Transporte de petróleo mediante oleoductos en Ecuador
Variable: Temperatura (°C) (cuantitativa continua)
*
2026-11-14
1 Carga de librerias
library(readxl)
library(dplyr)
library(gt)
library(e1071)2 Carga de datos
setwd("C:/Users/HP/Desktop/tabla_petro")
archivo <- list.files(pattern = "^tabla_transporte_petroleo.*\\.xlsx$")[1]
if (is.na(archivo)) stop("No se encontró el archivo .xlsx dentro de la carpeta tabla_petro")
datos <- as.data.frame(read_excel(archivo, sheet = "Datos_10000"))
dim(datos)## [1] 10000 203 Selección de la variable
Variable <- as.numeric(na.omit(datos$Temperatura_C))
if (length(Variable) == 0) {
stop("ERROR: No hay datos válidos para la variable seleccionada.")
}4 Frecuencia
Se agrupan los datos en K clases con la regla de Sturges.
N <- length(Variable)
K <- floor(1 + 3.322 * log10(N))
breaks_table <- seq(min(Variable), max(Variable), length.out = K + 1)
ni <- as.vector(table(cut(Variable, breaks = breaks_table, include.lowest = TRUE, right = FALSE)))
hi <- (ni / sum(ni)) * 100
Ni_asc <- cumsum(ni)
Ni_desc <- rev(cumsum(rev(ni)))
Hi_asc <- cumsum(hi)
Hi_desc <- rev(cumsum(rev(hi)))5 Tabla de Distribución de Frecuencia
A continuación se presenta la tabla de distribución de frecuencias obtenida.
5.1 Tabla General
TDF_General <- data.frame(
Li = round(breaks_table[1:K], 2),
Ls = round(breaks_table[2:(K + 1)], 2),
MC = round((breaks_table[1:K] + breaks_table[2:(K + 1)]) / 2, 2),
ni = ni,
hi = hi,
Ni_asc = Ni_asc,
Ni_desc = Ni_desc,
Hi_asc = Hi_asc,
Hi_desc = Hi_desc
)
TDF_General %>%
gt(rowname_col = "Li") %>%
tab_header(
title = md("**DISTRIBUCIÓN DE FRECUENCIAS: Temperatura (°C)**"),
subtitle = md("Variable: **Temperatura**")
) %>%
tab_source_note(source_note = "Autor: *") %>%
grand_summary_rows(
columns = c(ni, hi),
fns = list("TOTAL" = ~sum(., na.rm = TRUE))
) %>%
fmt_number(
columns = c(ni, Ni_asc, Ni_desc),
decimals = 0,
use_seps = TRUE
) %>%
fmt_number(
columns = c(hi, Hi_asc, Hi_desc),
decimals = 2
) %>%
cols_label(
Ls = "Lím. Sup", MC = "Marca Clase (Xi)",
ni = "ni", hi = "hi (%)",
Ni_asc = "Ni (Asc)", Ni_desc = "Ni (Desc)",
Hi_asc = "Hi (Asc)", Hi_desc = "Hi (Desc)"
) %>%
tab_stubhead(label = "Lím. Inf") %>%
cols_align(align = "center", columns = everything()) %>%
tab_style(
style = cell_text(align = "center"),
locations = cells_stub()
) %>%
tab_style(
style = list(cell_fill(color = "#235347"), cell_text(color = "white", weight = "bold")),
locations = list(cells_title(), cells_column_labels(), cells_stubhead())
) %>%
