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@@ -31,7 +31,7 @@
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                    paste("-", non_type, collapse = "\n"))
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  if (length(non_type) > 0) {
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  	stop(error_message)
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        stop(error_message)
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  }
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  cats <- unlist(lapply(gdata, is.factor))

@@ -51,7 +51,7 @@
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ds_summary.default <- function(data, variable) {
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	check_df(data)
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  check_df(data)
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  vary  <- rlang::enquo(variable)
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  var_name <- deparse(substitute(variable))
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  check_numeric(data, !! vary, var_name)
@@ -69,35 +69,35 @@
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  high_val <- ds_rindex(sdata, high)
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  result <- list(obs      = length(odata),
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    			 missing  = sum(is.na(odata)),
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			     avg      = mean(sdata),
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			     tavg     = mean(sdata, trim = 0.05),
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			     stdev    = stats::sd(sdata),
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			     variance = stats::var(sdata),
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			     skew     = ds_skewness(sdata),
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			     kurtosis = ds_kurtosis(sdata),
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			     uss      = stat_uss(sdata),
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			     css      = ds_css(sdata),
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			     cvar     = ds_cvar(sdata),
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			     sem      = ds_std_error(sdata),
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			     median   = stats::median(sdata),
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			     mode     = ds_mode(sdata),
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			     range    = ds_range(sdata),
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			     min      = min(sdata),
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			     Max      = max(sdata),
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			     iqrange  = stats::IQR(sdata),
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			     per99    = stats::quantile(sdata, 0.99),
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			     per90    = stats::quantile(sdata, 0.90),
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			     per95    = stats::quantile(sdata, 0.95),
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			     per75    = stats::quantile(sdata, 0.75),
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			     per25    = stats::quantile(sdata, 0.25),
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			     per10    = stats::quantile(sdata, 0.10),
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			     per5     = stats::quantile(sdata, 0.05),
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			     per1     = stats::quantile(sdata, 0.01),
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			     lowobs   = low,
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			     highobs  = high,
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			     lowobsi  = low_val,
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			     highobsi = high_val)
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           missing  = sum(is.na(odata)),
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           avg      = mean(sdata),
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           tavg     = mean(sdata, trim = 0.05),
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           stdev    = stats::sd(sdata),
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           variance = stats::var(sdata),
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           skew     = ds_skewness(sdata),
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           kurtosis = ds_kurtosis(sdata),
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           uss      = stat_uss(sdata),
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           css      = ds_css(sdata),
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           cvar     = ds_cvar(sdata),
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           sem      = ds_std_error(sdata),
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           median   = stats::median(sdata),
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           mode     = ds_mode(sdata),
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           range    = ds_range(sdata),
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           min      = min(sdata),
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           Max      = max(sdata),
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           iqrange  = stats::IQR(sdata),
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           per99    = stats::quantile(sdata, 0.99),
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           per90    = stats::quantile(sdata, 0.90),
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           per95    = stats::quantile(sdata, 0.95),
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           per75    = stats::quantile(sdata, 0.75),
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           per25    = stats::quantile(sdata, 0.25),
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           per10    = stats::quantile(sdata, 0.10),
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           per5     = stats::quantile(sdata, 0.05),
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           per1     = stats::quantile(sdata, 0.01),
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           lowobs   = low,
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           highobs  = high,
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           lowobsi  = low_val,
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           highobsi = high_val)
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  class(result) <- "ds_summary"
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  return(result)
Files Coverage
R 90.98%
Project Totals (19 files) 90.98%
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comment: false
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coverage:
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  status:
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    project:
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      default:
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        target: auto
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        threshold: 1%
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        informational: true
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    patch:
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      default:
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        target: auto
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        threshold: 1%
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        informational: true
Sunburst
The inner-most circle is the entire project, moving away from the center are folders then, finally, a single file. The size and color of each slice is representing the number of statements and the coverage, respectively.
Icicle
The top section represents the entire project. Proceeding with folders and finally individual files. The size and color of each slice is representing the number of statements and the coverage, respectively.
Grid
Each block represents a single file in the project. The size and color of each block is represented by the number of statements and the coverage, respectively.
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