ESSAWebinar.utf8

class: title-slide, center
background-image: url("2021 ESSA Webinar/Images/Front page.png")
background-size: Cover

<div>
<style type="text/css">.xaringan-extra-logo {
width: 90px;
height: 90px;
z-index: 0;
background-image: url(https://www.essa.org.au/images/ESSATemplate/logo.png);
background-size: contain;
background-repeat: no-repeat;
position: absolute;
top:1em;right:1em;
}
</style>
<script>(function () {
  let tries = 0
  function addLogo () {
    if (typeof slideshow === 'undefined') {
      tries += 1
      if (tries < 10) {
        setTimeout(addLogo, 100)
      }
    } else {
      document.querySelectorAll('.remark-slide-content:not(.title-slide):not(.inverse):not(.hide_logo)')
        .forEach(function (slide) {
          const logo = document.createElement('a')
          logo.classList = 'xaringan-extra-logo'
          logo.href = 'https://www.essa.org.au/images/ESSATemplate'
          slide.appendChild(logo)
        })
    }
  }
  document.addEventListener('DOMContentLoaded', addLogo)
})()</script>
</div>

---

class: left

background-image: url("2021 ESSA Webinar/Images/Home.jpg")
background-position: 100% 50%
background-size: 40% 65%

# About me
<br>
👋 I'm an applied sports scientist

I currently work with two 🏈teams

- Gold Coast Suns Football Club (AFL)

- Newcastle Knights Rugby League Club (NRL)

📍 I live in an pretty amazing place (Newcastle)

---

# How are you analysing your data?

<br>

.pull-left[

<img src="https://1xw7c62t8pgj1bq3qetvvsg1-wpengine.netdna-ssl.com/wp-content/uploads/2020/06/Coach-at-Whiteboard-1024x536.jpg" width="110%" />
]

.pull-right[
<img src="https://media.giphy.com/media/f5dv1g0Af3KNJneSC3/giphy.gif" width="94%" />
]

---

# Why learn a new program? 
<br>
.pull-left[
<svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:black;overflow:visible;position:relative;"><path d="M256,8C119,8,8,119,8,256S119,504,256,504,504,393,504,256,393,8,256,8Zm92.49,313h0l-20,25a16,16,0,0,1-22.49,2.5h0l-67-49.72a40,40,0,0,1-15-31.23V112a16,16,0,0,1,16-16h32a16,16,0,0,1,16,16V256l58,42.5A16,16,0,0,1,348.49,321Z"/></svg>  Save time
<br>

.pull-right[

<img src="https://media.giphy.com/media/PhZlwqetWEJe0tBZ1O/giphy.gif" width="65%" />
]

---

# But Excel is working for me
<br>
<svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:black;overflow:visible;position:relative;"><path d="M104 224H24c-13.255 0-24 10.745-24 24v240c0 13.255 10.745 24 24 24h80c13.255 0 24-10.745 24-24V248c0-13.255-10.745-24-24-24zM64 472c-13.255 0-24-10.745-24-24s10.745-24 24-24 24 10.745 24 24-10.745 24-24 24zM384 81.452c0 42.416-25.97 66.208-33.277 94.548h101.723c33.397 0 59.397 27.746 59.553 58.098.084 17.938-7.546 37.249-19.439 49.197l-.11.11c9.836 23.337 8.237 56.037-9.308 79.469 8.681 25.895-.069 57.704-16.382 74.757 4.298 17.598 2.244 32.575-6.148 44.632C440.202 511.587 389.616 512 346.839 512l-2.845-.001c-48.287-.017-87.806-17.598-119.56-31.725-15.957-7.099-36.821-15.887-52.651-16.178-6.54-.12-11.783-5.457-11.783-11.998v-213.77c0-3.2 1.282-6.271 3.558-8.521 39.614-39.144 56.648-80.587 89.117-113.111 14.804-14.832 20.188-37.236 25.393-58.902C282.515 39.293 291.817 0 312 0c24 0 72 8 72 81.452z"/></svg>   Excel is easy to use and has a place in data entry, analysis and visualisation

<br>

---
# But Excel is working for me

And soon enough....

<br>

.center[
Credit: Dr Jacquie Tran (ESSA Forum, 2019) and Dr Alice Sweeting (WCSF Workshop, 2019)
]

---

class: title-slide, top, hide_logo, inverse
background-image: url("2021 ESSA Webinar/Images/Knights.jpg")
background-size: cover

# Part 1 - R programming

---

# R programming

- Reproducibility -> automate workflows

- Simple to really complex analysis

- Its free

- Can create complex dashboards, or simple PDF/HTML outputs

- Fast and efficient analysis

- Handles big data sets really well (i.e., raw GPS files)

---
class: center
# What others think R looks like
<br>

.small[
Credit: Dr Alice Sweeting (WCSF Workshop, 2019)
]

---

# Where to start with R

.pull-left[
- Need to download [R](https://www.r-project.org/) first

<br>

- I recommend using [RStudio](https://www.rstudio.com/products/rstudio/) which is an interface for R, making it less 'scary' and more user friendly

<br>

- Practice using a data set you know and understand - start out with some simple plots

<br>

.pull-right[

<blockquote class="twitter-tweet"><p lang="en" dir="ltr">🚨 New post &amp; video tutorial 🚨<br><br>How sports scientists can use ggplot2 in R to make better visualisations 📊<br><br>Great visualisations help communicate your message more clearly. This post shows you an example of my process. <a href="https://twitter.com/hashtag/rstats?src=hash&amp;ref_src=twsrc%5Etfw">#rstats</a><a href="https://t.co/SsBae0nDU1">https://t.co/SsBae0nDU1</a> <a href="https://t.co/fwNdqrkDzZ">pic.twitter.com/fwNdqrkDzZ</a></p>&mdash; Mitch Henderson (@mitchhendo_) <a href="https://twitter.com/mitchhendo_/status/1254568836986048512?ref_src=twsrc%5Etfw">April 27, 2020</a></blockquote>

]

---
class: title-slide, right, hide_logo, inverse
background-image: url(https://www.newcastleknights.com.au/contentassets/b1476168d8634162aae7dda10e8ed390/kalyn-gym.jpg?center=0.3%2C0.5&preset=photo-inline)
background-size: cover

# Example 1 - strength data

---

# Create a project

.pull-left[
<svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:black;overflow:visible;position:relative;"><path d="M505.04 442.66l-99.71-99.69c-4.5-4.5-10.6-7-17-7h-16.3c27.6-35.3 44-79.69 44-127.99C416.03 93.09 322.92 0 208.02 0S0 93.09 0 207.98s93.11 207.98 208.02 207.98c48.3 0 92.71-16.4 128.01-44v16.3c0 6.4 2.5 12.5 7 17l99.71 99.69c9.4 9.4 24.6 9.4 33.9 0l28.3-28.3c9.4-9.4 9.4-24.59.1-33.99zm-297.02-90.7c-79.54 0-144-64.34-144-143.98 0-79.53 64.35-143.98 144-143.98 79.54 0 144 64.34 144 143.98 0 79.53-64.35 143.98-144 143.98zm.02-239.96c-40.78 0-73.84 33.05-73.84 73.83 0 32.96 48.26 93.05 66.75 114.86a9.24 9.24 0 0 0 14.18 0c18.49-21.81 66.75-81.89 66.75-114.86 0-40.78-33.06-73.83-73.84-73.83zm0 96c-13.26 0-24-10.75-24-24 0-13.26 10.75-24 24-24s24 10.74 24 24c0 13.25-10.75 24-24 24z"/></svg>  Projects help to organise files and locations

<br>

<br>

]

.pull-right[
<img src="2021 ESSA Webinar/Images/Project.png" width="100%" style="display: block; margin: auto;" />
]

---

# Import strength testing data

```r
install.packages("readr")
```

<br>

```r
library(readr)
Strength <- read_csv("2021 ESSA Webinar/Files/Strength.csv")
```

--
<br>

```r
library(readxl)
Strength_xlsx <- read_excel("C:/Users/Heidi.Thornton/OneDrive - GCFC Limited/Documents/Strength.xlsx")
```

---

# View data

```r
print(Strength, n=6)
```

```r
head(Strength, n=6)
```

```
## # A tibble: 6 x 8
##   Date      Name      Season Period `Bodyweight (kg~ `3RM Weighted C~ `3RM Bench Pres~
##   <chr>     <chr>      <dbl> <chr>             <dbl>            <dbl>            <dbl>
## 1 1/11/2020 Athlete 1   2021 Start~               85             120              95  
## 2 1/11/2020 Athlete 2   2021 Start~               86             125              97.5
## 3 1/11/2020 Athlete 3   2021 Start~               88             122.            100  
## 4 3/11/2020 Athlete 3   2021 Start~               89             125             102. 
## 5 1/11/2020 Athlete 4   2021 Start~               90             122             110  
## 6 1/11/2020 Athlete 5   2021 Start~              100              90             120  
## # ... with 1 more variable: 3RM Box Squat <dbl>
```

---

# Summarise data

```r
Strength_summary <- Strength %>%
      group_by(Name) %>% 
          filter(Season == 2021) %>%
            summarize(
                      'Max chins (kg)'= max(`3RM Weighted Chins`),
                      'Change chins (kg)' = (max(`3RM Weighted Chins`))-(min(`3RM Weighted Chins`)),
                      'Max bench (kg)'= max(`3RM Bench Press`),
                      'Change bench (kg)' = (max(`3RM Bench Press`))-(min(`3RM Bench Press`)),
                      'Max squat (kg)'= max(`3RM Box Squat`),
                      'Change squat (kg)' = (max(`3RM Box Squat`))-(min(`3RM Box Squat`)))
```
---

# Summarise data

```r
print(Strength_summary, n=10)
```

```
## # A tibble: 8 x 7
##   Name      `Max chins (kg)` `Change chins (k~ `Max bench (kg)` `Change bench (~
##   <chr>                <dbl>             <dbl>            <dbl>            <dbl>
## 1 Athlete 1             125                5               97.5              2.5
## 2 Athlete 2             128.               2.5            108.              10  
## 3 Athlete 3             130                7.5            105                5  
## 4 Athlete 4             128.               5.5            115                5  
## 5 Athlete 5              95                5              128.               7.5
## 6 Athlete 6             118.               7.5            108.               2.5
## 7 Athlete 7             110                5              120                5  
## 8 Athlete 8             115                2.5            100                2.5
## # ... with 2 more variables: Max squat (kg) <dbl>, Change squat (kg) <dbl>
```

---

# Visualise strength data

```r
library(formattable)

formattable(Strength_summary, align = c('c'), list(
  `Change chins (kg)`= color_tile("white", "#71CA97"),
  `Change bench (kg)`= color_tile ("white","#71CA97"),
  `Change squat (kg)`= color_tile("white","#71CA97")))
```
--
<br>

