Clase 2
Punto 1 gráficos de dispersión
pairs(cars)
pairs(iris)
pairs(iris3)
#Sacamos la variable nominal
pairs(iris[,1:4])
Punto 2 Colores
library(RColorBrewer)
paleta <- brewer.pal(3, "Set1")
paleta
## [1] "#E41A1C" "#377EB8" "#4DAF4A"
iris$Species
## [1] setosa setosa setosa setosa setosa setosa
## [7] setosa setosa setosa setosa setosa setosa
## [13] setosa setosa setosa setosa setosa setosa
## [19] setosa setosa setosa setosa setosa setosa
## [25] setosa setosa setosa setosa setosa setosa
## [31] setosa setosa setosa setosa setosa setosa
## [37] setosa setosa setosa setosa setosa setosa
## [43] setosa setosa setosa setosa setosa setosa
## [49] setosa setosa versicolor versicolor versicolor versicolor
## [55] versicolor versicolor versicolor versicolor versicolor versicolor
## [61] versicolor versicolor versicolor versicolor versicolor versicolor
## [67] versicolor versicolor versicolor versicolor versicolor versicolor
## [73] versicolor versicolor versicolor versicolor versicolor versicolor
## [79] versicolor versicolor versicolor versicolor versicolor versicolor
## [85] versicolor versicolor versicolor versicolor versicolor versicolor
## [91] versicolor versicolor versicolor versicolor versicolor versicolor
## [97] versicolor versicolor versicolor versicolor virginica virginica
## [103] virginica virginica virginica virginica virginica virginica
## [109] virginica virginica virginica virginica virginica virginica
## [115] virginica virginica virginica virginica virginica virginica
## [121] virginica virginica virginica virginica virginica virginica
## [127] virginica virginica virginica virginica virginica virginica
## [133] virginica virginica virginica virginica virginica virginica
## [139] virginica virginica virginica virginica virginica virginica
## [145] virginica virginica virginica virginica virginica virginica
## Levels: setosa versicolor virginica
tipo <- as.numeric(iris$Species)
tipo
## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [36] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [71] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3
## [106] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [141] 3 3 3 3 3 3 3 3 3 3
colores <- paleta[tipo]
colores
## [1] "#E41A1C" "#E41A1C" "#E41A1C" "#E41A1C" "#E41A1C" "#E41A1C" "#E41A1C"
## [8] "#E41A1C" "#E41A1C" "#E41A1C" "#E41A1C" "#E41A1C" "#E41A1C" "#E41A1C"
## [15] "#E41A1C" "#E41A1C" "#E41A1C" "#E41A1C" "#E41A1C" "#E41A1C" "#E41A1C"
## [22] "#E41A1C" "#E41A1C" "#E41A1C" "#E41A1C" "#E41A1C" "#E41A1C" "#E41A1C"
## [29] "#E41A1C" "#E41A1C" "#E41A1C" "#E41A1C" "#E41A1C" "#E41A1C" "#E41A1C"
## [36] "#E41A1C" "#E41A1C" "#E41A1C" "#E41A1C" "#E41A1C" "#E41A1C" "#E41A1C"
## [43] "#E41A1C" "#E41A1C" "#E41A1C" "#E41A1C" "#E41A1C" "#E41A1C" "#E41A1C"
## [50] "#E41A1C" "#377EB8" "#377EB8" "#377EB8" "#377EB8" "#377EB8" "#377EB8"
## [57] "#377EB8" "#377EB8" "#377EB8" "#377EB8" "#377EB8" "#377EB8" "#377EB8"
## [64] "#377EB8" "#377EB8" "#377EB8" "#377EB8" "#377EB8" "#377EB8" "#377EB8"
## [71] "#377EB8" "#377EB8" "#377EB8" "#377EB8" "#377EB8" "#377EB8" "#377EB8"
## [78] "#377EB8" "#377EB8" "#377EB8" "#377EB8" "#377EB8" "#377EB8" "#377EB8"
## [85] "#377EB8" "#377EB8" "#377EB8" "#377EB8" "#377EB8" "#377EB8" "#377EB8"
## [92] "#377EB8" "#377EB8" "#377EB8" "#377EB8" "#377EB8" "#377EB8" "#377EB8"
## [99] "#377EB8" "#377EB8" "#4DAF4A" "#4DAF4A" "#4DAF4A" "#4DAF4A" "#4DAF4A"
## [106] "#4DAF4A" "#4DAF4A" "#4DAF4A" "#4DAF4A" "#4DAF4A" "#4DAF4A" "#4DAF4A"
## [113] "#4DAF4A" "#4DAF4A" "#4DAF4A" "#4DAF4A" "#4DAF4A" "#4DAF4A" "#4DAF4A"
## [120] "#4DAF4A" "#4DAF4A" "#4DAF4A" "#4DAF4A" "#4DAF4A" "#4DAF4A" "#4DAF4A"
## [127] "#4DAF4A" "#4DAF4A" "#4DAF4A" "#4DAF4A" "#4DAF4A" "#4DAF4A" "#4DAF4A"
## [134] "#4DAF4A" "#4DAF4A" "#4DAF4A" "#4DAF4A" "#4DAF4A" "#4DAF4A" "#4DAF4A"
## [141] "#4DAF4A" "#4DAF4A" "#4DAF4A" "#4DAF4A" "#4DAF4A" "#4DAF4A" "#4DAF4A"
## [148] "#4DAF4A" "#4DAF4A" "#4DAF4A"
pairs(iris[,1:4],
# TÃtulo principal
main = "Relación entre las variables largo y ancho del sépalo y pétalo
para las variedades de flor setosa, versicolor y virginica.",
pch = 21, # Tipo de la forma
cex.main = .95, # Tamaño del tÃtulo
bg = colores) # Colores por especio.
