Introduction to R - PS 1

1. Practice: aggregation summaries. Use the aggregate function and the mtcars dataset to find: 1) average horsepower grouped by the number of cylinders 2) maximum weight per number of carburators.

attach(mtcars)
str(mtcars)

## 'data.frame':    32 obs. of  11 variables:
##  $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
##  $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
##  $ disp: num  160 160 108 258 360 ...
##  $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
##  $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
##  $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
##  $ qsec: num  16.5 17 18.6 19.4 17 ...
##  $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
##  $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
##  $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
##  $ carb: num  4 4 1 1 2 1 4 2 2 4 ...

#- average horsepower grouped by the number of cylinders
dataAggregation1 <- aggregate(mtcars$hp, by=list(cyl), FUN=mean, na.rm=TRUE)
dataAggregation1

##   Group.1      x
## 1       4  82.64
## 2       6 122.29
## 3       8 209.21

#- maximum weight per number of carburators
dataAggregation2 <- aggregate(mtcars$wt, by=list(carb), FUN=max, na.rm=TRUE)
dataAggregation2

##   Group.1     x
## 1       1 3.460
## 2       2 3.845
## 3       3 4.070
## 4       4 5.424
## 5       6 2.770
## 6       8 3.570

detach(mtcars)

2. Practice: comma-separated import from a url site. Use read.table but instead of naming a ???le on your working directory use a URL location. Import, for example, the List of Campus (Complementary Table 50) from the Upen Data UPF site.

file <- "http://data.upf.edu/storage/f/2013-06-13T13%3A56%3A38.399Z/tc-50-campus.csv"
data <- read.table(file, header=TRUE, sep=",", quote="")
str(data)

## 'data.frame':    120 obs. of  4 variables:
##  $ id         : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ codi       : int  401 402 403 404 405 406 407 408 409 410 ...
##  $ universitat: Factor w/ 12 levels "UAB","UAO","UB",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ descripcio : Factor w/ 114 levels "Alimentación de Torribera",..: 91 90 86 95 98 1 41 76 25 10 ...

tail(data)

##      id codi universitat
## 115 115 6002        Uvic
## 116 116 6003        Uvic
## 117 117 6004        Uvic
## 118 118 6201         UIC
## 119 119 6202         UIC
## 120 120 7001         UAO
##                                                               descripcio
## 115                                                           Miramarges
## 116                  Campus del centro 'BAU. Escuela Superior de Diseño'
## 117 Campus del centro 'EADA. Escuela de Alta Dirección y Administración'
## 118                                                            Barcelona
## 119                                                Sant Cugat del Vallès
## 120                                                          Bellesguard

summary(data)

##        id             codi       universitat
##  Min.   :  1.0   Min.   : 401   UAB    :34  
##  1st Qu.: 30.8   1st Qu.:2218   UPC    :15  
##  Median : 60.5   Median :2414   URV    :14  
##  Mean   : 60.5   Mean   :3125   UB     :13  
##  3rd Qu.: 90.2   3rd Qu.:4212   UdG    :11  
##  Max.   :120.0   Max.   :7001   UPF    :11  
##                                 (Other):22  
##                                                                   descripcio 
##  Barcelona                                                             :  3  
##  Campus del centro 'Barcelona Graduate School of Economics'            :  2  
##  Campus del centro 'Instituto Nacional de Educación Física de Cataluña':  2  
##  Sabadell                                                              :  2  
##  Sant Cugat del Vallès                                                 :  2  
##  Alimentación de Torribera                                             :  1  
##  (Other)                                                               :108

3. Practice: fixed-width import. Import all the columns of the RRHH09.txt ???le (but only 1000 rows). You?ll have to look at the metadata for the column widths and names. Summarize the columns, in particular the labor market situation (SITLAB): is the employment rate among respondents high? (decypher its code values reading the metadata).

