Nothing
tests/testthat/test-splash.R
In SwimmeR: Data Import, Cleaning, and Conversions for Swimming Results
# devtools::test_active_file("tests/testthat/test-splash.R")
test_that("Glenmark Senior Nationals 2019 whole meet", {
skip_on_cran()
file <-
"https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/Splash/Glenmark_Senior_Nationals_2019.pdf"
df_test <- file %>%
read_results() %>%
swim_parse() %>%
select(-Event)
df_standard <-
structure(list(Place = c("1", "2", "3", "4", "5", "6", "7", "8",
"1", "2", "3", "4", "5", "6", "7", "8", "1", "2", "3", "4", "5",
"6", "7", "8", "1", "2", "3", "4", "5", "6", "7", "8", "1", "2",
"3", "4", "5", "6", "7", "8", "1", "2", "3", "4", "5", "6", "7",
"8", "1", "2", "3", "4", "5", "6", "7", "1", "2", "3", "4", "5",
"6", "7", "8", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10",
"11", "12", "1", "2", "3", "4", "5", "6", "7", "8", "1", "2",
"3", "4", "5", "6", "7", "8", "1", "2", "3", "4", "5", "6", "7",
"8", "1", "2", "3", "4", "5", "6", "7", "8", "1", "2", "3", "4",
"5", "6", "7", "8", "1", "2", "3", "4", "5", "6", "7", "8", "1",
"2", "3", "4", "5", "6", "7", "8", "9", "10", "1", "2", "3",
"4", "5", "6", "7", "8", "1", "2", "3", "4", "5", "6", "7", "8",
"1", "2", "3", "4", "5", "6", "7", "8", "1", "2", "3", "4", "5",
"6", "7", "8", "1", "2", "3", "4", "5", "6", "7", "8", "1", "2",
"3", "4", "5", "6", "6", "8", "1", "2", "3", "4", "5", "6", "7",
"8", "1", "2", "3", "4", "5", "6", "7", "8", "1", "2", "3", "4",
"5", "6", "7", "8", "9", "10", "1", "2", "3", "4", "5", "6",
"7", "8", "1", "2", "3", "4", "5", "6", "7", "8", "1", "2", "3",
"4", "5", "6", "7", "8", "1", "2", "3", "4", "5", "6", "7", "8",
"1", "2", "3", "4", "5", "6", "7", "8", "1", "2", "3", "4", "5",
"6", "7", "8", "1", "2", "3", "4", "5", "1", "2", "3", "4", "5",
"6", "7", "8", "1", "2", "3", "4", "5", "6", "7", "8", "9", "1",
"2", "3", "4", "5", "6", "7", "8", "1", "2", "3", "4", "5", "6",
"7", "8", "1", "2", "3", "4", "5", "6", "7", "8", "1", "2", "3",
"4", "5", "6", "7", NA, "1", "2", "3", "4", "5", "6", "7", "8",
"1", "2", "3", "4", "5", "6", "7", "8", "1", "2", "3", "4", "5",
"6", "7", "8", "1", "2", "3", "4", "5", "6", "7", "8"), Name = c("Kushagra Rawat",
"ANAND AS", "AARON FERNANDES", "Vishal Grewal", "Anurag R. Singh",
"Aneesh S Gowda", "Sushrut. S. Kapse", "ARUN DEV", "Shivani Kataria",
"Shivangi Sarma", "Khushi Dinesh", "Bhavika Dugar", "Bhavya Sachdeva",
"Shakthi B", "RUTUJA TALEGAONKAR", "Kanya Nayyar", "Sajan Prakash",
"Shrihari Nataraj", "Siva S", "Sanu Debnath", "ARVIND MANI",
"JAYANT M", "Sethu Manickavel T", "Nanak Moolchandani", "Richa Mishra",
"KENISHA GUPTA", "APEKSHA FERNANDES", "Shrungi Bandekar", "Soubrity Mondal",
"Meenakshi V K R", "Firdoush Kayamkhani", "Astha Choudhury",
"Likith S P", "Ansh Arora", "Danush S", "SHWEJAL MANKAR", "VAISHNAV HEGDE B",
"ASHISH TOKAS", "Harnimrat Singh Bhinder", "Suneesh. S", "Jayaveena A V",
"Chahat Arora", "Annie Jain", "Saloni Dalal", "KAREENA SHANKTA",
"Arushi Manjunath", "SHARON SHAJU", "Shriya Ishwar Prasad", NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Kushagra Rawat",
"Sushrut. S. Kapse", "Aneesh S Gowda", "Anurag R. Singh", "Dhyan Balakrishna",
"SACHIN MAURYA", "ARUN DEV", "Saurabh Sangvekar", "Krishna Gadakh",
"DEV AMBOKAR", "SHUBHAM DHAYGUDE", "Sujan Chowdary P", "Richa Mishra",
"APEKSHA FERNANDES", "Shakthi B", "Shrungi Bandekar", "Firdoush Kayamkhani",
"Astha Choudhury", "Kanya Nayyar", "Kalyani Saxena", "Shrihari Nataraj",
"Siva S", "ARVIND MANI", "Anurag Dagar", "K. Abbasuddin", "Nanak Moolchandani",
"Soumyajit Saha", "Sethu Manickavel T", "Maana Patel", "Suvana C Baskar",
"Soubrity Mondal", "Meenakshi V K R", "YUGA BIRNALE", "Pratyasa Ray",
"Shreyanti Pan", "Shrungi Bandekar", "VIRDHAWAL KHADE", "Viraj Prabhu",
"MIHIR AMBRE", "Anshul Kothari", "ANAND AS", "Aaron D' Souza",
"GAURAV YADAV KA", "Samit Sejwal", NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, "Richa Mishra", "Bhavika Dugar",
"Khushi Dinesh", "Shakthi B", "Kanya Nayyar", "RUTUJA TALEGAONKAR",
"Navya Singal", "Divya Ghosh", "DOLLY PATIL", "Saachi Gramopadhye",
"Siva S", "T Emil Robin Singh", "JAYANT M", "Vishal Grewal",
"Yogeshwar Prasad", "Aneesh S Gowda", "Nanak Moolchandani", "Harshit Hooda",
"APEKSHA FERNANDES", "Saloni Dalal", "JYOTI PATIL", "Kalyani Saxena",
"Annie Jain", "Chahat Arora", "Saanvi S Rao", "Harshitha Jayaram",
"Likith S P", "Danush S", "M Lohith.", "ARUN S", "Oum Saxena",
"Tanish Kaswan", "AKASH POWAR", "Aditya Dubey", "Divya Satija",
"Nina Venkatesh", "JYOTSNA PANSARE", "Suvana C Baskar", "KENISHA GUPTA",
"Jayaveena A V", "Talasha Prabhu", "Avantika Chavan", "VIRDHAWAL KHADE",
"Supriya Mondal", "MIHIR AMBRE", "Aman Ghai", "Anshul Kothari",
"Viraj Prabhu", "Rakshith U shetty", "Tanish George Mathew",
"RUJUTA KHADE", "KENISHA GUPTA", "Avantika Chavan", "Aditi Dhumatkar",
"Mahi Swet Raj", "Jayaveena A V", "Talasha Prabhu", "Shivangi Sarma",
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
"Kushagra Rawat", "Soumyajit Saha", "Sushrut. S. Kapse", "Anurag R. Singh",
"Aneesh S Gowda", "SACHIN MAURYA", "Dhyan Balakrishna", "Gyan Sandhan Kashyap",
"Krishna Gadakh", "SHUBHAM DHAYGUDE", "Shivani Kataria", "KENISHA GUPTA",
"Khushi Dinesh", "Shivangi Sarma", "Bhavya Sachdeva", "Swarna K Harith",
"Navya Singal", "Smruthi Mahalingam", "Sajan Prakash", "MIHIR AMBRE",
"Supriya Mondal", "Tanish George Mathew", "Sanu Debnath", "SHIVAKSH SAHU",
"Bikram Changmai", "Priyank Rana", "Divya Satija", "APEKSHA FERNANDES",
"Nina Venkatesh", "Shivani Kataria", "Srishti Nag", "Damini K Gowda",
"Liyana Fathima Umer", "Uttara Gogoi", "Shrihari Nataraj", "Rakshith U shetty",
"MADHU P S", "Siddhant Sejwal", "ARVIND MANI", "Tanmay Das",
"Soumyajit Saha", "SHWEJAL MANKAR", "Maana Patel", "JYOTSNA PANSARE",
"Ridhima Veerendra Kumar", "Suvana C Baskar", "Meenakshi V K R",
"Shrungi Bandekar", "Pratyasa Ray", "Shreyanti Pan", "Shrihari Nataraj",
"ANAND AS", "VINAY SAHARAN", "AARON FERNANDES", "Viraj Prabhu",
"Anshul Kothari", "Vishal Grewal", "Prithvi M", NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, "Richa Mishra", "Khushi Dinesh",
"Bhavya Sachdeva", "Bhavika Dugar", "RUTUJA TALEGAONKAR", "Navya Singal",
"Divya Ghosh", "DOLLY PATIL", "Saachi Gramopadhye", "Kushagra Rawat",
"ANAND AS", "AARON FERNANDES", "Sanu Debnath", "VINAY SAHARAN",
"Vishal Grewal", "Avinash Mani", "Gyan Sandhan Kashyap", "KAREENA SHANKTA",
"Saloni Dalal", "Chahat Arora", "Annie Jain", "Harshitha Jayaram",
"Saanvi S Rao", "Kalyani Saxena", "SHARON SHAJU", "Likith S P",
"Danush S", "M Lohith.", "Ansh Arora", "ASHISH TOKAS", "Suneesh. S",
"T Emil Robin Singh", "Tanish Kaswan", "Ridhima Veerendra Kumar",
"Suvana C Baskar", "Soubrity Mondal", "Shreyanti Pan", "Pratyasa Ray",
"YUGA BIRNALE", "Shrungi Bandekar", "Maana Patel", "Shrihari Nataraj",
"MADHU P S", "Rakshith U shetty", "Tanmay Das", "Siddhant Sejwal",
"Soumyajit Saha", "Sethu Manickavel T", "Nanak Moolchandani",
"APEKSHA FERNANDES", "Anvesha Girish", "Shakthi B", "Richa Mishra",
"Srishti Nag", "Hema", "Kanya Nayyar", "Vanshika Sharma", "Sajan Prakash",
"Supriya Mondal", "Tanish George Mathew", "Sanu Debnath", "SHIVAKSH SAHU",
"Vinayak Parihar", "Bikram Changmai", "Saif Chandan Ali Kamcheru Satish",
"KENISHA GUPTA", "Shivani Kataria", "SADHVI DHURI", "Avantika Chavan",
"Shivangi Sarma", "Mahi Swet Raj", "Divya Satija", "Smruthi Mahalingam"
), Age = c("90", NA, NA, "90", NA, NA, NA, NA, "97", "02", NA,
"01", "90", "05", NA, "04", NA, NA, NA, NA, NA, NA, "07", "00",
NA, NA, NA, "03", NA, "06", NA, "04", NA, NA, "00", NA, NA, NA,
"96", NA, "08", "97", "02", NA, NA, NA, NA, "06", NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "90", NA, NA,
NA, NA, NA, NA, NA, "03", NA, NA, "02", NA, NA, "05", "03", NA,
"04", "04", "01", NA, NA, NA, "90", NA, "00", NA, "07", "00",
NA, NA, "06", NA, "02", NA, "03", NA, NA, NA, "89", NA, NA, NA,
"90", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, "01", NA, "05", "04", NA, NA, NA, NA, "00", NA, NA,
NA, "90", NA, NA, "00", "03", NA, NA, NA, "01", "02", "97", NA,
NA, NA, "00", NA, NA, NA, NA, NA, "90", "96", NA, NA, NA, NA,
"08", "93", NA, NA, NA, NA, NA, "89", NA, NA, NA, NA, NA, NA,
NA, "03", "08", "93", "02", NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, "90", NA, NA, NA, NA, NA, NA, "04",
"03", NA, "97", NA, NA, "02", "90", "04", NA, NA, NA, NA, NA,
NA, NA, NA, "04", "90", "96", NA, NA, "97", "96", NA, NA, "04",
NA, NA, NA, "90", NA, "90", NA, NA, "00", NA, NA, NA, "06", "03",
"02", NA, NA, NA, NA, NA, NA, "89", "90", NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "90", "01", NA, NA,
NA, NA, "00", "90", NA, NA, NA, NA, "90", NA, "04", NA, NA, "97",
"02", NA, NA, "01", NA, NA, "00", NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, "02", NA, "03", "00", NA, NA, NA, "90", "90", NA,
"07", "00", NA, NA, "05", NA, "96", "90", "04", "90", NA, NA,
NA, NA, NA, "90", "04", NA, NA, "97", NA, NA, "02", "03", "96",
NA), Team = c("DELHI", "SSCB", "MAHARASHTRA", "DELHI", "UTTAR PRADESH",
"KARNATAKA", "R.S.P.B", "SSCB", "HARYANA", "ASSAM", "KARNATAKA",
"TAMILNADU", "DELHI", "TAMILNADU", "MAHARASHTRA", "M.P.", "POLICE",
"KARNATAKA", "KARNATAKA", "R.S.P.B", "SSCB", "SSCB", "TAMILNADU",
"M.P.", "POLICE", "MAHARASHTRA", "MAHARASHTRA", "GOA", "BENGAL",
"TAMILNADU", "RAJASTHAN", "ASSAM", "KARNATAKA", "UTTAR PRADESH",
"TAMILNADU", "MAHARASHTRA", "SSCB", "SSCB", "PUNJAB", "KERALA",
"TAMILNADU", "PUNJAB", "MADHYA PRADESH", "KARNATAKA", "MAHARASHTRA",
"KARNATAKA", "MAHARASHTRA", "TAMILNADU", "KARNATAKA", "SSCB",
"R.S.P.B", "POLICE", "BENGAL", "MAHARASHTRA", "KERALA", "KARNATAKA",
"MAHARASHTRA", "R.S.P.B", "GUJARAT", "TAMILNADU", "DELHI", "BENGAL",
"KERALA", "DELHI", "R.S.P.B", "KARNATAKA", "UTTAR PRADESH", "KARNATAKA",
"SSCB", "SSCB", "R.S.P.B", "MADHYA PRADESH", "MAHARASHTRA", "MAHARASHTRA",
"ANDHRA PRADESH", "POLICE", "MAHARASHTRA", "TAMILNADU", "GOA",
"RAJASTHAN", "ASSAM", "M.P.", "GUJARAT", "KARNATAKA", "KARNATAKA",
"SSCB", "DELHI", "R.S.P.B", "M.P.", "R.S.P.B", "TAMILNADU", "GUJARAT",
"KARNATAKA", "BENGAL", "TAMILNADU", "MAHARASHTRA", "ODISHA",
"R.S.P.B", "GOA", "MAHARASHTRA", "R.S.P.B", "MAHARASHTRA", "GUJARAT",
"SSCB", "R.S.P.B", "SSCB", "DELHI", "MAHARASHTRA", "KARNATAKA",
"TAMILNADU", "DELHI", "BENGAL", "KERALA", "GUJARAT", "PUNJAB",
"KARNATAKA", "SSCB", "MAHARASHTRA", "R.S.P.B", "DELHI", "BENGAL",
"KERALA", "TELANGANA", "POLICE", "TAMILNADU", "KARNATAKA", "TAMILNADU",
"MADHYA PRADESH", "MAHARASHTRA", "UTTAR PRADESH", "KARNATAKA",
"MAHARASHTRA", "GOA", "KARNATAKA", "R.S.P.B", "SSCB", "DELHI",
"UTTAR PRADESH", "KARNATAKA", "M.P.", "HARYANA", "MAHARASHTRA",
"KARNATAKA", "MAHARASHTRA", "GUJARAT", "M.P.", "PUNJAB", "KARNATAKA",
"R.S.P.B", "KARNATAKA", "TAMILNADU", "R.S.P.B", "SSCB", "GUJARAT",
"RAJASTHAN", "SSCB", "DELHI", "HARYANA", "KARNATAKA", "MAHARASHTRA",
"KARNATAKA", "MAHARASHTRA", "TAMILNADU", "GOA", "R.S.P.B", "MAHARASHTRA",
"R.S.P.B", "MAHARASHTRA", "POLICE", "GUJARAT", "R.S.P.B", "KARNATAKA",
"KARNATAKA", "MAHARASHTRA", "MAHARASHTRA", "R.S.P.B", "R.S.P.B",
"BIHAR", "TAMILNADU", "GOA", "ASSAM", "KARNATAKA", "SSCB", "R.S.P.B",
"DELHI", "KERALA", "MAHARASHTRA", "BENGAL", "POLICE", "MAHARASHTRA",
"KARNATAKA", "TAMILNADU", "GUJARAT", "BENGAL", "DELHI", "KERALA",
"PUNJAB", "DELHI", "R.S.P.B", "R.S.P.B", "UTTAR PRADESH", "KARNATAKA",
"SSCB", "KARNATAKA", "ASSAM", "MADHYA PRADESH", "MAHARASHTRA",
"HARYANA", "MAHARASHTRA", "KARNATAKA", "ASSAM", "DELHI", "TAMILNADU",
"UTTAR PRADESH", "KARNATAKA", "POLICE", "MAHARASHTRA", "R.S.P.B",
"KARNATAKA", "R.S.P.B", "SSCB", "ASSAM", "DELHI", "HARYANA",
"MAHARASHTRA", "KARNATAKA", "HARYANA", "CHHATTISGARH", "KARNATAKA",
"KERALA", "ASSAM", "KARNATAKA", "KARNATAKA", "SSCB", "DELHI",
"SSCB", "DELHI", "R.S.P.B", "MAHARASHTRA", "GUJARAT", "MAHARASHTRA",
"KARNATAKA", "KARNATAKA", "TAMILNADU", "GOA", "ODISHA", "R.S.P.B",
"KARNATAKA", "SSCB", "SSCB", "MAHARASHTRA", "R.S.P.B", "GUJARAT",
"DELHI", "KARNATAKA", "KARNATAKA", "MAHARASHTRA", "TAMILNADU",
"BENGAL", "KERALA", "MAHARASHTRA", "R.S.P.B", "KARNATAKA", "DELHI",
"KERALA", "TAMILNADU", "BENGAL", "GUJARAT", "POLICE", "KARNATAKA",
"DELHI", "TAMILNADU", "MAHARASHTRA", "UTTAR PRADESH", "KARNATAKA",
"MAHARASHTRA", "GOA", "DELHI", "SSCB", "MAHARASHTRA", "R.S.P.B",
"SSCB", "DELHI", "KARNATAKA", "ASSAM", "MAHARASHTRA", "KARNATAKA",
"PUNJAB", "MADHYA PRADESH", "R.S.P.B", "KARNATAKA", "GUJARAT",
"MAHARASHTRA", "KARNATAKA", "TAMILNADU", "R.S.P.B", "UTTAR PRADESH",
"SSCB", "KERALA", "R.S.P.B", "RAJASTHAN", "KARNATAKA", "KARNATAKA",
"BENGAL", "R.S.P.B", "ODISHA", "MAHARASHTRA", "GOA", "GUJARAT",
"KARNATAKA", "SSCB", "KARNATAKA", "DELHI", "DELHI", "R.S.P.B",
"TAMILNADU", "MADHYA PRADESH", "MAHARASHTRA", "KARNATAKA", "TAMILNADU",
"POLICE", "CHHATTISGARH", "DELHI", "M.P.", "DELHI", "POLICE",
"R.S.P.B", "KARNATAKA", "R.S.P.B", "SSCB", "DELHI", "ASSAM",
"KARNATAKA", "MAHARASHTRA", "HARYANA", "MAHARASHTRA", "R.S.P.B",
"ASSAM", "BIHAR", "HARYANA", "KARNATAKA"), Finals = c("3:56.95",
"4:01.92", "4:04.71", "4:07.44", "4:09.34", "4:09.54", "4:11.74",
"4:13.00", "4:27.31", "4:30.55", "4:30.80", "4:37.18", "4:37.54",
"4:39.96", "4:45.87", "4:47.12", "2:05.89", "2:06.46", "2:06.69",
"2:09.13", "2:12.15", "2:12.49", "2:17.91", "2:20.87", "2:24.70",
"2:25.31", "2:26.53", "2:26.85", "2:30.44", "2:31.59", "2:33.44",
"2:34.17", "28.54", "29.38", "29.57", "29.60", "29.65", "29.81",
"29.88", "30.49", "33.86", "34.01", "34.30", "35.24", "35.32",
"35.38", "35.76", "36.11", "7:46.04", "7:49.31", "7:53.52", "8:19.53",
"8:20.32", "8:24.45", "8:46.12", "1:50.65", "1:52.82", "1:54.00",
"1:55.67", "1:57.23", "2:00.50", "2:01.71", "2:01.90", "15:41.45",
"16:29.27", "16:32.28", "16:44.68", "16:48.24", "16:52.77", "16:54.74",
"17:15.02", "17:34.64", "17:51.79", "18:41.31", "19:29.56", "5:05.49",
"5:12.91", "5:13.19", "5:18.92", "5:20.02", "5:25.12", "5:25.88",
"5:28.83", "2:02.29", "2:07.11", "2:10.24", "2:10.28", "2:10.66",
"2:11.87", "2:11.91", "2:13.42", "2:21.74", "2:22.30", "2:23.55",
"2:27.87", "2:30.03", "2:30.52", "2:31.62", "2:35.44", "22.44",
"23.39", "23.76", "23.77", "23.83", "23.93", "24.10", "24.21",
"4:02.90", "4:09.88", "4:13.25", "4:14.84", "4:15.54", "4:29.80",
"4:31.50", "4:47.63", "3:30.45", "3:31.04", "3:31.17", "3:33.22",
"3:37.07", "3:43.67", "3:45.01", "4:02.50", "17:55.55", "17:57.67",
"17:58.14", "18:29.40", "18:41.29", "19:01.60", "19:04.82", "19:53.91",
"20:15.27", "21:40.84", "4:33.01", "4:36.68", "4:41.21", "4:44.65",
"4:55.98", "4:56.07", "4:58.41", "4:59.06", "2:41.89", "2:42.50",
"2:42.76", "2:46.09", "2:49.11", "2:50.79", "2:51.90", "2:53.88",
"2:18.61", "2:19.11", "2:19.94", "2:20.18", "2:24.60", "2:25.38",
"2:27.26", "2:27.75", "28.33", "28.43", "28.74", "29.05", "29.06",
"29.52", "29.91", "30.09", "24.19", "24.55", "24.78", "25.30",
"25.60", "25.67", "25.68", "25.99", "26.72", "26.90", "27.39",
"27.57", "27.60", "27.67", "27.67", "27.68", "3:48.83", "3:52.11",
"3:52.93", "4:01.27", "4:02.53", "4:04.46", "4:07.31", "4:39.37",
"4:33.10", "4:34.84", "4:39.49", "4:40.06", "4:41.20", "4:52.31",
"4:56.41", "5:32.06", "8:09.47", "8:32.01", "8:33.58", "8:33.87",
"8:42.12", "8:52.91", "8:54.76", "8:57.24", "9:16.13", "9:22.83",
"2:05.80", "2:07.94", "2:10.49", "2:10.99", "2:15.13", "2:16.83",
"2:18.52", "2:20.33", "54.25", "54.38", "54.47", "56.15", "56.42",
