Create sample description table (also referred to as "Table 1").
Usage
report_sample(
data,
by = NULL,
centrality = "mean",
ci = NULL,
ci_method = "wilson",
ci_correct = FALSE,
select = NULL,
exclude = NULL,
weights = NULL,
total = TRUE,
digits = 2,
n = FALSE,
group_by = NULL,
...
)Arguments
- data
A data frame for which descriptive statistics should be created.
- by
Character vector, indicating the column(s) for possible grouping of the descriptive table. Note that weighting (see
weights) does not work with more than one grouping column.- centrality
Character, indicates the statistics that should be calculated for numeric variables. May be
"mean"(for mean and standard deviation) or"median"(for median and median absolute deviation) as summary.- ci
Level of confidence interval for relative frequencies (proportions). If not
NULL, confidence intervals are shown for proportions of factor levels.- ci_method
Character, indicating the method how to calculate confidence intervals for proportions. Currently implemented methods are
"wald"and"wilson". Note that"wald"can produce intervals outside the plausible range of [0, 1], and thus it is recommended to prefer the"wilson"method. The formulae for the confidence intervals are:"wald":$$p \pm z \sqrt{\frac{p (1 - p)}{n}}$$
"wilson":$$\frac{2np + z^2 \pm z \sqrt{z^2 + 4npq}}{2(n + z^2)}$$
where
pis the proportion (of a factor level),qis1-p,zis the critical z-score based on the interval level andnis the length of the vector (cf. Newcombe 1998, Wilson 1927).
- ci_correct
Logical, it
TRUE, applies continuity correction. See Newcombe 1998 for different correction-methods based on the chosenci_method.- select
Character vector, with column names that should be included in the descriptive table.
- exclude
Character vector, with column names that should be excluded from the descriptive table.
- weights
Character vector, indicating the name of a potential weight-variable. Reported descriptive statistics will be weighted by
weight.- total
Add a
Totalcolumn.- digits
Number of decimals.
- n
Logical, actual sample size used in the calculation of the reported descriptive statistics (i.e., without the missing values).
- group_by
Deprecated. Use
byinstead.- ...
Arguments passed to or from other methods.
References
Newcombe, R. G. (1998). Two-sided confidence intervals for the single proportion: comparison of seven methods. Statistics in Medicine. 17 (8): 857–872
Wilson, E. B. (1927). Probable inference, the law of succession, and statistical inference. Journal of the American Statistical Association. 22 (158): 209–212
Examples
library(report)
report_sample(iris[, 1:4])
#> # Descriptive Statistics
#>
#> Variable | Summary
#> ------------------------------------
#> Mean Sepal.Length (SD) | 5.84 (0.83)
#> Mean Sepal.Width (SD) | 3.06 (0.44)
#> Mean Petal.Length (SD) | 3.76 (1.77)
#> Mean Petal.Width (SD) | 1.20 (0.76)
report_sample(iris, select = c("Sepal.Length", "Petal.Length", "Species"))
#> # Descriptive Statistics
#>
#> Variable | Summary
#> -------------------------------------
#> Mean Sepal.Length (SD) | 5.84 (0.83)
#> Mean Petal.Length (SD) | 3.76 (1.77)
#> Species [setosa], % | 33.3
#> Species [versicolor], % | 33.3
#> Species [virginica], % | 33.3
report_sample(iris, by = "Species")
#> # Descriptive Statistics
#>
#> Variable | setosa (n=50) | versicolor (n=50) | virginica (n=50) | Total (n=150)
#> ---------------------------------------------------------------------------------------------
#> Mean Sepal.Length (SD) | 5.01 (0.35) | 5.94 (0.52) | 6.59 (0.64) | 5.84 (0.83)
#> Mean Sepal.Width (SD) | 3.43 (0.38) | 2.77 (0.31) | 2.97 (0.32) | 3.06 (0.44)
#> Mean Petal.Length (SD) | 1.46 (0.17) | 4.26 (0.47) | 5.55 (0.55) | 3.76 (1.77)
#> Mean Petal.Width (SD) | 0.25 (0.11) | 1.33 (0.20) | 2.03 (0.27) | 1.20 (0.76)
report_sample(airquality, by = "Month", n = TRUE, total = FALSE)
#> # Descriptive Statistics
#>
#> Variable | 5 (n=31) | 6 (n=30)
#> ---------------------------------------------------------------
#> Mean Ozone (SD), n | 23.62 (22.22), 26 | 29.44 (18.21), 9
#> Mean Solar.R (SD), n | 181.30 (115.08), 27 | 190.17 (92.88), 30
#> Mean Wind (SD), n | 11.62 (3.53), 31 | 10.27 (3.77), 30
#> Mean Temp (SD), n | 65.55 (6.85), 31 | 79.10 (6.60), 30
#> Mean Day (SD), n | 16.00 (9.09), 31 | 15.50 (8.80), 30
#>
#> Variable | 7 (n=31) | 8 (n=31) | 9 (n=30) (n=153)
#> -----------------------------------------------------------------------------------
#> Mean Ozone (SD), n | 59.12 (31.64), 26 | 59.96 (39.68), 26 | 31.45 (24.14), 29
#> Mean Solar.R (SD), n | 216.48 (80.57), 31 | 171.86 (76.83), 28 | 167.43 (79.12), 30
#> Mean Wind (SD), n | 8.94 (3.04), 31 | 8.79 (3.23), 31 | 10.18 (3.46), 30
#> Mean Temp (SD), n | 83.90 (4.32), 31 | 83.97 (6.59), 31 | 76.90 (8.36), 30
#> Mean Day (SD), n | 16.00 (9.09), 31 | 16.00 (9.09), 31 | 15.50 (8.80), 30
# confidence intervals for proportions
set.seed(123)
d <- data.frame(x = factor(sample(letters[1:3], 100, TRUE, c(0.01, 0.39, 0.6))))
report_sample(d, ci = 0.95, ci_method = "wald") # ups, negative CI
#> # Descriptive Statistics
#>
#> Variable | Summary
#> ----------------------------
#> x [a], % | 1.0 [-1.0, 3.0]
#> x [b], % | 39.0 [29.4, 48.6]
#> x [c], % | 60.0 [50.4, 69.6]
report_sample(d, ci = 0.95, ci_method = "wilson") # negative CI fixed
#> # Descriptive Statistics
#>
#> Variable | Summary
#> ----------------------------
#> x [a], % | 1.0 [0.2, 5.4]
#> x [b], % | 39.0 [30.0, 48.8]
#> x [c], % | 60.0 [50.2, 69.1]
report_sample(d, ci = 0.95, ci_correct = TRUE) # continuity correction
#> # Descriptive Statistics
#>
#> Variable | Summary
#> ----------------------------
#> x [a], % | 1.0 [0.1, 6.2]
#> x [b], % | 39.0 [29.6, 49.3]
#> x [c], % | 60.0 [49.7, 69.5]