cross_cases: Cross tabulation with support of labels, weights and multiple...

In expss: Tables, Labels and Some Useful Functions from Spreadsheets and 'SPSS' Statistics

Cross tabulation with support of labels, weights and multiple response variables.

Description

• cross_cases build a contingency table of the counts.

• cross_cpct, cross_cpct_responses build a contingency table of the column percent. These functions give different results only for multiple response variables. For cross_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 cross_cpct_responses base of percent is number of valid responses. Multiple response variables can have several responses for single case. Sum of percent of cross_cpct_responses always equals to 100%.

• cross_rpct build a contingency table of the row percent. Base for percent is number of valid cases.

• cross_tpct build a contingency table of the table percent. Base for percent is number of valid cases.

• cross_* functions evaluate their arguments in the context of the first argument data.

• cro_* functions use standard evaluation, e. g 'cro(mtcars$am, mtcars$vs)'.

• 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.

Usage

cross_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")
)

cross_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")
)

cross_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")
)

cross_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")
)

cross_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")
)

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")
)

total(x = 1, label = "#Total")

Arguments

data	data.frame in which context all other arguments will be evaluated (for cross_*).
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".
x	vector/data.frame of class 'category'/'dichotomy'.
label	character. Label for total variable.

Value

object of class 'etable'. Basically it's a data.frame but class is needed for custom methods.

See Also

tables, fre, cro_fun.

Examples

## Not run: 
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"
)

cross_cases(mtcars, am, vs) 
cro(mtcars$am, mtcars$vs) # the same result

# column percent with multiple banners
cross_cpct(mtcars, cyl, list(total(), vs, am)) 

# nested banner
cross_cpct(mtcars, cyl, list(total(), vs %nest% am))

# stacked variables
cross_cases(mtcars, list(cyl, carb), list(total(), vs %nest% am))

# nested variables
cross_cpct(mtcars, am %nest% cyl, list(total(), vs))

# row variables
cross_cpct(mtcars, cyl, list(total(), vs), row_vars = am)

# several totals above table
cross_cpct(mtcars, 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.table(t(replicate(20,sample(c(1:5,NA),4,replace = FALSE)))) %>% 
    setNames(paste0("brand_", 1:4))
# score - evaluation of tested product
brands = brands %>% 
    let(
        score = sample(-1:1,.N,replace = TRUE)
    ) %>% 
    apply_labels(
        brand_1 = "Used brands",
        brand_1 = num_lab("
                              1 Brand A
                              2 Brand B
                              3 Brand C
                              4 Brand D
                              5 Brand E
                              "),

        score = "Evaluation of tested brand",
        score = num_lab("
                             -1 Dislike it
                             0 So-so
                             1 Like it    
                             ")
)
cross_cpct(brands, mrset(brand_1 %to% brand_4), list(total(), score))
# responses
cross_cpct_responses(brands, mrset(brand_1 %to% brand_4), list(total(), score))

## End(Not run)

expss documentation built on July 26, 2023, 5:23 p.m.