Cross tabulation with support of labels, weights and multiple response variables.
cro, cro_cases build a contingency table of the counts.
cro_cpct, cro_cpct_responses build a contingency table
of the column percent. These functions give different results only for
multiple response variables. For cro_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 cro_cpct_responses base of percent is number of valid
responses. Multiple response variables can have several responses for single
case. Sum of percent of cro_cpct_responses always equals to 100%.
cro_rpct build a contingency table of the row percent. Base
for percent is number of valid cases.
cro_tpct build a contingency table of the table percent. Base
for percent is number of valid cases.
calc_cro_* are the same as above but evaluate their arguments
in the context of the first argument data.
total auxiliary function - creates variables with 1 for valid
case of its argument x and NA in opposite case.
You can combine tables with add_rows and merge.etable. For
sorting table see tab_sort_asc.
To provide multiple-response variables as arguments use mrset for
multiples with category encoding and mdset for multiples with
dichotomy (dummy) encoding. To compute statistics with nested
variables/banners use nest. For more sophisticated interface with
modern piping via magrittr see tables.
cro(
cell_vars,
col_vars = total(),
row_vars = NULL,
weight = NULL,
subgroup = NULL,
total_label = NULL,
total_statistic = "u_cases",
total_row_position = c("below", "above", "none")
)
cro_cases(
cell_vars,
col_vars = total(),
row_vars = NULL,
weight = NULL,
subgroup = NULL,
total_label = NULL,
total_statistic = "u_cases",
total_row_position = c("below", "above", "none")
)
cro_cpct(
cell_vars,
col_vars = total(),
row_vars = NULL,
weight = NULL,
subgroup = NULL,
total_label = NULL,
total_statistic = "u_cases",
total_row_position = c("below", "above", "none")
)
cro_rpct(
cell_vars,
col_vars = total(),
row_vars = NULL,
weight = NULL,
subgroup = NULL,
total_label = NULL,
total_statistic = "u_cases",
total_row_position = c("below", "above", "none")
)
cro_tpct(
cell_vars,
col_vars = total(),
row_vars = NULL,
weight = NULL,
subgroup = NULL,
total_label = NULL,
total_statistic = "u_cases",
total_row_position = c("below", "above", "none")
)
cro_cpct_responses(
cell_vars,
col_vars = total(),
row_vars = NULL,
weight = NULL,
subgroup = NULL,
total_label = NULL,
total_statistic = "u_responses",
total_row_position = c("below", "above", "none")
)
calc_cro(
data,
cell_vars,
col_vars = total(),
row_vars = NULL,
weight = NULL,
subgroup = NULL,
total_label = NULL,
total_statistic = "u_cases",
total_row_position = c("below", "above", "none")
)
calc_cro_cases(
data,
cell_vars,
col_vars = total(),
row_vars = NULL,
weight = NULL,
subgroup = NULL,
total_label = NULL,
total_statistic = "u_cases",
total_row_position = c("below", "above", "none")
)
calc_cro_cpct(
data,
cell_vars,
col_vars = total(),
row_vars = NULL,
weight = NULL,
subgroup = NULL,
total_label = NULL,
total_statistic = "u_cases",
total_row_position = c("below", "above", "none")
)
calc_cro_rpct(
data,
cell_vars,
col_vars = total(),
row_vars = NULL,
weight = NULL,
subgroup = NULL,
total_label = NULL,
total_statistic = "u_cases",
total_row_position = c("below", "above", "none")
)
calc_cro_tpct(
data,
cell_vars,
col_vars = total(),
row_vars = NULL,
weight = NULL,
subgroup = NULL,
total_label = NULL,
total_statistic = "u_cases",
total_row_position = c("below", "above", "none")
)
calc_cro_cpct_responses(
data,
cell_vars,
col_vars = total(),
row_vars = NULL,
weight = NULL,
subgroup = NULL,
total_label = NULL,
total_statistic = "u_responses",
total_row_position = c("below", "above", "none")
)
total(x = 1, label = "#Total")cell_vars |
vector/data.frame/list. Variables on which percentage/cases will be computed. Use mrset/mdset for multiple-response variables. |
col_vars |
vector/data.frame/list. Variables which breaks table by columns. Use mrset/mdset for multiple-response variables. |
row_vars |
vector/data.frame/list. Variables which breaks table by rows. Use mrset/mdset for multiple-response variables. |
weight |
numeric vector. Optional cases weights. Cases with NA's, negative and zero weights are removed before calculations. |
subgroup |
logical vector. You can specify subgroup on which table will be computed. |
total_label |
By default "#Total". You can provide several names - each name for each total statistics. |
total_statistic |
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. |
total_row_position |
Position of total row in the resulting table. Can be one of "below", "above", "none". |
data |
data.frame in which context all other arguments will be evaluated
(for |
x |
vector/data.frame of class 'category'/'dichotomy'. |
label |
character. Label for total variable. |
object of class 'etable'. Basically it's a data.frame but class is needed for custom methods.
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"
)
calculate(mtcars, cro(am, vs))
calc_cro(mtcars, am, vs) # the same result
# column percent with multiple banners
calculate(mtcars, cro_cpct(cyl, list(total(), vs, am)))
calc_cro_cpct(mtcars, cyl, list(total(), vs, am)) # the same result
# nested banner
calculate(mtcars, cro_cpct(cyl, list(total(), vs %nest% am)))
# stacked variables
calculate(mtcars, cro(list(cyl, carb), list(total(), vs %nest% am)))
# nested variables
calculate(mtcars, cro_cpct(am %nest% cyl, list(total(), vs)))
# row variables
calculate(mtcars, cro_cpct(cyl, list(total(), vs), row_vars = am))
# several totals above table
calculate(mtcars, cro_cpct(cyl,
list(total(), vs),
row_vars = am,
total_row_position = "above",
total_label = c("number of cases", "row %"),
total_statistic = c("u_cases", "u_rpct")
))
# multiple-choice variable
# brands - multiple response question
# Which brands do you use during last three months?
set.seed(123)
brands = data.frame(t(replicate(20,sample(c(1:5,NA),4,replace = FALSE))))
# score - evaluation of tested product
score = sample(-1:1,20,replace = TRUE)
var_lab(brands) = "Used brands"
val_lab(brands) = make_labels("
1 Brand A
2 Brand B
3 Brand C
4 Brand D
5 Brand E
")
var_lab(score) = "Evaluation of tested brand"
val_lab(score) = num_lab("
-1 Dislike it
0 So-so
1 Like it
")
cro_cpct(mrset(brands), list(total(), score))
# responses
cro_cpct_responses(mrset(brands), list(total(), score))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.