Dear all, I do not work with R. I need to create one contour plot for the purpose of comparison with an image in one publication. I will provide the source data. Can someone help me? Well thank you.
One good place to start is with the following two options,
ggplot2 has support for controur plots,
And then base-r also supports contour plots. Here's a little guide https://r-charts.com/correlation/contour-plot/
A good place to start when seeking help here is with a reprex, FAQ: How to do a minimal reproducible example ( reprex ) for beginners
Thank you very much. Since this is a one time task for me, just one graph, I am looking for a volunteer to build the graph. 
This forum is more about empowering applied statisticians and data scientists to use all the free and open source tooling to get this work done themselves, so you'll probably not have a huge amount of luck fully externalizing the work.
But your example plot seems to just be a contour and scatter plot, with custom contour colors and scatter shapes. That is, it's a pretty good entry point into R and ggplot.
Do you want to share a small subset of data and we can get you started?
Thanks for the explanation.
Example plot is from publication https://doi.org/10.1016/j.cjco.2022.09.007
My data is attached.
https://docs.google.com/document/d/e/2PACX-1vSQtYRkQFLrkOTE31qqbWoMSgt_YNJ0opdQfeJTJJNIYguikx7nNOMyZkkS-NpeHQTOUVbbd3xC6LwM/pub?embedded=true
Hi @Rudo, in order to following the @EconomiCurtis suggested,
you could try with this:
contour <- structure(list(Triglyceride = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5,
1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5,
1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5,
1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5,
1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5,
1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5,
2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5,
2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5,
2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5,
2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5,
2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5,
2.5, 2.5, 2.5, 2.5, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3.5,
3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5,
3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5,
3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5,
3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5,
3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5,
3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5,
4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5,
4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5,
4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5,
4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5,
