Introduction

  • The chess rating system invented and used for assessing relative strength of employment candidates by human resource departments
  • In this project, the unstructured data file (tournamentinfo.txt) is converted into structured data and find the average Pre Chess Rating of Opponents
  • The following libraries are used
    •       RCurl - Get the tournamentinfo.txt file from gihub repository
          sqldf - SQL operation
          stringr - String Manipulation,datatype conversion and extracting required data columns
          tidyr - Cleansing and formatting the data
          reshape2- Aggregate the data
          kable- Displaying data in table format

File Cleansing

  • The file is fetched from github repository using RCurl library
  • Removed the ‘-’ and ‘+’ symbols from file
  • Removed the empty lines and header information
  • Seperated the Line1 and Line2 and merged into single line with the help of stringr and sqldf library 
library(RCurl)
#Getting data from github repository
tour_info <-read.csv(text=getURL("https://raw.githubusercontent.com/thasleem1/DATA607/master/tournamentinfo.txt"))
#Formation of data frame with column
colnames(tour_info)<-c("tour_info")
#Removing the -- and + data
tour_data<-data.frame(gsub("-+","",tour_info$tour_info))

tournament <- tour_data[tour_data != ""]       # Remove the emtpy lines
tournament <- tour_data[c(3:nrow(tour_data)),] # Remove the header information

library(stringr)
#Extract Line1 and Line2
Line1 <- tournament[str_detect(substr(tournament, 1, 6), "[0-9]")]   
Line2 <- tournament[str_detect(substr(tournament, 1, 6), "[A-Z]{2,2}")]  

library("knitr")
library("kableExtra")
kable(data.frame(head(Line1, n=5))) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
  row_spec(0, bold = T, color = "white", background = "#ea7872")
head.Line1..n…5.
1 | GARY HUA |6.0 |W 39|W 21|W 18|W 14|W 7|D 12|D 4|
2 | DAKSHESH DARURI |6.0 |W 63|W 58|L 4|W 17|W 16|W 20|W 7|
3 | ADITYA BAJAJ |6.0 |L 8|W 61|W 25|W 21|W 11|W 13|W 12|
4 | PATRICK H SCHILLING |5.5 |W 23|D 28|W 2|W 26|D 5|W 19|D 1|
5 | HANSHI ZUO |5.5 |W 45|W 37|D 12|D 13|D 4|W 14|W 17| 
kable(data.frame(head(Line2, n=5))) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
  row_spec(0, bold = T, color = "white", background = "#ea7872")
head.Line2..n…5.
ON | 15445895 / R: 1794 >1817 |N:2 |W |B |W |B |W |B |W |
MI | 14598900 / R: 1553 >1663 |N:2 |B |W |B |W |B |W |B |
MI | 14959604 / R: 1384 >1640 |N:2 |W |B |W |B |W |B |W |
MI | 12616049 / R: 1716 >1744 |N:2 |W |B |W |B |W |B |B |
MI | 14601533 / R: 1655 >1690 |N:2 |B |W |B |W |B |W |B | 
#Merging Line1 and Line2 as a single record
merge <- data.frame(Line1, Line2)

#Concatenate the Line1 and Line2
library(sqldf)
player_data <- sqldf("Select Line1||Line2 as Line1_Line2_Merged from merge")

kable(data.frame(head(player_data, n=5))) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
  row_spec(0, bold = T, color = "white", background = "#ea7872")
Line1_Line2_Merged
1 | GARY HUA |6.0 |W 39|W 21|W 18|W 14|W 7|D 12|D 4| ON | 15445895 / R: 1794 >1817 |N:2 |W |B |W |B |W |B |W |
2 | DAKSHESH DARURI |6.0 |W 63|W 58|L 4|W 17|W 16|W 20|W 7| MI | 14598900 / R: 1553 >1663 |N:2 |B |W |B |W |B |W |B |
3 | ADITYA BAJAJ |6.0 |L 8|W 61|W 25|W 21|W 11|W 13|W 12| MI | 14959604 / R: 1384 >1640 |N:2 |W |B |W |B |W |B |W |
4 | PATRICK H SCHILLING |5.5 |W 23|D 28|W 2|W 26|D 5|W 19|D 1| MI | 12616049 / R: 1716 >1744 |N:2 |W |B |W |B |W |B |B |
5 | HANSHI ZUO |5.5 |W 45|W 37|D 12|D 13|D 4|W 14|W 17| MI | 14601533 / R: 1655 >1690 |N:2 |B |W |B |W |B |W |B |

