00.3-overview.Rmd

# The road ahead {.unnumbered}

Now that we know what R is and we have discussed some good alternatives, we will have a
look at what speaks for learning R. There are many good reasons to learn R! In the next
section of the book I will present ten of them, and we will also get hands-on with R. We
will skip over some details for the sake of showing you the whole spectrum of what R has
to offer and providing you with knowledge that is practically useful in as little time as
possible.

We will use modern R with *RStudio*, *RMarkdown* the *Tidyverse* --- more about that
later. This will help us to focus on data analysis quickly, without worrying to much about
some of the more technical aspects of programming.

## Overview {.unnumbered}

Here are the 10 things we will look at:

```{r, echo=FALSE, fig.alt="A hand-written list of 10 reasons to learn R"}
knitr::include_graphics("images/reasons.png")
```

#### 1. Free and open source {.unnumbered}

We will learn about free and open source software and why it is great.

#### 2. Easy to get started {.unnumbered}

We will install R and *RStudio*, setup an *RStudio project*, and start interacting with R
in the console and by writing our first script.

#### 3. A language made for data analysis {.unnumbered}

We will get to know the fundamental data types and data structures of R.

#### 4. Well-documented {.unnumbered}

We will learn how to get help in R.

#### 5. Expandable {.unnumbered}

We will learn how to add additional functions to R by installing packages and we will
learn how to load data into R.

#### 6. Powerful data wrangling functions {.unnumbered}

We will see how to transform data to prepare it for data analysis, join data.

#### 7. Powerful data visualization functions {.unnumbered}

We will make a complex statistical graph.

#### 8. Powerful statistical methods {.unnumbered}

We will run a statistical model.

#### 9. Encourages reproducible research {.unnumbered}

We will make our analysis reproducible.

#### 10. An awesome community {.unnumbered}

We will talk about where to find other R users and how to continue learning.

## Conventions {.unnumbered}

### Code formatting {.unnumbered}

In the text, names of R packages will look like this: `{tidyverse}`. R code will appear
inline like this `print("Hello R!")` or in a box, followed by its output like this:

```{r}
print("Hello R!")
```

### Callout Boxes {.unnumbered}

There are three different types of callout boxes:

::: {.goals}
At the beginning of each chapter, I will list the things we are going to do and learn. You
can use these to check your prior knowledge. Feel free to skip chapters that won't provide
any new insights for you.
:::

::: {.more}
This is an info box. It expands on a previous point with more details. It is not essential
to read these, but it could help you to see the connections between concepts.
:::

::: {.exercise}
This is an exercise. Do these to deepen your understanding. Can you figure out how to
reveal the solution in the box below?

::: {.answer}
```{r, eval=FALSE}
print("This is the solution")
```
:::
:::

### Data is singular {.unnumbered}

The word *"data"* will be used as a singular mass noun, as in *"The data looks
wonderful!"*. I apologize to those whose grammatical intuition this offends. I just cannot
help it!