Report Number

Report Number

library(knitr)
library(kableExtra)
library(dplyr)

## 
## Adjuntando el paquete: 'dplyr'

## The following object is masked from 'package:kableExtra':
## 
##     group_rows

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

#Tabla de frecuencia 
TDF_causas_raw <- as.data.frame(table(datos$Cause.Category))
colnames(TDF_causas_raw) <- c("Causa", "ni")
TDF_ordenada <- TDF_causas_raw %>%
  arrange(desc(ni)) 
# --- PASO 3: Preparar la Tabla Final 
# 3.1 Calcular Porcentajes
ni_total <- sum(TDF_ordenada$ni)
hi_num   <- (TDF_ordenada$ni / ni_total) * 100
TDF_ordenada$hi <- sprintf("%.2f", round(hi_num, 2))
TDF_limpia <- TDF_ordenada %>% 
  select(Causa, ni, hi)
Sumatoria <- data.frame(
  Causa = "TOTAL", 
  ni = ni_total, 
  hi = "100.00"
)
TDF_final <- rbind(TDF_limpia, Sumatoria)
colnames(TDF_final) <- c("Causa del Accidente", "ni", "hi (%)")
kable(TDF_final, align = 'c', 
      caption = "Gráfico 1 Frecuencia de Causas de Accidentes") %>%
  kable_styling(full_width = FALSE, position = "center", 
                bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(nrow(TDF_final), bold = TRUE, background = "#f2f2f2")

Gráfico 1 Frecuencia de Causas de Accidentes

Causa del Accidente	ni	hi (%)
MATERIAL/WELD/EQUIP FAILURE	1435	51.34
CORROSION	592	21.18
INCORRECT OPERATION	378	13.52
ALL OTHER CAUSES	118	4.22
NATURAL FORCE DAMAGE	118	4.22
EXCAVATION DAMAGE	97	3.47
OTHER OUTSIDE FORCE DAMAGE	57	2.04
TOTAL	2795	100.00

library(ggplot2)
library(dplyr)

datos_grafico <- TDF_limpia %>%
  mutate(
    ni = as.numeric(ni),
    Etiqueta_Leyenda = paste0(Causa, " (", hi, "%)"),
    Etiqueta_Leyenda = factor(Etiqueta_Leyenda, levels = unique(Etiqueta_Leyenda)) 
  )
ggplot(datos_grafico, aes(x = "", y = ni, fill = Etiqueta_Leyenda)) +  
  geom_bar(stat = "identity", width = 1, color = "white", alpha = 0.6) +
  
  coord_polar("y", start = 0) +
  theme_void() + 
  theme(
    legend.position = "right",             
    legend.text = element_text(size = 7),  
    legend.title = element_text(size = 9, face = "bold") 
  ) +
  
  labs(title = "Gráfico 2 Distribución de Report Numbers por Causa",
       fill = "Leyenda") + 
  scale_fill_brewer(palette = "Blues", direction = -1)

library(ggplot2)
library(dplyr)
#Gráfico de barras
datos_barras <- TDF_limpia %>%
  filter(Causa != "TOTAL") %>%
  mutate(
    ni = as.numeric(ni),
    Causa = reorder(Causa, ni) 
  )
ggplot(datos_barras, aes(x = Causa, y = ni)) +
  geom_bar(stat = "identity", fill = "blue", width = 0.7) +
  
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  labs(title = "Gráfica 3 Cantidad de Causas de Accidentes",
       subtitle = "Número de reportes",
       x = "Causa",
       y = "Número de Accidentes") +
  scale_y_continuous(expand = expansion(mult = c(0, 0.05)))

library(ggplot2)
library(dplyr)
#Gráfico barras
datos_barras <- TDF_limpia %>%
  filter(Causa != "TOTAL") %>%
  mutate(
    ni = as.numeric(ni),
    Causa = reorder(Causa, ni) 
  )
ggplot(datos_barras, aes(x = Causa, y = ni)) +
  geom_bar(stat = "identity", fill = "blue", width = 0.7) +
  
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  
  labs(title = "Gráfica 4 Causas generales de Accidentes",
       subtitle = "Número de reportes",
       x = "Causa",
       y = "Número de Accidentes") +
  scale_y_continuous(
    limits = c(0, 4000),  
    expand = expansion(mult = c(0, 0.05))
  )