File Formatting

  • The data is cleansed - seperating columns USCFID,PreRtg,PostRtg using tidyr library
  • Converting few columns from Character into Numeric datatype for calculating average
  • Subsetting the data for aggregate calculation
  • Reshaping the data to get the Pre Rating information of all players, used reshape2 library 
library(tidyr)
#Cleansing and formatting the data
cleansed_data <- player_data %>%
  # Split the single column of text into multiple columns
  separate(Line1_Line2_Merged, into = c("Pair", "Player_Name","Total","Round1","Round2","Round3","Round4","Round5","Round6","Round7","State","USCFID_Pre_Post_Rtg","Pts","R1","R2","R3","R4","R5","R6","R7"), sep = '\\|') 
cleansed_data$USCFID = as.numeric(substr(cleansed_data$USCFID_Pre_Post_Rtg, 1, 9))
cleansed_data$PreRtg = as.numeric(substr(cleansed_data$USCFID_Pre_Post_Rtg, 16, 19))
cleansed_data$PostRtg = as.numeric(substr(cleansed_data$USCFID_Pre_Post_Rtg, 24, 27))
cleansed_data$Pair = as.numeric(cleansed_data$Pair)

#library(stringi)
#Converting the Character to Number datatype
for (i in 4:10){ 
  cleansed_data[,i] <- as.numeric(gsub("[^0-9]+", "", cleansed_data[,i])) 
  }

kable(data.frame(head(cleansed_data))) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
  row_spec(0, bold = T, color = "white", background = "#ea7872") %>%
    scroll_box(width = "100%", height = "200px")
Pair Player_Name Total Round1 Round2 Round3 Round4 Round5 Round6 Round7 State USCFID_Pre_Post_Rtg Pts R1 R2 R3 R4 R5 R6 R7 USCFID PreRtg PostRtg
1 GARY HUA 6.0 39 21 18 14 7 12 4 ON 15445895 / R: 1794 >1817 N:2 W B W B W B W 15445895 1794 1817
2 DAKSHESH DARURI 6.0 63 58 4 17 16 20 7 MI 14598900 / R: 1553 >1663 N:2 B W B W B W B 14598900 1553 1663
3 ADITYA BAJAJ 6.0 8 61 25 21 11 13 12 MI 14959604 / R: 1384 >1640 N:2 W B W B W B W 14959604 1384 1640
4 PATRICK H SCHILLING 5.5 23 28 2 26 5 19 1 MI 12616049 / R: 1716 >1744 N:2 W B W B W B B 12616049 1716 1744
5 HANSHI ZUO 5.5 45 37 12 13 4 14 17 MI 14601533 / R: 1655 >1690 N:2 B W B W B W B 14601533 1655 1690
6 HANSEN SONG 5.0 34 29 11 35 10 27 21 OH 15055204 / R: 1686 >1687 N:3 W B W B B W B 15055204 1686 1687 
cleansed_data_subset <- subset(cleansed_data, select=c(Pair,PreRtg,Round1,Round2,Round3,Round4,Round5,Round6,Round7)) 

