##### UNIVERSIDAD CENTRAL DEL ECUADOR #####
#### AUTOR: LEONARDO RUIZ ####
### CARRERA: INGENIERÍA EN PETROLEOS #####
##1. Carga de Datos
library(readxl)
Datos <- read_xlsx("C:/Users/LEO/Documents/Antisana/weatherdataANTISANA.csv.xlsx")
##2.Extraer la variable continua
Min_Temperature <- Datos$Min_Temperature
Min_Temperature <- as.numeric(Min_Temperature)
Min_Temperature <- na.omit(Min_Temperature)
##3. Cálculo de intervalos (sturges)
R <- max(Min_Temperature) - min(Min_Temperature)
k <- floor(1 + (3.3 * log10(length(Min_Temperature))))
A <- R / k
liminf <- seq(from = min(Min_Temperature),
by = A,
length.out = k)
limsup <- liminf + A
limsup[k] <- max(Min_Temperature)
MC <- (liminf + limsup) / 2
##4.Tabla de distribución de frecuencias
#4.1 Frecuencia absoluta
ni <- numeric(k)
for (i in 1:k) {
if (i == k) {
ni[i] <- sum(Min_Temperature >= liminf[i] & Min_Temperature <= limsup[i])
} else {
ni[i] <- sum(Min_Temperature >= liminf[i] & Min_Temperature < limsup[i])
}
}
#4.2 Frecuencias relativas y acumuladas
hi <- (ni / length(Min_Temperature)) * 100
Niasc <- cumsum(ni)
Nidsc <- rev(cumsum(rev(ni)))
Hiasc <- cumsum(hi)
Hidsc <- rev(cumsum(rev(hi)))
#4.3 Tabla de frecuencias
tabla_Min_Temperature <- data.frame(
Límite_Inferior = round(liminf, 2),
Límite_Superior = round(limsup, 2),
Marca_Clase = round(MC, 2),
ni = ni,
hi_porc = round(hi, 2),
Ni_asc = Niasc,
Ni_dsc = Nidsc,
Hiasc_porc = round(Hiasc, 2),
Hidsc_porc = round(Hidsc, 2))
# TABLA 1 CON GT()
library(gt)
library(dplyr)
##
## Adjuntando el paquete: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(e1071)
tabla_Min_Temperature %>%
gt() %>%
tab_header(
title = md("**Tabla 1: Distribución de Frecuencias de Temperatura Mínima**"),
subtitle = md("Antisana | Método Sturges")
) %>%
tab_source_note(
source_note = md("**Antisana**")
) %>%
cols_label(
Límite_Inferior = "L. Inferior",
Límite_Superior = "L. Superior",
Marca_Clase = "Marca Clase",
hi_porc = "hi %",
Ni_asc = "Ni Asc.",
Ni_dsc = "Ni Desc.",
Hiasc_porc = "Hi Asc. %",
Hidsc_porc = "Hi Desc. %"
) %>%
fmt_number(
columns = c(Límite_Inferior, Límite_Superior, Marca_Clase),
decimals = 2
) %>%
fmt_number(
columns = c(hi_porc, Hiasc_porc, Hidsc_porc),
decimals = 2,
pattern = "{x}%"
)
| Tabla 1: Distribución de Frecuencias de Temperatura Mínima |
| Antisana | Método Sturges |
| L. Inferior |
L. Superior |
Marca Clase |
ni |
hi % |
Ni Asc. |
Ni Desc. |
Hi Asc. % |
Hi Desc. % |
| 2.65 |
3.56 |
3.11 |
2 |
0.55% |
2 |
366 |
0.55% |
100.00% |
| 3.56 |
4.47 |
4.02 |
4 |
1.09% |
6 |
364 |
1.64% |
99.45% |
| 4.47 |
5.38 |
4.93 |
5 |
1.37% |
11 |
360 |
3.01% |
98.36% |
| 5.38 |
6.29 |
5.84 |
21 |
5.74% |
32 |
355 |
8.74% |
96.99% |
| 6.29 |
7.21 |
6.75 |
55 |
15.03% |
87 |
334 |
23.77% |
91.26% |
| 7.21 |
8.12 |
7.66 |
108 |
29.51% |
195 |
279 |
53.28% |
76.23% |
| 8.12 |
9.03 |
8.57 |
80 |
21.86% |
275 |
171 |
75.14% |
46.72% |
| 9.03 |
9.94 |
9.48 |
60 |
16.39% |
335 |
91 |
91.53% |
24.86% |
| 9.94 |
10.85 |
10.39 |
31 |
8.47% |
366 |
31 |
100.00% |
8.47% |
| Antisana |
##5. Gráficos
#5.1 Histograma
hist(Min_Temperature,
main = "Gráfica No.1: Distribución de Temperatura Mínima",
breaks = seq(min(Min_Temperature), max(Min_Temperature) + A, by = A),
xlab = "Temperatura Mínima",
ylab = "Cantidad",
col = "lightblue",
border = "darkblue",
xaxt = "n")
# Eje X personalizado con MARCAS DE CLASE
axis(1, at = MC, # Posiciones: Marcas de Clase
labels = round(MC, 2), # Etiquetas: valores redondeados
las = 1) # Etiquetas horizontales
#5.2 Ojivas
x_asc <- c(min(liminf), limsup)
