useR2018

class: center, middle

# David Neuzerling
## 2018-08-17

<img src="hex/hexwall.jpg" style="width: 45%; height: 45%;"></img>

---
class: inverse, center, middle

<img src="hex/r2d3.jpg" style="width: 45%; height: 45%;"></img>   <img src="hex/recipes.jpg" style="width: 45%; height: 45%;"></img>

Hex logos are the cutest part of R

---
class: inverse, center, middle

# R vs. Python

![Fighting corgis](https://i.imgur.com/NzppaUe.gif)

---
class: inverse, center, middle

#Refresh-R: Piping data
<img src="https://i.giphy.com/Y8qTuvMUjSh7a.gif" style="width: 60%; height: 60%;"></img>

---
class: left
## Refresh-R: Ceci **est** pas une pipe

`%>%` is a *pipe*. It puts the object on the left into the *first* argument of
the function on the right:

* `4 %>% sqrt` is the same as `sqrt(4)`
* `data %>% head` is the same as `head(data)`

<img src="hex/magrittr.png" style="width: 15%; height: 15%;"></img>

---
class: left
## Refresh-R: Ceci **est** pas une pipe

`%>%` is a *pipe*. It puts the object on the left into the *first* argument of
the function on the right:

* `4 %>% sqrt` is the same as `sqrt(4)`
* `data %>% head` is the same as `head(data)`

Or, if we wanted to calculate the average BMI of all droids in the Star Wars movies:

```r
starwars %>% 
    filter(species == "Droid") %>%
    mutate(BMI = mass / (height / 100)^2) %>% 
    summarise(avg_BMI = mean(BMI, na.rm = TRUE))
```

is the same as

```r
summarise(mutate(filter(starwars, species == "Droid"), BMI = mass / (height / 100)^2), avg_BMI = mean(BMI, na.rm = TRUE))
```

It's 32.7.

---
class: inverse, center, middle

#Refresh-R: Visualisation
<img src="https://i.giphy.com/media/3og0IExSrnfW2kUaaI/source.gif" style="width: 70%; height: 70%;"></img>

---
class: center, middle

## Refresh-R: visualisation

<div id="htmlwidget-7784fcff432ee6ac69e2" style="width:100%;height:450px;" class="widgetframe html-widget"></div>
<script type="application/json" data-for="htmlwidget-7784fcff432ee6ac69e2">{"x":{"url":"user2018_full_files/figure-html//widgets/widget_bomrang_plotly.html","options":{"xdomain":"*","allowfullscreen":false,"lazyload":false}},"evals":[],"jsHooks":[]}</script>

---
class: left
## Refresh-R: Visualisation

```r
library(tidyverse)
library(bomrang)

weather <- get_historical(stationid = "090015", type = "max")
```

<img src="hex/tidyverse.png" style="width: 15%; height: 15%;"></img><img src="hex/bomrang.png" style="width: 15%; height: 15%;"></img>

---
class: left
## Refresh-R: Visualisation

```r
library(tidyverse)
library(bomrang)

weather <- get_historical(stationid = "090015", type = "max")
```

```r
weather %>% sample_n(6)
```

<table class="table" style="font-size: 18px; margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:left;">   </th>
   <th style="text-align:left;"> Product_code </th>
   <th style="text-align:right;"> Station_number </th>
   <th style="text-align:right;"> Year </th>
   <th style="text-align:right;"> Month </th>
   <th style="text-align:right;"> Day </th>
   <th style="text-align:right;"> Max_temperature </th>
   <th style="text-align:right;"> Accum_days_max </th>
   <th style="text-align:left;"> Quality </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> 5427 </td>
   <td style="text-align:left;"> IDCJAC0010 </td>
   <td style="text-align:right;"> 90015 </td>
   <td style="text-align:right;"> 1878 </td>
   <td style="text-align:right;"> 11 </td>
   <td style="text-align:right;"> 9 </td>
   <td style="text-align:right;"> 18.9 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:left;"> Y </td>
  </tr>
  <tr>
   <td style="text-align:left;"> 22746 </td>
   <td style="text-align:left;"> IDCJAC0010 </td>
   <td style="text-align:right;"> 90015 </td>
   <td style="text-align:right;"> 1926 </td>
   <td style="text-align:right;"> 4 </td>
   <td style="text-align:right;"> 11 </td>
   <td style="text-align:right;"> 17.3 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:left;"> Y </td>
  </tr>
  <tr>
   <td style="text-align:left;"> 52658 </td>
   <td style="text-align:left;"> IDCJAC0010 </td>
   <td style="text-align:right;"> 90015 </td>
   <td style="text-align:right;"> 2008 </td>
   <td style="text-align:right;"> 3 </td>
   <td style="text-align:right;"> 3 </td>
   <td style="text-align:right;"> 29.5 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:left;"> Y </td>
  </tr>
  <tr>
   <td style="text-align:left;"> 36199 </td>
   <td style="text-align:left;"> IDCJAC0010 </td>
   <td style="text-align:right;"> 90015 </td>
   <td style="text-align:right;"> 1963 </td>
   <td style="text-align:right;"> 2 </td>
   <td style="text-align:right;"> 9 </td>
   <td style="text-align:right;"> 19.4 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:left;"> Y </td>
  </tr>
  <tr>
   <td style="text-align:left;"> 26884 </td>
   <td style="text-align:left;"> IDCJAC0010 </td>
   <td style="text-align:right;"> 90015 </td>
   <td style="text-align:right;"> 1937 </td>
   <td style="text-align:right;"> 8 </td>
   <td style="text-align:right;"> 9 </td>
   <td style="text-align:right;"> 16.7 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:left;"> Y </td>
  </tr>
  <tr>
   <td style="text-align:left;"> 54953 </td>
   <td style="text-align:left;"> IDCJAC0010 </td>
   <td style="text-align:right;"> 90015 </td>
   <td style="text-align:right;"> 2014 </td>
   <td style="text-align:right;"> 6 </td>
   <td style="text-align:right;"> 15 </td>
   <td style="text-align:right;"> 13.7 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:left;"> Y </td>
  </tr>
</tbody>
</table>

