ACUMHPS.utf8

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# Using R and Power BI in Sport
#### Heidi Thornton, PhD | ACU High-Performance Sport Seminar

---

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

# About me

👋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 use R and Power BI daily

Outside of work, I love running and the gym 🏋️‍♀️

📌I live in an pretty amazing place (Newcastle)

---

<font size="20"> 'Start with the end in mind' </font>

### Credit to [Dr Jacquie Tran](https://jacquietran.github.io/2019_essa_forum/R/#1)

---
class: left

# Why not just use excel?

Both [Alice Sweeting](http://sportstatisticsrsweet.rbind.io/) and [Jacquie Tran](https://www.jacquietran.com/) have presented on this previously

BUT the general story is....

]
--

---

# Lets get started

❗ I'm skipping the setup of R and Power BI as there are **tonnes** of resources available (see end of presentation)

✔️ We will create a GPS match report in R **without** touching Excel
 
--

🔑 Firstly, we need to install a few R **packages**

```r
Packages <- c('readxl','tidyverse','dplyr','ggplot2', 'sparkline', 'kableExtra', 'data.table',
              'formattable', 'DT')

# Install listed packages
install.packages(Packages)
```
<br>

---

# CatapultR package

✔️ **`catapultR`** is a new R package developed by Catapult

🔨 **`catapultR`** is still a demo version

🔜 You can download it, but Catapult aren't accepting new users just yet

```r
# Install catapultR
devtools::install_bitbucket("catapultsports/catapultr")
library(catapultR)
```

---

# Pull sessions from the cloud

🔑 This token allows us to query your cloud and access data using the catapult API

```r
# token <- ofCloudGetToken(sRegion = "America", sName = "brian_wsoccer_demo", sPwd = "d3cCM4yvh6",
                         # sClientID = "h9vrcJ3FXxY71HDzdU0a6C4QLt5VjoOunABsRlK8", 
                         # sClientSecret = "STe67MbNGfkO5XH8jaZDyoQvnz4VBYiL0Clg2RcW")
```

There is a link to request access to catapultR - https://docs.google.com/forms/d/e/1FAIpQLScv8w7OHvzdR05_M-Piu1rNJU9OPzIwsBfLNvY56RZx_nnU_g/viewform

---

# View activities

✔️ Now we have saved our account token details, we can select what session/game we want to select

```r
# View activities
activities <- ofCloudGetActivities(token)
glimpse(activities)
```

```
## Rows: 1,210
## Columns: 19
## $ id             <chr> "6e035081-1e4b-4b13-b0ba-e27aa5a454e2", "77895525-cf54-~
## $ name           <chr> "210821SatAFLRd23vsSydney", "Activity 20210821075312", ~
## $ start_time     <int> 1629517523, 1629496354, 1629423083, 1629323771, 1629244~
## $ end_time       <int> 1629526735, 1629511575, 1629425226, 1629327783, 1629249~
## $ modified_at    <chr> "2021-08-27 01:56:35", "2021-08-21 02:36:28", "2021-08-~
## $ game_id        <chr> "6e035081-1e4b-4b13-b0ba-e27aa5a454e2", "77895525-cf54-~
## $ owner_id       <chr> "0d2d37ee-9879-4987-9189-bc0cd388bb98", "4b6264d6-cf87-~
## $ owner          <df[,10]> <data.frame[26 x 10]>
## $ periods        <list> [<data.frame[4 x 4]>], [<data.frame[1 x 4]>], [<dat~
## $ tags           <list> <"LPS", "Saturday", "Full", "T6Full">, "GPS", <"Friday~
## $ tag_list       <list> [<data.frame[4 x 5]>], [<data.frame[1 x 5]>], [<data.f~
## $ athlete_count  <int> 23, 8, 26, 8, 31, 3, 6, 23, 14, 28, 3, 39, 7, 32, 23, ~
## $ period_count   <int> 4, 1, 1, 9, 16, 1, 7, 4, 4, 1, 7, 16, 10, 5, 4, 7, 14, ~
## $ venue_name     <chr> "AFL Q", "AFL Q", "AFL Q", "AFL Q", "AFL Q", "AFL Q", "~
## $ venue_width    <int> 0, 0, 0, 0, 0, 0, 0, 0, 159, 0, 0, 0, 0, 0, 0, 0, 159, ~
## $ venue_length   <int> 0, 0, 0, 0, 0, 0, 0, 0, 159, 0, 0, 0, 0, 0, 0, 0, 159, ~
## $ venue_rotation <int> 0, 0, 0, 0, 0, 0, 0, 0, -179, 0, 0, 0, 0, 0, 0, 0, -179~
## $ venue_lat      <dbl> 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0~
## $ venue_lng      <dbl> 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,~
```

```r
# Select what activities we want to report on
target <- c("210529SatAFLRd11vsHawthorn","210522SatAFLRd10vsGeelong","210515SatAFLRd9vsBrisbane",
            "210508Rd8vsStKilda","210501SatAFLRd7vsCollingwood","210424SatAFLRd6vsSydney",
            "210417SatAFLRd5vsWesternBulldogs","210410SatAFLRd4vsCarlton",
            "210402FriAFLRd3vsAdelaide","210327Rd2vsNorthMelbourne","210321SunAFLRd1vsWestCoast")

activities <- filter(activities, name %in% target)
```

