Data tidying

# Data tidying
## - with {dplyr} and {tidyr} -
### Athanasia Mo Mowinckel
### 30.09.2019

---

![](https://www.oslobrains.no/wp-content/uploads/2017/09/LCBC_wide_compact_full.png)

---

<img src="http://weknowmemes.com/wp-content/uploads/2012/02/help-me-obi-wan.jpg" height="100%" />
]

## Me

<img src="https://media.makeameme.org/created/no-problem-at.jpg" height="500px" />
]

---
class: middle

.pull-left[
## Data as you expect
<img src="https://img.huffingtonpost.com/asset/5873b0c21b00008f016e65cf.jpg?ops=scalefit_820_noupscale" height="350px" />
]

.pull-right[
## Data you get
<img src="https://secure.i.telegraph.co.uk/multimedia/archive/02637/messy-desk_2637008b.jpg" height="350px" />
]

---
class: middle, center

---

# {dplyr} and {tidyr} 
# to the rescue

---

.pull-left[
# {dplyr}
> is a grammar of data manipulation, providing a consistent set of verbs that help you solve the most common data manipulation challenges

]

.pull-right[
# {tidyr}
> is a collection of functions to help you create "tidy" data, i.e. reshaping data structure to fit your needs  
<br>

<img src="https://d33wubrfki0l68.cloudfront.net/5f8c22ec53a1ac61684f3e8d59c623d09227d6b9/b15de/images/hex-tidyr.png" height="300px" />
]

---
class: middle, center, inverse

# {tidyr}

---

# {tidyr} - to help you create tidy data.

[From the tidyr site](https://tidyr.tidyverse.org/index.html)

** Tidy data is data where:**

- Every column is variable.

- Every row is an observation.

- Every cell is a single value.

### Tidy data describes a standard way of storing data that is used wherever possible throughout the tidyverse.

---

### Data with values in column headers

.left-column[
The data set `relig_income` gives us some information about the income levels of households of different religions.

Notice how the column headers are the income levels, and cell values are the number of households.

]

```r
library(tidyverse)
relig_income
```

```
## # A tibble: 18 x 11
##    religion `<$10k` `$10-20k` `$20-30k` `$30-40k` `$40-50k` `$50-75k`
##    <chr>      <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>
##  1 Agnostic      27        34        60        81        76       137
##  2 Atheist       12        27        37        52        35        70
##  3 Buddhist      27        21        30        34        33        58
##  4 Catholic     418       617       732       670       638      1116
##  5 Don’t k…      15        14        15        11        10        35
##  6 Evangel…     575       869      1064       982       881      1486
##  7 Hindu          1         9         7         9        11        34
##  8 Histori…     228       244       236       238       197       223
##  9 Jehovah…      20        27        24        24        21        30
## 10 Jewish        19        19        25        25        30        95
## 11 Mainlin…     289       495       619       655       651      1107
## 12 Mormon        29        40        48        51        56       112
## 13 Muslim         6         7         9        10         9        23
## 14 Orthodox      13        17        23        32        32        47
## 15 Other C…       9         7        11        13        13        14
## 16 Other F…      20        33        40        46        49        63
## 17 Other W…       5         2         3         4         2         7
## 18 Unaffil…     217       299       374       365       341       528
## # … with 4 more variables: `$75-100k` <dbl>, `$100-150k` <dbl>,
## #   `>150k` <dbl>, `Don't know/refused` <dbl>
```
]

---

### Data with values in column headers

.left-column[
We want the income to become its own column, and the frequency to be stored in a seperate column.

]

```r
relig_income %>%
  gather(income, frequency, -religion)
```

```
## # A tibble: 180 x 3
##    religion                income frequency
##    <chr>                   <chr>      <dbl>
##  1 Agnostic                <$10k         27
##  2 Atheist                 <$10k         12
##  3 Buddhist                <$10k         27
##  4 Catholic                <$10k        418
##  5 Don’t know/refused      <$10k         15
##  6 Evangelical Prot        <$10k        575
##  7 Hindu                   <$10k          1
##  8 Historically Black Prot <$10k        228
##  9 Jehovah's Witness       <$10k         20
## 10 Jewish                  <$10k         19
## # … with 170 more rows
```
]

---

### Data with values in column headers

.left-column[
The new `pivot_longer` function was recently released with syntax that might be a little more understandable (and memorable!)