legend("bottom", # leyenda en la perte inferior
title = "Especies :", # titulo de la leyenda
legend = as.character(unique(iris$Species)), # nombres de los grupos
fill = paleta, # colores de la leyenda
horiz = TRUE, # leyenda de forma horizontal
cex = .7, # tamaño del tÃtulo
xpd = T,
inset = -0.1) # ubicación de la leyenda
Punto 3 Correlación en tres dimensiones y con más datos
library(scatterplot3d) # paquete para realizar los gráficos en 3D
par(oma = c(3,2,1,2)) # se hace espacio extra para agregar la leyenda
scatterplot3d(x = iris$Petal.Width, # eje x
y = iris$Sepal.Length, # eje y
z = iris$Sepal.Width, # eje z
xlab = "Ancho Del Pétalo",
ylab = "Largo Del Sépalo",
zlab = "Ancho Del Sépalo",
bg = colores,
pch = 21,
angle = 190, # ángulo desde el que se obervan los datos.
main = "Dispersión de los individuos por largo del sépalo,
ancho del pétalo y ancho del sépalo.",
cex.main = 1) # tamaño del tÃtulo
library(GGally)
## Warning: package 'GGally' was built under R version 3.3.3
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 3.3.3
library(reshape)
ggpairs(data = iris, columns = 1:4,
title = "Relación entre las variables largo y ancho del sépalo y pétalo \n para las variedades de flor setosa, versicolor y virginica",
mapping = aes(color = Species, alpha = .7)) +
theme_bw()
Punto 4 GRáfico de Barras
library(MASS)
## Warning: package 'MASS' was built under R version 3.3.2
data("painters")
head(painters)
## Composition Drawing Colour Expression School
## Da Udine 10 8 16 3 A
## Da Vinci 15 16 4 14 A
## Del Piombo 8 13 16 7 A
## Del Sarto 12 16 9 8 A
## Fr. Penni 0 15 8 0 A
## Guilio Romano 15 16 4 14 A
datos <- as.matrix(painters[1,1:4]) # calificaciones del primer artista.
datos
## Composition Drawing Colour Expression
## Da Udine 10 8 16 3
barplot(datos,
main = "Calificaciones para el artista : Da Udine",
cex.main = 1, # tamaño del tÃtulo
ylab = "Calificación")
datos <- as.data.frame(painters[1:5, 1:4]) # se seleccionan los primeros 5 pintores de la tabla
datos
## Composition Drawing Colour Expression
## Da Udine 10 8 16 3
## Da Vinci 15 16 4 14
## Del Piombo 8 13 16 7
## Del Sarto 12 16 9 8
## Fr. Penni 0 15 8 0
promedio.composition<-mean(datos$Composition)
promedio.drawing<-mean(datos$Drawing)
promedio.color<-mean(datos$Colour)
promedio.expresion<-mean(datos$Expression)
datos.graficos<-cbind(promedio.composition, promedio.drawing, promedio.color, promedio.expresion)
barplot(datos.graficos)
datos.especies<- table(iris$Species)
datos.especies.prop<- round(prop.table(datos.especies),3) * 100
colores <- brewer.pal(3, "Greens")
pie(x = datos.especies,
labels = datos.especies.prop,
col = colores,
main = "Distribución de las especies en la tabla de datos iris")
Punto 5 Combinaciones de varios gráficos
layout(matrix(c(1,1,2,3), 2, 2, byrow = TRUE))
barplot(table(iris$Species))
title("Combinacion de varios graficos")
pie(table(iris$Species))
plot(iris$Sepal.Length,iris$Sepal.Width)
Punto 6 Graficos con volumen de casos
setwd("C:/Users/Acer/Desktop/EP_R")
datos <- read.table('WDS2014v2_1.csv', header=TRUE, sep=',',dec='.')
