Lectura de datos y librerias

Vamos a leer las librerias necesarias para seguir con el análisis de datos y las bases sobre las que vamos a trabajar.

library(readxl);
library(dplyr);
library(writexl);
library(knitr);
library(corrplot);


IE <- read_excel("union_europea_IE.xlsx", col_types = 'text');
PIB <- read_excel("union_europea_PIB.xlsx", col_types = 'text');

Muestra de datos

Se muestran los datos originales sobre los que vamos a trabajar.

Datos PIB original
DATAFLOW	LAST UPDATE	freq	unit	nace_r2	na_item	geo	TIME_PERIOD	OBS_VALUE	OBS_FLAG	CONF_STATUS
ESTAT:NAMA_10_A10(1.0)	27/02/25 23:00:00	Annual	Chain linked volumes (2005), million euro	Agriculture, forestry and fishing	Value added, gross	Albania	2019	2086.6	b	NA
ESTAT:NAMA_10_A10(1.0)	27/02/25 23:00:00	Annual	Chain linked volumes (2005), million euro	Agriculture, forestry and fishing	Value added, gross	Albania	2020	2124.1	NA	NA
ESTAT:NAMA_10_A10(1.0)	27/02/25 23:00:00	Annual	Chain linked volumes (2005), million euro	Agriculture, forestry and fishing	Value added, gross	Albania	2021	2088.3000000000002	NA	NA
ESTAT:NAMA_10_A10(1.0)	27/02/25 23:00:00	Annual	Chain linked volumes (2005), million euro	Agriculture, forestry and fishing	Value added, gross	Albania	2022	1987.6	NA	NA
ESTAT:NAMA_10_A10(1.0)	27/02/25 23:00:00	Annual	Chain linked volumes (2005), million euro	Agriculture, forestry and fishing	Value added, gross	Albania	2023	1951.3	p	NA
ESTAT:NAMA_10_A10(1.0)	27/02/25 23:00:00	Annual	Chain linked volumes (2005), million euro	Agriculture, forestry and fishing	Value added, gross	Austria	1995	2953.8	NA	NA

Datos IE original
DATAFLOW	LAST UPDATE	freq	unit	na_item	geo	TIME_PERIOD	OBS_VALUE	OBS_FLAG	CONF_STATUS
ESTAT:NAMA_10_EXI(1.0)	27/02/25 23:00:00	Annual	Chain linked volumes (2005), million euro	Exports of goods and services	Albania	2019	3678.6	b	NA
ESTAT:NAMA_10_EXI(1.0)	27/02/25 23:00:00	Annual	Chain linked volumes (2005), million euro	Exports of goods and services	Albania	2020	2658.7	NA	NA
ESTAT:NAMA_10_EXI(1.0)	27/02/25 23:00:00	Annual	Chain linked volumes (2005), million euro	Exports of goods and services	Albania	2021	4043.8	NA	NA
ESTAT:NAMA_10_EXI(1.0)	27/02/25 23:00:00	Annual	Chain linked volumes (2005), million euro	Exports of goods and services	Albania	2022	4733	NA	NA
ESTAT:NAMA_10_EXI(1.0)	27/02/25 23:00:00	Annual	Chain linked volumes (2005), million euro	Exports of goods and services	Albania	2023	5180.3999999999996	p	NA
ESTAT:NAMA_10_EXI(1.0)	27/02/25 23:00:00	Annual	Chain linked volumes (2005), million euro	Exports of goods and services	Austria	1995	63483.8	NA	NA

Análisis de calidad

Descarte de variables

Antes de empezar con el análisis de datos ya sabemos que vamos a descartar unas variables de la base de datos que no interesan como son: DATAFLOW, LAST_UPDATE, freq, unit, OBS_FLAG y CONF_STATUS

PIB2 <- PIB[, 4:9];
knitr::kable(head(PIB2), caption= 'Variables empleadas PIB');

Variables empleadas PIB
unit	nace_r2	na_item	geo	TIME_PERIOD	OBS_VALUE
Chain linked volumes (2005), million euro	Agriculture, forestry and fishing	Value added, gross	Albania	2019	2086.6
Chain linked volumes (2005), million euro	Agriculture, forestry and fishing	Value added, gross	Albania	2020	2124.1
Chain linked volumes (2005), million euro	Agriculture, forestry and fishing	Value added, gross	Albania	2021	2088.3000000000002
Chain linked volumes (2005), million euro	Agriculture, forestry and fishing	Value added, gross	Albania	2022	1987.6
Chain linked volumes (2005), million euro	Agriculture, forestry and fishing	Value added, gross	Albania	2023	1951.3
Chain linked volumes (2005), million euro	Agriculture, forestry and fishing	Value added, gross	Austria	1995	2953.8

