(R) ifelse()

ifelse() 

Using ifelse(), you can make a new variable based on the test you made. 

>library(ggplot2)
> summary(diamonds$carat) -------------carat: size of carat in each diamond


   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  0.2000  0.4000  0.7000  0.7979  1.0400  5.0100 

<EXAMPLE 1>

<new variable 'carattt' ------"small","big">

To make a new variable 'carattt' which consists of small and big, I will use a test in ifelse()

1st test: diamonds$carat<=0.7	Yes                                                    "small"
	No                                                     "big"

> diamonds$carattt<-ifelse(diamonds$carat<=0.7,"small","big")

> table(diamonds$carattt)

  big small 

26778 27162 

<Make a graph using a new variable 'carattt'>

> carattt_price<-diamonds%>%group_by(carattt)%>%summarise(mean_price=mean(price))

> ggplot(data=carattt_price,aes(x=carattt,y=mean_price))+geom_col()

<EXAMPLE 2>

<new variable 'caratt'  ------"small","middle","big">

To make a new variable "caratt" which consists of small, middle and big, I will use two tests in ifelse().

1st test:  diamonds$carat <=0.4	Yes              "small"
	No	2nd test: diamonds$carat<=1.04	Yes               "middle"
			No	"big"

> diamonds$caratt<-ifelse(diamonds$carat<=0.4,"small",ifelse(diamonds$carat<=1.04,"middle","big"))

> table(diamonds$caratt)

   big middle  small 

 13379  26170  14391 

<Make a graph using a new variable 'caratt'>

> caratt_price<-diamonds%>%group_by(caratt)%>%summarise(mean_price=mean(price))

> ggplot(data=caratt_price,aes(x=caratt,y=mean_price))+geom_col()