tabulation_concepts.R
In this vignette we will introduce some theory behind using layouts for table creation. Much of the theory also holds true when using other table packages. For this vignette we will use the following packages:
tabulation_concepts.R
The data we use is the following, created with random number generators:
add_subgroup <- function(x) paste0(tolower(x), sample(1:3, length(x), TRUE))
set.seed(1)
df <- tibble(
x = rnorm(100),
c1 = factor(sample(c("A", "B", "C"), 100, replace = TRUE), levels = c("A", "B", "C")),
r1 = factor(sample(c("U", "V", "W"), 100, replace = TRUE), levels = c("U", "V", "W"))
) %>%
mutate(
c2 = add_subgroup(c1),
r2 = add_subgroup(r1),
y = as.numeric(2 * as.numeric(c1) - 3 * as.numeric(r1))
) %>%
select(c1, c2, r1, r2, x, y)
df
# # A tibble: 100 × 6
# c1 c2 r1 r2 x y
# <fct> <chr> <fct> <chr> <dbl> <dbl>
# 1 B b2 U u3 -0.626 1
# 2 A a3 V v2 0.184 -4
# 3 B b1 V v2 -0.836 -2
# 4 B b3 V v2 1.60 -2
# 5 B b1 U u1 0.330 1
# 6 C c1 U u3 -0.820 3
# 7 A a3 U u3 0.487 -1
# 8 B b1 U u3 0.738 1
# 9 C c3 V v2 0.576 0
# 10 C c3 U u2 -0.305 3
# # ℹ 90 more rowstabulation_concepts.R
Let’s look at a table that has 3 columns and 3 rows. Each row
represents a different analysis (functions foo,
bar, zoo that return an rcell()
object):
A B C
------------------------------------------------
foo_label foo(df_A) foo(df_B) foo(df_C)
bar_label bar(df_A) bar(df_B) bar(df_C)
zoo_label zoo(df_A) zoo(df_B) zoo(df_C)The data passed to the analysis functions are a subset defined by the respective column and:
tabulation_concepts.R
Let’s do this on the concrete data with analyze():
foo <- prod
bar <- sum
zoo <- mean
lyt <- basic_table() %>%
split_cols_by("c1") %>%
analyze("x", function(df) foo(df$x), var_labels = "foo label", format = "xx.xx") %>%
analyze("x", function(df) bar(df$x), var_labels = "bar label", format = "xx.xx") %>%
analyze("x", function(df) zoo(df$x), var_labels = "zoo label", format = "xx.xx")
tbl <- build_table(lyt, df)
# Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times.
# occured at (row) path: root
tbl
# A B C
# ——————————————————————————————————
# foo label
# foo label 0.00 -0.00 -0.00
# bar label
# bar label 1.87 4.37 4.64
# zoo label
# zoo label 0.05 0.13 0.18tabulation_concepts.R
or if we wanted the x variable instead of the data
frame:
A B C
------------------------------------------------
foo_label foo(x_A) foo(x_B) foo(x_C)
bar_label bar(x_A) bar(x_B) bar(x_C)
zoo_label zoo(x_A) zoo(x_B) zoo(x_C)where:
tabulation_concepts.R
The function passed to afun is evaluated using argument
matching. If afun has an argument x the
analysis variable specified in vars in
analyze() is passed to the function, and if
afun has an argument df then a subset of the
dataset is passed to afun:
lyt2 <- basic_table() %>%
split_cols_by("c1") %>%
analyze("x", foo, var_labels = "foo label", format = "xx.xx") %>%
analyze("x", bar, var_labels = "bar label", format = "xx.xx") %>%
analyze("x", zoo, var_labels = "zoo label", format = "xx.xx")
tbl2 <- build_table(lyt2, df)
# Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times.
# occured at (row) path: root
tbl2
# A B C
# ————————————————————————————————
# foo label
# foo 0.00 -0.00 -0.00
# bar label
# bar 1.87 4.37 4.64
# zoo label
# zoo 0.05 0.13 0.18tabulation_concepts.R
Note that it is also possible that a function returns multiple rows
with in_rows():
lyt3 <- basic_table() %>%
split_cols_by("c1") %>%
analyze("x", function(x) {
in_rows(
"row 1" = rcell(mean(x), format = "xx.xx"),
"row 2" = rcell(sd(x), format = "xx.xxx")
)
}, var_labels = "foo label") %>%
analyze("x", function(x) {
in_rows(
"more rows 1" = rcell(median(x), format = "xx.x"),
"even more rows 1" = rcell(IQR(x), format = "xx.xx")
)
}, var_labels = "bar label", format = "xx.xx")
tbl3 <- build_table(lyt3, df)
# Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times.
