## Fabricated dataset.
dta_test <- data.frame(
USUBJID = rep(1:6, each = 3),
PARAMCD = rep("lab", 6 * 3),
AVISIT = rep(paste0("V", 1:3), 6),
ARM = rep(LETTERS[1:3], rep(6, 3)),
AVAL = c(9:1, rep(NA, 9))
)
# `analyze_vars()` in `rtables` pipelines
## Default output within a `rtables` pipeline.
l <- basic_table() %>%
split_cols_by(var = "ARM") %>%
split_rows_by(var = "AVISIT") %>%
analyze_vars(vars = "AVAL")
build_table(l, df = dta_test)
#> A B C
#> ————————————————————————————————————————
#> V1
#> n 2 1 0
#> Mean (SD) 7.5 (2.1) 3.0 (NA) NA
#> Median 7.5 3.0 NA
#> Min - Max 6.0 - 9.0 3.0 - 3.0 NA
#> V2
#> n 2 1 0
#> Mean (SD) 6.5 (2.1) 2.0 (NA) NA
#> Median 6.5 2.0 NA
#> Min - Max 5.0 - 8.0 2.0 - 2.0 NA
#> V3
#> n 2 1 0
#> Mean (SD) 5.5 (2.1) 1.0 (NA) NA
#> Median 5.5 1.0 NA
#> Min - Max 4.0 - 7.0 1.0 - 1.0 NA
## Select and format statistics output.
l <- basic_table() %>%
split_cols_by(var = "ARM") %>%
split_rows_by(var = "AVISIT") %>%
analyze_vars(
vars = "AVAL",
.stats = c("n", "mean_sd", "quantiles"),
.formats = c("mean_sd" = "xx.x, xx.x"),
.labels = c(n = "n", mean_sd = "Mean, SD", quantiles = c("Q1 - Q3"))
)
build_table(l, df = dta_test)
#> A B C
#> ———————————————————————————————————————
#> V1
#> n 2 1 0
#> Mean, SD 7.5, 2.1 3.0, NA NA
#> Q1 - Q3 6.0 - 9.0 3.0 - 3.0 NA
#> V2
#> n 2 1 0
#> Mean, SD 6.5, 2.1 2.0, NA NA
#> Q1 - Q3 5.0 - 8.0 2.0 - 2.0 NA
#> V3
#> n 2 1 0
#> Mean, SD 5.5, 2.1 1.0, NA NA
#> Q1 - Q3 4.0 - 7.0 1.0 - 1.0 NA
## Use arguments interpreted by `s_summary`.
l <- basic_table() %>%
split_cols_by(var = "ARM") %>%
split_rows_by(var = "AVISIT") %>%
analyze_vars(vars = "AVAL", na_rm = FALSE)
build_table(l, df = dta_test)
#> A B C
#> —————————————————————————————————
#> V1
#> n 2 2 2
#> Mean (SD) 7.5 (2.1) NA NA
#> Median 7.5 NA NA
#> Min - Max 6.0 - 9.0 NA NA
#> V2
#> n 2 2 2
#> Mean (SD) 6.5 (2.1) NA NA
#> Median 6.5 NA NA
#> Min - Max 5.0 - 8.0 NA NA
#> V3
#> n 2 2 2
#> Mean (SD) 5.5 (2.1) NA NA
#> Median 5.5 NA NA
#> Min - Max 4.0 - 7.0 NA NA
## Handle `NA` levels first when summarizing factors.
dta_test$AVISIT <- NA_character_
dta_test <- df_explicit_na(dta_test)
l <- basic_table() %>%
split_cols_by(var = "ARM") %>%
analyze_vars(vars = "AVISIT", na_rm = FALSE)
build_table(l, df = dta_test)
#> A B C
#> ——————————————————————————————————————————
#> n 6 6 6
#> <Missing> 6 (100%) 6 (100%) 6 (100%)
# auto format
dt <- data.frame("VAR" = c(0.001, 0.2, 0.0011000, 3, 4))
basic_table() %>%
analyze_vars(
vars = "VAR",
.stats = c("n", "mean", "mean_sd", "range"),
.formats = c("mean_sd" = "auto", "range" = "auto")
) %>%
build_table(dt)
#> all obs
#> —————————————————————————————
#> n 5
#> Mean 1.4
#> Mean (SD) 1.44042 (1.91481)
#> Min - Max 0.0010 - 4.0000
# `s_summary.numeric`
## Basic usage: empty numeric returns NA-filled items.
s_summary(numeric())
#> $n
#> n
#> 0
#>
#> $sum
#> sum
#> NA
#>
#> $mean
#> mean
#> NA
#>
#> $sd
#> sd
#> NA
#>
#> $se
#> se
#> NA
#>
#> $mean_sd
#> mean sd
#> NA NA
#>
#> $mean_se
#> mean se
#> NA NA
#>
#> $mean_ci
#> mean_ci_lwr mean_ci_upr
#> NA NA
#> attr(,"label")
#> [1] "Mean 95% CI"
#>
#> $mean_sei
#> mean_sei_lwr mean_sei_upr
#> NA NA
#> attr(,"label")
#> [1] "Mean -/+ 1xSE"
#>
#> $mean_sdi
#> mean_sdi_lwr mean_sdi_upr
#> NA NA
#> attr(,"label")
#> [1] "Mean -/+ 1xSD"
#>
#> $mean_ci_3d
#> mean mean_ci_lwr mean_ci_upr
#> NA NA NA
#> attr(,"label")
#> [1] "Mean (95% CI)"
#>
#> $mean_pval
#> p_value
#> NA
#> attr(,"label")
#> [1] "Mean p-value (H0: mean = 0)"
#>
#> $median
#> median
#> NA
#>
#> $mad
#> mad
#> NA
#>
#> $median_ci
#> median_ci_lwr median_ci_upr
#> NA NA
#> attr(,"conf_level")
#> [1] NA
#> attr(,"label")
#> [1] "Median 95% CI"
#>
#> $median_ci_3d
#> median median_ci_lwr median_ci_upr
#> NA NA NA
#> attr(,"label")
#> [1] "Median (95% CI)"
#>
#> $quantiles
#> quantile_0.25 quantile_0.75
#> NA NA
#> attr(,"label")
#> [1] "25% and 75%-ile"
#>
#> $iqr
#> iqr
#> NA
#>
#> $range
#> min max
#> NA NA
#>
#> $min
#> min
#> NA
#>
#> $max
#> max
#> NA
#>
#> $median_range
#> median min max
#> NA NA NA
#> attr(,"label")
#> [1] "Median (Min - Max)"
#>
#> $cv
#> cv
#> NA
#>
#> $geom_mean
#> geom_mean
#> NA
#>
#> $geom_sd
#> geom_sd
#> NA
#>
#> $geom_mean_sd
#> geom_mean geom_sd
#> NA NA
#>
#> $geom_mean_ci
#> mean_ci_lwr mean_ci_upr
#> NA NA
#> attr(,"label")
#> [1] "Geometric Mean 95% CI"
#>
#> $geom_cv
#> geom_cv
#> NA
#>
#> $geom_mean_ci_3d
#> geom_mean mean_ci_lwr mean_ci_upr
#> NA NA NA
#> attr(,"label")
#> [1] "Geometric Mean (95% CI)"