# Metadata for all the all the columns 
setwd("/Users/lauracozma/Desktop/Brushups/R")

RRHH09Widths <- c(6,  2,    4,  2,  4,  1,  4,  4,  4,  1,  1,  2,  2,  2,  2,  2,  1,  1,  1,  1,  1,  1,  1,  1,  2,  2,  2,  2,  2,  2,  1,  3,  1,  1,  1,  4,  2,  2,  2,  4,  4,  1,  1,  2,  2,  1,  2,  1,  1,  2,  4,  2,  4,  1,  3,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  3,  2,  2,  1,  1,  1,  1,  1,  1,  1,  1,  1,  3,  3,  3,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  4,  4,  4,  3,  4,  4,  4,  3,  4,  4,  4,  3,  4,  4,  4,  3,  4,  4,  4,  3,  4,  4,  4,  3,  4,  4,  4,  3,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  4,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  3,  3,  3,  2,  2,  2,  2,  2,  1,  1,  1,  1,  11)

RRHH09Names <- c('MUIDENT', 'CCAARESI', 'ANONAC',   'CCAANAC',  'CONTNACIM',    'RELA', 'CONTNAC1', 'CONTNAC2', 'CONTNAC3', 'SEXO', 'ESTADOCIVIL',  'DEPEN5',   'DEPEN18',  'DEPENMAS', 'NIVESTPA', 'NIVESTMA', 'NIVPROFPA',    'NIVPROFMA',    'ACTIPRIPA1',   'ACTIPRIMA1',   'EDUPRIM',  'EDUSECUN', 'BACHILLER',    'RES',  'CARES1',   'MESRES1',  'CARES2',   'MESRES2',  'CARES3',   'MESRES3',  'DOCTOR',   'CODIDOC',  'INFUN',    'INAPL',    'DESTEC',   'ANODOC',   'MESDOC',   'DURDOCANO',    'DURDOCMES',    'CONTEST',  'ANOEST',   'FINADOC',  'TRABDOC',  'ANOSINTRA',    'MESSINTRA',    'SITLAB',   'HTRAB',    'INGRESOS', 'AUTON',    'CNAE2',    'ANOINIALP',    'CAEMP',    'CONTEMP',  'SECTOR',   'CODALP',   'CATEGPROF',    'HLECT',    'TIPOCONT', 'JORLAB',   'BUSCATRAB',    'NIVELMIN', 'NIVELEST', 'RELTRABDOC',   'NIVELSAT1',    'NIVELSAT2',    'NIVELSAT3',    'NIVELSAT4',    'NIVELSAT5',    'NIVELSAT6',    'NIVELSAT7',    'NIVELSAT8',    'NIVELSAT9',    'NIVELSAT10',   'NIVELSAT11',   'NIVELSAT12',   'NIVELSAT13',   'ACTIANT',  'SECTORANT',    'ACTIPRIANT',   'ACTIANTANO',   'ACTIANTMES',   'TRABPOS',  'MOTIVOPOS1',   'MOTIVOPOS2',   'MOTIVOPOS3',   'MOTIVOPOS4',   'MOTIVOPOS5',   'MOTIVOPOS6',   'MOTIVOPOS7',   'MOTIVOPOS8',   'PORCINVES',    'PORCDOC',  'PORCOTRAS',    'FINANPOS', 'GESBUSEMP',    'PASOSEMP1',    'PASOSEMP2',    'PASOSEMP3',    'PASOSEMP4',    'PASOSEMP5',    'PASOSEMP6',    'PASOSEMP7',    'PASOSEMP8',    'PASOSEMP9',    'PASOSEMP10',   'PASOSEMP11',   'NOPASOSEMP1',  'NOPASOSEMP2',  'NOPASOSEMP3',  'NOPASOSEMP4',  'NOPASOSEMP5',  'NOPASOSEMP6',  'NOPASOSEMP7',  'NOPASOSEMP8',  'NOPASOSEMP9',  'CONT1',    'ANOIPAIS1',    'ANOFPAIS1',    'MESES1',   'CONT2',    'ANOIPAIS2',    'ANOFPAIS2',    'MESES2',   'CONT3',    'ANOIPAIS3',    'ANOFPAIS3',    'MESES3',   'CONT4',    'ANOIPAIS4',    'ANOFPAIS4',    'MESES4',   'CONT5',    'ANOIPAIS5',    'ANOFPAIS5',    'MESES5',   'CONT6',    'ANOIPAIS6',    'ANOFPAIS6',    'MESES6',   'CONT7',    'ANOIPAIS7',    'ANOFPAIS7',    'MESES7',   'MOTFUERA1',    'MOTFUERA2',    'MOTFUERA3',    'MOTFUERA4',    'MOTFUERA5',    'MOTFUERA6',    'MOTFUERA7',    'MOTVENIR1',    'MOTVENIR2',    'MOTVENIR3',    'MOTVENIR4',    'MOTVENIR5',    'MOTVENIR6',    'MOTVENIR7',    'PREVIRSE', 'MESANTESIR',   'MOTIRSE1', 'MOTIRSE2', 'MOTIRSE3', 'MOTIRSE4', 'MOTIRSE5', 'MOTIRSE6', 'CONTOUT',  'INV',  'MOTNOINV1',    'MOTNOINV2',    'MOTNOINV3',    'MOTNOINV4',    'MOTNOINV5',    'MOTNOINV6',    'MOTNOINV7',    'MOTNOINV8',    'MOTNOINV9',    'ALGINV',   'MOTINV1',  'MOTINV2',  'MOTINV3',  'MOTINV4',  'MOTINV5',  'MOTINV6',  'MOTINV7',  'MOTINV8',  'MOTINV9',  'MESESINV', 'ARTIC',    'LIBROS',   'NUMPAT',   'PATNAC',   'PATEU',    'PATCOOP',  'PATCOM',   'COMPA',    'TUTOR',    'COOPERA',  'INVFUT',   'FACTOR')


