Statistical Methods for Omics Assisted Breeding

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<br><br>

# Statistical Methods for Omics Assisted Breeding

## <b style='color:#FFEB3B'>Data Visualization in <span><i class="fab  fa-r-project faa-none animated "></i></span></b>

### <br>Emi Tanaka <br>emi.tanaka@sydney.edu.au <br>School of Mathematics and Statisitcs

### 2018/11/12 <br>
### <span style='font-size:14pt; color:pink!important'>These slides may take a while to render properly. You can find the pdf <a href="https://www.dropbox.com/s/hulz2ua8kszehvr/day1-session02-datavis.pdf?dl=1" download="day1-session02-datavis.pdf">here</a>.</span> <br><br>
### <a rel='license' href='http://creativecommons.org/licenses/by-sa/4.0/'><img alt='Creative Commons License' style='border-width:0; width:30pt' src='images/cc.svg' /><img alt='Creative Commons License' style='border-width:0; width:20pt' src='images/by.svg' /><img alt='Creative Commons License' style='border-width:0; width:20pt' src='images/sa.svg' /></a><span style='font-size:10pt'>&nbsp;&nbsp;&nbsp;&nbsp;This work by <span xmlns:cc='http://creativecommons.org/ns#' property='cc:attributionName'>Emi Tanaka</span> is licensed under a <a rel='license' href='http://creativecommons.org/licenses/by-sa/4.0/'>Creative Commons Attribution-ShareAlike 4.0 International License</a>.</span>

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<img src="images/anim.gif" style="position:absolute; top:43%; left:37%;width:240px">

---

* `R` has many contributed packages that extend from the standard `base` installation.

* Today we will learn about `ggplot2` and `desplot` R packages with a light touch on `plotly`.

# Why Data Visualisation in `R`?

* You can make .indigo[publication quality] graphs.

* The graphs are easily .indigo[reproducible].

<span><i class="fas  fa-exclamation-triangle faa-flash animated " style=" color:red;"></i></span> .red[`ggplot2` is quite stable now but `R` packages are contributed and can change in future iterations].

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<img src="images/rlogo.svg" width="80%"/>

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---

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]
.column.slide-in-right[
.sliderbox.bg-main2.vmiddle[
# .font_large[`ggplot`]
]]

---

# `ggplot2` R package

* `ggplot2` is a powerful data visualisation R  package with a large community following that is built on the .indigo[layered grammar of graphics] by Wickham (2008).  
* One of the reason that makes it powerful is because of its ease in extensibility resulting in many extension packages.
* `ggplot2` uses `qplot` or `ggplot` to make graphics
* `qplot` is useful for making quick graphs (especially when data is not in a `data.frame`) but `ggplot` is advisable for most occasions. 
* We will only cover `ggplot`.
* To get started, load the package:

```r
library(ggplot2) # or library(tidyverse)
```

.bottom_abs.width100.font_small[
Wickham (2008) Practical tools for exploring data and models. PhD Thesis.
]

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<img src="images/ggplot2.png" width="80%"/>
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---

# Layered Grammar of Graphics

* Every `ggplot2` object has three key components:
  1. .indigo[data],
  1. A set of .indigo[aesthestic mapping] between variables in the data and visual properties (e.g color, size etc) 
  1. At least one .indigo[layer] describing how to render each observation; usually created with .indigo[geom] function.

```r
str(iris)
```

```
## 'data.frame':	150 obs. of  5 variables:
##  $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
##  $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
##  $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
##  $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
##  $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
```
]]

.column.bg-blue[.content[

```r
ggplot(data=iris) + 
  aes(x=Sepal.Length,
      y=Sepal.Width) + 
  geom_point()
```
<img src="images/irisplot.svg" width="100%">

]]

---

.column[.content[
# Every .black[layer] has:
1. `geom` - the geometric object to use display the data, and `stat` - statistical transformation to use on the data for this layer.
1. .indigo[data] and .indigo[mapping] (aesthestics) which is usually inherited from `ggplot()` object.
1. `position` - position in the coordinate system.

```r
p <- ggplot(iris, aes(Sepal.Length, Sepal.Width))
p + geom_point() # blank + geom layer
```
which is a short-hand for:

```r
p + layer(geom="point", stat="identity", 
          position="identity")
```

]]
.column.bg-blue[.content[
Every `ggplot` object has:
1. Data
1. Aesthesitc mapping
1. Layer(s)

Purpose of a layer is to display:
* the raw .yellow[data],
* a .yellow[statistical summary], or 
* additional .yellow[metadata] such as context, annotations, and references.

