Part 1
Q1) How many data structures does R language have?
Answer:R language have 6 types of Data structures as follows:-
# Vector
# List
# Array
# Factor
# Matrix
# DataFrameQ2) What is the value of f(5) for the following R code?
b <- 4
f <- function (a)
{
b <- 3
b^3
}Ans: 27
Q3) 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:# Matrix is Homogeneous, It’s a m*n array with similar data type. EX: A = matrix (c(11, 22, 33, 44, 55, 66),
nrow = 2, ncol = 3) DataFrames are heterogeneous. It can contain multiple data types in multiple columns called fields. Ex: df <- data.frame( col1 = c(1:3, NA), col2 = c(“this”, NA, “is”, “text”), col3 = c(TRUE, FALSE, TRUE, TRUE), col4 = c(2.5, 4.2, 3.2, NA))
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 4Q6) 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))
df<-data.frame(v1=c(1:5),v2=c(2:6),v3=c(3:7),v4=c(4:8))
df1 <- df
df1[,c('v2','v3')] <- list(NULL)
df1## 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”?
st <- "contact@boston.in"
strsplit(st,".",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)
freq <- table(Sales)
cumsum(freq)## 2 10 12 13 14 34 35 40 56 67 134
## 1 2 4 5 6 7 8 9 11 12 13Part 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?
# import the dataset
setwd('E:/Data Science/R-programming/R-Test/Test in R/')
german_cr_data <- read.csv('german_credit_data_risk.csv')
#View(german_cr_data)
summary(german_cr_data) # shows the statistical summary of the dataset fields## 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. :72.0str(german_cr_data) ## shows the information about the data structure## '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" ...head(german_cr_data) ## shows the 1st six records of the 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 good1) Find missing value in the dataset
sum(is.na(german_cr_data)) ## shows the total missing values in the dataset## [1] 577colSums(is.na(german_cr_data)) ## shows 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 02) Impute missing values in the dataset using missforest package
## 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
table(german_cr_data$Saving.accounts)##
## little moderate quite rich rich
## 603 103 63 48german_cr_data$Saving.accounts[german_cr_data$Saving.accounts=='little'] <- c(1)
german_cr_data$Saving.accounts[german_cr_data$Saving.accounts=='moderate'] <- c(2)
german_cr_data$Saving.accounts[german_cr_data$Saving.accounts=='quite rich'] <- c(3)
german_cr_data$Saving.accounts[german_cr_data$Saving.accounts=='rich'] <- c(4)
class(german_cr_data$Saving.accounts)## [1] "character"#convert the datatype of Saving.accounts from character to numetic
german_cr_data$Saving.accounts <- as.numeric(german_cr_data$Saving.accounts)
class(german_cr_data$Saving.accounts)## [1] "numeric"## replacing each levels of Checking.account column with numeric
table(german_cr_data$Checking.account)##
## little moderate rich
## 274 269 63german_cr_data$Checking.account[german_cr_data$Checking.account=='little'] <- c(1)
german_cr_data$Checking.account[german_cr_data$Checking.account=='moderate'] <- c(2)
german_cr_data$Checking.account[german_cr_data$Checking.account=='rich'] <- c(4)
class(german_cr_data$Checking.account)## [1] "character"#convert the datatype of Checking.account from character to numetic
german_cr_data$Checking.account <- as.numeric(german_cr_data$Checking.account)
class(german_cr_data$Checking.account)## [1] "numeric"## selecting only numeric columns from the german_cr_data dataset
german_cr_data_num <- unlist(lapply(german_cr_data, is.numeric))
## impute missing values using missForest package
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.## Loading required package: foreach## Loading required package: itertools## Warning: package 'itertools' was built under R version 4.0.3## Loading required package: iteratorsmissForest(german_cr_data[,german_cr_data_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!## $ximp
