Data Visualization

# Data Visualization

## Designing effective plots

---
class: left, hide_logo, hide-count

## Setup

.pull-left[
**Option 1**
* Download and unzip today's materials into `glhlth562/materials`
* Find and open the `dataviz2_template.Rmd` file in `materials/dataviz2`.
]

.pull-right[
**Option 2**
* Pull updates from [github](https://github.com/ericpgreen/glhlth562) (assumed you cloned this repo previously)
* Find and open the `dataviz2_template.Rmd` file in `materials/dataviz2`.
]
---

# Labels, themes, annotations, scales

---

### Let's return to our FT example

---

### Load the data if it's not already loaded

```r
  covid <- read_csv("https://raw.githubusercontent.com/ericpgreen/glhlth562/main/materials/dataviz1/covid.csv")
  glimpse(covid)
```

```
## Rows: 624
## Columns: 6
## $ X1                <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1…
## $ Province_State    <chr> "Arizona", "Arizona", "Arizona", "Arizona", "Arizona…
## $ date              <date> 2020-09-01, 2020-09-02, 2020-09-03, 2020-09-04, 202…
## $ deaths_roll7      <dbl> 27.57143, 28.57143, 27.28571, 27.14286, 25.28571, 26…
## $ deaths_roll7_100k <dbl> 0.3787952, 0.3925339, 0.3748698, 0.3729072, 0.347392…
## $ label             <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
```

---

### Here's where we left off

```r
# see chunk 'resume'
p <- covid %>%                                    # data
  ggplot(aes(x = date, y = deaths_roll7_100k,     # mapping
             color = Province_State)) +
         geom_line() +                            # geom
         scale_color_manual(values = c(           # theme
                                       "#eb5e8d",   # pink 
                                       "#0f5599"    # blue
                                       )) 
p                              
```

]

<img src="data:image/png;base64,#dataviz2_deck_files/figure-html/resume_plot-1.png" width="70%" />
]
]

---

### Adjust the scales

```r
# see chunk 'scales'
p <- p +
  scale_y_continuous(breaks = seq(____, ____, _____)) +
  scale_x_date(date_breaks = "_______", #date_labels = "%b",
               # https://stackoverflow.com/a/50116294/841405
               labels = function(x) if_else(month(x) == 9 | month(x) == 1, 
                                            paste(month(x, label = TRUE),
                                                  "\n", year(x)), 
                                            paste(month(x, label = TRUE))))
p      
```

]

```r
# see chunk 'scales'
p <- p +
  scale_y_continuous(breaks = seq(0, 2.4, .2)) +
  scale_x_date(date_breaks = "1 month", #date_labels = "%b",
               # https://stackoverflow.com/a/50116294/841405
               labels = function(x) if_else(month(x) == 9 | month(x) == 1, 
                                            paste(month(x, label = TRUE),
                                                  "\n", year(x)), 
                                            paste(month(x, label = TRUE))))
p      
```

]

<img src="data:image/png;base64,#dataviz2_deck_files/figure-html/scales_plot-1.png" width="70%" />
]
]

---

### Annotate

```r
# see chunk 'annotate'
p <- p +
  geom_vline(xintercept = as.Date("2020-12-20"), 
             linetype="solid", color="black") +
  annotate("text", x = as.Date("2020-12-20"), y = ___, 
           label = "US vaccine launch", hjust=1.1) +
  geom_segment(aes(x=as.Date("2021-03-11"), xend=as.Date("2021-03-11"), 
                   y=___, yend=___), linetype="dashed", color="#0f5599") +
  annotate("text", x = as.Date("2021-03-11"), y = ___, label = "NC 10% fully vaccinated",
           color="#0f5599", hjust=-0.1) +
  geom_segment(aes(x=as.Date("2021-03-08"), xend=as.Date("2021-03-08"),       
                   y=0, yend=1.4), linetype="dashed", color="#eb5e8d") +
  annotate("text", x = as.Date("2021-03-08"), y = 1.4, label = "___",
           color="#eb5e8d", hjust=-0.1)
p  
```

]

<img src="data:image/png;base64,#dataviz2_deck_files/figure-html/annotate_plot-1.png" width="70%" />
]
]

---

### Labels

```r
# see chunk 'label'
p <- p +
  labs(title = "New deaths attributed to COVID-19 in North Carolina and Arizona",
       subtitle = "Seven-day rolling average of new deaths (per 100k)",
       y = NULL,
       x = NULL,
       caption = "Source: Financial Times analysis of data from the Johns Hopkins CSSE.") 
p
```

]

<img src="data:image/png;base64,#dataviz2_deck_files/figure-html/label_plot-1.png" width="70%" />
]
]

---

### Themes

```r
# see chunk 'theme'
p <- p +
  theme_minimal() +
  theme(panel.grid.minor = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")
p
```

]

<img src="data:image/png;base64,#dataviz2_deck_files/figure-html/theme_plot-1.png" width="70%" />
]
]

---

# Credits

Deck by Eric Green ([@ericpgreen](https://twitter.com/ericpgreen)), licensed under Creative Commons Attribution [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)

* {[`xaringan`](https://github.com/yihui/xaringan)} for slides with help from {[`xaringanExtra`](https://github.com/gadenbuie/xaringanExtra)}
* [JHU CSSE COVID-19 data](https://github.com/CSSEGISandData/COVID-19)