1 Configuración y Carga de Datos

En esta sección se cargan las librerías necesarias y se importa 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)
setwd("C:/Users/ronny/Downloads/Dataset")
datos <- read_excel("dataset_mundial_petro.xlsx")

cat("Número de registros:", nrow(datos), "\n")
## Número de registros: 49212
cat("Número de variables:", ncol(datos), "\n")
## Número de variables: 32

2 Extracción y Conteo Inicial

Se extrae la variable Country (País) del dataset y se realiza el conteo inicial de registros por país. Esta variable es de escala nominal, por lo que el análisis de distribución de frecuencias aplica categorías sin jerarquía intrínseca.

n <- nrow(datos)

conteo_inicial <- datos %>%
  count(Country, name = "fi") %>%
  arrange(desc(fi))

k <- nrow(conteo_inicial)
cat("Total de países (categorías):", k, "\n")
## Total de países (categorías): 104
cat("País más frecuente:", conteo_inicial$Country[1],
    "con", conteo_inicial$fi[1], "registros\n")
## País más frecuente: United States con 15036 registros
cat("País menos frecuente:", conteo_inicial$Country[k],
    "con", conteo_inicial$fi[k], "registro(s)\n")
## País menos frecuente: Grenada con 1 registro(s)

Vista previa del conteo (top 10 países):

head(conteo_inicial, 10)

3 Categorización y Ordenamiento Lógico

Dado que Country es una variable cualitativa nominal con 104 categorías, se organiza en orden descendente por frecuencia absoluta (fi), criterio estándar para variables nominales donde no existe un orden natural entre categorías.

tabla_freq <- conteo_inicial %>%
  mutate(
    hi_prop = fi / n,
    hi_pct  = hi_prop * 100,
    Fi      = cumsum(fi),
    Hi_prop = cumsum(hi_prop),
    Hi_pct  = cumsum(hi_pct)
  ) %>%
  mutate(i = row_number()) %>%
  select(i, Country, fi, hi_pct, hi_prop, Fi, Hi_pct, Hi_prop)

cat("Tabla generada con", nrow(tabla_freq), "países.\n")
## Tabla generada con 104 países.
cat("Verificación — suma de fi:", sum(tabla_freq$fi), "(debe ser", n, ")\n")
## Verificación — suma de fi: 49212 (debe ser 49212 )
cat("Verificación — suma de hi (%):", round(sum(tabla_freq$hi_pct), 3), "(debe ser 100)\n")
## Verificación — suma de hi (%): 100 (debe ser 100)