datos
## Country.Name P2014 TMI TFR EVT EVH EVM
## 1 Afghanistan 31627506 68.1 4.84 60.37 59.19 61.62
## 2 Albania 2894475 12.9 1.78 77.83 75.37 80.42
## 3 Algeria 38934334 22.0 2.86 74.81 72.51 77.22
## 4 Angola 24227524 98.8 6.08 52.27 50.80 53.81
## 5 Antigua and Barbuda 90900 6.1 2.08 75.94 73.54 78.45
## 6 Argentina 42980026 11.5 2.32 76.16 72.44 80.06
## 7 Armenia 3006154 13.2 1.53 74.68 70.94 78.59
## 8 Australia 23470118 3.2 1.86 82.25 80.30 84.30
## 9 Austria 8545908 3.0 1.44 81.34 78.80 84.00
## 10 Azerbaijan 9535079 28.9 2.00 70.76 67.69 73.99
## 11 Bahrain 1361930 5.6 2.06 76.68 75.77 77.64
## 12 Bangladesh 159077513 32.1 2.18 71.63 70.40 72.91
## 13 Barbados 283380 12.3 1.79 75.50 73.15 77.96
## 14 Belarus 9470000 3.5 1.62 72.98 68.00 78.20
## 15 Belgium 11231213 3.4 1.75 80.59 78.10 83.20
## 16 Belize 351706 14.6 2.58 70.08 67.36 72.94
## 17 Benin 10598482 65.7 4.77 59.51 58.11 60.98
## 18 Bhutan 765008 28.3 2.03 69.47 69.22 69.73
## 19 Bolivia 10561887 31.7 2.97 68.34 65.94 70.86
## 20 Bosnia and Herzegovina 3817554 5.4 1.26 76.43 73.96 79.03
## 21 Botswana 2219937 35.6 2.84 64.43 62.14 66.83
## 22 Brazil 206077898 14.4 1.79 74.40 70.74 78.25
## 23 Brunei Darussalam 417394 8.5 1.87 78.81 76.99 80.72
## 24 Bulgaria 7223938 9.7 1.48 75.41 71.70 79.30
## 25 Burkina Faso 17589198 62.2 5.52 58.59 57.31 59.93
## 26 Burundi 10816860 55.8 5.95 56.69 54.75 58.73
## 27 Cabo Verde 513906 21.3 2.30 73.15 71.37 75.02
## 28 Cambodia 15328136 26.3 2.64 68.21 66.22 70.30
## 29 Cameroon 22773014 58.6 4.70 55.49 54.36 56.68
## 30 Canada 35543658 4.4 1.61 81.96 80.02 83.99
## 31 Central African Republic 4804316 93.5 4.29 50.66 48.85 52.56
## 32 Chad 13587053 86.7 6.16 51.56 50.51 52.66
## 33 Chile 17762647 7.2 1.76 81.50 78.64 84.49
## 34 China 1364270000 9.8 1.56 75.78 74.29 77.35
## 35 Colombia 47791393 14.1 1.90 73.99 70.50 77.66
## 36 Comoros 769991 56.6 4.49 63.26 61.63 64.96
## 37 Costa Rica 4757606 8.6 1.82 79.40 77.01 81.91
## 38 Cote dIvoire 22157107 68.5 5.00 51.56 50.74 52.42
## 39 Croatia 4238389 3.8 1.46 77.33 74.50 80.30
## 40 Cuba 11379111 4.1 1.62 79.39 77.38 81.50
## 41 Cyprus 1153658 2.6 1.45 80.13 77.97 82.40
## 42 Czech Republic 10525347 2.9 1.46 78.28 75.30 81.40
## 43 Denmark 5638530 3.0 1.67 80.55 78.50 82.70
## 44 Djibouti 876174 55.8 3.20 62.02 60.44 63.67
## 45 Dominican Republic 10405943 26.2 2.48 73.50 70.44 76.71
## 46 Ecuador 15902916 19.0 2.54 75.87 73.19 78.69
## 47 Egypt Arab Rep 89579670 21.0 3.34 71.12 68.99 73.36
## 48 El Salvador 6107706 14.9 1.93 72.75 68.30 77.44
## 49 Equatorial Guinea 820885 70.3 4.84 57.65 56.33 59.03
## 50 Eritrea 5110444 35.0 4.28 63.66 61.56 65.87
## 51 Estonia 1314545 2.5 1.52 77.24 72.80 81.90
## 52 Ethiopia 96958732 42.9 4.40 64.04 62.16 66.01
## 53 Fiji 886450 19.4 2.56 70.09 67.15 73.17
## 54 Finland 5461512 2.0 1.75 81.13 78.30 84.10
## 55 France 66217509 3.6 1.99 82.37 79.30 85.60
## 56 Gabon 1687673 37.0 3.91 64.38 63.76 65.04
## 57 Gambia 1928201 48.6 5.72 60.23 58.91 61.62
## 58 Georgia 3727000 11.3 1.82 74.67 71.15 78.36
## 59 Germany 80970732 3.2 1.39 80.84 78.60 83.20
## 60 Ghana 26786598 44.2 4.17 61.31 60.36 62.31
## 61 Greece 10869637 3.7 1.30 81.29 78.70 84.00
## 62 Grenada 106349 11.1 2.15 73.37 71.00 75.85
## 63 Guatemala 16015494 25.1 3.21 71.72 68.28 75.34
## 64 Guinea 12275527 62.8 5.01 58.73 58.27 59.22
## 65 Guinea-Bissau 1800513 62.4 4.84 55.16 53.42 56.99
## 66 Guyana 763893 32.6 2.56 66.41 64.16 68.76
## 67 Haiti 10572029 53.5 3.03 62.75 60.67 64.93
## 68 Honduras 7961680 18.0 2.38 73.14 70.68 75.71
## 69 Hungary 9863183 5.3 1.35 75.87 72.80 79.10
## 70 Iceland 327386 1.6 1.93 82.06 80.50 83.70
## 71 India 1295291543 39.3 2.43 68.01 66.61 69.49
## 72 Indonesia 254454778 23.6 2.46 68.89 66.87 71.01
## 73 Iran 78143644 13.9 1.71 75.39 74.30 76.53
## 74 Iraq 34812326 27.2 4.57 69.40 67.23 71.68
## 75 Ireland 4615693 3.1 1.96 81.15 79.30 83.10
## 76 Israel 8215700 3.3 3.08 82.15 80.30 84.10
## 77 Italy 60789140 3.0 1.39 82.69 80.30 85.20
## 78 Jamaica 2720554 13.9 2.05 75.65 73.31 78.11
## 79 Japan 127131800 2.1 1.42 83.59 80.50 86.83
## 80 Jordan 6607000 15.8 