Match datasets based on precalculated principal components (R programming)

This needs to be done in R, so we need R code as a result. Attached is the file "[login to view URL]" The attached file consists of 12 columns and 1000 rows (incl. header) , The headline identifies each row with ID, cohort, PC1 to PC10. Cohort contains one of the 6 uniq values ("Control", "set_a", "set_b", "set_c", "set_d", "set_e") Take each "set" one-by-one and find the closest match to the PCs by finding the control with the minimum of sum over i of square (PCi-PCi) where i stands for PC1, 2 ... 10 and the difference is between the value for the "Control" and "set". So one needs to calculate these for case/set pairs. Once a control is selected it needs to be removed completely so it won't be selected for another case. Start with a case chosen from one series, and determine the best control. Then switch to another case series and find the best control for a chosen case. Continue until the end of all the cases. Then, start again finding a new control for each case until you reach controls for each case. Thank you!