library(tidyverse)
library(kableExtra)

My Approach

My approach for this project follow:

  • Import data using readr (used fixed length file)
  • Use the Tidyverse to wrangle data into the desired form
  • Write a CSV file using readr
  • Import csv file using Shiny App

Note: Inspiration and code for shiny app come from https://www.youtube.com/watch?v=HPZSunrSo5M

Import Data

columns <- c("player","c2","c3","c4","c5","c6","c7","c8","c9","c10")
delo <- read_fwf("https://raw.githubusercontent.com/MundyMSDS/DATA607/master/elodataset.txt", fwf_widths(c(6, 32, 7,7,6,6,6,6,6,6), columns),skip=4, comment="-")
glimpse(delo)
## Observations: 128
## Variables: 10
## $ player <chr> "1", "ON", "2", "MI", "3", "MI", "4", "MI", "5", "MI", ...
## $ c2     <chr> "| GARY HUA", "| 15445895 / R: 1794   ->1817", "| DAKSH...
## $ c3     <chr> "|6.0", "|N:2", "|6.0", "|N:2", "|6.0", "|N:2", "|5.5",...
## $ c4     <chr> "|W  39", "|W", "|W  63", "|B", "|L   8", "|W", "|W  23...
## $ c5     <chr> "|W  21", "|B", "|W  58", "|W", "|W  61", "|B", "|D  28...
## $ c6     <chr> "|W  18", "|W", "|L   4", "|B", "|W  25", "|W", "|W   2...
## $ c7     <chr> "|W  14", "|B", "|W  17", "|W", "|W  21", "|B", "|W  26...
## $ c8     <chr> "|W   7", "|W", "|W  16", "|B", "|W  11", "|W", "|D   5...
## $ c9     <chr> "|D  12", "|B", "|W  20", "|W", "|W  13", "|B", "|W  19...
## $ c10    <chr> "|D   4", "|W", "|W   7", "|B", "|W  12", "|W", "|D   1...

Wrangle Data

d <- delo %>% 
  mutate(c2= str_trim(str_sub(c2,2,29),side="both")) %>% 
  mutate(c3 = str_trim(str_sub(c3,2,6),side="both")) %>%
  mutate(c4 = str_trim(str_sub(c4,2,6),side="both")) %>%
  mutate(c5 = str_trim(str_sub(c5,2,6),side="both")) %>%
  mutate(c6 = str_trim(str_sub(c6,2,6),side="both")) %>%
  mutate(c7 = str_trim(str_sub(c7,2,6),side="both")) %>%
  mutate(c8 = str_trim(str_sub(c8,2,6),side="both")) %>%
  mutate(c9 = str_trim(str_sub(c9,2,6),side="both")) %>%
  mutate(c10 = str_trim(str_sub(c10,2,6),side="both")) %>% 
  mutate(c11 = if_else(str_detect(player,"\\d"),lead(player),"")) %>% 
  mutate(c12 = if_else(str_detect(player,"\\d"),lead(c2),"")) %>% 
  filter(str_detect(player,"\\d")) %>% 
  mutate(c4=as.integer(str_replace_all(c4,"[WLDHU]\\s",""))) %>%
  mutate(c5=as.integer(str_replace_all(c5,"[WLDHU]\\s",""))) %>%
  mutate(c6=as.integer(str_replace_all(c6,"[WLDHU]\\s",""))) %>%
  mutate(c7=as.integer(str_replace_all(c7,"[WLDHU]\\s",""))) %>%
  mutate(c8=as.integer(str_replace_all(c8,"[WLDHU]\\s",""))) %>%
  mutate(c9=as.integer(str_replace_all(c9,"[WLDHU]\\s",""))) %>%
  mutate(c10=as.integer(str_replace_all(c10,"[WLDHU]\\s",""))) %>% 
  mutate(c12 = str_replace_all(c12,"\\d{8}\\s\\/\\sR\\:\\s","")) %>% 
  separate(c12, into=c("c13", "c14"), sep = "->", remove = FALSE) %>% 
  mutate(c13 = str_extract(c13,"\\d{3,4}"))

d2 <- d  %>% 
  select(c2,c11,c3, c13,c4:c10) %>% 
  gather(c4:c10, key="match", value="player") %>% 
  arrange(c2) %>%
  mutate(player = as.character(player)) %>% 
  select(-match)
  

d3 <- d %>% 
  select (player, c13)
 
result <- left_join(d2, d3, by ="player") %>% 
  group_by(c2, c11,c3, c13.x) %>%  
  summarize(score = mean(as.integer(c13.y), na.rm = TRUE)) %>% 
  ungroup() %>% 
  mutate(score = as.integer(score)) %>% 
  rename(Name=c2, State=c11, Ttl_Points =c3, Pre_Rating = c13.x, Avg_Opp_Rating = score) %>% 
  arrange(desc(Avg_Opp_Rating))