---
class: left
## Refresh-R: Visualisation

```r
weather %>% 
    filter(!is.na(Max_temperature))
```

---
class: left
## Refresh-R: Visualisation

```r
weather %>% 
    filter(!is.na(Max_temperature)) %>%  
    mutate(
        Season = case_when(
            Month %in% c(12, 1, 2) ~ "Summer",
            Month %in% c(3, 4, 5) ~ "Autumn",
            Month %in% c(6, 7, 8) ~ "Winter",
            Month %in% c(9, 10, 11) ~ "Spring"
        ),
        Season = factor(Season, levels = c("Summer", "Autumn", "Winter", "Spring")) # ordering
    )
```

---
class: left
## Refresh-R: Visualisation

```r
weather %>% 
    filter(!is.na(Max_temperature)) %>%
    mutate(
        Season = case_when(
            Month %in% c(12, 1, 2) ~ "Summer",
            Month %in% c(3, 4, 5) ~ "Autumn",
            Month %in% c(6, 7, 8) ~ "Winter",
            Month %in% c(9, 10, 11) ~ "Spring"
        ),
        Season = factor(Season, levels = c("Summer", "Autumn", "Winter", "Spring")) # ordering
    ) %>%
    group_by(Year, Season) %>% 
    summarise(Average_max_temp = mean(Max_temperature)) -> weather_summarised
```

--
<table class="table" style="font-size: 18px; margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:right;"> Year </th>
   <th style="text-align:left;"> Season </th>
   <th style="text-align:right;"> Average_max_temp </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:right;"> 1979 </td>
   <td style="text-align:left;"> Winter </td>
   <td style="text-align:right;"> 13.25000 </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 1972 </td>
   <td style="text-align:left;"> Autumn </td>
   <td style="text-align:right;"> 17.70978 </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 1949 </td>
   <td style="text-align:left;"> Winter </td>
   <td style="text-align:right;"> 12.65761 </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 1899 </td>
   <td style="text-align:left;"> Spring </td>
   <td style="text-align:right;"> 17.22593 </td>
  </tr>
</tbody>
</table>

---
class: left
## Refresh-R: Visualisation

.pull-left[

```r
weather_summarised %>% 
    ggplot(
      aes(x = Year, y = Average_max_temp)
    )
```
]

.pull-right[
<img src="user2018_full_files/figure-html/weather_partial_plot_1-1.png" width="500" height="500" />
]

---
class: left
## Refresh-R: Visualisation

.pull-left[

```r
weather_summarised %>% 
    ggplot(
      aes(x = Year, y = Average_max_temp)
    ) + 
    facet_grid(Season ~ .)
```
]

.pull-right[
<img src="user2018_full_files/figure-html/weather_partial_plot_2-1.png" width="500" height="500" />
]

---
class: left
## Refresh-R: Visualisation

.pull-left[

```r
weather_summarised %>% 
    ggplot(
      aes(x = Year, y = Average_max_temp)
    ) + 
    facet_grid(Season ~ .) +
    geom_point() 
```
]

.pull-right[
<img src="user2018_full_files/figure-html/weather_partial_plot_3-1.png" width="500" height="500" />
]