IE2 <- IE[, 4:8];
knitr::kable(head(IE2), caption= 'Variables empleadas IE');

Variables empleadas IE
unit	na_item	geo	TIME_PERIOD	OBS_VALUE
Chain linked volumes (2005), million euro	Exports of goods and services	Albania	2019	3678.6
Chain linked volumes (2005), million euro	Exports of goods and services	Albania	2020	2658.7
Chain linked volumes (2005), million euro	Exports of goods and services	Albania	2021	4043.8
Chain linked volumes (2005), million euro	Exports of goods and services	Albania	2022	4733
Chain linked volumes (2005), million euro	Exports of goods and services	Albania	2023	5180.3999999999996
Chain linked volumes (2005), million euro	Exports of goods and services	Austria	1995	63483.8

Tipo de variables

Describimos de qué tipo son las variables de cada base de datos

Lo mostramos en estas tablas:

Variables IE
variable	tipo
unit	text
na_item	categorical
geo	categorical
TIME_PERIOD	numerical
OBS_VALUE	numerical

Variables PIB
variable	tipo
unit	text
nace_r2	categorical
na_item	text
geo	categorical
TIME_PERIOD	numerical
OBS_VALUE	numerical

PIB3 <- PIB2[PIB2$unit %in% c('Current prices, million euro', 'Percentage of gross domestic product(GDP)','Percentage of total'),]

PIB3 <- PIB3[!PIB3$geo %in% c('Euro area - 12 countries (2001-2006)', 'Euro area - 19 countries (2015-2022)', 'Euro area - 20 countries (from 2023)','European Union - 27 countries (from 2020)', 'Euro area (EA11-1999, EA12-2001, EA13-2007, EA15-208, EA16-2009, EA17-2011, EA18-2014, EA19-2015, EA15-2023'),]

PIB3 <- PIB3[!PIB3$nace_r2 == 'Total - all NACE activities',]

IE3 <- IE2[IE2$unit=='Current prices, million euro', ]

IE3 <- IE3[IE3$na_item %in% c('Exports of goods and services', 'Imports of goods and services'), ]

Valores Faltantes

Vamos a observar los valores faltantes de la base de datos IE por cada variable tanto en cantidad como en porcentaje sobre el total.

Valores faltantes IE
	Variable	numNA	percNA
OBS_VALUE	OBS_VALUE	320	0.44

Observamos que solo hay valores faltantes en la variable obs_value, en concreto 320, que es un 0.44% sobre el total. Como la variable que queremos estudiar es esta, no nos interesa mantener las observaciones sobre las cuales no tenemos esa información, más adelante tras analizar la base de datos PIB, eliminaremos aquellas observaciones en las que no se tenga información en la variable OBS_VALUE.
Ahora vamos a observar los valores faltantes de la base de datos PIB:

Valores Faltantes PIB
	Variable	numNA	percNA
OBS_VALUE	OBS_VALUE	6309	0.81

En esta base de datos encontramos 6309 casos faltantes únicamente en la variable OBS_VALUE, aunque pueda parecer una cantidad excesiva es únicamente el 0.81 % del total, como se repite la situación en la que es esta la variable que queremos estudiar, vamos a eliminar aquellas observaciones en las que no se tenga esta información.

Eliminación Valores Faltantes

Partiendo de que no nos interesan aquellas filas en las que falte aparezcan valores faltantes, debido a que la carencia de información en alguna de todas las variables, la observación no nos sirve. Se eliminan todas las filas en las que falte cualquier tipo de información.

PIB3 <- PIB3[!is.na(PIB3$OBS_VALUE),]
IE3 <- IE3[!is.na(IE3$OBS_VALUE),]

Commodities

Se vuelve a cambiar de base de datos y ahora trataremos la que muestra los datos de exportación e importación de cada país. La utilizaremos para comprobar si el índice de libertad, y el PIB tiene relación con el nivel de importaciones y exportaciones.

Lectura de Bases de datos

Se empieza con la lectura de las bases de datos.

## [1] "EXPORT" "IMPORT"

Tratado de variables

Se empieza a borrar aquellas variables que tenemos claro que queremos descartar, que son: ‘comm_code’, ‘quantity_name’, ‘quantity’ y ‘flow’.

