What’s my Game?

library(tidyverse)
library(readxl)
library(lubridate)

# https://blogs.reed.edu/ed-tech/2015/10/creating-nice-tables-using-r-markdown/
library(knitr) #kable
#library(xtable) # xtable: generate LaTeX code to output table into PDF  

library(highcharter)

My Statistical Analysis for My Poker Statistics

This visualization is interactive in the way that the plots are responsive to the move of the curser, with corresponding information displayed.

1. Data Preparation

#filePath <- file.choose()
filePath <- "/Users/apple/Desktop/YuqingRWork/Poker/DesciplinePokerAtheletes.xlsx"  # 
GoogleSheet <- read_excel(filePath)

Testimate <- GoogleSheet %>% mutate(Date = ymd(Date), #sesStart = ymd_hms(sesStart), sesEnd = ymd_hms(sesEnd),
                             #Stake = as.integer(Stake),
                             sesLength = round( as.numeric(sesEnd - sesStart), 2), #directly make substraction between times
                             Profit = cashOut - buyIn, 
                             profitPerHr = Profit/sesLength,
                             totalProfit = promoBonus + Profit,
                             totalProfitPerHr = totalProfit/sesLength, 
                             accuHour = cumsum(sesLength),
                             accuProfit = cumsum(Profit), 
                             accuProfitPerHr = accuProfit/accuHour,
                             accuTotalProfit = cumsum(totalProfit),
                             accuTotalProfitPerHr = accuTotalProfit/accuHour) 

#options(tibble.width = Inf)  
#options(tibble.print_max = 15, tibble.print_min = 5) # if more than 15 rows, print 5 rows
#Testimate

# Day of Profit
#Testimate %>% filter(Day == "Fri") 



# pending problem: accuProfit is incorrect (still goes with the whole stat) in specific day checking

My performance on Monday:

## # A tibble: 14 x 7
##    Day   Stake sesStart            sesEnd              sesLength Profit
##    <chr> <dbl> <dttm>              <dttm>                  <dbl>  <dbl>
##  1 Mon     3   2019-03-04 12:25:00 2019-03-04 19:24:00      6.98   1240
##  2 Mon     3   2019-11-19 00:50:00 2019-11-19 04:57:00      4.12    881
##  3 Mon     3   2019-05-13 13:35:00 2019-05-13 18:06:00      4.52    782
##  4 Mon     3   2019-02-18 10:38:00 2019-02-18 18:00:00      7.37    570
##  5 Mon     2   2018-12-03 14:15:00 2018-12-03 19:25:00      5.17    551
##  6 Mon     3   2019-03-11 19:05:00 2019-03-12 02:30:00      7.42    515
##  7 Mon     2   2019-11-25 21:55:00 2019-11-26 00:33:00      2.63    408
##  8 Mon     2   2019-05-20 12:32:00 2019-05-20 15:35:00      3.05    314
##  9 Mon     2   2019-07-01 20:54:00 2019-07-02 02:35:00      5.68    277
## 10 Mon     2.9 2019-08-19 12:41:00 2019-08-19 17:18:00      4.62    239
## 11 Mon     3   2019-08-26 11:52:00 2019-08-26 17:06:00      5.23    177
## 12 Mon     2   2019-07-08 22:20:00 2019-07-09 05:43:00      7.38   -138
## 13 Mon     3   2019-06-24 16:19:00 2019-06-24 18:53:00      2.57   -450
## 14 Mon     2.6 2019-09-02 09:14:00 2019-09-02 14:53:00      5.65  -1278
## # ... with 1 more variable: profitPerHr <dbl>

Testimate %>% 
  select(Day, Stake, sesStart,  sesLength, Profit, profitPerHr) %>% 
    arrange(desc(profitPerHr))  #%>% View()