#>
## Management of NA values.
x <- c(NA_real_, 1)
s_summary(x, na_rm = TRUE)
#> $n
#> n
#> 1
#>
#> $sum
#> sum
#> 1
#>
#> $mean
#> mean
#> 1
#>
#> $sd
#> sd
#> NA
#>
#> $se
#> se
#> NA
#>
#> $mean_sd
#> mean sd
#> 1 NA
#>
#> $mean_se
#> mean se
#> 1 NA
#>
#> $mean_ci
#> mean_ci_lwr mean_ci_upr
#> NA NA
#> attr(,"label")
#> [1] "Mean 95% CI"
#>
#> $mean_sei
#> mean_sei_lwr mean_sei_upr
#> NA NA
#> attr(,"label")
#> [1] "Mean -/+ 1xSE"
#>
#> $mean_sdi
#> mean_sdi_lwr mean_sdi_upr
#> NA NA
#> attr(,"label")
#> [1] "Mean -/+ 1xSD"
#>
#> $mean_ci_3d
#> mean mean_ci_lwr mean_ci_upr
#> 1 NA NA
#> attr(,"label")
#> [1] "Mean (95% CI)"
#>
#> $mean_pval
#> p_value
#> NA
#> attr(,"label")
#> [1] "Mean p-value (H0: mean = 0)"
#>
#> $median
#> median
#> 1
#>
#> $mad
#> mad
#> 0
#>
#> $median_ci
#> median_ci_lwr median_ci_upr
#> NA NA
#> attr(,"conf_level")
#> [1] NA
#> attr(,"label")
#> [1] "Median 95% CI"
#>
#> $median_ci_3d
#> median median_ci_lwr median_ci_upr
#> 1 NA NA
#> attr(,"label")
#> [1] "Median (95% CI)"
#>
#> $quantiles
#> quantile_0.25 quantile_0.75
#> 1 1
#> attr(,"label")
#> [1] "25% and 75%-ile"
#>
#> $iqr
#> iqr
#> 0
#>
#> $range
#> min max
#> 1 1
#>
#> $min
#> min
#> 1
#>
#> $max
#> max
#> 1
#>
#> $median_range
#> median min max
#> 1 1 1
#> attr(,"label")
#> [1] "Median (Min - Max)"
#>
#> $cv
#> cv
#> NA
#>
#> $geom_mean
#> geom_mean
#> 1
#>
#> $geom_sd
#> geom_sd
#> NA
#>
#> $geom_mean_sd
#> geom_mean geom_sd
#> 1 NA
#>
#> $geom_mean_ci
#> mean_ci_lwr mean_ci_upr
#> NA NA
#> attr(,"label")
#> [1] "Geometric Mean 95% CI"
#>
#> $geom_cv
#> geom_cv
#> NA
#>
#> $geom_mean_ci_3d
#> geom_mean mean_ci_lwr mean_ci_upr
#> 1 NA NA
#> attr(,"label")
#> [1] "Geometric Mean (95% CI)"
#>
s_summary(x, na_rm = FALSE)
#> $n
#> n
#> 2
#>
#> $sum
#> sum
#> NA
#>
#> $mean
#> mean
#> NA
#>
#> $sd
#> sd
#> NA
#>
#> $se
#> se
#> NA
#>
#> $mean_sd
#> mean sd
#> NA NA
#>
#> $mean_se
#> mean se
#> NA NA
#>
#> $mean_ci
#> mean_ci_lwr mean_ci_upr
#> NA NA
#> attr(,"label")
#> [1] "Mean 95% CI"
#>
#> $mean_sei
#> mean_sei_lwr mean_sei_upr
#> NA NA
#> attr(,"label")
#> [1] "Mean -/+ 1xSE"
#>
#> $mean_sdi
#> mean_sdi_lwr mean_sdi_upr
#> NA NA
#> attr(,"label")
#> [1] "Mean -/+ 1xSD"
#>
#> $mean_ci_3d
#> mean mean_ci_lwr mean_ci_upr
#> NA NA NA
#> attr(,"label")
#> [1] "Mean (95% CI)"
#>
#> $mean_pval
#> p_value
#> NA
#> attr(,"label")
#> [1] "Mean p-value (H0: mean = 0)"
#>
#> $median
#> median
#> NA
#>
#> $mad
#> mad
#> NA
#>
#> $median_ci
#> median_ci_lwr median_ci_upr
#> NA NA
#> attr(,"conf_level")
#> [1] NA
#> attr(,"label")
#> [1] "Median 95% CI"
#>
#> $median_ci_3d
#> median median_ci_lwr median_ci_upr
#> NA NA NA
#> attr(,"label")
#> [1] "Median (95% CI)"
#>
#> $quantiles
#> quantile_0.25 quantile_0.75
#> NA NA
#> attr(,"label")
#> [1] "25% and 75%-ile"
#>
#> $iqr
#> iqr
#> NA
#>
#> $range
#> min max
#> NA NA
#>
#> $min
#> min
#> NA
#>
#> $max
#> max
#> NA
#>
#> $median_range
#> median min max
#> NA NA NA
#> attr(,"label")
#> [1] "Median (Min - Max)"
#>
#> $cv
#> cv
#> NA
#>
#> $geom_mean
#> geom_mean
#> NA
#>
#> $geom_sd
#> geom_sd
#> NA
#>
#> $geom_mean_sd
#> geom_mean geom_sd
#> NA NA
#>
#> $geom_mean_ci
#> mean_ci_lwr mean_ci_upr
#> NA NA
#> attr(,"label")
#> [1] "Geometric Mean 95% CI"
#>
#> $geom_cv
#> geom_cv
#> NA
#>
#> $geom_mean_ci_3d
#> geom_mean mean_ci_lwr mean_ci_upr
#> NA NA NA
#> attr(,"label")
#> [1] "Geometric Mean (95% CI)"
#>
x <- c(NA_real_, 1, 2)
s_summary(x)
#> $n
#> n
#> 2
#>
#> $sum
#> sum
#> 3
#>
#> $mean
#> mean
#> 1.5
#>
#> $sd
#> sd
#> 0.7071068
#>
#> $se
#> se
#> 0.5
#>
#> $mean_sd
#> mean sd
#> 1.5000000 0.7071068
#>
#> $mean_se
#> mean se
#> 1.5 0.5
#>
#> $mean_ci
#> mean_ci_lwr mean_ci_upr
#> -4.853102 7.853102
#> attr(,"label")
#> [1] "Mean 95% CI"
#>
#> $mean_sei
#> mean_sei_lwr mean_sei_upr
#> 1 2
#> attr(,"label")
#> [1] "Mean -/+ 1xSE"
#>
#> $mean_sdi
#> mean_sdi_lwr mean_sdi_upr
#> 0.7928932 2.2071068
#> attr(,"label")
#> [1] "Mean -/+ 1xSD"
#>
#> $mean_ci_3d
#> mean mean_ci_lwr mean_ci_upr
#> 1.500000 -4.853102 7.853102
#> attr(,"label")
#> [1] "Mean (95% CI)"
#>
#> $mean_pval
#> p_value
#> 0.2048328
#> attr(,"label")
#> [1] "Mean p-value (H0: mean = 0)"
#>
#> $median
#> median
#> 1.5
#>
#> $mad
#> mad
#> 0
#>
#> $median_ci
#> median_ci_lwr median_ci_upr
#> NA NA
#> attr(,"conf_level")
#> [1] NA
#> attr(,"label")
#> [1] "Median 95% CI"
#>
#> $median_ci_3d
#> median median_ci_lwr median_ci_upr
#> 1.5 NA NA
#> attr(,"label")
#> [1] "Median (95% CI)"
#>
#> $quantiles
#> quantile_0.25 quantile_0.75
#> 1 2
#> attr(,"label")
#> [1] "25% and 75%-ile"
#>
#> $iqr
#> iqr
#> 1
#>
#> $range
#> min max
#> 1 2
#>
#> $min
#> min
#> 1
#>
#> $max
#> max
#> 2
#>
#> $median_range
#> median min max
#> 1.5 1.0 2.0
#> attr(,"label")
