source("https://raw.githubusercontent.com/ada2802/DATA-606-Lab-1/master/cdc.R")
cdc
## genhlth exerany hlthplan smoke100 height weight wtdesire age
## 1 good 0 1 0 70 175 175 77
## 2 good 0 1 1 64 125 115 33
## 3 good 1 1 1 60 105 105 49
## 4 good 1 1 0 66 132 124 42
## 5 very good 0 1 0 61 150 130 55
## 6 very good 1 1 0 64 114 114 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
## 20 very good 1 1 0 67 125 120 33
## 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
## 25 very good 0 0 1 64 105 120 27
## 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
## 29 very good 1 1 0 61 115 105 32
## 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
## 35 good 1 1 1 64 220 160 57
## 36 excellent 1 1 0 63 117 110 44
## 37 very good 1 0 1 70 160 140 18
## 38 excellent 1 1 1 69 190 180 65
## 39 very good 1 1 0 65 125 125 29
## 40 very good 1 1 0 67 160 145 29
## 41 very good 1 1 1 65 124 118 52
## 42 very good 1 1 0 66 143 130 45
## 43 excellent 1 0 0 64 118 118 35
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## 45 excellent 1 1 1 64 200 160 65
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## 52 very good 1 1 0 64 135 128 21
## 53 poor 1 1 1 62 140 130 64
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## 55 very good 1 1 0 68 150 150 43
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## 74 excellent 1 1 1 71 215 195 27
## 75 excellent 1 1 0 63 190 200 31
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## 79 poor 1 1 0 63 142 120 52
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## 94 very good 1 1 1 62 138 138 55
## 95 good 1 1 0 67 137 137 80
## 96 excellent 1 1 1 74 175 200 22
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## 98 very good 1 1 1 65 140 130 37
## 99 very good 0 1 0 74 180 180 58
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## 102 good 1 1 0 68 142 140 25
## 103 excellent 1 1 0 64 110 110 29
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## 106 very good 1 1 0 72 180 150 45
## 107 fair 0 0 1 66 140 136 24
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## 116 fair 1 1 1 73 207 195 65
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## 118 excellent 1 1 0 72 190 180 50
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## [ reached getOption("max.print") -- omitted 8889 rows ]
###Exercise 1: How many cases are there in this data set? How many variables? For each variable, identify its data type (e.g. categorical, discrete).
dim(cdc)[1] # number of cases
## [1] 20000
dim(cdc)[2] # number of variables
## [1] 9
str(cdc) # Height,weight, wtdesire and age are discrete. Genhlth, exerany, hlthplan, smoke100 and gender are categorical datas.
## 'data.frame': 20000 obs. of 9 variables:
## $ genhlth : Factor w/ 5 levels "excellent","very good",..: 3 3 3 3 2 2 2 2 3 3 ...
## $ exerany : num 0 0 1 1 0 1 1 0 0 1 ...
## $ hlthplan: num 1 1 1 1 1 1 1 1 1 1 ...
## $ smoke100: num 0 1 1 0 0 0 0 0 1 0 ...
## $ height : num 70 64 60 66 61 64 71 67 65 70 ...
## $ weight : int 175 125 105 132 150 114 194 170 150 180 ...
## $ wtdesire: int 175 115 105 124 130 114 185 160 130 170 ...
## $ age : int 77 33 49 42 55 55 31 45 27 44 ...
## $ gender : Factor w/ 2 levels "m","f": 1 2 2 2 2 2 1 1 2 1 ...
#Summaries and tables
###Exercise 2: Create a numerical summary for height and age, and compute the interquartile range for each. Compute the relative frequency distribution for gender and exerany. How many males are in the sample? What proportion of the sample reports being in excellent health?
