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

---

```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, 5, 5,…
$ challenge <chr> "showstopper", "signature", "showstopper", "signature", "sh…
$ bake_type <fct> cake, cake, pie_tart, pie_tart, cake, cake, pie_tart, pie_t…
$ num_bakes <dbl> 5, 12, 5, 4, 8, 21, 17, 7, 12, 24, 17, 12, 27, 11, 9, 15, 2…
```

---

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

Table: Data summary

-------------------------  -----------
Name                       cakes_tidy 
Number of rows             32         
Number of columns          4          
_______________________               
Column type frequency:                
character                  1          
factor                     2          
numeric                    1          
________________________              
Group variables            None       
-------------------------  -----------

**Variable type: character**

skim_variable    n_missing   complete_rate   min   max   empty   n_unique   whitespace
--------------  ----------  --------------  ----  ----  ------  ---------  -----------
challenge                0               1     9    11       0          2            0

**Variable type: factor**

skim_variable    n_missing   complete_rate  ordered    n_unique  top_counts             
--------------  ----------  --------------  --------  ---------  -----------------------
series                   0               1  FALSE             8  1: 4, 2: 4, 3: 4, 4: 4 
bake_type                0               1  FALSE             2  cak: 16, pie: 16

**Variable type: numeric**

skim_variable    n_missing   complete_rate    mean    sd   p0   p25   p50    p75   p100  hist  
--------------  ----------  --------------  ------  ----  ---  ----  ----  -----  -----  ------
num_bakes                0               1   12.56   7.1    0     7    12   17.5     27  ▆▇▇▇▃

---

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

Table: Data summary

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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()
```

Table: Data summary

-------------------------  -----------
Name                       Piped data 
Number of rows             32         
Number of columns          2          
_______________________               
Column type frequency:                
numeric                    1          
________________________              
Group variables            bake_type  
-------------------------  -----------

**Variable type: numeric**

skim_variable   bake_type    n_missing   complete_rate    mean     sd   p0     p25    p50     p75   p100  hist  
--------------  ----------  ----------  --------------  ------  -----  ---  ------  -----  ------  -----  ------
num_bakes       cake                 0               1   15.81   7.31    4   11.00   15.5   21.00     27  ▅▇▁▇▅ 
num_bakes       pie_tart             0               1    9.31   5.30    0    5.75    8.5   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