R 语言 数据帧
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数据帧
数据帧数据帧是表或二维数组状结构，其中每一列包含一个变量的值，每一行包含来自每一列的一组值。s以下是数据帧的特征。- 列名应为非空。
- 行名应该是唯一的。
- 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。
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创建数据帧
尝试一下# 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("2019-01-01", "2020-09-23", "2014-11-15", "2014-05-11", "2015-03-27")), stringsAsFactors = FALSE ) # Print the data frame. print(emp.data)当我们执行以上代码时，它产生以下结果-emp_id emp_name salary start_date 1 1 Rick 623.30 2019-01-01 2 2 Dan 515.20 2020-09-23 3 3 Michelle 611.00 2014-11-15 4 4 Ryan 729.00 2014-05-11 5 5 Gary 843.25 2015-03-27 -
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。
尝试一下# 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("2019-01-01", "2020-09-23", "2014-11-15", "2014-05-11", "2015-03-27")), stringsAsFactors = FALSE ) # Get the structure of the data frame. str(emp.data)当我们执行以上代码时，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: "2019-01-01" "2020-09-23" "2014-11-15" "2014-05-11" ... -
数据帧中的数据摘要
统计摘要和数据性质可以通过应用summary（）函数获得。
尝试一下# 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("2019-01-01", "2020-09-23", "2014-11-15", "2014-05-11", "2015-03-27")), stringsAsFactors = FALSE ) # Print the summary. print(summary(emp.data))当我们执行以上代码时，它产生以下结果-emp_id emp_name salary start_date Min. :1 Length:5 Min. :515.2 Min. :2019-01-01 1st Qu.:2 Class :character 1st Qu.:611.0 1st Qu.:2020-09-23 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
使用列名从数据帧中提取特定的列。
尝试一下# 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("2019-01-01","2020-09-23","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)当我们执行以上代码时，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，然后提取所有列
尝试一下# 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("2019-01-01", "2020-09-23", "2014-11-15", "2014-05-11", "2015-03-27")), stringsAsFactors = FALSE ) # Extract first two rows. result <- emp.data[1:2,] print(result)当我们执行以上代码时，它产生以下结果-emp_id emp_name salary start_date 1 1 Rick 623.3 2019-01-01 2 2 Dan 515.2 2020-09-23提取3次和5次行与2次和4次列
尝试一下# 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("2019-01-01", "2020-09-23", "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)当我们执行以上代码时，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
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添加列向量即可。
尝试一下# 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("2019-01-01", "2020-09-23", "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)当我们执行以上代码时，它产生以下结果-emp_id emp_name salary start_date dept 1 1 Rick 623.30 2019-01-01 IT 2 2 Dan 515.20 2020-09-23 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，我们需要以与现有数据帧相同的结构引入新行，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，并将其与现有数据帧合并以创建最终数据帧。
尝试一下# 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("2019-01-01", "2020-09-23", "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("2020-05-21","2020-07-30","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)当我们执行以上代码时，它产生以下结果-emp_id emp_name salary start_date dept 1 1 Rick 623.30 2019-01-01 IT 2 2 Dan 515.20 2020-09-23 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 2020-05-21 IT 7 7 Pranab 722.50 2020-07-30 Operations 8 8 Tusar 632.80 2014-06-17 Fianance