Advent of “Grammar”

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<i class="fas fa-exclamation-circle"></i> These slides are viewed best by Chrome and occasionally need to be refreshed if elements did not load properly. See <a href=edibble-slides.pdf>here for PDF <i class="fas fa-file-pdf"></i></a>. 
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---

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<br>

# .monash-blue.outline-text[Advent of "Grammar"]

<br>

<h2 style="font-weight:900!important;">Bridging Statistics and Data Science for the Design of Experiments</h2>

.bottom_abs.width100[

**Emi Tanaka**

Department of Econometrics and Business Statistics

<span><i class="fas  fa-envelope faa-float animated "></i></span>  emi.tanaka@monash.edu
<span><i class="fab  fa-twitter faa-float animated "></i></span>  @statsgen

27th Nov 2020 @ Statistical Society of Australia

<br>
]

]]

---

<div class="bg-white" style="width:900px;margin:auto;color:black;border:2px solid black;border-radius: 5px;padding:3px;background:">
<div class="center" style="color:white;border-radius: 5px;background:#006DAE;font-size:35px;font-weight:900;">NOTICE</div>
<div style="padding:10px;">
<ul>
<li> <b>This talk is most relevant for people who code</b> <i class="fas fa-code"></i></li>
<li><b>Examples are all in R but philosophy holds for other programming languages</b></li>
</ul>
</div>
</div>
<div>

<br>

</div>

---

# .circle-big[1]

# Grammar of Graphics

<i class="fab fa-r-project blue"></i> `ggplot2` 📦

.footnote.monash-bg-blue[
Wickham (2016) ggplot2: Elegant Graphics for Data Analysis. *Springer-Verlag New York*

Wilkinson (2005) The Grammar of Graphics. *Statistics and Computing. Springer, 2nd edition.*
]

---

## A typical Australian academic is expected to do 40% teaching, 40% research and 20% administration

.font_small[and like 200% external service work, outreach, writing grants, reviewing things, sitting on panels, learning how to do online teaching,...]

---

# What are the differences between these plots?

]
.item[

![](images/unnamed-chunk-2-1.png)

]
]

<div style="position:absolute; bottom:10%;left:30%;">
How do you construct these plots?
</div>

---

# Using Base R

```r
df
```

```
##       duty perc
## 1 Teaching   40
## 2 Research   40
## 3    Admin   20
```
]
.item.border-right[

```r
barplot(as.matrix(df$perc),
        legend = df$duty)
```

![](images/barplot-1.png)

]
.item[

```r
pie(df$perc, labels = df$duty)
```

![](images/pie-1.png)

]
]

.footnote[
R Core Team (2020) R: A Language and Environment for Statistical Computing https://www.R-project.org/
]

<div class="show-on-hover" style="padding:10px;position:absolute;bottom: 25px; right: 10px;">
<a href="#edibble" style="font-size:12pt"><i class="fas fa-caret-right"></i> edibble</a>
</div>

<div class="corner-box" style="bottom:50px;">
<ul>
<li><b>Single purpose functions</b> to generate "named plots"</li>
<li><b>Input</b> varies, here it is vector or matrix</li>
</ul>
</div>

---

# Using `ggplot2`

```r
df
```

```
##       duty perc
## 1 Teaching   40
## 2 Research   40
## 3    Admin   20
```
]
.item.border-right[

```r
ggplot(df, aes(x = "", # dummy
               y = perc, 
               fill = duty)) + 
  geom_col()
```

![](images/ggbarplot-1.png)

]
.item[

]
]

.footnote[
Wilkinson (2005) The Grammar of Graphics. *Statistics and Computing. Springer, 2nd edition.*

Wickham (2008) Practical Tools for Exploring Data and Models. *PhD Thesis Chapter 3: A layered grammar of graphics*.

Wickham (2010) A Layered Grammar of Graphics, *Journal of Computational and Graphical Statistics, 19:1, 3-28*

]

```r
ggplot(df, aes(x = "", # dummy
               y = perc, 
               fill = duty)) + 
  geom_col() + 
* coord_polar(theta = "y")
```

![](images/ggpie-1.png)

.corner-box[
* `ggplot2` implements the **grammar of graphics**
* the difference between a **stacked barplot** and a **pie chart** is that the coordinate system have been transformed from **Cartesian coordinate** to **polar coordinate**
]

---

# Data <i class="fas fa-exchange-alt"></i> Plot

* `ggplot` uses **tidy data** as input so plot construction is enforced by consistent thinking in relation to tidy data
<details style="font-size:15pt;">
<summary>Tidy data principles</summary>
<ol>
<li>Each variable must have its own column.</li>
<li>Each observation must have its own row.</li>
<li>Each value must have its own cell.</li>
</ol>
</details>
* Variables are mapped to a plot aesthetic
* Plots are constructed from its components expressed by the **grammar of graphics**

]
.item[

]
]

]

---

## Do you like potatoes?

<br>

## .gray[Let's have a look at a potato field experiment to understand the] infection rate .gray[when it's treated with] sulfur at different season and amount.gray[?]

