País

---

1 Carga de Datos y Librerías

Se cargan las librerías necesarias y el dataset Global Oil and Gas Extraction Tracker (GOGET), que contiene registros de unidades de extracción de petróleo y gas a nivel mundial.

library(readxl)
library(dplyr)
library(gt)
library(ggplot2)
library(scales)
library(forcats)
library(stringr)

setwd("C:/Users/ronny/Downloads/Dataset")
datos <- read_excel("dataset_mundial_petro.xlsx") %>%
  mutate(Country = trimws(Country)) %>%               
  # Ajustar si el nombre de columna difiere
  filter(!is.na(Country), Country != "NA", Country != "",
         !is.na(`Unit type`), `Unit type` != "NA")

cat("Registros válidos:", nrow(datos), "\n")

## Registros válidos: 8334

cat("Variables:", ncol(datos), "\n")

## Variables: 32

---

2 Extracción de la Variable

Se extrae la variable País (Country). Es una variable de escala nominal: sus categorías no tienen orden jerárquico intrínseco. Para el análisis regional, cada país se clasifica en su continente correspondiente mediante una función de asignación geográfica estándar.

asignar_continente <- function(pais) {
  case_when(
    # América del Norte
    pais %in% c("United States", "Canada", "Mexico", "Greenland") ~
      "América del Norte",

    # América del Sur y Caribe
    pais %in% c("Venezuela", "Brazil", "Colombia", "Argentina", "Ecuador",
                "Peru", "Bolivia", "Trinidad and Tobago", "Guyana", "Suriname",
                "Chile", "Cuba", "Paraguay", "Uruguay", "Panama", "Costa Rica",
                "Honduras", "Guatemala", "Nicaragua", "El Salvador",
                "Barbados", "Haiti", "Dominican Republic", "Jamaica",
                "Belize", "Bahamas") ~
      "América del Sur y Caribe",

    # Europa y Rusia
    pais %in% c("Norway", "United Kingdom", "Denmark", "Netherlands", "Germany",
                "Poland", "Romania", "Albania", "Serbia", "Croatia", "Hungary",
                "Czech Republic", "Austria", "Italy", "France", "Spain", "Greece",
                "Bulgaria", "Slovakia", "Ukraine", "Belarus", "Moldova",
                "Latvia", "Lithuania", "Estonia", "Finland", "Sweden",
                "Switzerland", "Belgium", "Portugal", "Ireland",
                "Bosnia and Herzegovina", "North Macedonia", "Montenegro",
                "Slovenia", "Kosovo", "Cyprus", "Russia") ~
      "Europa y Rusia",

    # Asia Central y Cáucaso
    pais %in% c("Kazakhstan", "Azerbaijan", "Turkmenistan", "Uzbekistan",
                "Kyrgyzstan", "Tajikistan", "Georgia", "Armenia") ~
      "Asia Central y Cáucaso",

    # Oriente Medio
    pais %in% c("Saudi Arabia", "Iraq", "Iran", "Kuwait",
                "United Arab Emirates", "Qatar", "Bahrain", "Oman",
                "Yemen", "Syria", "Jordan", "Israel", "Lebanon", "Turkey") ~
      "Oriente Medio",

    # África
    pais %in% c("Nigeria", "Angola", "Libya", "Algeria", "Egypt", "Tunisia",
                "Gabon", "Republic of the Congo",
                "Democratic Republic of the Congo", "Congo", "Cameroon",
                "Sudan", "South Sudan", "Chad", "Equatorial Guinea",
                "Mozambique", "Tanzania", "Cote d'Ivoire", "Ivory Coast",
                "Ghana", "Niger", "Somalia", "Morocco", "Namibia",
                "Madagascar", "Senegal", "Mauritania", "Uganda", "Kenya",
                "Ethiopia", "South Africa", "Zambia", "Zimbabwe") ~
      "África",