3.1 Agrupación por Continente

mapa_cont <- c(
  "United States" = "América del Norte", "Canada" = "América del Norte",
  "Mexico" = "América del Norte", "Cuba" = "América del Norte",
  "Jamaica" = "América del Norte", "Trinidad and Tobago" = "América del Norte",
  "Barbados" = "América del Norte", "Guatemala" = "América del Norte",
  "Grenada" = "América del Norte",
  "Argentina" = "América del Sur", "Colombia" = "América del Sur",
  "Venezuela" = "América del Sur", "Peru" = "América del Sur",
  "Bolivia" = "América del Sur", "Ecuador" = "América del Sur",
  "Brazil" = "América del Sur", "Guyana" = "América del Sur",
  "Suriname" = "América del Sur", "Chile" = "América del Sur",
  "Norway" = "Europa", "United Kingdom" = "Europa", "Netherlands" = "Europa",
  "Poland" = "Europa", "Denmark" = "Europa", "Romania" = "Europa",
  "Italy" = "Europa", "Germany" = "Europa", "Ireland" = "Europa",
  "Ukraine" = "Europa", "Hungary" = "Europa", "Albania" = "Europa",
  "Austria" = "Europa", "France" = "Europa", "Spain" = "Europa",
  "Cyprus" = "Europa",
  "Russia" = "Europa/Asia", "Russia-Kazakhstan" = "Europa/Asia",
  "Kazakhstan" = "Asia", "Iran" = "Asia", "Iraq" = "Asia",
  "Saudi Arabia" = "Asia", "China" = "Asia", "Indonesia" = "Asia",
  "Malaysia" = "Asia", "Thailand" = "Asia", "India" = "Asia",
  "Pakistan" = "Asia", "Qatar" = "Asia", "Turkmenistan" = "Asia",
  "Azerbaijan" = "Asia", "Vietnam" = "Asia", "Israel" = "Asia",
  "Kuwait" = "Asia", "United Arab Emirates" = "Asia", "Oman" = "Asia",
  "Brunei" = "Asia", "Bangladesh" = "Asia", "Myanmar" = "Asia",
  "Philippines" = "Asia", "Japan" = "Asia", "Syria" = "Asia",
  "Bahrain" = "Asia", "Timor-Leste" = "Asia", "Palestine" = "Asia",
  "Türkiye" = "Asia",
  "Thailand-Malaysia" = "Asia", "Kuwait-Saudi Arabia" = "Asia",
  "Saudi Arabia-Iran" = "Asia", "Iran-Iraq" = "Asia",
  "Kuwait-Saudi Arabia-Iran" = "Asia", "United Arab Emirates-Iran" = "Asia",
  "Vietnam-Malaysia" = "Asia", "China-Japan" = "Asia",
  "Saudi Arabia-Bahrain" = "Asia",
  "Nigeria" = "África", "Angola" = "África", "Algeria" = "África",
  "Libya" = "África", "Egypt" = "África", "Tanzania" = "África",
  "Mozambique" = "África", "Republic of the Congo" = "África",
  "Chad" = "África", "Cameroon" = "África", "Ghana" = "África",
  "Côte d'Ivoire" = "África", "South Sudan" = "África",
  "Senegal" = "África", "Uganda" = "África", "Ethiopia" = "África",
  "Namibia" = "África", "Mauritania" = "África", "South Africa" = "África",
  "Kenya" = "África", "Tunisia" = "África", "Gabon" = "África",
  "Morocco" = "África", "Zimbabwe" = "África", "Madagascar" = "África",
  "Senegal-Mauritania" = "África",
  "Australia" = "Oceanía", "New Zealand" = "Oceanía",
  "Papua New Guinea" = "Oceanía", "Timor Gap" = "Oceanía"
)

tabla_freq <- tabla_freq %>%
  mutate(Continente = mapa_cont[Country])

cat("Verificación — países sin continente asignado:",
    sum(is.na(tabla_freq$Continente)), "\n")
## Verificación — países sin continente asignado: 0

