Strategy

Two lines of the text file will be used to extract the data needed. The first line with the player name has the “Player Name” and “Total Pts.” The second line with “state” has the “state and pre_rtg that are required. The first line has the pair numbers for each of the pairs for each player. These numbers will be extracted and used as the index to get the pre_rtg for the pairs of each player. The pre_rtg’s will be summed along each row and divided by the number of pairs to find the mean.

Load required libraries and read the text file

The top 4 lines in the text file will be eliminated and every third line after that with dashes will be ignored.

#Load the required library
library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.3     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.3     ✔ tibble    3.2.1
## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(lubridate)
# Read the text file into a character vector
text_lines <- read_lines("https://raw.githubusercontent.com/hawa1983/DATA607/main/Chess%20Tournament.txt")

# Skip the first four lines
text_lines <- tail(text_lines, -4)

Create the initial data frame, df:

we created an empty data frame, extracted the values needed, and added a row at a time.

line_list = list()
# Create an empty data frame. Data will be added to this data frame later
df <- data.frame(player_no = character(0), name = character(0), state = character(0), score = character(0), current_rating = character(0), next_round_rating = character(0), pair_ids = list())

# Loop through the text_lines vector three lines at a time
for (i in seq(1, length(text_lines), by = 3)){
 
 
    # Save the first two lines as input strings. Ignore the third line with the dashes
    input_string1 <- text_lines[i]
    input_string2 <- text_lines[i + 1]
    
    line_list <- append(line_list, str_flatten(c(input_string1, input_string2)))
    
    # Define regular expressions to extract the information
    # Positive look behind and positive look ahead extension 
    # notations were used to isolate the values need. In all but pre_rtg and post_rtg, these 
    # notations are really not necessary since each value is the first occurrence of it's character type combination 
    # and str_extract only extract the first occurrence.
    
    player_no <- str_extract(input_string1, r"((?<=\s{3})(\d+)(?=\s\|))") 
    player_name <- str_extract(input_string1, r"((?<=\|\s)(\b[A-Z,\-,\s]+\b)(?=\s+\|\d+\.\d+\s+\|))") 
    state <- str_extract(input_string2, r"((\b[A-Z]{2}\b))") 
    rating <- str_extract(input_string1, r"((\b\d*\.\d*\b))") 
    current_rating <- str_extract(input_string2, r"((?<=R:\s{0,5})\d{3,4})")
    next_round_rating <- str_extract(input_string2, r"((?<=->\s{0,5})\d+)")
    pair_ids <- unlist(str_extract_all(input_string1, "(?<=[A-Z]\\s{1,4})(\\d*)(?=\\|)"))
      
    # Add rows one at a time to df data frame.
    new_row <- c(player_no, player_name, state, rating, current_rating, next_round_rating, pair_ids)
    df <- rbind(df, new_row)

}

# Rename the columns of df
names(df) <- c("id", "name", "state", "total_pts", "pre_rtg", "post_rtg", "pair_1", "pair_2", "pair_3", "pair_4", "pair_5", "pair_6", "pair_7")





# Display the data frame
options(kableExtra.latex.load_packages = FALSE)
library(kableExtra)

## 
## Attaching package: 'kableExtra'

## The following object is masked from 'package:dplyr':
## 
##     group_rows

kable(df, longtable = T, booktabs = T, caption = "Chess Game Data Extraction") |>
  kable_styling(latex_options = c("striped", "scale_down", "repeat_header")) |> 
  row_spec(0, background = "grey") |> 
  landscape()

