How to analyze datasets corresponding to 'Agilent-016332 Human Gene Expression 8X15k (AMADID: 16332)' in R?

You would most likely want to use limma. Using the only GEO data set for this array as an example:

> library(GEOquery)

> getGEOSuppFiles("GSE72246")
https://ftp.ncbi.nlm.nih.gov/geo/series/GSE72nnn/GSE72246/suppl/
OK
trying URL 'https://ftp.ncbi.nlm.nih.gov/geo/series/GSE72nnn/GSE72246/suppl//GSE72246_RAW.tar'
Content type 'application/x-tar' length 142499840 bytes (135.9 MB)
downloaded 135.9 MB

trying URL 'https://ftp.ncbi.nlm.nih.gov/geo/series/GSE72nnn/GSE72246/suppl//filelist.txt'
Content type 'text/plain' length 3621 bytes
downloaded 3621 bytes
> setwd("GSE72246/")
> untar("GSE72246_RAW.tar")

> dat <- read.maimages(dir(".", "txt.gz"), "agilent.median", green.only = TRUE)
Read GSM1858547_S08-0285_Day1_201.txt.gz
Read GSM1858548_S08-0296_Day1_101.txt.gz
Read GSM1858549_S08-0300_Day1_203.txt.gz
Read GSM1858550_S08-0308_Day57_201.txt.gz
Read GSM1858551_S08-0322_Day57_101.txt.gz
Read GSM1858552_S08-0326_Day1_104.txt.gz
Read GSM1858553_S08-0328_Day1_301.txt.gz
Read GSM1858554_S08-0329_Day57_203.txt.gz
Read GSM1858555_S08-0334_Day1_702.txt.gz
Read GSM1858556_S08-0337_Day1_204.txt.gz
Read GSM1858557_S08-0339_Day1_206.txt.gz
Read GSM1858558_S08-0341_Day1_502.txt.gz
Read GSM1858559_S08-0350_Day1_207.txt.gz
Read GSM1858560_S08-0351_Day1_208.txt.gz
Read GSM1858561_S08-0352_Day1_106.txt.gz
Read GSM1858562_S08-0358_Day1_304.txt.gz
Read GSM1858563_S08-0359_Day1_801.txt.gz
Read GSM1858564_S08-0363_Day57_104.txt.gz
Read GSM1858565_S08-0365_Day57_301.txt.gz
Read GSM1858566_S08-0370_Day1_802.txt.gz
Read GSM1858567_S08-0372_Day57_702.txt.gz
Read GSM1858568_S08-0377_Day1_308.txt.gz
Read GSM1858569_S08-0378_Day1_307.txt.gz
Read GSM1858570_S08-0379_Day1_107.txt.gz
Read GSM1858571_S08-0382_Day57_204.txt.gz
Read GSM1858572_S08-0383_Day57_206.txt.gz
Read GSM1858573_S08-0384_Day57_502.txt.gz
Read GSM1858574_S08-0385_Day1_505.txt.gz
Read GSM1858575_S08-0392_Day57_207.txt.gz
Read GSM1858576_S08-0394_Day57_208.txt.gz
Read GSM1858577_S08-0396_Day57_106.txt.gz
Read GSM1858578_S08-0405_Day57_801.txt.gz
Read GSM1858579_S08-0407_Day1_705.txt.gz
Read GSM1858580_S08-0410_Day57_304.txt.gz
Read GSM1858581_S08-0417_Day1_312.txt.gz
Read GSM1858582_S08-0420_Day1_209.txt.gz
Read GSM1858583_S09-0427_Day57_802.txt.gz
Read GSM1858584_S09-0430_Day57_107.txt.gz
Read GSM1858585_S09-0431_Day57_308.txt.gz
Read GSM1858586_S09-0434_Day57_307.txt.gz
Read GSM1858587_S09-0439_Day57_505.txt.gz
Read GSM1858588_S09-0440_Day1_210.txt.gz
Read GSM1858589_S09-0441_Day1_506.txt.gz
Read GSM1858590_S09-0459_Day57_705.txt.gz
Read GSM1858591_S09-0461_Day57_312.txt.gz
Read GSM1858592_S09-0463_Day57_209.txt.gz
Read GSM1858593_S09-0467_Day57_210.txt.gz
Read GSM1858594_S09-0468_Day57_506.txt.gz
>

And now you can make whatever comparisons you might like. The limma User's Guide has examples of reading in this sort of data on p. 19, and a worked example starting on p. 110. But after the normalization step, a single color Agilent analysis isn't materially different from any of the Affy examples, and you could use one or more of those as an example since the Agilent example is a time course.