数据帧是表或二维阵列状结构，其中每一列包含一个变量的值，并且每一行包含来自每一列的一组值。

以下是数据帧的特性。

列名称应为非空。

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。

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

当我们执行上面的代码，它产生以下结果 -

emp_id emp_name salary start_date1 1 Rick 623.30 2012-01-012 2 Dan 515.20 2013-09-233 3 Michelle 611.00 2014-11-154 4 Ryan 729.00 2014-05-115 5 Gary 843.25 2015-03-27获取数据帧的结构

通过使用str()函数可以看到数据帧的结构。

# 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)

当我们执行上面的代码，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" ...数据框中的数据摘要

可以通过应用summary()函数获取数据的统计摘要和性质。

# 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))

当我们执行上面的代码，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 从数据帧提取数据

使用列名称从数据框中提取特定列。

# 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)

当我们执行上面的代码，它产生以下结果 -

emp.data.emp_name emp.data.salary1 Rick 623.302 Dan 515.203 Michelle 611.004 Ryan 729.005 Gary 843.25

先提取前两行，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)

当我们执行上面的代码，它产生以下结果 -

emp_id emp_name salary start_date1 1 Rick 623.3 2012-01-012 2 Dan 515.2 2013-09-23

用第2和第4列提取第3和第5行

# 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)

当我们执行上面的代码，它产生以下结果 -

emp_name start_date3 Michelle 2014-11-155 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.dataprint(v)

当我们执行上面的代码，它产生以下结果 -

emp_id emp_name salary start_date dept1 1 Rick 623.30 2012-01-01 IT2 2 Dan 515.20 2013-09-23 Operations3 3 Michelle 611.00 2014-11-15 IT4 4 Ryan 729.00 2014-05-11 HR5 5 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，我们需要引入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，我们创建一个包含新行的数据帧，并将其与现有数据帧合并以创建最终数据帧。

# 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 frameemp.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)

当我们执行上面的代码，它产生以下结果 -

emp_id emp_name salary start_date dept1 1 Rick 623.30 2012-01-01 IT2 2 Dan 515.20 2013-09-23 Operations3 3 Michelle 611.00 2014-11-15 IT4 4 Ryan 729.00 2014-05-11 HR5 5 Gary 843.25 2015-03-27 Finance6 6 Rasmi 578.00 2013-05-21 IT7 7 Pranab 722.50 2013-07-30 Operations8 8 Tusar 632.80 2014-06-17 Fianance

• Q新闻丨中兴一程序员跳楼身亡；Kotlin和C成年度候选语言；谷歌在
• 阿里自研分布式强一致关系型数据库——X-DB
• 马克龙说法语将成世界第一语言 英国网民笑了…
• 邬贺铨：卖设备不如做服务，做服务不如做互联网
• 12月15日台州房产交易数据.
• 皮蓬：詹姆斯荣誉虽不及乔丹 但数据已超越篮球之神
• 最权威的2017全球挖机产销数据！日本竟然生产了全球40%的挖机…
• 五连红，近期18中14，2017-12-15周五竞彩大数据分析
• 超级数据暴赚后，投资心态回归理智！
• 申请日本留学为何要先进语言学校?