Lab 04: BMI 5/625

class: center, middle, inverse, title-slide

# Lab 04: BMI 5/625
## Working with Tidy Data
### Alison Hill (with modifications by Steven Bedrick)

---

class: middle, center, inverse

# ⌛️

## Let's review

---

## Data wrangling to date!

.pull-left[

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

]

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

- `select`

From `tidyr`:

- `pivot_longer`
- `pivot_wider`

Plus 1 other package:

- `skimr::skim`

]

---
# The Great British Baking Data Set

```
Rows: 1,772
Columns: 5
$ series    <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ episode   <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ baker     <chr> "Annetha", "David", "Edd", "Jasminder", "Jonathan", "Louise"…
$ challenge <chr> "signature", "signature", "signature", "signature", "signatu…
$ cake      <chr> "cake", "cake", "cake", "cake", "cake", "cake", "cake", "cak…
```

---

# Un-tidy cakes

.pull-left[

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

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

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

Four seasons, four datasets...

Each row: a challenge type ("signature" vs. "showstopper") and a count of entries by type

---

# Still un-tidy cakes

.pull-left[

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

```
# A tibble: 16 × 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
```
]

.pull-right[
At least now it's a single dataframe...
]
---

# Finally tidy cakes

```r
cakes_tidy <- cakes_untidy %>% 
  pivot_longer(cake:pie_tart, 
               names_to="bake_type", 
               values_to="num_bakes") %>% 
  arrange(series)
cakes_tidy
```

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

---

# What about changing types?

```r
cakes_tidy <- cakes_untidy %>% 
  pivot_longer(cake:pie_tart, 
               names_to="bake_type", 
               names_transform = list(bake_type=as.factor),
               values_to="num_bakes") %>% 
  arrange(series)
cakes_tidy
```

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

---
class: middle, inverse, center

## Know Your Tidy Data

---

```r
glimpse(cakes_tidy)
```

```
Rows: 32
Columns: 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", "showstopper", "signature", "signature", "sho…
$ bake_type <fct> cake, pie_tart, cake, pie_tart, cake, pie_tart, cake, pie_ta…
$ num_bakes <dbl> 5, 5, 12, 4, 8, 17, 21, 7, 12, 17, 24, 12, 27, 9, 11, 15, 20…
```

---

```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       |

---

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

**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

---
class: middle, inverse, center

## Benefits of Tidy Data

---

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

```
# A tibble: 4 × 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 × 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() %>% summary
```

Table: Data summary

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

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
```

---

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

---
class: middle, inverse, center

## `select()`: Your new best friend

---

# Selection Helpers

`dplyr` gives us helpful syntax for selecting columns:

```r
cakes_raw %>% head(4)
```

```
# A tibble: 4 × 5
  series episode baker     challenge cake 
   <dbl>   <dbl> <chr>     <chr>     <chr>
1      1       1 Annetha   signature cake 
2      1       1 David     signature cake 
3      1       1 Edd       signature cake 
4      1       1 Jasminder signature cake 
```

What if we only want _some_ of the columns?

---

# `dplyr::select()` to the rescue

```r
cakes_raw %>% select(cake)
```

```
# A tibble: 1,772 × 1
   cake 
   <chr>
 1 cake 
 2 cake 
 3 cake 
 4 cake 
 5 cake 
 6 cake 
 7 cake 
 8 cake 
 9 cake 
10 <NA> 
# … with 1,762 more rows
```

---

# `dplyr::select()` to the rescue

```r
cakes_raw %>% select(cake, baker) %>% head(4)
```

```
# A tibble: 4 × 2
  cake  baker    
  <chr> <chr>    
1 cake  Annetha  
2 cake  David    
3 cake  Edd      
4 cake  Jasminder
```

But this is only the beginning!

