R Cheat Sheet: Functions and Examples for Data Analysis with R, Summaries of Statistics

Download R Cheat Sheet: Functions and Examples for Data Analysis with R and more Summaries Statistics in PDF only on Docsity! R-cheatsheet 1 Help ? ?? example() apropos() help.search() Packages In order to use functionalities from a certain package, we need to first install and then load the package: # install package (do once): install.packages("") # load package (do once in every script): require(); library() Formula syntax Most of the functions that we need for this course uses a formula syntax: goal(y ~ x | z, data = mydata, ...) where goal may be a function for plotting, calculat- ing numerical summaries or making inference. For plots: • y is y-axis variable • x is x-axis variable • z a conditioning variable (separate panels). For other things: ‘y ~ x | z’ can usually be read ‘y is modeled by (or depends on) x differently for each z’. Numerical summaries These functions from the mosaic-package uses a for- mula syntax. favstats() # min/max, median, etc. tally() # tabulate data mean() median() sd() # standard deviation cor() # correlation General graphics gf_boxplot() gf_point() # scatter plot gf_histogram() gf_bar() # bar graph mplot(HELPrct)# different plots splom() # matrix of scatter plots Distributions plotDist() # plot theoretical distribution pdist() # find prob. from percentile qdist() # find percentile from prob. Hypothesis tests t.test() # t-test binom.test() # binomial (exact) test prop.test() # approximate test fisher.test() # Fisher's exact test cor.test() # correlation test chisq.test() # chi-square test Linear regression model <- lm() # fit linear model summary(model) # model fit summary coef(model) # estimated parameters confint(model) # CI for estimates anova(model) # F-tests, etc. drop1(model) rstudent(model) # Studentized residuals fitted(model) # fitted values plotModel(model)# plot regression lines model <- glm() # generalized linear model Data # Load data: read.file(); read.delim(); read.csv() # Data information nrow(); ncol() # data dimensions head() # extract first part of data tail() # extract last part of data colnames() # column names rownames() # row names summary() # Alter/create data: subset() # subset data by condition factor() # create grouping variable relevel() # change reference level cut() # cut numeric into intervals round() # rounding numbers c() # concatenate numerics seq() # create sequence with() aggregate() margin.table() # sum table entries