NRI 7350 - Lab 2

class: title-slide, nobar

![:img center, bottom: 25px, 50%, ,](https://github.com/allisonhorst/stats-illustrations/raw/master/rstats-artwork/ggplot2_masterpiece.png)

## NRI 7350
# Plots and Loading Data

.footnote[Artwork by [@allison_horst](https://github.com/allisonhorst/stats-illustrations)]

---
# Check-in

- Everyone getting emails? (e.g., email about these slides?)
- Everyone have access to these slides?  
  https://steffilazerte.ca/NRI_7350/slides.html

---
class: split-60
# Data for Assignments 2, 3 and 4

> Assignment 2 (first R assignment!) comes next week

### You'll need a data set with

- One *continuous* dependent variable (response)
  - One *categorical* independent variable with **at least three categories** (explanatory)
  - Two *continuous* independent variables (explanatory)

![:spacer 15px]()

.columnl[
> Example:   
> 
> - response = `frequency`
> - categorical explanatory = `site`
> - continuous explanatory = `noise` and `mass`
]

.columnr[
.medium[
<table class="table" style="margin-left: auto; margin-right: auto;">
 <thead>
 <tr>
 <th style="text-align:right;"> frequency </th>
 <th style="text-align:left;"> site </th>
 <th style="text-align:right;"> noise </th>
 <th style="text-align:right;"> mass </th>
 </tr>
 </thead>
<tbody>
 <tr>
 <td style="text-align:right;"> 3500 </td>
 <td style="text-align:left;"> rural </td>
 <td style="text-align:right;"> 45 </td>
 <td style="text-align:right;"> 11.0 </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 3600 </td>
 <td style="text-align:left;"> city </td>
 <td style="text-align:right;"> 65 </td>
 <td style="text-align:right;"> 10.0 </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 3555 </td>
 <td style="text-align:left;"> town </td>
 <td style="text-align:right;"> 55 </td>
 <td style="text-align:right;"> 10.5 </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 3650 </td>
 <td style="text-align:left;"> rural </td>
 <td style="text-align:right;"> 47 </td>
 <td style="text-align:right;"> 9.5 </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 3300 </td>
 <td style="text-align:left;"> town </td>
 <td style="text-align:right;"> 52 </td>
 <td style="text-align:right;"> 10.0 </td>
 </tr>
</tbody>
</table>
]]

---
# Data for Assignments 2, 3 and 4

### Don't have enough variables? You can...

- Create a categorical variable from continuous 
  - `noise` in dB = `quiet`, `regular`, `noisy`
  - `mass` in grams = `small`, `medium`, `large`
  - `concentration` in g/mL = `low`, `medium`, `high`
  - `amount` = `none`, `some`, `lots`
- Create a dummy continuous variable
  - `x` = random numbers between 1 and 250
  
  
### Don't have any data?

- Ask your supervisor for something related to your project
- Ask your fellow students
- Email Nicky and I together with a brief description of your project and its design and we'll figure something out

---
class: section

# Getting started (again)

Open RStudio  
Open your NRI project  
Open a **new** script for today:

File > New File > R Script

Make sure to load `tidyverse` at the top:  
`library(tidyverse)`

---
class: section

# Creating Figures

---
class: nobar
# `ggplot2`

![:img center, bottom: 0, 78%, ,](https://github.com/allisonhorst/stats-illustrations/raw/master/rstats-artwork/ggplot2_masterpiece.png)

.footnote[Artwork by [@allison_horst](https://github.com/allisonhorst/stats-illustrations)]

---
# Our data set: Palmer Penguins!

![:img center, bottom: 0, 78%, ,](https://raw.githubusercontent.com/allisonhorst/palmerpenguins/master/man/figures/lter_penguins.png)

.footnote[Artwork by [@allison_horst](https://github.com/allisonhorst/stats-illustrations)]

![:img right: 1%, top: 1%, 10%, ,](https://raw.githubusercontent.com/allisonhorst/palmerpenguins/master/man/figures/logo.png)

---
# Our data set: Palmer Penguins!