"57.03", "57.90", "58.13", "1:04.38", "1:05.53", "1:05.98", "1:06.35",
"1:06.53", "1:06.89", "1:07.26", "1:09.32", "25.58", "26.73",
"27.02", "27.13", "27.26", "27.31", "27.76", "28.03", "30.39",
"30.82", "30.87", "30.91", "31.91", "32.13", "32.32", "32.60",
"50.59", "51.49", "52.87", "52.92", "52.97", "53.54", "53.58",
"53.62", "9:04.86", "9:08.51", "9:09.18", "9:23.37", "9:56.91",
"1:39.69", "1:41.83", "1:41.92", "1:44.95", "1:47.70", "1:48.06",
"1:49.74", "1:50.08", "9:22.50", "9:24.81", "9:31.41", "9:47.27",
"9:49.61", "9:53.55", "10:20.97", "10:38.66", "10:43.53", "1:53.44",
"1:54.97", "1:55.71", "1:56.69", "1:57.75", "1:57.98", "1:59.14",
"2:00.39", "1:14.97", "1:15.89", "1:16.36", "1:16.58", "1:18.80",
"1:19.03", "1:19.59", "1:19.91", "1:02.69", "1:04.18", "1:05.08",
"1:05.12", "1:05.24", "1:06.41", "1:07.11", "1:07.13", "1:07.19",
"1:07.22", "1:07.41", "1:08.79", "1:09.56", "1:09.81", "1:10.05",
"1:05.35", "55.63", "58.30", "58.89", "59.01", "59.18", "59.37",
"59.80", "1:01.18", "2:23.17", "2:25.43", "2:25.78", "2:25.96",
"2:30.75", "2:31.81", "2:32.15", "2:34.32", "2:00.13", "2:03.41",
"2:04.23", "2:05.49", "2:08.39", "2:08.86", "2:09.40", "2:13.67",
"58.26", "59.21", "1:00.06", "1:00.14", "1:00.36", "1:00.42",
"1:00.93", "1:02.50"), Points = c("801", "752", "727", "703",
"687", "685", "668", "658", "692", "667", "665", "620", "618",
"602", "565", "558", "742", "732", "728", "688", "641", "637",
"564", "529", "662", "653", "637", "633", "589", "575", "555",
"547", "751", "689", "675", "673", "670", "659", "655", "616",
"654", "645", "629", "580", "576", "573", "555", "539", "724",
"709", "690", "588", "585", "571", "503", NA, NA, NA, NA, NA,
NA, NA, NA, "791", "682", "676", "651", "644", "636", "632",
"595", "563", "536", "468", "413", "662", "616", "615", "582",
"576", "549", "546", "531", "766", "682", "634", "634", "628",
"611", "610", "590", "670", "662", "645", "590", "565", "559",
"547", "508", "809", "714", "681", "680", "675", "667", "653",
"644", "646", "593", "570", "559", "555", "471", "463", "389",
"715", "709", "708", "688", "652", "596", "585", "467", "626",
"623", "622", "571", "553", "524", "519", "458", "434", "354",
"712", "684", "651", "628", "559", "558", "545", "542", "634",
"627", "624", "587", "556", "540", "529", "512", "763", "754",
"741", "737", "672", "661", "636", "630", "641", "634", "614",
"594", "594", "566", "544", "535", "780", "746", "725", "682",
"658", "652", "652", "629", "695", "681", "645", "632", "630",
"625", "625", "625", "743", "712", "704", "634", "624", "609",
"588", "408", "609", "597", "568", "565", "558", "497", "476",
"339", "788", "688", "682", "681", "649", "610", "604", "596",
"537", "518", "724", "688", "649", "641", "584", "562", "542",
"521", "774", "768", "765", "698", "688", "666", "637", "629",
"639", "606", "594", "584", "579", "570", "561", "512", "825",
"723", "700", "692", "682", "678", "646", "627", "705", "676",
"673", "670", "609", "597", "586", "571", "797", "756", "698",
"696", "694", "672", "671", "669", "609", "597", "595", "551",
"463", NA, NA, NA, NA, NA, NA, NA, NA, "640", "632", "610", "562",
"555", "544", "475", "437", "427", "726", "698", "684", "667",
"650", "646", "627", "608", "625", "603", "592", "587", "539",
"534", "523", "516", "755", "704", "675", "674", "670", "635",
"615", "615", "643", "642", "636", "599", "579", "573", "567",
NA, "809", "703", "682", "678", "672", "666", "651", "608", "615",
"587", "583", "581", "527", "516", "513", "491", "799", "737",
"723", "701", "655", "648", "639", "580", "699", "666", "638",
"635", "628", "626", "611", "566"), DQ = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)), row.names = c(NA,
-341L), class = "data.frame")
expect_equivalent(df_test, df_standard)
})
test_that("Khelo India Youth Games 2020 whole meet, has a tie, fully checked", {
skip_on_cran()
file <-
"https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/Splash/Khelo_India_Youth_Games_2020.pdf"
df_test <- file %>%
read_results() %>%
swim_parse() %>%
select(-Event)
df_standard <-
structure(list(Place = c("1", "2", "3", "4", "5", "6", "7", "8",
"1", "2", "3", "4", "5", "6", "7", "8", "1", "2", "3", "4", "5",
"6", "1", "2", "3", "4", "5", "1", "2", "3", "4", "5", "6", "7",
"8", "1", "2", "3", "4", "5", "6", "7", "8", "1", "2", "3", "4",
"5", "6", "1", "2", "3", "4", "1", "2", "3", "4", "5", "5", "7",
"8", "1", "2", "3", "4", "5", "6", "7", "8", "1", "2", "3", "4",
"5", "6", "7", "8", "1", "2", "3", "4", "5", "1", "2", "3", "4",
"5", "6", "7", "8", "9", "10", "1", "2", "3", "4", "5"), Name = c("SAMBHAVV R",
"ANEESH GOWDA S", "VEER KHATKAR", "VEDANT BAPNA", "GYAN SANDHAN KASHYAP",
"RAJDEEP GOGOI", "KRISHNA GADAKH", "BIKRAM CHANGMAI", "KHUSHI DINESH",
"KIARA BANGERA", "BHAVYA SACHDEVA", "SWARNA HARITH", "JHANVI CHOUDHARY",
"PALAK DHAMI", "MAHI SHWET RAJ", "ASHMITA CHANDRA", "AARON FERNANDES",
"SANJAY C J", "ANURAG SINGH", "RUNDRANSH MISHRA", "RUDRAKSH SAHU",
"JOY BARUA", "SHIVANGI SARMA", "BHAVIKA DUGAR", "JASMINE GURUNG",
"SAACHI GRAMOPAHYAY", "KIRAN SHET NARIYEKAR", "SWADESH MONDAL",
"ROHITH BENEDICTON", "BHARGAV PHUKAN", "SHRISH MAULIK", "AINESH RAY",
"YASH GULHANE", "JASHUA THOMAS", "DISHANT MEHTA", "KAREENA SHANKTA",
"APEKSHA FERNANDES", "ZARA JABBAR", "ARUSHI MANJUNATH", "ANVITA GOWDA",
"GUNN MATTA", "SHRIYA ISHWARPRASAD", "KHUSHPREET KAUR BHULLAR",
"DANUSH S", "M LOHITH", "VARUN PATEL", "MILANTON DUTTA", "SREEDIP MONDAL",
"R SUBRAMANIYAM", "KALYANI SAXENA", "AALIYAH SINGH", "RAJESHREE BURAGOHAIN",
"POOJA D", "ARYAN VARNEKAR", "HARSH SAROHA", "BIKRAM CHANGMAI",
"S HITEN MITTAL", "SAHIL LASKAR", "ANSHAV JINDAL", "ROHITH BENEDICTON",
"LAKSH PURI", "NINA VENKATESH", "KENISHA GUPTA", "NILABJAA GHOSH",
"SANJITI SAHA", "DISHA BHANDARI", "THITHIKSHAA H", "AEKA CHAKRA",
"MITHIKA KARAPURKAR", "MIHIR AMBRE", "D ADHITHYA", "PRIYANK RANA",
"M JAYA VIKESH", "ACHINTYA GHOSHAL", "RUDRAKSH SAHU", "SHUBHRANSHU DUTTA",
"A MIDHUNAN", "SUNAINA MANJUNATH", "SUMAN PATIL", "ANUBHUTI BARUAH",
"SUCHETNA CHAKRABORTY", "DOLPHI SARANG", "KHUSHI DINESH", "BHAVYA SACHDEVA",
"KIARA BANGERA", "AASTHA CHOUDHURY", "V VARSHA", "DIVYA GHOSH",
"KANYA NAYYAR", "CYNTHIA CHOUDHARY", "ASHMITA CHANDRA", "NIVRITI DATTA",
"SHIVANGI SARMA", "BHAVIKA DUGAR", "JASMINE GURUNG", "MOUMITRA KARAR",
"SAACHI GRAMOPAHYAY"), Age = c("04", "04", "04", "03", "04",
"04", "03", "04", "03", "06", "05", "04", "04", "05", "03", "05",
"00", "02", "02", "02", "02", "01", "02", "01", "00", "00", "00",
"04", "03", "04", "04", "03", "03", "05", "03", "04", "05", "05",
"04", "06", "04", "06", "04", "00", "01", "00", "01", "02", "00",
"00", "02", "02", "01", "04", "04", "04", "03", "05", "05", "03",
"03", "05", "03", "05", "05", "04", "05", "04", "04", "00", "02",
"99", "02", "02", "02", "01", "02", "02", "00", "02", "02", "01",
"03", "05", "06", "04", "03", "04", "04", "06", "05", "04", "02",
"01", "00", "01", "00"), Team = c("KARNATAKA", "KARNATAKA", "HARYANA",
"MAHARASHTRA", "DELHI", "ASSAM", "MADHYA PRADESH", "DELHI", "KARNATAKA",
"MAHARASHTRA", "DELHI", "TAMILNADU", "DELHI", "MAHARASHTRA",
"BIHAR", "KARNATAKA", "MAHARASHTRA", "KARNATAKA", "DELHI", "MAHARASHTRA",
"CHHATTISGARH", "ASSAM", "ASSAM", "TAMILNADU", "HARYANA", "GOA",
"GOA", "WEST BENGAL", "TAMILNADU", "DELHI", "MAHARASHTRA", "KARNATAKA",
"MAHARASHTRA", "TAMILNADU", "GUJARAT", "MAHARASHTRA", "MAHARASHTRA",
"MAHARASHTRA", "KARNATAKA", "KARNATAKA", "KARNATAKA", "TAMILNADU",
"MADHYA PRADESH", "TAMILNADU", "A.P.", "MADHYA PRADESH", "ASSAM",
"WEST BENGAL", "PUDUCHERRY", "GUJARAT", "UTTAR PRADESH", "ASSAM",
"PUDUCHERRY", "DELHI", "HARYANA", "DELHI", "KARNATAKA", "WEST BENGAL",
"PUNJAB", "TAMILNADU", "MAHARASHTRA", "KARNATAKA", "MAHARASHTRA",
"WEST BENGAL", "MAHARASHTRA", "UTTAR PRADESH", "KARNATAKA", "MAHARASHTRA",
"GOA", "MAHARASHTRA", "TAMILNADU", "DELHI", "TAMILNADU", "DELHI",
"CHHATTISGARH", "ASSAM", "PUDUCHERRY", "KARNATAKA", "GOA", "ASSAM",
"WEST BENGAL", "GUJARAT", "KARNATAKA", "DELHI", "MAHARASHTRA",
"DELHI", "TAMILNADU", "KARNATAKA", "MADHYA PRADESH", "GOA", "KARNATAKA",
"ASSAM", "ASSAM", "TAMILNADU", "HARYANA", "WEST BENGAL", "GOA"
), Finals = c("1:56.66", "1:57.46", "1:58.61", "1:58.88",
"1:59.06", "2:03.50", "2:03.52", "2:10.99", "2:10.29", "2:12.16",
"2:12.66", "2:13.21", "2:13.48", "2:16.52", "2:17.87", "2:18.09",
"1:56.51", "1:56.95", "1:58.37", "1:58.70", "2:05.01", "2:10.96",
"2:07.91", "2:22.97", "2:24.30", "2:35.28", "2:38.32", "1:06.33",
"1:08.56", "1:08.90", "1:09.75", "1:10.01", "1:10.83", "1:11.67",
"1:11.88", "1:14.66", "1:15.47", "1:19.02", "1:19.62", "1:20.71",
"1:20.90", "1:21.12", "1:21.57", "1:03.71", "1:05.31", "1:08.51",
"1:08.91", "1:10.24", "1:22.15", "1:20.25", "1:22.18", "1:30.76",
"1:57.40", "25.70", "25.88", "25.92", "26.19", "26.75", "26.75",
"26.95", "27.21", "28.58", "28.96", "30.01", "30.46", "30.64",
"31.08", "31.15", "31.31", "25.00", "26.03", "26.46", "26.64",
"26.96", "26.97", "27.43", "33.34", "30.78", "31.07", "31.11",
"31.22", "31.33", "9:26.19", "9:30.06", "9:36.02", "9:44.29",
"9:49.70", "9:53.19", "9:57.08", "10:05.72", "10:19.83", "11:33.67",
"9:31.22", "10:13.78", "10:36.19", "11:17.24", "11:31.89"), DQ = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)), row.names = c(NA, -97L), class = "data.frame")
expect_equivalent(df_test, df_standard)
})
test_that("2012 Euro Juniors", {
skip_on_cran()
file <-
"https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/Splash/European_Jr_Champs_2012.pdf"
df_test <- file %>%
read_results() %>%
swim_parse() %>%
select(-Event)
place_sum <- sum(as.numeric(df_test$Place), na.rm = TRUE)
dq_sum <- sum(df_test$DQ, na.rm = TRUE)
expect_equivalent(place_sum, 33438)
expect_equivalent(dq_sum, 46)
})
#
# test_that("2013 Euro Juniors, full meet checked", {
# skip_on_cran()
#
# # as of 3/18/23 pdf_test is not reading in rows where team names run into times correctly
# # I don't know how to fix this beyond using typo/replacement
#
# file <-
# "https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/Splash/Arena_European_Junior_Swimming_Champs_2013.pdf"
#
# df_test <- file %>%
# read_results() %>%
# swim_parse() %>%
# select(-Event)
#
# df_standard <-
# structure(list(Place = c("1", "2", "3", "4", "5", "6", "7", "7",
# "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19",
# "20", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "1",
# "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13",
# "14", "15", "16", "17", "18", "19", "20", "1", "2", "3", "4",
# "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15",
# "16", "17", "18", "19", "20", "1", "2", "3", "4", "5", "6", "7",
# "8", "8", "10", "11", "12", "13", "14", "15", "16", "17", "18",
# "19", "20", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10",
# "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12",
# "13", "14", "15", "16", "17", "18", "19", "20", "21", "1", "2",
# "3", "4", "5", "6", "6", "8", "9", "10", "1", "2", "3", "4",
# "5", "6", "7", "8", "9", "10", "1", "2", "3", "4", "5", "6",
# "7", "8", "9", "10", "1", "2", "3", "4", "5", "6", "7", "8",
# "9", "10", "1", "2", "3", "4", "5", "6", "7", "8", "9", NA),
# Name = c("STOLK Kyle", "WESTERMANN Magnus", "HOLUB Jan",
# "SZCZEPANSKI Sebastian", "SEDOV Evgeny", "HANSEN Morten Bak",
# "NIKULIN Iaroslav", "WIERLING Damian", "MYLONAS Fotios",
# "DEOLET Timothy", "HOLODA Peter", "SMITH Jack", "BROGLIA Alessandro",
# "PLAVIN Bogdan", "SORIC Lovre", "AYAR Kaan Tuerker", "HALDEMANN Alexandre",
# "NAGY Marcel", "NASCIMENTO Miguel Duarte", "ROGOZIN Jevgenij",
# "BECK Leonie Antonia", "KISS Nikoletta", "CAPONI Linda",
# "MASSONE Antonia", "SALAMATINA Valeria", "JONES Ellena",
# "VOLKODAVOVA Polina", "TETTAMANZI Alisia", "NATLACEN Gaja",
# "PEREZ BLANCO Jimena", "KUDASHEV Alexander", "TERRES ILLESCAS Pedro",
# "GRATZ Benjamin", "TWAROWSKI Jerzy", "JOHNSON Matthew", "SZABO Norbert",
# "KUNERT Alexander", "MUGNAINI Mattia", "KUSWIK Maciej", "RIVAS GALLEGO Javier",
# "GUREVICH Etay", "LIESS Nils", "KRYPAK MaksyM", "NOVAK Petr",
# "O'SULLIVAN David", "DIMITROV Nikola", "VATASESCU Stefan Andrei",
# "HECLAU Tom", "MARGEVICIUS Deividas", "WURZER Stefan", "SOLNTSEVA Viktoriya",
# "GUERRA Silvia", "MALYAVINA Anastasiya", "SEBESTYEN Dalma",
# "HUETHER Marlene", "KAZINA Polina", "REISAENEN Sofie", "HUMMEL Margarethe",
# "KAENSAEKOSKI Silja", "STEPANOVA Monika", "MIKKELBORG Silje",
# "HANSSON Sophie", "PINTAR Tjasa", "NABNEY Erin", "KADOGLU Georgiya",
# "MC NAMARA Dearbhail", "WIKIEL Angelika", "LEMKE Julia",
# "BAZYL Magdalena", "ELORANTA Emilia", "MAKOVICH Semen", "LITCHFIELD Max",
# "CHATRON Cyril", "CWIEK Kacper", "SZARANEK Mark", "STRAZDAS Povilas",
# "TAROCCHI Lorenzo", "ORNEK Alpkan", "SANCHEZ GTREZ-CABELLO",
# "MASLOV Andrey", "FOLDHAZI David", "CARAZO BARBERO Gonzalo",
# "BAIONI Andrea", "THOMASBERGER David", "SERES Edwin", "GREVEN Lucas",
# "STEPIEN Konrad", "VELOSO Tomas Miguel", "CHAIKOU Pavel",
# "FODOR Bogdan", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "MICKA Jan",
# "FURTEK Pawel", "HUGHES Caleb", "WOJDAK Wojciech", "BOUCHAUT Joris",
# "BOCK Maximilian", "ROMANCHUK Mykhaylo", "KNIGHT Joel", "CHRISTIANSEN Henrik",
# "BRZOSKOWSKI Maarten", "PIIROINEN Eetu", "SAEMUNDSSON Sven Arnar",
# "ACERENZA Domenico", "GIBBONS Brendan", "DMYTRIYEV Roman",
# "BJOERLYKKE Ole Martin", "MORTENSEN Oli", "SUHAREV Petar",
# "CHERNEV Teodosi", "HJELM Alvi", "FARKAS Tamas", "BAKLAKOVA Maria",
# "GALLARDO CARAPETO Fatima", "MEYNEN Julie-Marie", "SCHOLTISSEK Helen",
# "GOVEJSEK Nastja", "COOPER Harriet", "LEVISEN Julie", "HACHE Cloe",
# "KLEIN Nele", "LATHAM Katie", "GUZHENKOVA Anastasia", "DAY Emma",
# "SEBESTYEN Dalma", "WATTEL Marie", "NOVOSZATH Melinda", "RICO PEREZ Carmen",
# "AUSTIN Shauntelle", "PODEUS Elin", "KAETHNER Rosalie", "BENESOVA Alena",
# "TARASEVICH Grigory", "RAPSYS Danas", "SABBIONI Simone",
# "SCHWARZ Carl-Louis", "CHRISTOU Apostolos", "KONTIZAS Michail",
# "NASCIMENTO Miguel Duarte", "FOLDHAZI David", "THEODORIS Nathan",
# "MENCARINI Luca", "DORINOV Mikhail", "PALATOV Alexander",
# "LINDENBERG Yannick", "HORVATH David", "PILGER Max", "AURUSKEVICIUS Mantas",
# "ACKLAND Harry", "CALLAIS Quentin", "PFYFFER Luca", "NACHTMAN Mateusz",
# "OEZTUERK Sonnele", "MAZUTAITYTE Ugne", "RIEDEMANN Laura",
# "BRIGGS Megan", "GRUSOVA Tereza", "ZAMORANO SANZ Africa",
# "SVECENA Lucie", "MAGNER Agata", "HOPE Lucy", "USTINOVA Daria K"
# ), Age = c("96", "95", "96", "95", "96", "95", "95", "96",
# "96", "95", "96", "96", "96", "95", "95", "95", "95", "95",
# "95", "96", "97", "97", "98", "97", "98", "97", "98", "97",
# "97", "97", "95", "95", "96", "95", "95", "96", "96", "95",
# "96", "95", "95", "96", "95", "96", "95", "95", "95", "95",
# "95", "95", "98", "97", "97", "97", "98", "97", "97", "97",
# "97", "97", "98", "98", "97", "97", "98", "98", "98", "98",
# "97", "97", "95", "95", "95", "95", "95", "96", "95", "95",
# "96", "95", "95", "96", "95", "96", "95", "95", "96", "96",
# "96", "95", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "95",
# "95", "95", "96", "95", "95", "96", "95", "96", "95", "95",
# "96", "95", "95", "95", "96", "96", "95", "96", "96", "95",
# "97", "97", "97", "97", "97", "97", "97", "97", "98", "97",
# "97", "97", "97", "97", "98", "98", "97", "97", "97", "98",
# "95", "95", "96", "95", "96", "95", "95", "95", "95", "95",
# "95", "95", "95", "96", "96", "95", "96", "96", "96", "95",
