In Tangjiahui26/foofactors: Make Factors Less Annoying

This file works based on Jenny Bryan's coding, and I added some functions after it.

Factors are a very useful type of variable in R, but they can also drive you nuts. Especially the "stealth factor" that you think of as character.

Can we soften some of their sharp edges?

Binding two factors via fbind():

library(foofactors)
library(gapminder)
a <- factor(c("character", "hits", "your", "eyeballs"))
b <- factor(c("but", "integer", "where it", "counts"))

Simply catenating two factors leads to a result that most don't expect.

c(a, b)

The fbind() function glues two factors together and returns factor.

fbind(a, b)

Often we want a table of frequencies for the levels of a factor. The base table() function returns an object of class table, which can be inconvenient for downstream work. Processing with as.data.frame() can be helpful but it's a bit clunky.

set.seed(1234)
x <- factor(sample(letters[1:5], size = 100, replace = TRUE))
table(x)
as.data.frame(table(x))

The freq_out() function returns a frequency table as a well-named tbl_df:

freq_out(x)

Above codes are based on Jenny's notes. Here I created more functions about dealing with factor.

Most of time we often want to figure out if the factors should be character. We can use detect_factors() to check if some of the element of the factor are not unique. Otherwise, if all of the elements are unique, you probably should use it as character.

a <- factor(c("a","b","c")) ##Should be character
b <- factor(c("a","b","b")) ##Should be factor

detect_factors(a)
detect_factors(b)

We often want to know if our dataframe contains any factors that we don't want to use. The function check_factors() can be applied to check it.

check_factors(iris)
check_factors(gapminder)

Sometimes we may want to change the order of factors, and instead order them in descending way. The functionnew_reorder()can help you to realize it.

a <- factor(c("a","b","c"))
b <- factor(c("apple","banana","pear"))

levels(a)
levels(new_reorder(a))
levels(b)
levels(new_reorder(b))

If you want to order your factors as the way they appear in the data. The function new_factor() would like to set your factors to the order in which they appear in the data. In contrast, If you want the reverse order,new_factor_rev() can be used in your codes.

a <- factor(c("c","b","a"))
b <- factor(c("banana","apple","pear"))
levels(a)
new_factor(a)
levels(b)
new_factor(b)

Examples fornew_factor_rev():

new_factor_rev(a)
new_factor_rev(b)

Tangjiahui26/foofactors documentation built on May 18, 2019, 8:11 p.m.