    # Asia Pacífico y Oceanía
    pais %in% c("China", "India", "Indonesia", "Malaysia", "Vietnam",
                "Thailand", "Myanmar", "Bangladesh", "Pakistan", "Brunei",
                "Philippines", "Japan", "South Korea", "North Korea",
                "Taiwan", "Mongolia", "Papua New Guinea", "Timor-Leste",
                "East Timor", "Cambodia", "Laos", "Sri Lanka",
                "Afghanistan", "Nepal", "Australia", "New Zealand") ~
      "Asia Pacífico y Oceanía",

    TRUE ~ "Otro/No especificado"
  )
}

datos <- datos %>%
  mutate(Continente = asignar_continente(Country))

n <- nrow(datos)

cat("Variable analizada: País (Country)\n")

## Variable analizada: País (Country)

cat("Total de observaciones (n):", n, "\n")

## Total de observaciones (n): 8334

cat("Número de países únicos:", n_distinct(datos$Country), "\n")

## Número de países únicos: 104

cat("Continentes identificados:",
    paste(sort(unique(datos$Continente)), collapse = ", "), "\n")

## Continentes identificados: África, América del Norte, América del Sur y Caribe, Asia Central y Cáucaso, Asia Pacífico y Oceanía, Europa y Rusia, Oriente Medio, Otro/No especificado

---

3 Conteo y Cálculo

Se calculan la frecuencia absoluta (nᵢ), la frecuencia relativa en proporción (hᵢ) y en porcentaje (hᵢ %) para cada país y, de forma agregada, para cada continente, ordenadas de mayor a menor.

## ── Tabla por País ────────────────────────────────────────────────────────────
tabla_freq_pais <- datos %>%
  count(Country, name = "ni") %>%
  arrange(desc(ni)) %>%
  rename(Pais = Country) %>%
  mutate(
    hi_prop = ni / n,
    hi_pct  = hi_prop * 100,
    i       = row_number()
  ) %>%
  select(i, Pais, ni, hi_pct, hi_prop)

k_pais <- nrow(tabla_freq_pais)

cat("── Análisis por País ──\n")

## ── Análisis por País ──

cat("Número de países (k)     :", k_pais, "\n")

## Número de países (k)     : 104

cat("País más frecuente       :", tabla_freq_pais$Pais[1],
    "—", tabla_freq_pais$ni[1], "registros\n")

## País más frecuente       : United States — 3093 registros

cat("País menos frecuente     :", tabla_freq_pais$Pais[k_pais],
    "—", tabla_freq_pais$ni[k_pais], "registro(s)\n")

## País menos frecuente     : Zimbabwe — 1 registro(s)

cat("Verificación — Σnᵢ       :", sum(tabla_freq_pais$ni),
    "(debe ser", n, ")\n")

## Verificación — Σnᵢ       : 8334 (debe ser 8334 )

cat("Verificación — Σhᵢ%      :", round(sum(tabla_freq_pais$hi_pct), 2),
    "(debe ser 100)\n")

## Verificación — Σhᵢ%      : 100 (debe ser 100)

## ── Tabla por Continente ──────────────────────────────────────────────────────
tabla_freq_cont <- datos %>%
  count(Continente, name = "ni") %>%
  arrange(desc(ni)) %>%
  mutate(
    hi_prop = ni / n,
    hi_pct  = hi_prop * 100,
    i       = row_number()
  ) %>%
  select(i, Continente, ni, hi_pct, hi_prop)

k_cont <- nrow(tabla_freq_cont)

cat("── Análisis por Continente ──\n")

## ── Análisis por Continente ──

cat("Número de continentes (k)  :", k_cont, "\n")

## Número de continentes (k)  : 8

cat("Continente más frecuente   :", tabla_freq_cont$Continente[1],
    "—", tabla_freq_cont$ni[1], "registros\n")

## Continente más frecuente   : América del Norte — 4468 registros

cat("Continente menos frecuente :", tabla_freq_cont$Continente[k_cont],
    "—", tabla_freq_cont$ni[k_cont], "registro(s)\n")

