idataio input and output of data into R.
isummary - graphs and numerical summaries, with or without groups.
ihist - histogram
isplot - scatterplot, with or without groups
isubset - data subsetting
barchart - Barcharts, one or two Variables
bplot - Boxplot
change.order - Change Ordering of Categorical Variable
chi.gof.test - Chisquare Goodnes-of-fit Test
chi.ind.test - Chisquare Test for Independence
ci.mean.sim - Simulation of Confidence Intervals for one Mean
dlr - Least Squares Regression with one Dummy Variable
dlr.predict Prediction for SLR with Dummy Variable
fivenumber - Five Number Summary
flplot - Fitted Line Graph
get.moodle.data - read data from moodle quizzes
hplot - Histogram
iplot - Interaction Plot
kruskalwallis - Kruskal-Wallis test
mallows - Best Subset Regression
mlr Multiple Regression
mlr.predict - Prediction for Multiple Regression
mplot - Marginal Plot
multiple.graphs - Combine Several Graphs into one
nplot - Normal Probability Plot
one.sample.t - Infrerence for one Mean
one.sample.prop - Inference for one Proportion
one.sample.wilcoxon - Wilcoxon Rank Sum Test, non parametric alternative to one.sample.t
oneway - One-way ANOVA
pearson.cor - Test and Interval for Correlation
prop.ps - Power and Sample Size for one Proportion
slr - Regression for One Predictor
slr.predict Prediction for Regression with one Predictor
splot Scatterplot, also with groups
stat.table - Summary Statistics
t.ps - Power and Sample Size for one Mean
test.mean.sim - Simulation of Hypothesis esting for one Mean
tukey - Tukey Multiple Comparison, one or two Factors
twoway - Two-way ANOVA
These are apps that open a new window and then allow the user to do all the work using (mostly) point and click.
Most of these apps are called with data sets as arguments. They will accept any number of arguments, which can be either vectors, matrices or data frames. If any of the the later arguments do not match the first one in length they are ignored. Some apps also return a data set.
Most of the apps also show the commands that could be used in R directly to produce the same results, either with the Resma3 commands or without them.
Routine to read data into R and export data to a file. It allows for
data entered from the keyboard into a spreadsheet
data read from a file
data downloaded from the internet
data copied from another program such as a browser or an Excel spreadsheet
Almost all standard file formats are supported, such as csv, excel, html, etc. For a complete list see
Examples:
dta <- idataio() graphical and numerical summaries of one numerical vector, optionally rouped by a categorical variable
Examples
attach(mtcars)
isummary(mtcars)
isummary(mpg)
isummary(mpg, gears)draws histograms
Examples
ihist(mtcars)scatterplots
Examples
isplot(mtcars)
isplot(mpg, disp, gear, cyl) subsetting a data frame or vector
Examples:
new.mtcars <- isubset(mtcars)The routines I wrote for this course all use the following standard (where it makes sense)
first argument y is a numeric vector (“Response”)
second argument x is either a numeric or categorical vector or matrix (“Predictor” or “Factor”)
Sometimes there is a third argument z, always a categorical vector (“Group”)
Obvious exceptions: routines for categorical data analysis (barchart, chi.ind.test, chi.gof.test)
Many of the routines have the following arguments:
return.result=FALSE (Optional): if TRUE returns results as vector for further use. This allows storing the results, for example to do simulation.
You can get all the routines and data sets by downloading and opening Resma3.RData
sometimes you might make a mistake entering the data, or you want to change a few values. In that case use
students <- edit(students)Arguments
x: a data frame
makes column names “visible” to R
Examples:
attach(mothers)
mean(Length) Note: you need to do this only once in any R session, it will stay until you close R.
