Lab3: R Basics

A. Creating Vectors

In this section, you are expected to be able to shape data in vectors, perform basic mathematical operations, and also manipulate vectors.

Exercise 1

Create a vector A containing numeric values, starting from the last 2 digits of your student id up to 30.

A<- c(04:30)
A

##  [1]  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
## [26] 29 30

Exercise 2

Create a vector B containing 12 character values; all names of your classmate including yourself.

B<- c("Ardifo","Jocelyn","Julian","Kefas","Nikita","Sherly","Vanessa","Jeffry","Lala","Siana","Putri","Fallen")
B

##  [1] "Ardifo"  "Jocelyn" "Julian"  "Kefas"   "Nikita"  "Sherly"  "Vanessa"
##  [8] "Jeffry"  "Lala"    "Siana"   "Putri"   "Fallen"

Exercise 3

Create a vector C containing 12 numeric values, random number between 60 and 100.

C<- c(70,71,71,73,74,75,76,77,78,79,80,81) 
C

##  [1] 70 71 71 73 74 75 76 77 78 79 80 81

B. Creating Matrices

In this section, you are expected to be able to shape data in Matrices, perform basic mathematical operations, and also manipulate Matrices.

Exercise 4

Create a matrices M1 order by \(rows \times columns \space (4 \times 4)\) containing 16 numeric values, random number between 60 and 100.

M1<- matrix(70:85, nrow = 4, ncol = 4)  
M1

##      [,1] [,2] [,3] [,4]
## [1,]   70   74   78   82
## [2,]   71   75   79   83
## [3,]   72   76   80   84
## [4,]   73   77   81   85

Exercise 5

Create a matrices M2 order by \(rows \times columns \space (4 \times 4)\) containing 16 numeric values, random number between 30 and 60. Find out the following tasks:

• 3 * M1, give your opinion about the result.
• M1 + M2, give your opinion about the result.
• M1 - M2, give your opinion about the result.
• M1 * M2, give your opinion about the result.
• M1 / M2, give your opinion about the result.
• determinan of M1, give your opinion about the result.
• invers of M1, give your opinion about the result.

M2<- matrix(40:55, nrow = 4, ncol = 4)
M2

##      [,1] [,2] [,3] [,4]
## [1,]   40   44   48   52
## [2,]   41   45   49   53
## [3,]   42   46   50   54
## [4,]   43   47   51   55

3 * M1    # nilai yang ada pada matriks M1 dikali dengan 3

##      [,1] [,2] [,3] [,4]
## [1,]  210  222  234  246
## [2,]  213  225  237  249
## [3,]  216  228  240  252
## [4,]  219  231  243  255

M1 + M2   # penjumlahan antara matriks 1 dan matriks 2

##      [,1] [,2] [,3] [,4]
## [1,]  110  118  126  134
## [2,]  112  120  128  136
## [3,]  114  122  130  138
## [4,]  116  124  132  140

M1 - M2   # Pengurangan antara matriks 1 dengan matriks 2

##      [,1] [,2] [,3] [,4]
## [1,]   30   30   30   30
## [2,]   30   30   30   30
## [3,]   30   30   30   30
## [4,]   30   30   30   30

M1 * M2   # perkalian antara matriks 1 dan matriks 2

##      [,1] [,2] [,3] [,4]
## [1,] 2800 3256 3744 4264
## [2,] 2911 3375 3871 4399
## [3,] 3024 3496 4000 4536
## [4,] 3139 3619 4131 4675

M1 / M2   # pembagian antara matriks 1 dan matriks 2

##          [,1]     [,2]     [,3]     [,4]
## [1,] 1.750000 1.681818 1.625000 1.576923
## [2,] 1.731707 1.666667 1.612245 1.566038
## [3,] 1.714286 1.652174 1.600000 1.555556
## [4,] 1.697674 1.638298 1.588235 1.545455

det(M1)   # menghitung determinan dari matriks M1

## [1] -8.093713e-28

Exercise 6

Create a matrix data that is contain the following vectors:

• B that you has been created in the exercise 2. Name it as a ‘names’ variable
• C that you has been created in the exercise 3. Name it as a ‘scores’ variable.

names<- B
scores<- C
data<- cbind(names,scores)
data 

##       names     scores
##  [1,] "Ardifo"  "70"  
##  [2,] "Jocelyn" "71"  
##  [3,] "Julian"  "71"  
##  [4,] "Kefas"   "73"  
##  [5,] "Nikita"  "74"  
##  [6,] "Sherly"  "75"  
##  [7,] "Vanessa" "76"  
##  [8,] "Jeffry"  "77"  
##  [9,] "Lala"    "78"  
## [10,] "Siana"   "79"  
## [11,] "Putri"   "80"  
## [12,] "Fallen"  "81"

C. Lists

In this section, you are expected to be able to shape data by using the list() function, perform some basic manipulations.

Exercise 7

Please create a data set as the List variable by using the list() function, contain the following vectors:

• a variable name, the values including your classmate and yourself
• a variable age, the values including your classmate and yourself
• a variable gender, the values including your classmate and yourself

name<-B
age<-c(19, 19, 19, 19, 19, 19, 18, 19, 19, 19, 19, 20)
gender<-c("Male", "Female", "Male", "Male", "Female", "Female", "Female", "Male", "Female", "Female", "Female", "Male")
List<-list(name, age, gender)
List 

## [[1]]
##  [1] "Ardifo"  "Jocelyn" "Julian"  "Kefas"   "Nikita"  "Sherly"  "Vanessa"
##  [8] "Jeffry"  "Lala"    "Siana"   "Putri"   "Fallen" 
## 
## [[2]]
##  [1] 19 19 19 19 19 19 18 19 19 19 19 20
## 
## [[3]]
##  [1] "Male"   "Female" "Male"   "Male"   "Female" "Female" "Female" "Male"  
##  [9] "Female" "Female" "Female" "Male"

D. Factors

In this section, you are expected to be able to shape data by using the factor() function, perform some basic manipulations.

