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library(readr)
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library(ggplot2)
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library(dplyr)
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library(broom)
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library(Tmisc)
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library(caret)
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library(caret)
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library(splines)
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library(party)
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library(leaps)
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library(glmnet)
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(m=matrix, a=array, l=list; v=vector, d=dataframe)
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apply(x,index,fun) [input: m; output: a or l; applies function fun to rows/cols/cells (index) of x]
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lapply(x,fun) [input l; output l; apply fun to each element of list x]
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sapply(x,fun) [input l; output v; user friendly wrapper for lapply(); see also replicate()]
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tapply(x,index,fun) [input l output l; applies fun to subsets of x, as grouped based on index]
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plot(1:nc, wss, type="b", xlab="Number of Clusters", ylab="Within groups sum of squares")}
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wssplot <- function(data, nc=15, seed=1234){ wss <- (nrow(data)-1)*sum(apply(data,2,var)) for (i in 2:nc){ set.seed(seed) wss[i] <- sum(kmeans(data, centers=i)$withinss)}
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ggplot(mydata, aes(xvar, yvar)) + geom_point(aes(color=groupvar)) + geom_smooth(method="lm")
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qplot(x = cty, y = hwy, data = mpg, geom = “point") [Creates a complete plot with given data, geom, and mappings. Supplies many useful defaults]
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last_plot() [Returns the last plot]
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ggsave("plot.png", width = 5, height = 5) [Saves last plot as 5’ x 5’ file named "plot.png" in working directory. Matches file type to file extension]
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createDummyFeatures(obj=,target=,method=,cols=) [creates (0,1) flags for each non-numeric variable excluding target
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**normalizeFeatures(obj=,target=,method=,cols=,range=,on.constant=)
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center subtract mean
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scale divide by std. deviation
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standardize center and scale
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range linear scale to given range
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mergeSmallFactorLevels(task=,cols=,min.perc=) [combine infrequent factor levels into single merged level]
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read_csv("path/nhanes.csv")
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View(df)
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filter(df, ..,) [Filters data frame according to condition ]
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mean, median, range [na.rm=TRUE ]
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t.test(y~grp, data=df)
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wilcox.test(y~grp, data=df)
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anova(lmfit)
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TukeyHSD(aov(lmfit)) [ANOVA Post-hoc pairwise contrasts]
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xt <- xtabs(~x1+x2, data=df)
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addmargins(xt)
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prop.table(xt)
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chisq.test(xt)
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fisher.test(xt)
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mosaicplot(xt)
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factor(x, levels=c("wt", "mutant"))
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relevel(x, ref="wildtype")
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power.t.test(n, power, sd, delta)
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power.prop.test(n, power, p1, p2)
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tidy() augment() glance() [Model tidying functions in the broom package]
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aov(formula, data) [analysis of variance model]
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lm(formula, data) [fit linear models]
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glm(formula, family, data [fit generalized linear models]
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nls(formula, data) [nonlinear least-squares estimates of the nonlinear model parameters]
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lmer(formula, data) [fit mixed effects model]
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(lme4); lme() or (nlme)
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anova(fit, data...) [provides sequential sums of squares and corresponding F-test for objects]
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contrasts(fit, contrasts = TRUE) [view contrasts associated with a factor]
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contrasts(fit, how.many) <‐ value
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glht(fit, linfct) [makes multiple comparisons using a linear function linfct (mutcomp)]
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summary(fit) [summary of model, often w/ t-values]
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confint(parameter) [confidence intervals for one or more parameters in a fitted model]
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predict(fit,...) [predictions from fit]
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ctree(formula,data) [formula is a formula describing the predictor and response variables]
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is.na(x)
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is.nan(x)
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is.null(x)
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is.array(x)
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is.complex(x)
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is.character(x)
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is.data.frame(x)
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is.numeric(x)
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head(x)
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tail(x)
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summary(x)
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str(x)
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length(x)
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dim(x)
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dimnames(x)
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attr(x,which)
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nrow(x)
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ncol(x)
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NROW(x)
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NCOL(x)
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class(x)
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unclass(x)
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createDataPartition(y,p=0.8) [createDaIt splits a vector 'y' with 80 percent data in one part and 20 percent in other parttaPartition(y,p=0.8)]
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trainControl(summaryFunction = <R function>, classProbs = <logical>) [It is used for controlling training parameters like resampling, number of folds, iteration etc.]
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densityplot.rfe(x,data,...) [Lattice functions for plotting resampling results of recursive feature selection]
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featureplot(x,y,plot...) [A shortcut to produce lattice plots]
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medv=b0+b1∗lstat+b2∗lstat2^
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lm(medv ~ lstat + I(lstat^2), data = train.data)
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lm(medv ~ poly(lstat, 2, raw = TRUE), data = train.data)
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spline(x,y) [cubic spline interpolation]
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splineKnots(object)
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knots <- quantile(train.data$lstat, p = c(0.25, 0.5, 0.75))
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null<- lm(Formula~1, data=dtrain)
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full<-lm(Formula~.,data=dtrain)
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step(null, scope=list(lower=null, upper=full), direction="forward")
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step(full, scope=list(lower=full, upper=null), direction="backward")
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Transformations, filters, and other operations can be applied to the predictors with the preProc option.
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train(, preProc = c("method1", "method2"), ...)
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train determines the order of operations; the order that the methods are declared does not matter.
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recipes package has a more extensive list of preprocessing operations.
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