Summary statistics for quantitative data
Arguments
x: a numeric vector
na.rm = FALSE
Examples:
mean(Length)median(Length)sd(Length) IQR(Length) quantile(Length, c(0.25,0.75))Note: all these routines have an argument na.rm = FALSE, so if the data set has missing values (NA) the result is NA. Simply use na.rm = TRUE
Tables and cross-tabulation for categorical data
Arguments:
x: either a categorical vector or a data frame with two categorical columns
y: a second categorical vector (if x is a vector as well)
Examples:
head(rogaine,3) table(rogaine)Pearson’s correlation coefficient Arguments:
x: either a numeric vector or a data frame with two or more numeric columns
y: a second numeric vector (if x is a vector as well)
use = “everything”, set to use=“complete.obs” if NA’s in the data
Examples:
x <- rnorm(50)
y <- rnorm(50)
cor(x, y) cor(cbind(x,y)) find a subset of a data set based on some condition(s)
Arguments:
x: a data frame
cond: some logical condition
select (Optional): which columns should be returned, default is all of them
drop=FALSE, if just one column is selected as output use drop=TRUE
Examples:
head(subset(wrinccensus, Satisfaction>=4, select=Income),3)
head(subset(wrinccensus, Satisfaction>=4 & Gender=="Male"),3)
head(subset(wrinccensus, Satisfaction>=4 & Gender=="Male", select=c(Income,Job.Level)),3)
head(subset(wrinccensus, Satisfaction>=4 & Gender=="Male", select=Income),3)Note that the last one results in a data frame with one column. You might want it as a numeric vector:
head(subset(wrinccensus, Satisfaction>=4 & Gender=="Male", select=Income, drop=TRUE),3)NOTE: see also interactive app isubset
read data from moodle quizzes
highlight the data, use mouse to copy, switch to R and run
get.moodle.data()tables of summary statistics, with or without groups Arguments y: numeric vector (Required) x: categorical variable (Optional) Mean=TRUE: if set to FALSE table finds medians and IQRs Examples:
stat.table(Length)
stat.table(Length,Status)
stat.table(Length,Status,Mean=FALSE)five number summary and IQR, with or without groups
Arguments:
y: quantitative vector x: (optional) categorical vector
Example:
fivenumber(Length)Confidence interval or hypothesis test for one mean
Arguments:
y: either a vector with numbers or the sample mean of the data shat, n: standard deviation and sample size (only needed if y is sample mean)
mu.null: mean in null hypothesis (if missing confidence interval is found)
alternative = “equal”: alternative hypothesis
conf.level = 95
ndigit = 1 (number of digits for rounding)
Examples:
one.sample.t(Length, conf.level=90)
one.sample.t(49.55, 3.38, 94, conf.level=90, ndigit=2)
one.sample.t(Length, mu.null=50, alternative="less")power and sample size calculations for one mean
Arguments:
n: sample size diff: difference in means
sigma: standard deviation
power: power of test
E (optional): error of confidence interval (for sample size calculation only)
conf.level=90: confidence level of confidence interval (for sample size calculation only)
alpha = 0.05: type I error probability
alternative = “equal”: alternative hypothesis
routine finds whatever argument is left out (n, diff or power)
Examples:
t.ps(n=100, diff=1.23, sigma=5, alpha=0.1, alternative="greater")
t.ps(power=90, d=1, sigma=13, alpha=0.1, alternative="greater")
t.ps(sigma= 0.5, E=0.125, conf.level=99)Wilcoxon rank sum test for one quantitative variable - non parametric alternative to one.sample.t
Arguments:
y: quantitative vector
mu.null: mean in null hypothesis (if missing confidence interval is found)
alternative = “equal”: alternative hypothesis
conf.level = 95
Examples:
wilcoxon(Length, conf.level=90)
wilcoxon(Length, mu.null=50, alternative="greater")Confidence interval or hypothesis test for one proportion (percentage, probability)
Arguments:
x: number of successes
n: number of trials
pi.null: proportion in null hypothesis (if missing confidence interval is found)
alternative = “equal”: alternative hypothesis
conf.level = 95
Examples:
one.sample.prop(40, 100, conf.level=90)
one.sample.prop(40, 100, pi.null=0.5, alternative=less)Power and sample size calculations for one proportion
Arguments:
n: sample size phat: alternative proportion
pi.null: proportion under null hypothesis
power: power of test
E (optional): error of confidence interval (for sample size calculation only)
conf.level=90: confidence level of confidence interval (for sample size calculation only)
alpha = 0.05: type I error probability
alternative = “two.sided”: alternative hypothesis
routine finds whatever argument is left out (n, phat or power)
Examples:
prop.ps(n=100, phat=0.65, pi.null=0.5)
prop.ps(power=90, phat=0.65, pi.null=0.5)Chisquare test for multinomial proportions
Arguments:
x: observed counts p: hypothesized proportions
Example
chi.gof.test(c(12, 17, 20, 15, 10, 26), rep(1,6)/6) Confidence interval and hypothesis test for Pearson’s correlation coefficient
Arguments:
y: quantitative vector
x: quantitative vector
rho.null (if missing confidence interval is found, only rho.null = 0 accepted)
conf.level = 95 confidence level of interval
Note: when the routine is run R sometimes gives a
Warning message:
Continuous x aesthetic – did you forget aes(group=…)?
just ignore this
Example:
pearson.cor(Draft.Number, Day.of.Year, rho.null = 0)does a simulation for coverage of the t test confidence intervals
Arguments:
n : sample size mu: mean sigma: standard deviationconf.level: nominal coverage
Example:
ci.mean.sim(n=500,mu=75,sigma=30,conf.level=99)does a simulation of the p value of the t test. If mu.null=mu it finds the true type I error \(\alpha\), otherwise the power of the test. In either case it draws the histogram of p values.
