# NOT RUN {
## Wold Development Data
head(fselect(wlddev, Country = country, Year = year, ODA)) # Fast dplyr-like selecting
head(fselect(wlddev, -country, -year, -PCGDP))
head(fselect(wlddev, country, year, PCGDP:ODA))
head(fselect(wlddev, -(PCGDP:ODA)))
fselect(wlddev, country, year, PCGDP:ODA) <- NULL # Efficient deleting
head(wlddev)
rm(wlddev)
head(num_vars(wlddev)) # Select numeric variables
head(cat_vars(wlddev)) # Select categorical (non-numeric) vars
head(get_vars(wlddev, is_categorical)) # Same thing
num_vars(wlddev) <- num_vars(wlddev) # Replace Numeric Variables by themselves
get_vars(wlddev,is.numeric) <- get_vars(wlddev,is.numeric) # Same thing
head(get_vars(wlddev, 9:12)) # Select columns 9 through 12, 2x faster
head(get_vars(wlddev, -(9:12))) # All except columns 9 through 12
head(get_vars(wlddev, c("PCGDP","LIFEEX","GINI","ODA"))) # Select using column names
head(get_vars(wlddev, "[[:upper:]]", regex = TRUE)) # Same thing: match upper-case var. names
head(gvr(wlddev, "[[:upper:]]")) # Same thing
get_vars(wlddev, 9:12) <- get_vars(wlddev, 9:12) # 9x faster wlddev[9:12] <- wlddev[9:12]
add_vars(wlddev) <- STD(gv(wlddev,9:12), wlddev$iso3c) # Add Standardized columns 9 through 12
head(wlddev) # gv and av are shortcuts
get_vars(wlddev, 14:17) <- NULL # Efficient Deleting added columns again
av(wlddev, "front") <- STD(gv(wlddev,9:12), wlddev$iso3c) # Again adding in Front
head(wlddev)
get_vars(wlddev, 1:4) <- NULL # Deleting
av(wlddev,c(10,12,14,16)) <- W(wlddev,~iso3c, cols = 9:12, # Adding next to original variables
keep.by = FALSE)
head(wlddev)
get_vars(wlddev, c(10,12,14,16)) <- NULL # Deleting
# }
Run the code above in your browser using DataLab