R Quiz for Batch 1 & 2 Answer the following question and publish in rpubs and share the link
Part 1
Q1) How many data structures does R language have?
Answer:1.Atomic Vector 2.List 3.Array 4.Matrices 5.Data Frame & 6.Factors.
Q2) What is the value of f(5) for the following R code?
b <- 4
f <- function (a)
{
b <- 3
b^3
}
f(5)## [1] 27Q3) Fix the error in the code?
printmessage <- function (a) {
if (is.na (a))
print ("a is a missing value!")
else if (a < 0){
print ("a is less than zero")
}
else{
print ("a is greater than or equal to zero")
}
}
printmessage (NA)## [1] "a is a missing value!"Q4) What is the difference between data frame and a matrix in R?
Answer:In a data frame the columns contain different types of data.Data frame is more general than a matrix,in that different columns can have different modes(numeric,character,factor etc.).Just like a table in a database or excel sheet.
But,in a matrix all the elements are the same type of data.A matrix in R is like a mathematical matrix,containing all the same type of thing(usually numbers)
Q5) Two vectors X and Y are defined as follows – X <- c(3, 2, 4) and Y <- c(1, 2). What will be output of vector Z that is defined as Z <- X*Y.
X<-c(3,2,4)
Y<-c(1,2)
z<-X*Y## Warning in X * Y: longer object length is not a multiple of shorter object
## lengthz## [1] 3 4 4longer object length is not a multiple of shorter object length[1] 3 4 4
Q6) Drop variables v2 & v3 from the below dataframe df<-data.frame(v1=c(1:5),v2=c(2:6),v3=c(3:7),v4=c(4:8))
library(dplyr)## Warning: package 'dplyr' was built under R version 4.0.3##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, uniondf<-data.frame(v1=c(1:5),v2=c(2:6),v3=c(3:7),v4=c(4:8))
select(df,-v2,-v3)## v1 v4
## 1 1 4
## 2 2 5
## 3 3 6
## 4 4 7
## 5 5 8Q7) What will be the output of the following R programming code?
x<-5
if(x%%2==0){
print("X is an even number")
}else{
print("X is an odd number")
}## [1] "X is an odd number"Q8) I have a string “contact@boston.in”. Which string function can be used to split the string into two different strings “contact@boston” and “in”?
x<-"contact@boston.in"
strsplit(x,".",fixed=TRUE)## [[1]]
## [1] "contact@boston" "in"Q9) Write a R program to find the counts of uniques for the given vector
tt <- c("a", "b", "a", "a", "b", "c", "a1", "a1", "a1")
table(tt)## tt
## a a1 b c
## 3 3 2 1Q10) Write a R program to find the cumulative frequency for given vector?
Sales = c(10,2,40,13,34,12,35,67,12,56,14,56,134)
cumsum(Sales)## [1] 10 12 52 65 99 111 146 213 225 281 295 351 485Part 2 For the given dataset carrying out following tasks Dataset: ##The dataset is given in the link 1) Find missing value in the dataset 2) Impute missing values in the dataset using missforest package 3) Find summary statistics using dplyr package 4) What is the avg amount spend by Male and Females? 5) Using GGplots library build 5 graphs?
