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(11:30)
A

##  [1] 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("sherly","kefas","jeffry","julian","vanessa","angel","nikita","ardifo","siana","lala","fallen","jocelyn")  
B

##  [1] "sherly"  "kefas"   "jeffry"  "julian"  "vanessa" "angel"   "nikita" 
##  [8] "ardifo"  "siana"   "lala"    "fallen"  "jocelyn"

Exercise 3

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

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

##  [1] 74.17966 77.20349 85.75552 91.68635 63.67599 88.29406 98.92189 64.17309
##  [9] 66.66394 88.03700 91.59677 96.42593

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.

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

##          [,1]     [,2]     [,3]     [,4]
## [1,] 60.29262 75.61034 67.63119 71.84788
## [2,] 85.93878 99.48133 69.38620 71.81203
## [3,] 91.20837 77.05708 85.58754 90.72147
## [4,] 99.86933 73.96220 66.65561 77.23401

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.

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

##          [,1]     [,2]     [,3]     [,4]
## [1,] 54.35386 32.53674 52.42905 47.45018
## [2,] 39.63872 43.24592 37.28960 48.14137
## [3,] 36.36820 44.06140 43.81385 52.34423
## [4,] 49.70138 48.26752 49.55326 30.76793

3 * M1      #Mengalikan angka 3 dengan Matriks M1

##          [,1]     [,2]     [,3]     [,4]
## [1,] 180.8778 226.8310 202.8936 215.5436
## [2,] 257.8164 298.4440 208.1586 215.4361
## [3,] 273.6251 231.1712 256.7626 272.1644
## [4,] 299.6080 221.8866 199.9668 231.7020

M1 + M2     #Menjumlahkan Matriks M1 dan M2

##          [,1]     [,2]     [,3]     [,4]
## [1,] 114.6465 108.1471 120.0602 119.2981
## [2,] 125.5775 142.7273 106.6758 119.9534
## [3,] 127.5766 121.1185 129.4014 143.0657
## [4,] 149.5707 122.2297 116.2089 108.0019

M1 - M2     #Mengurangkan Matriks M1 dan M2

##           [,1]     [,2]     [,3]     [,4]
## [1,]  5.938754 43.07360 15.20214 24.39770
## [2,] 46.300065 56.23541 32.09660 23.67066
## [3,] 54.840174 32.99568 41.77369 38.37724
## [4,] 50.167942 25.69468 17.10236 46.46608

M1 * M2     #Mengalikan Matriks M1 dan M2

##          [,1]     [,2]     [,3]     [,4]
## [1,] 3277.137 2460.114 3545.839 3409.195
## [2,] 3406.503 4302.162 2587.383 3457.130
## [3,] 3317.085 3395.243 3749.920 4748.746
## [4,] 4963.644 3569.972 3303.003 2376.330

M1 / M2     #Membagikan Matriks M1 dan M2

##          [,1]     [,2]     [,3]     [,4]
## [1,] 1.109261 2.323845 1.289956 1.514175
## [2,] 2.168051 2.300363 1.860739 1.491691
## [3,] 2.507915 1.748857 1.953436 1.733170
## [4,] 2.009387 1.532339 1.345131 2.510212

det(M1)     #Mencari determinan dari M1

## [1] -444793

library(matlib)

inv(M1)     #Mencari Inverse dari M1

##             [,1]        [,2]        [,3]        [,4]
## [1,] -0.05152178  0.01820845  0.02123411  0.00605634
## [2,]  0.02482237  0.01580245 -0.03520345  0.00356666
## [3,] -0.15099149  0.08646208  0.16573403 -0.13460695
## [4,]  0.17316152 -0.11329765 -0.13677933  0.11787124

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,] "sherly"  "74.1796553414315"
##  [2,] "kefas"   "77.2034906595945"
##  [3,] "jeffry"  "85.7555165607482"
##  [4,] "julian"  "91.6863452177495"
##  [5,] "vanessa" "63.6759854387492"
##  [6,] "angel"   "88.2940642070025"
##  [7,] "nikita"  "98.9218910224736"
##  [8,] "ardifo"  "64.1730893123895"
##  [9,] "siana"   "66.6639427188784"
## [10,] "lala"    "88.0369957443327"
## [11,] "fallen"  "91.5967671852559"
## [12,] "jocelyn" "96.425931090489"

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("sherly","kefas","jeffry","julian","vanessa","angel","nikita","ardifo","siana","lala","fallen","jocelyn")
age <- c(19, 19, 19, 19, 18, 19, 18, 19, 19, 19, 21, 19)
gender <- c("female","male","male","male","female","female","female","male","female","female","male","female")
List <- list(name, age, gender)
List

## [[1]]
##  [1] "sherly"  "kefas"   "jeffry"  "julian"  "vanessa" "angel"   "nikita" 
##  [8] "ardifo"  "siana"   "lala"    "fallen"  "jocelyn"
## 
## [[2]]
##  [1] 19 19 19 19 18 19 18 19 19 19 21 19
## 
## [[3]]
##  [1] "female" "male"   "male"   "male"   "female" "female" "female" "male"  
##  [9] "female" "female" "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))

marital_status <- factor(c("yes","no","yes","no","yes","no","yes","no","yes","no","yes","no"))
marital_status