.small[

```r
library(palmerpenguins)
penguins
```

```
## # A tibble: 344 × 8
## species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g sex year
## <fct> <fct> <dbl> <dbl> <int> <int> <fct> <int>
## 1 Adelie Torgersen 39.1 18.7 181 3750 male 2007
## 2 Adelie Torgersen 39.5 17.4 186 3800 female 2007
## 3 Adelie Torgersen 40.3 18 195 3250 female 2007
## 4 Adelie Torgersen NA NA NA NA <NA> 2007
## 5 Adelie Torgersen 36.7 19.3 193 3450 female 2007
## 6 Adelie Torgersen 39.3 20.6 190 3650 male 2007
## 7 Adelie Torgersen 38.9 17.8 181 3625 female 2007
## 8 Adelie Torgersen 39.2 19.6 195 4675 male 2007
## 9 Adelie Torgersen 34.1 18.1 193 3475 <NA> 2007
## 10 Adelie Torgersen 42 20.2 190 4250 <NA> 2007
## # … with 334 more rows
```
]

![:img right: 10%, bottom: 0, 25%, ,](https://raw.githubusercontent.com/allisonhorst/palmerpenguins/master/man/figures/lter_penguins.png)

.footnote[Artwork by [@allison_horst](https://github.com/allisonhorst/stats-illustrations)]

![:img right: 1%, top: 1%, 10%, ,](https://raw.githubusercontent.com/allisonhorst/palmerpenguins/master/man/figures/logo.png)

> Your turn! Run this code and look at the output in the console

---
# A basic plot

<code class ='r hljs remark-code'>library(palmerpenguins) library(tidyverse) ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm)) + &nbsp;&nbsp;&nbsp;&nbsp;geom_point()</code>

---
# Break it down

<code class ='r hljs remark-code'>library(palmerpenguins) library(tidyverse) ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm)) + &nbsp;&nbsp;&nbsp;geom_point()</code>

![:spacer 10px]()

### `library(palmerpenguins)`
- Load the `palmerguins` package so we have access to `penguins` data

---
# Break it down

<code class ='r hljs remark-code'>library(palmerpenguins) library(tidyverse) ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm)) + &nbsp;&nbsp;&nbsp;geom_point()</code>

![:spacer 10px]()

### `library(tidyverse)`
- Load the `tidyverse` package (which loads the `ggplot2` package)

---
# Break it down

<code class ='r hljs remark-code'>library(palmerpenguins) library(tidyverse) ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm)) + &nbsp;&nbsp;&nbsp;geom_point()</code>

![:spacer 10px]()

### `ggplot()`
- Set the attributes of your plot
- **`data`** = Dataset
- **`aes`** = Aesthetics (how the data are used)
- Think of this as your plot defaults

---

# Break it down

<code class ='r hljs remark-code'>library(palmerpenguins) library(tidyverse) ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm)) + &nbsp;&nbsp;&nbsp;geom_point()</code>

![:spacer 10px]()

### `geom_point()`

- Choose a `geom` function to display the data
- Always *added* to a `ggplot()` call with **+**

> ggplots are essentially layered objects, starting with a call to `ggplot()`

---
class: split-50
# Plots are layered

.columnl[
.small[

```r
ggplot(data = penguins, aes(x = sex, y = body_mass_g))
```

<img src="2 Loading Data and Creating Figures - answers_files/figure-html/unnamed-chunk-8-1.png" width="90%" style="display: block; margin: auto;" />
]]

.columnr[
.small[

```r
ggplot(data = penguins, aes(x = sex, y = body_mass_g)) +
* geom_boxplot()
```

<img src="2 Loading Data and Creating Figures - answers_files/figure-html/unnamed-chunk-9-1.png" width="90%" style="display: block; margin: auto;" />
]]

---
class: split-50
# Plots are layered

.columnl[
.small[

```r
ggplot(data = penguins, aes(x = sex, y = body_mass_g))
```

<img src="2 Loading Data and Creating Figures - answers_files/figure-html/unnamed-chunk-10-1.png" width="90%" style="display: block; margin: auto;" />
]]

.columnr[
.small[

```r
ggplot(data = penguins, aes(x = sex, y = body_mass_g)) +
* geom_point()
```

