【教程】R语言数据帧 | 产品经理的私房菜让工作更简单

【教程】R语言数据帧

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数据帧是一个表或二维数组状结构,其中每一列包含一个可变的值和每行包含一组来自每列的值。

下面是一个数据帧的特征。

  • 列名应为非空。
  • 行的名称应该是唯一的。
  • 存储在数据帧中的数据可以是数字,因子或字符类型。
  • 每列应包含数据项的数量相同。

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("2012-01-01","2013-09-23","2014-11-15","2014-05-11","2015-03-27")),
	stringsAsFactors=FALSE
			)
# Print the data frame.			
print(emp.data) 

当我们上面的代码执行时,它产生以下结果:

  emp_id emp_name salary start_date
1      1     Rick 623.30 2012-01-01
2      2      Dan 515.20 2013-09-23
3      3 Michelle 611.00 2014-11-15
4      4     Ryan 729.00 2014-05-11
5      5     Gary 843.25 2015-03-27

得到数据帧的结构

数据帧的结构可以通过使用函数 str()了解(得到)

# 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
			)
# Get the structure of the data frame.
str(emp.data)

当我们上面的代码执行时,它产生以下结果:

'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: "2012-01-01" "2013-09-23" "2014-11-15" "2014-05-11" ...

数据在数据帧摘要

统计汇总数据和性质可通过应用 summary()函数来获得。

# 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("2012-01-01","2013-09-23","2014-11-15","2014-05-11","2015-03-27")),
	stringsAsFactors=FALSE
			)
# Print the summary.
print(summary(emp.data))  

当我们上面的代码执行时,它产生以下结果:

     emp_id    emp_name             salary        start_date        
 Min.   :1   Length:5           Min.   :515.2   Min.   :2012-01-01  
 1st Qu.:2   Class :character   1st Qu.:611.0   1st Qu.:2013-09-23  
 Median :3   Mode  :character   Median :623.3   Median :2014-05-11  
 Mean   :3                      Mean   :664.4   Mean   :2014-01-14  
 3rd Qu.:4                      3rd Qu.:729.0   3rd Qu.:2014-11-15  
 Max.   :5                      Max.   :843.2   Max.   :2015-03-27 

从数据帧中提取数据

使用列名称从数据帧提取特定的列。

# 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)

当我们上面的代码执行时,它产生以下结果:

  emp.data.emp_name emp.data.salary
1              Rick          623.30
2               Dan          515.20
3          Michelle          611.00
4              Ryan          729.00
5              Gary          843.25

提取前两行和所有列

# 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)

当我们上面的代码执行时,它产生以下结果:

  emp_id emp_name salary start_date
1      1     Rick  623.3 2012-01-01
2      2      Dan  515.2 2013-09-23

提取第3和第5行与第2和第4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)

当我们上面的代码执行时,它产生以下结果:

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

扩展数据帧

数据帧可以通过添加的列和行进行扩展。

添加列

只需使用新列名称添加列向量。

# 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)

当我们上面的代码执行时,它产生以下结果:

emp_id emp_name salary start_date       dept
1      1     Rick 623.30 2012-01-01         IT
2      2      Dan 515.20 2013-09-23 Operations
3      3 Michelle 611.00 2014-11-15         IT
4      4     Ryan 729.00 2014-05-11         HR
5      5     Gary 843.25 2015-03-27    Finance

添加行

要添加更多的行永久到现有的数据帧,我们需要引入新的行中的结构要与现有数据帧相同,并使用 rbind()函数。

在下面的例子中,我们创建一个新的行数据帧,现有的数据帧创建并与最终的数据帧合并。

# 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)

当我们上面的代码执行时,它产生以下结果:

  emp_id emp_name salary start_date       dept
1      1     Rick 623.30 2012-01-01         IT
2      2      Dan 515.20 2013-09-23 Operations
3      3 Michelle 611.00 2014-11-15         IT
4      4     Ryan 729.00 2014-05-11         HR
5      5     Gary 843.25 2015-03-27    Finance
6      6    Rasmi 578.00 2013-05-21         IT
7      7   Pranab 722.50 2013-07-30 Operations
8      8    Tusar 632.80 2014-06-17   Fianance

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