## # A tibble: 74 x 6
##    Day   Stake sesStart            sesLength Profit profitPerHr
##    <chr> <dbl> <dttm>                  <dbl>  <dbl>       <dbl>
##  1 Thu     3   2019-03-14 23:48:00      6.8    2320        341.
##  2 Fri     2.9 2018-12-14 22:24:00      3.93   1312        334.
##  3 Mon     3   2019-11-19 00:50:00      4.12    881        214.
##  4 Fri     2   2019-02-16 02:03:00      4.87    959        197.
##  5 Mon     3   2019-03-04 12:25:00      6.98   1240        178.
##  6 Mon     3   2019-05-13 13:35:00      4.52    782        173.
##  7 Fri     2.8 2019-08-30 19:21:00      3.63    568        156.
##  8 Thu     2.2 2018-12-06 21:50:00      3.22    501        156.
##  9 Mon     2   2019-11-25 21:55:00      2.63    408        155.
## 10 Wed     2   2019-06-05 21:28:00      2.95    391        133.
## # ... with 64 more rows

*Tibble output options https://r4ds.had.co.nz/tibbles.html 10.3.1

A separate data set to be analyzed: Session with “The Course” mindset

#Enlightened <- Testimate %>% filter(Date >= "2018-11-29") intuitive, but wrong, w.r.t cumulative values which are computed/had memory from overall dataset

EnlightenedEra <- GoogleSheet %>% mutate(Date = ymd(Date), #sesStart = ymd_hms(sesStart), sesEnd = ymd_hms(sesEnd),
                                  sesLength = round( as.numeric(sesEnd - sesStart), 2), #directly make substraction between times                             
                                  #Stake = as.integer(Stake),
                                  Profit = cashOut - buyIn, 
                                  profitPerHr = Profit/sesLength,
                                  totalProfit = promoBonus + Profit, # totalProfit add promotion earnings
                                  totalProfitPerHr = totalProfit/sesLength) %>% 
                                  filter(Date >= "2018-11-29") %>% mutate(accuHour = cumsum(sesLength),
                                                                   accuProfit = cumsum(Profit), 
                                                                   accuProfitPerHr = accuProfit/accuHour,
                                                                   accuTotalProfit = cumsum(totalProfit),
                                                                   accuTotalProfitPerHr = accuTotalProfit/accuHour) 
#options(tibble.width = Inf)  
#options(tibble.print_max = 15, tibble.print_min = 5) # if more than 15 rows, print 5 rows
EnlightenedEra

## # A tibble: 60 x 19
##    Date       Day   Stake Toughness sesStart            sesEnd             
##    <date>     <chr> <dbl>     <dbl> <dttm>              <dttm>             
##  1 2018-11-29 Thu     2           2 2018-11-29 20:44:00 2018-11-30 02:54:00
##  2 2018-11-30 Fri     3           3 2018-11-30 23:59:00 2018-12-01 03:45:00
##  3 2018-12-03 Mon     2           2 2018-12-03 14:15:00 2018-12-03 19:25:00
##  4 2018-12-06 Thu     2.2         3 2018-12-06 21:50:00 2018-12-07 01:03:00
##  5 2018-12-08 Fri     2.1         5 2018-12-08 00:48:00 2018-12-08 04:10:00
##  6 2018-12-12 Wed     2           3 2018-12-12 22:30:00 2018-12-13 03:15:00
##  7 2018-12-14 Fri     2.9         3 2018-12-14 22:24:00 2018-12-15 02:20:00
##  8 2018-12-15 Sat     2.2         3 2018-12-15 21:55:00 2018-12-16 04:55:00
##  9 2018-12-26 Wed     3           3 2018-12-26 21:45:00 2018-12-27 05:35:00
## 10 2019-01-03 Thu     2.4         2 2019-01-03 22:38:00 2019-01-04 06:28:00
## # ... with 50 more rows, and 13 more variables: buyIn <dbl>,
## #   promoBonus <dbl>, cashOut <dbl>, sesLength <dbl>, Profit <dbl>,
## #   profitPerHr <dbl>, totalProfit <dbl>, totalProfitPerHr <dbl>,
## #   accuHour <dbl>, accuProfit <dbl>, accuProfitPerHr <dbl>,
## #   accuTotalProfit <dbl>, accuTotalProfitPerHr <dbl>

# command+shift+C

EnlightenedEra %>% 
  select(Day, Stake, sesStart,  sesLength, Profit, profitPerHr) %>% 
    arrange(desc(Profit)) #%>% View() # arrange(desc(sesLength))

