Import the Stringr library and then pull in the data. I had an error saying that I am not authorized to pull data directly from the CUNY site that had the chess data, so I had to download it.
Then we view the data and change the column name to something simple.
library (stringr)
tournamentinfo <- read.csv("F:/Data/Project 1/tournamentinfo.txt")
head(tournamentinfo)## X.........................................................................................
## 1 Pair | Player Name |Total|Round|Round|Round|Round|Round|Round|Round|
## 2 Num | USCF ID / Rtg (Pre->Post) | Pts | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
## 3 -----------------------------------------------------------------------------------------
## 4 1 | GARY HUA |6.0 |W 39|W 21|W 18|W 14|W 7|D 12|D 4|
## 5 ON | 15445895 / R: 1794 ->1817 |N:2 |W |B |W |B |W |B |W |
## 6 -----------------------------------------------------------------------------------------names(tournamentinfo) <- c("x")Now that we have the data I will first break out the names of the different players using regular expressions.
Any time that we have a set of two or more characters separated by any number of characters followed by a set of another two or more characters, we have our name.
Upon looking at the data, we also have a couple rows of non relevant data (i.e. “Player Name”) so we can delete the rows with this column header information.
name <- unlist(str_extract_all(tournamentinfo$x, "[[:alpha:]]{2,}.+[[:alpha:]]{2,}"))
name## [1] "Pair | Player Name |Total|Round|Round|Round|Round|Round|Round|Round"
## [2] "Num | USCF ID / Rtg (Pre->Post) | Pts"
## [3] "GARY HUA"
## [4] "DAKSHESH DARURI"
## [5] "ADITYA BAJAJ"
## [6] "PATRICK H SCHILLING"
## [7] "HANSHI ZUO"
## [8] "HANSEN SONG"
## [9] "GARY DEE SWATHELL"
## [10] "EZEKIEL HOUGHTON"
## [11] "STEFANO LEE"
## [12] "ANVIT RAO"
## [13] "CAMERON WILLIAM MC LEMAN"
## [14] "KENNETH J TACK"
## [15] "TORRANCE HENRY JR"
## [16] "BRADLEY SHAW"
## [17] "ZACHARY JAMES HOUGHTON"
## [18] "MIKE NIKITIN"
## [19] "RONALD GRZEGORCZYK"
## [20] "DAVID SUNDEEN"
## [21] "DIPANKAR ROY"
## [22] "JASON ZHENG"
## [23] "DINH DANG BUI"
## [24] "EUGENE L MCCLURE"
## [25] "ALAN BUI"
## [26] "MICHAEL R ALDRICH"
## [27] "LOREN SCHWIEBERT"
## [28] "MAX ZHU"
## [29] "GAURAV GIDWANI"
## [30] "SOFIA ADINA STANESCU-BELLU"
## [31] "CHIEDOZIE OKORIE"
## [32] "GEORGE AVERY JONES"
## [33] "RISHI SHETTY"
## [34] "JOSHUA PHILIP MATHEWS"
## [35] "JADE GE"
## [36] "MICHAEL JEFFERY THOMAS"
## [37] "JOSHUA DAVID LEE"
## [38] "SIDDHARTH JHA"
## [39] "AMIYATOSH PWNANANDAM"
## [40] "BRIAN LIU"
## [41] "JOEL R HENDON"
## [42] "FOREST ZHANG"
## [43] "KYLE WILLIAM MURPHY"
## [44] "JARED GE"
## [45] "ROBERT GLEN VASEY"
## [46] "JUSTIN D SCHILLING"
## [47] "DEREK YAN"
## [48] "JACOB ALEXANDER LAVALLEY"
## [49] "ERIC WRIGHT"
## [50] "DANIEL KHAIN"
## [51] "MICHAEL J MARTIN"
## [52] "SHIVAM JHA"
## [53] "TEJAS AYYAGARI"
## [54] "ETHAN GUO"
## [55] "JOSE C YBARRA"
## [56] "LARRY HODGE"
## [57] "ALEX KONG"
## [58] "MARISA RICCI"
## [59] "MICHAEL LU"
## [60] "VIRAJ MOHILE"
## [61] "SEAN M MC CORMICK"
## [62] "JULIA SHEN"
## [63] "JEZZEL FARKAS"
## [64] "ASHWIN BALAJI"
## [65] "THOMAS JOSEPH HOSMER"
## [66] "BEN LI"name <- name[-(1:2)]
name## [1] "GARY HUA" "DAKSHESH DARURI"
