Assigment in R Dataframe
Archana Sundari
11/11/2020
1) Creating Vector and Covert Vector into List and List to Dataframe :
#Creating vectors:
x1<-c("Apple","Samsung","vivo")
x2<-c("Red","Black","Pink")
x3<-c(1,3,2)
# Creating list of vectors
x <- list(col1 = x1, col2 = x2, col2 = x3)
x ## $col1
## [1] "Apple" "Samsung" "vivo"
##
## $col2
## [1] "Red" "Black" "Pink"
##
## $col2
## [1] 1 3 2#Convert into Dataframe:
as.data.frame(x) ## col1 col2 col2.1
## 1 Apple Red 1
## 2 Samsung Black 3
## 3 vivo Pink 22) Converting predefined sets into Dataframe:
x <- as.list(BOD)# BOD (Biochemical Oxygen Demand) is pre-defined Vector in R.
x ## $Time
## [1] 1 2 3 4 5 7
##
## $demand
## [1] 8.3 10.3 19.0 16.0 15.6 19.8
##
## attr(,"reference")
## [1] "A1.4, p. 270"as.data.frame(x)## Time demand
## 1 1 8.3
## 2 2 10.3
## 3 3 19.0
## 4 4 16.0
## 5 5 15.6
## 6 7 19.83) Finding Dataframe:
data.frame() is used to identify whether the provided dataset is a Dataframe.
is.data.frame(BOD)## [1] TRUEis.data.frame(1)## [1] FALSEis.data.frame(x)## [1] FALSE4) Converting all the values in Dataframe into Matrix
data.matrix() function in R Language is used to create a matrix by converting all the values of a Data Frame into numeric mode and then binding them as a matrix.
df=data.frame(
"Name"=c("a","b","c","d"),
"Age"=c(12,34,23,67),
"BMI"=c(22,12,10,11),
"Country"=c("Malaysia","India","Spain","England"))
print(df)## Name Age BMI Country
## 1 a 12 22 Malaysia
## 2 b 34 12 India
## 3 c 23 10 Spain
## 4 d 67 11 Englanddf1<-data.matrix(df)
print(df1)## Name Age BMI Country
## [1,] 1 12 22 3
## [2,] 2 34 12 2
## [3,] 3 23 10 4
## [4,] 4 67 11 15) Converting matrix into DataFrame
as.data.frame() is used to convert matrix dataset as Dataframe.
df2<-as.data.frame(df1)
print(df2)## Name Age BMI Country
## 1 1 12 22 3
## 2 2 34 12 2
## 3 3 23 10 4
## 4 4 67 11 16) Creating a Dataframe using list , renaming Column names and Getting dimension of the data
Creating dataframe using data.frame() function. Renaming column names using names(), the same can also be achived using rename() dim() is used to get dimention of the existing dataframe
Friends=data.frame(
A=c("Raju","Kumar","Chandru","Deepak","Raina", "Dhoni", "Hema", "Vandana", "Bela","Anand" ),
B=c(12,34,23,67,23,43,23,38,34,12),
C=c("M","M","M","M","M","M","F","F","F","M"),
D=c("Malaysia","India","Spain","England", "Malaysia", "Malaysia", "India", "Egypt", "Italy", "Peru"))
names(Friends)<-c("Name", "Age", "Gender","Country" )
Friends## Name Age Gender Country
## 1 Raju 12 M Malaysia
## 2 Kumar 34 M India
## 3 Chandru 23 M Spain
## 4 Deepak 67 M England
## 5 Raina 23 M Malaysia
## 6 Dhoni 43 M Malaysia
## 7 Hema 23 F India
## 8 Vandana 38 F Egypt
## 9 Bela 34 F Italy
## 10 Anand 12 M Perudim(Friends)## [1] 10 47) Structure of data
str(Friends)## 'data.frame': 10 obs. of 4 variables:
## $ Name : chr "Raju" "Kumar" "Chandru" "Deepak" ...
## $ Age : num 12 34 23 67 23 43 23 38 34 12
## $ Gender : chr "M" "M" "M" "M" ...
## $ Country: chr "Malaysia" "India" "Spain" "England" ...8) Getting the first 6 rows:
head(Friends)## Name Age Gender Country
## 1 Raju 12 M Malaysia
## 2 Kumar 34 M India
## 3 Chandru 23 M Spain
## 4 Deepak 67 M England
## 5 Raina 23 M Malaysia
## 6 Dhoni 43 M Malaysia9) Getting the last 6 rows:
tail(Friends)## Name Age Gender Country
## 5 Raina 23 M Malaysia
## 6 Dhoni 43 M Malaysia
## 7 Hema 23 F India
## 8 Vandana 38 F Egypt
## 9 Bela 34 F Italy
## 10 Anand 12 M Peru10) Accessing 1st &2nd rows:
print(Friends[1:2,])## Name Age Gender Country
## 1 Raju 12 M Malaysia
## 2 Kumar 34 M India11) Accessing 1st &2nd col:
print(Friends[,1:2])## Name Age
## 1 Raju 12
## 2 Kumar 34
## 3 Chandru 23
## 4 Deepak 67
## 5 Raina 23
## 6 Dhoni 43
## 7 Hema 23
## 8 Vandana 38
## 9 Bela 34
## 10 Anand 1212) Selecting the subset of Dataframe
print(Friends)## Name Age Gender Country
## 1 Raju 12 M Malaysia
## 2 Kumar 34 M India
## 3 Chandru 23 M Spain
## 4 Deepak 67 M England
## 5 Raina 23 M Malaysia
## 6 Dhoni 43 M Malaysia
## 7 Hema 23 F India
## 8 Vandana 38 F Egypt
## 9 Bela 34 F Italy
## 10 Anand 12 M PeruFriendsinMalaysia<-subset(Friends,Country=="Malaysia")
print(FriendsinMalaysia)## Name Age Gender Country
## 1 Raju 12 M Malaysia
## 5 Raina 23 M Malaysia
## 6 Dhoni 43 M MalaysiaFriendsabove30 = subset(Friends, Age>30)
print(Friendsabove30)## Name Age Gender Country
## 2 Kumar 34 M India
## 4 Deepak 67 M England
## 6 Dhoni 43 M Malaysia
## 8 Vandana 38 F Egypt
## 9 Bela 34 F Italy13) Appending a column to an existing Dataframe
Employmentstatus <- c("Unemployed","Employed","Employed","Unemployed","Employed","Unemployed","Employed","Employed","Unemployed","Unemployed")
