R语言数据帧

数据帧是一个表或二维类似数组的结构，其中每列包含一个变量的值，每行包含来自每一列的一组值。

以下是数据帧的特征 -

• 列名称应该不为空。
• 行名称应该是唯一的。
• 存储在数据帧中的数据可以是数字，因子或字符类型。
• 每列应包含相同数量的数据项。

创建数据帧

# Create the data frame.
emp.data <- data.frame(
   emp_id = c (1:5), 
   emp_name = c("Rick","Dan","Michelle","Ryan","Gary"),
   salary = c(623.3,515.2,611.0,729.0,843.25), 

   start_date = as.Date(c("2017-01-01", "2017-09-23", "2017-11-15", "2017-05-11",
      "2018-03-27")),
   stringsAsFactors = FALSE
)
# Print the data frame.            
print(emp.data)

当我们执行上述代码时，会产生以下结果 -

  emp_id emp_name salary start_date
    Rick 623.30 2017-01-01
     Dan 515.20 2017-09-23
Michelle 611.00 2017-11-15
    Ryan 729.00 2017-05-11
    Gary 843.25 2018-03-27

获取数据帧的结构

通过使用str()函数可以查看数据帧的结构，参考以下代码实现 -

# Create the data frame.
emp.data <- data.frame(
   emp_id = c (1:5), 
   emp_name = c("Rick","Dan","Michelle","Ryan","Gary"),
   salary = c(623.3,515.2,611.0,729.0,843.25), 

   start_date = as.Date(c("2017-01-01", "2017-09-23", "2017-11-15", "2017-05-11",
      "2018-03-27")),
   stringsAsFactors = FALSE
)
# Get the structure of the data frame.
str(emp.data)

当我们执行上述代码时，会产生以下结果 -

'data.frame':   5 obs. of  4 variables:
 $ emp_id    : int  1 2 3 4 5
 $ emp_name  : chr  "Rick" "Dan" "Michelle" "Ryan" ...
 $ salary    : num  623 515 611 729 843
 $ start_date: Date, format: "2017-01-01" "2017-09-23" ...

数据帧数据摘要

数据的统计摘要和性质可以通过应用summary()函数获得。

# Create the data frame.
emp.data <- data.frame(
   emp_id = c (1:5), 
   emp_name = c("Rick","Dan","Michelle","Ryan","Gary"),
   salary = c(623.3,515.2,611.0,729.0,843.25), 

   start_date = as.Date(c("2015-01-01", "2016-09-23", "2017-11-15", "2018-05-11",
      "2018-03-27")),
   stringsAsFactors = FALSE
)
# Print the summary.

当我们执行上述代码时，会产生以下结果 -

     emp_id    emp_name             salary        start_date        
 Min.   :1   Length:5           Min.   :515.2   Min.   :2015-01-01  
 1st Qu.:2   Class :character   1st Qu.:611.0   1st Qu.:2016-09-23  
 Median :3   Mode  :character   Median :623.3   Median :2017-11-15  
 Mean   :3                      Mean   :664.4   Mean   :2017-03-28  
 3rd Qu.:4                      3rd Qu.:729.0   3rd Qu.:2018-03-27  
 Max.   :5                      Max.   :843.2   Max.   :2018-05-11

从数据帧提取数据

使用列名称从数据帧中提取特定列。

# Create the data frame.
emp.data <- data.frame(
   emp_id = c (1:5),
   emp_name = c("Rick","Dan","Michelle","Ryan","Gary"),
   salary = c(623.3,515.2,611.0,729.0,843.25),

   start_date = as.Date(c("2012-01-01","2013-09-23","2014-11-15","2014-05-11",
      "2015-03-27")),
   stringsAsFactors = FALSE
)
# Extract Specific columns.
result <- data.frame(emp.data$emp_name,emp.data$salary)
print(result)

当我们执行上述代码时，会产生以下结果 -

  emp.data.emp_name emp.data.salary
             Rick          623.30
              Dan          515.20
         Michelle          611.00
             Ryan          729.00
             Gary          843.25