---

# <i class="fas fa-puzzle-piece"></i> How do we get these graphs?

```r
data(cochran.crd,
     package = "agridat")

cochran.crd
```

```
##    inf trt row col
## 1    9  F3   4   1
## 2   12   O   4   2
## 3   18  S6   4   3
## 4   10 F12   4   4
## 5   24  S6   4   5
## 6   17 S12   4   6
## 7   30  S3   4   7
## 8   16  F6   4   8
## 9   10   O   3   1
## 10   7  S3   3   2
## 11   4 F12   3   3
## 12  10  F6   3   4
## 13  21  S3   3   5
## 14  24   O   3   6
## 15  29   O   3   7
## 16  12  S6   3   8
## 17   9  F3   2   1
## 18   7 S12   2   2
## 19  18  F6   2   3
## 20  30   O   2   4
## 21  18  F6   2   5
## 22  16 S12   2   6
## 23  16  F3   2   7
## 24   4 F12   2   8
## 25   9  S3   1   1
## 26  18   O   1   2
## 27  17 S12   1   3
## 28  19  S6   1   4
## 29  32   O   1   5
## 30   5 F12   1   6
## 31  26   O   1   7
## 32   4  F3   1   8
```

]
.item[

![](images/field1-1.png)
![](images/field2-1.png)

]
.item[

]
]

.footnote[
Kevin Wright (2020). agridat: Agricultural Datasets. R
  package version 1.17.
  
W.G. Cochran and G. Cox, 1957. Experimental Designs, 2nd ed. John Wiley, New York.
]

```r
ggplot(cochran.crd, 
       aes(x = col, 
           y = row, 
*          fill = inf)) +
  geom_tile(color = "black", size = 1.2) + 
* scale_fill_gradient(low = "gold",
*                     high = "firebrick4") +
  theme(text = element_text(size = 20))
```

```r
ggplot(cochran.crd, 
       aes(x = col, 
           y = row, 
*          fill = trt)) +
  geom_tile(color = "black", size = 1.2) +
* scale_fill_viridis_d() +
  theme(text = element_text(size = 20))
```

---

# <i class="fas fa-chalkboard-teacher"></i> Lessons from the grammar of graphics<br> in `ggplot2`

<br>
<div class="info-box" style="margin-right:100px;margin-left:100px;text-align:left;">
<ul>
<li> <b>Modifiable</b>: <code>ggplot</code> object can be modified</li>
{{content}}
</ul>
</div>

<li> <b>Generalisable</b>: Commands may feel verbose (compared to single-graphics functions) but you're less limited in the plots you can construct without an exhaustive list of functions (compared to the number of plots you can create)</li>
{{content}}
--

<li> <b>Extensible</b>: the system can be extended to make specialised plots or add more features if the same "grammar" is adopted</li>

---

# .circle-big[2]

# Grammar of <br>Data Manipulation

<i class="fab fa-r-project blue"></i> `dplyr` 📦

.footnote.monash-bg-blue[
Wickham, François, Henry & Müller (2020) dplyr: A Grammar of Data Manipulation. R-package version 1.0.0.
]

---

# What does the code below do?

```r
# part 1
tapply(cochran.crd$inf, cochran.crd$trt, mean)
```

```
#    F12     F3     F6      O    S12     S3     S6 
#  5.750  9.500 15.500 22.625 14.250 16.750 18.250
```

```r
df <- cochran.crd

# part 2
df$season <- ifelse(grepl("F", df$trt), 
                    "Fall", 
                    "Spring")
df$season[df$trt=="O"] <- "N/A"

# part 3
df$sulfur <- sapply(df$trt, 
    function(x) switch(as.character(x), 
          F3 = 300, F6 = 600, F12 = 1200,
          S3 = 300, S6 = 600, S12 = 1200,
          O = 0)) # pounds/acre
```
]
.item[

```r
# part 4, move trt after col
df[, c("inf", "row", "col", "trt", 
       "season", "sulfur")]
```

```
#    inf row col trt season sulfur
# 1    9   4   1  F3   Fall    300
# 2   12   4   2   O    N/A      0
# 3   18   4   3  S6 Spring    600
# 4   10   4   4 F12   Fall   1200
# 5   24   4   5  S6 Spring    600
# 6   17   4   6 S12 Spring   1200
# 7   30   4   7  S3 Spring    300
# 8   16   4   8  F6   Fall    600
# 9   10   3   1   O    N/A      0
# 10   7   3   2  S3 Spring    300
# 11   4   3   3 F12   Fall   1200
# 12  10   3   4  F6   Fall    600
# 13  21   3   5  S3 Spring    300
# 14  24   3   6   O    N/A      0
# 15  29   3   7   O    N/A      0
# 16  12   3   8  S6 Spring    600
# 17   9   2   1  F3   Fall    300
# 18   7   2   2 S12 Spring   1200
# 19  18   2   3  F6   Fall    600
# 20  30   2   4   O    N/A      0
# 21  18   2   5  F6   Fall    600
# 22  16   2   6 S12 Spring   1200
# 23  16   2   7  F3   Fall    300
# 24   4   2   8 F12   Fall   1200
# 25   9   1   1  S3 Spring    300
# 26  18   1   2   O    N/A      0
# 27  17   1   3 S12 Spring   1200
# 28  19   1   4  S6 Spring    600
# 29  32   1   5   O    N/A      0
# 30   5   1   6 F12   Fall   1200
# 31  26   1   7   O    N/A      0
# 32   4   1   8  F3   Fall    300
```

]

---

# Which is easier to read for you? .font_small[Part 1]

```r
# part 1
tapply(cochran.crd$inf, cochran.crd$trt, mean)
```

```
#    F12     F3     F6      O    S12     S3     S6 
#  5.750  9.500 15.500 22.625 14.250 16.750 18.250
```

]
.item[

```r
# part 1
cochran.crd %>% 
  group_by(trt) %>% 
  summarise(inf_mean = mean(inf))
```

```
# # A tibble: 7 x 2
#   trt   inf_mean
#   <fct>    <dbl>
# 1 F12       5.75
# 2 F3        9.5 
# 3 F6       15.5 
# 4 O        22.6 
# 5 S12      14.2 
# 6 S3       16.8 
# 7 S6       18.2
```
]

]

