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## Attaching package: 'h2o'## The following objects are masked from 'package:stats':
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## cor, sd, var## The following objects are masked from 'package:base':
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## %*%, %in%, &&, ||, apply, as.factor, as.numeric, colnames,
## colnames<-, ifelse, is.character, is.factor, is.numeric, log,
## log10, log1p, log2, round, signif, truncTEENS <- read.csv("http://www.sci.csueastbay.edu/~esuess/classes/Statistics_6620/Presentations/ml12/snsdata.csv")str(TEENS)## 'data.frame': 30000 obs. of 40 variables:
## $ gradyear : int 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 ...
## $ gender : Factor w/ 2 levels "F","M": 2 1 2 1 NA 1 1 2 1 1 ...
## $ age : num 19 18.8 18.3 18.9 19 ...
## $ friends : int 7 0 69 0 10 142 72 17 52 39 ...
## $ basketball : int 0 0 0 0 0 0 0 0 0 0 ...
## $ football : int 0 1 1 0 0 0 0 0 0 0 ...
## $ soccer : int 0 0 0 0 0 0 0 0 0 0 ...
## $ softball : int 0 0 0 0 0 0 0 1 0 0 ...
## $ volleyball : int 0 0 0 0 0 0 0 0 0 0 ...
## $ swimming : int 0 0 0 0 0 0 0 0 0 0 ...
## $ cheerleading: int 0 0 0 0 0 0 0 0 0 0 ...
## $ baseball : int 0 0 0 0 0 0 0 0 0 0 ...
## $ tennis : int 0 0 0 0 0 0 0 0 0 0 ...
## $ sports : int 0 0 0 0 0 0 0 0 0 0 ...
## $ cute : int 0 1 0 1 0 0 0 0 0 1 ...
## $ sex : int 0 0 0 0 1 1 0 2 0 0 ...
## $ sexy : int 0 0 0 0 0 0 0 1 0 0 ...
## $ hot : int 0 0 0 0 0 0 0 0 0 1 ...
## $ kissed : int 0 0 0 0 5 0 0 0 0 0 ...
## $ dance : int 1 0 0 0 1 0 0 0 0 0 ...
## $ band : int 0 0 2 0 1 0 1 0 0 0 ...
## $ marching : int 0 0 0 0 0 1 1 0 0 0 ...
## $ music : int 0 2 1 0 3 2 0 1 0 1 ...
## $ rock : int 0 2 0 1 0 0 0 1 0 1 ...
## $ god : int 0 1 0 0 1 0 0 0 0 6 ...
## $ church : int 0 0 0 0 0 0 0 0 0 0 ...
## $ jesus : int 0 0 0 0 0 0 0 0 0 2 ...
## $ bible : int 0 0 0 0 0 0 0 0 0 0 ...
## $ hair : int 0 6 0 0 1 0 0 0 0 1 ...
## $ dress : int 0 4 0 0 0 1 0 0 0 0 ...
## $ blonde : int 0 0 0 0 0 0 0 0 0 0 ...
## $ mall : int 0 1 0 0 0 0 2 0 0 0 ...
## $ shopping : int 0 0 0 0 2 1 0 0 0 1 ...
## $ clothes : int 0 0 0 0 0 0 0 0 0 0 ...
## $ hollister : int 0 0 0 0 0 0 2 0 0 0 ...
## $ abercrombie : int 0 0 0 0 0 0 0 0 0 0 ...
## $ die : int 0 0 0 0 0 0 0 0 0 0 ...
## $ death : int 0 0 1 0 0 0 0 0 0 0 ...
## $ drunk : int 0 0 0 0 1 1 0 0 0 0 ...
## $ drugs : int 0 0 0 0 1 0 0 0 0 0 ...
- We look at missing data for female variable.
table(TEENS$gender)##
## F M
## 22054 5222table(TEENS$gender, useNA = "ifany")##
## F M <NA>
## 22054 5222 2724
- Looking at the the missing data for age variable.
summary(TEENS$age)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 3.086 16.310 17.290 17.990 18.260 106.900 5086
- Eliminating age outliers.
TEENS$age <- ifelse(TEENS$age >= 13 & TEENS$age < 20,
TEENS$age, NA)summary(TEENS$age)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 13.03 16.30 17.26 17.25 18.22 20.00 5523
- Reassigning the missing gender values to “unknown”.
TEENS$female <- ifelse(TEENS$gender == "F" &
!is.na(TEENS$gender), 1, 0)TEENS$no_gender <- ifelse(is.na(TEENS$gender), 1, 0)
- Checking out the recoding work.
table(TEENS$gender, useNA = "ifany")##
## F M <NA>
## 22054 5222 2724table(TEENS$female, useNA = "ifany")##
## 0 1
## 7946 22054table(TEENS$no_gender, useNA = "ifany")##
## 0 1
## 27276 2724
- We then find the means age by cohort.