---

# ... All columns _other_ than `cake`

```r
cakes_raw %>% select(!cake) %>% head(4)
```

```
# A tibble: 4 × 4
  series episode baker     challenge
   <dbl>   <dbl> <chr>     <chr>    
1      1       1 Annetha   signature
2      1       1 David     signature
3      1       1 Edd       signature
4      1       1 Jasminder signature
```

---

# Columns that _start_ with a string?

```r
cakes_raw %>% select(starts_with("c"))
```

```
# A tibble: 1,772 × 2
   challenge cake 
   <chr>     <chr>
 1 signature cake 
 2 signature cake 
 3 signature cake 
 4 signature cake 
 5 signature cake 
 6 signature cake 
 7 signature cake 
 8 signature cake 
 9 signature cake 
10 signature <NA> 
# … with 1,762 more rows
```

---

# The last column...

```r
cakes_raw %>% select(last_col()) %>% head(4)
```

```
# A tibble: 4 × 1
  cake 
  <chr>
1 cake 
2 cake 
3 cake 
4 cake 
```

---

# A _range_ of contiguous columns

```r
cakes_raw %>% select(baker:cake) %>% head(4)
```

```
# A tibble: 4 × 3
  baker     challenge cake 
  <chr>     <chr>     <chr>
1 Annetha   signature cake 
2 David     signature cake 
3 Edd       signature cake 
4 Jasminder signature cake 
```

---

# There are many other helpers:

Matching columns by name:
* `starts_with()`/`ends_with()`
* `contains()`
* `num_range()` (for matching numerical ranges: think columns named for years, quarters, etc.)

See the `select` help page for more examples...

