Cheat Sheet for R and RStudio

Cheat Sheet for R and RStudio 1 Downloading and Installation • First download R for your OS: R • Next download RStudio for your OS: RStudio 2 Uploading Data into R-Studio R-Studio Makes uploading CSV files into R extremely simple. Just follow these instructions and you’ll be using R in no time. 1. Download your .csv data to a folder that you can easily find. 2. Open R-Studio. 3. In the interpreter (lower left-hand box of RStudio), type library(foreign) and hit Enter. This will install the package that reads your .csv files. 4. In the box on the upper-right hand corner of RStudio, click on the tab that says “Workspace”. 5. Then click on “Import Dataset > From Text File...”. Find your .csv dataset and open it. 6. In the interpreter (lower left-hand box), type in attach(name-of-dataset) and hit Enter. You can find the name of the dataset listed under the “Workspace” tab in the upper right-hand corner of RStudio. 7. To find the variable names in your dataset type names(name-of-dataset) and hit Enter. 1 3 Doing Statistics in RStudio After you have opened your data, doing statistics is really easy. Below is a list of commands that you will need to do any kind of statistics in RStudio. 3.1 Summary Statistics • summary(X) - Summary statistics such as mean,median,mode and quartiles for a variable. > summary(X) Min. 1st Qu. Median Mean 3rd Qu. -3.0360 -0.8855 -0.2475 -0.2382 0.3345 Max. 3.4460 • mean(X,na.rm=TRUE) - Produces the mean of the variable. Removes missing observations. > mean(X,na.rm=TRUE) [1] -0.2382041 • sd(X,na.rm=TRUE) - Produces the standard deviation of the variable. Removes missing observations. > sd(X,na.rm=TRUE) [1] 0.9604155 3.2 Regression • lm(Y ∼ X) - Runs a regression of Y on X where Y is your dependent variable and X is your independent variable. You need to save your model in R’s memory first and can get the regression coefficients and other info you need by using the summary() command. For example, for simple regression: > model1 = lm(Y~X) > summary(model1) Call: lm(formula = Y ~ X) Residuals: Min 1Q Median 3Q Max 2 -2.6068 -0.8068 0.0700 0.7027 3.3292 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.18866 0.11548 -1.634 X 0.07123 0.11726 0.607 0.106 0.545 Residual standard error: 1.121 on 98 degrees of freedom Multiple R-squared: 0.003752,Adjusted R-squared: -0.006414 F-statistic: 0.369 on 1 and 98 DF, p-value: 0.5449 for multiple regression... > model1.1 = lm(Y~X + Z) > summary(model1.1) Call: lm(formula = Y ~ X + Z) Residuals: Min 1Q Median -2.6534 -0.7729 0.0340 3Q Max 0.6860 3.2037 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.19525 0.11582 -1.686 0.095 . X 0.06916 0.11739 0.589 0.557 Z -0.10228 0.11333 -0.902 0.369 --Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.122 on 97 degrees of freedom Multiple R-squared: 0.01205,Adjusted R-squared: -0.008323 F-statistic: 0.5914 on 2 and 97 DF, p-value: 0.5555 • plot(X,Y) - Will produce a scatterplot of the variables X and Y with X on the x-axis and Y on the y-axis. • abline(regression model) - Will draw a regression line of the regression model that you saved through a scatterplot. For example: 3 > model2 = lm(Y~X) > plot(X,Y) > abline(model2) 3.3 Hypothesis Testing • t.test(X,Y) - Performs a t-test of means between two variables X and Y for the hypothesis H0 : µX = µY . Gives t-statistic, p-value and 95% confidence interval. Example: > t.test(X,Y) Welch Two Sample t-test data: X and Y t = -0.2212, df = 193.652, p-value = 0.8252 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.3231116 0.2579525 4 sample estimates: mean of x mean of y -0.2382041 -0.2056246 3.4 Graphics and Plots • hist(X) - Will produce a histogram of the variable X. > hist(X) • plot(X,Y) - Will produce a scatterplot of the variables X and Y with X on the x-axis and Y on the y-axis. > plot(X,Y) 5 6