#> [1] "Median (Min - Max)"
#>
#> $cv
#> cv
#> 47.14045
#>
#> $geom_mean
#> geom_mean
#> 1.414214
#>
#> $geom_sd
#> geom_sd
#> 1.632527
#>
#> $geom_mean_sd
#> geom_mean geom_sd
#> 1.414214 1.632527
#>
#> $geom_mean_ci
#> mean_ci_lwr mean_ci_upr
#> 0.01729978 115.60839614
#> attr(,"label")
#> [1] "Geometric Mean 95% CI"
#>
#> $geom_cv
#> geom_cv
#> 52.10922
#>
#> $geom_mean_ci_3d
#> geom_mean mean_ci_lwr mean_ci_upr
#> 1.41421356 0.01729978 115.60839614
#> attr(,"label")
#> [1] "Geometric Mean (95% CI)"
#>
## Benefits in `rtables` contructions:
dta_test <- data.frame(
Group = rep(LETTERS[seq(3)], each = 2),
sub_group = rep(letters[seq(2)], each = 3),
x = seq(6)
)
## The summary obtained in with `rtables`:
basic_table() %>%
split_cols_by(var = "Group") %>%
split_rows_by(var = "sub_group") %>%
analyze(vars = "x", afun = s_summary) %>%
build_table(df = dta_test)
#> A B C
#> —————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> a
#> n 2 1 0
#> sum 3 3 NA
#> mean 1.5 3 NA
#> sd 0.707106781186548 NA NA
#> se 0.5 NA NA
#> mean_sd 1.5, 0.707106781186548 3, NA NA
#> mean_se 1.5, 0.5 3, NA NA
#> Mean 95% CI -4.85310236808735, 7.85310236808735 NA NA
#> Mean -/+ 1xSE 1, 2 NA NA
#> Mean -/+ 1xSD 0.792893218813452, 2.20710678118655 NA NA
#> Mean (95% CI) 1.5, -4.85310236808735, 7.85310236808735 3, NA, NA NA
#> Mean p-value (H0: mean = 0) 0.204832764699133 NA NA
#> median 1.5 3 NA
#> mad 0 0 NA
#> Median 95% CI NA NA NA
#> Median (95% CI) 1.5, NA, NA 3, NA, NA NA
#> 25% and 75%-ile 1, 2 3, 3 NA
#> iqr 1 0 NA
#> range 1, 2 3, 3 NA
#> min 1 3 NA
#> max 2 3 NA
#> Median (Min - Max) 1.5, 1, 2 3, 3, 3 NA
#> cv 47.1404520791032 NA NA
#> geom_mean 1.41421356237309 3 NA
#> geom_sd 1.63252691943815 NA NA
#> geom_mean_sd 1.41421356237309, 1.63252691943815 3, NA NA
#> Geometric Mean 95% CI 0.0172997815631007, 115.608396135236 NA NA
#> geom_cv 52.1092246837487 NA NA
#> Geometric Mean (95% CI) 1.41421356237309, 0.0172997815631007, 115.608396135236 3, NA, NA NA
#> b
#> n 0 1 2
#> sum NA 4 11
#> mean NA 4 5.5
#> sd NA NA 0.707106781186548
#> se NA NA 0.5
#> mean_sd NA 4, NA 5.5, 0.707106781186548
#> mean_se NA 4, NA 5.5, 0.5
#> Mean 95% CI NA NA -0.853102368087347, 11.8531023680873
#> Mean -/+ 1xSE NA NA 5, 6
#> Mean -/+ 1xSD NA NA 4.79289321881345, 6.20710678118655
#> Mean (95% CI) NA 4, NA, NA 5.5, -0.853102368087347, 11.8531023680873
#> Mean p-value (H0: mean = 0) NA NA 0.0577158767526089
#> median NA 4 5.5
#> mad NA 0 0
#> Median 95% CI NA NA NA
#> Median (95% CI) NA 4, NA, NA 5.5, NA, NA
#> 25% and 75%-ile NA 4, 4 5, 6
#> iqr NA 0 1
#> range NA 4, 4 5, 6
#> min NA 4 5
#> max NA 4 6
#> Median (Min - Max) NA 4, 4, 4 5.5, 5, 6
#> cv NA NA 12.8564869306645
#> geom_mean NA 4 5.47722557505166
#> geom_sd NA NA 1.13760003310263
#> geom_mean_sd NA 4, NA 5.47722557505166, 1.13760003310263
#> Geometric Mean 95% CI NA NA 1.71994304449266, 17.4424380482025
#> geom_cv NA NA 12.945835316564
#> Geometric Mean (95% CI) NA 4, NA, NA 5.47722557505166, 1.71994304449266, 17.4424380482025
## By comparison with `lapply`:
X <- split(dta_test, f = with(dta_test, interaction(Group, sub_group)))
lapply(X, function(x) s_summary(x$x))
#> $A.a
#> $A.a$n
#> n
#> 2
#>
#> $A.a$sum
#> sum
#> 3
#>
#> $A.a$mean
#> mean
#> 1.5
#>
#> $A.a$sd
#> sd
#> 0.7071068
#>
#> $A.a$se
#> se
#> 0.5
#>
#> $A.a$mean_sd
#> mean sd
#> 1.5000000 0.7071068
#>
#> $A.a$mean_se
#> mean se
#> 1.5 0.5
#>
#> $A.a$mean_ci
#> mean_ci_lwr mean_ci_upr
#> -4.853102 7.853102
#> attr(,"label")
#> [1] "Mean 95% CI"
#>
#> $A.a$mean_sei
#> mean_sei_lwr mean_sei_upr
#> 1 2
#> attr(,"label")
#> [1] "Mean -/+ 1xSE"
#>
#> $A.a$mean_sdi
#> mean_sdi_lwr mean_sdi_upr
#> 0.7928932 2.2071068
#> attr(,"label")
#> [1] "Mean -/+ 1xSD"
#>
#> $A.a$mean_ci_3d
#> mean mean_ci_lwr mean_ci_upr
#> 1.500000 -4.853102 7.853102
#> attr(,"label")
#> [1] "Mean (95% CI)"
#>
#> $A.a$mean_pval
#> p_value
#> 0.2048328
#> attr(,"label")
#> [1] "Mean p-value (H0: mean = 0)"
#>
#> $A.a$median
#> median
#> 1.5
#>
#> $A.a$mad
#> mad
#> 0
#>
#> $A.a$median_ci
#> median_ci_lwr median_ci_upr
#> NA NA
#> attr(,"conf_level")
#> [1] NA
#> attr(,"label")
#> [1] "Median 95% CI"
#>
#> $A.a$median_ci_3d
#> median median_ci_lwr median_ci_upr
#> 1.5 NA NA
#> attr(,"label")
#> [1] "Median (95% CI)"
#>
#> $A.a$quantiles
#> quantile_0.25 quantile_0.75
#> 1 2
#> attr(,"label")
#> [1] "25% and 75%-ile"
#>
#> $A.a$iqr
#> iqr
#> 1
#>
#> $A.a$range
#> min max
#> 1 2
#>
#> $A.a$min
#> min
#> 1
#>
#> $A.a$max
#> max
#> 2
#>
#> $A.a$median_range
#> median min max
#> 1.5 1.0 2.0
#> attr(,"label")
#> [1] "Median (Min - Max)"
#>
#> $A.a$cv
#> cv
#> 47.14045
#>
#> $A.a$geom_mean
#> geom_mean
#> 1.414214
#>
#> $A.a$geom_sd
#> geom_sd
#> 1.632527
#>
#> $A.a$geom_mean_sd
#> geom_mean geom_sd