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

Factor <- List
Factor[[4]] <- marital_status
Factor

## [[1]]
##  [1] "sherly"  "kefas"   "jeffry"  "julian"  "vanessa" "angel"   "nikita" 
##  [8] "ardifo"  "siana"   "lala"    "fallen"  "jocelyn"
## 
## [[2]]
##  [1] 19 19 19 19 18 19 18 19 19 19 21 19
## 
## [[3]]
##  [1] "female" "male"   "male"   "male"   "female" "female" "female" "male"  
##  [9] "female" "female" "male"   "female"
## 
## [[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("angel","julian","vanessa","sherly","jeffry","jocelyn"),
                  gender = c("female","male","female","female","male","female"),
                  age = c(19,20,18,19,19,19),
                  marital_status = c("yes","no","yes","no","yes","no"),
                  address_by_city = c ("tangerang","tangerang","maluku","jakarta","tangerang","tangerang"),stringsAsFactors = F)
DF1                  

##   id    name gender age marital_status address_by_city
## 1  1   angel female  19            yes       tangerang
## 2  2  julian   male  20             no       tangerang
## 3  3 vanessa female  18            yes          maluku
## 4  4  sherly female  19             no         jakarta
## 5  5  jeffry   male  19            yes       tangerang
## 6  6 jocelyn 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("kefas","nikita","ardifo","fallen","ayu","siana"),
                  gender = c("male","female","male","male","female","female"),
                  age = c(19,19,19,21,20,19),
                  marital_status = c("yes","no","yes","no","yes","no"),
                  address_by_city = c ("tangerang","tangerang","kaltim","jakarta","tangerang","tangerang"),stringsAsFactors = F)
DF2

##   id   name gender age marital_status address_by_city
## 1  7  kefas   male  19            yes       tangerang
## 2  8 nikita female  19             no       tangerang
## 3  9 ardifo   male  19            yes          kaltim
## 4 10 fallen   male  21             no         jakarta
## 5 11    ayu female  20            yes       tangerang
## 6 12  siana female  19             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.
• 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  19            yes       tangerang
## 2   2  julian   male  20             no       tangerang
## 3   3 vanessa female  18            yes          maluku
## 4   4  sherly female  19             no         jakarta
## 5   5  jeffry   male  19            yes       tangerang
## 6   6 jocelyn female  19             no       tangerang
## 7   7   kefas   male  19            yes       tangerang
## 8   8  nikita female  19             no       tangerang
## 9   9  ardifo   male  19            yes          kaltim
## 10 10  fallen   male  21             no         jakarta
## 11 11     ayu female  20            yes       tangerang
## 12 12   siana female  19             no       tangerang

head(SB19,3)

##   id    name gender age marital_status address_by_city
## 1  1   angel female  19            yes       tangerang
## 2  2  julian   male  20             no       tangerang
## 3  3 vanessa female  18            yes          maluku

View(SB19)
class(SB19)

## [1] "data.frame"

str(SB19)

## 'data.frame':    12 obs. of  6 variables:
##  $ id             : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ name           : chr  "angel" "julian" "vanessa" "sherly" ...
##  $ gender         : chr  "female" "male" "female" "female" ...
##  $ age            : num  19 20 18 19 19 19 19 19 19 21 ...
##  $ marital_status : chr  "yes" "no" "yes" "no" ...
##  $ address_by_city: chr  "tangerang" "tangerang" "maluku" "jakarta" ...

dim(SB19)

## [1] 12  6

Piping

Filter (untuk memunculkan data yang sesuai dengan beberapa argumen / variable values)

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

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

##   id   name gender age marital_status address_by_city
## 1  2 julian   male  20             no       tangerang
## 2  5 jeffry   male  19            yes       tangerang
## 3  7  kefas   male  19            yes       tangerang
## 4  9 ardifo   male  19            yes          kaltim
## 5 10 fallen   male  21             no         jakarta

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

##   id    name gender age marital_status address_by_city
## 1  1   angel female  19            yes       tangerang
## 2  3 vanessa female  18            yes          maluku
## 3  4  sherly female  19             no         jakarta
## 4  6 jocelyn female  19             no       tangerang
## 5  8  nikita female  19             no       tangerang
## 6 11     ayu female  20            yes       tangerang
## 7 12   siana female  19             no       tangerang

Karena yang diminta hanya filter gender, jadi bisa menggunakan subset juga.

Subset (untuk memunculkan data dengan argumen tertentu)

pria1 <- SB19 %>%
    subset(gender=="male") %>%
    print()

##    id   name gender age marital_status address_by_city
## 2   2 julian   male  20             no       tangerang
## 5   5 jeffry   male  19            yes       tangerang
## 7   7  kefas   male  19            yes       tangerang
## 9   9 ardifo   male  19            yes          kaltim
## 10 10 fallen   male  21             no         jakarta

wanita1 <- SB19 %>%
    subset(gender=="female") %>%
    print()

##    id    name gender age marital_status address_by_city
## 1   1   angel female  19            yes       tangerang
## 3   3 vanessa female  18            yes          maluku
## 4   4  sherly female  19             no         jakarta
## 6   6 jocelyn female  19             no       tangerang
## 8   8  nikita female  19             no       tangerang
## 11 11     ayu female  20            yes       tangerang
## 12 12   siana female  19             no       tangerang