--
* Can you guess what `tapply` does if you don't know about it?
{{content}}
--
* <i class="fas fa-info-circle animated flash"></i> Functions are less accessible to broader audiences if function names and argument names are designed so:
  * specialist knowledge is required,
  * approach is ad-hoc, and/or
  * unintuitively designed for either input and/or output

---

# Which is easier to read for you? .font_small[Part 2]

```r
df <- cochran.crd

# part 2
df$season <- ifelse(grepl("F", df$trt), 
                    "Fall", 
                    "Spring")
df$season[df$trt=="O"] <- "N/A"

# part 3
df$sulfur <- sapply(df$trt, 
    function(x) switch(as.character(x), 
          F3 = 300, F6 = 600, F12 = 1200,
          S3 = 300, S6 = 600, S12 = 1200,
          O = 0)) # pounds/acre

# part 4, move trt after col
df[, c("inf", "row", "col", "trt", 
       "season", "sulfur")]
```

]
.item[

```r
cochran.crd %>% 
  # part 2 & 3
  mutate(season = case_when(
                    str_detect(trt, "F") ~ "Fall",
                    str_detect(trt, "S") ~ "Spring",
                                    TRUE ~ "N/A"),
         sulfur = case_when(
                    str_detect(trt, "12") ~ 1200,
                    str_detect(trt, "6") ~ 600,
                    str_detect(trt, "3") ~ 300,
                                     TRUE ~ 0)) %>% 
  # part 4
  relocate(trt, .after = col) %>% 
  as_tibble() # optional
```

```
# # A tibble: 32 x 6
#      inf   row   col trt   season sulfur
#    <int> <int> <int> <fct> <chr>   <dbl>
#  1     9     4     1 F3    Fall      300
#  2    12     4     2 O     N/A         0
#  3    18     4     3 S6    Spring    600
#  4    10     4     4 F12   Fall     1200
#  5    24     4     5 S6    Spring    600
#  6    17     4     6 S12   Spring   1200
#  7    30     4     7 S3    Spring    300
#  8    16     4     8 F6    Fall      600
#  9    10     3     1 O     N/A         0
# 10     7     3     2 S3    Spring    300
# # … with 22 more rows
```
]
]

---

# Which is easier to read for you? .font_small[Part 3]

.center[
🎯 find the mean of `inf` by the combination of variables `season` and `sulfur`<br>
**How would you modify the code below?**
]

```r
tapply(df$inf, df$trt, mean)
```

```
##    F12     F3     F6      O    S12     S3     S6 
##  5.750  9.500 15.500 22.625 14.250 16.750 18.250
```

]
.item[

```r
df %>% 
  group_by(trt) %>% 
  summarise(inf_mean = mean(inf))
```

```
## # A tibble: 7 x 2
##   trt   inf_mean
##   <fct>    <dbl>
## 1 F12       5.75
## 2 F3        9.5 
## 3 F6       15.5 
## 4 O        22.6 
## 5 S12      14.2 
## 6 S3       16.8 
## 7 S6       18.2
```
]

]

---

# Which is easier to read for you? .font_small[Part 3]

.center[
🎯 find the mean of `inf` by the combination of variables `season` and `sulfur`<br>
**How would you modify the code below?**
]

```r
tapply(df$inf, list(df$season, df$sulfur), mean)
```

```
##             0   300   600  1200
## Fall       NA  9.50 15.50  5.75
## N/A    22.625    NA    NA    NA
## Spring     NA 16.75 18.25 14.25
```
Notice that the above output is `matrix`.
{{content}}

]
.item[

```r
df %>% 
  group_by(season, sulfur) %>% 
  summarise(inf_mean = mean(inf))
```

```
## # A tibble: 7 x 3
## # Groups:   season [3]
##   season sulfur inf_mean
##   <chr>   <dbl>    <dbl>
## 1 Fall      300     9.5 
## 2 Fall      600    15.5 
## 3 Fall     1200     5.75
## 4 N/A         0    22.6 
## 5 Spring    300    16.8 
## 6 Spring    600    18.2 
## 7 Spring   1200    14.2
```
]

]

This works also:

```r
tapply(df$inf, paste0(df$season, df$sulfur), mean)
```

```
##   Fall1200    Fall300    Fall600       N/A0 Spring1200 
##      5.750      9.500     15.500     22.625     14.250 
##  Spring300  Spring600 
##     16.750     18.250
```

---

# Reading code like English grammar

```r
df <- cochran.crd %>% 
  mutate(season = case_when(
                    str_detect(trt, "F") ~ "Fall",
                    str_detect(trt, "S") ~ "Spring",
                                    TRUE ~ "N/A"),
         sulfur = case_when(
                    str_detect(trt, "12") ~ 1200,
                    str_detect(trt, "6") ~ 600,
                    str_detect(trt, "3") ~ 300,
                                     TRUE ~ 0)) %>% 
  relocate(trt, .after = col) %>% 
  as_tibble() # optional

df %>% 
  group_by(season, sulfur) %>% 
  summarise(inf_mean = mean(inf))
```

]
.item[

* Read `%>%` as "and then"
* The code may read more familiar to an English user without much specialist knowledge
* Of course this is biased in favour of people who know English
* Bonus: no intermediate naming to think or worry about

]
]

---

# <i class="fas fa-chalkboard-teacher"></i> Lessons from grammar of data manipulation<br> in `dplyr`

<br>
<div class="info-box" style="margin-right:100px;margin-left:100px;text-align:left;">
<ul>
<li><b>Leveraging existing knowledge</b> of an everyday user or an "average" statistician for the choice of function names and argument names can make it more accessible</li>
{{content}}
</ul>
</div>
--

<li><b>Making it more accessible</b>, by making code more expressive or otherwise, can promote statistical literacy</li>
{{content}}