---

# Many Tidyverse functions work with select helpers

```r
billboard %>% glimpse
```

```
Rows: 317
Columns: 79
$ artist       <chr> "2 Pac", "2Ge+her", "3 Doors Down", "3 Doors Down", "504 …
$ track        <chr> "Baby Don't Cry (Keep...", "The Hardest Part Of ...", "Kr…
$ date.entered <date> 2000-02-26, 2000-09-02, 2000-04-08, 2000-10-21, 2000-04-…
$ wk1          <dbl> 87, 91, 81, 76, 57, 51, 97, 84, 59, 76, 84, 57, 50, 71, 7…
$ wk2          <dbl> 82, 87, 70, 76, 34, 39, 97, 62, 53, 76, 84, 47, 39, 51, 6…
$ wk3          <dbl> 72, 92, 68, 72, 25, 34, 96, 51, 38, 74, 75, 45, 30, 28, 5…
$ wk4          <dbl> 77, NA, 67, 69, 17, 26, 95, 41, 28, 69, 73, 29, 28, 18, 4…
$ wk5          <dbl> 87, NA, 66, 67, 17, 26, 100, 38, 21, 68, 73, 23, 21, 13, …
$ wk6          <dbl> 94, NA, 57, 65, 31, 19, NA, 35, 18, 67, 69, 18, 19, 13, 3…
$ wk7          <dbl> 99, NA, 54, 55, 36, 2, NA, 35, 16, 61, 68, 11, 20, 11, 34…
$ wk8          <dbl> NA, NA, 53, 59, 49, 2, NA, 38, 14, 58, 65, 9, 17, 1, 29, …
$ wk9          <dbl> NA, NA, 51, 62, 53, 3, NA, 38, 12, 57, 73, 9, 17, 1, 27, …
$ wk10         <dbl> NA, NA, 51, 61, 57, 6, NA, 36, 10, 59, 83, 11, 17, 2, 30,…
$ wk11         <dbl> NA, NA, 51, 61, 64, 7, NA, 37, 9, 66, 92, 1, 17, 2, 36, N…
$ wk12         <dbl> NA, NA, 51, 59, 70, 22, NA, 37, 8, 68, NA, 1, 3, 3, 37, N…
$ wk13         <dbl> NA, NA, 47, 61, 75, 29, NA, 38, 6, 61, NA, 1, 3, 3, 39, N…
$ wk14         <dbl> NA, NA, 44, 66, 76, 36, NA, 49, 1, 67, NA, 1, 7, 4, 49, N…
$ wk15         <dbl> NA, NA, 38, 72, 78, 47, NA, 61, 2, 59, NA, 4, 10, 12, 57,…
$ wk16         <dbl> NA, NA, 28, 76, 85, 67, NA, 63, 2, 63, NA, 8, 17, 11, 63,…
$ wk17         <dbl> NA, NA, 22, 75, 92, 66, NA, 62, 2, 67, NA, 12, 25, 13, 65…
$ wk18         <dbl> NA, NA, 18, 67, 96, 84, NA, 67, 2, 71, NA, 22, 29, 15, 68…
$ wk19         <dbl> NA, NA, 18, 73, NA, 93, NA, 83, 3, 79, NA, 23, 29, 18, 79…
$ wk20         <dbl> NA, NA, 14, 70, NA, 94, NA, 86, 4, 89, NA, 43, 40, 20, 86…
$ wk21         <dbl> NA, NA, 12, NA, NA, NA, NA, NA, 5, NA, NA, 44, 43, 30, NA…
$ wk22         <dbl> NA, NA, 7, NA, NA, NA, NA, NA, 5, NA, NA, NA, 50, 40, NA,…
$ wk23         <dbl> NA, NA, 6, NA, NA, NA, NA, NA, 6, NA, NA, NA, NA, 39, NA,…
$ wk24         <dbl> NA, NA, 6, NA, NA, NA, NA, NA, 9, NA, NA, NA, NA, 44, NA,…
$ wk25         <dbl> NA, NA, 6, NA, NA, NA, NA, NA, 13, NA, NA, NA, NA, NA, NA…
$ wk26         <dbl> NA, NA, 5, NA, NA, NA, NA, NA, 14, NA, NA, NA, NA, NA, NA…
$ wk27         <dbl> NA, NA, 5, NA, NA, NA, NA, NA, 16, NA, NA, NA, NA, NA, NA…
$ wk28         <dbl> NA, NA, 4, NA, NA, NA, NA, NA, 23, NA, NA, NA, NA, NA, NA…
$ wk29         <dbl> NA, NA, 4, NA, NA, NA, NA, NA, 22, NA, NA, NA, NA, NA, NA…
$ wk30         <dbl> NA, NA, 4, NA, NA, NA, NA, NA, 33, NA, NA, NA, NA, NA, NA…
$ wk31         <dbl> NA, NA, 4, NA, NA, NA, NA, NA, 36, NA, NA, NA, NA, NA, NA…
$ wk32         <dbl> NA, NA, 3, NA, NA, NA, NA, NA, 43, NA, NA, NA, NA, NA, NA…
$ wk33         <dbl> NA, NA, 3, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ wk34         <dbl> NA, NA, 3, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ wk35         <dbl> NA, NA, 4, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ wk36         <dbl> NA, NA, 5, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ wk37         <dbl> NA, NA, 5, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ wk38         <dbl> NA, NA, 9, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ wk39         <dbl> NA, NA, 9, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ wk40         <dbl> NA, NA, 15, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ wk41         <dbl> NA, NA, 14, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ wk42         <dbl> NA, NA, 13, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ wk43         <dbl> NA, NA, 14, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ wk44         <dbl> NA, NA, 16, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ wk45         <dbl> NA, NA, 17, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ wk46         <dbl> NA, NA, 21, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ wk47         <dbl> NA, NA, 22, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ wk48         <dbl> NA, NA, 24, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ wk49         <dbl> NA, NA, 28, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ wk50         <dbl> NA, NA, 33, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ wk51         <dbl> NA, NA, 42, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ wk52         <dbl> NA, NA, 42, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ wk53         <dbl> NA, NA, 49, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ wk54         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ wk55         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ wk56         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ wk57         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ wk58         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ wk59         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ wk60         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ wk61         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ wk62         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ wk63         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ wk64         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ wk65         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ wk66         <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ wk67         <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ wk68         <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ wk69         <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ wk70         <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ wk71         <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ wk72         <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ wk73         <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ wk74         <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ wk75         <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ wk76         <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
```

