Generating QC-Ready Result Data Frames (ARDs) from Tables
Davide Garolini
2025-01-27
Source:vignettes/ard_how_to.Rmd
ard_how_to.RmdDisclaimer
This vignette is a work in progress and subject to change.
Creating an Example Table
In order to generate an ARD (Analysis Results Dataset), we first need to create a table from which all the necessary information will be retrieved. We will borrow a simple table from this vignette about clinical trials.
library(rtables)
# Loading required package: formatters
#
# Attaching package: 'formatters'
# The following object is masked from 'package:base':
#
# %||%
# Loading required package: magrittr
#
# Attaching package: 'rtables'
# The following object is masked from 'package:utils':
#
# str
ADSL <- ex_adsl # Example ADSL dataset
# Very simple table
lyt <- basic_table() %>%
split_cols_by("ARM") %>%
analyze(c("AGE", "SEX"))
tbl <- build_table(lyt, ADSL)
tbl
# A: Drug X B: Placebo C: Combination
# ————————————————————————————————————————————————————————————
# AGE
# Mean 33.77 35.43 35.43
# SEX
# F 79 77 66
# M 51 55 60
# U 3 2 4
# UNDIFFERENTIATED 1 0 2Converting the Table to a Result Data Frame (ARD)
The as_result_df() function is used to convert a table
to a result data frame. The result data frame is a data frame that
contains the result of the summary table and is ready to be used for
quality control purposes. This may be customized according to different
standards.
Let’s see how we can produce different result
data.frames. The following outputs can be returned by
setting different parameters in the as_results_df()
function, and these results can be transformed back into a table using
the df_to_tt() function.
as_result_df(tbl)
# group1 group1_level avar_name row_name label_name row_num
# 1 <NA> <NA> AGE Mean Mean 2
# 2 <NA> <NA> SEX F F 4
# 3 <NA> <NA> SEX M M 5
# 4 <NA> <NA> SEX U U 6
# 5 <NA> <NA> SEX UNDIFFERENTIATED UNDIFFERENTIATED 7
# is_group_summary node_class A: Drug X B: Placebo C: Combination
# 1 FALSE DataRow 33.76866 35.43284 35.43182
# 2 FALSE DataRow 79 77 66
# 3 FALSE DataRow 51 55 60
# 4 FALSE DataRow 3 2 4
# 5 FALSE DataRow 1 0 2
as_result_df(tbl, data_format = "strings")
# group1 group1_level avar_name row_name label_name row_num
# 1 <NA> <NA> AGE Mean Mean 2
# 2 <NA> <NA> SEX F F 4
# 3 <NA> <NA> SEX M M 5
# 4 <NA> <NA> SEX U U 6
# 5 <NA> <NA> SEX UNDIFFERENTIATED UNDIFFERENTIATED 7
# is_group_summary node_class A: Drug X B: Placebo C: Combination
# 1 FALSE DataRow 33.77 35.43 35.43
# 2 FALSE DataRow 79 77 66
# 3 FALSE DataRow 51 55 60
# 4 FALSE DataRow 3 2 4
# 5 FALSE DataRow 1 0 2
as_result_df(tbl, simplify = TRUE)
# label_name A: Drug X B: Placebo C: Combination
# 1 Mean 33.76866 35.43284 35.43182
# 2 F 79 77 66
# 3 M 51 55 60
# 4 U 3 2 4
# 5 UNDIFFERENTIATED 1 0 2
as_result_df(tbl, simplify = TRUE, keep_label_rows = TRUE)
# label_name A: Drug X B: Placebo C: Combination
# 1 AGE NA NA NA
# 2 Mean 33.76866 35.43284 35.43182
# 3 SEX NA NA NA
# 4 F 79 77 66
# 5 M 51 55 60
# 6 U 3 2 4
# 7 UNDIFFERENTIATED 1 0 2
as_result_df(tbl, simplify = TRUE, keep_label_rows = TRUE, expand_colnames = TRUE)
# label_name A: Drug X B: Placebo C: Combination
# 1 <only_for_column_names> A: Drug X B: Placebo C: Combination
# 2 AGE NA NA NA
# 3 Mean 33.76866 35.43284 35.43182
# 4 SEX NA NA NA
# 5 F 79 77 66
# 6 M 51 55 60
# 7 U 3 2 4
# 8 UNDIFFERENTIATED 1 0 2Now let’s generate our final ARD output, which is ready to be used for quality control purposes.
