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05 visualizing data.R
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05 visualizing data.R
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install.packages('tidyverse')
library(tidyverse)
install.packages('ggplot2')
library(ggplot2)
hotel_bookings <- read.csv("hotel_bookings.csv")
colnames(hotel_bookings)
ggplot(data = hotel_bookings) +
geom_bar(mapping = aes(x = distribution_channel))
#data %>%
# filter(variable1 == "DS") %>%
# ggplot(aes(x = weight, y = variable2, colour = variable1)) +
# geom_point(alpha = 0.3, position = position_jitter()) + stat_smooth(method = "lm")
ggplot(data = hotel_bookings) +
geom_point(mapping = aes(x = lead_time, y = children))
ggplot(data = hotel_bookings) +
geom_bar(mapping = aes(x = hotel, fill = market_segment))
ggplot(data = hotel_bookings) +
geom_bar(mapping = aes(x = hotel)) +
facet_wrap(~market_segment)
onlineta_city_hotels <- filter(hotel_bookings,
(hotel=="City Hotel" &
hotel_bookings$market_segment=="Online TA"))
View(onlineta_city_hotels)
onlineta_city_hotels_v2 <- hotel_bookings %>%
filter(hotel=="City Hotel") %>%
filter(market_segment=="Online TA")
View(onlineta_city_hotels_v2)
ggplot(data = onlineta_city_hotels_v2) +
geom_point(mapping = aes(x = lead_time, y = children))