Factor The Polynomial

Understanding factor the polynomial requires examining multiple perspectives and considerations. Why use as.factor () instead of just factor () - Stack Overflow. ‘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 ...

r - How to convert a factor to integer\numeric without loss of .... Another key aspect involves, 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. Equally important, r - Re-ordering factor levels in data frame - Stack Overflow.

factor () command in R is for categorical variables with hierarchy .... I'm quite confused on when to use factor (educ) or factor (agegroup) in R. Is it used for categorical ordered data? or can I just use to it a simple categorical data with no hierarchy? when to use factor () when plotting with ggplot in R?.

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. edit: I get this when I use the colour=gear: Drop unused factor levels in a subsetted data frame.

I have a data frame containing a factor. When I create a subset of this dataframe using subset or another indexing function, a new data frame is created. However, the factor variable retains all o... r - list all factor levels of a data.frame - Stack Overflow.

Another key aspect involves, 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 to force R to use a specified factor level as reference in a .... Moreover, when creating the factor from b you can specify the ordering of the levels using factor(b, levels = c(3,1,2,4,5)).

Building on this, 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.