R Notebook for the preliminary skate project by @Shuyin_ben twitter

Introduction

Last year, I started a project called Skate project on YouTube. Whose intent was to make myself and the viewer get good at skateboarding using data. I explain how i do it using software like R. I draw graphs and find the standard deviation which reveals a lot of information. That was the word for word explanation i extracted from YouTube. Now, this is a report of that analysis with a codebook - which i recommend the reader to look at before reading through this or replicating this.

Our goal is, to find out what tricks I’m good at and recommend which tricks i should start with in a competition like game of S.K.A.T.E sometimes called H.O.R.S.E or in skate lines using data. First things, first Importing and Cleaning.

raw data: google sheets download

tidy data: cleaner dataset download

Importing and Cleaning

Doing all the necessary imports of all the packages, we’ll require throughout the analysis. Plus, bringing the data into R from where you stored it in the computer.

library(psych)
library(ggplot2)

Attaching package: ‘ggplot2’

The following objects are masked from ‘package:psych’:

    %+%, alpha

library(magrittr)
library(dplyr)

Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union

library(lubridate)

Attaching package: ‘lubridate’

The following object is masked from ‘package:base’:

    date

library(tidyr)

Attaching package: ‘tidyr’

The following object is masked from ‘package:magrittr’:

    extract

library(Hmisc)

Loading required package: lattice
Loading required package: survival
Loading required package: Formula

Attaching package: ‘Hmisc’

The following objects are masked from ‘package:dplyr’:

    combine, src, summarize

The following object is masked from ‘package:psych’:

    describe

The following objects are masked from ‘package:base’:

    format.pval, round.POSIXt, trunc.POSIXt, units

library(gridExtra)

Attaching package: ‘gridExtra’

The following object is masked from ‘package:Hmisc’:

    combine

The following object is masked from ‘package:dplyr’:

    combine

# change to the new csv file with parsed dates and change the columns 
# place to numbers later 
path <- file.path("/Users/brendamainye/Downloads/datasets/skate_project1.csv")
skate_df <- read.csv(path)

Then here, we’re exploring the dataset for clues that we require to clean it in terms of: inconsistent column names(fixed this already), missing data (there’s one day i didn’t record. So i backfilled it with date ahead of it 2016-07-29 - refer to thecodebook for more information), outliers (the data is scaled already 0 - 5, this won’t be a problem this time), duplicate rows (none), Untidy (this is a tidy dataset, don’t you think?). Do you see anything strange?

dim(skate_df) # 29 rows and 23 columns

[1] 29 23

names(skate_df) # the column names do not require cleaning 

 [1] "X"               "ollie"           "fs.180"          "bs.180"         
 [5] "pop.shove"       "fs.shove"        "kickflip"        "heelflip"       
 [9] "f.180"           "f.ollie"         "f.shove"         "f.fs.shove"     
[13] "sw.fs.bone"      "sw.fs.no.comply" "f.bigspin"       "sw.bone"        
[17] "halfcab"         "fingerflip"      "sw.no.cmp"       "date"           
[21] "place"           "randomized"      "board"          

class(skate_df) # data.frame, we need to convert this later

[1] "data.frame"

glimpse(skate_df) # Understanding the data set

Observations: 29
Variables: 23
$ X               <int> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16...
$ ollie           <int> 5, 5, 5, 5, 5, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,...
$ fs.180          <int> 2, 4, 2, 5, 4, 3, 3, 3, 4, 3, 4, 1, 2, 4, 3, 3, 3, 4, 4,...
$ bs.180          <int> 1, 2, 4, 2, 3, 4, 4, 3, 1, 2, 3, 4, 5, 4, 1, 3, 3, 3, 4,...
$ pop.shove       <int> 4, 3, 2, 3, 1, 0, 2, 3, 1, 4, 2, 3, 3, 2, 1, 2, 4, 2, 0,...
$ fs.shove        <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 2,...
$ kickflip        <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ heelflip        <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ f.180           <int> 3, 2, 2, 3, 2, 2, 2, 0, 3, 1, 0, 2, 4, 3, 3, 3, 2, 2, 3,...
$ f.ollie         <int> 4, 4, 3, 4, 3, 4, 2, 4, 4, 4, 4, 3, 4, 4, 2, 4, 5, 2, 5,...
$ f.shove         <int> 4, 4, 3, 5, 3, 4, 3, 4, 5, 3, 5, 5, 2, 2, 2, 3, 3, 3, 3,...
$ f.fs.shove      <int> 2, 1, 1, 0, 1, 1, 2, 2, 2, 0, 3, 2, 1, 1, 1, 1, 1, 3, 2,...
$ sw.fs.bone      <int> 4, 4, 5, 5, 4, 5, 5, 4, 5, 5, 5, 5, 5, 5, 4, 5, 4, 5, 2,...
$ sw.fs.no.comply <int> 4, 3, 5, 5, 3, 3, 4, 2, 4, 2, 3, 3, 3, 2, 2, 4, 4, 3, 2,...
$ f.bigspin       <int> 0, 1, 1, 0, 2, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 2, 0,...
$ sw.bone         <int> 4, 4, 5, 5, 4, 2, 5, 4, 5, 4, 2, 5, 5, 5, 5, 5, 5, 5, 4,...
$ halfcab         <int> 4, 4, 3, 5, 5, 5, 4, 3, 4, 4, 5, 4, 2, 5, 5, 5, 5, 5, 5,...
$ fingerflip      <int> 3, 3, 3, 4, 4, 1, 2, 2, 1, 4, 3, 2, 5, 2, 3, 4, 2, 2, 1,...
$ sw.no.cmp       <int> 4, 4, 3, 5, 3, 2, 1, 3, 3, 3, 1, 4, 2, 3, 2, 2, 4, 4, 4,...
$ date            <fctr> 2016-10-07, 2016-07-29, 2016-07-29, 2016-05-08, 2016-08...
$ place           <fctr> marist, uhuru, comboni, marist, mparkkaren, comboni, ma...
$ randomized      <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
$ board           <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,...

summary(skate_df) # Understanding the data set: Summary statistics

       X          ollie           fs.180          bs.180        pop.shove    
 Min.   : 0   Min.   :3.000   Min.   :1.000   Min.   :1.000   Min.   :0.000  
 1st Qu.: 7   1st Qu.:5.000   1st Qu.:3.000   1st Qu.:2.000   1st Qu.:1.000  
 Median :14   Median :5.000   Median :3.000   Median :3.000   Median :2.000  
 Mean   :14   Mean   :4.931   Mean   :3.241   Mean   :2.966   Mean   :2.172  
 3rd Qu.:21   3rd Qu.:5.000   3rd Qu.:4.000   3rd Qu.:4.000   3rd Qu.:3.000  
 Max.   :28   Max.   :5.000   Max.   :5.000   Max.   :5.000   Max.   :4.000  
                                                                             
    fs.shove         kickflip    heelflip     f.180          f.ollie     
 Min.   :0.0000   Min.   :0   Min.   :0   Min.   :0.000   Min.   :2.000  
 1st Qu.:0.0000   1st Qu.:0   1st Qu.:0   1st Qu.:1.000   1st Qu.:3.000  
 Median :0.0000   Median :0   Median :0   Median :2.000   Median :4.000  
 Mean   :0.2414   Mean   :0   Mean   :0   Mean   :2.034   Mean   :3.759  
 3rd Qu.:0.0000   3rd Qu.:0   3rd Qu.:0   3rd Qu.:3.000   3rd Qu.:4.000  
 Max.   :2.0000   Max.   :0   Max.   :0   Max.   :4.000   Max.   :5.000  
                                                                         
    f.shove        f.fs.shove      sw.fs.bone    sw.fs.no.comply   f.bigspin     
 Min.   :2.000   Min.   :0.000   Min.   :2.000   Min.   :1.000   Min.   :0.0000  
 1st Qu.:3.000   1st Qu.:1.000   1st Qu.:4.000   1st Qu.:2.000   1st Qu.:0.0000  
 Median :4.000   Median :2.000   Median :5.000   Median :3.000   Median :1.0000  
 Mean   :3.621   Mean   :1.517   Mean   :4.517   Mean   :3.172   Mean   :0.6897  
 3rd Qu.:4.000   3rd Qu.:2.000   3rd Qu.:5.000   3rd Qu.:4.000   3rd Qu.:1.0000  
 Max.   :5.000   Max.   :3.000   Max.   :5.000   Max.   :5.000   Max.   :2.0000  
                                                                                 
