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Format content for data generated with summarizor()

Source: R/summarizor.R
fmt_2stats.Rd

This function was written to allow easy demonstrations of flextable's ability to produce table summaries (with summarizor()). It assumes that we have either a quantitative variable, in which case we will display the mean and the standard deviation, or a qualitative variable, in which case we will display the count and the percentage corresponding to each modality.

Usage

fmt_2stats(
  stat,
  num1,
  num2,
  cts,
  pcts,
  num1_mask = "%.01f",
  num2_mask = "(%.01f)",
  cts_mask = "%.0f",
  pcts_mask = "(%.02f%%)"
)

fmt_summarizor(
  stat,
  num1,
  num2,
  cts,
  pcts,
  num1_mask = "%.01f",
  num2_mask = "(%.01f)",
  cts_mask = "%.0f",
  pcts_mask = "(%.02f%%)"
)

Arguments

stat

a character column containing the name of statictics

num1

a numeric statistic to display such as a mean or a median

num2

a numeric statistic to display such as a standard deviation or a median absolute deviation.

cts

a count to display

pcts

a percentage to display

num1_mask

format associated with num1, a format string used by sprintf().

num2_mask

format associated with num2, a format string used by sprintf().

cts_mask

format associated with cts, a format string used by sprintf().

pcts_mask

format associated with pcts, a format string used by sprintf().

See also

summarizor(), tabulator(), mk_par()

Other text formatter functions: fmt_avg_dev(), fmt_dbl(), fmt_header_n(), fmt_int(), fmt_n_percent(), fmt_pct()

Examples

library(flextable)
z <- summarizor(iris, by = "Species")

tab_1 <- tabulator(
  x = z,
  rows = c("variable", "stat"),
  columns = "Species",
  blah = as_paragraph(
    as_chunk(
      fmt_summarizor(
        stat = stat,
        num1 = value1, num2 = value2,
        cts = cts, pcts = percent
      )
    )
  )
)

ft_1 <- as_flextable(x = tab_1, separate_with = "variable")
ft_1 <- labelizor(
  x = ft_1, j = "stat",
  labels = c(mean_sd = "Moyenne (ecart-type)",
             median_iqr = "Mediane (IQR)",
             range = "Etendue",
             missing = "Valeurs manquantes"
             )
)
ft_1 <- autofit(ft_1)
ft_1

setosa
(N=50)
versicolor
(N=50)
virginica
(N=50)
Sepal.Length
Mean (SD)
5.0 (0.4)
5.9 (0.5)
6.6 (0.6)
Median (IQR)
5.0 (0.4)
5.9 (0.7)
6.5 (0.7)
Range
4.3 - 5.8
4.9 - 7.0
4.9 - 7.9
Missing
0 (0.00%)
0 (0.00%)
0 (0.00%)
Sepal.Width
Mean (SD)
3.4 (0.4)
2.8 (0.3)
3.0 (0.3)
Median (IQR)
3.4 (0.5)
2.8 (0.5)
3.0 (0.4)
Range
2.3 - 4.4
2.0 - 3.4
2.2 - 3.8
Missing
0 (0.00%)
0 (0.00%)
0 (0.00%)
Petal.Length
Mean (SD)
1.5 (0.2)
4.3 (0.5)
5.6 (0.6)
Median (IQR)
1.5 (0.2)
4.3 (0.6)
5.5 (0.8)
Range
1.0 - 1.9
3.0 - 5.1
4.5 - 6.9
Missing
0 (0.00%)
0 (0.00%)
0 (0.00%)
Petal.Width
Mean (SD)
0.2 (0.1)
1.3 (0.2)
2.0 (0.3)
Median (IQR)
0.2 (0.1)
1.3 (0.3)
2.0 (0.5)
Range
0.1 - 0.6
1.0 - 1.8
1.4 - 2.5
Missing
0 (0.00%)
0 (0.00%)
0 (0.00%)