# Instalar plotly si aún no está instalado
if (!requireNamespace("plotly", quietly = TRUE)) {
install.packages("plotly")
}
# Instalación de librerías
#install.packages("ggplot2")
#install.packages("reshape2")
#install.packages("dplyr")
#install.packages("data.table")
# Carga de librerías
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.2.3
library(reshape2)
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.2.3
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(data.table)
##
## Attaching package: 'data.table'
## The following objects are masked from 'package:dplyr':
##
## between, first, last
## The following objects are masked from 'package:reshape2':
##
## dcast, melt
# Lectura del archivo CSV
energia_sus <- fread("global_data_sustainable_energy.csv")
# Visualización de los primeros y últimos 5 datos
head(energia_sus)
## Entity Year Access to electricity (% of population)
## 1: Afghanistan 2000 1.613591
## 2: Afghanistan 2001 4.074574
## 3: Afghanistan 2002 9.409158
## 4: Afghanistan 2003 14.738506
## 5: Afghanistan 2004 20.064968
## 6: Afghanistan 2005 25.390894
## Access to clean fuels for cooking
## 1: 6.2
## 2: 7.2
## 3: 8.2
## 4: 9.5
## 5: 10.9
## 6: 12.2
## Renewable-electricity-generating-capacity-per-capita
## 1: 9.22
## 2: 8.86
## 3: 8.47
## 4: 8.09
## 5: 7.75
## 6: 7.51
## Financial flows to developing countries (US $)
## 1: 20000
## 2: 130000
## 3: 3950000
## 4: 25970000
## 5: <NA>
## 6: 9830000
## Renewable energy share in the total final energy consumption (%)
## 1: 44.99
## 2: 45.60
## 3: 37.83
## 4: 36.66
## 5: 44.24
## 6: 33.88
## Electricity from fossil fuels (TWh) Electricity from nuclear (TWh)
## 1: 0.16 0
## 2: 0.09 0
## 3: 0.13 0
## 4: 0.31 0
## 5: 0.33 0
## 6: 0.34 0
## Electricity from renewables (TWh) Low-carbon electricity (% electricity)
## 1: 0.31 65.95744
## 2: 0.50 84.74577
## 3: 0.56 81.15942
## 4: 0.63 67.02128
## 5: 0.56 62.92135
## 6: 0.59 63.44086
## Primary energy consumption per capita (kWh/person)
## 1: 302.5948
## 2: 236.8919
## 3: 210.8622
## 4: 229.9682
## 5: 204.2312
## 6: 252.0691
## Energy intensity level of primary energy (MJ/$2017 PPP GDP)
## 1: 1.64
## 2: 1.74
## 3: 1.40
## 4: 1.40
## 5: 1.20
## 6: 1.41
## Value_co2_emissions_kt_by_country Renewables (% equivalent primary energy)
## 1: 760 NA
## 2: 730 NA
## 3: 1030 NA
## 4: 1220 NA
## 5: 1030 NA
## 6: 1550 NA
## gdp_growth gdp_per_capita Density\\n(P/Km2) Land Area(Km2) Latitude
## 1: NA NA 60 652230 33.93911
## 2: NA NA 60 652230 33.93911
## 3: NA 179.4266 60 652230 33.93911
## 4: 8.832278 190.6838 60 652230 33.93911
## 5: 1.414118 211.3821 60 652230 33.93911
## 6: 11.229715 242.0313 60 652230 33.93911
## Longitude
## 1: 67.70995
## 2: 67.70995
## 3: 67.70995
## 4: 67.70995
## 5: 67.70995
## 6: 67.70995
tail(energia_sus)
## Entity Year Access to electricity (% of population)
## 1: Zimbabwe 2015 33.70000
## 2: Zimbabwe 2016 42.56173
## 3: Zimbabwe 2017 44.17863
## 4: Zimbabwe 2018 45.57265
## 5: Zimbabwe 2019 46.78148
## 6: Zimbabwe 2020 52.74767
## Access to clean fuels for cooking
## 1: 29.5
## 2: 29.8
## 3: 29.8
## 4: 29.9
## 5: 30.1
## 6: 30.4
## Renewable-electricity-generating-capacity-per-capita
## 1: 63.54
## 2: 62.88
## 3: 62.33
## 4: 82.53
## 5: 81.40
## 6: 80.61
## Financial flows to developing countries (US $)
## 1: <NA>
## 2: 30000
## 3: 5570000
## 4: 10000
## 5: 250000
## 6: 30000
## Renewable energy share in the total final energy consumption (%)
## 1: 80.82
## 2: 81.90
## 3: 82.46
## 4: 80.23
## 5: 81.50
## 6: 81.90
## Electricity from fossil fuels (TWh) Electricity from nuclear (TWh)
## 1: 4.02 0
## 2: 3.50 0
## 3: 3.05 0
## 4: 3.73 0
## 5: 3.66 0
## 6: 3.40 0
## Electricity from renewables (TWh) Low-carbon electricity (% electricity)
## 1: 5.37 57.18850
## 2: 3.32 48.68035
## 3: 4.30 58.50341
## 4: 5.46 59.41241
## 5: 4.58 55.58253
## 6: 4.19 55.20422
## Primary energy consumption per capita (kWh/person)
## 1: 3860.920
## 2: 3227.680
## 3: 3068.012
## 4: 3441.986
## 5: 3003.655
## 6: 2680.132
## Energy intensity level of primary energy (MJ/$2017 PPP GDP)
## 1: 10.36
## 2: 10.00
## 3: 9.51
## 4: 9.83
## 5: 10.47
## 6: 10.00
## Value_co2_emissions_kt_by_country Renewables (% equivalent primary energy)
## 1: 12430 NA
## 2: 11020 NA
## 3: 10340 NA
## 4: 12380 NA
## 5: 11760 NA
## 6: NA NA
## gdp_growth gdp_per_capita Density\\n(P/Km2) Land Area(Km2) Latitude
## 1: 1.7798727 1445.070 38 390757 -19.01544
## 2: 0.7558693 1464.589 38 390757 -19.01544
## 3: 4.7094922 1235.189 38 390757 -19.01544
## 4: 4.8242105 1254.642 38 390757 -19.01544
## 5: -6.1442363 1316.741 38 390757 -19.01544
## 6: -6.2487482 1214.510 38 390757 -19.01544
## Longitude
## 1: 29.15486
## 2: 29.15486
## 3: 29.15486
## 4: 29.15486
## 5: 29.15486
## 6: 29.15486
# Información de la base de datos
str(energia_sus)
## Classes 'data.table' and 'data.frame': 3649 obs. of 21 variables:
## $ Entity : chr "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
## $ Year : int 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 ...
## $ Access to electricity (% of population) : num 1.61 4.07 9.41 14.74 20.06 ...
## $ Access to clean fuels for cooking : num 6.2 7.2 8.2 9.5 10.9 ...
## $ Renewable-electricity-generating-capacity-per-capita : num 9.22 8.86 8.47 8.09 7.75 7.51 7.4 7.25 7.49 7.5 ...
## $ Financial flows to developing countries (US $) :integer64 20000 130000 3950000 25970000 NA 9830000 10620000 15750000 ...
## $ Renewable energy share in the total final energy consumption (%): num 45 45.6 37.8 36.7 44.2 ...
## $ Electricity from fossil fuels (TWh) : num 0.16 0.09 0.13 0.31 0.33 0.34 0.2 0.2 0.19 0.16 ...
## $ Electricity from nuclear (TWh) : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Electricity from renewables (TWh) : num 0.31 0.5 0.56 0.63 0.56 0.59 0.64 0.75 0.54 0.78 ...