## # A tibble: 60 x 6
##    Day   Stake sesStart            sesLength Profit profitPerHr
##    <chr> <dbl> <dttm>                  <dbl>  <dbl>       <dbl>
##  1 Thu     3   2019-03-14 23:48:00      6.8    2320       341. 
##  2 Fri     2.9 2018-12-14 22:24:00      3.93   1312       334. 
##  3 Mon     3   2019-03-04 12:25:00      6.98   1240       178. 
##  4 Wed     2   2019-05-01 13:48:00      8.95    973       109. 
##  5 Fri     3   2020-01-05 02:50:00     10.4     963        92.4
##  6 Fri     2   2019-02-16 02:03:00      4.87    959       197. 
##  7 Mon     3   2019-11-19 00:50:00      4.12    881       214. 
##  8 Mon     3   2019-05-13 13:35:00      4.52    782       173. 
##  9 Tue     2   2019-05-07 19:52:00      9.83    640        65.1
## 10 Fri     3   2019-09-13 16:59:00      5.55    594       107. 
## # ... with 50 more rows

EnlightenedEra %>%
  select(Day, Stake, sesStart,  sesLength, Profit, profitPerHr) %>%
    filter(Stake > 2) %>% 
      arrange(desc(Profit))

## # A tibble: 35 x 6
##    Day   Stake sesStart            sesLength Profit profitPerHr
##    <chr> <dbl> <dttm>                  <dbl>  <dbl>       <dbl>
##  1 Thu     3   2019-03-14 23:48:00      6.8    2320       341. 
##  2 Fri     2.9 2018-12-14 22:24:00      3.93   1312       334. 
##  3 Mon     3   2019-03-04 12:25:00      6.98   1240       178. 
##  4 Fri     3   2020-01-05 02:50:00     10.4     963        92.4
##  5 Mon     3   2019-11-19 00:50:00      4.12    881       214. 
##  6 Mon     3   2019-05-13 13:35:00      4.52    782       173. 
##  7 Fri     3   2019-09-13 16:59:00      5.55    594       107. 
##  8 Mon     3   2019-02-18 10:38:00      7.37    570        77.3
##  9 Fri     2.8 2019-08-30 19:21:00      3.63    568       156. 
## 10 Mon     3   2019-03-11 19:05:00      7.42    515        69.4
## # ... with 25 more rows

Profit & total profit by day & by day per hour: Overall

options(tibble.width = Inf)  

Testimate %>% 
  group_by(Day) %>%
  summarise(
    count = n(),
    sesLengthByDay = round( mean(sesLength),2),
    profitByDay = round(mean(Profit),2),
    profitPerHrByDay = round(profitByDay/sesLengthByDay,2),
    totalProfitByDay = round(mean(totalProfit),2), # totalProfit including promotion earnings
    totalProfitPerHrByDay = round(totalProfitByDay/sesLengthByDay,2)
    ) %>% kable()

Day	count	sesLengthByDay	profitByDay	profitPerHrByDay	totalProfitByDay	totalProfitPerHrByDay
Feb	1	10.78	402.00	37.29	502.00	46.57
Fri	21	6.64	-26.43	-3.98	-26.43	-3.98
Mon	14	5.17	292.00	56.48	292.00	56.48
Sat	8	6.07	-189.12	-31.16	-114.12	-18.80
Thu	16	6.47	244.88	37.85	276.12	42.68
Tue	3	9.54	26.00	2.73	26.00	2.73
Wed	11	6.16	209.36	33.99	227.55	36.94

Pending: drop-down table, or straight go for Shiny.