---
class: left
## Refresh-R: Visualisation

.pull-left[

```r
weather_summarised %>% 
    ggplot(
      aes(x = Year, y = Average_max_temp)
    ) +
    facet_grid(Season ~ .) +
    geom_point() +
    geom_smooth() 
```

We can pass this to plotly with the `ggplotly` function.
]

.pull-right[
<img src="user2018_full_files/figure-html/weather_partial_plot_4-1.png" width="500" height="500" />
]

---
class: inverse, center, middle

#Refresh-R: Modelling
<img src="https://i.imgur.com/muNWT5A.gif" style="width: 40%; height: 40%;"></img>

---
class: left
## Refresh-R: Modelling

```r
diamonds %>% nrow
```

```
## [1] 53940
```

```r
diamonds %>% head
```

<table class="table" style="font-size: 18px; margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:right;"> carat </th>
   <th style="text-align:left;"> cut </th>
   <th style="text-align:left;"> color </th>
   <th style="text-align:left;"> clarity </th>
   <th style="text-align:right;"> depth </th>
   <th style="text-align:right;"> table </th>
   <th style="text-align:right;"> price </th>
   <th style="text-align:right;"> x </th>
   <th style="text-align:right;"> y </th>
   <th style="text-align:right;"> z </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:right;"> 0.23 </td>
   <td style="text-align:left;"> Ideal </td>
   <td style="text-align:left;"> E </td>
   <td style="text-align:left;"> SI2 </td>
   <td style="text-align:right;"> 61.5 </td>
   <td style="text-align:right;"> 55 </td>
   <td style="text-align:right;"> 326 </td>
   <td style="text-align:right;"> 3.95 </td>
   <td style="text-align:right;"> 3.98 </td>
   <td style="text-align:right;"> 2.43 </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 0.21 </td>
   <td style="text-align:left;"> Premium </td>
   <td style="text-align:left;"> E </td>
   <td style="text-align:left;"> SI1 </td>
   <td style="text-align:right;"> 59.8 </td>
   <td style="text-align:right;"> 61 </td>
   <td style="text-align:right;"> 326 </td>
   <td style="text-align:right;"> 3.89 </td>
   <td style="text-align:right;"> 3.84 </td>
   <td style="text-align:right;"> 2.31 </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 0.23 </td>
   <td style="text-align:left;"> Good </td>
   <td style="text-align:left;"> E </td>
   <td style="text-align:left;"> VS1 </td>
   <td style="text-align:right;"> 56.9 </td>
   <td style="text-align:right;"> 65 </td>
   <td style="text-align:right;"> 327 </td>
   <td style="text-align:right;"> 4.05 </td>
   <td style="text-align:right;"> 4.07 </td>
   <td style="text-align:right;"> 2.31 </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 0.29 </td>
   <td style="text-align:left;"> Premium </td>
   <td style="text-align:left;"> I </td>
   <td style="text-align:left;"> VS2 </td>
   <td style="text-align:right;"> 62.4 </td>
   <td style="text-align:right;"> 58 </td>
   <td style="text-align:right;"> 334 </td>
   <td style="text-align:right;"> 4.20 </td>
   <td style="text-align:right;"> 4.23 </td>
   <td style="text-align:right;"> 2.63 </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 0.31 </td>
   <td style="text-align:left;"> Good </td>
   <td style="text-align:left;"> J </td>
   <td style="text-align:left;"> SI2 </td>
   <td style="text-align:right;"> 63.3 </td>
   <td style="text-align:right;"> 58 </td>
   <td style="text-align:right;"> 335 </td>
   <td style="text-align:right;"> 4.34 </td>
   <td style="text-align:right;"> 4.35 </td>
   <td style="text-align:right;"> 2.75 </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 0.24 </td>
   <td style="text-align:left;"> Very Good </td>
   <td style="text-align:left;"> J </td>
   <td style="text-align:left;"> VVS2 </td>
   <td style="text-align:right;"> 62.8 </td>
   <td style="text-align:right;"> 57 </td>
   <td style="text-align:right;"> 336 </td>
   <td style="text-align:right;"> 3.94 </td>
   <td style="text-align:right;"> 3.96 </td>
   <td style="text-align:right;"> 2.48 </td>
  </tr>
</tbody>
</table>

---
class: left
## Refresh-R: Modelling

The `glance` function from the `broom` library makes model results tidy.

```r
library(broom)
```

```r
diamonds %>% 
    lm(price ~ color + carat + cut + clarity + depth + table, data = .) %>% 
    glance
```

