The function pcp_scale provides access to a set of transformations to use
in parallel coordinate plots. All transformations other than raw tend to
produce y values in the interval from 0 and 1.
pcp_scale(data, method = "uniminmax", .by_group = TRUE)data frame as returned by select_pcp
string specifying the method that should be used for scaling the values in a parallel coordinate plot (see Details).
logical value. If TRUE, scaling will respect any previous grouping variables. Applies to grouped data frames only.
data frame of the same size as the input data; values of pcp_y and
pcp_yend are scaled according to the specified method.
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.
method is a character string that denotes how to scale the variables
in the parallel coordinate plot. Options are named in the same way as the options in GGally::ggparcoord():
raw: raw data used, no scaling will be done.
std: univariately, subtract mean and divide by standard deviation. To get values into a unit interval we use a linear transformation of f(y) = y/4+0.5.
robust: univariately, subtract median and divide by median absolute deviation. To get values into an expected interval of unit interval we use a linear transformation of f(y) = y/4+0.5.
uniminmax: univariately, scale so the minimum of the variable is zero, and the maximum is one.
globalminmax: global scaling; the global maximum is mapped to 1,
global minimum across the variables is mapped to 0.
data(Carcinoma)
dim(Carcinoma)
#> [1] 118 9
# select all variables
pcp_data <- Carcinoma |> pcp_select(1:9)
summary(pcp_data)
#> pcp_id pcp_x pcp_level pcp_y
#> Min. : 1.0 No :118 Length:1062 Min. : 1.000
#> 1st Qu.: 30.0 Average:118 Class :character 1st Qu.: 1.607
#> Median : 59.5 A :118 Mode :character Median : 2.929
#> Mean : 59.5 B :118 Mean : 9.107
#> 3rd Qu.: 89.0 C :118 3rd Qu.: 3.000
#> Max. :118.0 D :118 Max. :126.000
#> (Other):354
#> pcp_yend pcp_class No Average A
#> Min. : 1.000 Length:1062 Min. : 1.00 Min. :1.000 1:234
#> 1st Qu.: 1.607 Class :character 1st Qu.: 33.00 1st Qu.:1.571 2:234
#> Median : 2.929 Mode :character Median : 63.50 Median :2.429 3:342
#> Mean : 9.107 Mean : 63.47 Mean :2.311 4:198
#> 3rd Qu.: 3.000 3rd Qu.: 94.00 3rd Qu.:3.000 5: 54
#> Max. :126.000 Max. :126.00 Max. :5.000
#>
#> B C D E F G
#> 1:243 1:279 1:342 1:144 1:558 1:288
#> 2:108 2:378 2:432 2:279 2:279 2:180
#> 3:621 3:333 3:207 3:477 3:180 3:549
#> 4: 63 4: 54 4: 72 4:126 4: 9 4: 27
#> 5: 27 5: 18 5: 9 5: 36 5: 36 5: 18
#>
#>
pcp_data |> pcp_scale() |> summary()
#> pcp_id pcp_x pcp_level pcp_y
#> Min. : 1.0 No :118 Length:1062 Min. :0.00000
#> 1st Qu.: 30.0 Average:118 Class :character 1st Qu.:0.07143
#> Median : 59.5 A :118 Mode :character Median :0.32143
#> Mean : 59.5 B :118 Mean :0.34690
#> 3rd Qu.: 89.0 C :118 3rd Qu.:0.50000
#> Max. :118.0 D :118 Max. :1.00000
#> (Other):354
#> pcp_yend pcp_class No Average A
#> Min. :0.00000 Length:1062 Min. : 1.00 Min. :1.000 1:234
#> 1st Qu.:0.07143 Class :character 1st Qu.: 33.00 1st Qu.:1.571 2:234
#> Median :0.32143 Mode :character Median : 63.50 Median :2.429 3:342
#> Mean :0.34690 Mean : 63.47 Mean :2.311 4:198
#> 3rd Qu.:0.50000 3rd Qu.: 94.00 3rd Qu.:3.000 5: 54
#> Max. :1.00000 Max. :126.00 Max. :5.000
#>
#> B C D E F G
#> 1:243 1:279 1:342 1:144 1:558 1:288
#> 2:108 2:378 2:432 2:279 2:279 2:180
#> 3:621 3:333 3:207 3:477 3:180 3:549
#> 4: 63 4: 54 4: 72 4:126 4: 9 4: 27
#> 5: 27 5: 18 5: 9 5: 36 5: 36 5: 18
#>
#>
# scaling gets values of pcp_y and pcp_yend between 0 and 1