Continuous summary table

Computes descriptive statistics (mean, SD, min, max, confidence interval of the mean, n) for one or many continuous variables selected with tidyselect syntax.

With by, produces grouped summaries with optional group-comparison tests (test), p-values (p_value), test statistics (statistic), and effect sizes (effect_size / effect_size_ci). Without by, produces one-way descriptive summaries.

Multiple output formats are available via output: a printed ASCII table ("default"), a plain numeric data.frame ("data.frame"), or publication-ready tables ("tinytable", "gt", "flextable", "excel", "clipboard", "word").

Usage

table_continuous(
  data,
  select = dplyr::everything(),
  by = NULL,
  exclude = NULL,
  regex = FALSE,
  test = c("welch", "student", "nonparametric"),
  p_value = FALSE,
  statistic = FALSE,
  effect_size = FALSE,
  effect_size_ci = FALSE,
  labels = NULL,
  ci_level = 0.95,
  digits = 2,
  decimal_mark = ".",
  output = c("default", "data.frame", "tinytable", "gt", "flextable", "excel",
    "clipboard", "word"),
  excel_path = NULL,
  excel_sheet = "Descriptives",
  clipboard_delim = "\t",
  word_path = NULL,
  verbose = FALSE
)

Arguments

data

A data.frame.

select

Columns to include. If regex = FALSE, use tidyselect syntax or a character vector of column names (default: dplyr::everything()). If regex = TRUE, provide a regular expression pattern (character string).

by

Optional grouping column. Accepts an unquoted column name or a single character column name. The column does not need to be numeric.

exclude

Columns to exclude. Supports tidyselect syntax and character vectors of column names.

regex

Logical. If FALSE (the default), uses tidyselect helpers. If TRUE, the select argument is treated as a regular expression.

test

Character. Statistical test to use when comparing groups. One of "welch" (default), "student", or "nonparametric".

"welch": Welch t-test (2 groups) or Welch one-way ANOVA (3+ groups). Does not assume equal variances.
"student": Student t-test (2 groups) or classic one-way ANOVA (3+ groups). Assumes equal variances.
"nonparametric": Wilcoxon rank-sum / Mann–Whitney U (2 groups) or Kruskal–Wallis H (3+ groups).

Used when by is supplied together with p_value = TRUE, statistic = TRUE, or effect_size = TRUE. Ignored otherwise.

p_value

Logical. If TRUE and by is used, adds a p-value column from the test specified by test. Defaults to FALSE. Ignored when by is not used.

statistic

Logical. If TRUE and by is used, the test statistic is shown in an additional column (e.g., t(df) = ..., F(df1, df2) = ..., W = ..., or H(df) = ...). Both p_value and statistic are independent; either or both can be enabled. Defaults to FALSE. Ignored when by is not used.

effect_size

Logical. If TRUE and by is used, adds an effect-size column ("ES"). The measure is chosen automatically:

Hedges' g (bias-corrected) - 2 groups, parametric (CI via Hedges & Olkin approximation).
Eta-squared (\(\eta^2\)) - 3+ groups, parametric (CI via noncentral F distribution).
Rank-biserial r (r_rb) - 2 groups, nonparametric (CI via Fisher z-transform).
Epsilon-squared (\(\varepsilon^2\)) - 3+ groups, nonparametric (CI via percentile bootstrap, 2 000 replicates).

Defaults to FALSE. Ignored when by is not used.

effect_size_ci

Logical. If TRUE, appends the confidence interval of the effect size in brackets (e.g., g = 0.45 [0.22, 0.68]). Implies effect_size = TRUE. Defaults to FALSE.

labels

An optional named character vector of variable labels. Names must match column names in data. When NULL (the default), labels are auto-detected from variable attributes (e.g., haven labels); if none are found, the column name is used.

ci_level

Confidence level for the mean confidence interval (default: 0.95). Must be between 0 and 1 exclusive.

digits

Number of decimal places for numeric output (default: 2).

decimal_mark

Character used as decimal separator. Either "." (default) or ",".

output

Output format. One of:

"default" (a printed ASCII table, returned invisibly)
"data.frame" (a plain numeric data.frame)
"tinytable" (requires tinytable)
"gt" (requires gt)
"flextable" (requires flextable)
"excel" (requires openxlsx2)
"clipboard" (requires clipr)
"word" (requires flextable and officer)

excel_path

File path for output = "excel".

excel_sheet

Sheet name for output = "excel" (default: "Descriptives").

clipboard_delim

Delimiter for output = "clipboard" (default: "\t").

word_path

File path for output = "word".

verbose

Logical. If TRUE, prints messages about excluded non-numeric columns (default: FALSE).