4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5,
4.5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5.5, 5.5, 5.5, 5.5,
5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5,
5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5,
5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5,
5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5,
5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5,
5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
6, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,
0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,
0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,
0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,
0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,
0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5), non_HDL_Cholesterol = c(0,
0, 0, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1.5, 1.5, 1.5, 1.5,
1.5, 1.5, 2, 2, 2, 2, 2, 2, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 3,
3, 3, 3, 3, 3, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 4, 4, 4, 4, 4, 4,
4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 5, 5, 5, 5, 5, 5, 5.5, 5.5, 5.5,
5.5, 5.5, 6, 6, 6, 6, 6.5, 6.5, 6.5, 7, 7, 7.5, 0, 0, 0, 0.5,
0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 2,
2, 2, 2, 2, 2, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 3, 3, 3, 3, 3, 3,
3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 4, 4, 4, 4, 4, 4, 4.5, 4.5, 4.5,
4.5, 4.5, 4.5, 5, 5, 5, 5, 5, 5, 5.5, 5.5, 5.5, 5.5, 5.5, 6,
6, 6, 6, 6.5, 6.5, 6.5, 7, 7, 7.5, 0, 0, 0, 0.5, 0.5, 0.5, 0.5,
1, 1, 1, 1, 1, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 2, 2, 2, 2, 2, 2,
2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 3, 3, 3, 3, 3, 3, 3.5, 3.5, 3.5,
3.5, 3.5, 3.5, 4, 4, 4, 4, 4, 4, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5,
5, 5, 5, 5, 5, 5, 5.5, 5.5, 5.5, 5.5, 5.5, 6, 6, 6, 6, 6.5, 6.5,
6.5, 7, 7, 7.5, 0, 0, 0, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1.5,
1.5, 1.5, 1.5, 1.5, 1.5, 2, 2, 2, 2, 2, 2, 2.5, 2.5, 2.5, 2.5,
2.5, 2.5, 3, 3, 3, 3, 3, 3, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 4,
4, 4, 4, 4, 4, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 5, 5, 5, 5, 5, 5,
5.5, 5.5, 5.5, 5.5, 5.5, 6, 6, 6, 6, 6.5, 6.5, 6.5, 7, 7, 7.5,
0, 0, 0, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1.5, 1.5, 1.5, 1.5,
1.5, 1.5, 2, 2, 2, 2, 2, 2, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 3,
3, 3, 3, 3, 3, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 4, 4, 4, 4, 4, 4,
4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 5, 5, 5, 5, 5, 5, 5.5, 5.5, 5.5,
5.5, 5.5, 6, 6, 6, 6, 6.5, 6.5, 6.5, 7, 7, 7.5, 0, 0, 0, 0.5,
0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 2,