#> 1.414214 1.632527
#>
#> $A.a$geom_mean_ci
#> mean_ci_lwr mean_ci_upr
#> 0.01729978 115.60839614
#> attr(,"label")
#> [1] "Geometric Mean 95% CI"
#>
#> $A.a$geom_cv
#> geom_cv
#> 52.10922
#>
#> $A.a$geom_mean_ci_3d
#> geom_mean mean_ci_lwr mean_ci_upr
#> 1.41421356 0.01729978 115.60839614
#> attr(,"label")
#> [1] "Geometric Mean (95% CI)"
#>
#>
#> $B.a
#> $B.a$n
#> n
#> 1
#>
#> $B.a$sum
#> sum
#> 3
#>
#> $B.a$mean
#> mean
#> 3
#>
#> $B.a$sd
#> sd
#> NA
#>
#> $B.a$se
#> se
#> NA
#>
#> $B.a$mean_sd
#> mean sd
#> 3 NA
#>
#> $B.a$mean_se
#> mean se
#> 3 NA
#>
#> $B.a$mean_ci
#> mean_ci_lwr mean_ci_upr
#> NA NA
#> attr(,"label")
#> [1] "Mean 95% CI"
#>
#> $B.a$mean_sei
#> mean_sei_lwr mean_sei_upr
#> NA NA
#> attr(,"label")
#> [1] "Mean -/+ 1xSE"
#>
#> $B.a$mean_sdi
#> mean_sdi_lwr mean_sdi_upr
#> NA NA
#> attr(,"label")
#> [1] "Mean -/+ 1xSD"
#>
#> $B.a$mean_ci_3d
#> mean mean_ci_lwr mean_ci_upr
#> 3 NA NA
#> attr(,"label")
#> [1] "Mean (95% CI)"
#>
#> $B.a$mean_pval
#> p_value
#> NA
#> attr(,"label")
#> [1] "Mean p-value (H0: mean = 0)"
#>
#> $B.a$median
#> median
#> 3
#>
#> $B.a$mad
#> mad
#> 0
#>
#> $B.a$median_ci
#> median_ci_lwr median_ci_upr
#> NA NA
#> attr(,"conf_level")
#> [1] NA
#> attr(,"label")
#> [1] "Median 95% CI"
#>
#> $B.a$median_ci_3d
#> median median_ci_lwr median_ci_upr
#> 3 NA NA
#> attr(,"label")
#> [1] "Median (95% CI)"
#>
#> $B.a$quantiles
#> quantile_0.25 quantile_0.75
#> 3 3
#> attr(,"label")
#> [1] "25% and 75%-ile"
#>
#> $B.a$iqr
#> iqr
#> 0
#>
#> $B.a$range
#> min max
#> 3 3
#>
#> $B.a$min
#> min
#> 3
#>
#> $B.a$max
#> max
#> 3
#>
#> $B.a$median_range
#> median min max
#> 3 3 3
#> attr(,"label")
#> [1] "Median (Min - Max)"
#>
#> $B.a$cv
#> cv
#> NA
#>
#> $B.a$geom_mean
#> geom_mean
#> 3
#>
#> $B.a$geom_sd
#> geom_sd
#> NA
#>
#> $B.a$geom_mean_sd
#> geom_mean geom_sd
#> 3 NA
#>
#> $B.a$geom_mean_ci
#> mean_ci_lwr mean_ci_upr
#> NA NA
#> attr(,"label")
#> [1] "Geometric Mean 95% CI"
#>
#> $B.a$geom_cv
#> geom_cv
#> NA
#>
#> $B.a$geom_mean_ci_3d
#> geom_mean mean_ci_lwr mean_ci_upr
#> 3 NA NA
#> attr(,"label")
#> [1] "Geometric Mean (95% CI)"
#>
#>
#> $C.a
#> $C.a$n
#> n
#> 0
#>
#> $C.a$sum
#> sum
#> NA
#>
#> $C.a$mean
#> mean
#> NA
#>
#> $C.a$sd
#> sd
#> NA
#>
#> $C.a$se
#> se
#> NA
#>
#> $C.a$mean_sd
#> mean sd
#> NA NA
#>
#> $C.a$mean_se
#> mean se
#> NA NA
#>
#> $C.a$mean_ci
#> mean_ci_lwr mean_ci_upr
#> NA NA
#> attr(,"label")
#> [1] "Mean 95% CI"
#>
#> $C.a$mean_sei
#> mean_sei_lwr mean_sei_upr
#> NA NA
#> attr(,"label")
#> [1] "Mean -/+ 1xSE"
#>
#> $C.a$mean_sdi
#> mean_sdi_lwr mean_sdi_upr
#> NA NA
#> attr(,"label")
#> [1] "Mean -/+ 1xSD"
#>
#> $C.a$mean_ci_3d
#> mean mean_ci_lwr mean_ci_upr
#> NA NA NA
#> attr(,"label")
#> [1] "Mean (95% CI)"
#>
#> $C.a$mean_pval
#> p_value
#> NA
#> attr(,"label")
#> [1] "Mean p-value (H0: mean = 0)"
#>
#> $C.a$median
#> median
#> NA
#>
#> $C.a$mad
#> mad
#> NA
#>
#> $C.a$median_ci
#> median_ci_lwr median_ci_upr
#> NA NA
#> attr(,"conf_level")
#> [1] NA
#> attr(,"label")
#> [1] "Median 95% CI"
#>
#> $C.a$median_ci_3d
#> median median_ci_lwr median_ci_upr
#> NA NA NA
#> attr(,"label")
#> [1] "Median (95% CI)"
#>
#> $C.a$quantiles
#> quantile_0.25 quantile_0.75
#> NA NA
#> attr(,"label")
#> [1] "25% and 75%-ile"
#>
#> $C.a$iqr
#> iqr
#> NA
#>
#> $C.a$range
#> min max
#> NA NA
#>
#> $C.a$min
#> min
#> NA
#>
#> $C.a$max
#> max
#> NA
#>
#> $C.a$median_range
#> median min max
#> NA NA NA
#> attr(,"label")
#> [1] "Median (Min - Max)"
#>
#> $C.a$cv
#> cv
#> NA
#>
#> $C.a$geom_mean
#> geom_mean
#> NaN
#>
#> $C.a$geom_sd
#> geom_sd
#> NA
#>
#> $C.a$geom_mean_sd
#> geom_mean geom_sd
#> NaN NA
#>
#> $C.a$geom_mean_ci
#> mean_ci_lwr mean_ci_upr
#> NA NA
#> attr(,"label")
#> [1] "Geometric Mean 95% CI"
#>
#> $C.a$geom_cv
#> geom_cv
#> NA
#>
#> $C.a$geom_mean_ci_3d
#> geom_mean mean_ci_lwr mean_ci_upr
#> NaN NA NA
#> attr(,"label")
#> [1] "Geometric Mean (95% CI)"
#>
#>
#> $A.b
#> $A.b$n
#> n
#> 0
#>
#> $A.b$sum
#> sum
#> NA
#>
#> $A.b$mean
#> mean
#> NA
#>
#> $A.b$sd
#> sd
#> NA
#>
#> $A.b$se
#> se
#> NA
#>
#> $A.b$mean_sd
#> mean sd
#> NA NA
#>
#> $A.b$mean_se
#> mean se
#> NA NA
#>
#> $A.b$mean_ci
#> mean_ci_lwr mean_ci_upr
#> NA NA
#> attr(,"label")
#> [1] "Mean 95% CI"
#>
#> $A.b$mean_sei
#> mean_sei_lwr mean_sei_upr
#> NA NA
#> attr(,"label")
#> [1] "Mean -/+ 1xSE"
#>
#> $A.b$mean_sdi
#> mean_sdi_lwr mean_sdi_upr
#> NA NA
#> attr(,"label")
#> [1] "Mean -/+ 1xSD"
#>
#> $A.b$mean_ci_3d
#> mean mean_ci_lwr mean_ci_upr
#> NA NA NA
#> attr(,"label")
#> [1] "Mean (95% CI)"
#>
#> $A.b$mean_pval
#> p_value
#> NA
#> attr(,"label")
#> [1] "Mean p-value (H0: mean = 0)"
#>
#> $A.b$median
#> median
#> NA
#>
#> $A.b$mad
#> mad
#> NA
#>
#> $A.b$median_ci
#> median_ci_lwr median_ci_upr
#> NA NA
