library(dplyr) library(ggplot2)
aberr_module <- params$aberr_module parsed_title <- dplyr::case_when( aberr_module == "dicentrics" ~ "Dicentrics curve fitting report", aberr_module == "translocations" ~ "Translocation FISH curve fitting report", aberr_module == "micronuclei" ~ "Micronuclei curve fitting report" )
r parsed_title
pander::panderOptions("table.style", "grid") pander::panderOptions("table.split.table", Inf) pander::panderOptions( "table.alignment.default", function(df) { ifelse(sapply(df, is.numeric), "center", "left") } ) render_table <- function(x, ...) { pander::pander(x) # TODO: handle alignments via justify parameter }
# General parameters count_data <- params$fit_results_list[["fit_raw_data"]] fit_model_statistics <- params$fit_results_list[["fit_model_statistics"]] fit_model_summary <- params$fit_results_list[["fit_model_summary"]] fit_coeffs <- params$fit_results_list[["fit_coeffs"]] fit_var_cov_mat <- params$fit_results_list[["fit_var_cov_mat"]] fit_cor_mat <- params$fit_results_list[["fit_cor_mat"]] fit_formula_tex <- params$fit_results_list[["fit_formula_tex"]] gg_curve <- params$fit_results_list[["gg_curve"]] irr_conds <- params$fit_results_list[["irr_conds"]] # detection_lims <- params$fit_results_list[["detection_lims"]] # Translocations genome_factor <- params$fit_results_list[["genome_factor"]] chromosome_table <- params$fit_results_list[["chromosome_table"]] trans_sex <- params$fit_results_list[["trans_sex"]] frequency_select <- params$fit_results_list[["frequency_select"]]
r if (aberr_module == "translocations") {"# Chromosome data"}
r if (aberr_module == "translocations") {
paste("The analysed blood sample comes from a", trans_sex, "individual.")
}
if (aberr_module == "translocations") { num_cols <- as.numeric(ncol(chromosome_table)) chromosome_table %>% dplyr::mutate( dplyr::across( .cols = dplyr::everything(), .fns = function(x) { x <- ifelse(is.na(x) | x == "FALSE", "", x) x <- ifelse(x == "TRUE", "$\\checkmark$", x) return(x) } ) ) %>% render_table(align = "c") }
r if (aberr_module == "translocations") {
if (num_cols == 1) {
"where each chromosome was stained using M-FISH."
}
}
data <- count_data %>% biodosetools:::fix_count_data_names(type = "count", output = "kable") data %>% render_table(align = "c") # TODO: handle u > 1.96 highlighting
r paste0("$$", fit_formula_tex, "$$")
r gsub("<=", "$\\\\leq$", fit_model_summary)
r if (aberr_module == "translocations") {"## Translocation frequency"}
r if (aberr_module == "translocations") {
paste("The fitting was performed using the ")
}
r if (aberr_module == "translocations") {
if (frequency_select == "full_gen_freq") {
paste("full genome translocation frequency.")
} else if (frequency_select == "measured_freq") {
paste("translocation frequency measured by FISH.")
}
}
r if (aberr_module == "translocations") {"## Genomic conversion factor"}
r if (aberr_module == "translocations") {
paste0("The genomic conversion factor to full genome is ", as.character(round(genome_factor, 3)), ".")
}
fit_coeffs %>% formatC(format = "e", digits = 3) %>% as.data.frame() %>% biodosetools:::fix_coeff_names(type = "rows", output = "kable") %>% render_table(align = "c")
fit_model_statistics %>% formatC(format = "f", digits = 3) %>% as.data.frame() %>% dplyr::mutate(df = as.integer(df)) %>% render_table(align = "c")
fit_cor_mat %>% biodosetools:::fix_coeff_names(type = "rows", output = "kable") %>% biodosetools:::fix_coeff_names(type = "cols", output = "kable") %>% formatC(format = "f", digits = 3) %>% as.data.frame() %>% render_table(align = "c")
fit_var_cov_mat %>% biodosetools:::fix_coeff_names(type = "rows", output = "kable") %>% biodosetools:::fix_coeff_names(type = "cols", output = "kable") %>% formatC(format = "e", digits = 3) %>% as.data.frame() %>% render_table(align = "c")
data.frame( matrix( unlist(irr_conds), nrow = length(irr_conds), byrow = TRUE ) ) %>% `colnames<-`(c("Characteristic", "Details")) %>% dplyr::mutate( Details = ifelse(Details == "", "Not specified", Details) ) %>% render_table(align = "l")
gg_curve
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