R Notebook
Paola Nieto (20150967)
DATA 1
library(readxl)
IDH <- read_excel("2018_Statistical_Annex_Table_1.xlsx")## New names:
## * `` -> ...1
## * `` -> ...3
## * `` -> ...4
## * `` -> ...5
## * `` -> ...6
## * ...str(IDH)## tibble [265 × 15] (S3: tbl_df/tbl/data.frame)
## $ ...1 : chr [1:265] NA NA "HDI rank" NA ...
## $ Table 1. Human Development Index and its components: chr [1:265] NA NA "Country" NA ...
## $ ...3 : chr [1:265] NA "Human Development Index (HDI)" "Value" "2017" ...
## $ ...4 : logi [1:265] NA NA NA NA NA NA ...
## $ ...5 : chr [1:265] "SDG 3" "Life expectancy at birth" "(years)" "2017" ...
## $ ...6 : chr [1:265] NA NA NA NA ...
## $ ...7 : chr [1:265] "SDG 4.3" "Expected years of schooling" "(years)" "2017" ...
## $ ...8 : chr [1:265] NA NA NA "a" ...
## $ ...9 : chr [1:265] "SDG 4.6" "Mean years of schooling" "(years)" "2017" ...
## $ ...10 : chr [1:265] NA NA NA "a" ...
## $ ...11 : chr [1:265] "SDG 8.5" "Gross national income (GNI) per capita" "(2011 PPP $)" "2017" ...
## $ ...12 : chr [1:265] NA NA NA NA ...
## $ ...13 : chr [1:265] NA "GNI per capita rank minus HDI rank" NA "2017" ...
## $ ...14 : logi [1:265] NA NA NA NA NA NA ...
## $ ...15 : chr [1:265] NA "HDI rank" NA "2016" ...IDH[,c(1,4,6,8,10,12,14)]=NULL
names(IDH) = c("country", "hdi", "Life expectancy", "Expected years of schooling", "Mean years of schooling", "GNI per capita", "GNI per capita rank minus HDI rank", "rank")HumanDevelopment = c("Very High")
VeryHigh = IDH[5:64,]
VeryHigh=data.frame(VeryHigh, HumanDevelopment, stringsAsFactors = F)
VeryHigh = VeryHigh [-1,]
HumanDevelopment = c("High")
High = IDH[65:118,]
High = data.frame(High, HumanDevelopment, stringsAsFactors = F)
High = High [-1,]
HumanDevelopment = c("Medium")
Medium = IDH[119:158,]
Medium = data.frame(Medium, HumanDevelopment, stringsAsFactors = F)
Medium = Medium [-1,]
HumanDevelopment = c("Low")
Low = IDH[159:197,]
Low =data.frame(Low, HumanDevelopment, stringsAsFactors = F)
Low = Low [-1,]
HumanDevelopment = c("Other")
Other = IDH[198:204,]
Other =data.frame(Other, HumanDevelopment, stringsAsFactors = F)
Other = Other [-1,]IDH2=rbind(VeryHigh, High, Medium, Low, Other)
IDH2[,c(7,8)]=NULL
str(IDH2)## 'data.frame': 195 obs. of 7 variables:
