The document discusses the dplyr R package, which offers a flexible grammar for data manipulation, aimed at enhancing performance and ease of use with data frames. It details core functionalities such as filtering, selecting columns, and mutating data, as well as the use of pipelines for improved code organization. The document also compares the dplyr pipe operator with the magrittr pipe operator, highlighting their differences.

1. Manipulating Data
Using DPLYR()
Rupak Roy

2. Dplyr() provides a flexible grammar of data manipulation. It's the next iteration
of plyr, focused on tools for working with data frames (hence the d in the
name).
It has three main goals:
 Identify the most important data manipulation verbs and make them easy to
use from R.
 Provide blazing fast performance for in-memory data i.e. large data by
writing key pieces in C++ (using Rcpp)
 Uses the same interface to work with data no matter where it's stored,
whether in a data frame, a data table or a database.
dplyr(): a grammar of data
manipulation
Rupak Roy

3. >install.packages(dplyr)
>library(dplyr)
#converting the variables to factors
>mtcars$cyl<- as.factor(mtcars$cyl);
>mtcars$am<-as.factor(mtcars$am);
>str(mtcars);
#using OR dpylr()
>dmtcars<-filter(mtcars,cyl==6|cyl==7)
#base R package
>dmtcarss<-mtcars[mtcars$cyl==6|mtcars$cyl == 7,]
>View(dmtcars)
#using AND dplyr()
>dmtcars<-filter(mtcars,cyl==6 & cyl==4)
#using base R package
>dmtcars<- dmtcars<-mtcars[mtcars$cyl==6 & mtcars$cyl ==4, ]
>View(dmtcars)
Subsetting: rows

4. #using dplyr()
>mtcars_col1<-select(mtcars, mpg, cyl, disp)
>View(mtcars_col)
#using base R-package
>mtcars_col1<-mtcars[ , c("mpg", "cyl", "disp")]
#adding new columns using dplyr:mutate()
>mtcars<-mutate(mtcars, newcol1= ifelse(mtcars$mpg<=15,"luxury",
ifelse(mtcars$mpg<= 20,"sports","economy")) )
#using base R-package where “newcol1” is the new column
>mtcars$newcol1<-ifelse(mtcars$mpg<=15,"luxury",ifelse (mtcars$mpg<=
20,"sports","economy"))
>View(mtcars)
>mtcars<-select(mtcars, -newcol1) #to delete a column
Sub-setting: columns

5. #arrange using dplyr()
>mtcars<-arrange(mtcars,cyl) #ascending order
>mtcars<-arrange(mtcars, desc(cyl))
#arrange using base R package
>mtcars<-mtcars[order(mtcars$cyl), ]
#group
>group_by(mtcars, cyl)
#summarize
>summarize(mtcars, mean(mpg), sd(mpg))
Order() and Group_by()
Rupak Roy

6.  Pipelines is a R package helps to better organize the code in
pipeline built with %>% structuring sequences of data operations
left-to-right which is much easier to read, write, and maintain.
 The dplyr R package uses %.% operator which is similar to %>%;
however, it has been deprecated and dplyr now recommends
magrittr that %>% which dplyr imports from magrittr.
Differences between %.%(dplyr) and %>%(magrittr):
> The magrittr package is a much more lightweight package that
exists to define only that pipe-like operator.
> Minimizing the need for local variables and function definitions.
Pipelines %>%(pipe operator)

7. #using base R package to find the average whose cylinder = 4
>mean(mtcars[mtcars$cyl=="4","mpg"])
Note: here we have use “4” as cyl data type is factor and not numeric else ==4
#using dpylr
>summarize(filter(mtcars,cyl=="4"), mean(mpg))
#using pipe
>mtcars%>%filter(cyl=="4")%>%summarize(mean(mpg))
#categorize the mtcars based on mpg in a new column
mtcars%>%mutate(newcol2=ifelse(mpg<=15,"luxury",ifelse (mpg<=
20,"sports","economy")))
magrittr()
Rupak Roy

8. Next:
We will see how to manipulate data using dates
Manipulating Data
Rupak Roy