## [3] "ADITYA BAJAJ" "PATRICK H SCHILLING"
## [5] "HANSHI ZUO" "HANSEN SONG"
## [7] "GARY DEE SWATHELL" "EZEKIEL HOUGHTON"
## [9] "STEFANO LEE" "ANVIT RAO"
## [11] "CAMERON WILLIAM MC LEMAN" "KENNETH J TACK"
## [13] "TORRANCE HENRY JR" "BRADLEY SHAW"
## [15] "ZACHARY JAMES HOUGHTON" "MIKE NIKITIN"
## [17] "RONALD GRZEGORCZYK" "DAVID SUNDEEN"
## [19] "DIPANKAR ROY" "JASON ZHENG"
## [21] "DINH DANG BUI" "EUGENE L MCCLURE"
## [23] "ALAN BUI" "MICHAEL R ALDRICH"
## [25] "LOREN SCHWIEBERT" "MAX ZHU"
## [27] "GAURAV GIDWANI" "SOFIA ADINA STANESCU-BELLU"
## [29] "CHIEDOZIE OKORIE" "GEORGE AVERY JONES"
## [31] "RISHI SHETTY" "JOSHUA PHILIP MATHEWS"
## [33] "JADE GE" "MICHAEL JEFFERY THOMAS"
## [35] "JOSHUA DAVID LEE" "SIDDHARTH JHA"
## [37] "AMIYATOSH PWNANANDAM" "BRIAN LIU"
## [39] "JOEL R HENDON" "FOREST ZHANG"
## [41] "KYLE WILLIAM MURPHY" "JARED GE"
## [43] "ROBERT GLEN VASEY" "JUSTIN D SCHILLING"
## [45] "DEREK YAN" "JACOB ALEXANDER LAVALLEY"
## [47] "ERIC WRIGHT" "DANIEL KHAIN"
## [49] "MICHAEL J MARTIN" "SHIVAM JHA"
## [51] "TEJAS AYYAGARI" "ETHAN GUO"
## [53] "JOSE C YBARRA" "LARRY HODGE"
## [55] "ALEX KONG" "MARISA RICCI"
## [57] "MICHAEL LU" "VIRAJ MOHILE"
## [59] "SEAN M MC CORMICK" "JULIA SHEN"
## [61] "JEZZEL FARKAS" "ASHWIN BALAJI"
## [63] "THOMAS JOSEPH HOSMER" "BEN LI"Next we extract the state values. State abbreviations are always two alphabetic characters in a row and they follow a space and then the “|”. After we have isolated any value that meets this criteria, we can then remove the “|” using another regular expression.
state <- unlist(str_extract_all(tournamentinfo$x, "( [[:alpha:]]{2,2} \\|)"))
state## [1] " ON |" " MI |" " MI |" " MI |" " MI |" " OH |" " MI |" " MI |"
## [9] " ON |" " MI |" " MI |" " MI |" " MI |" " MI |" " MI |" " MI |"
## [17] " MI |" " MI |" " MI |" " MI |" " ON |" " MI |" " ON |" " MI |"
## [25] " MI |" " ON |" " MI |" " MI |" " MI |" " ON |" " MI |" " ON |"
## [33] " MI |" " MI |" " MI |" " MI |" " MI |" " MI |" " MI |" " MI |"
## [41] " MI |" " MI |" " MI |" " MI |" " MI |" " MI |" " MI |" " MI |"
## [49] " MI |" " MI |" " MI |" " MI |" " MI |" " MI |" " MI |" " MI |"
## [57] " MI |" " MI |" " MI |" " MI |" " ON |" " MI |" " MI |" " MI |"state <- unlist(str_extract_all(state, "[[:alpha:]]{2}"))
state## [1] "ON" "MI" "MI" "MI" "MI" "OH" "MI" "MI" "ON" "MI" "MI" "MI" "MI" "MI"
## [15] "MI" "MI" "MI" "MI" "MI" "MI" "ON" "MI" "ON" "MI" "MI" "ON" "MI" "MI"
## [29] "MI" "ON" "MI" "ON" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI"
## [43] "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI"
## [57] "MI" "MI" "MI" "MI" "ON" "MI" "MI" "MI"The points figure can easily be broken out by identifying two characters separated by a literal “.”