as_result_df(tbl, make_ard = TRUE)
# group1 group1_level group2 group2_level variable variable_level
# 1 <NA> <NA> ARM A: Drug X AGE Mean
# 2 <NA> <NA> ARM A: Drug X SEX F
# 3 <NA> <NA> ARM A: Drug X SEX M
# 4 <NA> <NA> ARM A: Drug X SEX U
# 5 <NA> <NA> ARM A: Drug X SEX UNDIFFERENTIATED
# 6 <NA> <NA> ARM B: Placebo AGE Mean
# 7 <NA> <NA> ARM B: Placebo SEX F
# 8 <NA> <NA> ARM B: Placebo SEX M
# 9 <NA> <NA> ARM B: Placebo SEX U
# 10 <NA> <NA> ARM B: Placebo SEX UNDIFFERENTIATED
# 11 <NA> <NA> ARM C: Combination AGE Mean
# 12 <NA> <NA> ARM C: Combination SEX F
# 13 <NA> <NA> ARM C: Combination SEX M
# 14 <NA> <NA> ARM C: Combination SEX U
# 15 <NA> <NA> ARM C: Combination SEX UNDIFFERENTIATED
# variable_label stat_name stat stat_string
# 1 Mean mean 33.76866 33.77
# 2 F n 79.00000 79
# 3 M n 51.00000 51
# 4 U n 3.00000 3
# 5 UNDIFFERENTIATED n 1.00000 1
# 6 Mean mean 35.43284 35.43
# 7 F n 77.00000 77
# 8 M n 55.00000 55
# 9 U n 2.00000 2
# 10 UNDIFFERENTIATED n 0.00000 0
# 11 Mean mean 35.43182 35.43
# 12 F n 66.00000 66
# 13 M n 60.00000 60
# 14 U n 4.00000 4
# 15 UNDIFFERENTIATED n 2.00000 2Customizing the Output
as_result_df() and ARDs depend on the content of the
table, so it is possible to modify the table to customize the output.
For example, we can add some user-defined statistics with custom
names:
# rcell and in_rows are the core of any analysis function
rc <- rcell(c(1, 2), stat_names = c("Rand1", "Rand2"))
print(obj_stat_names(rc)) # c("Rand1", "Rand2")
# [1] "Rand1" "Rand2"
rc_row <- in_rows(
.list = list(a = c(NA, 1), b = c(1, NA)),
.formats = c("xx - xx", "xx.x - xx.x"),
.format_na_strs = list(c("asda", "lkjklj")),
.stat_names = list(c("A", "B"), c("B", "C"))
)
# Only a getter for this object
print(obj_stat_names(rc_row)) # list(a = c("A", "B"), b = c("B", "C"))
# $a
# [1] "A" "B"
#
# $b
# [1] "B" "C"
# if c("A", "B"), one for each row
# if single list, duplicated
rc_row <- in_rows(
.list = list(a = c(NA, 1), b = c(1, NA)),
.formats = c("xx - xx", "xx.x - xx.x"),
.format_na_strs = list(c("asda", "lkjklj")),
.stat_names = c("A", "B")
)
print(obj_stat_names(rc_row)) # c("A", "B") # one for each row
# $a
# [1] "A"
#
# $b
# [1] "B"
print(lapply(rc_row, obj_stat_names)) # identical to above + row names
# $a
# [1] "A"
#
# $b
# [1] "B"
rc_row <- in_rows(
.list = list(a = c(NA, 1), b = c(1, NA)),
.formats = c("xx - xx", "xx.x - xx.x"),
.format_na_strs = list(c("asda", "lkjklj")),
.stat_names = list(c("A", "B")) # It is duplicated, check it yourself!