2, 2, 2, 2, 2, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 3, 3, 3, 3, 3, 3,
3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 4, 4, 4, 4, 4, 4, 4.5, 4.5, 4.5,
4.5, 4.5, 4.5, 5, 5, 5, 5, 5, 5, 5.5, 5.5, 5.5, 5.5, 5.5, 6,
6, 6, 6, 6.5, 6.5, 6.5, 7, 7, 7.5, 0, 0, 0, 0.5, 0.5, 0.5, 0.5,
1, 1, 1, 1, 1, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 2, 2, 2, 2, 2, 2,
2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 3, 3, 3, 3, 3, 3, 3.5, 3.5, 3.5,
3.5, 3.5, 3.5, 4, 4, 4, 4, 4, 4, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5,
5, 5, 5, 5, 5, 5, 5.5, 5.5, 5.5, 5.5, 5.5, 6, 6, 6, 6, 6.5, 6.5,
6.5, 7, 7, 7.5, 0, 0, 0, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1.5,
1.5, 1.5, 1.5, 1.5, 1.5, 2, 2, 2, 2, 2, 2, 2.5, 2.5, 2.5, 2.5,
2.5, 2.5, 3, 3, 3, 3, 3, 3, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 4,
4, 4, 4, 4, 4, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 5, 5, 5, 5, 5, 5,
5.5, 5.5, 5.5, 5.5, 5.5, 6, 6, 6, 6, 6.5, 6.5, 6.5, 7, 7, 7.5,
0, 0, 0, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1.5, 1.5, 1.5, 1.5,
1.5, 1.5, 2, 2, 2, 2, 2, 2, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 3,
3, 3, 3, 3, 3, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 4, 4, 4, 4, 4, 4,
4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 5, 5, 5, 5, 5, 5, 5.5, 5.5, 5.5,
5.5, 5.5, 6, 6, 6, 6, 6.5, 6.5, 6.5, 7, 7, 7.5, 0, 0, 0, 0.5,
0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 2,
2, 2, 2, 2, 2, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 3, 3, 3, 3, 3, 3,
3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 4, 4, 4, 4, 4, 4, 4.5, 4.5, 4.5,
4.5, 4.5, 4.5, 5, 5, 5, 5, 5, 5, 5.5, 5.5, 5.5, 5.5, 5.5, 6,
6, 6, 6, 6.5, 6.5, 6.5, 7, 7, 7.5, 0, 0, 0, 0.5, 0.5, 0.5, 0.5,
1, 1, 1, 1, 1, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 2, 2, 2, 2, 2, 2,
2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 3, 3, 3, 3, 3, 3, 3.5, 3.5, 3.5,
3.5, 3.5, 3.5, 4, 4, 4, 4, 4, 4, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5,
5, 5, 5, 5, 5, 5, 5.5, 5.5, 5.5, 5.5, 5.5, 6, 6, 6, 6, 6.5, 6.5,
6.5, 7, 7, 7.5, 0, 0, 0, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1.5,
1.5, 1.5, 1.5, 1.5, 1.5, 2, 2, 2, 2, 2, 2, 2.5, 2.5, 2.5, 2.5,
2.5, 2.5, 3, 3, 3, 3, 3, 3, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 4,
4, 4, 4, 4, 4, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 5, 5, 5, 5, 5, 5,
5.5, 5.5, 5.5, 5.5, 5.5, 6, 6, 6, 6, 6.5, 6.5, 6.5, 7, 7, 7.5
), Difference_SampsonvsFriedewald_LDL_C = c(0.01, 0.02, 0.03,
0, 0.01, 0.03, 0.04, -0.01, 0.01, 0.02, 0.03, 0.04, -0.01, 0,
0.01, 0.03, 0.04, 0.05, 0, 0.01, 0.02, 0.03, 0.05, 0.06, 0, 0.01,
0.03, 0.04, 0.05, 0.06, 0.01, 0.02, 0.03, 0.05, 0.06, 0.07, 0.02,
0.03, 0.04, 0.05, 0.07, 0.08, 0.02, 0.03, 0.05, 0.06, 0.07, 0.08,
0.03, 0.04, 0.05, 0.07, 0.08, 0.09, 0.04, 0.05, 0.06, 0.07, 0.09,
