ETC1010: Data Modelling and Computing

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# ETC1010: Data Modelling and Computing
## Week of Tidy Data: Lecture 2
### Dr. Nicholas Tierney & Professor Di Cook
### EBS, Monash U.
### 2019-08-09

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.vvvhuge.white.center.middle[
What is this song?
]

.huge.white.center.middle[
(you can use your phone!)
]

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# recap: from ED survey

.large.pull-left[
- Traffic Light System: .green[Green = "good!"] ; .red[Red = "Help!"]
- R + Rstudio
- Tower of babel analogy for writing R code
- We are using ___, not ___ for ETC1010?
- Functions are  ___
- columns in data frames are accessed with ___ ?
]

.large.pull-right[
- packages are installed with ___ ?
- packages are loaded with ___ ?
- Why do we care about Reproducibility?
- Output + input of rmarkdown
- I have an assignment group
- If I have an assignment group, have recorded my assignment group in the ED survey
]

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Source: Artwork by @allison_horst
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# Overview

.vhuge.pull-left[
- `filter()`
- `select()`
- `mutate()`
- `arrange()`

]

.vhuge.pull-right[
- `group_by()`
- `summarise()`
- `count()`
]

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Artwork by @allison_horst
]

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.huge.white[
R Packages
]

.huge[

```r
avail_pkg <- available.packages()
dim(avail_pkg)
```

```
## [1] 14738    17
```
]

.vhuge.white[
As of 2019-08-09 there are 14738 R packages available
]

---

# Name clashes

```r
library(tidyverse)
```

```
## ── Attaching packages ────────────────────────────────────────────── tidyverse 1.2.1 ──
```

```
## ✔ ggplot2 3.2.0     ✔ purrr   0.3.2
## ✔ tibble  2.1.3     ✔ dplyr   0.8.3
## ✔ tidyr   0.8.3     ✔ stringr 1.4.0
## ✔ readr   1.3.1     ✔ forcats 0.4.0
```

```
## ── Conflicts ───────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
```

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# Many R packages

.huge[
- A blessing & a curse! 
- So many packages available, it can make it hard to choose!
- Many of the packages are designed to solve a specific problem
- The tidyverse is designed to work with many other packages following a consistent philosophy
- What this means is that you shouldn't notice it!
]

???

Extra reading:

We have been loading the `tidyverse` package. Its actually a suite of packages, and you can learn more about the individual packages at https://www.tidyverse.org. You could load each individually.

Because so many people contribute packages to R, it is a blessing and a curse.

???

The best techniques are available, but there can be conflicts between function names. When you load tidyverse it prints a great summary of conflicts that it knows about, between its functions and others.

For example, there is a `filter` function in the `stats` package that comes with the R distribution. This can cause confusion when you want to use the filter function in `dplyr` (part of tidyverse). To be sure the function you use is the one you want to use, you can prefix it with the package name, `dplyr::filter()`.

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.vvhuge[
Let's talk about data
]

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???

This was an actual experiment in Food Sciences at Iowa State University. The goal was to find out if some cheaper oil options could be used to make hot chips: that people would not be able to distinguish the difference between chips fried in the new oils relative to those fried in the current market leader.

Twelve tasters were recruited to sample two chips from each batch, over a period of ten weeks. The same oil was kept for a period of 10 weeks! May be a bit gross by the end!

This data set was brought to R by Hadley Wickham, and was one of the problems that inspired the thinking about tidy data and the plyr tools.

---

# Example: french fries

.huge[
* Experiment in Food Sciences at Iowa State University. 
* Aim: find if cheaper oil could be used to make hot chips
* Question: Can people distinguish between chips fried in the new oils relative to those current market leader oil.
* 12 tasters recruited 
* Each sampled two chips from each batch
* Over a period of ten weeks.

Same oil kept for a period of 10 weeks! May be a bit gross!

]

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# Example: french-fries - gathering into long form

```r
french_fries <- read_csv("data/french_fries.csv")
french_fries
```

```
## # A tibble: 6 x 9
##    time treatment subject   rep potato buttery grassy rancid painty
##   <dbl>     <dbl>   <dbl> <dbl>  <dbl>   <dbl>  <dbl>  <dbl>  <dbl>
## 1     1         1       3     1    2.9     0      0      0      5.5
## 2     1         1       3     2   14       0      0      1.1    0  
## 3     1         1      10     1   11       6.4    0      0      0  
## 4     1         1      10     2    9.9     5.9    2.9    2.2    0  
## 5     1         1      15     1    1.2     0.1    0      1.1    5.1
## 6     1         1      15     2    8.8     3      3.6    1.5    2.3
```

.huge[
This data set was brought to R by Hadley Wickham, and was one of the problems that inspired the thinking about tidy data and the plyr tools. 
]

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# French fries - gathering into long form

.left-code[

```r
fries_long <- french_fries %>% 
  gather(key = type, 
         value = rating, 
         -time, 
         -treatment, 
         -subject, 
         -rep) 
```
]

.right-plot[

```r
fries_long
```

```
## # A tibble: 3,480 x 6
##     time treatment subject   rep type   rating
##    <dbl>     <dbl>   <dbl> <dbl> <fct>   <dbl>
##  1     1         1       3     1 potato    2.9
##  2     1         1       3     2 potato   14  
##  3     1         1      10     1 potato   11  
##  4     1         1      10     2 potato    9.9
##  5     1         1      15     1 potato    1.2
##  6     1         1      15     2 potato    8.8
##  7     1         1      16     1 potato    9  
##  8     1         1      16     2 potato    8.2
##  9     1         1      19     1 potato    7  
## 10     1         1      19     2 potato   13  
## # … with 3,470 more rows
```

]

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# `filter()`: choose observations from your data

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# `filter()`: example

```r
fries_long %>%
  filter(subject == 10)
```

```
## # A tibble: 300 x 6
##     time treatment subject   rep type   rating
##    <dbl>     <dbl>   <dbl> <dbl> <fct>   <dbl>
##  1     1         1      10     1 potato   11  
##  2     1         1      10     2 potato    9.9
##  3     1         2      10     1 potato    9.3
##  4     1         2      10     2 potato   11  
##  5     1         3      10     1 potato   11.3
##  6     1         3      10     2 potato   10.1
##  7     2         1      10     1 potato    8  
##  8     2         1      10     2 potato   10.2
##  9     2         2      10     1 potato   11.2
## 10     2         2      10     2 potato    8.2
## # … with 290 more rows
```

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# `filter()`: details

.huge[
Filtering requires comparison to find the subset of observations of interest.  What do you think the following mean?

- `subject != 10` 
- `x > 10` 
- `x >= 10` 
- `class %in% c("A", "B")` 
- `!is.na(y)`
]

```r
countdown(minutes = 3, play_sound = TRUE)
```