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R/hytek_parse.R
In JumpeR: Importing and Working with Track and Field Data
Defines functions
hytek_parse
Documented in
hytek_parse
#' Parses Hytek format track and field results inside \code{tf_parse}
#'
#' Outputs a data frame of track and field results
#'
#' @importFrom dplyr mutate
#' @importFrom dplyr filter
#' @importFrom dplyr case_when
#' @importFrom dplyr na_if
#' @importFrom dplyr select
#' @importFrom dplyr bind_rows
#' @importFrom dplyr arrange
#' @importFrom dplyr across
#' @importFrom dplyr left_join
#' @importFrom dplyr all_of
#' @importFrom dplyr starts_with
#' @importFrom dplyr matches
#' @importFrom dplyr distinct
#' @importFrom stringr str_remove
#' @importFrom stringr str_remove_all
#' @importFrom stringr str_detect
#' @importFrom stringr str_split
#' @importFrom stringr str_length
#' @importFrom stringr str_replace_all
#' @importFrom stringr str_replace
#' @importFrom stringr str_subset
#' @importFrom stringr str_remove
#' @importFrom purrr map
#' @importFrom purrr map_lgl
#' @importFrom stats setNames
#' @importFrom SwimmeR `%!in%`
#'
#' @param hytek_file data with row numbers added
#' @param hytek_relay_athletes should \code{tf_parse} try to include the names
#' of relay athletes for relay events? Names will be listed in new columns
#' "Relay-Athlete_1", "Relay_Athlete_2" etc. Defaults to \code{FALSE}.
#' @param hytek_rounds should \code{tf_parse} try to include rounds for
#' jumping/throwing events? Please note this will add a significant number of
#' columns to the resulting data frame. Defaults to \code{FALSE}.
#' @param hytek_round_attempts should \code{tf_parse} try to include rounds
#' results (i.e. "PASS", "X", "O") for high jump and pole value events?
#' Please note this will add a significant number of columns to the resulting
#' data frame. Defaults to \code{FALSE}
#' @param hytek_split_attempts should \code{tf_parse} split attempts from each
#' round into separate columns? For example "XXO" would result in three
#' columns, one for "X', another for the second "X" and third for "O". There
#' will be a lot of columns. Defaults to \code{FALSE}
#' @param hytek_splits either \code{TRUE} or the default, \code{FALSE} - should
#' \code{hytek_parse} attempt to include splits.
#' @param hytek_split_length either the distance at which splits are collected
#' (must be constant distance) or the default, \code{1}, the length of track
#' at which splits are recorded. Not all results are internally consistent on
#' this issue. If in doubt use the default \code{1}
#'
#' @return a data frame of track and field results
#'
#' @seealso \code{hytek_parse} is runs inside
#' \code{\link{tf_parse}}
hytek_parse <-
function(hytek_file = file,
hytek_relay_athletes = relay_athletes,
hytek_rounds = rounds,
hytek_round_attempts = round_attempts,
hytek_split_attempts = split_attempts,
hytek_splits = splits,
hytek_split_length = split_length) {
#### testing setup ####
# hytek_file <-
# "http://results.deltatiming.com/ncaa/tf/2019-joe-walker-invitational/190412F009" %>%
# read_results() %>%
# add_row_numbers()
# hytek_file <-
# "http://tfresultsdata.deltatiming.com/2018-hurricane-invitational/180316F028.htm" %>%
# read_results() %>%
# add_row_numbers()
# hytek_file <- "http://tfresultsdata.deltatiming.com/2019-ysu-icebreaker/191206F031.htm" %>%
# read_results() %>%
# add_row_numbers()
#### Pulls out event labels from text ####
raw_results <- hytek_file
events <- event_parse(raw_results) %>%
dplyr::mutate(Event = stringr::str_remove(Event, " Women$| Men$"))
#### 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:\\.]*"
Result_String <- "\\d{0,2}\\:?\\-?\\d{1,2}\\.\\d{2}m?"
Result_Specials_String <-
paste0(Result_String, "$|^NT$|^NP$|^DQ$|^DNS$|^DNF$|^FOUL$|^NH$|^SCR$|^FS$|^ND$")
Wind_String <-
"\\+\\d\\.\\d|\\-\\d\\.\\d|^NWS$|^NWI$|^\\d\\.\\d$"
Age_String <- "^SR$|^JR$|^SO$|^FR$|^M?W?[:digit:]{1,3}$"
Tiebreaker_String <- "\\d\\.\\d{3}$"
suppressWarnings(
data_1 <- raw_results %>%
.[purrr::map(., length) > 0] %>%
.[stringr::str_length(.) > 50] %>%
.[stringr::str_count(., "\\d\\)") < 2] %>% # remove inline splits and team scores as 1) Alfred 2) Ithaca etc.
.[stringr::str_detect(., paste0(Result_String, "|DQ|DNS|DNF|FOUL|NH|SCR|FS|ND"))] %>% # must Results_String because all results do
.[purrr::map_lgl(., ~ !any(stringr::str_detect(., "\\d{3}\\.\\d{2}")))] %>% # closes loophole in Result_String where a number like 100.00 could get through even though it's not a valid result
.[purrr::map_lgl(., ~ !any(
stringr::str_detect(., "^[0-9\\(\\)\\.FOULPASSm\\s\\-\\+]+$")
))] %>% # closes loophole where throwing splits lines get in because they contain FOUL or PASS plus valid results
.[purrr::map_lgl(., ~ !any(
stringr::str_detect(., "Event .*\\d")
))] %>% # removes event titles that also include distances, like "Event 1 Short Hurdles 0.762m"
.[stringr::str_detect(., "[:alpha:]{2,}")] %>% # must have at least two letters in a row
# .[purrr::map_lgl(., ~ !any(stringr::str_detect(., avoid)))] %>% # remove lines contained in avoid
stringr::str_remove_all("\n\\s*") %>%
# stringr::str_remove_all("\\d{0,2}\\:?\\d{1,2}\\.\\d{3}") %>% # ties
# trimws() %>%
# stringr::str_replace_all(stats::setNames(replacement, typo)) %>%
# remove 'A', 'B' etc. relay designators
stringr::str_replace_all(" \\'[A-Z]\\' ", " ") %>% # tf specific - removes relay A, B etc. designators
stringr::str_replace_all(" [A-Z] ", " ") %>%
stringr::str_replace_all("\\'\\'", " ") %>%
# stringr::str_remove_all("(?<=\\.\\d{2})[Q|R|M|E|\\$|q](?=\\s)") %>% # tf specific - removes "q" or "Q" sometimes used to designate a qualifying result, also 'R', "M", "$", "E"
stringr::str_remove_all("(?<=\\.\\d{2})[A-Z|\\$|q](?=\\s)") %>% # tf specific - removes "q" or "Q" sometimes used to designate a qualifying result, also 'R', "M", "$", "E"
stringr::str_replace_all("(?<=\\s{3})[A-Z|\\$|q] (?=\\d)", " ") %>% # tf specific - removes upper case letters sometimes used before times to designate records etc.
