Plotting Multiple side-by-side Boxplots with dual y-axis for Parameters Containing Different Categories

Kaikash777 · March 6, 2019, 3:02pm

Overview:

In this instance, I am attempting to produce side-by-side boxplots showing four boxes per parameter per category underneath each parameter.

I have a data frame called 'CanStandPhenUrbFinal2' (see below) containing 4 columns: (1) Key_Parameters; (2) Latitude; (3) Canopy_Index; and (4) Category_Index

The idea is to establish how latitude affects the canopy cover index (%) of deciduous oak trees.

Key Parameters Index Categories:

Urbanisation Index: 1=Urban, 2=Suburban, 3=village, 4=rural
Stand Density Index:: 1=standing alone, 2=within a few trees or close proximity to other trees, 3=within a stand of 10-30 trees, and 4=large or woodland
Phenological Index: 1=no indication of autumn timing, 2=first autumn tinting, 3=partial autumn tinting (>25% of leaves), and 4=advanced autumn tinting (>75% of leaves)

The goal

Lattitude and Canopy Cover (dual y-axis)
Key Parameters (x-axis)
The boxes should denote the canopy cover index (%) per category per parameter

Any suggestions how to do that? Any help will be really appreciated!

R-code

     ##Produce a Plot for all key parameters
    ##Observation 1
    dev.new()
    QuercusParameterLat1<-ggplot(MeltedParameterLatitude1, aes(x = Key_Parameters, y = 
                                           Latitude, fill=Category_Index)) + 
                                 geom_boxplot() +
                                 theme(axis.text.x = element_text(angle = 15, hjust = 1), text = element_text(size=10)) + 
                                 scale_x_discrete(labels=c("Stand Density Index", "Urbansiation Index", "Phenological Index"))+
                                 theme(panel.background = element_blank(), 
                                 panel.grid.major = element_blank(), 
                                 panel.grid.minor = element_blank(),
                                 panel.border = element_blank()) + 
                                 theme(axis.line.x = element_line(color="black", size = 0.8),
                                 axis.line.y = element_line(color="black", size = 0.8)) + 
                                 labs(x = "Key Parameters", y = "Latitude", size = 0.5) +
                                 theme(legend.position="right")

    ##Produce the legend
    p11 <- QuercusParameterLat1 + guides(fill=guide_legend(title="Parameter Categories"))
    
    # now adding the secondary axis, following the example in the help file ?scale_y_continuous
    # and, very important, reverting the above transformation
    p <- p11 + scale_y_continuous(sec.axis = sec_axis(~.+1, name = "Canopy Index %"))