Value

Depends on output:

"default": prints a styled ASCII table and returns the underlying data.frame invisibly (S3 class "spicy_continuous_table").
"data.frame": a plain data.frame with columns variable, label, group (when by is used), mean, sd, min, max, ci_lower, ci_upper, n. When by is used together with p_value = TRUE, statistic = TRUE, or effect_size = TRUE, additional columns are appended (populated on the first row of each variable block only):
- test_type – test identifier (e.g., "welch_t", "welch_anova", "student_t", "anova", "wilcoxon", "kruskal").
- statistic, df1, df2, p.value – test results.
- es_type – effect-size identifier ("hedges_g", "eta_sq", "r_rb", or "epsilon_sq"), when effect_size = TRUE.
- es_value, es_ci_lower, es_ci_upper – effect-size estimate and confidence interval bounds.
"tinytable": a tinytable object.
"gt": a gt_tbl object.
"flextable": a flextable object.
"excel" / "word": writes to disk and returns the file path invisibly.
"clipboard": copies the table and returns the display data.frame invisibly.

Details

Non-numeric columns are silently dropped (set verbose = TRUE to see which columns were excluded). When a single constant column is passed, SD and CI are shown as "--" in the ASCII table.

Optional output engines require suggested packages:

tinytable for output = "tinytable"
gt for output = "gt"
flextable for output = "flextable"
flextable + officer for output = "word"
openxlsx2 for output = "excel"
clipr for output = "clipboard"

Examples

# Basic usage with all numeric columns
table_continuous(iris, output = "data.frame")
#>       variable        label     mean        sd min max ci_lower ci_upper   n
#> 1 Sepal.Length Sepal.Length 5.843333 0.8280661 4.3 7.9 5.709732 5.976934 150
#> 2  Sepal.Width  Sepal.Width 3.057333 0.4358663 2.0 4.4 2.987010 3.127656 150
#> 3 Petal.Length Petal.Length 3.758000 1.7652982 1.0 6.9 3.473185 4.042815 150
#> 4  Petal.Width  Petal.Width 1.199333 0.7622377 0.1 2.5 1.076353 1.322313 150

# Select specific columns with tidyselect
table_continuous(iris, select = c(Sepal.Length, Petal.Width), output = "data.frame")
#>       variable        label     mean        sd min max ci_lower ci_upper   n
#> 1 Sepal.Length Sepal.Length 5.843333 0.8280661 4.3 7.9 5.709732 5.976934 150
#> 2  Petal.Width  Petal.Width 1.199333 0.7622377 0.1 2.5 1.076353 1.322313 150

# Grouped descriptives
table_continuous(iris, select = c(Sepal.Length, Sepal.Width),
           by = Species, output = "data.frame")
#>       variable        label      group  mean        sd min max ci_lower
#> 1 Sepal.Length Sepal.Length     setosa 5.006 0.3524897 4.3 5.8 4.905824
#> 2 Sepal.Length Sepal.Length versicolor 5.936 0.5161711 4.9 7.0 5.789306
#> 3 Sepal.Length Sepal.Length  virginica 6.588 0.6358796 4.9 7.9 6.407285
#> 4  Sepal.Width  Sepal.Width     setosa 3.428 0.3790644 2.3 4.4 3.320271
#> 5  Sepal.Width  Sepal.Width versicolor 2.770 0.3137983 2.0 3.4 2.680820
#> 6  Sepal.Width  Sepal.Width  virginica 2.974 0.3224966 2.2 3.8 2.882347
#>   ci_upper  n
#> 1 5.106176 50
#> 2 6.082694 50
#> 3 6.768715 50
#> 4 3.535729 50
#> 5 2.859180 50
#> 6 3.065653 50