stringr::str_remove_all("(?<=\\s)[J|j](?=\\d)") %>% # tf specific - removes "j" or "J" sometimes used to designate a judged result
stringr::str_replace_all("-{2,5}", "10000") %>% #8/26
# stringr::str_replace_all("(\\.\\d{2})\\d+", "\\1 ") %>% # added 8/21 for illinois to deal with points column merging with final times column
# stringr::str_replace_all("\\d{1,2} (\\d{1,})$", " \\1 ") %>% # added 8/21 for illinois to deal with points column merging with final times column
stringr::str_replace_all("\\*", "_") %>%
stringr::str_replace_all(" \\+", " \\+") %>% # tf speciifc, for windspeed
stringr::str_replace_all(" \\-", " \\-") %>% # tf speciifc, for windspeed
stringr::str_replace_all("#", " ") %>% # tf specific, leading pound sign to spaces
stringr::str_replace_all("(?<=\\d) (?=[:alpha:])", " ") %>% # tf specific - split place and name
stringr::str_replace_all("(?<=\\dm) (?=[\\-|\\+|\\d])", " ") %>% # tf specific - split distance and windspeed
stringr::str_replace_all("(?<=\\d) (?=[\\-|\\+|\\d])", " ") %>% # tf specific - split time and windspeed
stringr::str_replace_all("(?<=\\d{3}) (?=[:alpha:])", " ") %>% # tf specific - split bib number and name
stringr::str_replace_all("(?<=\\d{2}) (?=[:alpha:])", " ") %>% # tf specific - split age and team
stringr::str_replace_all("(?<=\\.\\d{3})s ", " ") %>% # tf specific - split tiebreaker and s designator
stringr::str_replace_all(" FR ", " FR ") %>% # tf specific - split age and team
stringr::str_replace_all(" SO ", " SO ") %>% # tf specific - split age and team
stringr::str_replace_all(" JR ", " JR ") %>% # tf specific - split age and team
stringr::str_replace_all(" SR ", " SR ") %>% # tf specific - split age and team
stringr::str_replace_all("(?<=[:alpha:]) (?=\\d)", " ") %>% # tf specific - split name and age
stringr::str_replace_all("(?<=\\,) (?=\\d)", " ") %>% # tf specific - split name and age
stringr::str_replace_all(" (M\\d{1,3}) ", " \\1 ") %>% # tf specific - gendered ages M
stringr::str_replace_all(" (W\\d{1,3}) ", " \\1 ") %>% # tf specific - gendered ages W
stringr::str_replace_all("(?<=\\d\\.\\d) (?=\\d{1,2}\\s)", " ") %>% # tf specific - split off wind and heat number
stringr::str_replace_all("(?<=\\d) (?=\\d{1,}$)", " ") %>% # tf specific - split off row_numb
stringr::str_replace_all("(?<=\\dm)[:upper:](?=\\s)", " ") %>% # tf specific - sometimes an M for Meet record is added to a distance 1.23m as 1.23mM
stringr::str_replace_all("(?<=\\dm)[:graph:](?=\\s)", " ") %>% # tf specific - sometimes an M for Meet record is added to a distance 1.23m as 1.23mM
stringr::str_replace_all(" \\., ", " Period, ") %>% # for people with no first/last name, like Indian runners in some Singapore results
stringr::str_replace_all("([:alpha])(\\.[:alpha:])", "\\1 \\2") %>%
trimws()
)
#### if data_1 is empty ####
if(!length(data_1) > 0){
message("No results found in file")
} else {
#### splits data into variables by splitting at multiple (>= 2) spaces ####
data_1 <-
unlist(purrr::map(data_1, stringr::str_split, "\\s{2,}"),
recursive = FALSE)
# unique(map(data_1, length))
#### breaks data into subsets based on how many variables it has ####
data_length_4 <- data_1[purrr::map(data_1, length) == 4]
data_length_5 <- data_1[purrr::map(data_1, length) == 5]
data_length_6 <- data_1[purrr::map(data_1, length) == 6]
data_length_7 <- data_1[purrr::map(data_1, length) == 7]
data_length_8 <- data_1[purrr::map(data_1, length) == 8]
data_length_9 <- data_1[purrr::map(data_1, length) == 9]
data_length_10 <- data_1[purrr::map(data_1, length) == 10]
# treatment of DQs
# suppressWarnings(DQ <-
# data_1[stringr::str_detect(data_1, Time_Score_String, negate = TRUE) == TRUE])
# DQ_length_3 <- DQ[purrr::map(DQ, length) == 3]
# DQ_length_4 <- DQ[purrr::map(DQ, length) == 4]
#### ten variables ####
if (length(data_length_10) > 0) {
suppressWarnings(
df_10 <- data_length_10 %>%
list_transform() %>%
dplyr::mutate(Place = V1) %>%
dplyr::mutate(
Bib_Number = dplyr::case_when(stringr::str_detect(V2, "^\\d{1,6}$") ~ V2,
TRUE ~ "NA")
) %>%
dplyr::mutate(
Name = dplyr::case_when(
stringr::str_detect(V2, Name_String) ~ V2,
stringr::str_detect(V3, Name_String) ~ V3,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Age = dplyr::case_when(
stringr::str_detect(V3, Age_String) ~ V3,
stringr::str_detect(V4, Age_String) ~ V4,
stringr::str_detect(V5, Age_String) ~ V5,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Team = dplyr::case_when(
stringr::str_detect(V3, Age_String) &
stringr::str_detect(V4, "[:alpha:]{2,}") ~ V4,
stringr::str_detect(V4, Age_String) &
stringr::str_detect(V5, "[:alpha:]{2,}") ~ V5,
stringr::str_detect(V5, Age_String) &
stringr::str_detect(V6, "[:alpha:]{2,}") ~ V6,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Prelims_Result = dplyr::case_when(
stringr::str_detect(V5, Result_Specials_String) &
stringr::str_detect(V6, Result_Specials_String) ~ V5,
stringr::str_detect(V6, Result_Specials_String) &
stringr::str_detect(V7, Result_Specials_String) ~ V6,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Finals_Result = dplyr::case_when(
stringr::str_detect(V5, Result_Specials_String) == TRUE &
stringr::str_detect(V6, Result_Specials_String) == FALSE ~ V5,
stringr::str_detect(V6, Result_Specials_String) == TRUE &
stringr::str_detect(V5, Result_Specials_String) == FALSE &
stringr::str_detect(V7, Result_Specials_String) == FALSE ~ V6,
stringr::str_detect(V6, Result_Specials_String) &
stringr::str_detect(V7, Result_Specials_String) ~ V7,
stringr::str_detect(V7, Result_Specials_String) == TRUE &
stringr::str_detect(V6, Result_Specials_String) == FALSE &
stringr::str_detect(V8, Result_Specials_String) == FALSE ~ V7,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Wind_Speed = dplyr::case_when(stringr::str_detect(V8, Wind_String) ~ V8,
TRUE ~ "NA")
) %>%
dplyr::mutate(
Points = dplyr::case_when(
stringr::str_detect(V8, Wind_String) == FALSE &
stringr::str_detect(V8, "^\\d\\.?\\d?$") == TRUE &
stringr::str_detect(V9, "\\d{1,2}") == FALSE ~ V8,
stringr::str_detect(V8, Wind_String) == TRUE &
stringr::str_detect(V9, "^\\d\\.?\\d?$") == TRUE ~ V9,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Tiebreaker = dplyr::case_when(stringr::str_detect(V9, Tiebreaker_String) == TRUE ~ V9,
TRUE ~ "NA")
) %>%
dplyr::select(
Place,
Bib_Number,
Name,
Age,
Team,
Prelims_Result,
Finals_Result,
Wind_Speed,
Points,
Tiebreaker,
'Row_Numb' = V10
)
)
} else {
df_10 <- data.frame(Row_Numb = character(),
stringsAsFactors = FALSE)
}
#### nine variables ####
if (length(data_length_9) > 0) {
suppressWarnings(
df_9 <- data_length_9 %>%
list_transform() %>%
dplyr::mutate(Place = V1) %>%
dplyr::mutate(
Bib_Number = dplyr::case_when(stringr::str_detect(V2, "^\\d{1,6}$") ~ V2,
TRUE ~ "NA")
) %>%
dplyr::mutate(
Name = dplyr::case_when(
stringr::str_detect(V2, Name_String) ~ V2,
stringr::str_detect(V3, Name_String) ~ V3,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Age = dplyr::case_when(
stringr::str_detect(V3, Age_String) ~ V3,
stringr::str_detect(V4, Age_String) ~ V4,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Team = dplyr::case_when(
stringr::str_detect(V3, Age_String) &
stringr::str_detect(V4, "[:alpha:]{2,}") ~ V4,
stringr::str_detect(V4, Age_String) &
stringr::str_detect(V5, "[:alpha:]{2,}") ~ V5,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Prelims_Result = dplyr::case_when(
stringr::str_detect(V6, Result_Specials_String) == TRUE &
stringr::str_detect(V7, Result_Specials_String) == TRUE ~ V6,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Finals_Result = dplyr::case_when(
stringr::str_detect(V6, Result_Specials_String) &
stringr::str_detect(V7, Result_Specials_String) ~ V7,
stringr::str_detect(V6, Result_Specials_String) == TRUE &
stringr::str_detect(V5, Result_Specials_String) == FALSE &
stringr::str_detect(V7, Result_Specials_String) == FALSE ~ V6,
stringr::str_detect(V5, Result_Specials_String) == TRUE &
stringr::str_detect(V6, Result_Specials_String) == FALSE &
stringr::str_detect(V4, Result_Specials_String) == FALSE ~ V5,
stringr::str_detect(V6, Result_Specials_String) == TRUE &
stringr::str_detect(V5, Result_Specials_String) == TRUE &
stringr::str_detect(V5, "m") == TRUE &
stringr::str_detect(V6, "-") == TRUE ~ V5,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Wind_Speed = dplyr::case_when(
