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Round Data

sbf_round_data.Rd

Rounds the numeric columns of a data frame in the same way as the .md file saved by sbf_save_table() and sbf_save_plot().

Usage

sbf_round_data(
  x,
  proportion = FALSE,
  format_column = list(),
  round_as_estimate = character(0)
)

Arguments

x

A data frame.

proportion

A flag specifying whether the columns estimate, lower, upper and sd are proportions, in which case significant digits are counted from the nearer of 0 and 1 in the .md file.

format_column

A list of two-sided formulas of the form column ~ fun(.) specifying how to round and format numeric columns other than estimate, lower, upper and sd in the .md file. The function can return a numeric vector, which is formatted without further rounding, or a character vector, which is used as is. The values in the .rds and .csv files are not altered.

round_as_estimate

A character vector of numeric columns, other than estimate, lower, upper, sd and svalue, to round in the .md file to the same number of decimal places as estimate in the same row (e.g. an observed value compared with the estimate). Rows with a missing estimate are formatted as data. If proportion = TRUE the columns must also be proportions.

Value

x with its numeric columns rounded.

Details

The rounding is described in sbf_save_table(). The numeric columns stay numeric, so significant trailing zeros are not kept when the values are printed; use sbf_as_md() with round = TRUE to keep them. Integer columns and columns that are not numeric are not altered. Columns formatted by format_column are replaced by the values returned by the function, which are not rounded further.

See also

sbf_as_md()

Examples

x <- data.frame(
  estimate = c(0.53124, 12.3456),
  lower = c(0.21234, 9.8765),
  upper = c(0.87012, 15.4321),
  length = c(1.23456, 123.456)
)
sbf_round_data(x)
#>   estimate lower upper  length
#> 1     0.53  0.21  0.87   1.235
#> 2    12.30  9.90 15.00 123.500
sbf_round_data(x, format_column = list(length ~ round(., 1)))
#>   estimate lower upper length
#> 1     0.53  0.21  0.87    1.2
#> 2    12.30  9.90 15.00  123.5