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R/omega_swim_parse.R
In SwimmeR: Data Import, Cleaning, and Conversions for Swimming Results
Defines functions
swim_parse_omega
Documented in
swim_parse_omega
#' Formats Omega style swimming and diving data read with \code{read_results}
#' into a data frame
#'
#' Takes the output of \code{read_results} and cleans it, yielding a data frame
#' of swimming (and diving) results
#'
#' @importFrom dplyr mutate
#' @importFrom dplyr lag
#' @importFrom dplyr case_when
#' @importFrom dplyr na_if
#' @importFrom dplyr select
#' @importFrom dplyr arrange
#' @importFrom dplyr filter
#' @importFrom dplyr bind_rows
#' @importFrom dplyr everything
#' @importFrom dplyr pull
#' @importFrom dplyr select
#' @importFrom dplyr starts_with
#' @importFrom stringr str_replace_all
#' @importFrom stringr str_extract
#' @importFrom stringr str_split
#' @importFrom stringr str_detect
#' @importFrom stringr str_length
#' @importFrom stringr str_sort
#' @importFrom purrr map_lgl
#' @importFrom purrr map
#' @importFrom stats setNames
#'
#' @param file_omega output from \code{read_results}
#' @param avoid_omega a list of strings. Rows in \code{file_omega} containing
#' these strings will not be included. For example "Pool:", often used to
#' label pool records, could be passed to \code{avoid_omega}. The default is
#' \code{avoid_default}, which contains many strings similar to "Pool:", such
#' as "STATE:" and "Qual:". Users can supply their own lists to
#' \code{avoid_omega}. \code{avoid_omega} is handled before \code{typo_omega}
#' and \code{replacement_omega}.
#' @param typo_omega a list of strings that are typos in the original results.
#' \code{swim_parse} is particularly sensitive to accidental double spaces, so
#' "Central High School", with two spaces between "Central" and "High" is a
#' problem, which can be fixed. Pass "Central High School" to
#' \code{typo_omega}. Unexpected commas as also an issue, for example "Texas,
#' University of" should be fixed using \code{typo_omega} and
#' \code{replacement_omega}
#' @param replacement_omega a list of fixes for the strings in
#' \code{typo_omega}. Here one could pass "Central High School" (one space
#' between "Central" and "High") and "Texas" to \code{replacement_omega} fix
#' the issues described in \code{typo_omega}
#' @param format_results should the results be formatted for analysis (special
#' strings like \code{"DQ"} replaced with \code{NA}, \code{Finals} as
#' definitive column)? Default is \code{TRUE}
#' @param splits either \code{TRUE} or the default, \code{FALSE} - should
#' \code{swim_parse} attempt to include splits.
#' @param split_length_omega either \code{25} or the default, \code{50}, the
#' length of pool at which splits are recorded. Not all results are
#' internally consistent on this issue - some have races with splits by 50 and
#' other races with splits by 25.
#' @param relay_swimmers_omega should names of relay swimmers be captured?
#' Default is \code{FALSE}
#' @return returns a data frame with columns \code{Name}, \code{Place},
#' \code{Age}, \code{Team}, \code{Prelims}, \code{Finals},
#' \code{Points}, \code{Event} & \code{DQ}. Note all swims will have a
#' \code{Finals}, even if that time was actually swam in the prelims
#' (i.e. a swimmer did not qualify for finals). This is so that final results
#' for an event can be generated from just one column.
#'
#' @seealso \code{swim_parse_omega} must be run on the output of
#' \code{\link{read_results}}
swim_parse_omega <-
function(file_omega,
avoid_omega = avoid,
typo_omega = typo,
replacement_omega = replacement,
format_results = TRUE,
splits = FALSE,
split_length_omega = split_length,
relay_swimmers_omega = relay_swimmers) {
#### testing ####
# file_omega <- read_results("https://www.omegatiming.com/File/00011500030105EF01FFFFFFFFFFFF01.pdf")
# file_omega <- read_results("https://www.omegatiming.com/File/00011500030103EC02FFFFFFFFFFFF01.pdf")
# file_omega <- read_results("https://www.omegatiming.com/File/00011500030101EE01FFFFFFFFFFFF01.pdf")
# file_omega <- read_results("https://www.omegatiming.com/File/00011500030103EC06FFFFFFFFFFFF01.pdf")
# file_omega <- read_results("https://www.omegatiming.com/File/00011500030201EF04FFFFFFFFFFFF01.pdf")
# file_omega <- read_results("https://www.omegatiming.com/File/00011500030103EC04FFFFFFFFFFFF01.pdf") # men 100br final
# file_omega <- read_results("https://www.omegatiming.com/File/00011500020102EE01FFFFFFFFFFFF01.pdf") # wave 1 200 back heats
# file_omega <- read_results("https://www.omegatiming.com/File/00011500020201F104FFFFFFFFFFFF01.pdf") # wave 1 800 free
# file_omega <- read_results("https://www.omegatiming.com/File/00011500020201EB01FFFFFFFFFFFF01.pdf") # wave 1 50 heats
# file_omega <- read_results("https://www.omegatiming.com/File/00011500020101EB04FFFFFFFFFFFF01.pdf") # wave 1 50 final
# file_omega <- read_results("https://www.omegatiming.com/File/0001150002FFFFFFFFFFFFFFFFFFFF22.pdf") # wave 1 whole book
# file_omega <- read_results("https://www.omegatiming.com/File/00011500020201EC01FFFFFFFFFFFF01.pdf")
# file_omega <- read_results("https://www.omegatiming.com/File/00011500020204EE04FFFFFFFFFFFF01.pdf")
# file_omega <- read_results("https://www.omegatiming.com/File/00011500030103EC06FFFFFFFFFFFF01.pdf")
# file_omega <- read_results(system.file("extdata", "Omega_OT_100Br_Swimoff_2021.pdf", package = "SwimmeR"))
# file_omega <- read_results("https://www.omegatiming.com/File/00011500020101EB01FFFFFFFFFFFF01.pdf")
# file_omega <- read_results("https://www.omegatiming.com/File/00011500020102EE01FFFFFFFFFFFF01.pdf")
# file_omega <- read_results(system.file("extdata", "Omega_Wave1_1500_Finals_2021.pdf", package = "SwimmeR"))
# file_omega <- read_results("https://www.omegatiming.com/File/00011500030105EE02FFFFFFFFFFFF01.pdf")
# file_omega <- read_results("https://olympics.com/tokyo-2020/olympic-games/resOG2020-/pdf/OG2020-/SWM/OG2020-_SWM_C73A1_SWMM400MIM------------HEAT000100--.pdf")
# file_omega <- read_results("https://olympics.com/tokyo-2020/olympic-games/resOG2020-/pdf/OG2020-/SWM/OG2020-_SWM_C73A1_SWMW100MBR------------HEAT000100--.pdf")
# file_omega <- read_results(system.file("extdata", "Omega_OT_400IM_Prelims_2021.pdf", package = "SwimmeR"))
# file_omega <- read_results("https://olympics.com/tokyo-2020/olympic-games/resOG2020-/pdf/OG2020-/SWM/OG2020-_SWM_C73A2_SWMM400MIM------------HEAT--------.pdf")
# file_omega <- read_results("https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/Tokyo2020/OG2020-_SWM_C73A2_SWMM100MBF------------SFNL--------.pdf")
# file_omega <- read_results("https://olympics.com/tokyo-2020/olympic-games/resOG2020-/pdf/OG2020-/SWM/OG2020-_SWM_C73B1_SWMM4X200MFR----------FNL-000100--.pdf")
# file_omega <- read_results("https://olympics.com/tokyo-2020/olympic-games/resOG2020-/pdf/OG2020-/SWM/OG2020-_SWM_C73A1_SWMM100MFR------------FNL-000100--.pdf")
# file_omega <- read_results("https://olympics.com/tokyo-2020/olympic-games/resOG2020-/pdf/OG2020-/SWM/OG2020-_SWM_C73A1_SWMM200MFR------------FNL-000100--.pdf")
# file_omega <- read_results("https://olympics.com/tokyo-2020/olympic-games/resOG2020-/pdf/OG2020-/SWM/OG2020-_SWM_C73B1_SWMX4X100MMD----------FNL-000100--.pdf")
# file_omega <- read_results("https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/Tokyo2020/SWMW4X200MFR_HEAT.pdf")
# file_omega <- read_results("https://olympics.com/tokyo-2020/olympic-games/resOG2020-/pdf/OG2020-/SWM/OG2020-_SWM_C73A1_SWMM400MIM------------FNL-000100--.pdf")
# file_omega <- read_results("https://olympics.com/tokyo-2020/olympic-games/resOG2020-/pdf/OG2020-/SWM/OG2020-_SWM_C73A1_SWMM100MFR------------FNL-000100--.pdf")
# file_omega <- read_results("https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/Tokyo2020/SWMM50MFR_FNL.pdf")
# file_omega <- read_results("https://www.omegatiming.com/File/00011500020101EC01FFFFFFFFFFFF01.pdf")
# file_omega <- read_results("https://www.omegatiming.com/File/00011500020205EF01FFFFFFFFFFFF01.pdf")
# file_omega <- read_results("https://www.omegatiming.com/File/00011500020204EE01FFFFFFFFFFFF01.pdf")
# file_omega <- read_results("https://www.omegatiming.com/File/0001150003FFFFFFFFFFFFFFFFFFFF22.pdf")
# file_omega <- read_results("https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/Omega/Omega_OT_Wave2_FullResults_2021.pdf")
# file_omega <- read_results("https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/Tokyo2020/SWMM100MBF_SFNL.pdf")
# file_omega <- read_results("https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/Omega/Omega_OT_Wave2_W400Fr_Heats_2021.pdf")
# file_omega <- read_results("https://olympics.com/tokyo-2020/paralympic-games/resPG2020-/pdf/PG2020-/SWM/PG2020-_SWM_C73A1_SWMM400MFR--09010-----HEAT000100--.pdf")
# file_omega <- read_results("https://olympics.com/tokyo-2020/paralympic-games/resPG2020-/pdf/PG2020-/SWM/PG2020-_SWM_C73A2_SWMW100MBF--13030-----HEAT--------.pdf")
# file_omega <- read_results("https://olympics.com/tokyo-2020/paralympic-games/resPG2020-/pdf/PG2020-/SWM/PG2020-_SWM_C73A1_SWMM200MIM--06022-----FNL-000100--.pdf")
# file_omega <- read_results("https://olympics.com/tokyo-2020/olympic-games/resOG2020-/pdf/OG2020-/SWM/OG2020-_SWM_C73B1_SWMW4X100MFR----------HEAT000100--.pdf")
# file_omega <- read_results("https://olympics.com/tokyo-2020/paralympic-games/resPG2020-/pdf/PG2020-/SWM/PG2020-_SWM_C73A2_SWMM50MFR---11010-----HEAT--------.pdf")
# file_omega <- read_results("https://olympics.com/tokyo-2020/paralympic-games/resPG2020-/pdf/PG2020-/SWM/PG2020-_SWM_C73B1_SWMX4X100MFR13033-----FNL-000100--.pdf")
# file_omega <- read_results("https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/SwimmeR%20Test%20Files/PG2020_SWMM200MIM_FNL.pdf")
# file_omega <- read_results("https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/Paralympics2020/raw_files/PG2020_SWMW150MIM_04042_FNL.pdf")
# file_omega <- read_results("https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/Tokyo2020/SWMM4X200MFR_FNL.pdf")
# file_omega <- read_results("https://www.omegatiming.com/File/00011500100201EF01FFFFFFFFFFFF01.pdf")
# file_omega <- read_results(system.file("extdata", "RESULTS_BOOK.pdf", package = "SwimmeR"))
# file_omega <- read_results("https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/SwimmeR%20Test%20Files/PG2020_SWMM200MIM_FNL.pdf")
# avoid_omega <- c("abcxyz")
# typo_omega <- c("typo")
# replacement_omega <- c("typo")
# split_length_omega <- 50
# relay_swimmers_omega <- TRUE
# splits <- TRUE
#### assign row numbers ####
as_lines_list_2 <- file_omega %>%
.[stringr::str_detect(., "Early take-off", negate = TRUE)] %>% # removes DQ rational used in some relay DQs that messes up line spacing between relay and swimmers/splits - must happen before adding in row numbers
.[stringr::str_detect(., " 1?\\d50m ", negate = TRUE)] %>% # removes cumulative split rows for Omega results only but does not remove Women's 150m IM for para games
add_row_numbers() %>%
.[purrr::map_lgl(., ~ !any(stringr::str_detect(., avoid_omega)))] %>%
stringr::str_replace_all(stats::setNames(replacement_omega, typo_omega)) %>% # replace typos with replacements
# stringr::str_replace_all("DISQUAL", " DQ ") %>%
stringr::str_replace_all("EVENT\\:", "Event")
#### parsing omega files ####
#### Pulls out event labels from text ####
events <- event_parse(as_lines_list_2)
#### Pulls out heat labels from text ####
heats <- heat_parse_omega(as_lines_list_2)
#### Pulls out event date from text ####
Event_Date_String <- paste0("(?<=^\n)\\d{1,2}\\s(", paste(toupper(month.abb), collapse = "|"), ")\\s\\d{4}")
Event_Date <- stringr::str_extract(as_lines_list_2, Event_Date_String)
Event_Date <- Event_Date[!is.na(Event_Date)][1]
Event_Date <- as.Date(Event_Date, format = "%d %b %Y")
#### set up strings ####
Name_String <-
"_?[:alpha:]+\\s?\\'?[:alpha:\\-\\'\\.]*\\s?[:alpha:\\-\\'\\.]*\\s?[:alpha:\\-\\'\\.]*,?\\s?[:alpha:\\-\\'\\.]*\\s?[:alpha:]*\\s?[:alpha:]*\\s?[:alpha:]*\\.?,? [:alpha:]+\\s?[:alpha:\\-\\'\\.]*\\s?[:alpha:\\-\\']*\\s?[:alpha:]*\\s?[:alpha:]*\\s?[:alpha:\\.]*"
Time_Score_String <- "\\d{0,2}\\:?\\d{1,3}\\.\\d{2}"
Time_Score_Specials_String <- paste0("^NT$|^NP$|^DQ$|^DSQ$|^D?NS$|^SCR$|^x?X?", Time_Score_String, "x?X?$")
Time_Score_Specials_String_Extract <- paste0(Time_Score_String, "|^NT$|^NP$|^DQ$|^NS$|^SCR$")
Para_String <- "^SB?M?\\d{1,2}$"
Reaction_String <- "^\\+\\s?\\d\\.\\d{3}$|^\\-\\s?\\d\\.\\d{3}$|^[0-1]\\.00$|^[0-1]\\.\\d\\d$"
Record_String <- "^\\=?W[:upper:]$|^\\=?AR$|^\\=?US$|^\\=?CR$|^\\=?OT$|^\\=?OR$|^\\=?[:upper:]R$"
Header_string <- "\\sDisqualified\\s|\\sReaction\\sTime\\s"
Heat_String <- "Heat\\s\\d{1,}\\sof\\s\\d{1,}|Semifinal\\s+\\d{1,}|Final|(Heats?)(?![:alpha:])"
DOB_String_2 <- paste0("(?<=\\s)\\d{2}\\s(", paste(toupper(month.abb), collapse = "|"), ")\\s\\d{4}(?=\\s+\\d{1,2}\\.\\d{2})")
DOB_String_1 <- paste0("(?<=\\s)\\d{1}\\s(", paste(toupper(month.abb), collapse = "|"), ")\\s\\d{4}(?=\\s+\\d{1,2}\\.\\d{2})")
Age_String <- "^\\d{2}$"
#### Collect DOBs ####
DOBs_2 <- stringr::str_extract(as_lines_list_2, DOB_String_2)
DOBs_2 <- unique(DOBs_2[!is.na(DOBs_2)])
DOBs_2_Format <- as.Date(DOBs_2, format = "%d %b %Y")
Ages_2 <-
as.character(round(as.numeric(
difftime(Event_Date, DOBs_2_Format, units = "days") / 365
), 0))
DOBs_1 <- stringr::str_extract(as_lines_list_2, DOB_String_1)
DOBs_1 <- unique(DOBs_1[!is.na(DOBs_1)])
DOBs_1_Format <- as.Date(DOBs_1, format = "%d %b %Y")
Ages_1 <-
as.character(round(as.numeric(
difftime(Event_Date, DOBs_1_Format, units = "days") / 365
), 0))
#### clean input data ####
data_cleaned <- as_lines_list_2 %>%
stringr::str_remove("^\n\\s{0,}") %>%
.[stringr::str_length(.) > 50] %>% # slight speed boost from cutting down length of file
.[stringr::str_detect(., paste0(Time_Score_String,"|DS?Q|SCR|D?NS"))] %>% # must have \\.\\d\\d because all swimming and diving times do
.[stringr::str_detect(., "[:alpha:]{2,}.*[:alpha:]")] %>% # need some letters, need them to not just be a single instance of DQ etc.