Profit & total profit by day & by day per hour: Enlightened Era

options(tibble.width = Inf)  

EnlightenedEra %>% 
  group_by(Day) %>%
  summarise(
    count = n(),
    sesLengthByDay = round( mean(sesLength),2),
    profitByDay = round(mean(Profit),2),
    profitPerHrByDay = round(profitByDay/sesLengthByDay,2),
    totalProfitByDay = round(mean(totalProfit),2), # totalProfit including promotion earnings
    totalProfitPerHrByDay = round(totalProfitByDay/sesLengthByDay,2)
    ) %>% kable()

Day	count	sesLengthByDay	profitByDay	profitPerHrByDay	totalProfitByDay	totalProfitPerHrByDay
Feb	1	10.78	402.00	37.29	502.00	46.57
Fri	18	6.54	-16.56	-2.53	-16.56	-2.53
Mon	14	5.17	292.00	56.48	292.00	56.48
Sat	3	7.24	-568.00	-78.45	-468.00	-64.64
Thu	11	6.67	285.91	42.87	285.91	42.87
Tue	3	9.54	26.00	2.73	26.00	2.73
Wed	10	5.83	206.70	35.45	226.70	38.89

2. Interactive Visualization

Apply `htmlwidget` -> `highcharter`

Initially inspired by Philipp Spachtholz.

Package Author Joshua Kunst’s RPub https://rpubs.com/jbkunst/highcharter.

Additional `highcharter` learning material by Danton Noriega and Nana Boateng.

A.1 Session Profit (Overall)

#library(highcharter)

series = list(
  list(
    name = 'Total Profit for each session, stratified by stake (lemon chiffon: 1$/2$, peppermint: 1$/3$)',
    color = '#1E90FF', # <http://cloford.com/resources/colours/500col.htm>
    data = Testimate %>% select(totalProfit) %>% .[[1]]
  )
)

highchart() %>%
  hc_add_series_list(series) %>% 
  hc_xAxis(categories = Testimate %>% select(Day) %>% .[[1]], # or day
           plotBands = list(
              list(from = -0.5, to = 14.5, color = "#FFFACD"), # may be colored by day 
              list(from = 14.5, to = 15.5, color = "#BDFCC9"),
              list(from = 15.5, to = 16.5, color = "#FFFACD"),
              list(from = 16.5, to = 18.5, color = "#FFFACD"),
              list(from = 17.5, to = 18.5, color = "#FFFACD"),
              list(from = 18.5, to = 19.5, color = "#FFFACD"),
              list(from = 19.5, to = 20.5, color = "#BDFCC9"),
              list(from = 20.5, to = 21.5, color = "#FFFACD"),
              list(from = 21.5, to = 22.5, color = "#BDFCC9"),
              list(from = 22.5, to = 23.5, color = "#FFFACD"),
              list(from = 23.5, to = 24.5, color = "#FFFACD"),
              list(from = 24.5, to = 25.5, color = "#BDFCC9"),
              list(from = 25.5, to = 26.5, color = "#FFFACD"),
              list(from = 26.5, to = 27.5, color = "#FFFACD"),
              list(from = 27.5, to = 28.5, color = "#BDFCC9"),
              list(from = 28.5, to = 29.5, color = "#FFFACD"),
              list(from = 29.5, to = 34.5, color = "#BDFCC9"),
              list(from = 34.5, to = 35.5, color = "#FFFACD")
              )) %>%
  hc_yAxis(plotLines = list(
              list(value = 0, width = 5, color = 'black')
              ))

A.2 Session Profit (1/3)

#library(highcharter)

series = list(
  list(
    name = 'Total Profit for each 1$3$ session',
    color = '#1E90FF', # <http://cloford.com/resources/colours/500col.htm>
    data = Testimate %>% filter(Stake >= 2.5) %>% select(totalProfit)  %>% .[[1]]
  )
)

highchart() %>%
  hc_add_series_list(series) %>% 
  hc_xAxis(categories = Testimate %>% select(Day) %>% .[[1]] ) %>%
  hc_yAxis(plotLines = list(
              list(value = 0, width = 5, color = 'black')
              ))

A.3 Session Profit (1/2)

#library(highcharter)

series = list(
  list(
    name = 'Total Profit for each 1$2$ session',
    color = '#1E90FF', # <http://cloford.com/resources/colours/500col.htm>
    data = Testimate %>% filter(Stake < 2.5) %>% select(totalProfit)  %>% .[[1]]
  )
)

highchart() %>%
  hc_add_series_list(series) %>% 
  hc_xAxis(categories = Testimate %>% select(Day) %>% .[[1]] ) %>%
  hc_yAxis(plotLines = list(
              list(value = 0, width = 5, color = 'black')
              ))