# Fixed-width import function 
file <- "RRHH09.txt"
fwfDataFrame <- read.fwf(file, n = 1000, widths = RRHH09Widths, col.names = RRHH09Names)

summary(fwfDataFrame$SITLAB)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    1.00    1.00    1.00    1.07    1.00    3.00

SITLAB is a variable that takes value:

1 if respondent is employed
2 if respondent is unemployed
3 if respondent “Inactivo / Blanco” At this stage we cannot say anything about the employment rate among respondents since our variable is character. What we should do is convert it to a number and then recode it as a dummy variable (i.e. we should take care of the missing values, here coded as 3).

sitlab <- as.integer(fwfDataFrame$SITLAB)

fwfDataFrame$SITLAB[fwfDataFrame$SITLAB==3] <- NA
fwfDataFrame$SITLAB[fwfDataFrame$SITLAB==2] <- 0
sitlab <- as.integer(fwfDataFrame$SITLAB)
summary(sitlab)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   1.000   1.000   0.983   1.000   1.000      25

As we can notice above, employment rate among respondents is high, very close to 1 (mean=0.98).

4. Practice: describing BCN census data. Use the Demographic data of censal sections in Barcelona we imported earlier and… 1) produce stripcharts, histograms and kernel density estimates of a variable of your choice. Be creative: de???ne a new variable combin-ing existing ones, combine colors, explore optional parameters of the functions. 2) scatterplot two variables and add a linear regression line. Try adding a loess regression curve. Choose appropriate point char-acters and point dimensions (with lots of datapoints maybe blank-???lled smaller dots are most convenient). 3) challenging bit: use the layout function to display all the univari-ate plots in a single matrix of plots.

# data import & header translation into English
setwd("/Users/lauracozma/Desktop/Brushups/R")
censusBCN <- read.table("MAP_SCENSAL.csv", header = TRUE, sep = ";")
names(censusBCN) <- c("Date", "Men", "CensusDivision", "Women", "AGE_0_14", "AGE_15_A_24", "AGE_25_A_64", "AGE_65_plus", "NATIONALS", "EUCommunity", "Overseas")
str(censusBCN)

## 'data.frame':    1061 obs. of  11 variables:
##  $ Date          : Factor w/ 1 level "21/10/13": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Men           : int  780 898 1855 1676 1508 1014 1260 2274 1250 1455 ...
##  $ CensusDivision: int  1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 ...
##  $ Women         : int  585 730 1549 1262 1096 771 810 1429 1039 1015 ...
##  $ AGE_0_14      : int  139 179 599 363 294 159 223 484 268 331 ...
##  $ AGE_15_A_24   : int  113 196 352 278 198 153 199 422 251 292 ...
##  $ AGE_25_A_64   : int  885 1016 1969 1948 1708 1247 1478 2370 1449 1584 ...
##  $ AGE_65_plus   : int  228 237 484 349 404 226 170 427 321 263 ...
##  $ NATIONALS     : int  726 734 1811 1640 1356 791 922 1498 1151 971 ...
##  $ EUCommunity   : int  145 182 207 245 293 223 218 215 168 158 ...
##  $ Overseas      : int  494 712 1386 1053 955 771 930 1990 970 1341 ...