.[stringr::str_detect(., "[:alpha:]{2,} Record", negate = TRUE)] %>% # remove legend contents that will be included if their are DQs, DNS etc. Omega
.[stringr::str_detect(., "[:alpha:] DSQ [:alpha:]", negate = TRUE)] %>% # omega Wave II removes notes from when a DSQ is overturned
.[stringr::str_detect(., "\\d{2} [:upper:]{3} \\d{4} GOLD", negate = TRUE)] %>% # omega remove medalist results from the end of Wave II 2021 OTs
.[stringr::str_detect(., Heat_String, negate = TRUE)] %>% # omega removes weird formatting issue where results and heat labels are connected
{if (length(Ages_2) > 0) purrr::reduce2(.init = ., DOBs_2, Ages_2, stringr::str_replace_all) else .} %>%
{if (length(Ages_1) > 0) purrr::reduce2(.init = ., DOBs_1, Ages_1, stringr::str_replace_all) else .} %>%
stringr::str_replace_all("([:alpha:])\\. ", "\\1 ") %>% # remove periods from athlete names Omega
# .[!all(dplyr::between(purrr::map_int(., stringr::str_count, "\\.|\\("), 6.1, 7.9), purrr::map_int(., stringr::str_count, "\\.") != 6)] %>% # keeps relay swimmers + splits from 4x200 races from getting in Omega
.[stringr::str_detect(., "[:alpha:]\\s+\\-\\d\\.\\d{2}\\s+\\d{1,}$", negate = TRUE)] %>%
.[stringr::str_detect(., Header_string, negate = TRUE)] %>%
stringr::str_replace_all("( [:upper:]{2},\\s)*\\s?(?<![:upper:])\\=?[:upper:]{2}\\.?\\s(?=\\s{1,}\\d{1,}$)", " ") %>% # remove record strings like OR, OC but miss DNS, DSQ etc.
stringr::str_replace_all("\\s?[&%]\\s?", " ") %>% # added 8/21 for removing "&" and "%" as record designator
stringr::str_remove_all("(?<=\\d\\.\\d{2}\\s?)[:punct:]") %>% # remove symbols attached to times as record designator
stringr::str_remove_all("(?<=\\.\\d{2}\\s?)[A-WYZ|\\$|q](?=\\s)") %>% # remove letters attached to times as record designator
stringr::str_replace_all(" [qQ](?=\\d{1,5} )", " ") %>% # remove q|Q attached to points
stringr::str_replace_all("[A-WYZa-wyz]+(\\d{1,2}\\:\\d{2}\\.\\d{2})", "\\1") %>% # removed J etc. from next to swim, but does not remove X or x (for exhibition tracking)
stringr::str_replace_all("(\\d{1,2}\\:\\d{2}\\.\\d{2})[A-WYZa-wyz]+", "\\1") %>%
stringr::str_replace_all("[A-WYZa-wyz]+(\\d{2,3}\\.\\d{2})", "\\1") %>%
stringr::str_replace_all("(\\d{2,3}\\.\\d{2})[A-WYZa-wyz]+", "\\1") %>%
stringr::str_replace_all(" [:punct:]+(\\d{1,2}\\:\\d{2}\\.\\d{2})", " \\1") %>%
stringr::str_replace_all("(\\d{1,2}\\:\\d{2}\\.\\d{2})[:punct:]+", " \\1") %>%
stringr::str_replace_all(" [:punct:]+(\\d{2,3}\\.\\d{2})", " \\1") %>%
stringr::str_replace_all("(\\d{2,3}\\.\\d{2})[:punct:]+", "\\1 ") %>%
stringr::str_replace_all("\\(\\=?\\d\\)", " ") %>% # omega
stringr::str_remove_all("\\s{2}J\\s{2}") %>%
stringr::str_remove_all("\\=(?=\\d)") %>%
stringr::str_replace_all(" [A-WYZ]\\?? ", " ") %>% # omega
stringr::str_replace_all(" R?\\? ", " ") %>% # omega
stringr::str_replace_all(" \\d{4} ", " ") %>% # omega
stringr::str_replace_all("(?<=\\.\\d)(\\d)\\s+X(?=\\s+\\d{1,5}$)", "\\1X") %>% # brings Xs for exhibition that are spaced out in closer
# remove q from next to time 10/21/2020
stringr::str_remove_all(" q ") %>% # removes " q " sometimes used to designate a qualifying time
stringr::str_replace_all("-{2,5}", "10000") %>% #8/26
stringr::str_replace_all("\\*(?=[:alpha:])", "_") %>% # for * prior to name for foreign athletes
stringr::str_replace_all("\\*", " ") %>%
stringr::str_replace_all("(?<=\\s)\\+\\s(?=[:digit:])", " +") %>% # for reaction times
stringr::str_replace_all("(?<=\\s)\\-\\s(?=[:digit:])", " -") %>% # for reaction times
stringr::str_remove_all("\U2666") %>% # remove black diamond
trimws()
#### insert double spaces where needed ####
data_cleaned <- data_cleaned %>%
stringr::str_replace_all("(?<=\\d)\\s+[:upper:]?\\s+(?=\\d)", " ") %>% # letters like P or M to denote pool or meet record
stringr::str_replace_all(" \\=[:upper:]R\\,?", " ") %>% # omega remove record designators like =AR
stringr::str_replace_all("(?<=\\d) (?=[:alpha:])", " ") %>% # mostly to split place and name
stringr::str_replace_all("(?<=\\d) (?=_)", " ") %>% # mostly to split place and name, if name is preceded by "_" as a stand-in for "*"
stringr::str_replace_all("(?<=\\d) (?=\\d)", " ") %>% # mostly to split place team names that start with a number, like in NYS results (5-Fairport etc.)
stringr::str_replace_all("(?<=[:alpha:]),(?=[:alpha:])", ", ") %>% # split names that don't have a space between last,first
stringr::str_replace_all("(?<=[:alpha:])\\. (?=[:digit:])", "\\. ") %>% # split abbreviated team names like Southern Cal. and times
stringr::str_replace_all("(?<=\\d) (?=_)", " ") %>% # spacing between place and athletes with */_ leading name
stringr::str_replace_all("(?<=\\)) (?=[:alpha:])", " ") %>% # spacing between place) and names
stringr::str_replace_all("(SM?B?1\\d{1})(\\d{1,2})", "\\1 \\2") %>% # split para classification and age
stringr::str_replace_all(" NT ", " NT ") %>% # split prelim and final
stringr::str_replace_all("(?<=[:alpha:])\\s{2,3}(?=[:alpha:])", " ") %>% # testing 12/21/2020 would help with typos
stringr::str_replace_all("(?<=[:alpha:]) (?=\\d)", " ") %>% # split name and age
stringr::str_replace_all("(?<=[:alpha:])(\\d{1,3}\\-\\d{2})", " \\1 ") %>% # split name and yyy-mm age
stringr::str_replace_all("(?<=\\,) (?=\\d)", " ") %>% # split name and age if name is so long that it ends with a ","
stringr::str_replace_all("(?<=\\d) (?=\\d{1,}$)", " ") %>% # split off row_numb
stringr::str_replace_all("(?<=\\.\\d\\d\\s{1,10}\\d?\\d?\\:?\\d?\\d\\.\\d\\d)\\s{1,10}[:alpha:]{1,5}\\d?\\s{1,10}(?=\\d{1,})", " ") %>% # removes "AAC" or "AAA" or "NYS" or "SEC1" etc. from after finals time
stringr::str_replace_all("(?<=\\s)S(?=B?M?\\d{1,2})", " S") %>% # for para classifications
stringr::str_replace_all("([:alpha:])(SB?M?\\d{1,2})", "\\1 \\2") %>% # for splitting names and para classifications
# {if(stringr::str_detect(events$Event[1], "elay") == FALSE)
# stringr::str_replace_all(., "(^[:upper:]{3,})", "NA NA \\1") else . # for GUTIERREZ BERMUDEZ Juan Jose in 2020 Para Games 200IM Finals
# }
stringr::str_replace_all("(^[:upper:]{3,}.*\\s{3,}[:upper:]{3})", "7 6 \\1") # for GUTIERREZ BERMUDEZ Juan Jose in 2020 Para Games 200IM Finals
#### if data_cleaned is empty ####
if(!length(data_cleaned) > 0){
message("No results found in file")
} else {
#### splits data into variables by splitting at multiple (>= 2) spaces ####
data_cleaned <-
unlist(purrr::map(data_cleaned, stringr::str_split, "\\s{2,}"),
recursive = FALSE)
# unique(map(data_cleaned, length))
#### breaks data into subsets based on how many variables it has ####
data_length_5 <- data_cleaned[purrr::map(data_cleaned, length) == 5]
data_length_6 <- data_cleaned[purrr::map(data_cleaned, length) == 6]
data_length_7 <- data_cleaned[purrr::map(data_cleaned, length) == 7]
data_length_8 <- data_cleaned[purrr::map(data_cleaned, length) == 8]
data_length_9 <- data_cleaned[purrr::map(data_cleaned, length) == 9]
data_length_10 <- data_cleaned[purrr::map(data_cleaned, length) == 10]
data_length_11 <- data_cleaned[purrr::map(data_cleaned, length) == 11]
data_length_12 <- data_cleaned[purrr::map(data_cleaned, length) == 12]
data_length_13 <- data_cleaned[purrr::map(data_cleaned, length) == 13]
# data_length_14 <- data_cleaned[purrr::map(data_cleaned, length) == 14]
# treatment of DQs new 8/19
suppressWarnings(DQ <-
data_cleaned[stringr::str_detect(data_cleaned, Time_Score_String, negate = TRUE) == TRUE])
DQ_length_3 <- DQ[purrr::map(DQ, length) == 3]
DQ_length_4 <- DQ[purrr::map(DQ, length) == 4]
# DQ_length_5 <- DQ[purrr::map(DQ, length) == 5]
#### thirteen variables ####
if (length(data_length_13) > 0) {
suppressWarnings(
df_13 <- data_length_13 %>%
list_transform() %>%
dplyr::filter(stringr::str_detect(V1, Record_String) == FALSE) %>%
na_if_character("") %>%
na_if_character("NA") %>%
dplyr::select(
Place = V1,
Heat = V2,
Lane = V3,
Name = V4,
Team = V5,
Age = V6,
Reaction_Time = V7,
Finals = V11,
Row_Numb = V13
)
) %>%
dplyr::filter(stringr::str_detect(Place, "^\\d+$"))
} else {
df_13 <- data.frame(
Row_Numb = character(),
stringsAsFactors = FALSE
)
}
#### twelve variables ####
if (length(data_length_12) > 0) {
suppressWarnings(
df_12 <- data_length_12 %>%
list_transform() %>%
dplyr::filter(stringr::str_detect(V1, Record_String) == FALSE) %>%
dplyr::mutate(Heat = case_when(
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") ~ V2,
TRUE ~ "NA"
)) %>%
dplyr::mutate(Lane = case_when(
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") == TRUE ~ V3,
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") == FALSE ~ V2,
TRUE ~ "NA"
)) %>%
dplyr::mutate(Name = dplyr::case_when(stringr::str_detect(Heat, "NA") == TRUE ~ V3,
TRUE ~ V4)) %>%
dplyr::mutate(Para = dplyr::case_when(stringr::str_detect(V4, Para_String) == TRUE ~ V4,
TRUE ~ "NA")) %>%
dplyr::mutate(
Team = dplyr::case_when(
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V4, Para_String) == FALSE ~ V4,
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V4, Para_String) == TRUE ~ V5,
TRUE ~ V5
)
) %>%
dplyr::mutate(
Reaction_Time = dplyr::case_when(
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V4, Para_String) == FALSE ~ V5,
stringr::str_detect(V6, Reaction_String) == TRUE ~ V6,
stringr::str_detect(V7, Reaction_String) == TRUE ~ V7,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(Age = case_when(stringr::str_detect(V6, Age_String) == TRUE ~ V6,
TRUE ~ "NA")) %>%
dplyr::mutate(
Finals = dplyr::case_when(
stringr::str_detect(V11, Time_Score_Specials_String) == TRUE &
sec_format(V10) < sec_format(V11) ~ V11,
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V4, Para_String) == FALSE ~ V10,
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V4, Para_String) == FALSE ~ V10,
stringr::str_detect(V7, Time_Score_Specials_String) == TRUE &
stringr::str_detect(V8, Time_Score_Specials_String) == TRUE &
stringr::str_detect(V9, Time_Score_Specials_String) == TRUE &
stringr::str_detect(V10, Time_Score_Specials_String) == TRUE ~ V10,
stringr::str_detect(V10, Time_Score_Specials_String) == TRUE ~ V8,
TRUE ~ "NA"
)
) %>%
na_if_character("") %>%
na_if_character("NA") %>%
dplyr::select(
Place = V1,
Heat,
Lane,
Name,
Age,
Para,
Team,
Reaction_Time,
Finals,
Row_Numb = V12
) %>%
dplyr::filter(stringr::str_detect(Place, "^\\d+$"))
)
} else {
df_12 <- data.frame(
Row_Numb = character(),
stringsAsFactors = FALSE
)
}
#### eleven variables ####
if (length(data_length_11) > 0) {
suppressWarnings(
df_11 <- data_length_11 %>%
list_transform() %>%
dplyr::filter(stringr::str_detect(V1, Record_String) == FALSE) %>%
dplyr::filter(stringr::str_detect(V10, "Championship") == FALSE) %>%
na_if_character("") %>%
dplyr::mutate(Heat = case_when(
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") ~ V2,
TRUE ~ "NA"
)) %>%
dplyr::mutate(Lane = case_when(
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") == TRUE ~ V3,
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") == FALSE ~ V2,
TRUE ~ "NA"
)) %>%
dplyr::mutate(Name = dplyr::case_when(stringr::str_detect(Heat, "NA") == TRUE ~ V3,
TRUE ~ V4)) %>%
dplyr::mutate(Para = dplyr::case_when(stringr::str_detect(V4, Para_String) == TRUE ~ V4,
TRUE ~ "NA")) %>%
dplyr::mutate(
Team = dplyr::case_when(
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V4, Para_String) == FALSE ~ V4,
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V4, Para_String) == TRUE ~ V5,
TRUE ~ V5
)
) %>%
dplyr::mutate(
Reaction_Time = dplyr::case_when(
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V4, Para_String) == FALSE ~ V5,
stringr::str_detect(V6, Reaction_String) == TRUE ~ V6,
stringr::str_detect(V7, Reaction_String) == TRUE ~ V7,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(Age = case_when(stringr::str_detect(V6, Age_String) == TRUE ~ V6,
TRUE ~ "NA")) %>%
dplyr::mutate(
Finals = dplyr::case_when(
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V4, Para_String) == FALSE ~ V9,
stringr::str_detect(V5, Reaction_String) == TRUE &
stringr::str_detect(V9, Time_Score_Specials_String) == TRUE ~ V9,
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V4, Para_String) == TRUE &
stringr::str_detect(V9, Time_Score_Specials_String) == TRUE &
stringr::str_detect(V10, "\\:") == FALSE ~ V9,
stringr::str_detect(V10, Time_Score_Specials_String) == TRUE &
stringr::str_detect(V9, Time_Score_Specials_String) == TRUE &
(sec_format(V9) > sec_format(V10)) ~ V9,
stringr::str_detect(V10, Time_Score_Specials_String) == TRUE ~ V10,
TRUE ~ "NA"
)
) %>%
na_if_character("NA") %>%
dplyr::select(
Place = V1,
Heat,
Lane,
Name,
Age,
Para,
Team,
Reaction_Time,
Finals,
Row_Numb = V11
) %>%
dplyr::filter(stringr::str_detect(Place, "^\\d+$"))
)
} else {
df_11 <- data.frame(
Row_Numb = character(),
stringsAsFactors = FALSE
)
}
#### ten variables ####
if (length(data_length_10) > 0) {
suppressWarnings(
df_10 <- data_length_10 %>%
list_transform() %>%
dplyr::filter(stringr::str_detect(V1, Record_String) == FALSE) %>%
dplyr::filter(stringr::str_detect(V1, "en") == FALSE) %>% # remove Women's Men's etc.