4 Tabla de Distribución de Frecuencias

tabla_freq %>%
  select(i, Country, fi, hi_pct, hi_prop) %>%
  gt() %>%
  tab_header(
    title    = md("**Tabla N. 2.1**"),
    subtitle = md("Agrupación por países de yacimientos de extracción de petróleo y gas")
  ) %>%
  cols_label(
    i       = md("**N°**"),
    Country = md("**País**"),
    fi      = md("**ni**"),
    hi_pct  = md("**(%)** "),
    hi_prop = md("**(proporción)**")
  ) %>%
  tab_spanner(
    label   = md("**hi**"),
    columns = c(hi_pct, hi_prop)
  ) %>%
  fmt_number(columns = hi_pct,  decimals = 2) %>%
  fmt_number(columns = hi_prop, decimals = 3) %>%
  fmt_number(columns = fi,      decimals = 0, use_seps = TRUE) %>%
  grand_summary_rows(
    columns = c(fi, hi_pct, hi_prop),
    fns = list(label = "Total", fn = "sum"),
    fmt = list(
      ~ fmt_number(., columns = fi,      decimals = 0, use_seps = TRUE),
      ~ fmt_number(., columns = hi_pct,  decimals = 2),
      ~ fmt_number(., columns = hi_prop, decimals = 3)
    )
  ) %>%
  tab_source_note(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",
    heading.border.bottom.color       = "#AAAAAA",
    heading.border.bottom.width       = px(1),
    column_labels.font.weight         = "bold",
    column_labels.background.color    = "#FFFFFF",
    column_labels.border.top.color    = "#AAAAAA",
    column_labels.border.top.width    = px(1),
    column_labels.border.bottom.color = "#AAAAAA",
    column_labels.border.bottom.width = px(1),
    row.striping.include_table_body   = FALSE,
    source_notes.font.size            = px(11),
    source_notes.border.lr.color      = "transparent",
    table.border.top.color            = "#AAAAAA",
    table.border.top.width            = px(1),
    table.border.bottom.color         = "#AAAAAA",
    table.border.bottom.width         = px(1)
  ) %>%
  tab_style(
    style     = cell_text(color = "white", weight = "bold"),
    locations = cells_title(groups = c("title", "subtitle"))
  ) %>%
  tab_style(
    style     = cell_text(color = "#000000", weight = "bold"),
    locations = cells_column_labels()
  ) %>%
  tab_style(
    style     = cell_text(color = "#000000", weight = "bold"),
    locations = cells_column_spanners()
  ) %>%
  tab_style(
    style     = list(
      cell_text(weight = "bold", color = "#000000"),
      cell_borders(sides = "top", color = "#333333", weight = px(2))
    ),
    locations = cells_grand_summary()
  ) %>%
  tab_style(
    style     = cell_text(color = "#333333"),
    locations = cells_body()
  )
Tabla N. 2.1
Agrupación por países de yacimientos de extracción de petróleo y gas
N° País ni
hi
(%) (proporción)
1 United States 15,036 30.55 0.306
2 Canada 10,157 20.64 0.206
3 Norway 3,498 7.11 0.071
4 Argentina 2,724 5.54 0.055
5 Mexico 1,998 4.06 0.041
6 Russia 1,902 3.86 0.039
7 United Kingdom 1,343 2.73 0.027
8 Colombia 1,188 2.41 0.024
9 Netherlands 881 1.79 0.018
10 Nigeria 842 1.71 0.017
11 Venezuela 785 1.60 0.016
12 Poland 606 1.23 0.012
13 Iran 586 1.19 0.012
14 Ecuador 579 1.18 0.012
15 Brazil 506 1.03 0.010
16 Australia 468 0.95 0.010
17 Thailand 435 0.88 0.009
18 China 403 0.82 0.008
19 Egypt 357 0.73 0.007
20 Germany 313 0.64 0.006
21 Denmark 305 0.62 0.006
22 Kazakhstan 264 0.54 0.005
23 Indonesia 261 0.53 0.005
24 Angola 232 0.47 0.005
25 Malaysia 195 0.40 0.004
26 Algeria 193 0.39 0.004
27 Iraq 193 0.39 0.004
28 India 191 0.39 0.004
29 Libya 176 0.36 0.004
30 Peru 171 0.35 0.003
31 Romania 150 0.30 0.003
32 Saudi Arabia 141 0.29 0.003
33 Oman 130 0.26 0.003
34 United Arab Emirates 129 0.26 0.003
35 Pakistan 115 0.23 0.002
36 Trinidad and Tobago 106 0.22 0.002
37 Italy 105 0.21 0.002
38 Qatar 96 0.20 0.002
39 Turkmenistan 93 0.19 0.002
40 Azerbaijan 88 0.18 0.002
41 Vietnam 81 0.16 0.002
42 Tanzania 70 0.14 0.001
43 Mozambique 63 0.13 0.001
44 Bolivia 59 0.12 0.001
45 Barbados 58 0.12 0.001
46 Guyana 55 0.11 0.001
47 Republic of the Congo 50 0.10 0.001
48 Ukraine 48 0.10 0.001
49 Israel 47 0.10 0.001
50 New Zealand 45 0.09 0.001
51 Kuwait 44 0.09 0.001
52 Brunei 43 0.09 0.001
53 Ireland 38 0.08 0.001
54 Cuba 31 0.06 0.001
55 Tunisia 31 0.06 0.001
56 Bangladesh 30 0.06 0.001
57 Chad 25 0.05 0.001
58 Cyprus 25 0.05 0.001
59 Namibia 23 0.05 0.000
60 Thailand-Malaysia 23 0.05 0.000
61 Syria 18 0.04 0.000
62 Türkiye 18 0.04 0.000
63 Cameroon 17 0.03 0.000
64 Kuwait-Saudi Arabia 17 0.03 0.000
65 Myanmar 17 0.03 0.000
66 South Africa 17 0.03 0.000
67 Guatemala 16 0.03 0.000
68 Ethiopia 14 0.03 0.000
69 Mauritania 14 0.03 0.000
70 Saudi Arabia-Iran 14 0.03 0.000
71 Albania 13 0.03 0.000
72 Iran-Iraq 13 0.03 0.000
73 Papua New Guinea 13 0.03 0.000
74 Côte d'Ivoire 12 0.02 0.000
75 Ghana 12 0.02 0.000
76 Kuwait-Saudi Arabia-Iran 12 0.02 0.000
77 Hungary 11 0.02 0.000
78 Senegal 11 0.02 0.000
79 Philippines 10 0.02 0.000
80 South Sudan 10 0.02 0.000
81 Austria 9 0.02 0.000
82 Suriname 9 0.02 0.000
83 Bahrain 8 0.02 0.000
84 Uganda 8 0.02 0.000
85 Russia-Kazakhstan 6 0.01 0.000
86 Timor-Leste 6 0.01 0.000
87 Kenya 5 0.01 0.000
88 United Arab Emirates-Iran 5 0.01 0.000
89 Vietnam-Malaysia 5 0.01 0.000
90 China-Japan 4 0.01 0.000
91 France 4 0.01 0.000
92 Gabon 4 0.01 0.000
93 Chile 3 0.01 0.000
94 Japan 3 0.01 0.000
95 Morocco 3 0.01 0.000
96 Saudi Arabia-Bahrain 3 0.01 0.000
97 Senegal-Mauritania 3 0.01 0.000
98 Timor Gap 3 0.01 0.000
99 Zimbabwe 3 0.01 0.000
100 Jamaica 2 0.00 0.000
101 Madagascar 2 0.00 0.000
102 Palestine 2 0.00 0.000
103 Spain 2 0.00 0.000
104 Grenada 1 0.00 0.000
Total — — 49,212 100.00 1.000
Autor: Grupo 5 
tabla_cont <- tabla_freq %>%
  group_by(Continente) %>%
  summarise(
    fi = sum(fi),
    .groups = "drop"
  ) %>%
  mutate(
    hi_pct  = fi / n * 100,
    hi_prop = fi / n
  ) %>%
  arrange(desc(fi)) %>%
  mutate(i = row_number()) %>%
  select(i, Continente, fi, hi_pct, hi_prop)