---

# View parameters

Currently, there are **1046** metrics available

```r
# view parameters
parameters <- ofCloudGetParameters(token)
glimpse(parameters)
```

```
## Rows: 1,046
## Columns: 6
## $ id                <chr> "00025054-617c-45f1-ae03-a768ebfcd893", "003eb200-19~
## $ parameter_type_id <chr> "417654ed-209f-4c6f-a028-62c10f873d18", "65344b9a-85~
## $ name              <chr> "IMA Accel Medium", "Velocity Band 2 Average Effort ~
## $ original_name     <chr> "IMA Accel Medium", "Velocity Band 2 Average Effort ~
## $ slug              <chr> "ima_band2_accel_count", "velocity_band2_average_eff~
## $ calculation       <chr> "", "", "", "", "", "", "", "", "", "", "", "", "", ~
```

---

# Create dataset

```r
data <- ofCloudGetStatistics(
        token, params = c("date","athlete_name","total_duration","total_distance",
              "meterage_per_minute","velocity_band6_total_distance","max_vel"), 
  
  groupby = c("athlete", "activity"), 
            filters = list(name = "activity_id",
                           comparison = "=",
                           values = activities$id))
```

```r
head(data, n=3)
```

```
##         date       Name total_distance total_duration  max_vel
## 1 21/03/2021 Athlete 41       13000.07        5753.67 30.75762
## 2 27/03/2021 Athlete 41       12280.45        5510.38 33.12540
## 3 02/04/2021 Athlete 41       11235.87        4722.58 30.84264
##   velocity_band6_total_distance meterage_per_minute
## 1                        341.81            135.5664
## 2                        353.66            133.7162
## 3                        377.41            142.7508
```

---

# Summarise data

```r
data <- data %>%
    mutate(date=as.Date(as.character(date), format = "%d/%m/%Y")) %>%
  
    group_by(date, Name) %>%
              summarize(
                  Duration=sum(total_duration)/60,
                  Speed_mmin=mean(meterage_per_minute),
                  Distance=sum(total_distance),
                  HSR=sum(velocity_band6_total_distance),
                  Max_velocity=max(max_vel))
```

```
## # A tibble: 5 x 7
## # Groups:   date [1]
##   date       Name       Duration Speed_mmin Distance   HSR Max_velocity
##   <date>     <chr>         <dbl>      <dbl>    <dbl> <dbl>        <dbl>
## 1 2021-03-21 Athlete 13    100.        116.   11582.  99.9         27.9
## 2 2021-03-21 Athlete 14    101.        138.   13994. 337.          29.4
## 3 2021-03-21 Athlete 2      86.8       149.   12902.  60.6         26.7
## 4 2021-03-21 Athlete 20     89.7       127.   11403.  19.8         26.2
## 5 2021-03-21 Athlete 23     72.9       139.   10120. 272.          31.3
```