]]

---

```r
p <- ggplot(iris, 
            aes(Species, Sepal.Width))
class(p)
```

```
## [1] "gg"     "ggplot"
```

]]
.column[.content.vmiddle[
.img-fill[![](images/iris.png)]
<br>
.font_small[
Image source:<br>
http://suruchifialoke.com/2016-10-13-machine-learning-tutorial-iris-classification/
]
]]]
]
.row[
.split-four[

.column.center[

```r
p + geom_blank()
```

![](day1-session02-datavis_files/figure-html/unnamed-chunk-8-1.png)
]
.column.center[

```r
p + geom_point()
```

![](day1-session02-datavis_files/figure-html/unnamed-chunk-9-1.png)
]
.column.center[

```r
p + geom_boxplot()
```

![](day1-session02-datavis_files/figure-html/unnamed-chunk-10-1.png)
]
.column.center[

```r
p + geom_violin()
```

![](day1-session02-datavis_files/figure-html/unnamed-chunk-11-1.png)
]

]]

---

```r
p <- ggplot(iris, aes(Petal.Length, Petal.Width)) + geom_point(colour="gray")
```
]]
.row[
.split-two[
.row[
.split-two[
.column.center[

```r
p + geom_abline(intercept=-0.4,slope=0.4)
```

<img src="day1-session02-datavis_files/figure-html/unnamed-chunk-13-1.png" style="display: block; margin: auto;" />
]
.column.center[

```r
p + geom_smooth(method="lm")
```

<img src="day1-session02-datavis_files/figure-html/unnamed-chunk-14-1.png" style="display: block; margin: auto;" />
]
]]

.column.center[

```r
p + geom_hline(yintercept=0)
```

<img src="day1-session02-datavis_files/figure-html/unnamed-chunk-15-1.png" style="display: block; margin: auto;" />
]
.column.center[

```r
p + geom_vline(xintercept=0)
```

<img src="day1-session02-datavis_files/figure-html/unnamed-chunk-16-1.png" style="display: block; margin: auto;" />
]
]]]]

---

```r
p <- ggplot(iris, aes(Petal.Width, fill=Species))
```
]]
.row[
.split-two[
.row[
.split-two[
.column.center[

```r
p + geom_dotplot()
```

<img src="day1-session02-datavis_files/figure-html/unnamed-chunk-18-1.png" style="display: block; margin: auto;" />
]
.column.center[

```r
p + geom_histogram()
```

<img src="day1-session02-datavis_files/figure-html/unnamed-chunk-19-1.png" style="display: block; margin: auto;" />
]
]]

.column.center[

```r
p + geom_density()
```

<img src="day1-session02-datavis_files/figure-html/unnamed-chunk-20-1.png" style="display: block; margin: auto;" />
]
.column.center[

```r
p + geom_freqpoly(aes(color=Species))
```

<img src="day1-session02-datavis_files/figure-html/unnamed-chunk-21-1.png" style="display: block; margin: auto;" />
]
]]]]