# "98", "97", "98", "97", "98", "98", "97", "97", "97", "98"
# ), Team = c("Netherlands", "Denmark", "Poland", "Poland",
# "Russia", "Denmark", "Russia", "Germany", "Greece", "Belgium",
# "Hungary", "Great Britain", "Italy", "Ukraine", "Croatia",
# "Turkey", "Switzerland", "Germany", "Portugal", "Lithuania",
# "Germany", "Hungary", "Italy", "Germany", "Russia", "Great Britain",
# "Russia", "Italy", "Slovenia", "Spain", "Russia", "Spain",
# "Hungary", "Poland", "Great Britain", "Hungary", "Germany",
# "Italy", "Poland", "Spain", "Israel", "Switzerland", "Ukraine",
# "Czech Republic", "Ireland", "Bulgaria", "Romania", "Germany",
# "Lithuania", "Austria", "Ukraine", "Italy", "Ukraine", "Hungary",
# "Germany", "Russia", "Norway", "Germany", "Finland", "Czech Republic",
# "Norway", "Sweden", "Slovenia", "Great Britain", "Bulgaria",
# "Ireland", "Poland", "Belarus", "Poland", "Finland", "Russia",
# "Great Britain", "France", "Poland", "Great Britain", "Lithuania",
# "Italy", "Turkey", "Spain", "Russia", "Hungary", "Spain",
# "Italy", "Germany", "Hungary", "Netherlands", "Poland", "Portugal",
# "Belarus", "Romania", "Russia", "Great Britain", "Germany",
# "Poland", "Denmark", "France", "Croatia", "Austria", "Switzerland",
# "Finland", "Czech Republic", "Poland", "Great Britain", "Poland",
# "France", "Germany", "Ukraine", "Great Britain", "Norway",
# "Netherlands", "Finland", "Croatia", "Italy", "Ireland",
# "Czech Republic", "Norway", "Faroe Islands", "Bulgaria",
# "Bulgaria", "Faroe Islands", "Serbia", "Russia", "Spain",
# "Luxembourg", "Germany", "Slovenia", "Great Britain", "Denmark",
# "France", "Germany", "Great Britain", "Russia", "Great Britain",
# "Hungary", "France", "Hungary", "Spain", "Great Britain",
# "Sweden", "Germany", "Czech Republic", "Russia", "Lithuania",
# "Italy", "Germany", "Greece", "Greece", "Portugal", "Hungary",
# "Great Britain", "Italy", "Russia", "Russia", "Germany",
# "Hungary", "Germany", "Lithuania", "Great Britain", "France",
# "Switzerland", "Poland", "Germany", "Lithuania", "Germany",
# "Great Britain", "Czech Republic", "Spain", "Czech Republic",
# "Poland", "Great Britain", "Russia"), Finals = c("50.18",
# "50.19", "50.22", "50.33", "50.39", "50.65", "50.67", "50.67",
# "50.71", "50.73", "50.92", "50.96", "51.01", "51.05", "51.30",
# "51.36", "51.48", "51.49", "51.68", "52.09", "4:12.87", "4:13.43",
# "4:13.86", "4:14.37", "4:15.88", "4:17.02", "4:18.33", "4:18.78",
# "4:20.27", "4:20.30", "1:58.71", "1:59.54", "1:59.84", "2:00.34",
# "2:00.75", "2:00.79", "2:01.19", "2:01.52", "2:01.56", "2:01.62",
# "2:01.90", "2:02.14", "2:02.32", "2:02.50", "2:03.05", "2:03.39",
# "2:04.73", "2:04.79", "2:05.05", "2:05.55", "2:27.33", "2:29.91",
# "2:31.27", "2:31.34", "2:31.74", "2:32.23", "2:32.76", "2:33.39",
# "2:34.33", "2:34.45", "2:34.66", "2:35.28", "2:35.78", "2:36.40",
# "2:36.93", "2:37.31", "2:38.11", "2:39.85", "2:40.03", "2:41.08",
# "1:59.91", "2:01.60", "2:03.66", "2:03.85", "2:03.91", "2:04.11",
# "2:04.30", "2:04.59", "2:04.83", NA, "2:05.53", "2:05.54",
# "2:05.69", "2:05.74", "2:06.45", "2:07.15", "2:07.25", "2:07.41",
# "2:08.01", "2:08.31", "3:29.10", "3:33.25", "3:34.23", "3:34.43",
# "3:35.58", "3:36.12", "3:38.35", "3:38.59", "3:42.46", "3:42.83",
# "15:13.51", "15:13.85", "15:19.63", "15:27.06", "15:28.35",
# "15:28.79", "15:30.12", "15:30.17", "15:31.36", "15:43.69",
# "15:44.95", "15:49.85", "15:52.23", "15:52.96", "15:53.36",
# "16:13.03", "16:13.45", "16:15.38", "16:30.58", "16:38.38",
# "16:38.55", "54.78", "55.76", "55.92", "56.22", "56.31",
# "56.34", "56.34", "56.47", "56.97", "57.27", "2:11.24", "2:12.14",
# "2:12.75", "2:13.72", "2:13.82", "2:14.29", "2:15.51", "2:16.60",
# "2:16.79", "2:17.25", "55.08", "55.44", "55.73", "55.83",
# "55.87", "56.14", "56.45", "56.74", "56.86", "56.89", "2:12.27",
# "2:12.69", "2:13.92", "2:14.11", "2:15.07", "2:17.32", "2:17.43",
# "2:18.58", "2:18.69", "2:20.69", "2:13.90", "2:14.28", "2:14.61",
# "2:14.63", "2:14.99", "2:15.22", "2:17.70", "2:17.95", "2:18.51",
# NA), Points = c("816", "816", "815", "809", "806", "794",
# "793", "793", "791", "790", "781", "780", "777", "775", "764",
# "761", "756", "756", "747", "730", "845", "840", "836", "831",
# "816", "805", "793", "789", "775", "775", NA, NA, NA, NA,
# NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
# NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
# NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
# NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "729", "687",
# "678", "676", "665", "660", "640", "638", "605", "602", "866",
# "865", "849", "829", "825", "824", "821", "821", "817", "786",
# "783", "771", "765", "763", "762", "717", "716", "712", "679",
# "664", "663", "858", "814", "807", "794", "790", "789", "789",
# "783", "763", "751", NA, NA, NA, NA, NA, NA, NA, NA, NA,
# NA, "838", "822", "809", "805", "803", "791", "778", "767",
# "762", "761", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
# NA, NA, NA, NA, NA, NA, NA, NA, NA), DQ = c(0, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1)), row.names = c(NA,
# -171L), class = "data.frame")
#
#
# expect_equivalent(df_test, df_standard)
#
# place_sum <- sum(as.numeric(df_test$Place), na.rm = TRUE)
# expect_equivalent(place_sum, 1443)
#
# dq_sum <- sum(df_test$DQ, na.rm = TRUE)
# expect_equivalent(dq_sum, 1)
#
# })
test_that("2017 Open belgian Champs, has prelims, team-country", {
skip_on_cran()
file <-
"https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/Splash/Open_Belgian_Champs_2017.pdf"
df_test <- file %>%
read_results() %>%
swim_parse() %>%
select(-Event)
place_sum <- sum(as.numeric(df_test$Place), na.rm = TRUE)
dq_sum <- sum(df_test$DQ, na.rm = TRUE)
expect_equivalent(place_sum, 26332)
expect_equivalent(dq_sum, 42)
})
test_that("2017 Open belgian Champs, has prelims, team-country, splits", {
skip_on_cran()
file <-
"https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/Splash/Open_Belgian_Champs_2017.pdf"
df_test <- file %>%
read_results() %>%
swim_parse(splits = TRUE) %>%
select(-Event)
df_test_dim <- dim(df_test)
expect_equivalent(df_test_dim, c(1709, 108))
# rows 1709 fully checked
df_test_head <- df_test %>%
head(5)
df_standard <-
structure(list(Place = c("1", "2", "3", "4", "5"), Name = c("SMITS, Jade",
"HANSENNE, Nona", "JONGMAN, Indy", "DOBRIN, Alexandra", "VAN WALLENDAEL, Sarah"
), Age = c("01", "01", "01", "93", "02"), Team = c("BEL-BRABO",
"BEL-AART", "NED-KNZB", "ROU-CNBA", "BEL-BRABO"), Prelims = c("2:18.04",
"2:20.62", "2:18.24", "2:21.70", "2:23.61"), Finals = c("2:18.55",
"2:20.52", "2:21.93", "2:22.30", "2:22.96"), Points = c("717",
"688", "667", "662", "653"), DQ = c("0", "0", "0", "0", "0"),
Split_50 = c("31.01", "33.03", "32.89", "32.36", "33.69"),
Split_100 = c("1:05.93", "1:08.70", "1:09.00", "1:08.43",
"1:10.52"), Split_150 = c("1:42.36", "1:44.93", "1:46.12",
"1:45.40", "1:47.14"), Split_200 = c("2:18.55", "2:20.52",
"2:21.93", "2:22.30", "2:22.96"), Split_250 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_300 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_350 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_400 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_450 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_500 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_550 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_600 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_650 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_700 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_750 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_800 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_850 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_900 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_950 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_1000 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_1050 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_1100 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_1150 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_1200 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_1250 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_1300 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_1350 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_1400 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_1450 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_1500 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_1550 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_1600 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_1650 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_1700 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_1750 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_1800 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_1850 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_1900 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_1950 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_2000 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_2050 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_2100 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_2150 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_2200 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_2250 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_2300 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_2350 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_2400 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_2450 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_2500 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_2550 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_2600 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_2650 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_2700 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_2750 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_2800 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_2850 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_2900 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_2950 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_3000 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_3050 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_3100 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_3150 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_3200 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_3250 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_3300 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_3350 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_3400 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_3450 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_3500 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_3550 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_3600 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_3650 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_3700 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_3750 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_3800 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_3850 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_3900 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_3950 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_4000 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_4050 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_4100 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_4150 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_4200 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_4250 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_4300 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_4350 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_4400 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_4450 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_4500 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_4550 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_4600 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_4650 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_4700 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_4750 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_4800 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_4850 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_4900 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_4950 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_5000 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_)), row.names = c(NA, 5L), class = "data.frame")
expect_equivalent(df_test_head, df_standard)
})
test_that("Glenmark Senior Nationals 2019 relays", {
skip_on_cran()
file <-
"https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/Splash/Glenmark_Senior_Nationals_2019.pdf"
df_test <- file %>%
read_results() %>%
swim_parse(splits = TRUE, split_length = 100)
df_test_relay <- df_test %>%
filter(str_detect(Event, " x ")) %>%
select(where(~ !all(is.na(.x)))) %>%
select(-Event)
df_standard <-
structure(list(Place = c("1", "2", "3", "4", "5", "6", "7", "1",
"2", "3", "4", "5", "6", "7", "8", "1", "2", "3", "4", "5", "6",
"7", "8", "1", "2", "3", "4", "5", "6", "7", "8", "1", "2", "3",
"4", "5", "6", "7", "8", "1", "2", "3", "4", "5", "6", "7", "8",
"1", "2", "3", "4", "5", "1", "2", "3", "4", "5", "6", "7", "8"
), Team = c("KARNATAKA", "SSCB", "R.S.P.B", "POLICE", "BENGAL",
"MAHARASHTRA", "KERALA", "KARNATAKA", "MAHARASHTRA", "R.S.P.B",
"GUJARAT", "TAMILNADU", "DELHI", "BENGAL", "KERALA", "MAHARASHTRA",
"KARNATAKA", "TAMILNADU", "DELHI", "BENGAL", "KERALA", "GUJARAT",
"PUNJAB", "KARNATAKA", "SSCB", "MAHARASHTRA", "R.S.P.B", "DELHI",
"BENGAL", "KERALA", "TELANGANA", "KARNATAKA", "SSCB", "R.S.P.B",
"DELHI", "KERALA", "MAHARASHTRA", "BENGAL", "POLICE", "MAHARASHTRA",
"KARNATAKA", "TAMILNADU", "GUJARAT", "BENGAL", "DELHI", "KERALA",
"PUNJAB", "KARNATAKA", "MAHARASHTRA", "TAMILNADU", "BENGAL",
"KERALA", "MAHARASHTRA", "R.S.P.B", "KARNATAKA", "DELHI", "KERALA",
"TAMILNADU", "BENGAL", "GUJARAT"), Finals = c("7:46.04",
"7:49.31", "7:53.52", "8:19.53", "8:20.32", "8:24.45", "8:46.12",
"1:50.65", "1:52.82", "1:54.00", "1:55.67", "1:57.23", "2:00.50",
"2:01.71", "2:01.90", "4:02.90", "4:09.88", "4:13.25", "4:14.84",
"4:15.54", "4:29.80", "4:31.50", "4:47.63", "3:30.45", "3:31.04",
"3:31.17", "3:33.22", "3:37.07", "3:43.67", "3:45.01", "4:02.50",
"3:48.83", "3:52.11", "3:52.93", "4:01.27", "4:02.53", "4:04.46",
"4:07.31", "4:39.37", "4:33.10", "4:34.84", "4:39.49", "4:40.06",
"4:41.20", "4:52.31", "4:56.41", "5:32.06", "9:04.86", "9:08.51",
"9:09.18", "9:23.37", "9:56.91", "1:39.69", "1:41.83", "1:41.92",
"1:44.95", "1:47.70", "1:48.06", "1:49.74", "1:50.08"), Points = c("724",
"709", "690", "588", "585", "571", "503", NA, NA, NA, NA, NA,
NA, NA, NA, "646", "593", "570", "559", "555", "471", "463",
"389", "715", "709", "708", "688", "652", "596", "585", "467",
"743", "712", "704", "634", "624", "609", "588", "408", "609",
"597", "568", "565", "558", "497", "476", "339", "609", "597",
"595", "551", "463", NA, NA, NA, NA, NA, NA, NA, NA), DQ = c("0",
"0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0",