## $ Low-carbon electricity (% electricity) : num 66 84.7 81.2 67 62.9 ...
## $ Primary energy consumption per capita (kWh/person) : num 303 237 211 230 204 ...
## $ Energy intensity level of primary energy (MJ/$2017 PPP GDP) : num 1.64 1.74 1.4 1.4 1.2 1.41 1.5 1.53 1.94 2.25 ...
## $ Value_co2_emissions_kt_by_country : num 760 730 1030 1220 1030 ...
## $ Renewables (% equivalent primary energy) : num NA NA NA NA NA NA NA NA NA NA ...
## $ gdp_growth : num NA NA NA 8.83 1.41 ...
## $ gdp_per_capita : num NA NA 179 191 211 ...
## $ Density\n(P/Km2) : chr "60" "60" "60" "60" ...
## $ Land Area(Km2) : int 652230 652230 652230 652230 652230 652230 652230 652230 652230 652230 ...
## $ Latitude : num 33.9 33.9 33.9 33.9 33.9 ...
## $ Longitude : num 67.7 67.7 67.7 67.7 67.7 ...
## - attr(*, ".internal.selfref")=<externalptr>
# Dimensión de la base de datos
dimensiones <- dim(energia_sus)
print(dimensiones)
## [1] 3649 21
# Estadísticas básicas: media, desviación estándar, min, max, percentiles (25, 50, 75)
summary(energia_sus)
## Entity Year Access to electricity (% of population)
## Length:3649 Min. :2000 Min. : 1.252
## Class :character 1st Qu.:2005 1st Qu.: 59.801
## Mode :character Median :2010 Median : 98.362
## Mean :2010 Mean : 78.934
## 3rd Qu.:2015 3rd Qu.:100.000
## Max. :2020 Max. :100.000
## NA's :10
## Access to clean fuels for cooking
## Min. : 0.00
## 1st Qu.: 23.18
## Median : 83.15
## Mean : 63.26
## 3rd Qu.:100.00
## Max. :100.00
## NA's :169
## Renewable-electricity-generating-capacity-per-capita
## Min. : 0.00
## 1st Qu.: 3.54
## Median : 32.91
## Mean : 113.14
## 3rd Qu.: 112.21
## Max. :3060.19
## NA's :931
## Financial flows to developing countries (US $)
## Min. : 0
## 1st Qu.: 260000
## Median : 5660000
## Mean : 94224000
## 3rd Qu.: 55290000
## Max. :5202310000
## NA's : 2089
## Renewable energy share in the total final energy consumption (%)
## Min. : 0.000
## 1st Qu.: 6.515
## Median :23.300
## Mean :32.638
## 3rd Qu.:55.245
## Max. :96.040
## NA's :194
## Electricity from fossil fuels (TWh) Electricity from nuclear (TWh)
## Min. : 0.00 Min. : 0.00
## 1st Qu.: 0.29 1st Qu.: 0.00
## Median : 2.97 Median : 0.00
## Mean : 70.36 Mean : 13.45
## 3rd Qu.: 26.84 3rd Qu.: 0.00
## Max. :5184.13 Max. :809.41
## NA's :21 NA's :126
## Electricity from renewables (TWh) Low-carbon electricity (% electricity)
## Min. : 0.00 Min. : 0.000
## 1st Qu.: 0.04 1st Qu.: 2.878
## Median : 1.47 Median : 27.865
## Mean : 23.97 Mean : 36.801
## 3rd Qu.: 9.60 3rd Qu.: 64.404
## Max. :2184.94 Max. :100.000
## NA's :21 NA's :42
## Primary energy consumption per capita (kWh/person)
## Min. : 0
## 1st Qu.: 3117
## Median : 13121
## Mean : 25744
## 3rd Qu.: 33893
## Max. :262586
##
## Energy intensity level of primary energy (MJ/$2017 PPP GDP)
## Min. : 0.110
## 1st Qu.: 3.170
## Median : 4.300
## Mean : 5.307
## 3rd Qu.: 6.027
## Max. :32.570
## NA's :207
## Value_co2_emissions_kt_by_country Renewables (% equivalent primary energy)
## Min. : 10 Min. : 0.000
## 1st Qu.: 2020 1st Qu.: 2.137
## Median : 10500 Median : 6.291
## Mean : 159866 Mean :11.987
## 3rd Qu.: 60580 3rd Qu.:16.842
## Max. :10707220 Max. :86.837
## NA's :428 NA's :2137
## gdp_growth gdp_per_capita Density\\n(P/Km2) Land Area(Km2)
## Min. :-62.076 Min. : 111.9 Length:3649 Min. : 21
## 1st Qu.: 1.383 1st Qu.: 1337.8 Class :character 1st Qu.: 25713
## Median : 3.560 Median : 4578.6 Mode :character Median : 117600
## Mean : 3.442 Mean : 13283.8 Mean : 633213
## 3rd Qu.: 5.830 3rd Qu.: 15768.6 3rd Qu.: 513120
## Max. :123.140 Max. :123514.2 Max. :9984670
## NA's :317 NA's :282 NA's :1
## Latitude Longitude
## Min. :-40.901 Min. :-175.20
## 1st Qu.: 3.203 1st Qu.: -11.78
## Median : 17.190 Median : 19.15
## Mean : 18.246 Mean : 14.82
## 3rd Qu.: 38.970 3rd Qu.: 46.20
## Max. : 64.963 Max. : 178.07
## NA's :1 NA's :1
# Conteo de valores NaN (NA en R) en todo el DataFrame
nan_counts <- sapply(energia_sus, function(x) sum(is.na(x)))
cat("Número de valores NaN por columna:\n")
## Número de valores NaN por columna:
print(nan_counts)
## Entity
## 0
## Year
## 0
## Access to electricity (% of population)
## 10
## Access to clean fuels for cooking
## 169
## Renewable-electricity-generating-capacity-per-capita
## 931
## Financial flows to developing countries (US $)
## 2089
## Renewable energy share in the total final energy consumption (%)
## 194
## Electricity from fossil fuels (TWh)
## 21
## Electricity from nuclear (TWh)
## 126
## Electricity from renewables (TWh)
## 21
## Low-carbon electricity (% electricity)
## 42
## Primary energy consumption per capita (kWh/person)
## 0
## Energy intensity level of primary energy (MJ/$2017 PPP GDP)
## 207
## Value_co2_emissions_kt_by_country
## 428
## Renewables (% equivalent primary energy)
## 2137
## gdp_growth
## 317
## gdp_per_capita
## 282
## Density\\n(P/Km2)
## 0
## Land Area(Km2)
## 1
## Latitude
## 1
## Longitude
## 1
# Total de valores NaN en el DataFrame
total_nan <- sum(nan_counts)
cat("\nTotal de valores NaN en el DataFrame:", total_nan, "\n")
##
## Total de valores NaN en el DataFrame: 6977
# Nombres de las columnas
print(names(energia_sus))
## [1] "Entity"
## [2] "Year"
## [3] "Access to electricity (% of population)"
## [4] "Access to clean fuels for cooking"
## [5] "Renewable-electricity-generating-capacity-per-capita"
## [6] "Financial flows to developing countries (US $)"
## [7] "Renewable energy share in the total final energy consumption (%)"
## [8] "Electricity from fossil fuels (TWh)"
## [9] "Electricity from nuclear (TWh)"
## [10] "Electricity from renewables (TWh)"
## [11] "Low-carbon electricity (% electricity)"
## [12] "Primary energy consumption per capita (kWh/person)"
## [13] "Energy intensity level of primary energy (MJ/$2017 PPP GDP)"
## [14] "Value_co2_emissions_kt_by_country"
## [15] "Renewables (% equivalent primary energy)"
## [16] "gdp_growth"
## [17] "gdp_per_capita"
## [18] "Density\\n(P/Km2)"
## [19] "Land Area(Km2)"
## [20] "Latitude"
## [21] "Longitude"
# Renombrar columnas
energia_sus <- energia_sus %>%
rename(CO2 = Value_co2_emissions_kt_by_country, Country = Entity)
# Seleccionar columnas de interés para el boxplot
variables <- c(
'Access to electricity (% of population)',
'Access to clean fuels for cooking',
'Renewable-electricity-generating-capacity-per-capita',
'Financial flows to developing countries (US $)',
'Renewable energy share in the total final energy consumption (%)',
'Electricity from fossil fuels (TWh)',
'Electricity from nuclear (TWh)',
'Electricity from renewables (TWh)',
'Low-carbon electricity (% electricity)',
'Primary energy consumption per capita (kWh/person)',
'Energy intensity level of primary energy (MJ/$2017 PPP GDP)',
'CO2',
'Renewables (% equivalent primary energy)',
'gdp_growth',
'gdp_per_capita',
'Land Area(Km2)'
)
# Filtrar las columnas seleccionadas
df_selected <- energia_sus %>% select(all_of(variables))
# Derretir el DataFrame para el gráfico de caja
df_melted <- melt(df_selected, variable.name = 'Variable', value.name = 'Value')