library(missForest)## Warning: package 'missForest' was built under R version 4.0.3## Loading required package: randomForest## Warning: package 'randomForest' was built under R version 4.0.3## randomForest 4.6-14## Type rfNews() to see new features/changes/bug fixes.##
## Attaching package: 'randomForest'## The following object is masked from 'package:dplyr':
##
## combine## Loading required package: foreach## Loading required package: itertools## Warning: package 'itertools' was built under R version 4.0.3## Loading required package: iteratorsInport the Dataset
german_credit_dataset<-read.csv('C:/Users/MADHURIMA PANDE/Desktop/R/german_credit_data_risk.csv')Pre analysis of Dataset
View(german_credit_dataset)# View datasetsummary(german_credit_dataset)#Summarise dataset## X Age Sex Job
## Min. : 0.0 Min. :19.00 Length:1000 Min. :0.000
## 1st Qu.:249.8 1st Qu.:27.00 Class :character 1st Qu.:2.000
## Median :499.5 Median :33.00 Mode :character Median :2.000
## Mean :499.5 Mean :35.55 Mean :1.904
## 3rd Qu.:749.2 3rd Qu.:42.00 3rd Qu.:2.000
## Max. :999.0 Max. :75.00 Max. :3.000
## Housing Saving.accounts Checking.account Credit.amount
## Length:1000 Length:1000 Length:1000 Min. : 250
## Class :character Class :character Class :character 1st Qu.: 1366
## Mode :character Mode :character Mode :character Median : 2320
## Mean : 3271
## 3rd Qu.: 3972
## Max. :18424
## Duration Purpose Risk
## Min. : 4.0 Length:1000 Length:1000
## 1st Qu.:12.0 Class :character Class :character
## Median :18.0 Mode :character Mode :character
## Mean :20.9
## 3rd Qu.:24.0
## Max. :72.0head(german_credit_dataset)# 1st 6rows of dataset## X Age Sex Job Housing Saving.accounts Checking.account Credit.amount
## 1 0 67 male 2 own <NA> little 1169
## 2 1 22 female 2 own little moderate 5951
## 3 2 49 male 1 own little <NA> 2096
## 4 3 45 male 2 free little little 7882
## 5 4 53 male 2 free little little 4870
## 6 5 35 male 1 free <NA> <NA> 9055
## Duration Purpose Risk
## 1 6 radio/TV good
## 2 48 radio/TV bad
## 3 12 education good
## 4 42 furniture/equipment good
## 5 24 car bad
## 6 36 education goodtail(german_credit_dataset) # Last 6 rows of dataset## X Age Sex Job Housing Saving.accounts Checking.account Credit.amount
## 995 994 50 male 2 own <NA> <NA> 2390
## 996 995 31 female 1 own little <NA> 1736
## 997 996 40 male 3 own little little 3857
## 998 997 38 male 2 own little <NA> 804
## 999 998 23 male 2 free little little 1845
## 1000 999 27 male 2 own moderate moderate 4576
## Duration Purpose Risk
## 995 12 car good
## 996 12 furniture/equipment good
## 997 30 car good
## 998 12 radio/TV good
## 999 45 radio/TV bad
## 1000 45 car goodstr(german_credit_dataset)## 'data.frame': 1000 obs. of 11 variables:
## $ X : int 0 1 2 3 4 5 6 7 8 9 ...
## $ Age : int 67 22 49 45 53 35 53 35 61 28 ...
## $ Sex : chr "male" "female" "male" "male" ...
## $ Job : int 2 2 1 2 2 1 2 3 1 3 ...
## $ Housing : chr "own" "own" "own" "free" ...
## $ Saving.accounts : chr NA "little" "little" "little" ...
## $ Checking.account: chr "little" "moderate" NA "little" ...
## $ Credit.amount : int 1169 5951 2096 7882 4870 9055 2835 6948 3059 5234 ...
## $ Duration : int 6 48 12 42 24 36 24 36 12 30 ...
## $ Purpose : chr "radio/TV" "radio/TV" "education" "furniture/equipment" ...
## $ Risk : chr "good" "bad" "good" "good" ...1.Find missing value in the dataset
sum(is.na(german_credit_dataset)) #Total no. of missing values in the dataset## [1] 577colSums(is.na(german_credit_dataset)) # Columnwise missing values## X Age Sex Job
## 0 0 0 0
## Housing Saving.accounts Checking.account Credit.amount
## 0 183 394 0
## Duration Purpose Risk
## 0 0 0- Impute missing values in the dataset using missforest package
# Getting the unique count of Checking account
table(german_credit_dataset$Checking.account)##
## little moderate rich
## 274 269 63# replacing each levels of Checking.account column with numeric values like replace 'little' wih 1 'moderate' with 2 and 'rich' with 4
german_credit_dataset$Checking.account[german_credit_dataset$Checking.account=='little']<-c(1)
german_credit_dataset$Checking.account[german_credit_dataset$Checking.account=='moderate']<-c(2)
german_credit_dataset$Checking.account[german_credit_dataset$Checking.account=='rich']<-c(3)
class(german_credit_dataset$Checking.account)## [1] "character"#convert the datatype of Checking.account from character to numetic
german_credit_dataset$Checking.account<-as.numeric(german_credit_dataset$Checking.account)
class(german_credit_dataset$Checking.account)## [1] "numeric"# checking the unique count of Savings account
table(german_credit_dataset$Saving.accounts)##
## little moderate quite rich rich
## 603 103 63 48# replacing each levels of Saving.accounts column with numeric values like replace 'little' wih 1 'moderate' with 2, 'quite rich' with 3 and 'rich' with 4
german_credit_dataset$Saving.accounts[german_credit_dataset$Saving.accounts=='little']<-c(1)
german_credit_dataset$Saving.accounts[german_credit_dataset$Saving.accounts=='moderate']<-c(2)
german_credit_dataset$Saving.accounts[german_credit_dataset$Saving.accounts=='quite rich']<-c(3)
german_credit_dataset$Saving.accounts[german_credit_dataset$Saving.accounts=='rich']<-c(4)
class(german_credit_dataset$Saving.accounts)## [1] "character"german_credit_dataset$Saving.accounts<-as.numeric(german_credit_dataset$Saving.accounts)
class(german_credit_dataset$Saving.accounts)## [1] "numeric"german_credit_dataset_num<-unlist(lapply(german_credit_dataset,is.numeric))
german_credit_dataset_num## X Age Sex Job
## TRUE TRUE FALSE TRUE
## Housing Saving.accounts Checking.account Credit.amount
## FALSE TRUE TRUE TRUE
## Duration Purpose Risk
## TRUE FALSE FALSEmissForest(german_credit_dataset[,german_credit_dataset_num])## missForest iteration 1 in progress...## Warning in randomForest.default(x = obsX, y = obsY, ntree = ntree, mtry =
