7/18/16. Review. Review of Homework. Lecture 3: Programming Statistics in R. Questions from last lecture? Problems with Stata? Problems with Excel?

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1 Lecture 3: Programming Statistics in R Christopher S. Hollenbeak, PhD Jane R. Schubart, PhD The Outcomes Research Toolbox Review Questions from last lecture? Problems with Stata? Problems with Excel? 2 Review of Homework Create age and HLA mismatch categories Perform t tests for age, cold ischemia time, surgery duration, and HLA mismatches Perform chi-square tests for sex and race Use summarize command with if statement to get summaries Move summaries to Excel and format Make graph 3 1

2 cd /Volumes/Hollenbeak/Teaching/Residents/OutcomesResearch/ clear insheet using "ltd_data.csv" generate age039=0 replace age039=1 if age < 40 generate age4049=0 replace age4049=1 if age >= 40 & age < 50 generate age5059=0 replace age5059=1 if age >= 50 & age < 60 generate age60=0 replace age60=1 if age >= 60 generate ab0=0 replace ab0=1 if abmm==0 generate ab1=0 replace ab1=1 if abmm==1 generate ab2=0 replace ab2=1 if abmm==2 generate ab3=0 replace ab3=1 if abmm==3 generate ab4=0 replace ab4=1 if abmm==4 summarize age age039 age4049 age5059 age60 female male black nonblack coldtime dur_surg abmm ab0 ab1 ab2 ab3 ab4 if ssi==0, sep(0) summarize age age039 age4049 age5059 age60 female male black nonblack coldtime dur_surg abmm ab0 ab1 ab2 ab3 ab4 if ssi==1, sep(0) ttest age, by(ssi) ttest coldtime, by(ssi) ttest dur_surg, by(ssi) ttest abmm, by(ssi) tab female ssi, chi2 tab black ssi, chi2 twoway scatter los age, title("relationship between Age and LOS") xtitle("age (Years)") ytitle("hospital Stay (Days) ) 4 Stata Tip To find a variable quickly, use the command lookfor This will search all variable names and labels for your text For example: lookfor bmi will identify two variables in the data set that contain the text bmi bmi (which is patient s bmi at listing) bmi_d, which is donor BMI at hepatectomy 5 Overview R is an statistical computing environment Based on the S language (like S-Plus) Free and open source Extensive add-on packages available Also free and open source 2

3 Overview R has become the most widely used statistical software More flexible than Stata More extensible than Stata More up-to-date than Stata R language is object-oriented Usually requires fewer steps Can be unintuitive Did I mention it is free?? Common Tasks in R Import data Create variables Subset data Univariate statistics Chi-square tests T-tests ANOVA Graphics Histograms Scatterplots Boxplots R Interface We use RStudio to run R Process is (almost) the same as Stata Write a script file of commands Please make sure to save your script files Run blocks of commands and retrieve output Move to Word for cleaning Move output to Excel for formatting 3

4 R Interface Script Files Variables Console / Text output Browser Differences Between R and Stata Stata holds one data set only, and all commands apply to that data set R can hold multiple data sets Commands and variables must specific which data set Stata code is commands, separated by spaces, options after a single comma R code is functions, with parentheses, and options separated by multiple commas R is object oriented, so process is 1) create an object, 2) summarize the object Setting the Working Directory It is good practice to point R to a directory where it will thereafter retrieve files and write files The function is setwd() For example, if I have my.csv file with liver transplant data sitting in a projects folder Mac/Unix: setwd( ~/projects/ltd/ ) Windows: setwd( c:/users/chollenbeak/ projects/ltd/ ) Note that directories are separated by forward slashes / not backslashes \ even in Windows 4