# occured at (row) path: root
tbl3
# A B C
# ——————————————————————————————————————————
# foo label
# row 1 0.05 0.13 0.18
# row 2 0.985 0.815 0.890
# bar label
# more rows 1 -0.0 0.2 0.3
# even more rows 1 1.20 1.15 1.16tabulation_concepts.R
This is how we recommend you specify the row names explicitly.
Let’s say we would like to create the following table:
A B C
--------------------------------------
U foo(df_UA) foo(df_UB) foo(df_UC)
V foo(df_VA) foo(df_VB) foo(df_VC)
W foo(df_WA) foo(df_WB) foo(df_WC)where df_* are subsets of df as
follows:
df_UA <- df %>% filter(r1 == "U", c1 == "A")
df_VA <- df %>% filter(r1 == "V", c1 == "A")
df_WA <- df %>% filter(r1 == "W", c1 == "A")
df_UB <- df %>% filter(r1 == "U", c1 == "B")
df_VB <- df %>% filter(r1 == "V", c1 == "B")
df_WB <- df %>% filter(r1 == "W", c1 == "C")
df_UC <- df %>% filter(r1 == "U", c1 == "C")
df_VC <- df %>% filter(r1 == "V", c1 == "C")
df_WC <- df %>% filter(r1 == "W", c1 == "C")tabulation_concepts.R
further note that df_* are of the same class as
df, i.e. tibbles. Hence foo
aggregates the subset of our data to a cell value.
Given a function foo (ignore the ... for
now):
tabulation_concepts.R
we can start calculating the cell values individually:
foo(df_UA)
# [1] "17 x 6"
foo(df_VA)
# [1] "9 x 6"
foo(df_WA)
# [1] "14 x 6"
foo(df_UB)
# [1] "13 x 6"
foo(df_VB)
# [1] "15 x 6"
foo(df_WB)
# [1] "11 x 6"
foo(df_UC)
# [1] "10 x 6"
foo(df_VC)
# [1] "5 x 6"
foo(df_WC)
# [1] "11 x 6"tabulation_concepts.R
Now we are still missing the table structure:
matrix(
list(
foo(df_UA),
foo(df_VA),
foo(df_WA),
foo(df_UB),
foo(df_VB),
foo(df_WB),
foo(df_UC),
foo(df_VC),
foo(df_WC)
),
byrow = FALSE, ncol = 3
)
# [,1] [,2] [,3]
# [1,] "17 x 6" "13 x 6" "10 x 6"
# [2,] "9 x 6" "15 x 6" "5 x 6"
# [3,] "14 x 6" "11 x 6" "11 x 6"tabulation_concepts.R
In rtables this type of tabulation is done with
layouts:
lyt4 <- basic_table() %>%
split_cols_by("c1") %>%
split_rows_by("r1") %>%
analyze("x", foo)
tbl4 <- build_table(lyt4, df)
tbl4
# A B C
# ————————————————————————————————
# U
# foo 17 x 6 13 x 6 10 x 6
# V
# foo 9 x 6 15 x 6 5 x 6
# W
# foo 14 x 6 6 x 6 11 x 6tabulation_concepts.R
or if we would not want to see the foo label we would
have to use:
lyt5 <- basic_table() %>%
split_cols_by("c1") %>%
split_rows_by("r1") %>%
summarize_row_groups(cfun = foo, format = "xx")
tbl5 <- build_table(lyt5, df)
tbl5
# A B C
# ———————————————————————————
# 17 x 6 13 x 6 10 x 6
# 9 x 6 15 x 6 5 x 6
# 14 x 6 6 x 6 11 x 6tabulation_concepts.R
but now the row labels have disappeared. This is because
cfun needs to define its row label. So let’s redefine
foo:
foo <- function(df, labelstr) {
rcell(paste(dim(df), collapse = " x "), format = "xx", label = labelstr)
}
lyt6 <- basic_table() %>%
split_cols_by("c1") %>%
split_rows_by("r1") %>%
summarize_row_groups(cfun = foo)
tbl6 <- build_table(lyt6, df)
tbl6
# A B C
# ————————————————————————————
# U 17 x 6 13 x 6 10 x 6
# V 9 x 6 15 x 6 5 x 6
# W 14 x 6 6 x 6 11 x 6tabulation_concepts.R
Now let’s calculate the mean of df$y for pattern I:
foo <- function(df, labelstr) {
rcell(mean(df$y), label = labelstr, format = "xx.xx")
}
lyt7 <- basic_table() %>%
split_cols_by("c1") %>%
split_rows_by("r1") %>%