---

.column[.content.pad10px[
.font_sm55[
<table class="table table-striped table-hover table-condensed table-responsive" style="margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:left;"> geom </th>
   <th style="text-align:left;"> Description </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> geom_abline </td>
   <td style="text-align:left;"> Reference lines: horizontal, vertical, and diagonal </td>
  </tr>
  <tr>
   <td style="text-align:left;"> geom_bar </td>
   <td style="text-align:left;"> Bar charts </td>
  </tr>
  <tr>
   <td style="text-align:left;"> geom_bin2d </td>
   <td style="text-align:left;"> Heatmap of 2d bin counts </td>
  </tr>
  <tr>
   <td style="text-align:left;"> geom_blank </td>
   <td style="text-align:left;"> Draw nothing </td>
  </tr>
  <tr>
   <td style="text-align:left;"> geom_boxplot </td>
   <td style="text-align:left;"> A box and whiskers plot (in the style of Tukey) </td>
  </tr>
  <tr>
   <td style="text-align:left;"> geom_contour </td>
   <td style="text-align:left;"> 2d contours of a 3d surface </td>
  </tr>
  <tr>
   <td style="text-align:left;"> geom_count </td>
   <td style="text-align:left;"> Count overlapping points </td>
  </tr>
  <tr>
   <td style="text-align:left;"> geom_density </td>
   <td style="text-align:left;"> Smoothed density estimates </td>
  </tr>
  <tr>
   <td style="text-align:left;"> geom_density_2d </td>
   <td style="text-align:left;"> Contours of a 2d density estimate </td>
  </tr>
  <tr>
   <td style="text-align:left;"> geom_dotplot </td>
   <td style="text-align:left;"> Dot plot </td>
  </tr>
  <tr>
   <td style="text-align:left;"> geom_errorbarh </td>
   <td style="text-align:left;"> Horizontal error bars </td>
  </tr>
  <tr>
   <td style="text-align:left;"> geom_hex </td>
   <td style="text-align:left;"> Hexagonal heatmap of 2d bin counts </td>
  </tr>
  <tr>
   <td style="text-align:left;"> geom_freqpoly </td>
   <td style="text-align:left;"> Histograms and frequency polygons </td>
  </tr>
  <tr>
   <td style="text-align:left;"> geom_jitter </td>
   <td style="text-align:left;"> Jittered points </td>
  </tr>
  <tr>
   <td style="text-align:left;"> geom_crossbar </td>
   <td style="text-align:left;"> Vertical intervals: lines, crossbars &amp; errorbars </td>
  </tr>
  <tr>
   <td style="text-align:left;"> geom_map </td>
   <td style="text-align:left;"> Polygons from a reference map </td>
  </tr>
  <tr>
   <td style="text-align:left;"> geom_path </td>
   <td style="text-align:left;"> Connect observations </td>
  </tr>
  <tr>
   <td style="text-align:left;"> geom_point </td>
   <td style="text-align:left;"> Points </td>
  </tr>
  <tr>
   <td style="text-align:left;"> geom_polygon </td>
   <td style="text-align:left;"> Polygons </td>
  </tr>
  <tr>
   <td style="text-align:left;"> geom_qq_line </td>
   <td style="text-align:left;"> A quantile-quantile plot </td>
  </tr>
  <tr>
   <td style="text-align:left;"> geom_quantile </td>
   <td style="text-align:left;"> Quantile regression </td>
  </tr>
  <tr>
   <td style="text-align:left;"> geom_ribbon </td>
   <td style="text-align:left;"> Ribbons and area plots </td>
  </tr>
  <tr>
   <td style="text-align:left;"> geom_rug </td>
   <td style="text-align:left;"> Rug plots in the margins </td>
  </tr>
  <tr>
   <td style="text-align:left;"> geom_segment </td>
   <td style="text-align:left;"> Line segments and curves </td>
  </tr>
  <tr>
   <td style="text-align:left;"> geom_smooth </td>
   <td style="text-align:left;"> Smoothed conditional means </td>
  </tr>
  <tr>
   <td style="text-align:left;"> geom_spoke </td>
   <td style="text-align:left;"> Line segments parameterised by location, direction and distance </td>
  </tr>
  <tr>
   <td style="text-align:left;"> geom_label </td>
   <td style="text-align:left;"> Text </td>
  </tr>
  <tr>
   <td style="text-align:left;"> geom_raster </td>
   <td style="text-align:left;"> Rectangles </td>
  </tr>
  <tr>
   <td style="text-align:left;"> geom_violin </td>
   <td style="text-align:left;"> Violin plot </td>
  </tr>
</tbody>
</table>
]]]

.column.bg-blue[.content.vmiddle.white.center[
# .font-mono.font_large[geom]
]]
---

```r
head(iris[, c("Petal.Width", "Species")]) # raw data
```

```
##   Petal.Width Species
## 1         0.2  setosa
## 2         0.2  setosa
## 3         0.2  setosa
## 4         0.2  setosa
## 5         0.2  setosa
## 6         0.4  setosa
```
`stat_bin(bins=7, mapping=aes(Petal.Width, fill=Species))` <span><i class="fas  fa-arrow-down faa-none animated "></i></span>
Under the hood, the raw data is transformed into statistics and this is passed onto the `geom` where here `geom="bar"` is default.

```
##      fill  y count   x xmin xmax density    ncount
## 1 #619CFF  0     0 0.0 -0.2  0.2     0.0 0.0000000
## 2 #00BA38  0     0 0.0 -0.2  0.2     0.0 0.0000000
## 3 #F8766D 34    34 0.0 -0.2  0.2     1.7 1.0000000
## 4 #619CFF  0     0 0.4  0.2  0.6     0.0 0.0000000
## 5 #00BA38  0     0 0.4  0.2  0.6     0.0 0.0000000
## 6 #F8766D 16    16 0.4  0.2  0.6     0.8 0.4705882
```
]]
.column.bg-yellow[.vmiddle[
![](day1-session02-datavis_files/figure-html/unnamed-chunk-25-1.png)
]]

