Generate a codebook of a data frame. — data

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")),
for some functions, like data_select() or data_rename(), select can be a named character vector. In this case, the names are used to rename the columns in the output data frame. See 'Details' in the related functions to see where this option applies.
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. 1 or c(1, 3, 5)),
a vector of negative integers, giving the positions counting from the right (e.g., -1 or -1:-3),
one of the following select-helpers: starts_with(), ends_with(), contains(), a range using :, or regex(). starts_with(), ends_with(), and contains() accept several patterns, e.g starts_with("Sep", "Petal"). regex() can be used to define regular expression patterns.
a function testing for logical conditions, e.g. is.numeric() (or is.numeric), or any user-defined function that selects the variables for which the function returns TRUE (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.numeric or -(Sepal.Width:Petal.Length). Note: Negation means that matches are excluded, and thus, the exclude argument can be used alternatively. For instance, select=-ends_with("Length") (with -) is equivalent to exclude=ends_with("Length") (no -). In case negation should not work as expected, use the exclude argument 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 from exclude will be excluded instead of selected. If NULL (the default), excludes no columns.

variable_label_width

Length of variable labels. Longer labels will be wrapped at variable_label_width chars. If NULL, 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 TRUE and when one of the select-helpers or a regular expression is used in select, ignores lower/upper case in the search pattern when matching against variable names.

regex

Logical, if TRUE, the search pattern from select will be treated as regular expression. When regex = 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 = TRUE is comparable to using one of the two select-helpers, select = contains() or select = 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%)
#> ----------------------------------------------------