# doesn’t work
mean(TEENS$age)## [1] NA
- The second one calculates the means, since the logical value inicates whether the NA values should be the computing proceeds.
mean(TEENS$age, na.rm = TRUE)## [1] 17.25243
- CGetting the age by cohort.
aggregate(data = TEENS, age ~ gradyear, mean, na.rm = TRUE)## gradyear age
## 1 2006 18.65586
## 2 2007 17.70617
## 3 2008 16.76770
## 4 2009 15.81957
- Creating a vector with the average age for each gradyear, repeated by person.
ave_age <- ave(TEENS$age, TEENS$gradyear,
FUN = function(x) mean(x, na.rm = TRUE))TEENS$age <- ifelse(is.na(TEENS$age), ave_age, TEENS$age)
- To ensure missing values are eliminated, we check the summary results.
summary(TEENS$age)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 13.03 16.28 17.24 17.24 18.21 20.00interests <- TEENS[5:40]
interests_z <- as.data.frame(lapply(interests, scale))
- Setting the seed to guarantee results.
set.seed(2345)
TEEN_clusters <- kmeans(interests_z, 5)
- Looking at the size of the clusters
TEEN_clusters$size## [1] 871 600 5981 1034 21514
- Finding the cluster centers.
TEEN_clusters$centers## basketball football soccer softball volleyball swimming
## 1 0.16001227 0.2364174 0.10385512 0.07232021 0.18897158 0.23970234
## 2 -0.09195886 0.0652625 -0.09932124 -0.01739428 -0.06219308 0.03339844
## 3 0.52755083 0.4873480 0.29778605 0.37178877 0.37986175 0.29628671
## 4 0.34081039 0.3593965 0.12722250 0.16384661 0.11032200 0.26943332
## 5 -0.16695523 -0.1641499 -0.09033520 -0.11367669 -0.11682181 -0.10595448
## cheerleading baseball tennis sports cute
## 1 0.3931445 0.02993479 0.13532387 0.10257837 0.37884271
## 2 -0.1101103 -0.11487510 0.04062204 -0.09899231 -0.03265037
## 3 0.3303485 0.35231971 0.14057808 0.32967130 0.54442929
## 4 0.1856664 0.27527088 0.10980958 0.79711920 0.47866008
## 5 -0.1136077 -0.10918483 -0.05097057 -0.13135334 -0.18878627
## sex sexy hot kissed dance band
## 1 0.020042068 0.11740551 0.41389104 0.06787768 0.22780899 -0.10257102
## 2 -0.042486141 -0.04329091 -0.03812345 -0.04554933 0.04573186 4.06726666
## 3 0.002913623 0.24040196 0.38551819 -0.03356121 0.45662534 -0.02120728
## 4 2.028471066 0.51266080 0.31708549 2.97973077 0.45535061 0.38053621
## 5 -0.097928345 -0.09501817 -0.13810894 -0.13535855 -0.15932739 -0.12167214
## marching music rock god church jesus
## 1 -0.10942590 0.1378306 0.05905951 0.03651755 -0.00709374 0.01458533
## 2 5.25757242 0.4981238 0.15963917 0.09283620 0.06414651 0.04801941
## 3 -0.10880541 0.2844999 0.21436936 0.35014919 0.53739806 0.27843424
## 4 -0.02014608 1.1367885 1.21013948 0.41679142 0.16627797 0.12988313
## 5 -0.11098063 -0.1532006 -0.12460034 -0.12144246 -0.15889274 -0.08557822
## bible hair dress blonde mall shopping
## 1 -0.03692278 0.43807926 0.14905267 0.06137340 0.60368108 0.79806891
## 2 0.05863810 -0.04484083 0.07201611 -0.01146396 -0.08724304 -0.03865318
## 3 0.22990963 0.23612853 0.39407628 0.03471458 0.48318495 0.66327838
## 4 0.08478769 2.55623737 0.53852195 0.36134138 0.62256686 0.27101815
## 5 -0.06813159 -0.20498730 -0.14348036 -0.02918252 -0.18625656 -0.22865236
## clothes hollister abercrombie die death
## 1 0.5651537331 4.1521844 3.96493810 0.043475966 0.09857501
## 2 -0.0003526292 -0.1678300 -0.14129577 0.009447317 0.05135888
## 3 0.3759725120 -0.0553846 -0.07417839 0.037989066 0.11972190
## 4 1.2306917174 0.1610784 0.26324494 1.712181870 0.93631312
## 5 -0.1865419798 -0.1557662 -0.14861104 -0.094875180 -0.08370729
## drunk drugs
## 1 0.035614771 0.03443294
## 2 -0.086773220 -0.06878491
## 3 -0.009688746 -0.05973769
## 4 1.897388200 2.73326605
## 5 -0.087520105 -0.11423381
- Applying the cluster IDs to the original data frame.
TEENS$cluster <- TEEN_clusters$cluster
- Check out the first five records.
TEENS[1:5, c("cluster", "gender", "age", "friends")]## cluster gender age friends
## 1 5 M 18.982 7
## 2 3 F 18.801 0
## 3 5 M 18.335 69
## 4 5 F 18.875 0
## 5 4 <NA> 18.995 10
- Finding mean age by cluster.
aggregate(data = TEENS, age ~ cluster, mean)## cluster age
## 1 1 16.86497
## 2 2 17.39037
## 3 3 17.07656
## 4 4 17.11957
## 5 5 17.29849
- Finding the proportion of females by cluster.
aggregate(data = TEENS, female ~ cluster, mean)## cluster female
## 1 1 0.8381171
## 2 2 0.7250000
## 3 3 0.8378198
## 4 4 0.8027079
## 5 5 0.6994515
- Finding the mean number of friends by cluster.
aggregate(data = TEENS, friends ~ cluster, mean)## cluster friends
## 1 1 41.43054
## 2 2 32.57333
## 3 3 37.16185
## 4 4 30.50290
## 5 5 27.70052