#' Collects splits for relays within \code{swim_parse_splash}
#'
#' Takes the output of \code{read_results} and, inside of \code{swim_parse_splash},
#' extracts split times and associated row numbers
#'
#' @importFrom dplyr full_join
#' @importFrom dplyr bind_rows
#' @importFrom dplyr rename_at
#' @importFrom dplyr mutate_at
#' @importFrom dplyr rename
#' @importFrom dplyr vars
#' @importFrom dplyr na_if
#' @importFrom dplyr all_of
#' @importFrom stringr str_replace_all
#' @importFrom stringr str_replace
#' @importFrom stringr str_remove_all
#' @importFrom stringr str_extract_all
#' @importFrom stringr str_split
#' @importFrom stringr str_detect
#' @importFrom purrr map_lgl
#' @importFrom purrr map
#'
#' @param text output of \code{read_results} with row numbers appended by
#' \code{add_row_numbers}
#' @param split_len length of pool at which splits are measured - usually 25 or
#' 50
#' @return returns a dataframe with split times and row numbers
#'
#' @seealso \code{splits_parse} runs inside \code{\link{swim_parse_splash}} on the
#' output of \code{\link{read_results}} with row numbers from
#' \code{\link{add_row_numbers}}
splits_parse_splash_relays <-
function(text, split_len = split_length_splash) {
#### Testing ####
# text <- read_results("https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/Splash/Open_Belgian_Champs_2017.pdf") %>%
# add_row_numbers()
# split_len <- 50
# text <- read_results("https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/Splash/Glenmark_Senior_Nationals_2019.pdf") %>%
# add_row_numbers()
# split_len <- 50
#### Actual Function ####
### collect row numbers from rows containing splits ###
### define strings ###
split_string <- "(\\d?\\:?\\d{2}\\.\\d{2})|( NA )"
relay_swimmer_string <- "^\n\\s*[:alpha:]"
record_string <- "\n\\s+[:upper:]R\\s|\n\\s+US\\s|[:upper:][:alpha:]+ Record|\n\\s?[:upper:]{1,}\\s|\n\\s+NMR\\s+\\d{1,}$"
splash_string <- "Splash Meet Manager"
text <- text %>%
.[stringr::str_detect(., record_string, negate = TRUE)] %>%
stringr::str_replace_all(" \\:", " ") %>%
stringr::str_remove_all("\\(\\=?\\d?\\d?\\)\\s+\\d?\\:?\\d{2}\\.\\d{2}") %>%
stringr::str_replace("([:alpha:])\\s{6,}(\\d{1,}$)", "\\1 NA \\2") %>%
stringr::str_replace("(?<=\\d\\.\\d\\d)\\s{16,}(?=\\d?\\:?\\d{2}\\.\\d{2} )", " NA ")
### collect splits
row_numbs <- text %>%
.[purrr::map_lgl(.,
stringr::str_detect,
split_string)] %>%
.[purrr::map_lgl(.,
stringr::str_detect,
relay_swimmer_string)] %>%
.[!purrr::map_lgl(.,
stringr::str_detect,
record_string)] %>%
.[!purrr::map_lgl(.,
stringr::str_detect,
splash_string)] %>%
stringr::str_extract_all("\\d{1,}$")
flag <- FALSE
#### if there are still no valid splits return blank data frame ####
if (length(row_numbs) > 0) {
minimum_row <- min(as.numeric(row_numbs))
maximum_row <- as.numeric(length(text))
suppressWarnings(
data_1_splits <- text %>%
.[purrr::map_lgl(.,
stringr::str_detect,
split_string)] %>%
.[purrr::map_lgl(.,
stringr::str_detect,
relay_swimmer_string)] %>%
.[!purrr::map_lgl(.,
stringr::str_detect,
record_string)] %>%
.[!purrr::map_lgl(.,
stringr::str_detect,
splash_string)] %>%
stringr::str_extract_all(split_string) %>%
purrr::map(paste, collapse = " ") %>%
trimws()
)
#### add row numbers back in since they were removed ####
data_1_splits <- paste(row_numbs, data_1_splits, sep = " ")
#### break out by length ####
data_1_splits <-
unlist(purrr::map(data_1_splits, stringr::str_split, "\\s{2,}"),
recursive = FALSE)
data_splits_length_2 <-
data_1_splits[purrr::map(data_1_splits, length) == 2]
data_splits_length_3 <-