---

```r
p <- ggplot(iris, aes(Petal.Width, fill=Species))
```
]]
.row[
.split-two[
.row[
.split-two[
.column.center[

```r
p + stat_bin()
```

<img src="day1-session02-datavis_files/figure-html/unnamed-chunk-27-1.png" style="display: block; margin: auto;" />
]
.column.center[

```r
p + stat_bin(geom="bar")
```

<img src="day1-session02-datavis_files/figure-html/unnamed-chunk-28-1.png" style="display: block; margin: auto;" />
]
]]

.column.center[

```r
p + stat_bin(geom="point")
```

<img src="day1-session02-datavis_files/figure-html/unnamed-chunk-29-1.png" style="display: block; margin: auto;" />
]
.column.center[

```r
p + stat_bin(geom="line")
```

<img src="day1-session02-datavis_files/figure-html/unnamed-chunk-30-1.png" style="display: block; margin: auto;" />
]
]]]]

---

.column[.content.pad10px[
.font_sm74[
<table class="table table-striped table-hover table-condensed table-responsive" style="margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:left;"> stat </th>
   <th style="text-align:left;"> Description </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> stat_count </td>
   <td style="text-align:left;"> Bar charts </td>
  </tr>
  <tr>
   <td style="text-align:left;"> stat_bin_2d </td>
   <td style="text-align:left;"> Heatmap of 2d bin counts </td>
  </tr>
  <tr>
   <td style="text-align:left;"> stat_boxplot </td>
   <td style="text-align:left;"> A box and whiskers plot (in the style of Tukey) </td>
  </tr>
  <tr>
   <td style="text-align:left;"> stat_contour </td>
   <td style="text-align:left;"> 2d contours of a 3d surface </td>
  </tr>
  <tr>
   <td style="text-align:left;"> stat_sum </td>
   <td style="text-align:left;"> Count overlapping points </td>
  </tr>
  <tr>
   <td style="text-align:left;"> stat_density </td>
   <td style="text-align:left;"> Smoothed density estimates </td>
  </tr>
  <tr>
   <td style="text-align:left;"> stat_density_2d </td>
   <td style="text-align:left;"> Contours of a 2d density estimate </td>
  </tr>
  <tr>
   <td style="text-align:left;"> stat_bin_hex </td>
   <td style="text-align:left;"> Hexagonal heatmap of 2d bin counts </td>
  </tr>
  <tr>
   <td style="text-align:left;"> stat_bin </td>
   <td style="text-align:left;"> Histograms and frequency polygons </td>
  </tr>
  <tr>
   <td style="text-align:left;"> stat_qq_line </td>
   <td style="text-align:left;"> A quantile-quantile plot </td>
  </tr>
  <tr>
   <td style="text-align:left;"> stat_quantile </td>
   <td style="text-align:left;"> Quantile regression </td>
  </tr>
  <tr>
   <td style="text-align:left;"> stat_smooth </td>
   <td style="text-align:left;"> Smoothed conditional means </td>
  </tr>
  <tr>
   <td style="text-align:left;"> stat_spoke </td>
   <td style="text-align:left;"> Line segments parameterised by location, direction and distance </td>
  </tr>
  <tr>
   <td style="text-align:left;"> stat_ydensity </td>
   <td style="text-align:left;"> Violin plot </td>
  </tr>
  <tr>
   <td style="text-align:left;"> stat_sf </td>
   <td style="text-align:left;"> Visualise sf objects </td>
  </tr>
  <tr>
   <td style="text-align:left;"> stat_ecdf </td>
   <td style="text-align:left;"> Compute empirical cumulative distribution </td>
  </tr>
  <tr>
   <td style="text-align:left;"> stat_ellipse </td>
   <td style="text-align:left;"> Compute normal confidence ellipses </td>
  </tr>
  <tr>
   <td style="text-align:left;"> stat_function </td>
   <td style="text-align:left;"> Compute function for each x value </td>
  </tr>
  <tr>
   <td style="text-align:left;"> stat_identity </td>
   <td style="text-align:left;"> Leave data as is </td>
  </tr>
  <tr>
   <td style="text-align:left;"> stat_sf_coordinates </td>
   <td style="text-align:left;"> Extract coordinates from 'sf' objects </td>
  </tr>
  <tr>
   <td style="text-align:left;"> stat_summary_bin </td>
   <td style="text-align:left;"> Summarise y values at unique/binned x </td>
  </tr>
  <tr>
   <td style="text-align:left;"> stat_summary_2d </td>
   <td style="text-align:left;"> Bin and summarise in 2d (rectangle &amp; hexagons) </td>
  </tr>
  <tr>
   <td style="text-align:left;"> stat_unique </td>
   <td style="text-align:left;"> Remove duplicates </td>
  </tr>
</tbody>
</table>
]]]