library(reshape2)
#Aggregate the data
melt_data <- melt(cleansed_data_subset, id.vars=c("Pair", "PreRtg"))
agg_data <- melt_data[order(melt_data$Pair,melt_data$variable),]
kable(data.frame(agg_data)) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
  row_spec(0, bold = T, color = "white", background = "#ea7872") %>%
    scroll_box(width = "100%", height = "200px")
Pair PreRtg variable value
1 1 1794 Round1 39
65 1 1794 Round2 21
129 1 1794 Round3 18
193 1 1794 Round4 14
257 1 1794 Round5 7
321 1 1794 Round6 12
385 1 1794 Round7 4
2 2 1553 Round1 63
66 2 1553 Round2 58
130 2 1553 Round3 4
194 2 1553 Round4 17
258 2 1553 Round5 16
322 2 1553 Round6 20
386 2 1553 Round7 7
3 3 1384 Round1 8
67 3 1384 Round2 61
131 3 1384 Round3 25
195 3 1384 Round4 21
259 3 1384 Round5 11
323 3 1384 Round6 13
387 3 1384 Round7 12
4 4 1716 Round1 23
68 4 1716 Round2 28
132 4 1716 Round3 2
196 4 1716 Round4 26
260 4 1716 Round5 5
324 4 1716 Round6 19
388 4 1716 Round7 1
5 5 1655 Round1 45
69 5 1655 Round2 37
133 5 1655 Round3 12
197 5 1655 Round4 13
261 5 1655 Round5 4
325 5 1655 Round6 14
389 5 1655 Round7 17
6 6 1686 Round1 34
70 6 1686 Round2 29
134 6 1686 Round3 11
198 6 1686 Round4 35
262 6 1686 Round5 10
326 6 1686 Round6 27
390 6 1686 Round7 21
7 7 1649 Round1 57
71 7 1649 Round2 46
135 7 1649 Round3 13
199 7 1649 Round4 11
263 7 1649 Round5 1
327 7 1649 Round6 9
391 7 1649 Round7 2
8 8 1641 Round1 3
72 8 1641 Round2 32
136 8 1641 Round3 14
200 8 1641 Round4 9
264 8 1641 Round5 47
328 8 1641 Round6 28
392 8 1641 Round7 19
9 9 1411 Round1 25
73 9 1411 Round2 18
137 9 1411 Round3 59
201 9 1411 Round4 8
265 9 1411 Round5 26
329 9 1411 Round6 7
393 9 1411 Round7 20
10 10 1365 Round1 16
74 10 1365 Round2 19
138 10 1365 Round3 55
202 10 1365 Round4 31
266 10 1365 Round5 6
330 10 1365 Round6 25
394 10 1365 Round7 18
11 11 1712 Round1 38
75 11 1712 Round2 56
139 11 1712 Round3 6
203 11 1712 Round4 7
267 11 1712 Round5 3
331 11 1712 Round6 34
395 11 1712 Round7 26
12 12 1663 Round1 42
76 12 1663 Round2 33
140 12 1663 Round3 5
204 12 1663 Round4 38
268 12 1663 Round5 NA
332 12 1663 Round6 1
396 12 1663 Round7 3
13 13 1666 Round1 36
77 13 1666 Round2 27
141 13 1666 Round3 7
205 13 1666 Round4 5
269 13 1666 Round5 33
333 13 1666 Round6 3
397 13 1666 Round7 32
14 14 1610 Round1 54
78 14 1610 Round2 44
142 14 1610 Round3 8
206 14 1610 Round4 1
270 14 1610 Round5 27
334 14 1610 Round6 5
398 14 1610 Round7 31
15 15 1220 Round1 19
79 15 1220 Round2 16
143 15 1220 Round3 30