points <- unlist(str_extract_all(tournamentinfo$x, "(.\\..)"))
points## [1] "6.0" "6.0" "6.0" "5.5" "5.5" "5.0" "5.0" "5.0" "5.0" "5.0" "4.5"
## [12] "4.5" "4.5" "4.5" "4.5" "4.0" "4.0" "4.0" "4.0" "4.0" "4.0" "4.0"
## [23] "4.0" "4.0" "3.5" "3.5" "3.5" "3.5" "3.5" "3.5" "3.5" "3.5" "3.5"
## [34] "3.5" "3.5" "3.5" "3.5" "3.0" "3.0" "3.0" "3.0" "3.0" "3.0" "3.0"
## [45] "3.0" "3.0" "2.5" "2.5" "2.5" "2.5" "2.5" "2.5" "2.0" "2.0" "2.0"
## [56] "2.0" "2.0" "2.0" "2.0" "1.5" "1.5" "1.0" "1.0" "1.0"To identify the pre-score for each individual player we look for 3 to 4 consecutive numbers that come before “->”. We then ensure that this value is numeric
prescore <- unlist(str_extract_all(tournamentinfo$x, ":.*[[:digit:]]{3,4}.*->"))
prescore## [1] ": 1794 ->" ": 1553 ->" ": 1384 ->" ": 1716 ->" ": 1655 ->"
## [6] ": 1686 ->" ": 1649 ->" ": 1641P17->" ": 1411 ->" ": 1365 ->"
## [11] ": 1712 ->" ": 1663 ->" ": 1666 ->" ": 1610 ->" ": 1220P13->"
## [16] ": 1604 ->" ": 1629 ->" ": 1600 ->" ": 1564 ->" ": 1595 ->"
## [21] ": 1563P22->" ": 1555 ->" ": 1363 ->" ": 1229 ->" ": 1745 ->"
## [26] ": 1579 ->" ": 1552 ->" ": 1507 ->" ": 1602P6 ->" ": 1522 ->"
## [31] ": 1494 ->" ": 1441 ->" ": 1449 ->" ": 1399 ->" ": 1438 ->"
## [36] ": 1355 ->" ": 980P12->" ": 1423 ->" ": 1436P23->" ": 1348 ->"
## [41] ": 1403P5 ->" ": 1332 ->" ": 1283 ->" ": 1199 ->" ": 1242 ->"
## [46] ": 377P3 ->" ": 1362 ->" ": 1382 ->" ": 1291P12->" ": 1056 ->"
## [51] ": 1011 ->" ": 935 ->" ": 1393 ->" ": 1270 ->" ": 1186 ->"
## [56] ": 1153 ->" ": 1092 ->" ": 917 ->" ": 853 ->" ": 967 ->"
## [61] ": 955P11->" ": 1530 ->" ": 1175 ->" ": 1163 ->"prescore <- unlist(str_extract_all(prescore, "[[:digit:]]{3,4}"))
prescore <- as.numeric(prescore)Now we create a data frame with the pre-score and a new ID column which we create.
id <- as.factor(1:64)
predf <- data.frame(id, prescore)To extract the score and the individual rounds we look for the points value followed by characters or spaces and ending with one or more digits. An additional regular expression is used to remove spaces and any of the letters “WLDH”.