<table class="table table-condensed">
 <thead>
  <tr>
   <th style="text-align:center;"> Name </th>
   <th style="text-align:center;"> Max chins (kg) </th>
   <th style="text-align:center;"> Change chins (kg) </th>
   <th style="text-align:center;"> Max bench (kg) </th>
   <th style="text-align:center;"> Change bench (kg) </th>
   <th style="text-align:center;"> Max squat (kg) </th>
   <th style="text-align:center;"> Change squat (kg) </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:center;"> Athlete 1 </td>
   <td style="text-align:center;"> 125.0 </td>
   <td style="text-align:center;"> <span style="display: block; padding: 0 4px; border-radius: 4px; background-color: #b8e4cb">5.0</span> </td>
   <td style="text-align:center;"> 97.5 </td>
   <td style="text-align:center;"> <span style="display: block; padding: 0 4px; border-radius: 4px; background-color: #ffffff">2.5</span> </td>
   <td style="text-align:center;"> 150.0 </td>
   <td style="text-align:center;"> <span style="display: block; padding: 0 4px; border-radius: 4px; background-color: #e1f3e9">5.0</span> </td>
  </tr>
  <tr>
   <td style="text-align:center;"> Athlete 2 </td>
   <td style="text-align:center;"> 127.5 </td>
   <td style="text-align:center;"> <span style="display: block; padding: 0 4px; border-radius: 4px; background-color: #ffffff">2.5</span> </td>
   <td style="text-align:center;"> 107.5 </td>
   <td style="text-align:center;"> <span style="display: block; padding: 0 4px; border-radius: 4px; background-color: #71ca97">10.0</span> </td>
   <td style="text-align:center;"> 145.0 </td>
   <td style="text-align:center;"> <span style="display: block; padding: 0 4px; border-radius: 4px; background-color: #ffffff">3.0</span> </td>
  </tr>
  <tr>
   <td style="text-align:center;"> Athlete 3 </td>
   <td style="text-align:center;"> 130.0 </td>
   <td style="text-align:center;"> <span style="display: block; padding: 0 4px; border-radius: 4px; background-color: #71ca97">7.5</span> </td>
   <td style="text-align:center;"> 105.0 </td>
   <td style="text-align:center;"> <span style="display: block; padding: 0 4px; border-radius: 4px; background-color: #cfeddc">5.0</span> </td>
   <td style="text-align:center;"> 125.0 </td>
   <td style="text-align:center;"> <span style="display: block; padding: 0 4px; border-radius: 4px; background-color: #e1f3e9">5.0</span> </td>
  </tr>
  <tr>
   <td style="text-align:center;"> Athlete 4 </td>
   <td style="text-align:center;"> 127.5 </td>
   <td style="text-align:center;"> <span style="display: block; padding: 0 4px; border-radius: 4px; background-color: #a9dfc0">5.5</span> </td>
   <td style="text-align:center;"> 115.0 </td>
   <td style="text-align:center;"> <span style="display: block; padding: 0 4px; border-radius: 4px; background-color: #cfeddc">5.0</span> </td>
   <td style="text-align:center;"> 135.0 </td>
   <td style="text-align:center;"> <span style="display: block; padding: 0 4px; border-radius: 4px; background-color: #71ca97">12.5</span> </td>
  </tr>
  <tr>
   <td style="text-align:center;"> Athlete 5 </td>
   <td style="text-align:center;"> 95.0 </td>
   <td style="text-align:center;"> <span style="display: block; padding: 0 4px; border-radius: 4px; background-color: #b8e4cb">5.0</span> </td>
   <td style="text-align:center;"> 127.5 </td>
   <td style="text-align:center;"> <span style="display: block; padding: 0 4px; border-radius: 4px; background-color: #a0dbb9">7.5</span> </td>
   <td style="text-align:center;"> 130.0 </td>
   <td style="text-align:center;"> <span style="display: block; padding: 0 4px; border-radius: 4px; background-color: #e1f3e9">5.0</span> </td>
  </tr>
  <tr>
   <td style="text-align:center;"> Athlete 6 </td>
   <td style="text-align:center;"> 117.5 </td>
   <td style="text-align:center;"> <span style="display: block; padding: 0 4px; border-radius: 4px; background-color: #71ca97">7.5</span> </td>
   <td style="text-align:center;"> 107.5 </td>
   <td style="text-align:center;"> <span style="display: block; padding: 0 4px; border-radius: 4px; background-color: #ffffff">2.5</span> </td>
   <td style="text-align:center;"> 155.0 </td>
   <td style="text-align:center;"> <span style="display: block; padding: 0 4px; border-radius: 4px; background-color: #e1f3e9">5.0</span> </td>
  </tr>
  <tr>
   <td style="text-align:center;"> Athlete 7 </td>
   <td style="text-align:center;"> 110.0 </td>
   <td style="text-align:center;"> <span style="display: block; padding: 0 4px; border-radius: 4px; background-color: #b8e4cb">5.0</span> </td>
   <td style="text-align:center;"> 120.0 </td>
   <td style="text-align:center;"> <span style="display: block; padding: 0 4px; border-radius: 4px; background-color: #cfeddc">5.0</span> </td>
   <td style="text-align:center;"> 152.5 </td>
   <td style="text-align:center;"> <span style="display: block; padding: 0 4px; border-radius: 4px; background-color: #bbe5cd">7.5</span> </td>
  </tr>
  <tr>
   <td style="text-align:center;"> Athlete 8 </td>
   <td style="text-align:center;"> 115.0 </td>
   <td style="text-align:center;"> <span style="display: block; padding: 0 4px; border-radius: 4px; background-color: #ffffff">2.5</span> </td>
   <td style="text-align:center;"> 100.0 </td>
   <td style="text-align:center;"> <span style="display: block; padding: 0 4px; border-radius: 4px; background-color: #ffffff">2.5</span> </td>
   <td style="text-align:center;"> 135.0 </td>
   <td style="text-align:center;"> <span style="display: block; padding: 0 4px; border-radius: 4px; background-color: #e1f3e9">5.0</span> </td>
  </tr>
</tbody>
</table>

---

# Squat result by testing point

```r
Strength %>%
            filter(Season == 2021) %>%
  
# Dumbell plot using geom_line and geom_point
ggplot(aes(x= `3RM Box Squat`, y= Name)) +
          
        geom_line(aes(group = Name))+ # add line between testing points
        geom_point(aes(color=Period), size=4) + # a dot for each testing point
                 
        ggtitle("3RM box squat", subtitle = "By testing point") + # title
                xlab("Weight (kg)") + # axis label
        theme_classic()+ # simple theme
                theme(legend.position = "none", # hide legend
                      axis.title.y = element_blank()) # hide y axis title
```
---
# Squat result by testing point
<br>
<img src="2021 ESSA Webinar/2021-ESSA-Presentation_files/figure-html/dumbell plot2-1.png" style="display: block; margin: auto;" />

---

class: title-slide, left, hide_logo, inverse
background-image: url("https://athleticsweekly.com/wp-content/uploads/2016/12/nordbord-hamstring-750x450.jpg")
background-size: 100%

# Example 2 - Nordboard data

---

# Nordboard data

```r
Nordboard <- read_csv("2021 ESSA Webinar/2021 ESSA Webinar/Files/Nordboard.csv")
head(Nordboard, n=8)
```

```
## # A tibble: 8 x 8
##   Player    Date       RowIndex `Average Left F~ `Average Right ~ `Max Left Force`
##   <chr>     <chr>         <dbl>            <dbl>            <dbl>            <dbl>
## 1 Player 1  22/11/2019       10             394              415              405 
## 2 Player 10 7/12/2020         6             414              399.             438 
## 3 Player 10 15/12/2020        4             341.             331.             392.
## 4 Player 10 18/01/2021        2             498.             462.             508.
## 5 Player 10 3/02/2021         0             466.             439.             474.
## 6 Player 10 3/02/2021         2             459.             437.             471 
## 7 Player 10 8/02/2021         2             422.             446.             437.
## 8 Player 10 20/02/2021        4             406.             402.             422 
## # ... with 2 more variables: Max Right Force <dbl>,
## #   Difference_Max Force Imbalance <dbl>
```

---

# Clean Nordboard data

```r
Nordboard <- Nordboard %>% 
          group_by(Player, Date) %>% 
            mutate(Set = seq_along(RowIndex))

# Then reorder variables
Nordboard <- Nordboard[c(1:2,9,4:8)]
```

