Principles of Biostatistics and Data Analysis PHP 2510 Lab2

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1 Goals for Lab2: Familiarization with Do-file Editor (two important features: reproducible and analysis) Reviewing commands for summary statistics Visual depiction of data- bar chart and histograms Stata Commands used in this unit: cd Change directory dir or ls Show files in current directory insheet Read ASCII(text) data created by a spreadsheet use Load a Stata-format dataset list List values of variables describe Describe contents of data in memory or on disk clear Clear the entire dataset and everything else save Store the dataset currently in memory on disk in Stata data format summarize Descriptive statistics tabulate Produces one- and two-way tables graph bar Bar charts showing quantities or percentages by labeling columns histogram Histogram for continuous and categorical variables Importing data, creating do-files: We will begin by inputting a data file (.dta) into Stata..*set current directory.cd c:/data.*set current location for net.net from *install ancillary files from a package into current directory.net get agis.*open a dataset which asked a series of questions about drinking behavior among adolescents between the ages of 12 and 16.use "C:/data/nlsy97_selected_variables.dta", clear Stata has a window called a Do-file Editor in which you can enter a series of command. You can run all the commands in this file or just some of them. You can then edit, save, and open them at a later date. Saving these do-files means not only that you can replicate what you did and make any needed adjustments but also that you will develop templates you can draw on when you want to do a similar analysis. To open the Do-file Editor window, use Window Do-file Editor New Do-file. You can also open it by clicking on the icon that looks like a note pad with a pencil. The point is that Stata has written the actual command in the Results window, and you can copy this to the Do-file Editor. If the Do-file Editor window is not wide enough to hold the entire line, we can go over part way, insert three forward slashes, and press the Enter key to force the rest of the command to go to the next line. It s always good to add a comment, which can go anywhere in the program. One way to make a comment is to start the line with an asterisk. It could include a description of what the program does, path, date, and etc. Data summarizing:

2 Review the describe, list and inspect commands to see what the data looks like. You will find out that there are some missing values for some variables. Review the tabulate command to get frequency distributions for different variables such as:.tabulate drday97.tabulate drday97 ethnic97.tabulate drday97 race97 if race97==2 race97==3 This will give you the results of dropping cases if they have missing values on any of the variables. You could play around other tabulate commands with different variables and different options. Review the generate command to create new variables. gen x=cond(drday97<15,1,0) if!missing(drday97). gen y=hhin97/1000 Note: when you want to reproduce them by running these commands in do-files, you have to clear the data and start from the beginning of scripts, because those variables have been generated. summarize command allows the user to view the descriptive statistics for each of the variables in the dataset. Say for instance we are only concerned with viewing the summary statistics for the variable drday97. summarize drday97 drday Often times, in order to effectively get a feel for the data, we need more summary statistics than what the summarize command gives us. In these cases we can use the detail option. For instance:. summarize drday97, detail Percentiles Smallest 1% 0 0 5% % 0 0 Obs % 0 0 Sum of Wgt % 0 Mean Largest Std. Dev % % 5 30 Variance % Skewness % Kurtosis Because we need to eliminate any observations that did not answer all the items, we could drop cases if they have missing values on any of the variables.

3 . summarize age97 pdrink97 drday97 if!missing(age97, pdrink97, drday97) age pdrink drday What if we are only interested in some variables for the age group. summarize pdrink97 drday97 smday97 if age>=13&age<=15 pdrink drday smday Graphical Representation of Data: When analyzing a set of data, it is often helpful to look at a graphical representation of that data. One extremely useful depiction of data is achieved through the use of histogram. The data for creating a density histogram for variable drday97 is as follows,. histogram drday97 Density Perhaps we want to adjust the length of the intervals and axis of a frequency histogram using the command below:. histogram drday97, width(2) start(0) frequency Frequency

4 You could go to the menu bar Graphics Histogram to choose the histogram option. Another graphical display of date we might be interested in using is the bar charts. It shows quantities such as mean or percentages of one variable, based on another variable or the combinations of other variables.. graph bar smday97, over(age97) mean of smday As above, mean is by default. However, people might be concerned about the median of #smoking day for each age group. We could use following commands:. graph bar (median) smday97, over(age97) p 50 of smday Sometimes, it s important to know the information based on combination of different variables..graph bar drday97, over (gender97) over(ethnic97)

5 mean of drday Non-Hispanic Hispanic You could go to the menu bar Graphics Bar chart to choose its option. When you create some graphs and want to save it. You could right click to choose save graph or you could go to graph window s menu bar: File Save as to save the graph. Saving and using your work: After all the commands are pasted in the Do-files Editor, we could save it under the name lab3.do using File Save as You can run all or part of the program. To run the entire program, just click on the icon that has the page showing lines and. References: Accok, Alan. A Gentle Introduction to Stata. Texas: Stata Press, 2006 Resources to Help you Learn Stata. UCLA Academic Technology Services. 8 September