## Warning in melt.data.table(df_selected, variable.name = "Variable", value.name
## = "Value"): id.vars and measure.vars are internally guessed when both are
## 'NULL'. All non-numeric/integer/logical type columns are considered id.vars,
## which in this case are columns []. Consider providing at least one of 'id' or
## 'measure' vars in future.
## Warning in melt.data.table(df_selected, variable.name = "Variable", value.name
## = "Value"): 'measure.vars' [Access to electricity (% of population), Access to
## clean fuels for cooking, Renewable-electricity-generating-capacity-per-capita,
## Financial flows to developing countries (US $), ...] are not all of the same
## type. By order of hierarchy, the molten data value column will be of type
## 'double'. All measure variables not of type 'double' will be coerced too. Check
## DETAILS in ?melt.data.table for more on coercion.
library(ggplot2)
library(reshape2) # Para melt()
# Supongamos que tienes un data frame df_melted ya creado que contiene las columnas "Value" y "Variable"
# Crear el gráfico de caja
ggplot(df_melted, aes(x = Value, y = Variable)) +
geom_boxplot(outlier.colour = "red", outlier.size = 1, fill = "lightblue") +
geom_jitter(color = "black", size = 0.5, alpha = 0.5) +
labs(title = "Distribución de Variables de Energía y Desarrollo",
x = "Valores",
y = "Variables") +
theme_minimal()
## Warning: Removed 4886 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 4886 rows containing missing values or values outside the scale range
## (`geom_point()`).
## **Boxplot Individual para la variable 'Financial flows to developing countries (US $)'**
# Filtrar la columna específica
df_financial_flows <- energia_sus[, "Financial flows to developing countries (US $)"]
# Cargar la librería
library(plotly)
## Warning: package 'plotly' was built under R version 4.2.3
##
## 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':
##
## layout
# Crear el gráfico de caja
fig <- plot_ly(
data = df_financial_flows,
x = ~`Financial flows to developing countries (US $)`,
type = "box",
title = 'Distribución de los Flujos Financieros hacia los Países en Desarrollo (US $)',
xaxis = list(title = 'Flujos Financieros (US $)')
)
# Mostrar el gráfico
fig
## Warning: 'box' objects don't have these attributes: 'title'
## Valid attributes include:
## 'alignmentgroup', 'boxmean', 'boxpoints', 'customdata', 'customdatasrc', 'dx', 'dy', 'fillcolor', 'hoverinfo', 'hoverinfosrc', 'hoverlabel', 'hoveron', 'hovertemplate', 'hovertemplatesrc', 'hovertext', 'hovertextsrc', 'ids', 'idssrc', 'jitter', 'legendgroup', 'legendgrouptitle', 'legendrank', 'line', 'lowerfence', 'lowerfencesrc', 'marker', 'mean', 'meansrc', 'median', 'mediansrc', 'meta', 'metasrc', 'name', 'notched', 'notchspan', 'notchspansrc', 'notchwidth', 'offsetgroup', 'opacity', 'orientation', 'pointpos', 'q1', 'q1src', 'q3', 'q3src', 'quartilemethod', 'sd', 'sdsrc', 'selected', 'selectedpoints', 'showlegend', 'stream', 'text', 'textsrc', 'transforms', 'type', 'uid', 'uirevision', 'unselected', 'upperfence', 'upperfencesrc', 'visible', 'whiskerwidth', 'width', 'x', 'x0', 'xaxis', 'xcalendar', 'xhoverformat', 'xperiod', 'xperiod0', 'xperiodalignment', 'xsrc', 'y', 'y0', 'yaxis', 'ycalendar', 'yhoverformat', 'yperiod', 'yperiod0', 'yperiodalignment', 'ysrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'
## **Histograma de los flujos financieros para los países en desarrollo**
# Crear el histograma
fig_hist <- plot_ly(
data = df_financial_flows,
x = ~`Financial flows to developing countries (US $)`,
type = "histogram",
nbinsx = 100,
title = 'Histograma de los Flujos Financieros hacia los Países en Desarrollo (US $)',
xaxis = list(title = 'Flujos Financieros (US $)')
)
fig_hist
## Warning: 'histogram' objects don't have these attributes: 'title'
## Valid attributes include:
## '_deprecated', 'alignmentgroup', 'autobinx', 'autobiny', 'bingroup', 'cliponaxis', 'constraintext', 'cumulative', 'customdata', 'customdatasrc', 'error_x', 'error_y', 'histfunc', 'histnorm', 'hoverinfo', 'hoverinfosrc', 'hoverlabel', 'hovertemplate', 'hovertemplatesrc', 'hovertext', 'hovertextsrc', 'ids', 'idssrc', 'insidetextanchor', 'insidetextfont', 'legendgroup', 'legendgrouptitle', 'legendrank', 'marker', 'meta', 'metasrc', 'name', 'nbinsx', 'nbinsy', 'offsetgroup', 'opacity', 'orientation', 'outsidetextfont', 'selected', 'selectedpoints', 'showlegend', 'stream', 'text', 'textangle', 'textfont', 'textposition', 'textsrc', 'texttemplate', 'transforms', 'type', 'uid', 'uirevision', 'unselected', 'visible', 'x', 'xaxis', 'xbins', 'xcalendar', 'xhoverformat', 'xsrc', 'y', 'yaxis', 'ybins', 'ycalendar', 'yhoverformat', 'ysrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'
## **Boxplot para el valor de las emisiones de CO2 en Kt por país**
# Filtrar la columna específica
df_box_co2 <- energia_sus %>% select(CO2)
# Crear el gráfico de caja para la variable seleccionada
fig_co2 <- plot_ly(
data = df_box_co2,
x = ~CO2,
type = "box",
title = 'Valor de las emisiones de CO2 en kt por país',
xaxis = list(title = 'Valor de las emisiones de CO2 en kt por país')
)
fig_co2
## Warning: Ignoring 428 observations
## Warning: 'box' objects don't have these attributes: 'title'
## Valid attributes include:
## 'alignmentgroup', 'boxmean', 'boxpoints', 'customdata', 'customdatasrc', 'dx', 'dy', 'fillcolor', 'hoverinfo', 'hoverinfosrc', 'hoverlabel', 'hoveron', 'hovertemplate', 'hovertemplatesrc', 'hovertext', 'hovertextsrc', 'ids', 'idssrc', 'jitter', 'legendgroup', 'legendgrouptitle', 'legendrank', 'line', 'lowerfence', 'lowerfencesrc', 'marker', 'mean', 'meansrc', 'median', 'mediansrc', 'meta', 'metasrc', 'name', 'notched', 'notchspan', 'notchspansrc', 'notchwidth', 'offsetgroup', 'opacity', 'orientation', 'pointpos', 'q1', 'q1src', 'q3', 'q3src', 'quartilemethod', 'sd', 'sdsrc', 'selected', 'selectedpoints', 'showlegend', 'stream', 'text', 'textsrc', 'transforms', 'type', 'uid', 'uirevision', 'unselected', 'upperfence', 'upperfencesrc', 'visible', 'whiskerwidth', 'width', 'x', 'x0', 'xaxis', 'xcalendar', 'xhoverformat', 'xperiod', 'xperiod0', 'xperiodalignment', 'xsrc', 'y', 'y0', 'yaxis', 'ycalendar', 'yhoverformat', 'yperiod', 'yperiod0', 'yperiodalignment', 'ysrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'
## **Histograma del valor de las emisiones de CO2 en Kt por país**
# Crear el histograma
fig_hist_co2 <- plot_ly(
data = df_box_co2,
x = ~CO2,
type = "histogram",
nbinsx = 100,
title = 'Valor de las emisiones de CO2 en kt por país',
xaxis = list(title = 'Valor de las emisiones de CO2 en kt por país')
)
fig_hist_co2
## Warning: Ignoring 428 observations
## Warning: 'histogram' objects don't have these attributes: 'title'
## Valid attributes include:
## '_deprecated', 'alignmentgroup', 'autobinx', 'autobiny', 'bingroup', 'cliponaxis', 'constraintext', 'cumulative', 'customdata', 'customdatasrc', 'error_x', 'error_y', 'histfunc', 'histnorm', 'hoverinfo', 'hoverinfosrc', 'hoverlabel', 'hovertemplate', 'hovertemplatesrc', 'hovertext', 'hovertextsrc', 'ids', 'idssrc', 'insidetextanchor', 'insidetextfont', 'legendgroup', 'legendgrouptitle', 'legendrank', 'marker', 'meta', 'metasrc', 'name', 'nbinsx', 'nbinsy', 'offsetgroup', 'opacity', 'orientation', 'outsidetextfont', 'selected', 'selectedpoints', 'showlegend', 'stream', 'text', 'textangle', 'textfont', 'textposition', 'textsrc', 'texttemplate', 'transforms', 'type', 'uid', 'uirevision', 'unselected', 'visible', 'x', 'xaxis', 'xbins', 'xcalendar', 'xhoverformat', 'xsrc', 'y', 'yaxis', 'ybins', 'ycalendar', 'yhoverformat', 'ysrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'
## **Coeficiente de variación**
# Excluir las columnas específicas que no quieres analizar
columnas_excluir <- c('Year', 'Land Area(Km2)', 'Latitude', 'Longitude')
# Seleccionar solo las columnas numéricas excluyendo las que no queremos
columnas_incluidas <- setdiff(names(energia_sus)[sapply(energia_sus, is.numeric)], columnas_excluir)
# Calcular la media, desviación estándar y coeficiente de variación para las columnas seleccionadas
for (column in columnas_incluidas) {
