Descriptive Statistics and Visualizing Data in STATA

Transcription

1 Descriptive Statistics and Visualizing Data in STATA BIOS 514/517 R. Y. Coley Week of October 7, 2013

2 Log Files, Getting Data in STATA Log files save your commands cd /home/students/rycoley/bios To change directory log using stata-section-oct7, replace text To name log file (change stata-section-oct7) capture log close to close log file insheet using To get FEV data in

3 Defining, Labeling Variables table smoke Currently coded as 1 and 2 No missing data (would be coded as 9) label define smokelabel 1 "smoker" 2 "non-smoker" label values smoke smokelabel label define sexlabel 1 "male" 2 "female" label values sex sexlabel

4 Labeling Variables label variable age "Age (years)" label variable fev "FEV (L/s)" label variable height "Height (in)"

5 Descriptive Statistics Basic commands detailed in this week s lecture notes: summarize means centile tabstat tabulate

6 Descriptive Stats by Group bysort sex: tabstat fev, stat(n mean sd min p25 med p75 max) col(stat) format bysort sex: tabulate smoke

7 Defining New Variables A few ways: gen age9over = age>=9 gen age9over = 0 replace age9over=1 if age>=9 gen age9over = age==9 age==10 age==11... age==19

8 Measures of Spread Range: tabstat fev, stat(min max range) Variance: tabstat fev, stat(var) Standard Deviation: tabstat fev, stat(sd) Interquartile Range: tabstat fev, stat(p25, p75, iqr) IQR is the distance between the 25th and 75th percentiles of the data

9 Visualizing Data- Histograms histogram fev to save: graph export hist-fev.png, replace Height of each bar proportional to proportion of observations in that bin s range

10 Visualizing Data- Histograms histogram fev, kdensity by (sex) kdensity adds smooth line estimating density

11 Visualizing Data- Dotplots dotplot fev Each dot represents an observations

12 Visualizing Data- Box Plots a.k.a. Box and whiskers plots Box extends from lower quartile (25th percentile of data) to upper quartile (75th percentile) with a line at the median (50th percentile). Whiskers extend from lower quartile to lower adjacent value and from upper quartile to upper adjacent value LAV = lower quartile 3 2 IQR UAV = upper quartile IQR ( Observations outside the UAV and LAV plotted as points (Some box plots have whiskers extend to minimum and maximum observations.)

13 Visualizing Data- Box Plots graph box fev

14 Visualizing Data- Box Plots graph box fev, over(sex)

15 Visualizing Data- Scatterplots scatter fev height

16 Visualizing Data- Bar Charts gen one=1 graph bar (count) one, over(smoke) ytitle("frequency")

17 Another Example log using cause-of-death, text replace set obs 10 input float deaths str30 cause "Heart Disease" "Cancer" "Cerebrovascular Disease" "Chronic respiratory disease" "Accidental Death" "Diabetes" "Flu and pneumonia" "Alzheimer s disease" "Kidney disorder" "Septicemia"

18 Visualizing Data- Bar Chart gen dthou=deaths/1000 graph hbar dthou, over(cause) ytitle("annual deaths (thousands)")

19 Visualizing Data- Bar Charts gen dthou=deaths/1000 graph hbar dthou, over(cause, sort(1) descending) ytitle("annual deaths (thousands)")

20 Visualizing Data- Pie Charts graph pie deaths, over(cause) sort descending

21 Visualizing Data- Pie Charts

22 Visualizing Data- Pie Charts

23 Visualizing Data- Pie Charts

24 Doing it all over again in R! Look at the code I have posted on the discussion board. It is extensively commented (##)! Comments omitted here. data<-read.csv("fevdata.csv",header=true) names(data) dim(data) n<-dim(data)[1]

25 (Re-)defining variables Variables don t have labels like in Stata. But, we can improve upon the current coding of smoke and sex. data$smoke[data$smoke==2]<-0 \\ data$female<-data$sex==2 Creating a new variable: data$age9over<-data$age>=9

26 Descriptive Statistics summary(data$fev) #min, 1Q, Med, Mean, 3Q, Max mean(data$fev) quantile(data$fev, p=c(0.25, 0.5, 0.75)) table(data$smoke) xtabs(~data$smoke+data$female) #to get cross tabulation

27 Measures of Spread range(data$fev) #gives min and max var(data$fev) #variance sd(data$fev) #standard deviation

28 Histograms hist(data$fev, xlab="fev (L/s)", main="histogram of FEV") To save the graph: pdf(file="fev-hist-r.pdf") hist(data$fev, xlab="fev (L/s)", main="histogram of FEV") graphics.off() Histogram of FEV Frequency FEV (L/s)

29 Histograms hist(data$fev, xlab="fev (L/s)", main="histogram of FEV", prob=true) lines(density(data$fev)) Histogram of FEV Density FEV (L/s)

30 Histogram plot(hist(data$fev[data$female==0], xlab="fev (L/s)", main="males", ylim=c(0,80)), hist(data$fev[data$female==1], xlab="fev (L/s)", main="females", xlim=c(0,6))) Males Females Frequency Frequency FEV (L/s) FEV (L/s)

31 Boxplot boxplot(data$fev, ylab="fev (L/s)") FEV (L/s)

32 Boxplot boxplot(data$fev~data$female, ylab="fev (L/s)", xaxt="n") axis(1, at=c(1,2), labels=c("male", "Female")) FEV (L/s) Male Female

33 Scatter Plot plot(data$fev~data$height, ylab="fev (L/s)", xlab="height (in)") Height (in) FEV (L/s)

34 Bar Plot counts<-table(data$smoke) barplot(counts, xlab="smoker", xaxt="n") axis(1, at=c(1,2), labels=c("no","yes")) No Smoker Yes

35 Cause of Death Example in R n.deaths<-c(700142, , , , , 71372, 62034, 53852, 39480, 32238) cause<-c("heart Disease", "Cancer", "Cerebrovascular Disease", "Chronic Respiratory Diesease","Accidental death", "Diabetes", "Flu and Pneumonia", "Alzheimer s Disease", "Kidney Disorder","Septicemia") n.deaths<-n.deaths/1000

36 Cause of Death Example par(mar=c(4,6.5,1,1)) barplot(n.deaths, horiz=t, yaxt="n", xlab="number of Death (Thousands)", main="cause of Death") text(y=seq(1,11.35, 1.15), par("usr")[1], labels=cause, srt=45, pos=2, xpd=t, cex=0.75) Cause of Death Septicemia Kidney Disorder Alzheimer's Disease Flu and Pneumonia Diabetes Accidental death Chronic Respiratory Diesease Cerebrovascular Disease Cancer Heart Disease Number of Deaths (Thousands)

37 Cause of Death Example pie(n.deaths, cause, main="cause of Death" ) Cause of Death Heart Disease Cancer Septicemia Kidney Disorder Flu and Pneumonia Diabetes Alzheimer's Disease Cerebrovascular Disease Accidental death Chronic Respiratory Diesease