    sw.bone         halfcab        fingerflip      sw.no.cmp             date   
 Min.   :2.000   Min.   :2.000   Min.   :1.000   Min.   :1.000   2016-07-29: 2  
 1st Qu.:4.000   1st Qu.:4.000   1st Qu.:2.000   1st Qu.:2.000   2016-02-12: 1  
 Median :5.000   Median :5.000   Median :3.000   Median :3.000   2016-04-09: 1  
 Mean   :4.345   Mean   :4.414   Mean   :2.724   Mean   :3.069   2016-05-08: 1  
 3rd Qu.:5.000   3rd Qu.:5.000   3rd Qu.:4.000   3rd Qu.:4.000   2016-08-14: 1  
 Max.   :5.000   Max.   :5.000   Max.   :5.000   Max.   :5.000   2016-08-28: 1  
                                                                 (Other)   :22  
         place      randomized         board       
 comboni    : 8   Min.   :0.0000   Min.   :0.0000  
 hilltoprdng: 1   1st Qu.:0.0000   1st Qu.:0.0000  
 marist     :10   Median :1.0000   Median :0.0000  
 mparkkaren : 1   Mean   :0.6552   Mean   :0.4828  
 skatepark  : 5   3rd Qu.:1.0000   3rd Qu.:1.0000  
 uhuru      : 4   Max.   :1.0000   Max.   :1.0000  
                                                   

describe(skate_df) # Understanding the data set: Summary statistics continued answers so

no non-missing arguments to min; returning Infno non-missing arguments to min; returning Inf

skate_df 

 23  Variables      29  Observations
-------------------------------------------------------------------------------------
X 
       n  missing distinct     Info     Mean      Gmd      .05      .10      .25 
      29        0       29        1       14       10      1.4      2.8      7.0 
     .50      .75      .90      .95 
    14.0     21.0     25.2     26.6 

lowest :  0  1  2  3  4, highest: 24 25 26 27 28
-------------------------------------------------------------------------------------
ollie 
       n  missing distinct     Info     Mean      Gmd 
      29        0        2      0.1    4.931   0.1379 
                      
Value          3     5
Frequency      1    28
Proportion 0.034 0.966
-------------------------------------------------------------------------------------
fs.180 
       n  missing distinct     Info     Mean      Gmd 
      29        0        5      0.9    3.241    1.044 
                                        
Value          1     2     3     4     5
Frequency      1     5    11    10     2
Proportion 0.034 0.172 0.379 0.345 0.069
-------------------------------------------------------------------------------------
bs.180 
       n  missing distinct     Info     Mean      Gmd 
      29        0        5    0.931    2.966    1.291 
                                        
Value          1     2     3     4     5
Frequency      4     5    10     8     2
Proportion 0.138 0.172 0.345 0.276 0.069
-------------------------------------------------------------------------------------
pop.shove 
       n  missing distinct     Info     Mean      Gmd 
      29        0        5    0.932    2.172     1.31 
                                        
Value          0     1     2     3     4
Frequency      3     5     8    10     3
Proportion 0.103 0.172 0.276 0.345 0.103
-------------------------------------------------------------------------------------
fs.shove 
       n  missing distinct     Info     Mean      Gmd 
      29        0        3    0.432   0.2414   0.4286 
                            
Value          0     1     2
Frequency     24     3     2
Proportion 0.828 0.103 0.069
-------------------------------------------------------------------------------------
kickflip 
       n  missing distinct     Info     Mean      Gmd 
      29        0        1        0        0        0 
             
Value       0
Frequency  29
Proportion  1
-------------------------------------------------------------------------------------
heelflip 
       n  missing distinct     Info     Mean      Gmd 
      29        0        1        0        0        0 
             
Value       0
Frequency  29
Proportion  1
-------------------------------------------------------------------------------------
f.180 
       n  missing distinct     Info     Mean      Gmd 
      29        0        5    0.913    2.034    1.163 
                                        
Value          0     1     2     3     4
Frequency      3     5    10    10     1
Proportion 0.103 0.172 0.345 0.345 0.034
-------------------------------------------------------------------------------------
f.ollie 
       n  missing distinct     Info     Mean      Gmd 
      29        0        4    0.823    3.759   0.9557 
                                  
Value          2     3     4     5
Frequency      4     4    16     5
Proportion 0.138 0.138 0.552 0.172
-------------------------------------------------------------------------------------
f.shove 
       n  missing distinct     Info     Mean      Gmd 
      29        0        4    0.907    3.621    1.049 
                                  
Value          2     3     4     5
Frequency      3    11     9     6
Proportion 0.103 0.379 0.310 0.207
-------------------------------------------------------------------------------------
f.fs.shove 
       n  missing distinct     Info     Mean      Gmd 
      29        0        4      0.9    1.517     1.01 
                                  
Value          0     1     2     3
Frequency      4    10    11     4
Proportion 0.138 0.345 0.379 0.138
-------------------------------------------------------------------------------------
sw.fs.bone 
       n  missing distinct     Info     Mean      Gmd 
      29        0        4    0.705    4.517   0.7291 
                                  
Value          2     3     4     5
Frequency      1     2     7    19
Proportion 0.034 0.069 0.241 0.655
-------------------------------------------------------------------------------------
sw.fs.no.comply 
       n  missing distinct     Info     Mean      Gmd 
      29        0        5    0.924    3.172    1.163 
                                        
Value          1     2     3     4     5
Frequency      1     7    10     8     3
Proportion 0.034 0.241 0.345 0.276 0.103
-------------------------------------------------------------------------------------
f.bigspin 
       n  missing distinct     Info     Mean      Gmd 
      29        0        3    0.837   0.6897   0.7586 
                            
Value          0     1     2
Frequency     13    12     4
Proportion 0.448 0.414 0.138
-------------------------------------------------------------------------------------
sw.bone 
       n  missing distinct     Info     Mean      Gmd 
      29        0        4    0.802    4.345   0.8916 
                                  
Value          2     3     4     5
Frequency      2     2     9    16
Proportion 0.069 0.069 0.310 0.552
-------------------------------------------------------------------------------------
halfcab 
       n  missing distinct     Info     Mean      Gmd 
      29        0        4    0.777    4.414   0.8177 
                                  
Value          2     3     4     5
Frequency      1     3     8    17
Proportion 0.034 0.103 0.276 0.586
-------------------------------------------------------------------------------------
fingerflip 
       n  missing distinct     Info     Mean      Gmd 
      29        0        5    0.946    2.724    1.419 
                                        
Value          1     2     3     4     5
Frequency      5     9     7     5     3
Proportion 0.172 0.310 0.241 0.172 0.103
-------------------------------------------------------------------------------------
sw.no.cmp 
       n  missing distinct     Info     Mean      Gmd 
      29        0        5    0.927    3.069    1.197 
                                        
Value          1     2     3     4     5
Frequency      2     7     9     9     2
Proportion 0.069 0.241 0.310 0.310 0.069
-------------------------------------------------------------------------------------
date 
       n  missing distinct 
      29        0       28 

lowest : 2016-02-12 2016-04-09 2016-05-08 2016-07-29 2016-08-14
highest: 2017-06-30 2017-07-15 2017-07-22 2017-09-06 2017-12-02
-------------------------------------------------------------------------------------
place 
       n  missing distinct 
      29        0        6 
                                                                                  
Value          comboni hilltoprdng      marist  mparkkaren   skatepark       uhuru
Frequency            8           1          10           1           5           4
Proportion       0.276       0.034       0.345       0.034       0.172       0.138
-------------------------------------------------------------------------------------
randomized 
       n  missing distinct     Info      Sum     Mean      Gmd 
      29        0        2    0.679       19   0.6552    0.468 

-------------------------------------------------------------------------------------
board 
       n  missing distinct     Info      Sum     Mean      Gmd 
      29        0        2     0.75       14   0.4828   0.5172 

-------------------------------------------------------------------------------------

# many questions we'll explore in the dataset
skate_df <- tbl_df(skate_df) # converts to a format liked by dplyr
skate_df$board <- factor(skate_df$board) #convert into factors
skate_df$randomized <- factor(skate_df$randomized) #convert into factors 
(skate_df <- skate_df %>% select(-X)) # removing the X column since its just counts. And print results. skate_df <- skate_df[-1]

Exploratory data analysis

We’ll do a lot of summary statistics and plotting here. This is done to find patterns in the data. Which i hope you’ll see as we go through this analysis. We’ll see if the mean of the tricks vary in an unusual way grouped by the following variables namely randomized and place. We’ll not focus on this since i don’t want to develop some biases. But with time we shall. Do you see anything interesting?

