For this week’s project, I will be importing a text file of chess tournament results to then use the information to create a csv. file with the variables; Player’s Name, Player’s State, Total Number of Points, Player’s Pre-Rating, and Average Pre Chess Rating of Opponents.
I first imported the text file using ‘read.table’ which imported the file with 1 variable and 195 observations.
tournament <- read.table("C:\\Users\\nakes\\OneDrive\\Desktop\\607\\tournamentinfo.txt", sep="\t", header=TRUE, col.names = "all")I cleaned and tidyed the dataset a little by removing rows that were dashes. Then I created two datasets: df.t1 and df.t2. The df.t1 dataset has all the player ids, player names, total points and each opponent for the rounds. The df.t2 dataset has the state and each players pre-ratings. Creating two separate datasets makes it easier to extract the variables we need later on to combine.
#Remove all the rows with dashes
t1 <- tournament %>%
filter(!str_detect(all, pattern = "---"))
#Extract every other row
df.t1 <- data.frame(t1[seq(3, nrow(t1), 2), ])
df.t2 <- data.frame(t1[seq(4, nrow(t1), 2), ])In this next step, I used ‘substr’ to pull out specific length strings as different variables from df.t1 and df.t2. Then I changed the strings to individual dataframes to combine all the variables together.
#Extract the all the variables from df.new and df.other
id <- as.numeric(substr(df.t1$t1.seq.3..nrow.t1...2...., start = 2, stop = 5))
player_name <- (substr(df.t1$t1.seq.3..nrow.t1...2...., start = 9, stop = 35))
player_state <- (substr(df.t2$t1.seq.4..nrow.t1...2...., start = 2, stop = 5))
total_points <- (substr(df.t1$t1.seq.3..nrow.t1...2...., start = 42, stop = 45))
player_pre_rating <- as.numeric(substr(df.t2$t1.seq.4..nrow.t1...2...., start = 22, stop = 26))
round1 <- as.numeric(substr(df.t1$t1.seq.3..nrow.t1...2...., start = 50, stop = 52))
round2 <- as.numeric(substr(df.t1$t1.seq.3..nrow.t1...2...., start = 55, stop = 58))
round3 <- as.numeric(substr(df.t1$t1.seq.3..nrow.t1...2...., start = 61, stop = 64))
round4 <- as.numeric(substr(df.t1$t1.seq.3..nrow.t1...2...., start = 67, stop = 70))
round5 <- as.numeric(substr(df.t1$t1.seq.3..nrow.t1...2...., start = 73, stop = 76))
round6 <- as.numeric(substr(df.t1$t1.seq.3..nrow.t1...2...., start = 79, stop = 82))
round7 <- as.numeric(substr(df.t1$t1.seq.3..nrow.t1...2...., start = 85, stop = 88))
#Change all of the extracted strings to a dataframe