## $ country : chr "Norway" "Switzerland" "Australia" "Ireland" ...
## $ hdi : chr "0.95252201967581829" "0.94399757027811748" "0.9386312851065749" "0.93841005899505603" ...
## $ Life.expectancy : chr "82.328000000000003" "83.472999999999999" "83.067999999999998" "81.643000000000001" ...
## $ Expected.years.of.schooling: chr "17.852060000000002" "16.208819999999999" "22.921250000000001" "19.61374" ...
## $ Mean.years.of.schooling : chr "12.56682" "13.407999999999999" "12.855040000000001" "12.526289999999999" ...
## $ GNI.per.capita : chr "68012.492920000004" "57625.069710000003" "43560.057739999997" "53754.186260000002" ...
## $ HumanDevelopment : chr "Very High" "Very High" "Very High" "Very High" ...library(readr)
IDH2[,2:6]=lapply(IDH2[,2:6], as.numeric)## Warning in lapply(IDH2[, 2:6], as.numeric): NAs introduced by coercion
## Warning in lapply(IDH2[, 2:6], as.numeric): NAs introduced by coercion
## Warning in lapply(IDH2[, 2:6], as.numeric): NAs introduced by coercion
## Warning in lapply(IDH2[, 2:6], as.numeric): NAs introduced by coercion
## Warning in lapply(IDH2[, 2:6], as.numeric): NAs introduced by coercionstr(IDH2)## 'data.frame': 195 obs. of 7 variables:
## $ country : chr "Norway" "Switzerland" "Australia" "Ireland" ...
## $ hdi : num 0.953 0.944 0.939 0.938 0.936 ...
## $ Life.expectancy : num 82.3 83.5 83.1 81.6 81.2 ...
## $ Expected.years.of.schooling: num 17.9 16.2 22.9 19.6 17 ...
## $ Mean.years.of.schooling : num 12.6 13.4 12.9 12.5 14.1 ...
## $ GNI.per.capita : num 68012 57625 43560 53754 46136 ...
## $ HumanDevelopment : chr "Very High" "Very High" "Very High" "Very High" ...DATA 2
EPI <- read_excel("2018-epi.xlsx",
sheet = "2018EPI_ScoresCurrent")
EPI[,c(1:2)]=NULL
EPI[,c(3:14)]=NULL
EPI[,c(9:26)]=NULL
library(stringr)
names(EPI)=str_split(names(EPI),".cu",simplify = T)[,1]%>%gsub('\\s','',.)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(magrittr)
EPI[2:8]= replace(EPI[2:8], EPI[2:8]==-9999, NA)
str(EPI)## tibble [180 × 8] (S3: tbl_df/tbl/data.frame)
## $ country: chr [1:180] "Afghanistan" "Albania" "Algeria" "Angola" ...
## $ EPI : num [1:180] 37.7 65.5 57.2 37.4 59.2 ...
## $ HAD : num [1:180] 0 30.9 87.4 14.8 72.8 ...
## $ PME : num [1:180] 71.8 88.9 100 60.7 100 ...
## $ PMW : num [1:180] 77.1 88.1 96.7 68.5 100 ...
## $ USD : num [1:180] 24.88 67.15 64.36 9.42 54.69 ...
## $ UWD : num [1:180] 26.62 65.97 56.17 8.99 51.96 ...
## $ PBD : num [1:180] 0 62.9 35.6 40.1 52 ...MERGE
nrow(merge(IDH2,EPI))## [1] 162datajunta0=merge(IDH2,EPI,all.x=T, all.y=T)library(knitr)
library(kableExtra)##
## Attaching package: 'kableExtra'## The following object is masked from 'package:dplyr':
##
## group_rowskable(datajunta0[!complete.cases(datajunta0),],type='html')%>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
font_size = 10)| country | hdi | Life.expectancy | Expected.years.of.schooling | Mean.years.of.schooling | GNI.per.capita | HumanDevelopment | EPI | HAD | PME | PMW | USD | UWD | PBD | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4 | Andorra | 0.8576836 | 81.663 | 13.52402 | 10.155450 | 47573.8701 | Very High | NA | NA | NA | NA | NA | NA | NA |
| 21 | Bolivia | NA | NA | NA | NA | NA | NA | 55.98 | 30.12 | 100.00 | 96.22 | 44.96 | 44.18 | 43.51 |
| 22 | Bolivia (Plurinational State of) | 0.6925370 | 69.473 | 14.02423 | 8.915070 | 6714.0272 | Medium | NA | NA | NA | NA | NA | NA | NA |