stringr::str_detect(V7, Wind_String) ~ V7,
stringr::str_detect(V7, Wind_String) == FALSE &
stringr::str_detect(V8, Wind_String) == TRUE ~ V8,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Heat = dplyr::case_when(
stringr::str_detect(V7, Wind_String) == FALSE &
stringr::str_detect(V7, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V8, "^\\d\\d?\\.?\\d?$") == TRUE ~ V7,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Points = dplyr::case_when(
stringr::str_detect(V7, Heat) == TRUE &
stringr::str_detect(V8, "^\\d\\d?\\.?\\d?$") ~ V8,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Tiebreaker = dplyr::case_when(
stringr::str_detect(V8, Tiebreaker_String) == TRUE ~ V8,
TRUE ~ "NA"
)
) %>%
dplyr::select(
Place,
Bib_Number,
Name,
Age,
Team,
Prelims_Result,
Finals_Result,
Wind_Speed,
Heat,
Points,
Tiebreaker,
'Row_Numb' = V9
)
)
} else {
df_9 <- data.frame(Row_Numb = character(),
stringsAsFactors = FALSE)
}
#### eight variables ####
if (length(data_length_8) > 0) {
suppressWarnings(
df_8 <- data_length_8 %>%
list_transform() %>%
dplyr::mutate(Place = V1) %>%
dplyr::mutate(Bib_Number = dplyr::case_when(
stringr::str_detect(V2, "^\\d{1,6}$") ~ V2,
TRUE ~ "NA"
)) %>%
dplyr::mutate(
Name = dplyr::case_when(
stringr::str_detect(V2, Name_String) ~ V2,
stringr::str_detect(V3, Name_String) ~ V3,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Age = dplyr::case_when(
stringr::str_detect(V3, Age_String) ~ V3,
stringr::str_detect(V4, Age_String) ~ V4,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Team = dplyr::case_when(
stringr::str_detect(V3, Age_String) &
stringr::str_detect(V4, "[:alpha:]{2,}") ~ V4,
stringr::str_detect(V4, Age_String) &
stringr::str_detect(V5, "[:alpha:]{2,}") ~ V5,
stringr::str_detect(V2, Name_String) == TRUE &
stringr::str_detect(V3, Age_String) == FALSE &
stringr::str_detect(V3, "[:alpha:]{2,}") == TRUE ~ V3,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Finals_Result = dplyr::case_when(
stringr::str_detect(V4, Result_Specials_String) == TRUE &
stringr::str_detect(V5, Result_Specials_String) == FALSE ~ V4,
stringr::str_detect(V5, Result_Specials_String) == TRUE &
stringr::str_detect(V6, Result_Specials_String) == FALSE ~ V5,
stringr::str_detect(V6, Result_Specials_String) == TRUE &
stringr::str_detect(V5, Result_Specials_String) == FALSE &
stringr::str_detect(V7, Result_Specials_String) == FALSE ~ V6,
stringr::str_detect(V6, Result_Specials_String) == TRUE &
stringr::str_detect(V5, Result_Specials_String) == TRUE &
stringr::str_detect(V5, "m") == TRUE &
stringr::str_detect(V6, "-") == TRUE ~ V5,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Wind_Speed = dplyr::case_when(
stringr::str_detect(V5, Wind_String) == TRUE &
stringr::str_detect(V6, Wind_String) == FALSE ~ V5,
stringr::str_detect(V6, Wind_String) == TRUE &
stringr::str_detect(V7, Wind_String) == FALSE ~ V6,
stringr::str_detect(V7, Wind_String) == TRUE ~ V7,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Tiebreaker = dplyr::case_when(
stringr::str_detect(V7, Tiebreaker_String) == TRUE ~ V7,
TRUE ~ "NA"
)
) %>%
dplyr::select(
Place,
Bib_Number,
Name,
Age,
Team,
Finals_Result,
Wind_Speed,
Tiebreaker,
'Row_Numb' = V8
)
)
} 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::mutate(Place = V1) %>%
dplyr::mutate(
Bib_Number = dplyr::case_when(stringr::str_detect(V2, "^\\d{1,6}$") ~ V2,
TRUE ~ "NA")
) %>%
dplyr::mutate(
Name = dplyr::case_when(
stringr::str_detect(V2, Name_String) ~ V2,
stringr::str_detect(V3, Name_String) ~ V3,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Age = dplyr::case_when(
stringr::str_detect(V3, Age_String) ~ V3,
stringr::str_detect(V4, Age_String) ~ V4,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Team = dplyr::case_when(
stringr::str_detect(V3, Age_String) == FALSE &
# stringr::str_detect(V4, Age_String) == FALSE &
stringr::str_detect(V4, Result_Specials_String) == TRUE &
stringr::str_detect(V3, "[:alpha:]{2,}") == TRUE ~ V3,
stringr::str_detect(V3, Age_String) == TRUE &
stringr::str_detect(V4, "[:alpha:]{2,}") == TRUE ~ V4,
stringr::str_detect(V3, Name_String) == TRUE &
stringr::str_detect(V5, Result_Specials_String) == TRUE &
stringr::str_detect(V4, "[:alpha:]{2,}") == TRUE ~ V4,
stringr::str_detect(V4, Age_String) == TRUE &
stringr::str_detect(V5, "[:alpha:]{2,}") == TRUE ~ V5,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Finals_Result = dplyr::case_when(
stringr::str_detect(V4, Result_Specials_String) == TRUE ~ V4,
stringr::str_detect(V5, Result_Specials_String) == TRUE & # to deal with results that have both metric and imperial result - prefer metric
stringr::str_detect(V5, "m") == TRUE &
stringr::str_detect(V6, Result_Specials_String) == TRUE &
stringr::str_detect(V6, "m") == FALSE ~ V5,
stringr::str_detect(V5, Result_Specials_String) &
stringr::str_detect(V5, "m") == FALSE &
stringr::str_detect(V6, Result_Specials_String) == TRUE &
stringr::str_detect(V6, "m") == TRUE ~ V6,
stringr::str_detect(V5, Result_Specials_String) == TRUE &
stringr::str_detect(V6, Result_Specials_String) == FALSE ~ V5,
stringr::str_detect(V6, Result_Specials_String) == TRUE ~ V6,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Wind_Speed = dplyr::case_when(
stringr::str_detect(V5, Wind_String) == TRUE &
stringr::str_detect(V6, Wind_String) == FALSE ~ V5,
stringr::str_detect(V6, Wind_String) ~ V6,
TRUE ~ "NA"
)
) %>%
dplyr::select(
Place,
Bib_Number,
Name,
Age,
Team,
Finals_Result,
Wind_Speed,
'Row_Numb' = V7
)
)
} 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::mutate(Place = V1) %>%
dplyr::mutate(
Name = dplyr::case_when(
stringr::str_detect(V2, Name_String) ~ V2,
stringr::str_detect(V3, Name_String) ~ V3,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(Bib_Number = case_when(str_detect(V2, "^\\d{1,6}$") ~ V2,
TRUE ~ "NA")) %>%
dplyr::mutate(
Age = dplyr::case_when(
stringr::str_detect(V3, Age_String) ~ V3,
stringr::str_detect(V4, Age_String) ~ V4,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Team = dplyr::case_when(
stringr::str_detect(V3, Result_Specials_String) == TRUE ~ V2,
stringr::str_detect(V2, Name_String) == TRUE &
stringr::str_detect(V3, Age_String) == FALSE &
stringr::str_detect(V4, Result_Specials_String) == TRUE ~ V3,
stringr::str_detect(V3, Age_String) &
stringr::str_detect(V4, "[:alpha:]{2,}") ~ V4,
stringr::str_detect(V3, Name_String) == TRUE &
stringr::str_detect(V5, Result_Specials_String) == TRUE &
stringr::str_detect(V4, "[:alpha:]{2,}") ~ V4,
stringr::str_detect(V4, Age_String) &
stringr::str_detect(V3, Result_Specials_String) == FALSE &
stringr::str_detect(V5, "[:alpha:]{2,}") ~ V5,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Team = dplyr::case_when(Team == "NA" ~ Name,
TRUE ~ Team),
Name = dplyr::case_when(Team == Name ~ "NA",
TRUE ~ Name)
) %>%
dplyr::mutate(Age = dplyr::case_when(Name == "NA" ~ "NA",
TRUE ~ Age)) %>%
dplyr::mutate(
Prelims_Result = dplyr::case_when(
stringr::str_detect(V3, Result_Specials_String) == TRUE &
stringr::str_detect(V4, Result_Specials_String) == TRUE ~ V3,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Finals_Result = dplyr::case_when(
stringr::str_detect(V4, Result_Specials_String) &
stringr::str_detect(V4, "m") == TRUE &
stringr::str_detect(V5, Result_Specials_String) == TRUE &
stringr::str_detect(V5, "m") == FALSE ~ V4,
stringr::str_detect(V4, Result_Specials_String) &
stringr::str_detect(V4, "m") == FALSE &
stringr::str_detect(V5, Result_Specials_String) == TRUE &
stringr::str_detect(V5, "m") == TRUE ~ V5,
stringr::str_detect(V3, Result_Specials_String) == TRUE &
stringr::str_detect(V4, Result_Specials_String) == FALSE ~ V3,
stringr::str_detect(V3, Result_Specials_String) == TRUE &
stringr::str_detect(V4, Result_Specials_String) == TRUE ~ V4,
stringr::str_detect(V3, Result_Specials_String) == FALSE &
stringr::str_detect(V5, Result_Specials_String) == FALSE &
stringr::str_detect(V4, Result_Specials_String) == TRUE ~ V4,
stringr::str_detect(V5, Result_Specials_String) == TRUE ~ V5
)
) %>%
dplyr::select(
Place,
Bib_Number,
Name,
Age,
Team,
Prelims_Result,
Finals_Result,
'Row_Numb' = V6
)
)
} 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::mutate(
Name = dplyr::case_when(
stringr::str_detect(V2, Name_String) == TRUE &
stringr::str_detect(V4, Result_Specials_String) == TRUE ~ V2,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Team = dplyr::case_when(
