Assignment3: Esoph and Youth Survey
Barış Sivas
19 12 2020
1.Esoph Analysis
1.1 Preparation to The Data Analysis
There are some kind of packages that could be used in both analysis downloaded firstly.
With the aim of understanding what the data includes, a quick snapshot was taken as shown below. The data tells about the age distribution of the people who had an issue about cancer disease(cases or controls) with their alcohol and tobacco usage routines.
## agegp alcgp tobgp ncases ncontrols
## 1 25-34 0-39g/day 0-9g/day 0 40
## 2 25-34 0-39g/day 10-19 0 10
## 3 25-34 0-39g/day 20-29 0 6
## 4 25-34 0-39g/day 30+ 0 5
## 5 25-34 40-79 0-9g/day 0 27
## 6 25-34 40-79 10-19 0 7
## 7 25-34 40-79 20-29 0 4
## 8 25-34 40-79 30+ 0 7
## 9 25-34 80-119 0-9g/day 0 2
## 10 25-34 80-119 10-19 0 1## agegp alcgp tobgp ncases ncontrols
## 25-34:15 0-39g/day:23 0-9g/day:24 Min. : 0.000 Min. : 1.00
## 35-44:15 40-79 :23 10-19 :24 1st Qu.: 0.000 1st Qu.: 3.00
## 45-54:16 80-119 :21 20-29 :20 Median : 1.000 Median : 6.00
## 55-64:16 120+ :21 30+ :20 Mean : 2.273 Mean :11.08
## 65-74:15 3rd Qu.: 4.000 3rd Qu.:14.00
## 75+ :11 Max. :17.000 Max. :60.001.2. Distribution of The Cancer Case Among Age Groups
Definition of the Cancer Rate could be given as the number of ncases people who had token controls before the disease was known. After the calculation was done, a pie chart was drawn as it could be seen below. According to the chart the most risky age groups can be ordered as +75; %34, 65-74; %31, 55-64; % 30 …
dt1<- esoph %>%
group_by(agegp)%>%
summarise(total_ncases = sum(ncases), total_control = sum(ncontrols))%>%
mutate(Cancer_Rate = round(total_ncases*100/total_control))%>%
arrange(desc(Cancer_Rate))
dt1## # A tibble: 6 x 4
## agegp total_ncases total_control Cancer_Rate
## <ord> <dbl> <dbl> <dbl>
## 1 65-74 55 161 34
## 2 55-64 76 242 31
## 3 75+ 13 44 30
## 4 45-54 46 213 22
## 5 35-44 9 199 5
## 6 25-34 1 116 1ggplot(dt1, aes(x="", y=Cancer_Rate,"%", fill=agegp)) + geom_bar(stat="identity") +
geom_text(aes(x= 1.2, y = Cancer_Rate/2 + c(0, cumsum(Cancer_Rate)[-length(Cancer_Rate)]), label = Cancer_Rate), size=5, color = "white")+
coord_polar(theta = "y") +
scale_fill_brewer(palette = "Dark2")+
theme_void()+
labs(title = "%Cancer Rate & Age Range", fill = "Age Group")
1.3. Relationship Between The Cancer Case and Alcohol Consumption
This analyse sents a warn message to the people who takes 120 g alcohol per a day. Unfortunately high level alcohol consumption could be deadly especially for people who are older than 45 years old.
dt2<- esoph %>%
group_by(agegp,alcgp)%>%
summarise(total_ncases = sum(ncases), total_control = sum(ncontrols))%>%
mutate(Cancer_Rate = total_ncases*100/ total_control)%>%
arrange(desc(Cancer_Rate))
dt2## # A tibble: 24 x 5
## # Groups: agegp [6]
## agegp alcgp total_ncases total_control Cancer_Rate
## <ord> <ord> <dbl> <dbl> <dbl>
## 1 75+ 80-119 2 2 100
## 2 75+ 120+ 3 3 100
## 3 45-54 120+ 13 15 86.7
## 4 65-74 120+ 6 8 75
## 5 55-64 120+ 18 26 69.2
## 6 55-64 80-119 24 43 55.8
## 7 65-74 40-79 25 53 47.2
## 8 65-74 80-119 13 29 44.8
## 9 35-44 120+ 4 10 40
## 10 75+ 40-79 4 12 33.3
## # ... with 14 more rows ggplot(dt2, aes(x = agegp, y = Cancer_Rate, fill= alcgp))+
geom_bar(stat="identity", position = "dodge" ) +theme(axis.text.x = element_text(angle=0, size=9, vjust=0.5,hjust=1)) +
labs(title = "Cancer Rate Distribution", x = "Age Range", fill="Alcohol Consumption")
1.4. Relationship Between The Cancer Case and Tobacco Consumption
This analyse also sents a warn message to the people who takes 30 g tobacco per a day. It could be more dangerous especially for people who are older than 45 years old.
dt3<- esoph %>%
group_by(agegp,tobgp)%>%
summarise(total_ncases = sum(ncases), total_control = sum(ncontrols))%>%
mutate(Cancer_Rate = total_ncases*100/ total_control)%>%
arrange(desc(Cancer_Rate))
dt3## # A tibble: 24 x 5
## # Groups: agegp [6]
## agegp tobgp total_ncases total_control Cancer_Rate
## <ord> <ord> <dbl> <dbl> <dbl>
## 1 55-64 30+ 16 22 72.7
## 2 45-54 30+ 11 19 57.9
## 3 65-74 20-29 10 20 50
## 4 65-74 30+ 2 4 50
## 5 75+ 30+ 2 4 50
## 6 75+ 10-19 5 11 45.5
## 7 55-64 10-19 23 65 35.4
## 8 55-64 20-29 12 38 31.6
## 9 65-74 10-19 12 38 31.6
## 10 65-74 0-9g/day 31 99 31.3
## # ... with 14 more rows ggplot(dt3, aes(x = agegp, y = Cancer_Rate, fill= tobgp))+
geom_bar(stat="identity", position = "dodge") +theme(axis.text.x = element_text(angle=0, size=9, vjust=0.5,hjust=1)) +
labs(title = "Cancer Rate Distribution", x = "Age Range",fill="Tobacco Consumption")
1.5. Effectivness of Both Alcohol and Tobacco Consumption
According to the last two analysis not only tobacco but also alcohol consumption is too harmfull for especially elderly people. Now in this last analysis we tried to understand what will happen if people uses these two bad habit together. If a person take more than 40-79 g alcohol per a day with +30 g tobacco, s/he will definetely get cancer disease at the end. However even if a person with tobacco habit will use more thant 80 g alcohol per a day, s/he will definetely get cancer disease at the end also.