dplyr::filter(stringr::str_detect(V9, "Championship") == FALSE) %>%
na_if_character("") %>%
dplyr::mutate(Heat = case_when(
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") ~ V2,
TRUE ~ "NA"
)) %>%
dplyr::mutate(Lane = case_when(
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") == TRUE ~ V3,
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") == FALSE ~ V2,
TRUE ~ "NA"
)) %>%
dplyr::mutate(Name = dplyr::case_when(stringr::str_detect(Heat, "NA") == TRUE ~ V3,
TRUE ~ V4)) %>%
dplyr::mutate(Para = dplyr::case_when(stringr::str_detect(V4, Para_String) == TRUE ~ V4,
TRUE ~ "NA")) %>%
dplyr::mutate(
Team = dplyr::case_when(
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V4, Para_String) == FALSE ~ V4,
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V4, Para_String) == TRUE ~ V5,
TRUE ~ V5
)
) %>%
dplyr::mutate(
Reaction_Time = dplyr::case_when(
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V4, Para_String) == FALSE ~ V5,
stringr::str_detect(V6, Reaction_String) == TRUE ~ V6,
stringr::str_detect(V7, Reaction_String) == TRUE ~ V7,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(Age = case_when(stringr::str_detect(V6, Age_String) == TRUE ~ V6,
TRUE ~ "NA")) %>%
dplyr::mutate(
Finals = dplyr::case_when(
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V4, Para_String) == FALSE ~ V9,
stringr::str_detect(V5, Reaction_String) == TRUE & stringr::str_detect(V9, Time_Score_Specials_String) == TRUE ~ V9,
stringr::str_detect(Heat, "NA") == TRUE & stringr::str_detect(V4, Para_String) == TRUE & stringr::str_detect(V9, Time_Score_Specials_String) == TRUE ~ V9,
stringr::str_detect(V8, Time_Score_Specials_String) == TRUE & stringr::str_detect(V9, Time_Score_Specials_String) == TRUE &
(sec_format(V9) > sec_format(V8)) ~ V9,
stringr::str_detect(V8, Time_Score_Specials_String) == TRUE ~ V8,
TRUE ~ "NA"
)
) %>%
na_if_character("NA") %>%
dplyr::select(
Place = V1,
Heat,
Lane,
Name,
Age,
Para,
Team,
Reaction_Time,
Finals,
Row_Numb = V10
) %>%
dplyr::filter(stringr::str_detect(Place, "^\\d+$"))
)
} else {
df_10 <- data.frame(Row_Numb = character(),
stringsAsFactors = FALSE)
}
#### nine variables ####
if (length(data_length_9) > 0) {
suppressWarnings(
df_9 <- data_length_9 %>%
list_transform() %>%
dplyr::filter(stringr::str_detect(V1, Record_String) == FALSE) %>%
dplyr::filter(stringr::str_detect(V1, "(Women)|(Men)") == FALSE) %>% # remove Women's Men's etc.
dplyr::filter(stringr::str_detect(V3, "(Women)|(Men)") == FALSE) %>% # remove Women's Men's etc.
dplyr::filter(stringr::str_detect(V4, "(Women)|(Men)") == FALSE) %>% # remove Women's Men's etc.
na_if_character("") %>%
dplyr::mutate(Heat = case_when(
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") ~ V2,
TRUE ~ "NA"
)) %>%
dplyr::mutate(Lane = case_when(
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") == TRUE ~ V3,
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") == FALSE ~ V2,
TRUE ~ "NA"
)) %>%
dplyr::mutate(Name = dplyr::case_when(stringr::str_detect(Heat, "NA") == TRUE ~ V3,
TRUE ~ V4)) %>%
dplyr::mutate(Para = dplyr::case_when(stringr::str_detect(V4, Para_String) == TRUE ~ V4,
TRUE ~ "NA")) %>%
dplyr::mutate(
Team = dplyr::case_when(
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V4, Para_String) == FALSE ~ V4,
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V4, Para_String) == TRUE ~ V5,
TRUE ~ V5
)
) %>%
dplyr::mutate(
Reaction_Time = dplyr::case_when(
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V4, Para_String) == FALSE &
stringr::str_detect(V5, "^\\d\\.\\d{2}") == TRUE ~ V5,
# stringr::str_detect(Heat, "NA") == TRUE &
# stringr::str_detect(V4, Para_String) == TRUE &
stringr::str_detect(V6, "^\\d\\.\\d{2}") == TRUE ~ V6,
stringr::str_detect(V7, "^\\d\\.\\d{2}") == TRUE &
stringr::str_detect(V8, Time_Score_Specials_String) == TRUE ~ V7,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(Age = case_when(stringr::str_detect(V6, Age_String) == TRUE ~ V6,
TRUE ~ "NA")) %>%
dplyr::mutate(
Finals = dplyr::case_when(
stringr::str_detect(Heat, "NA") == TRUE & stringr::str_detect(V4, Para_String) == FALSE ~ V7,
stringr::str_detect(V7, "[1-9]\\:\\d\\d") == TRUE ~ V7,
stringr::str_detect(V6, Reaction_String) == TRUE & stringr::str_detect(V7, Time_Score_Specials_String) == TRUE &
stringr::str_detect(V8, "\\d\\d\\.\\d\\d") == FALSE ~ V7,
stringr::str_detect(V8, Time_Score_Specials_String) == TRUE ~ V8,
TRUE ~ "NA"
)
) %>%
na_if_character("NA") %>%
dplyr::select(
Place = V1,
Heat,
Lane,
Name,
Age,
Para,
Team,
Reaction_Time,
Finals,
Row_Numb = V9
) %>%
dplyr::filter(stringr::str_detect(Place, "^\\d+$"))
)
} else {
df_9 <- data.frame(
Row_Numb = character(),
stringsAsFactors = FALSE
)
}
#### eight variables ####
if (length(data_length_8) > 0) {
suppressWarnings(
df_8 <- data_length_8 %>%
list_transform() %>%
dplyr::filter(stringr::str_detect(V1, Record_String) == FALSE) %>%
dplyr::filter(stringr::str_detect(V1, "(Women)|(Men)") == FALSE) %>% # remove Women's Men's etc.
dplyr::filter(stringr::str_detect(V4, "(Women)|(Men)") == FALSE) %>%
na_if_character("") %>%
dplyr::mutate(Heat = case_when(
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") ~ V2,
TRUE ~ "NA"
)) %>%
dplyr::mutate(
Lane = case_when(
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") == TRUE ~ V3,
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") == FALSE ~ V2,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(Name = dplyr::case_when(stringr::str_detect(Heat, "NA") == TRUE ~ V3,
TRUE ~ V4)) %>%
dplyr::mutate(Para = dplyr::case_when(stringr::str_detect(V4, Para_String) == TRUE ~ V4,
TRUE ~ "NA")) %>%
dplyr::mutate(Team = dplyr::case_when(stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V4, Para_String) == FALSE ~ V4,
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V4, Para_String) == TRUE ~ V5,
TRUE ~ V5)) %>%
dplyr::mutate(
Reaction_Time = dplyr::case_when(
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V5, "^\\d\\.\\d\\d") == TRUE ~ V5,
stringr::str_detect(V6, "^\\d\\.\\d\\d") == TRUE &
str_detect(V5, "\\d\\d\\.\\d\\d") == FALSE ~ V6,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(Age = case_when(stringr::str_detect(V6, Age_String) == TRUE ~ V6,
TRUE ~ "NA")) %>%
dplyr::mutate(
Finals = dplyr::case_when(
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V5, Reaction_String) == TRUE &
stringr::str_detect(V6, Time_Score_Specials_String) == TRUE &
stringr::str_detect(V7, "^\\d\\d?\\.\\d\\d$") == TRUE &
(sec_format(V7) < sec_format(V6)) == TRUE ~ V6,
stringr::str_detect(Heat, "NA") == FALSE &
stringr::str_detect(V6, "[1-9]\\:\\d\\d") == TRUE &
stringr::str_detect(V7, "^\\d?\\d\\.\\d\\d") == TRUE &
(sec_format(V7) < sec_format(V6)) == TRUE ~ V6,
stringr::str_detect(Heat, "NA") == TRUE ~ V7,
stringr::str_detect(V7, "[1-9]\\:\\d\\d") == TRUE ~ V7,
stringr::str_detect(V6, Reaction_String) == TRUE &
stringr::str_detect(V7, Time_Score_Specials_String) == TRUE ~ V7,
stringr::str_detect(V8, Time_Score_Specials_String) == TRUE ~ V8,
TRUE ~ "NA"
)
) %>%
na_if_character("NA") %>%
dplyr::select(
Place = V1,
Heat,
Lane,
Name,
Age,
Para,
Team,
Reaction_Time,
Finals,
Row_Numb = V8
) %>%
dplyr::filter(stringr::str_detect(Place, "\\d")) # removes relay swimmer rows
)
} else {
df_8 <- data.frame(Row_Numb = character(),
stringsAsFactors = FALSE)
}
#### seven variables ####
if (length(data_length_7) > 0) {
suppressWarnings(
df_7 <- data_length_7 %>%
list_transform() %>%
dplyr::filter(stringr::str_detect(V1, Record_String) == FALSE) %>%
na_if_character("") %>%
dplyr::mutate(Heat = case_when(
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") ~ V2,
TRUE ~ "NA"
)) %>%
dplyr::mutate(Lane = case_when(
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") == TRUE ~ V3,
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") == FALSE ~ V2,
TRUE ~ "NA"
)) %>%
dplyr::mutate(
Name = dplyr::case_when(
stringr::str_detect(Heat, "NA") == TRUE ~ V3,
stringr::str_detect(Heat, V2) == TRUE &
stringr::str_detect(V5, Time_Score_Specials_String) == TRUE ~ "NA",
TRUE ~ V4
)
) %>%
dplyr::mutate(
Team = dplyr::case_when(
stringr::str_detect(Heat, "NA") == TRUE ~ V4,
stringr::str_detect(V5, Time_Score_Specials_String) == TRUE &
stringr::str_detect(V4, "[:alpha:]{3,}") == TRUE ~ V4,
TRUE ~ V5
)
) %>%
dplyr::mutate(
Reaction_Time = dplyr::case_when(
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V5, "^\\d\\.\\d\\d") == TRUE ~ V5,
stringr::str_detect(V6, "^\\d\\.\\d\\d") == TRUE &
str_detect(V5, "\\d\\d\\.\\d\\d") == FALSE ~ V6,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Age = case_when(
stringr::str_detect(V6, Age_String) == TRUE ~ V6,
stringr::str_detect(V6, Age_String) == FALSE &
stringr::str_detect(V5, Age_String) == TRUE ~ V5,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Finals = dplyr::case_when(
stringr::str_detect(V5, Time_Score_Specials_String) == TRUE &
stringr::str_detect(Reaction_Time, V5) == FALSE ~ V5,
stringr::str_detect(V6, Time_Score_Specials_String) == TRUE ~ V6,
TRUE ~ "NA"
)
) %>%
na_if_character("NA") %>%
dplyr::select(
Place = V1,
Heat,
Lane,
Name,
Team,
Age,
Reaction_Time,
Finals,
Row_Numb = V7
) %>%
dplyr::filter(!is.na(Finals)) %>%
dplyr::filter(stringr::str_detect(Place, "\\d")) # removes relay swimmer rows
)
} else {
df_7 <- data.frame(Row_Numb = character(),
stringsAsFactors = FALSE)
}
#### six variables ####
if (length(data_length_6) > 0) {
suppressWarnings(
df_6 <- data_length_6 %>%
list_transform() %>%
dplyr::filter(stringr::str_detect(V1, Record_String) == FALSE) %>%
na_if_character("") %>%
dplyr::mutate(
Place = dplyr::case_when(stringr::str_detect(V5, "DSQ|DNS") == TRUE ~ "10000",
TRUE ~ V1)
) %>%
dplyr::mutate(
Heat = dplyr::case_when(
stringr::str_detect(V1, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") == TRUE ~ V2,
stringr::str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V5, "DSQ|DNS") == FALSE ~ V2,
stringr::str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V1, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V5, "DSQ|DNS") == TRUE ~ V1,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Lane = case_when(
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") == TRUE ~ V3,
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") == FALSE ~ V2,
str_detect(V1, "^\\d{1,2}$") == TRUE &
stringr::str_detect(Place, "10000") == TRUE ~ V1,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Name = dplyr::case_when(
stringr::str_detect(Place, "10000") == TRUE &
stringr::str_detect(V2, Name_String) == TRUE ~ V2,
stringr::str_detect(V4, "\\d\\:\\d{2}\\.\\d{2}") == TRUE ~ "NA",
stringr::str_detect(Heat, "NA") == TRUE ~ V3,
stringr::str_detect(V5, "DSQ|DNS") == TRUE ~ V3,
stringr::str_detect(V3, Lane) == TRUE &
str_detect(V4, "[:upper:][:lower:]") == TRUE &
str_detect(V5, Time_Score_String) == TRUE ~ "NA",
TRUE ~ V4
)
) %>%
dplyr::mutate(Para = dplyr::case_when(stringr::str_detect(V3, Para_String) == TRUE ~ V3,
TRUE ~ "NA")) %>%
dplyr::mutate(
Team = dplyr::case_when(
stringr::str_detect(V4, "\\d\\:\\d{2}\\.\\d{2}") == TRUE ~ V3,
stringr::str_detect(Heat, "NA") == TRUE ~ V4,
stringr::str_detect(V5, Time_Score_Specials_String) == TRUE ~ V4,
TRUE ~ V5
)
) %>%
dplyr::mutate(
Reaction_Time = dplyr::case_when(stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V4, Time_Score_Specials_String) == FALSE &
stringr::str_detect(V5, "^\\d\\.\\d{2}") == TRUE ~ V5,
TRUE ~ "NA")
) %>%
dplyr::mutate(
Finals = dplyr::case_when(
str_detect(V4, "\\d\\:\\d{2}\\.\\d{2}") == TRUE &
str_detect(V5, "\\d\\.\\d{2}") ~ V4,
TRUE ~ V5
)
) %>%
na_if_character("NA") %>%
dplyr::select(
Place,
Heat,
Lane,
Name,
Para,
Team,
Reaction_Time,
Finals,
Row_Numb = V6
) %>%
dplyr::filter(stringr::str_detect(Place, "\\d")) # removes relay swimmer rows
)
} else {