<img src="2 Loading Data and Creating Figures - answers_files/figure-html/unnamed-chunk-11-1.png" width="90%" style="display: block; margin: auto;" />
]]

---
class:split-50
# Plots are layered

.columnl[
.small[

```r
ggplot(data = penguins, aes(x = sex, y = body_mass_g))
```

<img src="2 Loading Data and Creating Figures - answers_files/figure-html/unnamed-chunk-12-1.png" width="90%" style="display: block; margin: auto;" />
]]

.columnr[
.small[

```r
ggplot(data = penguins, aes(x = sex, y = body_mass_g)) +
* geom_violin()
```

<img src="2 Loading Data and Creating Figures - answers_files/figure-html/unnamed-chunk-13-1.png" width="90%" style="display: block; margin: auto;" />
]]

---
class: split-50
# Plots are layered

.columnl[
### You can add multiple layers

.small[

```r
ggplot(data = penguins, aes(x = sex, y = body_mass_g)) +
  geom_boxplot()
```

<img src="2 Loading Data and Creating Figures - answers_files/figure-html/unnamed-chunk-14-1.png" width="90%" style="display: block; margin: auto;" />
]]

---
class: split-50
# Plots are layered

.columnl[
### You can add multiple layers

.small[

```r
ggplot(data = penguins, aes(x = sex, y = body_mass_g)) +
  geom_boxplot() + 
* geom_point(size = 2, colour = "red")
```

<img src="2 Loading Data and Creating Figures - answers_files/figure-html/unnamed-chunk-15-1.png" width="90%" style="display: block; margin: auto;" />
]]

.columnr[

### Order matters

.small[

```r
ggplot(data = penguins, aes(x = sex, y = body_mass_g)) +
* geom_point(size = 2, colour = "red") +
  geom_boxplot()
```

<img src="2 Loading Data and Creating Figures - answers_files/figure-html/unnamed-chunk-16-1.png" width="90%" style="display: block; margin: auto;" />
]]

---
class: split-50
# Plots are objects

#### Any ggplot can be saved as an object

```r
g <- ggplot(data = penguins, aes(x = sex, y = body_mass_g))
```

.columnl[

```r
g
```

<img src="2 Loading Data and Creating Figures - answers_files/figure-html/unnamed-chunk-18-1.png" width="90%" style="display: block; margin: auto;" />
]

.columnr[

```r
g + geom_boxplot()
```

<img src="2 Loading Data and Creating Figures - answers_files/figure-html/unnamed-chunk-19-1.png" width="90%" style="display: block; margin: auto;" />
]

---
class: section

# More Geoms

### (Plot types)

---
# Geoms: Lines

```r
ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm)) +
* geom_line()
```

---
# Geoms: Histogram

```r
ggplot(data = penguins, aes(x = body_mass_g)) +
* geom_histogram(binwidth = 100)
```

![:label 50%, 18%, 25%](Note: We only need 1 aesthetic here (x&rpar;)

---
# Geoms: Barplots

### Let `ggplot` count your data

```r
ggplot(data = penguins, aes(x = species)) +
* geom_bar()
```

---
# Geoms: Barplots

### **Or**, you can provide the counts .small[(makes more sense when you already/only have counts)]

```r
species_counts <- count(penguins, species)

ggplot(data = species_counts, aes(x = species, y = n)) +
* geom_bar(stat = "identity")
```

---
# Side Note: `tidyverse` functions

### **Or**, you can provide the counts .small[(makes more sense when you already/only have counts)]

<code class ='r hljs remark-code'>species_counts <- count(penguins, species)</code>

### `count()`
- `tidyverse` functions always start with the **data**, followed by other arguments
- you can reference any **column** from '**data**'
- `count()` the number of observations per unique `column` category

```r
species_counts
```

```
## # A tibble: 3 × 2
## species n
## <fct> <int>
## 1 Adelie 152
## 2 Chinstrap 68
## 3 Gentoo 124
```

---
# Your Turn: Create this plot

<code class ='r hljs remark-code'>library(ggplot2) ggplot(data = &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;, aes(x = &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;, y = &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;)) + &nbsp;&nbsp;geom_ &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;( &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;)</code>

