chag | canonical source of change data

In the data frame below called MyData, 7 measurements (Sub1:Sub7) are taken on each individual. Individuals are identified by unique ID numbers. Several individuals are grouped into each level of Location. For each Sub in each Location, I want to set a threshold to define outliers, and return a list of those outliers. I could do this by sub-setting each unique level of Location : (APNG <- MyData%>%filter(Location == "APNG"), then using the summary() function to get the 1st and 3rd quartiles for each, create a threshold for each : thresh1 <- Q3+1.5*IQR(APNG$Sub1) + thresh2 <- Q1-1.5*IQR(APNG$Sub1), save the results into a different object, repeat for all Subs in each unique Location, and concatenate them all at the end. What is the best alternative to doing it this way?

here is an example of my data:

I have collected data on 216 individuals. I measured the concentration of the same 7 Substances in each individual, represented by Sub1:Sub7. The concentration of these Substances may be different in individuals from different Locations. I am interested in the level of refinement at which these individuals can be classified into groups based on their concentrations of these substances. I am also interested in seeing how these Substances may be correlated with each other, as the concentration of some may effect the concentration of others. Each Individual in my data set is represented by a unique ID number. Three "nested" grouping variables (Location, State, and Region) can be used to separate these individuals. Multiple Locations are in each State, and multiple States are part of larger Regions. For instance, the individuals in the Locations: APNG, BLEA, and NEAR are all in FL, while the individuals in the Locations: CACT, OYLE, and PIY are all in GA. The states FL and GA are both in Region A. I used this function to conduct an anova: