing names, employee IDs, and monthly salaries:

Example

table = data.frame(
    姓名 = c("张三", "李四"),
    工号 = c("001","002"),
    月薪 = c(1000, 2000)
)
print(table) # View the table data

Executing the above code outputs:

姓名 工号 月薪
1 张三  001 1000
2 李四  002 2000

The structure of the data frame can be displayed using the str() function:

Example

table = data.frame(
    姓名 = c("张三", "李四"),
    工号 = c("001","002"),
    月薪 = c(1000, 2000)
)
# Get the structure
str(table)

Executing the above code outputs:

'data.frame':   2 obs. of  3 variables:
 $ 姓名: chr  "张三" "李四"
 $ 工号: chr  "001" "002"
 $ 月薪: num  1000 2000

The summary() function can display a summary of the data frame:

Example

table = data.frame(
    姓名 = c("张三", "李四"),
    工号 = c("001","002"),
    月薪 = c(1000, 2000)
)
# Display summary
print(summary(table))

Executing the above code outputs:

姓名               工号                月薪     
Length:2           Length:2           Min.   :1000  
Class :character   Class :character   1st Qu.:1250  
Mode  :character   Mode  :character   Median :1500  
                                      Mean   :1500  
                                      3rd Qu.:1750  
                                      Max.   :2000

We can also extract specified columns:

Example

table = data.frame(
    姓名 = c("张三", "李四"),
    工号 = c("001","002"),
    月薪 = c(1000, 2000)
)
# Extract specified columns
result <- data.frame(table$姓名, table$月薪)
print(result)

Executing the above code outputs:

table.姓名 table.月薪
1       张三       1000
2       李四       2000

The following example shows the first two rows:

Example

table = data.frame(
    姓名 = c("张三", "李四", "王五"),
    工号 = c("001","002","003"),

This is a Chinese to English translation, please provide the English translation for this text. Do not provide any explanations or text apart from the translation.

Chinese: 月薪 = c(1000, 2000, 3000) print(table)

Extract the first two rows

print("---Output the first two rows----") result <- table[1:2,] print(result)

Executing the above code outputs the following result:

Name ID Salary
1 Zhang San 001 1000
2 Li Si 002 2000
3 Wang Wu 003 3000
[1] "---Output the first two rows----"
  Name ID Salary
1 Zhang San 001 1000
2 Li Si 002 2000

We can read data from a specified row and column using a coordinate-like format. Below, we read data from the 1st and 2nd columns of the 2nd and 3rd rows:

Example

table = data.frame(
    Name = c("Zhang San", "Li Si", "Wang Wu"),
    ID = c("001", "002", "003"),
    Salary = c(1000, 2000, 3000)
)
# Read data from the 1st and 2nd columns of the 2nd and 3rd rows:
result <- table[c(2, 3), c(1, 2)]
print(result)

Executing the above code outputs the following result:

Name ID
2 Li Si 002
3 Wang Wu 003

Expanding Data Frames

We can expand an existing data frame. Below is an example where we add a Department column:

Example

table = data.frame(
    Name = c("Zhang San", "Li Si", "Wang Wu"),
    ID = c("001", "002", "003"),
    Salary = c(1000, 2000, 3000)
)
# Add a Department column
table$Department <- c("Operations", "Technology", "Editing")

print(table)

Executing the above code outputs the following result:

Name ID Salary Department
1 Zhang San 001 1000 Operations
2 Li Si 002 2000 Technology
3 Wang Wu 003 3000 Editing

We can use the cbind() function to combine multiple vectors into a data frame:

Example

# Create vectors
sites <- c("Google", "tutorialpro", "Taobao")
likes <- c(222, 111, 123)
url <- c("www.google.com", "www.tutorialpro.org", "www.taobao.com")

# Combine vectors into a data frame
addresses <- cbind(sites, likes, url)

# View the data frame
print(addresses)

Executing the above code outputs the following result:

sites likes url
[1,] "Google" "222" "www.google.com"
[2,] "tutorialpro" "111" "www.tutorialpro.org"
[3,] "Taobao" "123" "www.taobao.com"

To merge two data frames, we can use the rbind() function:

Example

table = data.frame(
    Name = c("Zhang San", "Li Si", "Wang Wu"),
    ID = c("001", "002", "003"),
    Salary = c(1000, 2000, 3000)
)
newtable = data.frame(
    Name = c("Xiao Ming", "Xiao Bai"),
    ID = c("101", "102"),
    Salary = c(5000, 7000)
)
# Merge two data frames
result <- rbind(table, newtable)
print(result)

Executing the above code outputs the following result:

Name ID Salary
1 Zhang San 001 1000
2 Li Si 002 2000
3 Wang Wu 003 3000
4 Xiao Ming 101 5000
5 Xiao Bai 102 7000