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#' Selecting results
#'
#' Selects and aggregates over the \code{past_results_v2} data set or the
#' \code{results} input data set..
#' @return A data frame
#' @param results Default \code{NULL}. If \code{NULL} the \code{past_results_v2}
#' data set is used. Otherwise, the input data set.
#' @inheritParams plot_past
#' @export
#' @examples
#' select_results("prog", blas_optimize = NULL)
select_results = function(test_group,
results = NULL,
blas_optimize = NULL,
cores = 0) {
if (is.null(blas_optimize)) blas_optimize = c(FALSE, TRUE)
## Load past data
tmp_env = new.env()
data(past_results_v2, package = "benchmarkmeData", envir = tmp_env)
# Format data
results = results %>%
bind_rows(tmp_env$past_results_v2) %>%
filter(blas_optimize %in% !!blas_optimize) %>%
filter(test_group == !!test_group) %>%
filter(cores %in% !!cores) %>%
filter(!is.na(time)) %>%
group_by(id, cpu, date, sysname, blas_optimize, test_group, ram, cores) %>%
summarise(time = sum(time)) %>%
ungroup()
results = results %>%
arrange(time) %>%
mutate(rank = seq_along(time)) %>%
select(rank, everything())
results
}
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