#> attr(,"conf_level")
#> [1] NA
#> attr(,"label")
#> [1] "Median 95% CI"
#>
#> $A.b$median_ci_3d
#> median median_ci_lwr median_ci_upr
#> NA NA NA
#> attr(,"label")
#> [1] "Median (95% CI)"
#>
#> $A.b$quantiles
#> quantile_0.25 quantile_0.75
#> NA NA
#> attr(,"label")
#> [1] "25% and 75%-ile"
#>
#> $A.b$iqr
#> iqr
#> NA
#>
#> $A.b$range
#> min max
#> NA NA
#>
#> $A.b$min
#> min
#> NA
#>
#> $A.b$max
#> max
#> NA
#>
#> $A.b$median_range
#> median min max
#> NA NA NA
#> attr(,"label")
#> [1] "Median (Min - Max)"
#>
#> $A.b$cv
#> cv
#> NA
#>
#> $A.b$geom_mean
#> geom_mean
#> NaN
#>
#> $A.b$geom_sd
#> geom_sd
#> NA
#>
#> $A.b$geom_mean_sd
#> geom_mean geom_sd
#> NaN NA
#>
#> $A.b$geom_mean_ci
#> mean_ci_lwr mean_ci_upr
#> NA NA
#> attr(,"label")
#> [1] "Geometric Mean 95% CI"
#>
#> $A.b$geom_cv
#> geom_cv
#> NA
#>
#> $A.b$geom_mean_ci_3d
#> geom_mean mean_ci_lwr mean_ci_upr
#> NaN NA NA
#> attr(,"label")
#> [1] "Geometric Mean (95% CI)"
#>
#>
#> $B.b
#> $B.b$n
#> n
#> 1
#>
#> $B.b$sum
#> sum
#> 4
#>
#> $B.b$mean
#> mean
#> 4
#>
#> $B.b$sd
#> sd
#> NA
#>
#> $B.b$se
#> se
#> NA
#>
#> $B.b$mean_sd
#> mean sd
#> 4 NA
#>
#> $B.b$mean_se
#> mean se
#> 4 NA
#>
#> $B.b$mean_ci
#> mean_ci_lwr mean_ci_upr
#> NA NA
#> attr(,"label")
#> [1] "Mean 95% CI"
#>
#> $B.b$mean_sei
#> mean_sei_lwr mean_sei_upr
#> NA NA
#> attr(,"label")
#> [1] "Mean -/+ 1xSE"
#>
#> $B.b$mean_sdi
#> mean_sdi_lwr mean_sdi_upr
#> NA NA
#> attr(,"label")
#> [1] "Mean -/+ 1xSD"
#>
#> $B.b$mean_ci_3d
#> mean mean_ci_lwr mean_ci_upr
#> 4 NA NA
#> attr(,"label")
#> [1] "Mean (95% CI)"
#>
#> $B.b$mean_pval
#> p_value
#> NA
#> attr(,"label")
#> [1] "Mean p-value (H0: mean = 0)"
#>
#> $B.b$median
#> median
#> 4
#>
#> $B.b$mad
#> mad
#> 0
#>
#> $B.b$median_ci
#> median_ci_lwr median_ci_upr
#> NA NA
#> attr(,"conf_level")
#> [1] NA
#> attr(,"label")
#> [1] "Median 95% CI"
#>
#> $B.b$median_ci_3d
#> median median_ci_lwr median_ci_upr
#> 4 NA NA
#> attr(,"label")
#> [1] "Median (95% CI)"
#>
#> $B.b$quantiles
#> quantile_0.25 quantile_0.75
#> 4 4
#> attr(,"label")
#> [1] "25% and 75%-ile"
#>
#> $B.b$iqr
#> iqr
#> 0
#>
#> $B.b$range
#> min max
#> 4 4
#>
#> $B.b$min
#> min
#> 4
#>
#> $B.b$max
#> max
#> 4
#>
#> $B.b$median_range
#> median min max
#> 4 4 4
#> attr(,"label")
#> [1] "Median (Min - Max)"
#>
#> $B.b$cv
#> cv
#> NA
#>
#> $B.b$geom_mean
#> geom_mean
#> 4
#>
#> $B.b$geom_sd
#> geom_sd
#> NA
#>
#> $B.b$geom_mean_sd
#> geom_mean geom_sd
#> 4 NA
#>
#> $B.b$geom_mean_ci
#> mean_ci_lwr mean_ci_upr
#> NA NA
#> attr(,"label")
#> [1] "Geometric Mean 95% CI"
#>
#> $B.b$geom_cv
#> geom_cv
#> NA
#>
#> $B.b$geom_mean_ci_3d
#> geom_mean mean_ci_lwr mean_ci_upr
#> 4 NA NA
#> attr(,"label")
#> [1] "Geometric Mean (95% CI)"
#>
#>
#> $C.b
#> $C.b$n
#> n
#> 2
#>
#> $C.b$sum
#> sum
#> 11
#>
#> $C.b$mean
#> mean
#> 5.5
#>
#> $C.b$sd
#> sd
#> 0.7071068
#>
#> $C.b$se
#> se
#> 0.5
#>
#> $C.b$mean_sd
#> mean sd
#> 5.5000000 0.7071068
#>
#> $C.b$mean_se
#> mean se
#> 5.5 0.5
#>
#> $C.b$mean_ci
#> mean_ci_lwr mean_ci_upr
#> -0.8531024 11.8531024
#> attr(,"label")
#> [1] "Mean 95% CI"
#>
#> $C.b$mean_sei
#> mean_sei_lwr mean_sei_upr
#> 5 6
#> attr(,"label")
#> [1] "Mean -/+ 1xSE"
#>
#> $C.b$mean_sdi
#> mean_sdi_lwr mean_sdi_upr
#> 4.792893 6.207107
#> attr(,"label")
#> [1] "Mean -/+ 1xSD"
#>
#> $C.b$mean_ci_3d
#> mean mean_ci_lwr mean_ci_upr
#> 5.5000000 -0.8531024 11.8531024
#> attr(,"label")
#> [1] "Mean (95% CI)"
#>
#> $C.b$mean_pval
#> p_value
#> 0.05771588
#> attr(,"label")
#> [1] "Mean p-value (H0: mean = 0)"
#>
#> $C.b$median
#> median
#> 5.5
#>
#> $C.b$mad
#> mad
#> 0
#>
#> $C.b$median_ci
#> median_ci_lwr median_ci_upr
#> NA NA
#> attr(,"conf_level")
#> [1] NA
#> attr(,"label")
#> [1] "Median 95% CI"
#>
#> $C.b$median_ci_3d
#> median median_ci_lwr median_ci_upr
#> 5.5 NA NA
#> attr(,"label")
#> [1] "Median (95% CI)"
#>
#> $C.b$quantiles
#> quantile_0.25 quantile_0.75
#> 5 6
#> attr(,"label")
#> [1] "25% and 75%-ile"
#>
#> $C.b$iqr
#> iqr
#> 1
#>
#> $C.b$range
#> min max
#> 5 6
#>
#> $C.b$min
#> min
#> 5
#>
#> $C.b$max
#> max
#> 6
#>
#> $C.b$median_range
#> median min max
#> 5.5 5.0 6.0
#> attr(,"label")
#> [1] "Median (Min - Max)"
#>
#> $C.b$cv
#> cv
#> 12.85649
#>
#> $C.b$geom_mean
#> geom_mean
#> 5.477226
#>
#> $C.b$geom_sd
#> geom_sd
#> 1.1376
#>
#> $C.b$geom_mean_sd
#> geom_mean geom_sd
#> 5.477226 1.137600
#>
#> $C.b$geom_mean_ci
#> mean_ci_lwr mean_ci_upr
#> 1.719943 17.442438
#> attr(,"label")
#> [1] "Geometric Mean 95% CI"
#>
#> $C.b$geom_cv
#> geom_cv
#> 12.94584
#>
#> $C.b$geom_mean_ci_3d
#> geom_mean mean_ci_lwr mean_ci_upr
#> 5.477226 1.719943 17.442438
#> attr(,"label")
#> [1] "Geometric Mean (95% CI)"
#>
#>
# `s_summary.factor`
## Basic usage:
s_summary(factor(c("a", "a", "b", "c", "a")))
