CarlosGottfriedez.github.io

title: "R-Funktionen" output: html_document: theme: united toc: yes pdf_document: highlight: zenburn keep_tex: yes number_sections: yes toc: no

#R-Funktionen Diese Seite listet all die R-Funktionen auf die ich schonmal benutzt habe. Ich versuche so weit es geht die ausgeführten Beispiele mit anzuzeigen.

Um Java-Text anzuzeigen, brauch man!

public class HelloWorld{
    public static void main(String[] args){
        System.out.println{"Hello world"};
    }
}

Mit ? können wird die Dokumentation von R aufgerufen und weitere zusätzliche Informationen werden angezeigt.

# zum Beispiel
?row.names

c, nchar, data, str, dim, names, head, and tail.

# zeigt alle Observations an
row.names(mtcars)

Es folgen mehrere Beispiel mit R-Code

# Gibt die Werte der Variable mpg des Datenframes mtcars aus. (Also $ als Symbol).
mtcars$mpg

##Funktionen die das Zentrum beschreiben

# Gibt den Mittelwert der Daten an.
mean(mtcars$mpg)

# Gibt den Median der Daten an.
median(mtcars$mpg)

# Überblick über alle Daten
summary(cars)

##Funktionen um Datenframes zu laden

#Zeigt das Verzeichnis an in welchen wir uns befinden.
getwd()

#Wechsel des Verzeichnises setwd('~/Downloads') immer in **'' - Zeichen**.
setwd('~/Downloads') 
#cvs-Datei einlesen.
read.csv('reddit.csv')

##Andere Funktionen

# zeigt ein Subset
subset(mtcars, mtcars$mpg<=25 & mtcars$wt<=2.581 )

# Zeigt einen Überblick an
summary(mtcars)

# neue Spalte mit year als überschrifft und 1974 für alle
mtcars$year <- 1974

# Spalte löschen
mtcars <- subset(mtcars, select = -year)

# Ein Beispiel für Konditionen
mtcars$wt
cond <- mtcars$wt < 3
cond
mtcars$weight_class <- ifelse(cond, 'light', 'average')
mtcars$weight_class
cond <- mtcars$wt > 3.5
mtcars$weight_class <- ifelse(cond, 'heavy', mtcars$weight_class)
mtcars$weight_class

# entfernt code aus dem arbeitsbereich
rm(cond)
rm(efficient)

# zeigt die Anzahl der Fahrzeuge mit bestimmten Werten an
table(mtcars$mpg)

# Für Faktrone als Datentypen
levels(reddit$age.range)

##qplot

qplot is the basic plotting function in the ggplot2 package, designed to be familiar if you're used to plot from the base package. Parameter für qplot:

x = Variabelname data = Datenname xlim = Vektor(Von, Bis) binwidth = Balkendicke facet_wrap(~gender, ncol = 2) -- Aufteilen in Einzelne kleine Fenster

Um eine Plot zu zeichnen

#install.packages('ggplot2', dependencies = T)
library(ggplot2)
qplot(data= mtcars, x=wt)

Datentypen

@. Vektoren

Ein Beispiel für Vektoren

a <- c(1,2,5.3,6,-2,4) # numeric vector
b <- c("one","two","three") # character vector
c <- c(TRUE,TRUE,TRUE,FALSE,TRUE,FALSE) #logical vector

@. Matrizen

Ein Beispiel für Matrizen

# generates 5 x 4 numeric matrix 
y<-matrix(1:20, nrow=5,ncol=4)

# another example
cells <- c(1,26,24,68)
rnames <- c("R1", "R2")
cnames <- c("C1", "C2") 
mymatrix <- matrix(cells, nrow=2, ncol=2, byrow=TRUE,
  dimnames=list(rnames, cnames))
mymatrix[]

(@) Arrays

Sind wie Matrizen aufgebaut, nur sind mehrere Dimensionen möglich Arrays are similar to matrices but can have more than two dimensions. See help(array) for details.

(@) Data Frames

A data frame is more general than a matrix, in that different columns can have different modes (numeric, character, factor, etc.). This is similar to SAS and SPSS datasets.

d <- c(1,2,3,4)
e <- c("red", "white", "red", NA)
f <- c(TRUE,TRUE,TRUE,FALSE)
mydata <- data.frame(d,e,f)
names(mydata) <- c("ID","Color","Passed") # variable names
mydata

@. List

An ordered collection of objects (components). A list allows you to gather a variety of (possibly unrelated) objects under one name.