.column.bg-blue[.content.vmiddle.white.center[
# .font-mono.font_large[stat]
]]

---

.column.bg-main3[
]
.column.slide-in-right[
.sliderbox.bg-main2.vmiddle[
# .font_large[Customisation]
]]

---

```r
library(RColorBrewer)
ggplot(iris, aes(Petal.Width, 
*                fill=Species)) +
  geom_dotplot() + 
* scale_fill_brewer(palette="Set3")
```

![](day1-session02-datavis_files/figure-html/unnamed-chunk-32-1.png)
]]
.column[
![](day1-session02-datavis_files/figure-html/unnamed-chunk-33-1.png)
]

---

```r
ggplot(iris, aes(Petal.Width, 
                 fill=Species)) +
  geom_dotplot() + 
* scale_fill_grey()
```

]]
.column[.content[
# Manual scale
.font_sm85[

```r
ggplot(iris, aes(Petal.Width, 
                 fill=Species)) +
 geom_dotplot() + 
*scale_fill_manual(
* values=c("red","blue", "green"),
* labels=c("setosa", "versicolor", "virginica"))
```

<img src="day1-session02-datavis_files/figure-html/unnamed-chunk-35-1.png" style="display: block; margin: auto;" />
]]]

---

```r
ggplot(iris, aes(Petal.Width, 
                 Petal.Length,
*                color=Species)) +
  geom_point(size=2) + 
* scale_color_brewer(palette="Set1")
```

<img src="day1-session02-datavis_files/figure-html/unnamed-chunk-36-1.png" style="display: block; margin: auto;" />
]]]
.column[.content[
# Color variable is continuous 
.font_sm60[

```r
ggplot(iris, aes(Petal.Width, 
                 Petal.Length,
*                color=Sepal.Length)) +
  geom_point(size=2) + 
* scale_color_distiller(palette="YlGnBu")
```

<img src="day1-session02-datavis_files/figure-html/unnamed-chunk-37-1.png" style="display: block; margin: auto;" />
]]]

---

## Counts of yellow/white and sweet/starchy maize kernels
.font_sm60[
Here I am massaging the data to get the counts for the 8th maize ear by observer 1 (plant pathologist):

```r
library(agridat); library(dplyr)
(maize <- pearl.kernels %>% 
  filter(ear=="Ear08" & obs=="Obs01") %>% 
  select(ys, yt, ws, wt) %>% 
  tidyr::gather("Type", "Count", ys:wt) %>% 
  mutate(Color=case_when(
    Type %in% c("ys", "yt") ~ "Yellow",
    Type %in% c("ws", "wt") ~ "White"
  ),Kernel=case_when(
    Type %in% c("ys", "ws") ~ "Starchy",
    Type %in% c("yt", "wt") ~ "Sweet")))
```

```
##   Type Count  Color  Kernel
## 1   ys   352 Yellow Starchy
## 2   yt   102 Yellow   Sweet
## 3   ws    52  White Starchy
## 4   wt    26  White   Sweet
```
]
.bottom_abs.width100.font_small[
Pearl, Raymond (1911) The Personal Equation In Breeding Experiments Involving Certain Characters of Maize *Biological Bulletin* **21** 339-366
]
]]
.column.bg-blue[.content.vmiddle.nopadding[