Exercise 8

Please create a data set as the Factor variable as you have done at Exercise 7. Here, you add one more variable called marital_status by using the factor() function, as the following code:

marital_status <- factor(c("yes","no","yes","no", until 12 students))

E. Data Frames

In this section, you are expected to be able to shape data by using the data.frame() function, perform some basic manipulations.

Exercise 9

Please create a data set as the DF1 variable, contain the following vectors:

• id, assume 1 up to 6
• name the values according to your classmate and yourself
• gender the values according to your classmate and yourself
• age the values according to your classmate and yourself
• marital_status the values according to your classmate and yourself
• address_by_city the values according to your classmate and yourself

DF1<-data.frame(id=c(1:6),name=c("Ardifo","Julian","Vanessa","Kefas","Nikita","Jocelyn"),gender=c("Male","Male","Female","Male","Female","Female"),age=c(19,19,18,19,19,19),marital_status=c("single","single","single","single","single","single"),address_by_city=c("Kalimantan","Tangerang","Maluku","Tangerang","Tangerang","Tangerang"),stringsAsFactors = F)
DF1

##   id    name gender age marital_status address_by_city
## 1  1  Ardifo   Male  19         single      Kalimantan
## 2  2  Julian   Male  19         single       Tangerang
## 3  3 Vanessa Female  18         single          Maluku
## 4  4   Kefas   Male  19         single       Tangerang
## 5  5  Nikita Female  19         single       Tangerang
## 6  6 Jocelyn Female  19         single       Tangerang

Please create a data set as the DF2 variable, contain the following vectors:

• id, assume 7 up to 12
• name the values according to your classmate and yourself
• gender the values according to your classmate and yourself
• age the values according to your classmate and yourself
• marital_status the values according to your classmate and yourself
• address_by_city the values according to your classmate and yourself

DF2<-data.frame(id=c(1:6),name=c("Sherly","Putri","Jeffry","Siana","Lala","Falen"),gender=c("Female","Female","Male","Female","Female","Male"),age=c(19,19,19,19,19,20),marital_status=c("single","single","single","single","single","single"),address_by_city=c("Tangerang","Tangerang","Tangerang","Tangerang","Tangerang","Tangerang"),stringsAsFactors = F)
DF2

##   id   name gender age marital_status address_by_city
## 1  1 Sherly Female  19         single       Tangerang
## 2  2  Putri Female  19         single       Tangerang
## 3  3 Jeffry   Male  19         single       Tangerang
## 4  4  Siana Female  19         single       Tangerang
## 5  5   Lala Female  19         single       Tangerang
## 6  6  Falen   Male  20         single       Tangerang

Exercise 10

In this final exercise, please consider the following tasks:

• Combine DF1 and DF2, assign it as SB19 variable!
• Print the result of data frame SB19!
• Print first 3 rows of the SB19 dataset!
• How can you preview the SB19 dataset like an Excel file on your Rstudio?
• Review the structure of the data frame SB19!
• Check the dimension of the data.
• Please apply piping functions to the data frame SB19, filter it by their gender accordingly! (as you have learn last week)

SB19<-rbind(DF1,DF2)
print(SB19)

##    id    name gender age marital_status address_by_city
## 1   1  Ardifo   Male  19         single      Kalimantan
## 2   2  Julian   Male  19         single       Tangerang
## 3   3 Vanessa Female  18         single          Maluku
## 4   4   Kefas   Male  19         single       Tangerang
## 5   5  Nikita Female  19         single       Tangerang
## 6   6 Jocelyn Female  19         single       Tangerang
## 7   1  Sherly Female  19         single       Tangerang
## 8   2   Putri Female  19         single       Tangerang
## 9   3  Jeffry   Male  19         single       Tangerang
## 10  4   Siana Female  19         single       Tangerang
## 11  5    Lala Female  19         single       Tangerang
## 12  6   Falen   Male  20         single       Tangerang

head(SB19,3)

##   id    name gender age marital_status address_by_city
## 1  1  Ardifo   Male  19         single      Kalimantan
## 2  2  Julian   Male  19         single       Tangerang
## 3  3 Vanessa Female  18         single          Maluku

#View(SB19)
dim(SB19)

## [1] 12  6

library(magrittr)
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, union

data<-SB19 %>% filter(gender=="Male") %>% print()

##   id   name gender age marital_status address_by_city
## 1  1 Ardifo   Male  19         single      Kalimantan
## 2  2 Julian   Male  19         single       Tangerang
## 3  4  Kefas   Male  19         single       Tangerang
## 4  3 Jeffry   Male  19         single       Tangerang
## 5  6  Falen   Male  20         single       Tangerang

library(magrittr)
library(dplyr)
data<-SB19 %>% filter(gender=="Female") %>% print()

##   id    name gender age marital_status address_by_city
## 1  3 Vanessa Female  18         single          Maluku
## 2  5  Nikita Female  19         single       Tangerang
## 3  6 Jocelyn Female  19         single       Tangerang
## 4  1  Sherly Female  19         single       Tangerang
## 5  2   Putri Female  19         single       Tangerang
## 6  4   Siana Female  19         single       Tangerang
## 7  5    Lala Female  19         single       Tangerang