dt3<- esoph %>%
group_by(agegp,alcgp, tobgp)%>%
summarise(total_ncases = sum(ncases), total_control = sum(ncontrols))%>%
mutate(Cancer_Rate = total_ncases*100/ total_control)%>%
arrange(desc(Cancer_Rate))
dt2## # A tibble: 24 x 5
## # Groups: agegp [6]
## agegp alcgp total_ncases total_control Cancer_Rate
## <ord> <ord> <dbl> <dbl> <dbl>
## 1 75+ 80-119 2 2 100
## 2 75+ 120+ 3 3 100
## 3 45-54 120+ 13 15 86.7
## 4 65-74 120+ 6 8 75
## 5 55-64 120+ 18 26 69.2
## 6 55-64 80-119 24 43 55.8
## 7 65-74 40-79 25 53 47.2
## 8 65-74 80-119 13 29 44.8
## 9 35-44 120+ 4 10 40
## 10 75+ 40-79 4 12 33.3
## # ... with 14 more rows ggplot(dt3, aes(x = alcgp, y = Cancer_Rate, fill= tobgp))+
geom_bar(stat="identity", position = "dodge") +theme(axis.text.x = element_text(angle=0, size=9, vjust=0.5,hjust=1)) +
labs(title = "Cancer Rate Distribution", x = "Alcohol Consumption", fill = "Tobacco Consumption")
2.Young People Response Analysis
2.1. Preparation to The Data Analysis
After a quick research, the data of “Young People Responses” was downloaded from kaggle website and then it was uploaded to my progres journal in github for quick access those people who will try to make same work in the In order to get rid of missing values na.omit function was used on raw data to assign as raw_ys.
ys<- rio::import("https://github.com/pjournal/mef04-sivasbaris/blob/gh-pages/young_people_responses.csv?raw=True")
raw_ys<- na.omit(ys)