Data frame*

structure(list(Key_Parameters = structure(c(1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L), .Label = c("Stand_density_.index",
"Urbanisation_index", "Phenological_Index", "Canopy_Index"), class = "factor"),
Latitude = c(51.41752, 52.243806, 52.947709, 52.947709, 51.491811,
51.491811, 51.60157, 51.60157, 52.68959, 52.68959, 52.68959,
52.68959, 50.697802, 50.697802, 50.697802, 50.697802, 53.62417,
53.62417, 50.446841, 50.446841, 53.959679, 53.959679, 53.959679,
53.959679, 51.78375, 51.78375, 51.78375, 51.78375, 51.456965,
51.456965, 51.456965, 51.456965, 52.011812, 52.011812, 52.011812,
52.011812, 50.121978, 50.121978, 51.43474, 51.43474, 51.10708,
51.10708, 51.10708, 51.10708, 50.435984, 50.435984, 50.435984,
50.435984, 51.78666, 51.78666, 52.441088, 52.441088, 52.552344,
49.259471, 49.259471, 49.259471, 49.259471, 50.462, 50.462,
50.462, 50.462, 51.746642, 51.746642, 51.746642, 51.746642,
52.2501, 52.2501, 52.2501, 52.2501, 52.42646, 52.42646, 52.42646,
52.42646, 53.615575, 53.615575, 53.615575, 53.615575, 51.08478,
51.08478, 51.08478, 53.19329, 53.19329, 53.19329, 53.19329,
55.968437, 55.968437, 56.52664, 56.52664, 56.52664, 56.52664,
51.8113, 51.8113, 51.8113, 51.8113, 50.52008, 50.52008, 50.52008,
50.52008, 51.48417, 51.48417, 51.48417, 51.48417, 54.58243,
54.58243, 54.58243, 54.58243, 52.58839, 52.58839, 52.58839,
52.58839, 52.717283, 52.717283, 52.717283, 52.717283, 50.740764,
50.740764, 50.740764, 50.740764, 50.733412, 50.733412, 50.79926,
50.79926, 50.79926, 53.675788, 53.675788, 48.35079, 48.35079,
48.35079, 48.35079, 51.36445, 51.36445, 51.36445, 51.36445,
52.122402, 52.122402, 52.122402, 52.16104, 52.16104, 51.88468,
51.88468, 51.88468, 51.88468, 52.34015, 52.34015, 52.34015,
52.026042, 52.026042, 52.026042, 52.026042, 51.319032, 51.319032,
51.319032, 51.319032, 51.51365, 51.51365, 51.51365, 51.51365,
53.43202, 53.43202, 53.43202, 53.43202, 51.50797, 51.50797,
51.50797, 51.50797, 51.41752, 52.243806, 52.947709, 52.947709,
51.491811, 51.491811, 51.60157, 51.60157, 52.68959, 52.68959,
52.68959, 52.68959, 50.697802, 50.697802, 50.697802, 50.697802,
53.62417, 53.62417, 50.446841, 50.446841, 53.959679, 53.959679,
53.959679, 53.959679, 51.78375, 51.78375, 51.78375, 51.78375,
51.456965, 51.456965, 51.456965, 51.456965, 52.011812, 52.011812,
52.011812, 52.011812, 50.121978, 50.121978, 51.43474, 51.43474,
51.10708, 51.10708, 51.10708, 51.10708, 50.435984, 50.435984,
50.435984, 50.435984, 51.78666, 51.78666, 52.441088, 52.441088,
52.552344, 49.259471, 49.259471, 49.259471, 49.259471, 50.462,
50.462, 50.462, 50.462, 51.746642, 51.746642, 51.746642,
51.746642, 52.2501, 52.2501, 52.2501, 52.2501, 52.42646,
52.42646, 52.42646, 52.42646, 53.615575, 53.615575, 53.615575,
53.615575, 51.08478, 51.08478, 51.08478, 53.19329, 53.19329,
53.19329, 53.19329, 55.968437, 55.968437, 56.52664, 56.52664,
56.52664, 56.52664, 51.8113, 51.8113, 51.8113, 51.8113, 50.52008,
50.52008, 50.52008, 50.52008, 51.48417, 51.48417, 51.48417,
51.48417, 54.58243, 54.58243, 54.58243, 54.58243, 52.58839,
52.58839, 52.58839, 52.58839, 52.717283, 52.717283, 52.717283,
52.717283, 50.740764, 50.740764, 50.740764, 50.740764, 50.733412,
50.733412, 50.79926, 50.79926, 50.79926, 53.675788, 53.675788,
48.35079, 48.35079, 48.35079, 48.35079, 51.36445, 51.36445,
51.36445, 51.36445, 52.122402, 52.122402, 52.122402, 52.16104,
52.16104, 51.88468, 51.88468, 51.88468, 51.88468, 52.34015,
52.34015, 52.34015, 52.026042, 52.026042, 52.026042, 52.026042,
51.319032, 51.319032, 51.319032, 51.319032, 51.51365, 51.51365,
51.51365, 51.51365, 53.43202, 53.43202, 53.43202, 53.43202,
51.50797, 51.50797, 51.50797, 51.50797, 51.41752, 52.243806,
52.947709, 52.947709, 51.491811, 51.491811, 51.60157, 51.60157,
52.68959, 52.68959, 52.68959, 52.68959, 50.697802, 50.697802,
50.697802, 50.697802, 53.62417, 53.62417, 50.446841, 50.446841,
53.959679, 53.959679, 53.959679, 53.959679, 51.78375, 51.78375,
51.78375, 51.78375, 51.456965, 51.456965, 51.456965, 51.456965,
52.011812, 52.011812, 52.011812, 52.011812, 50.121978, 50.121978,
51.43474, 51.43474, 51.10708, 51.10708, 51.10708, 51.10708,
50.435984, 50.435984, 50.435984, 50.435984, 51.78666, 51.78666,
52.441088, 52.441088, 52.552344, 49.259471, 49.259471, 49.259471,
49.259471, 50.462, 50.462, 50.462, 50.462, 51.746642, 51.746642,
