====== Data frames ======

===== Basics about data frames =====


Data tables are represented in R as **data frames** ''data.frame'' - it is essentially a special ''list'' in which all items are vectors (columns, variables) of the same length. A data frame can be created with an assignment of the form:
<code R>
D <- data.frame(e1, e2, ... , ek, row.names=units)
</code>
where units is a vector containing names of units, and ei has either a form n<sub>i</sub>=v<sub>i</sub> or v<sub>i</sub> (n<sub>i</sub> is the name of the i-th column and v<sub>i</sub> is the vector of values).

<code R>
> units <- c('a','b','c','d','e')
> u <- c(1,2,3,5,5)
> v <- c(1,3,2,3,5)
> D <- data.frame(U=u,V=v,row.names=units)
> D
  U V
a 1 1
b 2 3
c 3 2
d 5 3
e 5 5
> plot(D)
> D$V
[1] 1 3 2 3 5
> D[[2]]
[1] 1 3 2 3 5 
</code>

We can get the i-th variable by expressions D$n<sub>i</sub> or <nowiki>D[[i]]</nowiki>.

A data frame D can be created also interactively using
<code>
D <- edit(data.frame())
</code>
We can also prepare it as an Excel spreadsheet, save it as a CSV file, and read it into R - see the next chapter.

==== Test ====

Represent the following data as a data frame

{{ru:7iss:labs:data1.jpg?400}}

and

{{ru:7iss:labs:data2.jpg?400}}

[[ru:7iss:labs:s:s2|Solution]]


===== Working directory =====

We get a current working directory using the function ''getwd()''. We change it with the function ''setwd(''//path//'')'', for example
<code R>
> getwd()
[1] "C:/Users/batagelj/test/ru/R"
> setwd("C:/Users/batagelj/work/R/Cluster")
> getwd()
[1] "C:/Users/batagelj/work/R/Cluster"
</code>
It can be changed also using the option  ''Change dir ...'' in the menu ''File''.

We can save the values of selected variables to the file //save//''.Rdata'' (in the working directory)
using the command
<code R>
dump(c("v1","v2",. . . ,"vk"),"save.Rdata")
</code>
When we need them again we can load them using 
<code R>
source("save.Rdata").
</code>
See also the functions  ''save'' and ''load''.

===== Data from R packages =====

<code R>
> data(package=.packages(all.available=TRUE))
> data(Pottery)
> help(Pottery)
> summary(Pottery)
          Site          Al              Fe              Mg              Ca               Na        
 AshleyRails: 5   Min.   :10.10   Min.   :0.920   Min.   :0.530   Min.   :0.0100   Min.   :0.0300  
 Caldicot   : 2   1st Qu.:11.95   1st Qu.:1.700   1st Qu.:0.670   1st Qu.:0.0600   1st Qu.:0.0500  
 IsleThorns : 5   Median :13.80   Median :5.465   Median :3.825   Median :0.1550   Median :0.1500  
 Llanedyrn  :14   Mean   :14.49   Mean   :4.468   Mean   :3.142   Mean   :0.1465   Mean   :0.1585  
                  3rd Qu.:17.45   3rd Qu.:6.590   3rd Qu.:4.503   3rd Qu.:0.2150   3rd Qu.:0.2150  
                  Max.   :20.80   Max.   :7.090   Max.   :7.230   Max.   :0.3100   Max.   :0.5400  
> dim(Pottery)
[1] 26  6
> library(cluster)
> help(ruspini)
> data(ruspini)
> head(ruspini)
> summary(ruspini)
> plot(ruspini)
</code>
See also data sets: flower, plantTraits, animals, iris, mtcars, etc.