| 31 | Côte d’Ivoire | NA | NA | NA | NA | NA | NA | 45.25 | 5.15 | 86.69 | 86.04 | 9.53 | 9.53 | 33.42 |
| 41 | Congo | 0.6062826 | 65.088 | 11.37073 | 6.310000 | 5694.2210 | Medium | NA | NA | NA | NA | NA | NA | NA |
| 42 | Congo (Democratic Republic of the) | 0.4574692 | 60.031 | 9.75000 | 6.759480 | 795.8264 | Low | NA | NA | NA | NA | NA | NA | NA |
| 44 | Côte d’Ivoire | 0.4923045 | 54.102 | 9.03737 | 5.192000 | 3481.2253 | Low | NA | NA | NA | NA | NA | NA | NA |
| 48 | Czech Republic | NA | NA | NA | NA | NA | NA | 67.68 | 72.86 | 56.56 | 65.55 | 75.00 | 64.96 | 98.82 |
| 49 | Czechia | 0.8875614 | 78.877 | 16.85478 | 12.740350 | 30588.3008 | Very High | NA | NA | NA | NA | NA | NA | NA |
| 50 | Dem. Rep. Congo | NA | NA | NA | NA | NA | NA | 30.41 | 5.97 | 31.83 | 35.45 | 9.80 | 10.21 | 40.47 |
| 61 | Eswatini (Kingdom of) | 0.5883164 | 58.268 | 11.19615 | 6.521370 | 7619.9435 | Medium | NA | NA | NA | NA | NA | NA | NA |
| 79 | Hong Kong, China (SAR) | 0.9325829 | 84.097 | 16.32567 | 12.038130 | 58419.7099 | Very High | NA | NA | NA | NA | NA | NA | NA |
| 84 | Iran | NA | NA | NA | NA | NA | NA | 58.16 | 84.14 | 86.08 | 85.46 | 62.45 | 55.03 | 21.21 |
| 85 | Iran (Islamic Republic of) | 0.7980573 | 76.153 | 14.88064 | 9.840175 | 19130.2400 | High | NA | NA | NA | NA | NA | NA | NA |
| 96 | Korea (Democratic People’s Rep. of) | NA | 71.887 | 12.00025 | NA | NA | Other | NA | NA | NA | NA | NA | NA | NA |
| 97 | Korea (Republic of) | 0.9025611 | 82.361 | 16.49749 | 12.116330 | 35944.7095 | Very High | NA | NA | NA | NA | NA | NA | NA |
| 100 | Lao People’s Democratic Republic | 0.6012757 | 67.021 | 11.20924 | 5.193850 | 6070.1156 | Medium | NA | NA | NA | NA | NA | NA | NA |
| 101 | Laos | NA | NA | NA | NA | NA | NA | 42.94 | 4.41 | 33.75 | 38.28 | 26.05 | 29.12 | 33.69 |
| 107 | Liechtenstein | 0.9160829 | 80.410 | 14.72093 | 12.548460 | 97335.7496 | Very High | NA | NA | NA | NA | NA | NA | NA |
| 110 | Macedonia | NA | NA | NA | NA | NA | NA | 61.06 | 30.74 | 90.78 | 89.67 | 69.78 | 68.53 | 70.11 |
| 117 | Marshall Islands | 0.7079474 | 73.620 | 13.00000 | 10.865770 | 5124.8962 | High | NA | NA | NA | NA | NA | NA | NA |
| 121 | Micronesia | NA | NA | NA | NA | NA | NA | 49.80 | 17.69 | 100.00 | 100.00 | 48.14 | 44.63 | 47.40 |
| 122 | Micronesia (Federated States of) | 0.6272547 | 69.316 | 11.70000 | 7.954740 | 3842.9071 | Medium | NA | NA | NA | NA | NA | NA | NA |
| 123 | Moldova | NA | NA | NA | NA | NA | NA | 51.97 | 45.65 | 68.56 | 70.31 | 58.20 | 63.83 | 60.77 |
| 124 | Moldova (Republic of) | 0.6997534 | 71.718 | 11.63386 | 11.595360 | 5553.8504 | High | NA | NA | NA | NA | NA | NA | NA |
| 125 | Monaco | NA | NA | NA | NA | NA | Other | NA | NA | NA | NA | NA | NA | NA |
| 132 | Nauru | NA | NA | 10.31429 | NA | 18572.9566 | Other | NA | NA | NA | NA | NA | NA | NA |
| 142 | Palau | 0.7984784 | 73.445 | 15.60380 | 12.327280 | 12830.5903 | High | NA | NA | NA | NA | NA | NA | NA |
| 143 | Palestine, State of | 0.6858355 | 73.646 | 12.82014 | 9.104640 | 5055.0862 | Medium | NA | NA | NA | NA | NA | NA | NA |
| 152 | Republic of Congo | NA | NA | NA | NA | NA | NA | 42.39 | 12.49 | 30.40 | 32.40 | 9.95 | 10.57 | 46.29 |
| 154 | Russia | NA | NA | NA | NA | NA | NA | 63.79 | 76.75 | 81.37 | 82.94 | 61.33 | 66.52 | 86.20 |
| 155 | Russian Federation | 0.8162755 | 71.222 | 15.53573 | 12.019990 | 24232.5558 | Very High | NA | NA | NA | NA | NA | NA | NA |
