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Table construction consists of at least of three functions chained with
magrittr pipe operator. At first we need to
specify variables for which statistics will be computed with
tab_cells. Secondary, we calculate statistics with one of
tab_stat_* functions. And last, we finalize table creation with
tab_pivot: dataset %>% tab_cells(variable) %>%
tab_stat_cases() %>% tab_pivot(). After that we can optionally sort table
with tab_sort_asc, drop empty rows/columns with drop_rc and
transpose with tab_transpose. Generally, table is just a data.frame so
we can use arbitrary operations on it. Statistic is always calculated with
the last cell, column/row variables, weight, missing values and subgroup. To
define new cell/column/row variables we can call appropriate function one more time.
tab_pivot defines how we combine different statistics and where
statistic labels will appear - inside/outside rows/columns. See examples.
For significance testing see significance.
tab_cols(data, ...)tab_cells(data, ...)
tab_rows(data, ...)
tab_weight(data, weight = NULL)
tab_mis_val(data, ...)
tab_total_label(data, ...)
tab_total_statistic(data, ...)
tab_total_row_position(data, total_row_position = c("below", "above", "none"))
tab_subgroup(data, subgroup = NULL)
tab_row_label(data, ..., label = NULL)
tab_stat_fun(data, ..., label = NULL, unsafe = FALSE)
tab_stat_mean_sd_n(
data,
weighted_valid_n = FALSE,
labels = c("Mean", "Std. dev.", ifelse(weighted_valid_n, "Valid N", "Unw. valid N")),
label = NULL
)
tab_stat_mean(data, label = "Mean")
tab_stat_median(data, label = "Median")
tab_stat_se(data, label = "S. E.")
tab_stat_sum(data, label = "Sum")
tab_stat_min(data, label = "Min.")
tab_stat_max(data, label = "Max.")
tab_stat_sd(data, label = "Std. dev.")
tab_stat_valid_n(data, label = "Valid N")
tab_stat_unweighted_valid_n(data, label = "Unw. valid N")
tab_stat_fun_df(data, ..., label = NULL, unsafe = FALSE)
tab_stat_cases(
data,
total_label = NULL,
total_statistic = "u_cases",
total_row_position = c("below", "above", "none"),
label = NULL
)
tab_stat_cpct(
data,
total_label = NULL,
total_statistic = "u_cases",
total_row_position = c("below", "above", "none"),
label = NULL
)
tab_stat_cpct_responses(
data,
total_label = NULL,
total_statistic = "u_responses",
total_row_position = c("below", "above", "none"),
label = NULL
)
tab_stat_tpct(
data,
total_label = NULL,
total_statistic = "u_cases",
total_row_position = c("below", "above", "none"),
label = NULL
)
tab_stat_rpct(
data,
total_label = NULL,
total_statistic = "u_cases",
total_row_position = c("below", "above", "none"),
label = NULL
)
tab_last_vstack(
data,
stat_position = c("outside_rows", "inside_rows"),
stat_label = c("inside", "outside"),
label = NULL
)
tab_last_hstack(
data,
stat_position = c("outside_columns", "inside_columns"),
stat_label = c("inside", "outside"),
label = NULL
)
tab_pivot(
data,
stat_position = c("outside_rows", "inside_rows", "outside_columns", "inside_columns"),
stat_label = c("inside", "outside")
)
tab_transpose(data)
tab_caption(data, ...)
All of these functions return object of class
intermediate_table except tab_pivot which returns final
result - object of class etable. Basically it's a data.frame but
class is needed for custom methods.
data.frame/intermediate_table
vector/data.frame/list. Variables for tables. Use mrset/mdset for multiple-response variables.
numeric vector in tab_weight. Cases with NA's, negative
and zero weights are removed before calculations.
Position of total row in the resulting table. Can be one of "below", "above", "none".
logical vector in tab_subgroup. You can specify
subgroup on which table will be computed.
character. Label for the statistic in the tab_stat_*.
logical If TRUE than fun will be evaluated as is. It can
lead to significant increase in the performance. But there are some
limitations. For tab_stat_fun it means that your function fun
should return vector of length one. Also there will be no attempts to make
labels for statistic. For tab_stat_fun_df your function should return
vector of length one or list/data.frame (optionally with 'row_labels'
element - statistic labels). If unsafe is TRUE then further
arguments (...) for fun will be ignored.
logical. Sould we show weighted valid N in
tab_stat_mean_sd_n? By default it is FALSE.
character vector of length 3. Labels for mean, standard
deviation and valid N in tab_stat_mean_sd_n.
By default "#Total". You can provide several names - each name for each total statistics.
By default it is "u_cases" (unweighted cases). Possible values are "u_cases", "u_responses", "u_cpct", "u_rpct", "u_tpct", "w_cases", "w_responses", "w_cpct", "w_rpct", "w_tpct". "u_" means unweighted statistics and "w_" means weighted statistics.
character one of the values "outside_rows",
"inside_rows", "outside_columns" or "inside_columns".
