Skate-aid Worldwide Highest Ollie Contest 2017

On 7th January 2017, Skate-aid organised a competition which involved the contestant(s). Jumping over an adjustable obstacle by Ollieing especially where they have established projects namely:

Additionally, the rules to govern the event worldwide can be found here rules. Here’s a skater from Kenya jumping over the obstacle. Which is an adjustible wood plank in this case.

Photo by Robert Opiyo, skateboarder Sydney Yongo

‘The aim of the contest was to unite skaters from all over the world and to help at-risk youths with a safe and constructive space that children require in their developmental ages.’ From page Skate-aid Shangilia Skatepark Nairobi - Kenya

From the results of the competition. Which i acquired using this link results. Here are the results.

  1. Copenhagen: Alexander Risrad - 103 cm (highest record)

  2. Cape Town: Ryan Naidoo -99 cm

  3. Münster: Georg G Anders -85 cm

  4. Nairobi: Kevin Maina - 75 cm

  5. San Isidro: William Morales -70 cm

  6. Dodoma: Gideon -54cm

  7. Bethlehem: Moyyed and Anas- 20 cm (Under 15 only)

  8. Kigali: 15cm (under 15 only)

  9. Uganda: from John Ray’s post here facebook post

cm- Centimeters in height.

I changed the results a bit so that they could be accomodated in the graph i’ve drawn below. Notice the changes in the lines of code below.

Making the dataframe

# making a dataframe of the results of the contest 
ctry <- c('DK','ZA','DE','KE','CR','TZ','UG','PS','RW','US') #different countries
hts <- c(103,99,85,75,70,54,88.4,20,15,114.3) # heights are in centimetres
names <- c('A.Risrad(103)','R.Naidoo(99)','G.Anders(85)','K.Maina(75)','W.Morales(70)','Gideon(54)','Peter(88.4)','Moyyed & Anas(20)','Na.RW(15)','A.Garcia(114.3)')
df <- data.frame(ctry,hts,names)
df
##    ctry   hts             names
## 1    DK 103.0     A.Risrad(103)
## 2    ZA  99.0      R.Naidoo(99)
## 3    DE  85.0      G.Anders(85)
## 4    KE  75.0       K.Maina(75)
## 5    CR  70.0     W.Morales(70)
## 6    TZ  54.0        Gideon(54)
## 7    UG  88.4       Peter(88.4)
## 8    PS  20.0 Moyyed & Anas(20)
## 9    RW  15.0         Na.RW(15)
## 10   US 114.3   A.Garcia(114.3)

Drawing the plot(dendogram)

# Make hierarichal clustering object and visualize
dist_mat <- dist(df[,2]) # selecting the second column
hc <- hclust(dist_mat)
plot(hc, labels = df$names, xlab = "Different heights of individuals in different countries", main = "How close the Ollie Heights were between countries \n
     Skate-aid International Annual Ollie Contest 2017")
legend(x = "bottomright", legend = "Close heights are paired in the leaves.")

Interpretation

The graph you’ve seen above puts the Ollie heights together with the ones most closest being in the same leaf. That is, Moyyes & Anas and Na.RW (i named it like that because the name of the person was missing and i didn’t find it anywhere). In addition, the heights of A.Risrad and R.Naidoo are really close to each other. Notice i also added the Guinness world record as well for comparison.

Conclusion

A.Garcia still holds the world record from Las Vegas, United States in 2011. But, if more practice & learning from mistakes could be used by A.Risrad and R.Naidoo the record could be beat as an example. Now, you know whose Ollie height was close to whom. Question is, where would yours be among these heights? Thanks for reading!

References

Text Mining course on Datacamp: Distance matrix and Dendrogram

Data Visualization in R course on Datacamp: Adding details to plots

To replicate use this

sessionInfo()
## R version 3.3.2 (2016-10-31)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: macOS Sierra 10.12.3
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## loaded via a namespace (and not attached):
##  [1] backports_1.0.5 magrittr_1.5    rprojroot_1.2   tools_3.3.2    
##  [5] htmltools_0.3.5 yaml_2.1.14     Rcpp_0.12.9     stringi_1.1.2  
##  [9] rmarkdown_1.3   knitr_1.15.1    stringr_1.2.0   digest_0.6.12  
## [13] evaluate_0.10