tabla_cont %>%
  gt() %>%
  tab_header(
    title    = md("**Tabla N. 2.2**"),
    subtitle = md("Agrupación por continentes de yacimientos de extracción de petróleo y gas")
  ) %>%
  cols_label(
    i          = md("**N°**"),
    Continente = md("**Continente**"),
    fi         = md("**ni**"),
    hi_pct     = md("**(%)** "),
    hi_prop    = md("**(proporción)**")
  ) %>%
  tab_spanner(
    label   = md("**hi**"),
    columns = c(hi_pct, hi_prop)
  ) %>%
  fmt_number(columns = hi_pct,  decimals = 2) %>%
  fmt_number(columns = hi_prop, decimals = 3) %>%
  fmt_number(columns = fi,      decimals = 0, use_seps = TRUE) %>%
  grand_summary_rows(
    columns = c(fi, hi_pct, hi_prop),
    fns = list(label = "Total", fn = "sum"),
    fmt = list(
      ~ fmt_number(., columns = fi,      decimals = 0, use_seps = TRUE),
      ~ fmt_number(., columns = hi_pct,  decimals = 2),
      ~ fmt_number(., columns = hi_prop, decimals = 3)
    )
  ) %>%
  tab_source_note(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",
    heading.border.bottom.color       = "#AAAAAA",
    heading.border.bottom.width       = px(1),
    column_labels.font.weight         = "bold",
    column_labels.background.color    = "#FFFFFF",
    column_labels.border.top.color    = "#AAAAAA",
    column_labels.border.top.width    = px(1),
    column_labels.border.bottom.color = "#AAAAAA",
    column_labels.border.bottom.width = px(1),
    row.striping.include_table_body   = FALSE,
    source_notes.font.size            = px(11),
    source_notes.border.lr.color      = "transparent",
    table.border.top.color            = "#AAAAAA",
    table.border.top.width            = px(1),
    table.border.bottom.color         = "#AAAAAA",
    table.border.bottom.width         = px(1)
  ) %>%
  tab_style(
    style     = cell_text(color = "white", weight = "bold"),
    locations = cells_title(groups = c("title", "subtitle"))
  ) %>%
  tab_style(
    style     = cell_text(color = "#000000", weight = "bold"),
    locations = cells_column_labels()
  ) %>%
  tab_style(
    style     = cell_text(color = "#000000", weight = "bold"),
    locations = cells_column_spanners()
  ) %>%
  tab_style(
    style     = list(
      cell_text(weight = "bold", color = "#000000"),
      cell_borders(sides = "top", color = "#333333", weight = px(2))
    ),
    locations = cells_grand_summary()
  ) %>%
  tab_style(
    style     = cell_text(color = "#333333"),
    locations = cells_body()
  )
Tabla N. 2.2
Agrupación por continentes de yacimientos de extracción de petróleo y gas
N° Continente ni
hi
(%) (proporción)
1 América del Norte 27,405 55.69 0.557
2 Europa 7,351 14.94 0.149
3 América del Sur 6,079 12.35 0.124
4 Asia 3,743 7.61 0.076
5 África 2,197 4.46 0.045
6 Europa/Asia 1,908 3.88 0.039
7 Oceanía 529 1.07 0.011
Total — — 49,212 100.00 1.000
Autor: Grupo 5