df1 <- data.frame(id)
df2 <- data.frame(player_name)
df3 <- data.frame(player_state)
df4 <- data.frame(total_points)
df5 <- data.frame(player_pre_rating)
df6 <- data.frame(round1)
df7 <- data.frame(round2)
df8 <- data.frame(round3)
df9 <- data.frame(round4)
df10 <- data.frame(round5)
df11 <- data.frame(round6)
df12 <- data.frame(round7)
# Combine all the dataframes into one dataframe
data <- cbind(df1, df2, df3, df4, df5, df6, df7, df8, df9, df10, df11, df12)
print(data)## id player_name player_state total_points player_pre_rating
## 1 1 GARY HUA ON 6.0 1794
## 2 2 DAKSHESH DARURI MI 6.0 1553
## 3 3 ADITYA BAJAJ MI 6.0 1384
## 4 4 PATRICK H SCHILLING MI 5.5 1716
## 5 5 HANSHI ZUO MI 5.5 1655
## 6 6 HANSEN SONG OH 5.0 1686
## 7 7 GARY DEE SWATHELL MI 5.0 1649
## 8 8 EZEKIEL HOUGHTON MI 5.0 1641
## 9 9 STEFANO LEE ON 5.0 1411
## 10 10 ANVIT RAO MI 5.0 1365
## 11 11 CAMERON WILLIAM MC LEMAN MI 4.5 1712
## 12 12 KENNETH J TACK MI 4.5 1663
## 13 13 TORRANCE HENRY JR MI 4.5 1666
## 14 14 BRADLEY SHAW MI 4.5 1610
## 15 15 ZACHARY JAMES HOUGHTON MI 4.5 1220
## 16 16 MIKE NIKITIN MI 4.0 1604
## 17 17 RONALD GRZEGORCZYK MI 4.0 1629
## 18 18 DAVID SUNDEEN MI 4.0 1600
## 19 19 DIPANKAR ROY MI 4.0 1564
## 20 20 JASON ZHENG MI 4.0 1595
## 21 21 DINH DANG BUI ON 4.0 1563
## 22 22 EUGENE L MCCLURE MI 4.0 1555
## 23 23 ALAN BUI ON 4.0 1363
## 24 24 MICHAEL R ALDRICH MI 4.0 1229
## 25 25 LOREN SCHWIEBERT MI 3.5 1745
## 26 26 MAX ZHU ON 3.5 1579
## 27 27 GAURAV GIDWANI MI 3.5 1552
## 28 28 SOFIA ADINA STANESCU-BELLU MI 3.5 1507
## 29 29 CHIEDOZIE OKORIE MI 3.5 1602
## 30 30 GEORGE AVERY JONES ON 3.5 1522
## 31 31 RISHI SHETTY MI 3.5 1494
## 32 32 JOSHUA PHILIP MATHEWS ON 3.5 1441
## 33 33 JADE GE MI 3.5 1449
## 34 34 MICHAEL JEFFERY THOMAS MI 3.5 1399
## 35 35 JOSHUA DAVID LEE MI 3.5 1438
## 36 36 SIDDHARTH JHA MI 3.5 1355
## 37 37 AMIYATOSH PWNANANDAM MI 3.5 980
## 38 38 BRIAN LIU MI 3.0 1423
## 39 39 JOEL R HENDON MI 3.0 1436
## 40 40 FOREST ZHANG MI 3.0 1348
## 41 41 KYLE WILLIAM MURPHY MI 3.0 1403
## 42 42 JARED GE MI 3.0 1332
## 43 43 ROBERT GLEN VASEY MI 3.0 1283
## 44 44 JUSTIN D SCHILLING MI 3.0 1199
## 45 45 DEREK YAN MI 3.0 1242
## 46 46 JACOB ALEXANDER LAVALLEY MI 3.0 377
## 47 47 ERIC WRIGHT MI 2.5 1362
## 48 48 DANIEL KHAIN MI 2.5 1382
## 49 49 MICHAEL J MARTIN MI 2.5 1291
## 50 50 SHIVAM JHA MI 2.5 1056
## 51 51 TEJAS AYYAGARI MI 2.5 1011
## 52 52 ETHAN GUO MI 2.5 935
## 53 53 JOSE C YBARRA MI 2.0 1393
## 54 54 LARRY