Lab 04: CS631

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# Lab 04: CS631
## Working with Tidy Data
### Alison Hill

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# ⌛️

## Let's review

---

## Data wrangling to date!

.pull-left[

From `dplyr`:
- `filter`
- `arrange`
- `mutate`
- `group_by`
- `summarize`
- `glimpse`
- `distinct`
- `count`
- `tally`
- `pull`
- `top_n`

]

.pull-right[
Let's add from `dplyr`:

- `select`
- `rename`
- `recode`
- `case_when`

From `tidyr`:

- `gather`
- `separate`
- `spread`
- `unite`

Plus 1 other package:

- `skimr::skim`

]

---

# Un-tidy cakes

.pull-left[

```
# A tibble: 2 x 4
  series challenge    cake pie_tart
  <fct>  <chr>       <dbl>    <dbl>
1 1      showstopper    5.       5.
2 1      signature     12.       4.
```

```
# A tibble: 2 x 4
  series challenge    cake pie_tart
  <fct>  <chr>       <dbl>    <dbl>
1 2      showstopper    8.      17.
2 2      signature     21.       7.
```
]

.pull-right[

```
# A tibble: 2 x 4
  series challenge    cake pie_tart
  <fct>  <chr>       <dbl>    <dbl>
1 3      showstopper   12.      17.
2 3      signature     24.      12.
```

```
# A tibble: 2 x 4
  series challenge    cake pie_tart
  <fct>  <chr>       <dbl>    <dbl>
1 4      showstopper   27.       9.
2 4      signature     11.      15.
```
]

---

# Still un-tidy cakes

```r
cakes_untidy %>% 
  bind_rows()
```

```
# A tibble: 16 x 4
   series challenge    cake pie_tart
   <fct>  <chr>       <dbl>    <dbl>
 1 1      showstopper    5.       5.
 2 1      signature     12.       4.
 3 2      showstopper    8.      17.
 4 2      signature     21.       7.
 5 3      showstopper   12.      17.
 6 3      signature     24.      12.
 7 4      showstopper   27.       9.
 8 4      signature     11.      15.
 9 5      showstopper   20.       6.
10 5      signature      4.       7.
11 6      showstopper   12.       0.
12 6      signature     20.      17.
13 7      showstopper   19.       3.
14 7      signature     11.      10.
15 8      showstopper   26.      12.
16 8      signature     21.       8.
```

---

# Finally tidy cakes

```r
cakes_tidy <- cakes_untidy %>% 
  gather(bake_type, num_bakes, cake:pie_tart, 
         factor_key = TRUE) %>% 
  arrange(series)
cakes_tidy
```

```
# A tibble: 32 x 4
   series challenge   bake_type num_bakes
   <fct>  <chr>       <fct>         <dbl>
 1 1      showstopper cake             5.
 2 1      signature   cake            12.
 3 1      showstopper pie_tart         5.
 4 1      signature   pie_tart         4.
 5 2      showstopper cake             8.
 6 2      signature   cake            21.
 7 2      showstopper pie_tart        17.
 8 2      signature   pie_tart         7.
 9 3      showstopper cake            12.
10 3      signature   cake            24.
# ... with 22 more rows
```

---
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## Know Your Tidy Data

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```r
glimpse(cakes_tidy)
```

```
Observations: 32
Variables: 4
$ series    <fct> 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5...
$ challenge <chr> "showstopper", "signature", "showstopper", "signatur...
$ bake_type <fct> cake, cake, pie_tart, pie_tart, cake, cake, pie_tart...
$ num_bakes <dbl> 5, 12, 5, 4, 8, 21, 17, 7, 12, 24, 17, 12, 27, 11, 9...
```

---

```r
library(skimr)
skim(cakes_tidy)
```

```
Skim summary statistics
 n obs: 32 
 n variables: 4

Variable type: character 
   variable missing complete  n min max empty n_unique
1 challenge       0       32 32   9  11     0        2

Variable type: factor 
   variable missing complete  n n_unique              top_counts ordered
1 bake_type       0       32 32        2 cak: 16, pie: 16, NA: 0   FALSE
2    series       0       32 32        8  1: 4, 2: 4, 3: 4, 4: 4   FALSE

Variable type: numeric 
   variable missing complete  n  mean  sd min p25 median  p75 max     hist
1 num_bakes       0       32 32 12.56 7.1   0   7     12 17.5  27 ▂▆▇▇▁▇▂▃
```

---

```r
skim(cakes_tidy) %>% 
  summary() 
```

```
A skim object

Name: cakes_tidy   
Number of Rows: 32   
Number of Columns: 4    
    
Column type frequency    
character: 1   
factor: 2   
numeric: 1
```

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## Benefits of Tidy Data

---

```r
cakes_tidy %>% 
  count(challenge, bake_type, wt = num_bakes, sort = TRUE)
```

```
# A tibble: 4 x 3
  challenge   bake_type     n
  <chr>       <fct>     <dbl>
1 showstopper cake       129.
2 signature   cake       124.
3 signature   pie_tart    80.
4 showstopper pie_tart    69.
```

---

```r
cakes_tidy %>% 
  count(series, bake_type, wt = num_bakes)
```

```
# A tibble: 16 x 3
   series bake_type     n
   <fct>  <fct>     <dbl>
 1 1      cake        17.
 2 1      pie_tart     9.
 3 2      cake        29.
 4 2      pie_tart    24.
 5 3      cake        36.
 6 3      pie_tart    29.
 7 4      cake        38.
 8 4      pie_tart    24.
 9 5      cake        24.
10 5      pie_tart    13.
11 6      cake        32.
12 6      pie_tart    17.
13 7      cake        30.
14 7      pie_tart    13.
15 8      cake        47.
16 8      pie_tart    20.
```

---

```r
library(skimr)
cakes_tidy %>% 
  group_by(bake_type) %>% 
  select_if(is.numeric) %>% 
  skim()
```

```
Skim summary statistics
 n obs: 32 
 n variables: 2 
 group variables: bake_type

Variable type: numeric 
  bake_type  variable missing complete  n  mean   sd min   p25 median
1      cake num_bakes       0       16 16 15.81 7.31   4 11      15.5
2  pie_tart num_bakes       0       16 16  9.31 5.3    0  5.75    8.5
    p75 max     hist
1 21     27 ▃▂▇▁▁▇▂▃
2 12.75  17 ▂▃▃▆▃▃▁▇
```

See: https://suzanbaert.netlify.com/2018/01/dplyr-tutorial-1/

---

```r
cakes_by_series <- cakes_tidy %>% 
  count(series, bake_type, wt = num_bakes) 
cakes_by_series
```

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```r
ggplot(cakes_by_series, aes(x = series, y = n, 
                            color = bake_type,
                            group = bake_type)) +
  geom_point() +
  geom_line() +
  expand_limits(y = 0)
```

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## You have 2 challenges today!

Described [here](04-challenge.html)
Reference lab [here](04-distributions.html)

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# 🍱

## Tidy Data:

http://r4ds.had.co.nz/tidy-data.html

http://moderndive.com/4-tidy.html

http://vita.had.co.nz/papers/tidy-data.html

https://github.com/jennybc/lotr-tidy#readme