The document provides an introduction to the dplyr package in R, detailing various functions for data manipulation such as select, filter, arrange, and summarise. It includes syntax examples and operations to transform and analyze datasets effectively. Additionally, it introduces the magrittr pipe operator for chaining commands in a more readable format.

1. dplyr Package

2. Introduction
Helps transform and manipulate data
Powerful tool to summarise data sets
Install: install.packages(dplyr)
Activate: library(dplyr)

3. File: Excel
Variables: 7
Observations: 153

4. ▪ select
▪ filter
▪ arrange
▪ distinct
▪ mutate
▪ transmute
▪ group_by
▪ summarise
▪ pipe operator (%>%)
▪ slice
▪ count
Functions in dplyr

5. • Keeps only those variables (columns) that you
want to retain/extract.
• Syntax: select(dataset,[column1],[column2],…)
Examples:
 Select columns Month, Dealer, Item, Quantity: select(sales,Month,Dealer,Item,Qty)
 Select columns from Month to Quantity: select(sales,Month:Qty)
 Deselect column Month from the dataset: select(sales,-Month)
 Select columns ending with the letter “r”: select(sales,ends_with("r"))
 Select columns containing the letter “r”: select(sales,contains("r"))
 Select columns starting the series “m”: select(sales,matches("m."))
 Select columns with multiple variables: select(sales,one_of(c("Month","Dealer")))
 Select columns starting with the letter “d”: select(sales,starts_with("d"))
select()

6. • Keeps only those records (rows) that you want to
retain/extract.
• Syntax: filter(dataset,criteria)
Examples:
 Item is Pen: filter(sales,Item==“Pen”)
 Quantity is more than 50: filter(sales,Qty>50)
 Item is Pencil and Quantity is more than 50: filter(sales,Item=="Pencil"&Qty>50)
 Quantity is between 50 and 80: filter(sales,Qty>50&Qty<80)
 Item is Pencil or Quantity is more than 50: filter(sales,Item=="Pencil"|Qty>50)
filter()

7. Examples:
 We want to extract the Sales Manager, Item and Quantity but only for Pencil:
i) k=select(sales,SalesManager,Item,Qty)
filter(k,Item=="Pencil")
ii) select(filter(sales,Item=="Pencil"),SalesManager,Item,Qty)
iii) filter(select(sales,SalesManager,Item,Qty),Item=="Pencil")
 We want to extract for the Month of May, Dealer, Item and Quantity:
i) filter(select(sales,Dealer,Item,Qty),sales$Month=="May")
ii) filter(select(sales,Dealer,Item,Qty),Month=="May")
select() and filter()

8. • Orders or sorts the records (rows) based on the
variable(s).
• By default the arrangement is in ascending order.
• Syntax: arrange(dataset,column1,[column2],…)
Examples:
 Sort the dataset based on Months: arrange(sales,Month)
 Sort the dataset based on Months and Dealer: arrange(sales,Month,Dealer)
 Arrange the data in descending order of Quantity: arrange(sales,desc(Qty))
arrange()

9. • Helps extract unique values from a variable.
• Syntax: distinct(dataset,by=column1)
Examples:
 Find the names of the Dealers: distinct(sales,Dealer)
 Find the items sold by each Dealer: arrange(distinct(sales,Dealer,Item),by=Dealer)
distinct()

10. • Adds a new variable (column) to the existing
dataset
• Syntax: mutate(dataset,newcolumn=criteria)
Example:
 Add a new column Target where it is twice of Quantity: mutate(sales,Target=Qty*2)
mutate()

11. • Creates a new variable (column) but drops the
existing ones
• Syntax: transmute(dataset,newcolumn=criteria)
Example:
 Create a new column Target where it is twice of Quantity: transmute(sales,tgt=2*Qty)
transmute()

12. • Helps create groups in a dataset based on a
varaible.
• Useful when nested with other functions.
• Syntax: group_by(dataset,column1,[column2]…)
• Ungroup Syntax: ungroup(dataset)
Example:
 Create groups in the data based on Items: group_by(sales,Item)
 Get the maximum units sold for each item: filter(group_by(sales,by=Item),Qty==max(Qty))
group_by()

13. • Helps generate a single number/statistic for the dataset
• Syntax: summarise(dataset,newvariable=function….)
Examples:
 Total number of units sold across all Items:
summarise(sales,total=sum(Qty))
 Total number of units sold and total amount:
summarise(sales,t_Qty=sum(Qty),t_Amount=sum(Amount))
 Total number of records in the dataset:
summarise(sales,rowscount=n())
 Get the total number of records, quantity sold and amount for each item:
summarise(group_by(sales,Item),rcount=n(),untiyqty=sum(Qty),totalamount=sum(Amount))
 Every statistic for each dealer and their respective items:
summarise(group_by(sales,Dealer,Item),rcount=n(),untiyqty=sum(Qty),totalamount=sum(Amount))
summarise()

14.  We want to extract the top 6 records for Dealers who have sold the Item Pen only:
filter((sales,Item=="Pen")
select(filter(sales,Item=="Pen"),Item,Dealer,Qty)
arrange(select(filter(sales,Item=="Pen"),Item,Dealer,Qty),by=Dealer)
head(arrange(select(filter(sales,Item=="Pen"),Item,Dealer,Qty),by=Dealer))
 We want the maximum quantity of every item for the month of May with just Dealer, Item and
Quantity variables:
select(sales,Dealer,Item,Qty)
filter(select(sales,Dealer,Item,Qty),sales$Month=="May")
group_by(filter(select(sales,Dealer,Item,Qty),sales$Month=="May"),Item,Dealer)
summarise(group_by(filter(select(sales,Dealer,Item,Qty),sales$Month=="May"),Item,Dealer),max(Qty))
Assignment

15. • Belongs to magrittr Package.
• Helps structure sequence of operations in a
single code from left to right.
• Helps avoid nesting of funtions.
• Operator: %>%
Examples:
 We want to extract the top 6 records for Dealers who have sold the Item Pen only:
sales%>%filter(Item=="Pen")%>%select(Dealer,Item,Qty)%>%arrange(Dealer)%>%head
 We want the maximum quantity of every item for the month of May with just Dealer, Item and
Quantity variables:
sales%>%select(Dealer,Item,Qty)%>%filter(sales$Month=="May")%>%group_by(Item,Dealer)%>%sum
marise(max(Qty))
pipe operator %>%

16. • Helps extract records (rows) based on their
position.
• Syntax: slice(dataset,row numbers)
Examples:
 Select first ten rows: slice(sales,1:10)
 Select rows fifteen to twenty: slice(sales,15:20)
slice()

17. • Helps count the number of times a values has
appeared in a variable.
• Syntax: count(dataset, [column1],[column2],…)
Examples:
 Count the number of times each Dealer has appeared: count(sales,Dealer)
 Count the number of times Pen has appeared: count(sales,Item=="Pen")
count()

18. Thanks!
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