B.1 Session Profit (Enlightened Era)

series = list(
  list(
    name = 'Total Profit for each session during the Enlightment Era, stratified by stake (lemon chiffon: 1$/2$, peppermint: 1$/3$)',
    color = '#1E90FF', # <http://cloford.com/resources/colours/500col.htm>
    data = EnlightenedEra %>% select(totalProfit) %>% .[[1]]
  )
)

highchart() %>%
  hc_add_series_list(series) %>% 
  hc_xAxis(categories = EnlightenedEra %>% select(Day) %>% .[[1]], # or day
           plotBands = list(
              list(from = -0.5, to = 0.5, color = "#FFFACD"), 
              list(from = 0.5, to = 1.5, color = "#BDFCC9"),
              list(from = 1.5, to = 3.5, color = "#FFFACD"),
              list(from = 3.5, to = 4.5, color = "#FFFACD"),
              list(from = 4.5, to = 5.5, color = "#FFFACD"),
              list(from = 5.5, to = 6.5, color = "#BDFCC9"),
              list(from = 6.5, to = 7.5, color = "#FFFACD"),
              list(from = 7.5, to = 8.5, color = "#BDFCC9"),
              list(from = 8.5, to = 9.5, color = "#FFFACD"),
              list(from = 9.5, to = 10.5, color = "#FFFACD"), 
              list(from = 10.5, to = 11.5, color = "#BDFCC9"),
              list(from = 11.5, to = 12.5, color = "#FFFACD"),
              list(from = 12.5, to = 13.5, color = "#FFFACD"),
              list(from = 13.5, to = 14.5, color = "#BDFCC9"),
              list(from = 14.5, to = 15.5, color = "#FFFACD"),
              list(from = 15.5, to = 20.5, color = "#BDFCC9"),
              list(from = 20.5, to = 21.5, color = "#FFFACD")
              )) %>%
  hc_yAxis(plotLines = list(
              list(value = 0, width = 5, color = 'black')
              ))

B.1 Cumulative Profit (Overall)

series = list(
  list(
    name = 'Cumulative Profit',
    color = '#EEAD0E', 
    data = Testimate %>% select(accuProfit) %>% .[[1]]
  ),
  list(
    name = 'Cumulative Total Profit (promo bonus included)',
    color = '#FF7D40', 
    data = Testimate %>% select(accuTotalProfit) %>% .[[1]]
  )
)

highchart() %>%
  hc_add_series_list(series) %>% 
  hc_xAxis(categories = Testimate %>% select(Day) %>% .[[1]],
           plotBands = list(
              list(from = -0.5, to = 14.5, color = "#FFFACD"), # may be colored by day 
              list(from = 14.5, to = 15.5, color = "#BDFCC9"),
              list(from = 15.5, to = 16.5, color = "#FFFACD"),
              list(from = 16.5, to = 18.5, color = "#FFFACD"),
              list(from = 17.5, to = 18.5, color = "#FFFACD"),
              list(from = 18.5, to = 19.5, color = "#FFFACD"),
              list(from = 19.5, to = 20.5, color = "#BDFCC9"),
              list(from = 20.5, to = 21.5, color = "#FFFACD"),
              list(from = 21.5, to = 22.5, color = "#BDFCC9"),
              list(from = 22.5, to = 23.5, color = "#FFFACD"),
              list(from = 23.5, to = 24.5, color = "#FFFACD"),
              list(from = 24.5, to = 25.5, color = "#BDFCC9"),
              list(from = 25.5, to = 26.5, color = "#FFFACD"),
              list(from = 26.5, to = 27.5, color = "#FFFACD"),
              list(from = 27.5, to = 28.5, color = "#BDFCC9"),
              list(from = 28.5, to = 29.5, color = "#FFFACD"),
              list(from = 29.5, to = 34.5, color = "#BDFCC9"),
              list(from = 34.5, to = 35.5, color = "#FFFACD")
              ))