5 Análisis Gráfico

top15 <- tabla_freq %>%
  slice_head(n = 15) %>%
  mutate(Country = fct_reorder(Country, fi))

cont_graf <- tabla_cont %>%
  mutate(Continente = fct_reorder(Continente, fi))

colores_cont <- c(
  "América del Norte" = "#AED6F1",
  "Europa"            = "#85C1E9",
  "América del Sur"   = "#5DADE2",
  "Asia"              = "#D6EAF8",
  "África"            = "#2E86C1",
  "Europa/Asia"       = "#1A5276",
  "Oceanía"           = "#154360"
)

colores_barras <- colorRampPalette(c("#D6EAF8", "#AED6F1", "#5DADE2", "#2E86C1"))(15)

tema_base <- theme_minimal(base_size = 12) +
  theme(
    legend.position    = "none",
    plot.title         = element_text(face = "bold", color = "#1A1A1A", size = 13),
    plot.subtitle      = element_text(color = "#555555", size = 10),
    plot.caption       = element_text(color = "#888888", size = 9, hjust = 0),
    axis.title         = element_text(face = "bold", color = "#333333", size = 11),
    axis.text          = element_text(color = "#333333"),
    axis.text.y        = element_text(face = "bold", color = "#222222"),
    panel.grid.major.y = element_blank(),
    panel.grid.major.x = element_line(color = "#EEEEEE"),
    panel.grid.minor   = element_blank(),
    plot.background    = element_rect(fill = "white", color = NA),
    panel.background   = element_rect(fill = "white", color = NA)
  )

5.1 Diagramas de Barras de Cantidad

5.1.1 Por Continente

ggplot(cont_graf, aes(x = Continente, y = fi, fill = Continente)) +
  geom_col(width = 0.65, color = "white", linewidth = 0.3) +
  geom_text(
    aes(label = format(fi, big.mark = ",")),
    hjust = -0.1, size = 3.4, color = "#222222", fontface = "bold"
  ) +
  coord_flip() +
  scale_fill_manual(values = colores_cont) +
  scale_y_continuous(
    labels = label_comma(),
    expand = expansion(mult = c(0, 0.18))
  ) +
  labs(
    title   = "Gráfica N. 1: Distribución de yacimientos de petróleo y gas por continentes",
    x       = "Continente",
    y       = "Frecuencia Absoluta (fᵢ)",
    caption = paste0("n = ", format(n, big.mark = ","),
                     " | Fuente: Global Energy Monitor — GOGET 2023")
  ) +
  tema_base

5.2 Diagramas de Barras Porcentual

5.2.1 Por Continente

ggplot(cont_graf, aes(x = Continente, y = hi_pct, fill = Continente)) +
  geom_col(width = 0.65, color = "white", linewidth = 0.3) +
  geom_text(
    aes(label = paste0(round(hi_pct, 2), "%")),
    hjust = -0.1, size = 3.4, color = "#222222", fontface = "bold"
  ) +
  coord_flip() +
  scale_fill_manual(values = colores_cont) +
  scale_y_continuous(
    labels = function(x) paste0(x, "%"),
    expand = expansion(mult = c(0, 0.18))
  ) +
  labs(
    title   = "Gráfica N. 2: Distribución porcentual de yacimientos de petróleo y gas por continentes",
    x       = "Continente",
    y       = "Frecuencia Relativa (%)",
    caption = paste0("n = ", format(n, big.mark = ","),
                     " | Fuente: Global Energy Monitor — GOGET 2023")
  ) +
  tema_base