glimpse(raw_ys)## Rows: 686
## Columns: 150
## $ Music <int> 5, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5...
## $ `Slow songs or fast songs` <int> 3, 4, 5, 3, 3, 5, 3, 3, 3, 3, 3, 3...
## $ Dance <int> 2, 2, 2, 4, 2, 5, 3, 2, 3, 1, 1, 5...
## $ Folk <int> 1, 1, 2, 3, 3, 3, 2, 5, 2, 1, 2, 3...
## $ Country <int> 2, 1, 3, 2, 2, 1, 1, 2, 1, 1, 1, 2...
## $ `Classical music` <int> 2, 1, 4, 4, 3, 2, 2, 2, 2, 4, 4, 1...
## $ Musical <int> 1, 2, 5, 3, 3, 2, 2, 5, 3, 1, 3, 5...
## $ Pop <int> 5, 3, 3, 5, 2, 5, 4, 3, 4, 2, 3, 5...
## $ Rock <int> 5, 5, 5, 3, 5, 3, 5, 5, 3, 5, 5, 2...
## $ `Metal or Hardrock` <int> 1, 4, 3, 1, 5, 1, 1, 2, 2, 1, 4, 1...
## $ Punk <int> 1, 4, 4, 2, 3, 1, 2, 3, 1, 1, 2, 1...
## $ `Hiphop, Rap` <int> 1, 1, 1, 5, 4, 3, 3, 2, 3, 1, 3, 2...
## $ `Reggae, Ska` <int> 1, 3, 4, 3, 3, 1, 2, 4, 2, 1, 1, 1...
## $ `Swing, Jazz` <int> 1, 1, 3, 2, 4, 1, 2, 4, 2, 2, 1, 3...
## $ `Rock n roll` <int> 3, 4, 5, 1, 4, 2, 3, 4, 3, 2, 4, 2...
## $ Alternative <int> 1, 4, 5, 2, 5, 3, 1, 4, 3, 5, 3, 1...
## $ Latino <int> 1, 2, 5, 4, 3, 3, 2, 5, 3, 2, 2, 3...
## $ `Techno, Trance` <int> 1, 1, 1, 2, 1, 5, 3, 1, 4, 1, 1, 1...
## $ Opera <int> 1, 1, 3, 2, 3, 2, 2, 2, 2, 2, 2, 1...
## $ Movies <int> 5, 5, 5, 5, 5, 4, 5, 5, 5, 5, 5, 5...
## $ Horror <int> 4, 2, 3, 4, 5, 2, 4, 2, 5, 3, 1, 3...
## $ Thriller <int> 2, 2, 4, 4, 5, 1, 4, 1, 4, 4, 5, 2...
## $ Comedy <int> 5, 4, 4, 5, 5, 5, 5, 5, 5, 4, 4, 5...
## $ Romantic <int> 4, 3, 2, 2, 2, 3, 2, 5, 3, 3, 3, 5...
## $ `Sci-fi` <int> 4, 4, 4, 3, 3, 1, 3, 1, 3, 2, 1, 2...
## $ War <int> 1, 1, 2, 3, 3, 3, 3, 3, 2, 5, 4, 1...
## $ `Fantasy/Fairy tales` <int> 5, 3, 5, 4, 4, 5, 4, 4, 5, 5, 5, 5...
## $ Animated <int> 5, 5, 5, 4, 3, 5, 4, 4, 5, 5, 3, 4...
## $ Documentary <int> 3, 4, 2, 3, 3, 3, 3, 4, 3, 5, 3, 2...
## $ Western <int> 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1...
## $ Action <int> 2, 4, 1, 4, 4, 2, 3, 2, 3, 4, 1, 3...
## $ History <int> 1, 1, 1, 3, 5, 3, 5, 3, 3, 2, 4, 2...
## $ Psychology <int> 5, 3, 2, 2, 3, 3, 2, 2, 3, 2, 4, 2...
## $ Politics <int> 1, 4, 1, 3, 4, 1, 3, 3, 3, 5, 4, 1...
## $ Mathematics <int> 3, 5, 5, 2, 2, 1, 1, 3, 2, 1, 1, 1...
## $ Physics <int> 3, 2, 2, 2, 3, 1, 1, 1, 1, 1, 1, 1...
## $ Internet <int> 5, 4, 4, 2, 4, 2, 5, 5, 4, 5, 3, 3...
## $ PC <int> 3, 4, 2, 2, 4, 1, 4, 1, 5, 4, 2, 3...
## $ `Economy Management` <int> 5, 5, 4, 2, 1, 3, 1, 4, 3, 1, 1, 3...
## $ Biology <int> 3, 1, 1, 3, 4, 5, 2, 2, 2, 1, 5, 1...
## $ Chemistry <int> 3, 1, 1, 3, 4, 5, 2, 1, 1, 1, 5, 1...
## $ Reading <int> 3, 4, 5, 5, 3, 3, 2, 4, 3, 3, 5, 4...
## $ Geography <int> 3, 4, 2, 2, 3, 3, 3, 4, 3, 5, 3, 1...
## $ `Foreign languages` <int> 5, 5, 5, 3, 4, 4, 4, 5, 5, 2, 5, 5...
## $ Medicine <int> 3, 1, 2, 3, 4, 5, 1, 1, 2, 1, 5, 1...
## $ Law <int> 1, 2, 3, 2, 3, 3, 2, 1, 4, 3, 2, 1...
## $ Cars <int> 1, 2, 1, 3, 5, 4, 1, 1, 2, 1, 3, 1...
## $ `Art exhibitions` <int> 1, 2, 5, 1, 2, 1, 1, 4, 2, 5, 1, 3...
## $ Religion <int> 1, 1, 5, 4, 2, 1, 2, 4, 2, 1, 1, 1...
## $ `Countryside, outdoors` <int> 5, 1, 5, 4, 5, 4, 2, 4, 4, 5, 5, 5...
## $ Dancing <int> 3, 1, 5, 1, 1, 3, 1, 5, 1, 1, 3, 3...
## $ `Musical instruments` <int> 3, 1, 5, 3, 5, 2, 1, 3, 1, 1, 4, 3...
## $ Writing <int> 2, 1, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1...
## $ `Passive sport` <int> 1, 1, 5, 3, 5, 5, 4, 4, 5, 5, 5, 3...
## $ `Active sport` <int> 5, 1, 2, 1, 4, 3, 5, 4, 1, 3, 3, 3...
## $ Gardening <int> 5, 1, 1, 4, 2, 3, 1, 1, 3, 1, 4, 1...
## $ Celebrities <int> 1, 2, 1, 3, 1, 1, 3, 2, 2, 2, 3, 5...
## $ Shopping <int> 4, 3, 4, 3, 2, 3, 3, 4, 5, 3, 2, 5...
## $ `Science and technology` <int> 4, 3, 2, 3, 3, 4, 2, 3, 4, 3, 3, 2...
## $ Theatre <int> 2, 2, 5, 2, 1, 3, 2, 5, 2, 1, 2, 3...
## $ `Fun with friends` <int> 5, 4, 5, 4, 3, 5, 4, 5, 4, 3, 4, 5...
## $ `Adrenaline sports` <int> 4, 2, 5, 2, 3, 1, 2, 2, 1, 1, 1, 4...
## $ Pets <int> 4, 5, 5, 1, 2, 5, 5, 2, 5, 1, 2, 5...
## $ Flying <int> 1, 1, 1, 1, 3, 1, 3, 4, 1, 4, 1, 2...
## $ Storm <int> 1, 1, 1, 2, 2, 3, 2, 5, 1, 1, 1, 3...
## $ Darkness <int> 1, 1, 1, 1, 2, 2, 4, 4, 2, 1, 1, 3...
## $ Heights <int> 1, 2, 1, 1, 2, 1, 3, 5, 2, 3, 1, 4...
## $ Spiders <int> 1, 1, 1, 1, 1, 1, 1, 3, 2, 5, 1, 3...
## $ Snakes <int> 5, 1, 1, 1, 2, 5, 5, 4, 1, 5, 3, 4...
## $ Rats <int> 3, 1, 1, 2, 2, 1, 3, 4, 1, 5, 1, 3...
## $ Ageing <int> 1, 3, 1, 2, 1, 4, 1, 3, 1, 5, 5, 2...
## $ `Dangerous dogs` <int> 3, 1, 1, 4, 1, 1, 2, 5, 4, 5, 2, 1...
## $ `Fear of public speaking` <int> 2, 4, 2, 3, 3, 1, 4, 3, 2, 5, 2, 3...
## $ Smoking <chr> "never smoked", "never smoked", "t...
## $ Alcohol <chr> "drink a lot", "drink a lot", "dri...
## $ `Healthy eating` <int> 4, 3, 3, 4, 2, 4, 2, 3, 3, 3, 3, 3...
## $ `Daily events` <int> 2, 3, 1, 3, 2, 3, 3, 4, 3, 3, 4, 5...
## $ `Prioritising workload` <int> 2, 2, 2, 1, 2, 5, 1, 2, 2, 1, 3, 3...
