Cheat Sheet

Statistical Analysis with Excel For Dummies

From Statistical Analysis with R For Dummies

By Joseph Schmuller

R provides a wide array of functions to help you with your work — from simple statistics to complex analyses. This cheat sheet lists a selection for you.

Base R Statistical Functions

Here’s a selection of statistical functions that come with the standard R installation. You’ll find many others in R packages.

Central Tendency and Variability
Function What it Calculates
mean(x) Mean of the numbers in vector x.
median(x) Median of the numbers in vector x
var(x) Estimated variance of the population from which the numbers in vector x are sampled
sd(x) Estimated standard deviation of the population from which the numbers in vector x are sampled
scale(x) Standard scores (z-scores) for the numbers in vector x
Relative Standing
Function What it Calculates
sort(x) The numbers in vector x in increasing order
sort(x)[n] The nth smallest number in vector x
rank(x) Ranks of the numbers (in increasing order) in vector x
rank(-x) Ranks of the numbers (in decreasing order) in vector x
rank(x, ties.method= “average”) Ranks of the numbers (in increasing order) in vector x, with tied numbers given the average of the ranks that the ties would have attained
rank(x, ties.method= “min”)
Ranks of the numbers (in increasing order) in vector x, with tied numbers given the minimum of the ranks that the ties would have attained
rank(x, ties.method = “max”) Ranks of the numbers (in increasing order) in vector x, with tied numbers given the maximum of the ranks that the ties would have attained
quantile(x)
The 0th, 25th, 50th, 75th, and 100th percentiles (i.e, the quartiles) of the numbers in vector x. (That’s not a misprint: quantile(x) returns the quartiles of x.)
t-tests
Function What it Calculates
t.test(x,mu=n, alternative = “two.sided”) Two-tailed t-test that the mean of the numbers in vector x is different from n.
t.test(x,mu=n, alternative = “greater”) One-tailed t-test that the mean of the numbers in vector x is greater than n.
t.test(x,mu=n, alternative = “less”) One-tailed t-test that the mean of the numbers in vector x is less than n.
t.test(x,y,mu=0, var.equal = TRUE, alternative = “two.sided”) Two-tailed t-test that the mean of the numbers in vector x is different from the mean of the numbers in vector y. The variances in the two vectors are assumed to be equal.
t.test(x,y,mu=0, alternative = “two.sided”, paired = TRUE) Two-tailed t-test that the mean of the numbers in vector x is different from the mean of the numbers in vector y. The vectors represent matched samples.
Analysis of Variance (ANOVA)
Function What it Calculates
aov(y~x, data = d) Single-factor ANOVA, with the numbers in vector y as the dependent variable and the elements of vector x as the levels of the independent variable. The data are in data frame d.
aov(y~x + Error(w/x), data = d) Repeated Measures ANOVA, with the numbers in vector y as the dependent variable and the elements in vector x as the levels of an independent variable. Error(w/x) indicates that each element in vector w experiences all the levels of x (i.e., x is a repeated measure). The data are in data frame d.
aov(y~x*z, data = d) Two-factor ANOVA, with the numbers in vector y as the dependent variable and the elements of vectors x and z as the levels of the two independent variables. The data are in data frame d.
aov(y~x*z + Error(w/z), data = d) Mixed ANOVA, with the numbers in vector z as the dependent variable and the elements of vectors x and y as the levels of the two independent variables. Error(w/z) indicates that each element in vector w experiences all the levels of z (i.e., z is a repeated measure). The data are in data frame d.
Correlation and Regression
Function What it Calculates
cor(x,y) Correlation coefficient between the numbers in vector x and the numbers in vector y
cor.test(x,y) Correlation coefficient between the numbers in vector x and the numbers in vector y, along with a t-test of the significance of the correlation coefficient.
lm(y~x, data = d) Linear regression analysis with the numbers in vector y as the dependent variable and the numbers in vector x as the independent variable. Data are in data frame d.
coefficients(a) Slope and intercept of linear regression model a.
confint(a) Confidence intervals of the slope and intercept of linear regression model a
lm(y~x+z, data = d) Multiple regression analysis with the numbers in vector y as the dependent variable and the numbers in vectors x and z as the independent variables. Data are in data frame d.

When you carry out an ANOVA or a regression analysis, store the analysis in a list. For example,
a <- lm(y~x, data = d)

Then, to see the tabled results, use the summary() function:

summary(a)