## mtry, : The response has five or fewer unique values. Are you sure you want to
## do regression?
## Warning in randomForest.default(x = obsX, y = obsY, ntree = ntree, mtry =
## mtry, : The response has five or fewer unique values. Are you sure you want to
## do regression?## done!
## missForest iteration 2 in progress...## Warning in randomForest.default(x = obsX, y = obsY, ntree = ntree, mtry =
## mtry, : The response has five or fewer unique values. Are you sure you want to
## do regression?
## Warning in randomForest.default(x = obsX, y = obsY, ntree = ntree, mtry =
## mtry, : The response has five or fewer unique values. Are you sure you want to
## do regression?## done!
## missForest iteration 3 in progress...## Warning in randomForest.default(x = obsX, y = obsY, ntree = ntree, mtry =
## mtry, : The response has five or fewer unique values. Are you sure you want to
## do regression?
## Warning in randomForest.default(x = obsX, y = obsY, ntree = ntree, mtry =
## mtry, : The response has five or fewer unique values. Are you sure you want to
## do regression?## done!
## missForest iteration 4 in progress...## Warning in randomForest.default(x = obsX, y = obsY, ntree = ntree, mtry =
## mtry, : The response has five or fewer unique values. Are you sure you want to
## do regression?
## Warning in randomForest.default(x = obsX, y = obsY, ntree = ntree, mtry =
## mtry, : The response has five or fewer unique values. Are you sure you want to
## do regression?## done!
## missForest iteration 5 in progress...## Warning in randomForest.default(x = obsX, y = obsY, ntree = ntree, mtry =
## mtry, : The response has five or fewer unique values. Are you sure you want to
## do regression?
## Warning in randomForest.default(x = obsX, y = obsY, ntree = ntree, mtry =
## mtry, : The response has five or fewer unique values. Are you sure you want to
## do regression?## done!
## missForest iteration 6 in progress...## Warning in randomForest.default(x = obsX, y = obsY, ntree = ntree, mtry =
## mtry, : The response has five or fewer unique values. Are you sure you want to
## do regression?
## Warning in randomForest.default(x = obsX, y = obsY, ntree = ntree, mtry =
## mtry, : The response has five or fewer unique values. Are you sure you want to
## do regression?## done!
## missForest iteration 7 in progress...## Warning in randomForest.default(x = obsX, y = obsY, ntree = ntree, mtry =
## mtry, : The response has five or fewer unique values. Are you sure you want to
## do regression?
## Warning in randomForest.default(x = obsX, y = obsY, ntree = ntree, mtry =
## mtry, : The response has five or fewer unique values. Are you sure you want to
## do regression?## done!## $ximp
## X Age Job Saving.accounts Checking.account Credit.amount Duration
## 1 0 67 2 1.592500 1.000000 1169 6
## 2 1 22 2 1.000000 2.000000 5951 48
## 3 2 49 1 1.000000 1.902312 2096 12
## 4 3 45 2 1.000000 1.000000 7882 42
## 5 4 53 2 1.000000 1.000000 4870 24
## 6 5 35 1 1.190000 1.242692 9055 36
## 7 6 53 2 3.000000 1.650000 2835 24
## 8 7 35 3 1.000000 2.000000 6948 36
## 9 8 61 1 4.000000 1.679444 3059 12
## 10 9 28 3 1.000000 2.000000 5234 30
## 11 10 25 2 1.000000 2.000000 1295 12
## 12 11 24 2 1.000000 1.000000 