Introducció a R
c(2, 8, 9)
## [1] 2 8 9
a <- c(2, 8, 9)
2*a
## [1] 4 16 18
head(mtcars)
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
mtcars$mpg
## [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2
## [15] 10.4 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4
## [29] 15.8 19.7 15.0 21.4
Classe
library(haven)
PAISOS <- read_sav("C:/Users/prlpz/Downloads/PAISOS.SAV")
PAISOS
## # A tibble: 160 x 14
## IDH NIVELL PAIS ESPVIDA PIB ALFAB CONT CALORIES HABMETG DIARIS
## <dbl> <dbl+l> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 167 1 Mozamb~ 46.4 70 36.9 2 1680 33333 1
## 2 147 1 Tanzan~ 52.1 100 64.4 2 2021 24970 1
## 3 171 1 Etiopia 47.5 110 32.7 2 1610 33333 0
## 4 173 1 Sierra~ 39 160 28.7 2 1695 13620 0
## 5 160 1 Butan 50.7 170 39.2 3 NA 11111 NA
## 6 151 1 Nepal 53.5 170 25.6 2 1957 16667 1
## 7 158 1 Uganda 44.9 180 58.6 2 2162 25000 0
## 8 165 1 Burundi 50.2 210 32.9 2 1941 16667 0
## 9 146 1 Bangla~ 55.6 220 36.4 3 2019 12500 1
## 10 163 1 Guinea~ 43.5 220 51.7 2 2556 7500 1
## # ... with 150 more rows, and 4 more variables: TV <dbl>, SANITAT <dbl>,
## # AGRICULT <dbl>, INDUST <dbl>
PAISOS$IDH
## [1] 167 147 171 173 160 151 158 165 146 163 162 135 157 156 138 174 134
## [18] 172 169 105 141 130 142 109 136 161 133 140 155 149 128 129 111 168
## [35] 139 150 97 116 121 131 107 145 104 118 125 113 152 100 127 123 102
## [52] 126 112 117 96 124 68 122 80 98 115 119 77 57 93 88 65 87
## [69] 108 75 58 85 51 28 66 40 79 46 70 67 29 74 49 69 38
## [86] 33 60 63 52 95 59 84 47 50 32 53 39 37 114 62 55 30
## [103] 25 91 31 22 36 76 44 23 26 17 19 21 9 24 56 35 11
## [120] 18 61 20 4 1 12 8 45 5 14 15 2 6 7 16 10 3
## [137] 27 13 82 34 101 106 73 78 72 110 83 170 137 120 148 132 143
## [154] 144 164 153 159 166 41 154
## attr(,"label")
## [1] "Índex de Desenvolupament Humà"
## attr(,"format.spss")
## [1] "F3.0"
## attr(,"display_width")
## [1] 0
summary(PAISOS$IDH)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 40.75 89.50 88.56 134.25 174.00
attach(PAISOS)
mean(IDH)
## [1] 88.55625
sd(IDH)
## [1] 52.05002
table(NIVELL)
## NIVELL
## 1 2 3
## 47 54 59
PAISOS[NIVELL==1,]
## # A tibble: 47 x 14
## IDH NIVELL PAIS ESPVIDA PIB ALFAB CONT CALORIES HABMETG DIARIS
## <dbl> <dbl+l> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 167 1 Mozamb~ 46.4 70 36.9 2 1680 33333 1
## 2 147 1 Tanzan~ 52.1 100 64.4 2 2021 24970 1
## 3 171 1 Etiopia 47.5 110 32.7 2 1610 33333 0
## 4 173 1 Sierra~ 39 160 28.7 2 1695 13620 0
## 5 160 1 Butan 50.7 170 39.2 3 NA 11111 NA
## 6 151 1 Nepal 53.5 170 25.6 2 1957 16667 1
## 7 158 1 Uganda 44.9 180 58.6 2 2162 25000 0
## 8 165 1 Burundi 50.2 210 32.9 2 1941 16667 0
## 9 146 1 Bangla~ 55.6 220 36.4 3 2019 12500 1
## 10 163 1 Guinea~ 43.5 220 51.7 2 2556 7500 1
## # ... with 37 more rows, and 4 more variables: TV <dbl>, SANITAT <dbl>,
## # AGRICULT <dbl>, INDUST <dbl>
table(CONT)
## CONT
## 1 2 3 4 5
## 26 52 37 34 8
table(NIVELL, CONT)