df_6 <- data.frame(Row_Numb = character(),
stringsAsFactors = FALSE)
}
#### five variables ####
if (length(data_length_5) > 0) {
suppressWarnings(
df_5 <- data_length_5 %>%
list_transform() %>%
dplyr::filter(stringr::str_detect(V1, Record_String) == FALSE) %>%
na_if_character("") %>%
dplyr::mutate(
Place = dplyr::case_when(stringr::str_detect(V4, "DSQ|DNS") == TRUE ~ "10000",
TRUE ~ V1)
) %>%
dplyr::mutate(
Heat = dplyr::case_when(
stringr::str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V5, "DSQ|DNS") == FALSE ~ V2,
stringr::str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V1, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V4, "DSQ") == TRUE ~ V1,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Lane = case_when(
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") == TRUE ~ V3,
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") == FALSE ~ V2,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Name = dplyr::case_when(
stringr::str_detect(V2, Name_String) == TRUE &
stringr::str_detect(V3, Time_Score_Specials_String) == FALSE &
stringr::str_detect(V4, Time_Score_Specials_String) == TRUE ~ V2,
stringr::str_detect(V4, Time_Score_Specials_String) == TRUE ~ "NA",
stringr::str_detect(Heat, "NA") == TRUE ~ V3,
stringr::str_detect(V5, "DSQ|DNS") == TRUE ~ V3,
TRUE ~ V4
)
) %>%
dplyr::mutate(
Team = dplyr::case_when(
stringr::str_detect(V4, Time_Score_Specials_String) == TRUE ~ V3,
stringr::str_detect(Heat, "NA") == TRUE ~ V4,
stringr::str_detect(V5, "DSQ|DNS") == TRUE ~ V4,
TRUE ~ V5
)
) %>%
# dplyr::mutate(Reaction_Time = dplyr::case_when(stringr::str_detect(Heat, "NA") == TRUE ~ V5,
# TRUE ~ "NA")) %>%
na_if_character("NA") %>%
dplyr::select(
Place,
Heat,
Lane,
Name,
Team,
# Reaction_Time,
Finals = V4,
Row_Numb = V5
) %>%
dplyr::filter(stringr::str_detect(Place, "\\d")) # removes relay swimmer rows
)
} else {
df_5 <- data.frame(Row_Numb = character(),
stringsAsFactors = FALSE)
}
#### DQ data ####
#### DQ 4 ####
if (length(DQ_length_4) > 0) {
suppressWarnings(
df_DQ_4 <- DQ_length_4 %>%
list_transform() %>%
dplyr::mutate(Place = "10000") %>%
dplyr::select(
Place = V1,
Team = V2,
Finals = V3,
Row_Numb = V4
) %>%
dplyr::mutate(DQ = 1)
)
} else {
df_DQ_4 <- data.frame(
Row_Numb = character(),
DQ = numeric(),
stringsAsFactors = FALSE
)
}
#### DQ 3 ####
if (length(DQ_length_3) > 0) {
suppressWarnings(
df_DQ_3 <- DQ_length_3 %>%
list_transform() %>%
dplyr::mutate(Place = "10000") %>%
dplyr::select(
Place,
Team = V1,
Finals = V2,
Row_Numb = V3
) %>%
dplyr::mutate(DQ = 1)
)
} else {
df_DQ_3 <- data.frame(
Row_Numb = character(),
stringsAsFactors = FALSE
)
}
#### Rejoin data frames from each number of variables ####
Min_Row_Numb <- min(events$Event_Row_Min)
suppressWarnings(
data <- dplyr::bind_rows(df_13, df_12) %>%
dplyr::bind_rows(df_11) %>%
dplyr::bind_rows(df_10) %>%
dplyr::bind_rows(df_9) %>%
dplyr::bind_rows(df_8) %>%
dplyr::bind_rows(df_7) %>%
dplyr::bind_rows(df_6) %>%
dplyr::bind_rows(df_5) %>%
dplyr::full_join(df_DQ_4) %>%
dplyr::full_join(df_DQ_3) %>%
dplyr::mutate(Row_Numb = as.numeric(Row_Numb)) %>%
dplyr::arrange(Row_Numb)
)
# if("Prelims" %in% names(data) == FALSE){
# data$Prelims <- NA
# }
# if("Age" %in% names(data) == FALSE){
# data$Age <- "NA"
# }
suppressWarnings(
data <- data %>%
dplyr::mutate(
Exhibition = dplyr::case_when(stringr::str_detect(Finals, "x|X") == TRUE ~ 1,
TRUE ~ 0),
) %>%
dplyr::mutate(
DQ = dplyr::case_when(
as.numeric(Place) == 10000 &
Exhibition == 0 ~ 1,
# added exhibition condition 8/27
stringr::str_detect(Finals, "DQ|DSQ|DNS|DFS") == TRUE ~ 1,
is.na(DQ) ~ 0,
TRUE ~ DQ
)
) %>%
# 3/14/23
na_if_character("10000") %>%
dplyr::mutate(dplyr::across(
c(Name, Team), ~ stringr::str_replace_all(., "10000", "--")
)) %>% # remove any "10000"s added in erroneously
####
dplyr::mutate(
Place = str_remove(Place, "\\)"),
Place = str_remove(Place, "_"),
Place = as.numeric(Place),
Place = dplyr::case_when(
is.na(dplyr::lag(Place)) == TRUE ~ Place,
dplyr::lag(Place) == Place ~ Place + 0.1,
dplyr::lag(Place) != Place ~ Place
),
Place = as.character(Place),
Row_Numb = as.numeric(Row_Numb)
) %>%
dplyr::filter(Team %!in% c("Gold", "Silver", "Bronze")) %>%
dplyr::filter(Name %!in% c("GOLD", "SILVER", "BRONZE"))
)
#### add in events based on row number ranges ####
if(min(data$Row_Numb) < min(events$Event_Row_Min)){
unknown_event <- data.frame(Event = "Unknown",
Event_Row_Min = min(data$Row_Numb),
Event_Row_Max = min(events$Event_Row_Min) - 1)
events <- dplyr::bind_rows(unknown_event, events)
}
data <-
transform(data, Event = events$Event[findInterval(Row_Numb, events$Event_Row_Min)]) %>%
na_if_character("Unknown")
#### adding heats based on row number ranges ####
if ("Heat" %in% names(data) == TRUE) {
if (all(is.na(data$Heat))) {
data <- data %>%
select(-Heat)
}
}
if ("Heat" %in% names(data) == FALSE) {
if (min(data$Row_Numb) < min(heats$Heat_Row_Min)) {
unknown_heat <- data.frame(
Heat = "NA",
Heat_Row_Min = min(data$Row_Numb),
Heat_Row_Max = min(heats$Heat_Row_Min) - 1
)
heats <- dplyr::bind_rows(unknown_heat, heats)
}
data <-
transform(data, Heat = heats$Heat[findInterval(Row_Numb, heats$Heat_Row_Min)]) %>%
na_if_character("NA")
}
#### cleaning up final results ####
suppressWarnings(
data <- data %>%
dplyr::mutate(
Name = stringr::str_replace_all(Name, "_", "\\*"),
Place = round(as.numeric(Place)),
Event = as.character(Event)
) %>%
dplyr::mutate(
Place = dplyr::case_when(is.na(Place) == TRUE &
DQ == 0 ~ dplyr::lag(Place) + 1,
TRUE ~ Place)
) %>%
dplyr::mutate(Reaction_Time = case_when(stringr::str_detect(Event, "(R|r)elay|\\dx\\d{3}") == TRUE ~ "NA",
TRUE ~ Reaction_Time)) %>%
na_if_character("NA")
)
#### cleaning ####
if(format_results == TRUE){
data <- format_results(data)
}
#### adding relay swimmers in ####
if (relay_swimmers_omega == TRUE) {
relay_swimmers_df <- collect_relay_swimmers_omega(as_lines_list_2)
relay_swimmers_df <-
transform(relay_swimmers_df, Row_Numb_Adjusted = data$Row_Numb[findInterval(Row_Numb, data$Row_Numb)]) %>%
dplyr::select(-Row_Numb)
data <- data %>%
dplyr::left_join(relay_swimmers_df, c("Row_Numb" = "Row_Numb_Adjusted"))
}
#### adding splits back in ####
if (splits == TRUE) {
data <- data %>%
dplyr::mutate(dplyr::across(where(is.numeric), as.character))
data_ind <- data %>%
dplyr::filter(stringr::str_detect(Event, "(R|r)elay|\\dx\\d{3}") == FALSE)
data_relay <- data %>%
dplyr::filter(stringr::str_detect(Event, "(R|r)elay|\\dx\\d{3}") == TRUE)
if(nrow(data_ind) > 0) {
# split_length <- 50
splits_df <-
splits_parse(as_lines_list_2, split_len = split_length_omega)
#### matches row numbers in splits_df to available row numbers in data
# helps a lot with relays, since their row numbers vary based on whether or not relay swimmers are included
# and if those swimmers are listed on one line or two
splits_df <-
transform(splits_df, Row_Numb_Adjusted = data_ind$Row_Numb[findInterval(Row_Numb, as.numeric(data_ind$Row_Numb))]) %>%
dplyr::mutate(Row_Numb_Adjusted = as.character(Row_Numb_Adjusted)) %>%
dplyr::select(-Row_Numb)
# new names for omega splits, because first 50 isn't captured
splits_df <- splits_df %>%
dplyr::rename_with(splits_rename_omega, dplyr::starts_with("Split"), split_len = split_length_omega) %>%
dplyr::mutate(dplyr::across(dplyr::starts_with("Split"), sec_format))
suppressWarnings(
data_ind <- data_ind %>%
dplyr::left_join(splits_df, by = c("Row_Numb" = "Row_Numb_Adjusted")) %>%
dplyr::mutate(dplyr::across(dplyr::starts_with("Split"), as.numeric)) %>%
dplyr::mutate(
Split_Total = rowSums(dplyr::across(dplyr::starts_with("Split")), na.rm = TRUE),
Finals_Sec = sec_format(Finals),
Split_50 = Finals_Sec - Split_Total,
Split_50 = dplyr::case_when(Split_50 < 0 ~ 10000,
TRUE ~ Split_50)
) %>%
na_if_numeric(10000) %>%
dplyr::select(-Split_Total, -Finals_Sec) %>%
dplyr::mutate(dplyr::across(
dplyr::starts_with("Split"), format, nsmall = 2
)) %>%
dplyr::mutate(dplyr::across(where(is.numeric), as.character)) %>%
dplyr::mutate(dplyr::across(where(is.character), trimws)) %>%
na_if_character("NA") %>%
dplyr::select(
!dplyr::starts_with("Split"),
stringr::str_sort(names(.), numeric = TRUE)
) # keep splits columns in order
)
}
if(nrow(data_relay) > 0){
splits_df <-
splits_parse_omega_relays(as_lines_list_2, split_len = split_length_omega)
#### matches row numbers in splits_df to available row numbers in data
# helps a lot with relays, since their row numbers vary based on whether or not relay swimmers are included
# and if those swimmers are listed on one line or two
splits_df <-
transform(splits_df, Row_Numb_Adjusted = data_relay$Row_Numb[findInterval(Row_Numb, as.numeric(data_relay$Row_Numb))]) %>%
dplyr::mutate(Row_Numb_Adjusted = as.character(Row_Numb_Adjusted)) %>%
dplyr::select(-Row_Numb)
data_relay <- data_relay %>%
# dplyr::filter(Finals != "DNS") %>%
# dplyr::filter(Finals != "DSQ") %>%
dplyr::left_join(splits_df, by = c("Row_Numb" = "Row_Numb_Adjusted")) %>%
na_if_numeric(10000) %>%
dplyr::mutate(dplyr::across(dplyr::starts_with("Split"), format, nsmall = 2)) %>%
dplyr::mutate(dplyr::across(where(is.numeric), as.character)) %>%
dplyr::mutate(dplyr::across(where(is.character), trimws)) %>%
na_if_character("NA") %>%
dplyr::select(!dplyr::starts_with("Split"),
stringr::str_sort(names(.), numeric = TRUE)) # keep splits columns in order
}
data <- data_ind %>%
dplyr::bind_rows(data_relay) %>%
dplyr::arrange(as.numeric(Row_Numb))
}
# remove reaction time from relays
if("Reaction_Time" %in% names(data)){
data <- data %>%
dplyr::mutate(Reaction_Time = dplyr::case_when(stringr::str_detect(Event, "Relay") == TRUE ~ "NA",
TRUE ~ Reaction_Time)) %>%
na_if_character("NA")
}
### remove empty columns (all values are NA) ###
data <- Filter(function(x)
!all(is.na(x)), data)
#### if there is a Place column it should be first ####
if("Place" %in% names(data)){
data <- data %>%
dplyr::select(Place, dplyr::everything())
}
if(all("Place" %in% names(data), "Heat" %in% names(data))){
data <- data %>%
dplyr::select(Place, Heat, dplyr::everything())
}
data$Row_Numb <- NULL
# message("Beginning with SwimmeR v0.6.0 the Grade and School output columns are renamed Age and Team respectively. Please adjust your work flows as needed.")
return(data)
}
}
#' @export
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Defines functions swim_parse_omega
Documented in swim_parse_omega
#' Formats Omega style swimming and diving data read with \code{read_results}
#' into a data frame
#'
#' Takes the output of \code{read_results} and cleans it, yielding a data frame
#' of swimming (and diving) results
#'
#' @importFrom dplyr mutate
#' @importFrom dplyr lag
#' @importFrom dplyr case_when
#' @importFrom dplyr na_if
#' @importFrom dplyr select
#' @importFrom dplyr arrange
#' @importFrom dplyr filter
#' @importFrom dplyr bind_rows
#' @importFrom dplyr everything
#' @importFrom dplyr pull
#' @importFrom dplyr select
#' @importFrom dplyr starts_with
#' @importFrom stringr str_replace_all
#' @importFrom stringr str_extract
#' @importFrom stringr str_split
#' @importFrom stringr str_detect
#' @importFrom stringr str_length
#' @importFrom stringr str_sort
#' @importFrom purrr map_lgl
#' @importFrom purrr map
#' @importFrom stats setNames
#'
#' @param file_omega output from \code{read_results}
#' @param avoid_omega a list of strings. Rows in \code{file_omega} containing
#' these strings will not be included. For example "Pool:", often used to
#' label pool records, could be passed to \code{avoid_omega}. The default is
#' \code{avoid_default}, which contains many strings similar to "Pool:", such
#' as "STATE:" and "Qual:". Users can supply their own lists to
#' \code{avoid_omega}. \code{avoid_omega} is handled before \code{typo_omega}
#' and \code{replacement_omega}.