---
exclude: FALSE
# Your Turn: Create this plot

<code class ='r hljs remark-code'>library(ggplot2) ggplot(data = penguins, aes(x = island, y = bill_depth_mm)) + &nbsp;&nbsp;geom_boxplot(colour = "blue")</code>

---
class: section
# Showing data by group

---
# Mapping aesthetics

```r
ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm)) +
  geom_point()
```

---
# Mapping aesthetics

<code class ='r hljs remark-code'>ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm, colour = sex)) + &nbsp;&nbsp;geom_point()</code>

---
# Mapping aesthetics

---
# Mapping aesthetics

### `ggplot` automatically populates the legends (combining where it can)

```r
ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm, colour = sex, shape = sex)) +
  geom_point()
```

---
# Faceting: `facet_wrap()`

```r
ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm, colour = sex)) +
  geom_point() +
* facet_wrap(~ species)
```

![:box 15%, 25%, 20%](Split plots by one grouping variable)

---
# Faceting: `facet_grid()`

```r
ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm, colour = sex)) +
  geom_point() +
* facet_grid(sex ~ species)
```

![:box 15%, 25%, 20%](Split plots by two grouping variables)

---
# Your Turn: Create this plot

<code class ='r hljs remark-code'>ggplot(data = &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;, aes( &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;)) + &nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; + &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</code>

![:spacer 15px]()

> **Hint:** `colour` is for outlining with a colour, `fill` is for 'filling' with a colour

---
exclude: FALSE
# Your Turn: Create this plot

<code class ='r hljs remark-code'>ggplot(data = penguins, aes(x = sex, y = flipper_length_mm, fill = sex)) + &nbsp;&nbsp;geom_boxplot() + &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;facet_wrap(~ species)</code>

![:spacer 15px]()

> **Hint:** `colour` is for outlining with a colour, `fill` is for 'filling' with a colour

---
exclude: true
class: section

# Adding Statistics to Plots

---
exclude: true
# Using stats: Summarizing data

### Add data means as points

```r
ggplot(data = penguins, aes(x = sex, y = body_mass_g)) +
* stat_summary(geom = "point", fun.y = mean)
```

---
exclude: true
# Using stats: Summarizing data

### Add error bars, calculated from the data

```r
ggplot(data = penguins, aes(x = sex, y = body_mass_g)) +
  stat_summary(geom = "point", fun.y = mean) +
* stat_summary(geom = "errorbar", width = 0.05, fun.data = mean_se)
```

---
class: section

# Trendlines / Regression Lines

---
# Trendlines / Regression lines

### `geom_line()` is connect-the-dots, not a trend or linear model

```r
ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm)) +
  geom_point() +
  geom_line()
```

![:box 25%, 25%, 20%](Not what we're looking for)

---
# Trendlines / Regression lines
### Let's add a trend line properly

Start with basic plot:

```r
g <- ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm)) +
 geom_point()
g
```

---
class: split-45
# Trendlines / Regression lines

.columnl[
### Add the `stat_smooth()`
- `lm` is for "linear model" (i.e. trendline)
- grey ribbon = standard error
]

.columnr[

<code class ='r hljs remark-code'>g + stat_smooth(method = "lm")</code>
]

![:spacer 55px]()

---
class: split-45
# Trendlines / Regression lines

.columnl[
### Add the `stat_smooth()`
- remove the grey ribbon `se = FALSE`
]

.columnr[

<code class ='r hljs remark-code'>g + stat_smooth(method = "lm", se = FALSE)</code>

]

![:spacer 55px]()

---
# Trendlines / Regression lines

### A line for each group
- Specify group (here we use `colour` to specify `sex`)

```r
g <- ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm, colour = species)) +
 geom_point()
g
```

---
# Using stats: Trendlines / Regression lines

### A line for each group
- `stat_smooth()` automatically uses the same grouping

```r
g + stat_smooth(method = "lm", se = FALSE)
```

---
# Trendlines / Regression lines

### A line for each group AND overall

```r
g +  
  stat_smooth(method = "lm", se = FALSE) + 
  stat_smooth(method = "lm", se = FALSE, colour = "black")
```