Arguments:
n : sample size
mu: mean
mu.null=mu: value of mean under null hypothesis
sigma: standard deviation
alpha: nominal alpha
Examples:
test.mean.sim(n=20, mu=5, sigma=1, alpha=0.1)
test.mean.sim(n=20, mu=5, mu.null=5.5, sigma=1, alpha=0.1)bar charts
Arguments:
y: a table (often from a call to the table routine)
Percent: if missing graph uses counts. Other values are “Grand”, “Row” or “Column” for respective percentages
new.order: for changing the order of the bars
Polygon = FALSE if TRUE adds polygon
Examples:
attach(rogaine)
barchart(table(Growth))
barchart(table(Growth), Percent="Grand")
barchart(table(Growth), Percent="Grand",Polygon=TRUE)
barchart(table(rogaine))
barchart(table(rogaine),Percent="Row")Histogram, if desired with fitted density
Arguments:
x: numerical data
f: name of distribution (Optional)
par: parameters of distribution(Optional)
n: number of bins (Optional) label_x, main_title: x axis label and graph title (Optional)
Examples:
hplot(Length)
hplot(Length, label_x = "Length of Babies (cm)", main_title = "Mothers, Babies and Cocain Use")
hplot(Length, f = "norm", par = c(mean(Length), sd(Length))) Boxplot / do.violinplot
Arguments:
y: numeric vector or matrix or data frame
x: catagorical vector (Optional)
do.violin = FALSE: if TRUE does violin plot
orientation=“vertical”, if orientation=“horizontal” boxplot is drawn horizontally
new_order: change the order of the boxes. Either a vector of position numbers or “Sort”, then sorted from smallest mean to largest.
label_x, label_y, main_title: axes labels and graph title (Optional)
Examples:
bplot(Length)
bplot(Length, Status)
bplot(Length, Status, label_y = "Length of Babies (cm)",
label_x = "Drug Status",
main_title = "Mothers, Babies and Cocain Use")Scatterplot, possibly with groups and fits
Arguments:
y: numeric vector , y axis
x: numeric vector, x axis
z: catagorical variable (Optional)
w: second catagorical variable (Optional)
plot.points=TRUE: if FALSE dots are not plotted add.line = 0: adds lines, if add.line=1 least squares regression line, if add.line=2 LOESS, if add.line=3 it does the line graph
jitter = FALSE: if true jitters dots
use.facets = FALSE: if TRUE usess facets instead of colors for z
errorbars = FALSE: if TRUE adds error band to fit
label_x, label_y, label_z, main_title: axes labels and graph title (Optional)
add.text, add.text_x, add.text_y: add text to graph (Optional)
plotting.size = 1: size of plotting symbols
plotting.symbols: change plotting symbols. can use either symbols added on keyboard or numbers corresponding to R symbols key(Optional)
plotting.colors: change colors, can use either numbers corresponding to R color key or explicit text : pcolor=“red” (Optional)
ref_x, ref_y: add reference lines (Optional)
log_x = FALSE, log_y = FALSE: change to log scale
no.legends = FALSE: rmove all alegends
Examples:
attach(salaries)
splot(Salary,Years)
splot(Salary,Years, add.line=1)
splot(Salary,Years, Level, add.line=1)
splot(Salary,Years, add.line=3)attach(upr)
splot(y = Freshmen.GPA, x = IGS, z = Gender, use.facets = TRUE, add.line = 1, label_y = "Freshmen GPA", label_x = "Indice de Ingreso", main_title = "UPR Admissions", jitter=TRUE, plotting.symbols = ".", plotting.colors = "blue", ref_x = 300, ref_y=3.5)NOTE: see also ineractive app isplot
Marginal plot with scatterplot and boxplots
Arguments:
y: numeric vector , y axis
x: numeric vector, x axis
z: catagorical variable (Optional)
add.line = 0: adds lines, if add.line=1 least squares regression line, if add.line=2 LOESS, if add.line=3 it does the line graph
Examples:
mplot(Salary, Years)Note: when the routine is run R sometimes gives a Warning message: Continuous x aesthetic – did you forget aes(group=…)? Just ignore that
Fitted line plot, allows for log transforms or polynomial fitting
Arguments:
y: numeric vector , y axis
x: numeric vector, x axis
z: catagorical variable (Optional)
additive = FALSE: if true fits parallel lines
logx = FALSE, logy = FALSE: if true applies log transforms
polydeg = 1: degree of polynomial to be fit
jitter = FALSE: if true jitters dots
Examples:
attach(longjump)
flplot(LongJump, Year)
flplot(LongJump, Year, polydeg=2)
attach(elusage)
flplot(elusage[,3], elusage[,4], logx=TRUE, logy=TRUE)Normal probability plot
Arguments:
y: numerical vector
x: categorical vector (Optional)
Examples:
nplot(euros[,1])Interaction plot