3.42 74.05 72.42 75.77
## 81 Kazakhstan 17289224 13.5 2.74 71.62 67.12 75.94
## 82 Kenya 44863583 36.6 4.33 61.58 59.85 63.39
## 83 Kiribati 110470 44.6 3.73 65.95 62.82 69.24
## 84 Korea Rep. 25026772 20.7 1.98 70.07 66.66 73.66
## 85 Korea S 50423955 3.0 1.21 82.16 78.99 85.48
## 86 Kuwait 3753121 7.7 2.11 74.59 73.47 75.76
## 87 Kyrgyz Republic 5835500 20.1 3.20 70.40 66.50 74.50
## 88 Lao PDR 6689300 52.3 2.99 66.12 64.77 67.53
## 89 Latvia 1993782 7.2 1.52 74.19 69.70 78.90
## 90 Lebanon 4546774 7.3 1.71 79.37 77.58 81.26
## 91 Lesotho 2109197 70.5 3.19 49.70 49.58 49.82
## 92 Liberia 4396554 54.7 4.72 60.83 59.87 61.85
## 93 Libya 6258984 11.9 2.47 71.72 68.94 74.63
## 94 Lithuania 2932367 3.6 1.59 73.97 68.60 79.60
## 95 Luxembourg 556319 1.6 1.55 82.21 80.50 84.00
## 96 Macedonia 2075625 5.2 1.52 75.34 73.10 77.70
## 97 Madagascar 23571713 37.0 4.41 65.09 63.61 66.64
## 98 Malawi 16695253 45.1 5.13 62.72 61.75 63.75
## 99 Malaysia 29901997 6.2 1.94 74.72 72.43 77.12
## 100 Maldives 401000 7.8 2.12 76.77 75.81 77.79
## 101 Mali 17086022 75.9 6.23 57.99 58.18 57.79
## 102 Malta 427364 5.2 1.38 81.75 79.60 84.00
## 103 Mauritania 3969625 66.1 4.60 63.02 61.56 64.55
## 104 Mauritius 1260934 12.2 1.43 74.19 70.97 77.58
## 105 Mexico 125385833 11.9 2.24 76.72 74.36 79.21
## 106 Micronesia Sts 104044 29.4 3.24 69.10 68.14 70.11
## 107 Moldova 3556397 13.9 1.26 71.46 67.43 75.68
## 108 Mongolia 2909871 19.9 2.66 69.46 65.28 73.86
## 109 Montenegro 621810 4.6 1.69 76.18 74.07 78.40
## 110 Morocco 33921203 24.6 2.52 74.02 73.02 75.06
## 111 Mozambique 27216276 58.5 5.36 55.03 53.58 56.54
## 112 Myanmar 53437159 40.7 2.20 65.86 63.85 67.96
## 113 Namibia 2402858 33.4 3.52 64.68 62.15 67.34
## 114 Nepal 28174724 30.5 2.22 69.60 68.21 71.07
## 115 Netherlands 16865008 3.3 1.68 81.30 79.50 83.20
## 116 New Zealand 4509700 4.8 1.92 81.40 79.60 83.30
## 117 Nicaragua 6013913 19.4 2.26 74.81 71.86 77.91
## 118 Niger 19113728 58.4 7.60 61.46 60.58 62.38
## 119 Nigeria 177475986 71.5 5.65 52.75 52.43 53.10
## 120 Norway 5136886 2.2 1.78 81.75 79.80 83.80
## 121 Oman 4236057 10.0 2.77 77.09 75.07 79.20
## 122 Pakistan 185044286 67.4 3.62 66.18 65.26 67.16
## 123 Panama 3867535 15.1 2.44 77.60 74.62 80.72
## 124 Papua New Guinea 7463577 45.7 3.76 62.61 60.52 64.79
## 125 Paraguay 6552518 18.1 2.54 72.92 70.83 75.12
## 126 Peru 30973148 13.6 2.46 74.53 71.95 77.23
## 127 Philippines 99138690 22.8 2.98 68.27 64.91 71.79
## 128 Poland 38011735 4.5 1.29 77.25 73.40 81.30
## 129 Portugal 10401062 3.0 1.21 80.72 77.60 84.00
## 130 Qatar 2172065 7.0 2.03 78.60 77.35 79.91
## 131 Romania 19904360 10.1 1.41 75.06 71.60 78.70
## 132 Russian Federation 143819569 8.5 1.70 70.37 65.00 76.00
## 133 Rwanda 11341544 32.7 3.90 63.97 61.05 67.03
## 134 Samoa 191845 15.4 4.09 73.51 70.41 76.77
## 135 Sao Tome and Principe 186342 35.5 4.58 66.38 64.43 68.44
## 136 Saudi Arabia 30886545 12.9 2.77 74.34 73.03 75.71
## 137 Senegal 14672557 42.3 5.09 66.37 64.52 68.32
## 138 Serbia 7129366 6.0 1.43 75.53 72.90 78.30
## 139 Seychelles 91400 11.9 2.30 73.23 68.40 78.30
## 140 Sierra Leone 6315627 90.2 4.63 50.88 50.35 51.43
## 141 Singapore 5469724 2.2 1.25 82.65 80.50 84.90
## 142 Slovak Republic 5418649 6.1 1.34 76.71 73.30 80.30
## 143 Slovenia 2061980 2.2 1.55 80.52 77.30 83.90
## 144 Solomon Islands 572171 24.2 3.97 67.93 66.52 69.41
## 145 Somalia 10517569 87.4 6.46 55.35 53.77 57.02
## 146 South Africa 54001953 34.4 2.36 57.18 55.17 59.30
## 147 South Sudan 11911184 62.0 5.02 55.68 54.73 56.68
## 148 Spain 46476032 3.6 1.27 83.08 80.20 86.10
## 149 Sri Lanka 20771000 8.6 2.08 74.79 71.51 78.25
## 150 St. Lucia 183645 13.0 1.89 75.05 72.40 77.83
## 151 St. Vincent and the Grenadines 109360 17.0 1.97 72.94 70.87 75.11
## 152 Sudan 39350274 48.8 4.35 63.46 61.98 65.01
## 153 Suriname 538248 19.5 2.36 71.15 68.03 74.43
## 154 Swaziland 1269112 45.8 3.27 48.93 49.62 48.21
## 155 Sweden 9696110 2.4 1.89 81.96 80.20 83.80
## 156 Switzerland 8188102 3.5 1.52 82.85 80.80 85.00
## 157 Syrian Arab Republic 22157800 11.7 2.95 70.07 63.97 76.48
## 158 Tajikistan 8295840 39.7 3.49 69.60 