---
# Sparklines

--
.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="M496 384H64V80c0-8.84-7.16-16-16-16H16C7.16 64 0 71.16 0 80v336c0 17.67 14.33 32 32 32h464c8.84 0 16-7.16 16-16v-32c0-8.84-7.16-16-16-16zM464 96H345.94c-21.38 0-32.09 25.85-16.97 40.97l32.4 32.4L288 242.75l-73.37-73.37c-12.5-12.5-32.76-12.5-45.25 0l-68.69 68.69c-6.25 6.25-6.25 16.38 0 22.63l22.62 22.62c6.25 6.25 16.38 6.25 22.63 0L192 237.25l73.37 73.37c12.5 12.5 32.76 12.5 45.25 0l96-96 32.4 32.4c15.12 15.12 40.97 4.41 40.97-16.97V112c.01-8.84-7.15-16-15.99-16z"/></svg> Sparklines are HTML widgets that can summarise data nicely in reports

I think of them as 'mini graphs' 🤷

<img src="ACU MHPS 2021/Images/sparklinetable.png" width="55%" />
]

--
.pull-right[

```r
sparklines <- data %>%
      group_by(Name) %>% 
      arrange(date) %>%
  summarize(
    Distance_SPK = spk_chr(
      round(Distance, 0),
        type='line',
        fillColor = "transparent",
        RangeMin = mean(Distance)-(sd(Distance)*0.5),
        RangeMax = mean(Distance)+(sd(Distance)*0.5),
        chartRangeClip = T,
        width = 100, height = 15),
    
    Duration_SPK = spk_chr(
        round(Duration, 0),
        type='line',
        fillColor = "transparent",
        RangeMin = mean(Duration)-(sd(Duration)*0.5),
        RangeMax = mean(Duration)+(sd(Duration)*0.5),
        chartRangeClip = T,
        width = 100,height = 15))
```

]

---
# Sparklines dataset

---

# Merge data frames

```r
combined <- full_join(data, sparklines, by = c("Name")) %>%
                      filter(date == "2021-05-29")

head(combined, n=5)
```

```
## # A tibble: 5 x 10
## # Groups:   date [1]
##   date       Name   Duration Speed_mmin Distance   HSR Max_velocity Distance_SPK
##   <date>     <chr>     <dbl>      <dbl>    <dbl> <dbl>        <dbl> <html>      
## 1 2021-05-29 Athle~     14.4       143.    2062. 173.          30.7 "<span id=\~
## 2 2021-05-29 Athle~    114.        134.   15259. 359.          32.1 "<span id=\~
## 3 2021-05-29 Athle~     96.9       123.   11920. 155.          32.3 "<span id=\~
## 4 2021-05-29 Athle~     92.2       132.   12187.  85.7         28.1 "<span id=\~
## 5 2021-05-29 Athle~     86.2       141.   12142.  50.6         28.6 "<span id=\~
## # ... with 2 more variables: Duration_SPK <html>, Speed_mmin_SPK <html>
```

---

# GPS report - Version 1

First we will create a basic table with limited formatting using the **`formattable`** package

```r
library(formattable)

formattable(combined, align = c('c'), list(options =list(dom ='tp')))
```