tab_options(
table.border.top.style = "none",
table.border.bottom.color = "#235347",
column_labels.border.bottom.color = "#235347",
data_row.padding = px(6)
)| DISTRIBUCIÓN DE FRECUENCIAS: Temperatura (°C) | ||||||||
| Variable: Temperatura | ||||||||
| Lím. Inf | Lím. Sup | Marca Clase (Xi) | ni | hi (%) | Ni (Asc) | Ni (Desc) | Hi (Asc) | Hi (Desc) |
|---|---|---|---|---|---|---|---|---|
| 17.98 | 20.62 | 19.30 | 2 | 0.02 | 2 | 10,000 | 0.02 | 100.00 |
| 20.62 | 23.26 | 21.94 | 22 | 0.22 | 24 | 9,998 | 0.24 | 99.98 |
| 23.26 | 25.89 | 24.57 | 80 | 0.80 | 104 | 9,976 | 1.04 | 99.76 |
| 25.89 | 28.53 | 27.21 | 354 | 3.54 | 458 | 9,896 | 4.58 | 98.96 |
| 28.53 | 31.17 | 29.85 | 889 | 8.89 | 1,347 | 9,542 | 13.47 | 95.42 |
| 31.17 | 33.81 | 32.49 | 1,716 | 17.16 | 3,063 | 8,653 | 30.63 | 86.53 |
| 33.81 | 36.44 | 35.13 | 2,286 | 22.86 | 5,349 | 6,937 | 53.49 | 69.37 |
| 36.44 | 39.08 | 37.76 | 2,105 | 21.05 | 7,454 | 4,651 | 74.54 | 46.51 |
| 39.08 | 41.72 | 40.40 | 1,407 | 14.07 | 8,861 | 2,546 | 88.61 | 25.46 |
| 41.72 | 44.36 | 43.04 | 682 | 6.82 | 9,543 | 1,139 | 95.43 | 11.39 |
| 44.36 | 47.00 | 45.68 | 299 | 2.99 | 9,842 | 457 | 98.42 | 4.57 |
| 47.00 | 49.63 | 48.32 | 111 | 1.11 | 9,953 | 158 | 99.53 | 1.58 |
| 49.63 | 52.27 | 50.95 | 32 | 0.32 | 9,985 | 47 | 99.85 | 0.47 |
| 52.27 | 54.91 | 53.59 | 15 | 0.15 | 10,000 | 15 | 100.00 | 0.15 |
| TOTAL | — | — | 10000 | 100 | — | — | — | — |
| Autor: * | ||||||||
5.2 Tabla Simplificada
Se redefinen las clases con límites redondeados y una amplitud constante.
breaks_table <- pretty(Variable, n = K)
amplitud <- breaks_table[2] - breaks_table[1]
K <- length(breaks_table) - 1
ni <- as.vector(table(cut(Variable, breaks = breaks_table, include.lowest = TRUE, right = FALSE)))
hi <- (ni / sum(ni)) * 100
Ni_asc <- cumsum(ni)
Ni_desc <- rev(cumsum(rev(ni)))
Hi_asc <- cumsum(hi)
Hi_desc <- rev(cumsum(rev(hi)))
TDF_Simple <- data.frame(
Li = round(breaks_table[1:K], 2),
Ls = round(breaks_table[2:(K + 1)], 2),
MC = round((breaks_table[1:K] + breaks_table[2:(K + 1)]) / 2, 2),
ni = ni,
hi = hi,
Ni_asc = Ni_asc,
Ni_desc = Ni_desc,
Hi_asc = Hi_asc,
Hi_desc = Hi_desc
)
TDF_Simple %>%
gt(rowname_col = "Li") %>%
tab_header(
title = md("**DISTRIBUCIÓN DE FRECUENCIAS SIMPLIFICADA: Temperatura (°C)**"),
subtitle = md(paste0("Variable: **Temperatura** (Amplitud = ", round(amplitud, 2), ")"))
) %>%
tab_source_note(source_note = "Autor: *") %>%
grand_summary_rows(
columns = c(ni, hi),
fns = list("TOTAL" = ~sum(., na.rm = TRUE))