stringr::str_detect(V2, Name_String) == TRUE &
stringr::str_detect(V4, Result_Specials_String) == TRUE ~ V3,
TRUE ~ V2
)
) %>%
dplyr::mutate(
Prelims_Result = dplyr::case_when(
stringr::str_detect(V3, Result_Specials_String) == TRUE &
stringr::str_detect(V4, Result_Specials_String) == TRUE ~ V3,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Finals_Result = dplyr::case_when(
stringr::str_detect(V3, Result_Specials_String) == TRUE &
stringr::str_detect(V4, Result_Specials_String) == FALSE ~ V3,
stringr::str_detect(V4, Result_Specials_String) == TRUE ~ V4,
TRUE ~ "NA"
)
) %>%
dplyr::select(
"Place" = V1,
Name,
Team,
Prelims_Result,
Finals_Result,
"Row_Numb" = V5
)
)
} else {
df_5 <- data.frame(Row_Numb = character(),
stringsAsFactors = FALSE)
}
#### four variables ####
if (length(data_length_4) > 0) {
suppressWarnings(
df_4 <- data_length_4 %>%
list_transform() %>%
dplyr::select(
"Place" = V1,
"Team" = V2,
"Finals_Result" = V3,
"Row_Numb" = V4
)
)
} else {
df_4 <- data.frame(Row_Numb = character(),
stringsAsFactors = FALSE)
}
Min_Row_Numb <- min(events$Event_Row_Min)
suppressWarnings(
data <-
dplyr::bind_rows(df_10, df_9, df_8, df_7, df_6, df_5, df_4) %>%
dplyr::mutate(Row_Numb = as.numeric(Row_Numb)) %>%
dplyr::arrange(Row_Numb) %>%
dplyr::mutate(Exhibition = 0) %>%
dplyr::mutate(DQ = 0) %>%
{ # Place column might or might not exist
if("Place" %!in% names(.)) dplyr::mutate(., Place = "NA") else . # sometimes there's no place column
} %>%
dplyr::filter(Place != "ND") %>%
### moved up from below for DQ work 8/20
dplyr::mutate(
DQ = dplyr::case_when(
Place == 10000 &
Exhibition == 0 & # added exhibition condition 8/27
stringr::str_detect(Finals_Result, "DNS") == FALSE ~ 1, # DNS is not charged as an event, is not a DQ
TRUE ~ DQ
)
) %>%
dplyr::na_if(10000) %>%
{ # Names column might or might not exist
if("Name" %!in% names(.)) dplyr::mutate(., Name = "NA") else . # relay entries don't have a team column
} %>%
dplyr::mutate(dplyr::across(
c(Name, Team), ~ stringr::str_replace_all(., "10000", "--")
)) %>% # remove any "10000"s added in erroneously
####
# dplyr::na_if("DNS") %>%
# dplyr::na_if("DNF") %>%
# dplyr::na_if("DQ") %>%
# dplyr::na_if("FOUL") %>%
# dplyr::na_if("NH") %>%
# dplyr::na_if("NWI") %>%
# dplyr::na_if("SCR") %>%
# dplyr::na_if("FS") %>%
dplyr::mutate(
# Place = as.numeric(Place), # from swim_parse for dealing with ties
# 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(Row_Numb >= Min_Row_Numb) %>%
dplyr::filter(stringr::str_detect(Place, "\\.") %in% c(FALSE, NA)) %>%
dplyr::na_if("NA")
)
#### Address Gendered Ages
if("Age" %in% names(data)){
data <- data %>%
dplyr::mutate(Gender = stringr::str_extract(Age, "^M|^W")) %>%
dplyr::mutate(Age = dplyr::case_when(
is.na(Gender) == FALSE ~ stringr::str_remove(Age, Gender),
TRUE ~ Age
))
}
#### Address Names with "." renamed to "Period"
if("Name" %in% names(data)){
data <- data %>%
dplyr::mutate(Name = stringr::str_replace(Name, "Period", "\\."))
}
#### added in to work with arrange/distinct calls after adding in events ####
if("Prelims_Result" %in% names(data) == FALSE){
data$Prelims_Result <- NA
}
if("Wind_Speed" %in% names(data) == FALSE){
data$Wind_Speed <- NA
}
#### add in events based on row number ranges ####
data <-
transform(data, Event = events$Event[findInterval(Row_Numb, events$Event_Row_Min)]) %>%
dplyr::arrange(Name, Team, is.na(Wind_Speed), is.na(Prelims_Result)) %>% # new 1/1/21 to deal with results presented by heat and as final on same page
dplyr::distinct(Name, Team, Event, Prelims_Result, Finals_Result, .keep_all = TRUE) %>% # new 1/1/21 to deal with results presented by heat and as final on same page
dplyr::arrange(Row_Numb)
#### adding relay athletes in ####
if (hytek_relay_athletes == TRUE) {
relay_athletes_df <- collect_relay_athletes(raw_results)
relay_athletes_df <-
transform(relay_athletes_df, Row_Numb_Adjusted = data$Row_Numb[findInterval(Row_Numb, data$Row_Numb)]) %>%
dplyr::select(-Row_Numb)
data <- data %>%
dplyr::left_join(relay_athletes_df, c("Row_Numb" = "Row_Numb_Adjusted"))
}
#### adding splits back in ####
if (hytek_splits == TRUE) {
splits_df <- splits_parse(raw_results, split_len = hytek_split_length) %>%
dplyr::distinct(dplyr::across(-Row_Numb), .keep_all = TRUE)
#### matches row numbers in splits_df to available row numbers in data
# helps a lot with relays, since their row numbers vary based on whether or not relay swimmers are included
# and if those swimmers are listed on one line or two
splits_df <-
transform(splits_df, Row_Numb_Adjusted = data$Row_Numb[findInterval(Row_Numb, data$Row_Numb)]) %>%
dplyr::select(-Row_Numb)
data <- data %>%
dplyr::left_join(splits_df, by = c("Row_Numb" = "Row_Numb_Adjusted")) %>%
dplyr::select(!dplyr::starts_with("Split"), stringr::str_sort(names(.), numeric = TRUE)) # keep splits columns in order
}
#### adding in rounds ####
if(hytek_rounds == TRUE){
rounds_df <- rounds_parse(raw_results)
rounds_df <-
transform(rounds_df, Row_Numb_Adjusted = data$Row_Numb[findInterval(Row_Numb, data$Row_Numb)]) %>%
dplyr::select(-Row_Numb)
data <- dplyr::left_join(data, rounds_df, by = c("Row_Numb" = "Row_Numb_Adjusted"))
}
#### adding in rounds results ####
if(hytek_round_attempts == TRUE){
round_attempts_df <- round_attempts_parse(raw_results)
round_attempts_df <-
transform(round_attempts_df, Row_Numb_Adjusted = data$Row_Numb[findInterval(Row_Numb, data$Row_Numb)]) %>%
dplyr::select(-Row_Numb)
data <- dplyr::left_join(data, round_attempts_df, by = c("Row_Numb" = "Row_Numb_Adjusted")) %>%
dplyr::mutate(dplyr::across(dplyr::contains("_Attempts"), ~ dplyr::case_when(stringr::str_detect(Event, "ong|riple") ~ "Unknown",
stringr::str_detect(Event, "ong|riple", negate = TRUE) ~ .))) %>%
na_if("Unknown")
}
if(hytek_split_attempts == TRUE){
suppressMessages(data <- attempts_split(data))
}
# removes unneeded Round_X columns (i.e. those that don't have an associated Round_Result)
if (any(stringr::str_detect(names(data), "Round_\\d{1,}_Attempt")) == TRUE) {
data <- remove_unneeded_rounds(data) %>%
dplyr::na_if("")
}
# add in wind for rounds
if(hytek_rounds == TRUE){
round_wind_df <- wind_parse_hytek(raw_results)
round_wind_df <-
transform(round_wind_df, Row_Numb_Adjusted = data$Row_Numb[findInterval(Row_Numb, data$Row_Numb)]) %>%
dplyr::select(-Row_Numb)
data <- dplyr::left_join(data, round_wind_df, by = c("Row_Numb" = "Row_Numb_Adjusted"))
}
#### ordering columns after adding rounds ####
if (hytek_rounds == TRUE) {
data <- data %>%
dplyr::select(colnames(.)[stringr::str_detect(names(.), "^Round", negate = TRUE)], stringr::str_sort(colnames(.)[stringr::str_detect(names(.), "^Round")]))
}
#### remove empty columns (all values are NA) ####
data <- Filter(function(x)
!all(is.na(x)), data)
#### remove unneeded columns ####
data <- data %>%
dplyr::select(which(SwimmeR::`%!in%`(
names(.), c("Row_Numb", "Exhibition", "Points", "Heat")
)))
row.names(data) <- NULL
return(data)
}
}
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Defines functions hytek_parse
Documented in hytek_parse
#' Parses Hytek format track and field results inside \code{tf_parse}
#'
#' Outputs a data frame of track and field results
#'
#' @importFrom dplyr mutate
#' @importFrom dplyr filter
#' @importFrom dplyr case_when
#' @importFrom dplyr na_if
#' @importFrom dplyr select
#' @importFrom dplyr bind_rows
#' @importFrom dplyr arrange
#' @importFrom dplyr across
#' @importFrom dplyr left_join
#' @importFrom dplyr all_of
#' @importFrom dplyr starts_with
#' @importFrom dplyr matches
#' @importFrom dplyr distinct
#' @importFrom stringr str_remove
#' @importFrom stringr str_remove_all
#' @importFrom stringr str_detect
#' @importFrom stringr str_split
#' @importFrom stringr str_length
#' @importFrom stringr str_replace_all
#' @importFrom stringr str_replace
#' @importFrom stringr str_subset
#' @importFrom stringr str_remove
#' @importFrom purrr map
#' @importFrom purrr map_lgl
#' @importFrom stats setNames
#' @importFrom SwimmeR `%!in%`
#'
#' @param hytek_file data with row numbers added
#' @param hytek_relay_athletes should \code{tf_parse} try to include the names
#' of relay athletes for relay events? Names will be listed in new columns
#' "Relay-Athlete_1", "Relay_Athlete_2" etc. Defaults to \code{FALSE}.