Filtrado de casos

De la información otorgada por esta base de datos, solo nos informa de cada país por específico, por eso eliminamos los casos de ‘EU-28’, y el de la suma de todos las commodities ‘ALL COMMODITIES’.

# Filtrar COMEXP
COMEXP <- COMEXP[!(COMEXP$commodity == "ALL COMMODITIES" & COMEXP$country_or_area == "EU-28"), ];

COMIMP <- COMIMP[!(COMIMP$commodity == "ALL COMMODITIES" & COMIMP$country_or_area == "EU-28"), ];

Objetivo 4

• Comparar y analizar el impacto de la crisis financiera de 2008 y la pandemia de COVID-19 en los flujos de exportación e importación de diferentes países seleccionados de distintas zonas geográficas, evaluando la permanencia de los efectos, las diferencias en la recuperación del sector comercial, y los factores que influyeron en la velocidad y el patrón de recuperación de cada país.

Gráficos de líneas exportaciones e importaciones Europa

library(dplyr)
library(ggplot2)

IE3 <- IE3 %>%
  mutate(
    OBS_VALUE = as.numeric(gsub(",", ".", gsub("[^0-9,\\.]", "", OBS_VALUE))),
    TIME_PERIOD = as.numeric(TIME_PERIOD)
  )

# Lista de países de la UE
paises_ue_lista <- c("Germany", "Spain", "France", "Italy", "Poland", "Sweden", "Netherlands",
                     "Portugal", "Greece", "Ireland", "Austria", "Belgium", "Croatia", "Hungary",
                     "Romania", "Finland", "Denmark", "Czechia", "Estonia", "Slovakia", "Slovenia",
                     "Lithuania", "Latvia", "Bulgaria", "Luxembourg")

# Detectar países con datos disponibles
paises_presentes <- IE3 %>%
  filter(TIME_PERIOD %in% 2006:2012 | TIME_PERIOD %in% 2018:2023,
         na_item %in% c("Exports of goods and services", "Imports of goods and services")) %>%
  distinct(geo) %>%
  pull(geo)

# Clasificación UE / No UE
paises_total <- data.frame(
  geo = paises_presentes,
  grupo = ifelse(paises_presentes %in% paises_ue_lista, "UE", "No UE")
)

# Filtrar y preparar datos
IE_crisis_pandemia <- IE3 %>%
  filter(geo %in% paises_total$geo,
         TIME_PERIOD %in% 2006:2012 | TIME_PERIOD %in% 2018:2023,
         na_item %in% c("Exports of goods and services", "Imports of goods and services"))

datos_combinados <- IE_crisis_pandemia %>%
  mutate(flujo = ifelse(na_item == "Exports of goods and services", "Exportaciones", "Importaciones")) %>%
  left_join(paises_total, by = "geo")

datos_combinados$grupo <- factor(datos_combinados$grupo, levels = c("UE", "No UE"))

# --- GRÁFICO UE ---
grafico_ue <- datos_combinados %>%
  filter(grupo == "UE") %>%
  ggplot(aes(x = TIME_PERIOD, y = OBS_VALUE/1000, color = flujo)) +
  geom_line(linewidth = 1.1) +
  geom_vline(xintercept = 2008, linetype = "dashed", color = "red", linewidth = 0.8) +
  geom_vline(xintercept = 2020, linetype = "dashed", color = "blue", linewidth = 0.8) +
  facet_wrap(~geo, ncol = 3, scales = "free_y") +
  labs(
    title = "Exportaciones e importaciones (2006–2012 y 2018–2023)",
    subtitle = "Países de la Unión Europea (líneas: 2008 y 2020)",
    x = "Año", y = "Valor en miles de millones €",
    color = "Tipo de flujo"
  ) +
  scale_x_continuous(breaks = seq(2006, 2023, by = 2)) +
  theme_minimal(base_size = 13) +
  theme(
    plot.title = element_text(size = 18, face = "bold"),
    plot.subtitle = element_text(size = 14),
    strip.text = element_text(size = 11, face = "bold"),
    axis.text.x = element_text(angle = 45, hjust = 1)
  )