]

```r
relig_income %>% 
  pivot_longer(-religion, names_to = "income", values_to = "frequency")
```

```
## # A tibble: 180 x 3
##    religion income             frequency
##    <chr>    <chr>                  <dbl>
##  1 Agnostic <$10k                     27
##  2 Agnostic $10-20k                   34
##  3 Agnostic $20-30k                   60
##  4 Agnostic $30-40k                   81
##  5 Agnostic $40-50k                   76
##  6 Agnostic $50-75k                  137
##  7 Agnostic $75-100k                 122
##  8 Agnostic $100-150k                109
##  9 Agnostic >150k                     84
## 10 Agnostic Don't know/refused        96
## # … with 170 more rows
```
]

---

### Data with columns of repeated observations

.left-column[
The data set `billboard` gives us some information about top billboard songs vor various time periods.

Notice how the column headers are numbers of weeks, one per song for the amounts of weeks that song was on the billboards.

]

```r
billboard
```

```
## # A tibble: 317 x 79
##    artist track date.entered   wk1   wk2   wk3   wk4   wk5   wk6   wk7
##    <chr>  <chr> <date>       <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
##  1 2 Pac  Baby… 2000-02-26      87    82    72    77    87    94    99
##  2 2Ge+h… The … 2000-09-02      91    87    92    NA    NA    NA    NA
##  3 3 Doo… Kryp… 2000-04-08      81    70    68    67    66    57    54
##  4 3 Doo… Loser 2000-10-21      76    76    72    69    67    65    55
##  5 504 B… Wobb… 2000-04-15      57    34    25    17    17    31    36
##  6 98^0   Give… 2000-08-19      51    39    34    26    26    19     2
##  7 A*Tee… Danc… 2000-07-08      97    97    96    95   100    NA    NA
##  8 Aaliy… I Do… 2000-01-29      84    62    51    41    38    35    35
##  9 Aaliy… Try … 2000-03-18      59    53    38    28    21    18    16
## 10 Adams… Open… 2000-08-26      76    76    74    69    68    67    61
## # … with 307 more rows, and 69 more variables: wk8 <dbl>, wk9 <dbl>,
## #   wk10 <dbl>, wk11 <dbl>, wk12 <dbl>, wk13 <dbl>, wk14 <dbl>,
## #   wk15 <dbl>, wk16 <dbl>, wk17 <dbl>, wk18 <dbl>, wk19 <dbl>,
## #   wk20 <dbl>, wk21 <dbl>, wk22 <dbl>, wk23 <dbl>, wk24 <dbl>,
## #   wk25 <dbl>, wk26 <dbl>, wk27 <dbl>, wk28 <dbl>, wk29 <dbl>,
## #   wk30 <dbl>, wk31 <dbl>, wk32 <dbl>, wk33 <dbl>, wk34 <dbl>,
## #   wk35 <dbl>, wk36 <dbl>, wk37 <dbl>, wk38 <dbl>, wk39 <dbl>,
## #   wk40 <dbl>, wk41 <dbl>, wk42 <dbl>, wk43 <dbl>, wk44 <dbl>,
## #   wk45 <dbl>, wk46 <dbl>, wk47 <dbl>, wk48 <dbl>, wk49 <dbl>,
## #   wk50 <dbl>, wk51 <dbl>, wk52 <dbl>, wk53 <dbl>, wk54 <dbl>,
## #   wk55 <dbl>, wk56 <dbl>, wk57 <dbl>, wk58 <dbl>, wk59 <dbl>,
## #   wk60 <dbl>, wk61 <dbl>, wk62 <dbl>, wk63 <dbl>, wk64 <dbl>,
## #   wk65 <dbl>, wk66 <lgl>, wk67 <lgl>, wk68 <lgl>, wk69 <lgl>,
## #   wk70 <lgl>, wk71 <lgl>, wk72 <lgl>, wk73 <lgl>, wk74 <lgl>,
## #   wk75 <lgl>, wk76 <lgl>
```
]

---

### Data with columns of repeated observations

.left-column[
We want the weeks to become their own column, and the weeks ranking to be stored in a seperate column.