# Grouped descriptives with p-value
table_continuous(iris, select = c(Sepal.Length, Sepal.Width),
           by = Species, p_value = TRUE, output = "data.frame")
#>       variable        label      group  mean        sd min max ci_lower
#> 1 Sepal.Length Sepal.Length     setosa 5.006 0.3524897 4.3 5.8 4.905824
#> 2 Sepal.Length Sepal.Length versicolor 5.936 0.5161711 4.9 7.0 5.789306
#> 3 Sepal.Length Sepal.Length  virginica 6.588 0.6358796 4.9 7.9 6.407285
#> 4  Sepal.Width  Sepal.Width     setosa 3.428 0.3790644 2.3 4.4 3.320271
#> 5  Sepal.Width  Sepal.Width versicolor 2.770 0.3137983 2.0 3.4 2.680820
#> 6  Sepal.Width  Sepal.Width  virginica 2.974 0.3224966 2.2 3.8 2.882347
#>   ci_upper  n   test_type statistic df1      df2      p.value
#> 1 5.106176 50 welch_anova 138.90829   2 92.21115 1.505059e-28
#> 2 6.082694 50        <NA>        NA  NA       NA           NA
#> 3 6.768715 50        <NA>        NA  NA       NA           NA
#> 4 3.535729 50 welch_anova  45.01204   2 97.40159 1.432735e-14
#> 5 2.859180 50        <NA>        NA  NA       NA           NA
#> 6 3.065653 50        <NA>        NA  NA       NA           NA

# Grouped descriptives with test statistic only
table_continuous(iris, select = c(Sepal.Length, Sepal.Width),
           by = Species, statistic = TRUE, output = "data.frame")
#>       variable        label      group  mean        sd min max ci_lower
#> 1 Sepal.Length Sepal.Length     setosa 5.006 0.3524897 4.3 5.8 4.905824
#> 2 Sepal.Length Sepal.Length versicolor 5.936 0.5161711 4.9 7.0 5.789306
#> 3 Sepal.Length Sepal.Length  virginica 6.588 0.6358796 4.9 7.9 6.407285
#> 4  Sepal.Width  Sepal.Width     setosa 3.428 0.3790644 2.3 4.4 3.320271
#> 5  Sepal.Width  Sepal.Width versicolor 2.770 0.3137983 2.0 3.4 2.680820
#> 6  Sepal.Width  Sepal.Width  virginica 2.974 0.3224966 2.2 3.8 2.882347
#>   ci_upper  n   test_type statistic df1      df2      p.value
#> 1 5.106176 50 welch_anova 138.90829   2 92.21115 1.505059e-28
#> 2 6.082694 50        <NA>        NA  NA       NA           NA
#> 3 6.768715 50        <NA>        NA  NA       NA           NA
#> 4 3.535729 50 welch_anova  45.01204   2 97.40159 1.432735e-14
#> 5 2.859180 50        <NA>        NA  NA       NA           NA
#> 6 3.065653 50        <NA>        NA  NA       NA           NA

# Grouped descriptives with both p-value and test statistic
table_continuous(iris, select = c(Sepal.Length, Sepal.Width),
           by = Species, p_value = TRUE, statistic = TRUE,
           output = "data.frame")
#>       variable        label      group  mean        sd min max ci_lower
#> 1 Sepal.Length Sepal.Length     setosa 5.006 0.3524897 4.3 5.8 4.905824
#> 2 Sepal.Length Sepal.Length versicolor 5.936 0.5161711 4.9 7.0 5.789306
#> 3 Sepal.Length Sepal.Length  virginica 6.588 0.6358796 4.9 7.9 6.407285
#> 4  Sepal.Width  Sepal.Width     setosa 3.428 0.3790644 2.3 4.4 3.320271
#> 5  Sepal.Width  Sepal.Width versicolor 2.770 0.3137983 2.0 3.4 2.680820
#> 6  Sepal.Width  Sepal.Width  virginica 2.974 0.3224966 2.2 3.8 2.882347
#>   ci_upper  n   test_type statistic df1      df2      p.value
#> 1 5.106176 50 welch_anova 138.90829   2 92.21115 1.505059e-28
#> 2 6.082694 50        <NA>        NA  NA       NA           NA
#> 3 6.768715 50        <NA>        NA  NA       NA           NA
#> 4 3.535729 50 welch_anova  45.01204   2 97.40159 1.432735e-14
#> 5 2.859180 50        <NA>        NA  NA       NA           NA
#> 6 3.065653 50        <NA>        NA  NA       NA           NA

# Student t-test / classic ANOVA (assumes equal variances)
table_continuous(iris, select = Sepal.Length, by = Species,
           test = "student", p_value = TRUE, output = "data.frame")
#>       variable        label      group  mean        sd min max ci_lower
#> 1 Sepal.Length Sepal.Length     setosa 5.006 0.3524897 4.3 5.8 4.905824
#> 2 Sepal.Length Sepal.Length versicolor 5.936 0.5161711 4.9 7.0 5.789306
#> 3 Sepal.Length Sepal.Length  virginica 6.588 0.6358796 4.9 7.9 6.407285
#>   ci_upper  n test_type statistic df1 df2      p.value
#> 1 5.106176 50     anova  119.2645   2 147 1.669669e-31
#> 2 6.082694 50      <NA>        NA  NA  NA           NA
#> 3 6.768715 50      <NA>        NA  NA  NA           NA