media <- mean(energia_sus[[column]], na.rm = TRUE)
desviacion <- sd(energia_sus[[column]], na.rm = TRUE)
# Evitar la división por cero
if (media != 0) {
coef_variacion <- (desviacion / media) * 100
cat("El coeficiente de variación para", column, "es:", round(coef_variacion, 2), "%\n")
} else {
cat("La media de la columna", column, "es cero, no se puede calcular el coeficiente de variación.\n")
}
}
## El coeficiente de variación para Access to electricity (% of population) es: 38.36 %
## El coeficiente de variación para Access to clean fuels for cooking es: 61.72 %
## El coeficiente de variación para Renewable-electricity-generating-capacity-per-capita es: 215.81 %
## El coeficiente de variación para Financial flows to developing countries (US $) es: 316.43 %
## El coeficiente de variación para Renewable energy share in the total final energy consumption (%) es: 91.59 %
## El coeficiente de variación para Electricity from fossil fuels (TWh) es: 494.64 %
## El coeficiente de variación para Electricity from nuclear (TWh) es: 542.79 %
## El coeficiente de variación para Electricity from renewables (TWh) es: 435.71 %
## El coeficiente de variación para Low-carbon electricity (% electricity) es: 93.24 %
## El coeficiente de variación para Primary energy consumption per capita (kWh/person) es: 135.07 %
## El coeficiente de variación para Energy intensity level of primary energy (MJ/$2017 PPP GDP) es: 66.55 %
## El coeficiente de variación para CO2 es: 483.94 %
## El coeficiente de variación para Renewables (% equivalent primary energy) es: 125.09 %
## El coeficiente de variación para gdp_growth es: 165.23 %
## El coeficiente de variación para gdp_per_capita es: 148.38 %
## **Coeficiente de asimetría de Pearson**
asimetria_pearson <- list()
# Calcular la media, mediana, desviación estándar y coeficiente de asimetría de Pearson para cada columna numérica
for (column in columnas_incluidas) {
media <- mean(energia_sus[[column]], na.rm = TRUE)
mediana <- median(energia_sus[[column]], na.rm = TRUE)
desviacion <- sd(energia_sus[[column]], na.rm = TRUE)
# Evitar la división por cero
if (desviacion != 0) {
coef_asimetria <- (3 * (media - mediana)) / desviacion
asimetria_pearson[[column]] <- coef_asimetria
cat("El coeficiente de asimetría de Pearson para", column, "es:", round(coef_asimetria, 2), "\n")
} else {
cat("La desviación estándar de la columna", column, "es cero, no se puede calcular el coeficiente de asimetría.\n")
}
}
## El coeficiente de asimetría de Pearson para Access to electricity (% of population) es: -1.93
## El coeficiente de asimetría de Pearson para Access to clean fuels for cooking es: -1.53
## El coeficiente de asimetría de Pearson para Renewable-electricity-generating-capacity-per-capita es: 0.99
## El coeficiente de asimetría de Pearson para Financial flows to developing countries (US $) es: 0.89
## El coeficiente de asimetría de Pearson para Renewable energy share in the total final energy consumption (%) es: 0.94
## El coeficiente de asimetría de Pearson para Electricity from fossil fuels (TWh) es: 0.58
## El coeficiente de asimetría de Pearson para Electricity from nuclear (TWh) es: 0.55
## El coeficiente de asimetría de Pearson para Electricity from renewables (TWh) es: 0.65
## El coeficiente de asimetría de Pearson para Low-carbon electricity (% electricity) es: 0.78
## El coeficiente de asimetría de Pearson para Primary energy consumption per capita (kWh/person) es: 1.09
## El coeficiente de asimetría de Pearson para Energy intensity level of primary energy (MJ/$2017 PPP GDP) es: 0.86
## El coeficiente de asimetría de Pearson para CO2 es: 0.58
## El coeficiente de asimetría de Pearson para Renewables (% equivalent primary energy) es: 1.14
## El coeficiente de asimetría de Pearson para gdp_growth es: -0.06
## El coeficiente de asimetría de Pearson para gdp_per_capita es: 1.32
# Mostrar los resultados como DataFrame
df_asimetria <- data.frame(
Variable = names(asimetria_pearson),
Coeficiente_Asimetria_de_Pearson = unlist(asimetria_pearson)
)
print(df_asimetria)
## Variable
## Access to electricity (% of population) Access to electricity (% of population)
## Access to clean fuels for cooking Access to clean fuels for cooking
## Renewable-electricity-generating-capacity-per-capita Renewable-electricity-generating-capacity-per-capita
## Financial flows to developing countries (US $) Financial flows to developing countries (US $)
## Renewable energy share in the total final energy consumption (%) Renewable energy share in the total final energy consumption (%)
## Electricity from fossil fuels (TWh) Electricity from fossil fuels (TWh)
## Electricity from nuclear (TWh) Electricity from nuclear (TWh)
## Electricity from renewables (TWh) Electricity from renewables (TWh)
## Low-carbon electricity (% electricity) Low-carbon electricity (% electricity)
## Primary energy consumption per capita (kWh/person) Primary energy consumption per capita (kWh/person)
## Energy intensity level of primary energy (MJ/$2017 PPP GDP) Energy intensity level of primary energy (MJ/$2017 PPP GDP)
## CO2 CO2
## Renewables (% equivalent primary energy) Renewables (% equivalent primary energy)
## gdp_growth gdp_growth
## gdp_per_capita gdp_per_capita
## Coeficiente_Asimetria_de_Pearson
## Access to electricity (% of population) -1.92510524
## Access to clean fuels for cooking -1.52865130
## Renewable-electricity-generating-capacity-per-capita 0.98572797
## Financial flows to developing countries (US $) 0.89112216
## Renewable energy share in the total final energy consumption (%) 0.93709943
## Electricity from fossil fuels (TWh) 0.58090482
## Electricity from nuclear (TWh) 0.55269740
## Electricity from renewables (TWh) 0.64630210
## Low-carbon electricity (% electricity) 0.78124529
## Primary energy consumption per capita (kWh/person) 1.08906319
## Energy intensity level of primary energy (MJ/$2017 PPP GDP) 0.85561098
## CO2 0.57919341
## Renewables (% equivalent primary energy) 1.13959506
## gdp_growth -0.06237959
## gdp_per_capita 1.32499239
if (!require("tidyr")) install.packages("tidyr")
## Loading required package: tidyr
##
## Attaching package: 'tidyr'
## The following object is masked from 'package:reshape2':
##
## smiths
library(tidyr)
## **Valores Faltantes**
# Contar valores faltantes por variable
missing_data <- energia_sus %>%
summarise(across(everything(), ~ sum(is.na(.)))) %>%
pivot_longer(cols = everything(), names_to = "variable", values_to = "missing_count")
# Crear el gráfico de valores faltantes
ggplot(missing_data, aes(x = reorder(variable, -missing_count), y = missing_count)) +
geom_bar(stat = "identity", fill = "steelblue") +
coord_flip() +
labs(title = "Cantidad de valores faltantes por variable", x = "Variables", y = "Cantidad de valores faltantes") +
theme_minimal()
# Calcular el porcentaje de valores faltantes
missing_percentage <- energia_sus %>%
summarise(across(everything(), ~ sum(is.na(.)) / nrow(energia_sus) * 100)) %>%
pivot_longer(cols = everything(), names_to = "variable", values_to = "missing_percentage")
# Crear el gráfico de porcentaje de valores faltantes
ggplot(missing_percentage, aes(x = reorder(variable, -missing_percentage), y = missing_percentage)) +
geom_bar(stat = "identity", fill = "steelblue") +
coord_flip() +
labs(title = "Porcentaje de valores faltantes por variable", x = "Variables", y = "Porcentaje de valores faltantes") +
theme_minimal()
# Gráfico de barras para visualizar el porcentaje de valores faltantes
ggplot(missing_percentage, aes(x = reorder(variable, -missing_percentage), y = missing_percentage)) +
geom_bar(stat = "identity", fill = "steelblue") +
coord_flip() +
labs(title = "Porcentaje de valores faltantes por variable en el DataFrame energia_sus",
x = "Variables",
y = "Porcentaje de valores faltantes") +
theme_minimal()
# Mapa de calor del patrón de datos faltantes
library(ggplot2)
library(reshape2)
# Mapa de calor para ver patrones de valores faltantes
missing_matrix <- is.na(energia_sus)
missing_heatmap <- melt(missing_matrix)