---
# Your Turn: Create this plot

- A scatter plot
- Comparing Flipper Length by Body Mass grouped by Species
- With *a single regression line for the overall trend*

---
exclude: FALSE

# Your Turn: Create this plot

- A scatter plot
- Comparing Flipper Length by Body Mass grouped by Species
- With *a single regression line for the overall trend*

```r
ggplot(data = penguins, aes(x = body_mass_g, y = flipper_length_mm, colour = species)) + 
  geom_point() +
  stat_smooth(se = FALSE, colour = "black", method = "lm")
```

---
class: section

# Customizing plots

---
# Customizing: Starting plot

### Let's work with this plot

```r
g <- ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm, colour = species)) +
 geom_point()
```

---
# Customizing: Labels

```r
g + labs(title = "Bill Length vs. Body Mass", 
         x = "Body Mass (g)", 
         y = "Bill Length (mm)",
         colour = "Species", tag = "A")
```

![:box 30%, 20%, 40%](Practice for later Add proper labels to some of your previous plots)

---
class: split-50
# Customizing: Built-in themes

.columnl[
<img src="2 Loading Data and Creating Figures - answers_files/figure-html/unnamed-chunk-51-1.png" width="95%" style="display: block; margin: auto;" /><img src="2 Loading Data and Creating Figures - answers_files/figure-html/unnamed-chunk-51-2.png" width="95%" style="display: block; margin: auto;" />
]

.columnr[
<img src="2 Loading Data and Creating Figures - answers_files/figure-html/unnamed-chunk-52-1.png" width="95%" style="display: block; margin: auto;" /><img src="2 Loading Data and Creating Figures - answers_files/figure-html/unnamed-chunk-52-2.png" width="95%" style="display: block; margin: auto;" />
]

---
# Customizing: Data range

### Limit the data (exclude data)

```r
g + xlim(c(4000, 5000))
```

```
## Warning: Removed 228 rows containing missing values (geom_point).
```

---
# Customizing: Axes

`scale_` + (`x` or `y`) + type (`contiuous`, `discrete`, `date`, `datetime`)

- `scale_x_continuous()`
- `scale_y_discrete()`
- etc.

### Common arguments

```r
g + scale_x_continuous(breaks = seq(0, 20, 10)) # Tick breaks
g + scale_x_continuous(limits = c(0, 15))       # xlim() is a shortcut for this
g + scale_x_continuous(expand = c(0, 0))        # Space between axis and data
```

---
# Customizing: Axes

---
# Customizing: Aesthetics

### Using scales

`scale_` + aesthetic (`colour`, `fill`, `size`, etc.) + type (`manual`, `continuous`, `datetime`, etc.)

```r
g + scale_colour_manual(name = "Type", values = c("green", "purple", "yellow"))
```

---
# Customizing: Aesthetics

### Using scales

Or be very explicit:

```r
g + scale_colour_manual(name = "Type", 
                        values = c("Adelie" = "green", "Gentoo" = "purple", "Chinstrap" = "yellow"), 
                        na.value = "black")
```

---
# Customizing: Aesthetics

### For colours, consider colour-blind-friendly scales

```r
library(ggthemes)
g + scale_colour_colorblind(name = "Type")
g + scale_colour_viridis_d(name = "Type")
```

---
# Customizing: Aesthetics

### Forcing

Remove the association between a variable and an aesthetic

<code class ='r hljs remark-code'>ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm, colour = sex)) + &nbsp;&nbsp;geom_point(colour = "green", size = 5) + &nbsp;&nbsp;stat_smooth(method = "lm", se = FALSE, colour = "red")</code>

![:box 15%, 60%, 20%](Note: When forcing, aesthetic is not inside <code>aes(&rpar;<code>)

---
class: split-50
# Customizing: Legends placement

.columnl[
### At the: top, bottom, left, right

```r
g + theme(legend.position = "top")
```

<img src="2 Loading Data and Creating Figures - answers_files/figure-html/unnamed-chunk-61-1.png" width="100%" style="display: block; margin: auto;" />
]

.columnr[
### Exactly here

```r
g + theme(legend.position = c(0.15, 0.7))
```

<img src="2 Loading Data and Creating Figures - answers_files/figure-html/unnamed-chunk-62-1.png" width="100%" style="display: block; margin: auto;" />
]

