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The nrow function in R returns the number of rows in an R object, such as a matrix or data frame. It is useful for quickly obtaining the dimensions of a data set, as it can be used to determine the number of observations in a data set before performing any data manipulation or analysis. It is important to remember that nrow counts the number of rows in a data set, not the number of observations. Therefore, if any observations have been deleted, nrow will not account for those observations.
You can use the nrow() function in R to count the number of rows in a data frame:
#count number of rows in data frame
nrow(df)
The following examples show how to use this function in practice with the following data frame:
#create data frame df <- data.frame(x=c(1, 2, 3, 3, 5, NA), y=c(8, 14, NA, 25, 29, NA)) #view data frame df x y 1 1 8 2 2 14 3 3 NA 4 3 25 5 5 29 6 NA NA
Example 1: Count Rows in Data Frame
The following code shows how to count the total number of rows in the data frame:
#count total rows in data frame
nrow(df)
[1] 6
There are 6 total rows.
Example 2: Count Rows with Condition in Data Frame
The following code shows how to count the number of rows where the value in the ‘x’ column is greater than 3 and is not blank:
#count total rows in data frame where 'x' is greater than 3 and not blank nrow(df[df$x>3 & !is.na(df$x), ]) [1] 1
There is 1 row in the data frame that satisfies this condition.
Example 3: Count Rows with no Missing Values
The following code shows how to use the function to count the number of rows with no missing values in the data frame:
#count total rows in data frame with no missing values in any column nrow(df[complete.cases(df), ]) [1] 4
There are 4 rows with no missing values in the data frame.
Example 4: Count Rows with Missing Values in Specific Column
#count total rows in with missing value in 'y' column nrow(df[is.na(df$y), ]) [1] 2
There are 2 rows with missing values in the ‘y’ column.