) %>%
fmt_number(
columns = c(ni, Ni_asc, Ni_desc),
decimals = 0,
use_seps = TRUE
) %>%
fmt_number(
columns = c(hi, Hi_asc, Hi_desc),
decimals = 2
) %>%
cols_label(
Ls = "Lím. Sup", MC = "Marca Clase (Xi)",
ni = "ni", hi = "hi (%)",
Ni_asc = "Ni (Asc)", Ni_desc = "Ni (Desc)",
Hi_asc = "Hi (Asc)", Hi_desc = "Hi (Desc)"
) %>%
tab_stubhead(label = "Lím. Inf") %>%
cols_align(align = "center", columns = everything()) %>%
tab_style(
style = cell_text(align = "center"),
locations = cells_stub()
) %>%
tab_style(
style = list(cell_fill(color = "#235347"), cell_text(color = "white", weight = "bold")),
locations = list(cells_title(), cells_column_labels(), cells_stubhead())
) %>%
tab_options(
table.border.top.style = "none",
table.border.bottom.color = "#235347",
column_labels.border.bottom.color = "#235347",
data_row.padding = px(6)
)| DISTRIBUCIÓN DE FRECUENCIAS SIMPLIFICADA: Temperatura (°C) | ||||||||
| Variable: Temperatura (Amplitud = 2) | ||||||||
| Lím. Inf | Lím. Sup | Marca Clase (Xi) | ni | hi (%) | Ni (Asc) | Ni (Desc) | Hi (Asc) | Hi (Desc) |
|---|---|---|---|---|---|---|---|---|
| 16 | 18 | 17 | 1 | 0.01 | 1 | 10,000 | 0.01 | 100.00 |
| 18 | 20 | 19 | 1 | 0.01 | 2 | 9,999 | 0.02 | 99.99 |
| 20 | 22 | 21 | 3 | 0.03 | 5 | 9,998 | 0.05 | 99.98 |
| 22 | 24 | 23 | 27 | 0.27 | 32 | 9,995 | 0.32 | 99.95 |
| 24 | 26 | 25 | 78 | 0.78 | 110 | 9,968 | 1.10 | 99.68 |
| 26 | 28 | 27 | 231 | 2.31 | 341 | 9,890 | 3.41 | 98.90 |
| 28 | 30 | 29 | 524 | 5.24 | 865 | 9,659 | 8.65 | 96.59 |
| 30 | 32 | 31 | 914 | 9.14 | 1,779 | 9,135 | 17.79 | 91.35 |
| 32 | 34 | 33 | 1,439 | 14.39 | 3,218 | 8,221 | 32.18 | 82.21 |
| 34 | 36 | 35 | 1,748 | 17.48 | 4,966 | 6,782 | 49.66 | 67.82 |
| 36 | 38 | 37 | 1,673 | 16.73 | 6,639 | 5,034 | 66.39 | 50.34 |
| 38 | 40 | 39 | 1,394 | 13.94 | 8,033 | 3,361 | 80.33 | 33.61 |
| 40 | 42 | 41 | 923 | 9.23 | 8,956 | 1,967 | 89.56 | 19.67 |
| 42 | 44 | 43 | 521 | 5.21 | 9,477 | 1,044 | 94.77 | 10.44 |
| 44 | 46 | 45 | 293 | 2.93 | 9,770 | 523 | 97.70 | 5.23 |
| 46 | 48 | 47 | 129 | 1.29 | 9,899 | 230 | 98.99 | 2.30 |
| 48 | 50 | 49 | 64 | 0.64 | 9,963 | 101 | 99.63 | 1.01 |
| 50 | 52 | 51 | 19 | 0.19 | 9,982 | 37 | 99.82 | 0.37 |
| 52 | 54 | 53 | 15 | 0.15 | 9,997 | 18 | 99.97 | 0.18 |
| 54 | 56 | 55 | 3 | 0.03 | 10,000 | 3 | 100.00 | 0.03 |
| TOTAL | — | — | 10000 | 100 | — | — | — | — |
| Autor: * | ||||||||
6 Gráficas De Distribución De Frecuencias
Esta sección presenta la visualización de la distribución de los datos.