rounds <- unlist(str_extract_all(tournamentinfo$x, "(.\\..).* *\\d{1,2}"))
rounds## [1] "6.0 |W 39|W 21|W 18|W 14|W 7|D 12|D 4"
## [2] "6.0 |W 63|W 58|L 4|W 17|W 16|W 20|W 7"
## [3] "6.0 |L 8|W 61|W 25|W 21|W 11|W 13|W 12"
## [4] "5.5 |W 23|D 28|W 2|W 26|D 5|W 19|D 1"
## [5] "5.5 |W 45|W 37|D 12|D 13|D 4|W 14|W 17"
## [6] "5.0 |W 34|D 29|L 11|W 35|D 10|W 27|W 21"
## [7] "5.0 |W 57|W 46|W 13|W 11|L 1|W 9|L 2"
## [8] "5.0 |W 3|W 32|L 14|L 9|W 47|W 28|W 19"
## [9] "5.0 |W 25|L 18|W 59|W 8|W 26|L 7|W 20"
## [10] "5.0 |D 16|L 19|W 55|W 31|D 6|W 25|W 18"
## [11] "4.5 |D 38|W 56|W 6|L 7|L 3|W 34|W 26"
## [12] "4.5 |W 42|W 33|D 5|W 38|H |D 1|L 3"
## [13] "4.5 |W 36|W 27|L 7|D 5|W 33|L 3|W 32"
## [14] "4.5 |W 54|W 44|W 8|L 1|D 27|L 5|W 31"
## [15] "4.5 |D 19|L 16|W 30|L 22|W 54|W 33|W 38"
## [16] "4.0 |D 10|W 15|H |W 39|L 2|W 36"
## [17] "4.0 |W 48|W 41|L 26|L 2|W 23|W 22|L 5"
## [18] "4.0 |W 47|W 9|L 1|W 32|L 19|W 38|L 10"
## [19] "4.0 |D 15|W 10|W 52|D 28|W 18|L 4|L 8"
## [20] "4.0 |L 40|W 49|W 23|W 41|W 28|L 2|L 9"
## [21] "4.0 |W 43|L 1|W 47|L 3|W 40|W 39|L 6"
## [22] "4.0 |W 64|D 52|L 28|W 15|H |L 17|W 40"
## [23] "4.0 |L 4|W 43|L 20|W 58|L 17|W 37|W 46"
## [24] "4.0 |L 28|L 47|W 43|L 25|W 60|W 44|W 39"
## [25] "3.5 |L 9|W 53|L 3|W 24|D 34|L 10|W 47"
## [26] "3.5 |W 49|W 40|W 17|L 4|L 9|D 32|L 11"
## [27] "3.5 |W 51|L 13|W 46|W 37|D 14|L 6"
## [28] "3.5 |W 24|D 4|W 22|D 19|L 20|L 8|D 36"
## [29] "3.5 |W 50|D 6|L 38|L 34|W 52|W 48"
## [30] "3.5 |L 52|D 64|L 15|W 55|L 31|W 61|W 50"
## [31] "3.5 |L 58|D 55|W 64|L 10|W 30|W 50|L 14"
## [32] "3.5 |W 61|L 8|W 44|L 18|W 51|D 26|L 13"
## [33] "3.5 |W 60|L 12|W 50|D 36|L 13|L 15|W 51"
## [34] "3.5 |L 6|W 60|L 37|W 29|D 25|L 11|W 52"
## [35] "3.5 |L 46|L 38|W 56|L 6|W 57|D 52|W 48"
## [36] "3.5 |L 13|W 57|W 51|D 33|H |L 16|D 28"
## [37] "3.5 |B |L 5|W 34|L 27|H |L 23|W 61"
## [38] "3.0 |D 11|W 35|W 29|L 12|H |L 18|L 15"
## [39] "3.0 |L 1|W 54|W 40|L 16|W 44|L 21|L 24"
## [40] "3.0 |W 20|L 26|L 39|W 59|L 21|W 56|L 22"
## [41] "3.0 |W 59|L 17|W 58|L 20"
## [42] "3.0 |L 12|L 50|L 57|D 60|D 61|W 64|W 56"
## [43] "3.0 |L 21|L 23|L 24|W 63|W 59|L 46|W 55"
## [44] "3.0 |B |L 