207 15 1220 Round4 22
271 15 1220 Round5 54
335 15 1220 Round6 33
399 15 1220 Round7 38
16 16 1604 Round1 10
80 16 1604 Round2 15
144 16 1604 Round3 NA
208 16 1604 Round4 39
272 16 1604 Round5 2
336 16 1604 Round6 36
400 16 1604 Round7 NA
17 17 1629 Round1 48
81 17 1629 Round2 41
145 17 1629 Round3 26
209 17 1629 Round4 2
273 17 1629 Round5 23
337 17 1629 Round6 22
401 17 1629 Round7 5
18 18 1600 Round1 47
82 18 1600 Round2 9
146 18 1600 Round3 1
210 18 1600 Round4 32
274 18 1600 Round5 19
338 18 1600 Round6 38
402 18 1600 Round7 10
19 19 1564 Round1 15
83 19 1564 Round2 10
147 19 1564 Round3 52
211 19 1564 Round4 28
275 19 1564 Round5 18
339 19 1564 Round6 4
403 19 1564 Round7 8
20 20 1595 Round1 40
84 20 1595 Round2 49
148 20 1595 Round3 23
212 20 1595 Round4 41
276 20 1595 Round5 28
340 20 1595 Round6 2
404 20 1595 Round7 9
21 21 1563 Round1 43
85 21 1563 Round2 1
149 21 1563 Round3 47
213 21 1563 Round4 3
277 21 1563 Round5 40
341 21 1563 Round6 39
405 21 1563 Round7 6
22 22 1555 Round1 64
86 22 1555 Round2 52
150 22 1555 Round3 28
214 22 1555 Round4 15
278 22 1555 Round5 NA
342 22 1555 Round6 17
406 22 1555 Round7 40
23 23 1363 Round1 4
87 23 1363 Round2 43
151 23 1363 Round3 20
215 23 1363 Round4 58
279 23 1363 Round5 17
343 23 1363 Round6 37
407 23 1363 Round7 46
24 24 1229 Round1 28
88 24 1229 Round2 47
152 24 1229 Round3 43
216 24 1229 Round4 25
280 24 1229 Round5 60
344 24 1229 Round6 44
408 24 1229 Round7 39
25 25 1745 Round1 9
89 25 1745 Round2 53
153 25 1745 Round3 3
217 25 1745 Round4 24
281 25 1745 Round5 34
345 25 1745 Round6 10
409 25 1745 Round7 47
26 26 1579 Round1 49
90 26 1579 Round2 40
154 26 1579 Round3 17
218 26 1579 Round4 4
282 26 1579 Round5 9
346 26 1579 Round6 32
410 26 1579 Round7 11
27 27 1552 Round1 51
91 27 1552 Round2 13
155 27 1552 Round3 46
219 27 1552 Round4 37
283 27 1552 Round5 14
347 27 1552 Round6 6
411 27 1552 Round7 NA
28 28 1507 Round1 24
92 28 1507 Round2 4
156 28 1507 Round3 22
220 28 1507 Round4 19
284 28 1507 Round5 20
348 28 1507 Round6 8
412 28 1507 Round7 36
29 29 1602 Round1 50
93 29 1602 Round2 6
157 29 1602 Round3 38
221 29 1602 Round4 34
285 29 1602 Round5 52
349 29 1602 Round6 48
413 29 1602 Round7 NA
30 30 1522 Round1 52
94 30 1522 Round2 64
158 30 1522 Round3 15
222 30 1522 Round4 55
286 30 1522 Round5 31
350 30 1522 Round6 61
414 30 1522 Round7 50
31 31 1494 Round1 58
95 31 1494 Round2 55
159 31 1494 Round3 64
223 31 1494 Round4 10
287 31 1494 Round5 30
351 31 1494 Round6 50
415 31 1494 Round7 14
