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 <- 10:30
A

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

Exercise 2

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

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

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

Exercise 3

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

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

##  [1] 62.38875 64.26517 81.30428 70.84725 81.99653 80.16449 76.58322 80.84223
##  [9] 97.96702 86.07844 99.16742 91.17518

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      <- runif(16,60,100)
dim(M1) <- c(4,4)
M1

##          [,1]     [,2]     [,3]     [,4]
## [1,] 93.52714 71.81038 70.84353 87.24286
## [2,] 87.16793 84.71045 94.39375 76.84868
## [3,] 78.24054 82.37344 97.57714 99.01813
## [4,] 94.29389 81.54247 72.91681 63.04105

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      <- runif(16,30,60)
dim(M2) <- c(4,4)
M2

##          [,1]     [,2]     [,3]     [,4]
## [1,] 55.97764 59.69429 30.35259 49.86018
## [2,] 58.27770 36.36451 52.19269 55.57889
## [3,] 35.56016 31.37194 49.52012 32.48177
## [4,] 41.50107 37.62186 51.02336 45.54659

3 * M1                        #     Matriks M1 dikalikan 3

##          [,1]     [,2]     [,3]     [,4]
## [1,] 280.5814 215.4311 212.5306 261.7286
## [2,] 261.5038 254.1313 283.1813 230.5460
## [3,] 234.7216 247.1203 292.7314 297.0544
## [4,] 282.8817 244.6274 218.7504 189.1231

M1 + M2                       #     Penjumlahan antara Matriks 1 dan Matriks 2

##          [,1]     [,2]     [,3]     [,4]
## [1,] 149.5048 131.5047 101.1961 137.1030
## [2,] 145.4456 121.0750 146.5864 132.4276
## [3,] 113.8007 113.7454 147.0973 131.4999
## [4,] 135.7950 119.1643 123.9402 108.5876

M1 - M2                       #     Pengurangan antara Matriks 1 dan Matriks 2

##          [,1]     [,2]     [,3]     [,4]
## [1,] 37.54949 12.11609 40.49095 37.38268
## [2,] 28.89024 48.34594 42.20106 21.26979
## [3,] 42.68038 51.00150 48.05702 66.53636
## [4,] 52.79282 43.92060 21.89344 17.49446

M1 * M2                       #     Perkalian antara Matriks 1 dan Matriks 2

##          [,1]     [,2]     [,3]     [,4]
## [1,] 5235.429 4286.669 2150.284 4349.944
## [2,] 5079.946 3080.454 4926.663 4271.164
## [3,] 2782.246 2584.215 4832.032 3216.284
## [4,] 3913.297 3067.780 3720.461 2871.305

M1 / M2                       #     Pembagian antara Matriks 1 dan Matriks 2

##          [,1]     [,2]     [,3]     [,4]
## [1,] 1.670794 1.202969 2.334020 1.749750
## [2,] 1.495734 2.329481 1.808563 1.382695
## [3,] 2.200230 2.625704 1.970454 3.048422
## [4,] 2.272083 2.167422 1.429087 1.384100

det(M1)                       #     Fungsi untuk mencari Determinan

## [1] 585721.4

library("matlib")
inv(M1)                       #     Fungsi untuk mencari invers

##             [,1]        [,2]        [,3]        [,4]
## [1,]  0.05389362  0.07302603 -0.07186368 -0.05072854
## [2,] -0.09209114 -0.15863981  0.11180118  0.14522617
## [3,]  0.02617028  0.12673674 -0.06423735 -0.08981543
## [4,]  0.00823665 -0.05062202  0.03717806  0.00777819

Exercise 6

Create a matrix data by column, 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
cbind(names,scores)

##       names     scores            
##  [1,] "Ardifo"  "62.3887493740767"
##  [2,] "Jeffry"  "64.2651687655598"
##  [3,] "Jocelyn" "81.3042829371989"
##  [4,] "Julian"  "70.8472461160272"
##  [5,] "Kefas"   "81.996531393379" 
##  [6,] "Nikita"  "80.1644925959408"
##  [7,] "Angel"   "76.5832183323801"
##  [8,] "Sherly"  "80.8422308415174"
##  [9,] "Vanessa" "97.9670208506286"
## [10,] "Siana"   "86.0784370265901"
## [11,] "Lala"    "99.1674230620265"
## [12,] "Fallen"  "91.1751762218773"

