1. Prof. Dr. Roberto Dantas de Pinho, roberto.pinho@mct.gov.br
26/jul/2012
This presentation is based on courses by
Dr. Paulo Justiniano Ribeiro Jr (UFPR) &
Dr. Cosme Marcelo Furtado Passos da Silva (FIOCRUZ)
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2.  A First R Session  Saving your work
 Objects  Changing data
 Data input  Sums e
 Now that we have aggregates
data...  Linear regression
 Some analyses
 Filter & select
And lots of other
things along the way
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3. Install, configuration etc.
R internals, structure etc.
Handling large datasets
Fancy plots beyond the basics
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4.  You can use R to evaluate some simple
expressions. Just type:
1 + 2 + 3
2 + 3 * 4
3/2 + 1
4 * 3**3
 R is an environment and a language
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5.  The R environment allows for you to submit
command and see results immediately.
 The R language is made by the set of rules
and functions that may be run by the R
environment.
 You may keep command sequences (scripts)
for latter use.
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6.  Several functions are available. A couple simple
examples:
 sqrt(2) 2
 abs(-10)  10
 sin(pi) sin( )
 pi is a constant in R, its value is already defined.
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7.  Results, input data, tables etc. are all stored
in R as Objects
 Objects have a name, content , type and are
stored in memory. Ex.
 Creates object “x” with the number 10:
x <- 10
 Show the content of x:
x
In R, abc is different of ABC
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8.  Try:
X <- sqrt(2)
<- and = are equivalent.
Y = sin(pi)
Z = sqrt(X+Y)
 In the above examples, X, Y and Z store
results from each operation.
In R, There is always many ways of
doing the same thing.
We will try to focus on a single way of doing each task.
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9.  What is the value of C at the end of the script?
A = 1
B = 2
C = A + B
A = 5
B = 5
 Why?
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10. SEXECASCAV|CGIN 10

11.  Tool that makes it easier to use R
 Manages work windows
 Easier access to objects, scripts, history of
commands and plots.
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12. Editing Scripts &
object view
Console
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13. Object list
& history
Help, plots,
files & packages
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14.  Object that hold multiple values that store
data of a single type
 Function c( ) (“c” from concatenate) groups
values to build a vector:
X = c(1,3,6)
 To access vector elements:
X[1] X[3]
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15.  Operations may be performed and functions
applied over the whole vector. Ex.
X = c(1,3,5)
Y = c(10,20,30)
X+Y
[1] 11 23 35
sum(X)
[1] 9
 How about X + 100 ?
[1] 101 103 105 due to the
Recycling law
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16.  When the size of an object required by an
operation is different from the actual size,
available data is repeated as needed.
 As X has 3 elements, X+100 is the same as
X + c(100,100,100)
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17. > X = 1:10
> [1] 1 2 3 4 5 6 7 8 9 10
> X = seq(0,1,by=0.1)
> [1] 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
> rep(“a”,5)
> “a” “a” “a” “a” “a”
> names = c("fulano", "beltrano",
"cicrano")
> names [1] "fulano" "beltrano" "cicrano"
> letras = letters[1:5]
> letras [1] "a" "b" "c" "d" "e"
> letras = LETTERS[1:5]
> letras [1] "A" "B" "C" "D" "E"
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18.  numeric  integer
 is.numeric( )  is.integer( )
 as.numeric( )  as.integer( )
 character  logical
 is.character( )  T == TRUE == 1
 as.character( )  F == FALSE == 0
A == B means “is A equal to B?”
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19.  A Vector arranged in rows & columns
m1 <- matrix(1:12, ncol = 3)
[,1] [,2] [,3]
[1,] 1 5 9
[2,] 2 6 10
[3,] 3 7 11
[4,] 4 8 12
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20.  length(m1)
 [1] 12
 dim(m1)
 [1] 4 3
 nrow(m1)
 [1] 4
 ncol(m1)
 [1] 3
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21.  m1[1, 2]
 [1] 5
 m1[2, 2]
 [1] 6
 m1[ , 2]
 [1] 5 6 7 8
 m1[3, ] m1[1,2]= 99
 [1] 3 7 11 changes the value of the cell
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22. m1[1:2, 2:3]
[,1] [,2]
[1,] 5 9
[2,] 6 10
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23. colnames(m1)
NULL
rownames(m1)
NULL
colnames(m1) = c("C1","C2","C3")
m1[,”C1”]
[1] 1 2 3 4 t(m1) transpose of m1
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24.  “matrix” with many dimensions. Ex. 3 dim.:
ar1 <- array(1:24, dim = c(3, 4, 2))
, , 1
1ª matrix
[,1] [,2] [,3] [,4]
[1,] 1 4 7 10
[2,] 2 5 8 11
[3,] 3 6 9 12 For a 3 dimention array, you
migth visualize the 3rd
, , 2 dimentions as a colections of
matrices.