# Nonparametric test (Kruskal-Wallis for 3+ groups)
table_continuous(iris, select = Sepal.Length, by = Species,
           test = "nonparametric", p_value = TRUE,
           statistic = TRUE, output = "data.frame")
#>       variable        label      group  mean        sd min max ci_lower
#> 1 Sepal.Length Sepal.Length     setosa 5.006 0.3524897 4.3 5.8 4.905824
#> 2 Sepal.Length Sepal.Length versicolor 5.936 0.5161711 4.9 7.0 5.789306
#> 3 Sepal.Length Sepal.Length  virginica 6.588 0.6358796 4.9 7.9 6.407285
#>   ci_upper  n test_type statistic df1 df2      p.value
#> 1 5.106176 50   kruskal  96.93744   2  NA 8.918734e-22
#> 2 6.082694 50      <NA>        NA  NA  NA           NA
#> 3 6.768715 50      <NA>        NA  NA  NA           NA

# Effect size (eta-squared for 3 groups)
table_continuous(iris, select = Sepal.Length, by = Species,
           effect_size = TRUE, output = "data.frame")
#>       variable        label      group  mean        sd min max ci_lower
#> 1 Sepal.Length Sepal.Length     setosa 5.006 0.3524897 4.3 5.8 4.905824
#> 2 Sepal.Length Sepal.Length versicolor 5.936 0.5161711 4.9 7.0 5.789306
#> 3 Sepal.Length Sepal.Length  virginica 6.588 0.6358796 4.9 7.9 6.407285
#>   ci_upper  n   test_type statistic df1      df2      p.value es_type  es_value
#> 1 5.106176 50 welch_anova  138.9083   2 92.21115 1.505059e-28  eta_sq 0.6187057
#> 2 6.082694 50        <NA>        NA  NA       NA           NA    <NA>        NA
#> 3 6.768715 50        <NA>        NA  NA       NA           NA    <NA>        NA
#>   es_ci_lower es_ci_upper
#> 1   0.5203092   0.6844361
#> 2          NA          NA
#> 3          NA          NA

# Effect size with confidence interval
table_continuous(iris, select = Sepal.Length, by = Species,
           p_value = TRUE, effect_size_ci = TRUE,
           output = "data.frame")
#> Warning: `effect_size_ci` implies `effect_size = TRUE`.
#>       variable        label      group  mean        sd min max ci_lower
#> 1 Sepal.Length Sepal.Length     setosa 5.006 0.3524897 4.3 5.8 4.905824
#> 2 Sepal.Length Sepal.Length versicolor 5.936 0.5161711 4.9 7.0 5.789306
#> 3 Sepal.Length Sepal.Length  virginica 6.588 0.6358796 4.9 7.9 6.407285
#>   ci_upper  n   test_type statistic df1      df2      p.value es_type  es_value
#> 1 5.106176 50 welch_anova  138.9083   2 92.21115 1.505059e-28  eta_sq 0.6187057
#> 2 6.082694 50        <NA>        NA  NA       NA           NA    <NA>        NA
#> 3 6.768715 50        <NA>        NA  NA       NA           NA    <NA>        NA
#>   es_ci_lower es_ci_upper
#> 1   0.5203092   0.6844361
#> 2          NA          NA
#> 3          NA          NA

# Nonparametric effect size (epsilon-squared with bootstrap CI)
# \donttest{
table_continuous(iris, select = Sepal.Length, by = Species,
           test = "nonparametric", effect_size_ci = TRUE,
           output = "data.frame")
#> Warning: `effect_size_ci` implies `effect_size = TRUE`.
#>       variable        label      group  mean        sd min max ci_lower
#> 1 Sepal.Length Sepal.Length     setosa 5.006 0.3524897 4.3 5.8 4.905824
#> 2 Sepal.Length Sepal.Length versicolor 5.936 0.5161711 4.9 7.0 5.789306
#> 3 Sepal.Length Sepal.Length  virginica 6.588 0.6358796 4.9 7.9 6.407285
#>   ci_upper  n test_type statistic df1 df2      p.value    es_type  es_value
#> 1 5.106176 50   kruskal  96.93744   2  NA 8.918734e-22 epsilon_sq 0.6458329
#> 2 6.082694 50      <NA>        NA  NA  NA           NA       <NA>        NA
#> 3 6.768715 50      <NA>        NA  NA  NA           NA       <NA>        NA
#>   es_ci_lower es_ci_upper
#> 1   0.5462008   0.7279627
#> 2          NA          NA
#> 3          NA          NA
# }

