‘factor(x, exclude = NULL)’ applied to a factor without ‘NA’s is a no-operation unless there are unused levels: in that case, a factor with the reduced level set is returned. ‘as.factor’ coerces its argument to a factor. It is an abbreviated (sometimes faster) form of ‘factor’. Performance: as.factor > factor when input is a factor The word "no-operation" is a bit ambiguous ...
See the Warning section of ?factor: In particular, as.numeric applied to a factor is meaningless, and may happen by implicit coercion. To transform a factor f to approximately its original numeric values, as.numeric(levels(f))[f] is recommended and slightly more efficient than as.numeric(as.character(f)). The FAQ on R has similar advice.
You should do the data processing step outside of the model formula/fitting. When creating the factor from b you can specify the ordering of the levels using factor(b, levels = c(3,1,2,4,5)). Do this in a data processing step outside the lm() call though. My answer below uses the relevel() function so you can create a factor and then shift the reference level around to suit as you need to.
A load factor=1 hashmap with number of entries=capacity will statistically have significant amount of collisions (=when multiple keys are producing the same hash). When collision occurs the lookup time increases, as in one bucket there will be >1 matching entries, for which the key must be individually checked for equality.
with dplyr::glimpse(data) I get more values, but no infos about number/values of factor-levels. Is there an automatic way to get all level informations of all factor vars in a data.frame?
How do I customize the tab-to-space conversion factor when using Visual Studio Code? For instance, right now in HTML it appears to produce two spaces per press of TAB, but in TypeScript it produces 4.
Factors (with as.factor) are variables that have discrete values, which may or may not be ordered. In other areas of science outside R they're often called categorical values. For example North South East and West could be factors. Numerics (with as.numeric) are numbers, with infinite other numbers between them. So for example 5 is a number, as is 6, but so are 5.01, 5.001, 5.0001 etc. To ...
ggplot(mtcars) + geom_point(aes(x=mpg, y=drat, colour=factor(gear))) Is the general rule to use factor when the variable being used to determine the shape/size/colour is discrete, and not continuous? Or is there another use of factor in this context? It seems like the first command can be made like the second with the right legend, even without factor. thanks. edit: I get this when I use the ...
A couple comments: reordering a factor is modifying a data column. The dplyr command to modify a data column is mutate. All arrange does is re-order rows, this has no effect on the levels of the factor and hence no effect on the order of a legend or axis in ggplot. All factors have an order for their levels. The difference between an ordered = TRUE factor and a regular factor is how the ...
