DATA606_W1_Lab_1_MatheeshaThambeliyagodage
Matheesha Thambeliyagodage
February 9, 2017
Begin by loading the data set of 20,000 observations into the R workspace.
library(DATA606)##
## Welcome to CUNY DATA606 Statistics and Probability for Data Analytics
## This package is designed to support this course. The text book used
## is OpenIntro Statistics, 3rd Edition. You can read this by typing
## vignette('os3') or visit www.OpenIntro.org.
##
## The getLabs() function will return a list of the labs available.
##
## The demo(package='DATA606') will list the demos that are available.##
## Attaching package: 'DATA606'## The following object is masked from 'package:utils':
##
## demo#source("more/cdc.R")
#source("https://github.com/mathsanu/CUNY_MSDA/blob/master/DATA606/W1/lab_1/cdc.R")
source("http://www.openintro.org/stat/data/cdc.R")
names(cdc)## [1] "genhlth" "exerany" "hlthplan" "smoke100" "height" "weight"
## [7] "wtdesire" "age" "gender"head(cdc)## genhlth exerany hlthplan smoke100 height weight wtdesire age gender
## 1 good 0 1 0 70 175 175 77 m
## 2 good 0 1 1 64 125 115 33 f
## 3 good 1 1 1 60 105 105 49 f
## 4 good 1 1 0 66 132 124 42 f
## 5 very good 0 1 0 61 150 130 55 f
## 6 very good 1 1 0 64 114 114 55 f1.Make a scatterplot of weight versus desired weight. Describe the relationship between these two variables.
plot(cdc$weight, main="Scatter Plot - Weight versus Desired Weight of Passengers", cdc$wtdesire, xlab = "Weight", ylab = "Desired Weight", col="purple", las = 1)
It appears to be a general positive correlation between the two variables weight versus desired weight, when desired weight increases weight also increses,although there is a great deal of spread. I assume they could be due to data entry mistakes (eg 700lb). – 2. Let’s consider a new variable: the difference between desired weight (wtdesire) and current weight (weight). Create this new variable by subtracting the two columns in the data frame and assigning them to a new object called wdiff.
cdc$wdiff <- cdc$wtdesire - cdc$weight
#cdc$wdiff
names(cdc)## [1] "genhlth" "exerany" "hlthplan" "smoke100" "height" "weight"
## [7] "wtdesire" "age" "gender" "wdiff"head(cdc)## genhlth exerany hlthplan smoke100 height weight wtdesire age gender
## 1 good 0 1 0 70 175 175 77 m
## 2 good 0 1 1 64 125 115 33 f
## 3 good 1 1 1 60 105 105 49 f
## 4 good 1 1 0 66 132 124 42 f
## 5 very good 0 1 0 61 150 130 55 f
## 6 very good 1 1 0 64 114 114 55 f
## wdiff
## 1 0
## 2 -10
## 3 0
## 4 -8
## 5 -20
## 6 03.What type of data is wdiff? If an observation wdiff is 0, what does this mean about the person’s weight and desired weight. What if wdiff is positive or negative?
wdiff is a scale variable an it is integer discrete because weights are integer values. Each record hase a wdiff value.
wdiff is equal to 0 means the person has the oplimal weight to the height or in their desired weight. wdiff > 0 then their weight is below their desired value, while wdiff < 0 implies that their actual weight is above their desired value. ——————– 4. Describe the distribution of wdiff in terms of its center, shape, and spread, including any plots you use. What does this tell us about how people feel about their current weight?