## Continente menos frecuente : Otro/No especificado — 35 registro(s)

cat("Verificación — Σnᵢ         :", sum(tabla_freq_cont$ni),
    "(debe ser", n, ")\n")

## Verificación — Σnᵢ         : 8334 (debe ser 8334 )

cat("Verificación — Σhᵢ%        :", round(sum(tabla_freq_cont$hi_pct), 2),
    "(debe ser 100)\n")

## Verificación — Σhᵢ%        : 100 (debe ser 100)

---

4 Tabla de Distribución de Frecuencias

4.1 Por País

tabla_freq_pais %>%
  gt() %>%
  tab_header(
    title    = md("**Tabla N. 1**"),
    subtitle = md("Distribución de frecuencias por país — yacimientos de petróleo y gas")
  ) %>%
  cols_label(
    i    = md("**N°**"),
    Pais = md("**País**"),
    ni   = md("**nᵢ**"),
    hi_pct  = md("**(%)** "),
    hi_prop = md("**(proporción)**")
  ) %>%
  tab_spanner(label = md("**hᵢ**"), columns = c(hi_pct, hi_prop)) %>%
  fmt_number(columns = ni,      decimals = 0, use_seps = TRUE) %>%
  fmt_number(columns = hi_pct,  decimals = 2) %>%
  fmt_number(columns = hi_prop, decimals = 4) %>%
  grand_summary_rows(
    columns = c(ni, hi_pct, hi_prop),
    fns = list(label = "Total", fn = "sum"),
    fmt = list(
      ~ fmt_number(., columns = ni,      decimals = 0, use_seps = TRUE),
      ~ fmt_number(., columns = hi_pct,  decimals = 2),
      ~ fmt_number(., columns = hi_prop, decimals = 4)
    )
  ) %>%
  tab_source_note("Autor: Grupo 5") %>%
  tab_options(
    table.width                       = pct(75),
    table.font.size                   = px(13),
    table.font.names                  = "Arial",
    heading.title.font.size           = px(15),
    heading.subtitle.font.size        = px(12),
    heading.align                     = "center",
    heading.background.color          = "#AAAAAA",
    column_labels.font.weight         = "bold",
    column_labels.background.color    = "#FFFFFF",
    column_labels.border.top.color    = "#AAAAAA",
    column_labels.border.bottom.color = "#AAAAAA",
    table.border.top.color            = "#AAAAAA",
    table.border.bottom.color         = "#AAAAAA"
  ) %>%
  tab_style(
    style     = cell_text(color = "white", weight = "bold"),
    locations = cells_title(groups = c("title", "subtitle"))
  ) %>%
  tab_style(
    style     = cell_text(weight = "bold"),
    locations = list(cells_column_labels(), cells_column_spanners(),
                     cells_grand_summary())
  )