#' @param typo_omega a list of strings that are typos in the original results.
#' \code{swim_parse} is particularly sensitive to accidental double spaces, so
#' "Central High School", with two spaces between "Central" and "High" is a
#' problem, which can be fixed. Pass "Central High School" to
#' \code{typo_omega}. Unexpected commas as also an issue, for example "Texas,
#' University of" should be fixed using \code{typo_omega} and
#' \code{replacement_omega}
#' @param replacement_omega a list of fixes for the strings in
#' \code{typo_omega}. Here one could pass "Central High School" (one space
#' between "Central" and "High") and "Texas" to \code{replacement_omega} fix
#' the issues described in \code{typo_omega}
#' @param format_results should the results be formatted for analysis (special
#' strings like \code{"DQ"} replaced with \code{NA}, \code{Finals} as
#' definitive column)? Default is \code{TRUE}
#' @param splits either \code{TRUE} or the default, \code{FALSE} - should
#' \code{swim_parse} attempt to include splits.
#' @param split_length_omega either \code{25} or the default, \code{50}, the
#' length of pool at which splits are recorded. Not all results are
#' internally consistent on this issue - some have races with splits by 50 and
#' other races with splits by 25.
#' @param relay_swimmers_omega should names of relay swimmers be captured?
#' Default is \code{FALSE}
#' @return returns a data frame with columns \code{Name}, \code{Place},
#' \code{Age}, \code{Team}, \code{Prelims}, \code{Finals},
#' \code{Points}, \code{Event} & \code{DQ}. Note all swims will have a
#' \code{Finals}, even if that time was actually swam in the prelims
#' (i.e. a swimmer did not qualify for finals). This is so that final results
#' for an event can be generated from just one column.
#'
#' @seealso \code{swim_parse_omega} must be run on the output of
#' \code{\link{read_results}}
swim_parse_omega <-
function(file_omega,
avoid_omega = avoid,
typo_omega = typo,
replacement_omega = replacement,
format_results = TRUE,
splits = FALSE,
split_length_omega = split_length,
relay_swimmers_omega = relay_swimmers) {
#### testing ####
# file_omega <- read_results("https://www.omegatiming.com/File/00011500030105EF01FFFFFFFFFFFF01.pdf")
# file_omega <- read_results("https://www.omegatiming.com/File/00011500030103EC02FFFFFFFFFFFF01.pdf")
# file_omega <- read_results("https://www.omegatiming.com/File/00011500030101EE01FFFFFFFFFFFF01.pdf")
# file_omega <- read_results("https://www.omegatiming.com/File/00011500030103EC06FFFFFFFFFFFF01.pdf")
# file_omega <- read_results("https://www.omegatiming.com/File/00011500030201EF04FFFFFFFFFFFF01.pdf")
# file_omega <- read_results("https://www.omegatiming.com/File/00011500030103EC04FFFFFFFFFFFF01.pdf") # men 100br final
# file_omega <- read_results("https://www.omegatiming.com/File/00011500020102EE01FFFFFFFFFFFF01.pdf") # wave 1 200 back heats
# file_omega <- read_results("https://www.omegatiming.com/File/00011500020201F104FFFFFFFFFFFF01.pdf") # wave 1 800 free
# file_omega <- read_results("https://www.omegatiming.com/File/00011500020201EB01FFFFFFFFFFFF01.pdf") # wave 1 50 heats
# file_omega <- read_results("https://www.omegatiming.com/File/00011500020101EB04FFFFFFFFFFFF01.pdf") # wave 1 50 final
# file_omega <- read_results("https://www.omegatiming.com/File/0001150002FFFFFFFFFFFFFFFFFFFF22.pdf") # wave 1 whole book
# file_omega <- read_results("https://www.omegatiming.com/File/00011500020201EC01FFFFFFFFFFFF01.pdf")
# file_omega <- read_results("https://www.omegatiming.com/File/00011500020204EE04FFFFFFFFFFFF01.pdf")
# file_omega <- read_results("https://www.omegatiming.com/File/00011500030103EC06FFFFFFFFFFFF01.pdf")
# file_omega <- read_results(system.file("extdata", "Omega_OT_100Br_Swimoff_2021.pdf", package = "SwimmeR"))
# file_omega <- read_results("https://www.omegatiming.com/File/00011500020101EB01FFFFFFFFFFFF01.pdf")
# file_omega <- read_results("https://www.omegatiming.com/File/00011500020102EE01FFFFFFFFFFFF01.pdf")
# file_omega <- read_results(system.file("extdata", "Omega_Wave1_1500_Finals_2021.pdf", package = "SwimmeR"))
# file_omega <- read_results("https://www.omegatiming.com/File/00011500030105EE02FFFFFFFFFFFF01.pdf")
# file_omega <- read_results("https://olympics.com/tokyo-2020/olympic-games/resOG2020-/pdf/OG2020-/SWM/OG2020-_SWM_C73A1_SWMM400MIM------------HEAT000100--.pdf")
# file_omega <- read_results("https://olympics.com/tokyo-2020/olympic-games/resOG2020-/pdf/OG2020-/SWM/OG2020-_SWM_C73A1_SWMW100MBR------------HEAT000100--.pdf")
# file_omega <- read_results(system.file("extdata", "Omega_OT_400IM_Prelims_2021.pdf", package = "SwimmeR"))
# file_omega <- read_results("https://olympics.com/tokyo-2020/olympic-games/resOG2020-/pdf/OG2020-/SWM/OG2020-_SWM_C73A2_SWMM400MIM------------HEAT--------.pdf")
# file_omega <- read_results("https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/Tokyo2020/OG2020-_SWM_C73A2_SWMM100MBF------------SFNL--------.pdf")
# file_omega <- read_results("https://olympics.com/tokyo-2020/olympic-games/resOG2020-/pdf/OG2020-/SWM/OG2020-_SWM_C73B1_SWMM4X200MFR----------FNL-000100--.pdf")
# file_omega <- read_results("https://olympics.com/tokyo-2020/olympic-games/resOG2020-/pdf/OG2020-/SWM/OG2020-_SWM_C73A1_SWMM100MFR------------FNL-000100--.pdf")
# file_omega <- read_results("https://olympics.com/tokyo-2020/olympic-games/resOG2020-/pdf/OG2020-/SWM/OG2020-_SWM_C73A1_SWMM200MFR------------FNL-000100--.pdf")
# file_omega <- read_results("https://olympics.com/tokyo-2020/olympic-games/resOG2020-/pdf/OG2020-/SWM/OG2020-_SWM_C73B1_SWMX4X100MMD----------FNL-000100--.pdf")
# file_omega <- read_results("https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/Tokyo2020/SWMW4X200MFR_HEAT.pdf")
# file_omega <- read_results("https://olympics.com/tokyo-2020/olympic-games/resOG2020-/pdf/OG2020-/SWM/OG2020-_SWM_C73A1_SWMM400MIM------------FNL-000100--.pdf")
# file_omega <- read_results("https://olympics.com/tokyo-2020/olympic-games/resOG2020-/pdf/OG2020-/SWM/OG2020-_SWM_C73A1_SWMM100MFR------------FNL-000100--.pdf")
# file_omega <- read_results("https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/Tokyo2020/SWMM50MFR_FNL.pdf")
# file_omega <- read_results("https://www.omegatiming.com/File/00011500020101EC01FFFFFFFFFFFF01.pdf")
# file_omega <- read_results("https://www.omegatiming.com/File/00011500020205EF01FFFFFFFFFFFF01.pdf")
# file_omega <- read_results("https://www.omegatiming.com/File/00011500020204EE01FFFFFFFFFFFF01.pdf")
# file_omega <- read_results("https://www.omegatiming.com/File/0001150003FFFFFFFFFFFFFFFFFFFF22.pdf")
# file_omega <- read_results("https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/Omega/Omega_OT_Wave2_FullResults_2021.pdf")
# file_omega <- read_results("https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/Tokyo2020/SWMM100MBF_SFNL.pdf")
# file_omega <- read_results("https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/Omega/Omega_OT_Wave2_W400Fr_Heats_2021.pdf")
# file_omega <- read_results("https://olympics.com/tokyo-2020/paralympic-games/resPG2020-/pdf/PG2020-/SWM/PG2020-_SWM_C73A1_SWMM400MFR--09010-----HEAT000100--.pdf")
# file_omega <- read_results("https://olympics.com/tokyo-2020/paralympic-games/resPG2020-/pdf/PG2020-/SWM/PG2020-_SWM_C73A2_SWMW100MBF--13030-----HEAT--------.pdf")
# file_omega <- read_results("https://olympics.com/tokyo-2020/paralympic-games/resPG2020-/pdf/PG2020-/SWM/PG2020-_SWM_C73A1_SWMM200MIM--06022-----FNL-000100--.pdf")
# file_omega <- read_results("https://olympics.com/tokyo-2020/olympic-games/resOG2020-/pdf/OG2020-/SWM/OG2020-_SWM_C73B1_SWMW4X100MFR----------HEAT000100--.pdf")
# file_omega <- read_results("https://olympics.com/tokyo-2020/paralympic-games/resPG2020-/pdf/PG2020-/SWM/PG2020-_SWM_C73A2_SWMM50MFR---11010-----HEAT--------.pdf")
# file_omega <- read_results("https://olympics.com/tokyo-2020/paralympic-games/resPG2020-/pdf/PG2020-/SWM/PG2020-_SWM_C73B1_SWMX4X100MFR13033-----FNL-000100--.pdf")
# file_omega <- read_results("https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/SwimmeR%20Test%20Files/PG2020_SWMM200MIM_FNL.pdf")
# file_omega <- read_results("https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/Paralympics2020/raw_files/PG2020_SWMW150MIM_04042_FNL.pdf")
# file_omega <- read_results("https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/Tokyo2020/SWMM4X200MFR_FNL.pdf")
# file_omega <- read_results("https://www.omegatiming.com/File/00011500100201EF01FFFFFFFFFFFF01.pdf")
# file_omega <- read_results(system.file("extdata", "RESULTS_BOOK.pdf", package = "SwimmeR"))
# file_omega <- read_results("https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/SwimmeR%20Test%20Files/PG2020_SWMM200MIM_FNL.pdf")
# avoid_omega <- c("abcxyz")
# typo_omega <- c("typo")
# replacement_omega <- c("typo")
# split_length_omega <- 50
# relay_swimmers_omega <- TRUE
# splits <- TRUE
#### assign row numbers ####
as_lines_list_2 <- file_omega %>%
.[stringr::str_detect(., "Early take-off", negate = TRUE)] %>% # removes DQ rational used in some relay DQs that messes up line spacing between relay and swimmers/splits - must happen before adding in row numbers
.[stringr::str_detect(., " 1?\\d50m ", negate = TRUE)] %>% # removes cumulative split rows for Omega results only but does not remove Women's 150m IM for para games
add_row_numbers() %>%
.[purrr::map_lgl(., ~ !any(stringr::str_detect(., avoid_omega)))] %>%
stringr::str_replace_all(stats::setNames(replacement_omega, typo_omega)) %>% # replace typos with replacements
# stringr::str_replace_all("DISQUAL", " DQ ") %>%
stringr::str_replace_all("EVENT\\:", "Event")
#### parsing omega files ####
#### Pulls out event labels from text ####
events <- event_parse(as_lines_list_2)
#### Pulls out heat labels from text ####
heats <- heat_parse_omega(as_lines_list_2)
#### Pulls out event date from text ####
Event_Date_String <- paste0("(?<=^\n)\\d{1,2}\\s(", paste(toupper(month.abb), collapse = "|"), ")\\s\\d{4}")
Event_Date <- stringr::str_extract(as_lines_list_2, Event_Date_String)
Event_Date <- Event_Date[!is.na(Event_Date)][1]
Event_Date <- as.Date(Event_Date, format = "%d %b %Y")
#### set up strings ####
Name_String <-
"_?[:alpha:]+\\s?\\'?[:alpha:\\-\\'\\.]*\\s?[:alpha:\\-\\'\\.]*\\s?[:alpha:\\-\\'\\.]*,?\\s?[:alpha:\\-\\'\\.]*\\s?[:alpha:]*\\s?[:alpha:]*\\s?[:alpha:]*\\.?,? [:alpha:]+\\s?[:alpha:\\-\\'\\.]*\\s?[:alpha:\\-\\']*\\s?[:alpha:]*\\s?[:alpha:]*\\s?[:alpha:\\.]*"
Time_Score_String <- "\\d{0,2}\\:?\\d{1,3}\\.\\d{2}"
Time_Score_Specials_String <- paste0("^NT$|^NP$|^DQ$|^DSQ$|^D?NS$|^SCR$|^x?X?", Time_Score_String, "x?X?$")
Time_Score_Specials_String_Extract <- paste0(Time_Score_String, "|^NT$|^NP$|^DQ$|^NS$|^SCR$")
Para_String <- "^SB?M?\\d{1,2}$"
Reaction_String <- "^\\+\\s?\\d\\.\\d{3}$|^\\-\\s?\\d\\.\\d{3}$|^[0-1]\\.00$|^[0-1]\\.\\d\\d$"
Record_String <- "^\\=?W[:upper:]$|^\\=?AR$|^\\=?US$|^\\=?CR$|^\\=?OT$|^\\=?OR$|^\\=?[:upper:]R$"
Header_string <- "\\sDisqualified\\s|\\sReaction\\sTime\\s"
Heat_String <- "Heat\\s\\d{1,}\\sof\\s\\d{1,}|Semifinal\\s+\\d{1,}|Final|(Heats?)(?![:alpha:])"
DOB_String_2 <- paste0("(?<=\\s)\\d{2}\\s(", paste(toupper(month.abb), collapse = "|"), ")\\s\\d{4}(?=\\s+\\d{1,2}\\.\\d{2})")
DOB_String_1 <- paste0("(?<=\\s)\\d{1}\\s(", paste(toupper(month.abb), collapse = "|"), ")\\s\\d{4}(?=\\s+\\d{1,2}\\.\\d{2})")
Age_String <- "^\\d{2}$"
#### Collect DOBs ####
DOBs_2 <- stringr::str_extract(as_lines_list_2, DOB_String_2)
DOBs_2 <- unique(DOBs_2[!is.na(DOBs_2)])
DOBs_2_Format <- as.Date(DOBs_2, format = "%d %b %Y")
Ages_2 <-
as.character(round(as.numeric(
difftime(Event_Date, DOBs_2_Format, units = "days") / 365
), 0))
DOBs_1 <- stringr::str_extract(as_lines_list_2, DOB_String_1)
DOBs_1 <- unique(DOBs_1[!is.na(DOBs_1)])
DOBs_1_Format <- as.Date(DOBs_1, format = "%d %b %Y")
Ages_1 <-
as.character(round(as.numeric(
difftime(Event_Date, DOBs_1_Format, units = "days") / 365
), 0))
#### clean input data ####
data_cleaned <- as_lines_list_2 %>%
stringr::str_remove("^\n\\s{0,}") %>%
.[stringr::str_length(.) > 50] %>% # slight speed boost from cutting down length of file
.[stringr::str_detect(., paste0(Time_Score_String,"|DS?Q|SCR|D?NS"))] %>% # must have \\.\\d\\d because all swimming and diving times do
.[stringr::str_detect(., "[:alpha:]{2,}.*[:alpha:]")] %>% # need some letters, need them to not just be a single instance of DQ etc.
