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HW 2
Q1a. Bring the file into R
HW<-
read.csv
(
"pepsicokes.csv"
)
HWdata<-
data.frame
(HW)
View
(HWdata)
## Warning: running command ''/usr/bin/otool' -L '/Library/Frameworks/
## R.framework/Resources/modules/R_de.so'' had status 1
Q1 b Create a dummy variable whitecollar ( 1 if white collar, 0 otherwise), representing stores with white collar percent > 60 and calculate frequencies for this variable; the white collar percentages on the dataset are represented by the variable WhiteCollarPctPen.
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
pepsicokes<-
mutate
(HWdata,
whitecollar=
ifelse
(WhiteCollarPctPen>
60
,
1
,
0
))
View
(pepsicokes)
## Warning: running command ''/usr/bin/otool' -L '/Library/Frameworks/
## R.framework/Resources/modules/R_de.so'' had status 1
count
(pepsicokes,
c
(whitecollar))
## # A tibble: 2 <U+00D7> 2
## `c(whitecollar)` n
## <dbl> <int>
## 1 0 87
## 2 1 49
Q1c. Do a crosstab of Prizm cluster (the 16 social groups) with Chain; and another crosstab of Prizm cluster with whitecollar
library
(gtable)
table1<-
table
(pepsicokes$Chain, pepsicokes$Prizm_Cluster)
View
(table1)
## Warning: running command ''/usr/bin/otool' -L '/Library/Frameworks/
## R.framework/Resources/modules/R_de.so'' had status 1
table2<-
table
(pepsicokes$whitecollar,pepsicokes$Prizm_Cluster)
View
(table2)
## Warning: running command ''/usr/bin/otool' -L '/Library/Frameworks/
## R.framework/Resources/modules/R_de.so'' had status 1
Q1d.Calculate descriptive statistics for any 5 continuous variables in the dataset.
library
(psych)
## Warning: package 'psych' was built under R version 3.3.2
describe
(
select
(pepsicokes,Corp_Pep_Volume_Sales,Corp_Coke_Volume
_Sales,Corp_Pep_Price,Corp_Coke_Price,Prizm_Cluster))
## vars n mean sd median trimmed
## Corp_Pep_Volume_Sales 1 136 69978.45 34613.79 64290.97 67037.20
## Corp_Coke_Volume_Sales 2 136 40126.02 29009.98 32740.59 36594.37
## Corp_Pep_Price 3 136 0.00 1.00 -0.06 0.02
## Corp_Coke_Price 4 136 0.00 1.00 -0.32 -0.05
## Prizm_Cluster* 5 136 6.57 3.23 6.00 6.43
## mad min max range skew kurtosis
## Corp_Pep_Volume_Sales 27941.09 9514.52 193510.58 183996.05 1.05 1.61
## Corp_Coke_Volume_Sales 24858.14 3999.10 140015.28 136016.18 1.10 0.63
## Corp_Pep_Price 1.09 -2.78 2.63 5.41 -
0.19 0.04
## Corp_Coke_Price 0.99 -2.26 2.16 4.42 0.41 -0.91
## Prizm_Cluster* 4.45 1.00 13.00 12.00 0.31 -0.88
## se
## Corp_Pep_Volume_Sales 2968.11
## Corp_Coke_Volume_Sales 2487.59
## Corp_Pep_Price 0.09
## Corp_Coke_Price 0.09
## Prizm_Cluster* 0.28
Q1e. Regress Corp_Pep_Volume_per__MM_ACV (million dollars All Commodity Volume = Total Store Sales) on a set of any 6 independent variables. (use lm() for linear regression)
Regression<-
lm
(Corp_Pep_Volume_per__MM_ACV~Corp_Pep_Volume_Sales+Corp_Coke_Vo
