This function creates a labelled flat table or flat proportional (marginal) table.
flat_table(
data,
...,
margin = c("counts", "cell", "row", "col"),
digits = 2,
show.values = FALSE,
weights = NULL
)A data frame. May also be a grouped data frame (see 'Note' and 'Examples').
One or more variables of data that should be printed as table.
Specify the table margin that should be computed for proportional
tables. By default, counts are printed. Use margin = "cell",
margin = "col" or margin = "row" to print cell,
column or row percentages of the table margins.
Numeric; for proportional tables, digits indicates the
number of decimal places.
Logical, if TRUE, value labels are prefixed by the
associated value.
Bare name, or name as string, of a variable in x
that indicates the vector of weights, which will be applied to weight all
observations. Default is NULL, so no weights are used.
An object of class ftable.
data may also be a grouped data frame (see group_by)
with up to two grouping variables. Cross tables are created for each subgroup then.
frq for simple frequency table of labelled vectors.
data(efc)
# flat table with counts
flat_table(efc, e42dep, c172code, e16sex)
#> e16sex male female
#> e42dep c172code
#> independent low level of education 5 7
#> intermediate level of education 15 30
#> high level of education 1 4
#> slightly dependent low level of education 16 25
#> intermediate level of education 39 96
#> high level of education 13 26
#> moderately dependent low level of education 28 33
#> intermediate level of education 59 104
#> high level of education 18 44
#> severely dependent low level of education 31 34
#> intermediate level of education 43 120
#> high level of education 11 39
# flat table with proportions
flat_table(efc, e42dep, c172code, e16sex, margin = "row")
#> e16sex male female
#> e42dep c172code
#> independent low level of education 41.67 58.33
#> intermediate level of education 33.33 66.67
#> high level of education 20.00 80.00
#> slightly dependent low level of education 39.02 60.98
#> intermediate level of education 28.89 71.11
#> high level of education 33.33 66.67
#> moderately dependent low level of education 45.90 54.10
#> intermediate level of education 36.20 63.80
#> high level of education 29.03 70.97
#> severely dependent low level of education 47.69 52.31
#> intermediate level of education 26.38 73.62
#> high level of education 22.00 78.00
# flat table from grouped data frame. You need to select
# the grouping variables and at least two more variables for
# cross tabulation.
library(dplyr)
efc %>%
group_by(e16sex) %>%
select(e16sex, c172code, e42dep) %>%
flat_table()
#>
#> Grouped by: male
#>
#> e42dep independent slightly dependent moderately dependent severely dependent
#> c172code
#> low level of education 5 16 28 31
#> intermediate level of education 15 39 59 43
#> high level of education 1 13 18 11
#>
#>
#> Grouped by: female
#>
#> e42dep independent slightly dependent moderately dependent severely dependent
#> c172code
#> low level of education 7 25 33 34
#> intermediate level of education 30 96 104 120
#> high level of education 4 26 44 39
#>
efc %>%
group_by(e16sex, e42dep) %>%
select(e16sex, e42dep, c172code, n4pstu) %>%
flat_table()