<table class="table" style="font-size: 16px; margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:right;"> r.squared </th>
   <th style="text-align:right;"> adj.r.squared </th>
   <th style="text-align:right;"> sigma </th>
   <th style="text-align:right;"> statistic </th>
   <th style="text-align:right;"> p.value </th>
   <th style="text-align:right;"> df </th>
   <th style="text-align:right;"> logLik </th>
   <th style="text-align:right;"> AIC </th>
   <th style="text-align:right;"> BIC </th>
   <th style="text-align:right;"> deviance </th>
   <th style="text-align:right;"> df.residual </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:right;"> 0.9160543 </td>
   <td style="text-align:right;"> 0.9160232 </td>
   <td style="text-align:right;"> 1156.09 </td>
   <td style="text-align:right;"> 29419.46 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 21 </td>
   <td style="text-align:right;"> -456955 </td>
   <td style="text-align:right;"> 913954 </td>
   <td style="text-align:right;"> 914149.7 </td>
   <td style="text-align:right;"> 72065109042 </td>
   <td style="text-align:right;"> 53919 </td>
  </tr>
</tbody>
</table>

<img src="hex/broom.png" style="width: 15%; height: 15%;"></img>

---
class: left
## Refresh-R: Modelling

Suppose we wanted to make a different model for every `color`.

`glance` gives one row per model.

```r
diamonds %>% 
    group_by(color) %>% 
    do(glance(lm(price ~ carat + cut + clarity + depth + table, data = .)))
```

<table class="table" style="font-size: 16px; margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:left;"> color </th>
   <th style="text-align:right;"> r.squared </th>
   <th style="text-align:right;"> adj.r.squared </th>
   <th style="text-align:right;"> sigma </th>
   <th style="text-align:right;"> statistic </th>
   <th style="text-align:right;"> p.value </th>
   <th style="text-align:right;"> df </th>
   <th style="text-align:right;"> logLik </th>
   <th style="text-align:right;"> AIC </th>
   <th style="text-align:right;"> BIC </th>
   <th style="text-align:right;"> deviance </th>
   <th style="text-align:right;"> df.residual </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> D </td>
   <td style="text-align:right;"> 0.8909225 </td>
   <td style="text-align:right;"> 0.8906966 </td>
   <td style="text-align:right;"> 1109.725 </td>
   <td style="text-align:right;"> 3943.879 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 15 </td>
   <td style="text-align:right;"> -57111.20 </td>
   <td style="text-align:right;"> 114254.40 </td>
   <td style="text-align:right;"> 114363.54 </td>
   <td style="text-align:right;"> 8324862365 </td>
   <td style="text-align:right;"> 6760 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> E </td>
   <td style="text-align:right;"> 0.9039023 </td>
   <td style="text-align:right;"> 0.9037647 </td>
   <td style="text-align:right;"> 1037.419 </td>
   <td style="text-align:right;"> 6572.156 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 15 </td>
   <td style="text-align:right;"> -81929.01 </td>
   <td style="text-align:right;"> 163890.02 </td>
   <td style="text-align:right;"> 164005.06 </td>
   <td style="text-align:right;"> 10527754413 </td>
   <td style="text-align:right;"> 9782 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> F </td>
   <td style="text-align:right;"> 0.9112486 </td>
   <td style="text-align:right;"> 0.9111182 </td>
   <td style="text-align:right;"> 1128.422 </td>
   <td style="text-align:right;"> 6986.982 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 15 </td>
   <td style="text-align:right;"> -80598.67 </td>
   <td style="text-align:right;"> 161229.34 </td>
   <td style="text-align:right;"> 161343.96 </td>
   <td style="text-align:right;"> 12131071813 </td>
   <td style="text-align:right;"> 9527 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> G </td>
   <td style="text-align:right;"> 0.9273524 </td>
   <td style="text-align:right;"> 0.9272622 </td>
   <td style="text-align:right;"> 1092.580 </td>
   <td style="text-align:right;"> 10282.267 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 15 </td>
   <td style="text-align:right;"> -95017.34 </td>
   <td style="text-align:right;"> 190066.68 </td>
   <td style="text-align:right;"> 190183.99 </td>
   <td style="text-align:right;"> 13461715023 </td>
   <td style="text-align:right;"> 11277 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> H </td>
   <td style="text-align:right;"> 0.9315567 </td>
   <td style="text-align:right;"> 0.9314411 </td>
   <td style="text-align:right;"> 1103.892 </td>
   <td style="text-align:right;"> 8058.471 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 15 </td>
   <td style="text-align:right;"> -69958.15 </td>
   <td style="text-align:right;"> 139948.29 </td>
   <td style="text-align:right;"> 140060.68 </td>
   <td style="text-align:right;"> 10100793783 </td>
   <td style="text-align:right;"> 8289 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> I </td>
   <td style="text-align:right;"> 0.9363188 </td>
   <td style="text-align:right;"> 0.9361540 </td>
   <td style="text-align:right;"> 1193.242 </td>
   <td style="text-align:right;"> 5678.599 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 15 </td>
   <td style="text-align:right;"> -46097.75 </td>
   <td style="text-align:right;"> 92227.50 </td>
   <td style="text-align:right;"> 92333.07 </td>
   <td style="text-align:right;"> 7698632142 </td>
   <td style="text-align:right;"> 5407 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> J </td>
   <td style="text-align:right;"> 0.9401123 </td>
   <td style="text-align:right;"> 0.9398121 </td>
   <td style="text-align:right;"> 1088.831 </td>
   <td style="text-align:right;"> 3131.733 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 15 </td>
   <td style="text-align:right;"> -23612.81 </td>
   <td style="text-align:right;"> 47257.62 </td>
   <td style="text-align:right;"> 47352.66 </td>
   <td style="text-align:right;"> 3311246600 </td>
   <td style="text-align:right;"> 2793 </td>
  </tr>
</tbody>
</table>

