
Hello all
I am fairly new to R and was hoping I could get some expert insight that would reduce my struggles with my data set.
I have data that consists of about 10K rows (each row representing 1 individual) and about 10 columns (ID, day, month, year, age [all as integers], season [with levels fall, winter, spring, summer], reason [with levels sport, regular, dlpk], field age [with levels adults, yearlings, senescent], zone [with 8 levels for each geographic region where each region is abbreviated with 2 letters], sex [F, M]. Since season, reason, field age, zone, sex have letters R uses these categorical data as factors with various levels, and I am having a hard time trying to calculate anything.
 
ID         Day    MO    YR        Age     season     reason        f_age        zone    sex
B1432   07     06    2002       3         FALL         SPORT        ADULT      BRA     F
B1265   19     10    2001      17        SUMMER  REG            YRLG        ROD    M......and so on
 
>From here I would like to compare the age between M/F for reason, and also whether there are reagional differences in the age composition, and if there is an interaction between region and reason.
Since the numbers are counts per year of reason, region, season, can I use linear regressions to examine long-term trends of age and year by sex, or age by reason and sex?
 
Thank you for any advice on this!
 
m
 

  		 	   		  
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