The pcp_select function allows a selection of variables from a data set.
These variables are transformed into an embellished long form of the data.
pcp_select(data, ...)a dataframe or tibble
choose the columns to be used in the parallel coordinate plot.
Variables can be selected by position, name or any of the tidyselect selector functions.
dataframe of a long form of the selected variables with extra columns:
| variable | functionality |
pcp_x, pcp_y | values for the mappings to x and y axes |
pcp_yend | vertical endpoint of a line segment |
pcp_class | type of each of the input variables |
pcp_level | preserves order of levels in categorical variables |
pcp_id | identifier for each observation |
The dimensions of the returned data set are: 6 + the number of input variables for its columns. The number of rows is given as the multiple of the number of selected variables and the number of rows in the original data.
The data pipeline feeding any of the geom layers in the ggpcp package is implemented in a three-step modularized
form rather than as the stat functions more typical for ggplot2 extensions.
The three steps of data pre-processing are:
| command | data processing step |
pcp_select | variable selection (and horizontal ordering) |
pcp_scale | (vertical) scaling of values |
pcp_arrange | dealing with tie-breaks on categorical axes |
Note that these data processing steps are executed before the call to ggplot2
and the identity function is used by default in all of the ggpcp specific layers.
Besides the speed-up by only executing the processing steps once for all layers,
the separation has the additional benefit, that it provides the users with the
possibility to make specific choices at each step in the process. Additionally,
separation allows for a cleaner user interface: parameters affecting the data
preparation process can be moved to the relevant (set of) function(s) only, thereby
reducing the number of arguments without any loss of functionality.
data(Carcinoma)
dim(Carcinoma)
#> [1] 118 9
# select all variables
pcp_data <- Carcinoma |> pcp_select(1:9)
dim(pcp_data) # 6 more columns, 9 times as many observations
#> [1] 1062 15
head(pcp_data)
#> # A tibble: 6 × 15
#> pcp_id pcp_x pcp_level pcp_y pcp_yend pcp_cl…¹ No Average A B C
#> <int> <fct> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <fct> <fct> <fct>
#> 1 1 No 1 1 1 numeric 1 3.14 4 3 4
#> 2 2 No 2 2 2 numeric 2 1 1 1 1
#> 3 3 No 3 3 3 numeric 3 3 3 3 3
#> 4 4 No 4 4 4 numeric 4 3.29 4 3 3
#> 5 5 No 5 5 5 numeric 5 3 3 3 3
#> 6 6 No 6 6 6 numeric 6 1.29 2 1 2
#> # … with 4 more variables: D <fct>, E <fct>, F <fct>, G <fct>, and abbreviated
#> # variable name ¹pcp_class