Round estimates and their corresponding errors

Though they are not easy to find in print, there are some general conventions for rounding numbers. When rounding a summary statistic such as the mean or median, the number of rounded digits shown should be governed by the precision of the statistic. For instance, authors are usually asked to present means plus or minus standard deviations in published research, or regression coefficients plus or minus the standard error. The convention applied here is to

1. find the place of the first significant digit of the error

2. round the estimate to that place

3. round the error to 1 additional place

4. present the combination in a form such as estimate (error) or estimate +/- error

table_ester(
  estimate,
  error,
  form = "{estimate} ± {error}",
  majority_rule = FALSE
)

table_estin(
  estimate,
  lower,
  upper,
  form = "{estimate} ({lower}, {upper})",
  majority_rule = FALSE
)

Arguments

estimate

a numeric vector of estimate values.

error

a numeric vector of error values. All errors should be >0.

form

a character value that indicates how the error and estimate should be formatted together. Users can specify anything they like as long as they use the terms estimate and error to refer to the estimate and error values, respectively, and encapsulate those terms inside of curly brackets, i.e., { } . For instance, if estimate = 1.23 and error = 0.45, then form = "{estimate} ({error})" will return "1.2 (0.45)", a common format used in presentation of the point and error combination. The default form gives output in the form of 1.2 +/- 0.45.

majority_rule

a logical value. If TRUE, then the most common digit used for rounding will be used to round every number given. Within a single table, consistency in saving digits may be desirable, so all numbers may be rounded to the place indicated by the majority of the numbers. Notably, if a user wants to exercise more control over the number of decimals shown, they should use table_glue() with a customized rounding specification (see round_spec).

lower

the lower-bound of an interval for the estimate.

upper

the upper-bound of an interval for the estimate.

Value

a character vector

References

Blackstone, Eugene H. "Rounding numbers" (2016): The Journal of Thoracic and Cardiovascular Surgery. DOI: https://doi.org/10.1016/j.jtcvs.2016.09.003

See also

Other table helpers: table_glue(), table_pvalue(), table_value()

Examples

# ---- examples are taken from Blackstone, 2016 ----

# Example 1: ----
# Mean age is 72.17986, and the standard deviation (SD) is 9.364132.
## Steps:
## - Nine is the first significant figure of the SD.
## - Nine is in the ones place. Thus...
##   + round the mean to the ones place (i.e., round(x, digits = 0))
##   + round the SD to the tenths place (i.e., round(x, digits = 1))
table_ester(estimate = 72.17986, error = 9.364132)
#> [1] "72 ± 9.4"
# > [1] 72 +/- 9.4

# an estimated lower and upper bound for 95% confidence limits
lower <- 72.17986 - 1.96 * 9.364132
upper <- 72.17986 + 1.96 * 9.364132
table_estin(estimate = 72.17986, lower = lower, upper = upper,
            form = "{estimate} (95% CI: {lower}, {upper})")
#> [1] "72 (95% CI: 54, 91)"
# > [1] "72 (95% CI: 54, 91)"

# Example 2: ----
# Mean cost is $72,347.23, and the standard deviation (SD) is $23,994.06.
## Steps:
## - Two is the first significant figure of the SD.
## - Nine is in the ten thousands place. Thus...
##   + round mean to the 10-thousands place (i.e., round(x, digits = -4))
##   + round SD to the thousands place (i.e., round(x, digits = -3))
table_ester(estimate = 72347.23, error = 23994.06)
#> [1] "70,000 ± 24,000"
# > [1] "70,000 +/- 24,000"

# an estimated lower and upper bound for 95% confidence limits
lower <- 72347.23 - 1.96 * 23994.06
upper <- 72347.23 + 1.96 * 23994.06
table_estin(estimate = 72347.23, lower = lower, upper = upper,
            form = "{estimate} (95% CI: {lower} - {upper})")
#> [1] "70,000 (95% CI: 30,000 - 120,000)"
# > [1] "70,000 (95% CI: 30,000 - 120,000)"