"0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0",
"0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0",
"0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0",
"0", "0", "0", "0", "0", "0", "0"), Split_100 = c("1:55.46",
"1:54.39", "1:58.53", NA, "2:03.92", "2:09.81", "2:04.90", NA,
NA, NA, NA, NA, NA, NA, NA, NA, "1:02.46", "1:03.01", NA, "1:04.60",
"1:08.63", NA, "1:09.59", "53.37", "53.99", NA, "52.56", "54.01",
"56.86", "56.59", NA, NA, "59.10", "1:00.03", "59.44", NA, "1:02.30",
"1:04.93", NA, "1:10.05", "1:08.93", "1:08.06", "1:10.09", "1:07.24",
NA, NA, "1:23.43", "2:17.38", "2:18.21", "2:21.40", "2:20.05",
"2:21.76", NA, NA, NA, NA, NA, NA, NA, NA), Split_200 = c("1:58.43",
"1:57.62", NA, NA, "2:06.58", "2:06.64", NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, "1:02.21", "1:04.40", NA, "1:06.10", "1:08.30",
NA, "1:11.60", "53.24", "54.04", NA, "53.94", "54.93", "56.22",
"58.22", NA, NA, "56.92", NA, "59.58", NA, "59.99", NA, NA, "1:05.60",
"1:06.97", "1:16.16", "1:02.48", NA, NA, NA, "1:24.38", "2:15.53",
"2:21.32", "2:21.77", "2:23.01", "2:31.97", NA, NA, NA, NA, NA,
NA, NA, NA), Split_300 = c("1:29.54", "1:31.42", NA, "2:05.96",
"2:06.84", "2:03.42", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
"1:03.42", "1:04.36", NA, "1:04.24", "1:07.29", NA, "1:16.70",
"53.20", "52.04", NA, "52.97", "55.14", "55.05", "55.50", NA,
NA, "1:04.97", "1:06.00", "1:08.29", NA, "1:07.49", "55.48",
NA, "1:14.61", "1:16.47", "1:19.67", NA, "1:01.15", NA, NA, "1:28.46",
"2:21.02", "2:15.75", "2:14.79", "2:18.45", "2:31.21", NA, NA,
NA, NA, NA, NA, NA, NA), Split_400 = c("2:22.61", "2:25.88",
NA, "2:07.68", "2:02.98", "2:04.58", "2:15.50", NA, NA, NA, NA,
NA, NA, NA, NA, NA, "1:01.79", "1:01.48", NA, "1:00.60", "1:05.58",
NA, "1:09.74", "50.64", "50.97", NA, "53.75", "52.99", "55.54",
"54.70", NA, NA, "51.12", NA, "53.96", NA, "54.68", NA, NA, "1:02.84",
"1:02.47", "55.60", NA, NA, NA, NA, "1:15.79", "2:10.93", "2:13.23",
"2:11.22", "2:21.86", "2:31.97", NA, NA, NA, NA, NA, NA, NA,
NA)), row.names = c(NA, -60L), class = "data.frame")
expect_equivalent(df_test_relay, df_standard)
df_test_ind <- df_test %>%
head(5) %>%
select(where(~ !all(is.na(.x))))
df_standard <-
structure(list(Place = c("1", "2", "3", "4", "5"), Name = c("Kushagra Rawat",
"ANAND AS", "AARON FERNANDES", "Vishal Grewal", "Anurag R. Singh"
), Age = c("90", NA, NA, "90", NA), Team = c("DELHI", "SSCB",
"MAHARASHTRA", "DELHI", "UTTAR PRADESH"), Finals = c("3:56.95",
"4:01.92", "4:04.71", "4:07.44", "4:09.34"), Points = c("801",
"752", "727", "703", "687"), DQ = c("0", "0", "0", "0", "0"),
Event = c("Men, 400m Freestyle", "Men, 400m Freestyle", "Men, 400m Freestyle",
"Men, 400m Freestyle", "Men, 400m Freestyle"), Split_100 = c("58.11",
"1:02.00", "1:00.47", "58.70", "1:00.54"), Split_200 = c("1:00.23",
"1:00.56", NA, "1:02.64", "1:03.75"), Split_300 = c("59.57",
"3:01.36", "3:04.24", "1:03.76", "1:04.16"), Split_400 = c("59.04",
"4:01.92", "4:04.71", "1:02.34", "1:00.89")), row.names = c(NA,
5L), class = "data.frame")
expect_equivalent(df_test_ind, df_standard)
})
test_that("Glenmark Senior Nationals 2019 with splits and relays", {
skip_on_cran()
file <-
"https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/Splash/Glenmark_Senior_Nationals_2019.pdf"
df <- file %>%
read_results() %>%
swim_parse(splits = TRUE, split_length = 100, relay_swimmers = TRUE)
df_test_relay <- df %>%
filter(str_detect(Event, " x ")) %>%
select(where( ~ !all(is.na(.x)))) %>%
select(-Event)
df_standard_relay <-
structure(list(Place = c("1", "2", "3", "4", "5", "6", "7", "1",
"2", "3", "4", "5", "6", "7", "8", "1", "2", "3", "4", "5", "6",
"7", "8", "1", "2", "3", "4", "5", "6", "7", "8", "1", "2", "3",
"4", "5", "6", "7", "8", "1", "2", "3", "4", "5", "6", "7", "8",
"1", "2", "3", "4", "5", "1", "2", "3", "4", "5", "6", "7", "8"
), Team = c("KARNATAKA", "SSCB", "R.S.P.B", "POLICE", "BENGAL",
"MAHARASHTRA", "KERALA", "KARNATAKA", "MAHARASHTRA", "R.S.P.B",
"GUJARAT", "TAMILNADU", "DELHI", "BENGAL", "KERALA", "MAHARASHTRA",
"KARNATAKA", "TAMILNADU", "DELHI", "BENGAL", "KERALA", "GUJARAT",
"PUNJAB", "KARNATAKA", "SSCB", "MAHARASHTRA", "R.S.P.B", "DELHI",
"BENGAL", "KERALA", "TELANGANA", "KARNATAKA", "SSCB", "R.S.P.B",
"DELHI", "KERALA", "MAHARASHTRA", "BENGAL", "POLICE", "MAHARASHTRA",
"KARNATAKA", "TAMILNADU", "GUJARAT", "BENGAL", "DELHI", "KERALA",
"PUNJAB", "KARNATAKA", "MAHARASHTRA", "TAMILNADU", "BENGAL",
"KERALA", "MAHARASHTRA", "R.S.P.B", "KARNATAKA", "DELHI", "KERALA",
"TAMILNADU", "BENGAL", "GUJARAT"), Finals = c("7:46.04",
"7:49.31", "7:53.52", "8:19.53", "8:20.32", "8:24.45", "8:46.12",
"1:50.65", "1:52.82", "1:54.00", "1:55.67", "1:57.23", "2:00.50",
"2:01.71", "2:01.90", "4:02.90", "4:09.88", "4:13.25", "4:14.84",
"4:15.54", "4:29.80", "4:31.50", "4:47.63", "3:30.45", "3:31.04",
"3:31.17", "3:33.22", "3:37.07", "3:43.67", "3:45.01", "4:02.50",
"3:48.83", "3:52.11", "3:52.93", "4:01.27", "4:02.53", "4:04.46",
"4:07.31", "4:39.37", "4:33.10", "4:34.84", "4:39.49", "4:40.06",
"4:41.20", "4:52.31", "4:56.41", "5:32.06", "9:04.86", "9:08.51",
"9:09.18", "9:23.37", "9:56.91", "1:39.69", "1:41.83", "1:41.92",
"1:44.95", "1:47.70", "1:48.06", "1:49.74", "1:50.08"), Points = c("724",
"709", "690", "588", "585", "571", "503", NA, NA, NA, NA, NA,
NA, NA, NA, "646", "593", "570", "559", "555", "471", "463",
"389", "715", "709", "708", "688", "652", "596", "585", "467",
"743", "712", "704", "634", "624", "609", "588", "408", "609",
"597", "568", "565", "558", "497", "476", "339", "609", "597",
"595", "551", "463", NA, NA, NA, NA, NA, NA, NA, NA), DQ = c("0",
"0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0",
"0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0",
"0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0",
"0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0",
"0", "0", "0", "0", "0", "0", "0"), Relay_Swimmer_1 = c("Siva S",
"ANAND AS", "Sanu Debnath", "Mandar Anandrao Divase", "Rajdip Roy",
"GOURAV CHAVAN", "Arjun Anesh K", "Shrihari Nataraj", "JYOTSNA PANSARE",
"Soumyajit Saha", "Maana Patel", "Sethu Manickavel T", "Siddhant Sejwal",
"Soubrity Mondal", "Aaron J Thomas", "RUJUTA KHADE", "Smruthi Mahalingam",
"Swarna K Harith", "Pallavi Sejwal", "Shristi Upadhyay", "Jomi George",
"Niva Sharma", "Gurnoor Kaur", "Tanish George Mathew", "NIMISH MULEY",
"VIRDHAWAL KHADE", "Viraj Prabhu", "Kushagra Rawat", "Rajdip Roy",
"Abhijith Gegarin", "Yash Varma", "Shrihari Nataraj", "ARVIND MANI",
"Soumyajit Saha", "Siddhant Sejwal", "Aaron J Thomas", "MEHLAM GHADIALY",
"Pratyay Bhattacharya", "Mandar Anandrao Divase", "YUGA BIRNALE",
"Ridhima Veerendra Kumar", "Meenakshi V K R", "Maana Patel",
"Soubrity Mondal", "Riniki Bordoloi", "Sanaa Mathew", "Deepali Attri",
"Damini K Gowda", "YUGA BIRNALE", "Swarna K Harith", "Shristi Upadhyay",
"Bhadrasudevan. S", "MIHIR AMBRE", "Aaron D' Souza", "Likith S P",
"Siddhant Sejwal", "Abhijith Gegarin", "Sethu Manickavel T",
"Shrinjan Pal", "Harshal Sarang"), Relay_Swimmer_2 = c("Avinash Mani",
"KUNTAL GIRI", "Soumyajit Saha", "Munish Kumar", "Army Pal",
"RISHABH CHAUBEY", "Akash. S", "Suvana C Baskar", "VIRDHAWAL KHADE",
"Avantika Chavan", "Anshul Kothari", "Meenakshi V K R", "Pallavi Sejwal",
"Kunal Basak", "Liyana Fathima Umer", "SADHVI DHURI", "Suvana C Baskar",
"Shakthi B", "Riniki Bordoloi", "Oendrila Banerjee", "Sanaa Mathew",
"SILKI NAGPURE", "Bhavjot Kaur", "Siva S", "GAURAV YADAV KA",
"MIHIR AMBRE", "Anoop Augustine", "Samit Sejwal", "Swapnil Chakraborty",
"Arjun Anesh K", "M. Tejasvin", "Rakshith U shetty", "SHIVAKSH SAHU",
"Supriya Mondal", "Sameer Sejwal", "Arjun. M", "MIHIR AMBRE",
"Swapnil Chakraborty", "Sandeep Panghal", "APEKSHA FERNANDES",
"Damini K Gowda", "Shakthi B", "Arushi S K M", "Nilabjaa Ghosh",
"Hema", "Shreya Mary Kamal", "Gurnoor Kaur", "Suvana C Baskar",
"SIYAA SHETTY", "Meenakshi V K R", "Oendrila Banerjee", "Jomi George",
"KENISHA GUPTA", "Avantika Chavan", "Deeksha Ramesh", "Priyank Rana",
"Greeshma.P", "Jayaveena A V", "Priyanka Manna", "Niva Sharma"
), Relay_Swimmer_3 = c("Tanish George Mathew", "JAYANT M", "Anoop Augustine",
"Aman Ghai", "Pratyay Bhattacharya", "AARYAN BHOSALE", "Arun. M",
"Likith S P", "SHWEJAL MANKAR", "Viraj Prabhu", "Oum Saxena",
"Danush S", "Aditya Dubey", "Srishti Basu", "Suneesh. S", "APEKSHA FERNANDES",
"Inchara B", "Jayaveena A V", "Saanvi Sood", "Priyanka Manna",
"Liyana Fathima Umer", "Arushi S K M", "Nishtha Sharma", "Prithvi M",
"VINAY SAHARAN", "SHWEJAL MANKAR", "Sanu Debnath", "Tanmay Das",
"Kunal Basak", "Amridhesh. U", "Tanneru Sai Tarun", "Likith S P",
"ASHISH TOKAS", "M Lohith.", "Aditya Dubey", "Suneesh. S", "JAY EKBOTE",
"Swadesh Mondal", "Munish Kumar", "KAREENA SHANKTA", "Saloni Dalal",
"Shriya Ishwar Prasad", "Kalyani Saxena", "Srishti Basu", "Saanvi Sood",
"Aradhana Bekal", "Chahat Arora", "Smruthi Mahalingam", "SADHVI DHURI",
"Shakthi B", "Priyanka Manna", "Arya G Nair", "RUJUTA KHADE",
"Aditi Dhumatkar", "Smruthi Mahalingam", "Pallavi Sejwal", "Arjun Sambu. A",
"Swarna K Harith", "Shristi Upadhyay", "Maana Patel"), Relay_Swimmer_4 = c("Shrihari Nataraj",
"VINAY SAHARAN", "Saurabh Sangvekar", "Sajan Prakash", "Kunal Basak",
"AARON FERNANDES", "Amal. A", "Deeksha Ramesh", "KENISHA GUPTA",
"Aditi Dhumatkar", "Niva Sharma", "Jayaveena A V", "Bhavya Sachdeva",
"Shrinjan Pal", "Greeshma.P", "KENISHA GUPTA", "Khushi Dinesh",
"Bhavika Dugar", "Bhavya Sachdeva", "Janhvi Choudhury", "Bhadrasudevan. S",
"Maana Patel", "Deepali Attri", "Shrihari Nataraj", "ANAND AS",
"AARON FERNANDES", "Saurabh Sangvekar", "Vishal Grewal", "Shrinjan Pal",
"Amal. A", "Challagani Abhilash", "Prithvi M", "ANAND AS", "Viraj Prabhu",
"Vishal Grewal", "Abhijith Gegarin", "AARON FERNANDES", "Shrinjan Pal",
"Aman Ghai", "KENISHA GUPTA", "Khushi Dinesh", "Swarna K Harith",
"Niva Sharma", "Janhvi Choudhury", "Bhavya Sachdeva", "Jomi George",
"Bhavjot Kaur", "Khushi Dinesh", "RUTUJA TALEGAONKAR", "Bhavika Dugar",
"Soubrity Mondal", "Iris Manoj", "VIRDHAWAL KHADE", "Viraj Prabhu",
"Shrihari Nataraj", "Bhavya Sachdeva", "Jomi George", "Danush S",
"Kunal Basak", "Anshul Kothari"), Split_100 = c("1:55.46", "1:54.39",
"1:58.53", NA, "2:03.92", "2:09.81", "2:04.90", NA, NA, NA, NA,
NA, NA, NA, NA, NA, "1:02.46", "1:03.01", NA, "1:04.60", "1:08.63",
NA, "1:09.59", "53.37", "53.99", NA, "52.56", "54.01", "56.86",
"56.59", NA, NA, "59.10", "1:00.03", "59.44", NA, "1:02.30",
"1:04.93", NA, "1:10.05", "1:08.93", "1:08.06", "1:10.09", "1:07.24",
NA, NA, "1:23.43", "2:17.38", "2:18.21", "2:21.40", "2:20.05",
"2:21.76", NA, NA, NA, NA, NA, NA, NA, NA), Split_200 = c("1:58.43",
"1:57.62", NA, NA, "2:06.58", "2:06.64", NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, "1:02.21", "1:04.40", NA, "1:06.10", "1:08.30",
NA, "1:11.60", "53.24", "54.04", NA, "53.94", "54.93", "56.22",
"58.22", NA, NA, "56.92", NA, "59.58", NA, "59.99", NA, NA, "1:05.60",
"1:06.97", "1:16.16", "1:02.48", NA, NA, NA, "1:24.38", "2:15.53",
"2:21.32", "2:21.77", "2:23.01", "2:31.97", NA, NA, NA, NA, NA,
NA, NA, NA), Split_300 = c("1:29.54", "1:31.42", NA, "2:05.96",
"2:06.84", "2:03.42", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
"1:03.42", "1:04.36", NA, "1:04.24", "1:07.29", NA, "1:16.70",
"53.20", "52.04", NA, "52.97", "55.14", "55.05", "55.50", NA,
NA, "1:04.97", "1:06.00", "1:08.29", NA, "1:07.49", "55.48",
NA, "1:14.61", "1:16.47", "1:19.67", NA, "1:01.15", NA, NA, "1:28.46",
"2:21.02", "2:15.75", "2:14.79", "2:18.45", "2:31.21", NA, NA,
NA, NA, NA, NA, NA, NA), Split_400 = c("2:22.61", "2:25.88",
NA, "2:07.68", "2:02.98", "2:04.58", "2:15.50", NA, NA, NA, NA,
NA, NA, NA, NA, NA, "1:01.79", "1:01.48", NA, "1:00.60", "1:05.58",
NA, "1:09.74", "50.64", "50.97", NA, "53.75", "52.99", "55.54",
"54.70", NA, NA, "51.12", NA, "53.96", NA, "54.68", NA, NA, "1:02.84",
"1:02.47", "55.60", NA, NA, NA, NA, "1:15.79", "2:10.93", "2:13.23",
"2:11.22", "2:21.86", "2:31.97", NA, NA, NA, NA, NA, NA, NA,
NA)), row.names = c(NA, -60L), class = "data.frame")
expect_equivalent(df_test_relay, df_standard_relay)
})
test_that("RBSF 100 free with merged teams/ages", {
skip_on_cran()
file <-
"https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/Splash/RBSF_100Free.pdf"
df_test <- file %>%
read_results() %>%
swim_parse(splits = TRUE)
df_standard <-
structure(list(Place = c("1", "2", "3", "4", "5", "6", "7", "8"
), Name = c("VERDONCK, Fleur", "DUREZ, Laure", "WOUTERS, Briana",
"HENVEAUX, Camille", "VAN DEN EEDE, Lisa", "DUJARDIN, Faye",
"SEYNAEVE, Marthe", "VANDEPOORTE, Pauline"), Age = c("06", "06",
"06", "06", "06", "06", "06", "06"), Team = c("ZGEEL", "ENLN",
"HZA", "LGN", "AZL", "GOLD", "BZK", "FAST"), Prelims = c("55.92",
"57.25", "57.81", "58.54", "1:00.96", "1:00.17", "1:00.59", "1:00.77"
), Finals = c("57.50", "57.77", "58.91", "59.53", "1:01.72",
"1:02.25", "1:02.55", "1:03.21"), DQ = c("0", "0", "0", "0",
"0", "0", "0", "0"), Event = c("Girls, 100m Freestyle 15 years",
"Girls, 100m Freestyle 15 years", "Girls, 100m Freestyle 15 years",
"Girls, 100m Freestyle 15 years", "Girls, 100m Freestyle 15 years",
"Girls, 100m Freestyle 15 years", "Girls, 100m Freestyle 15 years",
"Girls, 100m Freestyle 15 years"), Split_50 = c("27.73", "28.09",
"28.13", "28.95", "29.78", "29.53", "29.73", "30.07"), Split_100 = c("57.50",
"57.77", "58.91", "59.53", "1:01.72", "1:02.25", "1:02.55", "1:03.21"
)), row.names = c(NA, -8L), class = "data.frame")
expect_equivalent(df_test, df_standard)
})
test_that("RBSF 50 free with merged teams/ages, also has a tie", {
skip_on_cran()
file <-
"https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/Splash/RBSF_50Free.pdf"
df_test <- file %>%
read_results() %>%
swim_parse(splits = TRUE)
df_standard <-
structure(list(Place = c("1", "2", "3", "4", "5", "5", "7", "8"
), Name = c("SNEYERS, Kobe", "PETRE, Olivier", "WILLEMS, Dieter",
"BOETS, Victor", "VERCAUTEREN, Jasper", "DUFLOUCQ, Seppe", "OPDEBEECK, Thomas",
"EVENS, Thibo"), Age = c("06", "06", "06", "06", "06", "06",
"06", "06"), Team = c("SHARK", "ENLN", "AZV", "MOZKA", "BRABO",
"ISWIM", "BRABO", "KAZS"), Prelims = c("25.00", "26.15",
"25.79", "26.14", "26.29", "26.25", "26.15", "26.30"), Finals = c("25.69",
"26.05", "26.34", "26.57", "26.96", "26.96", "27.11", "27.66"
), DQ = c("0", "0", "0", "0", "0", "0", "0", "0"), Event = c("Boys, 50m Freestyle 15 years",
"Boys, 50m Freestyle 15 years", "Boys, 50m Freestyle 15 years",
"Boys, 50m Freestyle 15 years", "Boys, 50m Freestyle 15 years",
"Boys, 50m Freestyle 15 years", "Boys, 50m Freestyle 15 years",
"Boys, 50m Freestyle 15 years")), row.names = c(NA, -8L), class = "data.frame")
expect_equivalent(df_test, df_standard)
})
# test_that("Euro 2018 Champs - a giant mess", {
# skip_on_cran()
#
#
# # as of 3/18/23 pdf_test is not reading in rows where team names run into times correctly
# # I don't know how to fix this beyond using typo/replacement
#
# file <-
# "https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/Splash/EURO_MEET_2018.pdf"
#
# df_test <- file %>%
# read_results() %>%
# swim_parse(splits = TRUE, relay_swimmers = TRUE)
#
# df_standard <-
# readRDS(url("https://github.com/gpilgrim2670/Pilgrim_Data/raw/master/Splash/Euro_2018_Splash.rds"))
#
#
# expect_equivalent(df_test, df_standard)
#
# })
# testthat::test_file("tests/testthat/test-splash.R")
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# devtools::test_active_file("tests/testthat/test-splash.R")