6.1 Histogramas de Frecuencia
col_barras <- "#235347"
col_ejes <- "#163832"
col_claro <- "#8EB69B"
h_base <- hist(Variable, breaks = breaks_table, right = FALSE, include.lowest = TRUE, plot = FALSE)
limite_x <- range(breaks_table)
etiquetas_x <- format(breaks_table, scientific = FALSE, trim = TRUE)6.2 GRÁFICO 1: Histograma Absoluto (Local)
par(mar = c(8, 5, 4, 2))
plot(h_base,
main = "Gráfica N°1: Distribución de Temperatura",
xlab = "Temperatura (°C)", ylab = "Frecuencia absoluta",
col = col_barras, border = "white", axes = FALSE,
ylim = c(0, max(h_base$counts) * 1.1),
xlim = limite_x, cex.main = 1)
axis(1, at = breaks_table, labels = etiquetas_x, las = 2, cex.axis = 0.7)
axis(2)
grid(nx = NA, ny = NULL, col = "#D7DBDD", lty = "dotted")
6.3 GRÁFICO 2: Histograma Absoluto (Global)
par(mar = c(8, 5, 4, 2))
plot(h_base,
main = "Gráfica N°2: Distribución de Temperatura",
xlab = "Temperatura (°C)", ylab = "Total registros",
col = col_barras, border = "white", axes = FALSE,
ylim = c(0, sum(h_base$counts)),
xlim = limite_x, cex.main = 1)
axis(1, at = breaks_table, labels = etiquetas_x, las = 2, cex.axis = 0.7)
axis(2)
grid(nx = NA, ny = NULL, col = "#D7DBDD", lty = "dotted")
6.4 Gráficos Porcentuales
h_porc <- h_base
h_porc$counts <- (h_porc$counts / sum(h_porc$counts)) * 1006.5 GRÁFICO 3: Porcentajes (Local)
par(mar = c(8, 5, 4, 2))
plot(h_porc,
main = "Gráfica N°3: Distribución porcentual de Temperatura",
xlab = "Temperatura (°C)", ylab = "Porcentaje (%)",
col = col_barras, border = "white", axes = FALSE, freq = TRUE,
ylim = c(0, max(h_porc$counts) * 1.2),
xlim = limite_x, cex.main = 1)
axis(1, at = breaks_table, labels = etiquetas_x, las = 2, cex.axis = 0.7)
axis(2)
text(x = h_base$mids, y = h_porc$counts,
labels = paste0(round(h_porc$counts, 1), "%"),
pos = 3, cex = 0.6, col = col_ejes)
6.6 GRÁFICO 4: Porcentajes (Global)
par(mar = c(8, 5, 4, 2))
plot(h_porc,
main = "Gráfica N°4: Distribución porcentual de Temperatura",
xlab = "Temperatura (°C)", ylab = "% del total",
col = col_barras, border = "white", axes = FALSE, freq = TRUE,
ylim = c(0, 100),
xlim = limite_x, cex.main = 1)
axis(1, at = breaks_table, labels = etiquetas_x, las = 2, cex.axis = 0.7)
axis(2)
text(x = h_base$mids, y = h_porc$counts,
labels = paste0(round(h_porc$counts, 1), "%"),
pos = 3, cex = 0.6, col = col_ejes)
6.7 GRÁFICO 5: Boxplot
par(mar = c(8, 5, 4, 2))
boxplot(Variable, horizontal = TRUE, col = col_barras,
main = "Gráfica N°5: Diagrama de caja de Temperatura",
xlab = "Temperatura (°C)", outline = TRUE, outpch = 19,
outcol = col_claro, axes = FALSE, xlim = c(0.7, 1.3),
ylim = limite_x, cex.main = 1)
axis(1, at = breaks_table, labels = etiquetas_x, las = 2, cex.axis = 0.7)
box()
6.8 GRÁFICO 6: Ojivas
par(mar = c(8, 5, 4, 8), xpd = TRUE)
plot(breaks_table, c(0, Ni_asc), type = "o", col = col_barras,
lwd = 2, pch = 19, axes = FALSE,
main = "Gráfica N°6: Ojivas ascendente y descendente de Temperatura",
xlab = "Temperatura (°C)", ylab = "Frecuencia acumulada", cex.main = 1)
lines(breaks_table, c(Ni_desc, 0), type = "o", col = col_claro,
lwd = 2, pch = 19)
axis(1, at = breaks_table, labels = etiquetas_x, las = 2, cex.axis = 0.6)
axis(2)
legend("right", legend = c("Ascendente", "Descendente"),
col = c(col_barras, col_claro),
lty = 1, pch = 19, cex = 0.7, lwd = 2,
inset = c(-0.2, 0), bty = "n")
par(xpd = FALSE)7 Indicadores Estadísticos
media_val <- mean(Variable)
mediana_val <- median(Variable)
sd_val <- sd(Variable)
cv_val <- (sd_val / abs(media_val)) * 100
asimetria_val <- skewness(Variable, type = 2)
curtosis_val <- kurtosis(Variable, type = 2)
