I have a grain quality data (chalkiness, grain size, shape, head rice yield etc), analysed from around 900 rice seed samples screened under control and high temperature conditions in field in an augmented design. I wanted to plot a graph in R to see the relationship of the above grain quality parameters from these seeds from each genotype under each condition.
Is there a way I could develop best plots in R to view these quality data parameters, so I can understand the trends of these genotypes?
Please help. Thank you very much.
You can install FactomineR and perform principal component analysis. Which is a powerful statistic tool to explore large data variability.
As recommended by Soungalo Moustapha Drabo, principal component analysis (PCA) is a great tool. You can do it using R or Python, check this link:
https://www.analyticsvidhya.com/blog/2016/03/practical-guide-principal-component-analysis-python/ .
I recommend to use both and compare the results, both of them are free.
PCA will reduce your data into orthogonal vectors called principal component (PC), the first two component will explained mostly the total variance of your data. Please while carrying out PCA do not forget to standardize your data considering that you have properties of different dimensions (%, mm, etc).
After doing PCA, you will have an idea of your data and it will help to look forward to do other analyses.
And do not forget that PCA is just an exploratory tool,
All the best
Thank you Edenio Olivares Diaz and Soungalo Moustapha Drabo for your kind answers. Yes, PCA was one of the option I was thinking to do analysis with my data. Sure, I will take your recommendations when doing this analysis.
I will get back to you if I have questions.
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