# --- GRÁFICO No UE ---
grafico_no_ue <- datos_combinados %>%
  filter(grupo == "No UE") %>%
  ggplot(aes(x = TIME_PERIOD, y = OBS_VALUE/1000, color = flujo)) +
  geom_line(linewidth = 1.1) +
  geom_vline(xintercept = 2008, linetype = "dashed", color = "red", linewidth = 0.8) +
  geom_vline(xintercept = 2020, linetype = "dashed", color = "blue", linewidth = 0.8) +
  facet_wrap(~geo, ncol = 3, scales = "free_y") +
  labs(
    title = "Exportaciones e importaciones (2006–2012 y 2018–2023)",
    subtitle = "Países fuera de la Unión Europea (líneas: 2008 y 2020)",
    x = "Año", y = "Valor en miles de millones €",
    color = "Tipo de flujo"
  ) +
  scale_x_continuous(breaks = seq(2006, 2023, by = 2)) +
  theme_minimal(base_size = 13) +
  theme(
    plot.title = element_text(size = 18, face = "bold"),
    plot.subtitle = element_text(size = 14),
    strip.text = element_text(size = 11, face = "bold"),
    axis.text.x = element_text(angle = 45, hjust = 1)
  )

# Mostrar
print(grafico_ue)

print(grafico_no_ue)

PCA exportaciones

library(dplyr)
library(tidyr)
library(ggplot2)
library(tibble)
library(FactoMineR)
library(factoextra)


Paises = c("USA", "China", "Russian Federation", "Japan", "Brazil", "Australia",
                                             "Canada","Germany", "Spain", "France", "Italy", "Poland", "Sweden",
                                             "Netherlands","Portugal", "Greece", "Ireland", "Austria", "Belgium", 
                                             "Croatia", "Hungary","Romania", "Finland", "Denmark", "Czechia",
                                             "Estonia", "Slovakia", "Slovenia","Lithuania", "Latvia", "Bulgaria",
                                             "Luxembourg")

# filtrado de paises
COMEXP2 = COMEXP[COMEXP$country_or_area %in% Paises,]
COMIMP2 = COMIMP[COMIMP$country_or_area %in% Paises,]

EU_25 <- c("Germany", "Spain", "France", "Italy", "Poland", "Sweden",
           "Netherlands","Portugal", "Greece", "Ireland", "Austria", "Belgium", 
           "Croatia", "Hungary","Romania", "Finland", "Denmark", "Czechia",
           "Estonia", "Slovakia", "Slovenia","Lithuania", "Latvia", "Bulgaria", "Luxembourg")

# Crear nueva columna "grupo_mod"
COMEXP2 <- COMEXP2 %>%
  mutate(grupo_mod = ifelse(country_or_area %in% EU_25, "EU_25", country_or_area))

# Conversión de columnas numéricas
COMEXP2 <- COMEXP2 %>%
  mutate(
    trade_usd = as.numeric(trade_usd),
    year = as.numeric(year)
  )

COMEXP_wide <- COMEXP2 %>%
  group_by(grupo_mod, year, category) %>%
  summarise(trade_usd = sum(trade_usd, na.rm = TRUE), .groups = "drop") %>%
  pivot_wider(
    names_from = category,
    values_from = trade_usd,
    values_fill = 0
  )


# SEPARACION POR PERIODOS
PRECRISIS_EXP = COMEXP_wide[COMEXP_wide$year %in% c(2006:2008),]
CRISIS_EXP = COMEXP_wide[COMEXP_wide$year %in% c(2009:2011),]
POSTCRISIS_EXP = COMEXP_wide[COMEXP_wide$year %in% c(2012:2016),]

#Función PCA
HACER_PCA <- function(df, titulo) {
  # Detectar y eliminar columna de identificación (grupo_mod o country_or_area)
  id_cols <- c("country_or_area", "grupo_mod", "year")
  df <- df %>% select(-any_of(id_cols))

  # Ejecutar PCA
  res.pca <- PCA(scale(df), graph = FALSE)

  # Visualizaciones
  print(fviz_pca_var(res.pca, axes = c(1,2), title = titulo))
  print(fviz_contrib(res.pca, choice = "var", axes = 1, top = 10, 
                     title = paste("Contribución al PC1 -", titulo)))
  print(fviz_contrib(res.pca, choice = "var", axes = 2, top = 10, 
                     title = paste("Contribución al PC2 -", titulo)))