14|L 32|W 53|L 39|L 24|W 59"
## [45] "3.0 |L 5|L 51|D 60|L 56|W 63|D 55|W 58"
## [46] "3.0 |W 35|L 7|L 27|L 50|W 64|W 43|L 23"
## [47] "2.5 |L 18|W 24|L 21|W 61|L 8|D 51|L 25"
## [48] "2.5 |L 17|W 63|H |D 52|H |L 29|L 35"
## [49] "2.5 |L 26|L 20|D 63|D 64|W 58"
## [50] "2.5 |L 29|W 42|L 33|W 46|H |L 31|L 30"
## [51] "2.5 |L 27|W 45|L 36|W 57|L 32|D 47|L 33"
## [52] "2.5 |W 30|D 22|L 19|D 48|L 29|D 35|L 34"
## [53] "2.0 |H |L 25|H |L 44|U |W 57"
## [54] "2.0 |L 14|L 39|L 61|B |L 15|L 59|W 64"
## [55] "2.0 |L 62|D 31|L 10|L 30|B |D 45|L 43"
## [56] "2.0 |H |L 11|L 35|W 45|H |L 40|L 42"
## [57] "2.0 |L 7|L 36|W 42|L 51|L 35|L 53"
## [58] "2.0 |W 31|L 2|L 41|L 23|L 49|B |L 45"
## [59] "2.0 |L 41|B |L 9|L 40|L 43|W 54|L 44"
## [60] "1.5 |L 33|L 34|D 45|D 42|L 24"
## [61] "1.5 |L 32|L 3|W 54|L 47|D 42|L 30|L 37"
## [62] "1.0 |W 55"
## [63] "1.0 |L 2|L 48|D 49|L 43|L 45"
## [64] "1.0 |L 22|D 30|L 31|D 49|L 46|L 42|L 54"rounds <- str_replace_all(rounds, "W|L|D|H| ", replacement = "")
rounds## [1] "6.0|39|21|18|14|7|12|4" "6.0|63|58|4|17|16|20|7"
## [3] "6.0|8|61|25|21|11|13|12" "5.5|23|28|2|26|5|19|1"
## [5] "5.5|45|37|12|13|4|14|17" "5.0|34|29|11|35|10|27|21"
## [7] "5.0|57|46|13|11|1|9|2" "5.0|3|32|14|9|47|28|19"
## [9] "5.0|25|18|59|8|26|7|20" "5.0|16|19|55|31|6|25|18"
## [11] "4.5|38|56|6|7|3|34|26" "4.5|42|33|5|38||1|3"
## [13] "4.5|36|27|7|5|33|3|32" "4.5|54|44|8|1|27|5|31"
## [15] "4.5|19|16|30|22|54|33|38" "4.0|10|15||39|2|36"
## [17] "4.0|48|41|26|2|23|22|5" "4.0|47|9|1|32|19|38|10"
## [19] "4.0|15|10|52|28|18|4|8" "4.0|40|49|23|41|28|2|9"
## [21] "4.0|43|1|47|3|40|39|6" "4.0|64|52|28|15||17|40"
## [23] "4.0|4|43|20|58|17|37|46" "4.0|28|47|43|25|60|44|39"
## [25] "3.5|9|53|3|24|34|10|47" "3.5|49|40|17|4|9|32|11"
## [27] "3.5|51|13|46|37|14|6" "3.5|24|4|22|19|20|8|36"
## [29] "3.5|50|6|38|34|52|48" "3.5|52|64|15|55|31|61|50"
## [31] "3.5|58|55|64|10|30|50|14" "3.5|61|8|44|18|51|26|13"
## [33] "3.5|60|12|50|36|13|15|51" "3.5|6|60|37|29|25|11|52"
## [35] "3.5|46|38|56|6|57|52|48" "3.5|13|57|51|33||16|28"
## [37] "3.5|B|5|34|27||23|61" "3.0|11|35|29|12||18|15"
## [39] "3.0|1|54|40|16|44|21|24" "3.0|20|26|39|59|21|56|22"
