My (long) journey with R

# My (long) journey with R
## A lightning talk for the R-Ladies Sydney Group
### Sarah Romanes
### 20 June 2018

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# Keep in touch

<a href="mailto:sarah.romanes@hotmail.com">&nbsp; sarah.romanes@sydney.edu.au</a> 
<a href="http://twitter.com/sarah_romanes">&nbsp; @sarah_romanes</a> 
<a href="http://github.com/sarahromanes">&nbsp; @sarahromanes</a> 
<a href="http://www.maths.usyd.edu.au">&nbsp; School of Mathematics and Statistics @ The University of Sydney</a>
---

# About Me

* 3rd year PhD student at the University of Sydney.
* Lectures and tutors first year Statistics (also involves teaching R!)
* My research revolves around developing statistical machine learning methods for high dimensional data.

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# Throwback - 2012

My journey with **R** began back in 2012 (nearly 6 years ago!) in first year stats. **R** was introduced as a glorified calculator.

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# A typical first year question

$$\text{Suppose } X \sim \mathcal{N}(0,1). \text{What is } P(X \leq 0.8)? $$

![](index_files/figure-html/cars-1.svg)

Answer:

```r
pnorm(0.8)
```

```
## [1] 0.7881446
```

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# Along came sweave in second year... (2013)

---
# ... with its horrible syntax

```r
\documentclass{article}

\begin{document}
\SweaveOpts{concordance=TRUE}

\begin{center}
{\large \textbf{ STAT2011 Statistical Models} }\\
{\footnotesize\texttt{sydney.edu.au/science/maths/stat2011}}
\end{center}
\vspace{-1mm}
\begin{tabular*}{1.0\linewidth}{@{\extracolsep{\fill}}lr@{}}
  \hline\noalign{\smallskip}
Semester 1, 2013 & Username: \texttt{\Sexpr{Sys.getenv("USER")}} \\ %Michael Stewart\\
\hline
\end{tabular*}
\begin{center}
 \textbf{Computer Exercise Week 3}\\Due by the end of your week 4 session\\
 \textbf{Last compiled}: \today
\end{center}
```

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# What I was formally taught during my undergraduate degree in R (2012 - 2015)

Statistical modelling techniques

... and through that, baseR (by experience).

Basic report making via Sweave implementation

***

**Essentially - a strong focus on Statistics, and less focus on Data Science! R was a means to the end.**

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# What I was NOT formally taught during my undergraduate degree

tidyverse, eg. `ggplot2`, `dplyr`, `purrr` packages

rmarkdown/ xaringan slides/ blogdown

advanced R topics such as functions, object orientated programming, good programming practice

package development

git/ github/ version control

***

**I have had to learn (and not yet master) a lot of these things on my own accord during my PhD.**

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# So, why am I telling you all of this?

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## 1) Everyone still has something to learn in R, even if we have been using it for a long time

Especially those of us who learnt R 'formally'! For example, a lot of us are learning the `tidyverse` along side you! If you ask us for how to do things, we might give you a long baseR solution...

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## 2) Learning these things takes time and patience, and life does get in the way!

</center>
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# My tips on learning R

* My best improvements in R came from having clear motivations for WHY I was learning new skills
  + I learned `ggplot2` to make better data vis for presentations/publications/ packages

* Learn from examples! Eg, this presentation was based off Alison Hill's R Ladies talk about blogdown! Work smart, not hard.

* Having a mentor is extremely useful. I found my coding to improve drastically after my honours year as I had great guidance from an expert (my supervisor).

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# Thanks!

To R Ladies Sydney for allowing me to give this talk today!

To Alison Hill [**@apreshill**](https://twitter.com/apreshill) for the R ladies template I base this presentation off today!

Slides created via the R package [**xaringan**](https://github.com/yihui/xaringan).