It defines how we will combine statistics in the table.
character one of the values "inside" or
"outside". Where will be placed labels for the statistics relative
to column names/row labels? See examples.
tab_cells variables on which percentage/cases/summary
functions will be computed. Use mrset/mdset for
multiple-response variables.
tab_cols optional variables which breaks table by
columns. Use mrset/mdset for
multiple-response variables.
tab_rows optional variables which breaks table by rows. Use
mrset/mdset for multiple-response variables.
tab_weight optional weight for the statistic.
tab_mis_val optional missing values for the statistic. It will
be applied on variables specified by tab_cells. It works in the same
manner as na_if.
tab_subgroup optional logical vector/expression which specify
subset of data for table.
tab_row_label Add to table empty row with specified row
labels. It is usefull for making section headings and etc.
tab_total_row_position Default value for
total_row_position argument in tab_stat_cases and etc. Can be
one of "below", "above", "none".
tab_total_label Default value for total_label argument
in tab_stat_cases and etc. You can provide several names - each name
for each total statistics.
tab_total_statistic Default value for total_statistic
argument in tab_stat_cases and etc. You can provide several values.
Possible values are "u_cases", "u_responses", "u_cpct", "u_rpct", "u_tpct",
"w_cases", "w_responses", "w_cpct", "w_rpct", "w_tpct". "u_" means unweighted
statistics and "w_" means weighted statistics.
tab_stat_fun, tab_stat_fun_df tab_stat_fun
applies function on each variable in cells separately, tab_stat_fun_df
gives to function each data.frame in cells as a whole
data.table with all names converted to variable labels (if
labels exists). So it is not recommended to rely on original variables names
in your fun. For details see cross_fun. You can provide several
functions as arguments. They will be combined as with
combine_functions. So you can use method argument. For details
see documentation for combine_functions.
tab_stat_cases calculate counts.
tab_stat_cpct, tab_stat_cpct_responses calculate column
percent. These functions give different results only for multiple response
variables. For tab_stat_cpct base of percent is number of valid cases.
Case is considered as valid if it has at least one non-NA value. So for
multiple response variables sum of percent may be greater than 100. For
tab_stat_cpct_responses base of percent is number of valid responses.
Multiple response variables can have several responses for single case. Sum
of percent of tab_stat_cpct_responses always equals to 100%.
tab_stat_rpct calculate row percent. Base
for percent is number of valid cases.
tab_stat_tpct calculate table percent. Base
for percent is number of valid cases.
tab_stat_mean, tab_stat_median, tab_stat_se,
tab_stat_sum, tab_stat_min, tab_stat_max,
tab_stat_sd, tab_stat_valid_n,
tab_stat_unweighted_valid_n different summary statistics. NA's are
always omitted.
tab_pivot finalize table creation and define how different
tab_stat_* will be combined
tab_caption set caption on the table. Should be used after the tab_pivot.
tab_transpose transpose final table after tab_pivot or last
statistic.
fre, cross_cases, cross_fun, tab_sort_asc, drop_empty_rows, significance.
if (FALSE) {
data(mtcars)
mtcars = apply_labels(mtcars,
mpg = "Miles/(US) gallon",
cyl = "Number of cylinders",
disp = "Displacement (cu.in.)",
hp = "Gross horsepower",
drat = "Rear axle ratio",
wt = "Weight (1000 lbs)",
qsec = "1/4 mile time",
vs = "Engine",
vs = c("V-engine" = 0,
"Straight engine" = 1),
am = "Transmission",
am = c("Automatic" = 0,
"Manual"=1),
gear = "Number of forward gears",
carb = "Number of carburetors"
)
# some examples from 'cro'
# simple example - generally with 'cro' it can be made with less typing
mtcars %>%
tab_cells(cyl) %>%
tab_cols(vs) %>%
tab_stat_cpct() %>%
tab_pivot()
# split rows
mtcars %>%
tab_cells(cyl) %>%
tab_cols(vs) %>%
tab_rows(am) %>%
tab_stat_cpct() %>%
tab_pivot()
# multiple banners
mtcars %>%
tab_cells(cyl) %>%
tab_cols(total(), vs, am) %>%
tab_stat_cpct() %>%
tab_pivot()
# nested banners
mtcars %>%
tab_cells(cyl) %>%
tab_cols(total(), vs %nest% am) %>%