# Hedges' g for 2 groups
table_continuous(iris[iris$Species != "virginica", ],
           select = Sepal.Length, by = Species,
           effect_size_ci = TRUE, output = "data.frame")
#> Warning: `effect_size_ci` implies `effect_size = TRUE`.
#>       variable        label      group  mean        sd min max ci_lower
#> 1 Sepal.Length Sepal.Length     setosa 5.006 0.3524897 4.3 5.8 4.905824
#> 2 Sepal.Length Sepal.Length versicolor 5.936 0.5161711 4.9 7.0 5.789306
#> 3 Sepal.Length Sepal.Length  virginica    NA        NA  NA  NA       NA
#>   ci_upper  n test_type statistic    df1 df2      p.value  es_type  es_value
#> 1 5.106176 50   welch_t -10.52099 86.538  NA 3.746743e-17 hedges_g -2.088053
#> 2 6.082694 50      <NA>        NA     NA  NA           NA     <NA>        NA
#> 3       NA  0      <NA>        NA     NA  NA           NA     <NA>        NA
#>   es_ci_lower es_ci_upper
#> 1   -2.575291   -1.600814
#> 2          NA          NA
#> 3          NA          NA

# Regex column selection
table_continuous(iris, select = "^Sepal", regex = TRUE, output = "data.frame")
#>       variable        label     mean        sd min max ci_lower ci_upper   n
#> 1 Sepal.Length Sepal.Length 5.843333 0.8280661 4.3 7.9 5.709732 5.976934 150
#> 2  Sepal.Width  Sepal.Width 3.057333 0.4358663 2.0 4.4 2.987010 3.127656 150

# Custom labels
table_continuous(iris,
           select = c(Sepal.Length, Petal.Length),
           labels = c(Sepal.Length = "Sepal length (cm)",
                      Petal.Length = "Petal length (cm)"),
           output = "data.frame")
#>       variable             label     mean        sd min max ci_lower ci_upper
#> 1 Sepal.Length Sepal length (cm) 5.843333 0.8280661 4.3 7.9 5.709732 5.976934
#> 2 Petal.Length Petal length (cm) 3.758000 1.7652982 1.0 6.9 3.473185 4.042815
#>     n
#> 1 150
#> 2 150

# \donttest{
# ASCII table (default)
table_continuous(iris, select = starts_with("Sepal"))
#> Descriptive statistics
#> 
#>  Variable     │  M     SD   Min   Max   95% CI LL  95% CI UL    n 
#> ──────────────┼───────────────────────────────────────────────────
#>  Sepal.Length │ 5.84  0.83  4.30  7.90    5.71       5.98     150 
#>  Sepal.Width  │ 3.06  0.44  2.00  4.40    2.99       3.13     150 

# Grouped ASCII table
table_continuous(iris, select = starts_with("Sepal"), by = Species)
#> Descriptive statistics
#> 
#>  Variable     │ Group        M     SD   Min   Max   95% CI LL  95% CI UL   n 
#> ──────────────┼──────────────────────────────────────────────────────────────
#>  Sepal.Length │ setosa      5.01  0.35  4.30  5.80    4.91       5.11     50 
#>               │ versicolor  5.94  0.52  4.90  7.00    5.79       6.08     50 
#>               │ virginica   6.59  0.64  4.90  7.90    6.41       6.77     50 
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Sepal.Width  │ setosa      3.43  0.38  2.30  4.40    3.32       3.54     50 
#>               │ versicolor  2.77  0.31  2.00  3.40    2.68       2.86     50 
#>               │ virginica   2.97  0.32  2.20  3.80    2.88       3.07     50 