Chess Game Data Extraction
id	name	state	total_pts	pre_rtg	post_rtg	pair_1	pair_2	pair_3	pair_4	pair_5	pair_6	pair_7
1	GARY HUA	ON	6.0	1794	1817	39	21	18	14	7	12	4
2	DAKSHESH DARURI	MI	6.0	1553	1663	63	58	4	17	16	20	7
3	ADITYA BAJAJ	MI	6.0	1384	1640	8	61	25	21	11	13	12
4	PATRICK H SCHILLING	MI	5.5	1716	1744	23	28	2	26	5	19	1
5	HANSHI ZUO	MI	5.5	1655	1690	45	37	12	13	4	14	17
6	HANSEN SONG	OH	5.0	1686	1687	34	29	11	35	10	27	21
7	GARY DEE SWATHELL	MI	5.0	1649	1673	57	46	13	11	1	9	2
8	EZEKIEL HOUGHTON	MI	5.0	1641	1657	3	32	14	9	47	28	19
9	STEFANO LEE	ON	5.0	1411	1564	25	18	59	8	26	7	20
10	ANVIT RAO	MI	5.0	1365	1544	16	19	55	31	6	25	18
11	CAMERON WILLIAM MC LEMAN	MI	4.5	1712	1696	38	56	6	7	3	34	26
12	KENNETH J TACK	MI	4.5	1663	1670	42	33	5	38		1	3
13	TORRANCE HENRY JR	MI	4.5	1666	1662	36	27	7	5	33	3	32
14	BRADLEY SHAW	MI	4.5	1610	1618	54	44	8	1	27	5	31
15	ZACHARY JAMES HOUGHTON	MI	4.5	1220	1416	19	16	30	22	54	33	38
16	MIKE NIKITIN	MI	4.0	1604	1613	10	15		39	2	36
17	RONALD GRZEGORCZYK	MI	4.0	1629	1610	48	41	26	2	23	22	5
18	DAVID SUNDEEN	MI	4.0	1600	1600	47	9	1	32	19	38	10
19	DIPANKAR ROY	MI	4.0	1564	1570	15	10	52	28	18	4	8
20	JASON ZHENG	MI	4.0	1595	1569	40	49	23	41	28	2	9
21	DINH DANG BUI	ON	4.0	1563	1562	43	1	47	3	40	39	6
22	EUGENE L MCCLURE	MI	4.0	1555	1529	64	52	28	15		17	40
23	ALAN BUI	ON	4.0	1363	1371	4	43	20	58	17	37	46
24	MICHAEL R ALDRICH	MI	4.0	1229	1300	28	47	43	25	60	44	39
25	LOREN SCHWIEBERT	MI	3.5	1745	1681	9	53	3	24	34	10	47
26	MAX ZHU	ON	3.5	1579	1564	49	40	17	4	9	32	11
27	GAURAV GIDWANI	MI	3.5	1552	1539	51	13	46	37	14	6
28	SOFIA ADINA STANESCU-BELLU	MI	3.5	1507	1513	24	4	22	19	20	8	36
29	CHIEDOZIE OKORIE	MI	3.5	1602	1508	50	6	38	34	52	48
30	GEORGE AVERY JONES	ON	3.5	1522	1444	52	64	15	55	31	61	50