HODGE MI 2.0 1270
## 55 55 ALEX KONG MI 2.0 1186
## 56 56 MARISA RICCI MI 2.0 1153
## 57 57 MICHAEL LU MI 2.0 1092
## 58 58 VIRAJ MOHILE MI 2.0 917
## 59 59 SEAN M MC CORMICK MI 2.0 853
## 60 60 JULIA SHEN MI 1.5 967
## 61 61 JEZZEL FARKAS ON 1.5 955
## 62 62 ASHWIN BALAJI MI 1.0 1530
## 63 63 THOMAS JOSEPH HOSMER MI 1.0 1175
## 64 64 BEN LI MI 1.0 1163
## round1 round2 round3 round4 round5 round6 round7
## 1 39 21 18 14 7 12 4
## 2 63 58 4 17 16 20 7
## 3 8 61 25 21 11 13 12
## 4 23 28 2 26 5 19 1
## 5 45 37 12 13 4 14 17
## 6 34 29 11 35 10 27 21
## 7 57 46 13 11 1 9 2
## 8 3 32 14 9 47 28 19
## 9 25 18 59 8 26 7 20
## 10 16 19 55 31 6 25 18
## 11 38 56 6 7 3 34 26
## 12 42 33 5 38 NA 1 3
## 13 36 27 7 5 33 3 32
## 14 54 44 8 1 27 5 31
## 15 19 16 30 22 54 33 38
## 16 10 15 NA 39 2 36 NA
## 17 48 41 26 2 23 22 5
## 18 47 9 1 32 19 38 10
## 19 15 10 52 28 18 4 8
## 20 40 49 23 41 28 2 9
## 21 43 1 47 3 40 39 6
## 22 64 52 28 15 NA 17 40
## 23 4 43 20 58 17 37 46
## 24 28 47 43 25 60 44 39
## 25 9 53 3 24 34 10 47
## 26 49 40 17 4 9 32 11
## 27 51 13 46 37 14 6 NA
## 28 24 4 22 19 20 8 36
## 29 50 6 38 34 52 48 NA
## 30 52 64 15 55 31 61 50
## 31 58 55 64 10 30 50 14
## 32 61 8 44 18 51 26 13
## 33 60 12 50 36 13 15 51
## 34 6 60 37 29 25 11 52
## 35 46 38 56 6 57 52 48
## 36 13 57 51 33 NA 16 28
## 37 NA 5 34 27 NA 23 61
## 38 11 35 29 12 NA 18 15
## 39 1 54 40 16 44 21 24
## 40 20 26 39 59 21 56 22
## 41 59 17 58 20 NA NA NA
## 42 12 50 57 60 61 64 56
## 43 21 23 24 63 59 46 55
## 44 NA 14 32 53 39 24 59
## 45 5 51 60 56 63 55 58
## 46 35 7 27 50 64 43 23
## 47 18 24 21 61 8 51 25
## 48 17 63 NA 52 NA 29 35
## 49 26 20 63 64 58 NA NA
## 50 29 42 33 46 NA 31 30
## 51 27 45 36 57 32 47 33
## 52 30 22 19 48 29 35 34
## 53 NA 25 NA 44 NA 57 NA
## 54 14 39 61 NA 15 59 64
## 55 62 31 10 30 NA 45 43
## 56 NA 11 35 45 NA 40 42
## 57 7 36 42 51 35 53 NA
## 58 31 2 41 23 49 NA 45
## 59 41 NA 9 40 43 54 44
## 60 33 34 45 42 24 NA NA
## 61 32 3 54 47 42 30 37
## 62 55 NA NA NA NA NA NA
## 63 2 48 49 43 45 NA NA
## 64 22 30 31 49 46 42 54In the step, I created 7 new variables, round1_opp to round7_opp in the dataframe ‘data’. I used 7 loops to cycle through the data frame and input a value of the Players Pre-Rating in each correlating round using the rows. Then I used rowMean to calculate the Average Pre Chess Rating of Opponents, rounded the mean, and then selected only the variables I wanted to keep to export as a csv.