4308 48
## 13 12 22 2 1.000000 2.000000 1567 12
## 14 13 60 1 1.000000 1.000000 1199 24
## 15 14 28 2 1.000000 1.000000 1403 15
## 16 15 32 1 2.000000 1.000000 1282 24
## 17 16 53 2 2.026667 1.660000 2424 24
## 18 17 25 2 1.742500 1.000000 8072 30
## 19 18 44 3 1.000000 2.000000 12579 24
## 20 19 31 2 3.000000 1.450000 3430 24
## 21 20 48 2 1.000000 1.445690 2134 9
## 22 21 44 2 3.000000 1.000000 2647 6
## 23 22 48 1 1.000000 1.000000 2241 10
## 24 23 44 2 2.000000 2.000000 1804 12
## 25 24 26 2 1.320000 1.708690 2069 10
## 26 25 36 1 1.000000 1.000000 1374 6
## 27 26 39 1 1.000000 1.451645 426 6
## 28 27 42 2 4.000000 3.000000 409 12
## 29 28 34 2 1.000000 2.000000 2415 7
## 30 29 63 2 1.000000 1.000000 6836 60
## 31 30 36 2 4.000000 2.000000 1913 18
## 32 31 27 2 1.000000 1.000000 4020 24
## 33 32 30 2 2.000000 2.000000 5866 18
## 34 33 57 1 2.830000 2.080000 1264 12
## 35 34 33 3 1.000000 3.000000 1474 12
## 36 35 25 1 1.000000 2.000000 4746 45
## 37 36 31 2 1.000000 1.579075 6110 48
## 38 37 37 2 1.000000 3.000000 2100 18
## 39 38 37 2 1.000000 3.000000 1225 10
## 40 39 24 2 1.000000 2.000000 458 9
## 41 40 30 3 3.000000 1.680000 2333 30
## 42 41 26 2 3.000000 2.000000 1158 12
## 43 42 44 1 1.000000 2.000000 6204 18
## 44 43 24 2 2.000000 1.000000 6187 30
## 45 44 58 1 1.000000 1.000000 6143 48
## 46 45 35 3 1.000000 2.318086 1393 11
## 47 46 39 2 3.000000 1.890000 2299 36
## 48 47 23 0 3.000000 1.000000 1352 6
## 49 48 39 1 1.000000 1.818000 7228 11
## 50 49 28 2 2.000000 1.706667 2073 12
## 51 50 29 1 1.765000 2.000000 2333 24
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## 53 52 25 2 1.000000 1.980135 1262 12
## 54 53 31 2 1.991786 1.860000 3378 18
## 55 54 57 2 1.000000 2.000000 2225 36
## 56 55 26 1 1.764333 1.753333 783 6
## 57 56 52 3 1.450208 2.000000 6468 12
## 58 57 31 2 1.000000 1.566508 9566 36
## 59 58 23 3 1.000000 3.000000 1961 18
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## 66 65 48 2 1.990000 1.880000 5190 27
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## 69 68 37 2 1.000000 1.691508 1819 36
## 70 69 25 2 1.340000 1.590000 2394 36
## 71 70 30 2 1.000000 1.644508 8133 36
## 72 71 46 1 1.650000 1.530833 730 7
## 73 72 51 3 1.000000 1.000000 1164 8
## 74 73 41 1 1.000000 2.000000 5954 42
## 75 74 40 3 1.190000 1.000000 1977 36
## 76 75 66 3 1.000000 1.000000 1526 12
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## 78 77 51 2 1.000000 2.000000 4771 11
## 79 78 39 1 1.942500 1.878000 9436 54
## 80 79 22 2 1.000000 2.000000 3832 30
## 81 80 44 2 1.717500 1.850000 5943 24
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##
## $OOBerror
## NRMSE
## 0.0002453745
##
## attr(,"class")
## [1] "missForest"- Find summary statistics using dplyr package
library(dplyr)colSums(is.na(german_credit_dataset))## X Age Sex Job