<table class="table table-condensed">
 <thead>
  <tr>
   <th style="text-align:center;"> date </th>
   <th style="text-align:center;"> Name </th>
   <th style="text-align:center;"> Duration </th>
   <th style="text-align:center;"> Speed_mmin </th>
   <th style="text-align:center;"> Distance </th>
   <th style="text-align:center;"> HSR </th>
   <th style="text-align:center;"> Max_velocity </th>
   <th style="text-align:center;"> Distance_SPK </th>
   <th style="text-align:center;"> Duration_SPK </th>
   <th style="text-align:center;"> Speed_mmin_SPK </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:center;"> 2021-05-29 </td>
   <td style="text-align:center;"> Athlete 9 </td>
   <td style="text-align:center;"> 117.4733 </td>
   <td style="text-align:center;"> 116.0014 </td>
   <td style="text-align:center;"> 13627.07 </td>
   <td style="text-align:center;"> 53.62 </td>
   <td style="text-align:center;"> 29.68420 </td>
   <td style="text-align:center;"> <span id="htmlwidget-672495a6168ad6bd217d" class="sparkline html-widget"></span>
<script type="application/json" data-for="htmlwidget-672495a6168ad6bd217d">{"x":{"values":[11876,14013,13358,14210,14204,13443,12816,12681,11514,14292,13627],"options":{"type":"line","fillColor":"transparent","spotColor":false,"normalRangeMin":12798.9545612611,"normalRangeMax":13752.6425587389,"chartRangeClip":true,"chartRangeMinX":1,"chartRangeMinX.1":60,"height":15,"width":100},"width":100,"height":15},"evals":[],"jsHooks":[]}</script> </td>
   <td style="text-align:center;"> <span id="htmlwidget-b4d748fc9ebdb1432eba" class="sparkline html-widget"></span>
<script type="application/json" data-for="htmlwidget-b4d748fc9ebdb1432eba">{"x":{"values":[106,117,122,125,128,112,112,114,103,128,117],"options":{"type":"line","fillColor":"transparent","spotColor":false,"normalRangeMin":112.629401311607,"normalRangeMax":121.045901718696,"chartRangeClip":true,"height":15,"width":100},"width":100,"height":15},"evals":[],"jsHooks":[]}</script> </td>
   <td style="text-align:center;"> <span id="htmlwidget-e6f3cf4189aa10f83b57" class="sparkline html-widget"></span>
<script type="application/json" data-for="htmlwidget-e6f3cf4189aa10f83b57">{"x":{"values":[112,120,109,113,111,120,114,112,112,111,116],"options":{"type":"line","fillColor":"transparent","spotColor":false,"normalRangeMin":111.94199681885,"normalRangeMax":115.408412272059,"chartRangeClip":true,"height":15,"width":100},"width":100,"height":15},"evals":[],"jsHooks":[]}</script> </td>
  </tr>
  <tr>
   <td style="text-align:center;"> 2021-05-29 </td>
   <td style="text-align:center;"> Athlete 14 </td>
   <td style="text-align:center;"> 114.2290 </td>
   <td style="text-align:center;"> 133.5855 </td>
   <td style="text-align:center;"> 15259.34 </td>
   <td style="text-align:center;"> 359.05 </td>
   <td style="text-align:center;"> 32.12881 </td>
   <td style="text-align:center;"> <span id="htmlwidget-608102498d20e0b43937" class="sparkline html-widget"></span>
<script type="application/json" data-for="htmlwidget-608102498d20e0b43937">{"x":{"values":[13994,14273,14253,15225,16513,15746,14731,14435,14855,15339,15259],"options":{"type":"line","fillColor":"transparent","spotColor":false,"normalRangeMin":14592.3836661635,"normalRangeMax":15339.1089756547,"chartRangeClip":true,"chartRangeMinX":1,"chartRangeMinX.1":60,"height":15,"width":100},"width":100,"height":15},"evals":[],"jsHooks":[]}</script> </td>
   <td style="text-align:center;"> <span id="htmlwidget-33bd0fc6f1999a39d849" class="sparkline html-widget"></span>
<script type="application/json" data-for="htmlwidget-33bd0fc6f1999a39d849">{"x":{"values":[101,106,105,114,127,119,107,103,114,113,114],"options":{"type":"line","fillColor":"transparent","spotColor":false,"normalRangeMin":107.328021860506,"normalRangeMax":115.035493291009,"chartRangeClip":true,"height":15,"width":100},"width":100,"height":15},"evals":[],"jsHooks":[]}</script> </td>