---

# Clean Nordboard data

```
## # A tibble: 12 x 8
## # Groups:   Player, Date [11]
##    Player    Date         Set `Average Left F~ `Average Right ~ `Max Left Force`
##    <chr>     <chr>      <int>            <dbl>            <dbl>            <dbl>
##  1 Player 1  22/11/2019     1             394              415              405 
##  2 Player 10 7/12/2020      1             414              399.             438 
##  3 Player 10 15/12/2020     1             341.             331.             392.
##  4 Player 10 18/01/2021     1             498.             462.             508.
##  5 Player 10 3/02/2021      1             466.             439.             474.
##  6 Player 10 3/02/2021      2             459.             437.             471 
##  7 Player 10 8/02/2021      1             422.             446.             437.
##  8 Player 10 20/02/2021     1             406.             402.             422 
##  9 Player 10 22/02/2021     1             414.             444.             422.
## 10 Player 10 2/03/2021      1             398.             426.             418.
## 11 Player 11 11/01/2020     1             375.             365.             399 
## 12 Player 11 17/01/2020     1             397.             364.             404.
## # ... with 2 more variables: Max Right Force <dbl>,
## #   Difference_Max Force Imbalance <dbl>
```

---

# Clean Nordboard data

```r
# First we will rename a few columns to have the side (L/R) written first
Nordboard <- Nordboard %>% 
  rename("Right_Average Force" = `Average Right Force`, 
         "Left_Average Force" = `Average Left Force`,
         "Right_Max Force" = `Max Right Force`, 
         "Left_Max Force" = `Max Left Force`)

head(Nordboard, n=5)
```

```
## # A tibble: 5 x 8
## # Groups:   Player, Date [5]
##   Player    Date         Set `Left_Average Fo~ `Right_Average ~ `Left_Max Force`
##   <chr>     <chr>      <int>             <dbl>            <dbl>            <dbl>
## 1 Player 1  22/11/2019     1              394              415              405 
## 2 Player 10 7/12/2020      1              414              399.             438 
## 3 Player 10 15/12/2020     1              341.             331.             392.
## 4 Player 10 18/01/2021     1              498.             462.             508.
## 5 Player 10 3/02/2021      1              466.             439.             474.
## # ... with 2 more variables: Right_Max Force <dbl>,
## #   Difference_Max Force Imbalance <dbl>
```

---

# Long to wide format

<br>

---
# Long to wide format

```r
library(reshape2)
Nordboard <- melt(Nordboard, id.vars = c("Player", "Date", "Set"))
```

```
##       Player       Date Set           variable    value
## 1   Player 1 22/11/2019   1 Left_Average Force 394.0000
## 2  Player 10  7/12/2020   1 Left_Average Force 414.0000
## 3  Player 10 15/12/2020   1 Left_Average Force 340.6500
## 4  Player 10 18/01/2021   1 Left_Average Force 497.8125
## 5  Player 10  3/02/2021   1 Left_Average Force 466.2500
## 6  Player 10  3/02/2021   2 Left_Average Force 458.9375
## 7  Player 10  8/02/2021   1 Left_Average Force 422.5000
## 8  Player 10 20/02/2021   1 Left_Average Force 405.6875
## 9  Player 10 22/02/2021   1 Left_Average Force 413.6250
## 10 Player 10  2/03/2021   1 Left_Average Force 398.3750
## 11 Player 11 11/01/2020   1 Left_Average Force 374.9375
## 12 Player 11 17/01/2020   1 Left_Average Force 397.2500
```

---

# Add a new column (split text)

```r
Nordboard <- Nordboard %>% 
          separate(variable, into = c("Leg", "Variable"), sep = "_")
```
--

```
##       Player       Date Set  Leg      Variable    value
## 1   Player 1 22/11/2019   1 Left Average Force 394.0000
## 2  Player 10  7/12/2020   1 Left Average Force 414.0000
## 3  Player 10 15/12/2020   1 Left Average Force 340.6500
## 4  Player 10 18/01/2021   1 Left Average Force 497.8125
## 5  Player 10  3/02/2021   1 Left Average Force 466.2500
## 6  Player 10  3/02/2021   2 Left Average Force 458.9375
## 7  Player 10  8/02/2021   1 Left Average Force 422.5000
## 8  Player 10 20/02/2021   1 Left Average Force 405.6875
## 9  Player 10 22/02/2021   1 Left Average Force 413.6250
## 10 Player 10  2/03/2021   1 Left Average Force 398.3750
## 11 Player 11 11/01/2020   1 Left Average Force 374.9375
## 12 Player 11 17/01/2020   1 Left Average Force 397.2500
```

---
# Visualise Nordboard data

```r
filtNordboard <- Nordboard%>% 
                  filter(Player == "Player 10") %>%
                  filter(Variable == "Max Force")
```

--
<br>

```r
library(ggplot2)
  ggplot(data = filtNordboard, 
       aes(x=Date, y=value, group=Leg, colour = Leg)) +
        geom_line() +
        ylab("Max Force (N)") +
        theme_classic()
```
 
---
# Visualise Nordboard data

<img src="2021 ESSA Webinar/2021-ESSA-Presentation_files/figure-html/working code 18-1.png" style="display: block; margin: auto;" />
 
---
# Visualise Nordboard data

```r
Nordboard %>%
            filter(Player == "Player 10") %>% 
            filter(Variable == "Max Force Imbalance") %>%
          mutate(Direction = ifelse(value > 0,"Right", "Left")) %>%

ggplot(aes(x=Date, y=value, fill=Direction)) +
          geom_col(position = "identity") +
          ylab("Imbalance - Force (N)") +
          theme_classic() 
```

---

# Visualise Nordboard data

---
# Visualise Nordboard data

```r
# Filter our data by date and max force
Nordboard %>%
            filter(Date == "10/03/2021") %>% filter(Variable == "Max Force") %>%
  # Summarise it by player and leg for the day selected above
                group_by(Player, Leg) %>%
  # Now calculate max force by leg by player
                    summarise(maxForce = max(value))  %>%
  
ggplot(aes(x = Player, y = maxForce, fill = Leg)) + 
                          geom_bar(stat = "identity", width = .6) +  
                          coord_flip() +
                          theme_classic() +
                          labs(title="Max force (N) by leg") + ylab("Force (N)") +
                          theme(axis.title.y = element_blank())
```