66.20 73.17
## 159 Tanzania 51822621 36.2 5.15 64.94 63.54 66.42
## 160 Thailand 67725979 10.9 1.51 74.42 71.14 77.87
## 161 Timor-Leste 1212107 46.1 5.10 68.26 66.55 70.06
## 162 Togo 7115163 53.6 4.58 59.66 58.95 60.40
## 163 Tonga 105586 14.7 3.72 72.79 69.92 75.81
## 164 Trinidad and Tobago 1354483 18.7 1.78 70.44 67.02 74.03
## 165 Tunisia 10996600 12.6 2.20 74.14 71.90 76.50
## 166 Turkey 75932348 12.3 2.07 75.16 71.98 78.50
## 167 Turkmenistan 5307188 45.0 2.30 65.60 61.50 69.90
## 168 Uganda 37782971 39.1 5.78 58.47 56.74 60.28
## 169 Ukraine 45362900 8.1 1.50 71.19 66.25 76.37
## 170 United Arab Emirates 9086139 6.1 1.78 77.37 76.30 78.50
## 171 United Kingdom 64559135 3.7 1.83 81.06 79.30 82.90
## 172 United States 318857056 5.7 1.86 78.94 76.60 81.40
## 173 Uruguay 3419516 9.1 2.02 76.99 73.51 80.63
## 174 Uzbekistan 30757700 35.0 2.20 68.34 65.04 71.80
## 175 Venezuela 30693827 13.2 2.37 74.24 70.20 78.47
## 176 Vietnam 90728900 17.8 1.96 75.63 71.01 80.48
## 177 West Bank and Gaza 4294682 18.5 4.18 72.90 70.97 74.94
## 178 Yemen 26183676 35.1 4.16 63.82 62.49 65.22
## 179 Zambia 15721343 44.7 5.35 60.05 58.23 61.96
## 180 Zimbabwe 15245855 47.6 3.92 57.50 56.17 58.89
## REG ARE
## 1 South Asia Asia & Oceania
## 2 Europe & Central Asia Europe
## 3 Middle East & North Africa Africa
## 4 Sub-Saharan Africa Africa
## 5 Latin America & Caribbean America
## 6 Latin America & Caribbean America
## 7 Europe & Central Asia Europe
## 8 East Asia & Pacific Asia & Oceania
## 9 Europe & Central Asia Europe
## 10 Europe & Central Asia Europe
## 11 Middle East & North Africa Asia & Oceania
## 12 South Asia Asia & Oceania
## 13 Latin America & Caribbean America
## 14 Europe & Central Asia Europe
## 15 Europe & Central Asia Europe
## 16 Latin America & Caribbean America
## 17 Sub-Saharan Africa Africa
## 18 South Asia Asia & Oceania
## 19 Latin America & Caribbean America
## 20 Europe & Central Asia Europe
## 21 Sub-Saharan Africa Africa
## 22 Latin America & Caribbean America
## 23 East Asia & Pacific Asia & Oceania
## 24 Europe & Central Asia Europe
## 25 Sub-Saharan Africa Africa
## 26 Sub-Saharan Africa Africa
## 27 Sub-Saharan Africa Africa
## 28 East Asia & Pacific Asia & Oceania
## 29 Sub-Saharan Africa Africa
## 30 North America America
## 31 Sub-Saharan Africa Africa
## 32 Sub-Saharan Africa Africa
## 33 Latin America & Caribbean America
## 34 East Asia & Pacific Asia & Oceania
## 35 Latin America & Caribbean America
## 36 Sub-Saharan Africa Africa
## 37 Latin America & Caribbean America
## 38 Sub-Saharan Africa Africa
## 39 Europe & Central Asia Europe
## 40 Latin America & Caribbean America
## 41 Europe & Central Asia Europe
## 42 Europe & Central Asia Europe
## 43 Europe & Central Asia Europe
## 44 Middle East & North Africa Africa
## 45 Latin America & Caribbean America
## 46 Latin America & Caribbean America
## 47 Middle East & North Africa Africa
## 48 Latin America & Caribbean America
## 49 Sub-Saharan Africa Africa
## 50 Sub-Saharan Africa Africa
## 51 Europe & Central Asia Europe
## 52 Sub-Saharan Africa Africa
## 53 East Asia & Pacific Asia & Oceania
## 54 Europe & Central Asia Europe
## 55 Europe & Central Asia Europe
## 56 Sub-Saharan Africa Africa
## 57 Sub-Saharan Africa Africa
## 58 Europe & Central Asia Europe
## 59 Europe & Central Asia Europe
## 60 Sub-Saharan Africa Africa
## 61 Europe & Central Asia Europe
## 62 Latin America & Caribbean America
## 63 Latin America & Caribbean America
## 64 Sub-Saharan Africa Africa
## 65 Sub-Saharan Africa Africa
## 66 Latin America & Caribbean America
## 67 Latin America & Caribbean America
## 68 Latin America & Caribbean America
## 69 Europe & Central Asia Europe
## 70 Europe & Central Asia Europe
## 71 South Asia Asia & Oceania
## 72 East Asia & Pacific Asia & Oceania
## 73 Middle East & North Africa Asia & Oceania
## 74 Middle East & North Africa Asia & Oceania
## 75 Europe & Central Asia Europe
## 76 Middle East & North Africa Asia & Oceania
## 77 Europe & Central Asia Europe
## 78 Latin America & Caribbean America
## 79 East Asia & Pacific Asia & Oceania
## 80 Middle East & North