31	RISHI SHETTY	MI	3.5	1494	1444	58	55	64	10	30	50	14
32	JOSHUA PHILIP MATHEWS	ON	3.5	1441	1433	61	8	44	18	51	26	13
33	JADE GE	MI	3.5	1449	1421	60	12	50	36	13	15	51
34	MICHAEL JEFFERY THOMAS	MI	3.5	1399	1400	6	60	37	29	25	11	52
35	JOSHUA DAVID LEE	MI	3.5	1438	1392	46	38	56	6	57	52	48
36	SIDDHARTH JHA	MI	3.5	1355	1367	13	57	51	33		16	28
37	AMIYATOSH PWNANANDAM	MI	3.5	980	1077		5	34	27		23	61
38	BRIAN LIU	MI	3.0	1423	1439	11	35	29	12		18	15
39	JOEL R HENDON	MI	3.0	1436	1413	1	54	40	16	44	21	24
40	FOREST ZHANG	MI	3.0	1348	1346	20	26	39	59	21	56	22
41	KYLE WILLIAM MURPHY	MI	3.0	1403	1341	59	17	58	20
42	JARED GE	MI	3.0	1332	1256	12	50	57	60	61	64	56
43	ROBERT GLEN VASEY	MI	3.0	1283	1244	21	23	24	63	59	46	55
44	JUSTIN D SCHILLING	MI	3.0	1199	1199		14	32	53	39	24	59
45	DEREK YAN	MI	3.0	1242	1191	5	51	60	56	63	55	58
46	JACOB ALEXANDER LAVALLEY	MI	3.0	377	1076	35	7	27	50	64	43	23
47	ERIC WRIGHT	MI	2.5	1362	1341	18	24	21	61	8	51	25
48	DANIEL KHAIN	MI	2.5	1382	1335	17	63		52		29	35
49	MICHAEL J MARTIN	MI	2.5	1291	1259	26	20	63	64	58
50	SHIVAM JHA	MI	2.5	1056	1111	29	42	33	46		31	30
51	TEJAS AYYAGARI	MI	2.5	1011	1097	27	45	36	57	32	47	33
52	ETHAN GUO	MI	2.5	935	1092	30	22	19	48	29	35	34
53	JOSE C YBARRA	MI	2.0	1393	1359		25		44		57
54	LARRY HODGE	MI	2.0	1270	1200	14	39	61		15	59	64
55	ALEX KONG	MI	2.0	1186	1163	62	31	10	30		45	43
56	MARISA RICCI	MI	2.0	1153	1140		11	35	45		40	42
57	MICHAEL LU	MI	2.0	1092	1079	7	36	42	51	35	53
58	VIRAJ MOHILE	MI	2.0	917	941	31	2	41	23	49		45
59	SEAN M MC CORMICK	MI	2.0	853	878	41		9	40	43	54	44
60	JULIA SHEN	MI	1.5	967	984	33	34	45	42	24
61	JEZZEL FARKAS	ON	1.5	955	979	32	3	54	47	42	30	37
62	ASHWIN BALAJI	MI	1.0	1530	1535	55
63	THOMAS JOSEPH HOSMER	MI	1.0	1175	1125	2	48	49	43	45
64	BEN LI	MI	1.0	1163	1112	22	30	31	49	46	42	54

