{magittr} provides 4 special operators

• %>% - pipe operators
• %T>% - tee operator
• %$% - exposition operator
• %<>% - compound assignment pipe operator

• %*% - matrix multiplication, x %*% y
• %in% - value matching
• %% - modulus operator
• %o% - outer product, outer()
• %x% - Kronecker product, kronecker()
• %/% - integer division

R code can get hard to read

sapply(iris[iris$Sepal.Length < mean(iris$Sepal.Length),-5],FUN = mean)

The %>% is taking the output of the left-hand side and using that for the first argument of the right-hand side, or where it finds a .

Example using head(x, ...)

mtcars %>% head(.,2)  # same as using head(mtcars,2)

##               mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4      21   6  160 110  3.9 2.620 16.46  0  1    4    4
## Mazda RX4 Wag  21   6  160 110  3.9 2.875 17.02  0  1    4    4

mtcars %>% head(2)     # same as using head(mtcars,2)

##               mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4      21   6  160 110  3.9 2.620 16.46  0  1    4    4
## Mazda RX4 Wag  21   6  160 110  3.9 2.875 17.02  0  1    4    4

library(ggplot2)
mtcars %>%
  xtabs(~gear+carb,data=.) %>% 
  as.data.frame %>% 
  ggplot(.,aes(x=gear,y=carb,size=Freq)) +
  geom_point()

# Generate some sample data.
df <-
    data.frame(
        Price    = 1:100 %>% sample(replace = TRUE),
        Quantity = 1:10  %>% sample(replace = TRUE),
        Type     =
            0:1 %>%
            sample(replace = TRUE) %>%
            factor(labels = c("Buy", "Sell"))
    ) 

Source

• filter()
• summarize()
• arrange()
• mutate()

sapply(iris[iris$Sepal.Length < mean(iris$Sepal.Length),-5],FUN = mean)

## Sepal.Length  Sepal.Width Petal.Length  Petal.Width 
##      5.19875      3.13375      2.46250      0.66375

iris %>% 
  mutate(avg.length=mean(Sepal.Length)) %>% 
  filter(Sepal.Length<avg.length) %>% 
  select(-Species,-avg.length) %>%
  summarise_each(funs(mean))

##   Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1      5.19875     3.13375       2.4625     0.66375

• Similar to with() or attach()
  
• Useful for functions that don't take a data parameter
• Can execute several statements by wrapping them in {}

library(datasets)
table(CO2$Treatment,CO2$Type)

##             
##              Quebec Mississippi
##   nonchilled     21          21
##   chilled        21          21

# with(CO2,table(Treatment,Type))
CO2 %$% table(Treatment,Type)

##             Type
## Treatment    Quebec Mississippi
##   nonchilled     21          21
##   chilled        21          21

• Allows a "break" in the pipe.
  
• Executes right-hand side of %T>%, but will continue to pipe through to next statement

iris %>%
  filter(Species != 'virginica') %>% 
  select(Sepal.Width,Sepal.Length) %T>%
  plot %>%  # Make scatterplot and keep going
  colMeans

##  Sepal.Width Sepal.Length 
##        3.099        5.471

• Combines a pipe and an assignment operator
• Think i++ or x+=z from the C family, Python, Ruby, etc.

df <- rexp(5,.5) %>% data.frame(col1=.)
df

##        col1
## 1 3.6982916
## 2 0.3231815
## 3 1.0181722
## 4 1.9074081
## 5 1.6484035

df %<>% arrange(col1)
df

##        col1
## 1 0.3231815
## 2 1.0181722
## 3 1.6484035
## 4 1.9074081
## 5 3.6982916

• Shortcut for %>%: Ctrl+Shift+m
• ?%>% does not work, use ?'%>%'
• df %>% .something. %>% sytem.time does not work

• magrittr on GitHub
  
• dplyr and pipes: the basics
• pipeR vs. magrittr