#' @param hytek_rounds should \code{tf_parse} try to include rounds for
#' jumping/throwing events? Please note this will add a significant number of
#' columns to the resulting data frame. Defaults to \code{FALSE}.
#' @param hytek_round_attempts should \code{tf_parse} try to include rounds
#' results (i.e. "PASS", "X", "O") for high jump and pole value events?
#' Please note this will add a significant number of columns to the resulting
#' data frame. Defaults to \code{FALSE}
#' @param hytek_split_attempts should \code{tf_parse} split attempts from each
#' round into separate columns? For example "XXO" would result in three
#' columns, one for "X', another for the second "X" and third for "O". There
#' will be a lot of columns. Defaults to \code{FALSE}
#' @param hytek_splits either \code{TRUE} or the default, \code{FALSE} - should
#' \code{hytek_parse} attempt to include splits.
#' @param hytek_split_length either the distance at which splits are collected
#' (must be constant distance) or the default, \code{1}, the length of track
#' at which splits are recorded. Not all results are internally consistent on
#' this issue. If in doubt use the default \code{1}
#'
#' @return a data frame of track and field results
#'
#' @seealso \code{hytek_parse} is runs inside
#' \code{\link{tf_parse}}
hytek_parse <-
function(hytek_file = file,
hytek_relay_athletes = relay_athletes,
hytek_rounds = rounds,
hytek_round_attempts = round_attempts,
hytek_split_attempts = split_attempts,
hytek_splits = splits,
hytek_split_length = split_length) {
#### testing setup ####
# hytek_file <-
# "http://results.deltatiming.com/ncaa/tf/2019-joe-walker-invitational/190412F009" %>%
# read_results() %>%
# add_row_numbers()
# hytek_file <-
# "http://tfresultsdata.deltatiming.com/2018-hurricane-invitational/180316F028.htm" %>%
# read_results() %>%
# add_row_numbers()
# hytek_file <- "http://tfresultsdata.deltatiming.com/2019-ysu-icebreaker/191206F031.htm" %>%
# read_results() %>%
# add_row_numbers()
#### Pulls out event labels from text ####
raw_results <- hytek_file
events <- event_parse(raw_results) %>%
dplyr::mutate(Event = stringr::str_remove(Event, " Women$| Men$"))
#### set up strings ####
Name_String <-
"_?[:alpha:]+\\s?\\'?[:alpha:\\-\\'\\.]*\\s?[:alpha:\\-\\'\\.]*\\s?[:alpha:\\-\\'\\.]*,?\\s?[:alpha:\\-\\'\\.]*\\s?[:alpha:]*\\s?[:alpha:]*\\s?[:alpha:]*\\.?,? [:alpha:]+\\s?[:alpha:\\-\\'\\.]*\\s?[:alpha:\\-\\']*\\s?[:alpha:]*\\s?[:alpha:]*\\s?[:alpha:\\.]*"
Result_String <- "\\d{0,2}\\:?\\-?\\d{1,2}\\.\\d{2}m?"
Result_Specials_String <-
paste0(Result_String, "$|^NT$|^NP$|^DQ$|^DNS$|^DNF$|^FOUL$|^NH$|^SCR$|^FS$|^ND$")
Wind_String <-
"\\+\\d\\.\\d|\\-\\d\\.\\d|^NWS$|^NWI$|^\\d\\.\\d$"
Age_String <- "^SR$|^JR$|^SO$|^FR$|^M?W?[:digit:]{1,3}$"
Tiebreaker_String <- "\\d\\.\\d{3}$"
suppressWarnings(
data_1 <- raw_results %>%
.[purrr::map(., length) > 0] %>%
.[stringr::str_length(.) > 50] %>%
.[stringr::str_count(., "\\d\\)") < 2] %>% # remove inline splits and team scores as 1) Alfred 2) Ithaca etc.
.[stringr::str_detect(., paste0(Result_String, "|DQ|DNS|DNF|FOUL|NH|SCR|FS|ND"))] %>% # must Results_String because all results do
.[purrr::map_lgl(., ~ !any(stringr::str_detect(., "\\d{3}\\.\\d{2}")))] %>% # closes loophole in Result_String where a number like 100.00 could get through even though it's not a valid result
.[purrr::map_lgl(., ~ !any(
stringr::str_detect(., "^[0-9\\(\\)\\.FOULPASSm\\s\\-\\+]+$")
))] %>% # closes loophole where throwing splits lines get in because they contain FOUL or PASS plus valid results
.[purrr::map_lgl(., ~ !any(
stringr::str_detect(., "Event .*\\d")
))] %>% # removes event titles that also include distances, like "Event 1 Short Hurdles 0.762m"
.[stringr::str_detect(., "[:alpha:]{2,}")] %>% # must have at least two letters in a row
# .[purrr::map_lgl(., ~ !any(stringr::str_detect(., avoid)))] %>% # remove lines contained in avoid
stringr::str_remove_all("\n\\s*") %>%
# stringr::str_remove_all("\\d{0,2}\\:?\\d{1,2}\\.\\d{3}") %>% # ties
# trimws() %>%
# stringr::str_replace_all(stats::setNames(replacement, typo)) %>%
# remove 'A', 'B' etc. relay designators
stringr::str_replace_all(" \\'[A-Z]\\' ", " ") %>% # tf specific - removes relay A, B etc. designators
stringr::str_replace_all(" [A-Z] ", " ") %>%
stringr::str_replace_all("\\'\\'", " ") %>%
# stringr::str_remove_all("(?<=\\.\\d{2})[Q|R|M|E|\\$|q](?=\\s)") %>% # tf specific - removes "q" or "Q" sometimes used to designate a qualifying result, also 'R', "M", "$", "E"
stringr::str_remove_all("(?<=\\.\\d{2})[A-Z|\\$|q](?=\\s)") %>% # tf specific - removes "q" or "Q" sometimes used to designate a qualifying result, also 'R', "M", "$", "E"
stringr::str_replace_all("(?<=\\s{3})[A-Z|\\$|q] (?=\\d)", " ") %>% # tf specific - removes upper case letters sometimes used before times to designate records etc.
