preface.qmd

---
title: "Preface"
---

```{r, include=FALSE, message=FALSE}
library(ggplot2, quietly = T)
theme_set(theme_classic())
```

# R in applied demography

## Why R?

I've used R for twenty years. I was also trained in SPSS and SAS along
the way, by various mentors. Some tried to get me to learn more general
purpose languages like Delphi (of all things) or Perl, or Basic, and
I've been chastised for not knowing the depths of Python, but R presents
a nimble and rigorous platform to *do* demography. My top three reasons
for teaching and using R are:

1.  It's free - This is important, because, why should we pass along
    more costs to people, especially our students? This also make R code
    accessible to people, worldwide.

2.  It's the hotbed of methodological development. The R ecosystem has
    thousands of packages that represent the bleeding edge of data
    analysis, visualization and data science. This makes R attractive
    because it can pivot quickly to adopt new methods, which often lag
    in their development in other environments.

3.  It has a supportive community of users. While there are some debates
    over how friendly some R users are to new users, overall, after
    spending 20 years in the R community, I've personally assisted
    hundreds of users, and been personally helped by many others. The
    open source nature of R lends itself to sharing of ideas and
    collaboration between users.

### My assumptions in this book

In statistics we always make assumptions, often these are wrong, but we
adapt to our mistakes daily. My assumptions about who is reading this
book are:

1.  You are interested in learning more about R.

2.  You are likely a student or professional interested in demography or
    population research.

3.  You have likely been exposed to other statistical platforms and are
    curious about R, in conjunction with 1 and 2 above.

4.  You may be an avid R user from another strange and exotic
    discipline, but are interested in how demographers do research.

5.  You want to see *how* to do things instead of being bombarded with
    theoretical and often unnecessary gate-keeping mathematical
    treatments of statistical models.

I think if any of these assumptions are true, you're in the right place.
That being said, this book *is not* a review of all of statistics, nor
is it an encyclopedic coverage of the R language and ecosystem. I image
the latter being on the same scale of hopelessness as the search for the
Holy Grail or the fountain of youth. People have died for such fool
hearty quests, I'm not falling on my sword here folks.