## $ `Writing notes` <int> 5, 4, 5, 2, 3, 5, 3, 2, 4, 5, 5, 3...
## $ Workaholism <int> 4, 5, 3, 3, 3, 5, 2, 3, 2, 3, 4, 2...
## $ `Thinking ahead` <int> 2, 4, 5, 5, 3, 3, 4, 3, 3, 1, 5, 3...
## $ `Final judgement` <int> 5, 1, 3, 5, 1, 3, 3, 5, 3, 1, 1, 3...
## $ Reliability <int> 4, 4, 4, 5, 3, 4, 3, 4, 4, 3, 5, 5...
## $ `Keeping promises` <int> 4, 4, 5, 4, 4, 5, 3, 5, 4, 3, 5, 4...
## $ `Loss of interest` <int> 1, 3, 1, 2, 3, 3, 1, 3, 1, 3, 4, 3...
## $ `Friends versus money` <int> 3, 4, 5, 3, 2, 4, 4, 4, 3, 3, 4, 3...
## $ Funniness <int> 5, 3, 2, 3, 3, 4, 4, 3, 2, 5, 3, 3...
## $ Fake <int> 1, 2, 4, 2, 1, 1, 2, 1, 1, 3, 1, 2...
## $ `Criminal damage` <int> 1, 1, 1, 1, 4, 2, 1, 2, 1, 5, 2, 2...
## $ `Decision making` <int> 3, 2, 3, 3, 2, 2, 3, 5, 5, 3, 5, 5...
## $ Elections <int> 4, 5, 5, 5, 5, 5, 5, 5, 5, 1, 5, 3...
## $ `Self-criticism` <int> 1, 4, 4, 5, 4, 3, 3, 4, 4, 5, 5, 5...
## $ `Judgment calls` <int> 3, 4, 4, 5, 4, 5, 5, 5, 5, 3, 2, 5...
## $ Hypochondria <int> 1, 1, 1, 1, 1, 1, 2, 1, 2, 5, 5, 1...
## $ Empathy <int> 3, 2, 5, 3, 4, 4, 1, 4, 5, 5, 5, 4...
## $ `Eating to survive` <int> 1, 1, 5, 1, 2, 1, 2, 1, 2, 1, 3, 2...
## $ Giving <int> 4, 2, 5, 3, 3, 5, 3, 4, 3, 1, 2, 3...
## $ `Compassion to animals` <int> 5, 4, 4, 3, 5, 5, 5, 5, 5, 2, 5, 5...
## $ `Borrowed stuff` <int> 4, 3, 2, 4, 5, 5, 2, 4, 4, 2, 5, 5...
## $ Loneliness <int> 3, 2, 5, 3, 2, 3, 2, 2, 2, 4, 5, 2...
## $ `Cheating in school` <int> 2, 4, 3, 5, 4, 2, 5, 3, 3, 5, 2, 3...
## $ Health <int> 1, 4, 2, 3, 3, 3, 3, 4, 3, 2, 5, 3...
## $ `Changing the past` <int> 1, 4, 5, 4, 3, 1, 2, 2, 3, 3, 3, 2...
## $ God <int> 1, 1, 5, 5, 3, 5, 4, 5, 4, 1, 1, 3...
## $ Dreams <int> 4, 3, 1, 3, 3, 3, 4, 3, 3, 3, 3, 4...
## $ Charity <int> 2, 1, 3, 3, 2, 3, 1, 2, 1, 3, 2, 1...
## $ `Number of friends` <int> 3, 3, 3, 3, 3, 3, 4, 3, 3, 4, 3, 3...
## $ Punctuality <chr> "i am always on time", "i am often...
## $ Lying <chr> "never", "sometimes", "sometimes",...
## $ Waiting <int> 3, 3, 2, 3, 3, 4, 1, 1, 3, 3, 2, 3...
## $ `New environment` <int> 4, 4, 3, 4, 4, 5, 4, 4, 3, 5, 1, 2...
## $ `Mood swings` <int> 3, 4, 4, 2, 3, 5, 3, 4, 3, 5, 4, 3...
## $ `Appearence and gestures` <int> 4, 4, 3, 3, 3, 4, 4, 3, 4, 2, 4, 5...
## $ Socializing <int> 3, 4, 5, 3, 4, 5, 2, 4, 3, 5, 2, 3...
## $ Achievements <int> 4, 2, 3, 3, 2, 4, 4, 4, 3, 3, 3, 4...
## $ `Responding to a serious letter` <int> 3, 4, 4, 3, 2, 3, 3, 4, 4, 3, 4, 3...
## $ Children <int> 5, 2, 4, 5, 3, 2, 4, 3, 5, 5, 5, 3...
## $ Assertiveness <int> 1, 2, 3, 4, 4, 3, 3, 4, 2, 4, 5, 2...
## $ `Getting angry` <int> 1, 5, 4, 2, 3, 3, 1, 3, 1, 3, 5, 3...
## $ `Knowing the right people` <int> 3, 4, 3, 3, 4, 4, 4, 4, 3, 5, 3, 4...
## $ `Public speaking` <int> 5, 4, 2, 5, 4, 3, 5, 5, 3, 5, 3, 5...
## $ Unpopularity <int> 5, 4, 4, 5, 4, 3, 2, 3, 3, 2, 2, 3...
## $ `Life struggles` <int> 1, 1, 4, 2, 3, 5, 2, 5, 5, 4, 5, 4...
## $ `Happiness in life` <int> 4, 4, 4, 3, 3, 5, 4, 4, 4, 3, 4, 4...
## $ `Energy levels` <int> 5, 3, 4, 5, 4, 4, 4, 4, 3, 3, 3, 4...
## $ `Small - big dogs` <int> 1, 5, 3, 3, 4, 3, 3, 1, 2, 1, 4, 5...
## $ Personality <int> 4, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 4...
## $ `Finding lost valuables` <int> 3, 4, 3, 2, 3, 2, 2, 3, 2, 3, 1, 2...
## $ `Getting up` <int> 2, 5, 4, 4, 3, 2, 5, 4, 4, 5, 3, 5...
## $ `Interests or hobbies` <int> 3, 3, 5, 3, 5, 4, 4, 3, 3, 4, 2, 5...
## $ `Parents' advice` <int> 4, 2, 3, 3, 3, 4, 3, 3, 4, 4, 4, 3...
## $ `Questionnaires or polls` <int> 3, 3, 1, 3, 4, 5, 3, 3, 4, 4, 2, 2...
## $ `Internet usage` <chr> "few hours a day", "few hours a da...
## $ Finances <int> 3, 3, 2, 4, 2, 4, 3, 4, 2, 2, 4, 3...
## $ `Shopping centres` <int> 4, 4, 4, 3, 3, 3, 4, 4, 4, 2, 1, 5...
## $ `Branded clothing` <int> 5, 1, 1, 4, 3, 1, 4, 4, 2, 1, 1, 2...
## $ `Entertainment spending` <int> 3, 4, 4, 3, 3, 3, 4, 2, 3, 3, 2, 3...
## $ `Spending on looks` <int> 3, 2, 3, 3, 1, 4, 4, 3, 4, 1, 3, 4...
## $ `Spending on gadgets` <int> 1, 5, 4, 2, 4, 1, 3, 2, 2, 1, 2, 3...
## $ `Spending on healthy eating` <int> 3, 2, 2, 4, 4, 5, 2, 4, 2, 2, 3, 3...
## $ Age <int> 20, 19, 20, 20, 20, 20, 19, 19, 19...
## $ Height <int> 163, 163, 176, 170, 186, 177, 184,...
## $ Weight <int> 48, 58, 67, 59, 77, 50, 90, 60, 60...
## $ `Number of siblings` <int> 1, 2, 2, 1, 1, 1, 1, 3, 2, 1, 10, ...
## $ Gender <chr> "female", "female", "female", "fem...
## $ `Left - right handed` <chr> "right handed", "right handed", "r...
## $ Education <chr> "college/bachelor degree", "colleg...
## $ `Only child` <chr> "no", "no", "no", "no", "no", "no"...
## $ `Village - town` <chr> "village", "city", "city", "villag...
## $ `House - block of flats` <chr> "block of flats", "block of flats"...2.2. Selecting Data
After taking a snapshot to cleaned data, the rows between 32 to 63 which were assigned as History: Pets was selected.