32 32 1441 Round1 61
96 32 1441 Round2 8
160 32 1441 Round3 44
224 32 1441 Round4 18
288 32 1441 Round5 51
352 32 1441 Round6 26
416 32 1441 Round7 13
33 33 1449 Round1 60
97 33 1449 Round2 12
161 33 1449 Round3 50
225 33 1449 Round4 36
289 33 1449 Round5 13
353 33 1449 Round6 15
417 33 1449 Round7 51
34 34 1399 Round1 6
98 34 1399 Round2 60
162 34 1399 Round3 37
226 34 1399 Round4 29
290 34 1399 Round5 25
354 34 1399 Round6 11
418 34 1399 Round7 52
35 35 1438 Round1 46
99 35 1438 Round2 38
163 35 1438 Round3 56
227 35 1438 Round4 6
291 35 1438 Round5 57
355 35 1438 Round6 52
419 35 1438 Round7 48
36 36 1355 Round1 13
100 36 1355 Round2 57
164 36 1355 Round3 51
228 36 1355 Round4 33
292 36 1355 Round5 NA
356 36 1355 Round6 16
420 36 1355 Round7 28
37 37 980 Round1 NA
101 37 980 Round2 5
165 37 980 Round3 34
229 37 980 Round4 27
293 37 980 Round5 NA
357 37 980 Round6 23
421 37 980 Round7 61
38 38 1423 Round1 11
102 38 1423 Round2 35
166 38 1423 Round3 29
230 38 1423 Round4 12
294 38 1423 Round5 NA
358 38 1423 Round6 18
422 38 1423 Round7 15
39 39 1436 Round1 1
103 39 1436 Round2 54
167 39 1436 Round3 40
231 39 1436 Round4 16
295 39 1436 Round5 44
359 39 1436 Round6 21
423 39 1436 Round7 24
40 40 1348 Round1 20
104 40 1348 Round2 26
168 40 1348 Round3 39
232 40 1348 Round4 59
296 40 1348 Round5 21
360 40 1348 Round6 56
424 40 1348 Round7 22
41 41 1403 Round1 59
105 41 1403 Round2 17
169 41 1403 Round3 58
233 41 1403 Round4 20
297 41 1403 Round5 NA
361 41 1403 Round6 NA
425 41 1403 Round7 NA
42 42 1332 Round1 12
106 42 1332 Round2 50
170 42 1332 Round3 57
234 42 1332 Round4 60
298 42 1332 Round5 61
362 42 1332 Round6 64
426 42 1332 Round7 56
43 43 1283 Round1 21
107 43 1283 Round2 23
171 43 1283 Round3 24
235 43 1283 Round4 63
299 43 1283 Round5 59
363 43 1283 Round6 46
427 43 1283 Round7 55
44 44 1199 Round1 NA
108 44 1199 Round2 14
172 44 1199 Round3 32
236 44 1199 Round4 53
300 44 1199 Round5 39
364 44 1199 Round6 24
428 44 1199 Round7 59
45 45 1242 Round1 5
109 45 1242 Round2 51
173 45 1242 Round3 60
237 45 1242 Round4 56
301 45 1242 Round5 63
365 45 1242 Round6 55
429 45 1242 Round7 58
46 46 377 Round1 35
110 46 377 Round2 7
174 46 377 Round3 27
238 46 377 Round4 50
302 46 377 Round5 64
366 46 377 Round6 43
430 46 377 Round7 23
47 47 1362 Round1 18
111 47 1362 Round2 24
175 47 1362 Round3 21
239 47 1362 Round4 61
303 47 1362 Round5 8
367 47 1362 Round6 51
431 47 1362 Round7 25
48 48 1382 Round1 17
112 48 1382 Round2 63
176 48 1382 Round3 NA
