Convert a crosstable to a frequency or a propensity table

as.prop.table() is an S3 generic. It can be used on objects of class datawizard_crosstab created by data_tabulate() when it was run with the arguments by and proportions.

Usage

as.prop.table(x, ...)

# S3 method for class 'datawizard_crosstab'
as.prop.table(x, remove_na = TRUE, simplify = FALSE, verbose = TRUE, ...)

# S3 method for class 'datawizard_tables'
as.data.frame(
  x,
  row.names = NULL,
  optional = FALSE,
  ...,
  stringsAsFactors = FALSE,
  add_total = FALSE
)

# S3 method for class 'datawizard_table'
as.table(x, remove_na = TRUE, simplify = FALSE, verbose = TRUE, ...)

Arguments

x: An object created by data_tabulate(). It must be of class datawizard_crosstab for as.prop.table().
...: not used.
remove_na: Logical, if FALSE, missing values are included in the frequency or crosstable, else missing values are omitted. Note that the default for the as.table() method is remove_na = TRUE, so that missing values are not included in the returned table, which makes more sense for post-processing of the table, e.g. using chisq.test().
simplify: Logical, if TRUE, the returned table is simplified to a single table object if there is only one frequency or contingency table input. Else, always for multiple table inputs or when simplify = FALSE, a list of tables is returned. This is only relevant for the as.table() methods. To ensure consistent output, the default is FALSE.
verbose: Toggle warnings and messages.
row.names: NULL or a character vector giving the row names for the data frame. Missing values are not allowed.
optional: logical. If TRUE, setting row names and converting column names (to syntactic names: see make.names) is optional. Note that all of R's base package as.data.frame() methods use optional only for column names treatment, basically with the meaning of data.frame(*, check.names = !optional). See also the make.names argument of the matrix method.
stringsAsFactors: logical: should the character vector be converted to a factor?
add_total: For crosstables (i.e. when by is not NULL), a row and column with the total N values are added to the data frame. add_total has no effect in as.data.frame() for simple frequency tables.

Examples

data(efc)

# Some cross tabulation
cross <- data_tabulate(efc, select = "e42dep", by = "c172code", proportions = "row")
cross
#> e42dep | low level of education | intermediate level of education
#> -------+------------------------+--------------------------------
#> 1      |              0  (0.0%) |                      2 (100.0%)
#> 2      |              0  (0.0%) |                      4 (100.0%)
#> 3      |              4 (14.3%) |                     16  (57.1%)
#> 4      |              4  (6.3%) |                     42  (66.7%)
#> <NA>   |              0  (0.0%) |                      2  (66.7%)
#> -------+------------------------+--------------------------------
#> Total  |                      8 |                              66
#> 
#> e42dep | high level of education |      <NA> | Total
#> -------+-------------------------+-----------+------
#> 1      |               0  (0.0%) | 0  (0.0%) |     2
#> 2      |               0  (0.0%) | 0  (0.0%) |     4
#> 3      |               6 (21.4%) | 2  (7.1%) |    28
#> 4      |              10 (15.9%) | 7 (11.1%) |    63
#> <NA>   |               0  (0.0%) | 1 (33.3%) |     3
#> -------+-------------------------+-----------+------
#> Total  |                      16 |        10 |   100

# Convert to a propensity table
as.prop.table(cross)
#> Removing NA values from frequency table.
#> [[1]]
#>   low level of education intermediate level of education
#> 1             0.00000000                      1.00000000
#> 2             0.00000000                      1.00000000
#> 3             0.14285714                      0.57142857
#> 4             0.06349206                      0.66666667
#>   high level of education
#> 1              0.00000000
#> 2              0.00000000
#> 3              0.21428571
#> 4              0.15873016
#> 

# Convert to data.frame
result <- data_tabulate(efc, "c172code", by = "e16sex")
as.data.frame(result)
#>        var        table
#> 1 c172code c(1, 2, ....
as.data.frame(result)$table
#> [[1]]
#>   c172code male female NA
#> 1        1    5      3  0
#> 2        2   32     34  0
#> 3        3    4     12  0
#> 4     <NA>    5      5  0
#> 
as.data.frame(result, add_total = TRUE)$table
#> [[1]]
#>   c172code male female <NA> Total
#> 1        1    5      3    0     8
#> 2        2   32     34    0    66
#> 3        3    4     12    0    16
#> 4     <NA>    5      5    0    10
#> 5    Total   46     54    0   100
#> 

# Convert to a table that can be passed to chisq.test()

out <- data_tabulate(efc, "c172code", by = "e16sex")
# we need to simplify the output, else we get a list of tables
tbl <- as.table(out, simplify = TRUE)
#> Removing NA values from frequency table.
tbl
#>   male female
#> 1    5      3
#> 2   32     34
#> 3    4     12
suppressWarnings(chisq.test(tbl))
#> 
#> 	Pearson's Chi-squared test
#> 
#> data:  tbl
#> X-squared = 3.8802, df = 2, p-value = 0.1437
#> 

# apply chisq.test to each table
out <- data_tabulate(efc, c("c172code", "e16sex"))
suppressWarnings(lapply(as.table(out), chisq.test))
#> Removing NA values from frequency table.
#> [[1]]
#> 
#> 	Chi-squared test for given probabilities
#> 
#> data:  X[[i]]
#> X-squared = 65.867, df = 2, p-value = 4.98e-15
#> 
#> 
#> [[2]]
#> 
#> 	Chi-squared test for given probabilities
#> 
#> data:  X[[i]]
#> X-squared = 0.64, df = 1, p-value = 0.4237
#> 
#> 

# can also handle grouped data frames
d <- data_group(mtcars, "am")
x <- data_tabulate(d, "cyl", by = "gear")
as.table(x)
#> $`am (0)`
#>    3  4
#> 4  1  2
#> 6  2  2
#> 8 12  0
#> 
#> $`am (1)`
#>   4 5
#> 4 6 2
#> 6 2 1
#> 8 0 2
#>

Convert a crosstable to a frequency or a propensity table

Usage

Arguments

See also

Examples