Abstract.RdCompactly display the content and structure of a data.frame, including variable labels. str is optimised for lists and its output is relatively technical, when it comes to e.g. attributes. summary on the other side already calculates some basic statistics.
Abstract(x, sep = ", ", zero.form = ".", maxlevels = 5, trunc = TRUE, list.len = 999)a data.frame to be described
the separator for concatenating the levels of a factor
a symbol to be used, when a variable has zero NAs.
integer, defining how many factor levels are to be displayed. Default is 12. Set this to Inf, if all levels are needed.
logical, defining if level names excceeding the column with should be truncated. Default ist TRUE.
numeric; maximum number of list elements to display.
The levels of a factor and describing variable labels (as created by Label) will be wrapped within the columns.
The first 4 columns are printed with the needed fix width, the last 2 (Levels and Labels) are wrapped within the column. The width is calculated depending on the width of the screen as given by getOption("width").
ToWord has an interface for the class abstract.
an object of class abstract, essentially a character matrix with 5 or 6 columns
containing a sequential nr (Nr), the name of the column (ColName), the class (Class), the number of NAs (NAs), the levels if the variable is a factor (Levels) and - if there are any - descriptive labels for the column (Labels).
.
d.mydata <- d.pizza
# let's use some labels
Label(d.mydata) <- "Lorem ipsum dolor sit amet, consetetur sadipscing elitr,
sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat,
sed diam voluptua. At vero eos et accusam."
Label(d.mydata$temperature) <- "Amet, consetetur sadipscing elitr, sed diam nonumy "
Abstract(d.mydata)
#> ------------------------------------------------------------------------------
#> d.mydata :
#> Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam
#> nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam
#> erat, sed diam voluptua. At vero eos et accusam.
#>
#> data frame: 1209 obs. of 16 variables
#> 917 complete cases (75.8%)
#>
#> Nr ColName Class NAs Levels Label
#> 1 index integer . -
#> 2 date Date 32 (2.6%) -
#> 3 week numeric 32 (2.6%) -
#> 4 weekday numeric 32 (2.6%) -
#> 5 area factor 10 (0.8%) (3): -
#> 1-Brent,
#> 2-Camden,
#> 3-Westminst...
#> 6 count integer 12 (1.0%) -
#> 7 rabate logical 12 (1.0%) -
#> 8 price numeric 12 (1.0%) -
#> 9 operator factor 8 (0.7%) (3): -
#> 1-Allanah,
#> 2-Maria,
#> 3-Rhonda
#> 10 driver factor 5 (0.4%) (7): -
#> 1-Butcher,
#> 2-Carpenter...
#> 3-Carter,
#> 4-Farmer,
#> 5-Hunter,
#> ...
#> 11 delivery_min numeric . -
#> 12 temperature numeric 39 (3.2%) Amet,
#> consetetur
#> sadipscing
#> elitr, sed
#> diam
#> nonumy
#> 13 wine_ordered integer 12 (1.0%) -
#> 14 wine_delivered integer 12 (1.0%) -
#> 15 wrongpizza logical 4 (0.3%) -
#> 16 quality ordered, factor 201 (16.6%) (3): -
#> 1-low,
#> 2-medium,
#> 3-high
#>