]

```r
billboard %>% 
  gather(week, rank, starts_with("wk"), na.rm = TRUE)
```

```
## # A tibble: 5,307 x 5
##    artist         track                   date.entered week   rank
##    <chr>          <chr>                   <date>       <chr> <dbl>
##  1 2 Pac          Baby Don't Cry (Keep... 2000-02-26   wk1      87
##  2 2Ge+her        The Hardest Part Of ... 2000-09-02   wk1      91
##  3 3 Doors Down   Kryptonite              2000-04-08   wk1      81
##  4 3 Doors Down   Loser                   2000-10-21   wk1      76
##  5 504 Boyz       Wobble Wobble           2000-04-15   wk1      57
##  6 98^0           Give Me Just One Nig... 2000-08-19   wk1      51
##  7 A*Teens        Dancing Queen           2000-07-08   wk1      97
##  8 Aaliyah        I Don't Wanna           2000-01-29   wk1      84
##  9 Aaliyah        Try Again               2000-03-18   wk1      59
## 10 Adams, Yolanda Open My Heart           2000-08-26   wk1      76
## # … with 5,297 more rows
```
]

---

### Data with columns of repeated observations

.left-column[
With `pivot_longer` we can also do the same, now with perhaps some more transparent arguments.

]

```r
billboard %>% 
  pivot_longer(
    cols = starts_with("wk"), names_to = "week", 
    values_to = "rank", values_drop_na = TRUE
  )
```

```
## # A tibble: 5,307 x 5
##    artist  track                   date.entered week   rank
##    <chr>   <chr>                   <date>       <chr> <dbl>
##  1 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk1      87
##  2 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk2      82
##  3 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk3      72
##  4 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk4      77
##  5 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk5      87
##  6 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk6      94
##  7 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk7      99
##  8 2Ge+her The Hardest Part Of ... 2000-09-02   wk1      91
##  9 2Ge+her The Hardest Part Of ... 2000-09-02   wk2      87
## 10 2Ge+her The Hardest Part Of ... 2000-09-02   wk3      92
## # … with 5,297 more rows
```
]

---
### Data with columns of repeated observations

.left-column[
We can even extent the `pivot_longer` function, so that week becomes an integer immediately.

The alternative is to use a `mutate` function from {dplyr} after pivoting to do so. 
]

```r
billboard %>% 
  pivot_longer(
    cols = starts_with("wk"), names_to = "week", 
    names_prefix = "wk", names_ptypes = list(week = integer()),
    values_to = "rank", values_drop_na = TRUE
  )
```

```
## # A tibble: 5,307 x 5
##    artist  track                   date.entered  week  rank
##    <chr>   <chr>                   <date>       <int> <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 2Ge+her The Hardest Part Of ... 2000-09-02       1    91
##  9 2Ge+her The Hardest Part Of ... 2000-09-02       2    87
## 10 2Ge+her The Hardest Part Of ... 2000-09-02       3    92
## # … with 5,297 more rows
```
]

---
class: middle, center, inverse

# {dplyr}

---

# {dplyr} - to alter and add to data

[From the dplyr site](https://dplyr.tidyverse.org/)

- `mutate()` adds new variables that are functions of existing variables.

- `select()` picks variables based on their names.

- `filter()` picks cases based on their values.

- `summarise()` reduces multiple values down to a single summary.

- `arrange()` changes the ordering of the rows.

These all combine naturally with `group_by()` which allows you to perform any operation “by group”.

---

### Mutating data

.left-column[
If we go back to the billboards example we had before, and we don't do the step necessary to make `week` into an integer, we can alter (mutate) week to become an integer in a second step.

`mutate` is handy because you can use information from the data.frame it self to add or alter variables in the data.frame in a row-wise manner.