5 Running a Command To run a command in R: Highlight the line of code in your script file Mac: Cmd + Enter Windows: Ctrl + Enter Import Data Create a comma-separate-value text file Can be done in Excel Save As CSV Variable names in first row R stores data in objects called data frames You can import, create, and subset as many data frames as you want during an R session R Command: read.csv Import the data using the command read.csv() e.g. data1 <- read.csv( c:/projects/ltd_data.csv ) Or, if you have your working directory set to c:/projects/ already, you can simply use: data1 <- read.csv( ltd_data.csv ) This creates a data object called data1 When you want to use these data, refer to this object The <- is called the assignment operator and assigns a value to the object 5

6 Viewing Data To view your data, click on the name of the data frame in the Environment window The data set will then appear in the script window Referring to Variables All variables are associated with a data frame To call a variable, use object$variable For example, to compute the mean cost contained in data frame data1, use: mean(data1$cost1) Create New Variables Variables should be created inside a data frame To create a new variable, use the assignment operator Example: Assume you have a male dummy variable but no female dummy variable Create a female dummy variable using: data1$female <- 1 data$male 6

7 Create New Variables Assume you have a sex variable coded as M and F You need to create male and female dummy variables Use the ifelse() function data$newvar <- ifelse(condition, value_if_true, value_if_false) data1$male <- ifelse(data1$gender== M,1,0) data1$female <- 1- data1$male Create New Variables Assume you have a race variable coded as White, Black, Hispanic, Asian, and Other You need to create race/ethnicity dummy variables data1$white <- ifelse(data1$race== White,1,0) data1$black <- ifelse(data1$race== Black,1,0) data1$hispanic <- ifelse(data1$race== Hispanic,1,0) data1$asian <- ifelse(data1$race== Asian,1,0) data1$other <- ifelse(data1$race== Other,1,0) Create New Variables Assume you have a dummy variables for race and need a categorical version You can embed the ifelse() functions data1$race <- ifelse(data1$white==1, white, ifelse(data1$black==1, black, ifelse(data1$hispanic==1, hispanic, ifelse(data1$asian==1, asian, other )))) 7

8 Subsetting Data To pull out a subset of observations use the subset command Create a new data frame based on the original data_new <- subset(dataset_old, condition) e.g. data2 <- subset(data1, data1$male==1) Like Stata, R distinguishes between the logical equals (==) and the assignment equals (=) Summary Statistics R has functions for all the summary statistics you care to present N: length() Mean: mean() Standard Deviation: sd() Sum: sum() Minimum: min() Maximum: max() However, it lacks a single, easy function to create a table of descriptive statistics No equivalent of summarize in Stata) Summary Statistics The solution is to write your own You can create your own functions in R using the function() command Here is mine: summarize <- function(var){ n <- length(var) m <- mean(var) s <- sd(var) v <- sum(var) return(cbind(n, m, s, v)) } 8

9 Summary Statistics To create a table of summary statistics 1. Run the summarize command in R 2. Copy the output to Word 3. Replace spaces with tabs The special character for tab is ^t May need to replace two adjacent tabs (^t^t) with a single tab The special character for paragraph is ^p 4. Copy to Excel 5. Format in Excel Univariate Statistics Categorical variables Contingency tables Chi-square tests Continuous variables T-tests ANOVA Contingency Tables To obtain a tabulation of data, use the table() command For example, to get a tabulation of HLA A and B mismatches table(data1$abmm) Note that the output is not formatted neatly Do your formatting in Excel 9

10 Contingency Tables To get the percents, you can find the total observations using the length command and the divide the table table(data1$abmm)/length(data1$abmm) Contingency Tables To get a cross-tabulation, include both variables e.g. To study whether mortality differs between men and women: > table(data1$male, data1$died) Chi-square Test To test whether the difference in the distribution is significant, use the chisq.test() command Note that this command is applied to the table, not the variables > chisq.test(table(data1$male, data1$died)) Pearson's Chi-squared test with Yates' continuity correction data: table(data1$male, data1$died) X-squared = , df = 1, p-value =