0.1, 0.04, 0.05, 0.07, 0.08, 0.09, 0.05, 0.06, 0.07, 0.09, 0.06,
0.07, 0.08, 0.06, 0.07, 0.07, 0.12, 0.13, 0.14, 0.1, 0.11, 0.13,
0.14, 0.08, 0.1, 0.11, 0.12, 0.13, 0.07, 0.08, 0.09, 0.11, 0.12,
0.13, 0.06, 0.08, 0.09, 0.1, 0.11, 0.13, 0.06, 0.07, 0.09, 0.1,
0.11, 0.12, 0.06, 0.07, 0.08, 0.09, 0.11, 0.12, 0.05, 0.07, 0.08,
0.09, 0.1, 0.12, 0.05, 0.06, 0.07, 0.09, 0.1, 0.11, 0.05, 0.06,
0.07, 0.08, 0.1, 0.11, 0.04, 0.05, 0.07, 0.08, 0.09, 0.1, 0.04,
0.05, 0.06, 0.08, 0.09, 0.03, 0.05, 0.06, 0.07, 0.03, 0.04, 0.06,
0.03, 0.04, 0.02, 0.23, 0.25, 0.26, 0.21, 0.22, 0.23, 0.24, 0.18,
0.19, 0.2, 0.22, 0.23, 0.15, 0.17, 0.18, 0.19, 0.2, 0.22, 0.14,
0.15, 0.16, 0.18, 0.19, 0.2, 0.12, 0.14, 0.15, 0.16, 0.17, 0.19,
0.11, 0.12, 0.14, 0.15, 0.16, 0.17, 0.1, 0.11, 0.12, 0.13, 0.15,
0.16, 0.08, 0.09, 0.11, 0.12, 0.13, 0.14, 0.07, 0.08, 0.09, 0.11,
0.12, 0.13, 0.05, 0.07, 0.08, 0.09, 0.1, 0.12, 0.04, 0.05, 0.07,
0.08, 0.09, 0.03, 0.04, 0.05, 0.06, 0.01, 0.02, 0.04, 0, 0.01,
-0.02, 0.36, 0.37, 0.38, 0.32, 0.33, 0.34, 0.36, 0.28, 0.29,
0.31, 0.32, 0.33, 0.24, 0.26, 0.27, 0.28, 0.29, 0.31, 0.22, 0.23,
0.24, 0.26, 0.27, 0.28, 0.2, 0.21, 0.22, 0.23, 0.25, 0.26, 0.17,
0.18, 0.2, 0.21, 0.22, 0.23, 0.15, 0.16, 0.17, 0.18, 0.2, 0.21,
0.12, 0.13, 0.15, 0.16, 0.17, 0.18, 0.1, 0.11, 0.12, 0.13, 0.15,
0.16, 0.07, 0.09, 0.1, 0.11, 0.12, 0.14, 0.05, 0.06, 0.07, 0.09,
0.1, 0.02, 0.04, 0.05, 0.06, 0, 0.01, 0.02, -0.03, -0.01, -0.05,
0.48, 0.5, 0.51, 0.44, 0.45, 0.46, 0.47, 0.39, 0.4, 0.41, 0.43,
0.44, 0.34, 0.35, 0.37, 0.38, 0.39, 0.4, 0.31, 0.32, 0.33, 0.34,
0.36, 0.37, 0.27, 0.28, 0.3, 0.31, 0.32, 0.33, 0.24, 0.25, 0.26,
0.28, 0.29, 0.3, 0.2, 0.22, 0.23, 0.24, 0.25, 0.27, 0.17, 0.18,
0.19, 0.21, 0.22, 0.23, 0.13, 0.15, 0.16, 0.17, 0.18, 0.2, 0.1,
0.11, 0.12, 0.14, 0.15, 0.16, 0.06, 0.08, 0.09, 0.1, 0.11, 0.03,
0.04, 0.05, 0.07, -0.01, 0.01, 0.02, -0.04, -0.03, -0.08, 0.62,
0.63, 0.64, 0.56, 0.57, 0.59, 0.6, 0.5, 0.52, 0.53, 0.54, 0.55,
0.45, 0.46, 0.47, 0.48, 0.5, 0.51, 0.4, 0.41, 0.43, 0.44, 0.45,
0.46, 0.36, 0.37, 0.38, 0.39, 0.41, 0.42, 0.31, 0.32, 0.34, 0.35,
0.36, 0.37, 0.27, 0.28, 0.29, 0.3, 0.32, 0.33, 0.22, 0.23, 0.25,
0.26, 0.27, 0.28, 0.17, 0.19, 0.2, 0.21, 0.22, 0.24, 0.13, 0.14,
0.15, 0.17, 0.18, 0.19, 0.08, 0.1, 0.11, 0.12, 0.13, 0.04, 0.05,
0.06, 0.08, -0.01, 0.01, 0.02, -0.05, -0.04, -0.1, 0.76, 0.77,
0.79, 0.69, 0.71, 0.72, 0.73, 0.62, 0.64, 0.65, 0.66, 0.67, 0.56,
0.57, 0.58, 0.59, 0.61, 0.62, 0.5, 0.51, 0.53, 0.54, 0.55, 0.56,
0.45, 0.46, 0.47, 0.48, 0.5, 0.51, 0.39, 0.4, 0.41, 0.43, 0.44,
0.45, 0.33, 0.35, 0.36, 0.37, 0.38, 0.4, 0.28, 0.29, 0.3, 0.32,
0.33, 0.34, 0.22, 0.24, 0.25, 0.26, 0.27, 0.29, 0.17, 0.18, 0.19,
0.2, 0.22, 0.23, 0.11, 0.12, 0.14, 0.15, 0.16, 0.06, 0.07, 0.08,
0.09, 0, 0.01, 0.03, -0.06, -0.04, -0.11, 0.91, 0.92, 0.93, 0.83,