# Computing a new variable: number of women between 25-64 years old
censusBCN$percentWomen <- censusBCN$Women/(censusBCN$Men + censusBCN$Women) 
censusBCN$WomenAge_25_64 <- censusBCN$percentWomen * censusBCN$AGE_25_A_64
summary(censusBCN$WomenAge_25_64)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     124     378     442     461     516    2090

# 1. Stripchart,histogram and kernel density estimates of WomenAge_25_64
stripchart(censusBCN$WomenAge_25_64,     
           method="stack",
           pch=3, 
           main="Women aged 25-64", 
           col="Red",
           offset=0.5, 
           xlab="Women")

plot of chunk unnamed-chunk-8

hist(censusBCN$WomenAge_25_64,     
     col=rainbow(20),    
     main="Histogram: Number of Women between 25-64 years old", 
     xlab="Women")

plot of chunk unnamed-chunk-9

density <- density(censusBCN$WomenAge_25_64) 
plot(density, main = "")

plot of chunk unnamed-chunk-9

# histogram with fitted density curve
hist(censusBCN$WomenAge_25_64, prob=T, main="Histogram: Number of Women between 25-64 years old, bw=.5")
lines(density(censusBCN$WomenAge_25_64, bw=100),
      col="red",
      lwd=3)

plot of chunk unnamed-chunk-9

# 2. scatterplot 2 vars and add a linear regression line

plot(x = censusBCN$AGE_25_A_64, y = censusBCN$Women, ylab = "Women", xlab = "Age 25-64", pch = 1, cex=0.1)

# Linear regression line
abline(lm(censusBCN$Women ~ censusBCN$AGE_25_A_64), col = "red")

plot of chunk unnamed-chunk-10

# Loess regression 
lw1 <- loess(censusBCN$Women ~ censusBCN$AGE_25_A_64)
plot(loess(censusBCN$Women ~ censusBCN$AGE_25_A_64),pch=1,cex=0.1,ylab = "Women", xlab = "Age 25-64")
j <- order(censusBCN$AGE_25_A_64)
lines(censusBCN$AGE_25_A_64[j],lw1$fitted[j],col="blue",lwd=2)

plot of chunk unnamed-chunk-10

# 3. use the layout function to display all the univariate plots in a single matrix of plots
layout(matrix(c(1,1,2,3), 2, 2, byrow = TRUE), 
    widths=c(2,2), heights=c(1,2))


stripchart(censusBCN$WomenAge_25_64,     
           method="stack",
           pch=3, 
           main="Women aged 25-64", 
           col="Red",
           offset=0.5, 
           xlab="Women")

hist(censusBCN$WomenAge_25_64,     
     col=rainbow(20),    
     main="", 
     xlab="Women") 

plot(density, main = "")

plot of chunk unnamed-chunk-11

5. Practice: character to numeric when importing. the Ageing Population data imported earlier has some numerical columns stored as character. Use basic string manipulations to convert them back into numerical.

agingPopulation <- read.csv("aging-population-2008.csv", header = TRUE, sep = ",", quote = "\"", stringsAsFactors=FALSE)
str(agingPopulation)

## 'data.frame':    333 obs. of  7 variables:
##  $ SOA.Code                                              : chr  "E01031175" "E01031204" "E01031104" "E01031135" ...
##  $ SOA.Name                                              : chr  "Overslade South East" "Wootton Wawen" "St Nicolas North & College" "Bilton South Cock Robin" ...
##  $ Ward.Name                                             : chr  "Overslade" "Henley" "St. Nicolas" "Bilton" ...
##  $ Locality                                              : chr  "Rugby Town West" "Alcester, Studley & Henley" "Weddington & St. Nicolas" "Rugby Town West " ...
##  $ District.Borough                                      : chr  "Rugby" "Stratford-on-Avon" "Nuneaton & Bedworth" "Rugby" ...
##  $ Total.Population                                      : chr  "1,335" "1,286" "1,453" "1,588" ...
##  $ X..of.Total.Population.aged.60...Females...65...Males.: chr  "41.7%" "41.1%" "40.0%" "39.1%" ...