y_asc <- c(0, Niasc)
x_desc <- c(liminf, max(limsup))
y_desc <- c(Nidsc, 0)
x_range <- range(c(x_asc, x_desc))
y_range <- c(0, max(c(y_asc, y_desc)))
plot(x_asc, y_asc, type = "o", col = "skyblue",
main = "Gráfica No.2: Ojivas Ascendente y Descendente de Temperatura Mínima",
xlab = "Temperatura Mínima",
ylab = "Frecuencia acumulada",
xlim = x_range, ylim = y_range,
xaxt = "n", pch = 16, lwd = 2)
axis(1, at = pretty(x_range),
labels = format(pretty(x_range), scientific = FALSE))
axis(2, at = pretty(y_range))
lines(x_desc, y_desc, type = "o", col = "steelblue4", pch = 17, lwd = 2)
legend("right",
legend = c("Ojiva Ascendente", "Ojiva Descendente"),
col = c("skyblue", "steelblue4"),
pch = c(16, 17),
lty = 1,
lwd = 2,
cex = 0.8)
#5.3 Diagramas de cajas
boxplot(Min_Temperature,
horizontal = TRUE,
col = "steelblue",
main = "Gráfica No.3: Distribución de Temperatura Mínima",
xlab = "Temperatura Mínima",
xaxt = "n")
axis(1, at = pretty(Min_Temperature),
labels = format(pretty(Min_Temperature), scientific = FALSE))
# Outliers
outliers <- boxplot.stats(Min_Temperature)$out
cat("\nNúmero de outliers:", length(outliers), "\n")
##
## Número de outliers: 7
if(length(outliers) > 0) {
cat("Outliers:", round(outliers, 2), "\n")
}
## Outliers: 4.39 4.55 4.47 3.56 4.13 3.64 2.65
##6. Indicadores estadísticos
get_mode_interval <- function() {
idx <- which.max(ni)
return(paste0("[", round(liminf[idx], 2), ", ", round(limsup[idx], 2), "]"))
}
media <- mean(Min_Temperature)
mediana <- median(Min_Temperature)
moda_intervalo <- get_mode_interval()
desv <- sd(Min_Temperature)
varianza <- var(Min_Temperature)
cv <- (desv / media) * 100
asim <- skewness(Min_Temperature)
curt <- kurtosis(Min_Temperature)
# CREAR DATA.FRAME DE INDICADORES
indicadores <- data.frame(
Indicador = c("Mínimo", "Máximo", "Media", "Mediana", "Moda (intervalo)",
"Desviación Estándar", "Varianza", "Coef. Variación (%)",
"Asimetría", "Curtosis", "N° Outliers"),
Valor = c(round(min(Min_Temperature), 2), round(max(Min_Temperature), 2),
round(media, 2), round(mediana, 2), moda_intervalo,
round(desv, 2), round(varianza, 2), round(cv, 2),
round(asim, 2), round(curt, 2), length(outliers))
)
# TABLA 2 CON GT()
indicadores %>%
gt() %>%
tab_header(
title = md("**Tabla 2: Indicadores Estadísticos de Temperatura Mínima**")
) %>%
tab_source_note(
source_note = md("**Antisana**")
) %>%
cols_label(
Indicador = "Indicador",
Valor = "Valor"
) %>%
tab_style(
style = cell_text(weight = "bold"),
locations = cells_body(columns = Indicador)
)
| Tabla 2: Indicadores Estadísticos de Temperatura Mínima |
| Indicador |
Valor |
| Mínimo |
2.65 |
| Máximo |
10.85 |
| Media |
8.05 |
| Mediana |
8 |
| Moda (intervalo) |
[7.21, 8.12] |
| Desviación Estándar |
1.37 |
| Varianza |
1.89 |
| Coef. Variación (%) |
17.08 |
| Asimetría |
-0.43 |
| Curtosis |
0.57 |
| N° Outliers |
7 |
| Antisana |
##7. Conclusión
#La variable Min_Temperature fluctúa entre 2.65 y 10.85 y sus valores están en torno a los 8.00 (media = 8.05 ), con una desviación estándar de 1.37 siendo un conjunto de valores homogéneos (CV = 17.08%) cuyos valores se concentran en el intervalo modal [7.21, 8.12] y con distribución leptocúrtica (K = 0.57) y sesgo pronunciado hacia la izquierda (As = -0.43) a excepción de los 7 valores atípicos identificados, por lo tanto el comportamiento de la variable indica un proceso mayoritariamente estable con mediciones consistentes en el rango principal, aunque con presencia significativa de lecturas extremas que requieren análisis particular.