Table of Contents

1 Setting up

1.1 Packages

library() see the list of installed packages. library(class) load the package class. search() see the list of loaded packages. install.packages() and update.packages() install and update packages.

1.2 Emacs ESS

To start ESS session, run command S. ESS will create a command interface as a buffer. Execute ?foo will open the R-doc for the function foo.

There's a babel in org mode for R, so just C-c C-c would work. This will prompt to create a session. One special for this babel is you can evaluate by line, using C-<RET> in the edit buffer.

Keep the session using the header:

#+PROPERTY: session *R*

To export a graph:

:file image.png :results output graphics :exports both

To export ordinary result:

:exports both

To export some summary data:

:exports both :results output org

1.3 Interactive command

We need to know what's going on in the current workspace. getwd() and setwd() get and set the current dir. ls() list the objects currently stored. rm(x, y, z) remove objects rm(list=ls()) remove all objects. objects() create and store current objects.

We can perform some IO by save.image("mydata.Rdata") and load("mydata.Rdata") will save and load workspace in current directory respectively. source("a.R") loads a script. sink("a.lis") redirects the output to a file, and sink() restore that to standard output.

You can view documentation by calling help help(lm). ?lm and ??solve also shows documentation, while example(topic), as its name indicates, shows the examples. help.start() opens the html documentation page.

Finally, q() quit R.

1.4 Commonly used functions

  • str show the structure of arbitrary type
  • summary print the summary

To see the data, you can use:

  • dim()
  • length()
  • head()
  • tail()
  • summary(dataset) shows some information like max,min,mean
  • class(data$col) get type
  • levels(data$col) if it is factor, get the values

2 Objects

Objects have mode and length. The typeof gets the type of an object, while mode retrieves the mode of an object. length gets the length of the vector.

Objects have attributes. names is used for indexing dim is the dimension of a matrix dimnames is the character names of the dimensions

The missing values are NA, tested by is.na. Illegal computations produces NaN, e.g. 0/0.

Compound objects are factors (a.k.a. enumerators) and data frames (lists of objects all have the same length). Vectors are the array of objects of the same mode. List can contain the objects of different modes. A data frame is a list with class "data.frame".

Using bracket expression with predicates to select part of data is very useful. Inside the bracket, the column names do not need to have prefix.

Type Conversion: you can change a type of a vector by

  • as.factor(x)
  • as.numeric()

2.1 Vector

Create a vector by c: c(10.4, 5.6, 3.1). This connects elements end to end, e.g. c(x, 0, x). By a sequence 1:30: the same as: c(1, 2, ..., 29, 30). Colon operator can also specify a backward sequence: 30:1 Colon operator has higher priority: 2*1:15 is the same as c(2, 4, …, 28, 30) The colon operator works, if you want a more flexible and powerful expression, seq is what you want:

  • seq(2,10) produces 2:10.
  • seq(-5, 5, by=.2)
  • seq(length=51, from=-5, by=.2)

Repeating can be done by rep:

  • x <- c(1,2,3)
  • rep(x, times=5) : [1,2,3,1,2,3,...]
  • rep(x, each=5) : [1,1,1,1,1,2,2,2,2,2,...]

2.2 Indexing

Inside [], it can be a number or character, or a vector of them. For vectors, [] returns the element. For lists, [] will return the the element inside a list, while [[]] will return the single element. [] does not allow a vector as index.

If the index is integer, will select based on the position, start from 1. If it is negative, it means the elements other than those index. The index 0 will return empty. Other numeric values will be converted to integer towards zero.

The index can be a integer vector, which selects a bunch of values.

If the index is logical vector, the true ones will be returned. If the index is character, it is compared, partially, with the names attributes of the vector. $ can be used for indexing with character. The empty index [] will returns the entire vector with irrelevant attributes removed. The only retained ones are the names, dim and dimnames attributes.

fruit <- c(5, 10, 1, 20)
names(fruit) <- c("orange", "banana", "apple", "peach")
lunch <- fruit[c("apple","orange")]
# matrix
dim(z) <- c(3,5,100)~

Matrix can be created by the matrix function.

matrix(1:9, nrow=3,byrow=TRUE)

2.3 Data frame

can omit the NA values in data frame

A data frame is a list of equal-length vectors. When getting the data from read.csv, the result is a data frame. Use names to work on data frames will emit the names.

  • Since it is a list, using [] to index will give also the list, a.k.a. data frame, retaining names. You can use a vector as index.
  • Using [[]] to index will give the value, dropping names. You cannot use a vector as index.

2.4 data example

  ## (HEBI: Command line arguments)
  args = commandArgs(trailingOnly=TRUE)
  csvfile = args[1]
  csv = read.csv(csvfile, header=TRUE)

  total_test <- dim(csv)[[1]]
  sub = subset(csv, reach_code>=5)
  total_reach_poi <- dim(sub)[[1]]
  sub = subset(csv, reach_code==5 & status_code == 1)
  total_fail_poi <- dim(sub)[[1]]

  sub <- sub[1:(length(csv)-2)]
  ## (HEBI: callin ga function)
  funcs = TransferFunction(sub);

  ## (HEBI: define a function)
  Constant <- function(data) {
      ## (HEBI: return value as a vector)
      ret <- c()
      i <- 1
      ## (HEBI: a for loop using the vector as range)
      for (i in c(1:length(data))) {
          col = data[i];
          ## (HEBI: Get the name of a column)
          name = names(col);
          if (substr(name, 1, 6) == "output") {
              ## (HEBI: remove of NA)
              newcol = col[!is.na(col)];
              if (length(newcol) > 2) {
                  value <- newcol[1]
                  ## (HEBI: check the value of the vector is all the same)
                  if (length(newcol[newcol != value]) == 0) {
                      ## (HEBI: pushing a new value to the return vector)
                      ret <- c(ret, paste("name = ",  value))}}}}