0.84, 0.86, 0.87, 0.75, 0.76, 0.78, 0.79, 0.8, 0.67, 0.69, 0.7,
0.71, 0.72, 0.74, 0.61, 0.62, 0.63, 0.65, 0.66, 0.67, 0.54, 0.55,
0.57, 0.58, 0.59, 0.6, 0.48, 0.49, 0.5, 0.51, 0.53, 0.54, 0.41,
0.42, 0.43, 0.45, 0.46, 0.47, 0.34, 0.36, 0.37, 0.38, 0.39, 0.41,
0.28, 0.29, 0.3, 0.31, 0.33, 0.34, 0.21, 0.22, 0.24, 0.25, 0.26,
0.27, 0.15, 0.16, 0.17, 0.18, 0.2, 0.08, 0.09, 0.1, 0.12, 0.01,
0.03, 0.04, -0.05, -0.04, -0.12, 1.06, 1.08, 1.09, 0.97, 0.99,
1, 1.01, 0.89, 0.9, 0.91, 0.92, 0.94, 0.8, 0.81, 0.82, 0.83,
0.85, 0.86, 0.72, 0.73, 0.75, 0.76, 0.77, 0.78, 0.64, 0.66, 0.67,
0.68, 0.69, 0.71, 0.57, 0.58, 0.59, 0.61, 0.62, 0.63, 0.49, 0.5,
0.52, 0.53, 0.54, 0.55, 0.41, 0.43, 0.44, 0.45, 0.46, 0.48, 0.34,
0.35, 0.36, 0.38, 0.39, 0.4, 0.26, 0.27, 0.29, 0.3, 0.31, 0.32,
0.19, 0.2, 0.21, 0.22, 0.24, 0.11, 0.12, 0.13, 0.15, 0.03, 0.05,
0.06, -0.04, -0.03, -0.12, 1.22, 1.24, 1.25, 1.12, 1.14, 1.15,
1.16, 1.03, 1.04, 1.05, 1.06, 1.08, 0.93, 0.94, 0.95, 0.96, 0.98,
0.99, 0.84, 0.85, 0.86, 0.88, 0.89, 0.9, 0.75, 0.77, 0.78, 0.79,
0.8, 0.82, 0.67, 0.68, 0.69, 0.7, 0.72, 0.73, 0.58, 0.59, 0.6,
0.62, 0.63, 0.64, 0.49, 0.5, 0.52, 0.53, 0.54, 0.55, 0.41, 0.42,
0.43, 0.44, 0.46, 0.47, 0.32, 0.33, 0.34, 0.36, 0.37, 0.38, 0.23,
0.24, 0.26, 0.27, 0.28, 0.15, 0.16, 0.17, 0.18, 0.06, 0.07, 0.08,
-0.03, -0.02, -0.12, 1.39, 1.4, 1.42, 1.28, 1.29, 1.31, 1.32,
1.17, 1.18, 1.2, 1.21, 1.22, 1.06, 1.07, 1.09, 1.1, 1.11, 1.12,
0.97, 0.98, 0.99, 1, 1.01, 1.03, 0.87, 0.88, 0.89, 0.91, 0.92,
0.93, 0.77, 0.78, 0.8, 0.81, 0.82, 0.83, 0.67, 0.69, 0.7, 0.71,
0.72, 0.74, 0.58, 0.59, 0.6, 0.61, 0.63, 0.64, 0.48, 0.49, 0.5,
0.52, 0.53, 0.54, 0.38, 0.39, 0.41, 0.42, 0.43, 0.44, 0.28, 0.3,
0.31, 0.32, 0.33, 0.19, 0.2, 0.21, 0.23, 0.09, 0.1, 0.12, -0.01,
0.01, -0.1, -0.1, -0.08, -0.07, -0.09, -0.08, -0.07, -0.05, -0.09,
-0.08, -0.06, -0.05, -0.04, -0.08, -0.07, -0.06, -0.05, -0.03,
-0.02, -0.07, -0.05, -0.04, -0.03, -0.02, 0, -0.05, -0.04, -0.02,
-0.01, 0, 0.01, -0.03, -0.02, -0.01, 0.01, 0.02, 0.03, -0.02,
0, 0.01, 0.02, 0.03, 0.05, 0, 0.01, 0.03, 0.04, 0.05, 0.06, 0.02,
0.03, 0.04, 0.06, 0.07, 0.08, 0.04, 0.05, 0.06, 0.07, 0.09, 0.1,
0.05, 0.07, 0.08, 0.09, 0.1, 0.07, 0.08, 0.1, 0.11, 0.09, 0.1,
0.11, 0.1, 0.12, 0.12)), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -900L))
library(ggplot2)
ggplot(contour, aes(x=non_HDL_Cholesterol , y= Triglyceride, z= Difference_SampsonvsFriedewald_LDL_C )) +
geom_contour_filled()+
theme_classic()+
labs(title = 'Difference between Sampson vs Friedewald LDL-C')
Update:
Custom the axis, but dont see well:
#add
geom_point()
#add
geom_jitter()
Great! @MiguelÁngel thank you, you helped a lot. Is it possible to insert all values into the graph as empty circles? Is it possible to adjust the range, Y axis 0 to 4.5, X axis 0 to 12?
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