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Three of the eight charts in section 3.5 focus on who is booking and what they need on arrival. Chart 1 establishes baseline booking volumes for each hotel type. Chart 5 segments bookings by whether the party includes children or babies, using a derived binary variable. Chart 6 shows how many car parking spaces guests request, highlighting the distribution across zero and non-zero values.

Chart 1 — Cantidad de Reservas por Tipo de Hotel

This bar chart counts the total number of bookings for each level of the hotel factor — Resort Hotel and City Hotel. The fill = hotel mapping colors each bar differently, making the volume difference immediately visible. City Hotel typically shows a higher booking count than Resort Hotel in this dataset.
upc-grupo5-tb1.R
# 1. Reservas por tipo de hotel
ggplot(df, aes(x = hotel, fill = hotel)) +
  geom_bar() +
  labs(title = "Cantidad de Reservas por Tipo de Hotel", x = "Hotel", y = "Total")
geom_bar() with no y aesthetic counts rows by default (stat = "count"), so no aggregation step is needed before plotting.
Before plotting, the script derives a new column tiene_ninos that classifies each booking as either "Con Niños" (with children or babies) or "Solo Adultos" (adults only). The derivation checks whether children > 0 or babies > 0; either condition triggers the "Con Niños" label. This derivation runs immediately before the ggplot call.The resulting bar chart makes it easy to see what proportion of bookings involve traveling families. Because children was imputed with the median earlier in the script (section 3.4.1), there are no NA values that could silently fall into the "Solo Adultos" category.
upc-grupo5-tb1.R
# 5. Reservas con niños y/o bebés
df$tiene_ninos <- ifelse(df$children > 0 | df$babies > 0, "Con Niños", "Solo Adultos")
ggplot(df, aes(x = tiene_ninos, fill = tiene_ninos)) +
  geom_bar() +
  labs(title = "Proporción de Reservas con Menores")
The children column was imputed with its median value in section 3.4.1 (df$children[is.na(df$children)] <- median(df$children, na.rm = TRUE)), so tiene_ninos will never be NA.
Chart 6 treats required_car_parking_spaces as a categorical variable by wrapping it in as.factor(). This forces ggplot2 to draw one bar per distinct integer value (0, 1, 2, …) rather than mapping the column to a continuous x-axis. The purple fill is applied uniformly via fill = "purple" rather than a data mapping, since the interest is in frequency counts per category, not a comparison between groups.The chart reveals that the vast majority of bookings request zero parking spaces, with a sharp drop-off at higher values.
upc-grupo5-tb1.R
# 6. Espacios de estacionamiento requeridos
ggplot(df, aes(x = as.factor(required_car_parking_spaces))) +
  geom_bar(fill = "purple") +
  labs(title = "Demanda de Estacionamientos", x = "Espacios", y = "Frecuencia")

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