B.2 Cumulative Profit (Enlightened)

series = list(
  list(
    name = 'Cumulative Profit in Enlightened Era',
    color = '#EEAD0E', 
    data = EnlightenedEra %>% select(accuProfit) %>% .[[1]]
  ),
  list(
    name = 'Cumulative Total Profit during the Enlightened Era (promo bonus included)',
    color = '#FF7D40', 
    data = EnlightenedEra %>% select(accuTotalProfit) %>% .[[1]]
  )
)

highchart() %>%
  hc_add_series_list(series) %>% 
  hc_xAxis(categories = EnlightenedEra %>% select(Day) %>% .[[1]],
           plotBands = list(
              list(from = -0.5, to = 0.5, color = "#FFFACD"), 
              list(from = 0.5, to = 1.5, color = "#BDFCC9"),
              list(from = 1.5, to = 3.5, color = "#FFFACD"),
              list(from = 3.5, to = 4.5, color = "#FFFACD"),
              list(from = 4.5, to = 5.5, color = "#FFFACD"),
              list(from = 5.5, to = 6.5, color = "#BDFCC9"),
              list(from = 6.5, to = 7.5, color = "#FFFACD"),
              list(from = 7.5, to = 8.5, color = "#BDFCC9"),
              list(from = 8.5, to = 9.5, color = "#FFFACD"),
              list(from = 9.5, to = 10.5, color = "#FFFACD"), 
              list(from = 10.5, to = 11.5, color = "#BDFCC9"),
              list(from = 11.5, to = 12.5, color = "#FFFACD"),
              list(from = 12.5, to = 13.5, color = "#FFFACD"),
              list(from = 13.5, to = 14.5, color = "#BDFCC9"),
              list(from = 14.5, to = 15.5, color = "#FFFACD"),
              list(from = 15.5, to = 20.5, color = "#BDFCC9"),
              list(from = 20.5, to = 21.5, color = "#FFFACD")
              ))

C.1 Cumulative Profit per hour Rate (Overall)

series = list(
  list(
    name = 'Cumulative Profit Per Hour',
    color = '#EEAD0E',
    data = Testimate %>% select(accuProfitPerHr) %>% .[[1]] %>% round(2) 
  ),
  list(
    name = 'Cumulative Total Profit Per Hour (promo bonus included)',
    color = '#FF7D40',
    data = Testimate %>% select(accuTotalProfitPerHr) %>% .[[1]] %>% round(2)
  )
)

highchart() %>%
  hc_add_series_list(series) %>% 
  hc_xAxis(categories = Testimate %>% select(Day) %>% .[[1]],
           plotBands = list(
              list(from = -0.5, to = 14.5, color = "#FFFACD"), # may be colored by day 
              list(from = 14.5, to = 15.5, color = "#BDFCC9"),
              list(from = 15.5, to = 16.5, color = "#FFFACD"),
              list(from = 16.5, to = 18.5, color = "#FFFACD"),
              list(from = 17.5, to = 18.5, color = "#FFFACD"),
              list(from = 18.5, to = 19.5, color = "#FFFACD"),
              list(from = 19.5, to = 20.5, color = "#BDFCC9"),
              list(from = 20.5, to = 21.5, color = "#FFFACD"),
              list(from = 21.5, to = 22.5, color = "#BDFCC9"),
              list(from = 22.5, to = 23.5, color = "#FFFACD"),
              list(from = 23.5, to = 24.5, color = "#FFFACD"),
              list(from = 24.5, to = 25.5, color = "#BDFCC9"),
              list(from = 25.5, to = 26.5, color = "#FFFACD"),
              list(from = 26.5, to = 27.5, color = "#FFFACD"),
              list(from = 27.5, to = 28.5, color = "#BDFCC9"),
              list(from = 28.5, to = 29.5, color = "#FFFACD"),
              list(from = 29.5, to = 34.5, color = "#BDFCC9"),
              list(from = 34.5, to = 35.5, color = "#FFFACD")
              ))