# Create a loop for the 7 new variables using round and nrow
for (row in 1:nrow(data))
data$round1_opp[row] <- data$player_pre_rating[data$round1[row]]
for (row in 1:nrow(data))
data$round2_opp[row] <- data$player_pre_rating[data$round2[row]]
for (row in 1:nrow(data))
data$round3_opp[row] <- data$player_pre_rating[data$round3[row]]
for (row in 1:nrow(data))
data$round4_opp[row] <- data$player_pre_rating[data$round4[row]]
for (row in 1:nrow(data))
data$round5_opp[row] <- data$player_pre_rating[data$round5[row]]
for (row in 1:nrow(data))
data$round6_opp[row] <- data$player_pre_rating[data$round6[row]]
for (row in 1:nrow(data))
data$round7_opp[row] <- data$player_pre_rating[data$round7[row]]
# Find mean for a new variable avg_pre_chess_rating
data$mean <- rowMeans(data[,13:19], na.rm=TRUE)
data$avg_pre_chess_rating <- round(data$mean)
print(data)## id player_name player_state total_points player_pre_rating
## 1 1 GARY HUA ON 6.0 1794
## 2 2 DAKSHESH DARURI MI 6.0 1553
## 3 3 ADITYA BAJAJ MI 6.0 1384
## 4 4 PATRICK H SCHILLING MI 5.5 1716
## 5 5 HANSHI ZUO MI 5.5 1655
## 6 6 HANSEN SONG OH 5.0 1686
## 7 7 GARY DEE SWATHELL MI 5.0 1649
## 8 8 EZEKIEL HOUGHTON MI 5.0 1641
## 9 9 STEFANO LEE ON 5.0 1411
## 10 10 ANVIT RAO MI 5.0 1365
## 11 11 CAMERON WILLIAM MC LEMAN MI 4.5 1712
## 12 12 KENNETH J TACK MI 4.5 1663
## 13 13 TORRANCE HENRY JR MI 4.5 1666
## 14 14 BRADLEY SHAW MI 4.5 1610
## 15 15 ZACHARY JAMES HOUGHTON MI 4.5 1220
## 16 16 MIKE NIKITIN MI 4.0 1604
## 17 17 RONALD GRZEGORCZYK MI 4.0 1629
## 18 18 DAVID SUNDEEN MI 4.0 1600
## 19 19 DIPANKAR ROY MI 4.0 1564
## 20 20 JASON ZHENG MI 4.0 1595
## 21 21 DINH DANG BUI ON 4.0 1563
## 22 22 EUGENE L MCCLURE MI 4.0 1555
## 23 23 ALAN BUI ON 4.0 1363
## 24 24 MICHAEL R ALDRICH MI 4.0 1229
## 25 25 LOREN SCHWIEBERT MI 3.5 1745
## 26 26 MAX ZHU ON 3.5 1579
## 27 27 GAURAV GIDWANI MI 3.5 1552
## 28 28 SOFIA ADINA STANESCU-BELLU MI 3.5 1507
## 29 29 CHIEDOZIE OKORIE MI 3.5 1602
## 30 30 GEORGE AVERY JONES ON 3.5 1522
## 31 31 RISHI SHETTY MI 3.5 1494
## 32 32 JOSHUA PHILIP MATHEWS ON 3.5 1441
## 33 33 JADE GE MI 3.5 1449
## 34 34 MICHAEL JEFFERY THOMAS MI 3.5 1399
## 35 35 JOSHUA DAVID LEE MI 3.5 1438
## 36 36 SIDDHARTH JHA MI 3.5 1355
## 37 37 AMIYATOSH PWNANANDAM MI 3.5 980
## 38 38 BRIAN LIU MI 3.0 1423
## 39 39 JOEL R HENDON MI 3.0 1436
## 40 40 FOREST ZHANG MI 3.0 1348
## 41 41 KYLE WILLIAM MURPHY MI 3.0 1403
## 42 42 JARED GE MI 3.0 1332
## 43 43 ROBERT GLEN VASEY MI 3.0 1283
## 44 44 