Tabla N. 1
Distribución de frecuencias por país — yacimientos de petróleo y gas
	N°	País	nᵢ	hᵢ
				(%)	(proporción)
	1	United States	3,093	37.11	0.3711
	2	Canada	1,178	14.13	0.1413
	3	United Kingdom	317	3.80	0.0380
	4	Colombia	288	3.46	0.0346
	5	Russia	277	3.32	0.0332
	6	Nigeria	246	2.95	0.0295
	7	Argentina	202	2.42	0.0242
	8	Mexico	197	2.36	0.0236
	9	Australia	179	2.15	0.0215
	10	Netherlands	164	1.97	0.0197
	11	Venezuela	128	1.54	0.0154
	12	Norway	113	1.36	0.0136
	13	China	111	1.33	0.0133
	14	Poland	111	1.33	0.0133
	15	Iran	100	1.20	0.0120
	16	Brazil	97	1.16	0.0116
	17	Egypt	96	1.15	0.0115
	18	Ecuador	93	1.12	0.0112
	19	Angola	77	0.92	0.0092
	20	Indonesia	76	0.91	0.0091
	21	Malaysia	73	0.88	0.0088
	22	Romania	67	0.80	0.0080
	23	Algeria	58	0.70	0.0070
	24	Iraq	57	0.68	0.0068
	25	India	56	0.67	0.0067
	26	Germany	52	0.62	0.0062
	27	Kazakhstan	47	0.56	0.0056
	28	Thailand	42	0.50	0.0050
	29	United Arab Emirates	42	0.50	0.0050
	30	Libya	41	0.49	0.0049
	31	Oman	41	0.49	0.0049
	32	Italy	38	0.46	0.0046
	33	Pakistan	38	0.46	0.0046
	34	Trinidad and Tobago	35	0.42	0.0042
	35	Saudi Arabia	28	0.34	0.0034
	36	Peru	26	0.31	0.0031
	37	Vietnam	26	0.31	0.0031
	38	Qatar	25	0.30	0.0030
	39	Denmark	23	0.28	0.0028
	40	Turkmenistan	23	0.28	0.0028
	41	Azerbaijan	22	0.26	0.0026
	42	Tanzania	22	0.26	0.0026
	43	Guyana	19	0.23	0.0023
	44	Mozambique	17	0.20	0.0020
	45	Ukraine	16	0.19	0.0019
	46	Ireland	15	0.18	0.0018
	47	Brunei	14	0.17	0.0017
	48	Israel	14	0.17	0.0017
	49	Kuwait	14	0.17	0.0017
	50	Republic of the Congo	14	0.17	0.0017
	51	Bolivia	11	0.13	0.0013
	52	Chad	10	0.12	0.0012
	53	Cuba	10	0.12	0.0012
	54	Syria	8	0.10	0.0010
	55	Bangladesh	7	0.08	0.0008
	56	New Zealand	7	0.08	0.0008
	57	Barbados	6	0.07	0.0007
	58	Kuwait-Saudi Arabia	6	0.07	0.0007
	59	Myanmar	6	0.07	0.0007
	60	Türkiye	6	0.07	0.0007
	61	Cyprus	5	0.06	0.0006
	62	Côte d'Ivoire	5	0.06	0.0006
	63	Ethiopia	5	0.06	0.0006
	64	Namibia	5	0.06	0.0006
	65	Papua New Guinea	5	0.06	0.0006
	66	South Sudan	5	0.06	0.0006
	67	Suriname	5	0.06	0.0006
	68	Tunisia	5	0.06	0.0006
	69	Cameroon	4	0.05	0.0005
	70	Ghana	4	0.05	0.0005
	71	Guatemala	4	0.05	0.0005
	72	Mauritania	4	0.05	0.0005
	73	South Africa	4	0.05	0.0005
	74	Austria	3	0.04	0.0004
	75	Iran-Iraq	3	0.04	0.0004
	76	Senegal	3	0.04	0.0004
	77	Uganda	3	0.04	0.0004
	78	Bahrain	2	0.02	0.0002
	79	France	2	0.02	0.0002
	80	Gabon	2	0.02	0.0002
	81	Hungary	2	0.02	0.0002
	82	Kenya	2	0.02	0.0002
	83	Kuwait-Saudi Arabia-Iran	2	0.02	0.0002
	84	Philippines	2	0.02	0.0002
	85	Russia-Kazakhstan	2	0.02	0.0002
	86	Thailand-Malaysia	2	0.02	0.0002
	87	Timor-Leste	2	0.02	0.0002
	88	Albania	1	0.01	0.0001
	89	Chile	1	0.01	0.0001
	90	China-Japan	1	0.01	0.0001
	91	Grenada	1	0.01	0.0001
	92	Jamaica	1	0.01	0.0001
	93	Japan	1	0.01	0.0001
	94	Madagascar	1	0.01	0.0001
	95	Morocco	1	0.01	0.0001
	96	Palestine	1	0.01	0.0001
	97	Saudi Arabia-Bahrain	1	0.01	0.0001
	98	Saudi Arabia-Iran	1	0.01	0.0001
	99	Senegal-Mauritania	1	0.01	0.0001
	100	Spain	1	0.01	0.0001
	101	Timor Gap	1	0.01	0.0001
	102	United Arab Emirates-Iran	1	0.01	0.0001
	103	Vietnam-Malaysia	1	0.01	0.0001
	104	Zimbabwe	1	0.01	0.0001
Total	—	—	8,334	100.00	1.0000
Autor: Grupo 5

