So there are a couple of other functions that may be of use to you. One is the rgb function and the rgb function takes arguments for red, green and blue, and what it returns. So these are numbers between zero and one for red, green and blue and it will return, hexadecimal string that can be passed to functions like image or plot. And so that, so if you want to convert between red, gr, red, kind of ,um, decimal red green blue representation to this hexadecimal string you can use the rgb, function for that. Another thing you can use rgb for is to add transparency to your colors. And so, there is a fourth argument to rgb view which is a number between zero and one, the alpha parameter, which have been used, to specify transparency in your colors. Zero being the most transparent, or basically completely transparent, and one being not transparent at all. And lastly I'll just mention the color space package which can be used to, to have a different, to exert a different type of control over colors. You may want to explore it, but I won't talk about it here. So just a quick example of the transparency in r, so here's a plot of a, a, of a thousand points. And you could see that in the high density region here, I'm using the pch 19 which is a solid circle. In the high density region, all the plot, all the points just kind of overlap each other, so you just see a black,uh,blob there in the middle and it's, card, kind of hard to see what the resolution for the different points because there is no definition of points there because they are all overlapping. So if I add a little transparency to the plot you'll see that the circles become transparent so when they overlap each other they, you can kind of see through them. And so that the areas where there are lots of points overlapping they, they get darker and when there are areas where there are no points overlapping you just see kind of a transparent circle. So here I've got below, I've got the code that I've used to create this plot. And you can see that I've specified an alpha parameter of 0.2. And, and the r, the red, green and blue are all zeros so I can get black. Uh,and so point two is pretty close to zero, so there's a lot of transparency. Um,but you can see that when I use the transparency like thisiIt's kind of a cheap and easy way to get essentially a histogram estimator because, you know, the, the darker areas will represent higher density areas of the plot and the lighter areas will represent kind of lower density areas. So you can see a little bit more information when you have a plot with a lot of different points in it when you use a little bit of transparency. So that's just, that's the basic intro into the use of colors and transparency into r. I think that the careful use of colors and plots and maps and whatever just, can make it easier for the reader to get the point that you're trying to make, or to see the comparisons that you're trying to make. And and I think it's it's useful to have a good set of colors, especially to match kind of sequential colors and sequential data and diverging colors with diverging data. The color brewer, the RColorBrewer package is a very useful package that's available on CRAN, and it provides these uh,color palettes for the different types of data that you might be plotting. The color ramp and color palette functions are essential when you're trying to interpolate between colors, especially if you're using the RColor per package and you want interpolate between the colors on a given palette. And then finally, transparency is very useful because it can be used to clarify plots which have many many points on them by cre, by allowing the colors to be transparent and to kind of overlap each other.