## [41] "3.0|59|17|58|20" "3.0|12|50|57|60|61|64|56"
## [43] "3.0|21|23|24|63|59|46|55" "3.0|B|14|32|53|39|24|59"
## [45] "3.0|5|51|60|56|63|55|58" "3.0|35|7|27|50|64|43|23"
## [47] "2.5|18|24|21|61|8|51|25" "2.5|17|63||52||29|35"
## [49] "2.5|26|20|63|64|58" "2.5|29|42|33|46||31|30"
## [51] "2.5|27|45|36|57|32|47|33" "2.5|30|22|19|48|29|35|34"
## [53] "2.0||25||44|U|57" "2.0|14|39|61|B|15|59|64"
## [55] "2.0|62|31|10|30|B|45|43" "2.0||11|35|45||40|42"
## [57] "2.0|7|36|42|51|35|53" "2.0|31|2|41|23|49|B|45"
## [59] "2.0|41|B|9|40|43|54|44" "1.5|33|34|45|42|24"
## [61] "1.5|32|3|54|47|42|30|37" "1.0|55"
## [63] "1.0|2|48|49|43|45" "1.0|22|30|31|49|46|42|54"Found this solution at the following link: http://stackoverflow.com/questions/7069076/split-column-at-delimiter-in-data-frame
Attempted to use the tidyr package to separate the columns but was getting an error message that there were too few results for certain rows.
If I’m understanding it correctly, do.call allows us to apply a function (in this case rbind which binds the result to rows) to the results of the strsplit argument.
We then add an ID column and rename the columns
roundsdf <- data.frame(rounds)
roundsdf <- data.frame(do.call('rbind', strsplit(as.character(roundsdf$rounds),'|',fixed=TRUE)))## Warning in rbind(c("6.0", "39", "21", "18", "14", "7", "12", "4"),
## c("6.0", : number of columns of result is not a multiple of vector length
## (arg 16)roundsdf## X1 X2 X3 X4 X5 X6 X7 X8
## 1 6.0 39 21 18 14 7 12 4
## 2 6.0 63 58 4 17 16 20 7
## 3 6.0 8 61 25 21 11 13 12
## 4 5.5 23 28 2 26 5 19 1
## 5 5.5 45 37 12 13 4 14 17
## 6 5.0 34 29 11 35 10 27 21
## 7 5.0 57 46 13 11 1 9 2
## 8 5.0 3 32 14 9 47 28 19
## 9 5.0 25 18 59 8 26 7 20
## 10 5.0 16 19 55 31 6 25 18
## 11 4.5 38 56 6 7 3 34 26
## 12 4.5 42 33 5 38 1 3
## 13 4.5 36 27 7 5 33 3 32
## 14 4.5 54 44 8 1 27 5 31
## 15 4.5 19 16 30 22 54 33 38
## 16 4.0 10 15 39 2 36 4.0
## 17 4.0 48 41 26 2 23 22 5
## 18 4.0 47 9 1 32 19 38 10
## 19 4.0 15 10 52 28 18 4 8
## 20 4.0 40 49 23 41 28 2 9
## 21 4.0 43 1 47 3 40 39 6
## 22 4.0 64 52 28 15 17 40
## 23 4.0 4 43 20 58 17 37 46
## 24 4.0 