test_that("Glenmark Senior Nationals 2019 whole meet", {
skip_on_cran()
file <-
"https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/Splash/Glenmark_Senior_Nationals_2019.pdf"
df_test <- file %>%
read_results() %>%
swim_parse() %>%
select(-Event)
df_standard <-
structure(list(Place = c("1", "2", "3", "4", "5", "6", "7", "8",
"1", "2", "3", "4", "5", "6", "7", "8", "1", "2", "3", "4", "5",
"6", "7", "8", "1", "2", "3", "4", "5", "6", "7", "8", "1", "2",
"3", "4", "5", "6", "7", "8", "1", "2", "3", "4", "5", "6", "7",
"8", "1", "2", "3", "4", "5", "6", "7", "1", "2", "3", "4", "5",
"6", "7", "8", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10",
"11", "12", "1", "2", "3", "4", "5", "6", "7", "8", "1", "2",
"3", "4", "5", "6", "7", "8", "1", "2", "3", "4", "5", "6", "7",
"8", "1", "2", "3", "4", "5", "6", "7", "8", "1", "2", "3", "4",
"5", "6", "7", "8", "1", "2", "3", "4", "5", "6", "7", "8", "1",
"2", "3", "4", "5", "6", "7", "8", "9", "10", "1", "2", "3",
"4", "5", "6", "7", "8", "1", "2", "3", "4", "5", "6", "7", "8",
"1", "2", "3", "4", "5", "6", "7", "8", "1", "2", "3", "4", "5",
"6", "7", "8", "1", "2", "3", "4", "5", "6", "7", "8", "1", "2",
"3", "4", "5", "6", "6", "8", "1", "2", "3", "4", "5", "6", "7",
"8", "1", "2", "3", "4", "5", "6", "7", "8", "1", "2", "3", "4",
"5", "6", "7", "8", "9", "10", "1", "2", "3", "4", "5", "6",
"7", "8", "1", "2", "3", "4", "5", "6", "7", "8", "1", "2", "3",
"4", "5", "6", "7", "8", "1", "2", "3", "4", "5", "6", "7", "8",
"1", "2", "3", "4", "5", "6", "7", "8", "1", "2", "3", "4", "5",
"6", "7", "8", "1", "2", "3", "4", "5", "1", "2", "3", "4", "5",
"6", "7", "8", "1", "2", "3", "4", "5", "6", "7", "8", "9", "1",
"2", "3", "4", "5", "6", "7", "8", "1", "2", "3", "4", "5", "6",
"7", "8", "1", "2", "3", "4", "5", "6", "7", "8", "1", "2", "3",
"4", "5", "6", "7", NA, "1", "2", "3", "4", "5", "6", "7", "8",
"1", "2", "3", "4", "5", "6", "7", "8", "1", "2", "3", "4", "5",
"6", "7", "8", "1", "2", "3", "4", "5", "6", "7", "8"), Name = c("Kushagra Rawat",
"ANAND AS", "AARON FERNANDES", "Vishal Grewal", "Anurag R. Singh",
"Aneesh S Gowda", "Sushrut. S. Kapse", "ARUN DEV", "Shivani Kataria",
"Shivangi Sarma", "Khushi Dinesh", "Bhavika Dugar", "Bhavya Sachdeva",
"Shakthi B", "RUTUJA TALEGAONKAR", "Kanya Nayyar", "Sajan Prakash",
"Shrihari Nataraj", "Siva S", "Sanu Debnath", "ARVIND MANI",
"JAYANT M", "Sethu Manickavel T", "Nanak Moolchandani", "Richa Mishra",
"KENISHA GUPTA", "APEKSHA FERNANDES", "Shrungi Bandekar", "Soubrity Mondal",
"Meenakshi V K R", "Firdoush Kayamkhani", "Astha Choudhury",
"Likith S P", "Ansh Arora", "Danush S", "SHWEJAL MANKAR", "VAISHNAV HEGDE B",
"ASHISH TOKAS", "Harnimrat Singh Bhinder", "Suneesh. S", "Jayaveena A V",
"Chahat Arora", "Annie Jain", "Saloni Dalal", "KAREENA SHANKTA",
"Arushi Manjunath", "SHARON SHAJU", "Shriya Ishwar Prasad", NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Kushagra Rawat",
"Sushrut. S. Kapse", "Aneesh S Gowda", "Anurag R. Singh", "Dhyan Balakrishna",
"SACHIN MAURYA", "ARUN DEV", "Saurabh Sangvekar", "Krishna Gadakh",
"DEV AMBOKAR", "SHUBHAM DHAYGUDE", "Sujan Chowdary P", "Richa Mishra",
"APEKSHA FERNANDES", "Shakthi B", "Shrungi Bandekar", "Firdoush Kayamkhani",
"Astha Choudhury", "Kanya Nayyar", "Kalyani Saxena", "Shrihari Nataraj",
"Siva S", "ARVIND MANI", "Anurag Dagar", "K. Abbasuddin", "Nanak Moolchandani",
"Soumyajit Saha", "Sethu Manickavel T", "Maana Patel", "Suvana C Baskar",
"Soubrity Mondal", "Meenakshi V K R", "YUGA BIRNALE", "Pratyasa Ray",
"Shreyanti Pan", "Shrungi Bandekar", "VIRDHAWAL KHADE", "Viraj Prabhu",
"MIHIR AMBRE", "Anshul Kothari", "ANAND AS", "Aaron D' Souza",
"GAURAV YADAV KA", "Samit Sejwal", NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, "Richa Mishra", "Bhavika Dugar",
"Khushi Dinesh", "Shakthi B", "Kanya Nayyar", "RUTUJA TALEGAONKAR",
"Navya Singal", "Divya Ghosh", "DOLLY PATIL", "Saachi Gramopadhye",
"Siva S", "T Emil Robin Singh", "JAYANT M", "Vishal Grewal",
"Yogeshwar Prasad", "Aneesh S Gowda", "Nanak Moolchandani", "Harshit Hooda",
"APEKSHA FERNANDES", "Saloni Dalal", "JYOTI PATIL", "Kalyani Saxena",
"Annie Jain", "Chahat Arora", "Saanvi S Rao", "Harshitha Jayaram",
"Likith S P", "Danush S", "M Lohith.", "ARUN S", "Oum Saxena",
"Tanish Kaswan", "AKASH POWAR", "Aditya Dubey", "Divya Satija",
"Nina Venkatesh", "JYOTSNA PANSARE", "Suvana C Baskar", "KENISHA GUPTA",
"Jayaveena A V", "Talasha Prabhu", "Avantika Chavan", "VIRDHAWAL KHADE",
"Supriya Mondal", "MIHIR AMBRE", "Aman Ghai", "Anshul Kothari",
"Viraj Prabhu", "Rakshith U shetty", "Tanish George Mathew",
"RUJUTA KHADE", "KENISHA GUPTA", "Avantika Chavan", "Aditi Dhumatkar",
"Mahi Swet Raj", "Jayaveena A V", "Talasha Prabhu", "Shivangi Sarma",
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
"Kushagra Rawat", "Soumyajit Saha", "Sushrut. S. Kapse", "Anurag R. Singh",
"Aneesh S Gowda", "SACHIN MAURYA", "Dhyan Balakrishna", "Gyan Sandhan Kashyap",
"Krishna Gadakh", "SHUBHAM DHAYGUDE", "Shivani Kataria", "KENISHA GUPTA",
"Khushi Dinesh", "Shivangi Sarma", "Bhavya Sachdeva", "Swarna K Harith",
"Navya Singal", "Smruthi Mahalingam", "Sajan Prakash", "MIHIR AMBRE",
"Supriya Mondal", "Tanish George Mathew", "Sanu Debnath", "SHIVAKSH SAHU",
"Bikram Changmai", "Priyank Rana", "Divya Satija", "APEKSHA FERNANDES",
"Nina Venkatesh", "Shivani Kataria", "Srishti Nag", "Damini K Gowda",
"Liyana Fathima Umer", "Uttara Gogoi", "Shrihari Nataraj", "Rakshith U shetty",
"MADHU P S", "Siddhant Sejwal", "ARVIND MANI", "Tanmay Das",
"Soumyajit Saha", "SHWEJAL MANKAR", "Maana Patel", "JYOTSNA PANSARE",
"Ridhima Veerendra Kumar", "Suvana C Baskar", "Meenakshi V K R",
"Shrungi Bandekar", "Pratyasa Ray", "Shreyanti Pan", "Shrihari Nataraj",
"ANAND AS", "VINAY SAHARAN", "AARON FERNANDES", "Viraj Prabhu",
"Anshul Kothari", "Vishal Grewal", "Prithvi M", NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, "Richa Mishra", "Khushi Dinesh",
"Bhavya Sachdeva", "Bhavika Dugar", "RUTUJA TALEGAONKAR", "Navya Singal",
"Divya Ghosh", "DOLLY PATIL", "Saachi Gramopadhye", "Kushagra Rawat",
"ANAND AS", "AARON FERNANDES", "Sanu Debnath", "VINAY SAHARAN",
"Vishal Grewal", "Avinash Mani", "Gyan Sandhan Kashyap", "KAREENA SHANKTA",
"Saloni Dalal", "Chahat Arora", "Annie Jain", "Harshitha Jayaram",
"Saanvi S Rao", "Kalyani Saxena", "SHARON SHAJU", "Likith S P",
"Danush S", "M Lohith.", "Ansh Arora", "ASHISH TOKAS", "Suneesh. S",
"T Emil Robin Singh", "Tanish Kaswan", "Ridhima Veerendra Kumar",
"Suvana C Baskar", "Soubrity Mondal", "Shreyanti Pan", "Pratyasa Ray",
"YUGA BIRNALE", "Shrungi Bandekar", "Maana Patel", "Shrihari Nataraj",
"MADHU P S", "Rakshith U shetty", "Tanmay Das", "Siddhant Sejwal",
"Soumyajit Saha", "Sethu Manickavel T", "Nanak Moolchandani",
"APEKSHA FERNANDES", "Anvesha Girish", "Shakthi B", "Richa Mishra",
"Srishti Nag", "Hema", "Kanya Nayyar", "Vanshika Sharma", "Sajan Prakash",
"Supriya Mondal", "Tanish George Mathew", "Sanu Debnath", "SHIVAKSH SAHU",
"Vinayak Parihar", "Bikram Changmai", "Saif Chandan Ali Kamcheru Satish",
"KENISHA GUPTA", "Shivani Kataria", "SADHVI DHURI", "Avantika Chavan",
"Shivangi Sarma", "Mahi Swet Raj", "Divya Satija", "Smruthi Mahalingam"
), Age = c("90", NA, NA, "90", NA, NA, NA, NA, "97", "02", NA,
"01", "90", "05", NA, "04", NA, NA, NA, NA, NA, NA, "07", "00",
NA, NA, NA, "03", NA, "06", NA, "04", NA, NA, "00", NA, NA, NA,
"96", NA, "08", "97", "02", NA, NA, NA, NA, "06", NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "90", NA, NA,
NA, NA, NA, NA, NA, "03", NA, NA, "02", NA, NA, "05", "03", NA,
"04", "04", "01", NA, NA, NA, "90", NA, "00", NA, "07", "00",
NA, NA, "06", NA, "02", NA, "03", NA, NA, NA, "89", NA, NA, NA,
"90", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, "01", NA, "05", "04", NA, NA, NA, NA, "00", NA, NA,
NA, "90", NA, NA, "00", "03", NA, NA, NA, "01", "02", "97", NA,
NA, NA, "00", NA, NA, NA, NA, NA, "90", "96", NA, NA, NA, NA,
"08", "93", NA, NA, NA, NA, NA, "89", NA, NA, NA, NA, NA, NA,
NA, "03", "08", "93", "02", NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, "90", NA, NA, NA, NA, NA, NA, "04",
"03", NA, "97", NA, NA, "02", "90", "04", NA, NA, NA, NA, NA,
NA, NA, NA, "04", "90", "96", NA, NA, "97", "96", NA, NA, "04",
NA, NA, NA, "90", NA, "90", NA, NA, "00", NA, NA, NA, "06", "03",
"02", NA, NA, NA, NA, NA, NA, "89", "90", NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "90", "01", NA, NA,
NA, NA, "00", "90", NA, NA, NA, NA, "90", NA, "04", NA, NA, "97",
"02", NA, NA, "01", NA, NA, "00", NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, "02", NA, "03", "00", NA, NA, NA, "90", "90", NA,
"07", "00", NA, NA, "05", NA, "96", "90", "04", "90", NA, NA,
NA, NA, NA, "90", "04", NA, NA, "97", NA, NA, "02", "03", "96",
NA), Team = c("DELHI", "SSCB", "MAHARASHTRA", "DELHI", "UTTAR PRADESH",
"KARNATAKA", "R.S.P.B", "SSCB", "HARYANA", "ASSAM", "KARNATAKA",
"TAMILNADU", "DELHI", "TAMILNADU", "MAHARASHTRA", "M.P.", "POLICE",
"KARNATAKA", "KARNATAKA", "R.S.P.B", "SSCB", "SSCB", "TAMILNADU",
"M.P.", "POLICE", "MAHARASHTRA", "MAHARASHTRA", "GOA", "BENGAL",
"TAMILNADU", "RAJASTHAN", "ASSAM", "KARNATAKA", "UTTAR PRADESH",
"TAMILNADU", "MAHARASHTRA", "SSCB", "SSCB", "PUNJAB", "KERALA",
"TAMILNADU", "PUNJAB", "MADHYA PRADESH", "KARNATAKA", "MAHARASHTRA",
"KARNATAKA", "MAHARASHTRA", "TAMILNADU", "KARNATAKA", "SSCB",
"R.S.P.B", "POLICE", "BENGAL", "MAHARASHTRA", "KERALA", "KARNATAKA",
"MAHARASHTRA", "R.S.P.B", "GUJARAT", "TAMILNADU", "DELHI", "BENGAL",
"KERALA", "DELHI", "R.S.P.B", "KARNATAKA", "UTTAR PRADESH", "KARNATAKA",
"SSCB", "SSCB", "R.S.P.B", "MADHYA PRADESH", "MAHARASHTRA", "MAHARASHTRA",
"ANDHRA PRADESH", "POLICE", "MAHARASHTRA", "TAMILNADU", "GOA",
"RAJASTHAN", "ASSAM", "M.P.", "GUJARAT", "KARNATAKA", "KARNATAKA",
"SSCB", "DELHI", "R.S.P.B", "M.P.", "R.S.P.B", "TAMILNADU", "GUJARAT",
"KARNATAKA", "BENGAL", "TAMILNADU", "MAHARASHTRA", "ODISHA",
"R.S.P.B", "GOA", "MAHARASHTRA", "R.S.P.B", "MAHARASHTRA", "GUJARAT",
"SSCB", "R.S.P.B", "SSCB", "DELHI", "MAHARASHTRA", "KARNATAKA",
"TAMILNADU", "DELHI", "BENGAL", "KERALA", "GUJARAT", "PUNJAB",
"KARNATAKA", "SSCB", "MAHARASHTRA", "R.S.P.B", "DELHI", "BENGAL",
"KERALA", "TELANGANA", "POLICE", "TAMILNADU", "KARNATAKA", "TAMILNADU",
"MADHYA PRADESH", "MAHARASHTRA", "UTTAR PRADESH", "KARNATAKA",
"MAHARASHTRA", "GOA", "KARNATAKA", "R.S.P.B", "SSCB", "DELHI",
"UTTAR PRADESH", "KARNATAKA", "M.P.", "HARYANA", "MAHARASHTRA",
"KARNATAKA", "MAHARASHTRA", "GUJARAT", "M.P.", "PUNJAB", "KARNATAKA",
"R.S.P.B", "KARNATAKA", "TAMILNADU", "R.S.P.B", "SSCB", "GUJARAT",
"RAJASTHAN", "SSCB", "DELHI", "HARYANA", "KARNATAKA", "MAHARASHTRA",
"KARNATAKA", "MAHARASHTRA", "TAMILNADU", "GOA", "R.S.P.B", "MAHARASHTRA",
"R.S.P.B", "MAHARASHTRA", "POLICE", "GUJARAT", "R.S.P.B", "KARNATAKA",
"KARNATAKA", "MAHARASHTRA", "MAHARASHTRA", "R.S.P.B", "R.S.P.B",
"BIHAR", "TAMILNADU", "GOA", "ASSAM", "KARNATAKA", "SSCB", "R.S.P.B",
"DELHI", "KERALA", "MAHARASHTRA", "BENGAL", "POLICE", "MAHARASHTRA",
"KARNATAKA", "TAMILNADU", "GUJARAT", "BENGAL", "DELHI", "KERALA",
"PUNJAB", "DELHI", "R.S.P.B", "R.S.P.B", "UTTAR PRADESH", "KARNATAKA",
"SSCB", "KARNATAKA", "ASSAM", "MADHYA PRADESH", "MAHARASHTRA",
"HARYANA", "MAHARASHTRA", "KARNATAKA", "ASSAM", "DELHI", "TAMILNADU",
"UTTAR PRADESH", "KARNATAKA", "POLICE", "MAHARASHTRA", "R.S.P.B",
"KARNATAKA", "R.S.P.B", "SSCB", "ASSAM", "DELHI", "HARYANA",
"MAHARASHTRA", "KARNATAKA", "HARYANA", "CHHATTISGARH", "KARNATAKA",
"KERALA", "ASSAM", "KARNATAKA", "KARNATAKA", "SSCB", "DELHI",
"SSCB", "DELHI", "R.S.P.B", "MAHARASHTRA", "GUJARAT", "MAHARASHTRA",
"KARNATAKA", "KARNATAKA", "TAMILNADU", "GOA", "ODISHA", "R.S.P.B",
"KARNATAKA", "SSCB", "SSCB", "MAHARASHTRA", "R.S.P.B", "GUJARAT",
"DELHI", "KARNATAKA", "KARNATAKA", "MAHARASHTRA", "TAMILNADU",
"BENGAL", "KERALA", "MAHARASHTRA", "R.S.P.B", "KARNATAKA", "DELHI",
"KERALA", "TAMILNADU", "BENGAL", "GUJARAT", "POLICE", "KARNATAKA",
"DELHI", "TAMILNADU", "MAHARASHTRA", "UTTAR PRADESH", "KARNATAKA",
"MAHARASHTRA", "GOA", "DELHI", "SSCB", "MAHARASHTRA", "R.S.P.B",
"SSCB", "DELHI", "KARNATAKA", "ASSAM", "MAHARASHTRA", "KARNATAKA",
"PUNJAB", "MADHYA PRADESH", "R.S.P.B", "KARNATAKA", "GUJARAT",
"MAHARASHTRA", "KARNATAKA", "TAMILNADU", "R.S.P.B", "UTTAR PRADESH",
"SSCB", "KERALA", "R.S.P.B", "RAJASTHAN", "KARNATAKA", "KARNATAKA",
"BENGAL", "R.S.P.B", "ODISHA", "MAHARASHTRA", "GOA", "GUJARAT",
"KARNATAKA", "SSCB", "KARNATAKA", "DELHI", "DELHI", "R.S.P.B",
"TAMILNADU", "MADHYA PRADESH", "MAHARASHTRA", "KARNATAKA", "TAMILNADU",
"POLICE", "CHHATTISGARH", "DELHI", "M.P.", "DELHI", "POLICE",
"R.S.P.B", "KARNATAKA", "R.S.P.B", "SSCB", "DELHI", "ASSAM",
"KARNATAKA", "MAHARASHTRA", "HARYANA", "MAHARASHTRA", "R.S.P.B",
"ASSAM", "BIHAR", "HARYANA", "KARNATAKA"), Finals = c("3:56.95",
"4:01.92", "4:04.71", "4:07.44", "4:09.34", "4:09.54", "4:11.74",
"4:13.00", "4:27.31", "4:30.55", "4:30.80", "4:37.18", "4:37.54",
"4:39.96", "4:45.87", "4:47.12", "2:05.89", "2:06.46", "2:06.69",
"2:09.13", "2:12.15", "2:12.49", "2:17.91", "2:20.87", "2:24.70",
"2:25.31", "2:26.53", "2:26.85", "2:30.44", "2:31.59", "2:33.44",
"2:34.17", "28.54", "29.38", "29.57", "29.60", "29.65", "29.81",
"29.88", "30.49", "33.86", "34.01", "34.30", "35.24", "35.32",
"35.38", "35.76", "36.11", "7:46.04", "7:49.31", "7:53.52", "8:19.53",
"8:20.32", "8:24.45", "8:46.12", "1:50.65", "1:52.82", "1:54.00",
"1:55.67", "1:57.23", "2:00.50", "2:01.71", "2:01.90", "15:41.45",
"16:29.27", "16:32.28", "16:44.68", "16:48.24", "16:52.77", "16:54.74",
"17:15.02", "17:34.64", "17:51.79", "18:41.31", "19:29.56", "5:05.49",
"5:12.91", "5:13.19", "5:18.92", "5:20.02", "5:25.12", "5:25.88",
"5:28.83", "2:02.29", "2:07.11", "2:10.24", "2:10.28", "2:10.66",
"2:11.87", "2:11.91", "2:13.42", "2:21.74", "2:22.30", "2:23.55",
"2:27.87", "2:30.03", "2:30.52", "2:31.62", "2:35.44", "22.44",
"23.39", "23.76", "23.77", "23.83", "23.93", "24.10", "24.21",
"4:02.90", "4:09.88", "4:13.25", "4:14.84", "4:15.54", "4:29.80",
"4:31.50", "4:47.63", "3:30.45", "3:31.04", "3:31.17", "3:33.22",
"3:37.07", "3:43.67", "3:45.01", "4:02.50", "17:55.55", "17:57.67",
"17:58.14", "18:29.40", "18:41.29", "19:01.60", "19:04.82", "19:53.91",
"20:15.27", "21:40.84", "4:33.01", "4:36.68", "4:41.21", "4:44.65",
"4:55.98", "4:56.07", "4:58.41", "4:59.06", "2:41.89", "2:42.50",
"2:42.76", "2:46.09", "2:49.11", "2:50.79", "2:51.90", "2:53.88",
"2:18.61", "2:19.11", "2:19.94", "2:20.18", "2:24.60", "2:25.38",
"2:27.26", "2:27.75", "28.33", "28.43", "28.74", "29.05", "29.06",
"29.52", "29.91", "30.09", "24.19", "24.55", "24.78", "25.30",
"25.60", "25.67", "25.68", "25.99", "26.72", "26.90", "27.39",
"27.57", "27.60", "27.67", "27.67", "27.68", "3:48.83", "3:52.11",
"3:52.93", "4:01.27", "4:02.53", "4:04.46", "4:07.31", "4:39.37",
"4:33.10", "4:34.84", "4:39.49", "4:40.06", "4:41.20", "4:52.31",
"4:56.41", "5:32.06", "8:09.47", "8:32.01", "8:33.58", "8:33.87",
"8:42.12", "8:52.91", "8:54.76", "8:57.24", "9:16.13", "9:22.83",
"2:05.80", "2:07.94", "2:10.49", "2:10.99", "2:15.13", "2:16.83",
"2:18.52", "2:20.33", "54.25", "54.38", "54.47", "56.15", "56.42",
"57.03", "57.90", "58.13", "1:04.38", "1:05.53", "1:05.98", "1:06.35",