<li><b>Consistency in input and output</b> makes it easier for user expectation and helps to offload mental load</li>

---

## Can all this work be considered as statistics?

## Or data science?

<br>

<div class="info-box" style="margin-right:100px;margin-left:100px;text-align:left;font-size:18pt;">
Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to <b>extract knowledge and insights from many structural and unstructured data</b>. Data science is related to data mining, machine learning and big data.
<span style="float:right"><i class="fas fa-book"></i> <i>Wikipedia, accessed on 27th Nov 2020</i></span>
<br><br>
</div>

<br>

## Isn't this what statistics is about?

---

## There are two types of people...

<br>

---

## There are two types of people...

<br>

<br>

## But actually, it's hugely unbalanced

---

## Statistics by nature is inter-disciplinary and about extracting knowledge and insight from data 
.font_small[at least from my point of view]

## but communication largely became exclusive to selective set of statistically well-trained people governed by strict adherence to defintions or protocols in lieu of practicality
.font_small[which is not to say it is bad to do this]

---

## Far more people need to use statistics than just statistically well-trained people

<br>

.footnote[
Wickham (2015) Teaching Safe-Stats, Not Statistical Abstinence. *The American Statistician, Online Discussion*
]

## Statistics community need to go beyond talking among like-minded peers

---

# .circle-big[3]

# Grammar of <br>Experimental Design

<i class="fab fa-r-project blue"></i> `edibble` 📦

(Work-In-Progress)

---

# Typical course in experimental design <br>.font_small[(at least at University of Sydney in 2017-2019)]

Teach:

* Completely Randomised Design
* Randomised Complete Block Design
* Latin Square Design
* Balanced Incomplete Block Design
* Factorial Design
* <strike> 2</strike><sup>k</sup><strike> Factorial Design</strike> .font_small[(I removed this from 2018, I won't talk about this today)]
* Split-plot Design .font_small[(I added this from 2018 among other concepts)]

---

# Completely Randomised Design (CRD)

<br>

]

* `$t$` treatments randomised to `$n$` units

<br>

`$$\scriptsize \texttt{observation} = \texttt{mean} + \texttt{treatment} + \texttt{error}$$`

]

---

# Randomised Complete Block Design (RCBD)

<br>

]

* `$b$` blocks of size `$t$`
* `$t$` treatments randomised to `$t$` units within each block

`$$\scriptsize \texttt{observation} = \texttt{mean} + \texttt{treatment} + \texttt{block} + \texttt{error}$$`

]

---

# Latin Square Design (LSD)

<br>

]

* two orthogonal blocks of size `$t$`
* `$t$` treatments randomised to units such that every treatment appears exactly once in each block

`$$\scriptsize \texttt{observation} = \texttt{mean} + \texttt{treatment} + \texttt{row} + \texttt{column} + \texttt{error}$$`

]

---

# Balanced Incomplete Block Design (BIBD)
 
.grid[

<br>

]

* `$b$` blocks of size `$k < t$`
* `$t$` treatments randomised to units within each block such that every pair of treatment appears the same number of times across blocks

`$$\scriptsize \texttt{observation} = \texttt{mean} + \texttt{block} + \texttt{treatment} + \texttt{error}$$`

]

---

# Factorial Design

<br>

]

* `$ab = t$` treatments randomised to `$n$` units
* treatment is every combination of two factors A and B

`$$\scriptsize \texttt{observation} = \texttt{mean} + \texttt{A} + \texttt{B} + \texttt{A:B} + \texttt{error}$$`

<center>
<img src="images/factorial-eg1-anova-top.png" width = "850px"/>
<details style="font-size:4pt"><summary></summary>
<img src="images/factorial-eg1-anova-middle.png" width = "850px"/>
</details>
<img src="images/factorial-eg1-anova-bottom.png" width = "850px"/>
</center>
]

]

---

# Split-plot Design

<br>

]

<ul>
<li> $n_1$ whole plots consisting of $b$ sub plots</li>
<li>in total there are $n$ sub plots</li>
<li>treatment factor A is randomised to whole plots</li>
<li>treatment factor B is randomised to sub plots within each whole plot</li>
</ul>

`$$\scriptsize \texttt{observation} = \texttt{mean} + \texttt{A} + \texttt{WP} + \texttt{B} + \texttt{A:B} + \texttt{error}$$`

<center>
<img src="images/split-plot-eg1-anova.png" width = "850px"/>
</center>
]

</div>

]

---

# CRAN Task View of Design of Experiments

contains

# 📦 .yellow[229 R-packages ]

---

# Top 10 downloaded R-packages in 2018

![](images/top10_2018-1.png)

# `agricolae` is the most downloaded

---

# Top 10 downloaded R-packages in 2019

![](images/top10-1.png)

# Okay fine, results were different in 2019

---

# `agricolae::design.crd`

.blue[**Completely randomised design**] for `$t = 3$` treatments with `$2$` replicates each
<pre><code>
trt <- c("A", "B", "C")
agricolae::.bg-yellow[design.crd](trt = trt, r = 2) %>% glimpse()
</code></pre>

```
## List of 2
##  $ parameters:List of 7
##   ..$ design: chr "crd"
##   ..$ trt   : chr [1:3] "A" "B" "C"
##   ..$ r     : num [1:3] 2 2 2
##   ..$ serie : num 2
##   ..$ seed  : int 396269021
##   ..$ kinds : chr "Super-Duper"
##   ..$       : logi TRUE
##  $ book      :'data.frame':	6 obs. of  3 variables:
##   ..$ plots: num [1:6] 101 102 103 104 105 106
##   ..$ r    : int [1:6] 1 1 2 1 2 2
##   ..$ trt  : chr [1:6] "C" "A" "C" "B" ...
```
]