---

# Visualise Nordboard data

---
# Visualise Nordboard data

Interactive box plots per athlete and leg using **plotly**
<br>

```r
library(plotly)

target <- c("Player 10", "Player 21", "Player 24", "Player 18")

Nordboard1 <- Nordboard %>%
              filter(Variable == "Max Force") %>% 
              filter(Player %in% target) %>%
                     group_by(Player, Leg, Date) %>%
                     summarise(maxForce = max(value))

plot <- ggplot(Nordboard1, aes(x = Leg, y=maxForce, fill = Leg, colour = Leg)) +
          geom_boxplot() +
          theme_classic() +
          ylab("Force (N)") +
          theme(axis.title.y = element_blank()) +
          theme(panel.spacing = unit(2, "lines")) +
          facet_wrap(~Player, scales = "free")

fig <- ggplotly(plot)
fig
```

---

# Visualise Nordboard data

Interactive box plots per athlete and leg using **plotly**
<br>
<div id="htmlwidget-672ecfc259e5ddbe925c" style="width:864px;height:432px;" class="plotly html-widget"></div>
<script type="application/json" data-for="htmlwidget-672ecfc259e5ddbe925c">{"x":{"data":[{"x":[1,1,1,1,1,1,1,1],"y":[392.5,507.75,417.75,422,422.25,473.75,438,436.75],"hoverinfo":"y","type":"box","fillcolor":"rgba(248,118,109,1)","marker":{"opacity":null,"outliercolor":"rgba(0,0,0,1)","line":{"width":1.88976377952756,"color":"rgba(0,0,0,1)"},"size":5.66929133858268},"line":{"color":"rgba(248,118,109,1)","width":1.88976377952756},"name":"Left","legendgroup":"Left","showlegend":true,"xaxis":"x","yaxis":"y","frame":null},{"x":[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1],"y":[414,461.75,421.75,432.75,354.5,366,425.5,399.75,407.25,378.75,399,407,406.25,414,415,426,414,407.75,402.5,392.75,435],"hoverinfo":"y","type":"box","fillcolor":"rgba(248,118,109,1)","marker":{"opacity":null,"outliercolor":"rgba(0,0,0,1)","line":{"width":1.88976377952756,"color":"rgba(0,0,0,1)"},"size":5.66929133858268},"line":{"color":"rgba(248,118,109,1)","width":1.88976377952756},"name":"Left","legendgroup":"Left","showlegend":false,"xaxis":"x2","yaxis":"y2","frame":null},{"x":[1,1,1,1,1,1,1,1,1],"y":[472,445.25,350.75,434.25,508.75,444,451.75,418,424.75],"hoverinfo":"y","type":"box","fillcolor":"rgba(248,118,109,1)","marker":{"opacity":null,"outliercolor":"rgba(0,0,0,1)","line":{"width":1.88976377952756,"color":"rgba(0,0,0,1)"},"size":5.66929133858268},"line":{"color":"rgba(248,118,109,1)","width":1.88976377952756},"name":"Left","legendgroup":"Left","showlegend":false,"xaxis":"x3","yaxis":"y3","frame":null},{"x":[1,1,1,1,1,1,1,1,1,1,1,1,1],"y":[387.75,357.25,369,386.75,374.25,373.5,379.75,382,383,353.5,370,407.25,374],"hoverinfo":"y","type":"box","fillcolor":"rgba(248,118,109,1)","marker":{"opacity":null,"outliercolor":"rgba(0,0,0,1)","line":{"width":1.88976377952756,"color":"rgba(0,0,0,1)"},"size":5.66929133858268},"line":{"color":"rgba(248,118,109,1)","width":1.88976377952756},"name":"Left","legendgroup":"Left","showlegend":false,"xaxis":"x4","yaxis":"y4","frame":null},{"x":[2,2,2,2,2,2,2,2],"y":[382,481.25,444.75,413.5,473.5,485.75,426.5,451.75],"hoverinfo":"y","type":"box","fillcolor":"rgba(0,191,196,1)","marker":{"opacity":null,"outliercolor":"rgba(0,0,0,1)","line":{"width":1.88976377952756,"color":"rgba(0,0,0,1)"},"size":5.66929133858268},"line":{"color":"rgba(0,191,196,1)","width":1.88976377952756},"name":"Right","legendgroup":"Right","showlegend":true,"xaxis":"x","yaxis":"y","frame":null},{"x":[2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2],"y":[465,493.5,418.75,473.5,380.75,364.25,430,434.75,419.25,439.75,436.75,458.25,434.25,424.5,459.75,464.75,436.75,468.5,439.5,416.5,419.25],"hoverinfo":"y","type":"box","fillcolor":"rgba(0,191,196,1)","marker":{"opacity":null,"outliercolor":"rgba(0,0,0,1)","line":{"width":1.88976377952756,"color":"rgba(0,0,0,1)"},"size":5.66929133858268},"line":{"color":"rgba(0,191,196,1)","width":1.88976377952756},"name":"Right","legendgroup":"Right","showlegend":false,"xaxis":"x2","yaxis":"y2","frame":null},{"x":[2,2,2,2,2,2,2,2,2],"y":[483.75,485,443.75,460,547.25,419,495,444,419],"hoverinfo":"y","type":"box","fillcolor":"rgba(0,191,196,1)","marker":{"opacity":null,"outliercolor":"rgba(0,0,0,1)","line":{"width":1.88976377952756,"color":"rgba(0,0,0,1)"},"size":5.66929133858268},"line":{"color":"rgba(0,191,196,1)","width":1.88976377952756},"name":"Right","legendgroup":"Right","showlegend":false,"xaxis":"x3","yaxis":"y3","frame":null},{"x":[2,2,2,2,2,2,2,2,2,2,2,2,2],"y":[410.5,407.25,384,378,385.25,432.75,371.25,429,402.25,337,399,396,390],"hoverinfo":"y","type":"box","fillcolor":"rgba(0,191,196,1)","marker":{"opacity":null,"outliercolor":"rgba(0,0,0,1)","line":{"width":1.88976377952756,"color":"rgba(0,0,0,1)"},"size":5.66929133858268},"line":{"color":"rgba(0,191,196,1)","width":1.88976377952756},"name":"Right","legendgroup":"Right","showlegend":false,"xaxis":"x4","yaxis":"y4","frame":null}],"layout":{"margin":{"t":42.3013698630137,"r":7.30593607305936,"b":44.5662100456621,"l":28.4931506849315},"plot_bgcolor":"rgba(255,255,255,1)","paper_bgcolor":"rgba(255,255,255,1)","font":{"color":"rgba(0,0,0,1)","family":"","size":14.6118721461187},"xaxis":{"domain":[0,0.450869424842028],"automargin":true,"type":"linear","autorange":false,"range":[0.4,2.6],"tickmode":"array","ticktext":["Left","Right"],"tickvals":[1,2],"categoryorder":"array","categoryarray":["Left","Right"],"nticks":null,"ticks":"outside","tickcolor":"rgba(51,51,51,1)","ticklen":3.65296803652968,"tickwidth":0.66417600664176,"showticklabels":true,"tickfont":{"color":"rgba(77,77,77,1)","family":"","size":11.689497716895},"tickangle":-0,"showline":true,"linecolor":"rgba(0,0,0,1)","linewidth":0.66417600664176,"showgrid":false,"gridcolor":null,"gridwidth":0,"zeroline":false,"anchor":"y","title":"","hoverformat":".2f"},"annotations":[{"text":"Leg","x":0.5,"y":-0.0393201420598681,"showarrow":false,"ax":0,"ay":0,"font":{"color":"rgba(0,0,0,1)","family":"","size":14.6118721461187},"xref":"paper","yref":"paper","textangle":-0,"xanchor":"center","yanchor":"top","annotationType":"axis"},{"text":"Player 10","x":0.225434712421014,"y":1,"showarrow":false,"ax":0,"ay":0,"font":{"color":"rgba(26,26,26,1)","family":"","size":11.689497716895},"xref":"paper","yref":"paper","textangle":-0,"xanchor":"center","yanchor":"bottom"},{"text":"Player 18","x":0.774565287578986,"y":1,"showarrow":false,"ax":0,"ay":0,"font":{"color":"rgba(26,26,26,1)","family":"","size":11.689497716895},"xref":"paper","yref":"paper","textangle":-0,"xanchor":"center","yanchor":"bottom"},{"text":"Player 21","x":0.225434712421014,"y":0.39159171624925,"showarrow":false,"ax":0,"ay":0,"font":{"color":"rgba(26,26,26,1)","family":"","size":11.689497716895},"xref":"paper","yref":"paper","textangle":-0,"xanchor":"center","yanchor":"bottom"},{"text":"Player 24","x":0.774565287578986,"y":0.39159171624925,"showarrow":false,"ax":0,"ay":0,"font":{"color":"rgba(26,26,26,1)","family":"","size":11.689497716895},"xref":"paper","yref":"paper","textangle":-0,"xanchor":"center","yanchor":"bottom"},{"text":"Leg","x":1.02,"y":1,"showarrow":false,"ax":0,"ay":0,"font":{"color":"rgba(0,0,0,1)","family":"","size":14.6118721461187},"xref":"paper","yref":"paper","textangle":-0,"xanchor":"left","yanchor":"bottom","legendTitle":true}],"yaxis":{"domain":[0.60840828375075,1],"automargin":true,"type":"linear","autorange":false,"range":[375.7125,514.0375],"tickmode":"array","ticktext":["400","440","480"],"tickvals":[400,440,480],"categoryorder":"array","categoryarray":["400","440","480"],"nticks":null,"ticks":"outside","tickcolor":"rgba(51,51,51,1)","ticklen":3.65296803652968,"tickwidth":0.66417600664176,"showticklabels":true,"tickfont":{"color":"rgba(77,77,77,1)","family":"","size":11.689497716895},"tickangle":-0,"showline":true,"linecolor":"rgba(0,0,0,1)","linewidth":0.66417600664176,"showgrid":false,"gridcolor":null,"gridwidth":0,"zeroline":false,"anchor":"x","title":"","hoverformat":".2f"},"shapes":[{"type":"rect","fillcolor":null,"line":{"color":null,"width":0,"linetype":[]},"yref":"paper","xref":"paper","x0":0,"x1":0.450869424842028,"y0":0.60840828375075,"y1":1},{"type":"rect","fillcolor":"rgba(255,255,255,1)","line":{"color":"rgba(0,0,0,1)","width":1.32835201328352,"linetype":"solid"},"yref":"paper","xref":"paper","x0":0,"x1":0.450869424842028,"y0":0,"y1":23.37899543379,"yanchor":1,"ysizemode":"pixel"},{"type":"rect","fillcolor":null,"line":{"color":null,"width":0,"linetype":[]},"yref":"paper","xref":"paper","x0":0.549130575157972,"x1":1,"y0":0.60840828375075,"y1":1},{"type":"rect","fillcolor":"rgba(255,255,255,1)","line":{"color":"rgba(0,0,0,1)","width":1.32835201328352,"linetype":"solid"},"yref":"paper","xref":"paper","x0":0.549130575157972,"x1":1,"y0":0,"y1":23.37899543379,"yanchor":1,"ysizemode":"pixel"},{"type":"rect","fillcolor":null,"line":{"color":null,"width":0,"linetype":[]},"yref":"paper","xref":"paper","x0":0,"x1":0.450869424842028,"y0":0,"y1":0.39159171624925},{"type":"rect","fillcolor":"rgba(255,255,255,1)","line":{"color":"rgba(0,0,0,1)","width":1.32835201328352,"linetype":"solid"},"yref":"paper","xref":"paper","x0":0,"x1":0.450869424842028,"y0":0,"y1":23.37899543379,"yanchor":0.39159171624925,"ysizemode":"pixel"},{"type":"rect","fillcolor":null,"line":{"color":null,"width":0,"linetype":[]},"yref":"paper","xref":"paper","x0":0.549130575157972,"x1":1,"y0":0,"y1":0.39159171624925},{"type":"rect","fillcolor":"rgba(255,255,255,1)","line":{"color":"rgba(0,0,0,1)","width":1.32835201328352,"linetype":"solid"},"yref":"paper","xref":"paper","x0":0.549130575157972,"x1":1,"y0":0,"y1":23.37899543379,"yanchor":0.39159171624925,"ysizemode":"pixel"}],"xaxis2