如何在 R 中使用 Nrow函数？

在本文中，我们将讨论如何在 R 编程语言中使用 Nrow函数。此函数用于数据框或矩阵中以获取行数。

Syntax: nrow(data)

where, data can be a dataframe or a matrix.

示例 1：计算数据框中的行数

在此示例中，我们将计算数据框中的行数。

R

# create a dataframe with 4 rows 
# and 3 columns
data=data.frame(col1 = c(1,2,3,4),
                col2 = c(NA,NA,NA,NA),
                col3 = c(23,45,43,NA))
  
# display
print(data)
  
# count the number of rows
print(nrow(data))

R

# create a dataframe with 4 rows and 3 columns
data = data.frame(col1 = c(1,2,3,4),
                  col2 = c(NA,NA,NA,NA),
                  col3 = c(23,45,43,NA))
  
# display
print(data)
  
# count the number of rows 
# with condition column1 is greater than
# 3 and column3 is greater than 25
print(nrow(data[data$col1>3 & data$col3>25, ]))
  
# count the number of rows 
# with condition column1 is greater than 3
# or column3 is greater than 25
print(nrow(data[data$col1>3 | data$col3>25, ]))

R

# create a dataframe with 4 rows and 3 columns
data = data.frame(col1 = c(1,2,3,4),
                  col2 = c(89,NA,NA,67),
                  col3 = c(23,45,43,NA))
  
# display
print(data)
  
# total rows in dataframe  with no missing values
print(nrow(data[complete.cases(data), ]))

R

# create a dataframe with 4 rows and
# 3 columns
data = data.frame(col1 = c(1,2,3,4),
                  col2 = c(89,NA,NA,67),
                  col3 = c(23,45,43,NA))
  
# display
print(data)
  
# total rows in dataframe 
# with no missing values in column1
print(nrow(data[is.na(data$col1), ]))
  
# total rows in dataframe 
# with no missing values in column2
print(nrow(data[is.na(data$col2), ]))
  
# total rows in dataframe 
# with no missing values in column3
print(nrow(data[is.na(data$col3), ]))

输出：

示例 2：计算数据框中有条件的行

在这里，我们将在 nrow()函数中指定条件。

Syntax: nrow(data[condition, ])

where,

data is the input dataframe
condition is used to get the rows.

编程需要懂一点英语

R

# create a dataframe with 4 rows and 3 columns
data = data.frame(col1 = c(1,2,3,4),
                  col2 = c(NA,NA,NA,NA),
                  col3 = c(23,45,43,NA))
  
# display
print(data)
  
# count the number of rows 
# with condition column1 is greater than
# 3 and column3 is greater than 25
print(nrow(data[data$col1>3 & data$col3>25, ]))
  
# count the number of rows 
# with condition column1 is greater than 3
# or column3 is greater than 25
print(nrow(data[data$col1>3 | data$col3>25, ]))

输出：

示例 3：计算没有缺失值的行

在这里，我们将通过在 nrow 方法中使用 complete.cases() 来获取没有缺失值的总行数。

Syntax: nrow(data[complete.cases(data), ])

编程需要懂一点英语

R

# create a dataframe with 4 rows and 3 columns
data = data.frame(col1 = c(1,2,3,4),
                  col2 = c(89,NA,NA,67),
                  col3 = c(23,45,43,NA))
  
# display
print(data)
  
# total rows in dataframe  with no missing values
print(nrow(data[complete.cases(data), ]))

输出：

示例 4：计算特定列中缺失值的行

在这里，我们将使用 is.na() 方法计算特定列中丢失的行数。

Syntax: nrow(data[is.na(data$column_name), ])

where,

data is the input dataframe
column_name is the column to get missing value count

编程需要懂一点英语

R

# create a dataframe with 4 rows and
# 3 columns
data = data.frame(col1 = c(1,2,3,4),
                  col2 = c(89,NA,NA,67),
                  col3 = c(23,45,43,NA))
  
# display
print(data)
  
# total rows in dataframe 
# with no missing values in column1
print(nrow(data[is.na(data$col1), ]))
  
# total rows in dataframe 
# with no missing values in column2
print(nrow(data[is.na(data$col2), ]))
  
# total rows in dataframe 
# with no missing values in column3
print(nrow(data[is.na(data$col3), ]))

输出：