</div>

---

# `agricolae::design.rcbd`

<pre><code>
trt <- c("A", "B", "C")
agricolae::.bg-yellow[design.rcbd](trt = trt, r = 2) %>% glimpse()
</code></pre>

```
## List of 3
##  $ parameters:List of 7
##   ..$ design: chr "rcbd"
##   ..$ trt   : chr [1:3] "A" "B" "C"
##   ..$ r     : num 2
##   ..$ serie : num 2
##   ..$ seed  : int 973575053
##   ..$ kinds : chr "Super-Duper"
##   ..$       : logi TRUE
##  $ sketch    : chr [1:2, 1:3] "C" "A" "B" "C" ...
##  $ book      :'data.frame':	6 obs. of  3 variables:
##   ..$ plots: num [1:6] 101 102 103 201 202 203
##   ..$ block: Factor w/ 2 levels "1","2": 1 1 1 2 2 2
##   ..$ trt  : Factor w/ 3 levels "A","B","C": 3 2 1 1 3 2
```
]

<br>

</div>

---

# `agricolae::design.lsd()`

<pre><code>
trt <- c("A", "B", "C")
agricolae::.bg-yellow[design.lsd](trt = trt) %>% glimpse()
</code></pre>

```
## List of 3
##  $ parameters:List of 7
##   ..$ design: chr "lsd"
##   ..$ trt   : chr [1:3] "A" "B" "C"
##   ..$ r     : int 3
##   ..$ serie : num 2
##   ..$ seed  : int -1984440067
##   ..$ kinds : chr "Super-Duper"
##   ..$       : logi TRUE
##  $ sketch    : chr [1:3, 1:3] "B" "C" "A" "A" ...
##  $ book      :'data.frame':	9 obs. of  4 variables:
##   ..$ plots: num [1:9] 101 102 103 201 202 203 301 302 303
##   ..$ row  : Factor w/ 3 levels "1","2","3": 1 1 1 2 2 2 3 3 3
##   ..$ col  : Factor w/ 3 levels "1","2","3": 1 2 3 1 2 3 1 2 3
##   ..$ trt  : Factor w/ 3 levels "A","B","C": 2 1 3 3 2 1 1 3 2
```
]

<br>

</div>

---

# `agricolae::design.bib()`

<pre><code>
trt <- c("A", "B", "C")
agricolae::.bg-yellow[design.bib](trt = trt, k = 2) %>% glimpse()
</code></pre>

```
## [1] "No improvement over initial random design."
## 
## Parameters BIB
## ==============
## Lambda     : 1
## treatmeans : 3
## Block size : 2
## Blocks     : 3
## Replication: 2 
## 
## Efficiency factor 0.75 
## 
## <<< Book >>>
```

```
## List of 4
##  $ parameters:List of 6
##   ..$ design: chr "bib"
##   ..$ trt   : chr [1:3] "A" "B" "C"
##   ..$ k     : num 2
##   ..$ serie : num 2
##   ..$ seed  : int -509134623
##   ..$ kinds : chr "Super-Duper"
##  $ statistics:'data.frame':	1 obs. of  6 variables:
##   ..$ lambda    : num 1
##   ..$ treatmeans: int 3
##   ..$ blockSize : num 2
##   ..$ blocks    : int 3
##   ..$ r         : num 2
##   ..$ Efficiency: num 0.75
##  $ sketch    : chr [1:3, 1:2] "C" "B" "B" "A" ...
##  $ book      :'data.frame':	6 obs. of  3 variables:
##   ..$ plots: num [1:6] 101 102 201 202 301 302
##   ..$ block: Factor w/ 3 levels "1","2","3": 1 1 2 2 3 3
##   ..$ trt  : Factor w/ 3 levels "A","B","C": 3 1 2 1 2 3
```
]

<br>

</div>

---

# `agricolae::design.ab()`

.blue[**Factorial design**] for `$t = 3 \times 2$` treatments with `$2$` replication for each treatment

<pre><code>
agricolae::.bg-yellow[design.ab](trt = c(3, 2), r = 2, design = "crd") %>% glimpse()
</code></pre>

```
## List of 2
##  $ parameters:List of 8
##   ..$ design : chr "factorial"
##   ..$ trt    : chr [1:6] "1 1" "1 2" "2 1" "2 2" ...
##   ..$ r      : num [1:6] 2 2 2 2 2 2
##   ..$ serie  : num 2
##   ..$ seed   : int 801301585
##   ..$ kinds  : chr "Super-Duper"
##   ..$        : logi TRUE
##   ..$ applied: chr "crd"
##  $ book      :'data.frame':	12 obs. of  4 variables:
##   ..$ plots: num [1:12] 101 102 103 104 105 106 107 108 109 110 ...
##   ..$ r    : int [1:12] 1 1 1 2 1 1 1 2 2 2 ...
##   ..$ A    : chr [1:12] "2" "3" "2" "2" ...
##   ..$ B    : chr [1:12] "2" "2" "1" "2" ...
```
]

Note *not* A/B testing!

