In leppott/BCGcalc: Biological Condition Gradient, calculator

knitr::opts_chunk$set(results = 'asis', echo = FALSE, warning = FALSE)

cat("**Filename:** ", inFile$name, "\n\n", sep = "")

Plot

# use results before trimmed L1:6
df_plot <- df_results_model 

p1 <- ggplot2::ggplot(df_plot, ggplot2::aes(x = MTTI)) + 
  ggplot2::geom_boxplot() +
  ggplot2::theme_light() + 
  ggplot2::theme(axis.title.y = element_blank()
                 , axis.text.y = element_blank()
                 , axis.ticks.y = element_blank()
                 , panel.grid.major.y = element_blank()
                 , panel.grid.minor.y = element_blank())


# plotly::ggplotly(p1)
p1

Flags

cap_flags <- "Flag summary."
knitr::kable(table(df_met_flags[, "CHECKNAME"]
                   , df_met_flags[, "FLAG"]
                   , useNA = "ifany")
             , caption = cap_flags)

# Data
df_plot2 <- df_flags_wide
# Munge
## SampleID to row names 
row.names(df_plot2) <- df_plot2[, "SAMPLEID"]
## Remove columns
df_plot2 <- df_plot2[, -c(1:2)]
## Change terminology; NA/flag to 0/1
df_plot2[!is.na(df_plot2)] <- 1 
df_plot2[is.na(df_plot2)] <- 0
## Convert character to numeric
col2fix <- 2:ncol(df_plot2)
df_plot2[, col2fix] <- apply(df_plot2[, col2fix]
                  , 2
                  , function(x) as.numeric(as.character(x)))

# plot
p2 <- ComplexUpset::upset(df_plot2
                          , intersect = names(df_plot2)
                          , name = "Flags")
p2

Results

cap_data <- "Summary results."
DT::datatable(df_results 
              , filter = "top"
              , caption = cap_data
              , options = list(scrollX = TRUE
                               , lengthMenu = c(5, 10, 25, 50, 100, 1000)
                               , autoWidth = TRUE
              ))