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
1 1 Rick 623.30 2017-01-01
2 2 Dan 515.20 2017-09-23
3 3 Michelle 611.00 2017-11-15
4 4 Ryan 729.00 2017-05-11
5 5 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
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，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
1 1 Rick 623.3 2012-01-01
2 2 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
3 Michelle 2014-11-15
5 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
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
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