4.2 Por Continente

tabla_freq_cont %>%
  gt() %>%
  tab_header(
    title    = md("**Tabla N. 2**"),
    subtitle = md("Distribución de frecuencias por continente — yacimientos de petróleo y gas")
  ) %>%
  cols_label(
    i          = md("**N°**"),
    Continente = md("**Continente**"),
    ni         = md("**nᵢ**"),
    hi_pct     = md("**(%)** "),
    hi_prop    = md("**(proporción)**")
  ) %>%
  tab_spanner(label = md("**hᵢ**"), columns = c(hi_pct, hi_prop)) %>%
  fmt_number(columns = ni,      decimals = 0, use_seps = TRUE) %>%
  fmt_number(columns = hi_pct,  decimals = 2) %>%
  fmt_number(columns = hi_prop, decimals = 4) %>%
  grand_summary_rows(
    columns = c(ni, hi_pct, hi_prop),
    fns = list(label = "Total", fn = "sum"),
    fmt = list(
      ~ fmt_number(., columns = ni,      decimals = 0, use_seps = TRUE),
      ~ fmt_number(., columns = hi_pct,  decimals = 2),
      ~ fmt_number(., columns = hi_prop, decimals = 4)
    )
  ) %>%
  tab_source_note("Autor: Grupo 5") %>%
  tab_options(
    table.width                       = pct(75),
    table.font.size                   = px(13),
    table.font.names                  = "Arial",
    heading.title.font.size           = px(15),
    heading.subtitle.font.size        = px(12),
    heading.align                     = "center",
    heading.background.color          = "#AAAAAA",
    column_labels.font.weight         = "bold",
    column_labels.background.color    = "#FFFFFF",
    column_labels.border.top.color    = "#AAAAAA",
    column_labels.border.bottom.color = "#AAAAAA",
    table.border.top.color            = "#AAAAAA",
    table.border.bottom.color         = "#AAAAAA"
  ) %>%
  tab_style(
    style     = cell_text(color = "white", weight = "bold"),
    locations = cells_title(groups = c("title", "subtitle"))
  ) %>%
  tab_style(
    style     = cell_text(weight = "bold"),
    locations = list(cells_column_labels(), cells_column_spanners(),
                     cells_grand_summary())
  )

Tabla N. 2
Distribución de frecuencias por continente — yacimientos de petróleo y gas
	N°	Continente	nᵢ	hᵢ
				(%)	(proporción)
	1	América del Norte	4,468	53.61	0.5361
	2	Europa y Rusia	1,207	14.48	0.1448
	3	América del Sur y Caribe	926	11.11	0.1111
	4	Asia Pacífico y Oceanía	645	7.74	0.0774
	5	África	630	7.56	0.0756
	6	Oriente Medio	331	3.97	0.0397
	7	Asia Central y Cáucaso	92	1.10	0.0110
	8	Otro/No especificado	35	0.42	0.0042
Total	—	—	8,334	100.00	1.0000
Autor: Grupo 5

---

5 Gráficas

## ── Datos para graficar ───────────────────────────────────────────────────────

cont_graf <- tabla_freq_cont %>%
  mutate(Continente = fct_reorder(Continente, ni))