   <td style="text-align:center;"> <span id="htmlwidget-065a7324419a4b66103a" class="sparkline html-widget"></span>
<script type="application/json" data-for="htmlwidget-065a7324419a4b66103a">{"x":{"values":[138,135,135,134,130,132,138,140,130,136,134],"options":{"type":"line","fillColor":"transparent","spotColor":false,"normalRangeMin":133.197880505247,"normalRangeMax":136.375994040208,"chartRangeClip":true,"height":15,"width":100},"width":100,"height":15},"evals":[],"jsHooks":[]}</script> </td>
  </tr>
  <tr>
   <td style="text-align:center;"> 2021-05-29 </td>
   <td style="text-align:center;"> Athlete 32 </td>
   <td style="text-align:center;"> 112.0963 </td>
   <td style="text-align:center;"> 138.6244 </td>
   <td style="text-align:center;"> 15539.29 </td>
   <td style="text-align:center;"> 399.11 </td>
   <td style="text-align:center;"> 30.27415 </td>
   <td style="text-align:center;"> <span id="htmlwidget-0190b4b402ecae64bf95" class="sparkline html-widget"></span>
<script type="application/json" data-for="htmlwidget-0190b4b402ecae64bf95">{"x":{"values":[13203,14522,11999,15693,15305,14211,14477,12834,14669,15539],"options":{"type":"line","fillColor":"transparent","spotColor":false,"normalRangeMin":13636.7765758708,"normalRangeMax":14853.6352041292,"chartRangeClip":true,"chartRangeMinX":1,"chartRangeMinX.1":60,"height":15,"width":100},"width":100,"height":15},"evals":[],"jsHooks":[]}</script> </td>
   <td style="text-align:center;"> <span id="htmlwidget-cd9192812bac33d16f43" class="sparkline html-widget"></span>
<script type="application/json" data-for="htmlwidget-cd9192812bac33d16f43">{"x":{"values":[96,105,90,119,117,103,104,93,111,112],"options":{"type":"line","fillColor":"transparent","spotColor":false,"normalRangeMin":100.045973727323,"normalRangeMax":109.895626272677,"chartRangeClip":true,"height":15,"width":100},"width":100,"height":15},"evals":[],"jsHooks":[]}</script> </td>
   <td style="text-align:center;"> <span id="htmlwidget-b1f991bd895ab9ae2d23" class="sparkline html-widget"></span>
<script type="application/json" data-for="htmlwidget-b1f991bd895ab9ae2d23">{"x":{"values":[138,139,133,132,132,137,140,138,132,139],"options":{"type":"line","fillColor":"transparent","spotColor":false,"normalRangeMin":134.21596480739,"normalRangeMax":137.52307319261,"chartRangeClip":true,"height":15,"width":100},"width":100,"height":15},"evals":[],"jsHooks":[]}</script> </td>
  </tr>
  <tr>
   <td style="text-align:center;"> 2021-05-29 </td>
   <td style="text-align:center;"> Athlete 28 </td>
   <td style="text-align:center;"> 111.5465 </td>
   <td style="text-align:center;"> 121.7306 </td>
   <td style="text-align:center;"> 13578.62 </td>
   <td style="text-align:center;"> 235.78 </td>
   <td style="text-align:center;"> 28.60774 </td>
   <td style="text-align:center;"> <span id="htmlwidget-2aa13d0288cf7252a16a" class="sparkline html-widget"></span>
<script type="application/json" data-for="htmlwidget-2aa13d0288cf7252a16a">{"x":{"values":[12498,13583,13864,14485,15037,14073,12365,13150,13368,14441,13579],"options":{"type":"line","fillColor":"transparent","spotColor":false,"normalRangeMin":13264.7073252413,"normalRangeMax":14088.1855820314,"chartRangeClip":true,"chartRangeMinX":1,"chartRangeMinX.1":60,"height":15,"width":100},"width":100,"height":15},"evals":[],"jsHooks":[]}</script> </td>
   <td style="text-align:center;"> <span id="htmlwidget-69cfc1015d9ca9cb36ee" class="sparkline html-widget"></span>
<script type="application/json" data-for="htmlwidget-69cfc1015d9ca9cb36ee">{"x":{"values":[91,102,104,110,116,108,93,99,104,116,112],"options":{"type":"line","fillColor":"transparent","spotColor":false,"normalRangeMin":100.67910878847,"normalRangeMax":109.125982120621,"chartRangeClip":true,"height":15,"width":100},"width":100,"height":15},"evals":[],"jsHooks":[]}</script> </td>
   <td style="text-align:center;"> <span id="htmlwidget-b68a9ee0ba32d3ea09c9" class="sparkline html-widget"></span>