## Warning in melt(missing_matrix): The melt generic in data.table has been passed
## a matrix and will attempt to redirect to the relevant reshape2 method; please
## note that reshape2 is deprecated, and this redirection is now deprecated as
## well. To continue using melt methods from reshape2 while both libraries are
## attached, e.g. melt.list, you can prepend the namespace like
## reshape2::melt(missing_matrix). In the next version, this warning will become
## an error.
ggplot(missing_heatmap, aes(Var2, Var1, fill = value)) +
geom_tile() +
scale_fill_manual(values = c("white", "blue"), labels = c("Presente", "Faltante")) +
labs(title = "Mapa de calor del patrón de datos faltantes en 'energia_sus'",
x = "Variables",
y = "Observaciones") +
theme_minimal()
## **Mapa geo-referenciado del porcentaje de población por país que tiene acceso a la electricidad en 2014**
df_2014 <- filter(energia_sus, Year == 2014)
# Filtrar los datos para el año 2014
df_2014 <- energia_sus %>% filter(Year == 2014)
# Crear el mapa coroplético para el acceso a electricidad
fig <- plot_geo(df_2014) %>%
add_trace(
z = ~`Access to electricity (% of population)`,
locations = ~Country,
color = ~`Access to electricity (% of population)`,
colors = "Blues", # Cambiar 'Plasma' a 'Viridis'
colorbar = list(title = "Acceso a electricidad (%)")
) %>%
layout(title = "Porcentaje de población por país que tiene acceso a electricidad en 2014")
# Mostrar el gráfico
fig
## **Mapa geo-referenciado del porcentaje de población por país que tiene acceso a combustibles limpios 2014**
fig <- plot_geo(df_2014) %>%
add_trace(
z = ~`Access to clean fuels for cooking`,
locations = ~Country,
color = ~`Access to clean fuels for cooking`,
colors = "Blues"
) %>%
layout(title = "Porcentaje de población por país que tiene acceso a combustibles limpios para cocinar en 2014")
fig
## Warning: Ignoring 8 observations
## **Principales fuentes de electricidad en Colombia**
# Agrupar y transformar datos para Colombia
energia_agrupado <- energia_sus %>%
group_by(Country) %>%
summarise(
`Electricity from fossil fuels (TWh)` = sum(`Electricity from fossil fuels (TWh)`, na.rm = TRUE),
`Electricity from nuclear (TWh)` = sum(`Electricity from nuclear (TWh)`, na.rm = TRUE),
`Electricity from renewables (TWh)` = sum(`Electricity from renewables (TWh)`, na.rm = TRUE)
)
df_colombia <- filter(energia_agrupado, Country == "Colombia") %>%
pivot_longer(-Country, names_to = "Source", values_to = "Electricity (TWh)")
# Gráfico de pastel
fig <- plot_ly(df_colombia, labels = ~Source, values = ~`Electricity (TWh)`, type = 'pie') %>%
layout(title = 'Principales fuentes de electricidad en Colombia')
fig
## **Países que usan energías renovables**
fig <- plot_geo(energia_sus) %>%
add_trace(
z = ~`Electricity from renewables (TWh)`,
locations = ~Country,
color = ~`Electricity from renewables (TWh)`,
colors = "Blues"
) %>%
layout(title = "Países que usan energías renovables")
fig
## Warning: Ignoring 21 observations
## **Países que usan energía nuclear**
fig <- plot_geo(energia_sus) %>%
add_trace(
z = ~`Electricity from nuclear (TWh)`,
locations = ~Country,
color = ~`Electricity from nuclear (TWh)`,
colors = "Blues"
) %>%
layout(title = "Países que usan energía nuclear")
fig
## Warning: Ignoring 126 observations
## **Países que usan energía de combustibles fósiles**
fig <- plot_geo(energia_sus) %>%
add_trace(
z = ~`Electricity from fossil fuels (TWh)`,
locations = ~Country,
color = ~`Electricity from fossil fuels (TWh)`,
colors = "Blues"
) %>%
layout(title = "Países que usan energía de combustibles fósiles")
fig
## Warning: Ignoring 21 observations
## **Acceso a la electricidad en Colombia a través del (2000-2020)**
# Acceso a la electricidad en promedio global
electricidad_global <- energia_sus %>%
group_by(Year) %>%
summarise(`Access to electricity (% of population)` = mean(`Access to electricity (% of population)`, na.rm = TRUE))
fig <- plot_ly(electricidad_global, x = ~Year, y = ~`Access to electricity (% of population)`, type = 'scatter', mode = 'lines') %>%
layout(title = 'Acceso a la electricidad (2000-2020)', xaxis = list(title = 'Año'), yaxis = list(title = 'Acceso a la electricidad (%)'))
fig
# Acceso a la electricidad en Colombia
energia_colombia <- filter(energia_sus, Country == "Colombia")
electricidad_colombia <- energia_colombia %>%
group_by(Year) %>%
summarise(`Access to electricity (% of population)` = mean(`Access to electricity (% of population)`, na.rm = TRUE))
fig <- plot_ly(electricidad_colombia, x = ~Year, y = ~`Access to electricity (% of population)`, type = 'scatter', mode = 'lines') %>%
layout(title = 'Acceso a la electricidad en Colombia (2000-2020)', xaxis = list(title = 'Año'), yaxis = list(title = 'Acceso a la electricidad (%)'))
fig
## **Países con mayores emisiones de CO2**
# Agrupar y obtener los máximos de emisiones de CO2
maxco2 <- energia_sus %>%
group_by(Country) %>%
summarise(CO2 = max(CO2, na.rm = TRUE)) %>%
arrange(desc(CO2))
## Warning: There were 13 warnings in `summarise()`.
## The first warning was:
## ℹ In argument: `CO2 = max(CO2, na.rm = TRUE)`.
## ℹ In group 12: `Country = "Bahamas"`.
## Caused by warning in `max()`:
## ! ningun argumento finito para max; retornando -Inf
## ℹ Run ]8;;ide:run:dplyr::last_dplyr_warnings()dplyr::last_dplyr_warnings()]8;; to see the 12 remaining warnings.
top10co2 <- head(maxco2, 10)
# Crear el gráfico de barras
fig1 <- plot_ly(
top10co2,
x = ~Country,
y = ~CO2,
type = 'bar',
color = ~CO2,
title = 'Países con mayores emisiones de CO2',
labels = list(x = 'Países', y = 'Emisiones CO2 (kt)')
)
fig1 <- fig1 %>% layout(height = 800)
