```{r setup, include=FALSE}
library(methods)
knitr::opts_chunk$set(echo = TRUE, comment = "")
```

# R Basics 

## Data Classes

- Numeric - numbers (e.g. 1, 3.673)
- Character - strings or words (`"hey"`, `"I'm a string"`) in either single or double quotes
- Logicals - `TRUE` or `FALSE` - all capital letters and are **not** in quotes. 



## Data Types

- `vector` - 1-dimensional object of one class (all numeric or all character)
- `matrix` - 2-dimensional object of one class
- `data.frame` - 2-dimensional object, can be multiple classes (like Excel spreadsheet)
- `array` - object of > 2 dimensions of one class.  The data in a `nifti` object is one of these (usually 3-D)


## Initializing: vectors 

- Use `c()` to create a vector of numeric values: 
```{r vec}
v = c(1, 4, 3, 7, 8)
print(v)
```
- Shortcut for sequential numeric vector:
```{r vec2}
w = 1:5
print(w)
```

The gray boxes denote code, and the lines after denote the output.  

## Assignment

In `R`, you can assign using the equals `=` or arrow `<-` (aka assigment operator).

The above commands are equivalent to:

```{r, eval = FALSE}
w = 1:5
w <- 1:5
```

There are no differences in these 2 commands, but just preference (we use `=`).

Variable/object names:

- must start with a letter
- cannot contain spaces, `$`, quotes, or other special characters 
    - generally just alpha-numeric
- **can** contain periods (`.`) and underscores `_`


## Help

- To see the documentation for a function, use the `?` symbol before the name of the function.  This is a shortcut for the `help` command:
- For example, to see documentation for `c`:

```{r help, eval = FALSE}
?c
help(topic = "c")
```
- To search for help files, use a double `??` or `help.search`:

```{r help2, eval = FALSE}
??c
help.search(pattern = "c")
```

## Some Details

- R is case sensitive (e.g. `y` and `Y` are different)
- Commands separated by new line or by a colon (;)
- Use `#` to comment

You can also be explicit about which package you are using with the `::` operator, where the syntax is `package::function()`:

```{r colon_twice, eval=FALSE}
utils::help("c")
```

## Initializing: matrices and arrays

- Create a 3 x 4 numeric matrix and assign to variable `m`
    - note items are added column-wise 
```{r mat}
m = matrix(1:12, nrow = 3)
print(m)
```
- Create a 3 x 4 x 3 numeric array and assign to variable `a`
```{r arr}
a = array(1:36, dim = c(3, 4, 3))
```
- the `dim()` function returns the dimensions of the array
```{r dim}
dim(a)
```

## Subsetting: vectors

- Subsetting a vector (first index is `1`, not zero):
```{r vecSub}
print(v)
print(v[4])
print(v[1:3])
print(v[c(1,3,5)])
```

## Subsetting: matrices

- Subsetting a matrix - `[row,column]` format, 
```{r matSub}
print(m[1,3])
print(m[1:2,3:4])
```
- if `row` or `column` missing then all values printed:
```{r matsub2}
print(m[,4])
print(m[2,])
```

## Subsetting: arrays

- Subsetting - `[x,y,z]` format:
```{r arrSub}
print(a[1,1,1])
dim(a[,4,])
```

This will return an error - need to specify all dims:
```{r arrSub_bad, eval = FALSE}
a[,4]
```

## Operators in R: return numeric

- Arithmetic: `+`, `-`, `*`, `/`, `^` - exponents
- Standard math functions: `log`, `abs`, `sqrt`

```{r add_vw}
print(v); print(w)
print(v + 4)
print(v + w)
print(sqrt(w^2))
```

## Operators in R: return logical

- Comparison: `>`, `>=`, `<`, `<=`, `==` (equals), `!=` (not equal)
- Logical: `!` - not, `&` - and, `|` - or (a "pipe")
- `all()`: function to test all values `TRUE` and `any()`: (are any)

```{r true}
print(!FALSE)
print(TRUE | FALSE)
print(FALSE & FALSE)
c(all(c(TRUE, FALSE)), any(c(TRUE, FALSE)))
```


## Subsetting with logicals 

The `which` command takes a logical and gets the indices of `TRUE`:
```{r}
which(v > 5)
v[ which(v > 5) ]
```

Or directly pass in a vector of logicals to subset:
```{r}
v[ v > 5 ]
```

This method will be useful later when we are working with images.


## Website

<a href="../index.html" target="_blank">http://johnmuschelli.com/imaging_in_r</a>