]

```r
billboard %>% 
  pivot_longer(
    cols = starts_with("wk"), names_to = "week", 
    values_to = "rank", values_drop_na = TRUE
  ) %>% 
  mutate(week = parse_number(week))
```

```
## # A tibble: 5,307 x 5
##    artist  track                   date.entered  week  rank
##    <chr>   <chr>                   <date>       <dbl> <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 2Ge+her The Hardest Part Of ... 2000-09-02       1    91
##  9 2Ge+her The Hardest Part Of ... 2000-09-02       2    87
## 10 2Ge+her The Hardest Part Of ... 2000-09-02       3    92
## # … with 5,297 more rows
```
]

---

### Mutating data

The main {dplyr} functions also come with variants ending in `_at`, `_if`, `_all`, which allows you to do operations on multiple columns in a single step, as long as you want to do the same with all the selected columns.

For instance we can z-transform all the weekly rankings of all songs in one go.
]

```r
billboard %>% 
  mutate_at(vars(starts_with("wk")), scale)
```

```
## # A tibble: 317 x 79
##    artist track date.entered wk1[,1] wk2[,1] wk3[,1] wk4[,1] wk5[,1]
##    <chr>  <chr> <date>         <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
##  1 2 Pac  Baby… 2000-02-26    0.479   0.595    0.335   0.772   1.29 
##  2 2Ge+h… The … 2000-09-02    0.752   0.870    1.30   NA      NA    
##  3 3 Doo… Kryp… 2000-04-08    0.0709 -0.0645   0.142   0.324   0.406
##  4 3 Doo… Loser 2000-10-21   -0.270   0.265    0.335   0.414   0.448
##  5 504 B… Wobb… 2000-04-15   -1.56   -2.04    -1.93   -1.92   -1.65 
##  6 98^0   Give… 2000-08-19   -1.97   -1.77    -1.50   -1.51   -1.28 
##  7 A*Tee… Danc… 2000-07-08    1.16    1.42     1.49    1.58    1.84 
##  8 Aaliy… I Do… 2000-01-29    0.275  -0.504   -0.677  -0.840  -0.771
##  9 Aaliy… Try … 2000-03-18   -1.43   -0.998   -1.30   -1.42   -1.49 
## 10 Adams… Open… 2000-08-26   -0.270   0.265    0.431   0.414   0.490
## # … with 307 more rows, and 71 more variables: wk6[,1] <dbl>,
## #   wk7[,1] <dbl>, wk8[,1] <dbl>, wk9[,1] <dbl>, wk10[,1] <dbl>,
## #   wk11[,1] <dbl>, wk12[,1] <dbl>, wk13[,1] <dbl>, wk14[,1] <dbl>,
## #   wk15[,1] <dbl>, wk16[,1] <dbl>, wk17[,1] <dbl>, wk18[,1] <dbl>,
## #   wk19[,1] <dbl>, wk20[,1] <dbl>, wk21[,1] <dbl>, wk22[,1] <dbl>,
## #   wk23[,1] <dbl>, wk24[,1] <dbl>, wk25[,1] <dbl>, wk26[,1] <dbl>,
## #   wk27[,1] <dbl>, wk28[,1] <dbl>, wk29[,1] <dbl>, wk30[,1] <dbl>,
## #   wk31[,1] <dbl>, wk32[,1] <dbl>, wk33[,1] <dbl>, wk34[,1] <dbl>,
## #   wk35[,1] <dbl>, wk36[,1] <dbl>, wk37[,1] <dbl>, wk38[,1] <dbl>,
## #   wk39[,1] <dbl>, wk40[,1] <dbl>, wk41[,1] <dbl>, wk42[,1] <dbl>,
## #   wk43[,1] <dbl>, wk44[,1] <dbl>, wk45[,1] <dbl>, wk46[,1] <dbl>,
## #   wk47[,1] <dbl>, wk48[,1] <dbl>, wk49[,1] <dbl>, wk50[,1] <dbl>,
## #   wk51[,1] <dbl>, wk52[,1] <dbl>, wk53[,1] <dbl>, wk54[,1] <dbl>,
## #   wk55[,1] <dbl>, wk56[,1] <dbl>, wk57[,1] <dbl>, wk58[,1] <dbl>,
## #   wk59[,1] <dbl>, wk60[,1] <dbl>, wk61[,1] <dbl>, wk62[,1] <dbl>,
## #   wk63[,1] <dbl>, wk64[,1] <dbl>, wk65[,1] <dbl>, wk66[,1] <dbl>,
## #   wk67[,1] <dbl>, wk68[,1] <dbl>, wk69[,1] <dbl>, wk70[,1] <dbl>,
## #   wk71[,1] <dbl>, wk72[,1] <dbl>, wk73[,1] <dbl>, wk74[,1] <dbl>,
## #   wk75[,1] <dbl>, wk76[,1] <dbl>
```
]

