-
Notifications
You must be signed in to change notification settings - Fork 0
/
Create Data Dummy.R
68 lines (53 loc) · 2.31 KB
/
Create Data Dummy.R
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
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
# Conjoint Analysis
# Market Research Using Conjoint Analysis In R (Profile Book)
# References : https://www.coursera.org/learn/applying-data-analytics-business-in-marketing
# ============================================================================
# Install Library
library("conjoint")
library("fpc")
# ============================================================================
# Make A factorial Data
# Let’s start with an example. Imagine you want to determine which of the
# PAGES, GENRE and AUTHOR features is the most
# important for a successful Data Science Book.
# Here are feature values we will study:
# * PAGES (less than 500 Pages, 500-1000 Pages, +1000 Pages)
# * GENRE (FICTION, NON-FICTION)
# * AUTHOR (KNOWN, UNKNOWN)
# ============================================================================
# Declaration of features and feature values
pages <- c("LESS THAN 500 PAGES", "500 TO 1000 PAGES", "MORE THAN 1000 PAGES")
genre <- c("FICTION","NON-FICTION")
author <- c("KNOWN", "UNKNOWN")
factor_levels <- as.data.frame(c(pages,genre,author))
colnames(factor_levels) <- "Levels"
factor_levels
# All concept generation
data <- expand.grid(pages, genre, author)
colnames(data) <- c("Pages", "Genre", "Author")
data
# ============================================================================
# Make a Full Factorial Design
# Full of the combination of factor levels are considered
# ============================================================================
# Pemilihan konsep Relevan
facdesign <- caFactorialDesign(data = data, type = "full")
encdesign <- caEncodedDesign(facdesign)
# Memeriksa apakah konsep yang dipilih relevan untuk diteliti
cor(encdesign)
# ============================================================================
# Make A Respon for Each Profile books
# Make A 10 People respon for each Profile book to compare them
# ============================================================================
# Isi 10 responden secara acak dari seluruh 12 profil
set.seed(1)
response <- as.data.frame(sample(1:12, 12, rep=FALSE))
for (i in 2 : 10){
temp <- as.data.frame(sample(1:12, 12, rep=FALSE))
response <- cbind(response, temp)
}
response <- t(response)
row.names(response) <- c(1:10)
colnames(response) <- c(paste("Profile", c(1:12)))
response <- as.data.frame(response)
response