# Does the mean vary among the places i've visited?
describeBy(skate_df, group = skate_df$place)

 Descriptive statistics by group 
group: comboni
                vars n  mean   sd median trimmed   mad min max range  skew kurtosis
ollie              1 8  4.75 0.71    5.0    4.75  0.00   3   5     2 -1.86     1.70
fs.180             2 8  3.25 1.04    3.0    3.25  1.48   2   5     3  0.25    -1.38
bs.180             3 8  3.00 1.51    3.5    3.00  1.48   1   5     4 -0.22    -1.76
pop.shove          4 8  1.75 1.28    1.5    1.75  0.74   0   4     4  0.40    -1.22
fs.shove           5 8  0.38 0.74    0.0    0.38  0.00   0   2     2  1.28    -0.05
kickflip           6 8  0.00 0.00    0.0    0.00  0.00   0   0     0   NaN      NaN
heelflip           7 8  0.00 0.00    0.0    0.00  0.00   0   0     0   NaN      NaN
f.180              8 8  2.00 0.76    2.0    2.00  0.74   1   3     2  0.00    -1.47
f.ollie            9 8  4.00 1.07    4.0    4.00  1.48   2   5     3 -0.61    -1.09
f.shove           10 8  3.50 0.93    3.5    3.50  0.74   2   5     3  0.00    -1.21
f.fs.shove        11 8  1.25 0.71    1.0    1.25  0.74   0   2     2 -0.27    -1.30
sw.fs.bone        12 8  4.75 0.46    5.0    4.75  0.00   4   5     1 -0.95    -1.21
sw.fs.no.comply   13 8  3.38 1.30    4.0    3.38  0.74   1   5     4 -0.61    -1.13
f.bigspin         14 8  0.50 0.53    0.5    0.50  0.74   0   1     1  0.00    -2.23
sw.bone           15 8  4.25 1.16    5.0    4.25  0.00   2   5     3 -0.89    -0.99
halfcab           16 8  4.50 0.76    5.0    4.50  0.00   3   5     2 -0.87    -0.89
fingerflip        17 8  2.50 1.31    2.5    2.50  0.74   1   5     4  0.50    -0.89
sw.no.cmp         18 8  3.00 0.76    3.0    3.00  0.74   2   4     2  0.00    -1.47
date*             19 8 15.00 8.77   15.0   15.00 11.86   4  28    24  0.16    -1.68
place*            20 8  1.00 0.00    1.0    1.00  0.00   1   1     0   NaN      NaN
randomized*       21 8  1.62 0.52    2.0    1.62  0.00   1   2     1 -0.42    -2.03
board*            22 8  1.50 0.53    1.5    1.50  0.74   1   2     1  0.00    -2.23
                  se
ollie           0.25
fs.180          0.37
bs.180          0.53
pop.shove       0.45
fs.shove        0.26
kickflip        0.00
heelflip        0.00
f.180           0.27
f.ollie         0.38
f.shove         0.33
f.fs.shove      0.25
sw.fs.bone      0.16
sw.fs.no.comply 0.46
f.bigspin       0.19
sw.bone         0.41
halfcab         0.27
fingerflip      0.46
sw.no.cmp       0.27
date*           3.10
place*          0.00
randomized*     0.18
board*          0.19
--------------------------------------------------------------- 
group: hilltoprdng
                vars n mean sd median trimmed mad min max range skew kurtosis se
ollie              1 1    5 NA      5       5   0   5   5     0   NA       NA NA
fs.180             2 1    4 NA      4       4   0   4   4     0   NA       NA NA
bs.180             3 1    4 NA      4       4   0   4   4     0   NA       NA NA
pop.shove          4 1    0 NA      0       0   0   0   0     0   NA       NA NA
fs.shove           5 1    0 NA      0       0   0   0   0     0   NA       NA NA
kickflip           6 1    0 NA      0       0   0   0   0     0   NA       NA NA
heelflip           7 1    0 NA      0       0   0   0   0     0   NA       NA NA
f.180              8 1    3 NA      3       3   0   3   3     0   NA       NA NA
f.ollie            9 1    4 NA      4       4   0   4   4     0   NA       NA NA
f.shove           10 1    3 NA      3       3   0   3   3     0   NA       NA NA
f.fs.shove        11 1    0 NA      0       0   0   0   0     0   NA       NA NA
sw.fs.bone        12 1    5 NA      5       5   0   5   5     0   NA       NA NA
sw.fs.no.comply   13 1    3 NA      3       3   0   3   3     0   NA       NA NA
f.bigspin         14 1    0 NA      0       0   0   0   0     0   NA       NA NA
sw.bone           15 1    5 NA      5       5   0   5   5     0   NA       NA NA
halfcab           16 1    3 NA      3       3   0   3   3     0   NA       NA NA
fingerflip        17 1    5 NA      5       5   0   5   5     0   NA       NA NA
sw.no.cmp         18 1    2 NA      2       2   0   2   2     0   NA       NA NA
date*             19 1   21 NA     21      21   0  21  21     0   NA       NA NA
place*            20 1    2 NA      2       2   0   2   2     0   NA       NA NA
randomized*       21 1    2 NA      2       2   0   2   2     0   NA       NA NA
board*            22 1    2 NA      2       2   0   2   2     0   NA       NA NA
--------------------------------------------------------------- 
group: marist
                vars  n mean   sd median trimmed   mad min max range  skew kurtosis
ollie              1 10  5.0 0.00    5.0    5.00  0.00   5   5     0   NaN      NaN
fs.180             2 10  2.9 1.10    3.0    2.88  0.74   1   5     4  0.17    -0.60
bs.180             3 10  3.1 1.20    3.0    3.12  1.48   1   5     4 -0.17    -1.18
pop.shove          4 10  2.5 1.27    3.0    2.62  1.48   0   4     4 -0.59    -0.90
fs.shove           5 10  0.4 0.70    0.0    0.25  0.00   0   2     2  1.19    -0.07
kickflip           6 10  0.0 0.00    0.0    0.00  0.00   0   0     0   NaN      NaN
heelflip           7 10  0.0 0.00    0.0    0.00  0.00   0   0     0   NaN      NaN
f.180              8 10  2.4 1.17    3.0    2.50  0.74   0   4     4 -0.71    -0.67
f.ollie            9 10  3.7 0.82    4.0    3.75  0.00   2   5     3 -0.58    -0.45
f.shove           10 10  3.7 1.06    3.5    3.75  0.74   2   5     3  0.03    -1.58
f.fs.shove        11 10  1.4 0.97    1.5    1.38  0.74   0   3     3 -0.08    -1.30
sw.fs.bone        12 10  4.6 0.97    5.0    4.88  0.00   2   5     3 -1.92     2.28
sw.fs.no.comply   13 10  3.6 1.07    4.0    3.62  1.48   2   5     3 -0.23    -1.42
f.bigspin         14 10  0.4 0.70    0.0    0.25  0.00   0   2     2  1.19    -0.07
sw.bone           15 10  4.5 0.71    5.0    4.62  0.00   3   5     2 -0.85    -0.75
halfcab           16 10  4.3 0.95    4.5    4.50  0.74   2   5     3 -1.24     0.61
fingerflip        17 10  2.8 1.40    2.5    2.75  2.22   1   5     4  0.10    -1.64
sw.no.cmp         18 10  3.1 1.29    3.5    3.12  1.48   1   5     4 -0.16    -1.56
date*             19 10 11.7 8.33   11.0   11.50 11.12   1  24    23  0.13    -1.57
place*            20 10  3.0 0.00    3.0    3.00  0.00   3   3     0   NaN      NaN
randomized*       21 10  1.6 0.52    2.0    1.62  0.00   1   2     1 -0.35    -2.05
board*            22 10  1.4 0.52    1.0    1.38  0.00   1   2     1  0.35    -2.05
                  se
ollie           0.00
fs.180          0.35
bs.180          0.38
pop.shove       0.40
fs.shove        0.22
kickflip        0.00
heelflip        0.00
f.180           0.37
f.ollie         0.26
f.shove         0.33
f.fs.shove      0.31
sw.fs.bone      0.31
sw.fs.no.comply 0.34
f.bigspin       0.22
sw.bone         0.22
halfcab         0.30
fingerflip      0.44
sw.no.cmp       0.41
date*           2.63
place*          0.00