JUSTIN D SCHILLING MI 3.0 1199
## 45 45 DEREK YAN MI 3.0 1242
## 46 46 JACOB ALEXANDER LAVALLEY MI 3.0 377
## 47 47 ERIC WRIGHT MI 2.5 1362
## 48 48 DANIEL KHAIN MI 2.5 1382
## 49 49 MICHAEL J MARTIN MI 2.5 1291
## 50 50 SHIVAM JHA MI 2.5 1056
## 51 51 TEJAS AYYAGARI MI 2.5 1011
## 52 52 ETHAN GUO MI 2.5 935
## 53 53 JOSE C YBARRA MI 2.0 1393
## 54 54 LARRY HODGE MI 2.0 1270
## 55 55 ALEX KONG MI 2.0 1186
## 56 56 MARISA RICCI MI 2.0 1153
## 57 57 MICHAEL LU MI 2.0 1092
## 58 58 VIRAJ MOHILE MI 2.0 917
## 59 59 SEAN M MC CORMICK MI 2.0 853
## 60 60 JULIA SHEN MI 1.5 967
## 61 61 JEZZEL FARKAS ON 1.5 955
## 62 62 ASHWIN BALAJI MI 1.0 1530
## 63 63 THOMAS JOSEPH HOSMER MI 1.0 1175
## 64 64 BEN LI MI 1.0 1163
## round1 round2 round3 round4 round5 round6 round7 round1_opp round2_opp
## 1 39 21 18 14 7 12 4 1436 1563
## 2 63 58 4 17 16 20 7 1175 917
## 3 8 61 25 21 11 13 12 1641 955
## 4 23 28 2 26 5 19 1 1363 1507
## 5 45 37 12 13 4 14 17 1242 980
## 6 34 29 11 35 10 27 21 1399 1602
## 7 57 46 13 11 1 9 2 1092 377
## 8 3 32 14 9 47 28 19 1384 1441
## 9 25 18 59 8 26 7 20 1745 1600
## 10 16 19 55 31 6 25 18 1604 1564
## 11 38 56 6 7 3 34 26 1423 1153
## 12 42 33 5 38 NA 1 3 1332 1449
## 13 36 27 7 5 33 3 32 1355 1552
## 14 54 44 8 1 27 5 31 1270 1199
## 15 19 16 30 22 54 33 38 1564 1604
## 16 10 15 NA 39 2 36 NA 1365 1220
## 17 48 41 26 2 23 22 5 1382 1403
## 18 47 9 1 32 19 38 10 1362 1411
## 19 15 10 52 28 18 4 8 1220 1365
## 20 40 49 23 41 28 2 9 1348 1291
## 21 43 1 47 3 40 39 6 1283 1794
## 22 64 52 28 15 NA 17 40 1163 935
## 23 4 43 20 58 17 37 46 1716 1283
## 24 28 47 43 25 60 44 39 1507 1362
## 25 9 53 3 24 34 10 47 1411 1393
## 26 49 40 17 4 9 32 11 1291 1348
## 27 51 13 46 37 14 6 NA 1011 1666
## 28 24 4 22 19 20 8 36 1229 1716
## 29 50 6 38 34 52 48 NA 1056 1686
## 30 52 64 15 55 31 61 50 935 1163
## 31 58 55 64 10 30 50 14 917 1186
## 32 61 8 44 18 51 26 13 955 1641
## 33 60 12 50 36 13 15 51 967 1663
## 34 6 60 37 29 25 11 52 1686 967
## 35 46 38 56 6 57 52 48 377 1423
## 36 13 57 51 33 NA 16 28 1666 1092
## 37 NA 5 34 27 NA 23 61 NA 1655
## 38 11 35 29 12 NA 18 15 1712 1438
## 39 1 54 40 16 44 21 24 1794 1270
## 40 20 26 39 59 21 56 22 1595 1579
## 41 59 17 58 20 NA NA NA 853 1629
## 42 12 50 57 60 61 64 56 1663 1056
## 43 21 23 24 63 59 46 55 1563 1363
## 44 NA 14 32 53 39 24 59 NA 1610
## 45 5 51 60 56 63 55 58 1655 1011
## 46 35 7 27 50 64 43 23 1438 1649
## 47 18 24 21 61 8 51 25 1600 1229
## 48 17 63 NA 52 NA 29 35 1629 