)Let’s put it into practice:
mean_sd_custom <- function(x) {
mean <- mean(x, na.rm = FALSE)
sd <- sd(x, na.rm = FALSE)
rcell(c(mean, sd),
label = "Mean (SD)", format = "xx.x (xx.x)" # ,
# stat_names = c("Mean", "SD")
)
}
counts_percentage_custom <- function(x) {
cnts <- table(x)
out <- lapply(cnts, function(x) {
perc <- x / sum(cnts)
rcell(c(x, perc), format = "xx. (xx.%)")
})
in_rows(
.list = as.list(out), .labels = names(cnts),
.stat_names = list(c("Count", "Percentage"))
)
}
lyt <- basic_table(show_colcounts = TRUE, colcount_format = "N=xx") %>%
split_cols_by("ARM", split_fun = keep_split_levels(c("A: Drug X", "B: Placebo"))) %>%
analyze(vars = "AGE", afun = mean_sd_custom) %>%
analyze(vars = "SEX", afun = counts_percentage_custom)
tbl <- build_table(lyt, ex_adsl)
as_result_df(tbl, make_ard = TRUE)
# group1 group1_level group2 group2_level variable variable_level
# 1 <NA> <NA> ARM A: Drug X AGE Mean (SD)
# 2 <NA> <NA> ARM A: Drug X AGE Mean (SD)
# 3 <NA> <NA> ARM A: Drug X SEX F
# 4 <NA> <NA> ARM A: Drug X SEX F
# 5 <NA> <NA> ARM A: Drug X SEX M
# 6 <NA> <NA> ARM A: Drug X SEX M
# 7 <NA> <NA> ARM A: Drug X SEX U
# 8 <NA> <NA> ARM A: Drug X SEX U
# 9 <NA> <NA> ARM A: Drug X SEX UNDIFFERENTIATED
# 10 <NA> <NA> ARM A: Drug X SEX UNDIFFERENTIATED
# 11 <NA> <NA> ARM B: Placebo AGE Mean (SD)
# 12 <NA> <NA> ARM B: Placebo AGE Mean (SD)
# 13 <NA> <NA> ARM B: Placebo SEX F
# 14 <NA> <NA> ARM B: Placebo SEX F
# 15 <NA> <NA> ARM B: Placebo SEX M
# 16 <NA> <NA> ARM B: Placebo SEX M
# 17 <NA> <NA> ARM B: Placebo SEX U
# 18 <NA> <NA> ARM B: Placebo SEX U
# 19 <NA> <NA> ARM B: Placebo SEX UNDIFFERENTIATED
# 20 <NA> <NA> ARM B: Placebo SEX UNDIFFERENTIATED
# variable_label stat_name stat stat_string
# 1 Mean (SD) <NA> 33.768656716 33.8
# 2 Mean (SD) <NA> 6.553325712 6.6
# 3 F Count 79.000000000 79
# 4 F Percentage 0.589552239 59
# 5 M Count 51.000000000 51
# 6 M Percentage 0.380597015 38
# 7 U Count 3.000000000 3
# 8 U Percentage 0.022388060 2
# 9 UNDIFFERENTIATED Count 1.000000000 1
# 10 UNDIFFERENTIATED Percentage 0.007462687 1
# 11 Mean (SD) <NA> 35.432835821 35.4
# 12 Mean (SD) <NA> 7.895413879 7.9
# 13 F Count 77.000000000 77
# 14 F Percentage 0.574626866 57
# 15 M Count 55.000000000 55
# 16 M Percentage 0.410447761 41
# 17 U Count 2.000000000 2
# 18 U Percentage 0.014925373 1
# 19 UNDIFFERENTIATED Count 0.000000000 0
# 20 UNDIFFERENTIATED Percentage 0.000000000 0More Complex Outputs
Let’s add hierarchical row and column splits:
lyt <- basic_table() %>%
split_rows_by("STRATA2") %>%
summarize_row_groups() %>%
