Fontsize error when creating new Geom in ggplot2

Question

I am trying to create a new geom that will create a wind radii chart from hurricane data.

The data needed to run this came be generated from the following:

storm_observation <- data_frame(longitude = c(-89.6, -89.6, -89.6),
                            latitude = c(29.5, 29.5, 29.5),
                            wind_speed = c("34", "50", "64"),
                            ne = c(200, 120, 90),
                            nw = c(100, 75, 60),
                            se = c(200, 120, 90),
                            sw = c(150, 75, 60))

My code to create the new Geom is included below, but is throwing a strange error related to fontsize:

Error in check.length(gparname) : 'gpar' element 'fontsize' must not be length 0

I have attempted to include fontsize in the default_aes and gpar() functions, but it still resulted in the same error. Any help would be appreciated. NOTE: This requires tidyr, dplyr and geosphere packages.

GeomHurricane <- ggproto("GeomPolygon", Geom,
                     required_aes = c("x", "y", "r_ne", "r_se", "r_nw", "r_sw",
                                      "fill", "colour"),
                     default_aes = aes(scale_radii = 0.8, alpha = 0.8, linetype = 1, size = 0.5),


                     draw_group = function(data, panel_scales, coord) {

                       ## Create function for conditional mutation
                       mutate_cond <- function(.data, condition, ..., envir = parent.frame()) {
                         condition <- eval(substitute(condition), .data, envir)
                         .data[condition, ] <- .data[condition, ] %>% mutate(...)
                         .data
                       }

                       ## Create df of bearings for later joining
                       bearingDF <- tibble::data_frame(bearing = c(360,1:90,90:180,180:270,270:360),
                                                       direction = rep(c("r_ne", "r_se", "r_sw", "r_nw"),
                                                                       each = 91)) %>%
                         dplyr::bind_rows(tibble::data_frame(bearing = rep(0, 4),
                                                             direction = c("r_ne", "r_se", "r_sw", "r_nw")))

                       ## Transform data in tidy format and combine with bearings
                       data <- data %>%
                         dplyr::select(x, y, r_ne, r_nw, r_se, r_sw, colour, fill,
                                       PANEL, group, scale_radii, alpha, linetype,
                                       size) %>%
                         tidyr::gather(direction, distance, -x, -y, -colour, -fill,
                                       -PANEL, -group, -scale_radii, -alpha, -linetype,
                                       -size) %>%
                         dplyr::mutate(distance = distance * 1852 * scale_radii) %>%
                         dplyr::left_join(bearingDF, by = "direction") %>%
                         mutate_cond(bearing == 0, distance = 0)

                       ## Generate correct lat/lon for perimeter of polygons
                       data <- data %>%
                         dplyr::bind_cols(as.data.frame(geosphere::destPoint(as.matrix(data[,1:2]),
                                                                             data$bearing,
                                                                             data$distance))) %>%
                         dplyr::select(-x, -y) %>%
                         dplyr::rename(x = lon, y = lat)

                       ## Coord transform and take first row
                       coords <- coord$transform(data, panel_scales)
                       first_row <- coords[1, , drop = FALSE]

                       grid::polygonGrob(
                         coords$x, coords$y, 
                         default.units = "native",
                         gp = grid::gpar(
                           col = first_row$colour,
                           fill = scales::alpha(first_row$fill, first_row$alpha),
                           lwd = first_row$size * .pt,
                           lty = first_row$linetype
                         )
                       )
                     })

geom_hurricane <- function(mapping = NULL, data = NULL, stat = "identity", position = "identity",
                       na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ...) {
layer(geom = GeomHurricane, mapping = mapping, data = data, stat = stat,
position = position, show.legend = show.legend, inherit.aes = inherit.aes,
params = list(na.rm = na.rm, ...))}

Here is the code to create the map using the new geom:

get_map("Louisiana", zoom = 6, maptype = "toner-background", source = "stamen") %>%
  ggmap(extent = "device") +
  geom_hurricane(data = storm_observation,
             aes(x = longitude, y = latitude,
                 r_ne = ne, r_se = se, r_nw = nw, r_sw = sw,
                 fill = wind_speed, color = wind_speed)) +
  scale_color_manual(name = "Wind speed (kts)",
                 values = c("red", "orange", "yellow")) +
  scale_fill_manual(name = "Wind speed (kts)",
                values = c("red", "orange", "yellow"))

Ulrika

my friend who knows about your crazy dataset... you need to create a small reproducible example so that people can work with it.. – Introject

The entirety of the data needed was originally included, but I have changed it to a short code block that can create the dataframe. – Ulrika

@ Nobie, The dataset can be found here rammb.cira.colostate.edu/research/tropical_cyclones/… The goal is to extract the data and tidy the data set (convert wide to long). Once the data has been convert to long, the next step is to create a geom. I believe that is what G.Ambrose is trying to accomplish. – Northeastward

Answer 1

I figured it out. Had somehow forgotten to include draw_key = draw_key_polygon, once I added that back into the ggproto function everything worked. Thanks!

Ulrika