[,1] [,2] [,3] [,4]
[1,] 13 16 19 22
[2,] 14 17 20 23 2ª matrix
[3,] 15 18 21 24
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25.  How to work with this kind of data?
Ano Código do Órgão
UF Órgão Código da UO unidade orçamentária função subfunção programa ação
localizador descrição da ação valor P&D valor ACTC
Adm
direta e MODERNIZAÇÃO DO SISTEMA DE
2010 AC 1 indireta 1 Adm direta e indireta 19 121 2056 1548 PLANEJAMENTO E GESTÃO DA SDCT R$ - R$ 16.655,00
PROGRAMA DE COOPERAÇÃO TÉCNICA E
Adm FINANCEIRA COM INSTIT. NAC. INTERN.
direta e GOVERNAMENTAIS E NÃO
2010 AC 1 indireta 1 Adm direta e indireta 19 121 2056 1549 GOVERNAMENTAIS R$ - R$ 715.000,00
Adm
direta e MANUTENÇÃO DO GABINETE DO SECRETÁ
2010 AC 1 indireta 1 Adm direta e indireta 19 122 2009 2224 RIO R$ - R$ 27.732,11
Adm
direta e
2010 AC 1 indireta 1 Adm direta e indireta 19 122 2009 2227 DEPARTAMENTO DE GESTÃO INTERNA R$ - R$ 2.266.169,90
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26. colnames(d) [1] "letra" "num" "valor"
 Each column has its own data type
d = data.frame(letters[1:4], 1:4, 10.5)
letters.1.4. X1.4 X10.5
1 a 1 10.5 We will be using
2 b 2 10.5 data.frames most of
3 c 3 10.5 the time
4 d 4 10.5
 We can change column names:
colnames(d) = c("letra","num", "valor")
colnames(d)
[1] "letra" "num" "valor“
d$valor # selects column “valor” from d
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27.  list
 factor
latter...
27
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28.  Several possible sources.
 We will see:
 Keyboard x = scan( )
 Excel files
 CSV files
 SQL Databases
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29. require(XLConnect)
wb <- loadWorkbook(“AC_PDACTCaula.xls”)
plan1 <- readWorksheet(wb, sheet = 1)
str(plan1)
View(plan1)
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30. require(XLConnect)
 Loads package XLConnect
 Packages are sets of functions and data that
add capabilities to R.
 If the package is not installed:
setInternet2() #only on windows
install.packages("XLConnect", dep=T)
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31.  Creates an object “wb” that points to the
excel file:
wb <-
loadWorkbook(“AC_PDACTCaula.xls”)
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32.  Load the first sheet data into an object called
“plan1”
plan1 <- readWorksheet(wb, sheet = 1)
R functions
identify
parameters by Or by name, or
order both
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33.  Show the structure of the new object:
str(plan1) str() works with any R
Object. It is very useful.
 Show data on a window:
View(plan1) In RStudio, you may click on na
object from the objects list to the
same effect
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34. args(readWorksheet) #shows available
parameters
function (
object, #workbook “wb”
sheet, #number or name of the sheet
startRow, #
startCol, #
endRow, #
endCol, #
header # T or F: use first line to
name columns )
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35.  Comma-separated values
 Very popular format for data interchange
 ;
Other separators are also popular: <tab> <space>
 Example:
uf ano valido somaactc somapd
AC 2009 1 34296430.67 3630841.04
AC 2010 1 29397712.04 3579715.12
AL 2009 1 12650160.51 8903714.41
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36.  Example:
uf ano valido somaactc somapd
AC 2009 1 34296430,67 3630841,04
AC 2010 1 29397712,04 3579715,12
AL 2009 1 12650160,51 8903714,41
 To read this file:
d = read.csv(file="AgregaUF20110930_b.txt",
header=T, # uses first line as column names
sep="t", # separator is <tab>
dec="," # decimals uses comma
)
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37.  str(d) #structure
 summary(d) #Statistical summary
 head(d) #first rows
 tail(d) #last rows
 plot(d) #standard plot
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38. require(RODBC)
canal <- odbcConnect(
“base_ODBC",
case="tolower“,
uid=“user”,
pwd=“password”)
d <- sqlQuery(canal,
”select * from table where year = 2010”,
as.is=T)
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39.  How to get the sum of values from a
data.frame column?
sum(data.frame$column)
sum(d$somapd)
[1] NA
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40.  NA Not Available
 Missing values.