1175
## 49 26 20 63 64 58 NA NA 1579 1595
## 50 29 42 33 46 NA 31 30 1602 1332
## 51 27 45 36 57 32 47 33 1552 1242
## 52 30 22 19 48 29 35 34 1522 1555
## 53 NA 25 NA 44 NA 57 NA NA 1745
## 54 14 39 61 NA 15 59 64 1610 1436
## 55 62 31 10 30 NA 45 43 1530 1494
## 56 NA 11 35 45 NA 40 42 NA 1712
## 57 7 36 42 51 35 53 NA 1649 1355
## 58 31 2 41 23 49 NA 45 1494 1553
## 59 41 NA 9 40 43 54 44 1403 NA
## 60 33 34 45 42 24 NA NA 1449 1399
## 61 32 3 54 47 42 30 37 1441 1384
## 62 55 NA NA NA NA NA NA 1186 NA
## 63 2 48 49 43 45 NA NA 1553 1382
## 64 22 30 31 49 46 42 54 1555 1522
## round3_opp round4_opp round5_opp round6_opp round7_opp mean
## 1 1600 1610 1649 1663 1716 1605.286
## 2 1716 1629 1604 1595 1649 1469.286
## 3 1745 1563 1712 1666 1663 1563.571
## 4 1553 1579 1655 1564 1794 1573.571
## 5 1663 1666 1716 1610 1629 1500.857
## 6 1712 1438 1365 1552 1563 1518.714
## 7 1666 1712 1794 1411 1553 1372.143
## 8 1610 1411 1362 1507 1564 1468.429
## 9 853 1641 1579 1649 1595 1523.143
## 10 1186 1494 1686 1745 1600 1554.143
## 11 1686 1649 1384 1399 1579 1467.571
## 12 1655 1423 NA 1794 1384 1506.167
## 13 1649 1655 1449 1384 1441 1497.857
## 14 1641 1794 1552 1655 1494 1515.000
## 15 1522 1555 1270 1449 1423 1483.857
## 16 NA 1436 1553 1355 NA 1385.800
## 17 1579 1553 1363 1555 1655 1498.571
## 18 1794 1441 1564 1423 1365 1480.000
## 19 935 1507 1600 1716 1641 1426.286
## 20 1363 1403 1507 1553 1411 1410.857
## 21 1362 1384 1348 1436 1686 1470.429
## 22 1507 1220 NA 1629 1348 1300.333
## 23 1595 917 1629 980 377 1213.857
## 24 1283 1745 967 1199 1436 1357.000
## 25 1384 1229 1399 1365 1362 1363.286
## 26 1629 1716 1411 1441 1712 1506.857
## 27 377 980 1610 1686 NA 1221.667
## 28 1555 1564 1595 1641 1355 1522.143
## 29 1423 1399 935 1382 NA 1313.500
## 30 1220 1186 1494 955 1056 1144.143
## 31 1163 1365 1522 1056 1610 1259.857
## 32 1199 1600 1011 1579 1666 1378.714
## 33 1056 1355 1666 1220 1011 1276.857
## 34 980 1602 1745 1712 935 1375.286
## 35 1153 1686 1092 935 1382 1149.714
## 36 1011 1449 NA 1604 1507 1388.167
## 37 1399 1552 NA 1363 955 1384.800
## 38 1602 1663 NA 1600 1220 1539.167
## 39 1348 1604 1199 1563 1229 1429.571
## 40 1436 853 1563 1153 1555 1390.571
## 41 917 1595 NA NA NA 1248.500
## 42 1092 967 955 1163 1153 1149.857
## 43 1229 1175 853 377 1186 1106.571
## 44 1441 1393 1436 1229 853 1327.000
## 45 967 1153 1175 1186 917 1152.000
## 46 1552 1056 1163 1283 1363 1357.714
## 47 1563 955 1641 1011 1745 1392.000
## 48 