Elo Data Output

  kable(result, format = "markdown")
Name State Ttl_Points Pre_Rating Avg_Opp_Rating
GARY HUA ON 6.0 1794 1605
PATRICK H SCHILLING MI 5.5 1716 1573
ADITYA BAJAJ MI 6.0 1384 1563
ANVIT RAO MI 5.0 1365 1554
BRIAN LIU MI 3.0 1423 1539
STEFANO LEE ON 5.0 1411 1523
SOFIA ADINA STANESCU-BELLU MI 3.5 1507 1522
HANSEN SONG OH 5.0 1686 1518
BRADLEY SHAW MI 4.5 1610 1515
KENNETH J TACK MI 4.5 1663 1506
MAX ZHU ON 3.5 1579 1506
HANSHI ZUO MI 5.5 1655 1500
RONALD GRZEGORCZYK MI 4.0 1629 1498
TORRANCE HENRY JR MI 4.5 1666 1497
ETHAN GUO MI 2.5 935 1494
ZACHARY JAMES HOUGHTON MI 4.5 1220 1483
DAVID SUNDEEN MI 4.0 1600 1480
DINH DANG BUI ON 4.0 1563 1470
DAKSHESH DARURI MI 6.0 1553 1469
EZEKIEL HOUGHTON MI 5.0 1641 1468
CAMERON WILLIAM MC LEMAN MI 4.5 1712 1467
JOEL R HENDON MI 3.0 1436 1429
DIPANKAR ROY MI 4.0 1564 1426
MARISA RICCI MI 2.0 1153 1414
JASON ZHENG MI 4.0 1595 1410
ALEX KONG MI 2.0 1186 1406
ERIC WRIGHT MI 2.5 1362 1392
VIRAJ MOHILE MI 2.0 917 1391
FOREST ZHANG MI 3.0 1348 1390
SIDDHARTH JHA MI 3.5 1355 1388
MIKE NIKITIN MI 4.0 1604 1385
AMIYATOSH PWNANANDAM MI 3.5 980 1384
JOSHUA PHILIP MATHEWS ON 3.5 1441 1378
MICHAEL JEFFERY THOMAS MI 3.5 1399 1375
GARY DEE SWATHELL MI 5.0 1649 1372
LOREN SCHWIEBERT MI 3.5 1745 1363
MICHAEL LU MI 2.0 1092 1363
JACOB ALEXANDER LAVALLEY MI 3.0 377 1357
MICHAEL R ALDRICH MI 4.0 1229 1357
TEJAS AYYAGARI MI 2.5 1011 1356
DANIEL KHAIN MI 2.5 1382 1355
THOMAS JOSEPH HOSMER MI 1.0 1175 1350
JOSE C YBARRA MI 2.0 1393 1345
JULIA SHEN MI 1.5 967 1330
JEZZEL FARKAS ON 1.5 955 1327
JUSTIN D SCHILLING MI 3.0 1199 1327
SEAN M MC CORMICK MI 2.0 853 1319
CHIEDOZIE OKORIE MI 3.5 1602 1313
EUGENE L MCCLURE MI 4.0 1555 1300
SHIVAM JHA MI 2.5 1056 1296
MICHAEL J MARTIN MI 2.5 1291 1285
JADE GE MI 3.5 1449 1276
BEN LI MI 1.0 1163 1263
RISHI SHETTY MI 3.5 1494 1259
KYLE WILLIAM MURPHY MI 3.0 1403 1248
GAURAV GIDWANI MI 3.5 1552 1221
ALAN BUI ON 4.0 1363 1213
LARRY HODGE MI 2.0 1270 1206
ASHWIN BALAJI MI 1.0 1530 1186
DEREK YAN MI 3.0 1242 1152
JARED GE MI 3.0 1332 1149
JOSHUA DAVID LEE MI 3.5 1438 1149
GEORGE AVERY JONES ON 3.5 1522 1144
ROBERT GLEN VASEY MI 3.0 1283 1106

Write Data To CSV File

  result <- format_csv(result)
  setwd(file.path("C:","Users", "mutue", "OneDrive", "Documents", "Data607"))
  write_file(result,"elo_data.csv")

Shiny App

Click this link to access the Shiny App. Use the App to access your ELO CSV file or other csv file.

Link to App: https://mundymsds.shinyapps.io/EloApp/