<script type="application/json" data-for="htmlwidget-b68a9ee0ba32d3ea09c9">{"x":{"values":[138,133,134,132,130,131,133,133,128,125,122],"options":{"type":"line","fillColor":"transparent","spotColor":false,"normalRangeMin":128.398855615205,"normalRangeMax":132.892960748432,"chartRangeClip":true,"height":15,"width":100},"width":100,"height":15},"evals":[],"jsHooks":[]}</script> </td>
  </tr>
  <tr>
   <td style="text-align:center;"> 2021-05-29 </td>
   <td style="text-align:center;"> Athlete 4 </td>
   <td style="text-align:center;"> 108.8887 </td>
   <td style="text-align:center;"> 109.8003 </td>
   <td style="text-align:center;"> 11956.00 </td>
   <td style="text-align:center;"> 28.44 </td>
   <td style="text-align:center;"> 26.01875 </td>
   <td style="text-align:center;"> <span id="htmlwidget-ae666eca5a3212acddb9" class="sparkline html-widget"></span>
<script type="application/json" data-for="htmlwidget-ae666eca5a3212acddb9">{"x":{"values":[12133,13762,13052,11999,11912,11632,12141,12805,12673,11956],"options":{"type":"line","fillColor":"transparent","spotColor":false,"normalRangeMin":12079.7243109113,"normalRangeMax":12733.2584570887,"chartRangeClip":true,"chartRangeMinX":1,"chartRangeMinX.1":60,"height":15,"width":100},"width":100,"height":15},"evals":[],"jsHooks":[]}</script> </td>
   <td style="text-align:center;"> <span id="htmlwidget-9c033560708a323e607a" class="sparkline html-widget"></span>
<script type="application/json" data-for="htmlwidget-9c033560708a323e607a">{"x":{"values":[109,120,118,107,106,107,104,118,126,109],"options":{"type":"line","fillColor":"transparent","spotColor":false,"normalRangeMin":108.380223752687,"normalRangeMax":115.862142913979,"chartRangeClip":true,"height":15,"width":100},"width":100,"height":15},"evals":[],"jsHooks":[]}</script> </td>
   <td style="text-align:center;"> <span id="htmlwidget-4c1c3e7e9dd068f738a8" class="sparkline html-widget"></span>
<script type="application/json" data-for="htmlwidget-4c1c3e7e9dd068f738a8">{"x":{"values":[112,115,110,112,113,109,117,109,101,110],"options":{"type":"line","fillColor":"transparent","spotColor":false,"normalRangeMin":108.635743407367,"normalRangeMax":112.992846592633,"chartRangeClip":true,"height":15,"width":100},"width":100,"height":15},"evals":[],"jsHooks":[]}</script> </td>
  </tr>
  <tr>
   <td style="text-align:center;"> 2021-05-29 </td>
   <td style="text-align:center;"> Athlete 30 </td>
   <td style="text-align:center;"> 108.0282 </td>
   <td style="text-align:center;"> 134.8545 </td>
   <td style="text-align:center;"> 14568.09 </td>
   <td style="text-align:center;"> 548.45 </td>
   <td style="text-align:center;"> 35.88166 </td>
   <td style="text-align:center;"> <span id="htmlwidget-b3a77fd85c207384df5a" class="sparkline html-widget"></span>
<script type="application/json" data-for="htmlwidget-b3a77fd85c207384df5a">{"x":{"values":[13119,12377,13275,15047,14672,13715,12060,13362,14181,14952,14568],"options":{"type":"line","fillColor":"transparent","spotColor":false,"normalRangeMin":13248.5636421865,"normalRangeMax":14265.8789396317,"chartRangeClip":true,"chartRangeMinX":1,"chartRangeMinX.1":60,"height":15,"width":100},"width":100,"height":15},"evals":[],"jsHooks":[]}</script> </td>
   <td style="text-align:center;"> <span id="htmlwidget-0f2e5b0f9a65e6072251" class="sparkline html-widget"></span>
<script type="application/json" data-for="htmlwidget-0f2e5b0f9a65e6072251">{"x":{"values":[94,88,98,110,112,101,85,94,107,112,108],"options":{"type":"line","fillColor":"transparent","spotColor":false,"normalRangeMin":95.9916759859909,"normalRangeMax":105.544778559464,"chartRangeClip":true,"height":15,"width":100},"width":100,"height":15},"evals":[],"jsHooks":[]}</script> </td>
   <td style="text-align:center;"> <span id="htmlwidget-0bf004b8a36b4ff5ce68" class="sparkline html-widget"></span>
<script type="application/json" data-for="htmlwidget-0bf004b8a36b4ff5ce68">{"x":{"values":[140,140,136,137,132,136,141,142,133,133,135],"options":{"type":"line","fillColor":"transparent","spotColor":false,"normalRangeMin":135.006259655701,"normalRangeMax":138.594565798844,"chartRangeClip":true,"height":15,"width":100},"width":100,"height":15},"evals":[],"jsHooks":[]}</script> </td>