"1:06.53", "1:06.89", "1:07.26", "1:09.32", "25.58", "26.73",
"27.02", "27.13", "27.26", "27.31", "27.76", "28.03", "30.39",
"30.82", "30.87", "30.91", "31.91", "32.13", "32.32", "32.60",
"50.59", "51.49", "52.87", "52.92", "52.97", "53.54", "53.58",
"53.62", "9:04.86", "9:08.51", "9:09.18", "9:23.37", "9:56.91",
"1:39.69", "1:41.83", "1:41.92", "1:44.95", "1:47.70", "1:48.06",
"1:49.74", "1:50.08", "9:22.50", "9:24.81", "9:31.41", "9:47.27",
"9:49.61", "9:53.55", "10:20.97", "10:38.66", "10:43.53", "1:53.44",
"1:54.97", "1:55.71", "1:56.69", "1:57.75", "1:57.98", "1:59.14",
"2:00.39", "1:14.97", "1:15.89", "1:16.36", "1:16.58", "1:18.80",
"1:19.03", "1:19.59", "1:19.91", "1:02.69", "1:04.18", "1:05.08",
"1:05.12", "1:05.24", "1:06.41", "1:07.11", "1:07.13", "1:07.19",
"1:07.22", "1:07.41", "1:08.79", "1:09.56", "1:09.81", "1:10.05",
"1:05.35", "55.63", "58.30", "58.89", "59.01", "59.18", "59.37",
"59.80", "1:01.18", "2:23.17", "2:25.43", "2:25.78", "2:25.96",
"2:30.75", "2:31.81", "2:32.15", "2:34.32", "2:00.13", "2:03.41",
"2:04.23", "2:05.49", "2:08.39", "2:08.86", "2:09.40", "2:13.67",
"58.26", "59.21", "1:00.06", "1:00.14", "1:00.36", "1:00.42",
"1:00.93", "1:02.50"), Points = c("801", "752", "727", "703",
"687", "685", "668", "658", "692", "667", "665", "620", "618",
"602", "565", "558", "742", "732", "728", "688", "641", "637",
"564", "529", "662", "653", "637", "633", "589", "575", "555",
"547", "751", "689", "675", "673", "670", "659", "655", "616",
"654", "645", "629", "580", "576", "573", "555", "539", "724",
"709", "690", "588", "585", "571", "503", NA, NA, NA, NA, NA,
NA, NA, NA, "791", "682", "676", "651", "644", "636", "632",
"595", "563", "536", "468", "413", "662", "616", "615", "582",
"576", "549", "546", "531", "766", "682", "634", "634", "628",
"611", "610", "590", "670", "662", "645", "590", "565", "559",
"547", "508", "809", "714", "681", "680", "675", "667", "653",
"644", "646", "593", "570", "559", "555", "471", "463", "389",
"715", "709", "708", "688", "652", "596", "585", "467", "626",
"623", "622", "571", "553", "524", "519", "458", "434", "354",
"712", "684", "651", "628", "559", "558", "545", "542", "634",
"627", "624", "587", "556", "540", "529", "512", "763", "754",
"741", "737", "672", "661", "636", "630", "641", "634", "614",
"594", "594", "566", "544", "535", "780", "746", "725", "682",
"658", "652", "652", "629", "695", "681", "645", "632", "630",
"625", "625", "625", "743", "712", "704", "634", "624", "609",
"588", "408", "609", "597", "568", "565", "558", "497", "476",
"339", "788", "688", "682", "681", "649", "610", "604", "596",
"537", "518", "724", "688", "649", "641", "584", "562", "542",
"521", "774", "768", "765", "698", "688", "666", "637", "629",
"639", "606", "594", "584", "579", "570", "561", "512", "825",
"723", "700", "692", "682", "678", "646", "627", "705", "676",
"673", "670", "609", "597", "586", "571", "797", "756", "698",
"696", "694", "672", "671", "669", "609", "597", "595", "551",
"463", NA, NA, NA, NA, NA, NA, NA, NA, "640", "632", "610", "562",
"555", "544", "475", "437", "427", "726", "698", "684", "667",
"650", "646", "627", "608", "625", "603", "592", "587", "539",
"534", "523", "516", "755", "704", "675", "674", "670", "635",
"615", "615", "643", "642", "636", "599", "579", "573", "567",
NA, "809", "703", "682", "678", "672", "666", "651", "608", "615",
"587", "583", "581", "527", "516", "513", "491", "799", "737",
"723", "701", "655", "648", "639", "580", "699", "666", "638",
"635", "628", "626", "611", "566"), DQ = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)), row.names = c(NA,
-341L), class = "data.frame")
expect_equivalent(df_test, df_standard)
})
test_that("Khelo India Youth Games 2020 whole meet, has a tie, fully checked", {
skip_on_cran()
file <-
"https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/Splash/Khelo_India_Youth_Games_2020.pdf"
df_test <- file %>%
read_results() %>%
swim_parse() %>%
select(-Event)
df_standard <-
structure(list(Place = c("1", "2", "3", "4", "5", "6", "7", "8",
"1", "2", "3", "4", "5", "6", "7", "8", "1", "2", "3", "4", "5",
"6", "1", "2", "3", "4", "5", "1", "2", "3", "4", "5", "6", "7",
"8", "1", "2", "3", "4", "5", "6", "7", "8", "1", "2", "3", "4",
"5", "6", "1", "2", "3", "4", "1", "2", "3", "4", "5", "5", "7",
"8", "1", "2", "3", "4", "5", "6", "7", "8", "1", "2", "3", "4",
"5", "6", "7", "8", "1", "2", "3", "4", "5", "1", "2", "3", "4",
"5", "6", "7", "8", "9", "10", "1", "2", "3", "4", "5"), Name = c("SAMBHAVV R",
"ANEESH GOWDA S", "VEER KHATKAR", "VEDANT BAPNA", "GYAN SANDHAN KASHYAP",
"RAJDEEP GOGOI", "KRISHNA GADAKH", "BIKRAM CHANGMAI", "KHUSHI DINESH",
"KIARA BANGERA", "BHAVYA SACHDEVA", "SWARNA HARITH", "JHANVI CHOUDHARY",
"PALAK DHAMI", "MAHI SHWET RAJ", "ASHMITA CHANDRA", "AARON FERNANDES",
"SANJAY C J", "ANURAG SINGH", "RUNDRANSH MISHRA", "RUDRAKSH SAHU",
"JOY BARUA", "SHIVANGI SARMA", "BHAVIKA DUGAR", "JASMINE GURUNG",
"SAACHI GRAMOPAHYAY", "KIRAN SHET NARIYEKAR", "SWADESH MONDAL",
"ROHITH BENEDICTON", "BHARGAV PHUKAN", "SHRISH MAULIK", "AINESH RAY",
"YASH GULHANE", "JASHUA THOMAS", "DISHANT MEHTA", "KAREENA SHANKTA",
"APEKSHA FERNANDES", "ZARA JABBAR", "ARUSHI MANJUNATH", "ANVITA GOWDA",
"GUNN MATTA", "SHRIYA ISHWARPRASAD", "KHUSHPREET KAUR BHULLAR",
"DANUSH S", "M LOHITH", "VARUN PATEL", "MILANTON DUTTA", "SREEDIP MONDAL",
"R SUBRAMANIYAM", "KALYANI SAXENA", "AALIYAH SINGH", "RAJESHREE BURAGOHAIN",
"POOJA D", "ARYAN VARNEKAR", "HARSH SAROHA", "BIKRAM CHANGMAI",
"S HITEN MITTAL", "SAHIL LASKAR", "ANSHAV JINDAL", "ROHITH BENEDICTON",
"LAKSH PURI", "NINA VENKATESH", "KENISHA GUPTA", "NILABJAA GHOSH",
"SANJITI SAHA", "DISHA BHANDARI", "THITHIKSHAA H", "AEKA CHAKRA",
"MITHIKA KARAPURKAR", "MIHIR AMBRE", "D ADHITHYA", "PRIYANK RANA",
"M JAYA VIKESH", "ACHINTYA GHOSHAL", "RUDRAKSH SAHU", "SHUBHRANSHU DUTTA",
"A MIDHUNAN", "SUNAINA MANJUNATH", "SUMAN PATIL", "ANUBHUTI BARUAH",
"SUCHETNA CHAKRABORTY", "DOLPHI SARANG", "KHUSHI DINESH", "BHAVYA SACHDEVA",
"KIARA BANGERA", "AASTHA CHOUDHURY", "V VARSHA", "DIVYA GHOSH",
"KANYA NAYYAR", "CYNTHIA CHOUDHARY", "ASHMITA CHANDRA", "NIVRITI DATTA",
"SHIVANGI SARMA", "BHAVIKA DUGAR", "JASMINE GURUNG", "MOUMITRA KARAR",
"SAACHI GRAMOPAHYAY"), Age = c("04", "04", "04", "03", "04",
"04", "03", "04", "03", "06", "05", "04", "04", "05", "03", "05",
"00", "02", "02", "02", "02", "01", "02", "01", "00", "00", "00",
"04", "03", "04", "04", "03", "03", "05", "03", "04", "05", "05",
"04", "06", "04", "06", "04", "00", "01", "00", "01", "02", "00",
"00", "02", "02", "01", "04", "04", "04", "03", "05", "05", "03",
"03", "05", "03", "05", "05", "04", "05", "04", "04", "00", "02",
"99", "02", "02", "02", "01", "02", "02", "00", "02", "02", "01",
"03", "05", "06", "04", "03", "04", "04", "06", "05", "04", "02",
"01", "00", "01", "00"), Team = c("KARNATAKA", "KARNATAKA", "HARYANA",
"MAHARASHTRA", "DELHI", "ASSAM", "MADHYA PRADESH", "DELHI", "KARNATAKA",
"MAHARASHTRA", "DELHI", "TAMILNADU", "DELHI", "MAHARASHTRA",
"BIHAR", "KARNATAKA", "MAHARASHTRA", "KARNATAKA", "DELHI", "MAHARASHTRA",
"CHHATTISGARH", "ASSAM", "ASSAM", "TAMILNADU", "HARYANA", "GOA",
"GOA", "WEST BENGAL", "TAMILNADU", "DELHI", "MAHARASHTRA", "KARNATAKA",
"MAHARASHTRA", "TAMILNADU", "GUJARAT", "MAHARASHTRA", "MAHARASHTRA",
"MAHARASHTRA", "KARNATAKA", "KARNATAKA", "KARNATAKA", "TAMILNADU",
"MADHYA PRADESH", "TAMILNADU", "A.P.", "MADHYA PRADESH", "ASSAM",
"WEST BENGAL", "PUDUCHERRY", "GUJARAT", "UTTAR PRADESH", "ASSAM",
"PUDUCHERRY", "DELHI", "HARYANA", "DELHI", "KARNATAKA", "WEST BENGAL",
"PUNJAB", "TAMILNADU", "MAHARASHTRA", "KARNATAKA", "MAHARASHTRA",
"WEST BENGAL", "MAHARASHTRA", "UTTAR PRADESH", "KARNATAKA", "MAHARASHTRA",
"GOA", "MAHARASHTRA", "TAMILNADU", "DELHI", "TAMILNADU", "DELHI",
"CHHATTISGARH", "ASSAM", "PUDUCHERRY", "KARNATAKA", "GOA", "ASSAM",
"WEST BENGAL", "GUJARAT", "KARNATAKA", "DELHI", "MAHARASHTRA",
"DELHI", "TAMILNADU", "KARNATAKA", "MADHYA PRADESH", "GOA", "KARNATAKA",
"ASSAM", "ASSAM", "TAMILNADU", "HARYANA", "WEST BENGAL", "GOA"
), Finals = c("1:56.66", "1:57.46", "1:58.61", "1:58.88",
"1:59.06", "2:03.50", "2:03.52", "2:10.99", "2:10.29", "2:12.16",
"2:12.66", "2:13.21", "2:13.48", "2:16.52", "2:17.87", "2:18.09",
"1:56.51", "1:56.95", "1:58.37", "1:58.70", "2:05.01", "2:10.96",
"2:07.91", "2:22.97", "2:24.30", "2:35.28", "2:38.32", "1:06.33",
"1:08.56", "1:08.90", "1:09.75", "1:10.01", "1:10.83", "1:11.67",
"1:11.88", "1:14.66", "1:15.47", "1:19.02", "1:19.62", "1:20.71",
"1:20.90", "1:21.12", "1:21.57", "1:03.71", "1:05.31", "1:08.51",
"1:08.91", "1:10.24", "1:22.15", "1:20.25", "1:22.18", "1:30.76",
"1:57.40", "25.70", "25.88", "25.92", "26.19", "26.75", "26.75",
"26.95", "27.21", "28.58", "28.96", "30.01", "30.46", "30.64",
"31.08", "31.15", "31.31", "25.00", "26.03", "26.46", "26.64",
"26.96", "26.97", "27.43", "33.34", "30.78", "31.07", "31.11",
"31.22", "31.33", "9:26.19", "9:30.06", "9:36.02", "9:44.29",
"9:49.70", "9:53.19", "9:57.08", "10:05.72", "10:19.83", "11:33.67",
"9:31.22", "10:13.78", "10:36.19", "11:17.24", "11:31.89"), DQ = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)), row.names = c(NA, -97L), class = "data.frame")
expect_equivalent(df_test, df_standard)
})
test_that("2012 Euro Juniors", {
skip_on_cran()
file <-
"https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/Splash/European_Jr_Champs_2012.pdf"
df_test <- file %>%
read_results() %>%
swim_parse() %>%
select(-Event)
place_sum <- sum(as.numeric(df_test$Place), na.rm = TRUE)
dq_sum <- sum(df_test$DQ, na.rm = TRUE)
expect_equivalent(place_sum, 33438)
expect_equivalent(dq_sum, 46)
})
#
# test_that("2013 Euro Juniors, full meet checked", {
# skip_on_cran()
#
# # as of 3/18/23 pdf_test is not reading in rows where team names run into times correctly
# # I don't know how to fix this beyond using typo/replacement
#
# file <-
# "https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/Splash/Arena_European_Junior_Swimming_Champs_2013.pdf"
#
# df_test <- file %>%
# read_results() %>%
# swim_parse() %>%
# select(-Event)
#
# df_standard <-
# structure(list(Place = c("1", "2", "3", "4", "5", "6", "7", "7",
# "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19",
# "20", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "1",
# "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13",
# "14", "15", "16", "17", "18", "19", "20", "1", "2", "3", "4",
# "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15",
# "16", "17", "18", "19", "20", "1", "2", "3", "4", "5", "6", "7",
# "8", "8", "10", "11", "12", "13", "14", "15", "16", "17", "18",
# "19", "20", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10",
# "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12",
# "13", "14", "15", "16", "17", "18", "19", "20", "21", "1", "2",
# "3", "4", "5", "6", "6", "8", "9", "10", "1", "2", "3", "4",
# "5", "6", "7", "8", "9", "10", "1", "2", "3", "4", "5", "6",
# "7", "8", "9", "10", "1", "2", "3", "4", "5", "6", "7", "8",
# "9", "10", "1", "2", "3", "4", "5", "6", "7", "8", "9", NA),
# Name = c("STOLK Kyle", "WESTERMANN Magnus", "HOLUB Jan",
# "SZCZEPANSKI Sebastian", "SEDOV Evgeny", "HANSEN Morten Bak",
# "NIKULIN Iaroslav", "WIERLING Damian", "MYLONAS Fotios",
# "DEOLET Timothy", "HOLODA Peter", "SMITH Jack", "BROGLIA Alessandro",
# "PLAVIN Bogdan", "SORIC Lovre", "AYAR Kaan Tuerker", "HALDEMANN Alexandre",
# "NAGY Marcel", "NASCIMENTO Miguel Duarte", "ROGOZIN Jevgenij",
# "BECK Leonie Antonia", "KISS Nikoletta", "CAPONI Linda",
# "MASSONE Antonia", "SALAMATINA Valeria", "JONES Ellena",
# "VOLKODAVOVA Polina", "TETTAMANZI Alisia", "NATLACEN Gaja",
# "PEREZ BLANCO Jimena", "KUDASHEV Alexander", "TERRES ILLESCAS Pedro",
# "GRATZ Benjamin", "TWAROWSKI Jerzy", "JOHNSON Matthew", "SZABO Norbert",
# "KUNERT Alexander", "MUGNAINI Mattia", "KUSWIK Maciej", "RIVAS GALLEGO Javier",
# "GUREVICH Etay", "LIESS Nils", "KRYPAK MaksyM", "NOVAK Petr",
# "O'SULLIVAN David", "DIMITROV Nikola", "VATASESCU Stefan Andrei",
# "HECLAU Tom", "MARGEVICIUS Deividas", "WURZER Stefan", "SOLNTSEVA Viktoriya",
# "GUERRA Silvia", "MALYAVINA Anastasiya", "SEBESTYEN Dalma",
# "HUETHER Marlene", "KAZINA Polina", "REISAENEN Sofie", "HUMMEL Margarethe",
# "KAENSAEKOSKI Silja", "STEPANOVA Monika", "MIKKELBORG Silje",
# "HANSSON Sophie", "PINTAR Tjasa", "NABNEY Erin", "KADOGLU Georgiya",
# "MC NAMARA Dearbhail", "WIKIEL Angelika", "LEMKE Julia",
# "BAZYL Magdalena", "ELORANTA Emilia", "MAKOVICH Semen", "LITCHFIELD Max",
# "CHATRON Cyril", "CWIEK Kacper", "SZARANEK Mark", "STRAZDAS Povilas",
# "TAROCCHI Lorenzo", "ORNEK Alpkan", "SANCHEZ GTREZ-CABELLO",
# "MASLOV Andrey", "FOLDHAZI David", "CARAZO BARBERO Gonzalo",
# "BAIONI Andrea", "THOMASBERGER David", "SERES Edwin", "GREVEN Lucas",
# "STEPIEN Konrad", "VELOSO Tomas Miguel", "CHAIKOU Pavel",
# "FODOR Bogdan", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "MICKA Jan",
# "FURTEK Pawel", "HUGHES Caleb", "WOJDAK Wojciech", "BOUCHAUT Joris",
# "BOCK Maximilian", "ROMANCHUK Mykhaylo", "KNIGHT Joel", "CHRISTIANSEN Henrik",
# "BRZOSKOWSKI Maarten", "PIIROINEN Eetu", "SAEMUNDSSON Sven Arnar",
# "ACERENZA Domenico", "GIBBONS Brendan", "DMYTRIYEV Roman",
# "BJOERLYKKE Ole Martin", "MORTENSEN Oli", "SUHAREV Petar",
# "CHERNEV Teodosi", "HJELM Alvi", "FARKAS Tamas", "BAKLAKOVA Maria",
# "GALLARDO CARAPETO Fatima", "MEYNEN Julie-Marie", "SCHOLTISSEK Helen",
# "GOVEJSEK Nastja", "COOPER Harriet", "LEVISEN Julie", "HACHE Cloe",
# "KLEIN Nele", "LATHAM Katie", "GUZHENKOVA Anastasia", "DAY Emma",
# "SEBESTYEN Dalma", "WATTEL Marie", "NOVOSZATH Melinda", "RICO PEREZ Carmen",
# "AUSTIN Shauntelle", "PODEUS Elin", "KAETHNER Rosalie", "BENESOVA Alena",
# "TARASEVICH Grigory", "RAPSYS Danas", "SABBIONI Simone",
# "SCHWARZ Carl-Louis", "CHRISTOU Apostolos", "KONTIZAS Michail",
# "NASCIMENTO Miguel Duarte", "FOLDHAZI David", "THEODORIS Nathan",
# "MENCARINI Luca", "DORINOV Mikhail", "PALATOV Alexander",
# "LINDENBERG Yannick", "HORVATH David", "PILGER Max", "AURUSKEVICIUS Mantas",
# "ACKLAND Harry", "CALLAIS Quentin", "PFYFFER Luca", "NACHTMAN Mateusz",
# "OEZTUERK Sonnele", "MAZUTAITYTE Ugne", "RIEDEMANN Laura",
# "BRIGGS Megan", "GRUSOVA Tereza", "ZAMORANO SANZ Africa",
# "SVECENA Lucie", "MAGNER Agata", "HOPE Lucy", "USTINOVA Daria K"
# ), Age = c("96", "95", "96", "95", "96", "95", "95", "96",
# "96", "95", "96", "96", "96", "95", "95", "95", "95", "95",
# "95", "96", "97", "97", "98", "97", "98", "97", "98", "97",
# "97", "97", "95", "95", "96", "95", "95", "96", "96", "95",
# "96", "95", "95", "96", "95", "96", "95", "95", "95", "95",
# "95", "95", "98", "97", "97", "97", "98", "97", "97", "97",
# "97", "97", "98", "98", "97", "97", "98", "98", "98", "98",
# "97", "97", "95", "95", "95", "95", "95", "96", "95", "95",
# "96", "95", "95", "96", "95", "96", "95", "95", "96", "96",
# "96", "95", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "95",
# "95", "95", "96", "95", "95", "96", "95", "96", "95", "95",
# "96", "95", "95", "95", "96", "96", "95", "96", "96", "95",
# "97", "97", "97", "97", "97", "97", "97", "97", "98", "97",
# "97", "97", "97", "97", "98", "98", "97", "97", "97", "98",
# "95", "95", "96", "95", "96", "95", "95", "95", "95", "95",
# "95", "95", "95", "96", "96", "95", "96", "96", "96", "95",
# "98", "97", "98", "97", "98", "98", "97", "97", "97", "98"
# ), Team = c("Netherlands", "Denmark", "Poland", "Poland",
# "Russia", "Denmark", "Russia", "Germany", "Greece", "Belgium",
# "Hungary", "Great Britain", "Italy", "Ukraine", "Croatia",
# "Turkey", "Switzerland", "Germany", "Portugal", "Lithuania",
# "Germany", "Hungary", "Italy", "Germany", "Russia", "Great Britain",
# "Russia", "Italy", "Slovenia", "Spain", "Russia", "Spain",
# "Hungary", "Poland", "Great Britain", "Hungary", "Germany",
# "Italy", "Poland", "Spain", "Israel", "Switzerland", "Ukraine",
# "Czech Republic", "Ireland", "Bulgaria", "Romania", "Germany",
# "Lithuania", "Austria", "Ukraine", "Italy", "Ukraine", "Hungary",
# "Germany", "Russia", "Norway", "Germany", "Finland", "Czech Republic",
# "Norway", "Sweden", "Slovenia", "Great Britain", "Bulgaria",
# "Ireland", "Poland", "Belarus", "Poland", "Finland", "Russia",
# "Great Britain", "France", "Poland", "Great Britain", "Lithuania",
# "Italy", "Turkey", "Spain", "Russia", "Hungary", "Spain",
# "Italy", "Germany", "Hungary", "Netherlands", "Poland", "Portugal",
# "Belarus", "Romania", "Russia", "Great Britain", "Germany",
# "Poland", "Denmark", "France", "Croatia", "Austria", "Switzerland",
# "Finland", "Czech Republic", "Poland", "Great Britain", "Poland",
# "France", "Germany", "Ukraine", "Great Britain", "Norway",
# "Netherlands", "Finland", "Croatia", "Italy", "Ireland",
# "Czech Republic", "Norway", "Faroe Islands", "Bulgaria",
# "Bulgaria", "Faroe Islands", "Serbia", "Russia", "Spain",
# "Luxembourg", "Germany", "Slovenia", "Great Britain", "Denmark",
# "France", "Germany", "Great Britain", "Russia", "Great Britain",
# "Hungary", "France", "Hungary", "Spain", "Great Britain",
# "Sweden", "Germany", "Czech Republic", "Russia", "Lithuania",
# "Italy", "Germany", "Greece", "Greece", "Portugal", "Hungary",
# "Great Britain", "Italy", "Russia", "Russia", "Germany",
# "Hungary", "Germany", "Lithuania", "Great Britain", "France",
# "Switzerland", "Poland", "Germany", "Lithuania", "Germany",
# "Great Britain", "Czech Republic", "Spain", "Czech Republic",
# "Poland", "Great Britain", "Russia"), Finals = c("50.18",
# "50.19", "50.22", "50.33", "50.39", "50.65", "50.67", "50.67",
# "50.71", "50.73", "50.92", "50.96", "51.01", "51.05", "51.30",
# "51.36", "51.48", "51.49", "51.68", "52.09", "4:12.87", "4:13.43",
# "4:13.86", "4:14.37", "4:15.88", "4:17.02", "4:18.33", "4:18.78",
# "4:20.27", "4:20.30", "1:58.71", "1:59.54", "1:59.84", "2:00.34",
# "2:00.75", "2:00.79", "2:01.19", "2:01.52", "2:01.56", "2:01.62",
# "2:01.90", "2:02.14", "2:02.32", "2:02.50", "2:03.05", "2:03.39",
# "2:04.73", "2:04.79", "2:05.05", "2:05.55", "2:27.33", "2:29.91",
# "2:31.27", "2:31.34", "2:31.74", "2:32.23", "2:32.76", "2:33.39",
# "2:34.33", "2:34.45", "2:34.66", "2:35.28", "2:35.78", "2:36.40",
# "2:36.93", "2:37.31", "2:38.11", "2:39.85", "2:40.03", "2:41.08",
# "1:59.91", "2:01.60", "2:03.66", "2:03.85", "2:03.91", "2:04.11",
# "2:04.30", "2:04.59", "2:04.83", NA, "2:05.53", "2:05.54",
# "2:05.69", "2:05.74", "2:06.45", "2:07.15", "2:07.25", "2:07.41",
# "2:08.01", "2:08.31", "3:29.10", "3:33.25", "3:34.23", "3:34.43",
# "3:35.58", "3:36.12", "3:38.35", "3:38.59", "3:42.46", "3:42.83",
# "15:13.51", "15:13.85", "15:19.63", "15:27.06", "15:28.35",
# "15:28.79", "15:30.12", "15:30.17", "15:31.36", "15:43.69",
# "15:44.95", "15:49.85", "15:52.23", "15:52.96", "15:53.36",
# "16:13.03", "16:13.45", "16:15.38", "16:30.58", "16:38.38",
# "16:38.55", "54.78", "55.76", "55.92", "56.22", "56.31",
# "56.34", "56.34", "56.47", "56.97", "57.27", "2:11.24", "2:12.14",
# "2:12.75", "2:13.72", "2:13.82", "2:14.29", "2:15.51", "2:16.60",
# "2:16.79", "2:17.25", "55.08", "55.44", "55.73", "55.83",
# "55.87", "56.14", "56.45", "56.74", "56.86", "56.89", "2:12.27",
# "2:12.69", "2:13.92", "2:14.11", "2:15.07", "2:17.32", "2:17.43",
# "2:18.58", "2:18.69", "2:20.69", "2:13.90", "2:14.28", "2:14.61",
# "2:14.63", "2:14.99", "2:15.22", "2:17.70", "2:17.95", "2:18.51",
# NA), Points = c("816", "816", "815", "809", "806", "794",
# "793", "793", "791", "790", "781", "780", "777", "775", "764",
# "761", "756", "756", "747", "730", "845", "840", "836", "831",
# "816", "805", "793", "789", "775", "775", NA, NA, NA, NA,
# NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
# NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
# NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
# NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "729", "687",
# "678", "676", "665", "660", "640", "638", "605", "602", "866",
# "865", "849", "829", "825", "824", "821", "821", "817", "786",
# "783", "771", "765", "763", "762", "717", "716", "712", "679",
# "664", "663", "858", "814", "807", "794", "790", "789", "789",
# "783", "763", "751", NA, NA, NA, NA, NA, NA, NA, NA, NA,
# NA, "838", "822", "809", "805", "803", "791", "778", "767",
# "762", "761", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
# NA, NA, NA, NA, NA, NA, NA, NA, NA), DQ = c(0, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1)), row.names = c(NA,
# -171L), class = "data.frame")
#
#
# expect_equivalent(df_test, df_standard)
#
# place_sum <- sum(as.numeric(df_test$Place), na.rm = TRUE)
# expect_equivalent(place_sum, 1443)
#
# dq_sum <- sum(df_test$DQ, na.rm = TRUE)
# expect_equivalent(dq_sum, 1)
#
# })
test_that("2017 Open belgian Champs, has prelims, team-country", {
skip_on_cran()
file <-
"https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/Splash/Open_Belgian_Champs_2017.pdf"
df_test <- file %>%
read_results() %>%
swim_parse() %>%
select(-Event)
place_sum <- sum(as.numeric(df_test$Place), na.rm = TRUE)
dq_sum <- sum(df_test$DQ, na.rm = TRUE)
expect_equivalent(place_sum, 26332)
expect_equivalent(dq_sum, 42)
})
test_that("2017 Open belgian Champs, has prelims, team-country, splits", {
skip_on_cran()
file <-
"https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/Splash/Open_Belgian_Champs_2017.pdf"
df_test <- file %>%
read_results() %>%
swim_parse(splits = TRUE) %>%
select(-Event)
df_test_dim <- dim(df_test)
expect_equivalent(df_test_dim, c(1709, 108))
# rows 1709 fully checked
df_test_head <- df_test %>%
head(5)
df_standard <-
structure(list(Place = c("1", "2", "3", "4", "5"), Name = c("SMITS, Jade",
"HANSENNE, Nona", "JONGMAN, Indy", "DOBRIN, Alexandra", "VAN WALLENDAEL, Sarah"
), Age = c("01", "01", "01", "93", "02"), Team = c("BEL-BRABO",
"BEL-AART", "NED-KNZB", "ROU-CNBA", "BEL-BRABO"), Prelims = c("2:18.04",
"2:20.62", "2:18.24", "2:21.70", "2:23.61"), Finals = c("2:18.55",
"2:20.52", "2:21.93", "2:22.30", "2:22.96"), Points = c("717",
"688", "667", "662", "653"), DQ = c("0", "0", "0", "0", "0"),
Split_50 = c("31.01", "33.03", "32.89", "32.36", "33.69"),
Split_100 = c("1:05.93", "1:08.70", "1:09.00", "1:08.43",
"1:10.52"), Split_150 = c("1:42.36", "1:44.93", "1:46.12",
"1:45.40", "1:47.14"), Split_200 = c("2:18.55", "2:20.52",
"2:21.93", "2:22.30", "2:22.96"), Split_250 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_300 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_350 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_400 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_450 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_500 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_550 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_600 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_650 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_700 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_750 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_800 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_850 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_900 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_950 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_1000 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_1050 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_1100 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_1150 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_1200 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_1250 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_1300 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_1350 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_1400 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_1450 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_1500 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_1550 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_1600 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_1650 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_1700 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_1750 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_1800 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_1850 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_1900 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_1950 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_2000 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_2050 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_2100 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_2150 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_2200 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_2250 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_2300 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_2350 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_2400 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_2450 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_2500 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_2550 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_2600 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_2650 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_2700 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_2750 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_2800 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_2850 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_2900 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_2950 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_3000 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_3050 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_3100 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_3150 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_3200 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_3250 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_3300 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_3350 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_3400 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_3450 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_3500 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_3550 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_3600 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_3650 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_3700 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_3750 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_3800 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_3850 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_3900 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_3950 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_4000 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_4050 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_4100 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_4150 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_4200 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_4250 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_4300 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_4350 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_4400 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_4450 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_4500 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_4550 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_4600 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_4650 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_4700 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_4750 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_4800 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_4850 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_4900 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_), Split_4950 = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), Split_5000 = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_)), row.names = c(NA, 5L), class = "data.frame")
expect_equivalent(df_test_head, df_standard)
})
test_that("Glenmark Senior Nationals 2019 relays", {
skip_on_cran()
file <-
"https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/Splash/Glenmark_Senior_Nationals_2019.pdf"
df_test <- file %>%
read_results() %>%
swim_parse(splits = TRUE, split_length = 100)
df_test_relay <- df_test %>%
filter(str_detect(Event, " x ")) %>%
select(where(~ !all(is.na(.x)))) %>%
select(-Event)
df_standard <-
structure(list(Place = c("1", "2", "3", "4", "5", "6", "7", "1",
"2", "3", "4", "5", "6", "7", "8", "1", "2", "3", "4", "5", "6",
"7", "8", "1", "2", "3", "4", "5", "6", "7", "8", "1", "2", "3",
"4", "5", "6", "7", "8", "1", "2", "3", "4", "5", "6", "7", "8",
"1", "2", "3", "4", "5", "1", "2", "3", "4", "5", "6", "7", "8"
), Team = c("KARNATAKA", "SSCB", "R.S.P.B", "POLICE", "BENGAL",
"MAHARASHTRA", "KERALA", "KARNATAKA", "MAHARASHTRA", "R.S.P.B",
"GUJARAT", "TAMILNADU", "DELHI", "BENGAL", "KERALA", "MAHARASHTRA",
"KARNATAKA", "TAMILNADU", "DELHI", "BENGAL", "KERALA", "GUJARAT",
"PUNJAB", "KARNATAKA", "SSCB", "MAHARASHTRA", "R.S.P.B", "DELHI",
"BENGAL", "KERALA", "TELANGANA", "KARNATAKA", "SSCB", "R.S.P.B",
"DELHI", "KERALA", "MAHARASHTRA", "BENGAL", "POLICE", "MAHARASHTRA",
"KARNATAKA", "TAMILNADU", "GUJARAT", "BENGAL", "DELHI", "KERALA",
"PUNJAB", "KARNATAKA", "MAHARASHTRA", "TAMILNADU", "BENGAL",
"KERALA", "MAHARASHTRA", "R.S.P.B", "KARNATAKA", "DELHI", "KERALA",
"TAMILNADU", "BENGAL", "GUJARAT"), Finals = c("7:46.04",
"7:49.31", "7:53.52", "8:19.53", "8:20.32", "8:24.45", "8:46.12",
"1:50.65", "1:52.82", "1:54.00", "1:55.67", "1:57.23", "2:00.50",
"2:01.71", "2:01.90", "4:02.90", "4:09.88", "4:13.25", "4:14.84",
"4:15.54", "4:29.80", "4:31.50", "4:47.63", "3:30.45", "3:31.04",
"3:31.17", "3:33.22", "3:37.07", "3:43.67", "3:45.01", "4:02.50",
"3:48.83", "3:52.11", "3:52.93", "4:01.27", "4:02.53", "4:04.46",
"4:07.31", "4:39.37", "4:33.10", "4:34.84", "4:39.49", "4:40.06",
"4:41.20", "4:52.31", "4:56.41", "5:32.06", "9:04.86", "9:08.51",
"9:09.18", "9:23.37", "9:56.91", "1:39.69", "1:41.83", "1:41.92",
"1:44.95", "1:47.70", "1:48.06", "1:49.74", "1:50.08"), Points = c("724",
"709", "690", "588", "585", "571", "503", NA, NA, NA, NA, NA,
NA, NA, NA, "646", "593", "570", "559", "555", "471", "463",
"389", "715", "709", "708", "688", "652", "596", "585", "467",
"743", "712", "704", "634", "624", "609", "588", "408", "609",
"597", "568", "565", "558", "497", "476", "339", "609", "597",
"595", "551", "463", NA, NA, NA, NA, NA, NA, NA, NA), DQ = c("0",
"0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0",
"0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0",
