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03-ggplot2.Rmd
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03-ggplot2.Rmd
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---
title: "03-ggplot2"
author: "Silvia P. Canelón"
date: "9/19/2020"
output: html_document
---
class: penguin-tour
```{r, echo=FALSE, out.width=1200}
knitr::include_graphics("images/pptx/03-ggplot2.png")
```
.footnote[<span>Photo by <a href="https://unsplash.com/@eadesstudio?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">James Eades</a> on <a href="https://unsplash.com/collections/12240655/palmerpenguins/d5aed8c855e26061e5e651d3f180b76d?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a></span>
]
---
background-image: url(images/hex/ggplot2.png)
background-position: 1050px 50px
background-size: 80px
# ggplot2: info
.panelset[
.panel[.panel-name[Overview]
### Let's start by making a simple plot of our data!
### `ggplot2` uses the "Grammar of Graphics" and layers graphical components together to create a plot.
]
.panel[.panel-name[Cheatsheet]
`r icon::fa("file-pdf")` PDF: https://github.com/rstudio/cheatsheets/raw/master/data-visualization-2.1.pdf
]
.panel[.panel-name[Reading]
.left-column[
```{r echo=FALSE}
knitr::include_graphics("images/r4ds-cover.png")
```
]
.right-column[
### R for Data Science: [Ch 3 Data visualization](https://r4ds.had.co.nz/data-visualisation.html)
### Package documentation: https://ggplot2.tidyverse.org/
]
]
]
---
background-image: url(images/hex/ggplot2.png)
background-position: 1050px 50px
background-size: 80px
# ggplot2: exercise
.panelset[
.panel[.panel-name[View the data]
.pull-left[
### Get a full view of the dataset:
```{r eval=FALSE}
View(penguins)
```
]
.pull-right[
### Or catch a `glimpse`:
```{r}
glimpse(penguins)
```
]
]
.panel[.panel-name[Scatterplot]
Let's see if body mass varies by penguin sex
.pull-left[
```{r eval=FALSE}
ggplot(data = penguins,
aes(x = sex, y = body_mass_g)) + #<<
geom_point()
```
]
.pull-right[
```{r, echo=FALSE, warning=FALSE, fig.height=5}
ggplot(data = penguins,
aes(x = sex, y = body_mass_g)) +
geom_point()
```
]
]
.panel[.panel-name[Boxplot]
.pull-left[
```{r eval=FALSE}
ggplot(data = penguins,
aes(x = sex, y = body_mass_g)) +
geom_boxplot() #<<
```
]
.pull-right[
```{r echo=FALSE, warning=FALSE, fig.height=5}
ggplot(data = penguins,
aes(x = sex, y = body_mass_g)) +
geom_boxplot()
```
]
]
.panel[.panel-name[By Species]
.pull-left[
```{r eval=FALSE}
ggplot(data = penguins,
aes(x = sex, y = body_mass_g)) +
geom_boxplot(aes(fill = species)) #<<
```
### <br/> What do you notice?
]
.pull-right[
```{r echo=FALSE, warning=FALSE, fig.height=5}
ggplot(data = penguins,
aes(x = sex, y = body_mass_g)) +
geom_boxplot(aes(fill = species))
```
]
]
.panel[.panel-name[Chat]
### You might see...
.pull-left[
- Gentoo penguins have higher body mass than Adélie and Chinstrap penguins
- Higher body mass among male Gentoo penguins compared to female penguins
- Pattern not as discernible when comparing Adélie and Chinstrap penguins
- No *NA*s among Chinstrap penguin data points! **sex** was available for each observation
]
.pull-right[
```{r echo=FALSE, warning=FALSE, fig.height=5}
penguins %>%
ggplot(aes(x = sex, y = body_mass_g)) +
geom_boxplot(aes(fill = species))
```
]
]
]