Introduction to Stata 8 - (GRIPS

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10. Modifying data Don't misinterpret the title of this section: Never modify your original data, but add modifications by generating new variables from the original data. Not documenting modifications may lead to serious trouble. Therefore modifications: • should always be made with a do-file with a name reflecting what it does: gen.alfa2.do generates alfa2.dta. • The first command in the do-file reads data (eg. use, infix). • The last command saves the modified data set with a new name (save). • The do-file should be 'clean', ie. not include commands irrelevant to the modifications. See examples of modifying do-files in section 16 and in Take good care of your data. 10.1. Calculations Operators in expressions [GSW] 12; [U] 16.2 Arithmetic Relational Logical ^ power > greater than ! not * multiplication < less than ~ not / division >= > or equal | or + addition
generate; replace [R] generate Generate a new variable by: generate bmi=weight/(height^2) If the target variable (bmi) already exists in the data set, use replace: replace bmi=weight/(height^2) Do a conditional calculation (males only): generate mbmi=1.1*bmi if sex==1 Besides the standard operators there are a number of functions: [U] 16.3 generate y=abs(x) absolute value |x| gen y=exp(x) exponential, e x gen y=ln(x) natural logarithm gen y=log10(x) base 10 logarithm gen y=sqrt(x) square root gen y=int(x) integer part of x. int(5.8) = 5 gen y=round(x) nearest integer. round(5.8) = 6 gen y=round(x, 0.25) round(5.8, 0.25) = 5.75 gen y=mod(x1,x2) modulus; the remainder after dividing x1 by x2 gen y=max(x1,...xn) maximum value of arguments gen y=min(x1,...xn) minimum value of arguments gen y=sum(x) cumulative sum across observations, from first to current obs. gen y=_n _n is the observation number gen y=_N _N is the number of observations in the data set egen [R] egen egen (extensions to generate) gives some more useful functions. It may be confusing that functions working differently with generate and egen have the same names, so take care. Here are some examples: Generating the same value for all observations egen meanage=mean(age) Mean age across observations by sex: egen meange=mean(age) Mean age across observation, for each sex egen sumage=sum(age) Sum of age across all observations (unlike generate's sum) egen maxage=max(age) Maximum value of age across observations egen minage=min(age) Minimum value af age across observations Generating individual values for each observation (each row) egen minq=rmin(q1-q17) Min. value of q1-q17 for this observation egen maxq=rmax(q1-q17) Max. value of q1-q17 for this observation egen meanq=rmean(q1-q17) Mean value of q1-q17 for this observation egen sumq=rsum(q1-q17) Sum of q1-q17 for this observation egen valq=robs(q1-q17) Number of non-missing q1-q17 for this observation 21
Page 1 and 2: Introduction to Stata 8 Svend Juul
Page 3 and 4: Preface Stata is a software package
Page 5 and 6: 1. Installing, customizing and upda
Page 7 and 8: 2. Windows in Stata [GSW] 2 The org
Page 9 and 10: 3. Suggested mode of operation The
Page 11 and 12: 4. Getting help [GSW] 4, 19, 20; [U
Page 13 and 14: 6. Variables A Stata data set is re
Page 15 and 16: 7. Command syntax [U] 14.1 Stata is
Page 17 and 18: y and bysort prefix [U] 14.5 Makes
Page 19 and 20: Reading ASCII data [U] 24 Reading t
Page 21: See label definitions See the value
Page 25 and 26: 10.2. Selections Selecting observat
Page 27 and 28: 10.6. Combining files [U] 25 Append
Page 29 and 30: 11. Description and analysis This s
Page 31 and 32: tabi [R] tabulate tabi is an 'immed
Page 33 and 34: ttest [R] ttest T-test for comparis
Page 35 and 36: 12.2. Logistic regression logistic
Page 37 and 38: . summarize Variable | Obs Mean Std
Page 39 and 40: poisson [R] poisson The stsplit.can
Page 41 and 42: 14.2. The anatomy of graphs Figure
Page 43 and 44: You may define maximum and minimum
Page 45 and 46: 14.7. Examples On the following pag
Page 47 and 48: twoway scatter Mileage (mpg) 40 30
Page 49 and 50: twoway connected; twoway rcap Mean
Page 51 and 52: twoway dropline Patient number 20 1
Page 53 and 54: graph matrix 40 30 20 10 250 200 15
Page 55 and 56: Submitting graphs to journals etc.
Page 57 and 58: 15.2. String variables [U] 15.4; [U
Page 59 and 60: 15.3. Dates. Danish CPR numbers Dat
Page 61 and 62: 15.4. Random samples, simulations R
Page 63 and 64: 15.6. Sample size and study power s
Page 65 and 66: 15.8. Exchange of data with other p
Page 67 and 68: 16. Do-file examples Here follow sh
Page 69 and 70: Appendix 1 Purchasing Stata and man
Page 71 and 72: Variable names can have up to 8 cha
Page 73 and 74:
Index A ado-files .................
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Introduction to Stata 8 - (GRIPS

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