<code R>
> data(iris)
> help(iris)
> dim(iris)
[1] 150   5
> summary(iris)
  Sepal.Length    Sepal.Width     Petal.Length    Petal.Width          Species  
 Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100   setosa    :50  
 1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300   versicolor:50  
 Median :5.800   Median :3.000   Median :4.350   Median :1.300   virginica :50  
 Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199                  
 3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800                  
 Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500                                  
> head(iris)
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa
> tail(iris)
    Sepal.Length Sepal.Width Petal.Length Petal.Width   Species
145          6.7         3.3          5.7         2.5 virginica
146          6.7         3.0          5.2         2.3 virginica
147          6.3         2.5          5.0         1.9 virginica
148          6.5         3.0          5.2         2.0 virginica
149          6.2         3.4          5.4         2.3 virginica
150          5.9         3.0          5.1         1.8 virginica
> pairs(iris)

</code>

{{ru:7iss:labs:iris.png}}

{{ru:7iss:labs:iris.pdf}}









<html><!--
Ime razpredelnice lahko izpustimo, ce pred tem uporabimo funkcijo
attach(R), ki doda razpredelnico v seznam okolij po katerih R isce imena
spremenljivk. Seznam dobimo s funkcijo search(). Razpredelnico
odpnemo s funkcijo detach(R).


Ce razpredelnico pripravimo v Excelu, jo shranimo s File/Save as ... in
v Save as Type izberemo Text (Tab delimited). Excel sam doda
podaljsek .txt. V R lahko razpredelnico vnesemo z
R <- read.table("datoteka",header=TRUE)
Posebna razlicica read.csv je pripravljena za branje datotek .csv (comma
separated values).
Da bodo stvari tekle gladko, v razpredlnici ne uporabljamo imen, ki
vsebujejo presledke. Nadomestimo jih s podcrtajem ali piko ali sploh
opustimo. Seznam spremenljivk v razpredelnici dobimo z names(R).
V R-jevih paketih najdemo veliko ze pripravljenih testnih razpredelnic.
Njihov seznam dobimo z zahtevo
data(package=.packages(all.available=TRUE))
Izbrano razpredelnico R nalozimo z zahtevo data(R) in si njen zacetek
ogledamo z zahtevo head(R).

S funkcijo subset(R,pogoj) lahko dobimo iz razpredelnice R
podrazpredelnico sestavljeno iz vrstic, ki zadoscajo danemu pogoju.
Na razpredelnico R lahko pogledamo tudi kot na matriko z (morda)
raznovrstnimi vrednostmi. To nam omogoca indeksno doseganje njenih
sestavin:
R[i,] { opis i -te enote
R[,j] { j-ta spremenljivka
Uporabimo lahko vse, kar smo z indeksi vajeni poceti. Na primer, z
R[pogoj,] izberemo samo tiste enote, ki zadoscajo pogoju;
z R[,c("s1",". . . ","sk")] pa samo izbrane spremenljivke. Z izrazom
R[,sapply(R,is.numeric)] izberemo iz razpredelnice vse stevilske
spremenljivke.
Z R[order("s1",". . . ","sk"),] preuredimo razpredelnico glede na
vrednosti izbranih spremenljivk. Ce zelimo obratni vrstni red, nad order
uporabimo se funkcijo rev.
S summary(R) dobimo osnovne znacilnosti spremenljivk razpredelnice R.


<code R>
> data(package=.packages(all.available=TRUE))
> Orange
Tree age circumference > class(animals)
1 1 118 30 [1] "data.frame"
2 1 484 58 > names(animals)
3 1 664 87 [1] "war" "fly" "ver" "end" "gro" "hai"
4 1 1004 115 > row.names(animals)
...... [1] "ant" "bee" "cat" "cpl" "chi" "cow"
32 5 1004 125 [7] "duc" "eag" "ele" "fly" "fro" "her"
33 5 1231 142 [13] "lio" "liz" "lob" "man" "rab" "sal"
34 5 1372 174 [19] "spi" "wha"
35 5 1582 177 > animals$war
> (b <- subset(Orange,age<1000)) [1] 1 1 2 1 2 2 2 2 2 1 1 1 2 1 1 2 Tree age circumference > attributes(animals)
1 1 118 30 $names
2 1 484 58 [1] "war" "fly" "ver" "end" "gro" "hai"
3 1 664 87 $class
8 2 118 33 [1] "data.frame"
...... $row.names
31 5 664 81 [1] "ant" "bee" "cat" "cpl" "chi" "cow"
> data(animals,package="cluster") ......
> animals > war
war fly ver end gro hai Error: object "war" not found
ant 1 1 1 1 2 1 > attach(animals)
bee 1 2 1 1 2 2 > war
...... [1] 1 1 2 1 2 2 2 2 2 1 1 1 2 1 1 2 spi 1 1 1 NA 1 2
wha 2 1 2 2 2 1
</code>
--></html>
 
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