> For the complete documentation index, see [llms.txt](https://igb.mit.edu/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://igb.mit.edu/mini-courses/r/intro-to-r/simple-statistics-in-r.md).

# Simple Statistics in R

### Simple statistics <a href="#firstheading" id="firstheading"></a>

Let’s get a closer look at our data. Each column represents a sample in our experiment, and each sample has \~38K values corresponding to the expression of different transcripts. Suppose we wanted to compute the average value for a sample, or the minimum and maximum values? The R base package provides many built-in functions such as `mean`, `median`, `min`, `max`, and `range`. Try computing the mean for “sample1”

```r
baseDir<-getwd()
dataDir<-file.path(baseDir,"data")
metadata <- read.table(file.path(dataDir, 'mouse_exp_design.csv'), header=T, sep=",", row.names=1)
rpkm_data <- read.table(file.path(dataDir, 'counts.rpkm'), header=T, sep=",", row.names=1)
m <- match(row.names(metadata), colnames(rpkm_data))
data_ordered  <- rpkm_data[,m]
```

```r
mean(data_ordered[,'sample1'])
max(data_ordered[,'sample1'])
min(data_ordered[,'sample1'])
```

#### Missing values

By default, all R functions operating on vectors that contains missing data will return NA. It’s a way to make sure that users know they have missing data, and make a conscious decision on how to deal with it. When dealing with simple statistics like the mean, the easiest way to ignore NA (the missing data) is to use `na.rm=TRUE` (`rm` stands for remove). In some cases, it might be useful to remove the missing data from the vector. For this purpose, R comes with the function `na.omit` to generate a vector that has NA’s removed. For some applications, it’s useful to keep all observations, for others, it might be best to remove all observations that contain missing data. The function `complete.cases()` returns a logical vector indicating which rows have no missing values.

#### The apply Function

To obtain mean values for all samples we can use mean on each column individually, but there is also an easier way to go about it. The `apply` family of functions keep you from having to write loops (R is bad at looping) to perform some sort of operation on every row or column of a data matrix or a data frame. The family includes several functions, each differing slightly on the inputs or outputs.

```r
base::apply             Apply Functions Over Array Margins
base::by                Apply a Function to a Data Frame Split by Factors
base::eapply            Apply a Function Over Values in an Environment
base::lapply            Apply a Function over a List or Vector
base::mapply            Apply a Function to Multiple List or Vector Arguments
base::rapply            Recursively Apply a Function to a List
base::tapply            Apply a Function Over a Ragged Array
```

We will be using apply in our examples today, but do take a moment on your own to explore the many options that are available. The apply function returns a vector or array or list of values obtained by applying a function to margins of an array or matrix. We know about vectors/arrays and functions, but what are these “margins”? Margins are referring to either the rows (denoted by 1), the columns (denoted by 2) or both (1:2). By “both”, we mean apply the function to each individual value. Let’s try this with the `mean` function on our data:

```r
samplemeans <- apply(data_ordered, 2, mean) 
```