C.2 Cumulative Profit per hour rate (Enlightened)

series = list(
  list(
    name = 'Cumulative Profit Per Hour during the Enlightened Era',
    color = '#EEAD0E',
    data = EnlightenedEra %>% select(accuProfitPerHr) %>% .[[1]] %>% round(2) 
  ),
  list(
    name = 'Cumulative Total Profit Per Hour during the Enlightened Era (promo bonus included)',
    color = '#FF7D40',
    data = EnlightenedEra %>% select(accuTotalProfitPerHr) %>% .[[1]] %>% round(2)
  )
)

highchart() %>%
  hc_add_series_list(series) %>% 
  hc_xAxis(categories = EnlightenedEra %>% select(Day) %>% .[[1]],
           plotBands = list(
              list(from = -0.5, to = 0.5, color = "#FFFACD"), 
              list(from = 0.5, to = 1.5, color = "#BDFCC9"),
              list(from = 1.5, to = 3.5, color = "#FFFACD"),
              list(from = 3.5, to = 4.5, color = "#FFFACD"),
              list(from = 4.5, to = 5.5, color = "#FFFACD"),
              list(from = 5.5, to = 6.5, color = "#BDFCC9"),
              list(from = 6.5, to = 7.5, color = "#FFFACD"),
              list(from = 7.5, to = 8.5, color = "#BDFCC9"),
              list(from = 8.5, to = 9.5, color = "#FFFACD"),
              list(from = 9.5, to = 10.5, color = "#FFFACD"), 
              list(from = 10.5, to = 11.5, color = "#BDFCC9"),
              list(from = 11.5, to = 12.5, color = "#FFFACD"),
              list(from = 12.5, to = 13.5, color = "#FFFACD"),
              list(from = 13.5, to = 14.5, color = "#BDFCC9"),
              list(from = 14.5, to = 15.5, color = "#FFFACD"),
              list(from = 15.5, to = 20.5, color = "#BDFCC9"),
              list(from = 20.5, to = 21.5, color = "#FFFACD")
              ))

highcharter might be outdated on CRAN. Install from github directly by

library(devtools)
install_github("jbkunst/highcharter")

What’s my Game?

Yuqing Xue https://github.com/Testimate/Poker

Nov 16, 2018

My Statistical Analysis for My Poker Statistics

1. Data Preparation

My performance on Monday:

A separate data set to be analyzed: Session with “The Course” mindset

Profit & total profit by day & by day per hour: Overall

Profit & total profit by day & by day per hour: Enlightened Era

2. Interactive Visualization

Apply `htmlwidget` -> `highcharter`

Initially inspired by Philipp Spachtholz.

Package Author Joshua Kunst’s RPub https://rpubs.com/jbkunst/highcharter.

Additional `highcharter` learning material by Danton Noriega and Nana Boateng.

A.1 Session Profit (Overall)

A.2 Session Profit (1/3)

A.3 Session Profit (1/2)

B.1 Session Profit (Enlightened Era)

B.1 Cumulative Profit (Overall)

B.2 Cumulative Profit (Enlightened)

C.1 Cumulative Profit per hour Rate (Overall)

C.2 Cumulative Profit per hour rate (Enlightened)

What’s my Game?

Yuqing Xue https://github.com/Testimate/Poker

Nov 16, 2018

My Statistical Analysis for My Poker Statistics

1. Data Preparation

My performance on Monday:

A separate data set to be analyzed: Session with “The Course” mindset

Profit & total profit by day & by day per hour: Overall

Profit & total profit by day & by day per hour: Enlightened Era

2. Interactive Visualization

Apply htmlwidget -> highcharter

Initially inspired by Philipp Spachtholz.

Package Author Joshua Kunst’s RPub https://rpubs.com/jbkunst/highcharter.

Additional highcharter learning material by Danton Noriega and Nana Boateng.

A.1 Session Profit (Overall)

A.2 Session Profit (1/3)

A.3 Session Profit (1/2)

B.1 Session Profit (Enlightened Era)

B.1 Cumulative Profit (Overall)

B.2 Cumulative Profit (Enlightened)

C.1 Cumulative Profit per hour Rate (Overall)

C.2 Cumulative Profit per hour rate (Enlightened)

Apply `htmlwidget` -> `highcharter`

Additional `highcharter` learning material by Danton Noriega and Nana Boateng.