#> $n
#> $n$n
#> n
#> 5
#>
#>
#> $count
#> $count$a
#> count
#> 3
#>
#> $count$b
#> count
#> 1
#>
#> $count$c
#> count
#> 1
#>
#>
#> $count_fraction
#> $count_fraction$a
#> count p
#> 3.0 0.6
#>
#> $count_fraction$b
#> count p
#> 1.0 0.2
#>
#> $count_fraction$c
#> count p
#> 1.0 0.2
#>
#>
#> $count_fraction_fixed_dp
#> $count_fraction_fixed_dp$a
#> count p
#> 3.0 0.6
#>
#> $count_fraction_fixed_dp$b
#> count p
#> 1.0 0.2
#>
#> $count_fraction_fixed_dp$c
#> count p
#> 1.0 0.2
#>
#>
#> $fraction
#> $fraction$a
#> num denom
#> 3 5
#>
#> $fraction$b
#> num denom
#> 1 5
#>
#> $fraction$c
#> num denom
#> 1 5
#>
#>
#> $n_blq
#> $n_blq$n_blq
#> n_blq
#> 0
#>
#>
# Empty factor returns zero-filled items.
s_summary(factor(levels = c("a", "b", "c")))
#> $n
#> $n$n
#> n
#> 0
#>
#>
#> $count
#> $count$a
#> count
#> 0
#>
#> $count$b
#> count
#> 0
#>
#> $count$c
#> count
#> 0
#>
#>
#> $count_fraction
#> $count_fraction$a
#> count p
#> 0 0
#>
#> $count_fraction$b
#> count p
#> 0 0
#>
#> $count_fraction$c
#> count p
#> 0 0
#>
#>
#> $count_fraction_fixed_dp
#> $count_fraction_fixed_dp$a
#> count p
#> 0 0
#>
#> $count_fraction_fixed_dp$b
#> count p
#> 0 0
#>
#> $count_fraction_fixed_dp$c
#> count p
#> 0 0
#>
#>
#> $fraction
#> $fraction$a
#> num denom
#> 0 0
#>
#> $fraction$b
#> num denom
#> 0 0
#>
#> $fraction$c
#> num denom
#> 0 0
#>
#>
#> $n_blq
#> $n_blq$n_blq
#> n_blq
#> 0
#>
#>
## Management of NA values.
x <- factor(c(NA, "Female"))
x <- explicit_na(x)
s_summary(x, na_rm = TRUE)
#> $n
#> $n$n
#> n
#> 1
#>
#>
#> $count
#> $count$Female
#> count
#> 1
#>
#>
#> $count_fraction
#> $count_fraction$Female
#> count p
#> 1 1
#>
#>
#> $count_fraction_fixed_dp
#> $count_fraction_fixed_dp$Female
#> count p
#> 1 1
#>
#>
#> $fraction
#> $fraction$Female
#> num denom
#> 1 1
#>
#>
#> $n_blq
#> $n_blq$n_blq
#> n_blq
#> 0
#>
#>
s_summary(x, na_rm = FALSE)
#> $n
#> $n$n
#> n
#> 2
#>
#>
#> $count
#> $count$Female
#> count
#> 1
#>
#> $count$`<Missing>`
#> count
#> 1
#>
#>
#> $count_fraction
#> $count_fraction$Female
#> count p
#> 1.0 0.5
#>
#> $count_fraction$`<Missing>`
#> count p
#> 1.0 0.5
#>
#>
#> $count_fraction_fixed_dp
#> $count_fraction_fixed_dp$Female
#> count p
#> 1.0 0.5
#>
#> $count_fraction_fixed_dp$`<Missing>`
#> count p
#> 1.0 0.5
#>
#>
#> $fraction
#> $fraction$Female
#> num denom
#> 1 2
#>
#> $fraction$`<Missing>`
#> num denom
#> 1 2
#>
#>
#> $n_blq
#> $n_blq$n_blq
#> n_blq
#> 0
#>
#>
## Different denominators.
x <- factor(c("a", "a", "b", "c", "a"))
s_summary(x, denom = "N_row", .N_row = 10L)
#> $n
#> $n$n
#> n
#> 5
#>
#>
#> $count
#> $count$a
#> count
#> 3
#>
#> $count$b
#> count
#> 1
#>
#> $count$c
#> count
#> 1
#>
#>
#> $count_fraction
#> $count_fraction$a
#> count p
#> 3.0 0.3
#>
#> $count_fraction$b
#> count p
#> 1.0 0.1
#>
#> $count_fraction$c
#> count p
#> 1.0 0.1
#>
#>
#> $count_fraction_fixed_dp
#> $count_fraction_fixed_dp$a
#> count p
#> 3.0 0.3
#>
#> $count_fraction_fixed_dp$b
#> count p
#> 1.0 0.1
#>
#> $count_fraction_fixed_dp$c
#> count p
#> 1.0 0.1
#>
#>
#> $fraction
#> $fraction$a
#> num denom
#> 3 10
#>
#> $fraction$b
#> num denom
#> 1 10
#>
#> $fraction$c
#> num denom
#> 1 10
#>
#>
#> $n_blq
#> $n_blq$n_blq
#> n_blq
#> 0
#>
#>
s_summary(x, denom = "N_col", .N_col = 20L)
#> $n
#> $n$n
#> n
#> 5
#>
#>
#> $count
#> $count$a
#> count
#> 3
#>
#> $count$b
#> count
#> 1
#>
#> $count$c
#> count
#> 1
#>
#>
#> $count_fraction
#> $count_fraction$a
#> count p
#> 3.00 0.15
#>
#> $count_fraction$b
#> count p
#> 1.00 0.05
#>
#> $count_fraction$c
#> count p
#> 1.00 0.05
#>
#>
#> $count_fraction_fixed_dp
#> $count_fraction_fixed_dp$a
#> count p
#> 3.00 0.15
#>
#> $count_fraction_fixed_dp$b
#> count p
#> 1.00 0.05
#>
#> $count_fraction_fixed_dp$c
#> count p
#> 1.00 0.05
#>
#>
#> $fraction
#> $fraction$a
#> num denom
#> 3 20
#>
#> $fraction$b
#> num denom
#> 1 20
#>
#> $fraction$c
#> num denom
#> 1 20
#>
#>
#> $n_blq
#> $n_blq$n_blq
#> n_blq
#> 0
#>
#>
# `s_summary.character`
## Basic usage:
s_summary(c("a", "a", "b", "c", "a"), verbose = FALSE)
#> $n
#> $n$n
#> n
#> 5
#>
#>
#> $count
#> $count$a
#> count
#> 3
#>
#> $count$b
#> count
#> 1
#>
#> $count$c
#> count
#> 1
#>
#>
#> $count_fraction
#> $count_fraction$a
#> count p
#> 3.0 0.6
#>
#> $count_fraction$b
#> count p
#> 1.0 0.2
#>
#> $count_fraction$c
#> count p
#> 1.0 0.2
#>
#>
#> $count_fraction_fixed_dp
#> $count_fraction_fixed_dp$a
#> count p
#> 3.0 0.6
#>
#> $count_fraction_fixed_dp$b
#> count p
#> 1.0 0.2
#>
#> $count_fraction_fixed_dp$c
#> count p
#> 1.0 0.2
#>
#>
#> $fraction
#> $fraction$a
#> num denom
#> 3 5
#>
#> $fraction$b
#> num denom
#> 1 5
#>
#> $fraction$c
#> num denom
#> 1 5
#>
#>
#> $n_blq
#> $n_blq$n_blq
#> n_blq
#> 0
#>