# Add `quantity` to the `Friends` data frame
Friends$Employmentstatus <- Employmentstatus
Friends## Name Age Gender Country Employmentstatus
## 1 Raju 12 M Malaysia Unemployed
## 2 Kumar 34 M India Employed
## 3 Chandru 23 M Spain Employed
## 4 Deepak 67 M England Unemployed
## 5 Raina 23 M Malaysia Employed
## 6 Dhoni 43 M Malaysia Unemployed
## 7 Hema 23 F India Employed
## 8 Vandana 38 F Egypt Employed
## 9 Bela 34 F Italy Unemployed
## 10 Anand 12 M Peru Unemployed14) modifying indirectly by creating a table
Friends[[4]][2]="Australia" # change Country from India to Australia for Kumar
print(Friends)## Name Age Gender Country Employmentstatus
## 1 Raju 12 M Malaysia Unemployed
## 2 Kumar 34 M Australia Employed
## 3 Chandru 23 M Spain Employed
## 4 Deepak 67 M England Unemployed
## 5 Raina 23 M Malaysia Employed
## 6 Dhoni 43 M Malaysia Unemployed
## 7 Hema 23 F India Employed
## 8 Vandana 38 F Egypt Employed
## 9 Bela 34 F Italy Unemployed
## 10 Anand 12 M Peru Unemployed15) Create or Edit a DataFrame by direct assigning using table.
myTable=edit(Friends)
print(myTable)## Name Age Gender Country Employmentstatus
## 1 Raju 12 M Malaysia Unemployed
## 2 Kumar 34 M Australia Employed
## 3 Chandru 23 M Spain Employed
## 4 Deepak 67 M England Unemployed
## 5 Raina 23 M Malaysia Employed
## 6 Dhoni 43 M Malaysia Unemployed
## 7 Hema 23 F India Employed
## 8 Vandana 38 F Egypt Employed
## 9 Bela 34 F Italy Unemployed
## 10 Anand 12 M Peru Unemployed16) Sorting the dataframe
sorteddata <-myTable[order(myTable$Age),]
sorteddata## Name Age Gender Country Employmentstatus
## 1 Raju 12 M Malaysia Unemployed
## 10 Anand 12 M Peru Unemployed
## 3 Chandru 23 M Spain Employed
## 5 Raina 23 M Malaysia Employed
## 7 Hema 23 F India Employed
## 2 Kumar 34 M Australia Employed
## 9 Bela 34 F Italy Unemployed
## 8 Vandana 38 F Egypt Employed
## 6 Dhoni 43 M Malaysia Unemployed
## 4 Deepak 67 M England Unemployed17) Adding a column using cbind() function
newTable=cbind(myTable,Foodpreference=c("Veg","Veg","Veg","Non-Veg","Veg","Non-Veg","Veg","Veg","Non-Veg","Non-Veg"))
print(newTable)## Name Age Gender Country Employmentstatus Foodpreference
## 1 Raju 12 M Malaysia Unemployed Veg
## 2 Kumar 34 M Australia Employed Veg
## 3 Chandru 23 M Spain Employed Veg
## 4 Deepak 67 M England Unemployed Non-Veg
## 5 Raina 23 M Malaysia Employed Veg
## 6 Dhoni 43 M Malaysia Unemployed Non-Veg
## 7 Hema 23 F India Employed Veg
## 8 Vandana 38 F Egypt Employed Veg
## 9 Bela 34 F Italy Unemployed Non-Veg
## 10 Anand 12 M Peru Unemployed Non-Veg18) Adding rows using rbind() function:
newTable=rbind(newTable,data.frame(Name="Ely", Age=10, Gender="F", Country="Germany",Employmentstatus ="unemployed", Foodpreference = "Veg"))
print(newTable)## Name Age Gender Country Employmentstatus Foodpreference
## 1 Raju 12 M Malaysia Unemployed Veg
## 2 Kumar 34 M Australia Employed Veg
## 3 Chandru 23 M Spain Employed Veg
## 4 Deepak 67 M England Unemployed Non-Veg
## 5 Raina 23 M Malaysia Employed Veg
## 6 Dhoni 43 M Malaysia Unemployed Non-Veg
## 7 Hema 23 F India Employed Veg
## 8 Vandana 38 F Egypt Employed Veg
## 9 Bela 34 F Italy Unemployed Non-Veg
## 10 Anand 12 M Peru Unemployed Non-Veg
## 11 Ely 10 F Germany unemployed Veg19) Duplicating a column using mutate():
Using mutate(), we will create a new column and display the age in Year 2030.
#install.packages("dplyr")
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionnewTable=mutate(newTable,Agein2030=Age+10)
print(newTable)## Name Age Gender Country Employmentstatus Foodpreference Agein2030
## 1 Raju 12 M Malaysia Unemployed Veg 22
## 2 Kumar 34 M Australia Employed Veg 44
## 3 Chandru 23 M Spain Employed Veg 33
## 4 Deepak 67 M England Unemployed Non-Veg 77
## 5 Raina 23 M Malaysia Employed Veg 33
## 6 Dhoni 43 M Malaysia Unemployed Non-Veg 53
## 7 Hema 23 F India Employed Veg 33
## 8 Vandana 38 F Egypt Employed Veg 48
## 9 Bela 34 F Italy Unemployed Non-Veg 44
## 10 Anand 12 M Peru Unemployed Non-Veg 22
## 11 Ely 10 F Germany unemployed Veg 2020) transmute():
Using transmute(), we will change an existing column and display the age in Year 2050.
