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Peek at values and type of variables in a data frame

Source: R/data_peek.R
data_peek.Rd

This function creates a table a data frame, showing all column names, variable types and the first values (as many as fit into the screen).

Usage

data_peek(x, ...)

# S3 method for class 'data.frame'
data_peek(
  x,
  select = NULL,
  exclude = NULL,
  ignore_case = FALSE,
  regex = FALSE,
  width = NULL,
  verbose = TRUE,
  ...
)

Arguments

x

A data frame.

...

not used.

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. 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.

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.

width

Maximum width of line length to display. If NULL, width will be determined using options()$width.

verbose

Toggle warnings.

Value

A data frame with three columns, containing information about the name, type and first values of the input data frame.

Note

To show only specific or a limited number of variables, use the select argument, e.g. select = 1:5 to show only the first five variables.

Examples

data(efc)
data_peek(efc)
#> Data frame with 100 rows and 5 variables
#> 
#> Variable | Type    | Values                                           
#> ----------------------------------------------------------------------
#> c12hour  | numeric | 16, 148, 70, NA, 168, 16, 161, 110, 28, 40, ...  
#> e16sex   | numeric | 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, ... 
#> e42dep   | factor  | 3, 3, 3, NA, 4, 4, 4, 4, 4, 4, 4, 3, 4, 3, 3, ...
#> c172code | numeric | 2, 2, 1, 2, 2, 2, 2, 2, NA, 2, 2, 2, 3, 1, 3, ...
#> neg_c_7  | numeric | 12, 20, 11, 10, 12, 19, 15, 11, 15, 10, 28, ...  
# show variables two to four
data_peek(efc, select = 2:4)
#> Data frame with 100 rows and 5 variables
#> 
#> Variable | Type    | Values                                           
#> ----------------------------------------------------------------------
#> e16sex   | numeric | 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, ... 
#> e42dep   | factor  | 3, 3, 3, NA, 4, 4, 4, 4, 4, 4, 4, 3, 4, 3, 3, ...
#> c172code | numeric | 2, 2, 1, 2, 2, 2, 2, 2, NA, 2, 2, 2, 3, 1, 3, ...