51.746642, 51.746642, 52.2501, 52.2501, 52.2501, 52.2501,
52.42646, 52.42646, 52.42646, 52.42646, 53.615575, 53.615575,
53.615575, 53.615575, 51.08478, 51.08478, 51.08478, 53.19329,
53.19329, 53.19329, 53.19329, 55.968437, 55.968437, 56.52664,
56.52664, 56.52664, 56.52664, 51.8113, 51.8113, 51.8113,
51.8113, 50.52008, 50.52008, 50.52008, 50.52008, 51.48417,
51.48417, 51.48417, 51.48417, 54.58243, 54.58243, 54.58243,
54.58243, 52.58839, 52.58839, 52.58839, 52.58839, 52.717283,
52.717283, 52.717283, 52.717283, 50.740764, 50.740764, 50.740764,
50.740764, 50.733412, 50.733412, 50.79926, 50.79926, 50.79926,
53.675788, 53.675788, 48.35079, 48.35079, 48.35079, 48.35079,
51.36445, 51.36445, 51.36445, 51.36445, 52.122402, 52.122402,
52.122402, 52.16104, 52.16104, 51.88468, 51.88468, 51.88468,
51.88468, 52.34015, 52.34015, 52.34015, 52.026042, 52.026042,
52.026042, 52.026042, 51.319032, 51.319032, 51.319032, 51.319032,
51.51365, 51.51365, 51.51365, 51.51365, 53.43202, 53.43202,
53.43202, 53.43202, 51.50797, 51.50797, 51.50797, 51.50797,
51.41752, 52.243806, 52.947709, 52.947709, 51.491811, 51.491811,
51.60157, 51.60157, 52.68959, 52.68959, 52.68959, 52.68959,
50.697802, 50.697802, 50.697802, 50.697802, 53.62417, 53.62417,
50.446841, 50.446841, 53.959679, 53.959679, 53.959679, 53.959679,
51.78375, 51.78375, 51.78375, 51.78375, 51.456965, 51.456965,
51.456965, 51.456965, 52.011812, 52.011812, 52.011812, 52.011812,
50.121978, 50.121978, 51.43474, 51.43474, 51.10708, 51.10708,
51.10708, 51.10708, 50.435984, 50.435984, 50.435984, 50.435984,
51.78666, 51.78666, 52.441088, 52.441088, 52.552344, 49.259471,
49.259471, 49.259471, 49.259471, 50.462, 50.462, 50.462,
50.462, 51.746642, 51.746642, 51.746642, 51.746642, 52.2501,
52.2501, 52.2501, 52.2501, 52.42646, 52.42646, 52.42646,
52.42646, 53.615575, 53.615575, 53.615575, 53.615575, 51.08478,
51.08478, 51.08478, 53.19329, 53.19329, 53.19329, 53.19329,
55.968437, 55.968437, 56.52664, 56.52664, 56.52664, 56.52664,
51.8113, 51.8113, 51.8113, 51.8113, 50.52008, 50.52008, 50.52008,
50.52008, 51.48417, 51.48417, 51.48417, 51.48417, 54.58243,
54.58243, 54.58243, 54.58243, 52.58839, 52.58839, 52.58839,
52.58839, 52.717283, 52.717283, 52.717283, 52.717283, 50.740764,
50.740764, 50.740764, 50.740764, 50.733412, 50.733412, 50.79926,
50.79926, 50.79926, 53.675788, 53.675788, 48.35079, 48.35079,
48.35079, 48.35079, 51.36445, 51.36445, 51.36445, 51.36445,
52.122402, 52.122402, 52.122402, 52.16104, 52.16104, 51.88468,
51.88468, 51.88468, 51.88468, 52.34015, 52.34015, 52.34015,
52.026042, 52.026042, 52.026042, 52.026042, 51.319032, 51.319032,
51.319032, 51.319032, 51.51365, 51.51365, 51.51365, 51.51365,
53.43202, 53.43202, 53.43202, 53.43202, 51.50797, 51.50797,
51.50797, 51.50797), Category_Index = structure(c(3L, 4L,
2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 4L,
4L, 1L, 1L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 2L, 2L, 4L, 4L, 3L, 3L, 3L,
3L, 4L, 3L, 4L, 4L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 3L, 4L, 4L, 4L, 4L, 2L, 2L,
2L, 2L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L,
2L, 2L, 4L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L,
4L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 4L, 4L, 4L, 4L, 3L, 3L, 3L,
3L, 4L, 4L, 4L, 2L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 4L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L,
3L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 3L,
3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 4L, 4L, 4L, 4L, 1L, 1L, 4L, 4L, 4L, 4L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L,
2L, 3L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L,
4L, 1L, 1L, 1L, 1L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 4L, 4L, 4L,
4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 4L, 4L,
3L, 4L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 2L, 3L, 3L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), .Label = c("1",
"2", "3", "4"), class = "factor")), class = "data.frame", row.names = c(NA,
-660L))

system · March 27, 2019, 3:02pm

This topic was automatically closed 21 days after the last reply. New replies are no longer allowed.

If you have a query related to it or one of the replies, start a new topic and refer back with a link.