## History Psychology Politics Mathematics Physics Internet PC
## 1 1 5 1 3 3 5 3
## 2 1 3 4 5 2 4 4
## 3 1 2 1 5 2 4 2
## 5 3 2 3 2 2 2 2
## 6 5 3 4 2 3 4 4
## 7 3 3 1 1 1 2 1
## Economy Management Biology Chemistry Reading Geography Foreign languages
## 1 5 3 3 3 3 5
## 2 5 1 1 4 4 5
## 3 4 1 1 5 2 5
## 5 2 3 3 5 2 3
## 6 1 4 4 3 3 4
## 7 3 5 5 3 3 4
## Medicine Law Cars Art exhibitions Religion Countryside, outdoors Dancing
## 1 3 1 1 1 1 5 3
## 2 1 2 2 2 1 1 1
## 3 2 3 1 5 5 5 5
## 5 3 2 3 1 4 4 1
## 6 4 3 5 2 2 5 1
## 7 5 3 4 1 1 4 3
## Musical instruments Writing Passive sport Active sport Gardening Celebrities
## 1 3 2 1 5 5 1
## 2 1 1 1 1 1 2
## 3 5 5 5 2 1 1
## 5 3 1 3 1 4 3
## 6 5 1 5 4 2 1
## 7 2 1 5 3 3 1
## Shopping Science and technology Theatre Fun with friends Adrenaline sports
## 1 4 4 2 5 4
## 2 3 3 2 4 2
## 3 4 2 5 5 5
## 5 3 3 2 4 2
## 6 2 3 1 3 3
## 7 3 4 3 5 1
## Pets
## 1 4
## 2 5
## 3 5
## 5 1
## 6 2
## 7 5## History Psychology Politics Mathematics Physics
## Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.00 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:1.000
## Median :3.000 Median :3.00 Median :3.000 Median :2.000 Median :2.000
## Mean :3.226 Mean :3.14 Mean :2.627 Mean :2.401 Mean :2.096
## 3rd Qu.:4.000 3rd Qu.:4.00 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :5.000 Max. :5.00 Max. :5.000 Max. :5.000 Max. :5.000
## Internet PC Economy Management Biology
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:1.000
## Median :4.000 Median :3.000 Median :2.000 Median :2.000
## Mean :4.188 Mean :3.136 Mean :2.662 Mean :2.621
## 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## Chemistry Reading Geography Foreign languages
## Min. :1.000 Min. :1.0 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:2.0 1st Qu.:2.000 1st Qu.:3.000
## Median :2.000 Median :3.0 Median :3.000 Median :4.000
## Mean :2.121 Mean :3.2 Mean :3.109 Mean :3.813
## 3rd Qu.:3.000 3rd Qu.:5.0 3rd Qu.:4.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.0 Max. :5.000 Max. :5.000
## Medicine Law Cars Art exhibitions
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :2.000 Median :2.000 Median :2.000 Median :2.000
## Mean :2.475 Mean :2.224 Mean :2.634 Mean :2.617
## 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## Religion Countryside, outdoors Dancing Musical instruments
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:3.000 1st Qu.:1.000 1st Qu.:1.000
## Median :2.000 Median :4.000 Median :2.000 Median :2.000
## Mean :2.229 Mean :3.614 Mean :2.399 Mean :2.302
## 3rd Qu.:3.000 3rd Qu.:5.000 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## Writing Passive sport Active sport Gardening
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:1.000
## Median :1.000 Median :4.000 Median :3.000 Median :1.000
## Mean :1.866 Mean :3.394 Mean :3.236 Mean :1.872
## 3rd Qu.:2.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:2.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## Celebrities Shopping Science and technology Theatre
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :2.000 Median :3.000 Median :3.000 Median :3.000