## Warning: Specifying width/height in layout() is now deprecated.
## Please specify in ggplotly() or plot_ly()
fig1
## Warning: textfont.color doesn't (yet) support data arrays
## Warning: textfont.color doesn't (yet) support data arrays
## Warning: 'bar' objects don't have these attributes: 'title', 'labels'
## Valid attributes include:
## '_deprecated', 'alignmentgroup', 'base', 'basesrc', 'cliponaxis', 'constraintext', 'customdata', 'customdatasrc', 'dx', 'dy', 'error_x', 'error_y', 'hoverinfo', 'hoverinfosrc', 'hoverlabel', 'hovertemplate', 'hovertemplatesrc', 'hovertext', 'hovertextsrc', 'ids', 'idssrc', 'insidetextanchor', 'insidetextfont', 'legendgroup', 'legendgrouptitle', 'legendrank', 'marker', 'meta', 'metasrc', 'name', 'offset', 'offsetgroup', 'offsetsrc', 'opacity', 'orientation', 'outsidetextfont', 'selected', 'selectedpoints', 'showlegend', 'stream', 'text', 'textangle', 'textfont', 'textposition', 'textpositionsrc', 'textsrc', 'texttemplate', 'texttemplatesrc', 'transforms', 'type', 'uid', 'uirevision', 'unselected', 'visible', 'width', 'widthsrc', 'x', 'x0', 'xaxis', 'xcalendar', 'xhoverformat', 'xperiod', 'xperiod0', 'xperiodalignment', 'xsrc', 'y', 'y0', 'yaxis', 'ycalendar', 'yhoverformat', 'yperiod', 'yperiod0', 'yperiodalignment', 'ysrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'
## **Mapa geo-referenciado del porcentaje de población por países con mayores emisiones de CO2**
df_co2 <- energia_sus %>%
group_by(Country) %>%
summarise(CO2 = sum(CO2, na.rm = TRUE)) %>%
arrange(desc(CO2)) %>%
head(13)
fig2 <- plot_ly(
df_co2,
locations = ~Country,
locationmode = 'country names',
z = ~CO2,
type = 'choropleth',
color = ~CO2,
colorbar = list(title = 'Emisiones CO2 (kt)'),
title = 'Países con mayores emisiones de CO2 (2000-2020)'
)
fig2
## Warning: 'choropleth' objects don't have these attributes: 'title'
## Valid attributes include:
## 'autocolorscale', 'coloraxis', 'colorbar', 'colorscale', 'customdata', 'customdatasrc', 'featureidkey', 'geo', 'geojson', 'hoverinfo', 'hoverinfosrc', 'hoverlabel', 'hovertemplate', 'hovertemplatesrc', 'hovertext', 'hovertextsrc', 'ids', 'idssrc', 'legendgroup', 'legendgrouptitle', 'legendrank', 'locationmode', 'locations', 'locationssrc', 'marker', 'meta', 'metasrc', 'name', 'reversescale', 'selected', 'selectedpoints', 'showlegend', 'showscale', 'stream', 'text', 'textsrc', 'transforms', 'type', 'uid', 'uirevision', 'unselected', 'visible', 'z', 'zauto', 'zmax', 'zmid', 'zmin', 'zsrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'
## **Los principales países por consumo de electricidad a partir de combustibles fósiles (2000-2020)**
consumo_fossil_fuel <- energia_sus %>%
group_by(Country) %>%
summarise(Electricity_from_fossil_fuels = sum(`Electricity from fossil fuels (TWh)`, na.rm = TRUE)) %>%
arrange(desc(Electricity_from_fossil_fuels)) %>%
head(10)
fig3 <- plot_ly(
consumo_fossil_fuel,
x = ~Country,
y = ~Electricity_from_fossil_fuels,
type = 'bar',
title = 'Los principales países por consumo de electricidad a partir de combustibles fósiles (2000-2020)'
)
fig3
## Warning: 'bar' objects don't have these attributes: 'title'
## Valid attributes include:
## '_deprecated', 'alignmentgroup', 'base', 'basesrc', 'cliponaxis', 'constraintext', 'customdata', 'customdatasrc', 'dx', 'dy', 'error_x', 'error_y', 'hoverinfo', 'hoverinfosrc', 'hoverlabel', 'hovertemplate', 'hovertemplatesrc', 'hovertext', 'hovertextsrc', 'ids', 'idssrc', 'insidetextanchor', 'insidetextfont', 'legendgroup', 'legendgrouptitle', 'legendrank', 'marker', 'meta', 'metasrc', 'name', 'offset', 'offsetgroup', 'offsetsrc', 'opacity', 'orientation', 'outsidetextfont', 'selected', 'selectedpoints', 'showlegend', 'stream', 'text', 'textangle', 'textfont', 'textposition', 'textpositionsrc', 'textsrc', 'texttemplate', 'texttemplatesrc', 'transforms', 'type', 'uid', 'uirevision', 'unselected', 'visible', 'width', 'widthsrc', 'x', 'x0', 'xaxis', 'xcalendar', 'xhoverformat', 'xperiod', 'xperiod0', 'xperiodalignment', 'xsrc', 'y', 'y0', 'yaxis', 'ycalendar', 'yhoverformat', 'yperiod', 'yperiod0', 'yperiodalignment', 'ysrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'
## **Histograma del consumo de electricidad a partir de los combustibles fósiles (2000-2020)**
# Filtrar datos para Colombia
energia_colombia <- energia_sus %>% filter(Country == 'Colombia')
# Agrupar por año y calcular el consumo total de electricidad a partir de combustibles fósiles
fosil_colombia <- energia_colombia %>%
group_by(Year) %>%
summarise(Electricity_from_fossil_fuels = sum(`Electricity from fossil fuels (TWh)`, na.rm = TRUE))
fig4 <- plot_ly(
fosil_colombia,
x = ~Year,
y = ~Electricity_from_fossil_fuels,
type = 'bar',
title = 'Consumo de electricidad a partir de combustibles fósiles en Colombia (2000-2020)',
labels = list(x = 'Año', y = 'Electricidad de combustibles fósiles (TWh)')
)
fig4
## Warning: 'bar' objects don't have these attributes: 'title', 'labels'
## Valid attributes include:
## '_deprecated', 'alignmentgroup', 'base', 'basesrc', 'cliponaxis', 'constraintext', 'customdata', 'customdatasrc', 'dx', 'dy', 'error_x', 'error_y', 'hoverinfo', 'hoverinfosrc', 'hoverlabel', 'hovertemplate', 'hovertemplatesrc', 'hovertext', 'hovertextsrc', 'ids', 'idssrc', 'insidetextanchor', 'insidetextfont', 'legendgroup', 'legendgrouptitle', 'legendrank', 'marker', 'meta', 'metasrc', 'name', 'offset', 'offsetgroup', 'offsetsrc', 'opacity', 'orientation', 'outsidetextfont', 'selected', 'selectedpoints', 'showlegend', 'stream', 'text', 'textangle', 'textfont', 'textposition', 'textpositionsrc', 'textsrc', 'texttemplate', 'texttemplatesrc', 'transforms', 'type', 'uid', 'uirevision', 'unselected', 'visible', 'width', 'widthsrc', 'x', 'x0', 'xaxis', 'xcalendar', 'xhoverformat', 'xperiod', 'xperiod0', 'xperiodalignment', 'xsrc', 'y', 'y0', 'yaxis', 'ycalendar', 'yhoverformat', 'yperiod', 'yperiod0', 'yperiodalignment', 'ysrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'
## **Tasa de consumo de combustibles fósiles a lo largo de los años para los 10 principales países**
top_fossil_fuel_countries <- energia_sus %>%
group_by(Country) %>%
summarise(mean_fossil_fuels = mean(`Electricity from fossil fuels (TWh)`, na.rm = TRUE)) %>%
arrange(desc(mean_fossil_fuels))
top_fossil_fuel_countries <- head(top_fossil_fuel_countries, 10)
# Filtrar datos para los países seleccionados
filtered_data <- energia_sus %>%