randomized*     0.16
board*          0.16
--------------------------------------------------------------- 
group: mparkkaren
                vars n mean sd median trimmed mad min max range skew kurtosis se
ollie              1 1    5 NA      5       5   0   5   5     0   NA       NA NA
fs.180             2 1    4 NA      4       4   0   4   4     0   NA       NA NA
bs.180             3 1    3 NA      3       3   0   3   3     0   NA       NA NA
pop.shove          4 1    1 NA      1       1   0   1   1     0   NA       NA NA
fs.shove           5 1    0 NA      0       0   0   0   0     0   NA       NA NA
kickflip           6 1    0 NA      0       0   0   0   0     0   NA       NA NA
heelflip           7 1    0 NA      0       0   0   0   0     0   NA       NA NA
f.180              8 1    2 NA      2       2   0   2   2     0   NA       NA NA
f.ollie            9 1    3 NA      3       3   0   3   3     0   NA       NA NA
f.shove           10 1    3 NA      3       3   0   3   3     0   NA       NA NA
f.fs.shove        11 1    1 NA      1       1   0   1   1     0   NA       NA NA
sw.fs.bone        12 1    4 NA      4       4   0   4   4     0   NA       NA NA
sw.fs.no.comply   13 1    3 NA      3       3   0   3   3     0   NA       NA NA
f.bigspin         14 1    2 NA      2       2   0   2   2     0   NA       NA NA
sw.bone           15 1    4 NA      4       4   0   4   4     0   NA       NA NA
halfcab           16 1    5 NA      5       5   0   5   5     0   NA       NA NA
fingerflip        17 1    4 NA      4       4   0   4   4     0   NA       NA NA
sw.no.cmp         18 1    3 NA      3       3   0   3   3     0   NA       NA NA
date*             19 1    5 NA      5       5   0   5   5     0   NA       NA NA
place*            20 1    4 NA      4       4   0   4   4     0   NA       NA NA
randomized*       21 1    1 NA      1       1   0   1   1     0   NA       NA NA
board*            22 1    1 NA      1       1   0   1   1     0   NA       NA NA
--------------------------------------------------------------- 
group: skatepark
                vars n mean   sd median trimmed  mad min max range  skew kurtosis
ollie              1 5  5.0 0.00      5     5.0 0.00   5   5     0   NaN      NaN
fs.180             2 5  3.4 0.89      4     3.4 0.00   2   4     2 -0.60    -1.67
bs.180             3 5  3.0 0.71      3     3.0 0.00   2   4     2  0.00    -1.40
pop.shove          4 5  2.4 0.55      2     2.4 0.00   2   3     1  0.29    -2.25
fs.shove           5 5  0.0 0.00      0     0.0 0.00   0   0     0   NaN      NaN
kickflip           6 5  0.0 0.00      0     0.0 0.00   0   0     0   NaN      NaN
heelflip           7 5  0.0 0.00      0     0.0 0.00   0   0     0   NaN      NaN
f.180              8 5  1.6 1.14      2     1.6 1.48   0   3     3 -0.19    -1.75
f.ollie            9 5  3.4 1.34      4     3.4 1.48   2   5     3 -0.08    -2.11
f.shove           10 5  3.6 1.14      4     3.6 1.48   2   5     3 -0.19    -1.75
f.fs.shove        11 5  2.2 0.84      2     2.2 1.48   1   3     2 -0.25    -1.82
sw.fs.bone        12 5  4.4 0.89      5     4.4 0.00   3   5     2 -0.60    -1.67
sw.fs.no.comply   13 5  2.6 0.55      3     2.6 0.00   2   3     1 -0.29    -2.25
f.bigspin         14 5  1.2 0.84      1     1.2 1.48   0   2     2 -0.25    -1.82
sw.bone           15 5  4.0 1.22      4     4.0 1.48   2   5     3 -0.65    -1.40
halfcab           16 5  5.0 0.00      5     5.0 0.00   5   5     0   NaN      NaN
fingerflip        17 5  2.6 0.89      2     2.6 0.00   2   4     2  0.60    -1.67
sw.no.cmp         18 5  3.0 1.22      3     3.0 1.48   1   4     3 -0.65    -1.40
date*             19 5 17.8 6.22     17    17.8 7.41  11  26    15  0.17    -2.00
place*            20 5  5.0 0.00      5     5.0 0.00   5   5     0   NaN      NaN
randomized*       21 5  2.0 0.00      2     2.0 0.00   2   2     0   NaN      NaN
board*            22 5  1.6 0.55      2     1.6 0.00   1   2     1 -0.29    -2.25
                  se
ollie           0.00
fs.180          0.40
bs.180          0.32
pop.shove       0.24
fs.shove        0.00
kickflip        0.00
heelflip        0.00
f.180           0.51
f.ollie         0.60
f.shove         0.51
f.fs.shove      0.37
sw.fs.bone      0.40
sw.fs.no.comply 0.24
f.bigspin       0.37
sw.bone         0.55
halfcab         0.00
fingerflip      0.40
sw.no.cmp       0.55
date*           2.78
place*          0.00
randomized*     0.00
board*          0.24
--------------------------------------------------------------- 
group: uhuru
                vars n  mean    sd median trimmed   mad min max range  skew kurtosis
ollie              1 4  5.00  0.00    5.0    5.00  0.00   5   5     0   NaN      NaN
fs.180             2 4  3.50  0.58    3.5    3.50  0.74   3   4     1  0.00    -2.44
bs.180             3 4  2.25  0.96    2.5    2.25  0.74   1   3     2 -0.32    -2.08
pop.shove          4 4  2.75  0.50    3.0    2.75  0.00   2   3     1 -0.75    -1.69
fs.shove           5 4  0.00  0.00    0.0    0.00  0.00   0   0     0   NaN      NaN
kickflip           6 4  0.00  0.00    0.0    0.00  0.00   0   0     0   NaN      NaN
heelflip           7 4  0.00  0.00    0.0    0.00  0.00   0   0     0   NaN      NaN
f.180              8 4  1.50  1.29    1.5    1.50  1.48   0   3     3  0.00    -2.08
f.ollie            9 4  4.00  0.00    4.0    4.00  0.00   4   4     0   NaN      NaN
f.shove           10 4  4.00  0.82    4.0    4.00  0.74   3   5     2  0.00    -1.88
f.fs.shove        11 4  2.00  0.82    2.0    2.00  0.74   1   3     2  0.00    -1.88
sw.fs.bone        12 4  4.00  0.82    4.0    4.00  0.74   3   5     2  0.00    -1.88
sw.fs.no.comply   13 4  2.50  0.58    2.5    2.50  0.74   2   3     1  0.00    -2.44
f.bigspin         14 4  1.00  0.00    1.0    1.00  0.00   1   1     0   NaN      NaN
sw.bone           15 4  4.50  0.58    4.5    4.50  0.74   4   5     1  0.00    -2.44
halfcab           16 4  4.00  0.82    4.0    4.00  0.74   3   5     2  0.00    -1.88
fingerflip        17 4  2.25  0.96    2.5    2.25  0.74   1   3     2 -0.32    -2.08
sw.no.cmp         18 4  3.50  1.29    3.5    3.50  1.48   2   5     3  0.00    -2.08
date*             19 4 14.50 10.85   13.5   14.50 11.86   4  27    23  0.11    -2.27
place*            20 4  6.00  0.00    6.0    6.00  0.00   6   6     0   NaN      NaN
randomized*       21 4  1.50  0.58    1.5    1.50  0.74   1   2     1  0.00    -2.44
board*            22 4  1.50  0.58    1.5    1.50  0.74   1   2     1  0.00    -2.44
                  se
ollie           0.00
fs.180          0.29
bs.180          0.48
pop.shove       0.25
fs.shove        0.00
kickflip        0.00
heelflip        0.00
f.180           0.65
f.ollie         0.00
f.shove         0.41
f.fs.shove      0.41
sw.fs.bone      0.41
sw.fs.no.comply 0.29
f.bigspin       0.00
sw.bone         0.29
halfcab         0.41
fingerflip      0.48
sw.no.cmp       0.65
date*           5.42
place*          0.00
randomized*     0.29
board*          0.29