atipicos <- boxplot.stats(Variable)$out
status_atipicos <- if (length(atipicos) > 0) {
paste0(length(atipicos), " [ ", round(min(atipicos), 2), " ; ", round(max(atipicos), 2), " ]")
} else {
"0 (Sin atípicos)"
}
df_resumen <- data.frame(
Variable = "Temperatura",
Rango = paste0("[", round(min(Variable), 2), " ; ", round(max(Variable), 2), "]"),
X = media_val,
Me = mediana_val,
Mo = paste(round(TDF_Simple$MC[TDF_Simple$hi == max(TDF_Simple$hi)], 2), collapse = ", "),
Varianza = var(Variable),
sd = sd_val,
CV = cv_val,
As = asimetria_val,
K = curtosis_val,
Atipicos = status_atipicos
)
df_resumen %>%
gt() %>%
tab_header(title = md("**CONCLUSIONES Y ESTADÍSTICOS**"), subtitle = "Variable: Temperatura") %>%
tab_source_note(source_note = "Autor: *") %>%
cols_label(
X = "X",
Me = "Me",
Mo = "Mo",
sd = "sd",
CV = "CV",
As = "As",
K = "K",
Atipicos = "Valores atípicos"
) %>%
fmt_number(columns = c(X, Me, sd), decimals = 2) %>%
fmt_number(columns = Varianza, decimals = 2) %>%
fmt_number(columns = c(CV, K), decimals = 2) %>%
fmt_number(columns = As, decimals = 4) %>%
cols_width(
Variable ~ px(140),
Rango ~ px(150),
Mo ~ px(100),
Atipicos ~ px(220),
everything() ~ px(85)
) %>%
tab_options(
column_labels.background.color = "#235347",
table.border.top.style = "solid",
table.border.bottom.style = "solid",
heading.border.bottom.style = "solid",
column_labels.border.top.style = "solid",
column_labels.border.bottom.style = "solid",
data_row.padding = px(8)
) %>%
tab_style(
style = list(
cell_text(weight = "bold", color = "white"),
cell_borders(sides = c("left", "right"), color = "#D3D3D3", weight = px(1))
),
locations = cells_column_labels()
) %>%
tab_style(
style = cell_borders(sides = c("left", "right"), color = "#D3D3D3", weight = px(1)),
locations = cells_body()
)| CONCLUSIONES Y ESTADÍSTICOS | ||||||||||
| Variable: Temperatura | ||||||||||
| Variable | Rango | X | Me | Mo | Varianza | sd | CV | As | K | Valores atípicos |
|---|---|---|---|---|---|---|---|---|---|---|
| Temperatura | [17.98 ; 54.91] | 36.18 | 36.04 | 35 | 21.66 | 4.65 | 12.86 | 0.1976 | 0.28 | 130 [ 17.98 ; 54.91 ] |
| Autor: * | ||||||||||
txt_cv <- if (cv_val < 15) "homogéneo" else if (cv_val <= 30) "moderadamente heterogéneo" else "heterogéneo"
txt_ubic <- if (asimetria_val > 0.5) "se agrupan en la parte baja de la variable" else if (asimetria_val < -0.5) "se agrupan en la parte alta de la variable" else "se reparten de forma equilibrada alrededor de la media"
txt_asim <- if (abs(asimetria_val) < 0.5) "aproximadamente simétrica" else if (asimetria_val > 0) "asimétrica positiva (sesgo a la derecha)" else "asimétrica negativa (sesgo a la izquierda)"
txt_curt <- if (abs(curtosis_val) < 0.5) "mesocúrtica (similar a la normal)" else if (curtosis_val > 0) "leptocúrtica (más concentrada y con colas más pesadas)" else "platicúrtica (más aplanada)"
txt_atip <- if (length(atipicos) > 0) {
paste0("Se identificaron ", length(atipicos), " valores atípicos, en el rango de ", round(min(atipicos), 2), " a ", round(max(atipicos), 2), ".")
} else {
"No se identificaron valores atípicos."
}8 Conclusiones
Los valores de Temperatura fluctúan entre 17.98 y 54.91 y giran en torno a la media de 36.18 (mediana de 36.04), con una desviación estándar de 4.65. Con un coeficiente de variación de 12.86 %, el conjunto de datos es homogéneo y sus valores se reparten de forma equilibrada alrededor de la media. La distribución es aproximadamente simétrica (As = 0.1976) y mesocúrtica (similar a la normal) (K = 0.28). Se identificaron 130 valores atípicos, en el rango de 17.98 a 54.91.