Create README.md

README.md

@@ -0,0 +1,28 @@
+# r.data.table.funs
+A set of functions that I have found very useful for speeding up my analysis work. These are well designed (imo) functions that provide a
+fast alternative to conjuring up functions yourself. These functions are free to use, I don't care (mostly) what you do with it,
+are provided without any warranty yada yada, and do not give you the right to use it in closed source products (good luck doing that with
+R code though).
+
+# Contents
+## agg_by_period
+This is a useful case where you are required to aggregate value by a date index into separate periods,
+possibily for use in tools that work better without pivoted up data (eg. Excel). The syntax is:
+```
+agp(table_name, j, by, index)
+# j, by have their usual meaning as in data.table
+# index is the date column you want to aggregate by
+```
+
+The data can be aggregated for, say, recent 3, 6, 12 months and year to date by suitably defining the values of the `date_period` list
+(check the code for this variable definition).
+
+## deciles
+Often, in sales analytics, you are required to group categories (districts, doctors, countries) into 10 groups that have equal contributions,
+in decreasing order of contribution i.e. the first group will have the least members that contribute 10% of some value,
+the next will have more members that contribute the next 10%, all the way to the last group that will have the highest number of
+members that contirbute the bottom 10% of the value. This relates to percentiles, althought they are not identical.
+
+```
+decile_dt(x, decreasing = TRUE)
+```