split_cols_by("ARM") %>%
split_cols_by("STRATA1") %>%
analyze(c("AGE", "SEX"))
tbl <- build_table(lyt, ex_adsl)
as_result_df(tbl, make_ard = TRUE)
# group1 group1_level group2 group2_level group3 group3_level variable
# 1 STRATA2 S1 ARM A: Drug X STRATA1 A STRATA2
# 2 STRATA2 S1 ARM A: Drug X STRATA1 A STRATA2
# 3 STRATA2 S1 ARM A: Drug X STRATA1 A AGE
# 4 STRATA2 S1 ARM A: Drug X STRATA1 A SEX
# 5 STRATA2 S1 ARM A: Drug X STRATA1 A SEX
# 6 STRATA2 S1 ARM A: Drug X STRATA1 A SEX
# 7 STRATA2 S1 ARM A: Drug X STRATA1 A SEX
# 8 STRATA2 S2 ARM A: Drug X STRATA1 A STRATA2
# 9 STRATA2 S2 ARM A: Drug X STRATA1 A STRATA2
# 10 STRATA2 S2 ARM A: Drug X STRATA1 A AGE
# 11 STRATA2 S2 ARM A: Drug X STRATA1 A SEX
# 12 STRATA2 S2 ARM A: Drug X STRATA1 A SEX
# 13 STRATA2 S2 ARM A: Drug X STRATA1 A SEX
# 14 STRATA2 S2 ARM A: Drug X STRATA1 A SEX
# 15 STRATA2 S1 ARM A: Drug X STRATA1 B STRATA2
# 16 STRATA2 S1 ARM A: Drug X STRATA1 B STRATA2
# 17 STRATA2 S1 ARM A: Drug X STRATA1 B AGE
# 18 STRATA2 S1 ARM A: Drug X STRATA1 B SEX
# 19 STRATA2 S1 ARM A: Drug X STRATA1 B SEX
# 20 STRATA2 S1 ARM A: Drug X STRATA1 B SEX
# 21 STRATA2 S1 ARM A: Drug X STRATA1 B SEX
# 22 STRATA2 S2 ARM A: Drug X STRATA1 B STRATA2
# 23 STRATA2 S2 ARM A: Drug X STRATA1 B STRATA2
# 24 STRATA2 S2 ARM A: Drug X STRATA1 B AGE
# 25 STRATA2 S2 ARM A: Drug X STRATA1 B SEX
# 26 STRATA2 S2 ARM A: Drug X STRATA1 B SEX
# 27 STRATA2 S2 ARM A: Drug X STRATA1 B SEX
# 28 STRATA2 S2 ARM A: Drug X STRATA1 B SEX
# 29 STRATA2 S1 ARM A: Drug X STRATA1 C STRATA2
# 30 STRATA2 S1 ARM A: Drug X STRATA1 C STRATA2
# 31 STRATA2 S1 ARM A: Drug X STRATA1 C AGE
# 32 STRATA2 S1 ARM A: Drug X STRATA1 C SEX
# 33 STRATA2 S1 ARM A: Drug X STRATA1 C SEX
# 34 STRATA2 S1 ARM A: Drug X STRATA1 C SEX
# 35 STRATA2 S1 ARM A: Drug X STRATA1 C SEX
# 36 STRATA2 S2 ARM A: Drug X STRATA1 C STRATA2
# 37 STRATA2 S2 ARM A: Drug X STRATA1 C STRATA2
# 38 STRATA2 S2 ARM A: Drug X STRATA1 C AGE
# 39 STRATA2 S2 ARM A: Drug X STRATA1 C SEX
# 40 STRATA2 S2 ARM A: Drug X STRATA1 C SEX
# 41 STRATA2 S2 ARM A: Drug X STRATA1 C SEX
# 42 STRATA2 S2 ARM A: Drug X STRATA1 C SEX
# 43 STRATA2 S1 ARM B: Placebo STRATA1 A STRATA2
# 44 STRATA2 S1 ARM B: Placebo STRATA1 A STRATA2
# 45 STRATA2 S1 ARM B: Placebo STRATA1 A AGE
# 46 STRATA2 S1 ARM B: Placebo STRATA1 A SEX
# 47 STRATA2 S1 ARM B: Placebo STRATA1 A SEX
# 48 STRATA2 S1 ARM B: Placebo STRATA1 A SEX
# 49 STRATA2 S1 ARM B: Placebo STRATA1 A SEX
# 50 STRATA2 S2 ARM B: Placebo STRATA1 A STRATA2
# 51 STRATA2 S2 ARM B: Placebo STRATA1 A STRATA2
# 52 STRATA2 S2 ARM B: Placebo STRATA1 A AGE
# 53 STRATA2 S2 ARM B: Placebo STRATA1 A SEX
# 54 STRATA2 S2 ARM B: Placebo STRATA1 A SEX
# 55 STRATA2 S2 ARM B: Placebo STRATA1 A SEX
# 56 STRATA2 S2 ARM B: Placebo STRATA1 A SEX
# 57 STRATA2 S1 ARM B: Placebo STRATA1 B STRATA2
# 58 STRATA2 S1 ARM B: Placebo STRATA1 B STRATA2
# 59 STRATA2 S1 ARM B: Placebo STRATA1 B AGE
# 60 STRATA2 S1 ARM B: Placebo STRATA1 B SEX
# 61 STRATA2 S1 ARM B: Placebo STRATA1 B SEX
# 62 STRATA2 S1 ARM B: Placebo STRATA1 B SEX
# 63 STRATA2 S1 ARM B: Placebo STRATA1 B SEX
# 64 STRATA2 S2 ARM B: Placebo STRATA1 B STRATA2
# 65 STRATA2 S2 ARM B: Placebo STRATA1 B STRATA2
# 66 STRATA2 S2 ARM B: Placebo STRATA1 B AGE
# 67 STRATA2 S2 ARM B: Placebo STRATA1 B SEX
# 68 STRATA2 S2 ARM B: Placebo STRATA1 B SEX
# 69 STRATA2 S2 ARM B: Placebo STRATA1 B SEX
# 70 STRATA2 S2 ARM B: Placebo STRATA1 B SEX
# 71 STRATA2 S1 ARM B: Placebo STRATA1 C STRATA2
# 72 STRATA2 S1 ARM B: Placebo STRATA1 C STRATA2
# 73 STRATA2 S1 ARM B: Placebo STRATA1 C AGE
# 74 STRATA2 S1 ARM B: Placebo STRATA1 C SEX
# 75 STRATA2 S1 ARM B: Placebo STRATA1 C SEX
# 76 STRATA2 S1 ARM B: Placebo STRATA1 C SEX
# 77 STRATA2 S1 ARM B: Placebo STRATA1 C SEX
# 78 STRATA2 S2 ARM B: Placebo STRATA1 C STRATA2
# 79 STRATA2 S2 ARM B: Placebo STRATA1 C STRATA2
# 80 STRATA2 S2 ARM B: Placebo STRATA1 C AGE
# 81 STRATA2 S2 ARM B: Placebo STRATA1 C SEX
# 82 STRATA2 S2 ARM B: Placebo STRATA1 C SEX
# 83 STRATA2 S2 ARM B: Placebo STRATA1 C SEX
# 84 STRATA2 S2 ARM B: Placebo STRATA1 C SEX
# 85 STRATA2 S1 ARM C: Combination STRATA1 A STRATA2
# 86 STRATA2 S1 ARM C: Combination STRATA1 A STRATA2
# 87 STRATA2 S1 ARM C: Combination STRATA1 A AGE
# 88 STRATA2 S1 ARM C: Combination STRATA1 A SEX
# 89 STRATA2 S1 ARM C: Combination STRATA1 A SEX
# 90 STRATA2 S1 ARM C: Combination STRATA1 A SEX
# 91 STRATA2 S1 ARM C: Combination STRATA1 A SEX
# 92 STRATA2 S2 ARM C: Combination STRATA1 A STRATA2
# 93 STRATA2 S2 ARM C: Combination STRATA1 A STRATA2
# 94 STRATA2 S2 ARM C: Combination STRATA1 A AGE
# 95 STRATA2 S2 ARM C: Combination STRATA1 A SEX
# 96 STRATA2 S2 ARM C: Combination STRATA1 A SEX
# 97 STRATA2 S2 ARM C: Combination STRATA1 A SEX
# 98 STRATA2 S2 ARM C: Combination STRATA1 A SEX
# 99 STRATA2 S1 ARM C: Combination STRATA1 B STRATA2
# 100 STRATA2 S1 ARM C: Combination STRATA1 B STRATA2
# 101 STRATA2 S1 ARM C: Combination STRATA1 B AGE
# 102 STRATA2 S1 ARM C: Combination STRATA1 B SEX
# 103 STRATA2 S1 ARM C: Combination STRATA1 B SEX
# 104 STRATA2 S1 ARM C: Combination STRATA1 B SEX
# 105 