Create spoof matrix form from a data frame — test_matrix

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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