":{"type":"linear","autorange":false,"range":[0.4,2.6],"tickmode":"array","ticktext":["Left","Right"],"tickvals":[1,2],"categoryorder":"array","categoryarray":["Left","Right"],"nticks":null,"ticks":"outside","tickcolor":"rgba(51,51,51,1)","ticklen":3.65296803652968,"tickwidth":0.66417600664176,"showticklabels":true,"tickfont":{"color":"rgba(77,77,77,1)","family":"","size":11.689497716895},"tickangle":-0,"showline":true,"linecolor":"rgba(0,0,0,1)","linewidth":0.66417600664176,"showgrid":false,"domain":[0.549130575157972,1],"gridcolor":null,"gridwidth":0,"zeroline":false,"anchor":"y2","title":"","hoverformat":".2f"},"yaxis2":{"type":"linear","autorange":false,"range":[347.55,500.45],"tickmode":"array","ticktext":["350","400","450","500"],"tickvals":[350,400,450,500],"categoryorder":"array","categoryarray":["350","400","450","500"],"nticks":null,"ticks":"outside","tickcolor":"rgba(51,51,51,1)","ticklen":3.65296803652968,"tickwidth":0.66417600664176,"showticklabels":true,"tickfont":{"color":"rgba(77,77,77,1)","family":"","size":11.689497716895},"tickangle":-0,"showline":true,"linecolor":"rgba(0,0,0,1)","linewidth":0.66417600664176,"showgrid":false,"domain":[0.60840828375075,1],"gridcolor":null,"gridwidth":0,"zeroline":false,"anchor":"x2","title":"","hoverformat":".2f"},"xaxis3":{"type":"linear","autorange":false,"range":[0.4,2.6],"tickmode":"array","ticktext":["Left","Right"],"tickvals":[1,2],"categoryorder":"array","categoryarray":["Left","Right"],"nticks":null,"ticks":"outside","tickcolor":"rgba(51,51,51,1)","ticklen":3.65296803652968,"tickwidth":0.66417600664176,"showticklabels":true,"tickfont":{"color":"rgba(77,77,77,1)","family":"","size":11.689497716895},"tickangle":-0,"showline":true,"linecolor":"rgba(0,0,0,1)","linewidth":0.66417600664176,"showgrid":false,"domain":[0,0.450869424842028],"gridcolor":null,"gridwidth":0,"zeroline":false,"anchor":"y3","title":"","hoverformat":".2f"},"yaxis3":{"type":"linear","autorange":false,"range":[340.925,557.075],"tickmode":"array","ticktext":["350","400","450","500","550"],"tickvals":[350,400,450,500,550],"categoryorder":"array","categoryarray":["350","400","450","500","550"],"nticks":null,"ticks":"outside","tickcolor":"rgba(51,51,51,1)","ticklen":3.65296803652968,"tickwidth":0.66417600664176,"showticklabels":true,"tickfont":{"color":"rgba(77,77,77,1)","family":"","size":11.689497716895},"tickangle":-0,"showline":true,"linecolor":"rgba(0,0,0,1)","linewidth":0.66417600664176,"showgrid":false,"domain":[0,0.39159171624925],"gridcolor":null,"gridwidth":0,"zeroline":false,"anchor":"x3","title":"","hoverformat":".2f"},"xaxis4":{"type":"linear","autorange":false,"range":[0.4,2.6],"tickmode":"array","ticktext":["Left","Right"],"tickvals":[1,2],"categoryorder":"array","categoryarray":["Left","Right"],"nticks":null,"ticks":"outside","tickcolor":"rgba(51,51,51,1)","ticklen":3.65296803652968,"tickwidth":0.66417600664176,"showticklabels":true,"tickfont":{"color":"rgba(77,77,77,1)","family":"","size":11.689497716895},"tickangle":-0,"showline":true,"linecolor":"rgba(0,0,0,1)","linewidth":0.66417600664176,"showgrid":false,"domain":[0.549130575157972,1],"gridcolor":null,"gridwidth":0,"zeroline":false,"anchor":"y4","title":"","hoverformat":".2f"},"yaxis4":{"type":"linear","autorange":false,"range":[332.2125,437.5375],"tickmode":"array","ticktext":["340","360","380","400","420"],"tickvals":[340,360,380,400,420],"categoryorder":"array","categoryarray":["340","360","380","400","420"],"nticks":null,"ticks":"outside","tickcolor":"rgba(51,51,51,1)","ticklen":3.65296803652968,"tickwidth":0.66417600664176,"showticklabels":true,"tickfont":{"color":"rgba(77,77,77,1)","family":"","size":11.689497716895},"tickangle":-0,"showline":true,"linecolor":"rgba(0,0,0,1)","linewidth":0.66417600664176,"showgrid":false,"domain":[0,0.39159171624925],"gridcolor":null,"gridwidth":0,"zeroline":false,"anchor":"x4","title":"","hoverformat":".2f"},"showlegend":true,"legend":{"bgcolor":"rgba(255,255,255,1)","bordercolor":"transparent","borderwidth":1.88976377952756,"font":{"color":"rgba(0,0,0,1)","family":"","size":11.689497716895},"y":0.927821522309711},"hovermode":"closest","barmode":"relative"},"config":{"doubleClick":"reset","showSendToCloud":false},"source":"A","attrs":{"1d1880a7d45":{"x":{},"y":{},"fill":{},"colour":{},"type":"box"}},"cur_data":"1d1880a7d45","visdat":{"1d1880a7d45":["function (y) ","x"]},"highlight":{"on":"plotly_click","persistent":false,"dynamic":false,"selectize":false,"opacityDim":0.2,"selected":{"opacity":1},"debounce":0},"shinyEvents":["plotly_hover","plotly_click","plotly_selected","plotly_relayout","plotly_brushed","plotly_brushing","plotly_clickannotation","plotly_doubleclick","plotly_deselect","plotly_afterplot","plotly_sunburstclick"],"base_url":"https://plot.ly"},"evals":[],"jsHooks":[]}</script>