Arguments:
y: numerical vector
x and z: categorical vectors
Examples:
attach(fermentation)
iplot(Ethanol, Sugar, Oxygen)combine (up to four graphs) in one
ggplt objects, likely generated using other graph functions with the argument returnGraph=TRUE
titles (Optional) titles for each graph
Examples:
attach(gasoline)
plt1 <- bplot(MPG, Gasoline, returnGraph=TRUE)
plt2 <- bplot(MPG, Automobile, returnGraph=TRUE)
multiple.graphs(plt1,plt2)x<-rnorm(1000)
multiple.graphs(
hplot(x, n=10, returnGraph=TRUE),
hplot(x, n=25, returnGraph=TRUE),
hplot(x, n=50, returnGraph=TRUE),
hplot(x, n=100, returnGraph=TRUE),
titles = paste(c(10, 25, 50, 100), "bins")
)Chisquare test of independence
Arguments:
x: a table of counts
Examples:
chi.ind.test(table(rogaine))ANOVA with one factor
Arguments:
y: numeric vector
x: categorical vector
ndigit = 1: rounding answer to 1 digit
var.equal = TRUE: assume equal variance
conf.level = 95: in the case of a categorical variable with 2 levels finds a 95% confidence interval for the difference in means
Examples:
oneway(Length, Status)Non-parametric ANOVA
Arguments:
y: numeric vector
x: categorical vector
Examples:
kruskalwallis(Length, Status)ANOVA with two factors
Arguments:
y: numeric vector
x, z: categorical vectors
with.interaction = TRUE: assume interaction is present (defaults to FALSE if there are no repeated measurements)
Examples:
attach(gasoline)
twoway(MPG, Gasoline, Automobile)
twoway(MPG, Gasoline, Automobile, with.interaction="FALSE")Tukey’s Multiple Comparison in ANOVA
Arguments:
y: numeric vector
x : categorical vector
z : second categorical vector (Optional)
with.interaction = TRUE: assume interaction is present (defaults to FALSE if there are no repeated measurements)
which=“first”: do comparison for first categorical variable (x), or change to which=“second” or which=“interaction”
Examples:
tukey(mothers[,2], mothers[,1])
tukey(MPG, Gasoline, Automobile, which="first")
tukey(MPG, Gasoline, Automobile, which="interaction")Linear Regression with one predictor, including polynomial regression
Arguments:
y, x: numerical vectors
no.intercept = FALSE: fit intercept?
polydeg = 1: fit polynomial of higher degree?
show.tests=FALSE: if TRUE t tests for coefficients are shown
Examples:
slr(wine[,3],wine[,2])
slr(wine[,3],wine[,2],polydeg=2)
slr(log(wine[,3]),wine[,2],polydeg=2)Prediction for simple linear regression
Arguments:
same as slr. In addition:
newx = x: predict for values for x (can be vector). If missing predict for values in data set.
interval: either “PI” for prediction intervals or “CI” for confidence intervals
conf.level = 95
Examples:
slr.predict(wine[,3], wine[,2],newx=c(2,2.5,3), interval="PI", conf.level=90) Linear Regression with more than one predictor
Arguments:
y: numerical vector
x: numeric matrix with predictors in columns
show.tests=FALSE: if TRUE t tests for coefficients are shown
returnModel=FALSE, if TRUE fit object is returned (and can be used in other routines)
Examples:
mlr(houseprice[,1], houseprice[, -1])Prediction for regression with more than one predictor
Arguments:
same as slr.predict but here x and newx are matrices
Examples:
newx <- cbind(c(2000, 2100, 2200), rep(1, 3), rep(2, 3), rep(2, 3))
mlr.predict(houseprice[,1], houseprice[, -1], newx=newx, interval="PI", conf.level = 99)Best subset regression with Mallow’s Cp
Arguments:
same as mlr
Examples:
mallows(houseprice[,1], houseprice[, -1] ) Linear regression with one dummy variable
Arguments:
y: numerical vector
x: numeric vectorz: categorical vector
additive = FALSE: if parallel lines set to TRUE
show.tests=FALSE: if TRUE t tests for coefficients are shown
Examples:
dlr(salaries[,1], salaries[,2], salaries[,3])
dlr(salaries[,1], salaries[,2], salaries[,3], additive=T)Prediction for regression with a dummy variable
Arguments:
same as slr.predict but also needs newz: values of categorical variable for prediction
Examples:
dlr.predict(salaries[, 1], salaries[, 2], salaries[, 3],
newx=5, newz="Low", interval="PI")Change the order of a categorical variable
Arguments:
z: categorical variable
NewOrder: can be a numeric vector specifying a certain order or a categorical vector with ordered values of z
Examples:
bplot(Length, Status)
bplot(Length, change.order(Status,c(2,1,3)))
bplot(Length, change.order(Status,c("Throughout","First Trimester","Drug Free")))