.font_small[Image source: http://corncommentary.com/2012/05/22/using-the-kfc-kernel-for-cellulosic/
]]]

---

```r
p <- ggplot(maize, aes(Kernel, Count, fill=Color)) + 
  geom_bar(stat="identity")
```
<img src="day1-session02-datavis_files/figure-html/unnamed-chunk-40-1.png" style="display: block; margin: auto;" />
]]
.column.bg-blue[.content.vmiddle[
<img src="images/corn-farm.jpg" width="114%" style="margin-right: -7%; margin-left: -7%;">
.font_small[
Image Source:<br>
https://agrifarmingtips.com/maize-cultivation-process/]

]]

---

```r
p <- ggplot(maize, aes(Kernel, Count, fill=Color)) + 
  geom_bar(stat="identity", color="black") + 
  scale_fill_manual(values=c("white", "yellow"), 
                    label=c("White", "Yellow")) + 
  guides(fill=FALSE)
```
<img src="day1-session02-datavis_files/figure-html/unnamed-chunk-42-1.png" style="display: block; margin: auto;" />
]]
.column.bg-blue[.content.vmiddle[
<img src="images/corn-farm.jpg" width="114%" style="margin-right: -7%; margin-left: -7%;">
.font_small[
Image Source:<br>
https://agrifarmingtips.com/maize-cultivation-process/]
]]

---

.column[.content[
# Example: Observer 1 for Maize Ear 8
<pre>
<code class="r hljs remark-code"><div class="remark-code-line">p &lt;- ggplot(maize, aes(Kernel, Count, fill=Color)) + </div>
<div class="remark-code-line"><s>  geom_bar(stat=<span class="hljs-string">"identity"</span>, color=<span class="hljs-string">"black"</span>) + </s></div>
<div class="remark-code-line">  scale_fill_manual(values=c(<span class="hljs-string">"white"</span>, <span class="hljs-string">"yellow"</span>), </div>
<div class="remark-code-line">                    label=c(<span class="hljs-string">"White"</span>, <span class="hljs-string">"Yellow"</span>)) + </div>
<div class="remark-code-line">  guides(fill=<span class="hljs-literal">FALSE</span>)</div>
</code></pre>
<img src="day1-session02-datavis_files/figure-html/unnamed-chunk-43-1.png" style="display: block; margin: auto;" />

]]
.column.bg-blue[.content.vmiddle[
<img src="images/corn-farm.jpg" width="114%" style="margin-right: -7%; margin-left: -7%;">
.font_small[
Image Source:<br>
https://agrifarmingtips.com/maize-cultivation-process/]
]]

---

```r
p + geom_bar()
```
<img src="day1-session02-datavis_files/figure-html/unnamed-chunk-45-1.png" style="display: block; margin: auto;" />

]]]
.column[.content.center[
## &nbsp;

```r
p + geom_bar(position="stack")
```
<img src="day1-session02-datavis_files/figure-html/unnamed-chunk-47-1.png" style="display: block; margin: auto;" />
]]]]
.row[
.split-two[
.column[.content.center[

```r
p + geom_bar(position="dodge")
```
<img src="day1-session02-datavis_files/figure-html/unnamed-chunk-49-1.png" style="display: block; margin: auto;" />
]]
.column[.content.center[

```r
p + geom_bar(position="fill")
```
<img src="day1-session02-datavis_files/figure-html/unnamed-chunk-51-1.png" style="display: block; margin: auto;" />
]]]]

.bottom_abs.width100.font_sm[All `geom_bar` include the arguments `stat="identity"` and `color="black"`.]

---

```r
p2 <- ggplot(maize, aes(1, Count, fill=Type)) + 
  guides(fill=FALSE) + theme_void()
```
]]
.row[.content[

```r
p + geom_bar()
```
<img src="day1-session02-datavis_files/figure-html/unnamed-chunk-54-1.png" style="display: block; margin: auto;" />

]]]
.column[.content.center[

```r
p + geom_bar() + coord_polar(theta="y")
```
<img src="day1-session02-datavis_files/figure-html/unnamed-chunk-56-1.png" style="display: block; margin: auto;" />
]]]]
.row[
.split-two[
.column[.content.center[

```r
p + geom_bar() + coord_flip()
```
<img src="day1-session02-datavis_files/figure-html/unnamed-chunk-58-1.png" style="display: block; margin: auto;" />

]]
.column[.content.center[

```r
p + geom_bar() + 
  coord_polar(theta="y", direction=-1)
```
<img src="day1-session02-datavis_files/figure-html/unnamed-chunk-60-1.png" style="display: block; margin: auto;" />
]]]]]]]
.bottom_abs.width100.font_small[All `geom_bar` include the arguments `stat="identity"` and `color="black"`.]