5.3 Diagrama Circular

5.3.1 Por Continente

datos_pie_cont <- tabla_cont %>%
  arrange(desc(fi)) %>%
  mutate(
    Continente = fct_reorder(Continente, hi_pct),
    etiqueta   = paste0(round(hi_pct, 1), "%")
  )

ggplot(datos_pie_cont, aes(x = "", y = hi_pct, fill = Continente)) +
  geom_col(width = 1, color = "white", linewidth = 0.6) +
  geom_text(
    aes(label = etiqueta),
    position = position_stack(vjust = 0.5),
    size = 3.5, color = "#1A1A1A", fontface = "bold"
  ) +
  coord_polar(theta = "y", start = 0) +
  scale_fill_manual(values = colores_cont) +
  labs(
    title   = "Gráfica N.3: Distribución Porcentual por Continente",
    fill    = "Continentes",
    caption = paste0("n = ", format(n, big.mark = ","),
                     " | Fuente: Global Energy Monitor — GOGET 2023")
  ) +
  theme_void(base_size = 12) +
  theme(
    plot.title      = element_text(face = "bold", color = "#1A1A1A",
                                   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", color = "#1A1A1A", size = 10),
    legend.text     = element_text(size = 9, color = "#333333"),
    plot.background = element_rect(fill = "white", color = NA)
  )

6 Indicadores Estadísticos

# Moda por continente
moda_continentes <- tabla_cont$Continente[which.max(tabla_cont$fi)]

# Tabla de indicadores estilo imagen de referencia
tabla_indicadores <- data.frame(
  "Variable"        = "País",
  "Rango"           = "Continentes",
  "Media (X)"       = "-",
  "Mediana (Me)"    = "-",
  "Moda (Mo)"       = moda_continentes,
  "Varianza (V)"    = "-",
  "Desv. Est. (Sd)" = "-",
  "C.V. (%)"        = "-",
  "Asimetría (As)"  = "-",
  "Curtosis (K)"    = "-",
  check.names = FALSE
)
tabla_indicadores %>%
  gt() %>%
  tab_header(
    title = md("**Tabla N°3 de Conclusiones yacimientos de petróleo y gas por continentes**")
  ) %>%
  tab_source_note(source_note = "Autor: Grupo 5") %>%
  tab_options(
    table.width                       = pct(92),
    table.font.size                   = px(13),
    table.font.names                  = "Arial",
    heading.title.font.size           = px(15),
    heading.align                     = "left",
    heading.border.bottom.color       = "#CCCCCC",
    heading.border.bottom.width       = px(1),
    column_labels.font.weight         = "bold",
    column_labels.background.color    = "#F0F0F0",
    column_labels.border.top.color    = "#AAAAAA",
    column_labels.border.top.width    = px(1),
    column_labels.border.bottom.color = "#AAAAAA",
    column_labels.border.bottom.width = px(1),
    row.striping.include_table_body   = FALSE,
    source_notes.font.size            = px(11),
    source_notes.border.lr.color      = "transparent",
    table.border.top.color            = "#AAAAAA",
    table.border.top.width            = px(1),
    table.border.bottom.color         = "#AAAAAA",
    table.border.bottom.width         = px(2)
  ) %>%
  tab_style(
    style     = cell_text(color = "#000000", weight = "bold"),
    locations = cells_column_labels()
  ) %>%
  tab_style(
    style     = cell_text(color = "#333333"),
    locations = cells_body()
  )
Tabla N°3 de Conclusiones yacimientos de petróleo y gas por continentes
Variable Rango Media (X) Mediana (Me) Moda (Mo) Varianza (V) Desv. Est. (Sd) C.V. (%) Asimetría (As) Curtosis (K)
País Continentes - - América del Norte - - - - -
Autor: Grupo 5