## Mean :2.319 Mean :3.257 Mean :3.271 Mean :3.023
## 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## Fun with friends Adrenaline sports Pets
## Min. :2.000 Min. :1.00 Min. :1.000
## 1st Qu.:4.000 1st Qu.:2.00 1st Qu.:2.000
## Median :5.000 Median :3.00 Median :4.000
## Mean :4.552 Mean :2.88 Mean :3.324
## 3rd Qu.:5.000 3rd Qu.:4.00 3rd Qu.:5.000
## Max. :5.000 Max. :5.00 Max. :5.000## Rows: 686
## Columns: 32
## $ History <int> 1, 1, 1, 3, 5, 3, 5, 3, 3, 2, 4, 2, 2, 1, ...
## $ Psychology <int> 5, 3, 2, 2, 3, 3, 2, 2, 3, 2, 4, 2, 5, 1, ...
## $ Politics <int> 1, 4, 1, 3, 4, 1, 3, 3, 3, 5, 4, 1, 1, 1, ...
## $ Mathematics <int> 3, 5, 5, 2, 2, 1, 1, 3, 2, 1, 1, 1, 1, 1, ...
## $ Physics <int> 3, 2, 2, 2, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
## $ Internet <int> 5, 4, 4, 2, 4, 2, 5, 5, 4, 5, 3, 3, 4, 5, ...
## $ PC <int> 3, 4, 2, 2, 4, 1, 4, 1, 5, 4, 2, 3, 2, 2, ...
## $ `Economy Management` <int> 5, 5, 4, 2, 1, 3, 1, 4, 3, 1, 1, 3, 3, 1, ...
## $ Biology <int> 3, 1, 1, 3, 4, 5, 2, 2, 2, 1, 5, 1, 2, 4, ...
## $ Chemistry <int> 3, 1, 1, 3, 4, 5, 2, 1, 1, 1, 5, 1, 1, 1, ...
## $ Reading <int> 3, 4, 5, 5, 3, 3, 2, 4, 3, 3, 5, 4, 4, 1, ...
## $ Geography <int> 3, 4, 2, 2, 3, 3, 3, 4, 3, 5, 3, 1, 1, 1, ...
## $ `Foreign languages` <int> 5, 5, 5, 3, 4, 4, 4, 5, 5, 2, 5, 5, 3, 3, ...
## $ Medicine <int> 3, 1, 2, 3, 4, 5, 1, 1, 2, 1, 5, 1, 1, 5, ...
## $ Law <int> 1, 2, 3, 2, 3, 3, 2, 1, 4, 3, 2, 1, 1, 1, ...
## $ Cars <int> 1, 2, 1, 3, 5, 4, 1, 1, 2, 1, 3, 1, 1, 1, ...
## $ `Art exhibitions` <int> 1, 2, 5, 1, 2, 1, 1, 4, 2, 5, 1, 3, 4, 1, ...
## $ Religion <int> 1, 1, 5, 4, 2, 1, 2, 4, 2, 1, 1, 1, 2, 2, ...
## $ `Countryside, outdoors` <int> 5, 1, 5, 4, 5, 4, 2, 4, 4, 5, 5, 5, 3, 5, ...
## $ Dancing <int> 3, 1, 5, 1, 1, 3, 1, 5, 1, 1, 3, 3, 1, 2, ...
## $ `Musical instruments` <int> 3, 1, 5, 3, 5, 2, 1, 3, 1, 1, 4, 3, 1, 2, ...
## $ Writing <int> 2, 1, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 1, ...
## $ `Passive sport` <int> 1, 1, 5, 3, 5, 5, 4, 4, 5, 5, 5, 3, 3, 5, ...
## $ `Active sport` <int> 5, 1, 2, 1, 4, 3, 5, 4, 1, 3, 3, 3, 1, 1, ...
## $ Gardening <int> 5, 1, 1, 4, 2, 3, 1, 1, 3, 1, 4, 1, 5, 1, ...
## $ Celebrities <int> 1, 2, 1, 3, 1, 1, 3, 2, 2, 2, 3, 5, 5, 3, ...
## $ Shopping <int> 4, 3, 4, 3, 2, 3, 3, 4, 5, 3, 2, 5, 5, 4, ...
## $ `Science and technology` <int> 4, 3, 2, 3, 3, 4, 2, 3, 4, 3, 3, 2, 2, 2, ...
## $ Theatre <int> 2, 2, 5, 2, 1, 3, 2, 5, 2, 1, 2, 3, 4, 1, ...
## $ `Fun with friends` <int> 5, 4, 5, 4, 3, 5, 4, 5, 4, 3, 4, 5, 5, 5, ...
## $ `Adrenaline sports` <int> 4, 2, 5, 2, 3, 1, 2, 2, 1, 1, 1, 4, 1, 4, ...
## $ Pets <int> 4, 5, 5, 1, 2, 5, 5, 2, 5, 1, 2, 5, 5, 5, ...2.3. Principal Component Analysis
To understand which component is more effective on analysis, princomp function used on surveyed_data. Between 32 components, the first 14 components were chosen for PCA analysis due to the their cumulative proportion with 72%.
## Importance of components:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5
## Standard deviation 2.0374787 1.8233387 1.60327816 1.48530949 1.26023134
## Proportion of Variance 0.1297287 0.1038926 0.08032815 0.06894201 0.04963072
## Cumulative Proportion 0.1297287 0.2336214 0.31394951 0.38289152 0.43252224
## Comp.6 Comp.7 Comp.8 Comp.9 Comp.10
## Standard deviation 1.20132035 1.07959740 1.06930606 1.05342776 0.98848494
## Proportion of Variance 0.04509908 0.03642283 0.03573173 0.03467844 0.03053445
## Cumulative Proportion 0.47762132 0.51404415 0.54977588 0.58445432 0.61498878
## Comp.11 Comp.12 Comp.13 Comp.14 Comp.15
## Standard deviation 0.96843882 0.94342106 0.92688407 0.89350216 0.87546384
## Proportion of Variance 0.02930855 0.02781385 0.02684731 0.02494832 0.02395115
## Cumulative Proportion 0.64429733 0.67211118 0.69895850 0.72390681 0.74785797
## Comp.16 Comp.17 Comp.18 Comp.19 Comp.20
## Standard deviation 0.85981220 0.8379842 0.81377184 0.7619490 0.74936288
## Proportion of Variance 0.02310241 0.0219443 0.02069452 0.0181427 0.01754827
## Cumulative Proportion 0.77096037 0.7929047 0.81359919 0.8317419 0.84929016
## Comp.21 Comp.22 Comp.23 Comp.24 Comp.25
## Standard deviation 0.73524390 0.72622112 0.70400198 0.70113254 0.67137253
## Proportion of Variance 0.01689324 0.01648116 0.01548809 0.01536209 0.01408566
## Cumulative Proportion 0.86618340 0.88266456 0.89815265 0.91351474 0.92760039
## Comp.26 Comp.27 Comp.28 Comp.29 Comp.30
## Standard deviation 0.64706583 0.62748835 0.58745885 0.58160258 0.553117299
## Proportion of Variance 0.01308419 0.01230443 0.01078462 0.01057067 0.009560586
## Cumulative Proportion 0.94068459 0.95298901 0.96377363 0.97434431 0.983904895
## Comp.31 Comp.32
## Standard deviation 0.53278768 0.480812500
## Proportion of Variance 0.00887071 0.007224396
## Cumulative Proportion 0.99277560 1.000000000
##
## Loadings:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8
## History 0.184 0.117 0.196 0.225 0.146 0.222 0.252
## Psychology 0.239 0.108 0.168 -0.127 0.201
## Politics 0.220 0.289 0.143 0.280 0.137 0.111
## Mathematics 0.317 -0.119 0.101 -0.292 -0.287
## Physics 0.355 -0.231 0.104 -0.135 -0.188 0.127
## Internet 0.210 0.120 -0.435 0.213 -0.228
## PC 0.363 -0.176 -0.287
## Economy Management 0.166 0.310 0.206 -0.196 -0.255
## Biology 0.273 -0.371 -0.159 0.211
## Chemistry 0.192 -0.414 -0.108 0.240
## Reading 0.279 -0.170 0.195 -0.124
## Geography 0.140 0.160 0.152 0.154 0.337 -0.358
## Foreign languages 0.204 0.199 -0.145 -0.108 -0.470
## Medicine 0.258 -0.316 -0.132 0.280 -0.122
## Law 0.123 0.143 0.283 0.395 0.178