# 0 means old skateboard Nocto - it's broken now and 1 one means Darcy the new skateboard
describeBy(skate_df, group = skate_df$board)

 Descriptive statistics by group 
group: 0
                vars  n mean   sd median trimmed  mad min max range  skew kurtosis
ollie              1 15 4.87 0.52      5    5.00 0.00   3   5     2 -3.13     8.39
fs.180             2 15 3.13 1.06      3    3.15 1.48   1   5     4 -0.24    -0.86
bs.180             3 15 2.87 1.30      3    2.85 1.48   1   5     4 -0.14    -1.44
pop.shove          4 15 2.27 1.16      2    2.31 1.48   0   4     4 -0.23    -1.04
fs.shove           5 15 0.00 0.00      0    0.00 0.00   0   0     0   NaN      NaN
kickflip           6 15 0.00 0.00      0    0.00 0.00   0   0     0   NaN      NaN
heelflip           7 15 0.00 0.00      0    0.00 0.00   0   0     0   NaN      NaN
f.180              8 15 2.13 1.13      2    2.15 1.48   0   4     4 -0.52    -0.59
f.ollie            9 15 3.53 0.74      4    3.62 0.00   2   4     2 -1.08    -0.43
f.shove           10 15 3.60 1.12      4    3.62 1.48   2   5     3 -0.09    -1.50
f.fs.shove        11 15 1.33 0.82      1    1.31 1.48   0   3     3  0.14    -0.73
sw.fs.bone        12 15 4.67 0.49      5    4.69 0.00   4   5     1 -0.64    -1.69
sw.fs.no.comply   13 15 3.20 1.01      3    3.15 1.48   2   5     3  0.40    -1.08
f.bigspin         14 15 0.60 0.63      1    0.54 1.48   0   2     2  0.44    -0.95
sw.bone           15 15 4.27 1.03      5    4.38 0.00   2   5     3 -1.22     0.23
halfcab           16 15 4.13 0.92      4    4.23 1.48   2   5     3 -0.76    -0.40
fingerflip        17 15 2.80 1.15      3    2.77 1.48   1   5     4  0.10    -0.98
sw.no.cmp         18 15 2.87 1.13      3    2.85 1.48   1   5     4 -0.04    -0.85
date*             19 15 7.27 4.13      7    7.23 4.45   1  14    13  0.12    -1.44
place*            20 15 3.20 1.74      3    3.15 2.97   1   6     5  0.17    -1.27
randomized*       21 15 1.33 0.49      1    1.31 0.00   1   2     1  0.64    -1.69
board*            22 15 1.00 0.00      1    1.00 0.00   1   1     0   NaN      NaN
                  se
ollie           0.13
fs.180          0.27
bs.180          0.34
pop.shove       0.30
fs.shove        0.00
kickflip        0.00
heelflip        0.00
f.180           0.29
f.ollie         0.19
f.shove         0.29
f.fs.shove      0.21
sw.fs.bone      0.13
sw.fs.no.comply 0.26
f.bigspin       0.16
sw.bone         0.27
halfcab         0.24
fingerflip      0.30
sw.no.cmp       0.29
date*           1.07
place*          0.45
randomized*     0.13
board*          0.00
--------------------------------------------------------------- 
group: 1
                vars  n  mean   sd median trimmed  mad min max range  skew kurtosis
ollie              1 14  5.00 0.00    5.0    5.00 0.00   5   5     0   NaN      NaN
fs.180             2 14  3.36 0.84    3.0    3.33 1.48   2   5     3  0.06    -0.86
bs.180             3 14  3.07 1.00    3.0    3.08 0.74   1   5     4 -0.13    -0.32
pop.shove          4 14  2.07 1.21    2.0    2.08 1.48   0   4     4 -0.37    -1.08
fs.shove           5 14  0.50 0.76    0.0    0.42 0.00   0   2     2  0.98    -0.67
kickflip           6 14  0.00 0.00    0.0    0.00 0.00   0   0     0   NaN      NaN
heelflip           7 14  0.00 0.00    0.0    0.00 0.00   0   0     0   NaN      NaN
f.180              8 14  1.93 1.00    2.0    2.00 1.48   0   3     3 -0.30    -1.31
f.ollie            9 14  4.00 1.04    4.0    4.08 1.48   2   5     3 -0.77    -0.66
f.shove           10 14  3.64 0.74    3.5    3.58 0.74   3   5     2  0.58    -1.13
f.fs.shove        11 14  1.71 0.99    2.0    1.75 1.48   0   3     3 -0.34    -1.08
sw.fs.bone        12 14  4.36 1.01    5.0    4.50 0.00   2   5     3 -1.10    -0.29
sw.fs.no.comply   13 14  3.14 1.10    3.0    3.17 1.48   1   5     4 -0.26    -1.01
f.bigspin         14 14  0.79 0.80    1.0    0.75 1.48   0   2     2  0.35    -1.48
sw.bone           15 14  4.43 0.76    5.0    4.50 0.00   3   5     2 -0.77    -0.96
halfcab           16 14  4.71 0.61    5.0    4.83 0.00   3   5     2 -1.72     1.72
fingerflip        17 14  2.64 1.39    2.0    2.58 1.48   1   5     4  0.44    -1.27
sw.no.cmp         18 14  3.29 0.99    3.5    3.25 0.74   2   5     3 -0.10    -1.46
date*             19 14 21.50 4.18   21.5   21.50 5.19  15  28    13  0.00    -1.46
place*            20 14  3.21 1.89    3.0    3.17 2.97   1   6     5  0.16    -1.60
randomized*       21 14  2.00 0.00    2.0    2.00 0.00   2   2     0   NaN      NaN
board*            22 14  2.00 0.00    2.0    2.00 0.00   2   2     0   NaN      NaN
                  se
ollie           0.00
fs.180          0.23
bs.180          0.27
pop.shove       0.32
fs.shove        0.20
kickflip        0.00
heelflip        0.00
f.180           0.27
f.ollie         0.28
f.shove         0.20
f.fs.shove      0.27
sw.fs.bone      0.27
sw.fs.no.comply 0.29
f.bigspin       0.21
sw.bone         0.20
halfcab         0.16
fingerflip      0.37
sw.no.cmp       0.27
date*           1.12
place*          0.50
randomized*     0.00
board*          0.00

# the codebook has more information about what O and 1 means
describeBy(skate_df, group = skate_df$randomized)

 Descriptive statistics by group 
group: 0
                vars  n mean   sd median trimmed  mad min max range  skew kurtosis
ollie              1 10  4.8 0.63    5.0    5.00 0.00   3   5     2 -2.28     3.57
fs.180             2 10  3.3 0.95    3.0    3.25 1.48   2   5     3  0.17    -1.17
bs.180             3 10  2.6 1.17    2.5    2.62 1.48   1   4     3 -0.03    -1.68
pop.shove          4 10  2.3 1.34    2.5    2.38 1.48   0   4     4 -0.24    -1.40
fs.shove           5 10  0.0 0.00    0.0    0.00 0.00   0   0     0   NaN      NaN
kickflip           6 10  0.0 0.00    0.0    0.00 0.00   0   0     0   NaN      NaN
heelflip           7 10  0.0 0.00    0.0    0.00 0.00   0   0     0   NaN      NaN
f.180              8 10  2.0 0.94    2.0    2.12 0.74   0   3     3 -0.72    -0.47
f.ollie            9 10  3.6 0.70    4.0    3.75 0.00   2   4     2 -1.19    -0.07
f.shove           10 10  3.8 0.79    4.0    3.75 1.48   3   5     2  0.29    -1.50
f.fs.shove        11 10  1.2 0.79    1.0    1.25 1.48   0   2     2 -0.29    -1.50
sw.fs.bone        12 10  4.6 0.52    5.0    4.62 0.00   4   5     1 -0.35    -2.06
sw.fs.no.comply   13 10  3.5 1.08    3.5    3.50 0.74   2   5     3  0.00    -1.48
f.bigspin         14 10  0.7 0.67    1.0    0.62 0.74   0   2     2  0.31    -1.14
sw.bone           15 10  4.2 0.92    4.0    4.38 0.74   2   5     3 -1.11     0.52
halfcab           16 10  4.1 0.74    4.0    4.12 0.74   3   5     2 -0.12    -1.35
fingerflip        17 10  2.7 1.16    3.0    2.75 1.48   1   4     3 -0.25    -1.57
sw.no.cmp         18 10  3.1 1.10    3.0    3.12 0.74   1   5     4 -0.17    -0.60
date*             19 10  5.8 2.66    5.5    5.75 2.97   2  10     8  0.16    -1.53
place*            20 10  3.1 1.85    3.0    3.00 2.22   1   6     5  0.34    -1.30
randomized*       21 10  1.0 0.00    1.0    1.00 0.00   1   1     0   NaN      NaN
board*            22 10  1.0 0.00    1.0    1.00 0.00   1   1     0   NaN      NaN
                  se
ollie           0.20
fs.180          0.30
bs.180          0.37
pop.shove       0.42
fs.shove        0.00
kickflip        0.00
heelflip        0.00
f.180           0.30
f.ollie         0.22
f.shove         0.25
f.fs.shove      0.25
sw.fs.bone      0.16
sw.fs.no.comply 0.34
f.bigspin       0.21
sw.bone         0.29
halfcab         0.23
fingerflip      0.37
sw.no.cmp       0.35
date*           0.84
place*          0.59
randomized*     0.00
board*          0.00
--------------------------------------------------------------- 
group: 1
                vars  n  mean   sd median trimmed  mad min max range  skew kurtosis
ollie              1 19  5.00 0.00      5    5.00 0.00   5   5     0   NaN      NaN
fs.180             2 19  3.21 0.98      3    3.24 1.48   1   5     4 -0.40    -0.49
bs.180             3 19  3.16 1.12      3    3.18 1.48   1   5     4 -0.29    -0.56
pop.shove          4 19  2.11 1.10      2    2.12 1.48   0   4     4 -0.43    -0.80
fs.shove           5 19  0.37 0.68      0    0.29 0.00   0   2     2  1.44     0.58
kickflip           6 19  0.00 0.00      0    0.00 0.00   0   0     0   NaN      NaN
heelflip           7 19  0.00 0.00      0    0.00 0.00   0   0     0   NaN      NaN
f.180              8 19  2.05 1.13      2    2.06 1.48   0   4     4 -0.32    -1.04
f.ollie            9 19  3.84 1.01      4    3.88 1.48   2   5     3 -0.61    -0.79
f.shove           10 19  3.53 1.02      3    3.53 1.48   2   5     3  0.08    -1.25
f.fs.shove        11 19  1.68 0.95      2    1.71 1.48   0   3     3 -0.13    -1.06
sw.fs.bone        12 19  4.47 0.90      5    4.59 0.00   2   5     3 -1.42     0.77
sw.fs.no.comply   13 19  3.00 1.00      3    3.00 1.48   1   5     4  0.00    -0.79
f.bigspin         14 19  0.68 0.75      1    0.65 1.48   0   2     2  0.52    -1.16
sw.bone           15 19  4.42 0.90      5    4.53 0.00   2   5     3 -1.29     0.50
halfcab           16 19  4.58 0.84      5    4.71 0.00   2   5     3 -1.85     2.47
fingerflip        17 19  2.74 1.33      2    2.71 1.48   1   5     4  0.46    -1.08
sw.no.cmp         18 19  3.05 1.08      3    3.06 1.48   1   5     4 -0.10    -1.25
date*             19 19 18.53 6.70     19   19.00 7.41   1  28    27 -0.69     0.14
place*            20 19  3.26 1.79      3    3.24 2.97   1   6     5  0.07    -1.51
randomized*       21 19  2.00 0.00      2    2.00 0.00   2   2     0   NaN      NaN
board*            22 19  1.74 0.45      2    1.76 0.00   1   2     1 -0.99    -1.06
                  se
ollie           0.00
fs.180          0.22
bs.180          0.26
pop.shove       0.25
fs.shove        0.16
kickflip        0.00
heelflip        0.00
f.180           0.26
f.ollie         0.23
f.shove         0.23
f.fs.shove      0.22
sw.fs.bone      0.21
sw.fs.no.comply 0.23
f.bigspin       0.17
sw.bone         0.21
halfcab         0.19
fingerflip      0.30
sw.no.cmp       0.25
date*           1.54
place*          0.41
randomized*     0.00
board*          0.10