<br>

<br>

</div>

---

# `agricolae::design.split()`

.blue[**Split-plot design**] for `$t = 2 \times 4$` treatments with `$2$` replication for each treatment

<pre><code>
trt1 <- c("I", "R"); trt2 <- LETTERS[1:4]
agricolae::.bg-yellow[design.split](trt1 = trt1, trt2 = trt2, r = 2, design = "crd") %>% 
    glimpse()
</code></pre>

```
## List of 2
##  $ parameters:List of 8
##   ..$ design : chr "split"
##   ..$        : logi TRUE
##   ..$ trt1   : chr [1:2] "I" "R"
##   ..$ applied: chr "crd"
##   ..$ r      : num [1:2] 2 2
##   ..$ serie  : num 2
##   ..$ seed   : int -765020087
##   ..$ kinds  : chr "Super-Duper"
##  $ book      :'data.frame':	16 obs. of  5 variables:
##   ..$ plots : num [1:16] 101 101 101 101 102 102 102 102 103 103 ...
##   ..$ splots: Factor w/ 4 levels "1","2","3","4": 1 2 3 4 1 2 3 4 1 2 ...
##   ..$ r     : int [1:16] 1 1 1 1 1 1 1 1 2 2 ...
##   ..$ trt1  : chr [1:16] "I" "I" "I" "I" ...
##   ..$ trt2  : chr [1:16] "D" "A" "C" "B" ...
```
]

</div>

---

# Grammar of experimental design

* Remember Base R plots? <a href="#base-plot" style="font-size:12pt"><i class="fas fa-caret-right"></i> base plot</a>
--

* 🤔 "named experimental design" functions (`agricolae::design.crd`, etc.) are like "named statistical graphic" functions (`pie`, `barplot`)
--

* 💡 construction of experimental design needs to be made more accessible, modifiable, extensible and generalisable
--

* This is the concept for the development of the `edibble` R-package <br>https://github.com/emitanaka/edibble<br>(<i class="fas fa-wrench"></i> WIP, sorry code not ready yet, please enjoy prototype and live demo instead)
--

* `tibble` R-package is a modern reimagining of the `data.frame`
* `edibble` (WIP) creates experimental design tibbles
{{content}}

---

# Prototype for grammar of experimental design

* Consider a field experiment with 32 plots

]
.item[

```r
library(edibble)
initiate_design(seed = 2020) %>% 
* set_units(plot = 32)
```

```
## An edibble design
## └─plot (32 levels)
```
]
]

---

# Prototype Grammar of Experimental Design

* Consider a field experiment with 32 plots
* There are 7 different treatments

]
.item50[

```r
library(edibble)
initiate_design(seed = 2020) %>% 
  set_units(plot = 32) %>% 
* set_trts(trt = c("F12", "F3", "F6", "O",
*                  "S12", "S3", "S6"))
```

```
## An edibble design
## ├─plot (32 levels)
## └─trt (7 levels)
```

]
]

---

# Prototype Grammar of Experimental Design .circle[1]

* Consider a field experiment with 32 plots
* There are 7 different treatments
* Apply treatments to plots

]
.item50[

```r
library(edibble)
initiate_design(seed = 2020) %>% 
  set_units(plot = 32) %>% 
  set_trts(trt = c("F12", "F3", "F6", "O", 
                   "S12", "S3", "S6")) %>% 
* apply_trts(~ plot)
```

```
## An edibble design
## ├─plot (32 levels)
## └─trt (7 levels)
```

]
]

---

# Prototype Grammar of Experimental Design .circle[1]

* Consider a field experiment with 32 plots
* There are 7 different treatments
* Apply treatments to plots
* Randomise treatments to plots

]
.item50[

```r
library(edibble)
initiate_design(seed = 2020) %>% 
  set_units(plot = 32) %>% 
  set_trts(trt = c("F12", "F3", "F6", "O", 
                   "S12", "S3", "S6")) %>% 
  apply_trts(~ plot) %>% 
* randomise_trts()
```

```
## An edibble design
## ├─plot (32 levels)
## └─trt (7 levels)
```

]
]

---

# Prototype Grammar of Experimental Design .circle[1]

* Consider a field experiment with 32 plots
* There are 7 different treatments
* Apply treatments to plots
* Randomise treatments to plots
* The result is a **completely randomised design**

]
.item50[

```r
library(edibble)
initiate_design(seed = 2020) %>% 
  set_units(plot = 32) %>% 
  set_trts(trt = c("F12", "F3", "F6", "O", 
                   "S12", "S3", "S6")) %>% 
  apply_trts(~ plot) %>% 
  randomise_trts() %>% 
* serve_table()
```

```
## # An edibble: 32 x 2
##    plot   trt  
##    <fct>  <fct>
##  1 plot1  F12  
##  2 plot2  S3   
##  3 plot3  O    
##  4 plot4  S6   
##  5 plot5  F3   
##  6 plot6  S12  
##  7 plot7  F6   
##  8 plot8  S6   
##  9 plot9  F3   
## 10 plot10 S3   
## # … with 22 more rows
```

]
]

---

# Prototype Grammar of Experimental Design .circle[2]

* Consider a field experiment with 2 blocks each with 16 plots
* There are 7 different treatments
* Apply treatments to plots
* Randomise treatments to plots within blocks
* Resulting design is a **randomised block design**

<br>

Can you see how it differs from the previous design?

]
.item50[

```r
library(edibble)
initiate_design(seed = 2020) %>% 
* set_units(block = c("B1", "B2"),
*           plot = nested_in(block, 16)) %>%
  set_trts(trt = c("F12", "F3", "F6", "O", 
                   "S12", "S3", "S6")) %>% 
  apply_trts(~ plot) %>% 
  randomise_trts() %>% 
  serve_table() 
```

```
## # An edibble: 32 x 3
##    block plot   trt  
##    <fct> <fct>  <fct>
##  1 B1    plot1  F12  
##  2 B1    plot2  S3   
##  3 B1    plot3  S12  
##  4 B1    plot4  S6   
##  5 B1    plot5  F3   
##  6 B1    plot6  S3   
##  7 B1    plot7  F6   
##  8 B1    plot8  S6   
##  9 B1    plot9  F3   
## 10 B1    plot10 S6   
## # … with 22 more rows
```

]
]

---

.animated.flash.font_large[
# LIVE DEMO
]