## X Age Job Saving.accounts Checking.account Credit.amount Duration
## 1 0 67 2 1.838889 1.000000 1169 6
## 2 1 22 2 1.000000 2.000000 5951 48
## 3 2 49 1 1.000000 1.957321 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 1.270000 1.541083 9055 36
## 7 6 53 2 3.000000 1.687000 2835 24
## 8 7 35 3 1.000000 2.000000 6948 36
## 9 8 61 1 4.000000 2.070000 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 2.324286 1.810000 2424 24
## 18 17 25 2 1.448333 1.000000 8072 30
## 19 18 44 3 1.000000 2.000000 12579 24
## 20 19 31 2 3.000000 1.562000 3430 24
## 21 20 48 2 1.000000 1.735455 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 1.530000 1.820000 2069 10
## 26 25 36 1 1.000000 1.000000 1374 6
## 27 26 39 1 1.000000 1.618571 426 6
## 28 27 42 2 4.000000 4.000000 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 2.510000 2.547500 1264 12
## 35 34 33 3 1.000000 4.000000 1474 12
## 36 35 25 1 1.000000 2.000000 4746 45
## 37 36 31 2 1.000000 1.599434 6110 48
## 38 37 37 2 1.000000 4.000000 2100 18
## 39 38 37 2 1.000000 4.000000 1225 10
## 40 39 24 2 1.000000 2.000000 458 9
## 41 40 30 3 3.000000 1.575000 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 2.652020 1393 11
## 47 46 39 2 3.000000 2.057000 2299 36
## 48 47 23 0 3.000000 1.000000 1352 6
## 49 48 39 1 1.000000 2.045655 7228 11
## 50 49 28 2 2.000000 1.830000 2073 12
## 51 50 29 1 1.630000 2.000000 2333 24
## 52 51 30 3 1.000000 2.000000 5965 27
## 53 52 25 2 1.000000 2.033667 1262 12
## 54 53 31 2 1.540000 2.070000 3378 18
## 55 54 57 2 1.000000 2.000000 2225 36
## 56 55 26 1 1.945000 1.942000 783 6
## 57 56 52 3 1.200000 2.000000 6468 12
## 58 57 31 2 1.000000 1.731882 9566 36
## 59 58 23 3 1.000000 4.000000 1961 18
## 60 59 23 1 1.000000 1.000000 6229 36
## 61 60 27 2 1.000000 2.000000 1391 9
## 62 61 50 2 2.482000 2.000000 1537 15
## 63 62 61 3 1.000000 2.000000 1953 36
## 64 63 25 2 1.000000 2.000000 14421 48
## 65 64 26 2 1.000000 1.686685 3181 24
## 66 65 48 2 1.480000 2.003333 5190 27
## 67 66 29 2 1.000000 1.601559 2171 12
## 68 67 22 2 4.000000 2.000000 1007 12
## 69 68 37 2 1.000000 1.792308 1819 36
## 70 69 25 2 1.630000 1.750000 2394 36
## 71 70 30 2 1.000000 1.534549 8133 36
## 72 71 46 1 1.890000 1.656000 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 1.160000 1.000000 1977 36
## 76 75 66 3 1.000000 1.000000 1526 12
## 77 76 34 2 1.000000 1.000000 3965 42
## 78 77 51 2 1.000000 2.000000 4771 11
## 79 78 39 1 1.680000 1.950000 9436 54
## 80 79 22 2 1.000000 2.000000 3832 30
## 81 80 44 2 1.135000 1.487727 5943 24
## 82 81 47 2 3.000000 2.177500 1213 15
## 83 82 24 1 2.000000 1.810000 1568 18
## 84 83 58 1 1.000000 1.000000 1755 24
## 85 84 52 1 1.000000 1.000000 2315 10
## 86 85 29 3 1.000000 1.913353 1412 12
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## 88 87 47 2 2.000000 2.000000 12612 36
## 89 88 30 3 2.000000 1.000000 2249 18
## 90 89 28 2 1.000000 1.000000 1108 12
## 91 90 56 2 1.000000 1.756667 618 12
## 92 91 54 2 1.000000 1.000000 1409 12
## 93 92 33 1 1.410000 1.880000 797 12
## 94 93 20 2 1.733750 4.000000 3617 24
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## 116 115 47 2 1.636471 2.050000 3578 48
## 117 116 30 3 1.240000 1.000000 7174 42
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## 122 121 41 3 1.000000 1.973004 3868 24
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## 136 135 38 2 2.130000 2.240833 1240 12
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##
## $OOBerror
## NRMSE
## 0.0002888457
##
## attr(,"class")
## [1] "missForest"3) Find summary statistics using dplyr package
library(dplyr)##
## Attaching package: 'dplyr'## The following object is masked from 'package:randomForest':
##
## combine## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, uniongerman_cr_data %>% group_by(Sex) %>%
summarise(Mean_age=mean(Age),StndDev=sd(Age),Max_age=max(Age),Min_age = min(Age),Quartile_1=quantile(Age,prob=c(0.25)),Median=quantile(Age,prob=c(0.50)),Quartile_3=quantile(Age,prob=c(0.75)))## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 2 x 8
## Sex Mean_age StndDev Max_age Min_age Quartile_1 Median Quartile_3
## <chr> <dbl> <dbl> <int> <int> <dbl> <dbl> <dbl>
## 1 female 32.8 11.8 75 19 24 29 37
## 2 male 36.8 11.0 75 20 28 35 43The above table shows the statistical information of the Age column.
german_cr_data %>% group_by(Sex) %>%
summarise(Mean_age=mean(Credit.amount),StndDev=sd(Credit.amount),Max_credit=max(Credit.amount),Min_credit = min(Credit.amount),Quartile_1=quantile(Credit.amount,prob=c(0.25)),Median=quantile(Credit.amount,prob=c(0.50)),Quartile_3=quantile(Credit.amount,prob=c(0.75)))## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 2 x 8
