search for: miset1

...using MIM, but I've decided to go cold turkey on other stats packages as a incentive for learning more about R. Most of the recoding is for nominal variables, like race, religion, urbanicity, and the like. So, for example, to recode race for my first dataset, inmi1, I would do the following: miset1$white <- recode(miset1$RACE, '1=1; else=0; ') miset1$black <- recode(miset1$RACE, '2=1; else=0; ') miset1$asian <- recode(miset1$RACE, '3=1; else=0; ') miset1$hispanic <- recode(miset1$RACE, '4=1; else=0; ') miset1$raceother <- recode(miset1$RACE, &...

Can someone help me understand this results? > levels(as.factor(miset1$facts_convict)) [1] "1" "1" "2" "3" "4" "5" "6" converting to numeric and back doesn't seem to help: > levels(as.factor(as.numeric(miset1$facts_convict))) [1] "1" "1" "2" "3" &quot...