# Total Population
agingPopulation$TotalPopulation <- as.numeric(gsub('![[:alnum:]]*[[:space:]]|[[:punct:]]', '', agingPopulation$Total.Population))


# % of Total Population aged 60+ (Females), 65+ (Males)
#replace the % from the string variable
agingPopulation$PercentTotal.Population.aged.60Females..65Males <- as.numeric(gsub("%", "", cat(agingPopulation$X..of.Total.Population.aged.60...Females...65...Males.)))

## 41.7% 41.1% 40.0% 39.1% 38.7% 37.2% 36.3% 36.2% 35.6% 34.9% 34.9% 33.7% 33.5% 33.4% 32.4% 32.4% 32.2% 32.0% 31.9% 31.8% 31.8% 31.8% 31.6% 31.4% 31.3% 31.2% 31.2% 31.0% 30.7% 30.3% 29.8% 29.8% 29.7% 29.7% 29.7% 29.6% 29.4% 29.3% 29.3% 29.3% 29.0% 28.9% 28.9% 28.9% 28.9% 28.8% 28.8% 28.7% 28.5% 28.4% 28.1% 27.7% 27.7% 27.7% 27.4% 27.3% 27.2% 27.2% 27.1% 27.0% 27.0% 27.0% 26.8% 26.7% 26.6% 26.5% 26.5% 26.4% 26.2% 26.1% 26.1% 26.0% 25.9% 25.9% 25.9% 25.9% 25.8% 25.8% 25.7% 25.6% 25.5% 25.5% 25.4% 25.4% 25.4% 25.3% 25.2% 25.2% 25.2% 25.1% 25.1% 25.0% 25.0% 24.9% 24.8% 24.7% 24.6% 24.6% 24.5% 24.5% 24.5% 24.5% 24.4% 24.4% 24.3% 24.2% 24.1% 24.1% 24.0% 24.0% 23.9% 23.8% 23.7% 23.7% 23.7% 23.6% 23.6% 23.6% 23.5% 23.5% 23.5% 23.4% 23.0% 22.9% 22.9% 22.7% 22.7% 22.6% 22.5% 22.5% 22.4% 22.4% 22.4% 22.4% 22.3% 22.3% 22.3% 22.3% 22.2% 22.2% 22.1% 22.0% 22.0% 21.9% 21.9% 21.9% 21.8% 21.7% 21.7% 21.7% 21.7% 21.7% 21.7% 21.6% 21.6% 21.6% 21.5% 21.5% 21.4% 21.4% 21.3% 21.3% 21.3% 21.2% 21.1% 21.1% 21.0% 21.0% 20.9% 20.8% 20.8% 20.7% 20.7% 20.7% 20.5% 20.5% 20.4% 20.4% 20.4% 20.2% 20.2% 20.1% 20.0% 20.0% 19.9% 19.7% 19.6% 19.5% 19.5% 19.4% 19.3% 19.3% 19.2% 19.2% 19.1% 19.0% 19.0% 19.0% 19.0% 19.0% 19.0% 18.9% 18.8% 18.7% 18.7% 18.6% 18.6% 18.6% 18.5% 18.5% 18.5% 18.4% 18.3% 18.3% 18.2% 18.2% 18.2% 18.2% 18.1% 18.0% 17.9% 17.8% 17.7% 17.6% 17.6% 17.6% 17.6% 17.5% 17.5% 17.4% 17.4% 17.3% 17.3% 17.1% 17.1% 17.1% 17.1% 17.0% 17.0% 17.0% 17.0% 16.9% 16.9% 16.8% 16.5% 16.4% 16.4% 16.3% 16.3% 16.2% 16.1% 16.0% 16.0% 16.0% 15.9% 15.9% 15.9% 15.8% 15.8% 15.8% 15.8% 15.7% 15.5% 15.4% 15.3% 15.1% 15.1% 15.1% 15.0% 15.0% 15.0% 14.8% 14.7% 14.4% 14.3% 14.3% 14.3% 14.3% 14.2% 14.2% 14.2% 14.2% 14.1% 14.1% 14.0% 13.6% 13.4% 13.4% 13.3% 13.3% 13.3% 12.9% 12.8% 12.7% 12.7% 12.7% 12.6% 12.2% 12.1% 12.0% 11.8% 11.8% 11.7% 11.7% 11.7% 11.4% 11.3% 11.3% 11.1% 11.0% 10.8% 10.7% 10.6% 10.6% 10.5% 10.4% 10.0% 10.0% 10.0% 9.8% 9.7% 9.4% 9.0% 8.6% 8.2% 7.9% 7.9% 7.5% 7.0% 6.7% 5.9% 5.6% 5.4%