## ── Paletas de colores ────────────────────────────────────────────────────────

# Continentes: color distinto por región
colores_cont <- c(
  "América del Norte"        = "#1A5276",
  "América del Sur y Caribe" = "#1E8449",
  "Europa y Rusia"           = "#C0392B",
  "Asia Central y Cáucaso"   = "#7D3C98",
  "Oriente Medio"            = "#D68910",
  "África"                   = "#D4AC0D",
  "Asia Pacífico y Oceanía"  = "#148F77",
  "Otro/No especificado"     = "#AAB7B8"
)

## ── Etiqueta de fuente ────────────────────────────────────────────────────────
pie_label <- paste0("n = ", format(n, big.mark = ","),
                    " | Fuente: Global Energy Monitor — GOGET 2023")

## ── Tema para barras verticales (continentes) ─────────────────────────────────
tema_base <- theme_minimal(base_size = 12) +
  theme(
    legend.position    = "none",
    plot.title         = element_text(face = "bold", size = 13),
    plot.caption       = element_text(color = "#888888", size = 9, hjust = 0),
    axis.title         = element_text(face = "bold", size = 11),
    axis.text.x        = element_text(face = "bold", angle = 20, hjust = 1),
    panel.grid.major.x = element_blank(),
    panel.grid.major.y = element_line(color = "#EEEEEE"),
    panel.grid.minor   = element_blank(),
    plot.background    = element_rect(fill = "white", color = NA)
  )

5.1 Análisis por Continente

5.1.1 Diagrama de Barras — Frecuencia Absoluta

ggplot(cont_graf, aes(x = Continente, y = ni, fill = Continente)) +
  geom_col(width = 0.55, color = "white") +
  geom_text(aes(label = format(ni, big.mark = ",")),
            vjust = -0.4, size = 3.5, fontface = "bold") +
  scale_fill_manual(values = colores_cont) +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 12)) +
  scale_y_continuous(labels = label_comma(),
                     expand = expansion(mult = c(0, 0.12))) +
  labs(title   = "Gráfica N. 4: Distribución de yacimientos por continente",
       x = "Continente", y = "Frecuencia Absoluta (nᵢ)",
       caption = pie_label) +
  tema_base

5.1.2 Diagrama de Barras — Frecuencia Relativa Porcentual

ggplot(cont_graf, aes(x = Continente, y = hi_pct, fill = Continente)) +
  geom_col(width = 0.55, color = "white") +
  geom_text(aes(label = paste0(round(hi_pct, 2), "%")),
            vjust = -0.4, size = 3.5, fontface = "bold") +
  scale_fill_manual(values = colores_cont) +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 12)) +
  scale_y_continuous(labels = function(x) paste0(x, "%"),
                     expand = expansion(mult = c(0, 0.12))) +
  labs(title   = "Gráfica N. 5: Distribución porcentual por continente",
       x = "Continente", y = "Frecuencia Relativa (%)",
       caption = pie_label) +
  tema_base

5.1.3 Diagrama Circular

tabla_freq_cont %>%
  mutate(Continente = fct_reorder(Continente, hi_pct),
         etiqueta   = paste0(round(hi_pct, 1), "%")) %>%
  ggplot(aes(x = "", y = hi_pct, fill = Continente)) +
  geom_col(width = 1, color = "white") +
  geom_text(aes(label = etiqueta),
            position = position_stack(vjust = 0.5),
            size = 4, fontface = "bold") +
  coord_polar(theta = "y") +
  scale_fill_manual(values = colores_cont) +
  labs(title   = "Gráfica N. 6: Distribución porcentual por continente",
       fill    = "Continente",
       caption = pie_label) +
  theme_void(base_size = 12) +
  theme(
    plot.title      = element_text(face = "bold", size = 13, hjust = 0.5),
    plot.caption    = element_text(color = "#888888", size = 9, hjust = 0.5),
    legend.position = "right",
    legend.title    = element_text(face = "bold", size = 10),
    plot.background = element_rect(fill = "white", color = NA)
  )

---

6 Indicadores Estadísticos

La variable País es cualitativa nominal. Para este tipo de variable, el único indicador de tendencia central aplicable es la moda, evaluada tanto a nivel de país individual como a nivel de continente.

