Skip to contents

Court zones to x, y coordinates

Source: R/plot.R
dv_xy.Rd

Generate x and y coordinates for plotting, from DataVolley numbered zones

Usage

dv_xy(
  zones,
  end = "lower",
  xynames = c("x", "y"),
  as_for_serve = FALSE,
  subzones
)

Arguments

zones

numeric: zones numbers 1-9 to convert to x and y coordinates

end

string: use the "lower" or "upper" part of the figure

xynames

character: names to use for the x and y columns of the returned data.frame

as_for_serve

logical: if TRUE, treat positions as for serving. Only zones 1,5,6,7,9 are meaningful in this case

subzones

character: if supplied, coordinates will be adjusted for subzones. Values other than "A" to "D" will be ignored

Value

data.frame with columns "x" and "y" (or other names if specified in xynames)

Details

For a description of the court dimensions and coordinates used for plotting, see ggcourt

See also

ggcourt, dv_flip_xy, dv_xy2index, dv_index2xy, dv_cone2xy, dv_xy2zone, dv_xy2subzone

Examples

if (FALSE) {
x <- dv_read(dv_example_file(), insert_technical_timeouts = FALSE)

library(ggplot2)
library(dplyr)

## Example 1: attack frequency by zone, per team

attack_rate <- plays(x) %>% dplyr::filter(skill == "Attack") %>%
  group_by(team, start_zone) %>% dplyr::summarize(n_attacks = n()) %>%
  mutate(rate = n_attacks/sum(n_attacks)) %>% ungroup

## add columns "x" and "y" for the x, y coordinates associated with the zones
attack_rate <- cbind(attack_rate, dv_xy(attack_rate$start_zone, end = "lower"))

## for team 2, these need to be on the top half of the diagram
tm2 <- attack_rate$team == teams(x)[2]
attack_rate[tm2, c("x", "y")] <- dv_xy(attack_rate$start_zone, end = "upper")[tm2, ]

## plot this
ggplot(attack_rate, aes(x, y, fill = rate)) + geom_tile() + ggcourt(labels = teams(x)) +
  scale_fill_gradient2(name = "Attack rate")


## Example 2: map of starting and ending zones of attacks using arrows

## first tabulate attacks by starting and ending zone
attack_rate <- plays(x) %>% dplyr::filter(team == teams(x)[1] & skill == "Attack") %>%
  group_by(start_zone, end_zone) %>% tally() %>% ungroup

## convert counts to rates
attack_rate$rate <- attack_rate$n/sum(attack_rate$n)

## discard zones with zero attacks or missing location information
attack_rate <- attack_rate %>% dplyr::filter(rate>0 & !is.na(start_zone) & !is.na(end_zone))

## add starting x,y coordinates
attack_rate <- cbind(attack_rate,
     dv_xy(attack_rate$start_zone, end = "lower", xynames = c("sx","sy")))

## and ending x,y coordinates
attack_rate <- cbind(attack_rate,
     dv_xy(attack_rate$end_zone, end = "upper", xynames = c("ex","ey")))

## plot in reverse order so largest arrows are on the bottom
attack_rate <- attack_rate %>% dplyr::arrange(desc(rate))

p <- ggplot(attack_rate,aes(x,y,col = rate)) + ggcourt(labels = c(teams(x)[1],""))
for (n in 1:nrow(attack_rate))
    p <- p + geom_path(data = data.frame(x = c(attack_rate$sx[n], attack_rate$ex[n]),
                                       y = c(attack_rate$sy[n],attack_rate$ey[n]),
                                       rate = attack_rate$rate[n]),
        aes(size = rate), lineend = "round", arrow = arrow(ends = "last", type = "closed"))
p + scale_fill_gradient(name = "Attack rate") + guides(size = "none")
}