.[stringr::str_detect(., "[:alpha:]{2,} Record", negate = TRUE)] %>% # remove legend contents that will be included if their are DQs, DNS etc. Omega
.[stringr::str_detect(., "[:alpha:] DSQ [:alpha:]", negate = TRUE)] %>% # omega Wave II removes notes from when a DSQ is overturned
.[stringr::str_detect(., "\\d{2} [:upper:]{3} \\d{4} GOLD", negate = TRUE)] %>% # omega remove medalist results from the end of Wave II 2021 OTs
.[stringr::str_detect(., Heat_String, negate = TRUE)] %>% # omega removes weird formatting issue where results and heat labels are connected
{if (length(Ages_2) > 0) purrr::reduce2(.init = ., DOBs_2, Ages_2, stringr::str_replace_all) else .} %>%
{if (length(Ages_1) > 0) purrr::reduce2(.init = ., DOBs_1, Ages_1, stringr::str_replace_all) else .} %>%
stringr::str_replace_all("([:alpha:])\\. ", "\\1 ") %>% # remove periods from athlete names Omega
# .[!all(dplyr::between(purrr::map_int(., stringr::str_count, "\\.|\\("), 6.1, 7.9), purrr::map_int(., stringr::str_count, "\\.") != 6)] %>% # keeps relay swimmers + splits from 4x200 races from getting in Omega
.[stringr::str_detect(., "[:alpha:]\\s+\\-\\d\\.\\d{2}\\s+\\d{1,}$", negate = TRUE)] %>%
.[stringr::str_detect(., Header_string, negate = TRUE)] %>%
stringr::str_replace_all("( [:upper:]{2},\\s)*\\s?(?<![:upper:])\\=?[:upper:]{2}\\.?\\s(?=\\s{1,}\\d{1,}$)", " ") %>% # remove record strings like OR, OC but miss DNS, DSQ etc.
stringr::str_replace_all("\\s?[&%]\\s?", " ") %>% # added 8/21 for removing "&" and "%" as record designator
stringr::str_remove_all("(?<=\\d\\.\\d{2}\\s?)[:punct:]") %>% # remove symbols attached to times as record designator
stringr::str_remove_all("(?<=\\.\\d{2}\\s?)[A-WYZ|\\$|q](?=\\s)") %>% # remove letters attached to times as record designator
stringr::str_replace_all(" [qQ](?=\\d{1,5} )", " ") %>% # remove q|Q attached to points
stringr::str_replace_all("[A-WYZa-wyz]+(\\d{1,2}\\:\\d{2}\\.\\d{2})", "\\1") %>% # removed J etc. from next to swim, but does not remove X or x (for exhibition tracking)
stringr::str_replace_all("(\\d{1,2}\\:\\d{2}\\.\\d{2})[A-WYZa-wyz]+", "\\1") %>%
stringr::str_replace_all("[A-WYZa-wyz]+(\\d{2,3}\\.\\d{2})", "\\1") %>%
stringr::str_replace_all("(\\d{2,3}\\.\\d{2})[A-WYZa-wyz]+", "\\1") %>%
stringr::str_replace_all(" [:punct:]+(\\d{1,2}\\:\\d{2}\\.\\d{2})", " \\1") %>%
stringr::str_replace_all("(\\d{1,2}\\:\\d{2}\\.\\d{2})[:punct:]+", " \\1") %>%
stringr::str_replace_all(" [:punct:]+(\\d{2,3}\\.\\d{2})", " \\1") %>%
stringr::str_replace_all("(\\d{2,3}\\.\\d{2})[:punct:]+", "\\1 ") %>%
stringr::str_replace_all("\\(\\=?\\d\\)", " ") %>% # omega
stringr::str_remove_all("\\s{2}J\\s{2}") %>%
stringr::str_remove_all("\\=(?=\\d)") %>%
stringr::str_replace_all(" [A-WYZ]\\?? ", " ") %>% # omega
stringr::str_replace_all(" R?\\? ", " ") %>% # omega
stringr::str_replace_all(" \\d{4} ", " ") %>% # omega
stringr::str_replace_all("(?<=\\.\\d)(\\d)\\s+X(?=\\s+\\d{1,5}$)", "\\1X") %>% # brings Xs for exhibition that are spaced out in closer
# remove q from next to time 10/21/2020
stringr::str_remove_all(" q ") %>% # removes " q " sometimes used to designate a qualifying time
stringr::str_replace_all("-{2,5}", "10000") %>% #8/26
stringr::str_replace_all("\\*(?=[:alpha:])", "_") %>% # for * prior to name for foreign athletes
stringr::str_replace_all("\\*", " ") %>%
stringr::str_replace_all("(?<=\\s)\\+\\s(?=[:digit:])", " +") %>% # for reaction times
stringr::str_replace_all("(?<=\\s)\\-\\s(?=[:digit:])", " -") %>% # for reaction times
stringr::str_remove_all("\U2666") %>% # remove black diamond
trimws()
#### insert double spaces where needed ####
data_cleaned <- data_cleaned %>%
stringr::str_replace_all("(?<=\\d)\\s+[:upper:]?\\s+(?=\\d)", " ") %>% # letters like P or M to denote pool or meet record
stringr::str_replace_all(" \\=[:upper:]R\\,?", " ") %>% # omega remove record designators like =AR
stringr::str_replace_all("(?<=\\d) (?=[:alpha:])", " ") %>% # mostly to split place and name
stringr::str_replace_all("(?<=\\d) (?=_)", " ") %>% # mostly to split place and name, if name is preceded by "_" as a stand-in for "*"
stringr::str_replace_all("(?<=\\d) (?=\\d)", " ") %>% # mostly to split place team names that start with a number, like in NYS results (5-Fairport etc.)
stringr::str_replace_all("(?<=[:alpha:]),(?=[:alpha:])", ", ") %>% # split names that don't have a space between last,first
stringr::str_replace_all("(?<=[:alpha:])\\. (?=[:digit:])", "\\. ") %>% # split abbreviated team names like Southern Cal. and times
stringr::str_replace_all("(?<=\\d) (?=_)", " ") %>% # spacing between place and athletes with */_ leading name
stringr::str_replace_all("(?<=\\)) (?=[:alpha:])", " ") %>% # spacing between place) and names
stringr::str_replace_all("(SM?B?1\\d{1})(\\d{1,2})", "\\1 \\2") %>% # split para classification and age
stringr::str_replace_all(" NT ", " NT ") %>% # split prelim and final
stringr::str_replace_all("(?<=[:alpha:])\\s{2,3}(?=[:alpha:])", " ") %>% # testing 12/21/2020 would help with typos
stringr::str_replace_all("(?<=[:alpha:]) (?=\\d)", " ") %>% # split name and age
stringr::str_replace_all("(?<=[:alpha:])(\\d{1,3}\\-\\d{2})", " \\1 ") %>% # split name and yyy-mm age
stringr::str_replace_all("(?<=\\,) (?=\\d)", " ") %>% # split name and age if name is so long that it ends with a ","
stringr::str_replace_all("(?<=\\d) (?=\\d{1,}$)", " ") %>% # split off row_numb
stringr::str_replace_all("(?<=\\.\\d\\d\\s{1,10}\\d?\\d?\\:?\\d?\\d\\.\\d\\d)\\s{1,10}[:alpha:]{1,5}\\d?\\s{1,10}(?=\\d{1,})", " ") %>% # removes "AAC" or "AAA" or "NYS" or "SEC1" etc. from after finals time
stringr::str_replace_all("(?<=\\s)S(?=B?M?\\d{1,2})", " S") %>% # for para classifications
stringr::str_replace_all("([:alpha:])(SB?M?\\d{1,2})", "\\1 \\2") %>% # for splitting names and para classifications
# {if(stringr::str_detect(events$Event[1], "elay") == FALSE)
# stringr::str_replace_all(., "(^[:upper:]{3,})", "NA NA \\1") else . # for GUTIERREZ BERMUDEZ Juan Jose in 2020 Para Games 200IM Finals
# }
stringr::str_replace_all("(^[:upper:]{3,}.*\\s{3,}[:upper:]{3})", "7 6 \\1") # for GUTIERREZ BERMUDEZ Juan Jose in 2020 Para Games 200IM Finals
#### if data_cleaned is empty ####
if(!length(data_cleaned) > 0){
message("No results found in file")
} else {
#### splits data into variables by splitting at multiple (>= 2) spaces ####
data_cleaned <-
unlist(purrr::map(data_cleaned, stringr::str_split, "\\s{2,}"),
recursive = FALSE)
# unique(map(data_cleaned, length))
#### breaks data into subsets based on how many variables it has ####
data_length_5 <- data_cleaned[purrr::map(data_cleaned, length) == 5]
data_length_6 <- data_cleaned[purrr::map(data_cleaned, length) == 6]
data_length_7 <- data_cleaned[purrr::map(data_cleaned, length) == 7]
data_length_8 <- data_cleaned[purrr::map(data_cleaned, length) == 8]
data_length_9 <- data_cleaned[purrr::map(data_cleaned, length) == 9]
data_length_10 <- data_cleaned[purrr::map(data_cleaned, length) == 10]
data_length_11 <- data_cleaned[purrr::map(data_cleaned, length) == 11]
data_length_12 <- data_cleaned[purrr::map(data_cleaned, length) == 12]
data_length_13 <- data_cleaned[purrr::map(data_cleaned, length) == 13]
# data_length_14 <- data_cleaned[purrr::map(data_cleaned, length) == 14]
# treatment of DQs new 8/19
suppressWarnings(DQ <-
data_cleaned[stringr::str_detect(data_cleaned, Time_Score_String, negate = TRUE) == TRUE])
DQ_length_3 <- DQ[purrr::map(DQ, length) == 3]
DQ_length_4 <- DQ[purrr::map(DQ, length) == 4]
# DQ_length_5 <- DQ[purrr::map(DQ, length) == 5]
#### thirteen variables ####
if (length(data_length_13) > 0) {
suppressWarnings(
df_13 <- data_length_13 %>%
list_transform() %>%
dplyr::filter(stringr::str_detect(V1, Record_String) == FALSE) %>%
na_if_character("") %>%
na_if_character("NA") %>%
dplyr::select(
Place = V1,
Heat = V2,
Lane = V3,
Name = V4,
Team = V5,
Age = V6,
Reaction_Time = V7,
Finals = V11,
Row_Numb = V13
)
) %>%
dplyr::filter(stringr::str_detect(Place, "^\\d+$"))
} else {
df_13 <- data.frame(
Row_Numb = character(),
stringsAsFactors = FALSE
)
}
#### twelve variables ####
if (length(data_length_12) > 0) {
suppressWarnings(
df_12 <- data_length_12 %>%
list_transform() %>%
dplyr::filter(stringr::str_detect(V1, Record_String) == FALSE) %>%
dplyr::mutate(Heat = case_when(
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") ~ V2,
TRUE ~ "NA"
)) %>%
dplyr::mutate(Lane = case_when(
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") == TRUE ~ V3,
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") == FALSE ~ V2,
TRUE ~ "NA"
)) %>%
dplyr::mutate(Name = dplyr::case_when(stringr::str_detect(Heat, "NA") == TRUE ~ V3,
TRUE ~ V4)) %>%
dplyr::mutate(Para = dplyr::case_when(stringr::str_detect(V4, Para_String) == TRUE ~ V4,
TRUE ~ "NA")) %>%
dplyr::mutate(
Team = dplyr::case_when(
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V4, Para_String) == FALSE ~ V4,
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V4, Para_String) == TRUE ~ V5,
TRUE ~ V5
)
) %>%
dplyr::mutate(
Reaction_Time = dplyr::case_when(
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V4, Para_String) == FALSE ~ V5,
stringr::str_detect(V6, Reaction_String) == TRUE ~ V6,
stringr::str_detect(V7, Reaction_String) == TRUE ~ V7,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(Age = case_when(stringr::str_detect(V6, Age_String) == TRUE ~ V6,
TRUE ~ "NA")) %>%
dplyr::mutate(
Finals = dplyr::case_when(
stringr::str_detect(V11, Time_Score_Specials_String) == TRUE &
sec_format(V10) < sec_format(V11) ~ V11,
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V4, Para_String) == FALSE ~ V10,
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V4, Para_String) == FALSE ~ V10,
stringr::str_detect(V7, Time_Score_Specials_String) == TRUE &
stringr::str_detect(V8, Time_Score_Specials_String) == TRUE &
stringr::str_detect(V9, Time_Score_Specials_String) == TRUE &
stringr::str_detect(V10, Time_Score_Specials_String) == TRUE ~ V10,
stringr::str_detect(V10, Time_Score_Specials_String) == TRUE ~ V8,
TRUE ~ "NA"
)
) %>%
na_if_character("") %>%
na_if_character("NA") %>%
dplyr::select(
Place = V1,
Heat,
Lane,
Name,
Age,
Para,
Team,
Reaction_Time,
Finals,
Row_Numb = V12
) %>%
dplyr::filter(stringr::str_detect(Place, "^\\d+$"))
)
} else {
df_12 <- data.frame(
Row_Numb = character(),
stringsAsFactors = FALSE
)
}
#### eleven variables ####
if (length(data_length_11) > 0) {
suppressWarnings(
df_11 <- data_length_11 %>%
list_transform() %>%
dplyr::filter(stringr::str_detect(V1, Record_String) == FALSE) %>%
dplyr::filter(stringr::str_detect(V10, "Championship") == FALSE) %>%
na_if_character("") %>%
dplyr::mutate(Heat = case_when(
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") ~ V2,
TRUE ~ "NA"
)) %>%
dplyr::mutate(Lane = case_when(
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") == TRUE ~ V3,
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") == FALSE ~ V2,
TRUE ~ "NA"
)) %>%
dplyr::mutate(Name = dplyr::case_when(stringr::str_detect(Heat, "NA") == TRUE ~ V3,
TRUE ~ V4)) %>%
dplyr::mutate(Para = dplyr::case_when(stringr::str_detect(V4, Para_String) == TRUE ~ V4,
TRUE ~ "NA")) %>%
dplyr::mutate(
Team = dplyr::case_when(
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V4, Para_String) == FALSE ~ V4,
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V4, Para_String) == TRUE ~ V5,
TRUE ~ V5
)
) %>%
dplyr::mutate(
Reaction_Time = dplyr::case_when(
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V4, Para_String) == FALSE ~ V5,
stringr::str_detect(V6, Reaction_String) == TRUE ~ V6,
stringr::str_detect(V7, Reaction_String) == TRUE ~ V7,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(Age = case_when(stringr::str_detect(V6, Age_String) == TRUE ~ V6,
TRUE ~ "NA")) %>%
dplyr::mutate(
Finals = dplyr::case_when(
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V4, Para_String) == FALSE ~ V9,
stringr::str_detect(V5, Reaction_String) == TRUE &
stringr::str_detect(V9, Time_Score_Specials_String) == TRUE ~ V9,
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V4, Para_String) == TRUE &
stringr::str_detect(V9, Time_Score_Specials_String) == TRUE &
stringr::str_detect(V10, "\\:") == FALSE ~ V9,
stringr::str_detect(V10, Time_Score_Specials_String) == TRUE &
stringr::str_detect(V9, Time_Score_Specials_String) == TRUE &
(sec_format(V9) > sec_format(V10)) ~ V9,
stringr::str_detect(V10, Time_Score_Specials_String) == TRUE ~ V10,
TRUE ~ "NA"
)
) %>%
na_if_character("NA") %>%
dplyr::select(
Place = V1,
Heat,
Lane,
Name,
Age,
Para,
Team,
Reaction_Time,
Finals,
Row_Numb = V11
) %>%
dplyr::filter(stringr::str_detect(Place, "^\\d+$"))
)
} else {
df_11 <- data.frame(
Row_Numb = character(),
stringsAsFactors = FALSE
)
}
#### ten variables ####
if (length(data_length_10) > 0) {
suppressWarnings(
df_10 <- data_length_10 %>%
list_transform() %>%
dplyr::filter(stringr::str_detect(V1, Record_String) == FALSE) %>%
dplyr::filter(stringr::str_detect(V1, "en") == FALSE) %>% # remove Women's Men's etc.