#>
s_summary(c("a", "a", "b", "c", "a", ""), .var = "x", na_rm = FALSE, verbose = FALSE)
#> $n
#> $n$n
#> n
#> 6
#>
#>
#> $count
#> $count$a
#> count
#> 3
#>
#> $count$b
#> count
#> 1
#>
#> $count$c
#> count
#> 1
#>
#> $count$`NA`
#> count
#> 1
#>
#>
#> $count_fraction
#> $count_fraction$a
#> count p
#> 3.0 0.5
#>
#> $count_fraction$b
#> count p
#> 1.0000000 0.1666667
#>
#> $count_fraction$c
#> count p
#> 1.0000000 0.1666667
#>
#> $count_fraction$`NA`
#> count p
#> 1.0000000 0.1666667
#>
#>
#> $count_fraction_fixed_dp
#> $count_fraction_fixed_dp$a
#> count p
#> 3.0 0.5
#>
#> $count_fraction_fixed_dp$b
#> count p
#> 1.0000000 0.1666667
#>
#> $count_fraction_fixed_dp$c
#> count p
#> 1.0000000 0.1666667
#>
#> $count_fraction_fixed_dp$`NA`
#> count p
#> 1.0000000 0.1666667
#>
#>
#> $fraction
#> $fraction$a
#> num denom
#> 3 6
#>
#> $fraction$b
#> num denom
#> 1 6
#>
#> $fraction$c
#> num denom
#> 1 6
#>
#> $fraction$`NA`
#> num denom
#> 1 6
#>
#>
#> $n_blq
#> $n_blq$n_blq
#> n_blq
#> 0
#>
#>
# `s_summary.logical`
## Basic usage:
s_summary(c(TRUE, FALSE, TRUE, TRUE))
#> $n
#> n
#> 4
#>
#> $count
#> count
#> 3
#>
#> $count_fraction
#> count fraction
#> 3.00 0.75
#>
#> $count_fraction_fixed_dp
#> count fraction
#> 3.00 0.75
#>
#> $fraction
#> num denom
#> 3 4
#>
#> $n_blq
#> n_blq
#> 0
#>
# Empty factor returns zero-filled items.
s_summary(as.logical(c()))
#> $n
#> n
#> 0
#>
#> $count
#> count
#> 0
#>
#> $count_fraction
#> count fraction
#> 0 0
#>
#> $count_fraction_fixed_dp
#> count fraction
#> 0 0
#>
#> $fraction
#> num denom
#> 0 0
#>
#> $n_blq
#> n_blq
#> 0
#>
## Management of NA values.
x <- c(NA, TRUE, FALSE)
s_summary(x, na_rm = TRUE)
#> $n
#> n
#> 2
#>
#> $count
#> count
#> 1
#>
#> $count_fraction
#> count fraction
#> 1.0 0.5
#>
#> $count_fraction_fixed_dp
#> count fraction
#> 1.0 0.5
#>
#> $fraction
#> num denom
#> 1 2
#>
#> $n_blq
#> n_blq
#> 0
#>
s_summary(x, na_rm = FALSE)
#> $n
#> n
#> 3
#>
#> $count
#> count
#> 1
#>
#> $count_fraction
#> count fraction
#> 1.0000000 0.3333333
#>
#> $count_fraction_fixed_dp
#> count fraction
#> 1.0000000 0.3333333
#>
#> $fraction
#> num denom
#> 1 3
#>
#> $n_blq
#> n_blq
#> 0
#>
## Different denominators.
x <- c(TRUE, FALSE, TRUE, TRUE)
s_summary(x, denom = "N_row", .N_row = 10L)
#> $n
#> n
#> 4
#>
#> $count
#> count
#> 3
#>
#> $count_fraction
#> count fraction
#> 3.0 0.3
#>
#> $count_fraction_fixed_dp
#> count fraction
#> 3.0 0.3
#>
#> $fraction
#> num denom
#> 3 10
#>
#> $n_blq
#> n_blq
#> 0
#>
s_summary(x, denom = "N_col", .N_col = 20L)
#> $n
#> n
#> 4
#>
#> $count
#> count
#> 3
#>
#> $count_fraction
#> count fraction
#> 3.00 0.15
#>
#> $count_fraction_fixed_dp
#> count fraction
#> 3.00 0.15
#>
#> $fraction
#> num denom
#> 3 20
#>
#> $n_blq
#> n_blq
#> 0
#>
a_summary(factor(c("a", "a", "b", "c", "a")), .N_row = 10, .N_col = 10)
#> RowsVerticalSection (in_rows) object print method:
#> ----------------------------
#> row_name formatted_cell indent_mod row_label
#> 1 n 5 0 n
#> 2 count.a 3 0 a
#> 3 count.b 1 0 b
#> 4 count.c 1 0 c
#> 5 count_fraction.a 3 (60%) 0 a
#> 6 count_fraction.b 1 (20%) 0 b
#> 7 count_fraction.c 1 (20%) 0 c
#> 8 count_fraction_fixed_dp.a 3 (60.0%) 0 a
#> 9 count_fraction_fixed_dp.b 1 (20.0%) 0 b
#> 10 count_fraction_fixed_dp.c 1 (20.0%) 0 c
#> 11 fraction.a 3/5 (60.0%) 0 a
#> 12 fraction.b 1/5 (20.0%) 0 b
#> 13 fraction.c 1/5 (20.0%) 0 c
#> 14 n_blq 0 0 n_blq
a_summary(
factor(c("a", "a", "b", "c", "a")),
.ref_group = factor(c("a", "a", "b", "c")), compare_with_ref_group = TRUE, .in_ref_col = TRUE
)
#> RowsVerticalSection (in_rows) object print method:
#> ----------------------------
#> row_name formatted_cell indent_mod
#> 1 n 5 0
#> 2 count.a 3 0
#> 3 count.b 1 0
#> 4 count.c 1 0
#> 5 count_fraction.a 3 (60%) 0
#> 6 count_fraction.b 1 (20%) 0
#> 7 count_fraction.c 1 (20%) 0
#> 8 count_fraction_fixed_dp.a 3 (60.0%) 0
#> 9 count_fraction_fixed_dp.b 1 (20.0%) 0
#> 10 count_fraction_fixed_dp.c 1 (20.0%) 0
#> 11 fraction.a 3/5 (60.0%) 0
#> 12 fraction.b 1/5 (20.0%) 0
#> 13 fraction.c 1/5 (20.0%) 0
#> 14 n_blq 0 0
#> 15 pval_counts 0
#> row_label
#> 1 n
#> 2 a
#> 3 b
#> 4 c
#> 5 a
#> 6 b
#> 7 c
#> 8 a
#> 9 b
#> 10 c
#> 11 a
#> 12 b
#> 13 c
#> 14 n_blq
#> 15 p-value (chi-squared test)
a_summary(c("A", "B", "A", "C"), .var = "x", .N_col = 10, .N_row = 10, verbose = FALSE)
#> RowsVerticalSection (in_rows) object print method:
#> ----------------------------
#> row_name formatted_cell indent_mod row_label
#> 1 n 4 0 n
#> 2 count.A 2 0 A
#> 3 count.B 1 0 B
#> 4 count.C 1 0 C
#> 5 count_fraction.A 2 (50%) 0 A
#> 6 count_fraction.B 1 (25%) 0 B
#> 7 count_fraction.C 1 (25%) 0 C
#> 8 count_fraction_fixed_dp.A 2 (50.0%) 0 A
