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Correct format for 01-supp-data-structures lesson #101

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May 14, 2015
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Correct format for 01-supp-data-structures lesson #101

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Learning objectives
dhaine May 13, 2015
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61 changes: 29 additions & 32 deletions 01-supp-data-structures.Rmd
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Expand Up @@ -5,18 +5,15 @@ subtitle: Data types and structures
minutes: 45
---

> ## Objectives {.objectives}
> Expose learners to the different data types in R
> ## Learning Objectives {.objectives}
>
> Learn how to create vectors of different types
>
> Be able to check the type of vector
>
> Learn about missing data and other special values
>
> Getting familiar with the different data structures (lists, matrices, data frames)
> * Expose learners to the different data types in R
> * Learn how to create vectors of different types
> * Be able to check the type of vector
> * Learn about missing data and other special values
> * Getting familiar with the different data structures (lists, matrices, data frames)

### Understanding basic data types in R
### Understanding Basic Data Types in R

To make the best of the R language, you'll need a strong understanding of the
basic data types and data structures and how to operate on those.
Expand Down Expand Up @@ -86,8 +83,7 @@ workhorse of R. Technically, vectors can be one of two types:

although the term "vector" most commonly refers to the atomic types not to lists.


#### The different vector modes
### The Different Vector Modes

A vector is a collection of elements that are most commonly of mode `character`,
`logical`, `integer` or `numeric`.
Expand Down Expand Up @@ -132,7 +128,7 @@ While using quoted text will create a vector of mode `character`:
z <- c("Sarah", "Tracy", "Jon")
```

#### Examining vectors
### Examining Vectors

The functions `typeof()`, `length()`, `class()` and `str()` provide useful
information about your vectors and R objects in general.
Expand All @@ -148,7 +144,7 @@ str(z)
>
> Do you see a property that's common to all these vectors above?

#### Adding elements
### Adding Elements

The function `c()` (for combine) can also be used to add elements to a vector.

Expand All @@ -159,7 +155,7 @@ z <- c("Greg", z)
z
```

#### Vectors from a sequence of numbers
### Vectors from a Sequence of Numbers

You can create vectors as a sequence of numbers.

Expand All @@ -169,7 +165,7 @@ seq(10)
seq(from = 1, to = 10, by = 0.1)
```

#### Missing data
### Missing Data

R supports missing data in vectors. They are represented as `NA` (Not Available)
and can be used for all the vector types covered in this lesson:
Expand All @@ -194,8 +190,7 @@ anyNA(x)
anyNA(y)
```

#### Other special values

### Other Special Values

`Inf` is infinity. You can have either positive or negative infinity.

Expand All @@ -209,7 +204,7 @@ anyNA(y)
0/0
```

#### What happens when you mix types inside a vector?
### What Happens When You Mix Types Inside a Vector?

R will create a resulting vector with a mode that can most easily accommodate
all the elements it contains. This conversion between modes of storage is called
Expand All @@ -231,7 +226,7 @@ as.numeric("1")
as.character(1:2)
```

#### Object attributes
### Objects Attributes

Objects can have __attributes__. Attributes are part of the object. These include:

Expand Down Expand Up @@ -338,7 +333,7 @@ Elements are indexed by double brackets. Single brackets will still return
a(nother) list.


### Data frame
### Data Frame

A data frame is a very important data type in R. It's pretty much the *de facto*
data structure for most tabular data and what we use for statistics.
Expand All @@ -358,23 +353,25 @@ Some additional information on data frames:
* Find the number of rows and columns with `nrow(dat)` and `ncol(dat)`, respectively.
* Rownames are usually 1, 2, ..., n.

#### Creating data frames by hand
### Creating Data Frames by Hand

To create data frames by hand:

```{r}
dat <- data.frame(id = letters[1:10], x = 1:10, y = 11:20)
dat
```

#### Useful data frame functions

* `head()` - shown first 6 rows
* `tail()` - show last 6 rows
* `dim()` - returns the dimensions
* `nrow()` - number of rows
* `ncol()` - number of columns
* `str()` - structure of each column
* `names()` - shows the `names` attribute for a data frame, which gives the
column names.
> ## Useful data frame functions {.callout}
>
> * `head()` - shown first 6 rows
> * `tail()` - show last 6 rows
> * `dim()` - returns the dimensions
> * `nrow()` - number of rows
> * `ncol()` - number of columns
> * `str()` - structure of each column
> * `names()` - shows the `names` attribute for a data frame, which gives the
>column names.

See that it is actually a special list:

Expand Down