After dissecting the data set a bit. Not much cleaning is needed since i preprocessed the columns in pandas especially the date column. Notice, i did some backfilling which wasn’t really necessary but i didn’t want to lose that row. So to avoid losing it, i pushed the date “backwards” 2016-07-29 appears twice; A necessary conversion. I also wanted to make different variations of the data set; We’re gonna side step that for now. Let’s draw some plots. :)

# where did i skate the most? describe already told us this.
# Why is it decimals and not whole numbers?
ggplot(skate_df, aes(x = place)) + geom_bar(fill = "black", col = "black") +
  labs(x = "place", y = "number of times i skated",
title = "Plot of how many times i skated somewhere", caption = "mostly marist") + theme_classic()

# theme_classic overides the classic ggplot theme

The funnest part of the analysis beckons. Taking the columns of interest doing basic math finding the sum of each column representing a trick being done in sum1 variable. Then dividing what we’ll get with total if i got the tricks right all the time. So that makes the number 145(29 times 5), then we’ll divide each item by 145 (see the variable prob_trick). Furthermore, we’ll find the standard deviation for each item using sapply - which applies a function to the columns we just made (var_trick)

# select the columns to use later in the analysis
skate_df_cols <- skate_df %>% select(ollie, fs.180, bs.180, pop.shove,fs.shove, kickflip,        heelflip,f.180,f.ollie,f.shove,f.fs.shove, sw.fs.bone, sw.fs.no.comply,f.bigspin,          sw.bone,halfcab,fingerflip,sw.no.cmp)
(sum1 <- skate_df_cols %>% summarise_all(funs(sum))) # find sum of each trick

(sds <- skate_df_cols %>% summarise_all(funs(sd))) # use brackets instead of calling the 

# variable a second time or using a print statement
class(sum1) # what's the class of the resultant data set

[1] "tbl_df"     "tbl"        "data.frame"

# This function divides x being the row item that is, the trick ollie
# by the sum of trick scores if i got them all down. 
divide <- function(x,y){
  divide_by(x,145)
}
sum2 <- sapply(sum1,divide)
#prob_trick <- round(sort(sum2,decreasing = TRUE),2) another way of doing it
prob_trick <- sum2 %>% sort(decreasing = TRUE) %>% round(2)
# we can't do much with standard deviation so let's try what we've done for prob_trick
sds2 <- sapply(skate_df_cols,sd)
#var_trick <- round(sort(sum2,decreasing = TRUE),2))
(var_trick <- sds2 %>% sort() %>% round(2))

       kickflip        heelflip           ollie        fs.shove       f.bigspin 
           0.00            0.00            0.37            0.58            0.71 
     sw.fs.bone         halfcab         sw.bone      f.fs.shove         f.ollie 
           0.78            0.82            0.90            0.91            0.91 
        f.shove          fs.180 sw.fs.no.comply           f.180       sw.no.cmp 
           0.94            0.95            1.04            1.05            1.07 
         bs.180       pop.shove      fingerflip 
           1.15            1.17            1.25 

med <- sapply(skate_df_cols,median) # calculating medians again
#med_trick <- round(sort(med,decreasing = TRUE),2))
(med_trick <- med %>% sort(decreasing = TRUE))

          ollie      sw.fs.bone         sw.bone         halfcab         f.ollie 
              5               5               5               5               4 
        f.shove          fs.180          bs.180 sw.fs.no.comply      fingerflip 
              4               3               3               3               3 
      sw.no.cmp       pop.shove           f.180      f.fs.shove       f.bigspin 
              3               2               2               2               1 
       fs.shove        kickflip        heelflip 
              0               0               0 

prob_trick # we're comparing what we got to see if this can tell me what trick i should have

          ollie      sw.fs.bone         halfcab         sw.bone         f.ollie 
           0.99            0.90            0.88            0.87            0.75 
        f.shove          fs.180 sw.fs.no.comply       sw.no.cmp          bs.180 
           0.72            0.65            0.63            0.61            0.59 
     fingerflip       pop.shove           f.180      f.fs.shove       f.bigspin 
           0.54            0.43            0.41            0.30            0.14 
       fs.shove        kickflip        heelflip 
           0.05            0.00            0.00 

# more confidence in games of S.K.A.T.E or H.O.R.S.E.
# So from this, this is the arrangement we're getting from probabilities but the median
# gives a tie, the plots could give us a clues how to proceed or confidence intervals
trick_line_up <- c('ollie','sw.fs.bone','halfcab','sw.bone','f.ollie','f.shove','fs.180','bs.180','sw.fs.no.comply','sw.no.cmp','bs.180','fingerflip','pop.shove','f.180','f.fs.shove','f.bigspin','fs.shove')

We can’t use multiplot function from the scater package by Davis McCarthy and the grid.arrange() function from gridExtra shrinks the plots and we won’t be able to see the plots nicely. This one by one, approach is not good for comparison. If you have a better way of doing this holler! Before, i used par which did it very well.