stringr::str_remove_all("(?<=\\s)[J|j](?=\\d)") %>% # tf specific - removes "j" or "J" sometimes used to designate a judged result
stringr::str_replace_all("-{2,5}", "10000") %>% #8/26
# stringr::str_replace_all("(\\.\\d{2})\\d+", "\\1 ") %>% # added 8/21 for illinois to deal with points column merging with final times column
# stringr::str_replace_all("\\d{1,2} (\\d{1,})$", " \\1 ") %>% # added 8/21 for illinois to deal with points column merging with final times column
stringr::str_replace_all("\\*", "_") %>%
stringr::str_replace_all(" \\+", " \\+") %>% # tf speciifc, for windspeed
stringr::str_replace_all(" \\-", " \\-") %>% # tf speciifc, for windspeed
stringr::str_replace_all("#", " ") %>% # tf specific, leading pound sign to spaces
stringr::str_replace_all("(?<=\\d) (?=[:alpha:])", " ") %>% # tf specific - split place and name
stringr::str_replace_all("(?<=\\dm) (?=[\\-|\\+|\\d])", " ") %>% # tf specific - split distance and windspeed
stringr::str_replace_all("(?<=\\d) (?=[\\-|\\+|\\d])", " ") %>% # tf specific - split time and windspeed
stringr::str_replace_all("(?<=\\d{3}) (?=[:alpha:])", " ") %>% # tf specific - split bib number and name
stringr::str_replace_all("(?<=\\d{2}) (?=[:alpha:])", " ") %>% # tf specific - split age and team
stringr::str_replace_all("(?<=\\.\\d{3})s ", " ") %>% # tf specific - split tiebreaker and s designator
stringr::str_replace_all(" FR ", " FR ") %>% # tf specific - split age and team
stringr::str_replace_all(" SO ", " SO ") %>% # tf specific - split age and team
stringr::str_replace_all(" JR ", " JR ") %>% # tf specific - split age and team
stringr::str_replace_all(" SR ", " SR ") %>% # tf specific - split age and team
stringr::str_replace_all("(?<=[:alpha:]) (?=\\d)", " ") %>% # tf specific - split name and age
stringr::str_replace_all("(?<=\\,) (?=\\d)", " ") %>% # tf specific - split name and age
stringr::str_replace_all(" (M\\d{1,3}) ", " \\1 ") %>% # tf specific - gendered ages M
stringr::str_replace_all(" (W\\d{1,3}) ", " \\1 ") %>% # tf specific - gendered ages W
stringr::str_replace_all("(?<=\\d\\.\\d) (?=\\d{1,2}\\s)", " ") %>% # tf specific - split off wind and heat number
stringr::str_replace_all("(?<=\\d) (?=\\d{1,}$)", " ") %>% # tf specific - split off row_numb
stringr::str_replace_all("(?<=\\dm)[:upper:](?=\\s)", " ") %>% # tf specific - sometimes an M for Meet record is added to a distance 1.23m as 1.23mM
stringr::str_replace_all("(?<=\\dm)[:graph:](?=\\s)", " ") %>% # tf specific - sometimes an M for Meet record is added to a distance 1.23m as 1.23mM
stringr::str_replace_all(" \\., ", " Period, ") %>% # for people with no first/last name, like Indian runners in some Singapore results
stringr::str_replace_all("([:alpha])(\\.[:alpha:])", "\\1 \\2") %>%
trimws()
)
#### if data_1 is empty ####
if(!length(data_1) > 0){
message("No results found in file")
} else {
#### splits data into variables by splitting at multiple (>= 2) spaces ####
data_1 <-
unlist(purrr::map(data_1, stringr::str_split, "\\s{2,}"),
recursive = FALSE)
# unique(map(data_1, length))
#### breaks data into subsets based on how many variables it has ####
data_length_4 <- data_1[purrr::map(data_1, length) == 4]
data_length_5 <- data_1[purrr::map(data_1, length) == 5]
data_length_6 <- data_1[purrr::map(data_1, length) == 6]
data_length_7 <- data_1[purrr::map(data_1, length) == 7]
data_length_8 <- data_1[purrr::map(data_1, length) == 8]
data_length_9 <- data_1[purrr::map(data_1, length) == 9]
data_length_10 <- data_1[purrr::map(data_1, length) == 10]
# treatment of DQs
# suppressWarnings(DQ <-
# data_1[stringr::str_detect(data_1, Time_Score_String, negate = TRUE) == TRUE])
# DQ_length_3 <- DQ[purrr::map(DQ, length) == 3]
# DQ_length_4 <- DQ[purrr::map(DQ, length) == 4]
#### ten variables ####
if (length(data_length_10) > 0) {
suppressWarnings(
df_10 <- data_length_10 %>%
list_transform() %>%
dplyr::mutate(Place = V1) %>%
dplyr::mutate(
Bib_Number = dplyr::case_when(stringr::str_detect(V2, "^\\d{1,6}$") ~ V2,
TRUE ~ "NA")
) %>%
dplyr::mutate(
Name = dplyr::case_when(
stringr::str_detect(V2, Name_String) ~ V2,
stringr::str_detect(V3, Name_String) ~ V3,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Age = dplyr::case_when(
stringr::str_detect(V3, Age_String) ~ V3,
stringr::str_detect(V4, Age_String) ~ V4,
stringr::str_detect(V5, Age_String) ~ V5,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Team = dplyr::case_when(
stringr::str_detect(V3, Age_String) &
stringr::str_detect(V4, "[:alpha:]{2,}") ~ V4,
stringr::str_detect(V4, Age_String) &
stringr::str_detect(V5, "[:alpha:]{2,}") ~ V5,
stringr::str_detect(V5, Age_String) &
stringr::str_detect(V6, "[:alpha:]{2,}") ~ V6,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Prelims_Result = dplyr::case_when(
stringr::str_detect(V5, Result_Specials_String) &
stringr::str_detect(V6, Result_Specials_String) ~ V5,
stringr::str_detect(V6, Result_Specials_String) &
stringr::str_detect(V7, Result_Specials_String) ~ V6,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Finals_Result = dplyr::case_when(
stringr::str_detect(V5, Result_Specials_String) == TRUE &
stringr::str_detect(V6, Result_Specials_String) == FALSE ~ V5,
stringr::str_detect(V6, Result_Specials_String) == TRUE &
stringr::str_detect(V5, Result_Specials_String) == FALSE &
stringr::str_detect(V7, Result_Specials_String) == FALSE ~ V6,
stringr::str_detect(V6, Result_Specials_String) &
stringr::str_detect(V7, Result_Specials_String) ~ V7,
stringr::str_detect(V7, Result_Specials_String) == TRUE &
stringr::str_detect(V6, Result_Specials_String) == FALSE &
stringr::str_detect(V8, Result_Specials_String) == FALSE ~ V7,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Wind_Speed = dplyr::case_when(stringr::str_detect(V8, Wind_String) ~ V8,
TRUE ~ "NA")
) %>%
dplyr::mutate(
Points = dplyr::case_when(
stringr::str_detect(V8, Wind_String) == FALSE &
stringr::str_detect(V8, "^\\d\\.?\\d?$") == TRUE &
stringr::str_detect(V9, "\\d{1,2}") == FALSE ~ V8,
stringr::str_detect(V8, Wind_String) == TRUE &
stringr::str_detect(V9, "^\\d\\.?\\d?$") == TRUE ~ V9,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Tiebreaker = dplyr::case_when(stringr::str_detect(V9, Tiebreaker_String) == TRUE ~ V9,
TRUE ~ "NA")
) %>%
dplyr::select(
Place,
Bib_Number,
Name,
Age,
Team,
Prelims_Result,
Finals_Result,
Wind_Speed,
Points,
Tiebreaker,
'Row_Numb' = V10
)
)
} else {
df_10 <- data.frame(Row_Numb = character(),
stringsAsFactors = FALSE)
}
#### nine variables ####
if (length(data_length_9) > 0) {
suppressWarnings(
df_9 <- data_length_9 %>%
list_transform() %>%
dplyr::mutate(Place = V1) %>%
dplyr::mutate(
Bib_Number = dplyr::case_when(stringr::str_detect(V2, "^\\d{1,6}$") ~ V2,
TRUE ~ "NA")
) %>%
dplyr::mutate(
Name = dplyr::case_when(
stringr::str_detect(V2, Name_String) ~ V2,
stringr::str_detect(V3, Name_String) ~ V3,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Age = dplyr::case_when(
stringr::str_detect(V3, Age_String) ~ V3,
stringr::str_detect(V4, Age_String) ~ V4,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Team = dplyr::case_when(
stringr::str_detect(V3, Age_String) &
stringr::str_detect(V4, "[:alpha:]{2,}") ~ V4,
stringr::str_detect(V4, Age_String) &
stringr::str_detect(V5, "[:alpha:]{2,}") ~ V5,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Prelims_Result = dplyr::case_when(
stringr::str_detect(V6, Result_Specials_String) == TRUE &
stringr::str_detect(V7, Result_Specials_String) == TRUE ~ V6,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Finals_Result = dplyr::case_when(
stringr::str_detect(V6, Result_Specials_String) &
stringr::str_detect(V7, Result_Specials_String) ~ V7,
stringr::str_detect(V6, Result_Specials_String) == TRUE &
stringr::str_detect(V5, Result_Specials_String) == FALSE &
stringr::str_detect(V7, Result_Specials_String) == FALSE ~ V6,
stringr::str_detect(V5, Result_Specials_String) == TRUE &