Replace the columns pair_1 to pair_7 with the pre_rtg value that match the values of the column

Apply the mutate_at function column-wise for columns pair_1 to pair_7 to perform the following operations: - Replace the values of each of the columns that start with pair_ with the pre_rtg value using the column value as the index to select the pre_rtg value. Also change their data type from character to numeric. - Change the data type of numeric columns. - Display the data.

df <- df %>%
   mutate_at(vars(starts_with("pair_")), ~ as.double(pre_rtg[as.integer(.)])
             ) |> mutate_at(
             vars(4:6), ~ as.double(.)
             )

options(kableExtra.latex.load_packages = FALSE)
library(kableExtra)

kable(df, longtable = T, booktabs = T, caption = "Chess Game Data Extraction") |>
  kable_styling(latex_options = c("striped", "scale_down", "repeat_header")) |> 
  row_spec(0, background = "grey") |> 
  landscape()

Chess Game Data Extraction
id	name	state	total_pts	pre_rtg	post_rtg	pair_1	pair_2	pair_3	pair_4	pair_5	pair_6	pair_7
1	GARY HUA	ON	6.0	1794	1817	1436	1563	1600	1610	1649	1663	1716
2	DAKSHESH DARURI	MI	6.0	1553	1663	1175	917	1716	1629	1604	1595	1649
3	ADITYA BAJAJ	MI	6.0	1384	1640	1641	955	1745	1563	1712	1666	1663
4	PATRICK H SCHILLING	MI	5.5	1716	1744	1363	1507	1553	1579	1655	1564	1794
5	HANSHI ZUO	MI	5.5	1655	1690	1242	980	1663	1666	1716	1610	1629
6	HANSEN SONG	OH	5.0	1686	1687	1399	1602	1712	1438	1365	1552	1563
7	GARY DEE SWATHELL	MI	5.0	1649	1673	1092	377	1666	1712	1794	1411	1553
8	EZEKIEL HOUGHTON	MI	5.0	1641	1657	1384	1441	1610	1411	1362	1507	1564
9	STEFANO LEE	ON	5.0	1411	1564	1745	1600	853	1641	1579	1649	1595
10	ANVIT RAO	MI	5.0	1365	1544	1604	1564	1186	1494	1686	1745	1600
11	CAMERON WILLIAM MC LEMAN	MI	4.5	1712	1696	1423	1153	1686	1649	1384	1399	1579
12	KENNETH J TACK	MI	4.5	1663	1670	1332	1449	1655	1423	NA	1794	1384
13	TORRANCE HENRY JR	MI	4.5	1666	1662	1355	1552	1649	1655	1449	1384	1441
14	BRADLEY SHAW	MI	4.5	1610	1618	1270	1199	1641	1794	1552	1655	1494
15	ZACHARY JAMES HOUGHTON	MI	4.5	1220	1416	1564	1604	1522	1555	1270	1449	1423
16	MIKE NIKITIN	MI	4.0	1604	1613	1365	1220	NA	1436	1553	1355	NA
17	RONALD GRZEGORCZYK	MI	4.0	1629	1610	1382	1403	1579	1553	1363	1555	1655
18	DAVID SUNDEEN	MI	4.0	1600	1600	1362	1411	1794	1441	1564	1423	1365
19	DIPANKAR ROY	MI	4.0	1564	1570	1220	1365	935	1507	1600	1716	1641
20	JASON ZHENG	MI	4.0	1595	1569	1348	1291	1363	1403	1507	1553	1411
21	DINH DANG BUI	ON	4.0	1563	1562	1283	1794	1362	1384	1348	1436	1686
22	EUGENE L MCCLURE	MI	4.0	1555	1529	1163	935	1507	1220	NA	1629	1348
23	ALAN BUI	ON	4.0	1363	1371	1716	1283	1595	917	1629	980	377
24	MICHAEL R ALDRICH	MI	4.0	1229	1300	1507	1362	1283	1745	967	1199	1436