summarize_row_groups(cfun = foo)
tbl7 <- build_table(lyt7, df)
tbl7
# A B C
# —————————————————————————
# U -1.00 1.00 3.00
# V -4.00 -2.00 0.00
# W -7.00 -5.00 -3.00tabulation_concepts.R
Note that foo has the variable information hard-encoded
in the function body. Let’s try some alternatives returning to
analyze():
lyt8 <- basic_table() %>%
split_cols_by("c1") %>%
split_rows_by("r1") %>%
analyze("y", afun = mean)
tbl8 <- build_table(lyt8, df)
tbl8
# A B C
# —————————————————————
# U
# mean -1 1 3
# V
# mean -4 -2 0
# W
# mean -7 -5 -3tabulation_concepts.R
Note that the subset of the y variable is passed as the
x argument to mean(). We could also get the
data.frame instead of the variable:
lyt9 <- basic_table() %>%
split_cols_by("c1") %>%
split_rows_by("r1") %>%
analyze("y", afun = function(df) mean(df$y))
tbl9 <- build_table(lyt9, df)
tbl9
# A B C
# ——————————————————
# U
# y -1 1 3
# V
# y -4 -2 0
# W
# y -7 -5 -3tabulation_concepts.R
which is in contrast to:
lyt10 <- basic_table() %>%
split_cols_by("c1") %>%
split_rows_by("r1") %>%
analyze("y", afun = function(x) mean(x))
tbl10 <- build_table(lyt10, df)
tbl10
# A B C
# ——————————————————
# U
# y -1 1 3
# V
# y -4 -2 0
# W
# y -7 -5 -3tabulation_concepts.R
where the function receives the subset of y.
Pattern I is an interesting one as we can add more row structure (with further splits). Consider the following table:
A B C
--------------------------------------
U
u1 foo(<>) foo(<>) foo(<>)
u2 foo(<>) foo(<>) foo(<>)
u3 foo(<>) foo(<>) foo(<>)
V
v1 foo(<>) foo(<>) foo(<>)
v2 foo(<>) foo(<>) foo(<>)
v3 foo(<>) foo(<>) foo(<>)
W
w1 foo(<>) foo(<>) foo(<>)
w2 foo(<>) foo(<>) foo(<>)
w3 foo(<>) foo(<>) foo(<>)where <> represents the data that is represented
by the cell. So for the cell U > u1, A we would have the
subset:
df %>%
filter(r1 == "U", r2 == "u1", c1 == "A")
# # A tibble: 2 × 6
# c1 c2 r1 r2 x y
# <fct> <chr> <fct> <chr> <dbl> <dbl>
# 1 A a2 U u1 1.12 -1
# 2 A a1 U u1 0.594 -1tabulation_concepts.R
and so on. We can get this table as follows:
lyt11 <- basic_table() %>%
split_cols_by("c1") %>%
split_rows_by("r1") %>%
split_rows_by("r2") %>%
summarize_row_groups(cfun = function(df, labelstr) {
rcell(mean(df$x), format = "xx.xx", label = paste("mean x for", labelstr))
})
tbl11 <- build_table(lyt11, df)
tbl11
# A B C
# ———————————————————————————————————————
# U
# mean x for u3 -0.04 0.36 -0.25
# mean x for u1 0.86 0.32 NA
# mean x for u2 -0.28 0.38 0.08
# V
# mean x for v2 0.01 0.55 0.60
# mean x for v3 -0.03 -0.30 1.06
# mean x for v1 0.56 -0.27 -0.54
# W
# mean x for w1 -0.58 0.42 0.67
# mean x for w3 0.56 0.69 -0.39
# mean x for w2 -1.99 -0.10 0.53tabulation_concepts.R
or, if we wanted to calculate two summaries per row split:
s_mean_sd <- function(x) {
in_rows("mean (sd)" = rcell(c(mean(x), sd(x)), format = "xx.xx (xx.xx)"))
}
s_range <- function(x) {
in_rows("range" = rcell(range(x), format = "xx.xx - xx.xx"))
}
lyt12 <- basic_table() %>%
split_cols_by("c1") %>%
split_rows_by("r1") %>%
split_rows_by("r2") %>%
analyze("x", s_mean_sd, show_labels = "hidden") %>%
analyze("x", s_range, show_labels = "hidden")
tbl12 <- build_table(lyt12, df)
# Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times.
