df <- data.frame(x = rnorm(5), x1 = rnorm(5), x2 = rnorm(5))
> df
x x1 x2
1 1.2222897 0.3904347 -0.05835815
2 -1.7002094 -1.5195555 -0.79265835
3 0.5570183 -1.3191265 0.26198408
4 -0.2457016 0.1461557 -0.05567788
5 -0.7689870 -0.6361940 -0.80780107
library(plyr)
library(reshape2)
library(ggplot2)
# melting the data.frame turns your data.frame from wide to tall
# d_ply takes a data frame, applies a function each variable (in this case column)
# creates a plot, writes it to disk but returns nothing.
d_ply(melt(df), .(variable), function(x){
pl <- qplot(x$value, geom = "histogram", main = unique(x$variable))
# Change the png to jpg, eps or pdf if you prefer a different format
ggsave(pl, file = paste0(unique(x$variable),".png"))
})
# Now we can see that my working dir has 3 plots, each named after a column
> dir(pattern = "png")
[1] "x.png" "x1.png" "x2.png"