## 0 0 0 0
## Housing Saving.accounts Checking.account Credit.amount
## 0 183 394 0
## Duration Purpose Risk
## 0 0 0german_credit_dataset %>% group_by(Sex) %>% summarise(Min_Age=min(Age),Quantile_1=quantile(Age,prob=c(0.25)),Median_Age=median(Age),Mean_Age=mean(Age),StndDev=sd(Age),Quantile_3=quantile(Age,prob=c(0.75)),Max_Age=max(Age))## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 2 x 8
## Sex Min_Age Quantile_1 Median_Age Mean_Age StndDev Quantile_3 Max_Age
## <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 female 19 24 29 32.8 11.8 37 75
## 2 male 20 28 35 36.8 11.0 43 75The above table shows the summary statistics of age column
german_credit_dataset %>% group_by(Sex) %>% summarise(Min_Credit=min(Credit.amount),Quantile_1=quantile(Credit.amount,prob=c(0.25)),Median_Credit=median(Credit.amount),Mean_Credit=mean(Credit.amount),StndDev=sd(Credit.amount),Quantile_3=quantile(Credit.amount,prob=c(0.75)),Max_Credit=max(Credit.amount))## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 2 x 8
## Sex Min_Credit Quantile_1 Median_Credit Mean_Credit StndDev Quantile_3
## <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 fema~ 250 1248. 1959 2878. 2603. 3606.
## 2 male 276 1442. 2444. 3448. 2900. 4266.
## # ... with 1 more variable: Max_Credit <int>The above table shows the summary statistics of Credit.amount column
- What is the avg amount spend by Male and Female?
german_credit_dataset %>% group_by(Sex) %>% summarise(Avg_cr_amt=mean(Credit.amount))## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 2 x 2
## Sex Avg_cr_amt
## <chr> <dbl>
## 1 female 2878.
## 2 male 3448.library(ggplot2)## Warning: package 'ggplot2' was built under R version 4.0.3##
## Attaching package: 'ggplot2'## The following object is masked from 'package:randomForest':
##
## marginggplot(german_credit_dataset,aes(Sex,Credit.amount))+geom_bar(stat="identity")+theme(axis.text.x=element_text(angle=70,vjust=0.5,color="red"))+xlab("Sex")+ylab("Credit.amount")+ggtitle("Sex Vs Credit amount")
- Using GGplots library build 5 graphs
Statistical Age data of male and female
ggplot(german_credit_dataset,aes(Age,Sex,fill=Sex))+ geom_boxplot()+ggtitle("Box plot of statistical age data of male and female")+theme(axis.text.x = element_text(angle=70,vjust=0.5,color="blue"))+xlab("Sex")+ylab("Age")+coord_flip()
In the above box plot, age of maximum female customers lies between 23 to 37 and age of maximum male customers lies between 28 to 43.The outlier of female customers can be considered after 53 and outlier of male customers can be considered after 65.
Ratio Of Housing type
ggplot(german_credit_dataset,aes(x=Housing,fill=factor(Housing)))+geom_bar(width=2)## Warning: position_stack requires non-overlapping x intervals
From above plot, we can infer that, No of Own house>No of rented house> No of free house
Ratio Of male & female
ggplot(german_credit_dataset, aes(x =factor(1), fill = factor(Sex))) + geom_bar(width = 1) + coord_polar(theta = "y")
Ratio of male is greater than female
Relationship Of Duration & Credit amount
ggplot(german_credit_dataset, aes(Duration,Credit.amount)) + geom_point(aes(color =Purpose)) +
scale_x_continuous("Duration", breaks = seq(0,0.35,0.05))+
scale_y_continuous("Credit.amount", breaks = seq(0,270,by = 30))+
theme_bw() + labs(title="Scatterplot")
The above plot is the relationship between Credit amount & Duration w.r.t Purpose
Relationship between Risk & Age
ggplot(german_credit_dataset, aes(Age, fill = Risk)) + geom_bar()+
labs(title = "Risk Count According to Age", x = "Age", y = "Count of Risk")
We can infer that, Risk factor is going to be lower with increasing of age.