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Generating new variables and manipulating data with STATA Biostatistics 212 Lecture 3.

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Presentation on theme: "Generating new variables and manipulating data with STATA Biostatistics 212 Lecture 3."— Presentation transcript:

1 Generating new variables and manipulating data with STATA Biostatistics 212 Lecture 3

2 Housekeeping Lab 1 handed back today –Think of red ink as teaching points, not penalties… Do and Log files –Understand each command! –Order them appropriately

3 The.do file template –Tell STATA where to look for things and where to put things –Run correct version of Stata –Stop STATA from prompting you to push a button to continue –Tell STATA to clear any datasets in memory and increase its mem capacity –Since your do file may not be perfect, tell STATA to close any logs that are open when you try to run your do file –Tell STATA to create a log of your output for you and what you’re going to call that log. Tell it to overwrite it each time –Stick in some comments to remind you what this do file is for –Tell STATA what dataset to work on –Leave some SPACE for putting in analysis commands you want to keep –Lastly, tell STATA to close the log and go back to its usual “more” mode cd “C:\data\biostat212\” version 11 set more off clear set memory 10m capture log close log using “name of your log.log”, replace /* here are my comments */ use “name of your dataset”, clear summarize this browse that tabulate this log close set more on

4 New issues “” vs. “” Other?

5 Today... What we did in Lab 1, and why it was unrealistic What does “data cleaning” mean? Importing data into Stata How to generate a variable How to manipulate the data in your new variable How to label variables and otherwise document your work Examples

6 Last time… What was unrealistic?

7 Last time… What was unrealistic? –The dataset came as a Stata.dta file

8 Last time… What was unrealistic? –The dataset came as a Stata.dta file –The variables were ready to analyze

9 Last time… What was unrealistic? –The dataset came as a Stata.dta file –The variables were ready to analyze –Most variables were labeled

10 Last time… i.e. – The data was “clean”

11 How your data will arrive On paper forms In a text file (comma or tab delimited) In Excel In Access In another data format (SAS, etc)

12 Importing into Stata Options: –Copy and Paste –insheet, infile, fdause, other flexible Stata commands –A convenience program like “Stat/Transfer”

13 Importing into Stata Make sure it worked –Look at the data

14 Importing into Stata Demo – neonatal opiate withdrawal data –Import with cut and paste from Excel –Import with insheet (save as.csv file first)

15 Exploring your data Figure out what all those variables mean Options –Browse, describe, summarize, list in STATA –Refer to a data dictionary –Refer to a data collection form –Guess, or ask the person who gave it to you

16 Exploring your data Demo: Neonatal opiate withdrawal data

17 Exploring your data Demo: Neonatal opiate withdrawal data Problems arise… –Sex is m/f, not 1/0 –Gestational age has nonsense values (0, 60) –Breastfeeding has a bunch of weird text values –Drug variables coded y or blank –Many variable names are obscure

18 Cleaning your data You must “clean” your data so it is ready to analyze.

19 Cleaning your data What does the variable measure? –rename and/or label var so it’s clear Find nonsense values and outliers –recode as missing or track down real value? Deal with missing values –Too many values missing in some subjects? Coding consistent? –drop variable or observation? Categorize as needed –generate a new numeric variable –recode (dichotomous variables coded as 1/0, watch missing values) –label define and then label values –Check tab oldvar newvar, missing bysort catvar: sum contvar

20 Cleaning your data The importance of documentation –Retracing your steps Document every step using a “do” file

21 Data cleaning Basic skill 1 – Making a new variable Creating new variables generate newvar = expression

22 Data cleaning Basic skill 1 – Making a new variable Creating new variables generate newvar = expression An “expression” can be: –A number (constant) - generate allzeros = 0 –A variable - generate ageclone = age –A function - generate agesqrt = sqrt(age)

23 Data cleaning Basic skill 2 – Manipulating values of a variable Changing the values of a variable replace var = exp [if boolean_expression] A boolean expression evaluates to true or false for each observation

24 Data cleaning Basic skill 2 – Manipulating values of a variable Examples generate bmi = weight/(height^2) generate male = 0 replace male = 1 if sex==“male” generate ageover50 = 0 replace ageover 50 = 1 if age>50 generate complexvar = age replace complexvar = (ln(age)*3) if (age>30 | male==1) & (othervar1>=othervar2)

25 Data cleaning Basic skill 2 – Manipulating values of a variable Logical operators for boolean expressions: EnglishStata Equal to == Not equal to! =, ~= Greater than> Greater than/equal to> = Less than < Less than/equal to <= And & Or |

26 Data cleaning Basic skill 2 – Manipulating values of a variable Mathematical operators: EnglishStata Add + Subtract - Multiply * Divide/ To the power of ^ Natural log of ln(expression) Base 10 log of log10(expression) Etcetera…

27 Data cleaning Basic skill 2 – Manipulating values of a variable Another way to manipulate data recode var oldvalue1=newvalue1 [oldvalue2=newvalue2] [if boolean_expression] More complicated, but more flexible command than replace

28 Data cleaning Basic skill 2 – Manipulating values of a variable Examples generate male = 0 recode male 0=1 if sex==“male” generate female = male recode female 1=0 0=1

29 Data cleaning Basic skill 2 – Manipulating values of a variable Examples generate raceethnic = race recode raceethnic 1=6 if ethnic==“hispanic” (replace raceethnic = 6 if ethnic==“hispanic” & race==1) generate tertilescac = cac recode tertilescac min/54=1 55/82=2 83/max=3

30 Data cleaning Basic skill 3 – Getting rid of variables/observations Getting rid of a variable drop var Getting rid of observations drop if boolean_expression

31 Data cleaning Basic skill 4 – Labeling things You can label: –A dataset label data “label” –A variable label var varname “label” –Values of a variable (2-step process) label define labelname value1 “label1” [value2 “value2”…] label values varname labelname label define caccatlabel 0 “0” 1 “1-100” 2 “ ” 3 “>400” label values caccat caccatlabel

32 Data cleaning Basic skill 5 –Dealing with missing values Missing values are important, easy to forget –. for numbers –“” for text –tab var1 var2, missing –Watch the total “n” for tab, summarize commands, regression analyses, etc.

33 Data cleaning Demo: Neonatal opiate withdrawal data

34 Cleaning your data What does the variable measure? –rename or label var so it’s clear Find nonsense values and outliers –recode as missing or track down real value? Deal with missing values –Too many? Coding consistent? –drop variable or observation? Categorize as needed –generate a new numeric variable –recode (dichotomous variables coded as 1/0, watch missing values) –label define and then label values –Check tab oldvar newvar, missing bysort catvar: sum contvar

35 Data cleaning At the end of the day you have: –1 raw data file, original format –1 raw data file, Stata format –1 do file that cleans it up –1 log file that documents the cleaning –1 clean data file, Stata format

36 Summary Data cleaning –ALWAYS necessary to some extent –ALWAYS use a do file –NEVER overwrite original data –Check your work –Watch out for missing values –Label as much as you can

37 Lab this week It’s long It’s hard It’s important lab to your section leader’s Due at the beginning of lecture next week

38 Preview of next week… Using Excel –What is it good for? –Formulas –Designing a good spreadsheet –Formatting


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