3 Operators

+-*/, ^ for exp, %% for modulus
%*% matrix product, %o% outer product
!, &, | for vector, &&, || for no vector
>, <, ==, <=, >=
<-, -> assignments, $ list subset, : sequence, ~ for model formula

Built-in functions:

  • log, exp, sin, cos, tan, sqrt
  • min, max
  • range: same as c(min(x),max(x))
  • length(x), sum(x), prod(x) (product)
  • mean(x): sum(x)/length(x)
  • var(x): sum((x-mean(x))^2)/(length(x)-1)
  • sort(x): increasing order
  • order() or sort.list()
  • paste(sep" ")= function takes an arbitrary number of arguments and concatenates them one by one into character strings. The argument can be numeric.
  • toString(8): convert integer to string
  • round(x, digits=0)

4 Control Structure

The compound statements are the same as C, can be a single statement without the braces.

4.1 Conditional

if (STMT) STMT else if (STMT) STMT else STMT
switch (STMT, LIST)
  • the STMT is first evaluated
  • if the value is within 1 and the length of the LIST, evaluate LIST[i], and return
  • return NULL
  • Notice that the LIST can be a comma separated argument of switch … which means switch actually accepts ...

4.2 Loop

=while (STMT) STMT
repeat STMT
break, next

5 Evaluation rules

recycling rules
the shortest list is recycled to the length of longest.
dimensional attributes
the dimension of matrix must match. No recycle for a matrix.

6 Function

function (ARGLIST) BODY

The argument list can be a symbol, a symbol=value, or a .... The body is a compound expression, surrounded with {}. Function can be assigned to a symbol.

The matching of formals and actual are pretty tricky.

  1. exact matching on tags
  2. partial matching on tags
  3. positional matching for ...

Partial matching result must be unique, but the exact matched ones are excluded before this step is entered.

7 Quote

The quote will wrap the expression into an object without evaluating it. The resulting object has the mode of call. The eval is used to evaluate it.

8 Debugging

The print function can output the value of a variable.

To enter the debugger, a call to browser function suffices. This allows you to browse the values at that point. A more powerful debugger is by a call to debug with the function name as argument. Each time that function is called, you enter the debug and can control the execution. Tracing can be registered by trace or untrace with the name of the function. It might need to be quoted in some case, so you'd better quote it, with double quotes. Every time the function is invoked, the return value will be printed as trace.

9 Data IO

  • write
  • write.table
  • write.csv
  • read.table("filename", header=TRUE, sep=",")
    • this ignores blank lines,
    • and expect the header to be one field less than the body.
    • # as comments
  • read.delim
  • cat outputs the data, no index, no newline
make the columns into this namespace
remove those

10 Models

10.1 Linear model.

 fm = lm(y ~ x1 + x2, data = mydataframe)

The fitted model in the variable fm can be accessed by:

extract the coefficients
the Residual Sum of Square
extract the model formula
produce four plots: residuals, fitted values, diagnostics.
predict(OBJECT, newdata=DATA.FRAME)
use the model to predict
extract the residuals

The models can be updated, if the formula only changes a little bit. In the following example, the . means the corresponding part of the original formula.

fs <- lm(y~x1 + x2, data=mydata)
fs <- update(fs, . ~ . + x3)
fs <- update(fs, sqrt(.) ~ .)

11 Plot

Process data:

  • table
  • cut(data, breaks=c(1,3,8))

11.1 Decoration

  • box
  • axis
  • las attribute
  • legend
  • par
  • text
  • mtext
  • points

11.2 Plot Types

11.2.1 plot

  • lines
  • abline

11.2.2 barplot

11.2.3 pie

11.2.4 boxplot

  • quantile

11.2.5 hist

  • lines(density(data))

11.2.6 TODO stem

11.2.7 TODO mosaicplot

11.2.8 pairs

11.3 Device Driver

When outputting some image, you have to tell R which format you want to use. The default on linux is X11, that's why it opens a image immediately after plotting. To use a device, call the device function, and after that all graphics output will be sent to that device.

  • X11
  • pdf
  • png
  • jpeg

When you have finished with a device, terminate it by dev.off().

To output to a file TODO to open plot in emacs:

  dev.control(displaylist = "enable")
  dev.copy(pdf, "test2.pdf")
  # should now have a valid test2.pdf
  dev.off() # finished

12 Packages

12.1 ggplot2

qplot(totbill, tip, geom="point", data=tips) # scatter plot
qplot(totbill, tip, geom="point", data=tips) + geom_smooth(method="lm") # with linear relationship line
qplot(tip, geom="histogram", data=tip) # histogram
qplot(tip, geom="histogram", binwidth=1, data=tips) # with custom binwidth
# box plots
qplot(sex, tipperc, geom="boxplot", data=tips)
qplot(smoker, tipperc, geom="boxplot", data=tips)
qplot(sex:smoker, tipperc, geom="boxplot", data=tips) # combine! plot the two sets of graph in two one graph
qplot(totbill, tip, geom="point", colour=day, data=tips) # scatter plot with colors, in regard to "day" column

12.2 plot(x, y, …)

Possible ... arguments:

  • type what type of plot:
    • p for points,
    • l for lines,
    • b for both,
    • h for histogram like (or high-density) vertical lines,
  • main an overall title for the plot: see title.
  • xlab a title for the x axis: see title.
  • ylab a title for the y axis: see title.