---
class: left
## Refresh-R: Modelling

We can use the `augment` function from the `broom` package to get 
*observation-level* statistics for our linear models.

```r
diamonds %>% 
    group_by(color) %>% 
    do(augment(lm(price ~ carat + cut + clarity + depth + table, data = .))) %>% 
    head
```

<table class="table" style="font-size: 16px; margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:left;"> color </th>
   <th style="text-align:right;"> price </th>
   <th style="text-align:right;"> carat </th>
   <th style="text-align:left;"> cut </th>
   <th style="text-align:left;"> clarity </th>
   <th style="text-align:right;"> depth </th>
   <th style="text-align:right;"> table </th>
   <th style="text-align:right;"> .fitted </th>
   <th style="text-align:right;"> .se.fit </th>
   <th style="text-align:right;"> .resid </th>
   <th style="text-align:right;"> .hat </th>
   <th style="text-align:right;"> .sigma </th>
   <th style="text-align:right;"> .cooksd </th>
   <th style="text-align:right;"> .std.resid </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> D </td>
   <td style="text-align:right;"> 357 </td>
   <td style="text-align:right;"> 0.23 </td>
   <td style="text-align:left;"> Very Good </td>
   <td style="text-align:left;"> VS2 </td>
   <td style="text-align:right;"> 60.5 </td>
   <td style="text-align:right;"> 61 </td>
   <td style="text-align:right;"> -738.0208 </td>
   <td style="text-align:right;"> 48.04253 </td>
   <td style="text-align:right;"> 1095.0208 </td>
   <td style="text-align:right;"> 0.0018742 </td>
   <td style="text-align:right;"> 1109.727 </td>
   <td style="text-align:right;"> 0.0001221 </td>
   <td style="text-align:right;"> 0.9876762 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> D </td>
   <td style="text-align:right;"> 402 </td>
   <td style="text-align:right;"> 0.23 </td>
   <td style="text-align:left;"> Very Good </td>
   <td style="text-align:left;"> VS1 </td>
   <td style="text-align:right;"> 61.9 </td>
   <td style="text-align:right;"> 58 </td>
   <td style="text-align:right;"> -348.5558 </td>
   <td style="text-align:right;"> 51.35991 </td>
   <td style="text-align:right;"> 750.5558 </td>
   <td style="text-align:right;"> 0.0021420 </td>
   <td style="text-align:right;"> 1109.769 </td>
   <td style="text-align:right;"> 0.0000656 </td>
   <td style="text-align:right;"> 0.6770698 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> D </td>
   <td style="text-align:right;"> 403 </td>
   <td style="text-align:right;"> 0.26 </td>
   <td style="text-align:left;"> Very Good </td>
   <td style="text-align:left;"> VS2 </td>
   <td style="text-align:right;"> 60.8 </td>
   <td style="text-align:right;"> 59 </td>
   <td style="text-align:right;"> -369.9372 </td>
   <td style="text-align:right;"> 42.32684 </td>
   <td style="text-align:right;"> 772.9372 </td>
   <td style="text-align:right;"> 0.0014548 </td>
   <td style="text-align:right;"> 1109.767 </td>
   <td style="text-align:right;"> 0.0000472 </td>
   <td style="text-align:right;"> 0.6970199 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> D </td>
   <td style="text-align:right;"> 403 </td>
   <td style="text-align:right;"> 0.26 </td>
   <td style="text-align:left;"> Good </td>
   <td style="text-align:left;"> VS2 </td>
   <td style="text-align:right;"> 65.2 </td>
   <td style="text-align:right;"> 56 </td>
   <td style="text-align:right;"> -287.9032 </td>
   <td style="text-align:right;"> 60.17899 </td>
   <td style="text-align:right;"> 690.9032 </td>
   <td style="text-align:right;"> 0.0029408 </td>
   <td style="text-align:right;"> 1109.775 </td>
   <td style="text-align:right;"> 0.0000764 </td>
   <td style="text-align:right;"> 0.6235073 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> D </td>
   <td style="text-align:right;"> 403 </td>
   <td style="text-align:right;"> 0.26 </td>
   <td style="text-align:left;"> Good </td>
   <td style="text-align:left;"> VS1 </td>
   <td style="text-align:right;"> 58.4 </td>
   <td style="text-align:right;"> 63 </td>
   <td style="text-align:right;"> -557.2384 </td>
   <td style="text-align:right;"> 76.61276 </td>
   <td style="text-align:right;"> 960.2384 </td>
   <td style="text-align:right;"> 0.0047662 </td>
   <td style="text-align:right;"> 1109.745 </td>
   <td style="text-align:right;"> 0.0002402 </td>
   <td style="text-align:right;"> 0.8673639 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> D </td>
   <td style="text-align:right;"> 404 </td>
   <td style="text-align:right;"> 0.22 </td>
   <td style="text-align:left;"> Premium </td>
   <td style="text-align:left;"> VS2 </td>
   <td style="text-align:right;"> 59.3 </td>
   <td style="text-align:right;"> 62 </td>
   <td style="text-align:right;"> -732.0338 </td>
   <td style="text-align:right;"> 49.90248 </td>
   <td style="text-align:right;"> 1136.0338 </td>
   <td style="text-align:right;"> 0.0020222 </td>
   <td style="text-align:right;"> 1109.720 </td>
   <td style="text-align:right;"> 0.0001419 </td>
   <td style="text-align:right;"> 1.0247445 </td>
  </tr>
</tbody>
</table>