---
# Many Tidyverse functions work with select helpers

```r
billboard %>% 
  pivot_longer(cols=starts_with("wk"),
               names_to = "week",
               names_prefix = "wk",
               values_to = "rank"
  ) %>% head(10)
```

```
# A tibble: 10 × 5
   artist track                   date.entered week   rank
   <chr>  <chr>                   <date>       <chr> <dbl>
 1 2 Pac  Baby Don't Cry (Keep... 2000-02-26   1        87
 2 2 Pac  Baby Don't Cry (Keep... 2000-02-26   2        82
 3 2 Pac  Baby Don't Cry (Keep... 2000-02-26   3        72
 4 2 Pac  Baby Don't Cry (Keep... 2000-02-26   4        77
 5 2 Pac  Baby Don't Cry (Keep... 2000-02-26   5        87
 6 2 Pac  Baby Don't Cry (Keep... 2000-02-26   6        94
 7 2 Pac  Baby Don't Cry (Keep... 2000-02-26   7        99
 8 2 Pac  Baby Don't Cry (Keep... 2000-02-26   8        NA
 9 2 Pac  Baby Don't Cry (Keep... 2000-02-26   9        NA
10 2 Pac  Baby Don't Cry (Keep... 2000-02-26   10       NA
```
---
class: middle, inverse, center

## `janitor`: Your _other_ new best friend

---

Often, data comes to us in... a less than pristine state:

```r
glimpse(gapmnd)
```

```
Rows: 213
Columns: 15
$ `Fixed broadband Internet subscribers (per 100 people)` <chr> "Afghanistan",…
$ `1998`                                                  <dbl> NA, NA, NA, NA…
$ `1999`                                                  <dbl> NA, NA, NA, NA…
$ `2000`                                                  <dbl> NA, NA, NA, NA…
$ `2001`                                                  <dbl> 0.0000000000, …
$ `2002`                                                  <dbl> 0.0000000000, …
$ `2003`                                                  <dbl> 0.000000e+00, …
$ `2004`                                                  <dbl> 6.880265e-04, …
$ `2005`                                                  <dbl> 7.356639e-04, …
$ `2006`                                                  <dbl> 0.001625928, N…
$ `2007`                                                  <dbl> 0.001581161, 0…
$ `2008`                                                  <dbl> 0.001537626, 2…
$ `2009`                                                  <dbl> 0.00299058, 2.…
$ `2010`                                                  <dbl> 0.004362367, 3…
$ `2011`                                                  <lgl> NA, NA, NA, NA…
```

Note the very inconvenient column names...

---

## The `janitor` package is here to help!

```r
gapmnd %>% janitor::clean_names() %>% glimpse()
```

```
Rows: 213
Columns: 15
$ fixed_broadband_internet_subscribers_per_100_people <chr> "Afghanistan", "Al…
$ x1998                                               <dbl> NA, NA, NA, NA, NA…
$ x1999                                               <dbl> NA, NA, NA, NA, NA…
$ x2000                                               <dbl> NA, NA, NA, NA, NA…
$ x2001                                               <dbl> 0.0000000000, 0.00…
$ x2002                                               <dbl> 0.0000000000, 0.00…
$ x2003                                               <dbl> 0.000000e+00, 0.00…
$ x2004                                               <dbl> 6.880265e-04, 0.00…
$ x2005                                               <dbl> 7.356639e-04, 8.65…
$ x2006                                               <dbl> 0.001625928, NA, 0…
$ x2007                                               <dbl> 0.001581161, 0.315…
$ x2008                                               <dbl> 0.001537626, 2.011…
$ x2009                                               <dbl> 0.00299058, 2.8815…
$ x2010                                               <dbl> 0.004362367, 3.292…
$ x2011                                               <lgl> NA, NA, NA, NA, NA…
```

---

### `janitor` has many other capabilities...

* Transforming columns

* Removing empty rows/columns

* Collapsing sets of values values to `NA`, as needed

It also has a very nice cross-tabulation syntax (`tabyl()`)!

---

## You have 2 _challenges_ today!

Described [here](04-challenge_rev.html)

Also see a reference walkthrough [here](04-distributions.html)

---
class: middle, inverse, center

# 🍱

## 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