---

```r
g <- ggplot(pearl.kernels, aes(ear, ys, color=ear, size=3)) + xlab(NULL) + 
 guides(color=FALSE, size=FALSE) + ylab("No. of Yellow\n Starchy Kernel")
```
]]
.row[
.split-two[
.row[
.split-two[
.column[.content.center[

```r
g + geom_point()
```

![](day1-session02-datavis_files/figure-html/unnamed-chunk-62-1.png)
]]
.column[.content.center[

```r
g + geom_point(position="jitter")
```

![](day1-session02-datavis_files/figure-html/unnamed-chunk-63-1.png)
]]]]
.row[
.split-two[
.column[.content.center[

```r
g + geom_point(alpha=1 / 3)
```

![](day1-session02-datavis_files/figure-html/unnamed-chunk-64-1.png)
]]
.column[.content.center[

```r
g + geom_point(alpha=1 / 6)
```

![](day1-session02-datavis_files/figure-html/unnamed-chunk-65-1.png)
]]]]
]]

---

```r
maize2 <- pearl.kernels %>% 
  tidyr::gather("Type", "Count", ys:wt) %>% 
  mutate(Color=ifelse(substr(Type, 1, 1)=="y", 
                      "Yellow", "White"),
         Kernel=ifelse(substr(Type, 2, 2)=="s", 
                      "Starchy", "Sweet"),
         obs=factor(as.integer(substring(obs, 4, 5))))
```
]]
.column[.content[
.img-fill[![](images/tidyr-spread-gather.gif)]
]]
]]

```r
head(maize2)
```

```
    ear obs Type Count  Color  Kernel
1 Ear08   1   ys   352 Yellow Starchy
2 Ear08   2   ys   322 Yellow Starchy
3 Ear08   3   ys   298 Yellow Starchy
4 Ear08   4   ys   332 Yellow Starchy
5 Ear08   5   ys   305 Yellow Starchy
6 Ear08   6   ys   313 Yellow Starchy
```
]]
.column[.content[

```r
head(pearl.kernels)
```

```
    ear   obs  ys  yt  ws wt
1 Ear08 Obs01 352 102  52 26
2 Ear08 Obs02 322  49  82 79
3 Ear08 Obs03 298  75 108 51
4 Ear08 Obs04 332 101  71 28
5 Ear08 Obs05 305 101  86 40
6 Ear08 Obs06 313 100  90 29
```
]]
]]

---

```r
ggplot(maize2, 
       aes(obs, Count, fill=Type)) + 
  geom_bar(stat="identity") +  
  xlab("Observer") +
* facet_wrap(~ear)
```

<img src="day1-session02-datavis_files/figure-html/unnamed-chunk-69-1.png" style="display: block; margin: auto;" />
]]
.column[.content[
# &nbsp;

```r
ear8 <- maize2 %>% filter(ear=="Ear08") %>% 
  ggplot(aes(obs, Count, fill=Type)) + 
  geom_bar(stat="identity", show.legend=F) + 
  labs(tag="(A)", title="Ear 8", x="Observer") + 
* facet_grid(Color ~ Kernel)
```
<img src="day1-session02-datavis_files/figure-html/unnamed-chunk-71-1.png" style="display: block; margin: auto;" />
]]

---

```r
library(patchwork)
ear8 + ear9 + ear10 + ear11 + plot_layout(ncol = 2)
```

![](day1-session02-datavis_files/figure-html/unnamed-chunk-73-1.png)
]]
.column.bg-blue[.content.vmiddle.center[
<img src="images/patchwork.png" width="80%">
]]

