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<!DOCTYPE html>
<html lang="" xml:lang="">
<head>
<title>Tidy data</title>
<meta charset="utf-8" />
<meta name="author" content="" />
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class: center, middle, inverse, title-slide
# Tidy data
## <br><br> College of the Atlantic
###
---
## 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?
]
<img src="img/hyperwar-airplanes-on-hand.png" width="70%" style="display: block; margin: auto;" />
.footnote[
Source: [Army Air Forces Statistical Digest, WW II](https://www.ibiblio.org/hyperwar/AAF/StatDigest/aafsd-3.html)
]
---
.question[
What makes this data not tidy?
]
<br>
<img src="img/hiv-est-prevalence-15-49.png" width="70%" style="display: block; margin: auto;" />
.footnote[
Source: [Gapminder, Estimated HIV prevalence among 15-49 year olds](https://www.gapminder.org/data)
]
---
.question[
What makes this data not tidy?
]
<br>
<img src="img/us-general-economic-characteristic-acs-2017.png" width="85%" style="display: block; margin: auto;" />
.footnote[
Source: [US Census Fact Finder, General Economic Characteristics, ACS 2017](https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ACS_17_5YR_DP03&src=pt)
]
---
## Displaying vs. summarising data
.panelset[
.panel[.panel-name[Output]
.pull-left[
```
## name height mass
## 1 Luke Skywalker 172 77.0
## 2 C-3PO 167 75.0
## 3 R2-D2 96 32.0
## 4 Darth Vader 202 136.0
## 5 Leia Organa 150 49.0
## 6 Owen Lars 178 120.0
## 7 Beru Whitesun lars 165 75.0
## 8 R5-D4 97 32.0
## 9 Biggs Darklighter 183 84.0
## 10 Obi-Wan Kenobi 182 77.0
## 11 Anakin Skywalker 188 84.0
## 12 Wilhuff Tarkin 180 NA
## 13 Chewbacca 228 112.0
## 14 Han Solo 180 80.0
## 15 Greedo 173 74.0
## 16 Jabba Desilijic Tiure 175 1358.0
## 17 Wedge Antilles 170 77.0
## 18 Jek Tono Porkins 180 110.0
## 19 Yoda 66 17.0
## 20 Palpatine 170 75.0
## 21 Boba Fett 183 78.2
## 22 IG-88 200 140.0
## 23 Bossk 190 113.0
## 24 Lando Calrissian 177 79.0
## 25 Lobot 175 79.0
## 26 Ackbar 180 83.0
## 27 Mon Mothma 150 NA
## 28 Arvel Crynyd NA NA
## 29 Wicket Systri Warrick 88 20.0
## 30 Nien Nunb 160 68.0
## 31 Qui-Gon Jinn 193 89.0
## 32 Nute Gunray 191 90.0
## 33 Finis Valorum 170 NA
## 34 Jar Jar Binks 196 66.0
## 35 Roos Tarpals 224 82.0
## 36 Rugor Nass 206 NA
## 37 Ric Olié 183 NA
## 38 Watto 137 NA
## 39 Sebulba 112 40.0
## 40 Quarsh Panaka 183 NA
## 41 Shmi Skywalker 163 NA
## 42 Darth Maul 175 80.0
## 43 Bib Fortuna 180 NA
## 44 Ayla Secura 178 55.0
## 45 Dud Bolt 94 45.0
## 46 Gasgano 122 NA
## 47 Ben Quadinaros 163 65.0
## 48 Mace Windu 188 84.0
## 49 Ki-Adi-Mundi 198 82.0
## 50 Kit Fisto 196 87.0
## 51 Eeth Koth 171 NA
## 52 Adi Gallia 184 50.0
## 53 Saesee Tiin 188 NA
## 54 Yarael Poof 264 NA
## 55 Plo Koon 188 80.0
## 56 Mas Amedda 196 NA
## 57 Gregar Typho 185 85.0
## 58 Cordé 157 NA
## 59 Cliegg Lars 183 NA
## 60 Poggle the Lesser 183 80.0
## 61 Luminara Unduli 170 56.2
## 62 Barriss Offee 166 50.0
## 63 Dormé 165 NA
## 64 Dooku 193 80.0
## 65 Bail Prestor Organa 191 NA
## 66 Jango Fett 183 79.0
## 67 Zam Wesell 168 55.0
## 68 Dexter Jettster 198 102.0
## 69 Lama Su 229 88.0
## 70 Taun We 213 NA
## 71 Jocasta Nu 167 NA
## 72 Ratts Tyerell 79 15.0
## 73 R4-P17 96 NA
## 74 Wat Tambor 193 48.0
## 75 San Hill 191 NA
## 76 Shaak Ti 178 57.0
## 77 Grievous 216 159.0
## 78 Tarfful 234 136.0
## 79 Raymus Antilles 188 79.0
## 80 Sly Moore 178 48.0
## 81 Tion Medon 206 80.0
## 82 Finn NA NA
## 83 Rey NA NA
## 84 Poe Dameron NA NA
## 85 BB8 NA NA
## 86 Captain Phasma NA NA
## 87 Padmé Amidala 165 45.0
```
]
.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)
)
```
]
]
]
---
## Acknowledgements
* This course builds on the materials from [Data Science in a Box](https://datasciencebox.org/) developed by Mine Çetinkaya-Rundel and are adapted under the [Creative Commons Attribution Share Alike 4.0 International](https://github.com/rstudio-education/datascience-box/blob/master/LICENSE.md)
</textarea>
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