---
class: section

# Combining plots with `patchwork`

## Further Reading: <https://patchwork.data-imaginist.com/>

---
# Combining plots with `patchwork`

### Setup
- Load `patchwork`
- Create a couple of different plots

```r
library(patchwork)

g1 <- ggplot(data = penguins, aes(x = bill_length_mm, y = bill_depth_mm, colour = species)) + 
 geom_point()

g2 <- ggplot(data = penguins, aes(x = species, y = flipper_length_mm)) + 
 geom_boxplot()

g3 <- ggplot(data = penguins, aes(x = flipper_length_mm, y = body_mass_g, colour = species)) + 
 geom_point()
```

---
# Combining plots with `patchwork`

### Side-by-Side 2 plots

```r
g1 + g2
```

---
# Combining plots with `patchwork`

### More complex arrangements

```r
g2 / (g1 + g3)
```

---
# Combining plots with `patchwork`

### "collect" common legends

```r
g2 / (g1 + g3) + plot_layout(guides = "collect")
```

---
class: split-55
# Combining plots with `patchwork`

.columnl[
### Annotate
.small[

```r
g2 / (g1 + g3) + 
  plot_layout(guides = "collect") +
  plot_annotation(title = "Penguins Data Summary",          
                  caption = "Fig 1. Penguins Data Summary", 
                  tag_levels = "A",                         
                  tag_suffix = ")")                         
```
]]

.columnr[
<img src="2 Loading Data and Creating Figures - answers_files/figure-html/unnamed-chunk-67-1.png" width="100%" style="display: block; margin: auto;" />

]

---
class: section
# Saving plots

---
# Saving plots

## RStudio Export
**Demo**

## `ggsave()`

```r
g <- ggplot(penguins, aes(x = sex, y = bill_length_mm, fill = year)) +
 geom_boxplot()

ggsave(filename = "penguins_mass.png", plot = g)
```

```
## Saving 8 x 3.6 in image
```

---
# Saving plots

## Publication quality plots

- Many publications require 'lossless' (pdf, svg, eps, ps) or high quality formats (tiff, png)
- Specific sizes corresponding to columns widths
- Minimum resolutions

```r
g <- ggplot(penguins, aes(x = sex, y = body_mass_g)) +
 geom_boxplot() +
 labs(x = "Sex", y = "Body Mass (g)") + 
 theme(axis.text.x = element_text(angle = 45, hjust = 1))

ggsave(filename = "penguin_mass.pdf", plot = g, dpi = 300,
       height = 80, width = 129, units = "mm")
```

---
class: section

# Loading Data

---
class: full-width
# Data types: What kind of data do you have?

## Specific program files

Type             | R Package         | Function (example usage)
---------------- | ----------------- | -------------
Excel (.xls, .xlsx)            | `readxl`          | `read_excel("file.xlsx", sheet = 1)`
Comma separated (.csv) | `readr`          | `read_csv("file.csv")` 
Tab separated   (e.g, .txt, .dat) | `readr`          | `read_tsv("file.txt")`
Space separated (e.g, .txt, .dat) | `readr`          | `read_delim("file.dat", delim = " ")`
Fixed-width     (e.g, .txt, .dat) | `readr`          | `read_fwf("file.dat")`

.small[
> **Notes**  
> 1. You may be familiar with base functions (i.e. `read.csv()`, `read.table()`)  
>    These are perfectly acceptable, but `readr` is a bit more powerful and quick
> 
> 2. It can be quicker and safer to save Excel files as a *.csv (Comma-separated-variables file)
>    and then use `readr` package and `read_csv()` function
> 
> 3. `readr` is a tidyverse package
]

---
# Where is my data?

```r
library(tidyverse) # Load tidyverse which includes readr package

my_data <- read_csv("weather.csv")
```

```
## Error: 'weather.csv' does not exist in current working directory ('/home/steffi/Projects/Teaching/UofM - NRI/NRI_7350/_labs').
```

