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(02:30)
A

##  [1]  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
## [26] 27 28 29 30

Exercise 2

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

B <- c("Angel", "Irene", "Jeffry", "Julian", "Kefas", "Lala", "Nikita", "Sherly", "Siana", "Supit", "Ardifo", "Fallen")
B

##  [1] "Angel"  "Irene"  "Jeffry" "Julian" "Kefas"  "Lala"   "Nikita" "Sherly"
##  [9] "Siana"  "Supit"  "Ardifo" "Fallen"

Exercise 3

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

C <- runif(12,60,100)
C

##  [1] 83.06539 72.71245 94.20521 73.14817 92.73011 86.79504 96.19773 78.06197
##  [9] 87.26555 96.34301 70.37458 67.14578

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.

 d <- runif(16,60,100)
M1 <- matrix(d, nrow = 4, ncol = 4)
M1

##          [,1]     [,2]     [,3]     [,4]
## [1,] 73.90680 71.43729 70.82227 66.26045
## [2,] 98.04732 72.10280 74.11385 95.80472
## [3,] 81.68304 80.22039 76.32970 71.64045
## [4,] 60.87155 75.40760 80.17859 75.58125

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. library(matlib)
• invers of M1, give your opinion about the result.

d <- runif(16,30,60)
M2 <- matrix(d, nrow = 4, ncol = 4)
M2

##          [,1]     [,2]     [,3]     [,4]
## [1,] 56.11872 58.41173 58.17010 31.41097
## [2,] 43.05740 39.01182 56.41392 52.72082
## [3,] 46.22255 36.71104 56.13462 36.78458
## [4,] 52.05576 53.14185 56.76434 32.06309

3*M1          #semua hasil data dari M1 akan dikalikan dengan 3

##          [,1]     [,2]     [,3]     [,4]
## [1,] 221.7204 214.3119 212.4668 198.7814
## [2,] 294.1420 216.3084 222.3416 287.4142
## [3,] 245.0491 240.6612 228.9891 214.9214
## [4,] 182.6147 226.2228 240.5358 226.7438

M1+M2         #semua data dari M1 akan ditambah dengan data M2

##          [,1]     [,2]     [,3]      [,4]
## [1,] 130.0255 129.8490 128.9924  97.67142
## [2,] 141.1047 111.1146 130.5278 148.52555
## [3,] 127.9056 116.9314 132.4643 108.42504
## [4,] 112.9273 128.5494 136.9429 107.64434

M1-M2         #semua data dari M1 akan dikurang dengan data M2

##           [,1]     [,2]     [,3]     [,4]
## [1,] 17.788080 13.02556 12.65217 34.84948
## [2,] 54.989916 33.09098 17.69993 43.08390
## [3,] 35.460499 43.50935 20.19508 34.85587
## [4,]  8.815789 22.26574 23.41425 43.51816

M1*M2         #semua data dari M1 akan dikalikan dengan data M2

##          [,1]     [,2]     [,3]     [,4]
## [1,] 4147.554 4172.776 4119.739 2081.305
## [2,] 4221.663 2812.861 4181.053 5050.904
## [3,] 3775.598 2944.974 4284.738 2635.264
## [4,] 3168.715 4007.299 4551.285 2423.368

M1/M2         #semua data dari M1 akan dibagi dengan data M2

##          [,1]     [,2]     [,3]     [,4]
## [1,] 1.316972 1.222996 1.217503 2.109468
## [2,] 2.277130 1.848230 1.313751 1.817208
## [3,] 1.767169 2.185184 1.359762 1.947567
## [4,] 1.169353 1.418987 1.412482 2.357267

det(M1)       #akan menghasilkan determinan dari M1

## [1] 122839.2

library("matlib")
inv(M1)    #akan menghasilkan invers dari M1

##            [,1]        [,2]        [,3]        [,4]
## [1,]  0.1432684  0.00177201 -0.08255202 -0.04959873
## [2,] -0.4103731 -0.00365155  0.35464041  0.02824433
## [3,]  0.4589275 -0.03187841 -0.36528230 -0.01568714
## [4,] -0.1927977  0.03603349  0.10016123  0.04163846

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,] "Angel"  "83.0653871688992"
##  [2,] "Irene"  "72.7124506328255"
##  [3,] "Jeffry" "94.2052064090967"
##  [4,] "Julian" "73.1481749005616"
##  [5,] "Kefas"  "92.7301126066595"
##  [6,] "Lala"   "86.7950434144586"
##  [7,] "Nikita" "96.1977332923561"
##  [8,] "Sherly" "78.0619682371616"
##  [9,] "Siana"  "87.2655522078276"
## [10,] "Supit"  "96.3430074974895"
## [11,] "Ardifo" "70.3745798580348"
## [12,] "Fallen" "67.1457812003791"