---

class: left, hide_logo, inverse
background-image: url("https://content.api.news/v3/images/bin/c1e6072e5a224657d0f9af62860978db")
background-size: 100%

# Part 2 - Power BI

---

# Why Power BI? 
<br>
<svg aria-hidden="true" role="img" viewBox="0 0 384 512" style="height:1em;width:0.75em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M224 136V0H24C10.7 0 0 10.7 0 24v464c0 13.3 10.7 24 24 24h336c13.3 0 24-10.7 24-24V160H248c-13.2 0-24-10.8-24-24zm60.1 106.5L224 336l60.1 93.5c5.1 8-.6 18.5-10.1 18.5h-34.9c-4.4 0-8.5-2.4-10.6-6.3C208.9 405.5 192 373 192 373c-6.4 14.8-10 20-36.6 68.8-2.1 3.9-6.1 6.3-10.5 6.3H110c-9.5 0-15.2-10.5-10.1-18.5l60.3-93.5-60.3-93.5c-5.2-8 .6-18.5 10.1-18.5h34.8c4.4 0 8.5 2.4 10.6 6.3 26.1 48.8 20 33.6 36.6 68.5 0 0 6.1-11.7 36.6-68.5 2.1-3.9 6.2-6.3 10.6-6.3H274c9.5-.1 15.2 10.4 10.1 18.4zM384 121.9v6.1H256V0h6.1c6.4 0 12.5 2.5 17 7l97.9 98c4.5 4.5 7 10.6 7 16.9z"/></svg>  If you know how to use **Microsoft Excel**, you'll already know parts of **Microsoft Power BI**

<br>

---

class: left, hide_logo, inverse
background-image: url("https://resources.goldcoastfc.com.au/photo-resources/2021/02/12/ac430f86-6d20-4d83-8b90-35817d0789a8/14GCNM20CH832463414_best.JPG?width=952&height=592")
background-size: 100%

# Power BI exmaple - wellness report

---

# Wellness report

.pull-left[

**Example will show**
- How to import data

- Create a table

- Add filters

- Create calculated columns

- Formatting

<br>

.pull-right[

<blockquote class="twitter-tweet"><p lang="en" dir="ltr">1/ Are you using a wellness monitoring dashboard with your team? <br><br>Does it start conversations with athletes or does it stop them from training? <a href="https://twitter.com/hashtag/PowerBiDesktop?src=hash&amp;ref_src=twsrc%5Etfw">#PowerBiDesktop</a> <a href="https://twitter.com/hashtag/PowerBi?src=hash&amp;ref_src=twsrc%5Etfw">#PowerBi</a> <a href="https://twitter.com/hashtag/PowerBiForSportScience?src=hash&amp;ref_src=twsrc%5Etfw">#PowerBiForSportScience</a> <a href="https://twitter.com/hashtag/PowerBiForSport?src=hash&amp;ref_src=twsrc%5Etfw">#PowerBiForSport</a> <a href="https://twitter.com/hashtag/DAX?src=hash&amp;ref_src=twsrc%5Etfw">#DAX</a> <a href="https://twitter.com/hashtag/DAXMeasure?src=hash&amp;ref_src=twsrc%5Etfw">#DAXMeasure</a> <a href="https://twitter.com/hashtag/SportScience?src=hash&amp;ref_src=twsrc%5Etfw">#SportScience</a> <a href="https://twitter.com/hashtag/WellnessMonitoring?src=hash&amp;ref_src=twsrc%5Etfw">#WellnessMonitoring</a> <a href="https://twitter.com/hashtag/Monitoring?src=hash&amp;ref_src=twsrc%5Etfw">#Monitoring</a> <a href="https://t.co/Scbj2WYvS6">pic.twitter.com/Scbj2WYvS6</a></p>&mdash; Futbol AnalysR (@FutbolAnalysR) <a href="https://twitter.com/FutbolAnalysR/status/1410303765480046595?ref_src=twsrc%5Etfw">June 30, 2021</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
]

---

# Useful resources
<br>

.pull-left[
**R Studio**

- [Mitch Henderson](https://www.mitchhenderson.org/)

- [Alice Sweeting](http://sportstatisticsrsweet.rbind.io/)

- [Neil Collins](https://www.sportscidata.com/)

- [Jose Fernandez](https://twitter.com/jfernandez__)

]

.pull-right[
**Power BI**
- [Josh Trewin](http://futbolanalysr.rbind.io/)

- [Roberto D'Onofrio Rondón](https://twitter.com/robertodono)

- [Complementary training](https://complementarytraining.net/powerbi-course-video-1/)

]

---
class: right, hide_logo, inverse, bottom

background-image: url("https://resources.afl.com.au/photo-resources/2019/11/14/2c9782b4-2794-4956-9ae7-916d0e018502/GFRiGW19RCP759699483.JPG?width=952&height=592")
background-size: 100%

# Thank you and good luck!

#### <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:white;overflow:visible;position:relative;"><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 32.4 173.4 128.7 16.8 12.2 50.2 41.8 73.4 41.4z"/></svg>  [heidi.thornton@goldcoastfc.com.au](mailto:heidi.thornton@goldcoastfc.com.au)<br>
#### <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:white;overflow:visible;position:relative;"><path d="M459.37 151.716c.325 4.548.325 9.097.325 13.645 0 138.72-105.583 298.558-298.558 298.558-59.452 0-114.68-17.219-161.137-47.106 8.447.974 16.568 1.299 25.34 1.299 49.055 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.112-72.772 6.498.974 12.995 1.624 19.818 1.624 9.421 0 18.843-1.3 27.614-3.573-48.081-9.747-84.143-51.98-84.143-102.985v-1.299c13.969 7.797 30.214 12.67 47.431 13.319-28.264-18.843-46.781-51.005-46.781-87.391 0-19.492 5.197-37.36 14.294-52.954 51.655 63.675 129.3 105.258 216.365 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.828 46.782-104.934 104.934-104.934 30.213 0 57.502 12.67 76.67 33.137 23.715-4.548 46.456-13.32 66.599-25.34-7.798 24.366-24.366 44.833-46.132 57.827 21.117-2.273 41.584-8.122 60.426-16.243-14.292 20.791-32.161 39.308-52.628 54.253z"/></svg>  [heidithornton09](https://twitter.com/heidithornton09)<br>
#### <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:white;overflow:visible;position:relative;"><path d="M416 32H31.9C14.3 32 0 46.5 0 64.3v383.4C0 465.5 14.3 480 31.9 480H416c17.6 0 32-14.5 32-32.3V64.3c0-17.8-14.4-32.3-32-32.3zM135.4 416H69V202.2h66.5V416zm-33.2-243c-21.3 0-38.5-17.3-38.5-38.5S80.9 96 102.2 96c21.2 0 38.5 17.3 38.5 38.5 0 21.3-17.2 38.5-38.5 38.5zm282.1 243h-66.4V312c0-24.8-.5-56.7-34.5-56.7-34.6 0-39.9 27-39.9 54.9V416h-66.4V202.2h63.7v29.2h.9c8.9-16.8 30.6-34.5 62.9-34.5 67.2 0 79.7 44.3 79.7 101.9V416z"/></svg>  [Heidi Thornton](https://www.linkedin.com/in/heidi-thornton-956354a0/)