---

### Mutating data

We're going to use the `relig_income` dataset again, and use it in its untidy form, so highlight some cool things you can do with `mutate` and `select` .

Here, we use mutate to add a total row, and use `select_if` to select all numeric columns to create a sum of.

Then, we use `mutate_if` to alter each of the numeric columns, so they become a percentage
    
]

```r
relig_income %>% 
  mutate(total = rowSums(select_if(., is.numeric))) %>% 
  mutate_if(is.numeric, function(x) (x/.$total)*100 )
```

```
## # A tibble: 18 x 12
##    religion `<$10k` `$10-20k` `$20-30k` `$30-40k` `$40-50k` `$50-75k`
##    <chr>      <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>
##  1 Agnostic   3.27       4.12      7.26      9.81      9.20      16.6
##  2 Atheist    2.33       5.24      7.18     10.1       6.80      13.6
##  3 Buddhist   6.57       5.11      7.30      8.27      8.03      14.1
##  4 Catholic   5.19       7.66      9.09      8.32      7.92      13.9
##  5 Don’t k…   5.51       5.15      5.51      4.04      3.68      12.9
##  6 Evangel…   6.07       9.17     11.2      10.4       9.30      15.7
##  7 Hindu      0.389      3.50      2.72      3.50      4.28      13.2
##  8 Histori…  11.4       12.2      11.8      11.9       9.87      11.2
##  9 Jehovah…   9.30      12.6      11.2      11.2       9.77      14.0
## 10 Jewish     2.79       2.79      3.67      3.67      4.40      13.9
## 11 Mainlin…   3.87       6.63      8.29      8.77      8.71      14.8
## 12 Mormon     4.99       6.88      8.26      8.78      9.64      19.3
## 13 Muslim     5.17       6.03      7.76      8.62      7.76      19.8
## 14 Orthodox   3.58       4.68      6.34      8.82      8.82      12.9
## 15 Other C…   6.98       5.43      8.53     10.1      10.1       10.9
## 16 Other F…   4.45       7.35      8.91     10.2      10.9       14.0
## 17 Other W…  11.9        4.76      7.14      9.52      4.76      16.7
## 18 Unaffil…   5.85       8.07     10.1       9.85      9.20      14.2
## # … with 5 more variables: `$75-100k` <dbl>, `$100-150k` <dbl>,
## #   `>150k` <dbl>, `Don't know/refused` <dbl>, total <dbl>
```
]

---

### Summarising data

```r
relig_income %>% 
  pivot_longer(-religion, 
               names_to = "income", 
               values_to = "frequency") %>% 
*   group_by(religion) %>%
  summarise_at(vars(frequency), 
               list(Mean = mean, 
                    Min = min, 
                    Max = max,
                    Sum = sum))
```

```
## # A tibble: 18 x 5
##    religion                 Mean   Min   Max   Sum
##    <chr>                   <dbl> <dbl> <dbl> <dbl>
##  1 Agnostic                 82.6    27   137   826
##  2 Atheist                  51.5    12    76   515
##  3 Buddhist                 41.1    21    62   411
##  4 Catholic                805.    418  1489  8054
##  5 Don’t know/refused       27.2    10   116   272
##  6 Evangelical Prot        947.    414  1529  9472
##  7 Hindu                    25.7     1    54   257
##  8 Historically Black Prot 200.     78   339  1995
##  9 Jehovah's Witness        21.5     6    37   215
## 10 Jewish                   68.2    19   162   682
## 11 Mainline Prot           747     289  1328  7470
## 12 Mormon                   58.1    29   112   581
## 13 Muslim                   11.6     6    23   116
## 14 Orthodox                 36.3    13    73   363
## 15 Other Christian          12.9     7    18   129
## 16 Other Faiths             44.9    20    71   449
## 17 Other World Religions     4.2     2     8    42
## 18 Unaffiliated            371.    217   597  3707
```

