DPLYR

mydata <- read.csv(“C:/Users/leelavathi.a/Desktop/sampledata.csv”) al<-sample_n(mydata,10) ###### distict ######is used to remove duplicates x1<-distinct(mydata) x2<-select(mydata,3:5)

####### filter the data############ my<-filter(mydata,Index %in% c(“A”,“C”)) my2<-filter(mydata,!Index %in% c(“A”,“C”)) my1<-filter(mydata,Index %in% c(“A”,“C”)& Y2008<1945229)

SELECT

NN<-select(mydata,contains(“N”)) ### SELECTING RANDOMLY BASED ON MULTIPLE COLOUMNS #### selecting<-mydata %>% select(Index,State,Y2008) %>% sample_n(15)

filtering data using a specific keyword"

st<-filter(mydata,grepl(“Ar”,State)) bnk<-filter(UTI_Distributer_2015_16324536,grepl(“Bank”,Name.of.the.ARN.Holder,ignore.case=TRUE))

ARRANGE FUNCTION FOR SORTING THE DATA

ascending<-mydata %>% arrange(Index) descending<-mydata %>% arrange(desc(Index)) ### SORTING MULTIPLE COLOUMNS ####### asc1<-mydata %>% arrange(Index,State,Y2008) ## SORTING HTE DATA IN DESCENDING ORDER FOR MULTIPLE VARIABLES ### DESC1<-mydata %>% arrange(desc(Index,State,Y2008,Y2009))

GR<-mydata %>% group_by(Index,State) %>% summarise_at(Y2002)

Filter Data within a Categorical Variable

al<-mydata %>% filter(Index %in% c(“A”,“c”)) %>% group_by(Index) %>% do(head(.,2))

Selecting 3rd Maximum Value by Categorical Variable

third<-mydata %>% select(Index,Y2015) %>% filter(Index %in% c(“A”,“C”,“I”)) %>% group_by(Index) %>% arrange(desc(Y2015)) %>% slice(1)

third1<-mydata %>% select(Index,Y2015) %>% filter(Index %in% c(“A”,“C”,“I”)) %>% group_by(Index) %>% filter(min_rank(desc(Y2015)) == 3)

USING MUTATE FOR PERFORMIMNG ARTHIMATIC OPERATIONS

nwer<-mutate(mydata,change=Y2006-Y2007) nwww<-data.frame(mydata\(Y2002/mydata\)Y2003) ########## #mydata11 = mutate_all(mydata, funs(“new” = .* 1000))

Calculate Rank for Variables

mydata12 = mutate_at(mydata, vars(Y2008:Y2010), funs(Rank=min_rank(.)))

Select State that generated highest income among the variable ‘Index’

out = mydata %>% group_by(Index) %>% filter(min_rank(desc(Y2015)) == 1) %>% select(Index, Y2015)

Cumulative Income of ‘Index’ variable

out2 = mydata %>% group_by(Index) %>% mutate(Total=cumsum(Y2015)) %>% select(Index,Y2013:Y2015)

JOINS IN R

df1 <- data.frame(ID = c(1, 2, 3, 4, 5), w = c(‘a’, ‘b’, ‘c’, ‘d’, ‘e’), x = c(1, 1, 0, 0, 1), y=rnorm(5), z=letters[1:5]) df2 <- data.frame(ID = c(1, 7, 3, 6, 8), a = c(‘z’, ‘b’, ‘k’, ‘d’, ‘l’), b = c(1, 2, 3, 0, 4), c =rnorm(5), d =letters[2:6]) ### INNER JOIN ######### df3 = inner_join(df1, df2, by = “ID”)

LEFT_JOIN

RETURNS ALL THE ROWS IN FIRST DATA FRAME AND ALL COLOUMNS IN SECOND DF

LEFTJOIN<-left_join(df1, df2, by = “ID”)

UNINON

COMBINES DATA VERTICALLY

x=data.frame(ID = 1:6, ID1= 1:6) y=data.frame(ID = 1:6, ID1 = 1:6,ID2=1:6) UN<-union(x,y) un1<-union_all(x,y)