dplyr::filter(stringr::str_detect(V9, "Championship") == FALSE) %>%
na_if_character("") %>%
dplyr::mutate(Heat = case_when(
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") ~ V2,
TRUE ~ "NA"
)) %>%
dplyr::mutate(Lane = case_when(
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") == TRUE ~ V3,
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") == FALSE ~ V2,
TRUE ~ "NA"
)) %>%
dplyr::mutate(Name = dplyr::case_when(stringr::str_detect(Heat, "NA") == TRUE ~ V3,
TRUE ~ V4)) %>%
dplyr::mutate(Para = dplyr::case_when(stringr::str_detect(V4, Para_String) == TRUE ~ V4,
TRUE ~ "NA")) %>%
dplyr::mutate(
Team = dplyr::case_when(
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V4, Para_String) == FALSE ~ V4,
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V4, Para_String) == TRUE ~ V5,
TRUE ~ V5
)
) %>%
dplyr::mutate(
Reaction_Time = dplyr::case_when(
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V4, Para_String) == FALSE ~ V5,
stringr::str_detect(V6, Reaction_String) == TRUE ~ V6,
stringr::str_detect(V7, Reaction_String) == TRUE ~ V7,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(Age = case_when(stringr::str_detect(V6, Age_String) == TRUE ~ V6,
TRUE ~ "NA")) %>%
dplyr::mutate(
Finals = dplyr::case_when(
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V4, Para_String) == FALSE ~ V9,
stringr::str_detect(V5, Reaction_String) == TRUE & stringr::str_detect(V9, Time_Score_Specials_String) == TRUE ~ V9,
stringr::str_detect(Heat, "NA") == TRUE & stringr::str_detect(V4, Para_String) == TRUE & stringr::str_detect(V9, Time_Score_Specials_String) == TRUE ~ V9,
stringr::str_detect(V8, Time_Score_Specials_String) == TRUE & stringr::str_detect(V9, Time_Score_Specials_String) == TRUE &
(sec_format(V9) > sec_format(V8)) ~ V9,
stringr::str_detect(V8, Time_Score_Specials_String) == TRUE ~ V8,
TRUE ~ "NA"
)
) %>%
na_if_character("NA") %>%
dplyr::select(
Place = V1,
Heat,
Lane,
Name,
Age,
Para,
Team,
Reaction_Time,
Finals,
Row_Numb = V10
) %>%
dplyr::filter(stringr::str_detect(Place, "^\\d+$"))
)
} else {
df_10 <- data.frame(Row_Numb = character(),
stringsAsFactors = FALSE)
}
#### nine variables ####
if (length(data_length_9) > 0) {
suppressWarnings(
df_9 <- data_length_9 %>%
list_transform() %>%
dplyr::filter(stringr::str_detect(V1, Record_String) == FALSE) %>%
dplyr::filter(stringr::str_detect(V1, "(Women)|(Men)") == FALSE) %>% # remove Women's Men's etc.
dplyr::filter(stringr::str_detect(V3, "(Women)|(Men)") == FALSE) %>% # remove Women's Men's etc.
dplyr::filter(stringr::str_detect(V4, "(Women)|(Men)") == FALSE) %>% # remove Women's Men's etc.
na_if_character("") %>%
dplyr::mutate(Heat = case_when(
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") ~ V2,
TRUE ~ "NA"
)) %>%
dplyr::mutate(Lane = case_when(
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") == TRUE ~ V3,
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") == FALSE ~ V2,
TRUE ~ "NA"
)) %>%
dplyr::mutate(Name = dplyr::case_when(stringr::str_detect(Heat, "NA") == TRUE ~ V3,
TRUE ~ V4)) %>%
dplyr::mutate(Para = dplyr::case_when(stringr::str_detect(V4, Para_String) == TRUE ~ V4,
TRUE ~ "NA")) %>%
dplyr::mutate(
Team = dplyr::case_when(
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V4, Para_String) == FALSE ~ V4,
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V4, Para_String) == TRUE ~ V5,
TRUE ~ V5
)
) %>%
dplyr::mutate(
Reaction_Time = dplyr::case_when(
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V4, Para_String) == FALSE &
stringr::str_detect(V5, "^\\d\\.\\d{2}") == TRUE ~ V5,
# stringr::str_detect(Heat, "NA") == TRUE &
# stringr::str_detect(V4, Para_String) == TRUE &
stringr::str_detect(V6, "^\\d\\.\\d{2}") == TRUE ~ V6,
stringr::str_detect(V7, "^\\d\\.\\d{2}") == TRUE &
stringr::str_detect(V8, Time_Score_Specials_String) == TRUE ~ V7,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(Age = case_when(stringr::str_detect(V6, Age_String) == TRUE ~ V6,
TRUE ~ "NA")) %>%
dplyr::mutate(
Finals = dplyr::case_when(
stringr::str_detect(Heat, "NA") == TRUE & stringr::str_detect(V4, Para_String) == FALSE ~ V7,
stringr::str_detect(V7, "[1-9]\\:\\d\\d") == TRUE ~ V7,
stringr::str_detect(V6, Reaction_String) == TRUE & stringr::str_detect(V7, Time_Score_Specials_String) == TRUE &
stringr::str_detect(V8, "\\d\\d\\.\\d\\d") == FALSE ~ V7,
stringr::str_detect(V8, Time_Score_Specials_String) == TRUE ~ V8,
TRUE ~ "NA"
)
) %>%
na_if_character("NA") %>%
dplyr::select(
Place = V1,
Heat,
Lane,
Name,
Age,
Para,
Team,
Reaction_Time,
Finals,
Row_Numb = V9
) %>%
dplyr::filter(stringr::str_detect(Place, "^\\d+$"))
)
} else {
df_9 <- data.frame(
Row_Numb = character(),
stringsAsFactors = FALSE
)
}
#### eight variables ####
if (length(data_length_8) > 0) {
suppressWarnings(
df_8 <- data_length_8 %>%
list_transform() %>%
dplyr::filter(stringr::str_detect(V1, Record_String) == FALSE) %>%
dplyr::filter(stringr::str_detect(V1, "(Women)|(Men)") == FALSE) %>% # remove Women's Men's etc.
dplyr::filter(stringr::str_detect(V4, "(Women)|(Men)") == FALSE) %>%
na_if_character("") %>%
dplyr::mutate(Heat = case_when(
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") ~ V2,
TRUE ~ "NA"
)) %>%
dplyr::mutate(
Lane = case_when(
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") == TRUE ~ V3,
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") == FALSE ~ V2,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(Name = dplyr::case_when(stringr::str_detect(Heat, "NA") == TRUE ~ V3,
TRUE ~ V4)) %>%
dplyr::mutate(Para = dplyr::case_when(stringr::str_detect(V4, Para_String) == TRUE ~ V4,
TRUE ~ "NA")) %>%
dplyr::mutate(Team = dplyr::case_when(stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V4, Para_String) == FALSE ~ V4,
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V4, Para_String) == TRUE ~ V5,
TRUE ~ V5)) %>%
dplyr::mutate(
Reaction_Time = dplyr::case_when(
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V5, "^\\d\\.\\d\\d") == TRUE ~ V5,
stringr::str_detect(V6, "^\\d\\.\\d\\d") == TRUE &
str_detect(V5, "\\d\\d\\.\\d\\d") == FALSE ~ V6,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(Age = case_when(stringr::str_detect(V6, Age_String) == TRUE ~ V6,
TRUE ~ "NA")) %>%
dplyr::mutate(
Finals = dplyr::case_when(
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V5, Reaction_String) == TRUE &
stringr::str_detect(V6, Time_Score_Specials_String) == TRUE &
stringr::str_detect(V7, "^\\d\\d?\\.\\d\\d$") == TRUE &
(sec_format(V7) < sec_format(V6)) == TRUE ~ V6,
stringr::str_detect(Heat, "NA") == FALSE &
stringr::str_detect(V6, "[1-9]\\:\\d\\d") == TRUE &
stringr::str_detect(V7, "^\\d?\\d\\.\\d\\d") == TRUE &
(sec_format(V7) < sec_format(V6)) == TRUE ~ V6,
stringr::str_detect(Heat, "NA") == TRUE ~ V7,
stringr::str_detect(V7, "[1-9]\\:\\d\\d") == TRUE ~ V7,
stringr::str_detect(V6, Reaction_String) == TRUE &
stringr::str_detect(V7, Time_Score_Specials_String) == TRUE ~ V7,
stringr::str_detect(V8, Time_Score_Specials_String) == TRUE ~ V8,
TRUE ~ "NA"
)
) %>%
na_if_character("NA") %>%
dplyr::select(
Place = V1,
Heat,
Lane,
Name,
Age,
Para,
Team,
Reaction_Time,
Finals,
Row_Numb = V8
) %>%
dplyr::filter(stringr::str_detect(Place, "\\d")) # removes relay swimmer rows
)
} else {
df_8 <- data.frame(Row_Numb = character(),
stringsAsFactors = FALSE)
}
#### seven variables ####
if (length(data_length_7) > 0) {
suppressWarnings(
df_7 <- data_length_7 %>%
list_transform() %>%
dplyr::filter(stringr::str_detect(V1, Record_String) == FALSE) %>%
na_if_character("") %>%
dplyr::mutate(Heat = case_when(
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") ~ V2,
TRUE ~ "NA"
)) %>%
dplyr::mutate(Lane = case_when(
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") == TRUE ~ V3,
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") == FALSE ~ V2,
TRUE ~ "NA"
)) %>%
dplyr::mutate(
Name = dplyr::case_when(
stringr::str_detect(Heat, "NA") == TRUE ~ V3,
stringr::str_detect(Heat, V2) == TRUE &
stringr::str_detect(V5, Time_Score_Specials_String) == TRUE ~ "NA",
TRUE ~ V4
)
) %>%
dplyr::mutate(
Team = dplyr::case_when(
stringr::str_detect(Heat, "NA") == TRUE ~ V4,
stringr::str_detect(V5, Time_Score_Specials_String) == TRUE &
stringr::str_detect(V4, "[:alpha:]{3,}") == TRUE ~ V4,
TRUE ~ V5
)
) %>%
dplyr::mutate(
Reaction_Time = dplyr::case_when(
stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V5, "^\\d\\.\\d\\d") == TRUE ~ V5,
stringr::str_detect(V6, "^\\d\\.\\d\\d") == TRUE &
str_detect(V5, "\\d\\d\\.\\d\\d") == FALSE ~ V6,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Age = case_when(
stringr::str_detect(V6, Age_String) == TRUE ~ V6,
stringr::str_detect(V6, Age_String) == FALSE &
stringr::str_detect(V5, Age_String) == TRUE ~ V5,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Finals = dplyr::case_when(
stringr::str_detect(V5, Time_Score_Specials_String) == TRUE &
stringr::str_detect(Reaction_Time, V5) == FALSE ~ V5,
stringr::str_detect(V6, Time_Score_Specials_String) == TRUE ~ V6,
TRUE ~ "NA"
)
) %>%
na_if_character("NA") %>%
dplyr::select(
Place = V1,
Heat,
Lane,
Name,
Team,
Age,
Reaction_Time,
Finals,
Row_Numb = V7
) %>%
dplyr::filter(!is.na(Finals)) %>%
dplyr::filter(stringr::str_detect(Place, "\\d")) # removes relay swimmer rows
)
} else {
df_7 <- data.frame(Row_Numb = character(),
stringsAsFactors = FALSE)
}
#### six variables ####
if (length(data_length_6) > 0) {
suppressWarnings(
df_6 <- data_length_6 %>%
list_transform() %>%
dplyr::filter(stringr::str_detect(V1, Record_String) == FALSE) %>%
na_if_character("") %>%
dplyr::mutate(
Place = dplyr::case_when(stringr::str_detect(V5, "DSQ|DNS") == TRUE ~ "10000",
TRUE ~ V1)
) %>%
dplyr::mutate(
Heat = dplyr::case_when(
stringr::str_detect(V1, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") == TRUE ~ V2,
stringr::str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V5, "DSQ|DNS") == FALSE ~ V2,
stringr::str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V1, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V5, "DSQ|DNS") == TRUE ~ V1,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Lane = case_when(
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") == TRUE ~ V3,
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") == FALSE ~ V2,
str_detect(V1, "^\\d{1,2}$") == TRUE &
stringr::str_detect(Place, "10000") == TRUE ~ V1,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Name = dplyr::case_when(
stringr::str_detect(Place, "10000") == TRUE &
stringr::str_detect(V2, Name_String) == TRUE ~ V2,
stringr::str_detect(V4, "\\d\\:\\d{2}\\.\\d{2}") == TRUE ~ "NA",
stringr::str_detect(Heat, "NA") == TRUE ~ V3,
stringr::str_detect(V5, "DSQ|DNS") == TRUE ~ V3,
stringr::str_detect(V3, Lane) == TRUE &
str_detect(V4, "[:upper:][:lower:]") == TRUE &
str_detect(V5, Time_Score_String) == TRUE ~ "NA",
TRUE ~ V4
)
) %>%
dplyr::mutate(Para = dplyr::case_when(stringr::str_detect(V3, Para_String) == TRUE ~ V3,
TRUE ~ "NA")) %>%
dplyr::mutate(
Team = dplyr::case_when(
stringr::str_detect(V4, "\\d\\:\\d{2}\\.\\d{2}") == TRUE ~ V3,
