I have to aggregate (of course with a categorical break variable) a quite big data table containing some continuous variables by resulting the mean, median, standard deviation and interquartile range (IQR) of the required variables.
The first three is an easy one with the SPSS Aggregate command, but I have no idea how to compute IQR by aggregating the data table.
I know I could compute IQR by using Descriptives (by quartiles), but as I need the calculations in aggregation - this is not an option. Unfortunately using R fails also thanks to some odd circumstances (not able to load a huge comma separated file in R neither with base:: read.table, neither with sqldf, neither with bigmemory and neither with ff packages).
Any idea is welcomed! And of course: thank you in advance.
P.S.: I thought about estimating IQR by multiplying the standard deviation by 1.5, but that method would not work as the distributions are skewed, so assuming normality does not stands.
P.S.: do you think using R within SPSS would not result in memory problems like while opening the dataset in pure R?
OMS can capture any pivot table as a dataset, so any statistical results displayed that way can be used as a dataset. Another approach, however, in this case would be to use the RANK command. RANK allows for grouping variables, so you could get rank within group, and it can compute the quartiles and percentiles within group. For example, RANK VARIABLES=salary (A) BY jobcat minority /RANK /NTILES(4) /PERCENT. Then aggregating with FIRST and the group variables as breaks would give you a dataset of the quartiles by group from which to compute the iqr.
Many ways to skin a cat.
-Jon Peck