I am trying to make a map of global model predictions with the outline of the world on it.
The data I have cover most of the worlds oceans and the grid cells have a resolution of 4 degrees. This is my desired output: Map of global predictions
This is the code I have tried using:
# Packages
library(rnaturalearth)
library(ggplot2)
library(sp)
library(ggspatial)
# Subset of data (500 rows, 4 columns; Longitude, Latitude, Prediction, Grid cell area)
data <- structure(list(Longitude = c(-10.16956752, 126.1689468, -61.99876686,
-94.00000289, 126.0007703, 170.000033, 82.55726146, -6.495772811,
-149.9999879, -61.99897757, -89.99998854, 2.000011459, 122.1211726,
-2.593090202, -17.99998854, -170.0001433, 121.8262874, 10.21256204,
166.0000115, 158.3111356, 134.3679695, -13.73648415, 170.0000423,
92.91243971, 5.996191823, 62.00001146, -53.99998854, -21.99998854,
125.9910976, 154.0000115, 149.9995576, -77.88731531, -45.99998854,
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-138.5853402, -17.99998854, -69.99998854, -56.79830902, 110.0502773,
50.00001146, 39.56890984, -146.0004371, -5.999521688, 18.72192101,
-69.77517581, 21.3289268, 42.00001146, 162.0000115, 165.9995769,
-162.0365539, 170.0000138, 142.0000446, -45.99998854, -165.9999885,
110.0000115, -25.99998854, 6.000011459, -89.99998854, -53.99998854,
50.00187439, 146.0000115, -134.3123896, 165.9543502, -169.9999885,
-81.89972865, 72.42284406, 134.0000115, -170.0197871, -96.6556298,
17.92812, -152.9139565, -9.999988541, -73.99998854, 158.0000115,
54.20603343, 157.9995854, 74.00034763, -157.9999902, 102.4769658,
-73.99191424, 6.000011459, 162.0000177, -10.07971318, -93.99998854,
90.00001146, 153.9999475, 162.0000115, 117.9915582, -33.99998854,
38.00001146, -77.99998854, -81.99998854, 74.00022314, 114.1420482,
-173.9974719, 118.4796001, 178.0206217, 42.00001146, 178, -138.000174,
146.6226982, 165.999998, -49.73916727, -37.99998854, 137.9999465,
38.00001146, -82.04904949, 119.0280587, -169.9999946, 162.855343,
113.9999563, -61.9672714, 50.12191729, -173.9999885, 41.85382837,
130.0000115, 126.5154259, -37.93913378, 62.00001146, 129.9747567,
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13.72012633, -37.59668977, 162.0000061, -29.99998854, -177.9999885,
117.9999058, -149.9999885, 114.0000063, 96.91839389, -29.99998854,
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130.3202993, 5.888053059, 70.00001146, -137.9971843, -57.99998854,
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165.951299, 174.0000115, 58.00001146, 54.00001146, -78.41571925,
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166.0000115, -177.9999885, 165.9998751, 122.5803857, -1.832865108,
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), Latitude = c(3.820825091, 12.08126747, -55.97816596, 0.022599112,
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), class = "data.frame")
# X and Y limits
x_min <- min(data$Longitude)
x_max <- max(data$Longitude)
y_min <- min(data$Latitude)
y_max <- max(data$Latitude)
# World map
world <- ne_countries(scale = "medium", returnclass = "sf")
# CRF
crf <- CRS(projargs = "+proj=longlat +datum=NAD83 +no_defs")
# Plot
ggplot() +
geom_raster(data = data, aes(x = Longitude, y = Latitude, fill = pred)) +
scale_fill_viridis_c(option = "plasma") +
geom_sf(data = world, fill = alpha("lightgrey", 1), color = "lightgrey") +
coord_sf(xlim=c(x_min, x_max), ylim=c(y_min, y_max), crs = crf, expand = FALSE)
I get this warning when I run the above code:
Error in matrix(NA_character_, nrow = nrow, ncol = ncol) :
invalid 'nrow' value (too large or NA)
In addition: Warning messages:
1: Raster pixels are placed at uneven horizontal intervals and will be shifted. Consider using geom_tile() instead.
2: Raster pixels are placed at uneven vertical intervals and will be shifted. Consider using geom_tile() instead.
3: In f(...) : NAs introduced by coercion to integer range
4: In f(...) : NAs introduced by coercion to integer range
I have tried using geom_tile / geom_stars and rounding my lat/lon values to no avail.
I think the issue might be arising due to the projection used which means the area of a grid cell decreases as you move away from the equator, hence I have also included grid cell area in the data (4th column).
Any help/suggestions would be greatly appreciated! Ideally without interpolating as these are already model predictions.
If you need the full dataset it can be found here: https://github.com/Rawizard/stack_overflow/blob/main/SO_question_data1.RDS
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