---

# Limitations

.pad20[
* Generalising experimental designs is extremely hard
* A lot of domains have their own specialised design
{{content}}

]
--
* Some specialist designs that have certain properties cannot be easily replicated by `edibble`
* An ad-hoc approach may still be required to create design with specific properties
{{content}}
--
but the hope is that this can be achieved with replacement in one of the steps (e.g. replacing `randomise_trts`)
{{content}}
--
* _So why would anyone buy into this framework?_
{{content}}
--
* Hopefully, developers and users can see the benefit in being more expressive in code; and also a pay-off like easy visualisation of designs becomes worthwhile

---

# <i class="fas fa-clock"></i> Timeline

.grid[.item[
* By end of 2020, I intend to have `edibble` be able to construct textbook (mostly orthogonal) designs.
* From 2021, I plan to hold (free) workshops in experimental design with `edibble` to generate certain designs.
* The initial development for `edibble` is focussed on designs with "fixed" structures. 
* For survey and sampling designs you should check out [DeclareDesign](https://declaredesign.org/).

]
.item[
.center[
<br><br>

# <i class="fas fa-comments"></i>

Feedback is welcomed!

]
<br>

&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<i class="fab fa-github"></i> <a href="https://github.com/emitanaka/edibble/issues" style="color:black!important">emitanaka/edibble/issues</a>
<br>
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<i class="fas fa-envelope"></i> emi.tanaka@monash.edu

<br><br>
<div class="center">
<div style="border: 2px dotted #006DAE; width:400px;padding:10px;margin:auto;background:#D2F1FF;">
<i class="fas fa-link"></i> Slides can be found at

<a href="https://www.emitanaka.org/slides/SSA2020/" style="font-size:20pt">emitanaka.org/slides/SSA2020/</a>
</div>
</div>
]
]

