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Useful functions for writing tests and examples, and a starting point for more sophisticated custom matrix_form methods.

Usage

basic_matrix_form(
  df,
  indent_rownames = FALSE,
  parent_path = NULL,
  ignore_rownames = FALSE,
  add_decoration = FALSE,
  fontspec = font_spec(),
  split_labels = NULL,
  data_labels = NULL,
  num_rep_cols = 0L
)

basic_listing_mf(
  df,
  keycols = names(df)[1],
  add_decoration = TRUE,
  fontspec = font_spec()
)

Arguments

df

(data.frame)
a data frame.

indent_rownames

(flag)
whether row names should be indented. Being this used for testing purposes, it defaults to FALSE. If TRUE, it assigns label rows on even lines (also format is "-" and value strings are ""). Indentation works only if split labels are used (see parameters split_labels and data_labels).

parent_path

(string)
parent path that all rows should be "children of". Defaults to NULL, as usually this is not needed. It may be necessary to use "root", for some specific scenarios.

ignore_rownames

(flag)
whether row names should be ignored.

add_decoration

(flag)
whether adds title and footer decorations should be added to the matrix form.

fontspec

(font_spec)
a font_spec object specifying the font information to use for calculating string widths and heights, as returned by font_spec().

split_labels

(string)
indicates which column to use as split labels. If NULL, no split labels are used.

data_labels

(string)
indicates which column to use as data labels. It is ignored if no split_labels is present and is automatically assigned to "Analysis method" when split_labels is present, but data_labels is NULL. Its direct column name is used as node name in "DataRow" pathing. See mf_rinfo() for more information.

num_rep_cols

(numeric(1))
Number of columns to be treated as repeating columns. Defaults to 0 for basic_matrix_form and length(keycols) for basic_listing_mf. Note repeating columns are separate from row labels if present.

keycols

(character)
a vector of df column names that are printed first and for which repeated values are assigned "". This format is characteristic of a listing matrix form.

Value

A valid MatrixPrintForm object representing df that is ready for ASCII rendering.

A valid MatrixPrintForm object representing df as a listing that is ready for ASCII rendering.

Details

If some of the column has a obj_format assigned, it will be respected for all column values except for label rows, if present (see parameter split_labels).

Functions

  • basic_listing_mf(): Create a MatrixPrintForm object from data frame df that respects the default formats for a listing object.

Examples

mform <- basic_matrix_form(mtcars)
cat(toString(mform))
#>                       mpg    cyl   disp    hp    drat    wt     qsec    vs   am   gear   carb
#> ooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooo
#> Mazda RX4              21     6     160    110   3.9    2.62    16.46   0    1     4      4  
#> Mazda RX4 Wag          21     6     160    110   3.9    2.875   17.02   0    1     4      4  
#> Datsun 710            22.8    4     108    93    3.85   2.32    18.61   1    1     4      1  
#> Hornet 4 Drive        21.4    6     258    110   3.08   3.215   19.44   1    0     3      1  
#> Hornet Sportabout     18.7    8     360    175   3.15   3.44    17.02   0    0     3      2  
#> Valiant               18.1    6     225    105   2.76   3.46    20.22   1    0     3      1  
#> Duster 360            14.3    8     360    245   3.21   3.57    15.84   0    0     3      4  
#> Merc 240D             24.4    4    146.7   62    3.69   3.19     20     1    0     4      2  
#> Merc 230              22.8    4    140.8   95    3.92   3.15    22.9    1    0     4      2  
#> Merc 280              19.2    6    167.6   123   3.92   3.44    18.3    1    0     4      4  
#> Merc 280C             17.8    6    167.6   123   3.92   3.44    18.9    1    0     4      4  
#> Merc 450SE            16.4    8    275.8   180   3.07   4.07    17.4    0    0     3      3  
#> Merc 450SL            17.3    8    275.8   180   3.07   3.73    17.6    0    0     3      3  
#> Merc 450SLC           15.2    8    275.8   180   3.07   3.78     18     0    0     3      3  
#> Cadillac Fleetwood    10.4    8     472    205   2.93   5.25    17.98   0    0     3      4  
#> Lincoln Continental   10.4    8     460    215    3     5.424   17.82   0    0     3      4  
#> Chrysler Imperial     14.7    8     440    230   3.23   5.345   17.42   0    0     3      4  
#> Fiat 128              32.4    4    78.7    66    4.08    2.2    19.47   1    1     4      1  
#> Honda Civic           30.4    4    75.7    52    4.93   1.615   18.52   1    1     4      2  
#> Toyota Corolla        33.9    4    71.1    65    4.22   1.835   19.9    1    1     4      1  
#> Toyota Corona         21.5    4    120.1   97    3.7    2.465   20.01   1    0     3      1  
#> Dodge Challenger      15.5    8     318    150   2.76   3.52    16.87   0    0     3      2  
#> AMC Javelin           15.2    8     304    150   3.15   3.435   17.3    0    0     3      2  
#> Camaro Z28            13.3    8     350    245   3.73   3.84    15.41   0    0     3      4  
#> Pontiac Firebird      19.2    8     400    175   3.08   3.845   17.05   0    0     3      2  
#> Fiat X1-9             27.3    4     79     66    4.08   1.935   18.9    1    1     4      1  
#> Porsche 914-2          26     4    120.3   91    4.43   2.14    16.7    0    1     5      2  
#> Lotus Europa          30.4    4    95.1    113   3.77   1.513   16.9    1    1     5      2  
#> Ford Pantera L        15.8    8     351    264   4.22   3.17    14.5    0    1     5      4  
#> Ferrari Dino          19.7    6     145    175   3.62   2.77    15.5    0    1     5      6  
#> Maserati Bora          15     8     301    335   3.54   3.57    14.6    0    1     5      8  
#> Volvo 142E            21.4    4     121    109   4.11   2.78    18.6    1    1     4      2  