#install.packages("dplyr")
library(dplyr)
Table2=transmute(newTable,Name, Age, Gender, Country, Employmentstatus, Foodpreference, Agein2050=Agein2030+20)
print(Table2)## Name Age Gender Country Employmentstatus Foodpreference Agein2050
## 1 Raju 12 M Malaysia Unemployed Veg 42
## 2 Kumar 34 M Australia Employed Veg 64
## 3 Chandru 23 M Spain Employed Veg 53
## 4 Deepak 67 M England Unemployed Non-Veg 97
## 5 Raina 23 M Malaysia Employed Veg 53
## 6 Dhoni 43 M Malaysia Unemployed Non-Veg 73
## 7 Hema 23 F India Employed Veg 53
## 8 Vandana 38 F Egypt Employed Veg 68
## 9 Bela 34 F Italy Unemployed Non-Veg 64
## 10 Anand 12 M Peru Unemployed Non-Veg 42
## 11 Ely 10 F Germany unemployed Veg 4021) Adding column:
Adding numeric columns
Table2$"NewAge"<-NA
Table2[["NewAge"]]<-Table2[["Age"]] + Table2[["Agein2050"]]
print(Table2)## Name Age Gender Country Employmentstatus Foodpreference Agein2050
## 1 Raju 12 M Malaysia Unemployed Veg 42
## 2 Kumar 34 M Australia Employed Veg 64
## 3 Chandru 23 M Spain Employed Veg 53
## 4 Deepak 67 M England Unemployed Non-Veg 97
## 5 Raina 23 M Malaysia Employed Veg 53
## 6 Dhoni 43 M Malaysia Unemployed Non-Veg 73
## 7 Hema 23 F India Employed Veg 53
## 8 Vandana 38 F Egypt Employed Veg 68
## 9 Bela 34 F Italy Unemployed Non-Veg 64
## 10 Anand 12 M Peru Unemployed Non-Veg 42
## 11 Ely 10 F Germany unemployed Veg 40
## NewAge
## 1 54
## 2 98
## 3 76
## 4 164
## 5 76
## 6 116
## 7 76
## 8 106
## 9 98
## 10 54
## 11 5022) Deleting a column by assigning NULL to it.
Here we will delete column “NewAge” by assigning NULL to it.
Table2$NewAge <- NULL
Table2## Name Age Gender Country Employmentstatus Foodpreference Agein2050
## 1 Raju 12 M Malaysia Unemployed Veg 42
## 2 Kumar 34 M Australia Employed Veg 64
## 3 Chandru 23 M Spain Employed Veg 53
## 4 Deepak 67 M England Unemployed Non-Veg 97
## 5 Raina 23 M Malaysia Employed Veg 53
## 6 Dhoni 43 M Malaysia Unemployed Non-Veg 73
## 7 Hema 23 F India Employed Veg 53
## 8 Vandana 38 F Egypt Employed Veg 68
## 9 Bela 34 F Italy Unemployed Non-Veg 64
## 10 Anand 12 M Peru Unemployed Non-Veg 42
## 11 Ely 10 F Germany unemployed Veg 4023) Deleting a column by assigning NULL to it.
Here we will delete specific row by proving the corresponding row number. Here we will delete 2nd row.
Table2 <- Table2[-2,]
Table2## Name Age Gender Country Employmentstatus Foodpreference Agein2050
## 1 Raju 12 M Malaysia Unemployed Veg 42
## 3 Chandru 23 M Spain Employed Veg 53
## 4 Deepak 67 M England Unemployed Non-Veg 97
## 5 Raina 23 M Malaysia Employed Veg 53
## 6 Dhoni 43 M Malaysia Unemployed Non-Veg 73
## 7 Hema 23 F India Employed Veg 53
## 8 Vandana 38 F Egypt Employed Veg 68
## 9 Bela 34 F Italy Unemployed Non-Veg 64
## 10 Anand 12 M Peru Unemployed Non-Veg 42
## 11 Ely 10 F Germany unemployed Veg 4024) Summarize Dataframe :
summary() function is used to summarize each column in the Dataframe. If a column is Character, then it will provide us size/length of the dataframe, class, mode. If a column is a number, then it provides minimum, Maximum, Mean, Median etc.
summary(Table2)## Name Age Gender Country
## Length:10 Min. :10.00 Length:10 Length:10
## Class :character 1st Qu.:14.75 Class :character Class :character
## Mode :character Median :23.00 Mode :character Mode :character
## Mean :28.50
## 3rd Qu.:37.00
## Max. :67.00
## Employmentstatus Foodpreference Agein2050
## Length:10 Length:10 Min. :40.00
## Class :character Class :character 1st Qu.:44.75
## Mode :character Mode :character Median :53.00
## Mean :58.50
## 3rd Qu.:67.00
## Max. :97.0025) Visualize the Dataframe using plot() :
Table2$Gender <- factor(Table2$Gender)
plot(Table2$Gender, Table2$Age)