# tinytable output
if (requireNamespace("tinytable", quietly = TRUE)) {
  table_continuous(iris, output = "tinytable")
  table_continuous(iris, select = starts_with("Sepal"),
             by = Species, output = "tinytable")
}
#> +--------------+------------+------+------+------+------+------+------+----+
#> | Variable     | Group      | M    | SD   | Min  | Max  | 95% CI      | n  |
#> +--------------+------------+------+------+------+------+------+------+----+
#> |              |            |      |      |      |      | LL   | UL   |    |
#> +==============+============+======+======+======+======+======+======+====+
#> | Sepal.Length | setosa     | 5.01 | 0.35 | 4.30 | 5.80 | 4.91 | 5.11 | 50 |
#> +--------------+------------+------+------+------+------+------+------+----+
#> |              | versicolor | 5.94 | 0.52 | 4.90 | 7.00 | 5.79 | 6.08 | 50 |
#> +--------------+------------+------+------+------+------+------+------+----+
#> |              | virginica  | 6.59 | 0.64 | 4.90 | 7.90 | 6.41 | 6.77 | 50 |
#> +--------------+------------+------+------+------+------+------+------+----+
#> | Sepal.Width  | setosa     | 3.43 | 0.38 | 2.30 | 4.40 | 3.32 | 3.54 | 50 |
#> +--------------+------------+------+------+------+------+------+------+----+
#> |              | versicolor | 2.77 | 0.31 | 2.00 | 3.40 | 2.68 | 2.86 | 50 |
#> +--------------+------------+------+------+------+------+------+------+----+
#> |              | virginica  | 2.97 | 0.32 | 2.20 | 3.80 | 2.88 | 3.07 | 50 |
#> +--------------+------------+------+------+------+------+------+------+----+ 

# gt output
if (requireNamespace("gt", quietly = TRUE)) {
  table_continuous(iris, output = "gt")
  table_continuous(iris, select = starts_with("Sepal"),
             by = Species, output = "gt")
}


  
        Variable
      
      
        Group
      
      
        M
      
      
        SD
      
      
        Min
      
      
        Max
      
      
        95% CI
      
      
        n
      
    

      
      
      
      
      
      LL
      UL
      
    
Sepal.Length
setosa
5.01
0.35
4.30
5.80
4.91
5.11
50

versicolor
5.94
0.52
4.90
7.00
5.79
6.08
50

virginica
6.59
0.64
4.90
7.90
6.41
6.77
50
Sepal.Width
setosa
3.43
0.38
2.30
4.40
3.32
3.54
50

versicolor
2.77
0.31
2.00
3.40
2.68
2.86
50

virginica
2.97
0.32
2.20
3.80
2.88
3.07
50

# flextable output
if (requireNamespace("flextable", quietly = TRUE)) {
  table_continuous(iris, output = "flextable")
  table_continuous(iris, by = Species, output = "flextable")
}
Variable Group M SD Min Max 95% CI n
LL UL 
Sepal.Length setosa 5.01 0.35 4.30 5.80 4.91 5.11 50
versicolor 5.94 0.52 4.90 7.00 5.79 6.08 50
virginica 6.59 0.64 4.90 7.90 6.41 6.77 50
Sepal.Width setosa 3.43 0.38 2.30 4.40 3.32 3.54 50
versicolor 2.77 0.31 2.00 3.40 2.68 2.86 50
virginica 2.97 0.32 2.20 3.80 2.88 3.07 50
Petal.Length setosa 1.46 0.17 1.00 1.90 1.41 1.51 50
versicolor 4.26 0.47 3.00 5.10 4.13 4.39 50
virginica 5.55 0.55 4.50 6.90 5.40 5.71 50
Petal.Width setosa 0.25 0.11 0.10 0.60 0.22 0.28 50
versicolor 1.33 0.20 1.00 1.80 1.27 1.38 50
virginica 2.03 0.27 1.40 2.50 1.95 2.10 50

# Word output
if (requireNamespace("flextable", quietly = TRUE) &&
    requireNamespace("officer", quietly = TRUE)) {
  table_continuous(iris, select = starts_with("Sepal"),
             by = Species, output = "word",
             word_path = tempfile(fileext = ".docx"))
}

# Excel output
if (requireNamespace("openxlsx2", quietly = TRUE)) {
  table_continuous(iris, select = starts_with("Sepal"),
             by = Species, output = "excel",
             excel_path = tempfile(fileext = ".xlsx"))
}
# }

Variable	Group	M	SD	Min	Max	95% CI		n
						LL	UL
Sepal.Length	setosa	5.01	0.35	4.30	5.80	4.91	5.11	50
	versicolor	5.94	0.52	4.90	7.00	5.79	6.08	50
	virginica	6.59	0.64	4.90	7.90	6.41	6.77	50
Sepal.Width	setosa	3.43	0.38	2.30	4.40	3.32	3.54	50
	versicolor	2.77	0.31	2.00	3.40	2.68	2.86	50
	virginica	2.97	0.32	2.20	3.80	2.88	3.07	50

Usage

Arguments

Value

Details

See also

Examples