stringr::str_detect(V6, Result_Specials_String) == FALSE &
stringr::str_detect(V4, Result_Specials_String) == FALSE ~ V5,
stringr::str_detect(V6, Result_Specials_String) == TRUE &
stringr::str_detect(V5, Result_Specials_String) == TRUE &
stringr::str_detect(V5, "m") == TRUE &
stringr::str_detect(V6, "-") == TRUE ~ V5,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Wind_Speed = dplyr::case_when(
stringr::str_detect(V7, Wind_String) ~ V7,
stringr::str_detect(V7, Wind_String) == FALSE &
stringr::str_detect(V8, Wind_String) == TRUE ~ V8,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Heat = dplyr::case_when(
stringr::str_detect(V7, Wind_String) == FALSE &
stringr::str_detect(V7, "^\\d{1,2}$") == TRUE &
stringr::str_detect(V8, "^\\d\\d?\\.?\\d?$") == TRUE ~ V7,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Points = dplyr::case_when(
stringr::str_detect(V7, Heat) == TRUE &
stringr::str_detect(V8, "^\\d\\d?\\.?\\d?$") ~ V8,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Tiebreaker = dplyr::case_when(
stringr::str_detect(V8, Tiebreaker_String) == TRUE ~ V8,
TRUE ~ "NA"
)
) %>%
dplyr::select(
Place,
Bib_Number,
Name,
Age,
Team,
Prelims_Result,
Finals_Result,
Wind_Speed,
Heat,
Points,
Tiebreaker,
'Row_Numb' = V9
)
)
} else {
df_9 <- data.frame(Row_Numb = character(),
stringsAsFactors = FALSE)
}
#### eight variables ####
if (length(data_length_8) > 0) {
suppressWarnings(
df_8 <- data_length_8 %>%
list_transform() %>%
dplyr::mutate(Place = V1) %>%
dplyr::mutate(Bib_Number = dplyr::case_when(
stringr::str_detect(V2, "^\\d{1,6}$") ~ V2,
TRUE ~ "NA"
)) %>%
dplyr::mutate(
Name = dplyr::case_when(
stringr::str_detect(V2, Name_String) ~ V2,
stringr::str_detect(V3, Name_String) ~ V3,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Age = dplyr::case_when(
stringr::str_detect(V3, Age_String) ~ V3,
stringr::str_detect(V4, Age_String) ~ V4,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Team = dplyr::case_when(
stringr::str_detect(V3, Age_String) &
stringr::str_detect(V4, "[:alpha:]{2,}") ~ V4,
stringr::str_detect(V4, Age_String) &
stringr::str_detect(V5, "[:alpha:]{2,}") ~ V5,
stringr::str_detect(V2, Name_String) == TRUE &
stringr::str_detect(V3, Age_String) == FALSE &
stringr::str_detect(V3, "[:alpha:]{2,}") == TRUE ~ V3,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Finals_Result = dplyr::case_when(
stringr::str_detect(V4, Result_Specials_String) == TRUE &
stringr::str_detect(V5, Result_Specials_String) == FALSE ~ V4,
stringr::str_detect(V5, Result_Specials_String) == TRUE &
stringr::str_detect(V6, Result_Specials_String) == FALSE ~ V5,
stringr::str_detect(V6, Result_Specials_String) == TRUE &
stringr::str_detect(V5, Result_Specials_String) == FALSE &
stringr::str_detect(V7, Result_Specials_String) == FALSE ~ V6,
stringr::str_detect(V6, Result_Specials_String) == TRUE &
stringr::str_detect(V5, Result_Specials_String) == TRUE &
stringr::str_detect(V5, "m") == TRUE &
stringr::str_detect(V6, "-") == TRUE ~ V5,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Wind_Speed = dplyr::case_when(
stringr::str_detect(V5, Wind_String) == TRUE &
stringr::str_detect(V6, Wind_String) == FALSE ~ V5,
stringr::str_detect(V6, Wind_String) == TRUE &
stringr::str_detect(V7, Wind_String) == FALSE ~ V6,
stringr::str_detect(V7, Wind_String) == TRUE ~ V7,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Tiebreaker = dplyr::case_when(
stringr::str_detect(V7, Tiebreaker_String) == TRUE ~ V7,
TRUE ~ "NA"
)
) %>%
dplyr::select(
Place,
Bib_Number,
Name,
Age,
Team,
Finals_Result,
Wind_Speed,
Tiebreaker,
'Row_Numb' = V8
)
)
} 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::mutate(Place = V1) %>%
dplyr::mutate(
Bib_Number = dplyr::case_when(stringr::str_detect(V2, "^\\d{1,6}$") ~ V2,
TRUE ~ "NA")
) %>%
dplyr::mutate(
Name = dplyr::case_when(
stringr::str_detect(V2, Name_String) ~ V2,
stringr::str_detect(V3, Name_String) ~ V3,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Age = dplyr::case_when(
stringr::str_detect(V3, Age_String) ~ V3,
stringr::str_detect(V4, Age_String) ~ V4,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Team = dplyr::case_when(
stringr::str_detect(V3, Age_String) == FALSE &
# stringr::str_detect(V4, Age_String) == FALSE &
stringr::str_detect(V4, Result_Specials_String) == TRUE &
stringr::str_detect(V3, "[:alpha:]{2,}") == TRUE ~ V3,
stringr::str_detect(V3, Age_String) == TRUE &
stringr::str_detect(V4, "[:alpha:]{2,}") == TRUE ~ V4,
stringr::str_detect(V3, Name_String) == TRUE &
stringr::str_detect(V5, Result_Specials_String) == TRUE &
stringr::str_detect(V4, "[:alpha:]{2,}") == TRUE ~ V4,
stringr::str_detect(V4, Age_String) == TRUE &
stringr::str_detect(V5, "[:alpha:]{2,}") == TRUE ~ V5,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Finals_Result = dplyr::case_when(
stringr::str_detect(V4, Result_Specials_String) == TRUE ~ V4,
stringr::str_detect(V5, Result_Specials_String) == TRUE & # to deal with results that have both metric and imperial result - prefer metric
stringr::str_detect(V5, "m") == TRUE &
stringr::str_detect(V6, Result_Specials_String) == TRUE &
stringr::str_detect(V6, "m") == FALSE ~ V5,
stringr::str_detect(V5, Result_Specials_String) &
stringr::str_detect(V5, "m") == FALSE &
stringr::str_detect(V6, Result_Specials_String) == TRUE &
stringr::str_detect(V6, "m") == TRUE ~ V6,
stringr::str_detect(V5, Result_Specials_String) == TRUE &
stringr::str_detect(V6, Result_Specials_String) == FALSE ~ V5,
stringr::str_detect(V6, Result_Specials_String) == TRUE ~ V6,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Wind_Speed = dplyr::case_when(
stringr::str_detect(V5, Wind_String) == TRUE &
stringr::str_detect(V6, Wind_String) == FALSE ~ V5,
stringr::str_detect(V6, Wind_String) ~ V6,
TRUE ~ "NA"
)
) %>%
dplyr::select(
Place,
Bib_Number,
Name,
Age,
Team,
Finals_Result,
Wind_Speed,
'Row_Numb' = V7
)
)
} 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::mutate(Place = V1) %>%
dplyr::mutate(
Name = dplyr::case_when(
stringr::str_detect(V2, Name_String) ~ V2,
stringr::str_detect(V3, Name_String) ~ V3,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(Bib_Number = case_when(str_detect(V2, "^\\d{1,6}$") ~ V2,
TRUE ~ "NA")) %>%
dplyr::mutate(
Age = dplyr::case_when(
stringr::str_detect(V3, Age_String) ~ V3,
stringr::str_detect(V4, Age_String) ~ V4,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Team = dplyr::case_when(
stringr::str_detect(V3, Result_Specials_String) == TRUE ~ V2,
stringr::str_detect(V2, Name_String) == TRUE &
stringr::str_detect(V3, Age_String) == FALSE &
stringr::str_detect(V4, Result_Specials_String) == TRUE ~ V3,
stringr::str_detect(V3, Age_String) &
stringr::str_detect(V4, "[:alpha:]{2,}") ~ V4,
stringr::str_detect(V3, Name_String) == TRUE &
stringr::str_detect(V5, Result_Specials_String) == TRUE &
stringr::str_detect(V4, "[:alpha:]{2,}") ~ V4,
stringr::str_detect(V4, Age_String) &
stringr::str_detect(V3, Result_Specials_String) == FALSE &
stringr::str_detect(V5, "[:alpha:]{2,}") ~ V5,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Team = dplyr::case_when(Team == "NA" ~ Name,
TRUE ~ Team),
Name = dplyr::case_when(Team == Name ~ "NA",
TRUE ~ Name)
) %>%
dplyr::mutate(Age = dplyr::case_when(Name == "NA" ~ "NA",
TRUE ~ Age)) %>%
dplyr::mutate(
Prelims_Result = dplyr::case_when(
stringr::str_detect(V3, Result_Specials_String) == TRUE &
stringr::str_detect(V4, Result_Specials_String) == TRUE ~ V3,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Finals_Result = dplyr::case_when(
stringr::str_detect(V4, Result_Specials_String) &
stringr::str_detect(V4, "m") == TRUE &
stringr::str_detect(V5, Result_Specials_String) == TRUE &
stringr::str_detect(V5, "m") == FALSE ~ V4,
stringr::str_detect(V4, Result_Specials_String) &
stringr::str_detect(V4, "m") == FALSE &
stringr::str_detect(V5, Result_Specials_String) == TRUE &