25	LOREN SCHWIEBERT	MI	3.5	1745	1681	1411	1393	1384	1229	1399	1365	1362
26	MAX ZHU	ON	3.5	1579	1564	1291	1348	1629	1716	1411	1441	1712
27	GAURAV GIDWANI	MI	3.5	1552	1539	1011	1666	377	980	1610	1686	NA
28	SOFIA ADINA STANESCU-BELLU	MI	3.5	1507	1513	1229	1716	1555	1564	1595	1641	1355
29	CHIEDOZIE OKORIE	MI	3.5	1602	1508	1056	1686	1423	1399	935	1382	NA
30	GEORGE AVERY JONES	ON	3.5	1522	1444	935	1163	1220	1186	1494	955	1056
31	RISHI SHETTY	MI	3.5	1494	1444	917	1186	1163	1365	1522	1056	1610
32	JOSHUA PHILIP MATHEWS	ON	3.5	1441	1433	955	1641	1199	1600	1011	1579	1666
33	JADE GE	MI	3.5	1449	1421	967	1663	1056	1355	1666	1220	1011
34	MICHAEL JEFFERY THOMAS	MI	3.5	1399	1400	1686	967	980	1602	1745	1712	935
35	JOSHUA DAVID LEE	MI	3.5	1438	1392	377	1423	1153	1686	1092	935	1382
36	SIDDHARTH JHA	MI	3.5	1355	1367	1666	1092	1011	1449	NA	1604	1507
37	AMIYATOSH PWNANANDAM	MI	3.5	980	1077	NA	1655	1399	1552	NA	1363	955
38	BRIAN LIU	MI	3.0	1423	1439	1712	1438	1602	1663	NA	1600	1220
39	JOEL R HENDON	MI	3.0	1436	1413	1794	1270	1348	1604	1199	1563	1229
40	FOREST ZHANG	MI	3.0	1348	1346	1595	1579	1436	853	1563	1153	1555
41	KYLE WILLIAM MURPHY	MI	3.0	1403	1341	853	1629	917	1595	NA	NA	NA
42	JARED GE	MI	3.0	1332	1256	1663	1056	1092	967	955	1163	1153
43	ROBERT GLEN VASEY	MI	3.0	1283	1244	1563	1363	1229	1175	853	377	1186
44	JUSTIN D SCHILLING	MI	3.0	1199	1199	NA	1610	1441	1393	1436	1229	853
45	DEREK YAN	MI	3.0	1242	1191	1655	1011	967	1153	1175	1186	917
46	JACOB ALEXANDER LAVALLEY	MI	3.0	377	1076	1438	1649	1552	1056	1163	1283	1363
47	ERIC WRIGHT	MI	2.5	1362	1341	1600	1229	1563	955	1641	1011	1745
48	DANIEL KHAIN	MI	2.5	1382	1335	1629	1175	NA	935	NA	1602	1438
49	MICHAEL J MARTIN	MI	2.5	1291	1259	1579	1595	1175	1163	917	NA	NA
50	SHIVAM JHA	MI	2.5	1056	1111	1602	1332	1449	377	NA	1494	1522
51	TEJAS AYYAGARI	MI	2.5	1011	1097	1552	1242	1355	1092	1441	1362	1449
52	ETHAN GUO	MI	2.5	935	1092	1522	1555	1564	1382	1602	1438	1399
53	JOSE C YBARRA	MI	2.0	1393	1359	NA	1745	NA	1199	NA	1092	NA
54	LARRY HODGE	MI	2.0	1270	1200	1610	1436	955	NA	1220	853	1163
55	ALEX KONG	MI	2.0	1186	1163	1530	1494	1365	1522	NA	1242	1283
56	MARISA RICCI	MI	2.0	1153	1140	NA	1712	1438	1242	NA	1348	1332
57	MICHAEL LU	MI	2.0	1092	1079	1649	1355	1332	1011	1438	1393	NA
58	VIRAJ MOHILE	MI	2.0	917	941	1494	1553	1403	1363	1291	NA	1242
59	SEAN M MC CORMICK	MI	2.0	853	878	1403	NA	1411	1348	1283	1270	1199
60	JULIA SHEN	MI	1.5	967	984	1449	1399	1242	1332	1229	NA	NA
61	JEZZEL FARKAS	ON	1.5	955	979	1441	1384	1270	1362	1332	1522	980
62	ASHWIN BALAJI	MI	1.0	1530	1535	1186	NA	NA	NA	NA	NA	NA
63	THOMAS JOSEPH HOSMER	MI	1.0	1175	1125	1553	1382	1291	1283	1242	NA	NA
64	BEN LI	MI	1.0	1163	1112	1555	1522	1494	1291	377	1332	1270

