You can manipulate the axes by changing the limits e.g. This is a glossary of basic R commands/functions that I have used to introduce R to students. Data in R are often stored in data frames, because they can store multiple types of data. scale – how to expand the number of bins presented (default, scale = 1). Further details about the dataset can be read from the command: #Dataset description ?pbc We start with a direct application of the Surv() function and pass it to the survfit() function. Content Blog #FunDataFriday About Social. There are many additional parameters that “tweak” the legend! A short list of the most useful R commands. R commands for meta-analysis and sensitivity analyses have been described in the previous section. R offers multiple packages for performing data analysis. If your x-axis data are numeric your line plots will look “normal”. install.packages(“Name of the Desired Package”) 1.3 Loading the Data set. You’ll need to make a custom axis with the axis() command but first you need to re-draw the plot without any axes: The bottom (x-axis) is the one that needs some work. R Commands for – Analysis of Variance, Design, and Regression: Linear Modeling of Unbalanced Data Ronald Christensen Department of Mathematics and Statistics University of New Mexico c 2020. vii This is a work in progress! bg – if using open symbols you use bg to specify the fill (background) colour. In this tutorial, we will learn how to analyze and display data using R statistical language. R is one of the most widely used programming languages for data and statistical analysis. R objects may be data or other things, such as custom R commands or results. If you have even more exotic data, consult the CRAN guide to data import and export. R has more data analysis functionality built-in, Python relies on packages. angle – the starting point for the first slice of pie. Data munging, classification & regression, image processing and everything in between. Notice how the exact break points are specified in the c(x1, x2, x3) format. ), confint(model1, parm="x") #CI for the coefficient of x, exp(confint(model1, parm="x")) #CI for odds ratio, shortmodel=glm(cbind(y1,y2)~x, family=binomial) binomial inputs, dresid=residuals(model1, type="deviance") #deviance residuals, presid=residuals(model1, type="pearson") #Pearson residuals, plot(residuals(model1, type="deviance")) #plot of deviance residuals, newx=data.frame(X=20) #set (X=20) for an upcoming prediction, predict(mymodel, newx, type="response") #get predicted probability at X=20, t.test(y~x, var.equal=TRUE) #pooled t-test where x is a factor, x=as.factor(x) #coerce x to be a factor variable, tapply(y, x, mean) #get mean of y at each level of x, tapply(y, x, sd) #get stadard deviations of y at each level of x, tapply(y, x, length) #get sample sizes of y at each level of x, plotmeans(y~x) #means and 95% confidence intervals, oneway.test(y~x, var.equal=TRUE) #one-way test output, levene.test(y,x) #Levene's test for equal variances, blockmodel=aov(y~x+block) #Randomized block design model with "block" as a variable, tapply(lm(y~x1:x2,mean) #get the mean of y for each cell of x1 by x2, anova(lm(y~x1+x2)) #a way to get a two-way ANOVA table, interaction.plot(FactorA, FactorB, y) #get an interaction plot, pairwise.t.test(y,x,p.adj="none") #pairwise t tests, pairwise.t.test(y,x,p.adj="bonferroni") #pairwise t tests, TukeyHSD(AOVmodel) #get Tukey CIs and P-values, plot(TukeyHSD(AOVmodel)) #get 95% family-wise CIs, contrast=rbind(c(.5,.5,-1/3,-1/3,-1/3)) #set up a contrast, summary(glht(AOVmodel, linfct=mcp(x=contrast))) #test a contrast, confint(glht(AOVmodel, linfct=mcp(x=contrast))) #CI for a contrast, friedman.test(y,x,block) #Friedman test for block design, setwd("P:/Data/MATH/Hartlaub/DataAnalysis"), str(mydata) #shows the variable names and types, ls() #shows a list of objects that are available, attach(mydata) #attaches the dataframe to the R search path, which makes it easy to access variable names, mean(x) #computes the mean of the variable x, median(x) #computes the median of the variable x, sd(x) #computes the standard deviation of the variable x, IQR(x) #computer the IQR of the variable x, summary(x) #computes the 5-number summary and the mean of the variable x, t.test(x, y, paired=TRUE) #get a paired t test, cor(x,y) #computes the correlation coefficient, cor(mydata) #computes a correlation matrix, windows(record=TRUE) #records your work, including plots, hist(x) #creates a histogram for the variable x, boxplot(x) # creates a boxplot for the variable x, boxplot(y~x) # creates side-by-side boxplots, stem(x) #creates a stem plot for the variable x, plot(y~x) #creates a scatterplot of y versus x, plot(mydata) #provides a scatterplot matrix, abline(lm(y~x)) #adds regression line to plot, lines(lowess(x,y)) # adds lowess line (x,y) to plot, summary(regmodel) #get results from fitting the regression model, anova(regmodel) #get the ANOVA table fro the regression fit, plot(regmodel) #get four plots, including normal probability plot, of residuals, fits=regmodel$fitted #store the fitted values in variable named "fits", resids=regmodel$residuals #store the residual values in a varaible named "resids", sresids=rstandard(regmodel) #store the standardized residuals in a variable named "sresids", studresids=rstudent(regmodel) #store the studentized residuals in a variable named "studresids", beta1hat=regmodel$coeff[2] #assign the slope coefficient to the name "beta1hat", qt(.975,15) # find the 97.5% percentile for a t distribution with 15 df, confint(regmodel) #CIs for all parameters, newx=data.frame(X=41) #create a new data frame with one new x* value of 41, predict.lm(regmodel,newx,interval="confidence") #get a CI for the mean at the value x*, predict.lm(model,newx,interval="prediction") #get a prediction interval for an individual Y value at the value x*, hatvalues(regmodel) #get the leverage values (hi), allmods = regsubsets(y~x1+x2+x3+x4, nbest=2, data=mydata) #(leaps package must be loaded), identify best two models for 1, 2, 3 predictors, summary(allmods) # get summary of best subsets, summary(allmods)$adjr2 #adjusted R^2 for some models, plot(allmods, scale="adjr2") # plot that identifies models, plot(allmods, scale="Cp") # plot that identifies models, fullmodel=lm(y~., data=mydata) # regress y on everything in mydata, MSE=(summary(fullmodel)$sigma)^2 # store MSE for the full model, extractAIC(lm(y~x1+x2+x3), scale=MSE) #get Cp (equivalent to AIC), step(fullmodel, scale=MSE, direction="backward") #backward elimination, step(fullmodel, scale=MSE, direction="forward") #forward elimination, step(fullmodel, scale=MSE, direction="both") #stepwise regression, none(lm(y~1) #regress y on the constant only, step(none, scope=list(upper=fullmodel), scale=MSE) #use Cp in stepwise regression. The size of the plotted points is manipulated using the cex= n parameter, where n = the ‘magnification’ factor. R provides a wide array of functions to help you with statistical analysis with R—from simple statistics to complex analyses. The development version is always available at the pmc repository.. 8 Workflow: projects. breaks – how to split the break-points. beside – used in multi-category plots. The colMeans () command has produced a single sample of 4 values from the dataset VADeaths (these data are built-in to R). r owmeans () command gives the mean of values in the row while rowsums () command gives the sum of values in the row. The action of quitting from an R session uses the function call q(). The command is plot(). Today’s post highlights some common functions in R that I like to use to explore a data frame before I conduct any statistical analysis. Following steps will be performed to achieve our goal. But it should be useful as is. 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