According to these models, some diamonds have... negative prices?

---
class: left
## Refresh-R: Modelling

```r
diamonds %>% 
    group_by(color) %>% 
    do(augment(lm(price ~ carat + cut + clarity + depth + table, data = .))) %>%
    ggplot(aes(x = .fitted, y = .resid)) +
        geom_hline(yintercept = 0) +
        geom_point() + 
        facet_wrap(color ~ ., ncol = 2)
```

---
class: center
## Refresh-R: Modelling

<img src="user2018_full_files/figure-html/diamonds_ggplot_display-1.png" width="1000" height="500" />

---
class: center
## Refresh-R: Modelling

.pull-left[
<img src="user2018_full_files/figure-html/diamond_unmodified-1.png" width="500" height="500" />
]

.pull-right[
<img src="user2018_full_files/figure-html/diamond_modified-1.png" width="500" height="500" />
]

---
class: inverse, center, middle
# Modifying data

<img src="https://imgs.xkcd.com/comics/machine_learning.png" style="width: 35%; height: 35%;">

https://xkcd.com/1838/

---
class: left
## Modifying data

* Centering
* Scaling
* Filters
* PCA
* Encoding/decoding
* Missing value imputation
* Feature engineering

```r
library(recipes)
```

<img src="hex/recipes.png" style="width: 18%; height: 18%;"></img>

---
## `ames` housing data

.pull-left[

```r
library(leaflet)

leaflet(ames) %>% 
    addTiles() %>% 
    addCircleMarkers(radius = 1)
```
]