28 47 43 25 60 44 39
## 25 3.5 9 53 3 24 34 10 47
## 26 3.5 49 40 17 4 9 32 11
## 27 3.5 51 13 46 37 14 6 3.5
## 28 3.5 24 4 22 19 20 8 36
## 29 3.5 50 6 38 34 52 48 3.5
## 30 3.5 52 64 15 55 31 61 50
## 31 3.5 58 55 64 10 30 50 14
## 32 3.5 61 8 44 18 51 26 13
## 33 3.5 60 12 50 36 13 15 51
## 34 3.5 6 60 37 29 25 11 52
## 35 3.5 46 38 56 6 57 52 48
## 36 3.5 13 57 51 33 16 28
## 37 3.5 B 5 34 27 23 61
## 38 3.0 11 35 29 12 18 15
## 39 3.0 1 54 40 16 44 21 24
## 40 3.0 20 26 39 59 21 56 22
## 41 3.0 59 17 58 20 3.0 59 17
## 42 3.0 12 50 57 60 61 64 56
## 43 3.0 21 23 24 63 59 46 55
## 44 3.0 B 14 32 53 39 24 59
## 45 3.0 5 51 60 56 63 55 58
## 46 3.0 35 7 27 50 64 43 23
## 47 2.5 18 24 21 61 8 51 25
## 48 2.5 17 63 52 29 35
## 49 2.5 26 20 63 64 58 2.5 26
## 50 2.5 29 42 33 46 31 30
## 51 2.5 27 45 36 57 32 47 33
## 52 2.5 30 22 19 48 29 35 34
## 53 2.0 25 44 U 57 2.0
## 54 2.0 14 39 61 B 15 59 64
## 55 2.0 62 31 10 30 B 45 43
## 56 2.0 11 35 45 40 42
## 57 2.0 7 36 42 51 35 53 2.0
## 58 2.0 31 2 41 23 49 B 45
## 59 2.0 41 B 9 40 43 54 44
## 60 1.5 33 34 45 42 24 1.5 33
## 61 1.5 32 3 54 47 42 30 37
## 62 1.0 55 1.0 55 1.0 55 1.0 55
## 63 1.0 2 48 49 43 45 1.0 2
## 64 1.0 22 30 31 49 46 42 54roundsdf$id <- as.factor(1:64)
names(roundsdf) <- c("points", "r1", "r2", "r3", "r4", "r5", "r6", "r7", "id")The match function is then used to replace the opponent id with their pre-score, as shown below.
roundsdf$r1 <- predf$prescore[match(predf$id, roundsdf$r1)]
roundsdf$r2 <- predf$prescore[match(predf$id, roundsdf$r2)]
roundsdf$r3 <- predf$prescore[match(predf$id, roundsdf$r3)]
roundsdf$r4 <- predf$prescore[match(predf$id, roundsdf$r4)]
roundsdf$r5 <- predf$prescore[match(predf$id, roundsdf$r5)]
roundsdf$r6 <- predf$prescore[match(predf$id, roundsdf$r6)]
roundsdf$r7 <- predf$prescore[match(predf$id, roundsdf$r7)]Add an average column to average the pre-score of each persons opponents (omitting NA values from the calculation)
roundsdf$avg <- rowMeans(roundsdf[,2:8], na.rm = TRUE)
roundsdf$avg <- round(roundsdf$avg, digits=0) Create the final data frame, rename the last column and write the results to a CSV file.