lume_Sales+Corp_Pep_Price+CSD_Volume_per__MM_ACV+CSD_Price+Corp_C
oke_Price,
data=
pepsicokes)
summary
(Regression)
## ## Call:
## lm(formula = Corp_Pep_Volume_per__MM_ACV ~ Corp_Pep_Volume_Sales + ## Corp_Coke_Volume_Sales + Corp_Pep_Price + CSD_Volume_per__MM_ACV + ## CSD_Price + Corp_Coke_Price, data = pepsicokes)
## ## Residuals:
## Min 1Q Median 3Q Max ## -0.7273 -0.1781 -0.0249 0.1698 0.6004 ## ## Coefficients:
## Estimate Std. Error t value Pr(>|t|) ## (Intercept) -1.163e+01 7.673e-01 -15.156 < 2e-16 ***
## Corp_Pep_Volume_Sales 1.239e-06 7.190e-07 1.723 0.0873 . ## Corp_Coke_Volume_Sales -7.485e-07 1.284e-06 -0.583 0.5611 ## Corp_Pep_Price -2.784e-01 3.649e-02 -7.630 4.57e-12 ***
## CSD_Volume_per__MM_ACV 4.660e-04 1.768e-05 26.357 < 2e-16 ***
## CSD_Price 1.574e+00 1.391e-01 11.314 < 2e-16 ***
## Corp_Coke_Price 3.184e-03 2.590e-02 0.123 0.9023 ## ---
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## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## ## Residual standard error: 0.2503 on 129 degrees of freedom
## Multiple R-squared: 0.9401, Adjusted R-squared: 0.9373 ## F-statistic: 337.5 on 6 and 129 DF, p-value: < 2.2e-16
Q1f Run a Hierarchical Cluster Analysis using the variables Pepsi Volume per MM ACV, Pepsi Price per MM ACV, Coke Volume per
MM ACV, Coke Price per MM ACV. These variables were all standardized to mean 0, standard deviation 1 (Zscores) before clustering. Generate a Scree plot and find the kink point to determine the optimal number of clusters.
pepsicokes_use<-pepsicokes[,
c
(
36
,
37
,
39
,
40
)]
scaled_pepsicokes<-
scale
(pepsicokes_use,
center=
TRUE
,
scale=
TRUE
)
View
(scaled_pepsicokes)
## Warning: running command ''/usr/bin/otool' -L '/Library/Frameworks/
## R.framework/Resources/modules/R_de.so'' had status 1
pepsicokes_dist<-
dist
(scaled_pepsicokes,
method =
"euclidean"
)
pepsicokes_hclust<-
hclust
(pepsicokes_dist,
method=
"complete"
)
summary
(pepsicokes_hclust)
## Length Class Mode ## merge 270 -none- numeric ## height 135 -none- numeric ## order 136 -none- numeric ## labels 0 -none- NULL ## method 1 -none- character
## call 3 -none- call ## dist.method 1 -none- character
plot
(pepsicokes_hclust)
plot
(pepsicokes_hclust,
label=
pepsicokes$TDLinx)
pepsicokes_hclust$height
## [1] 0.1386584 0.1396047 0.1631199 0.1671819 0.1725537 0.2093707 0.2136051
## [8] 0.2148434 0.2169044 0.2264955 0.2350235 0.2414639 0.2458700 0.2512878
## [15] 0.2605308 0.2642873 0.2765192 0.2772353 0.2809642 0.2848664 0.2913083
## [22] 0.3031009 0.3107680 0.3188676 0.3194838 0.3220497 0.3317241 0.3341909
## [29] 0.3407441 0.3471289 0.3824845 0.3842518 0.3857692 0.4052727 0.4096676
## [36] 0.4096954 0.4146928 0.4179864 0.4398404 0.4435938 0.4625907 0.4696793
## [43] 0.4714327 0.4741556 0.4928168 0.4999340 0.5045603 0.5071533 0.5138479
## [50] 0.5186018 0.5286290 0.5339792 0.5356408 0.5608411 0.5741377 0.5767585
## [57] 0.5769589 0.5846019 0.5870278 0.5970021 0.6284375 0.6359602 0.6647388
## [64] 0.6675389 0.6711418 0.6757199 0.6989639 0.7024853 0.7113948 0.7334023
## [71] 0.7504464 0.7522828 0.7757182 0.7794874 0.8166034
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0.8207481 0.8216293