summary(cdc$height) # summary of height
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 48.00 64.00 67.00 67.18 70.00 93.00
summary(cdc$age) # summary of age
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 18.00 31.00 43.00 45.07 57.00 99.00
IQR(cdc$height) # the interquartile range for hieght
## [1] 6
IQR(cdc$age) # the interquartile range for age
## [1] 26
table(cdc$gender)/dim(cdc)[1] # the relative frequency distribution for gender and exerany
##
## m f
## 0.47845 0.52155
table(cdc$exerany)/dim(cdc)[1] # the relative frequency distribution for exerany
##
## 0 1
## 0.2543 0.7457
table(cdc$gender,cdc$exerany)/dim(cdc)[1] #the relative frequency distribution for gender and exerany
##
## 0 1
## m 0.10745 0.37100
## f 0.14685 0.37470
dim(subset(cdc, cdc$gender == "m"))[1] # number of males
## [1] 9569
dim(subset(cdc, cdc$genhlth == "excellent"))[1] # number of the sample in excellent health
## [1] 4657
dim(subset(cdc, cdc$genhlth == "excellent"))[1]/dim(cdc)[1] # proportion of the sample in excellent health
## [1] 0.23285
###Exercise 3: What does the mosaic plot reveal about smoking habits and gender?
#Answer: Each column in the mosaic plot represents the catagory of gender and each row correspond to the proportion of smoke100 for each gender.The resoult shows proportion of males who smok 100 or more cigarettes is greater than female's.
mosaicplot(table(cdc$gender,cdc$smoke100))
#A little more on subsetting
###Exercise 4: Create a new object called under23_and_smoke that contains all observations of respondents under the age of 23 that have smoked 100 cigarettes in their lifetime. Write the command you used to create the new object as the answer to this exercise.
under23_and_smoke <- subset(cdc, age < 23 & smoke100 == 1)
#Quantitative data
###Exercise 5: What does this box plot show? Pick another categorical variable from the data set and see how it relates to BMI. List the variable you chose, why you might think it would have a relationship to BMI, and indicate what the figure seems to suggest.
###Answer: The box plot shows minimum, first quartile, median, third quartile and maximum of each health condition value for all 20,000 numbers in the cdc data set. The varibale I chose is genhlth since the health condition has a closed correlation to weight control and BMI is a prox to measure whether a people is over weight or under weight.
bmi <- (cdc$weight / cdc$height^2) * 703
boxplot(bmi ~ cdc$genhlth)
#On Your Own
###1. Make a scatterplot of weight versus desired weight. Describe the relationship between these two variables.
###Answer: They have positive correlation.
plot(cdc$weight, cdc$wtdesire, main="Weight vs. Desired Weight", xlab="Weight ", ylab="Desired Weight", pch=19)
###2. Letâs 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.
wdiff <- cdc$weight - cdc$wtdesire
###3. What type of data is wdiff? If an observation wdiff is 0, what does this mean about the personâs weight and desired weight. What if wdiff is positive or negative?
#Answer: if wdiff is 0, that means the person doesn't desired to loss weight (if positive) or to gain more weight (if negative) , which his/her actual weight is the same as the desired weight .
typeof(wdiff)
## [1] "integer"
###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?
###Answer:People feel they have a bit over weight and desire to loss 0 to 50 ponds.
summary(wdiff)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -500.00 0.00 10.00 14.59 21.00 300.00
hist(wdiff)
wdiff_no_outliers=subset(wdiff, wdiff> -200 & wdiff <200)
summary(wdiff_no_outliers)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -110.0 0.0 10.0 14.5 21.0 190.0
hist(wdiff_no_outliers)
abline(v=0,col='red')
abline (v=21, col='red')
###5. Using numerical summaries and a side-by-side box plot, determine if men tend to view their weight differently than women.
cdc_no_outlier=subset(cdc, abs(cdc$wtdesire-cdc$weight)<200)
cdc_no_outlier_male=subset(cdc_no_outlier, gender=='m')
cdc_no_outlier_female=subset(cdc_no_outlier, gender=='f')
summary(cdc_no_outlier_male$weight)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 78.0 165.0 185.0 189.2 210.0 400.0
summary(cdc_no_outlier_female$weight)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 68.0 128.0 145.0 151.6 170.0 380.0
boxplot((cdc_no_outlier$weight-cdc_no_outlier$wtdesire) ~ cdc_no_outlier$gender, main="Difference Between Actual and Desired Weight, by Gender", xlab="Gender")
###6. Now itâs time to get creative. Find the mean and standard deviation of weight and determine what proportion of the weights are within one standard deviation of the mean.
weightMean <- mean(cdc$weight)
weightSD <- (cdc$weight)
cdc_one_sd=subset(cdc, cdc$weight>(weightMean-weightSD) & cdc$weight<weightMean+weightSD)
dim(cdc_one_sd)[1]/dim(cdc)[1]
## [1] 0.99935