filter(Country %in% top_fossil_fuel_countries$Country)
fig5 <- plot_ly(
filtered_data,
x = ~Year,
y = ~`Electricity from fossil fuels (TWh)`,
color = ~Country,
type = 'scatter',
mode = 'lines',
title = 'Tendencias en la Generación de Electricidad de Combustibles Fósiles entre los Principales 10 Países'
)
fig5
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning: 'scatter' objects don't have these attributes: 'title'
## Valid attributes include:
## 'cliponaxis', 'connectgaps', 'customdata', 'customdatasrc', 'dx', 'dy', 'error_x', 'error_y', 'fill', 'fillcolor', 'fillpattern', 'groupnorm', 'hoverinfo', 'hoverinfosrc', 'hoverlabel', 'hoveron', 'hovertemplate', 'hovertemplatesrc', 'hovertext', 'hovertextsrc', 'ids', 'idssrc', 'legendgroup', 'legendgrouptitle', 'legendrank', 'line', 'marker', 'meta', 'metasrc', 'mode', 'name', 'opacity', 'orientation', 'selected', 'selectedpoints', 'showlegend', 'stackgaps', 'stackgroup', 'stream', 'text', 'textfont', 'textposition', 'textpositionsrc', 'textsrc', 'texttemplate', 'texttemplatesrc', 'transforms', 'type', 'uid', 'uirevision', 'unselected', 'visible', 'x', 'x0', 'xaxis', 'xcalendar', 'xhoverformat', 'xperiod', 'xperiod0', 'xperiodalignment', 'xsrc', 'y', 'y0', 'yaxis', 'ycalendar', 'yhoverformat', 'yperiod', 'yperiod0', 'yperiodalignment', 'ysrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'
## Warning: 'scatter' objects don't have these attributes: 'title'
## Valid attributes include:
## 'cliponaxis', 'connectgaps', 'customdata', 'customdatasrc', 'dx', 'dy', 'error_x', 'error_y', 'fill', 'fillcolor', 'fillpattern', 'groupnorm', 'hoverinfo', 'hoverinfosrc', 'hoverlabel', 'hoveron', 'hovertemplate', 'hovertemplatesrc', 'hovertext', 'hovertextsrc', 'ids', 'idssrc', 'legendgroup', 'legendgrouptitle', 'legendrank', 'line', 'marker', 'meta', 'metasrc', 'mode', 'name', 'opacity', 'orientation', 'selected', 'selectedpoints', 'showlegend', 'stackgaps', 'stackgroup', 'stream', 'text', 'textfont', 'textposition', 'textpositionsrc', 'textsrc', 'texttemplate', 'texttemplatesrc', 'transforms', 'type', 'uid', 'uirevision', 'unselected', 'visible', 'x', 'x0', 'xaxis', 'xcalendar', 'xhoverformat', 'xperiod', 'xperiod0', 'xperiodalignment', 'xsrc', 'y', 'y0', 'yaxis', 'ycalendar', 'yhoverformat', 'yperiod', 'yperiod0', 'yperiodalignment', 'ysrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'
## Warning: 'scatter' objects don't have these attributes: 'title'
## Valid attributes include:
## 'cliponaxis', 'connectgaps', 'customdata', 'customdatasrc', 'dx', 'dy', 'error_x', 'error_y', 'fill', 'fillcolor', 'fillpattern', 'groupnorm', 'hoverinfo', 'hoverinfosrc', 'hoverlabel', 'hoveron', 'hovertemplate', 'hovertemplatesrc', 'hovertext', 'hovertextsrc', 'ids', 'idssrc', 'legendgroup', 'legendgrouptitle', 'legendrank', 'line', 'marker', 'meta', 'metasrc', 'mode', 'name', 'opacity', 'orientation', 'selected', 'selectedpoints', 'showlegend', 'stackgaps', 'stackgroup', 'stream', 'text', 'textfont', 'textposition', 'textpositionsrc', 'textsrc', 'texttemplate', 'texttemplatesrc', 'transforms', 'type', 'uid', 'uirevision', 'unselected', 'visible', 'x', 'x0', 'xaxis', 'xcalendar', 'xhoverformat', 'xperiod', 'xperiod0', 'xperiodalignment', 'xsrc', 'y', 'y0', 'yaxis', 'ycalendar', 'yhoverformat', 'yperiod', 'yperiod0', 'yperiodalignment', 'ysrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'
## Warning: 'scatter' objects don't have these attributes: 'title'
## Valid attributes include:
## 'cliponaxis', 'connectgaps', 'customdata', 'customdatasrc', 'dx', 'dy', 'error_x', 'error_y', 'fill', 'fillcolor', 'fillpattern', 'groupnorm', 'hoverinfo', 'hoverinfosrc', 'hoverlabel', 'hoveron', 'hovertemplate', 'hovertemplatesrc', 'hovertext', 'hovertextsrc', 'ids', 'idssrc', 'legendgroup', 'legendgrouptitle', 'legendrank', 'line', 'marker', 'meta', 'metasrc', 'mode', 'name', 'opacity', 'orientation', 'selected', 'selectedpoints', 'showlegend', 'stackgaps', 'stackgroup', 'stream', 'text', 'textfont', 'textposition', 'textpositionsrc', 'textsrc', 'texttemplate', 'texttemplatesrc', 'transforms', 'type', 'uid', 'uirevision', 'unselected', 'visible', 'x', 'x0', 'xaxis', 'xcalendar', 'xhoverformat', 'xperiod', 'xperiod0', 'xperiodalignment', 'xsrc', 'y', 'y0', 'yaxis', 'ycalendar', 'yhoverformat', 'yperiod', 'yperiod0', 'yperiodalignment', 'ysrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'
## Warning: 'scatter' objects don't have these attributes: 'title'
## Valid attributes include:
## 'cliponaxis', 'connectgaps', 'customdata', 'customdatasrc', 'dx', 'dy', 'error_x', 'error_y', 'fill', 'fillcolor', 'fillpattern', 'groupnorm', 'hoverinfo', 'hoverinfosrc', 'hoverlabel', 'hoveron', 'hovertemplate', 'hovertemplatesrc', 'hovertext', 'hovertextsrc', 'ids', 'idssrc', 'legendgroup', 'legendgrouptitle', 'legendrank', 'line', 'marker', 'meta', 'metasrc', 'mode', 'name', 'opacity', 'orientation', 'selected', 'selectedpoints', 'showlegend', 'stackgaps', 'stackgroup', 'stream', 'text', 'textfont', 'textposition', 'textpositionsrc', 'textsrc', 'texttemplate', 'texttemplatesrc', 'transforms', 'type', 'uid', 'uirevision', 'unselected', 'visible', 'x', 'x0', 'xaxis', 'xcalendar', 'xhoverformat', 'xperiod', 'xperiod0', 'xperiodalignment', 'xsrc', 'y', 'y0', 'yaxis', 'ycalendar', 'yhoverformat', 'yperiod', 'yperiod0', 'yperiodalignment', 'ysrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'
## Warning: 'scatter' objects don't have these attributes: 'title'
## Valid attributes include:
## 'cliponaxis', 'connectgaps', 'customdata', 'customdatasrc', 'dx', 'dy', 'error_x', 'error_y', 'fill', 'fillcolor', 'fillpattern', 'groupnorm', 'hoverinfo', 'hoverinfosrc', 'hoverlabel', 'hoveron', 'hovertemplate', 'hovertemplatesrc', 'hovertext', 'hovertextsrc', 'ids', 'idssrc', 'legendgroup', 'legendgrouptitle', 'legendrank', 'line', 'marker', 'meta', 'metasrc', 'mode', 'name', 'opacity', 'orientation', 'selected', 'selectedpoints', 'showlegend', 'stackgaps', 'stackgroup', 'stream', 'text', 'textfont', 'textposition', 'textpositionsrc', 'textsrc', 'texttemplate', 'texttemplatesrc', 'transforms', 'type', 'uid', 'uirevision', 'unselected', 'visible', 'x', 'x0', 'xaxis', 'xcalendar', 'xhoverformat', 'xperiod', 'xperiod0', 'xperiodalignment', 'xsrc', 'y', 'y0', 'yaxis', 'ycalendar', 'yhoverformat', 'yperiod', 'yperiod0', 'yperiodalignment', 'ysrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'