---

# Acknowledgements

This slide was made using `xaringan` R-package and the following systems.

```r
sessioninfo::session_info()
```

```
## ─ Session info ───────────────────────────────────────────────────────────────
##  setting  value                       
##  version  R version 4.0.1 (2020-06-06)
##  os       macOS Catalina 10.15.7      
##  system   x86_64, darwin17.0          
##  ui       X11                         
##  language (EN)                        
##  collate  en_AU.UTF-8                 
##  ctype    en_AU.UTF-8                 
##  tz       Australia/Melbourne         
##  date     2020-11-27                  
## 
## ─ Packages ───────────────────────────────────────────────────────────────────
##  package     * version    date       lib source                              
##  agricolae     1.3-3      2020-06-07 [1] CRAN (R 4.0.1)                      
##  AlgDesign     1.2.0      2019-11-29 [1] CRAN (R 4.0.1)                      
##  anicon        0.1.0      2020-06-21 [1] Github (emitanaka/anicon@0b756df)   
##  assertthat    0.2.1      2019-03-21 [2] CRAN (R 4.0.0)                      
##  backports     1.2.0      2020-11-02 [1] CRAN (R 4.0.2)                      
##  broom         0.7.2      2020-10-20 [1] CRAN (R 4.0.2)                      
##  callr         3.5.1      2020-10-13 [1] CRAN (R 4.0.2)                      
##  cellranger    1.1.0      2016-07-27 [2] CRAN (R 4.0.0)                      
##  cli           2.2.0      2020-11-20 [1] CRAN (R 4.0.1)                      
##  cluster       2.1.0      2019-06-19 [2] CRAN (R 4.0.1)                      
##  colorspace    2.0-0      2020-11-11 [1] CRAN (R 4.0.2)                      
##  combinat      0.0-8      2012-10-29 [1] CRAN (R 4.0.1)                      
##  countdown     0.3.5      2020-07-20 [1] Github (gadenbuie/countdown@a544fa4)
##  crayon        1.3.4      2017-09-16 [2] CRAN (R 4.0.0)                      
##  DBI           1.1.0      2019-12-15 [1] CRAN (R 4.0.2)                      
##  dbplyr        2.0.0      2020-11-03 [1] CRAN (R 4.0.2)                      
##  digest        0.6.27     2020-10-24 [1] CRAN (R 4.0.2)                      
##  dplyr       * 1.0.2      2020-08-18 [1] CRAN (R 4.0.2)                      
##  edibble     * 0.0.0.9000 2020-11-15 [1] local                               
##  ellipsis      0.3.1      2020-05-15 [2] CRAN (R 4.0.0)                      
##  emo           0.0.0.9000 2020-06-26 [1] Github (hadley/emo@3f03b11)         
##  evaluate      0.14       2019-05-28 [2] CRAN (R 4.0.0)                      
##  fansi         0.4.1      2020-01-08 [2] CRAN (R 4.0.0)                      
##  farver        2.0.3.9000 2020-07-24 [1] Github (thomasp85/farver@f1bcb56)   
##  fastmap       1.0.1      2019-10-08 [2] CRAN (R 4.0.0)                      
##  forcats     * 0.5.0      2020-03-01 [2] CRAN (R 4.0.0)                      
##  fs            1.5.0      2020-07-31 [1] CRAN (R 4.0.2)                      
##  generics      0.1.0      2020-10-31 [2] CRAN (R 4.0.2)                      
##  ggplot2     * 3.3.2      2020-06-19 [1] CRAN (R 4.0.2)                      
##  glue          1.4.2      2020-08-27 [1] CRAN (R 4.0.2)                      
##  gtable        0.3.0      2019-03-25 [2] CRAN (R 4.0.0)                      
##  haven         2.3.1      2020-06-01 [2] CRAN (R 4.0.0)                      
##  highr         0.8        2019-03-20 [2] CRAN (R 4.0.0)                      
##  hms           0.5.3      2020-01-08 [2] CRAN (R 4.0.0)                      
##  htmltools     0.5.0      2020-06-16 [1] CRAN (R 4.0.2)                      
##  httpuv        1.5.4      2020-06-06 [2] CRAN (R 4.0.1)                      
##  httr          1.4.2      2020-07-20 [1] CRAN (R 4.0.2)                      
##  icon          0.1.0      2020-06-21 [1] Github (emitanaka/icon@8458546)     
##  igraph        1.2.6      2020-10-06 [1] CRAN (R 4.0.2)                      
##  jsonlite      1.7.1      2020-09-07 [1] CRAN (R 4.0.2)                      
##  klaR          0.6-15     2020-02-19 [1] CRAN (R 4.0.1)                      
##  knitr         1.30       2020-09-22 [1] CRAN (R 4.0.2)                      
##  labeling      0.4.2      2020-10-20 [1] CRAN (R 4.0.2)                      
##  labelled      2.7.0      2020-09-21 [1] CRAN (R 4.0.2)                      
##  later         1.1.0.1    2020-06-05 [2] CRAN (R 4.0.1)                      
##  lattice       0.20-41    2020-04-02 [2] CRAN (R 4.0.1)                      
##  lifecycle     0.2.0      2020-03-06 [1] CRAN (R 4.0.0)                      
##  lubridate     1.7.9      2020-06-08 [2] CRAN (R 4.0.1)                      
##  magrittr      2.0.1      2020-11-17 [1] CRAN (R 4.0.2)                      
##  MASS          7.3-53     2020-09-09 [1] CRAN (R 4.0.2)                      
##  mime          0.9        2020-02-04 [2] CRAN (R 4.0.0)                      
##  miniUI        0.1.1.1    2018-05-18 [1] CRAN (R 4.0.0)                      
##  modelr        0.1.8      2020-05-19 [2] CRAN (R 4.0.0)                      
##  munsell       0.5.0      2018-06-12 [2] CRAN (R 4.0.0)                      
##  nlme          3.1-150    2020-10-24 [2] CRAN (R 4.0.2)                      
##  pillar        1.4.7      2020-11-20 [1] CRAN (R 4.0.1)                      
##  pkgconfig     2.0.3      2019-09-22 [2] CRAN (R 4.0.0)                      
##  processx      3.4.4      2020-09-03 [1] CRAN (R 4.0.2)                      
##  promises      1.1.1      2020-06-09 [1] CRAN (R 4.0.0)                      
##  ps            1.4.0      2020-10-07 [1] CRAN (R 4.0.2)                      
##  purrr       * 0.3.4      2020-04-17 [2] CRAN (R 4.0.0)                      
##  questionr     0.7.3      2020-10-05 [1] CRAN (R 4.0.2)                      
##  R6            2.5.0      2020-10-28 [1] CRAN (R 4.0.2)                      
##  Rcpp          1.0.5      2020-07-06 [1] CRAN (R 4.0.0)                      
##  readr       * 1.4.0      2020-10-05 [2] CRAN (R 4.0.2)                      
##  readxl        1.3.1      2019-03-13 [2] CRAN (R 4.0.0)                      
##  reprex        0.3.0.9001 2020-08-08 [1] Github (tidyverse/reprex@9594ee9)   
##  rlang         0.4.8      2020-10-08 [1] CRAN (R 4.0.2)                      
##  rmarkdown     2.5        2020-10-21 [1] CRAN (R 4.0.1)                      
##  rstudioapi    0.13       2020-11-12 [1] CRAN (R 4.0.1)                      
##  rvest         0.3.6      2020-07-25 [1] CRAN (R 4.0.2)                      
##  scales        1.1.1      2020-05-11 [2] CRAN (R 4.0.0)                      
##  sessioninfo   1.1.1      2018-11-05 [2] CRAN (R 4.0.0)                      
##  shiny         1.5.0      2020-06-23 [1] CRAN (R 4.0.2)                      
##  stringi       1.5.3      2020-09-09 [2] CRAN (R 4.0.2)                      
##  stringr     * 1.4.0      2019-02-10 [2] CRAN (R 4.0.0)                      
##  tibble      * 3.0.4.9000 2020-11-26 [1] Github (tidyverse/tibble@9eeef4d)   
##  tidyr       * 1.1.2      2020-08-27 [1] CRAN (R 4.0.2)                      
##  tidyselect    1.1.0      2020-05-11 [2] CRAN (R 4.0.0)                      
##  tidyverse   * 1.3.0      2019-11-21 [1] CRAN (R 4.0.2)                      
##  utf8          1.1.4      2018-05-24 [2] CRAN (R 4.0.0)                      
##  vctrs         0.3.5.9000 2020-11-26 [1] Github (r-lib/vctrs@957baf7)        
##  viridisLite   0.3.0      2018-02-01 [2] CRAN (R 4.0.0)                      
##  whisker       0.4        2019-08-28 [2] CRAN (R 4.0.0)                      
##  withr         2.3.0      2020-09-22 [1] CRAN (R 4.0.2)                      
##  xaringan      0.18       2020-10-21 [1] CRAN (R 4.0.2)                      
##  xfun          0.19       2020-10-30 [1] CRAN (R 4.0.2)                      
##  xml2          1.3.2      2020-04-23 [2] CRAN (R 4.0.0)                      
##  xtable        1.8-4      2019-04-21 [2] CRAN (R 4.0.0)                      
##  yaml          2.2.1      2020-02-01 [1] CRAN (R 4.0.2)                      
## 
## [1] /Users/etan0038/Library/R/4.0/library
## [2] /Library/Frameworks/R.framework/Versions/4.0/Resources/library
```

]

(Scroll on html slide to see all)