---
title: "Visualizing the nature of data sets"
author: Abhijit Dasgupta, PhD
abstract: We explore patterns in data sets
---

```{r setup, include=F, child = here::here('slides/templates/setup.Rmd')}
```


```{r setup1, include=FALSE}
setwd(here::here('slides/lectures'))
theme_set(theme_classic()+theme(axis.text = element_text(size=14),
                                axis.title = element_text(size=16),
                                legend.text = element_text(size=14),
                                legend.title = element_text(size=16)))
```

layout: true

<div class="my-header">
<span>BIOF 339: Practical R</span></div>

---
class: middle, center

# The nature of a data set

---

## Data characteristics

Some of the things we care about in a data set are 

+ Nature of each column
+ Missing data patterns
+ Correlation patterns

The **visdat** package and the **naniar** package help us with visualizing these.

---

## Without visualization

.pull-left[
```{r}
summary(airquality)
```
]
.pull-right[
```{r}
glimpse(airquality, width=40)
```

]

These give us a variable-by-variable view.

---

## Visualizing a dataset

.pull-left[

```{r}
visdat::vis_dat(airquality)
```
]
.pull-right[

+ What kinds of variables are in the dataset
+ Which elements are missing
+ A sense of missing patterns
]

---

## Correlation patterns

```{r, fig.height=6}
visdat::vis_cor(airquality)
```

---

## Focus on missing data patterns

```{r, fig.height=6}
visdat::vis_miss(airquality)
```

---
class: middle, center

# A deeper look at missing data

---

```{r}
library(naniar)
gg_miss_upset(airquality)
```

---

```{r}
gg_miss_upset(riskfactors)
```

---

## Missing at random? 

Does missingness in one variable depend on values of another variable?

.pull-left[
```{r, fig.height=5}
ggplot(airquality, 
       aes(Ozone, Solar.R))+
  geom_miss_point()
```
]
.pull-right[
The red points are the values of one variable when the other variable is missing
]

---

## Missing at random?

.pull-left[
```{r, fig.height=6}
gg_miss_fct(x = riskfactors, fct=marital)
```

]
.pull-right[
Percent missing in each variable by levels of a factor

What you're looking for is relatively even colors across
]

---

## Further exploration

1. The **naniar** [website](http://naniar.njtierney.com/)