Reading data (Creating a dataframe)

• mydata=read.csv(file=file.choose())

• mydata #shows the entire data set
• head(mydata) #shows the first 6 rows
• tail(mydata) #shows the last 6 rows
• str(mydata) #shows the variable names and types
• names(mydata) #shows the variable names
• ls() #shows a list of objects that are available

• 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
• cor(x,y) #computes the correlation coefficient

Graphical Displays

• 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
• abline(lm(y~x)) #adds regression line to plot
• lines(lowess(x,y)) # adds lowess line (x,y) to plot

Probability Distributions

• dbinom(x, n, p) #binomial density, probability
• pbinom(x, n, p) #cumulative binomial probability
• pnorm(x, mean, sd) #cumulative probability for a Normal distribution
• psignrank(x, n) #cumulative probability for Wilcoxon signed rank statistic with sample size = n
• pwilcox(x, n, m) #cumulative probability for Wilcoxon rank sum statistic with sample sizes n and m

Nonparametric Tests

• binom.test(x, n, p) #Binomial test for proportions
• binom.confint(x, n) #NEED binom package, confidence intervals for a proportion
• SIGN.test(x, md=0) #NEED BSDA package, Sign test
• wilcox.test(x) #Wilcoxon signed rank test
• owa(pre, post) #NEED NSM3 package, Ordered Walsh Averages
• HollBivSym(x,y) #Hollander's test for bivariate symmetry
• wilcox.test(x, y) #Wilcoxon rank sum test
• wilcox_test(x, y) # NEED coin package - Exact conditional distribution
• waerden.test(response, treatement) #NEED agricolae package, van der Waerden's Test
• pFligPoli(x, y, method="Monte Carlo") #NEED NSM3 package; approximate p-value for Fligner-Policello test)
• pFligPoli(x, y, method="Asymptotic") #NEED NSM3 package; LSA for Fligner-Policello test)
• ansari.test(x, y) #Ansari-Bradley test
• cAnsBrad(alpha, m, n) #NEED NSM3 package; provides critical value
• pAnsBrad(x, y) #NEED NSM3 package; provides p-value for Ansari-Bradley test
• MillerJack(x, y) #NEED NSM3 package; provides Miller Jackknife Q
• pLepage(x, y) #NEED NSM3 package; provides Lepage's test
• fligner.test(x, g) #Fligner-Killeen test of equal variances; g is a grouping variable
• ks.test(x,y) #Kolmogorov-Smirnov test
• kruskal.test(x, g) #Kruskal-Wallis rank sum test; g is a grouping variable
• cKW(alpha, n) #NEED NSM3 package; provides critical value for Kruskal-Wallis statistic
• pKW(x, g) #NEED NSM3 package; provides Kruskal-Wallis rank sum test; g is a grouping variable
• kruskalmc(response, group) #Need pgirmess package; g is a grouping variable
• cSDCFlig(alpha, n) #NEED NSM3 package; provides critical value for Dwass, Steel, Critchlow, Fligner statistic
• JT.test(data, g) #Need SAGx package; Jonckheere-Terpstra test; data is a matrix; g is a grouping variable
• pJCK(x, g) #NEED NSM3 package; provides Jonckheere-Terpstra test; g is a grouping variable
• cHaySton(alpha, n) #NEED NSM3 package; provides critical value for ordered alternatives multiple comparisons
• pHaySton(x, g) #NEED NSM3 package; provides ordered alternatives multiple comparisons; g is a grouping variable
• cNDWol(alpha, n)#NEED NSM3 package; provides critical value for treatment versus control multiple comparisons
• cMaxCorrNor(alpha, n)#NEED NSM3 package; provides LSA critical value for treatment versus control multiple comparisons
• cUmbrPK(alpha, n, p) #NEED NSM3 package; provides critical value for Mack-Wolfe Umbrella peak known statistic
• pUmbrPK(x, peak, g) #NEED NSM3 package; provides Mack-Wolfe Umbrella peak known test; g is a grouping variable
• cUmbrPU(alpha, n, p) #NEED NSM3 package; provides critical value for Mack-Wolfe Umbrella peak unknown statistic
• pUmbrPU(x, g) #NEED NSM3 package; provides Mack-Wolfe Umbrella peak uknown test; g is a grouping variable
• friedman.test(y~A|B) # y are the data values, A is a grouping factor, and B is a blocking factor
• cPage(alpha, k, n) #NEED NSM3 package; provides critical value for Page's test, k is number of treatments, n is number of blocks
• pPage(x, b, trt) #NEED NSM3 package; provides Page's test for ordered alternatives; b identifies blocks, trt identifies treatments
• cWNMT(alpha, k, n) #NEED NSM3 pacakge; provides critical value for two-sided follow-up muliple comparisons for a RCBD; k is number of treatments, n is number of blocks
• pWNMT(x, b, trt) #NEED NSM3 pacakge; provides two-sided follow-up muliple comparisons for a RCBD; b identifies blocks, trt identifies treatments
• cNWWM(alpha, k, n) #NEED NSM3 pacakge; provides critical value for one-sided treatment versus control muliple comparisons for a RCBD; k is number of treatments, n is number of blocks
• pNWWM(x, b, trt) #NEED NSM3 pacakge; provides one-sided treatment versus control muliple comparisons for a RCBD; b identifies blocks, trt identifies treatments
• cor.test(x,y) #correlation test plus CI for several measures of association (r, rho, tau)
• kendall.ci(x, y) #NEED NSM3 package; provides a confidence interval for Kendall's tau