data_1_splits[purrr::map(data_1_splits, length) == 3]
data_splits_length_4 <-
data_1_splits[purrr::map(data_1_splits, length) == 4]
data_splits_length_5 <-
data_1_splits[purrr::map(data_1_splits, length) == 5]
data_splits_length_6 <-
data_1_splits[purrr::map(data_1_splits, length) == 6]
data_splits_length_7 <-
data_1_splits[purrr::map(data_1_splits, length) == 7]
data_splits_length_8 <-
data_1_splits[purrr::map(data_1_splits, length) == 8]
data_splits_length_9 <-
data_1_splits[purrr::map(data_1_splits, length) == 9]
data_splits_length_10 <-
data_1_splits[purrr::map(data_1_splits, length) == 10]
#### transform all lists to dataframes ####
if (length(data_splits_length_10) > 0) {
df_10_splits <- data_splits_length_10 %>%
list_transform()
} else {
df_10_splits <- data.frame(Row_Numb = character(),
stringsAsFactors = FALSE)
}
if (length(data_splits_length_9) > 0) {
df_9_splits <- data_splits_length_9 %>%
list_transform()
} else {
df_9_splits <- data.frame(Row_Numb = character(),
stringsAsFactors = FALSE)
}
if (length(data_splits_length_8) > 0) {
df_8_splits <- data_splits_length_8 %>%
list_transform()
} else {
df_8_splits <- data.frame(Row_Numb = character(),
stringsAsFactors = FALSE)
}
if (length(data_splits_length_7) > 0) {
df_7_splits <- data_splits_length_7 %>%
list_transform()
} else {
df_7_splits <- data.frame(Row_Numb = character(),
stringsAsFactors = FALSE)
}
if (length(data_splits_length_6) > 0) {
df_6_splits <- data_splits_length_6 %>%
list_transform()
} else {
df_6_splits <- data.frame(Row_Numb = character(),
stringsAsFactors = FALSE)
}
if (length(data_splits_length_5) > 0) {
df_5_splits <- data_splits_length_5 %>%
list_transform()
} else {
df_5_splits <- data.frame(Row_Numb = character(),
stringsAsFactors = FALSE)
}
if (length(data_splits_length_4) > 0) {
df_4_splits <- data_splits_length_4 %>%
list_transform()
} else {
df_4_splits <- data.frame(Row_Numb = character(),
stringsAsFactors = FALSE)
}
if (length(data_splits_length_3) > 0) {
df_3_splits <- data_splits_length_3 %>%
list_transform()
} else {
df_3_splits <- data.frame(Row_Numb = character(),
stringsAsFactors = FALSE)
}
if (length(data_splits_length_2) > 0) {
df_2_splits <- data_splits_length_2 %>%
list_transform()
} else {
df_2_splits <- data.frame(Row_Numb = character(),
stringsAsFactors = FALSE)
}
#### bind up results ####
# results are bound before going to lines_sort so that in cases where there are multiple rows with splits for the same race,
# like in longer events with many splits, those splits can be collected and treated together
data_splits <-
dplyr::bind_rows(
df_10_splits,
df_9_splits,
df_8_splits,
df_7_splits,
df_6_splits,
df_5_splits,
df_4_splits,
df_3_splits,
df_2_splits
) %>%
lines_sort(min_row = minimum_row) %>%
dplyr::mutate(Row_Numb = as.numeric(Row_Numb) - 1) # make row number of split match row number of performance
if ("V1" %in% names(data_splits) &
any(stringr::str_detect(data_splits$V1, "\\.") == FALSE)) {
data_splits <- data_splits %>%
dplyr::rename("Row_Numb" = V1)
}
#### rename columns V1, V2 etc. by 50 ####
old_names <- names(data_splits)[grep("^V", names(data_splits))]
new_names <-
paste("Split", seq(1, length(names(data_splits)) - 1) * split_len, sep = "_")
data_splits <- data_splits %>%
dplyr::rename_at(dplyr::vars(dplyr::all_of(old_names)), ~ new_names)
data_splits <- data_splits %>%
na_if_character("NA")
row.names(data_splits) <- NULL
} else {
# if there are no rows with valid splits return blank data frame
data_splits <- data.frame(Row_Numb = as.numeric())
}
return(data_splits)
}
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