"0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0",
"0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0",
"0", "0", "0", "0", "0", "0", "0"), Split_100 = c("1:55.46",
"1:54.39", "1:58.53", NA, "2:03.92", "2:09.81", "2:04.90", NA,
NA, NA, NA, NA, NA, NA, NA, NA, "1:02.46", "1:03.01", NA, "1:04.60",
"1:08.63", NA, "1:09.59", "53.37", "53.99", NA, "52.56", "54.01",
"56.86", "56.59", NA, NA, "59.10", "1:00.03", "59.44", NA, "1:02.30",
"1:04.93", NA, "1:10.05", "1:08.93", "1:08.06", "1:10.09", "1:07.24",
NA, NA, "1:23.43", "2:17.38", "2:18.21", "2:21.40", "2:20.05",
"2:21.76", NA, NA, NA, NA, NA, NA, NA, NA), Split_200 = c("1:58.43",
"1:57.62", NA, NA, "2:06.58", "2:06.64", NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, "1:02.21", "1:04.40", NA, "1:06.10", "1:08.30",
NA, "1:11.60", "53.24", "54.04", NA, "53.94", "54.93", "56.22",
"58.22", NA, NA, "56.92", NA, "59.58", NA, "59.99", NA, NA, "1:05.60",
"1:06.97", "1:16.16", "1:02.48", NA, NA, NA, "1:24.38", "2:15.53",
"2:21.32", "2:21.77", "2:23.01", "2:31.97", NA, NA, NA, NA, NA,
NA, NA, NA), Split_300 = c("1:29.54", "1:31.42", NA, "2:05.96",
"2:06.84", "2:03.42", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
"1:03.42", "1:04.36", NA, "1:04.24", "1:07.29", NA, "1:16.70",
"53.20", "52.04", NA, "52.97", "55.14", "55.05", "55.50", NA,
NA, "1:04.97", "1:06.00", "1:08.29", NA, "1:07.49", "55.48",
NA, "1:14.61", "1:16.47", "1:19.67", NA, "1:01.15", NA, NA, "1:28.46",
"2:21.02", "2:15.75", "2:14.79", "2:18.45", "2:31.21", NA, NA,
NA, NA, NA, NA, NA, NA), Split_400 = c("2:22.61", "2:25.88",
NA, "2:07.68", "2:02.98", "2:04.58", "2:15.50", NA, NA, NA, NA,
NA, NA, NA, NA, NA, "1:01.79", "1:01.48", NA, "1:00.60", "1:05.58",
NA, "1:09.74", "50.64", "50.97", NA, "53.75", "52.99", "55.54",
"54.70", NA, NA, "51.12", NA, "53.96", NA, "54.68", NA, NA, "1:02.84",
"1:02.47", "55.60", NA, NA, NA, NA, "1:15.79", "2:10.93", "2:13.23",
"2:11.22", "2:21.86", "2:31.97", NA, NA, NA, NA, NA, NA, NA,
NA)), row.names = c(NA, -60L), class = "data.frame")
expect_equivalent(df_test_relay, df_standard)
df_test_ind <- df_test %>%
head(5) %>%
select(where(~ !all(is.na(.x))))
df_standard <-
structure(list(Place = c("1", "2", "3", "4", "5"), Name = c("Kushagra Rawat",
"ANAND AS", "AARON FERNANDES", "Vishal Grewal", "Anurag R. Singh"
), Age = c("90", NA, NA, "90", NA), Team = c("DELHI", "SSCB",
"MAHARASHTRA", "DELHI", "UTTAR PRADESH"), Finals = c("3:56.95",
"4:01.92", "4:04.71", "4:07.44", "4:09.34"), Points = c("801",
"752", "727", "703", "687"), DQ = c("0", "0", "0", "0", "0"),
Event = c("Men, 400m Freestyle", "Men, 400m Freestyle", "Men, 400m Freestyle",
"Men, 400m Freestyle", "Men, 400m Freestyle"), Split_100 = c("58.11",
"1:02.00", "1:00.47", "58.70", "1:00.54"), Split_200 = c("1:00.23",
"1:00.56", NA, "1:02.64", "1:03.75"), Split_300 = c("59.57",
"3:01.36", "3:04.24", "1:03.76", "1:04.16"), Split_400 = c("59.04",
"4:01.92", "4:04.71", "1:02.34", "1:00.89")), row.names = c(NA,
5L), class = "data.frame")
expect_equivalent(df_test_ind, df_standard)
})
test_that("Glenmark Senior Nationals 2019 with splits and relays", {
skip_on_cran()
file <-
"https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/Splash/Glenmark_Senior_Nationals_2019.pdf"
df <- file %>%
read_results() %>%
swim_parse(splits = TRUE, split_length = 100, relay_swimmers = TRUE)
df_test_relay <- df %>%
filter(str_detect(Event, " x ")) %>%
select(where( ~ !all(is.na(.x)))) %>%
select(-Event)
df_standard_relay <-
structure(list(Place = c("1", "2", "3", "4", "5", "6", "7", "1",
"2", "3", "4", "5", "6", "7", "8", "1", "2", "3", "4", "5", "6",
"7", "8", "1", "2", "3", "4", "5", "6", "7", "8", "1", "2", "3",
"4", "5", "6", "7", "8", "1", "2", "3", "4", "5", "6", "7", "8",
"1", "2", "3", "4", "5", "1", "2", "3", "4", "5", "6", "7", "8"
), Team = c("KARNATAKA", "SSCB", "R.S.P.B", "POLICE", "BENGAL",
"MAHARASHTRA", "KERALA", "KARNATAKA", "MAHARASHTRA", "R.S.P.B",
"GUJARAT", "TAMILNADU", "DELHI", "BENGAL", "KERALA", "MAHARASHTRA",
"KARNATAKA", "TAMILNADU", "DELHI", "BENGAL", "KERALA", "GUJARAT",
"PUNJAB", "KARNATAKA", "SSCB", "MAHARASHTRA", "R.S.P.B", "DELHI",
"BENGAL", "KERALA", "TELANGANA", "KARNATAKA", "SSCB", "R.S.P.B",
"DELHI", "KERALA", "MAHARASHTRA", "BENGAL", "POLICE", "MAHARASHTRA",
"KARNATAKA", "TAMILNADU", "GUJARAT", "BENGAL", "DELHI", "KERALA",
"PUNJAB", "KARNATAKA", "MAHARASHTRA", "TAMILNADU", "BENGAL",
"KERALA", "MAHARASHTRA", "R.S.P.B", "KARNATAKA", "DELHI", "KERALA",
"TAMILNADU", "BENGAL", "GUJARAT"), Finals = c("7:46.04",
"7:49.31", "7:53.52", "8:19.53", "8:20.32", "8:24.45", "8:46.12",
"1:50.65", "1:52.82", "1:54.00", "1:55.67", "1:57.23", "2:00.50",
"2:01.71", "2:01.90", "4:02.90", "4:09.88", "4:13.25", "4:14.84",
"4:15.54", "4:29.80", "4:31.50", "4:47.63", "3:30.45", "3:31.04",
"3:31.17", "3:33.22", "3:37.07", "3:43.67", "3:45.01", "4:02.50",
"3:48.83", "3:52.11", "3:52.93", "4:01.27", "4:02.53", "4:04.46",
"4:07.31", "4:39.37", "4:33.10", "4:34.84", "4:39.49", "4:40.06",
"4:41.20", "4:52.31", "4:56.41", "5:32.06", "9:04.86", "9:08.51",
"9:09.18", "9:23.37", "9:56.91", "1:39.69", "1:41.83", "1:41.92",
"1:44.95", "1:47.70", "1:48.06", "1:49.74", "1:50.08"), Points = c("724",
"709", "690", "588", "585", "571", "503", NA, NA, NA, NA, NA,
NA, NA, NA, "646", "593", "570", "559", "555", "471", "463",
"389", "715", "709", "708", "688", "652", "596", "585", "467",
"743", "712", "704", "634", "624", "609", "588", "408", "609",
"597", "568", "565", "558", "497", "476", "339", "609", "597",
"595", "551", "463", NA, NA, NA, NA, NA, NA, NA, NA), DQ = c("0",
"0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0",
"0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0",
"0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0",
"0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0",
"0", "0", "0", "0", "0", "0", "0"), Relay_Swimmer_1 = c("Siva S",
"ANAND AS", "Sanu Debnath", "Mandar Anandrao Divase", "Rajdip Roy",
"GOURAV CHAVAN", "Arjun Anesh K", "Shrihari Nataraj", "JYOTSNA PANSARE",
"Soumyajit Saha", "Maana Patel", "Sethu Manickavel T", "Siddhant Sejwal",
"Soubrity Mondal", "Aaron J Thomas", "RUJUTA KHADE", "Smruthi Mahalingam",
"Swarna K Harith", "Pallavi Sejwal", "Shristi Upadhyay", "Jomi George",
"Niva Sharma", "Gurnoor Kaur", "Tanish George Mathew", "NIMISH MULEY",
"VIRDHAWAL KHADE", "Viraj Prabhu", "Kushagra Rawat", "Rajdip Roy",
"Abhijith Gegarin", "Yash Varma", "Shrihari Nataraj", "ARVIND MANI",
"Soumyajit Saha", "Siddhant Sejwal", "Aaron J Thomas", "MEHLAM GHADIALY",
"Pratyay Bhattacharya", "Mandar Anandrao Divase", "YUGA BIRNALE",
"Ridhima Veerendra Kumar", "Meenakshi V K R", "Maana Patel",
"Soubrity Mondal", "Riniki Bordoloi", "Sanaa Mathew", "Deepali Attri",
"Damini K Gowda", "YUGA BIRNALE", "Swarna K Harith", "Shristi Upadhyay",
"Bhadrasudevan. S", "MIHIR AMBRE", "Aaron D' Souza", "Likith S P",
"Siddhant Sejwal", "Abhijith Gegarin", "Sethu Manickavel T",
"Shrinjan Pal", "Harshal Sarang"), Relay_Swimmer_2 = c("Avinash Mani",
"KUNTAL GIRI", "Soumyajit Saha", "Munish Kumar", "Army Pal",
"RISHABH CHAUBEY", "Akash. S", "Suvana C Baskar", "VIRDHAWAL KHADE",
"Avantika Chavan", "Anshul Kothari", "Meenakshi V K R", "Pallavi Sejwal",
"Kunal Basak", "Liyana Fathima Umer", "SADHVI DHURI", "Suvana C Baskar",
"Shakthi B", "Riniki Bordoloi", "Oendrila Banerjee", "Sanaa Mathew",
"SILKI NAGPURE", "Bhavjot Kaur", "Siva S", "GAURAV YADAV KA",
"MIHIR AMBRE", "Anoop Augustine", "Samit Sejwal", "Swapnil Chakraborty",
"Arjun Anesh K", "M. Tejasvin", "Rakshith U shetty", "SHIVAKSH SAHU",
"Supriya Mondal", "Sameer Sejwal", "Arjun. M", "MIHIR AMBRE",
"Swapnil Chakraborty", "Sandeep Panghal", "APEKSHA FERNANDES",
"Damini K Gowda", "Shakthi B", "Arushi S K M", "Nilabjaa Ghosh",
"Hema", "Shreya Mary Kamal", "Gurnoor Kaur", "Suvana C Baskar",
"SIYAA SHETTY", "Meenakshi V K R", "Oendrila Banerjee", "Jomi George",
"KENISHA GUPTA", "Avantika Chavan", "Deeksha Ramesh", "Priyank Rana",
"Greeshma.P", "Jayaveena A V", "Priyanka Manna", "Niva Sharma"
), Relay_Swimmer_3 = c("Tanish George Mathew", "JAYANT M", "Anoop Augustine",
"Aman Ghai", "Pratyay Bhattacharya", "AARYAN BHOSALE", "Arun. M",
"Likith S P", "SHWEJAL MANKAR", "Viraj Prabhu", "Oum Saxena",
"Danush S", "Aditya Dubey", "Srishti Basu", "Suneesh. S", "APEKSHA FERNANDES",
"Inchara B", "Jayaveena A V", "Saanvi Sood", "Priyanka Manna",
"Liyana Fathima Umer", "Arushi S K M", "Nishtha Sharma", "Prithvi M",
"VINAY SAHARAN", "SHWEJAL MANKAR", "Sanu Debnath", "Tanmay Das",
"Kunal Basak", "Amridhesh. U", "Tanneru Sai Tarun", "Likith S P",
"ASHISH TOKAS", "M Lohith.", "Aditya Dubey", "Suneesh. S", "JAY EKBOTE",
"Swadesh Mondal", "Munish Kumar", "KAREENA SHANKTA", "Saloni Dalal",
"Shriya Ishwar Prasad", "Kalyani Saxena", "Srishti Basu", "Saanvi Sood",
"Aradhana Bekal", "Chahat Arora", "Smruthi Mahalingam", "SADHVI DHURI",
"Shakthi B", "Priyanka Manna", "Arya G Nair", "RUJUTA KHADE",
"Aditi Dhumatkar", "Smruthi Mahalingam", "Pallavi Sejwal", "Arjun Sambu. A",
"Swarna K Harith", "Shristi Upadhyay", "Maana Patel"), Relay_Swimmer_4 = c("Shrihari Nataraj",
"VINAY SAHARAN", "Saurabh Sangvekar", "Sajan Prakash", "Kunal Basak",
"AARON FERNANDES", "Amal. A", "Deeksha Ramesh", "KENISHA GUPTA",
"Aditi Dhumatkar", "Niva Sharma", "Jayaveena A V", "Bhavya Sachdeva",
"Shrinjan Pal", "Greeshma.P", "KENISHA GUPTA", "Khushi Dinesh",
"Bhavika Dugar", "Bhavya Sachdeva", "Janhvi Choudhury", "Bhadrasudevan. S",
"Maana Patel", "Deepali Attri", "Shrihari Nataraj", "ANAND AS",
"AARON FERNANDES", "Saurabh Sangvekar", "Vishal Grewal", "Shrinjan Pal",
"Amal. A", "Challagani Abhilash", "Prithvi M", "ANAND AS", "Viraj Prabhu",
"Vishal Grewal", "Abhijith Gegarin", "AARON FERNANDES", "Shrinjan Pal",
"Aman Ghai", "KENISHA GUPTA", "Khushi Dinesh", "Swarna K Harith",
"Niva Sharma", "Janhvi Choudhury", "Bhavya Sachdeva", "Jomi George",
"Bhavjot Kaur", "Khushi Dinesh", "RUTUJA TALEGAONKAR", "Bhavika Dugar",
"Soubrity Mondal", "Iris Manoj", "VIRDHAWAL KHADE", "Viraj Prabhu",
"Shrihari Nataraj", "Bhavya Sachdeva", "Jomi George", "Danush S",
"Kunal Basak", "Anshul Kothari"), Split_100 = c("1:55.46", "1:54.39",
"1:58.53", NA, "2:03.92", "2:09.81", "2:04.90", NA, NA, NA, NA,
NA, NA, NA, NA, NA, "1:02.46", "1:03.01", NA, "1:04.60", "1:08.63",
NA, "1:09.59", "53.37", "53.99", NA, "52.56", "54.01", "56.86",
"56.59", NA, NA, "59.10", "1:00.03", "59.44", NA, "1:02.30",
"1:04.93", NA, "1:10.05", "1:08.93", "1:08.06", "1:10.09", "1:07.24",
NA, NA, "1:23.43", "2:17.38", "2:18.21", "2:21.40", "2:20.05",
"2:21.76", NA, NA, NA, NA, NA, NA, NA, NA), Split_200 = c("1:58.43",
"1:57.62", NA, NA, "2:06.58", "2:06.64", NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, "1:02.21", "1:04.40", NA, "1:06.10", "1:08.30",
NA, "1:11.60", "53.24", "54.04", NA, "53.94", "54.93", "56.22",
"58.22", NA, NA, "56.92", NA, "59.58", NA, "59.99", NA, NA, "1:05.60",
"1:06.97", "1:16.16", "1:02.48", NA, NA, NA, "1:24.38", "2:15.53",
"2:21.32", "2:21.77", "2:23.01", "2:31.97", NA, NA, NA, NA, NA,
NA, NA, NA), Split_300 = c("1:29.54", "1:31.42", NA, "2:05.96",
"2:06.84", "2:03.42", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
"1:03.42", "1:04.36", NA, "1:04.24", "1:07.29", NA, "1:16.70",
"53.20", "52.04", NA, "52.97", "55.14", "55.05", "55.50", NA,
NA, "1:04.97", "1:06.00", "1:08.29", NA, "1:07.49", "55.48",
NA, "1:14.61", "1:16.47", "1:19.67", NA, "1:01.15", NA, NA, "1:28.46",
"2:21.02", "2:15.75", "2:14.79", "2:18.45", "2:31.21", NA, NA,
NA, NA, NA, NA, NA, NA), Split_400 = c("2:22.61", "2:25.88",
NA, "2:07.68", "2:02.98", "2:04.58", "2:15.50", NA, NA, NA, NA,
NA, NA, NA, NA, NA, "1:01.79", "1:01.48", NA, "1:00.60", "1:05.58",
NA, "1:09.74", "50.64", "50.97", NA, "53.75", "52.99", "55.54",
"54.70", NA, NA, "51.12", NA, "53.96", NA, "54.68", NA, NA, "1:02.84",
"1:02.47", "55.60", NA, NA, NA, NA, "1:15.79", "2:10.93", "2:13.23",
"2:11.22", "2:21.86", "2:31.97", NA, NA, NA, NA, NA, NA, NA,
NA)), row.names = c(NA, -60L), class = "data.frame")
expect_equivalent(df_test_relay, df_standard_relay)
})
test_that("RBSF 100 free with merged teams/ages", {
skip_on_cran()
file <-
"https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/Splash/RBSF_100Free.pdf"
df_test <- file %>%
read_results() %>%
swim_parse(splits = TRUE)
df_standard <-
structure(list(Place = c("1", "2", "3", "4", "5", "6", "7", "8"
), Name = c("VERDONCK, Fleur", "DUREZ, Laure", "WOUTERS, Briana",
"HENVEAUX, Camille", "VAN DEN EEDE, Lisa", "DUJARDIN, Faye",
"SEYNAEVE, Marthe", "VANDEPOORTE, Pauline"), Age = c("06", "06",
"06", "06", "06", "06", "06", "06"), Team = c("ZGEEL", "ENLN",
"HZA", "LGN", "AZL", "GOLD", "BZK", "FAST"), Prelims = c("55.92",
"57.25", "57.81", "58.54", "1:00.96", "1:00.17", "1:00.59", "1:00.77"
), Finals = c("57.50", "57.77", "58.91", "59.53", "1:01.72",
"1:02.25", "1:02.55", "1:03.21"), DQ = c("0", "0", "0", "0",
"0", "0", "0", "0"), Event = c("Girls, 100m Freestyle 15 years",
"Girls, 100m Freestyle 15 years", "Girls, 100m Freestyle 15 years",
"Girls, 100m Freestyle 15 years", "Girls, 100m Freestyle 15 years",
"Girls, 100m Freestyle 15 years", "Girls, 100m Freestyle 15 years",
"Girls, 100m Freestyle 15 years"), Split_50 = c("27.73", "28.09",
"28.13", "28.95", "29.78", "29.53", "29.73", "30.07"), Split_100 = c("57.50",
"57.77", "58.91", "59.53", "1:01.72", "1:02.25", "1:02.55", "1:03.21"
)), row.names = c(NA, -8L), class = "data.frame")
expect_equivalent(df_test, df_standard)
})
test_that("RBSF 50 free with merged teams/ages, also has a tie", {
skip_on_cran()
file <-
"https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/Splash/RBSF_50Free.pdf"
df_test <- file %>%
read_results() %>%
swim_parse(splits = TRUE)
df_standard <-
structure(list(Place = c("1", "2", "3", "4", "5", "5", "7", "8"
), Name = c("SNEYERS, Kobe", "PETRE, Olivier", "WILLEMS, Dieter",
"BOETS, Victor", "VERCAUTEREN, Jasper", "DUFLOUCQ, Seppe", "OPDEBEECK, Thomas",
"EVENS, Thibo"), Age = c("06", "06", "06", "06", "06", "06",
"06", "06"), Team = c("SHARK", "ENLN", "AZV", "MOZKA", "BRABO",
"ISWIM", "BRABO", "KAZS"), Prelims = c("25.00", "26.15",
"25.79", "26.14", "26.29", "26.25", "26.15", "26.30"), Finals = c("25.69",
"26.05", "26.34", "26.57", "26.96", "26.96", "27.11", "27.66"
), DQ = c("0", "0", "0", "0", "0", "0", "0", "0"), Event = c("Boys, 50m Freestyle 15 years",
"Boys, 50m Freestyle 15 years", "Boys, 50m Freestyle 15 years",
"Boys, 50m Freestyle 15 years", "Boys, 50m Freestyle 15 years",
"Boys, 50m Freestyle 15 years", "Boys, 50m Freestyle 15 years",
"Boys, 50m Freestyle 15 years")), row.names = c(NA, -8L), class = "data.frame")
expect_equivalent(df_test, df_standard)
})
# test_that("Euro 2018 Champs - a giant mess", {
# skip_on_cran()
#
#
# # as of 3/18/23 pdf_test is not reading in rows where team names run into times correctly
# # I don't know how to fix this beyond using typo/replacement
#
# file <-
# "https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/Splash/EURO_MEET_2018.pdf"
#
# df_test <- file %>%
# read_results() %>%
# swim_parse(splits = TRUE, relay_swimmers = TRUE)
#
# df_standard <-
# readRDS(url("https://github.com/gpilgrim2670/Pilgrim_Data/raw/master/Splash/Euro_2018_Splash.rds"))
#
#
# expect_equivalent(df_test, df_standard)
#
# })
# testthat::test_file("tests/testthat/test-splash.R")
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