  # Mostrar tabla de contribuciones combinadas PC1+PC2
  contrib_total <- rowSums(res.pca$var$contrib[, 1:2])
  top10 = (sort(contrib_total, decreasing = TRUE)[1:10])
  print(knitr::kable(top10))

  return(list(res.pca, names(top10)))
}


# Aplicar a cada periodo
pca_pre <- HACER_PCA(PRECRISIS_EXP, "Exportaciones 2006–2008 (Pre-crisis)")

## 
## 
## |                                                      |        x|
## |:-----------------------------------------------------|--------:|
## |63_other_made_textile_articles_sets_worn_clothing_etc | 4.955416|
## |52_cotton                                             | 4.939733|
## |46_manufactures_of_plaiting_material_basketwork_etc   | 4.870621|
## |67_bird_skin_feathers_artificial_flowers_human_hair   | 4.807887|
## |55_manmade_staple_fibres                              | 4.704015|
## |66_umbrellas_walking_sticks_seat_sticks_whips_etc     | 4.573384|
## |54_manmade_filaments                                  | 4.548866|
## |96_miscellaneous_manufactured_articles                | 4.267263|
## |82_tools_implements_cutlery_etc_of_base_metal         | 4.204634|
## |95_toys_games_sports_requisites                       | 3.917670|

pca_cri <- HACER_PCA(CRISIS_EXP, "Exportaciones 2009–2011 (Crisis)")

## 
## 
## |                                                      |        x|
## |:-----------------------------------------------------|--------:|
## |63_other_made_textile_articles_sets_worn_clothing_etc | 4.120717|
## |52_cotton                                             | 4.052948|
## |67_bird_skin_feathers_artificial_flowers_human_hair   | 4.029999|
## |54_manmade_filaments                                  | 3.983768|
## |46_manufactures_of_plaiting_material_basketwork_etc   | 3.975048|
## |55_manmade_staple_fibres                              | 3.960142|
## |66_umbrellas_walking_sticks_seat_sticks_whips_etc     | 3.929465|
## |82_tools_implements_cutlery_etc_of_base_metal         | 3.730290|
## |96_miscellaneous_manufactured_articles                | 3.722950|
## |50_silk                                               | 3.332857|

pca_post <- HACER_PCA(POSTCRISIS_EXP, "Exportaciones 2012–2016 (Post-crisis)")

## 
## 
## |                                                      |        x|
## |:-----------------------------------------------------|--------:|
## |63_other_made_textile_articles_sets_worn_clothing_etc | 3.611137|
## |67_bird_skin_feathers_artificial_flowers_human_hair   | 3.578768|
## |54_manmade_filaments                                  | 3.571676|
## |52_cotton                                             | 3.552475|
## |55_manmade_staple_fibres                              | 3.502750|
## |66_umbrellas_walking_sticks_seat_sticks_whips_etc     | 3.476160|
## |46_manufactures_of_plaiting_material_basketwork_etc   | 3.472516|
## |82_tools_implements_cutlery_etc_of_base_metal         | 3.377921|
## |96_miscellaneous_manufactured_articles                | 3.354027|
## |70_glass_and_glassware                                | 3.294894|

PCA importación

# --- PASO 1: preparar y agrupar COMIMP ---
COMIMP_mod <- COMIMP2 %>%
  mutate(
    trade_usd = as.numeric(trade_usd),
    year = as.numeric(year),
    grupo_mod = ifelse(country_or_area %in% EU_25, "EU_25", country_or_area)
  ) %>%
  group_by(grupo_mod, year, category) %>%
  summarise(trade_usd = sum(trade_usd, na.rm = TRUE), .groups = "drop") %>%
  pivot_wider(names_from = category, values_from = trade_usd, values_fill = 0)

# --- PASO 2: separar por periodos ---
PRECRISIS_IMP <- COMIMP_mod %>% filter(year %in% 2006:2008)
CRISIS_IMP    <- COMIMP_mod %>% filter(year %in% 2009:2011)
POSTCRISIS_IMP<- COMIMP_mod %>% filter(year %in% 2012:2016)

# --- PASO 4: aplicar PCA ---
pca_imp_pre  <- HACER_PCA(PRECRISIS_IMP, "PCA Importaciones Pre-Crisis (2006–2008)")

## 
## 
## |                                                      |        x|
## |:-----------------------------------------------------|--------:|
## |52_cotton                                             | 7.813841|
## |55_manmade_staple_fibres                              | 7.640172|
## |40_rubber_and_articles_thereof                        | 6.415096|
## |25_salt_sulphur_earth_stone_plaster_lime_and_cement   | 5.933868|
## |54_manmade_filaments                                  | 5.810276|
## |12_oil_seed_oleagic_fruits_grain_seed_fruit_etc_ne    | 5.622188|
## |26_ores_slag_and_ash                                  | 5.496725|
## |03_fish_crustaceans_molluscs_aquatic_invertebrates_ne | 5.180095|
## |44_wood_and_articles_of_wood_wood_charcoal            | 4.430021|
## |74_copper_and_articles_thereof                        | 4.127749|

pca_imp_cri  <- HACER_PCA(CRISIS_IMP,    "PCA Importaciones Crisis (2009–2011)")