提取前两行，然后提取所有列 -

# Create the data frame.
emp.data <- data.frame(
   emp_id = c (1:5),
   emp_name = c("Rick","Dan","Michelle","Ryan","Gary"),
   salary = c(623.3,515.2,611.0,729.0,843.25),

   start_date = as.Date(c("2012-01-01", "2013-09-23", "2014-11-15", "2014-05-11",
      "2015-03-27")),
   stringsAsFactors = FALSE
)
# Extract first two rows.
result <- emp.data[1:2,]
print(result)

当我们执行上述代码时，会产生以下结果 -

  emp_id    emp_name   salary    start_date
    Rick      623.3     2012-01-01
    Dan       515.2     2013-09-23

提取第2列和第4列和第3行和第5列

# Create the data frame.
emp.data <- data.frame(
   emp_id = c (1:5), 
   emp_name = c("Rick","Dan","Michelle","Ryan","Gary"),
   salary = c(623.3,515.2,611.0,729.0,843.25), 

    start_date = as.Date(c("2012-01-01", "2013-09-23", "2014-11-15", "2014-05-11",
      "2015-03-27")),
   stringsAsFactors = FALSE
)

# Extract 3rd and 5th row with 2nd and 4th column.
result <- emp.data[c(3,5),c(2,4)]
print(result)

当我们执行上述代码时，会产生以下结果 -

  emp_name start_date
Michelle 2014-11-15
    Gary 2015-03-27

扩展数据帧

可以通过添加列和行来扩展数据帧。

添加列

只需使用新的列名来添加列向量。参考以下示例代码 -

# Create the data frame.
emp.data <- data.frame(
   emp_id = c (1:5), 
   emp_name = c("Rick","Dan","Michelle","Ryan","Gary"),
   salary = c(623.3,515.2,611.0,729.0,843.25), 

   start_date = as.Date(c("2012-01-01", "2013-09-23", "2014-11-15", "2014-05-11",
      "2015-03-27")),
   stringsAsFactors = FALSE
)

# Add the "dept" coulmn.
emp.data$dept <- c("IT","Operations","IT","HR","Finance")
v <- emp.data
print(v)

当我们执行上述代码时，会产生以下结果 -

  emp_id   emp_name    salary    start_date       dept
   Rick        623.30    2012-01-01       IT
   Dan         515.20    2013-09-23       Operations
   Michelle    611.00    2014-11-15       IT
   Ryan        729.00    2014-05-11       HR
   Gary        843.25    2015-03-27       Finance

添加行

要将更多行永久添加到现有数据帧，需要使用与现有数据帧相同结构的新行，并使用rbind()函数。

在下面的示例中，我们使用新行创建一个数据帧，并将其与现有的数据帧进行合并，以创建最终的数据帧。

# Create the first data frame.
emp.data <- data.frame(
   emp_id = c (1:5), 
   emp_name = c("Rick","Dan","Michelle","Ryan","Gary"),
   salary = c(623.3,515.2,611.0,729.0,843.25), 

   start_date = as.Date(c("2012-01-01", "2013-09-23", "2014-11-15", "2014-05-11",
      "2015-03-27")),
   dept = c("IT","Operations","IT","HR","Finance"),
   stringsAsFactors = FALSE
)

# Create the second data frame
emp.newdata <-     data.frame(
   emp_id = c (6:8), 
   emp_name = c("Rasmi","Pranab","Tusar"),
   salary = c(578.0,722.5,632.8), 
   start_date = as.Date(c("2013-05-21","2013-07-30","2014-06-17")),
   dept = c("IT","Operations","Fianance"),
   stringsAsFactors = FALSE
)

# Bind the two data frames.
emp.finaldata <- rbind(emp.data,emp.newdata)
print(emp.finaldata)

当我们执行上述代码时，会产生以下结果 -

  emp_id     emp_name    salary     start_date       dept
    Rick        623.30     2012-01-01       IT
    Dan         515.20     2013-09-23       Operations
    Michelle    611.00     2014-11-15       IT
    Ryan        729.00     2014-05-11       HR
    Gary        843.25     2015-03-27       Finance
    Rasmi       578.00     2013-05-21       IT
    Pranab      722.50     2013-07-30       Operations
    Tusar       632.80     2014-06-17       Fianance