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<-c("Angel", "Irene", "Jeffry", "Julian", "Kefas", "Lala", "Nikita", "Sherly", "Siana", "Supit", "Ardifo", "Fallen", "sofia")
age<-c(18,18,19,19,19,19,18,19,19,18,18,20,20)
gender<-c("female","female","male","male","male","female","female","female","female","female","male","male","female")
list<-list(name,age,gender)
list

## [[1]]
##  [1] "Angel"  "Irene"  "Jeffry" "Julian" "Kefas"  "Lala"   "Nikita" "Sherly"
##  [9] "Siana"  "Supit"  "Ardifo" "Fallen" "sofia" 
## 
## [[2]]
##  [1] 18 18 19 19 19 19 18 19 19 18 18 20 20
## 
## [[3]]
##  [1] "female" "female" "male"   "male"   "male"   "female" "female" "female"
##  [9] "female" "female" "male"   "male"   "female"

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))

name<-c("Angel", "Irene", "Jeffry", "Julian", "Kefas", "Lala", "Nikita", "Sherly", "Siana", "Supit", "Ardifo", "Fallen", "sofia")
marital_status<-factor(c("no","no","no","no","no","yes","no","no","yes","yes","no","yes","no"))
marital_status

##  [1] no  no  no  no  no  yes no  no  yes yes no  yes no 
## Levels: no yes

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("Angel", "Irene", "Jeffry", "Julian", "Kefas", "Lala"),
                   gender = c("female","female","male","male","male","female"),
                   age = c(18,18,19,19,19,19),
                   marital_status = c("no","no","no","no","no","yes"),
                   address_by_city = c("Tangerang","Tangerang","Tangerang","Tangerang","Tangerang","Tangerang"), stringsAsFactors = F)
df1

##   id   name gender age marital_status address_by_city
## 1  1  Angel female  18             no       Tangerang
## 2  2  Irene female  18             no       Tangerang
## 3  3 Jeffry   male  19             no       Tangerang
## 4  4 Julian   male  19             no       Tangerang
## 5  5  Kefas   male  19             no       Tangerang
## 6  6   Lala female  19            yes       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("Nikita", "Sherly", "Siana", "Supit", "Ardifo", "Fallen"),
                   gender = c("female","female","female","female","male","male"),
                   age = c(18,19,18,18,19,20),
                   marital_status = c("no","no","yes","yes","no","yes"),
                   address_by_city = c("Tangerang","Tangerang","Tangerang","Manado","Kalimantan","Tangerang"), stringsAsFactors = F)
df2

##   id   name gender age marital_status address_by_city
## 1  1 Nikita female  18             no       Tangerang
## 2  2 Sherly female  19             no       Tangerang
## 3  3  Siana female  18            yes       Tangerang
## 4  4  Supit female  18            yes          Manado
## 5  5 Ardifo   male  19             no      Kalimantan
## 6  6 Fallen   male  20            yes       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  Angel female  18             no       Tangerang
## 2   2  Irene female  18             no       Tangerang
## 3   3 Jeffry   male  19             no       Tangerang
## 4   4 Julian   male  19             no       Tangerang
## 5   5  Kefas   male  19             no       Tangerang
## 6   6   Lala female  19            yes       Tangerang
## 7   1 Nikita female  18             no       Tangerang
## 8   2 Sherly female  19             no       Tangerang
## 9   3  Siana female  18            yes       Tangerang
## 10  4  Supit female  18            yes          Manado
## 11  5 Ardifo   male  19             no      Kalimantan
## 12  6 Fallen   male  20            yes       Tangerang

head(SB19)

##   id   name gender age marital_status address_by_city
## 1  1  Angel female  18             no       Tangerang
## 2  2  Irene female  18             no       Tangerang
## 3  3 Jeffry   male  19             no       Tangerang
## 4  4 Julian   male  19             no       Tangerang
## 5  5  Kefas   male  19             no       Tangerang
## 6  6   Lala female  19            yes       Tangerang

#View(SB19)                            
str(SB19)                             

## 'data.frame':    12 obs. of  6 variables:
##  $ id             : int  1 2 3 4 5 6 1 2 3 4 ...
##  $ name           : chr  "Angel" "Irene" "Jeffry" "Julian" ...
##  $ gender         : chr  "female" "female" "male" "male" ...
##  $ age            : num  18 18 19 19 19 19 18 19 18 18 ...
##  $ marital_status : chr  "no" "no" "no" "no" ...
##  $ address_by_city: chr  "Tangerang" "Tangerang" "Tangerang" "Tangerang" ...

dim(SB19) 

## [1] 12  6

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

male<-SB19 %>% filter(gender == 'Male') %>% print()

## [1] id              name            gender          age            
## [5] marital_status  address_by_city
## <0 rows> (or 0-length row.names)