## 
## 
## |                                                      |        x|
## |:-----------------------------------------------------|--------:|
## |52_cotton                                             | 6.738719|
## |55_manmade_staple_fibres                              | 6.576824|
## |40_rubber_and_articles_thereof                        | 6.074507|
## |26_ores_slag_and_ash                                  | 5.752115|
## |12_oil_seed_oleagic_fruits_grain_seed_fruit_etc_ne    | 5.726721|
## |74_copper_and_articles_thereof                        | 5.292766|
## |54_manmade_filaments                                  | 5.282262|
## |25_salt_sulphur_earth_stone_plaster_lime_and_cement   | 5.275753|
## |44_wood_and_articles_of_wood_wood_charcoal            | 5.104751|
## |03_fish_crustaceans_molluscs_aquatic_invertebrates_ne | 4.911336|

pca_imp_post <- HACER_PCA(POSTCRISIS_IMP,"PCA Importaciones Post-Crisis (2012–2016)")

## 
## 
## |                                                      |        x|
## |:-----------------------------------------------------|--------:|
## |55_manmade_staple_fibres                              | 5.748922|
## |52_cotton                                             | 5.647229|
## |40_rubber_and_articles_thereof                        | 5.444360|
## |25_salt_sulphur_earth_stone_plaster_lime_and_cement   | 5.375902|
## |12_oil_seed_oleagic_fruits_grain_seed_fruit_etc_ne    | 5.366515|
## |70_glass_and_glassware                                | 5.308241|
## |26_ores_slag_and_ash                                  | 5.234925|
## |44_wood_and_articles_of_wood_wood_charcoal            | 5.172552|
## |03_fish_crustaceans_molluscs_aquatic_invertebrates_ne | 4.806955|
## |74_copper_and_articles_thereof                        | 4.770071|

Gráficos valor categorías

library(dplyr)
library(ggplot2)
library(tidyr)

graficar_top_categoria <- function(df, categorias,titulo) {
  df %>%
    select(any_of(categorias)) %>%
    summarise(across(everything(), ~ sum(.x, na.rm = TRUE))) %>%
    pivot_longer(everything(), names_to = "categoria", values_to = "valor_total") %>%
    arrange(desc(valor_total)) %>%
    ggplot(aes(x = reorder(categoria, valor_total), y = valor_total / 1e9)) +
    geom_bar(stat = "identity", fill = "darkorange") +
    coord_flip() +
    labs(
      title = titulo,
      x = "Categoría",
      y = "Valor total (miles de millones USD)"
    ) +
    theme_minimal(base_size = 10)
}


prexp = pca_pre[[2]]
graficar_top_categoria(PRECRISIS_EXP, prexp, 'Exportaciones globales pre-crisis')

primp = pca_imp_pre[[2]]
graficar_top_categoria(PRECRISIS_IMP,primp, 'Importaciones globales pre-crisis')

crexp = pca_cri[[2]]
graficar_top_categoria(CRISIS_EXP, crexp, 'Exportaciones globales crisis')

crimp = pca_imp_cri[[2]]
graficar_top_categoria(CRISIS_IMP,crimp, 'Importaciones globales crisis')

postexp = pca_post[[2]]
graficar_top_categoria(POSTCRISIS_EXP, postexp, 'Exportaciones globales post-crisis')

postimp = pca_imp_post[[2]]
graficar_top_categoria(POSTCRISIS_IMP,postimp, 'Importaciones globales post-crisis')

Objetivo 4

Isaac Herranz Blasco

2025-05-29

Lectura de datos y librerias

Muestra de datos

Análisis de calidad

Descarte de variables

Tipo de variables

Valores Faltantes

Eliminación Valores Faltantes

Commodities

Lectura de Bases de datos

Tratado de variables

Filtrado de casos

Objetivo 4

Gráficos de líneas exportaciones e importaciones Europa

PCA exportaciones

PCA importación

Gráficos valor categorías