## Error: replacement has 0 rows, data has 333

str(agingPopulation)

## 'data.frame':    333 obs. of  8 variables:
##  $ SOA.Code                                              : chr  "E01031175" "E01031204" "E01031104" "E01031135" ...
##  $ SOA.Name                                              : chr  "Overslade South East" "Wootton Wawen" "St Nicolas North & College" "Bilton South Cock Robin" ...
##  $ Ward.Name                                             : chr  "Overslade" "Henley" "St. Nicolas" "Bilton" ...
##  $ Locality                                              : chr  "Rugby Town West" "Alcester, Studley & Henley" "Weddington & St. Nicolas" "Rugby Town West " ...
##  $ District.Borough                                      : chr  "Rugby" "Stratford-on-Avon" "Nuneaton & Bedworth" "Rugby" ...
##  $ Total.Population                                      : chr  "1,335" "1,286" "1,453" "1,588" ...
##  $ X..of.Total.Population.aged.60...Females...65...Males.: chr  "41.7%" "41.1%" "40.0%" "39.1%" ...
##  $ TotalPopulation                                       : num  1335 1286 1453 1588 1487 ...

First, when I have imported the csv file, R sees string variables as factors. In order to avoid this i have added the stringsAsFactors=FALSE option to the read.csv function. Then I have tried convert the Total Population to a numeric variable. In order to do that, I got rid of the commas (that separated the thousands) and the quotes and then converted to numeric. As far as the other variable which we could convert to numeric concerned, I have tried to write the code above, but it gives me an error. Basically what I have done is: eliminating the commas and the percentual sign and then apply the as.numeric(). It gives me an error.

6. Practice: proper formatting of dates in imports. Create a new column in the BCN census data containing the day after the ???rst column date. You must use the as.Date function.

censusBCN <- read.table("MAP_SCENSAL.csv", header = TRUE, sep = ";")
names(censusBCN) <- c("Date", "Men", "CensusDivision", "Women", "AGE_0_14", "AGE_15_A_24", "AGE_25_A_64", "AGE_65_plus", "NATIONALS", "EUCommunity", "Overseas")
censusBCN$Date<-strftime(strptime(censusBCN$Date,f="%d/%m/%y"),f="%d/%m/%Y")
censusBCN$TheDayAfter <- as.Date(censusBCN$Date, f="%d/%m/%Y")+1
str(censusBCN)

## 'data.frame':    1061 obs. of  12 variables:
##  $ Date          : chr  "21/10/2013" "21/10/2013" "21/10/2013" "21/10/2013" ...
##  $ Men           : int  780 898 1855 1676 1508 1014 1260 2274 1250 1455 ...
##  $ CensusDivision: int  1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 ...
##  $ Women         : int  585 730 1549 1262 1096 771 810 1429 1039 1015 ...
##  $ AGE_0_14      : int  139 179 599 363 294 159 223 484 268 331 ...
##  $ AGE_15_A_24   : int  113 196 352 278 198 153 199 422 251 292 ...
##  $ AGE_25_A_64   : int  885 1016 1969 1948 1708 1247 1478 2370 1449 1584 ...
##  $ AGE_65_plus   : int  228 237 484 349 404 226 170 427 321 263 ...
##  $ NATIONALS     : int  726 734 1811 1640 1356 791 922 1498 1151 971 ...
##  $ EUCommunity   : int  145 182 207 245 293 223 218 215 168 158 ...
##  $ Overseas      : int  494 712 1386 1053 955 771 930 1990 970 1341 ...
##  $ TheDayAfter   : Date, format: "2013-10-22" "2013-10-22" ...