moda_pais     <- tabla_freq_pais$Pais[1]
moda_pais_n   <- tabla_freq_pais$ni[1]
moda_pais_pct <- round(tabla_freq_pais$hi_pct[1], 2)

moda_cont     <- tabla_freq_cont$Continente[1]
moda_cont_n   <- tabla_freq_cont$ni[1]
moda_cont_pct <- round(tabla_freq_cont$hi_pct[1], 2)

data.frame(
  "Variable"        = c("País (Country)", "Continente"),
  "Rango"           = c(paste0("D = {", k_pais, " países únicos}"),
                        paste0("D = {", paste(tabla_freq_cont$Continente,
                                              collapse = ", "), "}")),
  "Media (X)"       = c("-", "-"),
  "Mediana (Me)"    = c("-", "-"),
  "Moda (Mo)"       = c(moda_pais, moda_cont),
  "Varianza (V)"    = c("-", "-"),
  "Desv. Est. (Sd)" = c("-", "-"),
  "C.V. (%)"        = c("-", "-"),
  "Asimetría (As)"  = c("-", "-"),
  "Curtosis (K)"    = c("-", "-"),
  check.names = FALSE
) %>%
  gt() %>%
  tab_header(
    title = md("**Tabla N°3 de Conclusiones — País en yacimientos de petróleo y gas**")
  ) %>%
  tab_source_note("Autor: Grupo 5") %>%
  tab_options(
    table.width                       = pct(100),
    table.font.size                   = px(12),
    table.font.names                  = "Arial",
    heading.align                     = "center",
    heading.title.font.size           = px(13),
    heading.background.color          = "#AAAAAA",
    heading.border.bottom.color       = "#AAAAAA",
    column_labels.font.weight         = "normal",
    column_labels.background.color    = "#FFFFFF",
    column_labels.border.top.color    = "#CCCCCC",
    column_labels.border.top.width    = px(1),
    column_labels.border.bottom.color = "#CCCCCC",
    column_labels.border.bottom.width = px(1),
    table_body.border.bottom.color    = "#CCCCCC",
    table_body.border.bottom.width    = px(1),
    table.border.top.color            = "#AAAAAA",
    table.border.top.width            = px(1),
    table.border.bottom.color         = "#AAAAAA",
    table.border.bottom.width         = px(1),
    source_notes.font.size            = px(11),
    source_notes.border.lr.color      = "transparent",
    data_row.padding                  = px(5)
  ) %>%
  tab_style(
    style     = cell_text(color = "white", weight = "bold"),
    locations = cells_title(groups = "title")
  ) %>%
  tab_style(
    style     = cell_text(color = "#333333", align = "center"),
    locations = cells_column_labels()
  ) %>%
  tab_style(
    style     = cell_text(color = "#333333", align = "center"),
    locations = cells_body(columns = c("Media (X)", "Mediana (Me)", "Moda (Mo)",
                                       "Varianza (V)", "Desv. Est. (Sd)",
                                       "C.V. (%)", "Asimetría (As)", "Curtosis (K)"))
  )

Tabla N°3 de Conclusiones — País en yacimientos de petróleo y gas
Variable	Rango	Media (X)	Mediana (Me)	Moda (Mo)	Varianza (V)	Desv. Est. (Sd)	C.V. (%)	Asimetría (As)	Curtosis (K)
País (Country)	D = {104 países únicos}	-	-	United States	-	-	-	-	-
Continente	D = {América del Norte, Europa y Rusia, América del Sur y Caribe, Asia Pacífico y Oceanía, África, Oriente Medio, Asia Central y Cáucaso, Otro/No especificado}	-	-	América del Norte	-	-	-	-	-
Autor: Grupo 5

---

7 Conclusiones

La variable “país” tiene como valor más frecuente “United States”, con una participación de 37.11% del total de yacimientos registrados. A nivel continental, América del Norte concentra la mayor proporción de unidades de extracción (53.61%).