# occured at (row) path: r1[U]->r2[u3]
# Warning in min(x): no non-missing arguments to min; returning Inf
# Warning in max(x): no non-missing arguments to max; returning -Inf
# Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times.
# occured at (row) path: r1[U]->r2[u1]
# Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times.
# occured at (row) path: r1[U]->r2[u2]
# Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times.
# occured at (row) path: r1[V]->r2[v2]
# Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times.
# occured at (row) path: r1[V]->r2[v3]
# Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times.
# occured at (row) path: r1[V]->r2[v1]
# Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times.
# occured at (row) path: r1[W]->r2[w1]
# Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times.
# occured at (row) path: r1[W]->r2[w3]
# Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times.
# occured at (row) path: r1[W]->r2[w2]
tbl12
# A B C
# ———————————————————————————————————————————————————————————
# U
# u3
# mean (sd) -0.04 (1.18) 0.36 (1.41) -0.25 (0.72)
# range -1.80 - 1.47 -1.28 - 2.40 -0.82 - 0.56
# u1
# mean (sd) 0.86 (0.38) 0.32 (0.51) NA
# range 0.59 - 1.12 -0.48 - 0.94 Inf - -Inf
# u2
# mean (sd) -0.28 (0.96) 0.38 (0.67) 0.08 (0.91)
# range -1.52 - 1.43 -0.39 - 0.82 -0.93 - 1.51
# V
# v2
# mean (sd) 0.01 (0.25) 0.55 (1.14) 0.60 (0.03)
# range -0.16 - 0.18 -0.84 - 1.60 0.58 - 0.62
# v3
# mean (sd) -0.03 (0.37) -0.30 (0.36) 1.06 (NA)
# range -0.41 - 0.33 -0.62 - 0.03 1.06 - 1.06
# v1
# mean (sd) 0.56 (1.10) -0.27 (0.73) -0.54 (1.18)
# range -0.16 - 2.17 -1.22 - 0.59 -1.38 - 0.29
# W
# w1
# mean (sd) -0.58 (0.85) 0.42 (NA) 0.67 (0.39)
# range -1.25 - 0.61 0.42 - 0.42 0.37 - 1.21
# w3
# mean (sd) 0.56 (0.85) 0.69 (NA) -0.39 (1.68)
# range -0.71 - 1.98 0.69 - 0.69 -2.21 - 1.10
# w2
# mean (sd) -1.99 (NA) -0.10 (0.47) 0.53 (0.60)
# range -1.99 - -1.99 -0.61 - 0.39 -0.10 - 1.16tabulation_concepts.R
Which has the following structure:
A B C
---------------------------------------------------------
U
u1
mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>)
range s_range(<>) s_range(<>) s_range(<>)
u2
mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>)
range s_range(<>) s_range(<>) s_range(<>)
u3
mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>)
range s_range(<>) s_range(<>) s_range(<>)
V
v1
mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>)
range s_range(<>) s_range(<>) s_range(<>)
v2
mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>)
range s_range(<>) s_range(<>) s_range(<>)
v3
mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>)
range s_range(<>) s_range(<>) s_range(<>)
W
w1
mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>)
range s_range(<>) s_range(<>) s_range(<>)
w2
mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>)
range s_range(<>) s_range(<>) s_range(<>)
w3
mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>)
range s_range(<>) s_range(<>) s_range(<>)The rows U, u1, u2, …,
W, w1, w2, w3 are
label rows and the other rows (with mean_sd and
range) are data rows. Currently we do not have content rows
in the table. Content rows summarize the data defined by their splitting
(i.e. V > v1, B). So if we wanted to add content rows at
the r2 split level then we would get:
A B C
---------------------------------------------------------
U
u1 s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>)
mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>)
range s_range(<>) s_range(<>) s_range(<>)
u2 s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>)
mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>)
range s_range(<>) s_range(<>) s_range(<>)
u3 s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>)
mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>)
range s_range(<>) s_range(<>) s_range(<>)
V
v1 s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>)
mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>)
range s_range(<>) s_range(<>) s_range(<>)
v2 s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>)
mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>)
range s_range(<>) s_range(<>) s_range(<>)
v3 s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>)
mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>)
range s_range(<>) s_range(<>) s_range(<>)
W
w1 s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>)
mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>)
range s_range(<>) s_range(<>) s_range(<>)
w2 s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>)
mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>)
range s_range(<>) s_range(<>) s_range(<>)
w3 s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>)
mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>)
range s_range(<>) s_range(<>) s_range(<>)where s_cfun_2 is the content function and either
returns one row via rcell() or multiple rows via
in_rows(). The data represented by <>
for the content rows is same data as for it’s descendant, i.e. for the
U > u1, A content row cell it is
df %>% filter(r1 == "U", r2 == "u1", c1 == "A"). Note
that content functions cfun operate only on data frames and
not on vectors/variables so they must take the df argument.