STRATA2 S1 ARM C: Combination STRATA1 B SEX
# 106 STRATA2 S2 ARM C: Combination STRATA1 B STRATA2
# 107 STRATA2 S2 ARM C: Combination STRATA1 B STRATA2
# 108 STRATA2 S2 ARM C: Combination STRATA1 B AGE
# 109 STRATA2 S2 ARM C: Combination STRATA1 B SEX
# 110 STRATA2 S2 ARM C: Combination STRATA1 B SEX
# 111 STRATA2 S2 ARM C: Combination STRATA1 B SEX
# 112 STRATA2 S2 ARM C: Combination STRATA1 B SEX
# 113 STRATA2 S1 ARM C: Combination STRATA1 C STRATA2
# 114 STRATA2 S1 ARM C: Combination STRATA1 C STRATA2
# 115 STRATA2 S1 ARM C: Combination STRATA1 C AGE
# 116 STRATA2 S1 ARM C: Combination STRATA1 C SEX
# 117 STRATA2 S1 ARM C: Combination STRATA1 C SEX
# 118 STRATA2 S1 ARM C: Combination STRATA1 C SEX
# 119 STRATA2 S1 ARM C: Combination STRATA1 C SEX
# 120 STRATA2 S2 ARM C: Combination STRATA1 C STRATA2
# 121 STRATA2 S2 ARM C: Combination STRATA1 C STRATA2
# 122 STRATA2 S2 ARM C: Combination STRATA1 C AGE
# 123 STRATA2 S2 ARM C: Combination STRATA1 C SEX
# 124 STRATA2 S2 ARM C: Combination STRATA1 C SEX
# 125 STRATA2 S2 ARM C: Combination STRATA1 C SEX
# 126 STRATA2 S2 ARM C: Combination STRATA1 C SEX
# variable_level variable_label stat_name stat stat_string
# 1 S1 S1 n 18.0000000 18
# 2 S1 S1 p 0.4736842 47.4
# 3 Mean Mean mean 31.6111111 31.61
# 4 F F n 12.0000000 12
# 5 M M n 5.0000000 5
# 6 U U n 1.0000000 1
# 7 UNDIFFERENTIATED UNDIFFERENTIATED n 0.0000000 0
# 8 S2 S2 n 20.0000000 20
# 9 S2 S2 p 0.5263158 52.6
# 10 Mean Mean mean 34.4000000 34.4
# 11 F F n 9.0000000 9
# 12 M M n 11.0000000 11
# 13 U U n 0.0000000 0
# 14 UNDIFFERENTIATED UNDIFFERENTIATED n 0.0000000 0
# 15 S1 S1 n 28.0000000 28
# 16 S1 S1 p 0.5957447 59.6
# 17 Mean Mean mean 34.5714286 34.57
# 18 F F n 14.0000000 14
# 19 M M n 13.0000000 13
# 20 U U n 1.0000000 1
# 21 UNDIFFERENTIATED UNDIFFERENTIATED n 0.0000000 0
# 22 S2 S2 n 19.0000000 19
# 23 S2 S2 p 0.4042553 40.4
# 24 Mean Mean mean 32.7894737 32.79
# 25 F F n 11.0000000 11
# 26 M M n 8.0000000 8
# 27 U U n 0.0000000 0
# 28 UNDIFFERENTIATED UNDIFFERENTIATED n 0.0000000 0
# 29 S1 S1 n 27.0000000 27
# 30 S1 S1 p 0.5510204 55.1
# 31 Mean Mean mean 35.2592593 35.26
# 32 F F n 17.0000000 17
# 33 M M n 8.0000000 8
# 34 U U n 1.0000000 1
# 35 UNDIFFERENTIATED UNDIFFERENTIATED n 1.0000000 1
# 36 S2 S2 n 22.0000000 22
# 37 S2 S2 p 0.4489796 44.9
# 38 Mean Mean mean 32.9545455 32.95
# 39 F F n 16.0000000 16
# 40 M M n 6.0000000 6
# 41 U U n 0.0000000 0
# 42 UNDIFFERENTIATED UNDIFFERENTIATED n 0.0000000 0
# 43 S1 S1 n 22.0000000 22
# 44 S1 S1 p 0.5000000 50
# 45 Mean Mean mean 