## CONT
## NIVELL 1 2 3 4 5
## 1 0 37 8 1 0
## 2 3 13 16 17 5
## 3 23 2 13 16 3
plot(PIB, ESPVIDA)
plot(log10(PIB), ESPVIDA)
SERVEIS <- 100-AGRICULT-INDUST
table(SERVEIS>70)
##
## FALSE TRUE
## 138 7
PIB[SERVEIS>70]
## [1] NA NA NA NA NA NA NA NA NA 3470 NA
## [12] NA NA 6210 7940 11670 13460 NA 21070 23830 NA NA
mean(PIB[SERVEIS>70], na.rm=TRUE)
## [1] 12521.43
PAIS[SERVEIS>70]
## [1] NA NA NA NA NA NA
## [7] NA NA NA "Uruguay" NA NA
## [13] NA "Barbados" "Bahrain" "Bahamas" "Israel" NA
## [19] "Canada" "USA" NA NA
PAIS[SERVEIS>70 & !is.na(SERVEIS)]
## [1] "Uruguay" "Barbados" "Bahrain" "Bahamas" "Israel" "Canada"
## [7] "USA"
hist(SANITAT)
hist(SANITAT, breaks=30)
boxplot(SANITAT)
boxplot(SANITAT, INDUST)
boxplot(SANITAT, INDUST, names=c("Sanitat", "Indústria"))
boxplot(AGRICULT~CONT)
CONT <- factor(CONT, labels=c("Europa",
"Àfrica", "Àsia",
"Amèrica", "Oceania"))
boxplot(INDUST~CONT)
Lliurar
table(CONT)
## CONT
## Europa Àfrica Àsia Amèrica Oceania
## 26 52 37 34 8
pie(table(CONT))
aggregate(ESPVIDA, by=list(CONT), mean)
## Group.1 x
## 1 Europa 74.90385
## 2 Àfrica 53.46154
## 3 Àsia 66.62432
## 4 Amèrica 70.05294
## 5 Oceania 68.73750
aggregate(ESPVIDA, by=list(CONT), sd)
## Group.1 x
## 1 Europa 3.115058
## 2 Àfrica 7.674970
## 3 Àsia 8.647652
## 4 Amèrica 5.030889
## 5 Oceania 6.780210
median(HABMETG, na.rm = TRUE)
## [1] 2000
aggregate(HABMETG, by=list(NIVELL), median, na.rm=TRUE)
## Group.1 x
## 1 1 13620
## 2 2 1776
## 3 3 585
summary(PIB)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 70.0 532.5 1715.0 5778.5 6337.5 36730.0 22
summary(PAISOS)
## IDH NIVELL PAIS ESPVIDA
## Min. : 1.00 Min. :1.000 Length:160 Min. :39.00
## 1st Qu.: 40.75 1st Qu.:1.000 Class :character 1st Qu.:55.67
## Median : 89.50 Median :2.000 Mode :character Median :67.60
## Mean : 88.56 Mean :2.075 Mean :64.50
## 3rd Qu.:134.25 3rd Qu.:3.000 3rd Qu.:72.58
## Max. :174.00 Max. :3.000 Max. :79.50
##
## PIB ALFAB CONT CALORIES
## Min. : 70.0 Min. :12.40 Min. :1.000 Min. :1505
## 1st Qu.: 532.5 1st Qu.:54.50 1st Qu.:2.000 1st Qu.:2248
## Median : 1715.0 Median :81.30 Median :3.000 Median :2614
## Mean : 5778.5 Mean :73.39 Mean :2.656 Mean :2661
## 3rd Qu.: 6337.5 3rd Qu.:94.35 3rd Qu.:4.000 3rd Qu.:3166
## Max. :36730.0 Max. :99.00 Max. :5.000 Max. :3947
## NA's :22 NA's :3 NA's :8
## HABMETG DIARIS TV SANITAT
## Min. : 211.0 Min. : 0.00 Min. : 0.00 Min. :0.400
## 1st Qu.: 642.2 1st Qu.: 1.00 1st Qu.: 2.00 1st Qu.:1.300
## Median : 2000.0 Median : 5.00 Median : 9.00 Median :2.050
## Mean : 6709.6 Mean :11.17 Mean :16.17 Mean :2.228
## 3rd Qu.: 8901.5 3rd Qu.:15.75 3rd Qu.:25.50 3rd Qu.:2.800
## Max. :50000.0 Max. :82.00 Max. :82.00 Max. :7.000
## NA's :18 NA's :22 NA's :10 NA's :88
## AGRICULT INDUST
## Min. : 0.00 Min. : 1.00
## 1st Qu.:13.00 1st Qu.: 9.00
## Median :35.00 Median :20.00
## Mean :40.71 Mean :18.84
## 3rd Qu.:70.00 3rd Qu.:28.00
## Max. :93.00 Max. :46.00
## NA's :15 NA's :14