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The nrow function in R is a useful tool for analyzing data by providing the number of rows in a given dataset. It can be used to quickly determine the size of a dataset and to identify any potential issues with missing or duplicated data. This function is particularly useful for data exploration and quality control purposes. For example, it can be used to check the integrity of a dataset by comparing the number of rows to the expected number of observations. Additionally, the nrow function can be used in combination with other R functions, such as filter or select, to perform more complex analyses. For instance, it can be used to subset a dataset based on a specific number of rows, or to calculate the proportion of missing data in a dataset. Overall, the nrow function is a versatile tool that can aid in various data analysis tasks.
Use nrow Function in R (With Examples)
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.