]

```r
relig_income %>% 
  pivot_longer(-religion, 
               names_to = "income", 
               values_to = "frequency") %>% 
* group_by(income) %>%
  summarise_at(vars(frequency), 
               list(Mean = mean,
                    Min = min, 
                    Max = max,
                    Sum = sum))
```

```
## # A tibble: 10 x 5
##    income              Mean   Min   Max   Sum
##    <chr>              <dbl> <dbl> <dbl> <dbl>
##  1 <$10k               107.     1   575  1930
##  2 >150k               145.     4   634  2608
##  3 $10-20k             154.     2   869  2781
##  4 $100-150k           178.     4   792  3197
##  5 $20-30k             186.     3  1064  3357
##  6 $30-40k             183.     4   982  3302
##  7 $40-50k             171.     2   881  3085
##  8 $50-75k             288.     7  1486  5185
##  9 $75-100k            222.     3   949  3990
## 10 Don't know/refused  340.     8  1529  6121
```
]

---

### Summarising data

```r
billboard %>% 
  summarise_at(vars(wk1, wk5, wk10, wk20, wk30), 
               list(Mean = mean, 
                    Max = min, 
                    Min = max,
                    Sum = sum),
               na.rm = TRUE)
```

```
## # A tibble: 1 x 20
##   wk1_Mean wk5_Mean wk10_Mean wk20_Mean wk30_Mean wk1_Max wk5_Max wk10_Max
##      <dbl>    <dbl>     <dbl>     <dbl>     <dbl>   <dbl>   <dbl>    <dbl>
## 1     80.0     56.3      45.8      54.3      26.4      15       2        1
## # … with 12 more variables: wk20_Max <dbl>, wk30_Max <dbl>, wk1_Min <dbl>,
## #   wk5_Min <dbl>, wk10_Min <dbl>, wk20_Min <dbl>, wk30_Min <dbl>,
## #   wk1_Sum <dbl>, wk5_Sum <dbl>, wk10_Sum <dbl>, wk20_Sum <dbl>,
## #   wk30_Sum <dbl>
```

---

### Summarising data

```r
billboard %>% 
  pivot_longer(starts_with("wk"),
               names_to = "week",
               values_to = "rank") %>% 
  mutate(week = parse_number(week)) %>% 
  summarise_at(vars(rank),
               list(Mean = mean, Max = min, Min = max),
               na.rm = TRUE)
```

```
## # A tibble: 1 x 3
##    Mean   Max   Min
##   <dbl> <dbl> <dbl>
## 1  51.1     1   100
```

---

### Summarising data

```r
billboard %>% 
  pivot_longer(starts_with("wk"), names_to = "week", values_to = "rank") %>% 
  mutate(week = parse_number(week)) %>% 
* group_by(artist, track) %>%
  summarise_at(vars(rank),
               list(Mean = mean, Max = min, Min = max),
               na.rm = TRUE)
```

```
## # A tibble: 317 x 5
## # Groups:   artist [228]
##    artist         track                    Mean   Max   Min
##    <chr>          <chr>                   <dbl> <dbl> <dbl>
##  1 2 Pac          Baby Don't Cry (Keep...  85.4    72    99
##  2 2Ge+her        The Hardest Part Of ...  90      87    92
##  3 3 Doors Down   Kryptonite               26.5     3    81
##  4 3 Doors Down   Loser                    67.1    55    76
##  5 504 Boyz       Wobble Wobble            56.2    17    96
##  6 98^0           Give Me Just One Nig...  37.6     2    94
##  7 A*Teens        Dancing Queen            97      95   100
##  8 Aaliyah        I Don't Wanna            52.0    35    86
##  9 Aaliyah        Try Again                16.7     1    59
## 10 Adams, Yolanda Open My Heart            67.8    57    89
## # … with 307 more rows
```

---
class: inverse, center, middle

# There you go!

### A small intro to tidying data