summary(cdc$wdiff)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -300.00 -21.00 -10.00 -14.59 0.00 500.00table(cdc$wdiff)##
## -300 -246 -235 -220 -210 -200 -190 -180 -175 -170 -165 -160 -155 -152 -150
## 2 1 1 1 1 6 1 1 1 4 2 3 1 4 17
## -148 -147 -145 -142 -140 -139 -135 -133 -132 -130 -128 -126 -125 -122 -120
## 1 1 4 2 8 1 4 1 3 15 1 2 9 2 27
## -117 -115 -113 -112 -110 -109 -108 -107 -105 -103 -100 -99 -98 -97 -96
## 1 11 3 3 16 3 3 1 12 1 115 1 3 2 1
## -95 -94 -93 -92 -91 -90 -88 -87 -86 -85 -84 -83 -82 -81 -80
## 11 2 2 2 1 46 6 2 5 22 3 4 5 2 72
## -79 -78 -77 -76 -75 -74 -73 -72 -71 -70 -69 -68 -67 -66 -65
## 4 6 5 3 43 6 5 8 2 125 6 9 5 9 62
## -64 -63 -62 -61 -60 -59 -58 -57 -56 -55 -54 -53 -52 -51 -50
## 8 8 10 4 184 8 15 8 12 101 13 11 14 4 395
## -49 -48 -47 -46 -45 -44 -43 -42 -41 -40 -39 -38 -37 -36 -35
## 12 23 18 13 189 18 12 21 8 556 18 31 34 23 360
## -34 -33 -32 -31 -30 -29 -28 -27 -26 -25 -24 -23 -22 -21 -20
## 32 30 28 22 893 36 70 65 26 677 47 70 74 41 1467
## -19 -18 -17 -16 -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5
## 43 141 69 73 1173 71 157 171 64 1856 103 234 234 132 1253
## -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10
## 131 188 125 51 5616 19 39 33 21 232 34 58 43 17 333
## 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## 20 32 23 8 208 5 13 14 9 151 2 17 9 5 79
## 26 27 28 29 30 31 32 33 35 36 37 38 39 40 41
## 3 9 9 1 46 2 5 6 26 3 3 1 1 21 3
## 42 43 45 47 50 52 53 55 60 61 63 64 65 68 70
## 2 1 7 1 9 1 1 2 6 1 1 2 5 1 3
## 72 73 75 80 83 85 86 90 91 110 311 500
## 1 1 1 2 1 2 1 1 1 1 1 1hist(cdc$wdiff, ,main="Histogram for wdiff", breaks = 100,xlab="Gap between Desired Weight and Actual Weight ", ylab = "Person Count",border="blue", col="yellow")
–This distribution shows the “Gap between Desired Weight and Actual Weight” heavily distributed between 0-10. And majority of the people are overweight. Median is -10 with the Mean -14.5
–5.Using numerical summaries and a side-by-side box plot, determine if men tend to view their weight differently than women.
summary(cdc$wdiff[cdc$gender == "m"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -300.00 -20.00 -5.00 -10.71 0.00 500.00summary(cdc$wdiff[cdc$gender == "f"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -300.00 -27.00 -10.00 -18.15 0.00 83.00boxplot(main="Box Plot for Men and Wemen weight Distribution", cdc$wdiff ~ cdc$gender, xlab="Gender", ylab = "wdiff",col="Red", outline = F)
Above box plot and summary calculations shows median of women is lower than men. This means women are away from the desired weight than men (overweight women)
–6.Now it’s time to get creative. Find the mean and standard deviation of weight and determine what proportion of the weights are within one standard deviation of the mean.
mean_weight <- mean(cdc$weight)
mean_weight## [1] 169.683sd_weight <- sd(cdc$weight)
sd_weight## [1] 40.08097weights_within_one_sd <- subset(cdc, weight < mean_weight + sd_weight & weight > mean_weight -
+ sd_weight)
weights_within_one_sd## genhlth exerany hlthplan smoke100 height weight wtdesire age
## 1 good 0 1 0 70 175 175 77
## 4 good 1 1 0 66 132 124 42
## 5 very good 0 1 0 61 150 130 55
## 7 very good 1 1 0 71 194 185 31
## 8 very good 0 1 0 67 170 160 45
## 9 good 0 1 1 65 150 130 27
## 10 good 1 1 0 70 180 170 44
## 11 excellent 1 1 1 69 186 175 46
## 12 fair 1 1 1 69 168 148 62
## 13 excellent 1 0 1 66 185 220 21
## 14 excellent 1 1 1 70 170 170 69
## 15 fair 1 0 0 69 170 170 23
## 16 good 1 1 1 73 185 175 79
## 17 good 0 0 1 67 156 150 47
## 18 fair 0 1 1 71 185 185 76
## 19 good 1 1 1 75 200 190 43
## 21 very good 1 1 0 69 200 150 48
## 22 good 1 1 1 65 160 140 54
## 23 very good 0 1 1 73 160 160 43
## 24 good 1 1 1 67 165 158 30
## 26 good 0 1 0 68 190 150 33
## 27 excellent 1 0 1 67 190 165 44
## 28 excellent 1 0 1 69 160 150 42
## 30 excellent 1 1 1 74 185 175 63
## 31 very good 1 1 0 67 166 160 74
## 32 excellent 1 1 0 71 180 175 41
## 33 very good 1 1 0 71 182 182 36
## 34 good 1 1 1 68 185 160 67
## 37 very good 1 0 1 70 160 140 18
## 38 excellent 1 1 1 69 190 180 65
## 40 very good 1 1 0 67 160 145 29
## 42 very good 1 1 0 66 143 130 45
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## 46 excellent 0 1 0 59 145 110 36
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## 48 good 1 1 0 68 130 130 47
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## 51 good 0 1 0 65 179 150 33
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## [ reached getOption("max.print") -- omitted 4153 rows ]dim(weights_within_one_sd)## [1] 14152 10There are 14152 observations in the required subset, out of a total of 20000 observations, this represents a proportion of 14152/20000 = 0.7076, or about 71%. Thus 71% of weights are within 1 standard deviation of the mean.