How can I plot ggplot2 columns in a loop? - r

Question

I have a dataframe with lat, long and dozens of other columns. Basically, I want to write a loop where X=Long,Y=Lat remains constant, but the color changes in every loop. The color is basically the other columns, one for every plot. How can I do this?
library(maps)
library(ggplot2)
library(RColorBrewer)
library(reshape)
usamap <- ggplot2::map_data("state")
myPalette <- colorRampPalette(rev(brewer.pal(11, "Spectral")))
simplefun<-function(colname){
ggplot()+
geom_polygon( data=usamap, aes(x=long, y=lat, group=group),colour="black",fill="white")+
geom_point(data=stat,aes_string(x=stat$longitude,y=stat$latitude,color=colname))+
scale_colour_gradientn(name="name",colours = myPalette(10))+
xlab('Longitude')+
ylab('Latitude')+
coord_map(projection = "mercator")+
theme_bw()+
theme(line = element_blank())+
theme(legend.position = c(.93,.20),panel.grid.major = element_line(colour = "#808080"))+
ggsave(paste0(colname,".png"),width=10, height=8,dpi=300)
}
colname<-names(stat[4:16])
lapply(colname,simplefun)
dput(droplevels(stat))
structure(list(siteId = structure(1:16, .Label = c("US1NYAB0001",
"US1NYAB0006", "US1NYAB0010", "US1NYAB0021", "US1NYAB0023", "US1NYAB0028",
"US1NYAB0032", "US1NYAL0002", "US1NYBM0004", "US1NYBM0007", "US1NYBM0011",
"US1NYBM0014", "US1NYBM0021", "US1NYBM0024", "US1NYBM0032", "US1NYBM0034"
), class = "factor"), latitude = c(42.667, 42.7198, 42.5455,
42.6918, 42.6602, 42.7243, 42.5754, 42.2705, 42.0296, 42.0493,
42.0735, 42.3084, 42.0099, 42.1098, 42.1415, 42.0826), longitude = c(-74.0509,
-73.9304, -74.1475, -73.8311, -73.8103, -73.757, -73.7995, -77.9419,
-76.0213, -76.0288, -75.9296, -75.9569, -75.5142, -75.8858, -75.889,
-75.9912), no = c(2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 1L), min_obs = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0), min_mod = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0), avg_obs = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0.15,
0, 0, 0, 0, 0, 0), avg_mod = c(3136.8388671875, 2997.28173828125,
3258.61840820312, 2970.74340820312, 2992.9765625, 0, 3075.54443359375,
2701.03662109375, 2974.23413085938, 2967.5029296875, 3004.57861328125,
2965.07470703125, 3260.25463867188, 3028.55590820312, 2981.8876953125,
0), max_obs = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0,
0), max_mod = c(6273.677734375, 5994.5634765625, 6517.23681640625,
5941.48681640625, 5985.953125, 0, 6151.0888671875, 5402.0732421875,
5948.46826171875, 5935.005859375, 6009.1572265625, 5930.1494140625,
6520.50927734375, 6057.11181640625, 5963.775390625, 0), mean_bias = c(0,
0, 0, 0, 0, NaN, 0, 0, 0, 5.05475490855863e-05, 0, 0, 0, 0, 0,
NaN), corr_coef = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, NA,
NA, NA, NA, NA, NA), additive_bias = c(-6273.677734375, -5994.5634765625,
-6517.23681640625, -5941.48681640625, -5985.953125, 0, -6151.0888671875,
-5402.0732421875, -5948.46826171875, -5934.705859375, -6009.1572265625,
-5930.1494140625, -6520.50927734375, -6057.11181640625, -5963.775390625,
0), mean_error = c(-3136.8388671875, -2997.28173828125, -3258.61840820312,
-2970.74340820312, -2992.9765625, 0, -3075.54443359375, -2701.03662109375,
-2974.23413085938, -2967.3529296875, -3004.57861328125, -2965.07470703125,
-3260.25463867188, -3028.55590820312, -2981.8876953125, 0), mean_abs_error = c(3136.8388671875,
2997.28173828125, 3258.61840820312, 2970.74340820312, 2992.9765625,
0, 3075.54443359375, 2701.03662109375, 2974.23413085938, 2967.3529296875,
3004.57861328125, 2965.07470703125, 3260.25463867188, 3028.55590820312,
2981.8876953125, 0), rmse = c(4436.16006895562, 4238.79648453055,
4608.38234747949, 4201.26561821133, 4232.7080465523, 0, 4349.47664966936,
3819.84262201718, 4206.20224553428, 4196.4707575116, 4249.11582411849,
4193.24886413303, 4610.69632679956, 4283.02483978603, 4217.02602018439,
0)), .Names = c("siteId", "latitude", "longitude", "no", "min_obs",
"min_mod", "avg_obs", "avg_mod", "max_obs", "max_mod", "mean_bias",
"corr_coef", "additive_bias", "mean_error", "mean_abs_error",
"rmse"), row.names = c(NA, -16L), class = "data.frame")

Answer 1

I had the same problem and I solve it like this:
Let's assume ggplotdata in my code is like your dataframe (with more than two columns) in the second post. (dput(droplevels(stat))?)
library(reshape) # package for melting data
shapes <- 1:ncol(ggplot_data) # number of shapes
ggplot_data <- melt(ggplot_data, id = "X1") # melt data together
p1 <- ggplot(ggplot_data, aes(X1,value))
p1 <- p1 +aes(shape = factor(variable))+ # different shapes
geom_point(aes(colour = factor(variable)))+ # different colors
scale_shape_manual(labels=colname, values = shapes)+ # same for the legend
scale_color_manual(labels=colname, values = mypalette) # same for legend