#> 9 count_fraction_fixed_dp.B 1 (25.0%) 0 B
#> 10 count_fraction_fixed_dp.C 1 (25.0%) 0 C
#> 11 fraction.A 2/4 (50.0%) 0 A
#> 12 fraction.B 1/4 (25.0%) 0 B
#> 13 fraction.C 1/4 (25.0%) 0 C
#> 14 n_blq 0 0 n_blq
a_summary(
c("A", "B", "A", "C"),
.ref_group = c("B", "A", "C"), .var = "x", compare_with_ref_group = TRUE, verbose = FALSE,
.in_ref_col = FALSE
)
#> RowsVerticalSection (in_rows) object print method:
#> ----------------------------
#> row_name formatted_cell indent_mod
#> 1 n 4 0
#> 2 count.A 2 0
#> 3 count.B 1 0
#> 4 count.C 1 0
#> 5 count_fraction.A 2 (50%) 0
#> 6 count_fraction.B 1 (25%) 0
#> 7 count_fraction.C 1 (25%) 0
#> 8 count_fraction_fixed_dp.A 2 (50.0%) 0
#> 9 count_fraction_fixed_dp.B 1 (25.0%) 0
#> 10 count_fraction_fixed_dp.C 1 (25.0%) 0
#> 11 fraction.A 2/4 (50.0%) 0
#> 12 fraction.B 1/4 (25.0%) 0
#> 13 fraction.C 1/4 (25.0%) 0
#> 14 n_blq 0 0
#> 15 pval_counts 0.9074 0
#> row_label
#> 1 n
#> 2 A
#> 3 B
#> 4 C
#> 5 A
#> 6 B
#> 7 C
#> 8 A
#> 9 B
#> 10 C
#> 11 A
#> 12 B
#> 13 C
#> 14 n_blq
#> 15 p-value (chi-squared test)
a_summary(c(TRUE, FALSE, FALSE, TRUE, TRUE), .N_row = 10, .N_col = 10)
#> RowsVerticalSection (in_rows) object print method:
#> ----------------------------
#> row_name formatted_cell indent_mod row_label
#> 1 n 5 0 n
#> 2 count 3 0 count
#> 3 count_fraction 3 (60%) 0 count_fraction
#> 4 count_fraction_fixed_dp 3 (60.0%) 0 count_fraction_fixed_dp
#> 5 fraction 3/5 (60.0%) 0 fraction
#> 6 n_blq 0 0 n_blq
a_summary(
c(TRUE, FALSE, FALSE, TRUE, TRUE),
.ref_group = c(TRUE, FALSE), .in_ref_col = TRUE, compare_with_ref_group = TRUE,
.in_ref_col = FALSE
)
#> RowsVerticalSection (in_rows) object print method:
#> ----------------------------
#> row_name formatted_cell indent_mod row_label
#> 1 n 5 0 n
#> 2 count 3 0 count
#> 3 count_fraction 3 (60%) 0 count_fraction
#> 4 count_fraction_fixed_dp 3 (60.0%) 0 count_fraction_fixed_dp
#> 5 fraction 3/5 (60.0%) 0 fraction
#> 6 n_blq 0 0 n_blq
#> 7 pval_counts 0 p-value (chi-squared test)
a_summary(rnorm(10), .N_col = 10, .N_row = 20, .var = "bla")
#> RowsVerticalSection (in_rows) object print method:
#> ----------------------------
#> row_name formatted_cell indent_mod row_label
#> 1 n 10 0 n
#> 2 sum -1.2 0 Sum
#> 3 mean -0.1 0 Mean
#> 4 sd 1.1 0 SD
#> 5 se 0.4 0 SE
#> 6 mean_sd -0.1 (1.1) 0 Mean (SD)
#> 7 mean_se -0.1 (0.4) 0 Mean (SE)
#> 8 mean_ci (-0.94, 0.71) 0 Mean 95% CI
#> 9 mean_sei (-0.48, 0.25) 0 Mean -/+ 1xSE
#> 10 mean_sdi (-1.26, 1.03) 0 Mean -/+ 1xSD
#> 11 mean_pval 0.7577 0 Mean p-value (H0: mean = 0)
#> 12 median -0.2 0 Median
#> 13 mad 0.0 0 Median Absolute Deviation
#> 14 median_ci (-1.63, 0.63) 0 Median 95% CI
#> 15 quantiles -0.6 - 0.5 0 25% and 75%-ile
#> 16 iqr 1.1 0 IQR
#> 17 range -1.9 - 2.1 0 Min - Max
#> 18 min -1.9 0 Minimum
#> 19 max 2.1 0 Maximum
#> 20 median_range -0.2 (-1.9 - 2.1) 0 Median (Min - Max)
#> 21 cv -994.1 0 CV (%)
#> 22 geom_mean NA 0 Geometric Mean
#> 23 geom_sd NA 0 Geometric SD
#> 24 geom_mean_sd NA 0 Geometric Mean (SD)
#> 25 geom_mean_ci NA 0 Geometric Mean 95% CI
#> 26 geom_cv NA 0 CV % Geometric Mean
#> 27 median_ci_3d -0.17 (-1.63 - 0.63) 0 Median (95% CI)
#> 28 mean_ci_3d -0.12 (-0.94 - 0.71) 0 Mean (95% CI)
#> 29 geom_mean_ci_3d NA 0 Geometric Mean (95% CI)
a_summary(rnorm(10, 5, 1),
.ref_group = rnorm(20, -5, 1), .var = "bla", compare_with_ref_group = TRUE,
.in_ref_col = FALSE
)
#> RowsVerticalSection (in_rows) object print method:
#> ----------------------------
#> row_name formatted_cell indent_mod row_label
#> 1 n 10 0 n
#> 2 sum 54.1 0 Sum
#> 3 mean 5.4 0 Mean
#> 4 sd 0.9 0 SD
#> 5 se 0.3 0 SE
#> 6 mean_sd 5.4 (0.9) 0 Mean (SD)
#> 7 mean_se 5.4 (0.3) 0 Mean (SE)
#> 8 mean_ci (4.74, 6.09) 0 Mean 95% CI
#> 9 mean_sei (5.11, 5.71) 0 Mean -/+ 1xSE
#> 10 mean_sdi (4.47, 6.36) 0 Mean -/+ 1xSD
#> 11 mean_pval <0.0001 0 Mean p-value (H0: mean = 0)
#> 12 median 5.1 0 Median
#> 13 mad -0.0 0 Median Absolute Deviation
#> 14 median_ci (4.75, 6.07) 0 Median 95% CI
#> 15 quantiles 5.0 - 5.6 0 25% and 75%-ile
#> 16 iqr 0.6 0 IQR
#> 17 range 4.4 - 7.8 0 Min - Max
#> 18 min 4.4 0 Minimum
#> 19 max 7.8 0 Maximum
#> 20 median_range 5.1 (4.4 - 7.8) 0 Median (Min - Max)
#> 21 cv 17.5 0 CV (%)
#> 22 geom_mean 5.3 0 Geometric Mean
#> 23 geom_sd 1.2 0 Geometric SD
#> 24 geom_mean_sd 5.3 (1.2) 0 Geometric Mean (SD)
#> 25 geom_mean_ci (4.78, 5.99) 0 Geometric Mean 95% CI
#> 26 geom_cv 15.9 0 CV % Geometric Mean
#> 27 median_ci_3d 5.09 (4.75 - 6.07) 0 Median (95% CI)
#> 28 mean_ci_3d 5.41 (4.74 - 6.09) 0 Mean (95% CI)
#> 29 geom_mean_ci_3d 5.35 (4.78 - 5.99) 0 Geometric Mean (95% CI)
#> 30 pval <0.0001 0 p-value (t-test)
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