11 T-Test To compare the means between two groups, use the t.test() command Syntax is t.test(vdepvar ~ indvar) For example, to test whether the LOS differs between men and women: t.test(data1$los ~ data1$male) Welch Two Sample t-test data: data1$los by data1$male t = , df = , p-value = alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: sample estimates: mean in group 0 mean in group ANOVA To compare a continuous variable across more than two groups use the command aov() For example, to test whether LOS varies over HLA A and B mismatches: anova1 <- aov(data1$los ~ data1$abmm) summary(anova1) 11

12 ANOVA aov() creates an ANOVA object called anova1 summary() gives us the results > anova1 <- aov(data1$los ~ data1$abmm) > summary(anova1) Df Sum Sq Mean Sq F value Pr(>F) data1$abmm *** Residuals Signif. codes: 0 *** ** 0.01 * Missing Data R stores missing values as NA R cannot run analyses on missing data It will not produce output and will not tell you why You must identify variables with missing data and drop those observations Use the is.na() function to identify an observation with missing values Missing Data Example: Assume you can tell that the male dummy variable has some missing values. You need to drop those observations Strategy 1: Create a subset with no missing values data2 <- subset(data1, is.na(data1$male)==false) t.test(data2$los ~ data2$male) Strategy 2: Use original data set but limit the analysis to non-missing observations t.test(data1$los[is.na(data1$male)==false] ~ data1$male[is.na(data1$male)==false]) 12

13 Graphics in R R has fantastic graphing capabilities Almost any aspect of the graph can be controlled by the user The opposite of the Stata approach We will discuss three types of graphs Histograms Scatterplots Boxplots Histogram To summarize the distribution of a continuous variable, plot a histogram The R command is hist() To summarize the age of transplant recipients: hist(data1$age) Histogram of data1$age Frequency data1$age 13

14 Histograms Can improve this by changing options: main= Main Title ylab= Y axis label xlab= X axis label col= Gives the boxes color box() Adds a box around the inner plot R graphics are extremely flexible and customizable! hist(data1$cci_index, ylab="frequency", xlab="comorbidities", main="distribution of Charlson Comorbidity Index", col="red, breaks=25) box() Distribution of Age Frequency age 14

15 Scatterplot To create a scatterplot use the plot() function For example, to look at the correlation between one-year cost and five-year cost plot(data1$age, data1$los) data1$los data1$age Scatterplot Again we can customize with additional options pch=x Change the plot symbol xlim=c(lower,upper) Set x axis limits ylim=c(lower,upper) Set y axis limits cex=x Symbol scaling factor Default is 1, Half size is.5, Double size is 2, etc. 15

16 plot(data1$age, data1$los, ylab= Total Length of Stay", xlab= Age at Transplant", main="correlation Between Age and LOS", pch=16, cex=1.25, xlim=c(0,80), ylim=c(0,400)) Correlation Between Age and LOS Total Length of Stay Age at Transplant Boxplot To summarize distributions across strata Use boxplot(variable ~ strata) command For example, to plot the distribution of costs across stage: boxplot(data1$age ~ data1$abmm) 16

17 Boxplot We can customize outline=false Turn off outliers notch=true Adds notches to the boxes col= color Fills boxes with color boxplot(data1$los ~ data1$abmm, outline=false, notch=true, ylim=c(0,60), xlab="hla-a and -B Mismatches", ylab="inpatient Length of Stay", col= turquoise") Inpatient Length of Stay HLA A and B Mismatches 17

18 Annotating Graphs R has a few functions that make annotating graphs particularly easy 1. Add a vertical or horizontal line abline(v=x) puts a vertical line at x abline(h=y) puts a horizontal line at y We usually add these lines to the zeros or a null value 2. Add text to a graph text(x, y, text ) puts text at (x,y) We frequently add p-values to graphs Exporting Graphics Graphics can be exported directly from Rstudio PDF is the preferred format Export Save as PDF Other formats are available (eps, tiff, etc.) Export Save as image Homework Revisit the homework from Lecture 2 Do it all in R 18