Africa Asia & Oceania
## 81 Europe & Central Asia Asia & Oceania
## 82 Sub-Saharan Africa Africa
## 83 East Asia & Pacific Asia & Oceania
## 84 East Asia & Pacific Asia & Oceania
## 85 East Asia & Pacific Asia & Oceania
## 86 Middle East & North Africa Asia & Oceania
## 87 Europe & Central Asia Europe
## 88 East Asia & Pacific Asia & Oceania
## 89 Europe & Central Asia Europe
## 90 Middle East & North Africa Asia & Oceania
## 91 Sub-Saharan Africa Africa
## 92 Sub-Saharan Africa Africa
## 93 Middle East & North Africa Africa
## 94 Europe & Central Asia Europe
## 95 Europe & Central Asia Europe
## 96 Europe & Central Asia Europe
## 97 Sub-Saharan Africa Africa
## 98 Sub-Saharan Africa Africa
## 99 East Asia & Pacific Asia & Oceania
## 100 South Asia Asia & Oceania
## 101 Sub-Saharan Africa Africa
## 102 Middle East & North Africa Asia & Oceania
## 103 Sub-Saharan Africa Africa
## 104 Sub-Saharan Africa Africa
## 105 Latin America & Caribbean America
## 106 East Asia & Pacific Asia & Oceania
## 107 Europe & Central Asia Europe
## 108 East Asia & Pacific Asia & Oceania
## 109 Europe & Central Asia Europe
## 110 Middle East & North Africa Africa
## 111 Sub-Saharan Africa Africa
## 112 East Asia & Pacific Asia & Oceania
## 113 Sub-Saharan Africa Africa
## 114 South Asia Asia & Oceania
## 115 Europe & Central Asia Europe
## 116 East Asia & Pacific Asia & Oceania
## 117 Latin America & Caribbean America
## 118 Sub-Saharan Africa Africa
## 119 Sub-Saharan Africa Africa
## 120 Europe & Central Asia Europe
## 121 Middle East & North Africa Asia & Oceania
## 122 South Asia Asia & Oceania
## 123 Latin America & Caribbean America
## 124 East Asia & Pacific Asia & Oceania
## 125 Latin America & Caribbean America
## 126 Latin America & Caribbean America
## 127 East Asia & Pacific Asia & Oceania
## 128 Europe & Central Asia Europe
## 129 Europe & Central Asia Europe
## 130 Middle East & North Africa Asia & Oceania
## 131 Europe & Central Asia Europe
## 132 East Asia & Pacific Asia & Oceania
## 133 Sub-Saharan Africa Africa
## 134 East Asia & Pacific Asia & Oceania
## 135 Sub-Saharan Africa Africa
## 136 Middle East & North Africa Asia & Oceania
## 137 Sub-Saharan Africa Africa
## 138 Europe & Central Asia Europe
## 139 Sub-Saharan Africa Africa
## 140 Sub-Saharan Africa Africa
## 141 East Asia & Pacific Asia & Oceania
## 142 Europe & Central Asia Europe
## 143 Europe & Central Asia Europe
## 144 East Asia & Pacific Asia & Oceania
## 145 Sub-Saharan Africa Africa
## 146 Sub-Saharan Africa Africa
## 147 Sub-Saharan Africa Africa
## 148 Europe & Central Asia Europe
## 149 South Asia Asia & Oceania
## 150 Latin America & Caribbean America
## 151 Latin America & Caribbean America
## 152 Sub-Saharan Africa Africa
## 153 Latin America & Caribbean America
## 154 Sub-Saharan Africa Africa
## 155 Europe & Central Asia Europe
## 156 Europe & Central Asia Europe
## 157 Middle East & North Africa Asia & Oceania
## 158 Europe & Central Asia Europe
## 159 Sub-Saharan Africa Africa
## 160 East Asia & Pacific Asia & Oceania
## 161 East Asia & Pacific Asia & Oceania
## 162 Sub-Saharan Africa Africa
## 163 East Asia & Pacific Asia & Oceania
## 164 Latin America & Caribbean America
## 165 Middle East & North Africa Africa
## 166 Europe & Central Asia Europe
## 167 Europe & Central Asia Asia & Oceania
## 168 Sub-Saharan Africa Africa
## 169 Europe & Central Asia Europe
## 170 Middle East & North Africa Asia & Oceania
## 171 Europe & Central Asia Europe
## 172 North America America
## 173 Latin America & Caribbean America
## 174 Europe & Central Asia Asia & Oceania
## 175 Latin America & Caribbean America
## 176 East Asia & Pacific Asia & Oceania
## 177 Middle East & North Africa Asia & Oceania
## 178 Middle East & North Africa Asia & Oceania
## 179 Sub-Saharan Africa Africa
## 180 Sub-Saharan Africa Africa
P2014rec2 <- (datos$P2014/1000000)
P2014rec2
