stringsAsFactors = HELLNO

fbind()

Binding two factors via fbind():

library(foofactors)
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)

## [1] 1 3 4 2 1 3 4 2

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

fbind(a, b)

## [1] character hits      your      eyeballs  but       integer   where it 
## [8] counts   
## Levels: but character counts eyeballs hits integer where it your

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)

## x
##  a  b  c  d  e 
## 25 26 17 17 15

as.data.frame(table(x))

##   x Freq
## 1 a   25
## 2 b   26
## 3 c   17
## 4 d   17
## 5 e   15

freq_out()

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

freq_out(x)

## # A tibble: 5 x 2
##   x         n
##   <fct> <int>
## 1 a        25
## 2 b        26
## 3 c        17
## 4 d        17
## 5 e        15

first_upper()

The first_upper() function returns the same factor but with the first letter of each character value capitalized.

months <- factor(c("january","february","march","april","may","june","july",
         "august","september","october","november","december"))
first_upper(months)

##  [1] January   February  March     April     May       June      July     
##  [8] August    September October   November  December 
## 12 Levels: April August December February January July June March ... September

subfactor()

The subfactor() This function returns a factor with just the first three letters of each element.

library(tidyverse)
library(scales)
library(gapminder)
Euro <- gapminder %>%
  filter(year == 2007, continent == "Europe" ) %>% 
  head(20)

Euro %>%
  ggplot(aes(x = country, y = lifeExp)) +
  geom_point(shape = 23, fill = 'cornflowerblue', size = 3) +
  guides(fill=F) +
  labs(x = "Country", y = "Life Expectancy (years)") +
  theme_bw()

And now lets see after applying the subfactor() function

Euro %>%
  ggplot(aes(x = subfactor(country), y = lifeExp)) +
  geom_point(shape = 23, fill = 'cornflowerblue', size = 3) +
  labs(x = "Country", y = "Life Expectancy (years)") +
  theme_bw()

factor_asis()

The factor_asis() function encodes a vector as a factor with levels in the same order as they appear in the data instead of the alphabetical order.

continents <- factor(c("Asia", "Europe", "Africa", "Americas", "Oceania"))
levels(continents)

## [1] "Africa"   "Americas" "Asia"     "Europe"   "Oceania"

continents_asis <- factor_asis(c("Asia", "Europe", "Africa", "Americas", "Oceania"))
levels(continents_asis)

## [1] "Asia"     "Europe"   "Africa"   "Americas" "Oceania"

This function has the same effect when plotting. To demonstrate this, let’s look at a plot of the number of countries in each continent from the gapminder dataset without modification.

gapminder %>%
  filter(year == 2007) %>%
  ggplot(aes(x = continent, fill = continent)) +
  scale_fill_brewer(palette = "Pastel2") +
  geom_bar() +
  guides(fill=F) +
  labs(x = "Continent", y = "Number of countries") +
  theme_bw()

And now the same plot using factor_asis().

gapminder %>%
  filter(year == 2007) %>%
  ggplot(aes(x = factor_asis(continent), fill = continent)) +
  scale_fill_brewer(palette = "Pastel2") +
  geom_bar() +
  guides(fill=F) +
  labs(x = "Continent", y = "Number of countries") +
  theme_bw()

As noted, the order of the continents corresponds to the order of appearance in the dataset.

reorder_desc()

The reorder_desc() function reorders the levels of a factor in a descending order.

alpha_continents <- factor(c("Africa", "Americas", "Asia", "Europe", "Oceania"))
alpha_continents

## [1] Africa   Americas Asia     Europe   Oceania 
## Levels: Africa Americas Asia Europe Oceania

reorder_desc(alpha_continents)

## [1] Africa   Americas Asia     Europe   Oceania 
## attr(,"scores")
##   Africa Americas     Asia   Europe  Oceania 
##       -1       -2       -3       -4       -5 
## Levels: Oceania Europe Asia Americas Africa

This function mantains levels in a descending order even when plotting:

gapminder %>%
  filter(year == 2007) %>%
  ggplot(aes(x = reorder_desc(continent), fill = continent)) +
  scale_fill_brewer(palette = "Pastel2") +
  geom_bar() +
  guides(fill=F) +
  labs(x = "Continent", y = "Number of countries") +
  theme_bw()

```