## Sex Mean_age StndDev Max_credit Min_credit Quartile_1 Median Quartile_3
## <chr> <dbl> <dbl> <int> <int> <dbl> <dbl> <dbl>
## 1 female 2878. 2603. 18424 250 1248. 1959 3606.
## 2 male 3448. 2900. 15945 276 1442. 2444. 4266.The above table the statistical information of the Credit.amount column
4) What is the avg amount spend by Male and Females?
german_cr_data %>% group_by(Sex) %>%
summarise(Max_cr_amt=max(Credit.amount),Min_cr_amt=min(Credit.amount),Avg_cr_amt=mean(Credit.amount))## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 2 x 4
## Sex Max_cr_amt Min_cr_amt Avg_cr_amt
## <chr> <int> <int> <dbl>
## 1 female 18424 250 2878.
## 2 male 15945 276 3448.library(ggplot2)##
## Attaching package: 'ggplot2'## The following object is masked from 'package:randomForest':
##
## marginggplot(german_cr_data, aes(Sex, mean(Credit.amount)))+geom_bar(stat = "identity") + ggtitle("Average credit amount of Male and Female customers") + scale_y_continuous("Average credit", breaks = seq(0,4000,by = 500)) + theme_bw()
Insight : From the above bar chart it is clear that the average credit is more by Male customers than by Female.
5) Using GGplots library build 5 graphs?
library(ggplot2)
ggplot(german_cr_data, aes(Age,Sex,fill=Sex)) +geom_boxplot(outlier.colour="red") +ggtitle("Box Plot showing statistical Age data of Male and Female") + theme(axis.text.x = element_text(angle = 70, vjust = 0.5, colour = "red")) + xlab("Age") + ylab("Sex") + stat_summary(fun.y=mean, geom="point")+ coord_flip()## Warning: `fun.y` is deprecated. Use `fun` instead.
Insight: The above box plot indicates that the 50% female customers age lie between 24 to 37 and male customers 28 to 43. Average age of female customer is around 32 and male customer is 36. Age of female customers after 55 can be consider as outliers and for male customers age after 65 are outliers.
ggplot(german_cr_data, aes(x = factor(1), fill = factor(Sex))) + geom_bar(width = 1) + coord_polar(theta = "y")
a <- table(german_cr_data$Sex)
pct = round(a / sum(a) * 100)
lbs = paste(c("Female", "Male"), " ", pct, "%", sep = " ")
library(plotrix)## Warning: package 'plotrix' was built under R version 4.0.3pie3D(a, labels = lbs,
main = "Pie Chart Showing Ratio of Female and Male")
Insight: Percentage of male customers data available in the dataset are more than the female.
ggplot(german_cr_data, aes(Sex, fill = Risk)) + geom_bar()+
labs(title = "Risk Count for male and female category", x = "Sex", y = "Count of Risk")
Insight:The above stacked bar chart indicates that the good risk is higher than the bad risk for both female and male customers. Also, risk from female customer is around 30% which is less than the male customers which is around 70% ()[calculation done as follows:-]
contable1<-table(german_cr_data$Sex,german_cr_data$Risk)
contable1##
## bad good
## female 109 201
## male 191 499prop.table(contable1)##
## bad good
## female 0.109 0.201
## male 0.191 0.499ggplot(german_cr_data, aes(Duration, Credit.amount)) + geom_point(aes(color = Purpose)) +
scale_x_continuous("Duration", breaks = seq(0,80,10))+
scale_y_continuous("Credit.amount", breaks = seq(200,20000,by = 1000))+
theme_bw() + labs(title="Scatter Plot for Duration and Purpose of Credit")
Insight: This scatter plot shows the co-relationship between the credit and duration of the data for different purposes.
ggplot(german_cr_data, aes(x = Risk, y=Credit.amount))+ ggtitle("Bar Plot showing Credit Risk for different Purposes")+
geom_bar(
aes(fill = Purpose), stat = "identity", color = "white",
position = position_dodge(0.9)
)+
facet_wrap(~Sex) 
Insight:The above bar chart shows the good and bad risk of different purposes and their corresponding credit amount.
ggplot(german_cr_data, aes(Housing, Risk))+
geom_raster(aes(fill = Credit.amount))+
labs(title ="Heat Map showing Risk based on Housing", x = "Housing", y = "Risk")+
scale_fill_continuous(name = "Credit.amount") 