## Cars 0.333 -0.199
## Art exhibitions 0.324 -0.200
## Religion 0.222 0.176
## Countryside, outdoors 0.203 -0.369 0.165 -0.143
## Dancing 0.254 -0.216 -0.237
## Musical instruments 0.213 -0.315
## Writing 0.230 0.186 -0.147 -0.107 0.319
## Passive sport 0.155 -0.215 -0.119 0.245 -0.167
## Active sport 0.178 -0.258 -0.144 0.364 -0.115 0.203
## Gardening 0.191 -0.144 -0.160 0.333 0.334
## Celebrities -0.104 0.167 -0.356 0.119 -0.323 0.136 0.119
## Shopping -0.168 0.144 -0.410 -0.228
## Science and technology 0.364
## Theatre 0.316 -0.112 -0.126 -0.162
## Fun with friends 0.129 -0.270 -0.102 -0.308 -0.266
## Adrenaline sports 0.225 -0.230 -0.181 0.322 -0.195
## Pets -0.249 0.274 0.205
## Comp.9 Comp.10 Comp.11 Comp.12 Comp.13 Comp.14 Comp.15
## History 0.170 0.224 0.163
## Psychology 0.130 -0.272 0.123 0.447 -0.226 0.218 -0.260
## Politics -0.202 0.132 0.158
## Mathematics -0.149 0.332 -0.216
## Physics 0.221 -0.114
## Internet 0.107 -0.241 0.184 0.266 0.195
## PC 0.103 -0.172 0.119 0.121 0.133
## Economy Management -0.263 0.176 -0.192
## Biology
## Chemistry -0.113 0.129
## Reading 0.167 0.184 0.143
## Geography -0.177 0.205 -0.346 -0.196
## Foreign languages 0.171 -0.170 -0.407 0.101
## Medicine -0.231
## Law -0.158 0.140 -0.178
## Cars 0.320 -0.163 -0.177
## Art exhibitions 0.141 -0.172 0.105 -0.308
## Religion -0.358 -0.135 0.362 0.352 0.162
## Countryside, outdoors -0.298 0.275 0.197 0.279 -0.124
## Dancing -0.311 0.147 0.263
## Musical instruments -0.308 -0.333 -0.513 0.210
## Writing 0.164 -0.290 -0.228 -0.331
## Passive sport -0.647 0.105 -0.351 0.111 -0.306
## Active sport -0.137 0.215 -0.128 -0.118 0.101
## Gardening -0.171 0.195 0.161 0.322
## Celebrities -0.255 0.108 -0.103
## Shopping -0.103 0.157 -0.169
## Science and technology 0.281 0.206 0.199 -0.101
## Theatre 0.219 0.203 -0.131 -0.116 0.212 -0.268
## Fun with friends 0.336 0.104 -0.247 0.133 0.272 0.392
## Adrenaline sports 0.118 0.201 -0.228
## Pets 0.319 0.354 0.102 0.419 -0.167 -0.375
## Comp.16 Comp.17 Comp.18 Comp.19 Comp.20 Comp.21 Comp.22
## History 0.289 0.426 0.149
## Psychology 0.167 0.280 0.217 -0.109 -0.107
## Politics -0.124 0.156 0.157 0.191
## Mathematics 0.236 -0.135 -0.186 0.106
## Physics 0.171 -0.151 -0.240
## Internet 0.168 0.118 -0.285 -0.116
## PC 0.208 -0.119 0.278
## Economy Management -0.303 0.133 -0.202 -0.229 0.346
## Biology -0.144 0.155
## Chemistry
## Reading 0.219 -0.348 0.110 -0.216 0.332
## Geography 0.138 -0.326 0.104 -0.306 -0.131
## Foreign languages -0.163 0.150 -0.265 0.280 -0.178
## Medicine -0.174 -0.103
## Law 0.119 -0.344 -0.287
## Cars 0.144 -0.155 0.115 0.115 -0.192 -0.436
## Art exhibitions 0.120 -0.113 -0.442
## Religion -0.354 -0.505 -0.230
## Countryside, outdoors 0.357 0.212 0.298
## Dancing 0.244 0.183 0.245 -0.349 -0.254
## Musical instruments 0.235 -0.141 0.175 0.119 0.147 0.124
## Writing -0.199 -0.217 -0.236 0.139 -0.189
## Passive sport 0.130 -0.125 -0.146 0.107 0.218
## Active sport 0.228 0.308 -0.335 -0.110 0.227
## Gardening -0.466 0.128 0.237 0.113 -0.146 -0.116
## Celebrities 0.216 -0.232 -0.102 0.322 0.184 0.265
## Shopping -0.142 -0.127 0.327 0.180 -0.135
## Science and technology -0.266 0.108 -0.358 0.172 0.285
## Theatre 0.155 -0.148 -0.157 0.115
## Fun with friends -0.158 -0.286 -0.131
## Adrenaline sports -0.162 -0.263 0.261 -0.316 0.175
## Pets 0.195 -0.167 -0.272 -0.120 -0.168
## Comp.23 Comp.24 Comp.25 Comp.26 Comp.27 Comp.28 Comp.29
## History 0.227 0.281 0.236 0.246 0.108 0.131
## Psychology -0.241 -0.139 -0.145 0.113 -0.165 -0.104
## Politics -0.250 -0.302 0.158 -0.214 -0.460 0.165 -0.187
## Mathematics 0.169
## Physics -0.141 0.265 -0.183
## Internet 0.177 0.185 -0.253 0.219 0.272
## PC -0.166 0.321 -0.145 -0.569
## Economy Management 0.365 0.201
## Biology -0.128
## Chemistry 0.101 0.159 0.251 -0.102 -0.145 -0.126
## Reading -0.162 0.209 0.278 -0.234 -0.329 -0.160
## Geography -0.116 -0.157 0.201 -0.148 -0.150
## Foreign languages -0.185 -0.243 0.133 0.226
## Medicine 0.107 -0.121 0.218
## Law 0.376 -0.267 0.296 -0.173
## Cars -0.277 0.430 0.104 -0.211
## Art exhibitions 0.206 -0.121 0.154 -0.405 -0.223 -0.253 0.187
## Religion
## Countryside, outdoors 0.142 0.182 -0.282 -0.185 -0.131
## Dancing -0.304 0.244 -0.171 0.172 -0.162
## Musical instruments 0.348 -0.116
## Writing 0.362 -0.133 -0.250 0.210
## Passive sport 0.136
## Active sport -0.159 -0.338 -0.212 0.183
## Gardening -0.224 0.110 0.181 0.109
## Celebrities -0.338 -0.159 0.186 0.207
## Shopping 0.406 -0.142 0.186 -0.214 -0.344
## Science and technology -0.164 -0.191 -0.112 -0.307 0.399
## Theatre -0.142 0.308 0.223 0.459
## Fun with friends -0.331 -0.125
## Adrenaline sports 0.124 0.483 0.128
## Pets
## Comp.30 Comp.31 Comp.32
## History 0.177
## Psychology -0.165
## Politics 0.106
## Mathematics 0.592
## Physics 0.169 -0.609
## Internet
## PC 0.115 -0.115
## Economy Management -0.247
## Biology 0.777
## Chemistry -0.627 -0.108 -0.295
## Reading 0.167 -0.134
## Geography -0.102 -0.107
## Foreign languages
## Medicine 0.508 0.136 -0.475
## Law -0.152
## Cars
## Art exhibitions -0.132
## Religion -0.127
## Countryside, outdoors
## Dancing 0.163
## Musical instruments
## Writing 0.133
## Passive sport
## Active sport -0.120
## Gardening
## Celebrities -0.116
## Shopping 0.160 0.118
## Science and technology
## Theatre -0.250 0.192
## Fun with friends
## Adrenaline sports
## Pets2.4. Preparation of Limited Components for Correlation
As it can be seen from the graph which was drawn at below, the first 7 PC are responsible for 78.18% of the variation.