## Warning: 'scatter' objects don't have these attributes: 'title'
## Valid attributes include:
## 'cliponaxis', 'connectgaps', 'customdata', 'customdatasrc', 'dx', 'dy', 'error_x', 'error_y', 'fill', 'fillcolor', 'fillpattern', 'groupnorm', 'hoverinfo', 'hoverinfosrc', 'hoverlabel', 'hoveron', 'hovertemplate', 'hovertemplatesrc', 'hovertext', 'hovertextsrc', 'ids', 'idssrc', 'legendgroup', 'legendgrouptitle', 'legendrank', 'line', 'marker', 'meta', 'metasrc', 'mode', 'name', 'opacity', 'orientation', 'selected', 'selectedpoints', 'showlegend', 'stackgaps', 'stackgroup', 'stream', 'text', 'textfont', 'textposition', 'textpositionsrc', 'textsrc', 'texttemplate', 'texttemplatesrc', 'transforms', 'type', 'uid', 'uirevision', 'unselected', 'visible', 'x', 'x0', 'xaxis', 'xcalendar', 'xhoverformat', 'xperiod', 'xperiod0', 'xperiodalignment', 'xsrc', 'y', 'y0', 'yaxis', 'ycalendar', 'yhoverformat', 'yperiod', 'yperiod0', 'yperiodalignment', 'ysrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'
## Warning: 'scatter' objects don't have these attributes: 'title'
## Valid attributes include:
## 'cliponaxis', 'connectgaps', 'customdata', 'customdatasrc', 'dx', 'dy', 'error_x', 'error_y', 'fill', 'fillcolor', 'fillpattern', 'groupnorm', 'hoverinfo', 'hoverinfosrc', 'hoverlabel', 'hoveron', 'hovertemplate', 'hovertemplatesrc', 'hovertext', 'hovertextsrc', 'ids', 'idssrc', 'legendgroup', 'legendgrouptitle', 'legendrank', 'line', 'marker', 'meta', 'metasrc', 'mode', 'name', 'opacity', 'orientation', 'selected', 'selectedpoints', 'showlegend', 'stackgaps', 'stackgroup', 'stream', 'text', 'textfont', 'textposition', 'textpositionsrc', 'textsrc', 'texttemplate', 'texttemplatesrc', 'transforms', 'type', 'uid', 'uirevision', 'unselected', 'visible', 'x', 'x0', 'xaxis', 'xcalendar', 'xhoverformat', 'xperiod', 'xperiod0', 'xperiodalignment', 'xsrc', 'y', 'y0', 'yaxis', 'ycalendar', 'yhoverformat', 'yperiod', 'yperiod0', 'yperiodalignment', 'ysrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'
## Warning: 'scatter' objects don't have these attributes: 'title'
## Valid attributes include:
## 'cliponaxis', 'connectgaps', 'customdata', 'customdatasrc', 'dx', 'dy', 'error_x', 'error_y', 'fill', 'fillcolor', 'fillpattern', 'groupnorm', 'hoverinfo', 'hoverinfosrc', 'hoverlabel', 'hoveron', 'hovertemplate', 'hovertemplatesrc', 'hovertext', 'hovertextsrc', 'ids', 'idssrc', 'legendgroup', 'legendgrouptitle', 'legendrank', 'line', 'marker', 'meta', 'metasrc', 'mode', 'name', 'opacity', 'orientation', 'selected', 'selectedpoints', 'showlegend', 'stackgaps', 'stackgroup', 'stream', 'text', 'textfont', 'textposition', 'textpositionsrc', 'textsrc', 'texttemplate', 'texttemplatesrc', 'transforms', 'type', 'uid', 'uirevision', 'unselected', 'visible', 'x', 'x0', 'xaxis', 'xcalendar', 'xhoverformat', 'xperiod', 'xperiod0', 'xperiodalignment', 'xsrc', 'y', 'y0', 'yaxis', 'ycalendar', 'yhoverformat', 'yperiod', 'yperiod0', 'yperiodalignment', 'ysrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'
## Warning: 'scatter' objects don't have these attributes: 'title'
## Valid attributes include:
## 'cliponaxis', 'connectgaps', 'customdata', 'customdatasrc', 'dx', 'dy', 'error_x', 'error_y', 'fill', 'fillcolor', 'fillpattern', 'groupnorm', 'hoverinfo', 'hoverinfosrc', 'hoverlabel', 'hoveron', 'hovertemplate', 'hovertemplatesrc', 'hovertext', 'hovertextsrc', 'ids', 'idssrc', 'legendgroup', 'legendgrouptitle', 'legendrank', 'line', 'marker', 'meta', 'metasrc', 'mode', 'name', 'opacity', 'orientation', 'selected', 'selectedpoints', 'showlegend', 'stackgaps', 'stackgroup', 'stream', 'text', 'textfont', 'textposition', 'textpositionsrc', 'textsrc', 'texttemplate', 'texttemplatesrc', 'transforms', 'type', 'uid', 'uirevision', 'unselected', 'visible', 'x', 'x0', 'xaxis', 'xcalendar', 'xhoverformat', 'xperiod', 'xperiod0', 'xperiodalignment', 'xsrc', 'y', 'y0', 'yaxis', 'ycalendar', 'yhoverformat', 'yperiod', 'yperiod0', 'yperiodalignment', 'ysrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'
## **Tendencias en la generación de electricidad de combustibles fósiles entre los 10 principales países**
# Asegurar que Colombia esté en la lista de países seleccionados
# Asegurar que Colombia esté en la lista de países seleccionados
top_fossil_fuel_countries <- energia_sus %>%
group_by(Country) %>%
summarise(mean_fossil_fuels = mean(`Electricity from fossil fuels (TWh)`, na.rm = TRUE)) %>%
arrange(desc(mean_fossil_fuels)) %>%
slice(1:3) # Selecciona los 3 principales países
# Filtrar los datos para los países seleccionados (top 3 + Colombia)
filtered_data <- energia_sus %>%
filter(Country %in% top_fossil_fuel_countries$Country)
# Crear el gráfico de líneas para mostrar la tendencia en el uso de electricidad de combustibles fósiles
fig <- plot_ly(
data = filtered_data,
x = ~Year,
y = ~`Electricity from fossil fuels (TWh)`,
color = ~Country,
type = 'scatter',
mode = 'lines+markers'
) %>%
layout(
title = 'Tendencias en la Generación de Electricidad de Combustibles Fósiles entre los Principales 10 Países y Colombia',
xaxis = list(title = 'Año'),
yaxis = list(title = 'Electricidad de Combustibles Fósiles (TWh)')
)
fig
# Definir los países que se quieren visualizar
paises <- c('United States', 'Colombia', 'China', 'Brazil')
# Filtrar los datos para los países seleccionados
tenden_co2 <- energia_sus %>%
filter(Country %in% paises)
# Crear el gráfico de líneas para mostrar la tendencia de emisiones de CO2
ggplot(tenden_co2, aes(x = Year, y = CO2, color = Country)) +
geom_line() +
ggtitle('Tendencia de Emisiones de CO2 para Estados Unidos, Colombia, China y Brasil') +
theme_minimal()
## Warning: Removed 4 rows containing missing values or values outside the scale range
## (`geom_line()`).
# **Tendencia de emisiones de CO2 para Colombia y Brasil**
# Definir los países que se quieren visualizar
paises <- c('Colombia', 'Brazil', 'Argentina', 'Chile', 'Ecuador', 'Uruguay')
# Filtrar los datos para los países seleccionados
tenden_co2 <- energia_sus %>%
filter(Country %in% paises)
# Crear el gráfico de líneas para mostrar la tendencia de emisiones de CO2
ggplot(tenden_co2, aes(x = Year, y = CO2, color = Country)) +
geom_line() +
ggtitle('Tendencia de Emisiones de CO2 para Colombia y Brasil') +
theme_minimal()
## Warning: Removed 6 rows containing missing values or values outside the scale range
## (`geom_line()`).