  </tr>
  <tr>
   <td style="text-align:center;"> 2021-05-29 </td>
   <td style="text-align:center;"> Athlete 41 </td>
   <td style="text-align:center;"> 106.3490 </td>
   <td style="text-align:center;"> 137.5965 </td>
   <td style="text-align:center;"> 14633.25 </td>
   <td style="text-align:center;"> 468.82 </td>
   <td style="text-align:center;"> 31.78578 </td>
   <td style="text-align:center;"> <span id="htmlwidget-b3a83c5f4cafb59a6a28" class="sparkline html-widget"></span>
<script type="application/json" data-for="htmlwidget-b3a83c5f4cafb59a6a28">{"x":{"values":[13000,12280,11236,13889,11920,5017,13810,14633],"options":{"type":"line","fillColor":"transparent","spotColor":false,"normalRangeMin":10458.9621259303,"normalRangeMax":13487.4840440697,"chartRangeClip":true,"chartRangeMinX":1,"chartRangeMinX.1":60,"height":15,"width":100},"width":100,"height":15},"evals":[],"jsHooks":[]}</script> </td>
   <td style="text-align:center;"> <span id="htmlwidget-8eab2712c89ccef6a250" class="sparkline html-widget"></span>
<script type="application/json" data-for="htmlwidget-8eab2712c89ccef6a250">{"x":{"values":[96,92,79,105,88,31,105,106],"options":{"type":"line","fillColor":"transparent","spotColor":false,"normalRangeMin":75.2942319238201,"normalRangeMax":100.394476409513,"chartRangeClip":true,"height":15,"width":100},"width":100,"height":15},"evals":[],"jsHooks":[]}</script> </td>
   <td style="text-align:center;"> <span id="htmlwidget-8482f2dc34ac0d7c99a2" class="sparkline html-widget"></span>
<script type="application/json" data-for="htmlwidget-8482f2dc34ac0d7c99a2">{"x":{"values":[136,134,143,132,135,164,131,138],"options":{"type":"line","fillColor":"transparent","spotColor":false,"normalRangeMin":133.48800222144,"normalRangeMax":144.33161027856,"chartRangeClip":true,"height":15,"width":100},"width":100,"height":15},"evals":[],"jsHooks":[]}</script> </td>
  </tr>
  <tr>
   <td style="text-align:center;"> 2021-05-29 </td>
   <td style="text-align:center;"> Athlete 37 </td>
   <td style="text-align:center;"> 105.3603 </td>
   <td style="text-align:center;"> 132.4269 </td>
   <td style="text-align:center;"> 13952.54 </td>
   <td style="text-align:center;"> 300.39 </td>
   <td style="text-align:center;"> 32.25472 </td>
   <td style="text-align:center;"> <span id="htmlwidget-2b2758da4aa006e2e9da" class="sparkline html-widget"></span>
<script type="application/json" data-for="htmlwidget-2b2758da4aa006e2e9da">{"x":{"values":[11451,13563,13783,14861,15244,13930,13749,12628,13533,14477,13953],"options":{"type":"line","fillColor":"transparent","spotColor":false,"normalRangeMin":13225.9629221477,"normalRangeMax":14260.0916505796,"chartRangeClip":true,"chartRangeMinX":1,"chartRangeMinX.1":60,"height":15,"width":100},"width":100,"height":15},"evals":[],"jsHooks":[]}</script> </td>
   <td style="text-align:center;"> <span id="htmlwidget-9eb11f4bbf1576e36e95" class="sparkline html-widget"></span>
<script type="application/json" data-for="htmlwidget-9eb11f4bbf1576e36e95">{"x":{"values":[83,103,103,117,117,106,103,97,107,110,105],"options":{"type":"line","fillColor":"transparent","spotColor":false,"normalRangeMin":99.9734519562204,"normalRangeMax":109.291426831658,"chartRangeClip":true,"height":15,"width":100},"width":100,"height":15},"evals":[],"jsHooks":[]}</script> </td>
   <td style="text-align:center;"> <span id="htmlwidget-f85cb51e8e516809487c" class="sparkline html-widget"></span>
<script type="application/json" data-for="htmlwidget-f85cb51e8e516809487c">{"x":{"values":[138,132,133,127,131,132,134,130,126,132,132],"options":{"type":"line","fillColor":"transparent","spotColor":false,"normalRangeMin":129.964895052553,"normalRangeMax":133.146946765629,"chartRangeClip":true,"height":15,"width":100},"width":100,"height":15},"evals":[],"jsHooks":[]}</script> </td>
  </tr>
</tbody>
</table>