With no folder (just file name) R expects file to be in **Working directory**

## Working directory is:
- Where your RStudio project is
- Your home directory (My Documents, etc.) [If not using RStudio Projects]
- Where you've set it (using `setwd()` or RStudio's Session > Set Working Directory)

> Using Projects in RStudio is a great idea

---
class: full-width

# Where is my data?

## **Absolute** Paths

.medium[
  OS        | Absolute Path                                      
----------- | ---------------------------------------------------
**LINUX**   | /home/steffi/Documents/R Projects/mydata.csv       
**WINDOWS** | C:/Users/steffi/My Documents/R Projects/mydata.csv
**MAC**     | /users/steffi/Documents/R Projects/mydata.csv
]

## **Relative** Paths

.medium[
Path               | Where to look
------------------ | --------
./mydata.csv       | Here (current directory) (./)
../mydata.csv      | Go up one directory (../)
./data/mydata.csv  | Stay here (./), go into "data" folder (data/)
../data/mydata.csv | Go up one directory (../), then into "data" folder (data/)
]

![:box 25%, 50%, 35%](With RStudio 'Projects' only need to use relative paths)

---
# Keep yourself organized

- Create an RStudio Project for each Project (e.g. `My Project`)
- Create a specific `Data` folder within each project (one per project)

Folders look like:
```
- My Project
    - Data
        - mydata1.csv
        - mydata2.csv
    - myscript.R
    - My Project.Rproj
```

Now when you load data, you can use something like this: `"Data/mydata1.csv"`

---
# Checking / Cleaning your data

```r
library(readxl)
my_data <- read_excel("my_data.xlsx")
```

```r
head(my_data)
```

```
## # A tibble: 6 × 6
## `Sample Number` Stage `Date Egg` `Body Mass (g)` ...5 ...6 
## <dbl> <chr> <dttm> <dbl> <lgl> <lgl>
## 1 1 Adult, 1 Egg Stage 2007-11-11 00:00:00 3750 NA NA 
## 2 2 Adult, 1 Egg Stage 2007-11-11 00:00:00 3800 NA NA 
## 3 3 Adult, 1 Egg Stage 2007-11-16 00:00:00 3250 NA NA 
## 4 4 Adult, 1 Egg Stage 2007-11-16 00:00:00 NA NA NA 
## 5 5 Adult, 1 Egg Stage 2007-11-16 00:00:00 3450 NA NA 
## 6 6 Adult, 1 Egg Stage 2007-11-16 00:00:00 3650 NA NA
```

---
#  Checking / Cleaning your data

```r
tail(my_data)
```

```
## # A tibble: 6 × 6
## `Sample Number` Stage `Date Egg` `Body Mass (g)` ...5 ...6 
## <dbl> <chr> <dttm> <dbl> <lgl> <lgl>
## 1 63 Adult, 1 Egg Stage 2009-11-19 00:00:00 3650 NA NA 
## 2 64 Adult, 1 Egg Stage 2009-11-19 00:00:00 4000 NA NA 
## 3 65 Adult, 1 Egg Stage 2009-11-21 00:00:00 3400 NA NA 
## 4 66 Adult, 1 Egg Stage 2009-11-21 00:00:00 3775 NA NA 
## 5 67 Adult, 1 Egg Stage 2009-11-21 00:00:00 4100 NA NA 
## 6 68 Adult, 1 Egg Stage 2009-11-21 00:00:00 3775 NA NA
```

> - Looks like we have some extra, empty, columns... (`..5`, `..6`)
> - Also looks like some column names might not work well in R
>   - (Anything with a space or special character, i.e. `Date Egg` and `Body Mass (g)`

---
# Checking / Cleaning your data

- When loading data that was in Excel (etc.) it can often have some funky things going on
- Use the `janitor` package to quickly fix some of those problems

### Column names

```r
library(janitor)

my_data <- clean_names(my_data)
head(my_data)
```

```
## # A tibble: 6 × 6
## sample_number stage date_egg body_mass_g x5 x6 
## <dbl> <chr> <dttm> <dbl> <lgl> <lgl>
## 1 1 Adult, 1 Egg Stage 2007-11-11 00:00:00 3750 NA NA 
## 2 2 Adult, 1 Egg Stage 2007-11-11 00:00:00 3800 NA NA 
## 3 3 Adult, 1 Egg Stage 2007-11-16 00:00:00 3250 NA NA 
## 4 4 Adult, 1 Egg Stage 2007-11-16 00:00:00 NA NA NA 
## 5 5 Adult, 1 Egg Stage 2007-11-16 00:00:00 3450 NA NA 
## 6 6 Adult, 1 Egg Stage 2007-11-16 00:00:00 3650 NA NA
```

