data_codebook() generates codebooks from data frames, i.e. overviews
of all variables and some more information about each variable (like
labels, values or value range, frequencies, amount of missing values).
Usage
data_codebook(
data,
select = NULL,
exclude = NULL,
variable_label_width = NULL,
value_label_width = NULL,
max_values = 10,
range_at = 6,
ignore_case = FALSE,
regex = FALSE,
verbose = TRUE,
...
)
# S3 method for class 'data_codebook'
print_html(
x,
font_size = "100%",
line_padding = 3,
row_color = "#eeeeee",
...
)Arguments
- data
A data frame, or an object that can be coerced to a data frame.
- select
Variables that will be included when performing the required tasks. Can be either
a variable specified as a literal variable name (e.g.,
column_name),a string with the variable name (e.g.,
"column_name"), a character vector of variable names (e.g.,c("col1", "col2", "col3")), or a character vector of variable names including ranges specified via:(e.g.,c("col1:col3", "col5")),a formula with variable names (e.g.,
~column_1 + column_2),a vector of positive integers, giving the positions counting from the left (e.g.
1orc(1, 3, 5)),a vector of negative integers, giving the positions counting from the right (e.g.,
-1or-1:-3),one of the following select-helpers:
starts_with(),ends_with(),contains(), a range using:orregex("").starts_with(),ends_with(), andcontains()accept several patterns, e.gstarts_with("Sep", "Petal").or a function testing for logical conditions, e.g.
is.numeric()(oris.numeric), or any user-defined function that selects the variables for which the function returnsTRUE(like:foo <- function(x) mean(x) > 3),ranges specified via literal variable names, select-helpers (except
regex()) and (user-defined) functions can be negated, i.e. return non-matching elements, when prefixed with a-, e.g.-ends_with(""),-is.numericor-(Sepal.Width:Petal.Length). Note: Negation means that matches are excluded, and thus, theexcludeargument can be used alternatively. For instance,select=-ends_with("Length")(with-) is equivalent toexclude=ends_with("Length")(no-). In case negation should not work as expected, use theexcludeargument instead.
If
NULL, selects all columns. Patterns that found no matches are silently ignored, e.g.extract_column_names(iris, select = c("Species", "Test"))will just return"Species".- exclude
See
select, however, column names matched by the pattern fromexcludewill be excluded instead of selected. IfNULL(the default), excludes no columns.- variable_label_width
Length of variable labels. Longer labels will be wrapped at
variable_label_widthchars. IfNULL, longer labels will not be split into multiple lines. Only applies to labelled data.- value_label_width
Length of value labels. Longer labels will be shortened, where the remaining part is truncated. Only applies to labelled data or factor levels.
- max_values
Number of maximum values that should be displayed. Can be used to avoid too many rows when variables have lots of unique values.
- range_at
Indicates how many unique values in a numeric vector are needed in order to print a range for that variable instead of a frequency table for all numeric values. Can be useful if the data contains numeric variables with only a few unique values and where full frequency tables instead of value ranges should be displayed.
- ignore_case
Logical, if
TRUEand when one of the select-helpers or a regular expression is used inselect, ignores lower/upper case in the search pattern when matching against variable names.- regex
Logical, if
TRUE, the search pattern fromselectwill be treated as regular expression. Whenregex = TRUE, select must be a character string (or a variable containing a character string) and is not allowed to be one of the supported select-helpers or a character vector of length > 1.regex = TRUEis comparable to using one of the two select-helpers,select = contains("")orselect = regex(""), however, since the select-helpers may not work when called from inside other functions (see 'Details'), this argument may be used as workaround.- verbose
Toggle warnings and messages on or off.
- ...
Arguments passed to or from other methods.
- x
A (grouped) data frame, a vector or a statistical model (for
unstandardize()cannot be a model).- font_size
For HTML tables, the font size.
- line_padding
For HTML tables, the distance (in pixel) between lines.
- row_color
For HTML tables, the fill color for odd rows.
Value
A formatted data frame, summarizing the content of the data frame.
Returned columns include the column index of the variables in the original
data frame (ID), column name, variable label (if data is labelled), type
of variable, number of missing values, unique values (or value range),
value labels (for labelled data), and a frequency table (N for each value).
Most columns are formatted as character vectors.
Note
There are methods to print() the data frame in a nicer output, as
well methods for printing in markdown or HTML format (print_md() and
print_html()). The print() method for text outputs passes arguments in
... to insight::export_table().