NA 935 NA 1602 1438 1355.800
## 49 1175 1163 917 NA NA 1285.800
## 50 1449 377 NA 1494 1522 1296.000
## 51 1355 1092 1441 1362 1449 1356.143
## 52 1564 1382 1602 1438 1399 1494.571
## 53 NA 1199 NA 1092 NA 1345.333
## 54 955 NA 1220 853 1163 1206.167
## 55 1365 1522 NA 1242 1283 1406.000
## 56 1438 1242 NA 1348 1332 1414.400
## 57 1332 1011 1438 1393 NA 1363.000
## 58 1403 1363 1291 NA 1242 1391.000
## 59 1411 1348 1283 1270 1199 1319.000
## 60 1242 1332 1229 NA NA 1330.200
## 61 1270 1362 1332 1522 980 1327.286
## 62 NA NA NA NA NA 1186.000
## 63 1291 1283 1242 NA NA 1350.200
## 64 1494 1291 377 1332 1270 1263.000
## avg_pre_chess_rating
## 1 1605
## 2 1469
## 3 1564
## 4 1574
## 5 1501
## 6 1519
## 7 1372
## 8 1468
## 9 1523
## 10 1554
## 11 1468
## 12 1506
## 13 1498
## 14 1515
## 15 1484
## 16 1386
## 17 1499
## 18 1480
## 19 1426
## 20 1411
## 21 1470
## 22 1300
## 23 1214
## 24 1357
## 25 1363
## 26 1507
## 27 1222
## 28 1522
## 29 1314
## 30 1144
## 31 1260
## 32 1379
## 33 1277
## 34 1375
## 35 1150
## 36 1388
## 37 1385
## 38 1539
## 39 1430
## 40 1391
## 41 1248
## 42 1150
## 43 1107
## 44 1327
## 45 1152
## 46 1358
## 47 1392
## 48 1356
## 49 1286
## 50 1296
## 51 1356
## 52 1495
## 53 1345
## 54 1206
## 55 1406
## 56 1414
## 57 1363
## 58 1391
## 59 1319
## 60 1330
## 61 1327
## 62 1186
## 63 1350
## 64 1263#Select only the variables we want in new dataframe
df = subset(data, select = c(player_name, player_state, total_points, player_pre_rating, avg_pre_chess_rating) )
print(df)## player_name player_state total_points player_pre_rating
## 1 GARY HUA ON 6.0 1794
## 2 DAKSHESH DARURI MI 6.0 1553
## 3 ADITYA BAJAJ MI 6.0 1384
## 4 PATRICK H SCHILLING MI 5.5 1716
## 5 HANSHI ZUO MI 5.5 1655
## 6 HANSEN SONG OH 5.0 1686
## 7 GARY DEE SWATHELL MI 5.0 1649
## 8 EZEKIEL HOUGHTON MI 5.0 1641
## 9 STEFANO LEE ON 5.0 1411
## 10 ANVIT RAO MI 5.0 1365
## 11 CAMERON WILLIAM MC LEMAN MI 4.5 1712
## 12 KENNETH J TACK MI 4.5 1663
## 13 TORRANCE HENRY JR MI 4.5 1666
## 14 BRADLEY SHAW MI 4.5 1610
## 15 ZACHARY JAMES HOUGHTON MI 4.5 1220
## 16 MIKE NIKITIN MI 4.0 1604
## 17 RONALD GRZEGORCZYK MI 4.0 1629
## 18 DAVID SUNDEEN MI 4.0 1600
## 19 DIPANKAR ROY MI 4.0 1564
## 20 JASON ZHENG MI 4.0 1595
## 21 DINH DANG BUI ON 4.0 1563
## 22 EUGENE L MCCLURE MI 4.0 1555
## 23 ALAN BUI ON 4.0 1363
## 24 MICHAEL R ALDRICH MI 4.0 1229
## 25 LOREN SCHWIEBERT MI 3.5 1745
## 26 MAX ZHU ON 3.5 1579
## 27 GAURAV GIDWANI MI 3.5 1552
## 28 SOFIA ADINA