#>
#> Grouped by: male, independent
#>
#> n4pstu No Care Level Care Level 1 Care Level 2 Care Level 3 Care Level 3+
#> c172code
#> low level of education 4 1 0 0 0
#> intermediate level of education 7 2 4 2 0
#> high level of education 1 0 0 0 0
#>
#>
#> Grouped by: male, slightly dependent
#>
#> n4pstu No Care Level Care Level 1 Care Level 2 Care Level 3 Care Level 3+
#> c172code
#> low level of education 9 3 1 2 0
#> intermediate level of education 22 7 8 2 0
#> high level of education 8 1 2 2 0
#>
#>
#> Grouped by: male, moderately dependent
#>
#> n4pstu No Care Level Care Level 1 Care Level 2 Care Level 3 Care Level 3+
#> c172code
#> low level of education 15 6 4 2 0
#> intermediate level of education 27 14 13 4 1
#> high level of education 11 2 5 0 0
#>
#>
#> Grouped by: male, severely dependent
#>
#> n4pstu No Care Level Care Level 1 Care Level 2 Care Level 3 Care Level 3+
#> c172code
#> low level of education 8 5 10 7 0
#> intermediate level of education 7 3 12 20 1
#> high level of education 4 2 3 2 0
#>
#>
#> Grouped by: female, independent
#>
#> n4pstu No Care Level Care Level 1 Care Level 2 Care Level 3 Care Level 3+
#> c172code
#> low level of education 3 1 2 1 0
#> intermediate level of education 19 6 5 0 0
#> high level of education 2 2 0 0 0
#>
#>
#> Grouped by: female, slightly dependent
#>
#> n4pstu No Care Level Care Level 1 Care Level 2 Care Level 3 Care Level 3+
#> c172code
#> low level of education 17 4 2 1 0
#> intermediate level of education 63 13 14 4 1
#> high level of education 18 3 4 1 0
#>
#>
#> Grouped by: female, moderately dependent
#>
#> n4pstu No Care Level Care Level 1 Care Level 2 Care Level 3 Care Level 3+
#> c172code
#> low level of education 16 11 5 1 0
#> intermediate level of education 44 36 17 7 0
#> high level of education 22 12 8 2 0
#>
#>
#> Grouped by: female, severely dependent
#>
#> n4pstu No Care Level Care Level 1 Care Level 2 Care Level 3 Care Level 3+
#> c172code
#> low level of education 14 4 8 7 0
#> intermediate level of education 39 18 35 23 2
#> high level of education 7 8 16 8 0
#>
# now it gets weird...
efc %>%
group_by(e16sex, e42dep) %>%
select(e16sex, e42dep, c172code, n4pstu, c161sex) %>%
flat_table()
#>
#> Grouped by: male, independent
#>
#> c161sex Male Female
#> c172code n4pstu
#> low level of education No Care Level 1 3
#> Care Level 1 0 1
#> Care Level 2 0 0
#> Care Level 3 0 0
#> Care Level 3+ 0 0
#> intermediate level of education No Care Level 3 4
#> Care Level 1 0 2
#> Care Level 2 1 3
#> Care Level 3 2 0
#> Care Level 3+ 0 0
#> high level of education No Care Level 0 1
#> Care Level 1 0 0
#> Care Level 2 0 0
#> Care Level 3 0 0
#> Care Level 3+ 0 0
#>
#>
#> Grouped by: male, slightly dependent
#>
#> c161sex Male Female
#> c172code n4pstu
#> low level of education No Care Level 3 6
#> Care Level 1 1 1
#> Care Level 2 1 0
#> Care Level 3 0 2
#> Care Level 3+ 0 0
#> intermediate level of education No Care Level 7 15
#> Care Level 1 2 5
#> Care Level 2 1 7
#> Care Level 3 2 0
#> Care Level 3+ 0 0
#> high level of education No Care Level 1 7
#> Care Level 1 0 1
#> Care Level 2 0 2
#> Care Level 3 1 1
#> Care Level 3+ 0 0
#>
#>
#> Grouped by: male, moderately dependent
#>
#> c161sex Male Female
#> c172code n4pstu
#> low level of education No Care Level 7 8
#> Care Level 1 0 6
#> Care Level 2 0 4
#> Care Level 3 0 2
#> Care Level 3+ 0 0
#> intermediate level of education No Care Level 3 24
#> Care Level 1 1 13
#> Care Level 2 4 9
#> Care Level 3 1 3
#> Care Level 3+ 1 0
#> high level of education No Care Level 5 6
#> Care Level 1 2 0
#> Care Level 2 1 4
#> Care Level 3 0 0
#> Care Level 3+ 0 0
#>
#>
#> Grouped by: male, severely dependent
#>
#> c161sex Male Female
#> c172code n4pstu
#> low level of education No Care Level 0 8
#> Care Level 1 1 4
#> Care Level 2 1 9
#> Care Level 3 0 7
#> Care Level 3+ 0 0
#> intermediate level of education No Care Level 1 6
#> Care Level 1 0 3
#> Care Level 2 2 10
#> Care Level 3 1 19
#> Care Level 3+ 0 1
#> high level of education No Care Level 1 3
#> Care Level 1 2 0
#> Care Level 2 0 3
#> Care Level 3 0 2
#> Care Level 3+ 0 0
#>
#>
#> Grouped by: female, independent
#>
#> c161sex Male Female
#> c172code n4pstu
#> low level of education No Care Level 1 2
#> Care Level 1 0 1
#> Care Level 2 0 2
#> Care Level 3 1 0
#> Care Level 3+ 0 0
#> intermediate level of education No Care Level 4 15
#> Care Level 1 2 4
#> Care Level 2 2 3
#> Care Level 3 0 0
#> Care Level 3+ 0 0
#> high level of education No Care Level 0 2
#> Care Level 1 0 2
#> Care Level 2 0 0
#> Care Level 3 0 0
#> Care Level 3+ 0 0
#>
#>
#> Grouped by: female, slightly dependent
#>
#> c161sex Male Female
#> c172code n4pstu
#> low level of education No Care Level 3 14
#> Care Level 1 0 4
#> Care Level 2 1 1
#> Care Level 3 0 1
#> Care Level 3+ 0 0
#> intermediate level of education No Care Level 12 51
#> Care Level 1 3 10
#> Care Level 2 3 11
#> Care Level 3 1 3
#> Care Level 3+ 1 0
#> high level of education No Care Level 6 12
#> Care Level 1 0 3
#> Care Level 2 1 3
#> Care Level 3 1 0
#> Care Level 3+ 0 0
#>
#>
#> Grouped by: female, moderately dependent
#>
#> c161sex Male Female
#> c172code n4pstu
#> low level of education No Care Level 6 10
#> Care Level 1 2 9
#> Care Level 2 1 4
#> Care Level 3 0 1
#> Care Level 3+ 0 0
#> intermediate level of education No Care Level 8 36
#> Care Level 1 11 25
#> Care Level 2 8 9
#> Care Level 3 0 7
#> Care Level 3+ 0 0
#> high level of education No Care Level 7 15
#> Care Level 1 4 8
#> Care Level 2 2 6
#> Care Level 3 0 2
#> Care Level 3+ 0 0
#>
#>
#> Grouped by: female, severely dependent
#>
#> c161sex Male Female
#> c172code n4pstu
#> low level of education No Care Level 4 10
#> Care Level 1 2 2
#> Care Level 2 2 6
#> Care Level 3 2 5
#> Care Level 3+ 0 0
#> intermediate level of education No Care Level 6 33
#> Care Level 1 3 15
#> Care Level 2 5 30
#> Care Level 3 11 12
#> Care Level 3+ 1 1
#> high level of education No Care Level 1 6
#> Care Level 1 2 6
#> Care Level 2 7 9
#> Care Level 3 3 5
#> Care Level 3+ 0 0
#>