36.6818182 36.68
# 46 F F n 11.0000000 11
# 47 M M n 10.0000000 10
# 48 U U n 1.0000000 1
# 49 UNDIFFERENTIATED UNDIFFERENTIATED n 0.0000000 0
# 50 S2 S2 n 22.0000000 22
# 51 S2 S2 p 0.5000000 50
# 52 Mean Mean mean 33.5454545 33.55
# 53 F F n 13.0000000 13
# 54 M M n 9.0000000 9
# 55 U U n 0.0000000 0
# 56 UNDIFFERENTIATED UNDIFFERENTIATED n 0.0000000 0
# 57 S1 S1 n 19.0000000 19
# 58 S1 S1 p 0.4222222 42.2
# 59 Mean Mean mean 37.6842105 37.68
# 60 F F n 12.0000000 12
# 61 M M n 7.0000000 7
# 62 U U n 0.0000000 0
# 63 UNDIFFERENTIATED UNDIFFERENTIATED n 0.0000000 0
# 64 S2 S2 n 26.0000000 26
# 65 S2 S2 p 0.5777778 57.8
# 66 Mean Mean mean 34.7692308 34.77
# 67 F F n 15.0000000 15
# 68 M M n 10.0000000 10
# 69 U U n 1.0000000 1
# 70 UNDIFFERENTIATED UNDIFFERENTIATED n 0.0000000 0
# 71 S1 S1 n 26.0000000 26
# 72 S1 S1 p 0.5777778 57.8
# 73 Mean Mean mean 35.3846154 35.38
# 74 F F n 13.0000000 13
# 75 M M n 13.0000000 13
# 76 U U n 0.0000000 0
# 77 UNDIFFERENTIATED UNDIFFERENTIATED n 0.0000000 0
# 78 S2 S2 n 19.0000000 19
# 79 S2 S2 p 0.4222222 42.2
# 80 Mean Mean mean 34.8947368 34.89
# 81 F F n 13.0000000 13
# 82 M M n 6.0000000 6
# 83 U U n 0.0000000 0
# 84 UNDIFFERENTIATED UNDIFFERENTIATED n 0.0000000 0
# 85 S1 S1 n 14.0000000 14
# 86 S1 S1 p 0.3500000 35
# 87 Mean Mean mean 34.0000000 34
# 88 F F n 7.0000000 7
# 89 M M n 6.0000000 6
# 90 U U n 1.0000000 1
# 91 UNDIFFERENTIATED UNDIFFERENTIATED n 0.0000000 0
# 92 S2 S2 n 26.0000000 26
# 93 S2 S2 p 0.6500000 65
# 94 Mean Mean mean 34.3461538 34.35
# 95 F F n 11.0000000 11
# 96 M M n 14.0000000 14
# 97 U U n 0.0000000 0
# 98 UNDIFFERENTIATED UNDIFFERENTIATED n 1.0000000 1
# 99 S1 S1 n 18.0000000 18
# 100 S1 S1 p 0.4186047 41.9
# 101 Mean Mean mean 35.8333333 35.83
# 102 F F n 9.0000000 9
# 103 M M n 9.0000000 9
# 104 U U n 0.0000000 0
# 105 UNDIFFERENTIATED UNDIFFERENTIATED n 0.0000000 0
# 106 S2 S2 n 25.0000000 25
# 107 S2 S2 p 0.5813953 58.1
# 108 Mean Mean mean 36.6800000 36.68
# 109 F F n 12.0000000 12
# 110 M M n 12.0000000 12
# 111 U U n 1.0000000 1
# 112 UNDIFFERENTIATED UNDIFFERENTIATED n 0.0000000 0
# 113 S1 S1 n 24.0000000 24
# 114 S1 S1 p 0.4897959 49
# 115 Mean Mean mean 36.5833333 36.58
# 116 F F n 14.0000000 14
# 117 M M n 8.0000000 8
# 118 U U n 1.0000000 1
# 119 UNDIFFERENTIATED UNDIFFERENTIATED n 1.0000000 1
# 120 S2 S2 n 25.0000000 25
# 121 S2 S2 p 0.5102041 51
# 122 Mean Mean mean 34.7200000 34.72
# 123 F F n 13.0000000 13
# 124 M M n 11.0000000 11
# 125 U U n 1.0000000 1
# 126 UNDIFFERENTIATED UNDIFFERENTIATED n 0.0000000 0