240 48 1382 Round4 52
304 48 1382 Round5 NA
368 48 1382 Round6 29
432 48 1382 Round7 35
49 49 1291 Round1 26
113 49 1291 Round2 20
177 49 1291 Round3 63
241 49 1291 Round4 64
305 49 1291 Round5 58
369 49 1291 Round6 NA
433 49 1291 Round7 NA
50 50 1056 Round1 29
114 50 1056 Round2 42
178 50 1056 Round3 33
242 50 1056 Round4 46
306 50 1056 Round5 NA
370 50 1056 Round6 31
434 50 1056 Round7 30
51 51 1011 Round1 27
115 51 1011 Round2 45
179 51 1011 Round3 36
243 51 1011 Round4 57
307 51 1011 Round5 32
371 51 1011 Round6 47
435 51 1011 Round7 33
52 52 935 Round1 30
116 52 935 Round2 22
180 52 935 Round3 19
244 52 935 Round4 48
308 52 935 Round5 29
372 52 935 Round6 35
436 52 935 Round7 34
53 53 1393 Round1 NA
117 53 1393 Round2 25
181 53 1393 Round3 NA
245 53 1393 Round4 44
309 53 1393 Round5 NA
373 53 1393 Round6 57
437 53 1393 Round7 NA
54 54 1270 Round1 14
118 54 1270 Round2 39
182 54 1270 Round3 61
246 54 1270 Round4 NA
310 54 1270 Round5 15
374 54 1270 Round6 59
438 54 1270 Round7 64
55 55 1186 Round1 62
119 55 1186 Round2 31
183 55 1186 Round3 10
247 55 1186 Round4 30
311 55 1186 Round5 NA
375 55 1186 Round6 45
439 55 1186 Round7 43
56 56 1153 Round1 NA
120 56 1153 Round2 11
184 56 1153 Round3 35
248 56 1153 Round4 45
312 56 1153 Round5 NA
376 56 1153 Round6 40
440 56 1153 Round7 42
57 57 1092 Round1 7
121 57 1092 Round2 36
185 57 1092 Round3 42
249 57 1092 Round4 51
313 57 1092 Round5 35
377 57 1092 Round6 53
441 57 1092 Round7 NA
58 58 917 Round1 31
122 58 917 Round2 2
186 58 917 Round3 41
250 58 917 Round4 23
314 58 917 Round5 49
378 58 917 Round6 NA
442 58 917 Round7 45
59 59 853 Round1 41
123 59 853 Round2 NA
187 59 853 Round3 9
251 59 853 Round4 40
315 59 853 Round5 43
379 59 853 Round6 54
443 59 853 Round7 44
60 60 967 Round1 33
124 60 967 Round2 34
188 60 967 Round3 45
252 60 967 Round4 42
316 60 967 Round5 24
380 60 967 Round6 NA
444 60 967 Round7 NA
61 61 955 Round1 32
125 61 955 Round2 3
189 61 955 Round3 54
253 61 955 Round4 47
317 61 955 Round5 42
381 61 955 Round6 30
445 61 955 Round7 37
62 62 1530 Round1 55
126 62 1530 Round2 NA
190 62 1530 Round3 NA
254 62 1530 Round4 NA
318 62 1530 Round5 NA
382 62 1530 Round6 NA
446 62 1530 Round7 NA
63 63 1175 Round1 2
127 63 1175 Round2 48
191 63 1175 Round3 49
255 63 1175 Round4 43
319 63 1175 Round5 45
383 63 1175 Round6 NA
447 63 1175 Round7 NA
64 64 1163 Round1 22
128 64 1163 Round2 30
192 64 1163 Round3 31
256 64 1163 Round4 49
320 64 1163 Round5 46
384 64 1163 Round6 42
448 64 1163 Round7 54