## [1] 31.627506 2.894475 38.934334 24.227524 0.090900
## [6] 42.980026 3.006154 23.470118 8.545908 9.535079
## [11] 1.361930 159.077513 0.283380 9.470000 11.231213
## [16] 0.351706 10.598482 0.765008 10.561887 3.817554
## [21] 2.219937 206.077898 0.417394 7.223938 17.589198
## [26] 10.816860 0.513906 15.328136 22.773014 35.543658
## [31] 4.804316 13.587053 17.762647 1364.270000 47.791393
## [36] 0.769991 4.757606 22.157107 4.238389 11.379111
## [41] 1.153658 10.525347 5.638530 0.876174 10.405943
## [46] 15.902916 89.579670 6.107706 0.820885 5.110444
## [51] 1.314545 96.958732 0.886450 5.461512 66.217509
## [56] 1.687673 1.928201 3.727000 80.970732 26.786598
## [61] 10.869637 0.106349 16.015494 12.275527 1.800513
## [66] 0.763893 10.572029 7.961680 9.863183 0.327386
## [71] 1295.291543 254.454778 78.143644 34.812326 4.615693
## [76] 8.215700 60.789140 2.720554 127.131800 6.607000
## [81] 17.289224 44.863583 0.110470 25.026772 50.423955
## [86] 3.753121 5.835500 6.689300 1.993782 4.546774
## [91] 2.109197 4.396554 6.258984 2.932367 0.556319
## [96] 2.075625 23.571713 16.695253 29.901997 0.401000
## [101] 17.086022 0.427364 3.969625 1.260934 125.385833
## [106] 0.104044 3.556397 2.909871 0.621810 33.921203
## [111] 27.216276 53.437159 2.402858 28.174724 16.865008
## [116] 4.509700 6.013913 19.113728 177.475986 5.136886
## [121] 4.236057 185.044286 3.867535 7.463577 6.552518
## [126] 30.973148 99.138690 38.011735 10.401062 2.172065
## [131] 19.904360 143.819569 11.341544 0.191845 0.186342
## [136] 30.886545 14.672557 7.129366 0.091400 6.315627
## [141] 5.469724 5.418649 2.061980 0.572171 10.517569
## [146] 54.001953 11.911184 46.476032 20.771000 0.183645
## [151] 0.109360 39.350274 0.538248 1.269112 9.696110
## [156] 8.188102 22.157800 8.295840 51.822621 67.725979
## [161] 1.212107 7.115163 0.105586 1.354483 10.996600
## [166] 75.932348 5.307188 37.782971 45.362900 9.086139
## [171] 64.559135 318.857056 3.419516 30.757700 30.693827
## [176] 90.728900 4.294682 26.183676 15.721343 15.245855
ggplot(data = datos, aes(x=ARE, y=TFR, size=P2014)) + geom_jitter(shape=21) + xlab("Area Geografica") + ylab("Tasa de fertilidad") + ggtitle("Relacion Area Geografica - Fertibilidad - Poblacion Paises en millones") + scale_y_continuous(breaks = 0:7) + theme_bw() + scale_size_continuous(name="Poblacion")
P2014rec2 <- (datos$P2014/1000000)
P2014rec2
## [1] 31.627506 2.894475 38.934334 24.227524 0.090900
## [6] 42.980026 3.006154 23.470118 8.545908 9.535079
## [11] 1.361930 159.077513 0.283380 9.470000 11.231213
## [16] 0.351706 10.598482 0.765008 10.561887 3.817554
## [21] 2.219937 206.077898 0.417394 7.223938 17.589198
## [26] 10.816860 0.513906 15.328136 22.773014 35.543658
## [31] 4.804316 13.587053 17.762647 1364.270000 47.791393
## [36] 0.769991 4.757606 22.157107 4.238389 11.379111
## [41] 1.153658 10.525347 5.638530 0.876174 10.405943
## [46] 15.902916 89.579670 6.107706 0.820885 5.110444
## [51] 1.314545 96.958732 0.886450 5.461512 66.217509
## [56] 1.687673 1.928201 3.727000 80.970732 26.786598
## [61] 10.869637 0.106349 16.015494 12.275527 1.800513
## [66] 0.763893 10.572029 7.961680 9.863183 0.327386
## [71] 1295.291543 254.454778 78.143644 34.812326 4.615693
## [76] 8.215700 60.789140 2.720554 127.131800 6.607000
## [81] 17.289224 44.863583 0.110470 25.026772 50.423955
## [86] 3.753121 5.835500 6.689300 1.993782 4.546774
## [91] 2.109197 4.396554 6.258984 2.932367 0.556319
## [96] 2.075625 23.571713 16.695253 29.901997 0.401000
## [101] 17.086022 0.427364 3.969625 1.260934 125.385833
## [106] 0.104044 3.556397 2.909871 0.621810 33.921203
## [111] 27.216276 53.437159 2.402858 28.174724 16.865008
## [116] 4.509700 6.013913 19.113728 177.475986 5.136886
## [121] 4.236057 185.044286 3.867535 7.463577 6.552518
## [126] 30.973148 99.138690 38.011735 10.401062 2.172065
## [131] 19.904360 143.819569 11.341544 0.191845 0.186342
## [136] 30.886545 14.672557 7.129366 0.091400 6.315627
## [141] 5.469724 5.418649 2.061980 0.572171 10.517569
## [146] 54.001953 11.911184 46.476032 20.771000 0.183645
## [151] 0.109360 39.350274 0.538248 1.269112 9.696110
## [156] 8.188102 22.157800 8.295840 51.822621 67.725979
## [161] 1.212107 7.115163 0.105586 1.354483 10.996600
## [166] 75.932348 5.307188 37.782971 45.362900 9.086139
## [171] 64.559135 318.857056 3.419516 30.757700 30.693827
## [176] 90.728900 4.294682 26.183676 15.721343 15.245855
ggplot(data = datos, aes(x=ARE, y=TFR, size=P2014rec2)) + geom_jitter(shape=21) + xlab("Area Geografica") + ylab("Tasa de fertilidad") + ggtitle("Relacion Area Geografica - Fertibilidad - Poblacion Paises en millones") + scale_y_continuous(breaks = 0:7) + theme_bw() + scale_size_continuous(name="Poblacion")