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

## [[1]]
##  [1] "Ardifo"  "Jeffry"  "Jocelyn" "Julian"  "Kefas"   "Nikita"  "Angel"  
##  [8] "Sherly"  "Vanessa" "Siana"   "Lala"    "Fallen" 
## 
## [[2]]
##  [1] 19 19 19 19 19 19 19 19 18 19 19 20
## 
## [[3]]
##  [1] "male"   "male"   "female" "male"   "male"   "female" "female" "female"
##  [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))

martial_status  <- factor(c("yes","no","yes","no","yes","no","yes","no","yes","no","yes","no"))
"Factor"        <- List
Factor[[4]]     <- martial_status
Factor

## [[1]]
##  [1] "Ardifo"  "Jeffry"  "Jocelyn" "Julian"  "Kefas"   "Nikita"  "Angel"  
##  [8] "Sherly"  "Vanessa" "Siana"   "Lala"    "Fallen" 
## 
## [[2]]
##  [1] 19 19 19 19 19 19 19 19 18 19 19 20
## 
## [[3]]
##  [1] "male"   "male"   "female" "male"   "male"   "female" "female" "female"
##  [9] "female" "female" "female" "male"  
## 
## [[4]]
##  [1] yes no  yes no  yes no  yes no  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("Ardifo","Jeffry","Jocelyn","Julian","Kefas","Nikita"),
                  gender = c("male","male","female","male","male","female"),
                  age = c(19,19,19,19,19,19),
                  martial_status = c("yes","no","yes","no","yes","no"),
                  address_by_city = c("Kalimantan","Tangerang","Tangerang","Tangerang","Tangerang","Tangerang"),
                  stringsAsFactors = F)
print(DF1)

##   id    name gender age martial_status address_by_city
## 1  1  Ardifo   male  19            yes      Kalimantan
## 2  2  Jeffry   male  19             no       Tangerang
## 3  3 Jocelyn female  19            yes       Tangerang
## 4  4  Julian   male  19             no       Tangerang
## 5  5   Kefas   male  19            yes       Tangerang
## 6  6  Nikita female  19             no       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(7:12),
                  name = c("Angel","Sherly","Vanessa","Siana","Lala","Fallen"),
                  gender = c("female","female","female","female","female","male"),
                  age = c(19,19,18,19,19,20),
                  martial_status = c("yes","no","yes","no","yes","no"),
                  address_by_city = c("Tangerang","Jakarta","Maluku","Tangerang","Tangerang","Tangerang"),
                  stringsAsFactors = F)
print(DF2)

##   id    name gender age martial_status address_by_city
## 1  7   Angel female  19            yes       Tangerang
## 2  8  Sherly female  19             no         Jakarta
## 3  9 Vanessa female  18            yes          Maluku
## 4 10   Siana female  19             no       Tangerang
## 5 11    Lala female  19            yes       Tangerang
## 6 12  Fallen   male  20             no       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.

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

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

head(SB19,3)

##   id    name gender age martial_status address_by_city
## 1  1  Ardifo   male  19            yes      Kalimantan
## 2  2  Jeffry   male  19             no       Tangerang
## 3  3 Jocelyn female  19            yes       Tangerang

View(SB19)

str(SB19)

## 'data.frame':    12 obs. of  6 variables:
##  $ id             : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ name           : chr  "Ardifo" "Jeffry" "Jocelyn" "Julian" ...
##  $ gender         : chr  "male" "male" "female" "male" ...
##  $ age            : num  19 19 19 19 19 19 19 19 18 19 ...
##  $ martial_status : chr  "yes" "no" "yes" "no" ...
##  $ address_by_city: chr  "Kalimantan" "Tangerang" "Tangerang" "Tangerang" ...

dim(SB19)

## [1] 12  6

• Please apply piping functions to the data frame SB19, filter it by their gender accordingly! (as you have learn last week)

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

Male_data   <- SB19 %>% 
    filter(gender=="male")%>% 
    print()

##   id   name gender age martial_status address_by_city
## 1  1 Ardifo   male  19            yes      Kalimantan
## 2  2 Jeffry   male  19             no       Tangerang
## 3  4 Julian   male  19             no       Tangerang
## 4  5  Kefas   male  19            yes       Tangerang
## 5 12 Fallen   male  20             no       Tangerang

Female_data <- SB19 %>% 
    filter(gender=="female")%>% 
    print()

##   id    name gender age martial_status address_by_city
## 1  3 Jocelyn female  19            yes       Tangerang
## 2  6  Nikita female  19             no       Tangerang
## 3  7   Angel female  19            yes       Tangerang
## 4  8  Sherly female  19             no         Jakarta
## 5  9 Vanessa female  18            yes          Maluku
## 6 10   Siana female  19             no       Tangerang
## 7 11    Lala female  19            yes       Tangerang