# example of a list with 4 components - 
# a string, a numeric vector, a matrix, and a scaler 
w <- list(name="Fred", mynumbers=a, mymatrix=y, age=5.3)

# example of a list containing two lists 
v <- c(list1,list2)

@. Factors

Tell R that a variable is nominal by making it a factor. The factor stores the nominal values as a vector of integers in the range [ 1... k ] (where k is the number of unique values in the nominal variable), and an internal vector of character strings (the original values) mapped to these integers.

Nominale Variablen

# variable gender with 20 "male" entries and 
# 30 "female" entries 
gender <- c(rep("male",20), rep("female", 30)) 
gender <- factor(gender) 
# stores gender as 20 1s and 30 2s and associates
# 1=female, 2=male internally (alphabetically)
# R now treats gender as a nominal variable 
summary(gender)

Ordinale Variablen

An ordered factor is used to represent an ordinal variable.

# variable rating coded as "large", "medium", "small'
rating <- c(rep("large"), rep("medium"), rep("small"))
rating <- ordered(rating)

summary(rating)
# recodes rating to 1,2,3 and associates
# 1=large, 2=medium, 3=small internally
# R now treats rating as ordinal

R will treat factors as nominal variables and ordered factors as ordinal variables in statistical proceedures and graphical analyses. You can use options in the factor( ) and ordered( ) functions to control the mapping of integers to strings (overiding the alphabetical ordering). You can also use factors to create value labels. For more on factors see the UCLA page.

length(mtcars) # number of the variables or components
str(mtcars)    # structure of an object 
class(mtcars$wt)  # class or type of an object
class(mtcars) # many options
names(mtcars) # names of the Variables

mtcars     # prints the object mtcars
ls()   # list current objects

c(object,object,...)       # combine objects into a vector
cbind(object, object, ...) # combine objects as columns
rbind(object, object, ...) # combine objects as rows 

newobject <- edit(object) # edit copy and save as newobject 
fix(object)               # edit in place

#Tabellen

Zum Einbinden von Tabellen eignet sich R-Markdown ebenfalls, ess muss nur die Tabelle in der Datei haben ein Beispiel:

man kann die Layouts verändern, wenn man dies in die Meta daten der Datei schreibt

rmarkdown::tufte_handout:
  highlight: zenburn
  
wenn man die Folgenden Daten im Chunkout schreibt wird die Tabele im R ausgeführt.

library(xtable)
options(xtable.comment = FALSE)
options(xtable.booktabs = TRUE)
xtable(head(mtcars[, 1:6]), caption = "First rows of mtcars")

##Funktionen für das Setup des Pdf´s unter R-Studion

Weitere Einstellungen für die R-Chunks

All diese Einstellungen werden hier {r, echo=FALSE} im R-Chunk verändert.

Source Code Display

echo: (TRUE; logical or numeric) whether to include R source code in the output file; besides TRUE/FALSE which completely turns on/off the source code, we can also use a numeric vector to select which R expression(s) to echo in a chunk, e.g. echo=2:3 means only echo the 2nd and 3rd expressions, and echo=-4 means to exclude the 4th expression.

results: ('markup'; character) takes three possible values:

markup: mark up the results using the output hook, e.g. put results in a special LaTeX environment. asis: output as-is, i.e., write raw results from R into the output document. hide hide results; this option only applies to normal R output (not warnings, messages or errors). note markup and asis are equivalent to verbatim and tex in Sweave respectively (you can still use the latter two, but they can be misleading, e.g., verbatim does not really mean verbatim in R, and tex seems to be restricted to LaTeX).

# This code chunk is evaluated
# and the source code is displayed.
# However, the results are hidden.

x <- "Hello World"
y <- "!"
cat(c(x, rep(y,3)), sep="")

###Plots

fig.width, fig.height: (both are 7; numeric) width and height of the plot, to be used in the graphics device (in inches) and have to be numeric

# Plot a mandelbrot set with adjusted width and height.
mandelbrot(10, 10)

You can also embed plots, for example:

plot(cars)

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.

###Code Evaluation

eval: (TRUE; logical) whether to evaluate the code chunk.

# This code chunk is not evaluated, however the source code is displayed.

# Sort columns of a random 10,000 x 10,000 matrix
foo <- matrix(rnorm(1e+08), 10000, 10000)
system.time(bar <- apply(foo, 2, sort))

###Code Decoration

prompt: (FALSE; logical) whether to add the prompt characters in the R code (see prompt and continue in ?base::options; note that adding prompts can make it difficult for readers to copy R code from the output, so prompt=FALSE may be a better choice.

 # The R command prompt is shown.
 
x <- rnorm(1000, mean=100, sd=5)
y <- rnorm(1000, mean=100, sd=5)

####So macht man links

[Udacity website](https://www.udacity.com/course/viewer#!/c-ud651/l-729069797/e-804129319/m-811719066)

This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.

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