# trying to make a plot with summary stats
#plot(skate$fs180, main = "Freq of Fs180 as days passed", xlab = "day",ylab = "Frequency",type = "both")
ggplot(skate_df, aes(x = ymd(date), y = ollie))  + geom_point() +
  geom_line() + geom_hline(data = skate_df, aes(yintercept = median(ollie), color = "red")) + labs(x = "day", y = "Frequency",
title = "Freq of ollie as days passed", caption = "More confident to start with Ollie") + scale_color_manual(name = "median", values = "red") + theme_classic()

ggplot(skate_df, aes(x = ymd(date), y = fs.180))  + geom_point() +
  geom_line() + geom_hline(data = skate_df, aes(yintercept = median(fs.180), col = "red")) + labs(x = "day", y = "Frequency",
title = "Freq of fs.180 as days passed", caption = "fs.180 is shaky")  + scale_color_manual(name = "median", values = "red") + theme_classic()

ggplot(skate_df, aes(x = ymd(date), y = bs.180))  + geom_point() +
  geom_line() + geom_hline(data = skate_df, aes(yintercept = median(bs.180), col = "red")) + labs(x = "day", y = "Frequency",
title = "Freq of bs.180 as days passed", caption = "bs.180 are a bit similiar median is equal")  + scale_color_manual(name = "median", values = "red") + theme_classic()

# we can use grid arrange on a separate code chunks
#-----------------------------------------------------------------------------------------
# it's time we made a function
# Instead of writing the code over and over again i made a function that just 
# requires you to type something y_axis_label (The y axis coordinate), y_intercept_med(the median of the trick), title_of_graph and caption_of_graph
plot_gg_tricks <- function(y_axis_label,y_intercept_med,title_of_graph = readline(), caption_of_graph = readline()){
  ggplot(skate_df, aes(x = ymd(date), y = y_axis_label))  + geom_point() +
  geom_line() + geom_hline(data = skate_df, aes(yintercept = median(y_intercept_med), col = "red")) + labs(x = "day", y = "Frequency",
title = title_of_graph, caption = caption_of_graph) + scale_color_manual(name = "median", values = "red") + theme_classic()
}
# pop.shove
plot_gg_tricks(y_axis_label = skate_df$pop.shove,y_intercept_med = skate_df$pop.shove,"Freq of pop.shove as days passed", "pop.shove come along way not so confident with this.")

#fs.shove
plot_gg_tricks(y_axis_label = skate_df$fs.shove,y_intercept_med = skate_df$fs.shove,"Freq of fs.shove as days passed", "fs.shove still learning it notice the many zeros at the start.")

#kickflip
plot_gg_tricks(y_axis_label = skate_df$kickflip,y_intercept_med = skate_df$kickflip,"Freq of kickflip as days passed", "kickflip didn't do any")

# heelflip
plot_gg_tricks(y_axis_label = skate_df$heelflip,y_intercept_med = skate_df$heelflip,"Freq of heelflip as days passed", "heelflip didn't do any")

# f.180
plot_gg_tricks(y_axis_label = skate_df$f.180,y_intercept_med = skate_df$f.180,"Freq of f.180 as days passed", "f.180 is confusing summary statistics to the rescue.")

#f.ollie
plot_gg_tricks(y_axis_label = skate_df$f.ollie,y_intercept_med = skate_df$f.ollie,"Freq of f.ollie as days passed", "f.ollie i'm improving at this one of my top 5 tricks")

# f.shove
plot_gg_tricks(y_axis_label = skate_df$f.shove,y_intercept_med = skate_df$f.shove,"Freq of f.shove as days passed", "f.shove looking good.")

#f.fs.shove
plot_gg_tricks(y_axis_label = skate_df$f.fs.shove,y_intercept_med = skate_df$f.fs.shove,"Freq of f.fs.shove as days passed", "f.fs.shove learning this one as well never done it more than thrice.")

# sw.fs.bone
plot_gg_tricks(y_axis_label = skate_df$sw.fs.bone,y_intercept_med = skate_df$sw.fs.bone,"Freq of sw.fs.bone as days passed", "sw.fs.bone looks like a top 5 trick.")

# sw.fs.no.comply
plot_gg_tricks(y_axis_label = skate_df$sw.fs.no.comply,y_intercept_med = skate_df$sw.fs.no.comply,"Freq of sw.fs.no.comply as days passed", "sw.fs.no.comply seems that i can improve this")

#f.bigspin
plot_gg_tricks(y_axis_label = skate_df$f.bigspin,y_intercept_med = skate_df$f.bigspin,"Freq of f.bigspin as days passed", "f.bigspin very bad at these max times done 2.")

#sw.bone
plot_gg_tricks(y_axis_label = skate_df$sw.bone,y_intercept_med = skate_df$sw.bone,"Freq of sw.bone as days passed", "sw.bone another good trick to start with look at those 5's")

#halfcab
plot_gg_tricks(y_axis_label = skate_df$halfcab,y_intercept_med = skate_df$halfcab,"Freq of halfcab as days passed", "halfcab started shaky but stabilizing slowly need more data.")

# fingerflip
plot_gg_tricks(y_axis_label = skate_df$fingerflip,y_intercept_med = skate_df$fingerflip,"Freq of fingerflip as days passed", "fingerflip i learned this from playing a videogame and did it.")

#sw.no.cmp
plot_gg_tricks(y_axis_label = skate_df$sw.no.cmp, y_intercept_med = skate_df$sw.no.cmp, "Freq of sw.no.cmp as days passed", "sw.no.cmp looks like i'm better off doing this first than the fingerflip.")

Graphical statistical analysis

The data doesn’t meet the requirements of the central limit theorem but we can try make confidence intervals with boot strapping(sampling within a sample) to see what we get especially where they were ties. About what trick to do next. Let me try with ggplot2. We’ll use summary functions from the Hmisc package specifically the smean.cl.boot function and smedian.hilow. If you’re not acquainted to these functions use this in the console to find out what they do ?smean.cl.boot and ?smedian.hilow. The green line represents the range plausible range of means if we did this study again 1000 times for example sw.fs.bone(switch frontside bone) between 4 and 5 times whereas, the green dot represents the mean. This is so cool!

# remember we made this, let's see if it still holds
(trick_line_up)

 [1] "ollie"           "sw.fs.bone"      "halfcab"         "sw.bone"        
 [5] "f.ollie"         "f.shove"         "fs.180"          "bs.180"         
 [9] "sw.fs.no.comply" "sw.no.cmp"       "bs.180"          "fingerflip"     
[13] "pop.shove"       "f.180"           "f.fs.shove"      "f.bigspin"      
[17] "fs.shove"       

# this is a graphical statistical visualization
# according to the documentation mean.cl.boot is a very fast implementation of the basic nonparametric bootstrap for obtaining confidence limits for the population mean without assuming normality
# I don't recommend this test since i'm getting errors about the geom_pointrange not being
# specified. If you have a better of doing this let me know. The fastest way to reach me is twitter or youtube
plot_gg_tricks2 <- function(y_axis_label,title_of_graph = readline(), caption_of_graph = readline()){
  ggplot(skate_df, aes(x = ymd(date), y = y_axis_label))  + geom_point() +
  geom_line() +  stat_summary(fun.data = mean_cl_boot, col = "green") + labs(x = "day", y = "Frequency", title = title_of_graph, caption = caption_of_graph) + theme_classic()
}
# Less repetition i suppose and typing
# You know the drill, this time it will based on the tricks that we found are good start
# with based on median and probability
# ollie
# Cl means confidence interval i'm using 0.95 the default and bootstrapped 1000 times
plot_gg_tricks2(y_axis_label = skate_df$ollie, title_of_graph = 'Freq of ollie as days passed with mean CI',caption_of_graph = "start with ollie got it.")

# sw.fs.bone
plot_gg_tricks2(y_axis_label = skate_df$sw.fs.bone, title_of_graph = 'Freq of ollie as days passed with mean CI',caption_of_graph = "Then do a sw.fs.bone.")

# sw.bone
plot_gg_tricks2(y_axis_label = skate_df$sw.bone, title_of_graph = 'Freq of sw.bone as days passed with mean CI',caption_of_graph = "so we have a tie, i'm more confident with sw.fs.bone.")

# f.ollie
plot_gg_tricks2(y_axis_label = skate_df$f.ollie, title_of_graph = 'Freq of f.ollie as days passed with mean CI',caption_of_graph = "i had a feeling this one of my top 5.")

# f.shove
plot_gg_tricks2(y_axis_label = skate_df$f.shove, title_of_graph = 'Freq of f.shove as days passed with mean CI',caption_of_graph = "yup, this one should be next after f.ollie.")

# fs.180
plot_gg_tricks2(y_axis_label = skate_df$fs.180, title_of_graph = 'Freq of fs.180 as days passed with mean CI',caption_of_graph = "I learned this first, so naturally, i do it first.")

# bs.180
plot_gg_tricks2(y_axis_label = skate_df$bs.180, title_of_graph = 'Freq of bs.180 as days passed with mean CI',caption_of_graph = "either way sort of trick, can do between 2/4.")

# sw.fs.no.comply
plot_gg_tricks2(y_axis_label = skate_df$sw.fs.no.comply, title_of_graph = 'Freq of sw.fs.no.comply as days passed with mean CI',caption_of_graph = "ties need more data for this one.")

# sw.no.comp
plot_gg_tricks2(y_axis_label = skate_df$sw.no.cmp, title_of_graph = 'Freq of sw.no.cmp as days passed with mean CI',caption_of_graph = "ties need more data for this one.")

