How to plot spatial data when grid cell size varies

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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-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, 
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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, 
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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