A view on R Capabilities

Dear R Programmers,


I have tried to capture R capabilites in a single slide. I welcome your comments/suggestions on the same.

Please feel free to reach author at mavuluri.pradeep@gmail.com for more on R.

Analytical Adoption (Past & Now)

Today, in the evening, there was a sudden request from my old colleague, who said a re-surveyed data of old study (done past three and half years) has arrived and asked for help in updating it. As data was sent in structrued data, hardly it took forty-five mins to finalize the results and another thirty mins to summarize it. While, summarzing I found extreme changes happened in just three and half years of anlaytical solution adoption/implementation. Few, I would like to bring forth and expressing my views on the same.
 

As, it is evident from above, I, feel change in decsion driver of analytical adoption precisely to  line-of-business heads makes more sense as these are the actual people who deal with project requirements as-well as deliverables. However, preferring multiple vendors against single vendor, makes me to think that now most of companies feel that no single vendor can provide all analytical solutions they required (herein, I doubt, whether companies has eased their data policies as earlier I understood, reason for sticking with single vendor is data privacy?). Finally, pace with which analytical solution adoption/implementation has increased drastically, is this means, now more companies are understanding their business values or going with wave in the market, I would like to understand more.

Also, I have observed, the survey got response from same-old 98% of respondents leaving only 2% of them to be new.

Usage of R functions "table" & "ifelse" when NA's exist

Most of the time I came across now and then in help posts questions regarding the mismatching total count of observations after employing the R functions "table" and "ifelse". This usually creates frustration among fresh/part-time practitioners which ends up doubting the application and reverting back to their earlier tool.

However, this mismatching of total count happens only when you have NA's in the data.

Thus, to always get the total count figures, we should make practice of use following options with respect to the R functions mentioned above:
table(varname1, varname2, useNA = c("ifany")) # in "table" usage of "useNA" option
ifelse(is.na(varname1) == T, ***, ifelse(varname1 > 100 & varname1 <= 110, 1, 0))
# in "ifelse" usage of "is.na" option

***-- here you need to provide which value to be taken if variable has values of NA.
Happy R Programming. Author can be reached at mavuluri.pradeep@gmail.com.