finaldf <- data.frame(name, state, points, prescore, roundsdf$avg)
colnames(finaldf)[5] <- "prescoreopp"
finaldf## name state points prescore prescoreopp
## 1 GARY HUA ON 6.0 1794 1605
## 2 DAKSHESH DARURI MI 6.0 1553 1469
## 3 ADITYA BAJAJ MI 6.0 1384 1564
## 4 PATRICK H SCHILLING MI 5.5 1716 1574
## 5 HANSHI ZUO MI 5.5 1655 1501
## 6 HANSEN SONG OH 5.0 1686 1519
## 7 GARY DEE SWATHELL MI 5.0 1649 1372
## 8 EZEKIEL HOUGHTON MI 5.0 1641 1468
## 9 STEFANO LEE ON 5.0 1411 1523
## 10 ANVIT RAO MI 5.0 1365 1554
## 11 CAMERON WILLIAM MC LEMAN MI 4.5 1712 1468
## 12 KENNETH J TACK MI 4.5 1663 1506
## 13 TORRANCE HENRY JR MI 4.5 1666 1498
## 14 BRADLEY SHAW MI 4.5 1610 1515
## 15 ZACHARY JAMES HOUGHTON MI 4.5 1220 1484
## 16 MIKE NIKITIN MI 4.0 1604 1386
## 17 RONALD GRZEGORCZYK MI 4.0 1629 1499
## 18 DAVID SUNDEEN MI 4.0 1600 1480
## 19 DIPANKAR ROY MI 4.0 1564 1426
## 20 JASON ZHENG MI 4.0 1595 1411
## 21 DINH DANG BUI ON 4.0 1563 1470
## 22 EUGENE L MCCLURE MI 4.0 1555 1300
## 23 ALAN BUI ON 4.0 1363 1214
## 24 MICHAEL R ALDRICH MI 4.0 1229 1357
## 25 LOREN SCHWIEBERT MI 3.5 1745 1363
## 26 MAX ZHU ON 3.5 1579 1507
## 27 GAURAV GIDWANI MI 3.5 1552 1222
## 28 SOFIA ADINA STANESCU-BELLU MI 3.5 1507 1522
## 29 CHIEDOZIE OKORIE MI 3.5 1602 1314
## 30 GEORGE AVERY JONES ON 3.5 1522 1144
## 31 RISHI SHETTY MI 3.5 1494 1260
## 32 JOSHUA PHILIP MATHEWS ON 3.5 1441 1379
## 33 JADE GE MI 3.5 1449 1277
## 34 MICHAEL JEFFERY THOMAS MI 3.5 1399 1375
## 35 JOSHUA DAVID LEE MI 3.5 1438 1150
## 36 SIDDHARTH JHA MI 3.5 1355 1388
## 37 AMIYATOSH PWNANANDAM MI 3.5 980 1385
## 38 BRIAN LIU MI 3.0 1423 1539
## 39 JOEL R HENDON MI 3.0 1436 1430
## 40 FOREST ZHANG MI 3.0 1348 1391
## 41 KYLE WILLIAM MURPHY MI 3.0 1403 1248
## 42 JARED GE MI 3.0 1332 1150
## 43 ROBERT GLEN VASEY MI 3.0 1283 1107
## 44 JUSTIN D SCHILLING MI 3.0 1199 1327
## 45 DEREK YAN MI 3.0 1242 1152
## 46 JACOB ALEXANDER LAVALLEY MI 3.0 377 1358
## 47 ERIC WRIGHT MI 2.5 1362 1392
## 48 DANIEL KHAIN MI 2.5 1382 1356
## 49 MICHAEL J MARTIN MI 2.5 1291 1286
## 50 SHIVAM JHA MI 2.5 1056 1296
## 51 TEJAS AYYAGARI MI 2.5 1011 1356
## 52 ETHAN GUO MI 2.5 935 1495
## 53 JOSE C YBARRA MI 2.0 1393 1345
## 54 LARRY HODGE MI 2.0 1270 1206
## 55 ALEX KONG MI 2.0 1186 1424
## 56 MARISA RICCI MI 2.0 1153 1414
## 57 MICHAEL LU MI 2.0 1092 1363
## 58 VIRAJ MOHILE MI 2.0 917 1391
## 59 SEAN M MC CORMICK MI 2.0 853 1341
## 60 JULIA SHEN MI 1.5 967 1330
## 61 JEZZEL FARKAS ON 1.5 955 1327
## 62 ASHWIN BALAJI MI 1.0 1530 1186
## 63 THOMAS JOSEPH HOSMER MI 1.0 1175 1350
## 64 BEN LI MI 1.0 1163 1263write.csv(finaldf, file = "Chess.csv",row.names=FALSE)