# fingerflip
plot_gg_tricks2(y_axis_label = skate_df$fingerflip, title_of_graph = 'Freq of fingerflip as days passed with mean CI',caption_of_graph = "needs more data.")

# pop shove
plot_gg_tricks2(y_axis_label = skate_df$pop.shove, title_of_graph = 'Freq of pop.shove as days passed with mean CI',caption_of_graph = "pop shove is better than my since the limits are better numbers.")

# f.180
plot_gg_tricks2(y_axis_label = skate_df$f.180, title_of_graph = 'Freq of f.180 as days passed with mean CI',caption_of_graph = "you can do it twice with confidence.")

# f.fs.shove
plot_gg_tricks2(y_axis_label = skate_df$f.fs.shove, title_of_graph = 'Freq of f.fs.shove as days passed with mean CI',caption_of_graph = "this trick belongs here. I concur with this.")

# f.bigspin
plot_gg_tricks2(y_axis_label = skate_df$f.bigspin, title_of_graph = 'Freq of f.bigspin as days passed with mean CI',caption_of_graph = "Needs more training.")

# fs.shove
plot_gg_tricks2(y_axis_label = skate_df$fs.shove, title_of_graph = 'Freq of fs.shove as days passed with mean CI',caption_of_graph = "yeah, this one should be the last.Those zeros prove it.")

# do for the trick tie breakers that is smedian.hilow, with quantile calculation 
# we're expecting less variability is better
# read the docs abouts smedidan.hilow()
# I wanted to do this test instead check it out ?wilcoxtest()
print("ollie");smedian.hilow(skate_df$ollie) #1

[1] "ollie"
Median  Lower  Upper 
   5.0    4.4    5.0 

print("sw.fs.bone"); smedian.hilow(skate_df$sw.fs.bone) #2

[1] "sw.fs.bone"
Median  Lower  Upper 
   5.0    2.7    5.0 

print("halfcab"); smedian.hilow(skate_df$halfcab) #3

[1] "halfcab"
Median  Lower  Upper 
   5.0    2.7    5.0 

print("sw.bone"); smedian.hilow(skate_df$sw.bone) #4

[1] "sw.bone"
Median  Lower  Upper 
     5      2      5 

print("f.ollie"); smedian.hilow(skate_df$f.ollie) #5

[1] "f.ollie"
Median  Lower  Upper 
     4      2      5 

print("f.shove"); smedian.hilow(skate_df$f.shove) #6

[1] "f.shove"
Median  Lower  Upper 
     4      2      5 

print("fs.180"); smedian.hilow(skate_df$fs.180) #7

[1] "fs.180"
Median  Lower  Upper 
   3.0    1.7    5.0 

print("bs.180"); smedian.hilow(skate_df$bs.180) #8

[1] "bs.180"
Median  Lower  Upper 
     3      1      5 

print("sw.fs.no.comply"); smedian.hilow(skate_df$sw.fs.no.comply) #8

[1] "sw.fs.no.comply"
Median  Lower  Upper 
   3.0    1.7    5.0 

print("sw.no.cmp"); smedian.hilow(skate_df$sw.no.cmp) #9

[1] "sw.no.cmp"
Median  Lower  Upper 
     3      1      5 

print("fingerflip"); smedian.hilow(skate_df$fingerflip) #10

[1] "fingerflip"
Median  Lower  Upper 
     3      1      5 

print("pop.shove"); smedian.hilow(skate_df$pop.shove) #12

[1] "pop.shove"
Median  Lower  Upper 
     2      0      4 

print("f.180"); smedian.hilow(skate_df$f.180) #11

[1] "f.180"
Median  Lower  Upper 
   2.0    0.0    3.3 

print("f.fs.shove"); smedian.hilow(skate_df$f.fs.shove) #13

[1] "f.fs.shove"
Median  Lower  Upper 
     2      0      3 

print("f.bigspin"); smedian.hilow(skate_df$f.bigspin) #14

[1] "f.bigspin"
Median  Lower  Upper 
     1      0      2 

print("fs.shove"); smedian.hilow(skate_df$fs.shove) #15

[1] "fs.shove"
Median  Lower  Upper 
     0      0      2 

Conclusion

One interesting thing, i should point out is that I’m not confident with the following tricks fakie bigspin(f.bigspin), frontside shove(fs.shove) and sometimes fakie frontside shove(f.fs.shove). Yet with our numerous tests this is evident. We used data to confirm this! That makes me excited. Otherwise, after inspecting everything this is how i think my line up of tricks should be with some freedom to alternate ollie,sw.fs.bone,halfcab,sw.bone,f.ollie,f.shove,fs.180,bs.180,sw.fs.no.comply,sw.no.cmp,fingerflip,pop.shove,f.180,f.fs.shove, f.bigspin, fs.shove. I’ll proceed to test this line up with two constraints just flatground at marist lane and with a game of S.K.A.T.E with a friend. Thinking of doing tests that involve p values soon.

References

R Core Team (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

Revelle, W. (2017) psych: Procedures for Personality and Psychological Research, Northwestern University, Evanston, Illinois, USA,https://CRAN.R-project.org/package=psych Version = 1.7.5.

H.Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2009.

Stefan Milton Bache and Hadley Wickham (2014). magrittr: A Forward-Pipe Operator for R. R package version 1.5. https://CRAN.R-project.org/package=magrittr.

Hadley Wickham, Romain Francois, Lionel Henry and Kirill Müller (2017). dplyr: A Grammar of Data Manipulation. R package version 0.7.2. https://CRAN.R-project.org/package=dplyr.

Garrett Grolemund, Hadley Wickham (2011). Dates and Times Made Easy with lubridate. Journal of Statistical Software, 40(3), 1-25. URL http://www.jstatsoft.org/v40/i03/.

Hadley Wickham and Lionel Henry (2017). tidyr: Easily Tidy Data with ‘spread()’ and ‘gather()’ Functions. R package version 0.7.0. https://CRAN.R-project.org/package=tidyr

Frank E Harrell Jr, with contributions from Charles Dupont and many others. (2017). Hmisc: Harrell Miscellaneous. R package version 4.0-3. https://CRAN.R-project.org/package=Hmisc

Baptiste Auguie (2016). gridExtra: Miscellaneous Functions for “Grid” Graphics. R package version 2.2.1. https://CRAN.R-project.org/package=gridExtra

https://www.datacamp.com/courses/data-visualization-with-ggplot2-1

https://www.datacamp.com/courses/data-visualization-with-ggplot2-2

https://stackoverflow.com/questions/26890354/lineplot-legend-abline-ggplot

https://stats.stackexchange.com/questions/112829/how-do-i-calculate-confidence-intervals-for-a-non-normal-distribution

https://stackoverflow.com/questions/5226807/multiple-graphs-in-one-canvas-using-ggplot2

https://github.com/Shuyib/datasharing

Session information

sessionInfo()

R version 3.3.2 (2016-10-31)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: OS X El Capitan 10.11.6

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     

other attached packages:
 [1] gridExtra_2.2.1 Hmisc_4.0-3     Formula_1.2-2   survival_2.41-3 lattice_0.20-35
 [6] tidyr_0.7.1     lubridate_1.6.0 dplyr_0.7.2     magrittr_1.5    ggplot2_2.2.1  
[11] psych_1.7.5    

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.12        RColorBrewer_1.1-2  plyr_1.8.4          bindr_0.1          
 [5] base64enc_0.1-3     tools_3.3.2         rpart_4.1-11        digest_0.6.12      
 [9] checkmate_1.8.3     htmlTable_1.9       jsonlite_1.5        evaluate_0.10.1    
[13] tibble_1.3.4        gtable_0.2.0        nlme_3.1-131        pkgconfig_2.0.1    
[17] rlang_0.1.2         Matrix_1.2-11       yaml_2.1.14         parallel_3.3.2     
[21] bindrcpp_0.2        cluster_2.0.6       stringr_1.2.0       knitr_1.17         
[25] htmlwidgets_0.9     nnet_7.3-12         rprojroot_1.2       grid_3.3.2         
[29] data.table_1.10.4   glue_1.1.1          R6_2.2.2            foreign_0.8-69     
[33] rmarkdown_1.6       latticeExtra_0.6-28 purrr_0.2.3         backports_1.1.0    
[37] scales_0.5.0        htmltools_0.3.6     splines_3.3.2       assertthat_0.2.0   
[41] mnormt_1.5-5        colorspace_1.3-2    labeling_0.3        stringi_1.1.5      
[45] acepack_1.4.1       lazyeval_0.2.0      munsell_0.4.3