stringr::str_detect(Heat, "NA") == TRUE ~ V4,
stringr::str_detect(V5, Time_Score_Specials_String) == TRUE ~ V4,
TRUE ~ V5
)
) %>%
dplyr::mutate(
Reaction_Time = dplyr::case_when(stringr::str_detect(Heat, "NA") == TRUE &
stringr::str_detect(V4, Time_Score_Specials_String) == FALSE &
stringr::str_detect(V5, "^\\d\\.\\d{2}") == TRUE ~ V5,
TRUE ~ "NA")
) %>%
dplyr::mutate(
Finals = dplyr::case_when(
str_detect(V4, "\\d\\:\\d{2}\\.\\d{2}") == TRUE &
str_detect(V5, "\\d\\.\\d{2}") ~ V4,
TRUE ~ V5
)
) %>%
na_if_character("NA") %>%
dplyr::select(
Place,
Heat,
Lane,
Name,
Para,
Team,
Reaction_Time,
Finals,
Row_Numb = V6
) %>%
dplyr::filter(stringr::str_detect(Place, "\\d")) # removes relay swimmer rows
)
} else {
df_6 <- data.frame(Row_Numb = character(),
stringsAsFactors = FALSE)
}
#### five variables ####
if (length(data_length_5) > 0) {
suppressWarnings(
df_5 <- data_length_5 %>%
list_transform() %>%
dplyr::filter(stringr::str_detect(V1, Record_String) == FALSE) %>%
na_if_character("") %>%
dplyr::mutate(
Place = dplyr::case_when(stringr::str_detect(V4, "DSQ|DNS") == TRUE ~ "10000",
TRUE ~ V1)
) %>%
dplyr::mutate(
Heat = dplyr::case_when(
stringr::str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V5, "DSQ|DNS") == FALSE ~ V2,
stringr::str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V1, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V4, "DSQ") == TRUE ~ V1,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Lane = case_when(
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") == TRUE ~ V3,
str_detect(V2, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V3, "^\\d{1,2}$") == FALSE ~ V2,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Name = dplyr::case_when(
stringr::str_detect(V2, Name_String) == TRUE &
stringr::str_detect(V3, Time_Score_Specials_String) == FALSE &
stringr::str_detect(V4, Time_Score_Specials_String) == TRUE ~ V2,
stringr::str_detect(V4, Time_Score_Specials_String) == TRUE ~ "NA",
stringr::str_detect(Heat, "NA") == TRUE ~ V3,
stringr::str_detect(V5, "DSQ|DNS") == TRUE ~ V3,
TRUE ~ V4
)
) %>%
dplyr::mutate(
Team = dplyr::case_when(
stringr::str_detect(V4, Time_Score_Specials_String) == TRUE ~ V3,
stringr::str_detect(Heat, "NA") == TRUE ~ V4,
stringr::str_detect(V5, "DSQ|DNS") == TRUE ~ V4,
TRUE ~ V5
)
) %>%
# dplyr::mutate(Reaction_Time = dplyr::case_when(stringr::str_detect(Heat, "NA") == TRUE ~ V5,
# TRUE ~ "NA")) %>%
na_if_character("NA") %>%
dplyr::select(
Place,
Heat,
Lane,
Name,
Team,
# Reaction_Time,
Finals = V4,
Row_Numb = V5
) %>%
dplyr::filter(stringr::str_detect(Place, "\\d")) # removes relay swimmer rows
)
} else {
df_5 <- data.frame(Row_Numb = character(),
stringsAsFactors = FALSE)
}
#### DQ data ####
#### DQ 4 ####
if (length(DQ_length_4) > 0) {
suppressWarnings(
df_DQ_4 <- DQ_length_4 %>%
list_transform() %>%
dplyr::mutate(Place = "10000") %>%
dplyr::select(
Place = V1,
Team = V2,
Finals = V3,
Row_Numb = V4
) %>%
dplyr::mutate(DQ = 1)
)
} else {
df_DQ_4 <- data.frame(
Row_Numb = character(),
DQ = numeric(),
stringsAsFactors = FALSE
)
}
#### DQ 3 ####
if (length(DQ_length_3) > 0) {
suppressWarnings(
df_DQ_3 <- DQ_length_3 %>%
list_transform() %>%
dplyr::mutate(Place = "10000") %>%
dplyr::select(
Place,
Team = V1,
Finals = V2,
Row_Numb = V3
) %>%
dplyr::mutate(DQ = 1)
)
} else {
df_DQ_3 <- data.frame(
Row_Numb = character(),
stringsAsFactors = FALSE
)
}
#### Rejoin data frames from each number of variables ####
Min_Row_Numb <- min(events$Event_Row_Min)
suppressWarnings(
data <- dplyr::bind_rows(df_13, df_12) %>%
dplyr::bind_rows(df_11) %>%
dplyr::bind_rows(df_10) %>%
dplyr::bind_rows(df_9) %>%
dplyr::bind_rows(df_8) %>%
dplyr::bind_rows(df_7) %>%
dplyr::bind_rows(df_6) %>%
dplyr::bind_rows(df_5) %>%
dplyr::full_join(df_DQ_4) %>%
dplyr::full_join(df_DQ_3) %>%
dplyr::mutate(Row_Numb = as.numeric(Row_Numb)) %>%
dplyr::arrange(Row_Numb)
)
# if("Prelims" %in% names(data) == FALSE){
# data$Prelims <- NA
# }
# if("Age" %in% names(data) == FALSE){
# data$Age <- "NA"
# }
suppressWarnings(
data <- data %>%
dplyr::mutate(
Exhibition = dplyr::case_when(stringr::str_detect(Finals, "x|X") == TRUE ~ 1,
TRUE ~ 0),
) %>%
dplyr::mutate(
DQ = dplyr::case_when(
as.numeric(Place) == 10000 &
Exhibition == 0 ~ 1,
# added exhibition condition 8/27
stringr::str_detect(Finals, "DQ|DSQ|DNS|DFS") == TRUE ~ 1,
is.na(DQ) ~ 0,
TRUE ~ DQ
)
) %>%
# 3/14/23
na_if_character("10000") %>%
dplyr::mutate(dplyr::across(
c(Name, Team), ~ stringr::str_replace_all(., "10000", "--")
)) %>% # remove any "10000"s added in erroneously
####
dplyr::mutate(
Place = str_remove(Place, "\\)"),
Place = str_remove(Place, "_"),
Place = as.numeric(Place),
Place = dplyr::case_when(
is.na(dplyr::lag(Place)) == TRUE ~ Place,
dplyr::lag(Place) == Place ~ Place + 0.1,
dplyr::lag(Place) != Place ~ Place
),
Place = as.character(Place),
Row_Numb = as.numeric(Row_Numb)
) %>%
dplyr::filter(Team %!in% c("Gold", "Silver", "Bronze")) %>%
dplyr::filter(Name %!in% c("GOLD", "SILVER", "BRONZE"))
)
#### add in events based on row number ranges ####
if(min(data$Row_Numb) < min(events$Event_Row_Min)){
unknown_event <- data.frame(Event = "Unknown",
Event_Row_Min = min(data$Row_Numb),
Event_Row_Max = min(events$Event_Row_Min) - 1)
events <- dplyr::bind_rows(unknown_event, events)
}
data <-
transform(data, Event = events$Event[findInterval(Row_Numb, events$Event_Row_Min)]) %>%
na_if_character("Unknown")
#### adding heats based on row number ranges ####
if ("Heat" %in% names(data) == TRUE) {
if (all(is.na(data$Heat))) {
data <- data %>%
select(-Heat)
}
}
if ("Heat" %in% names(data) == FALSE) {
if (min(data$Row_Numb) < min(heats$Heat_Row_Min)) {
unknown_heat <- data.frame(
Heat = "NA",
Heat_Row_Min = min(data$Row_Numb),
Heat_Row_Max = min(heats$Heat_Row_Min) - 1
)
heats <- dplyr::bind_rows(unknown_heat, heats)
}
data <-
transform(data, Heat = heats$Heat[findInterval(Row_Numb, heats$Heat_Row_Min)]) %>%
na_if_character("NA")
}
#### cleaning up final results ####
suppressWarnings(
data <- data %>%
dplyr::mutate(
Name = stringr::str_replace_all(Name, "_", "\\*"),
Place = round(as.numeric(Place)),
Event = as.character(Event)
) %>%
dplyr::mutate(
Place = dplyr::case_when(is.na(Place) == TRUE &
DQ == 0 ~ dplyr::lag(Place) + 1,
TRUE ~ Place)
) %>%
dplyr::mutate(Reaction_Time = case_when(stringr::str_detect(Event, "(R|r)elay|\\dx\\d{3}") == TRUE ~ "NA",
TRUE ~ Reaction_Time)) %>%
na_if_character("NA")
)
#### cleaning ####
if(format_results == TRUE){
data <- format_results(data)
}
#### adding relay swimmers in ####
if (relay_swimmers_omega == TRUE) {
relay_swimmers_df <- collect_relay_swimmers_omega(as_lines_list_2)
relay_swimmers_df <-
transform(relay_swimmers_df, Row_Numb_Adjusted = data$Row_Numb[findInterval(Row_Numb, data$Row_Numb)]) %>%
dplyr::select(-Row_Numb)
data <- data %>%
dplyr::left_join(relay_swimmers_df, c("Row_Numb" = "Row_Numb_Adjusted"))
}
#### adding splits back in ####
if (splits == TRUE) {
data <- data %>%
dplyr::mutate(dplyr::across(where(is.numeric), as.character))
data_ind <- data %>%
dplyr::filter(stringr::str_detect(Event, "(R|r)elay|\\dx\\d{3}") == FALSE)
data_relay <- data %>%
dplyr::filter(stringr::str_detect(Event, "(R|r)elay|\\dx\\d{3}") == TRUE)
if(nrow(data_ind) > 0) {
# split_length <- 50
splits_df <-
splits_parse(as_lines_list_2, split_len = split_length_omega)
#### matches row numbers in splits_df to available row numbers in data
# helps a lot with relays, since their row numbers vary based on whether or not relay swimmers are included
# and if those swimmers are listed on one line or two
splits_df <-
transform(splits_df, Row_Numb_Adjusted = data_ind$Row_Numb[findInterval(Row_Numb, as.numeric(data_ind$Row_Numb))]) %>%
dplyr::mutate(Row_Numb_Adjusted = as.character(Row_Numb_Adjusted)) %>%
dplyr::select(-Row_Numb)
# new names for omega splits, because first 50 isn't captured
splits_df <- splits_df %>%
dplyr::rename_with(splits_rename_omega, dplyr::starts_with("Split"), split_len = split_length_omega) %>%
dplyr::mutate(dplyr::across(dplyr::starts_with("Split"), sec_format))
suppressWarnings(
data_ind <- data_ind %>%
dplyr::left_join(splits_df, by = c("Row_Numb" = "Row_Numb_Adjusted")) %>%
dplyr::mutate(dplyr::across(dplyr::starts_with("Split"), as.numeric)) %>%
dplyr::mutate(
Split_Total = rowSums(dplyr::across(dplyr::starts_with("Split")), na.rm = TRUE),
Finals_Sec = sec_format(Finals),
Split_50 = Finals_Sec - Split_Total,
Split_50 = dplyr::case_when(Split_50 < 0 ~ 10000,
TRUE ~ Split_50)
) %>%
na_if_numeric(10000) %>%
dplyr::select(-Split_Total, -Finals_Sec) %>%
dplyr::mutate(dplyr::across(
dplyr::starts_with("Split"), format, nsmall = 2
)) %>%
dplyr::mutate(dplyr::across(where(is.numeric), as.character)) %>%
dplyr::mutate(dplyr::across(where(is.character), trimws)) %>%
na_if_character("NA") %>%
dplyr::select(
!dplyr::starts_with("Split"),
stringr::str_sort(names(.), numeric = TRUE)
) # keep splits columns in order
)
}
if(nrow(data_relay) > 0){
splits_df <-
splits_parse_omega_relays(as_lines_list_2, split_len = split_length_omega)
#### matches row numbers in splits_df to available row numbers in data
# helps a lot with relays, since their row numbers vary based on whether or not relay swimmers are included
# and if those swimmers are listed on one line or two
splits_df <-
transform(splits_df, Row_Numb_Adjusted = data_relay$Row_Numb[findInterval(Row_Numb, as.numeric(data_relay$Row_Numb))]) %>%
dplyr::mutate(Row_Numb_Adjusted = as.character(Row_Numb_Adjusted)) %>%
dplyr::select(-Row_Numb)
data_relay <- data_relay %>%
# dplyr::filter(Finals != "DNS") %>%
# dplyr::filter(Finals != "DSQ") %>%
dplyr::left_join(splits_df, by = c("Row_Numb" = "Row_Numb_Adjusted")) %>%
na_if_numeric(10000) %>%
dplyr::mutate(dplyr::across(dplyr::starts_with("Split"), format, nsmall = 2)) %>%
dplyr::mutate(dplyr::across(where(is.numeric), as.character)) %>%
dplyr::mutate(dplyr::across(where(is.character), trimws)) %>%
na_if_character("NA") %>%
dplyr::select(!dplyr::starts_with("Split"),
stringr::str_sort(names(.), numeric = TRUE)) # keep splits columns in order
}
data <- data_ind %>%
dplyr::bind_rows(data_relay) %>%
dplyr::arrange(as.numeric(Row_Numb))
}
# remove reaction time from relays
if("Reaction_Time" %in% names(data)){
data <- data %>%
dplyr::mutate(Reaction_Time = dplyr::case_when(stringr::str_detect(Event, "Relay") == TRUE ~ "NA",
TRUE ~ Reaction_Time)) %>%
na_if_character("NA")
}
### remove empty columns (all values are NA) ###
data <- Filter(function(x)
!all(is.na(x)), data)
#### if there is a Place column it should be first ####
if("Place" %in% names(data)){
data <- data %>%
dplyr::select(Place, dplyr::everything())
}
if(all("Place" %in% names(data), "Heat" %in% names(data))){
data <- data %>%
dplyr::select(Place, Heat, dplyr::everything())
}
data$Row_Numb <- NULL
# message("Beginning with SwimmeR v0.6.0 the Grade and School output columns are renamed Age and Team respectively. Please adjust your work flows as needed.")
return(data)
}
}
#' @export
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