Calculate the average pre_rtg for each player

Specify the range of columns whose values I want to average. Find the average of columns pair_1 to pair_7 across each row. Ignore columns with NA. Select and display the required columns

df_final <- df |>
  rowwise() |>
  mutate(mean_rtg = round(mean(c_across(starts_with("pair_")), na.rm = TRUE))) |> 
  select("Player Name" = name, 
         "Player State" = state, 
         "Total Points" = total_pts, 
         "Pre-Rating" = pre_rtg, 
         "Average Pre-Rating" = mean_rtg
         )

#write the dataframe to a csv file
write_csv(df_final, "ratings.csv")

options(kableExtra.latex.load_packages = FALSE)
library(kableExtra)

kable(df_final, longtable = T, booktabs = T, caption = "Chess Game Average Pre-game Rating") |>
  kable_styling(latex_options = c("striped", "scale_down", "repeat_header")) |> 
  row_spec(0, background = "grey") |> 
  landscape()

Chess Game Average Pre-game Rating
Player Name	Player State	Total Points	Pre-Rating	Average Pre-Rating
GARY HUA	ON	6.0	1794	1605
DAKSHESH DARURI	MI	6.0	1553	1469
ADITYA BAJAJ	MI	6.0	1384	1564
PATRICK H SCHILLING	MI	5.5	1716	1574
HANSHI ZUO	MI	5.5	1655	1501
HANSEN SONG	OH	5.0	1686	1519
GARY DEE SWATHELL	MI	5.0	1649	1372
EZEKIEL HOUGHTON	MI	5.0	1641	1468
STEFANO LEE	ON	5.0	1411	1523
ANVIT RAO	MI	5.0	1365	1554
CAMERON WILLIAM MC LEMAN	MI	4.5	1712	1468
KENNETH J TACK	MI	4.5	1663	1506
TORRANCE HENRY JR	MI	4.5	1666	1498
BRADLEY SHAW	MI	4.5	1610	1515
ZACHARY JAMES HOUGHTON	MI	4.5	1220	1484
MIKE NIKITIN	MI	4.0	1604	1386
RONALD GRZEGORCZYK	MI	4.0	1629	1499
DAVID SUNDEEN	MI	4.0	1600	1480
DIPANKAR ROY	MI	4.0	1564	1426
JASON ZHENG	MI	4.0	1595	1411
DINH DANG BUI	ON	4.0	1563	1470
EUGENE L MCCLURE	MI	4.0	1555	1300
ALAN BUI	ON	4.0	1363	1214
MICHAEL R ALDRICH	MI	4.0	1229	1357
LOREN SCHWIEBERT	MI	3.5	1745	1363
MAX ZHU	ON	3.5	1579	1507
GAURAV GIDWANI	MI	3.5	1552	1222
SOFIA ADINA STANESCU-BELLU	MI	3.5	1507	1522
CHIEDOZIE OKORIE	MI	3.5	1602	1314
GEORGE AVERY JONES	ON	3.5	1522	1144
RISHI SHETTY	MI	3.5	1494	1260
JOSHUA PHILIP MATHEWS	ON	3.5	1441	1379
JADE GE	MI	3.5	1449	1277
MICHAEL JEFFERY THOMAS	MI	3.5	1399	1375
JOSHUA DAVID LEE	MI	3.5	1438	1150
SIDDHARTH JHA	MI	3.5	1355	1388
AMIYATOSH PWNANANDAM	MI	3.5	980	1385
BRIAN LIU	MI	3.0	1423	1539
JOEL R HENDON	MI	3.0	1436	1430
FOREST ZHANG	MI	3.0	1348	1391
KYLE WILLIAM MURPHY	MI	3.0	1403	1248
JARED GE	MI	3.0	1332	1150
ROBERT GLEN VASEY	MI	3.0	1283	1107
JUSTIN D SCHILLING	MI	3.0	1199	1327
DEREK YAN	MI	3.0	1242	1152
JACOB ALEXANDER LAVALLEY	MI	3.0	377	1358
ERIC WRIGHT	MI	2.5	1362	1392
DANIEL KHAIN	MI	2.5	1382	1356
MICHAEL J MARTIN	MI	2.5	1291	1286
SHIVAM JHA	MI	2.5	1056	1296
TEJAS AYYAGARI	MI	2.5	1011	1356
ETHAN GUO	MI	2.5	935	1495
JOSE C YBARRA	MI	2.0	1393	1345
LARRY HODGE	MI	2.0	1270	1206
ALEX KONG	MI	2.0	1186	1406
MARISA RICCI	MI	2.0	1153	1414
MICHAEL LU	MI	2.0	1092	1363
VIRAJ MOHILE	MI	2.0	917	1391
SEAN M MC CORMICK	MI	2.0	853	1319
JULIA SHEN	MI	1.5	967	1330
JEZZEL FARKAS	ON	1.5	955	1327
ASHWIN BALAJI	MI	1.0	1530	1186
THOMAS JOSEPH HOSMER	MI	1.0	1175	1350
BEN LI	MI	1.0	1163	1263

Project 1

Fomba Kassoh, Group member: Souleymane Doumbia

2023-09-25

Strategy

Load required libraries and read the text file

Create the initial data frame, df:

Replace the columns pair_1 to pair_7 with the pre_rtg value that match the values of the column

Calculate the average pre_rtg for each player