datos1<-subset(datos, ARE=="America")
datos1
## Country.Name P2014 TMI TFR EVT EVH EVM
## 5 Antigua and Barbuda 90900 6.1 2.08 75.94 73.54 78.45
## 6 Argentina 42980026 11.5 2.32 76.16 72.44 80.06
## 13 Barbados 283380 12.3 1.79 75.50 73.15 77.96
## 16 Belize 351706 14.6 2.58 70.08 67.36 72.94
## 19 Bolivia 10561887 31.7 2.97 68.34 65.94 70.86
## 22 Brazil 206077898 14.4 1.79 74.40 70.74 78.25
## 30 Canada 35543658 4.4 1.61 81.96 80.02 83.99
## 33 Chile 17762647 7.2 1.76 81.50 78.64 84.49
## 35 Colombia 47791393 14.1 1.90 73.99 70.50 77.66
## 37 Costa Rica 4757606 8.6 1.82 79.40 77.01 81.91
## 40 Cuba 11379111 4.1 1.62 79.39 77.38 81.50
## 45 Dominican Republic 10405943 26.2 2.48 73.50 70.44 76.71
## 46 Ecuador 15902916 19.0 2.54 75.87 73.19 78.69
## 48 El Salvador 6107706 14.9 1.93 72.75 68.30 77.44
## 62 Grenada 106349 11.1 2.15 73.37 71.00 75.85
## 63 Guatemala 16015494 25.1 3.21 71.72 68.28 75.34
## 66 Guyana 763893 32.6 2.56 66.41 64.16 68.76
## 67 Haiti 10572029 53.5 3.03 62.75 60.67 64.93
## 68 Honduras 7961680 18.0 2.38 73.14 70.68 75.71
## 78 Jamaica 2720554 13.9 2.05 75.65 73.31 78.11
## 105 Mexico 125385833 11.9 2.24 76.72 74.36 79.21
## 117 Nicaragua 6013913 19.4 2.26 74.81 71.86 77.91
## 123 Panama 3867535 15.1 2.44 77.60 74.62 80.72
## 125 Paraguay 6552518 18.1 2.54 72.92 70.83 75.12
## 126 Peru 30973148 13.6 2.46 74.53 71.95 77.23
## 150 St. Lucia 183645 13.0 1.89 75.05 72.40 77.83
## 151 St. Vincent and the Grenadines 109360 17.0 1.97 72.94 70.87 75.11
## 153 Suriname 538248 19.5 2.36 71.15 68.03 74.43
## 164 Trinidad and Tobago 1354483 18.7 1.78 70.44 67.02 74.03
## 172 United States 318857056 5.7 1.86 78.94 76.60 81.40
## 173 Uruguay 3419516 9.1 2.02 76.99 73.51 80.63
## 175 Venezuela 30693827 13.2 2.37 74.24 70.20 78.47
## REG ARE
## 5 Latin America & Caribbean America
## 6 Latin America & Caribbean America
## 13 Latin America & Caribbean America
## 16 Latin America & Caribbean America
## 19 Latin America & Caribbean America
## 22 Latin America & Caribbean America
## 30 North America America
## 33 Latin America & Caribbean America
## 35 Latin America & Caribbean America
## 37 Latin America & Caribbean America
## 40 Latin America & Caribbean America
## 45 Latin America & Caribbean America
## 46 Latin America & Caribbean America
## 48 Latin America & Caribbean America
## 62 Latin America & Caribbean America
## 63 Latin America & Caribbean America
## 66 Latin America & Caribbean America
## 67 Latin America & Caribbean America
## 68 Latin America & Caribbean America
## 78 Latin America & Caribbean America
## 105 Latin America & Caribbean America
## 117 Latin America & Caribbean America
## 123 Latin America & Caribbean America
## 125 Latin America & Caribbean America
## 126 Latin America & Caribbean America
## 150 Latin America & Caribbean America
## 151 Latin America & Caribbean America
## 153 Latin America & Caribbean America
## 164 Latin America & Caribbean America
## 172 North America America
## 173 Latin America & Caribbean America
## 175 Latin America & Caribbean America
P2014rec2 <- (datos1$P2014/1000000)
P2014rec2
## [1] 0.090900 42.980026 0.283380 0.351706 10.561887 206.077898
## [7] 35.543658 17.762647 47.791393 4.757606 11.379111 10.405943
## [13] 15.902916 6.107706 0.106349 16.015494 0.763893 10.572029
## [19] 7.961680 2.720554 125.385833 6.013913 3.867535 6.552518
## [25] 30.973148 0.183645 0.109360 0.538248 1.354483 318.857056
## [31] 3.419516 30.693827
ggplot(data = datos1, aes(x=Country.Name, y=TFR, size=P2014rec2)) + geom_jitter(shape=21) + xlab("Area Geografica") + ylab("Tasa de fertilidad") + ggtitle("Relacion Area Geografica - Fertibilidad - Poblacion Paises en millones") + scale_y_continuous(breaks = 0:32) + theme_bw() + scale_size_continuous(name="Poblacion")