## Importance of components:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5
## Standard deviation 1.6452835 1.5142414 1.4271869 1.03350621 1.0063014
## Proportion of Variance 0.1933541 0.1637805 0.1454902 0.07629536 0.0723316
## Cumulative Proportion 0.1933541 0.3571346 0.5026248 0.57892018 0.6512518
## Comp.6 Comp.7 Comp.8 Comp.9 Comp.10
## Standard deviation 0.96137744 0.95114911 0.79989367 0.74166663 0.70773166
## Proportion of Variance 0.06601761 0.06462033 0.04570213 0.03929067 0.03577744
## Cumulative Proportion 0.71726939 0.78188972 0.82759186 0.86688253 0.90265997
## Comp.11 Comp.12 Comp.13 Comp.14
## Standard deviation 0.67387024 0.58045711 0.56374032 0.50391048
## Proportion of Variance 0.03243579 0.02406646 0.02270023 0.01813756
## Cumulative Proportion 0.93509576 0.95916222 0.98186244 1.00000000
##
## Loadings:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8
## History 0.129 0.225 0.389 0.426 0.123 0.283 0.172
## Psychology 0.208 0.293 -0.104 -0.422 0.374 0.422 -0.524
## Politics 0.358 0.300 0.311 -0.229 0.286 0.392
## Mathematics 0.161 0.398 -0.270 -0.344 -0.384
## Physics 0.266 0.338 -0.313 0.236 -0.171 0.114 -0.243 -0.136
## Internet 0.346 -0.124 -0.431 0.383 0.310 0.235
## PC 0.432 -0.260 -0.150 0.213 0.252 0.192
## Economy Management -0.105 0.354 0.109 -0.241 -0.356 -0.549 0.163
## Biology 0.517 -0.118 0.101 -0.107 0.151
## Chemistry 0.485 -0.187 0.264
## Reading 0.199 0.393 -0.147 -0.117 0.343 -0.449 0.333
## Geography 0.113 0.243 0.259 0.163 0.547 -0.285 -0.255 -0.539
## Foreign languages 0.124 0.110 0.374 -0.559 -0.131 -0.296
## Medicine 0.499 -0.134 0.105 -0.134 0.244
## Comp.9 Comp.10 Comp.11 Comp.12 Comp.13 Comp.14
## History 0.596 0.249 0.216
## Psychology -0.242
## Politics -0.191 -0.575 0.100
## Mathematics 0.136 0.207 0.636
## Physics -0.151 -0.127 0.182 -0.679
## Internet 0.420 -0.207 0.365
## PC -0.362 0.228 -0.581 -0.165 0.126
## Economy Management 0.371 0.327 -0.187 -0.228
## Biology -0.132 0.111 0.784
## Chemistry 0.103 0.137 0.229 -0.647 -0.107 -0.354
## Reading 0.366 -0.204 -0.389
## Geography 0.154 -0.191 -0.148 -0.105
## Foreign languages -0.544 0.321
## Medicine -0.143 0.613 0.111 -0.462The plot at below shows that with the first 10 component, nearly 90% of the variation is explained.
ggplot(data.frame(pc=1:14,cum_var=c(0.1933541, 0.3571346, 0.5026248, 0.57892018, 0.6512518, 0.71726939, 0.78188972, 0.82759186, 0.86688253, 0.90265997, 0.93509576, 0.95916222, 0.98186244, 1.00000000)),aes(x=pc,y=cum_var)) +
geom_point() +
geom_line()
2.5. Multidimensional Scaling Analysis
survey_mds_distance <- 1 - cor(survey_mds_data)
survey_mds <- cmdscale(survey_mds_distance,k=2)
colnames(survey_mds) <- c("x","y")
print(survey_mds)## x y
## History 0.031306226 -0.057585867
## Psychology 0.221673919 -0.034255291
## Politics -0.210706395 0.084207670
## Mathematics -0.366106787 -0.301011922
## Physics -0.338602615 -0.488946202
## Internet -0.435984609 0.193575251
## PC -0.602886441 -0.104205122
## Economy Management -0.313464728 0.369369369
## Biology 0.249496019 -0.352229723
## Chemistry 0.111150203 -0.427188341
## Reading 0.525002226 -0.036403993
## Geography -0.082496760 -0.010735122
## Foreign languages 0.197742370 0.164499851
## Medicine 0.190107055 -0.319538506
## Law -0.091720302 0.234211611
## Cars -0.593017958 0.091655704
## Art exhibitions 0.380132671 -0.025937391
## Religion 0.177085206 -0.229380678
## Countryside, outdoors 0.159766147 -0.101417980
## Dancing 0.326087243 0.151882648
## Musical instruments 0.165516710 -0.156630640
## Writing 0.291051922 -0.065898705
## Passive sport -0.288889494 0.153049302
## Active sport -0.180541543 0.077474365
## Gardening 0.203069385 -0.051532921
## Celebrities 0.059109759 0.515094714
## Shopping 0.248971459 0.505415685
## Science and technology -0.368579088 -0.307211932
## Theatre 0.452232345 0.006680821
## Fun with friends 0.002109219 0.282818197
## Adrenaline sports -0.251016420 0.072054588
## Pets 0.132403055 0.168120560ggplot(data.frame(survey_mds),aes(x=x,y=y)) +
geom_text(label=rownames(survey_mds),size=3.5) +
labs(x="x",y="y", title="MDS - Grouping Hobies By Relational Distance") 
2.6. In Conclusion
To sum up, it can be said that;
- Celebrities- Shopping
- Active Sports- Adrenaline- Pssive Sports- Politics
- Science and technology- Mathematics
- Medicine-Biology-Chemistry
- countryside, Outdoors-History- Musical Instruments- Religion
- Art exhibitions- Theatre- Reading
- Foreign Language- Dancing
- Gardening- Psychology- Writing
- Geography-History
categories are the most relational to each other.