tab_stat_cpct() %>%
tab_pivot()
# summary statistics
mtcars %>%
tab_cells(mpg, disp, hp, wt, qsec) %>%
tab_cols(am) %>%
tab_stat_fun(Mean = w_mean, "Std. dev." = w_sd, "Valid N" = w_n) %>%
tab_pivot()
# summary statistics - labels in columns
mtcars %>%
tab_cells(mpg, disp, hp, wt, qsec) %>%
tab_cols(am) %>%
tab_stat_fun(Mean = w_mean, "Std. dev." = w_sd, "Valid N" = w_n, method = list) %>%
tab_pivot()
# subgroup with droping empty columns
mtcars %>%
tab_subgroup(am == 0) %>%
tab_cells(cyl) %>%
tab_cols(total(), vs %nest% am) %>%
tab_stat_cpct() %>%
tab_pivot() %>%
drop_empty_columns()
# total position at the top of the table
mtcars %>%
tab_cells(cyl) %>%
tab_cols(total(), vs) %>%
tab_rows(am) %>%
tab_stat_cpct(total_row_position = "above",
total_label = c("number of cases", "row %"),
total_statistic = c("u_cases", "u_rpct")) %>%
tab_pivot()
# this example cannot be made easily with 'cro'
mtcars %>%
tab_cells(am) %>%
tab_cols(total(), vs) %>%
tab_total_row_position("none") %>%
tab_stat_cpct(label = "col %") %>%
tab_stat_rpct(label = "row %") %>%
tab_stat_tpct(label = "table %") %>%
tab_pivot(stat_position = "inside_rows")
# statistic labels inside columns
mtcars %>%
tab_cells(am) %>%
tab_cols(total(), vs) %>%
tab_total_row_position("none") %>%
tab_stat_cpct(label = "col %") %>%
tab_stat_rpct(label = "row %") %>%
tab_stat_tpct(label = "table %") %>%
tab_pivot(stat_position = "inside_columns")
# stacked statistics
mtcars %>%
tab_cells(cyl) %>%
tab_cols(total(), am) %>%
tab_stat_mean() %>%
tab_stat_se() %>%
tab_stat_valid_n() %>%
tab_stat_cpct() %>%
tab_pivot()
# stacked statistics with section headings
mtcars %>%
tab_cells(cyl) %>%
tab_cols(total(), am) %>%
tab_row_label("#Summary statistics") %>%
tab_stat_mean() %>%
tab_stat_se() %>%
tab_stat_valid_n() %>%
tab_row_label("#Column percent") %>%
tab_stat_cpct() %>%
tab_pivot()
# stacked statistics with different variables
mtcars %>%
tab_cols(total(), am) %>%
tab_cells(mpg, hp, qsec) %>%
tab_stat_mean() %>%
tab_cells(cyl, carb) %>%
tab_stat_cpct() %>%
tab_pivot()
# stacked statistics - label position outside row labels
mtcars %>%
tab_cells(cyl) %>%
tab_cols(total(), am) %>%
tab_stat_mean() %>%
tab_stat_se %>%
tab_stat_valid_n() %>%
tab_stat_cpct(label = "Col %") %>%
tab_pivot(stat_label = "outside")
# example from 'cross_fun_df' - linear regression by groups with sorting
mtcars %>%
tab_cells(sheet(mpg, disp, hp, wt, qsec)) %>%
tab_cols(total(), am) %>%
tab_stat_fun_df(
function(x){
frm = reformulate(".", response = as.name(names(x)[1]))
model = lm(frm, data = x)
sheet('Coef.' = coef(model),
confint(model)
)
}
) %>%
tab_pivot() %>%
tab_sort_desc()
# multiple-response variables and weight
data(product_test)
codeframe_likes = num_lab("
1 Liked everything
2 Disliked everything
3 Chocolate
4 Appearance
5 Taste
6 Stuffing
7 Nuts
8 Consistency
98 Other
99 Hard to answer
")
set.seed(1)
product_test = product_test %>%
let(
# recode age by groups
age_cat = recode(s2a, lo %thru% 25 ~ 1, lo %thru% hi ~ 2),
wgt = runif(.N, 0.25, 4),
wgt = wgt/sum(wgt)*.N
) %>%
apply_labels(
age_cat = "Age",
age_cat = c("18 - 25" = 1, "26 - 35" = 2),
a1_1 = "Likes. VSX123",
b1_1 = "Likes. SDF456",
a1_1 = codeframe_likes,
b1_1 = codeframe_likes
)
product_test %>%
tab_cells(mrset(a1_1 %to% a1_6), mrset(b1_1 %to% b1_6)) %>%
tab_cols(total(), age_cat) %>%
tab_weight(wgt) %>%
tab_stat_cpct() %>%
tab_sort_desc() %>%
tab_pivot()
# trick to place cell variables labels inside columns
# useful to compare two variables
# '|' is needed to prevent automatic labels creation from argument
# alternatively we can use list(...) to avoid this
product_test %>%
tab_cols(total(), age_cat) %>%
tab_weight(wgt) %>%
tab_cells("|" = unvr(mrset(a1_1 %to% a1_6))) %>%
tab_stat_cpct(label = var_lab(a1_1)) %>%
tab_cells("|" = unvr(mrset(b1_1 %to% b1_6))) %>%
tab_stat_cpct(label = var_lab(b1_1)) %>%
tab_pivot(stat_position = "inside_columns")
# if you need standard evaluation, use 'vars'
tables = mtcars %>%
tab_cols(total(), am %nest% vs)
for(each in c("mpg", "disp", "hp", "qsec")){
tables = tables %>% tab_cells(vars(each)) %>%
tab_stat_fun(Mean = w_mean, "Std. dev." = w_sd, "Valid N" = w_n)
}
tables %>% tab_pivot()
}
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