.pull-right[
<div id="htmlwidget-a036e520aac72992ba38" style="width:420px;height:420px;" class="leaflet html-widget"></div>
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href=\"http://creativecommons.org/licenses/by-sa/2.0/\">CC-BY-SA<\/a>"}]},{"method":"addCircleMarkers","args":[[42.054035,42.053014,42.052659,42.051245,42.060899,42.060779,42.062978,42.060728,42.06112,42.059193,42.05848,42.057419,42.058151,42.057268,42.059169,42.061239,42.0603514,42.05885,42.056673,42.054453,42.050346,42.05027,42.049297,42.055147,42.054592,42.055227,42.053395,42.056375,42.055318,42.051835,42.051684,42.051685,42.049672,42.050683,42.050681,42.049811,42.062352,42.062109,42.0611331,42.0612885,42.060872,42.0608079,42.060879,42.060298,42.059617,42.058388,42.058599,42.057102,42.057164,42.058252,42.057181,42.061928,42.06257,42.062303,42.061443,42.060977,42.061193,42.060936,42.057103,42.054386,42.053243,42.051721,42.051674,42.052457,42.052437,42.050909,42.05306,42.051827,42.051653,42.050084,42.052347,42.051429,42.036255,42.036113,42.036026,42.03548,42.036659,42.034589,42.0347,42.036633,42.034555,42.034678,42.0360754,42.036535,42.034793,42.035685,42.034856,42.03457,42.035333,42.034603,42.048253,42.046825,42.045773,42.047452,42.048086,42.048566,42.047899,42.047876,42.047782,42.0471748,42.046145,42.046145,42.046424,42.046114,42.0470316,42.04638,42.042565,42.043704,42.034561,42.048606,42.046322,42.043038,42.043306,42.043306,42.043329,42.043822,42.043821,42.046574,42.042205,42.043363,42.039085,42.038971,42.040326,42.041146,42.040391,42.0418387,42.039211,42.039211,42.038538,42.035256,42.035482,42.035943,42.036095,42.037424,42.048494,42.0459375,42.049514,42.047447,42.044895,42.043984,42.042908,42.0430839,42.044847,42.043969,42.044052,42.044176,42.044883,42.0435919,42.042308,42.040396,42.04137,42.038515,42.039841,42.038443,42.038521,42.038249,42.0381282,42.035869,42.0372097,42.040085,42.041119,42.035867,42.03594,42.0347571,42.034848,42.034748,42.034851,42.036873,42.037093,42.033313,42.032134,42.031363,42.031381,42.0303783,42.033279,42.033644,42.032391,42.03128,42.0303113,42.030383,42.030467,42.030388,42.030331,42.028099,42.029146,42.028145,42.028083,42.03005,42.028164,42.02916,42.028165,42.033642,42.030187,42.033621,42.033563,42.0336,42.033665,42.033631,42.031556,42.031337,42.032302,42.027986,42.028888,42.028875,42.026914,42.020693,42.024141,42.026741,42.033454,42.032432,42.028256,42.026752,42.02616,42.025255,42.022825,42.034451,42.033386,42.033387,42.033008,42.03237,42.029867,42.03069,42.029924,42.029731,42.030519,42.031187,42.03136,42.03128,42.030082,42.03009,42.03387,42.032164,42.026547,42.024673,42.023079,42.028226,42.026533,42.024208,42.025173,42.02526,42.0247497,42.024126,42.025019,42.024162,42.03374,42.032076,42.033783,42.03068,42.0311198,42.025747,42.024614,42.024604,42.024604,42.030601,42.0298532,42.024318,42.021321,42.018411,42.01867,42.022186,42.022045,42.021395,42.021133,42.018662,42.019069,42.016121,42.018721,42.017749,42.016275,42.016253,42.01404,42.014092,42.014023,42.0203585,42.021245,42.021041,42.020635,42.021163,42.020306,42.019849,42.019629,42.020196,42.021793,42.021583,42.017726,42.022674,42.022702,42.022089,42.017737,42.0177987,42.0163944,42.019091,42.019093,42.016226,42.014937,42.014682,42.011005,42.010606,42.009345,42.009337,42.010797,42.022772,42.021414,42.021474,42.021456,42.022653,42.021327,42.02152,42.008687,41.99706,41.997052,41.996065,41.994784,41.994013,41.997741,41.995346,41.995278,41.994284,41.99353,41.995489,41.99435,41.992989,41.99321,41.990934,41.993253,41.991004,41.991861,41.991758,41.991876,41.992212,41.992119,41.9917,41.991824,41.991558,41.988729,41.9889358,41.98766,41.986647,41.9913,41.989848,41.989185,42.0530363,42.05139,42.049322,42.059956,42.063294,42.060896,42.062956,42.060598,42.062903,42.061981,42.061633,42.061178,42.059283,42.059129,42.059161,42.058274,42.058059,42.059645,42.058194,42.05793,42.058173,42.057854,42.058201,42.058747,42.059242,42.060265,42.059817,42.058439,42.056796,42.053532,42.055408,42.055081,42.055572,42.05184,42.052507,42.052102,42.052719,42.05288,42.052915,42.050345,42.0496539,42.049394,42.049106,42.051619,42.0503771,42.049406,42.053166,42.052355,42.052448,42.05267,42.04891,42.056398,42.054626,42.055228,42.055184,42.055154,42.05337,42.053306,42.053307,42.0564419,42.056461,42.052758,42.052338,42.051683,42.051661,42.051823,42.051822,42.050615,42.050705,42.050705,42.050682,42.050533,42.050213,42.05009,42.050134,42.050222,42.050397,42.0488934,42.048486,42.0623099,42.063304,42.063025,42.061902,42.06128,42.062991,42.062992,42.062994,42.062942,42.062258,42.062257,42.062776,42.062789,42.0623985,42.062108,42.060934,42.060353,42.061382,42.061437,42.060576,42.060575,42.061678,42.060426,42.060178,42.0599123,42.0598989,42.061404,42.059241,42.058782,42.058886,42.0592,42.059177,42.059179,42.059183,42.058339,42.058336,42.057177,42.05851,42.057164,42.057089,42.057019,42.056983,42.057277,42.057467,42.06232,42.063183,42.062058,42.062404,42.062562,42.062557,42.062305,42.062266,42.062081,42.062086,42.061562,42.059377,42.061739,42.061149,42.061114,42.061656,42.061365,42.063283,42.058368,42.05859,42.059481,42.059068,42.059605,42.057839,42.057834,42.057833,42.057098,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]