# Advanced test case with label rows
library(dplyr)
iris_output <- iris %>%
  group_by(Species) %>%
  summarize("all obs" = round(mean(Petal.Length), 2)) %>%
  mutate("DataRow_label" = "Mean")
mf <- basic_matrix_form(iris_output,
  indent_rownames = TRUE,
  split_labels = "Species", data_labels = "DataRow_label"
)
cat(toString(mf))
#>              all obs
#> oooooooooooooooooooo
#> setosa              
#>   Mean        1.46  
#> versicolor          
#>   Mean        4.26  
#> virginica           
#>   Mean        5.55  
mform <- basic_listing_mf(mtcars)
cat(toString(mform))
#> main title
#> sub
#> titles
#> 
#> ooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooo
#> mpg    cyl   disp    hp    drat    wt     qsec    vs   am   gear   carb
#> ooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooo
#> 10.4    8     472    205   2.93   5.25    17.98   0    0     3      4  
#>         8     460    215    3     5.424   17.82   0    0     3      4  
#> 13.3    8     350    245   3.73   3.84    15.41   0    0     3      4  
#> 14.3    8     360    245   3.21   3.57    15.84   0    0     3      4  
#> 14.7    8     440    230   3.23   5.345   17.42   0    0     3      4  
#>  15     8     301    335   3.54   3.57    14.6    0    1     5      8  
#> 15.2    8    275.8   180   3.07   3.78     18     0    0     3      3  
#>         8     304    150   3.15   3.435   17.3    0    0     3      2  
#> 15.5    8     318    150   2.76   3.52    16.87   0    0     3      2  
#> 15.8    8     351    264   4.22   3.17    14.5    0    1     5      4  
#> 16.4    8    275.8   180   3.07   4.07    17.4    0    0     3      3  
#> 17.3    8    275.8   180   3.07   3.73    17.6    0    0     3      3  
#> 17.8    6    167.6   123   3.92   3.44    18.9    1    0     4      4  
#> 18.1    6     225    105   2.76   3.46    20.22   1    0     3      1  
#> 18.7    8     360    175   3.15   3.44    17.02   0    0     3      2  
#> 19.2    6    167.6   123   3.92   3.44    18.3    1    0     4      4  
#>         8     400    175   3.08   3.845   17.05   0    0     3      2  
#> 19.7    6     145    175   3.62   2.77    15.5    0    1     5      6  
#>  21     6     160    110   3.9    2.62    16.46   0    1     4      4  
#>         6     160    110   3.9    2.875   17.02   0    1     4      4  
#> 21.4    6     258    110   3.08   3.215   19.44   1    0     3      1  
#>         4     121    109   4.11   2.78    18.6    1    1     4      2  
#> 21.5    4    120.1   97    3.7    2.465   20.01   1    0     3      1  
#> 22.8    4     108    93    3.85   2.32    18.61   1    1     4      1  
#>         4    140.8   95    3.92   3.15    22.9    1    0     4      2  
#> 24.4    4    146.7   62    3.69   3.19     20     1    0     4      2  
#>  26     4    120.3   91    4.43   2.14    16.7    0    1     5      2  
#> 27.3    4     79     66    4.08   1.935   18.9    1    1     4      1  
#> 30.4    4    75.7    52    4.93   1.615   18.52   1    1     4      2  
#>         4    95.1    113   3.77   1.513   16.9    1    1     5      2  
#> 32.4    4    78.7    66    4.08    2.2    19.47   1    1     4      1  
#> 33.9    4    71.1    65    4.22   1.835   19.9    1    1     4      1  
#> ooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooo
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