---

.column[.content[
# Example: Barley in Norway
.font_sm80[Average height each year for 15 genotypes of barley in Norway from 1974-1982.]

```r
str(aastveit.barley.height) # from agridat
```

```
## 'data.frame':	135 obs. of  3 variables:
##  $ year  : int  1974 1975 1976 1977 1978 1979 1980 1981 1982 1974 ...
##  $ gen   : Factor w/ 15 levels "G01","G02","G03",..: 1 1 1 1 1 1 1 1 1 2 ...
##  $ height: num  81 67.3 71.5 64.3 55.8 84.9 86.2 88 72 72.3 ...
```

.font_sm80[The covariate information are found in `aastveit.barley.covs`.<br>
Use below command to find information about the data:

```r
?aastveit.barley.height
?aastveit.barley.covs
```
]
.bottom_abs.width100.font_small[
Aastveit, A. H. and Martens, H. (1986). ANOVA interactions interpreted by partial least squares regression. *Biometrics* **42** 829–844. 
]]]

.column.bg-white[.content[

```r
barley <- aastveit.barley.height %>% 
  left_join(aastveit.barley.covs, 
            by="year")
```
<img src="images/left-join.gif">
]]

---

```r
(maxh_df <- barley %>% 
   select(year, height, gen, T4) %>% 
   group_by(year) %>% 
   filter(height==max(height)) %>% 
   arrange(year))
```

```
## # A tibble: 9 x 4
## # Groups:   year [9]
##    year height gen      T4
##   <int>  <dbl> <fct> <dbl>
## 1  1974   97   G15    12.1
## 2  1975   83.3 G15    16.0
## 3  1976   86.8 G15    17.4
## 4  1977   80   G10    17.4
## 5  1978   75.5 G11    13.9
## 6  1979   97   G14    14.0
## 7  1980  106.  G11    13.9
## 8  1981  106.  G07    13.6
## 9  1982   90.3 G14    11.6
```
]]
.column.bg-white[.content[
# Labels with `geom_label`

```r
g <- ggplot(barley, 
       aes(T4, height)) + 
  geom_point(size=4, aes(color=factor(year))) + 
  guides(color=FALSE) + 
  xlab("Avg temp (Celsius) in the 4-th period") + 
  ylab("Height")
g + geom_label(data=maxh_df, size=4,
             aes(T4, height, label=year)) 
```

<img src="day1-session02-datavis_files/figure-html/unnamed-chunk-78-1.png" style="display: block; margin: auto;" />
]]

---

```r
g + 
* geom_text(
    data=maxh_df, size=4,
*   nudge_y=10,
    aes(T4, height, label=year)) 
```

]]
.column[.content[
# `ggrepel`

```r
library(ggrepel)
g + 
* geom_label_repel(
    data=maxh_df, size=4,
    aes(T4, height, label=year)) 
```

<img src="day1-session02-datavis_files/figure-html/unnamed-chunk-80-1.png" style="display: block; margin: auto;" />
]]

---

```r
g + annotate("text", 
             x=12, y=100, 
             label="1974", size=12)
```

![](day1-session02-datavis_files/figure-html/unnamed-chunk-81-1.png)
]]
.column[.content[
# Annotation Rectangle

```r
g + annotate("rect", xmin=15, xmax=18, 
             ymin=-Inf, ymax=Inf, 
             alpha=0.2, fill="red")
```

![](day1-session02-datavis_files/figure-html/unnamed-chunk-82-1.png)
]]

---

.column.bg-blue[.content[
# Changing Labels

```r
g <- ggplot(vargas.wheat1.traits, 
       aes(NGS, yield)) + 
  geom_point(size=3) + 
  geom_point(aes(colour=gen)) + 
  geom_smooth(se=F, method="lm") + 
  facet_wrap(~year) + 
* labs(colour="Genotype") +
  # changes the label name for color legend
* labs(x="Number of grains per spikelet") +
  # same as xlab(..)
* labs(y="Yield (kg/ha)") +
  # same as ylab(..)
* labs(title="Durum Wheat at Ciudad Obregon, Mexico 1990-1995") +
  # same as ggtitle(..)
* labs(subtitle="Source: Vargas et al. (1998) Interpreting Genotype x Environment Interaction in Wheat by Partial Least Squares Regression.")
  # same as ggtitle(subtitle=..)
```
]]
.column[.content.vmiddle.center[
![](day1-session02-datavis_files/figure-html/unnamed-chunk-84-1.png)
]]

---