 NaN Not a Number
 Value not able to be presented as a number.
 Inf & -Inf
 plus and minus infinite
Try: c(-1,0,1)/0
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41.  Sum:
sum(d$somapd, na.rm=T)
[1] 4836882446
 Mean:
mean(d$somapd, na.rm=T)
 Median:
median(d$somapd, na.rm=T)
 Standard deviation:
sd(d$somapd, na.rm=T)
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42.  For these examples:
milsa = read.csv("milsaText.txt",
sep="t", head=T, dec=".")
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43.  Absolute frequencies
table(milsa$civil)
 Relative frequencies
table(milsa$civil) /
length(milsa$civil)
or
prop.table(milsa$civil)
 Pie chart
pie(table(milsa$civil))
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44.  With attach(milsa)
 Absolute frequencies
table(civil)
 Relative frequencies
table(civil) /
length(civil)
or
prop.table(civil)
 Pie Chart
after: detach(milsa)
pie(table(civil))
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45.  Bar plot:
barplot(table(instrucao))
 remember:
 I may save any result as an object to use it later.
instrucao.tb = table(instrucao)
barplot(instrucao.tb)
pie(instrucao.tb)
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46.  Try:
prop.table(filhos)
 Solution:
prop.table(table(filhos))
 Other solution:
 Filter out elements with NA
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47.  mean(filhos, na.rm=T)
 median(filhos, na.rm=T)
 range(filhos, na.rm=T)
 var(filhos, na.rm=T) #variance
 sd(filhos, na.rm=T) #standard
deviation
 Quantiles:
 filhos.quartis = quantile(filhos, na.rm=T)
 interquartile range:
 filhos.quartis [4] -filhos.quartis [1]
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48.  plot(milsa)
 plot(salario ~ ano)
 hist(salario)
 boxplot(salario)
 stem(salario)
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49.  Selecting some rows
 milsaNovo = milsa[c(1,3,5,6) , ]
 Selecting some columns
 milsaNovo = milsa[ , c(1,3,5)]
 milsaNovo = milsa[ , c(“funcionario”,
”instrucao“, “salario”)]
 Attention:
 New copy
 milsaNovo=milsa[c(1,3,5,6) ,]
 Replaces previous
 milsa=milsa[c(1,3,5,6) , ]
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50.  Who earns above median
 acimamediana = milsa[ salario >
median(salario), ]
 Who is married and has higher education
degree?
 casadoEsuperior = milsa[
civil==“casado” & instrucao ==
“Superior”, ]
AND: both must be true
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51.  Who is married or has higher education
degree?
 casadoOUsuperior = milsa[
civil==“casado” | instrucao ==
“Superior”, ]
OR: at least one must
be true
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52. NOT
 milsaLimpo=milsa[!is.na(salario), ]
 In English:
 New Table milsaLimpo
 equals =
 Old table milsa
 Select [
 Rows where
 Salary is not NA ! is.na(salario)
 And all columns , ]
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53. How many are married?
sum(civil==“casado”)
 or
table(civil)["casado"]
How may are married and has higher ed.
degree?
sum(civil==“casado” & instrucao ==
“Superior” )
 or
table(civil,instrucao)["casado","S
uperior"]
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54.  milsaNovo is equal to milsa, without
rows 1,2 & 5 & without columns 1 &
8:
milsaNovo =
milsa[-c(1,2,5), -c(1,8)]
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55. Which rows where this
is TRUE
 sup = which(instrucao=="Superior“)
 [1] 19 24 31 33 34 36
 May use it again later:
 mean(milsa[sup,”salario”])
 Mean salary for those with higher education
advantage: it is not a copy!!