STANESCU-BELLU MI 3.5 1507
## 29 CHIEDOZIE OKORIE MI 3.5 1602
## 30 GEORGE AVERY JONES ON 3.5 1522
## 31 RISHI SHETTY MI 3.5 1494
## 32 JOSHUA PHILIP MATHEWS ON 3.5 1441
## 33 JADE GE MI 3.5 1449
## 34 MICHAEL JEFFERY THOMAS MI 3.5 1399
## 35 JOSHUA DAVID LEE MI 3.5 1438
## 36 SIDDHARTH JHA MI 3.5 1355
## 37 AMIYATOSH PWNANANDAM MI 3.5 980
## 38 BRIAN LIU MI 3.0 1423
## 39 JOEL R HENDON MI 3.0 1436
## 40 FOREST ZHANG MI 3.0 1348
## 41 KYLE WILLIAM MURPHY MI 3.0 1403
## 42 JARED GE MI 3.0 1332
## 43 ROBERT GLEN VASEY MI 3.0 1283
## 44 JUSTIN D SCHILLING MI 3.0 1199
## 45 DEREK YAN MI 3.0 1242
## 46 JACOB ALEXANDER LAVALLEY MI 3.0 377
## 47 ERIC WRIGHT MI 2.5 1362
## 48 DANIEL KHAIN MI 2.5 1382
## 49 MICHAEL J MARTIN MI 2.5 1291
## 50 SHIVAM JHA MI 2.5 1056
## 51 TEJAS AYYAGARI MI 2.5 1011
## 52 ETHAN GUO MI 2.5 935
## 53 JOSE C YBARRA MI 2.0 1393
## 54 LARRY HODGE MI 2.0 1270
## 55 ALEX KONG MI 2.0 1186
## 56 MARISA RICCI MI 2.0 1153
## 57 MICHAEL LU MI 2.0 1092
## 58 VIRAJ MOHILE MI 2.0 917
## 59 SEAN M MC CORMICK MI 2.0 853
## 60 JULIA SHEN MI 1.5 967
## 61 JEZZEL FARKAS ON 1.5 955
## 62 ASHWIN BALAJI MI 1.0 1530
## 63 THOMAS JOSEPH HOSMER MI 1.0 1175
## 64 BEN LI MI 1.0 1163
## avg_pre_chess_rating
## 1 1605
## 2 1469
## 3 1564
## 4 1574
## 5 1501
## 6 1519
## 7 1372
## 8 1468
## 9 1523
## 10 1554
## 11 1468
## 12 1506
## 13 1498
## 14 1515
## 15 1484
## 16 1386
## 17 1499
## 18 1480
## 19 1426
## 20 1411
## 21 1470
## 22 1300
## 23 1214
## 24 1357
## 25 1363
## 26 1507
## 27 1222
## 28 1522
## 29 1314
## 30 1144
## 31 1260
## 32 1379
## 33 1277
## 34 1375
## 35 1150
## 36 1388
## 37 1385
## 38 1539
## 39 1430
## 40 1391
## 41 1248
## 42 1150
## 43 1107
## 44 1327
## 45 1152
## 46 1358
## 47 1392
## 48 1356
## 49 1286
## 50 1296
## 51 1356
## 52 1495
## 53 1345
## 54 1206
## 55 1406
## 56 1414
## 57 1363
## 58 1391
## 59 1319
## 60 1330
## 61 1327
## 62 1186
## 63 1350
## 64 1263#write csv.
write.csv(df,"C:\\Users\\nakes\\OneDrive\\Desktop\\607\\data607_chess_project1.csv")I was able to create the final dataset using the materials from our 607 class and the information available to us online. In the final dataset we have: Player’s Name (player_name), Player’s State (player_state), Total Number of Points (total_points), Player’s Pre-Rating (player_pre_rating), and Average Pre Chess Rating of Opponents (avg_pre_chess_rating). This dataset is now more easy to use and manipulate as csv. and dataframe to plot or do some descriptive analysis.