---
# Checking / Cleaning your data

- When loading data that was in Excel (etc.) it can often have some funky things going on
- Use the `janitor` package to quickly fix some of those problems

### Empty rows/columns

```r
my_data <- remove_empty(my_data, which = c("rows", "cols"))
```

```r
head(my_data)
```

```
## # A tibble: 6 × 4
## sample_number stage date_egg body_mass_g
## <dbl> <chr> <dttm> <dbl>
## 1 1 Adult, 1 Egg Stage 2007-11-11 00:00:00 3750
## 2 2 Adult, 1 Egg Stage 2007-11-11 00:00:00 3800
## 3 3 Adult, 1 Egg Stage 2007-11-16 00:00:00 3250
## 4 4 Adult, 1 Egg Stage 2007-11-16 00:00:00 NA
## 5 5 Adult, 1 Egg Stage 2007-11-16 00:00:00 3450
## 6 6 Adult, 1 Egg Stage 2007-11-16 00:00:00 3650
```

---
# Loading your data

![:spacer 40px]()

.center[
> This blazing fast intro to loading/cleaning will not cover the 
> many, *many*, **many**, **MANY** ways that data can be weird.
> 
> 
> 
> Let me know if (when) you run into problems and we can trouble shoot together!
]

---
# Your Turn!

## Prep for next class .small[(be ready for class, but you don't have to share with me unless you want to!)]

- Create a **new RStudio Project** for your class project
- Create a **"Data" folder** inside this project folder
  - Files pane > New Folder
- **Add data** to it (if you have data)
  - Use your computers folder navigator for this
- **Create a new script** in the main folder
  - [Menu] File > New > R Script
- Add code to this script to **load your data into R**
  - Load the appropriate packages (`tidyverse`, `readxl`, `janitor`)
  - Use the appropriate function given your data type (e.g., `read_csv()` for .csv, `read_excel` for .xlsx)
  - Use the appropriate file location (e.g, `"Data/my_data.csv"`)  
      Remember quotes (" ") around the *entire* file location
- **Explore your data** - Click on your data in the Environment pane and take a look!

---
class: section

# Wrapping Up!

---
# Wrapping up: Common mistakes

### Figures
- The **package** is `ggplot`**2**, the function is just **`ggplot()`**
- Did you remember to put the **`+`** at the **end** of the line?
- Order matters! If you're using custom `theme()`'s, make sure you put these lines **after** bundled themes like `theme_bw()`, or they will be overwritten
- Variables like 'year' are treated as continuous, but are really categories
  - Wrap them in `factor()`, i.e. `ggplot(data = penguins, aes(x = factor(year), y = body_mass_g))`

### Loading data
- Not using RProjects which makes it hard to find your data
- Expecting your data to be something it's not
  - (open your data in a text editor or spreadsheet program to take a look)
- Using the wrong function (i.e you used `read_csv()` when you should have used `read_tsv()`)

---
# Wrapping up: Further reading (all **Free**!)
- RStudio > Help > Cheatsheets > Data Visualization with `ggplot2`
- [`ggplot2` book v3](https://ggplot2-book.org)
  - By Hadley Wickham, Danielle Navarro, and Thomas Lin Pedersen
- [Cookbook for R](http://www.cookbook-r.com) - by Winston Chang
  - See also R Graphics Cookbook by Winston Chang
- [`patchwork` site](https://patchwork.data-imaginist.com/)

- [R for Data Science](https://r4ds.had.co.nz)
  - [Data Visualization](http://r4ds.had.co.nz/data-visualisation.html)
  - [Workflow and Projects](https://r4ds.had.co.nz/workflow-projects.html)
  - [Data Import](https://r4ds.had.co.nz/data-import.html)