## [78] 0.8292620 0.8311296 0.8529778 0.8792595 0.8822876 0.8951340 0.9143303
## [85] 0.9182950 0.9205618 0.9396085 0.9538322 0.9644107 1.0058766 1.0164154
## [92] 1.0295904 1.0479720 1.0735370 1.0840720 1.1018983 1.1283187 1.1427112
## [99] 1.1778581 1.2029170 1.2308914 1.2843263 1.2881743 1.3136084 1.3149742
## [106] 1.3430558 1.3446066 1.3554758 1.4109480 1.4166235 1.5335090 1.7497358
## [113] 1.7634136 1.8420395 1.8874882 1.9327043 1.9705814 1.9982353 2.0039138
## [120] 2.0143144 2.0690615 2.1516141 2.2145405 2.3907431 2.7105632 2.9240202
## [127] 3.1327974 3.2017816 3.6972456 3.8005397 3.9446471 4.3989273 4.9564989
## [134] 5.3277902 6.4419282
plot
(pepsicokes_hclust$height)
pepsicokes_hclust_scree<-
rev
(pepsicokes_hclust$height)
plot
(pepsicokes_hclust_scree)
plot
(pepsicokes_hclust_scree,
main=
"Scree Plot"
, type=
"l"
)
rect.hclust
(pepsicokes_hclust,
h=
2.5
)
clusters<-
cutree
(pepsicokes_hclust,
4
)
table
(clusters)
## clusters
## 1 2 3 4 ## 16 53 39 28
pepsicokes_cluster<-
data.frame
(pepsicokes,clusters)
table
(
cutree
(pepsicokes_hclust,
4
),
cutree
(pepsicokes_hclust,
3
))
## ## 1 2 3
## 1 16 0 0
## 2 0 53 0
## 3 0 39 0
## 4 0 0 28
View
(pepsicokes)
## Warning: running command ''/usr/bin/otool' -L '/Library/Frameworks/
## R.framework/Resources/modules/R_de.so'' had status 1
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Q1g Run a K-means Cluster Analysis using the number of clusters
determined in d
k_cluster<-
kmeans
(pepsicokes_use,
4
)
k_cluster
## K-means clustering with 4 clusters of sizes 31, 39, 45, 21
## ## Cluster means:
## Corp_Pep_Volume_per__MM_ACV Corp_Pep_Price Corp_Coke_Volume_per__MM_ACV
## 1 0.3042666 -0.55226207 -1.2223726
## 2 -0.9092102 1.14747095 0.8399118
## 3 -0.1669167 -0.05773223 -0.1685815
## 4 1.5970566 -1.19206155 0.6058648
## Corp_Coke_Price
## 1 -0.7113567
## 2 -0.8676354
## 3 1.1737700
## 4 0.1461995
## ## Clustering vector:
## [1] 4 4 3 4 4 4 4 4 4 4 4 3 4 4 4 4 4 4 3 2 2 3 2 2 3 2 2 2 3 2 2 2 4 3 4
## [36] 1 1 4 3 1 1 3 3 1 3 1 1 1 1 1 1 1 1 1 4 1 1 1 1 1 3 1 3 1 1 3 1 3 1 1
## [71] 3 1 1 1 3 1 3 1 1 3 3 2 2 2 2 3 2 2 3 3 3 2 3 2 3 2 3 2 3 2 2 3 3 2 2
## [106] 3 2 2 3 2 2 3 2 3 2 2 2 3 3 2 2 3 2 3 3 4 3 3 2 2 2 3 3 2 3 3
## ## Within cluster sum of squares by cluster:
## [1] 27.35084 29.36915 64.34683 47.89759
## (between_SS / total_SS = 68.7 %)
## ## Available components:
## ## [1] "cluster" "centers" "totss" "withinss" ## [5] "tot.withinss" "betweenss" "size" "iter" ## [9] "ifault"
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