| 157 | São Tomé and PrÃÂÂncipe | NA | NA | NA | NA | NA | NA | 54.01 | 12.74 | 100.00 | 96.57 | 30.40 | 28.24 | 45.76 |
| 158 | Saint Kitts and Nevis | 0.7778446 | 74.372 | 14.38947 | 8.400000 | 23977.6260 | High | NA | NA | NA | NA | NA | NA | NA |
| 162 | San Marino | NA | NA | 15.11120 | NA | NA | Other | NA | NA | NA | NA | NA | NA | NA |
| 163 | Sao Tome and Principe | 0.5894763 | 66.762 | 12.46685 | 6.333470 | 2941.3128 | Medium | NA | NA | NA | NA | NA | NA | NA |
| 173 | Somalia | NA | 56.714 | NA | NA | NA | Other | NA | NA | NA | NA | NA | NA | NA |
| 175 | South Korea | NA | NA | NA | NA | NA | NA | 62.30 | 100.00 | 30.21 | 40.43 | 100.00 | 93.05 | 91.48 |
| 176 | South Sudan | 0.3877252 | 57.288 | 4.87162 | 4.849130 | 963.1741 | Low | NA | NA | NA | NA | NA | NA | NA |
| 181 | Swaziland | NA | NA | NA | NA | NA | NA | 40.32 | 15.38 | 54.85 | 65.66 | 9.03 | 9.68 | 44.73 |
| 184 | Syrian Arab Republic | 0.5357037 | 70.963 | 8.75280 | 5.062500 | 2337.1701 | Low | NA | NA | NA | NA | NA | NA | NA |
| 185 | Taiwan | NA | NA | NA | NA | NA | NA | 72.84 | 65.32 | 67.35 | 75.70 | 74.41 | 65.01 | 81.17 |
| 187 | Tanzania | NA | NA | NA | NA | NA | NA | 50.83 | 7.64 | 100.00 | 91.65 | 11.43 | 12.18 | 42.14 |
| 188 | Tanzania (United Republic of) | 0.5377147 | 66.310 | 8.92369 | 5.780000 | 2655.3938 | Low | NA | NA | NA | NA | NA | NA | NA |
| 190 | The former Yugoslav Republic of Macedonia | 0.7566872 | 75.851 | 13.32826 | 9.631960 | 12504.9456 | High | NA | NA | NA | NA | NA | NA | NA |
| 198 | Tuvalu | NA | NA | NA | NA | 5887.7243 | Other | NA | NA | NA | NA | NA | NA | NA |
| 203 | United States | 0.9239136 | 79.541 | 16.46821 | 13.379990 | 54941.1093 | Very High | NA | NA | NA | NA | NA | NA | NA |
| 204 | United States of America | NA | NA | NA | NA | NA | NA | 71.19 | 100.00 | 99.01 | 92.71 | 81.84 | 100.00 | 64.99 |
| 208 | Venezuela | NA | NA | NA | NA | NA | NA | 63.89 | 76.03 | 100.00 | 99.93 | 52.40 | 48.38 | 37.32 |
| 209 | Venezuela (Bolivarian Republic of) | 0.7607730 | 74.726 | 14.30000 | 10.323430 | 10671.5415 | High | NA | NA | NA | NA | NA | NA | NA |
| 211 | Yemen | 0.4519004 | 65.157 | 8.97700 | 3.000000 | 1239.2914 | Low | NA | NA | NA | NA | NA | NA | NA |
IDH2[IDH2$country=="United States",'country']="United States of America"
IDH2[IDH2$country=="Bolivia (Plurinational State of)",'country']="Bolivia"
IDH2[IDH2$country=="Venezuela (Bolivarian Republic of)",'country']="Venezuela"
IDH2[IDH2$country=="The former Yugoslav Republic of Macedonia",'country']="Macedonia"
IDH2[IDH2$country=="Tanzania (United Republic of)",'country']="Tanzania"
IDH2[IDH2$country=="Eswatini (Kingdom of)",'country']="Swaziland"
IDH2[IDH2$country=="Korea (Republic of)",'country']="South Korea"
IDH2[IDH2$country=="Russian Federation",'country']="Russia"
IDH2[IDH2$country=="Lao People's Democratic Republic",'country']="Laos"
EPI[EPI$country=="Côte d'Ivoire",'country']="Côte d'Ivoire"
EPI[EPI$country=="Czech Republic",'country']="Czechia"
EPI[EPI$country=="Republic of Congo",'country']="Congo"
IDH2[IDH2$country=="Congo (Democratic Republic of the)",'country']="Dem. Rep. Congo"
IDH2[IDH2$country=="Iran (Islamic Republic of)",'country']="Iran"
IDH2[IDH2$country=="Micronesia (Federated States of)",'country']="Micronesia"
IDH2[IDH2$country=="Moldova (Republic of)",'country']="Moldova"Nuevo merge
datajunta=merge(IDH2,EPI)
nrow(datajunta)## [1] 178str(datajunta)## 'data.frame': 178 obs. of 14 variables:
## $ country : chr "Afghanistan" "Albania" "Algeria" "Angola" ...
## $ hdi : num 0.498 0.785 0.754 0.581 0.78 ...
## $ Life.expectancy : num 64 78.5 76.3 61.8 76.5 ...
## $ Expected.years.of.schooling: num 10.4 14.8 14.4 11.8 13.2 ...
## $ Mean.years.of.schooling : num 3.78 10.03 7.97 5.13 9.24 ...
## $ GNI.per.capita : num 1824 11886 13802 5790 20764 ...
## $ HumanDevelopment : chr "Low" "High" "High" "Medium" ...
## $ EPI : num 37.7 65.5 57.2 37.4 59.2 ...
## $ HAD : num 0 30.9 87.4 14.8 72.8 ...
## $ PME : num 71.8 88.9 100 60.7 100 ...
## $ PMW : num 77.1 88.1 96.7 68.5 100 ...
## $ USD : num 24.88 67.15 64.36 9.42 54.69 ...
## $ UWD : num 26.62 65.97 56.17 8.99 51.96 ...
## $ PBD : num 0 62.9 35.6 40.1 52 ...Calculamos y graficamos la matriz de correlación.
dontselect=c("country","hdi","HumanDevelopment","EPI")
select=setdiff(names(datajunta),dontselect)
theData= datajunta[,select]
# esta es:
library(polycor)
corMatrix=polycor::hetcor(theData)$correlations
#Explorar correlaciones:
#Sin evaluar significancia:
library(ggcorrplot)## Loading required package: ggplot2ggcorrplot(corMatrix)
#Evaluando significancia:
ggcorrplot(corMatrix,
p.mat = cor_pmat(corMatrix),
insig = "blank")
Calcule el KMO
library(psych)##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha## The following object is masked from 'package:polycor':
##
## polyserialpsych::KMO(corMatrix) ## Kaiser-Meyer-Olkin factor adequacy
## Call: psych::KMO(r = corMatrix)
## Overall MSA = 0.87
## MSA for each item =
## Life.expectancy Expected.years.of.schooling
## 0.96 0.97
## Mean.years.of.schooling GNI.per.capita
## 0.94 0.93
## HAD PME
## 0.93 0.51
## PMW USD
## 0.51 0.86
## UWD PBD
## 0.87 0.92# Es mayor de 0.5, si podemos factorizarPrueba de identidad
cortest.bartlett(corMatrix,n=nrow(theData))$p.value>0.05## [1] FALSEPrueba de singularidad
library(matrixcalc)
is.singular.matrix(corMatrix)## [1] FALSEfa.parallel(theData,fm = 'ML', fa = 'fa')
## Parallel analysis suggests that the number of factors = 2 and the number of components = NA#Redimensionamos
library(GPArotation)
resfa <- fa(theData,nfactors = 2,cor = 'mixed',rotate = "varimax",fm="minres")print(resfa$loadings)##
## Loadings:
## MR1 MR2
## Life.expectancy 0.896
## Expected.years.of.schooling 0.878
## Mean.years.of.schooling 0.871
## GNI.per.capita 0.764
## HAD 0.869 0.126
## PME 0.996
## PMW 0.988
## USD 0.983
## UWD 0.978
## PBD 0.735
##
## MR1 MR2
## SS loadings 6.141 1.997
## Proportion Var 0.614 0.200
## Cumulative Var 0.614 0.814print(resfa$loadings,cutoff = 0.5) #cortando solo los que aportan más (kmo mayor a 0.5)##
## Loadings:
## MR1 MR2
## Life.expectancy 0.896
## Expected.years.of.schooling 0.878
## Mean.years.of.schooling 0.871
## GNI.per.capita 0.764
## HAD 0.869
## PME 0.996
## PMW 0.988
## USD 0.983
## UWD 0.978
## PBD 0.735
##
## MR1 MR2
## SS loadings 6.141 1.997
## Proportion Var 0.614 0.200
## Cumulative Var 0.614 0.814Resultado visual:
fa.diagram(resfa)
Si bien todas las variables de HDI fueron factorizadas juntas, a estas se agregaron las variables relacionadas con agua y saneamiento, y expocisión a metales pesados
El otro facto creado agrupa a las variables relacionadas con calidad del aire, pero excluye a la variable de combustibles solidos domésticos (está incluida en el primer factor)
Evaluando el resultado
¿La RaÃz del error cuadrático medio corregida está cerca a cero?