---

# GPS report - Version 2

✔️ A few more steps with this one, but worth it for the final product

```r
combined <- combined[c(1,3,10,4,11,5,9,7)] %>%
    filter(Duration > 98) %>%
        ungroup() %>%

formattable::formattable
        list(Duration = color_tile("white","indianred"),
             Speed_mmin = color_tile("white","indianred"),
             Distance = color_tile("white","indianred"),
             Max_velocity = color_tile("white","lightgreen"))) %>%
  
formattable::as.datatable(rownames = FALSE, 
              class = 'white-space: nowrap',
              colnames = c("Name","Duration (mins)","Duration trend","Speed (m.min) ","Speed trend",
                           " Distance (m)","Distance trend","Max speed (m.s)"), 
              options = list(dom = 'tp', bPaginate=FALSE, 
              columnDefs = list(className = 'dt-center', targets = "_all")),
                    fnDrawCallback = htmlwidgets::
                    JS('function(){HTMLWidgets.staticRender();}')) %>% 
      spk_add_deps() %>%
      formatStyle(c(1,3,5,7), `border-right` = "solid 1px", `border-color` = "darkgrey")
```

---

# GPS Report - Version 2 outut

<div id="htmlwidget-4ed32ce9b37d411372f3" style="width:100%;height:auto;" class="datatables html-widget"></div>
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---

# Power BI

📈 Create simple, powerful visualisations, in a time efficient manner

👉 All calculations/formulas can be done directly in **Power BI**

✔️ Download direct from the internet

<br>

---

# Power BI report

--
<img src="2021-ACU-HT_files/figure-html/power bi report 2-1.png" width="55%" style="display: block; margin: auto;" />

---

# Power BI data

👉 Repeat similiar steps using **`catapultR`** and added in **period name** (drill name)

```r
# token <- ofCloudGetToken(sRegion = "APAC, sName = " Acc name", sPwd = "acc password",
                        # sClientID = "ID", ClientSecret = "secret")

activities <- ofCloudGetActivities(token)
activities <-filter(activities, activities$name=="210609Wed")

# Pull through most recent data
data <- ofCloudGetStatistics(token, 
                             
  params = c("date", "activity_name","athlete_name","period_name","start_time_h","end_time_h", 
             "total_duration","meterage_per_minute","hirmin","acceleration_density"), 
  
  groupby = c("athlete", "period", "activity"), 
  filters = list(name = "activity_id",
                 comparison = "=",
                 values = activities$id[1]))

data$total_duration <- as.numeric(data$total_duration)/60
write.csv(data, row.names=FALSE, "assets/data.csv") # Export data
```

---

# Create Power BI report

- Open power BI and connect data using **Get Data** button

- Calculate **% game intensity** columns using pre-determined **intercept** (peak intensity) and **slope** (decrement) values

- Add a table and select columns you want (averages)
--

<br>

]

]

.footnote[
**References** <br>
Delaney et al. (2017). Modelling the decrement in running intensity within professional soccer players. Science and Medicine in Football. <br>
Duthie et al. (2018). Running intensities in elite youth soccer by age. Journla of Strength and Conditioning Research. 
]
---

# Formatting

🚦	Colour code the table to reflect the intensity of each drill using **conditional formatting** rules 
 
- Below: 0-89%
- At: 90-109%
- Above: 110-129%
- Above: 130%+

---

# Power BI report version 2

💹 Alternatively, we can select other options such as data bars, icons, font colour etc

---

# Useful Resources

catapultR - http://catapultr.catapultsports.com/download_catapultR.html

Mitch Henderson - https://www.mitchhenderson.org/

Alice Sweeting - http://sportstatisticsrsweet.rbind.io/

Josh Trewin - http://futbolanalysr.rbind.io/

Neil Collins - https://www.sportscidata.com/

Jose Fernandez - https://twitter.com/jfernandez__

---

# 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:black;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:black;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>