Data visualisation, reporting, and processing with R

class: center, middle, inverse, title-slide

# Data visualisation, reporting, and processing with R
## Closing remarks
### Kirill Müller, cynkra GmbH

---

# EDA

Understand your data

- Generate questions

- Search for answers

- Rinse and repeat

---

# Important questions

- Variation

- Covariation

???

Tabular data

- Distribution
- Typica values
- Unusual values, outliers
- Missing values

---

# Discrete variables

.pull-left[

```r
ggplot(data = mpg) +
  geom_bar(
    mapping = aes(x = class)
  )
```

![](outro_files/figure-html/outro-1-1.png)
]

.pull-right[

```r
mpg %>% 
  count(class)
```

```
## # A tibble: 7 x 2
##   class          n
##   <chr>      <int>
## 1 2seater        5
## 2 compact       47
## 3 midsize       41
## 4 minivan       11
## 5 pickup        33
## 6 subcompact    35
## 7 suv           62
```
]

---

# Continuous variables

.pull-left[

```r
ggplot(data = mpg) +
  geom_histogram(
    mapping = aes(x = displ),
    binwidth = 0.05
  )
```

![](outro_files/figure-html/outro-3-1.png)
]

.pull-right[

```r
ggplot(data = mpg) +
  geom_density(
    mapping = aes(x = displ)
  )
```

![](outro_files/figure-html/outro-4-1.png)
]

---

# Categorical vs. continuous variables

```r
ggplot(data = mpg) +
  geom_density(
    mapping = aes(x = displ, y = ..scaled.., color = class),
  )
```

![](outro_files/figure-html/outro-5-1.png)

---

# Categorical vs. continuous variables

```r
ggplot(data = mpg) +
  geom_boxplot(
    mapping = aes(x = class, y = displ),
  )
```

![](outro_files/figure-html/outro-6-1.png)

---

# Categorical variables

.pull-left[

```r
ggplot(data = mpg) +
  geom_bin2d(
    mapping = aes(x = drv, y = class),
  )
```

![](outro_files/figure-html/outro-7-1.png)
]

.pull-right[

```r
mpg %>% 
  count(drv, class)
```

```
## # A tibble: 12 x 3
##    drv   class          n
##    <chr> <chr>      <int>
##  1 4     compact       12
##  2 4     midsize        3
##  3 4     pickup        33
##  4 4     subcompact     4
##  5 4     suv           51
##  6 f     compact       35
##  7 f     midsize       38
##  8 f     minivan       11
##  9 f     subcompact    22
## 10 r     2seater        5
## 11 r     subcompact     9
## 12 r     suv           11
```
]

---

# Continuous variables

```r
ggplot(data = mpg) +
  geom_jitter(
    mapping = aes(x = hwy, y = cty),
    alpha = 0.3
  )
```

![](outro_files/figure-html/outro-9-1.png)

---

class: inverse

???

---

# More transformations

- Grouped `mutate()` and `filter()`, see r4ds 5.7.1
- Scoped functions, see `?scoped`
- `complete()` and `fill()`
- `extract()`

## Same syntax for working with databases

- *dbplyr* package
- Transformation operations are translated to SQL

---

# Joins

r4ds, chapter 13

```r
flights %>%
  select(year, month, day, carrier) %>%
  left_join(airlines)
```

```
## Joining, by = "carrier"
```

```
## # A tibble: 336,776 x 5
##     year month   day carrier name                    
##    <int> <int> <int> <chr>   <chr>                   
##  1  2013     1     1 UA      United Air Lines Inc.   
##  2  2013     1     1 UA      United Air Lines Inc.   
##  3  2013     1     1 AA      American Airlines Inc.  
##  4  2013     1     1 B6      JetBlue Airways         
##  5  2013     1     1 DL      Delta Air Lines Inc.    
##  6  2013     1     1 UA      United Air Lines Inc.   
##  7  2013     1     1 B6      JetBlue Airways         
##  8  2013     1     1 EV      ExpressJet Airlines Inc.
##  9  2013     1     1 B6      JetBlue Airways         
## 10  2013     1     1 AA      American Airlines Inc.  
## # … with 336,766 more rows
```

---

# Nested data frames

r4ds, chapter 25

```r
flights %>%
  nest(-month) %>% 
  arrange(month)
```

```
## Warning: All elements of `...` must be named.
## Did you want `data = c(year, day, dep_time, sched_dep_time, dep_delay, arr_time, sched_arr_time, 
##     arr_delay, carrier, flight, tailnum, origin, dest, air_time, 
##     distance, hour, minute, time_hour)`?
```

```
## # A tibble: 12 x 2
##    month            data
##    <int> <list<df[,18]>>
##  1     1   [27,004 × 18]
##  2     2   [24,951 × 18]
##  3     3   [28,834 × 18]
##  4     4   [28,330 × 18]
##  5     5   [28,796 × 18]
##  6     6   [28,243 × 18]
##  7     7   [29,425 × 18]
##  8     8   [29,327 × 18]
##  9     9   [27,574 × 18]
## 10    10   [28,889 × 18]
## 11    11   [27,268 × 18]
## 12    12   [28,135 × 18]
```

---

# Visualizations not covered

.pull-left[

## Position adjustments

jitter, dodge, stack, nudge

## Scales

labeling, color, range

]

.pull-right[

## Coordinate systems

flipping, aspect ratio, polar

## Theming

tweaking plots, standard appearance

]

---

# Extension for ggplot2

- Useful `ggplot2` extensions, https://www.ggplot2-exts.org/gallery/
    - ggstance
    - ggrepel
    - gganimate
    - GGally

- Useful `ggplot2` themes
    - ggpubr
    - ggthemr
    - ggpomological

---

# Pointers

Working directory hell:
- https://www.tidyverse.org/articles/2017/12/workflow-vs-script/

Symbolic link to data directory:
- Linux and OS X: `file.symlink()`
- Windows: `Sys.junction()`

R markdown: http://rmarkdown.rstudio.com/gallery.html

---

# Pointers 2

Literature:

- Quick-R [http://www.statmethods.net](http://www.statmethods.net)

- Advanced R [http://adv-r.had.co.nz](http://adv-r.had.co.nz)

- Packages suggested by RStudio [https://github.com/rstudio/RStartHere](https://github.com/rstudio/RStartHere)

- moderndive: [https://moderndive.com/](https://moderndive.com/)