resfa$crms## [1] 0.04354232¿La RaÃz del error cuadrático medio de aproximación es menor a 0.05?
resfa$RMSEA## RMSEA lower upper confidence
## 0.1764266 0.1522265 0.2027940 0.9000000¿El Ãndice de Tucker-Lewis es mayor a 0.9?
resfa$TLI## [1] 0.9020002¿Qué variables aportaron mas a los factores?
sort(resfa$communality)## PBD GNI.per.capita
## 0.5415986 0.5845982
## Mean.years.of.schooling HAD
## 0.7586788 0.7711137
## Expected.years.of.schooling Life.expectancy
## 0.7711331 0.8053404
## UWD USD
## 0.9606300 0.9695515
## PMW PME
## 0.9796408 0.9956592¿Qué variables contribuyen a mas de un factor?
sort(resfa$complexity)## Mean.years.of.schooling Expected.years.of.schooling
## 1.000101 1.001614
## PBD GNI.per.capita
## 1.004478 1.004634
## Life.expectancy PME
## 1.006266 1.007479
## PMW USD
## 1.007744 1.007872
## UWD HAD
## 1.008031 1.041965Posibles valores proyectados:
Darles nombres
as.data.frame(resfa$scores)%>%head()datajuntaFA=cbind(datajunta[1],as.data.frame(resfa$scores))
library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutplot_ly(data=datajuntaFA, x = ~MR1, y = ~MR2, text=~country) %>%
add_markers() %>%
layout(scene = list(xaxis = list(title = 'Factor1'),
yaxis = list(title = 'Factor2')))## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
## Please use `arrange()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.library(fpc)
library(cluster)
library(dbscan)##
## Attaching package: 'dbscan'## The following object is masked from 'package:fpc':
##
## dbscang.dist.cmd = daisy(datajuntaFA[,c(2:3)], metric = 'euclidean')
kNNdistplot(g.dist.cmd, k=2)
abline(h=0.63,col='red')
Para tener una idea de cada quien:
resDB=fpc::dbscan(g.dist.cmd, eps=0.63, MinPts=2,method = 'dist')
datajuntaFA$clustDB=as.factor(resDB$cluster)
aggregate(cbind(MR1, MR2) # dependientes
~ clustDB, # nivel
data = datajuntaFA, # data
max) # operacionplot_ly(data=datajuntaFA, x = ~MR1, y = ~MR2, text=~country, color = ~clustDB) %>%
add_markers() %>%
layout(scene = list(xaxis = list(title = 'Factor1'),
yaxis = list(title = 'Factor2')))Finalmente, veamos relaciones:
library(BBmisc)##
## Attaching package: 'BBmisc'## The following objects are masked from 'package:dplyr':
##
## coalesce, collapse## The following object is masked from 'package:base':
##
## isFALSEdatajunta$FA1=normalize(datajuntaFA$MR1,
method = "range",
margin=2, # by column
range = c(0, 10))
datajunta$FA2=normalize(datajuntaFA$MR2,
method = "range",
margin=2, # by column
range = c(0, 10))You can see them all here:
plot(datajunta[,c("hdi","EPI","FA1","FA2")])