Load data from cdc.R located in working directory/more and create data frame “cdc”.
source("more/cdc.R")Q 1: Make a scatterplot of weight versus desired weight. Describe the relationship between these two variables.
names(cdc)## [1] "genhlth" "exerany" "hlthplan" "smoke100" "height" "weight"
## [7] "wtdesire" "age" "gender"plot(x = cdc$wtdesire, y = cdc$weight, ylab = "Current Weight", xlab = "Desired Weight")
abline(lm(cdc$weight ~ cdc$wtdesire, data = cdc), col="red") #Added Linear regression line to make analysis easier.
There is positive relation between current weight and desired weight. As Weight coordinate is increasing Desired Weight coordinate is decreasing. To conclude, as weight is increasing, desire to lose weight is also increasing.
lm(cdc$weight ~ cdc$wtdesire, data = cdc)##
## Call:
## lm(formula = cdc$weight ~ cdc$wtdesire, data = cdc)
##
## Coefficients:
## (Intercept) cdc$wtdesire
## 14.330 1.002This tells us slope is 1 and intercept is 14.33.
Q 2: Let’s consider a new variable: the difference between desired weight (wtdesire) and current weight (weight). Create this new variable by subtracting the two columns in the data frame and assigning them to a new object called wdiff.
Adding the new column as difference of desired weight and weight.
cdc$wdiff <- cdc$wtdesire - cdc$weightQ 3: 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?
Structure command will tell us data type of each column. And wdiff is int data type.
str(cdc)## 'data.frame': 20000 obs. of 10 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 ...
## $ wdiff : int 0 -10 0 -8 -20 0 -9 -10 -20 -10 ...cdc$wdiff## [1] 0 -10 0 -8 -20 0 -9 -10 -20 -10 -11 -20 35
## [14] 0 0 -10 -6 0 -10 -5 -50 -20 0 -7 15 -40
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## [40] -15 -6 -13 0 -40 -40 -35 -10 0 3 -1 -29 -7
## [53] -10 -2 0 0 -10 -48 -10 -40 -11 -24 -50 -20 0
## [66] -15 10 -5 -10 0 -25 0 -10 -20 10 -30 12 -6
## [79] -22 -10 20 0 -25 -10 -28 -8 -30 0 -23 -20 -100
## [92] -16 -15 0 0 25 -20 -10 0 0 0 -2 0 0
## [105] 0 -30 -4 -28 -11 -20 0 0 0 -13 0 -12 -10
## [118] -10 -45 5 -80 0 -5 -44 -16 0 -5 -70 -5 -5
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## [157] -15 0 0 3 -5 -40 -20 -12 0 -30 -10 0 -30
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## [196] 0 -42 -25 0 -30 -15 -5 -25 -10 -75 0 0 5
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## [18357] 0 -30 -25 -15 -18 14 0 0 -20 -5 0 25 -17
## [18370] -20 -55 10 20 0 0 -34 -3 0 -10 -5 0 -10
## [18383] 0 -3 11 -30 0 -10 -15 15 -25 -10 -19 0 -20
## [18396] -10 0 0 -15 0 0 -5 -5 0 10 0 0 0
## [18409] 0 -20 0 -20 -10 -8 -5 0 0 -15 -5 -4 -16
## [18422] -20 -56 -24 -40 -15 -5 0 -12 -70 0 -26 0 -5
## [18435] -32 -10 0 25 0 -30 -10 -40 0 -10 5 -10 -80
## [18448] 0 -20 0 -5 0 -7 0 -20 0 30 -15 -4 -5
## [18461] -40 -13 -15 -10 0 -30 0 -10 -25 -10 -10 -20 10
## [18474] 0 -5 0 -55 -10 8 0 -2 0 0 0 0 0
## [18487] -25 -45 -25 5 -25 -6 -9 0 -10 -10 -20 -30 0
## [18500] -35 -8 -15 -10 -13 -15 -30 -39 -20 -20 -15 -25 -20
## [18513] -20 -1 0 -100 -15 0 -10 -25 -55 -15 -15 -4 -20
## [18526] -10 -20 -8 -20 -25 20 20 -28 -20 -5 0 0 0
## [18539] -14 0 -17 -7 -8 -5 -20 -15 0 15 0 -7 -5
## [18552] -14 -20 -40 -100 0 0 0 -25 -10 0 0 0 0
## [18565] 0 0 -10 12 -80 0 0 -15 -25 -10 10 -10 -20
## [18578] -20 -15 -30 -25 -15 -15 -8 0 -16 -30 -21 -110 0
## [18591] -30 19 -8 0 -45 -20 7 0 2 -15 -80 0 -30
## [18604] -10 -3 0 -7 -40 -100 22 0 -60 -10 -30 -5 -10
## [18617] -30 -20 -15 -2 -52 -10 -20 -20 0 0 -25 0 -8
## [18630] -5 -3 -16 -45 -10 -20 -10 -15 -6 -10 -20 -11 0
## [18643] 0 -10 0 0 0 -5 0 -15 -39 0 0 0 0
## [18656] -10 -20 -20 0 0 -15 -13 0 -15 -22 -20 0 -4
## [18669] -10 -9 -15 -25 -7 5 50 -15 -10 0 0 -60 -20
## [18682] -15 -25 -7 -68 -15 0 -30 0 -30 -6 -42 0 20
## [18695] -23 -20 0 -50 -1 -40 0 -20 -25 -15 -28 -20 -15
## [18708] -25 0 0 -25 -10 -50 0 -10 0 0 -35 -10 -19
## [18721] -10 0 -130 -10 0 -45 0 -45 -27 0 12 -10 -5
## [18734] -15 -30 -15 0 3 -2 -4 10 10 0 -50 -15 -20
## [18747] -20 -30 0 -7 15 -5 -25 0 0 -10 -10 0 -75
## [18760] 0 -5 -24 -42 -20 -25 -15 5 0 -10 0 -10 -7
## [18773] -25 -5 -20 0 0 -16 -40 0 -50 -28 -75 -45 -10
## [18786] 0 -15 10 -20 -36 0 -40 -35 0 -15 -35 -20 0
## [18799] -126 -15 -25 -80 -7 -5 -5 -30 -40 -20 -15 -20 -14
## [18812] -10 0 -20 0 0 0 -100 -30 -15 -13 0 -64 0
## [18825] 10 -5 0 0 -5 -25 -40 -20 -3 -44 0 -5 -20
## [18838] -20 0 0 1 0 -14 -25 0 0 -2 -50 -18 -40
## [18851] 0 -50 -5 -40 -27 0 -45 0 -27 -25 0 -40 0
## [18864] -15 -5 11 -20 25 -20 -5 0 0 -20 -40 7 -30
## [18877] -25 0 -22 16 0 0 -15 -105 0 0 -10 -50 0
## [18890] -85 0 -23 -34 0 -5 -5 -15 -40 -30 -10 0 -41
## [18903] -20 -30 10 -80 -5 -10 -25 0 -25 -67 -5 -165 0
## [18916] -10 -5 -20 -15 -13 0 -74 -5 2 -65 -14 0 0
## [18929] -25 0 -10 0 -20 0 0 15 -15 0 2 -120 0
## [18942] -37 0 0 -5 15 -10 -20 0 -100 -40 -30 -85 -10
## [18955] -30 -30 0 -10 0 -7 0 0 -34 -15 -35 10 0
## [18968] -5 -35 0 -10 -20 -30 -40 -5 0 -9 -25 -15 0
## [18981] 0 0 0 0 -2 15 0 -20 -50 -13 -10 -8 -10
## [18994] -19 -75 6 -55 10 -5 0 18 -6 -20 -7 5 -20
## [19007] 0 -40 0 -40 -13 -100 -20 -20 -7 -35 -3 0 0
## [19020] -13 0 -75 0 -4 -20 -16 -5 30 -25 0 0 0
## [19033] -9 7 -20 -15 -15 -30 0 -15 -17 -120 -10 0 -5
## [19046] -10 -35 -12 -40 -100 -5 0 33 -30 -5 0 -20 0
## [19059] -15 -40 -10 -30 -5 -5 -5 0 -30 -5 -50 0 0
## [19072] -64 10 0 -3 -30 -18 -5 -9 0 -55 -8 0 -38
## [19085] -10 -15 0 -20 -30 0 0 -66 -13 0 -50 -5 -15
## [19098] -10 -60 -25 -10 15 -15 -40 -60 -13 15 -20 -10 -50
## [19111] -30 0 0 -32 -40 -10 0 -20 -30 -60 -80 0 0
## [19124] -15 -15 0 0 0 -30 0 0 -20 10 -30 -15 30
## [19137] 0 -57 -10 -20 -30 -35 -15 0 -5 0 20 0 -5
## [19150] 0 -5 0 -2 -100 -25 0 -12 0 0 0 -39 -15
## [19163] 0 -10 -10 -10 -50 0 0 0 -20 0 -20 0 -20
## [19176] 0 0 0 -40 0 0 -20 -15 0 0 0 -20 -37
## [19189] -10 -15 35 -5 -10 -8 