# Mean function in R: Mean()

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
• Mean of multiple columns of a 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:

mean(x, na.rm = FALSE, …)
• 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)
```
 0.4105

#### 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)
```
 0.4105

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

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

```### create the dataframe
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))
```

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

```

so the resultant mean of “Price” column will be

output:

 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

```

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
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)

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)

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 For further understanding of mean() function in R using dplyr one can refer the dplyr documentation