---
## Infrequently occurring levels

.pull-left[

```r
ames %>% 
    ggplot(aes(x = Neighborhood)) +
    geom_bar(fill = "#6d1e3b") + 
    coord_flip()
```
]

.pull-right[
<img src="user2018_full_files/figure-html/neighbourhood_plot-1.png" width="500" height="500" />
]

---
## Non-normally distributed data

.pull-left[

```r
ames %>% 
    ggplot(aes(x = Lot_Area)) + 
    geom_density() + 
    xlim(0, 30000)
```
]

.pull-right[
<img src="user2018_full_files/figure-html/lot_area_plot-1.png" width="500" height="500" />
]

---
## Preparing and baking a recipe

```r
library(rsample)
data_split <- initial_split(ames, strata = "Sale_Price", p = 0.75)
ames_train <- training(data_split)
ames_test <- testing(data_split)
```

<img src="hex/rsample.png" style="width: 15%; height: 15%;"></img>

---
## Preparing and baking a recipe

```r
library(rsample)
data_split <- initial_split(ames, strata = "Sale_Price", p = 0.75)
ames_train <- training(data_split)
ames_test <- testing(data_split)
```

```r
recipe(
        Sale_Price ~ Longitude + Latitude + Neighborhood + Lot_Area, 
        data = ames_train
    ) %>% 
    step_other(Neighborhood, threshold = 0.05) %>%
    step_log(Sale_Price, base = 10) %>%
    step_YeoJohnson(Lot_Area) %>% 
    prep(training = ames_train) -> ames_recipe
```

--

```r
ames_train_baked <- ames_recipe %>% bake(ames_train)
ames_test_baked <- ames_recipe %>% bake(ames_test)
```

---
## Linear models

.pull-left[
<img src="user2018_full_files/figure-html/ames_unmodified_lm-1.png" width="500" height="500" />
]

.pull-right[
<img src="user2018_full_files/figure-html/ames_modified_lm-1.png" width="500" height="500" />
]

---
class: inverse, middle, center

# Missing values

<img src = "missing_value_imputation.jpg" style="width: 30%; height: 30%;"></img>

Mean value imputation

---
## Ozone data

.pull-left[

```r
library(naniar)
ozone <- read_csv("ozoneNA.csv") %>% 
    select(-X1, -WindDirection)
vis_miss(ozone)
```

<img src="hex/naniar.png" style="width: 30%; height: 30%;"></img>

]

.pull-right[
<img src="user2018_full_files/figure-html/naniar_plot-1.png" width="500" height="500" />
]

---
class: middle, center
## Visualising missing data

<img src="user2018_full_files/figure-html/feed_visdat-1.png" width="1000" height="500" />

---
class: inverse, center, middle
# Time series

<img src="https://media.giphy.com/media/QYumYBnNm3Qw8/giphy.gif" style="width: 70%; height: 70%;"></img>

---
class: left
## `fable`

```r
library(tsibble) 
library(fable) 
```

```r
cafe <- fpp2::auscafe %>% as_tsibble
cafe %>% head
```

.pull-left[
<table class="table" style="font-size: 18px; margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:left;"> index </th>
   <th style="text-align:right;"> value </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> 1982 Apr </td>
   <td style="text-align:right;"> 0.3424 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> 1982 May </td>
   <td style="text-align:right;"> 0.3421 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> 1982 Jun </td>
   <td style="text-align:right;"> 0.3287 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> 1982 Jul </td>
   <td style="text-align:right;"> 0.3385 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> 1982 Aug </td>
   <td style="text-align:right;"> 0.3315 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> 1982 Sep </td>
   <td style="text-align:right;"> 0.3419 </td>
  </tr>
</tbody>
</table>
]

.pull-right[
<img src="hex/tsibble.png" style="width: 30%; height: 30%;"></img><img src="hex/fable.png" style="width: 30%; height: 30%;"></img>
]

---
class: left
## `fable`

```r
# Example by Rob Hyndman
cafe %>% ARIMA(log(value) ~ pdq(2, 1, 1) + PDQ(2, 1, 2)) %>% forecast() %>% autoplot()
```

<img src="user2018_full_files/figure-html/fable_plot-1.png" width="1000" height="400" />

---
class: inverse, center, middle

<img src="hex/shiny.png" style="width: 50%; height: 50%;"></img>