stringr::str_detect(V5, "m") == TRUE ~ V5,
stringr::str_detect(V3, Result_Specials_String) == TRUE &
stringr::str_detect(V4, Result_Specials_String) == FALSE ~ V3,
stringr::str_detect(V3, Result_Specials_String) == TRUE &
stringr::str_detect(V4, Result_Specials_String) == TRUE ~ V4,
stringr::str_detect(V3, Result_Specials_String) == FALSE &
stringr::str_detect(V5, Result_Specials_String) == FALSE &
stringr::str_detect(V4, Result_Specials_String) == TRUE ~ V4,
stringr::str_detect(V5, Result_Specials_String) == TRUE ~ V5
)
) %>%
dplyr::select(
Place,
Bib_Number,
Name,
Age,
Team,
Prelims_Result,
Finals_Result,
'Row_Numb' = V6
)
)
} 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::mutate(
Name = dplyr::case_when(
stringr::str_detect(V2, Name_String) == TRUE &
stringr::str_detect(V4, Result_Specials_String) == TRUE ~ V2,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Team = dplyr::case_when(
stringr::str_detect(V2, Name_String) == TRUE &
stringr::str_detect(V4, Result_Specials_String) == TRUE ~ V3,
TRUE ~ V2
)
) %>%
dplyr::mutate(
Prelims_Result = dplyr::case_when(
stringr::str_detect(V3, Result_Specials_String) == TRUE &
stringr::str_detect(V4, Result_Specials_String) == TRUE ~ V3,
TRUE ~ "NA"
)
) %>%
dplyr::mutate(
Finals_Result = dplyr::case_when(
stringr::str_detect(V3, Result_Specials_String) == TRUE &
stringr::str_detect(V4, Result_Specials_String) == FALSE ~ V3,
stringr::str_detect(V4, Result_Specials_String) == TRUE ~ V4,
TRUE ~ "NA"
)
) %>%
dplyr::select(
"Place" = V1,
Name,
Team,
Prelims_Result,
Finals_Result,
"Row_Numb" = V5
)
)
} else {
df_5 <- data.frame(Row_Numb = character(),
stringsAsFactors = FALSE)
}
#### four variables ####
if (length(data_length_4) > 0) {
suppressWarnings(
df_4 <- data_length_4 %>%
list_transform() %>%
dplyr::select(
"Place" = V1,
"Team" = V2,
"Finals_Result" = V3,
"Row_Numb" = V4
)
)
} else {
df_4 <- data.frame(Row_Numb = character(),
stringsAsFactors = FALSE)
}
Min_Row_Numb <- min(events$Event_Row_Min)
suppressWarnings(
data <-
dplyr::bind_rows(df_10, df_9, df_8, df_7, df_6, df_5, df_4) %>%
dplyr::mutate(Row_Numb = as.numeric(Row_Numb)) %>%
dplyr::arrange(Row_Numb) %>%
dplyr::mutate(Exhibition = 0) %>%
dplyr::mutate(DQ = 0) %>%
{ # Place column might or might not exist
if("Place" %!in% names(.)) dplyr::mutate(., Place = "NA") else . # sometimes there's no place column
} %>%
dplyr::filter(Place != "ND") %>%
### moved up from below for DQ work 8/20
dplyr::mutate(
DQ = dplyr::case_when(
Place == 10000 &
Exhibition == 0 & # added exhibition condition 8/27
stringr::str_detect(Finals_Result, "DNS") == FALSE ~ 1, # DNS is not charged as an event, is not a DQ
TRUE ~ DQ
)
) %>%
dplyr::na_if(10000) %>%
{ # Names column might or might not exist
if("Name" %!in% names(.)) dplyr::mutate(., Name = "NA") else . # relay entries don't have a team column
} %>%
dplyr::mutate(dplyr::across(
c(Name, Team), ~ stringr::str_replace_all(., "10000", "--")
)) %>% # remove any "10000"s added in erroneously
####
# dplyr::na_if("DNS") %>%
# dplyr::na_if("DNF") %>%
# dplyr::na_if("DQ") %>%
# dplyr::na_if("FOUL") %>%
# dplyr::na_if("NH") %>%
# dplyr::na_if("NWI") %>%
# dplyr::na_if("SCR") %>%
# dplyr::na_if("FS") %>%
dplyr::mutate(
# Place = as.numeric(Place), # from swim_parse for dealing with ties
# 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(Row_Numb >= Min_Row_Numb) %>%
dplyr::filter(stringr::str_detect(Place, "\\.") %in% c(FALSE, NA)) %>%
dplyr::na_if("NA")
)
#### Address Gendered Ages
if("Age" %in% names(data)){
data <- data %>%
dplyr::mutate(Gender = stringr::str_extract(Age, "^M|^W")) %>%
dplyr::mutate(Age = dplyr::case_when(
is.na(Gender) == FALSE ~ stringr::str_remove(Age, Gender),
TRUE ~ Age
))
}
#### Address Names with "." renamed to "Period"
if("Name" %in% names(data)){
data <- data %>%
dplyr::mutate(Name = stringr::str_replace(Name, "Period", "\\."))
}
#### added in to work with arrange/distinct calls after adding in events ####
if("Prelims_Result" %in% names(data) == FALSE){
data$Prelims_Result <- NA
}
if("Wind_Speed" %in% names(data) == FALSE){
data$Wind_Speed <- NA
}
#### add in events based on row number ranges ####
data <-
transform(data, Event = events$Event[findInterval(Row_Numb, events$Event_Row_Min)]) %>%
dplyr::arrange(Name, Team, is.na(Wind_Speed), is.na(Prelims_Result)) %>% # new 1/1/21 to deal with results presented by heat and as final on same page
dplyr::distinct(Name, Team, Event, Prelims_Result, Finals_Result, .keep_all = TRUE) %>% # new 1/1/21 to deal with results presented by heat and as final on same page
dplyr::arrange(Row_Numb)
#### adding relay athletes in ####
if (hytek_relay_athletes == TRUE) {
relay_athletes_df <- collect_relay_athletes(raw_results)
relay_athletes_df <-
transform(relay_athletes_df, Row_Numb_Adjusted = data$Row_Numb[findInterval(Row_Numb, data$Row_Numb)]) %>%
dplyr::select(-Row_Numb)
data <- data %>%
dplyr::left_join(relay_athletes_df, c("Row_Numb" = "Row_Numb_Adjusted"))
}
#### adding splits back in ####
if (hytek_splits == TRUE) {
splits_df <- splits_parse(raw_results, split_len = hytek_split_length) %>%
dplyr::distinct(dplyr::across(-Row_Numb), .keep_all = TRUE)
#### matches row numbers in splits_df to available row numbers in data
# helps a lot with relays, since their row numbers vary based on whether or not relay swimmers are included
# and if those swimmers are listed on one line or two
splits_df <-
transform(splits_df, Row_Numb_Adjusted = data$Row_Numb[findInterval(Row_Numb, data$Row_Numb)]) %>%
dplyr::select(-Row_Numb)
data <- data %>%
dplyr::left_join(splits_df, by = c("Row_Numb" = "Row_Numb_Adjusted")) %>%
dplyr::select(!dplyr::starts_with("Split"), stringr::str_sort(names(.), numeric = TRUE)) # keep splits columns in order
}
#### adding in rounds ####
if(hytek_rounds == TRUE){
rounds_df <- rounds_parse(raw_results)
rounds_df <-
transform(rounds_df, Row_Numb_Adjusted = data$Row_Numb[findInterval(Row_Numb, data$Row_Numb)]) %>%
dplyr::select(-Row_Numb)
data <- dplyr::left_join(data, rounds_df, by = c("Row_Numb" = "Row_Numb_Adjusted"))
}
#### adding in rounds results ####
if(hytek_round_attempts == TRUE){
round_attempts_df <- round_attempts_parse(raw_results)
round_attempts_df <-
transform(round_attempts_df, Row_Numb_Adjusted = data$Row_Numb[findInterval(Row_Numb, data$Row_Numb)]) %>%
dplyr::select(-Row_Numb)
data <- dplyr::left_join(data, round_attempts_df, by = c("Row_Numb" = "Row_Numb_Adjusted")) %>%
dplyr::mutate(dplyr::across(dplyr::contains("_Attempts"), ~ dplyr::case_when(stringr::str_detect(Event, "ong|riple") ~ "Unknown",
stringr::str_detect(Event, "ong|riple", negate = TRUE) ~ .))) %>%
na_if("Unknown")
}
if(hytek_split_attempts == TRUE){
suppressMessages(data <- attempts_split(data))
}
# removes unneeded Round_X columns (i.e. those that don't have an associated Round_Result)
if (any(stringr::str_detect(names(data), "Round_\\d{1,}_Attempt")) == TRUE) {
data <- remove_unneeded_rounds(data) %>%
dplyr::na_if("")
}
# add in wind for rounds
if(hytek_rounds == TRUE){
round_wind_df <- wind_parse_hytek(raw_results)
round_wind_df <-
transform(round_wind_df, Row_Numb_Adjusted = data$Row_Numb[findInterval(Row_Numb, data$Row_Numb)]) %>%
dplyr::select(-Row_Numb)
data <- dplyr::left_join(data, round_wind_df, by = c("Row_Numb" = "Row_Numb_Adjusted"))
}
#### ordering columns after adding rounds ####
if (hytek_rounds == TRUE) {
data <- data %>%
dplyr::select(colnames(.)[stringr::str_detect(names(.), "^Round", negate = TRUE)], stringr::str_sort(colnames(.)[stringr::str_detect(names(.), "^Round")]))
}
#### remove empty columns (all values are NA) ####
data <- Filter(function(x)
!all(is.na(x)), data)
#### remove unneeded columns ####
data <- data %>%
dplyr::select(which(SwimmeR::`%!in%`(
names(.), c("Row_Numb", "Exhibition", "Points", "Heat")
)))
row.names(data) <- NULL
return(data)
}
}
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