Description Usage Arguments Value See Also Examples

`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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 | ```
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.

tables, fre, cro_fun.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | ```
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))
``` |

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