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presentation.Rpres
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presentation.Rpres
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Overview of software learning tools
========================================================
author: Mark Hanly
date: Friday 15 February 2019
autosize: true
Let's face it. Software is scary.
========================================================
```{r, echo=FALSE, fig.width=8, fig.height=12, out.extra='style="float:left; position:fixed"'}
knitr::include_graphics("images/error-5.png")
```
```{r, echo=FALSE, fig.width=10, fig.height=10, out.extra='style="float:left; position:absolute; top:100px; left: 1200px"'}
knitr::include_graphics("images/error-2.jpg")
```
```{r, echo=FALSE, fig.width=8, fig.height=12, out.extra='style="float:left; position:absolute; top:200px; left: 400px"'}
knitr::include_graphics("images/error-3.jpg")
```
```{r, echo=FALSE, fig.width=10, fig.height=10, out.extra='style="float:left; position:absolute; top:500px; left: 1300px"'}
knitr::include_graphics("images/error-4.jpg")
```
```{r, echo=FALSE, fig.width=8, fig.height=10, out.extra='style="float:left; position:absolute; top:500px; left: -100px"'}
knitr::include_graphics("images/error-1.jpg")
```
Thinking about the end point
========================================================
- Assignment
- Thesis
- Research report
- Presentation
- Academic paper
- Webpage/interactive tool
[Here's an example](https://www.mja.com.au/system/files/issues/202_11/sch00103.pdf)
Two software tasks
========================================================
## Statistical Analysis
- Excel/Numbers
- SPSS
- **SAS**
- Stata
- **R**
- **Python**
***
## Communicating Results
- Word/Pages
- **Jupyter Notebooks**
- **RMarkdown/knitr**
Two approaches to producing an output
========================================================
## The **Office** model
- Analyse data in Excel
- Create a graph
- Cut and paste the graph into Word
- Write your report in Word
***
## The **Plain Text** model
-
Package wars
========================================================
Python
========================================================
```{r, echo=FALSE}
knitr::include_graphics("images/python.png")
```
- HDAT 9300 Principles of Programming
- HDAT 9500 Machine Learning and Data Mining
***
## Pros
- General purpose programming language
- Open source and free
- Widely used in many specialised areas
- Machine learning
- Deep learning
- Natural lanaguage processing
- Many online resources
## Cons
- Immature
- Compatibility issues
SAS
========================================================
- HDAT 9400 Management and Curation of Health Data
***
## Pros
- Handles large datasets well
- Data management is easy and transparent
- Well documented
- Widely used in many industries
- Government
- Pharmaceuticals
## Cons
- Not open source
- Expensive
- Slow to integrate novel methods
- Clunky
- Sub-par graphics
R
========================================================
- HDAT 9200 Statistical Foundations of HDS
- HDAT 9600 Statistical Modelling I
- HDAT 9700 Statistical Modelling II
- HDAT 9800 Visualisation and Communication of Health Data
***
## Pros
- Open source
- Free
- Easy to implement new methods
- Cutting edge
- Huge amount of plug-in libraries for a variety of goals
- Excellent graphical capabilities
## Cons
- Poorly documented
Communicating Results
========================================================
- Rmarkdown/knitr
- jupyter Notebooks
- Shiny interactive web applications
Communicating Results
========================================================
- Rmarkdown/knitr
- jupyter Notebooks
- Shiny interactive web applications
Further reading
========================================================