Generating output

  • Using SQL Query, the average pre-rating of each player is identified by joining aggregated data performed on previous steps 
#Query to achive the output using SQL
final_data <- sqldf("SELECT c.Player_Name
    ,c.State
    ,c.Total
    ,c.PreRtg
    ,p3.Oppo_PreRtg
FROM cleansed_data c
INNER JOIN (
    SELECT p1.Pair,p1.PreRtg,avg(p2.PreRtg) AS Oppo_PreRtg
    FROM (
        SELECT * FROM agg_data
        ) p1
    LEFT JOIN (
        SELECT DISTINCT Pair,PreRtg
        FROM agg_data
        ) p2 ON p1.value = p2.Pair
    GROUP BY p1.Pair,p1.PreRtg
    ) p3 ON c.Pair = p3.Pair")

kable(data.frame(final_data)) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
  row_spec(0, bold = T, color = "white", background = "#c65953") %>% 
column_spec(5, background = "#f7f1e1") %>%
    scroll_box(width = "100%", height = "400px")
Player_Name State Total PreRtg Oppo_PreRtg
GARY HUA ON 6.0 1794 1605.286
DAKSHESH DARURI MI 6.0 1553 1469.286
ADITYA BAJAJ MI 6.0 1384 1563.571
PATRICK H SCHILLING MI 5.5 1716 1573.571
HANSHI ZUO MI 5.5 1655 1500.857
HANSEN SONG OH 5.0 1686 1518.714
GARY DEE SWATHELL MI 5.0 1649 1372.143
EZEKIEL HOUGHTON MI 5.0 1641 1468.429
STEFANO LEE ON 5.0 1411 1523.143
ANVIT RAO MI 5.0 1365 1554.143
CAMERON WILLIAM MC LEMAN MI 4.5 1712 1467.571
KENNETH J TACK MI 4.5 1663 1506.167
TORRANCE HENRY JR MI 4.5 1666 1497.857
BRADLEY SHAW MI 4.5 1610 1515.000
ZACHARY JAMES HOUGHTON MI 4.5 1220 1483.857
MIKE NIKITIN MI 4.0 1604 1385.800
RONALD GRZEGORCZYK MI 4.0 1629 1498.571
DAVID SUNDEEN MI 4.0 1600 1480.000
DIPANKAR ROY MI 4.0 1564 1426.286
JASON ZHENG MI 4.0 1595 1410.857
DINH DANG BUI ON 4.0 1563 1470.429
EUGENE L MCCLURE MI 4.0 1555 1300.333
ALAN BUI ON 4.0 1363 1213.857
MICHAEL R ALDRICH MI 4.0 1229 1357.000
LOREN SCHWIEBERT MI 3.5 1745 1363.286
MAX ZHU ON 3.5 1579 1506.857
GAURAV GIDWANI MI 3.5 1552 1221.667
SOFIA ADINA STANESCUBELLU MI 3.5 1507 1522.143
CHIEDOZIE OKORIE MI 3.5 1602 1313.500
GEORGE AVERY JONES ON 3.5 1522 1144.143
RISHI SHETTY MI 3.5 1494 1259.857
JOSHUA PHILIP MATHEWS ON 3.5 1441 1378.714
JADE GE MI 3.5 1449 1276.857
MICHAEL JEFFERY THOMAS MI 3.5 1399 1375.286
JOSHUA DAVID LEE MI 3.5 1438 1149.714
SIDDHARTH JHA MI 3.5 1355 1388.167
AMIYATOSH PWNANANDAM MI 3.5 980 1384.800
BRIAN LIU MI 3.0 1423 1539.167
JOEL R HENDON MI 3.0 1436 1429.571
FOREST ZHANG MI 3.0 1348 1390.571
KYLE WILLIAM MURPHY MI 3.0 1403 1248.500
JARED GE MI 3.0 1332 1149.857
ROBERT GLEN VASEY MI 3.0 1283 1106.571
JUSTIN D SCHILLING MI 3.0 1199 1327.000
DEREK YAN MI 3.0 1242 1152.000
JACOB ALEXANDER LAVALLEY MI 3.0 377 1357.714
ERIC WRIGHT MI 2.5 1362 1392.000
DANIEL KHAIN MI 2.5 1382 1355.800
MICHAEL J MARTIN MI 2.5 1291 1285.800
SHIVAM JHA MI 2.5 1056 1296.000
TEJAS AYYAGARI MI 2.5 1011 1356.143
ETHAN GUO MI 2.5 935 1494.571
JOSE C YBARRA MI 2.0 1393 1345.333
LARRY HODGE MI 2.0 1270 1206.167
ALEX KONG MI 2.0 1186 1406.000
MARISA RICCI MI 2.0 1153 1414.400
MICHAEL LU MI 2.0 1092 1363.000
VIRAJ MOHILE MI 2.0 917 1391.000
SEAN M MC CORMICK MI 2.0 853 1319.000
JULIA SHEN MI 1.5 967 1330.200
JEZZEL FARKAS ON 1.5 955 1327.286
ASHWIN BALAJI MI 1.0 1530 1186.000
THOMAS JOSEPH HOSMER MI 1.0 1175 1350.200
BEN LI MI 1.0 1163 1263.000

Generating csv file

  • setwd - Setting working directory
  • write.csv - Write the file based on the given setwd directory 
#Creating the .csv file
#Please change the working directory accordingly incase of writing in to local machine
setwd("C:/Users/aisha/Dropbox/CUNY/Semester1/DATA607_Data_Acquisition_and_Management/Week4")
write.csv(final_data,"tournament.csv")