Showing 1 of 4 files from the diff.

@@ -249,7 +249,7 @@
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  do_calc_on_num_cols(obj, data, cols = cols, groups = groups,
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                      other_cols = other_cols, out_names = out_names,
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                      func =  function(count_table, cols = cols, groups = groups) {
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                        tibble::as.tibble(lapply(split(cols, groups), function(col_index) {
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                        tibble::as_tibble(lapply(split(cols, groups), function(col_index) {
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                          apply(count_table[, col_index], MARGIN = 1, FUN = func)
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                        }))
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                      }
@@ -418,7 +418,7 @@
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      }
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      count_table[to_zero] <- 0
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    }
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    names(count_table) <- groups # needed because do_calc_on_num_cols assumes column named by groups even though this function does not use groups currently
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    names(count_table) <- as.character(groups) # needed because do_calc_on_num_cols assumes column named by groups even though this function does not use groups currently
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    return(count_table)
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  }
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@@ -489,8 +489,8 @@
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                          sample_size <- min(colSums(count_table)) # Calculate minimum count if no sample size is given
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                          my_print("Rarefying to ", sample_size, " since that is the lowest sample total.")
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                        }
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                        output <- tibble::as.tibble(t(vegan::rrarefy(t(count_table), sample = sample_size)))
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                        names(output) <- groups # needed because do_calc_on_num_cols assumes column named by groups even though this function does not use groups currently
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                        output <- tibble::as_tibble(t(vegan::rrarefy(t(count_table), sample = sample_size)))
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                        names(output) <- as.character(groups) # needed because do_calc_on_num_cols assumes column named by groups even though this function does not use groups currently
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                        return(output)
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                      }
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  )
@@ -781,7 +781,7 @@
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        }
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      }, numeric(1))
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    })
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    output <- tibble::as.tibble(output, stringsAsFactors = FALSE)
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    output <- tibble::as_tibble(output, stringsAsFactors = FALSE)
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    return(output)
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  }
Files Coverage
R 57.75%
src/repel_boxes.cpp 59.07%
Project Totals (24 files) 57.86%
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