Further, a cfun must also have the labelstr
argument which is the split level. This way, the cfun can
define its own row name. In order to get the table above we can use the
layout framework as follows:
s_mean_sd <- function(x) {
in_rows("mean (sd)" = rcell(c(mean(x), sd(x)), format = "xx.xx (xx.xx)"))
}
s_range <- function(x) {
in_rows("range" = rcell(range(x), format = "xx.xx - xx.xx"))
}
s_cfun_2 <- function(df, labelstr) {
rcell(nrow(df), format = "xx", label = paste(labelstr, "(n)"))
}
lyt13 <- basic_table() %>%
split_cols_by("c1") %>%
split_rows_by("r1") %>%
split_rows_by("r2") %>%
summarize_row_groups(cfun = s_cfun_2) %>%
analyze("x", s_mean_sd, show_labels = "hidden") %>%
analyze("x", s_range, show_labels = "hidden")
tbl13 <- build_table(lyt13, df)
# Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times.
# occured at (row) path: r1[U]->r2[u3]
# Warning in min(x): no non-missing arguments to min; returning Inf
# Warning in max(x): no non-missing arguments to max; returning -Inf
# Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times.
# occured at (row) path: r1[U]->r2[u1]
# Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times.
# occured at (row) path: r1[U]->r2[u2]
# Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times.
# occured at (row) path: r1[V]->r2[v2]
# Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times.
# occured at (row) path: r1[V]->r2[v3]
# Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times.
# occured at (row) path: r1[V]->r2[v1]
# Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times.
# occured at (row) path: r1[W]->r2[w1]
# Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times.
# occured at (row) path: r1[W]->r2[w3]
# Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times.
# occured at (row) path: r1[W]->r2[w2]
tbl13
# A B C
# ———————————————————————————————————————————————————————————
# U
# u3 (n) 6 5 3
# mean (sd) -0.04 (1.18) 0.36 (1.41) -0.25 (0.72)
# range -1.80 - 1.47 -1.28 - 2.40 -0.82 - 0.56
# u1 (n) 2 5 0
# mean (sd) 0.86 (0.38) 0.32 (0.51) NA
# range 0.59 - 1.12 -0.48 - 0.94 Inf - -Inf
# u2 (n) 9 3 7
# mean (sd) -0.28 (0.96) 0.38 (0.67) 0.08 (0.91)
# range -1.52 - 1.43 -0.39 - 0.82 -0.93 - 1.51
# V
# v2 (n) 2 4 2
# mean (sd) 0.01 (0.25) 0.55 (1.14) 0.60 (0.03)
# range -0.16 - 0.18 -0.84 - 1.60 0.58 - 0.62
# v3 (n) 3 4 1
# mean (sd) -0.03 (0.37) -0.30 (0.36) 1.06 (NA)
# range -0.41 - 0.33 -0.62 - 0.03 1.06 - 1.06
# v1 (n) 4 7 2
# mean (sd) 0.56 (1.10) -0.27 (0.73) -0.54 (1.18)
# range -0.16 - 2.17 -1.22 - 0.59 -1.38 - 0.29
# W
# w1 (n) 4 1 4
# mean (sd) -0.58 (0.85) 0.42 (NA) 0.67 (0.39)
# range -1.25 - 0.61 0.42 - 0.42 0.37 - 1.21
# w3 (n) 9 1 3
# mean (sd) 0.56 (0.85) 0.69 (NA) -0.39 (1.68)
# range -0.71 - 1.98 0.69 - 0.69 -2.21 - 1.10
# w2 (n) 1 4 4
# mean (sd) -1.99 (NA) -0.10 (0.47) 0.53 (0.60)
# range -1.99 - -1.99 -0.61 - 0.39 -0.10 - 1.16tabulation_concepts.R
In the same manner, if we want content rows for the r1
split we can do it at as follows:
lyt14 <- basic_table() %>%
split_cols_by("c1") %>%
split_rows_by("r1") %>%
summarize_row_groups(cfun = s_cfun_2) %>%
split_rows_by("r2") %>%
summarize_row_groups(cfun = s_cfun_2) %>%
analyze("x", s_mean_sd, show_labels = "hidden") %>%
analyze("x", s_range, show_labels = "hidden")
tbl14 <- build_table(lyt14, df)
# Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times.