Examples
data(iris)
data_codebook(iris, select = starts_with("Sepal"))
#> iris (150 rows and 5 variables, 2 shown)
#>
#> ID | Name | Type | Missings | Values | N
#> ---+--------------+---------+----------+------------+----
#> 1 | Sepal.Length | numeric | 0 (0.0%) | [4.3, 7.9] | 150
#> ---+--------------+---------+----------+------------+----
#> 2 | Sepal.Width | numeric | 0 (0.0%) | [2, 4.4] | 150
#> ---------------------------------------------------------
data(efc)
data_codebook(efc)
#> efc (100 rows and 5 variables, 5 shown)
#>
#> ID | Name | Label | Type
#> ---+----------+------------------------------------------+------------
#> 1 | c12hour | average number of hours of care per week | numeric
#> ---+----------+------------------------------------------+------------
#> 2 | e16sex | elder's gender | numeric
#> | | |
#> ---+----------+------------------------------------------+------------
#> 3 | e42dep | elder's dependency | categorical
#> | | |
#> | | |
#> | | |
#> ---+----------+------------------------------------------+------------
#> 4 | c172code | carer's level of education | numeric
#> | | |
#> | | |
#> ---+----------+------------------------------------------+------------
#> 5 | neg_c_7 | Negative impact with 7 items | numeric
#> ----------------------------------------------------------------------
#>
#> ID | Missings | Values | Value Labels | N
#> ---+------------+----------+---------------------------------+-----------
#> 1 | 2 (2.0%) | [5, 168] | | 98
#> ---+------------+----------+---------------------------------+-----------
#> 2 | 0 (0.0%) | 1 | male | 46 (46.0%)
#> | | 2 | female | 54 (54.0%)
#> ---+------------+----------+---------------------------------+-----------
#> 3 | 3 (3.0%) | 1 | independent | 2 ( 2.1%)
#> | | 2 | slightly dependent | 4 ( 4.1%)
#> | | 3 | moderately dependent | 28 (28.9%)
#> | | 4 | severely dependent | 63 (64.9%)
#> ---+------------+----------+---------------------------------+-----------
#> 4 | 10 (10.0%) | 1 | low level of education | 8 ( 8.9%)
#> | | 2 | intermediate level of education | 66 (73.3%)
#> | | 3 | high level of education | 16 (17.8%)
#> ---+------------+----------+---------------------------------+-----------
#> 5 | 3 (3.0%) | [7, 28] | | 97
#> -------------------------------------------------------------------------
# shorten labels
data_codebook(efc, variable_label_width = 20, value_label_width = 15)
#> efc (100 rows and 5 variables, 5 shown)
#>
#> ID | Name | Label | Type | Missings | Values
#> ---+----------+--------------------+-------------+------------+---------
#> 1 | c12hour | average number of | numeric | 2 (2.0%) | [5, 168]
#> | | hours of care per | | |
#> | | week | | |
#> ---+----------+--------------------+-------------+------------+---------
#> 2 | e16sex | elder's gender | numeric | 0 (0.0%) | 1
#> | | | | | 2
#> ---+----------+--------------------+-------------+------------+---------
#> 3 | e42dep | elder's dependency | categorical | 3 (3.0%) | 1
#> | | | | | 2
#> | | | | | 3
#> | | | | | 4
#> ---+----------+--------------------+-------------+------------+---------
#> 4 | c172code | carer's level of | numeric | 10 (10.0%) | 1
#> | | education | | | 2
#> | | | | | 3
#> ---+----------+--------------------+-------------+------------+---------
#> 5 | neg_c_7 | Negative impact | numeric | 3 (3.0%) | [7, 28]
#> | | with 7 items | | |
#> ------------------------------------------------------------------------
#>
#> ID | Value Labels | N
#> ---+------------------+-----------
#> 1 | | 98
#> | |
#> | |
#> ---+------------------+-----------
#> 2 | male | 46 (46.0%)
#> | female | 54 (54.0%)
#> ---+------------------+-----------
#> 3 | independent | 2 ( 2.1%)
#> | slightly... | 4 ( 4.1%)
#> | moderately... | 28 (28.9%)
#> | severely... | 63 (64.9%)
#> ---+------------------+-----------
#> 4 | low level of... | 8 ( 8.9%)
#> | intermediate... | 66 (73.3%)
#> | high level of... | 16 (17.8%)
#> ---+------------------+-----------
#> 5 | | 97
#> | |
#> ----------------------------------
# automatic range for numerics at more than 5 unique values
data(mtcars)
data_codebook(mtcars, select = starts_with("c"))
#> mtcars (32 rows and 11 variables, 2 shown)
#>
#> ID | Name | Type | Missings | Values | N
#> ---+------+---------+----------+--------+-----------
#> 2 | cyl | numeric | 0 (0.0%) | 4 | 11 (34.4%)
#> | | | | 6 | 7 (21.9%)
#> | | | | 8 | 14 (43.8%)
#> ---+------+---------+----------+--------+-----------
#> 11 | carb | numeric | 0 (0.0%) | [1, 8] | 32
#> ----------------------------------------------------
# force all values to be displayed
data_codebook(mtcars, select = starts_with("c"), range_at = 100)
#> mtcars (32 rows and 11 variables, 2 shown)
#>
#> ID | Name | Type | Missings | Values | N
#> ---+------+---------+----------+--------+-----------
#> 2 | cyl | numeric | 0 (0.0%) | 4 | 11 (34.4%)
#> | | | | 6 | 7 (21.9%)
#> | | | | 8 | 14 (43.8%)
#> ---+------+---------+----------+--------+-----------
#> 11 | carb | numeric | 0 (0.0%) | 1 | 7 (21.9%)
#> | | | | 2 | 10 (31.2%)
#> | | | | 3 | 3 ( 9.4%)
#> | | | | 4 | 10 (31.2%)
#> | | | | 6 | 1 ( 3.1%)
#> | | | | 8 | 1 ( 3.1%)
#> ----------------------------------------------------