0 -10 20 -45 -27 -15 0
## [19202] -20 0 -20 -5 -7 -30 -50 -14 -5 -35 -35 0 0
## [19215] -10 -20 -100 40 -10 10 -10 -24 15 -80 -33 0 0
## [19228] -8 -11 -35 -5 0 -60 10 -8 -14 0 0 -5 -40
## [19241] -30 -60 0 -8 0 -50 0 -15 -30 7 -15 0 -15
## [19254] 0 0 -25 0 -20 -10 0 -15 0 -5 0 0 5
## [19267] 0 -20 -25 0 -20 -10 -20 -5 -11 -60 -25 0 0
## [19280] 0 -14 -35 -1 -8 0 -35 0 0 -25 -15 0 0
## [19293] -10 -35 -25 -35 25 -42 1 0 -5 -50 0 0 0
## [19306] -10 -10 -40 -25 -20 0 0 -20 -10 -2 -30 20 0
## [19319] -200 -20 0 28 0 -50 -85 0 -40 0 0 -12 0
## [19332] -45 -40 0 0 -15 0 -3 -15 -15 0 -40 0 0
## [19345] -20 0 0 0 -48 0 -15 -17 0 0 0 -10 -25
## [19358] 0 -20 -35 -3 0 -55 0 -2 -51 -10 -6 -50 -20
## [19371] -21 -5 -28 -15 10 25 -25 -10 0 -150 0 0 -20
## [19384] -33 20 0 -10 -5 -10 0 -30 0 -50 -31 -2 -60
## [19397] 6 -80 8 -140 -20 -10 -10 5 -115 -20 0 0 0
## [19410] -21 20 -10 -15 -10 -60 -10 -35 8 0 -15 -34 -10
## [19423] -36 -10 -60 0 0 0 -20 -12 0 30 -15 -5 -20
## [19436] -15 -30 0 -15 -10 -10 -6 -100 -7 -20 -20 -8 -5
## [19449] -10 -20 -23 20 -5 -10 -18 -10 -40 6 0 10 0
## [19462] -30 -10 0 -20 0 -60 -25 25 0 -5 -15 -10 0
## [19475] -3 0 -50 -60 -35 35 0 0 -10 -4 35 -5 -7
## [19488] -15 -3 22 -6 -15 -27 0 -40 -15 -5 0 -15 -130
## [19501] 0 -10 0 5 0 -10 0 10 -10 10 -34 -30 -36
## [19514] -60 -20 -5 -70 -10 -20 -10 -23 -10 -75 -60 8 4
## [19527] -5 -73 5 -3 10 -120 -70 -80 -20 -20 -20 -33 -10
## [19540] 0 -43 -30 -80 0 0 10 0 -30 0 12 -12 -10
## [19553] 5 -30 -10 0 0 -30 -115 0 0 0 0 -10 0
## [19566] -50 -60 -20 -5 -5 0 0 -30 -48 -10 -69 0 -17
## [19579] 0 -40 -55 -40 -27 -55 -20 -5 -6 -5 -30 10 -30
## [19592] -7 -5 0 -3 0 0 0 10 -20 -25 0 -30 -30
## [19605] 0 -31 -20 0 -15 -14 -20 0 -65 -70 -22 0 -9
## [19618] 0 -35 -15 -128 5 -10 -5 -18 -2 -5 0 0 -3
## [19631] -5 0 -20 -15 0 -25 10 -10 -15 -70 0 -10 15
## [19644] 0 -9 0 0 -30 -15 -10 -19 -40 -5 20 -35 -15
## [19657] 0 0 -30 -35 -5 -20 0 0 0 -18 0 0 0
## [19670] -18 -30 0 -35 -15 -2 35 0 -10 -10 -45 0 0
## [19683] -12 0 -50 -7 -39 -10 -10 -60 0 -20 -5 -10 -60
## [19696] -25 -40 -20 -10 -20 30 -11 0 0 0 0 -10 -3
## [19709] -5 -20 0 -45 -17 -10 0 -2 -10 -5 0 -20 -50
## [19722] 0 -10 -60 0 -5 0 0 -35 0 -20 -13 -13 9
## [19735] 5 15 8 -3 -10 0 0 -15 -3 0 0 -65 -40
## [19748] -50 0 -5 -55 0 0 5 0 -25 0 -20 -30 -8
## [19761] 0 -28 -5 -30 -10 -10 -9 -13 -20 -20 0 10 0
## [19774] -15 32 -15 11 0 -40 -50 -5 0 -75 0 -5 0
## [19787] 15 -20 0 -5 0 0 -16 -10 -40 -60 0 0 -20
## [19800] 0 0 0 0 -15 -25 -12 -11 18 5 -52 -30 0
## [19813] -7 -8 -25 5 -40 0 -25 10 15 15 -55 10 10
## [19826] -80 -5 1 -54 -10 0 11 -20 -10 -2 -20 0 -25
## [19839] 0 0 -7 -8 -25 -15 -55 -2 -20 -15 -25 -18 -39
## [19852] 0 32 0 -42 -15 -10 0 0 30 -10 -30 -30 -7
## [19865] -100 -30 -58 -24 0 0 -23 -50 -8 -40 0 -5 -5
## [19878] -30 0 -18 -20 -5 -20 12 0 -110 -20 -8 -20 5
## [19891] -15 0 -70 -30 -20 0 -5 0 -35 -20 -20 0 0
## [19904] 0 -8 -44 -35 0 0 -15 -6 -10 0 0 0 -10
## [19917] -20 -35 20 0 0 15 -152 0 0 0 -20 -25 -75
## [19930] -30 0 -20 -5 -40 0 -20 5 0 -10 20 -5 23
## [19943] -20 -15 -6 -9 -20 -10 0 0 -25 -142 -40 0 -15
## [19956] -22 0 -13 -20 -20 -5 0 -3 0 -43 0 -30 -75
## [19969] 0 8 10 15 -10 10 -6 -40 -58 -30 0 -65 0
## [19982] 0 -41 -10 -20 -5 -25 -5 -40 -30 -5 -35 0 -45
## [19995] 0 -75 -15 -66 0 -5If wdiff value is 0 person is satisfied with their weight. If wdiff value is negative person wants to lose weight and if positive person wants to gain weight.
Q 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?
Lets visualize wdiff using histogram.
hist(cdc$wdiff, breaks = 50)
Applying summary command.
summary(cdc$wdiff)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -300.00 -21.00 -10.00 -14.59 0.00 500.00Since median and mean are negative, seems people want to lose 10 to ~ 15 pounds. Histogram is skewed to left describing there are more people who want to lose weight and fewer people who want to gain weight.
Q 5: Using numerical summaries and a side-by-side box plot, determine if men tend to view their weight differently than women.
A: Summary for men
summary(subset(cdc$wdiff, cdc$gender == "m"))## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -300.00 -20.00 -5.00 -10.71 0.00 500.00Summary for women
summary(subset(cdc$wdiff, cdc$gender == "f"))## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -300.00 -27.00 -10.00 -18.15 0.00 83.00Box plot
boxplot(cdc$wdiff ~ cdc$gender)
Men have median and mean as -5 and -10.71 and women have median and mean as -10 and -18.15. Women seem to want to lose more pounds than men.
Q 6: Now it’s time to get creative. Find the mean and standard deviation of weight and determine what proportion of the weights are within one standard deviation of the mean.
mean(cdc$weight)## [1] 169.683sd(cdc$weight)## [1] 40.08097Number of people with weight that falls within one standard deviation
PepWithinSD <- subset(cdc, cdc$weight < (mean(cdc$weight) + sd(cdc$weight)) & cdc$weight > (mean(cdc$weight) - sd(cdc$weight)))
(dim(PepWithinSD)[1]/dim(cdc)[1]) * 100## [1] 70.76There are 70.76% of people with weights that are within one standard deviation of the mean.
Apendix
source("more/cdc.R")
names(cdc)
plot(x = cdc$wtdesire, y = cdc$weight, ylab = "Current Weight", xlab = "Desired Weight")
abline(lm(cdc$weight ~ cdc$wtdesire, data = cdc), col="red") #Added Linear regression line to make analysis easier.
lm(cdc$weight ~ cdc$wtdesire, data = cdc)
cdc$wdiff <- cdc$wtdesire - cdc$weight
str(cdc)
cdc$wdiff
hist(cdc$wdiff, breaks = 50)
summary(cdc$wdiff)
summary(subset(cdc$wdiff, cdc$gender == "m"))
summary(subset(cdc$wdiff, cdc$gender == "f"))
boxplot(cdc$wdiff ~ cdc$gender)
mean(cdc$weight)
sd(cdc$weight)
PepWithinSD <- subset(cdc, cdc$weight < (mean(cdc$weight) + sd(cdc$weight)) & cdc$weight > (mean(cdc$weight) - sd(cdc$weight)))
(dim(PepWithinSD)[1]/dim(cdc)[1]) * 100