# occured at (row) path: r1[U]->r2[u3]
# Warning in min(x): no non-missing arguments to min; returning Inf
# Warning in max(x): no non-missing arguments to max; returning -Inf
# Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times.
# occured at (row) path: r1[U]->r2[u1]
# Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times.
# occured at (row) path: r1[U]->r2[u2]
# Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times.
# occured at (row) path: r1[V]->r2[v2]
# Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times.
# occured at (row) path: r1[V]->r2[v3]
# Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times.
# occured at (row) path: r1[V]->r2[v1]
# Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times.
# occured at (row) path: r1[W]->r2[w1]
# Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times.
# occured at (row) path: r1[W]->r2[w3]
# Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times.
# occured at (row) path: r1[W]->r2[w2]
tbl14
# A B C
# ———————————————————————————————————————————————————————————
# U (n) 17 13 10
# u3 (n) 6 5 3
# mean (sd) -0.04 (1.18) 0.36 (1.41) -0.25 (0.72)
# range -1.80 - 1.47 -1.28 - 2.40 -0.82 - 0.56
# u1 (n) 2 5 0
# mean (sd) 0.86 (0.38) 0.32 (0.51) NA
# range 0.59 - 1.12 -0.48 - 0.94 Inf - -Inf
# u2 (n) 9 3 7
# mean (sd) -0.28 (0.96) 0.38 (0.67) 0.08 (0.91)
# range -1.52 - 1.43 -0.39 - 0.82 -0.93 - 1.51
# V (n) 9 15 5
# v2 (n) 2 4 2
# mean (sd) 0.01 (0.25) 0.55 (1.14) 0.60 (0.03)
# range -0.16 - 0.18 -0.84 - 1.60 0.58 - 0.62
# v3 (n) 3 4 1
# mean (sd) -0.03 (0.37) -0.30 (0.36) 1.06 (NA)
# range -0.41 - 0.33 -0.62 - 0.03 1.06 - 1.06
# v1 (n) 4 7 2
# mean (sd) 0.56 (1.10) -0.27 (0.73) -0.54 (1.18)
# range -0.16 - 2.17 -1.22 - 0.59 -1.38 - 0.29
# W (n) 14 6 11
# w1 (n) 4 1 4
# mean (sd) -0.58 (0.85) 0.42 (NA) 0.67 (0.39)
# range -1.25 - 0.61 0.42 - 0.42 0.37 - 1.21
# w3 (n) 9 1 3
# mean (sd) 0.56 (0.85) 0.69 (NA) -0.39 (1.68)
# range -0.71 - 1.98 0.69 - 0.69 -2.21 - 1.10
# w2 (n) 1 4 4
# mean (sd) -1.99 (NA) -0.10 (0.47) 0.53 (0.60)
# range -1.99 - -1.99 -0.61 - 0.39 -0.10 - 1.16tabulation_concepts.R
In pagination, content rows and label rows get repeated if a page is
split in a descendant of a content row. So, for example, if we were to
split the following table at ***:
A B C
---------------------------------------------------------
U
u1 (n) s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>)
mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>)
***
range s_range(<>) s_range(<>) s_range(<>)
u2 (n) s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>)
mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>)
range s_range(<>) s_range(<>) s_range(<>)Then we would get the following two tables:
A B C
---------------------------------------------------------
U
u1 (n) s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>)
mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>)and
A B C
---------------------------------------------------------
U
u1 (n) s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>)
range s_range(<>) s_range(<>) s_range(<>)
u2 (n) s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>)
mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>)
range s_range(<>) s_range(<>) s_range(<>)Let’s consider the following tabulation pattern:
A B C
------------------------------------------------
label 1 foo(x_A) bar(x_B) zoo(x_C)
label 2 foo(x_A) bar(x_B) zoo(x_C)
label 3 foo(x_A) bar(x_B) zoo(x_C)We will discuss that in a future release of rtables.