Since this is a standard study, there is a base R function which you can use to calculate the mean in R. It is, quite appropriately, titled “mean”.

```
# find mean in r - example
> test <- c(41,34,39,34,34,32,37,32,43,43,24,32)
> mean(test)
[1] 35.41667
```

A common annoyance is missing values in your data. Fortunately, this is fairly easy to address with the na.rm option, as shown below.

```
# find mean in r - example
> test <- c(41,34,39,34,34,32,37,32,43,43,24,32, NA,NA)
# mean in R - calculation fails due to missing values
> find mean(test)
[1] NA
# find mean in R - success with na.rm=True option
> mean(test, na.rm=TRUE)
[1] 35.41667
```

You can also obtain the mean of a sample as part of the “summary” descriptive statistics function. This is a handy tool for checking out a new set of information.

```
# find mean in R
> test <- c(41,34,39,34,34,32,37,32,43,43,24,32, NA,NA)
> summary(test)
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
24.00 32.00 34.00 35.42 39.50 43.00 2
```

Need to run mean for multiple columns? You can either run summary (see below) or use sapply to map it across the columns of the dataframe. Both approaches are demonstrated below.

```
# find mean in R - multiple columns
> head(ChickWeight)
weight Time Chick Diet
1 42 0 1 1
2 51 2 1 1
3 59 4 1 1
4 64 6 1 1
5 76 8 1 1
6 93 10 1 1
# find mean in R - calculated using summary
> summary(ChickWeight)
weight Time Chick Diet
Min. : 35.0 Min. : 0.00 13 : 12 1:220
1st Qu.: 63.0 1st Qu.: 4.00 9 : 12 2:120
Median :103.0 Median :10.00 20 : 12 3:120
Mean :121.8 Mean :10.72 10 : 12 4:118
3rd Qu.:163.8 3rd Qu.:16.00 17 : 12
Max. :373.0 Max. :21.00 19 : 12
(Other):506
# find mean in R - calculated using sapply; limit to first two columns
> sapply(ChickWeight[,1:2], mean)
weight Time
121.81834 10.71799
```

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