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56.  A random sample of 10 rows from
milsa:
amostra =
sample(x=nrow(milsa),size=10)
[1] 12 29 1 3 17 14 26 33 20 31
 Mean salary for the sample:
mean(milsa[amostra,”salario”])
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57.  By number of children:
milsa[order(filhos),]
 Decreasing:
milsa[order(filhos, decreasing=T),]
 By number of children and then age:
milsa[order(filhos,ano),]
 10 youngest:
head(milsa[order(ano),], 10)
 10 older:
tail(milsa[order(ano),], 10)
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58.  Removing an object
 rm(milsaNovo)
 Removing every object
 rm(list = ls())
ls() : list of current
objects
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59.  List objects are collections that may include different
types of objects.
lis = list(A=1:10, B=“Text”,
C = matrix(1:9,ncol=3)
 They are often used as parameters to functions or as
result sets from them.
 lis[1:2]
 A list with the two first objects from lis (A & B)
 lis[[1]]:
 object stored at the first position of the list ( the content of
A). The same as lis$A
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60.  Saving all objects:
save.image(“file.RData”)
 Saving selected objects:
save( x, y, file=“file.RData”)
 loading:
load(“file.RData“)
Several “loads”: objects with distinct
names are kept in memory
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61.  Saving a script “.R” that reproduces the desired
output.
 Advantage:
 It may be used to document the work performed;
 It may be used again over updated data to update
results.
 Hybrid model:
 Save intermediate results that take long time to
process. Update them less often.
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62.  Add a column to a data.frame:
milsa$idade =
milsa$ano + milsa$mes/12
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63. X Y
6+3+5=14
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64. X Y
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65. X Y
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66. X Y
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67. X Y
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68.  Example:
&
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69.  Only rows found in both data.frames:
merge(x=milsa,
y=tabInst,by.x="instrucao", by.y="desc“,
all=F)
All rows from data.frame X:
merge(x=milsa,
y=tabInst,by.x="instrucao", by.y="desc",
all.x=T)
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70. All rows from data.frame y:
merge(x=milsa,
y=tabInst,by.x="instrucao", by.y="desc",
all.y=T)
All rows from data.frames x & y:
merge(x=milsa,
y=tabInst,by.x="instrucao",
by.y="desc", all=T)
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71.  From text to numeric
d.f$novaColuna = as.numeric(d.f$coluna)
 From numeric to text:
d.f$novaColuna=as.character(d.f$coluna)
 From text or numeric to integer:
d.f$novaColuna = as.integer(d.f$coluna)
Integers save memory
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72.  Representation for categorical data
 Nominal
▪ “married”, “single”
 Ordinal Factors save memory
▪ “tall”, “short”
 Assure proper treatment for these variables
by many R functions
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73. Nominal:
milsa$fatorcivil=factor(milsa$civil, ordered=F)
$fatorcivil : Factor w/ 2 levels
"casado","solteiro": 2 1 1 2 2 1 2 2 1 2
Ordinal:
milsa$fatormes = factor(milsa$mes, ordered=T)
$fatormes : Ord.factor w/ 12 levels
"0"<"1"<"2"<"3"<..: 4 11 6 11 8 1 1 5 11 7 ...
It is possible to define a custom order: ?factor
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74.  From factor to text:
d.f$novaColuna =
as.character(d.f$colunaFator)
 From factor to numeric:
d.f$novaColuna =
as.numeric(
as.character(d.f$colunaFator))
The internal representation of a factor
is different from its text description
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75.  Using:
m1 <- matrix(1:12, ncol = 3)
 Sum of columns (a value for each column):
colSums(m1)
[1] 10 26 42
 or
apply(m1,2,sum)
[1] 10 26 42
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76.  Sum of rows (one value for each row):
rowSums(m1)
[1] 15 18 21 24
 or
apply(m1,1,sum)
[1] 15 18 21 24
May use any
function, even
your own.
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77. aggregate(salario ~ instrucao,
data = milsa, mean)
instrucao salario
1 1oGrau 7.836667
2 2oGrau 11.528333
3 Superior 16.475000
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78. aggregate(
salario ~ instrucao + civil,
data = milsa, mean)
instrucao civil salario
1 1oGrau casado 7.044000
2 2oGrau casado 12.825000
3 Superior casado 17.783333
4 1oGrau solteiro 8.402857
5 2oGrau solteiro 8.935000
6 Superior solteiro 15.166667
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79. model = lm(
formula = salario ~ ano + instrucao,
data = milsa)
summary(model)
Just one line!!!
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80. Prof. Dr. Roberto Dantas de Pinho, roberto.pinho@mct.gov.br
This presentation is based on courses by
Dr. Paulo Justiniano Ribeiro Jr (UFPR) &
Dr. Cosme Marcelo Furtado Passos da Silva (FIOCRUZ)
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