Mean function in R -mean() calculates the arithmetic mean. mean() function calculates arithmetic mean of vector with NA values and arithmetic mean of column in data frame. mean of a group can also calculated using mean() function in R by providing it inside the aggregate function. with mean() function we can also perform row wise mean using dplyr package and also column wise mean lets see an example of each.

- mean of the list of vector elements with NA values
- mean of a particular column of the dataframe in R
- column wise mean of the dataframe using mean() function
- mean of the group in R dataframe using aggregate() and dplyr package
- Row wise mean of the dataframe in R using mean() function

**Syntax for mean() function in R:**

**x –**numeric vector**rm-**whether NA should be removed, if not, NA will be returned

**Example of R Mean() function:**

# R mean function x <-c(1.234,2.342,-4.562,5.671,12.345,-14.567) mean(x)

**output:**

** **

**Example of R Mean() function with NA:**

Mean function doesn’t give desired output, If NAs are present in the vector. so it has to be handled by using na.rm=TRUE in mean() function

# mean function for input vector which has NA. x <-c(1.234,2.342,-4.562,5.671,12.345,-14.567,NA) mean(x,na.rm=TRUE)

**output:**

** **

**Example of mean() function in R dataframe: **

Lets create the data frame to demonstrate mean function – mean() in r

### create the dataframe my_basket = data.frame(ITEM_GROUP = c("Fruit","Fruit","Fruit","Fruit","Fruit","Vegetable","Vegetable","Vegetable","Vegetable","Dairy","Dairy","Dairy","Dairy","Dairy"), ITEM_NAME = c("Apple","Banana","Orange","Mango","Papaya","Carrot","Potato","Brinjal","Raddish","Milk","Curd","Cheese","Milk","Paneer"), Price = c(100,80,80,90,65,70,60,70,25,60,40,35,50,120), Tax = c(2,4,5,6,2,3,5,1,3,4,5,6,4,3)) my_basket

so the resultant dataframe will be

**mean of a column in R data frame using mean() function :**

mean() function takes the column name as argument and calculates the mean of that particular column

# mean() function in R : mean of a column in data frame mean(my_basket$Price)

so the resultant mean of “Price” column will be

**output:**

**[1] 67.5**

**column wise mean using mean() function:**

mean() function is applied to the required column through mapply() function, so that it calculates the mean of required column as shown below.

# mean() function in R : mean of multiple column in data frame mapply(mean,my_basket[,c(-1,-2)])

so the resultant mean of “Price” and “Tax” columns will be

#### Mean of the column by group using mean() function

aggregate() function along with the mean() function calculates the mean of a group. here mean of “Price” column, for “Item_Group” is calculated.

##### Mean of the column by group aggregate(x= my_basket$Price, by= list(my_basket$ITEM_GROUP), FUN=mean)

Item_group has three groups “Dairy”,”Fruit” & “Vegetable”. mean of price for each group is calculated as shown below

#### Mean of the column by group and populate it by using mean() function:

group_by() function along with the mean() function calculates the mean of a group. here mean of “Price” column, for “Item_Group” is calculated and populated across as shown below

#### mean of the column by group and populate it using dplyr library(dplyr) my_basket %>% group_by(ITEM_GROUP) %>% mutate(mean_by_group = mean(Price))

Item_group has three groups “Dairy”,”Fruit” & “Vegetable”. mean of price for each group is calculated and populated as shown below

#### Row wise mean using mean() function along with dplyr

Row wise mean is calculated with the help rowwise() function of dplyr package and mean() function as shown below

## row wise mean using dplyr library(dplyr) my_basket %>% rowwise() %>% mutate( Mean_price = mean(c(Price,Tax)) )

row wise mean of “Price” and “Tax” is calculated and populated for each row as shown below