class: center, middle, inverse, title-slide

# Data wrangling
## Aggregation
### Ben Baumer
### SDS 192</br>Sep 30, 2020</br>(<a href="http://beanumber.github.io/sds192/lectures/mdsr_wrangling_04-group_by.html" class="uri">http://beanumber.github.io/sds192/lectures/mdsr_wrangling_04-group_by.html</a>)

---

class: center, middle, inverse

![](https://raw.githubusercontent.com/tidyverse/dplyr/master/man/figures/logo.png)

---
## The Five Verbs

.pull-left[
The Five Verbs

- `select()`

- `filter()`

- `mutate()`

- `arrange()`

- `summarize()`

]

.pull-right[
Plus:

- **`group_by()`**

- `rename()`

- `inner_join()`

- `left_join()`

]

---
## `summarize()`: collapse into **a single row**

.center[![](../gfx/summarize.png)]

---
## `group_by()`: group by a variable

.center[![](../gfx/group_by.png)]

---
background-image: url("../gfx/summarize_funs.png")
background-size: contain
background-position: 100% 0%
## Summary funs

.pull-left[

- take a **vector**

- output a single value

]

---
## Example: average fuel economy

```r
mtcars %>%
  group_by(cyl) %>%
  summarize(
    num_cars = n(),
    avg_mpg = mean(mpg)
  )
```

```
## `summarise()` ungrouping output (override with `.groups` argument)
```

```
## # A tibble: 3 x 3
##     cyl num_cars avg_mpg
##   <dbl>    <int>   <dbl>
## 1     4       11    26.7
## 2     6        7    19.7
## 3     8       14    15.1
```

Notes for current slide

Notes for next slide

Data wrangling

Aggregation

Ben Baumer

SDS 192
Sep 30, 2020
(http://beanumber.github.io/sds192/lectures/mdsr_wrangling_04-group_by.html)

1 / 7

2 / 7

The Five Verbs

select()
filter()
mutate()
arrange()
summarize()

Plus:

group_by()
rename()
inner_join()
left_join()

3 / 7

`summarize()`: collapse into a single row

4 / 7

`group_by()`: group by a variable

5 / 7

Summary funs

take a vector
output a single value

6 / 7

Example: average fuel economy

mtcars %>%
  group_by(cyl) %>%
  summarize(
    num_cars = n(),
    avg_mpg = mean(mpg)
  )

## `summarise()` ungrouping output (override with `.groups` argument)

## # A tibble: 3 x 3
##     cyl num_cars avg_mpg
##   <dbl>    <int>   <dbl>
## 1     4       11    26.7
## 2     6        7    19.7
## 3     8       14    15.1

7 / 7

2 / 7

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