Tidy data

class: center, middle, inverse, title-slide

# Tidy data
## <br><br> Introduction to Data Science with R and Tidyverse
### based on datasciencebox.org

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<div class="my-footer">
<span>
Introduction to Data Science with R and Tidyverse | Lukas Jürgensmeier, Matteo Fina, Jan Bischoff | based on
<a href="https://datasciencebox.org" target="_blank">datasciencebox.org</a>
</span>
</div>

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## Tidy data

> *Happy families are all alike; every unhappy family is unhappy in its own way.* 
>
> — Leo Tolstoy

.pull-left[
**Characteristics of tidy data:**

- Each variable forms a *column*.
- Each observation forms a *row*.
- Each type of observational unit forms a *table*.
]
--
.pull-right[
**Characteristics of untidy data:**

!@#$%^&*()
]

---

.question[
What makes this data not tidy?
]

<br>

.footnote[
Source: [Gapminder, Estimated HIV prevalence among 15-49 year olds](https://www.gapminder.org/data)
]

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## Displaying vs. summarising data

.panelset[

.panel[.panel-name[Output]

.pull-left[

```
## # A tibble: 87 x 3
##   name           height  mass
##   <chr>           <int> <dbl>
## 1 Luke Skywalker    172    77
## 2 C-3PO             167    75
## 3 R2-D2              96    32
## 4 Darth Vader       202   136
## 5 Leia Organa       150    49
## 6 Owen Lars         178   120
## # ... with 81 more rows
```
]
.pull-right[

```
## # A tibble: 3 x 2
##   gender    avg_ht
##   <chr>      <dbl>
## 1 feminine    165.
## 2 masculine   177.
## 3 <NA>        181.
```
]

]

.panel[.panel-name[Code]

.pull-left[

```r
starwars %>%
  select(name, height, mass)
```
]
.pull-right[

```r
starwars %>%
  group_by(gender) %>%
  summarize(
    avg_ht = mean(height, na.rm = TRUE)
    )
```
]

]