Data 606 Lab1 - Introduction to data
Kleber Perez - February 10, 2019
The BRFSS Web site (http://www.cdc.gov/brfss)
The Behavioral Risk Factor Surveillance System (BRFSS) is an annual telephone survey of 350,000 people in the United States. As its name implies, the BRFSS is designed to identify risk factors in the adult population and report emerging health trends.
We begin by loading the data set of 20,000 observations into the R workspace.
source("more/cdc.R")The data set cdc that shows up in your workspace is a data matrix, with each row representing a case and each column representing a variable. R calls this data format a data frame, which is a term that will be used throughout the labs.
To view the names of the variables, type the command
names(cdc)## [1] "genhlth" "exerany" "hlthplan" "smoke100" "height" "weight"
## [7] "wtdesire" "age" "gender"a) How many cases are there in this data set?
nrow(cdc)## [1] 20000b) How many variables?
ncol(cdc)## [1] 9c) For each variable, identify its data type (e.g. categorical, discrete).
str(cdc)## '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 ...a) Create a numerical summary for ‘height’ and ‘age’, and compute the interquartile range for each.
summary(cdc$height)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 48.00 64.00 67.00 67.18 70.00 93.00summary(cdc$age)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 18.00 31.00 43.00 45.07 57.00 99.00b) Compute the relative frequency distribution for ‘gender’ and ‘exerany’.
table(cdc$gender)/nrow(cdc)##
## m f
## 0.47845 0.52155table(cdc$exerany) / nrow(cdc)##
## 0 1
## 0.2543 0.7457c) How many males are in the sample?
table(cdc$gender)##
## m f
## 9569 10431d) What proportion of the sample reports being in excellent health?
table(cdc$genhlth)/nrow(cdc)##
## excellent very good good fair poor
## 0.23285 0.34860 0.28375 0.10095 0.03385barplot(table(cdc$genhlth)/nrow(cdc))
What does the mosaic plot reveal about smoking habits and gender?
table(cdc$smoke100,cdc$gender)##
## m f
## 0 4547 6012
## 1 5022 4419mosaicplot(table(cdc$smoke100,cdc$gender),main="Smoking habits and gender",xlab="Smoking habit",ylab="Gender")
Create a new object called under23_and_smoke that contains all observations of respondents under the age of 23 that have smoked 100 cigarettes in their lifetime. Write the command you used to create the new object as the answer to this exercise.
under23_and_smoke <- subset(cdc,cdc$age<23 & cdc$smoke100==1)
head(under23_and_smoke)## genhlth exerany hlthplan smoke100 height weight wtdesire age gender
## 13 excellent 1 0 1 66 185 220 21 m
## 37 very good 1 0 1 70 160 140 18 f
## 96 excellent 1 1 1 74 175 200 22 m
## 180 good 1 1 1 64 190 140 20 f
## 182 very good 1 1 1 62 92 92 21 f
## 240 very good 1 0 1 64 125 115 22 fWhat does this box plot show? Pick another categorical variable from the data set and see how it relates to BMI. List the variable you chose, why you might think it would have a relationship to BMI, and indicate what the figure seems to suggest.
bmi <- (cdc$weight/cdc$height^2)*703
boxplot(bmi ~ cdc$exerany,main="BMI vs Exercise",ylab="BMI",xlab="Exercise")
According to the above plot and data, it shows that the people who excercise under normal BMI (18.5 - 24.9). People who dont excercise have a higher BMI than normal.
On Your Own
Make a scatterplot of weight versus desired weight. Describe the relationship between these two variables.
plot(cdc$weight,cdc$wtdesire)
There is a strong corelation between weight vs desired weight. Both are linear.
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.
wdiff <- cdc$wtdesire - cdc$weightWhat 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?
If the widff is 0, then the desired weight and current weight is equal. So he is on ideal weight. if possitive, then the weight of the person is less. He still need to gain this much weight to be in ideal range. if negative, then the weight of the person is more. He need to reduce this much weight to be in ideal range.
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?
hist(wdiff,breaks=50)
Using numerical summaries and a side-by-side box plot, determine if men tend to view their weight differently than women.
cdcc_withdiff <- data.frame(cdc,wdiff)
plot(cdcc_withdiff$gender,cdcc_withdiff$wdiff)
The median is almost equal for men and women.
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.
cdc_meanweight <- mean(cdc$weight); cdc_sdweight <- sd(cdc$weight)
nrow(subset(cdc,cdc$weight > (cdc_meanweight - cdc_sdweight) & (cdc_meanweight - cdc_sdweight)))/nrow(cdc)## [1] 0.86085Around 86% fall under one Standard deviation.
This is a product of OpenIntro that is released under a Creative Commons Attribution-ShareAlike 3.0 Unported. This lab was adapted for OpenIntro by Andrew Bray and Mine Çetinkaya-Rundel from a lab written by Mark Hansen of UCLA Statistics.
Please email to: kleber.perez@live.com for any suggestion.