06-lesson-06-ggplot2.Rmd

---
title: "GGPLOT2"
output: revealjs::revealjs_presentation
---

```{r setup}
library(tidyverse)
library(ggplot2)
```

# Recap from Yesterday

- Intro to R
- Tidy data practices
- Getting Data into R
- *Basic wrangling*

## Wrangling Data

- "verbs" from the tidyverse
    - select()
    - filter()
    - group_by()
    - summarise()

***

```{r data_import}
outreach <- read_csv("~/Downloads/outreach.csv")
```

***

```{r data_inspect}
head(outreach[, 1:5])
```

***

Are patients sicker at weekends?

***

```{r dplyr_example}
outreach$weekend <- ifelse(
  outreach$wday == "Sat" | outreach$wday == "Sun",
  TRUE, FALSE)

outreach_f <- filter(outreach, icu_accept == 1)
outreach_s <- select(outreach_f, age, male, weekend, sofa_score)
outreach_g <- group_by(outreach_s, weekend)
outreach_x <- summarise(outreach_g, mean.sofa = mean(sofa_score),
                        sd.sofa = sd(sofa_score))

outreach_x
```

***

A better way?

***

```{r magrittr_example}
outreach %>%
  filter(icu_accept == 1) %>%
  select(age, male, weekend, sofa_score) %>%
  group_by(weekend) %>%
  summarise(mean.sofa = mean(sofa_score),
              sd.sofa = sd(sofa_score))
```

# The Grammar of Graphics

- GGPLOT2 is an r library that allows us to easily make publication quality plots
- Based on the "Grammar of Graphics"

## Building Layers

- The Data
    - The thing we are plotting
- Mapping Aesthetics
    - How we are mapping the data to a visual dimention
- Geometric Objects
    - How the mapping is presented to us

## The Data

- Should be "tidy"
- Think carefully about unit of observation

***

```{r ggplot_data, eval = FALSE}
outreach %>%
  ggplot()

# ggplot(data = outreach)
```

***

```{r ggplot_data_eval, echo = FALSE}
outreach %>%
  ggplot()

# ggplot(data = outreach)
```

## Mapping Aesthetics
 
- This is how the data is "mapped" to a visual dimention
- We are most familiar with `x` and `y` mappings
- Others might include:
    - Size
    - Shape
    - Colour/Fill

```{r aesthetics, eval = FALSE}
outreach %>%
  ggplot(mapping = aes(x = sofa_score, fill = weekend))

# ggplot(data = outreach,
#        mapping = aes(x = sofa_score, fill = weekend))
```

***

```{r aesthetics_eval, echo = FALSE}
outreach %>%
  ggplot(mapping = aes(x = sofa_score, fill = weekend))

# ggplot(data = outreach,
#        mapping = aes(x = sofa_score, fill = weekend))
```

## Geometric Objects

- This allows us to display our data
- Points, lines, bars etc.

```{r geom, eval = FALSE}
outreach %>%
  ggplot(mapping = aes(x = sofa_score, fill = weekend)) +
  geom_density()

# ggplot(data = outreach,
#        mapping = aes(x = sofa_score, fill = weekend)) +
#   geom_density()
```

***

```{r geom_eval, echo = FALSE}
outreach %>%
  ggplot(mapping = aes(x = sofa_score, fill = weekend)) +
  geom_density(bw = 0.6, alpha = 0.8)

# ggplot(data = outreach,
#        mapping = aes(x = sofa_score, fill = weekend)) +
#   geom_density()
```

## Review of layers

1. data
2. aesthetic mapping
3. geoms

## Let's Play

```{r play1, eval=FALSE}



outreach %>%
  ggplot(mapping = aes(x = news_score,
                       y = sofa_score)) +
  geom_point()






```

***

```{r play1_eval, echo=FALSE}
outreach %>%
ggplot(mapping = aes(x = news_score,
                     y = sofa_score)) +
  geom_point()
```

***

```{r play2, eval=FALSE}
outreach %>%
ggplot(mapping = aes(x = news_score,
                     y = sofa_score)) +
  geom_jitter()
```

***

```{r play2_eval, echo=FALSE}
outreach %>%
ggplot(mapping = aes(x = news_score,
                     y = sofa_score)) +
  geom_jitter()
```

## Exercise

- There are many different anaesthetics (x, y, col, size, shape etc.)
- There are many different geoms
- Check out the cheat sheet and see if you can construct your own