如何正確分析 plot 我在 R 中的酒店預訂數據？

Question

我是 R 的初學者，我的數據是一家擁有 3 間不同公寓的酒店及其在 2018 年至 2022 年之間的預訂數據。對於每個預訂，我都有一個到達日期和一個離開日期（已經采用日期格式），預訂的總晚數以及他們住在哪間公寓。我現在想分析和 plot 數據，以了解預訂數量隨時間的整體變化以及每個不同公寓的變化。 我真的很想在 ggplot 中得到一個看起來像 geom_line 圖的圖，但我不知道最好的方法，以及如何將“預訂數量”放入變量中。

謝謝！

這是我的數據的負責人：

  arr_date   dep_date total_nights hochsaison mittelsaison nebensaison ost west sued ost.west sued.west sued.ost gesamtes_haus year month wday
1 2018-01-12 2018-01-14            2          0            0           2   0    0    0        0         0        2             0 2018   Jan   Fr
2 2018-01-17 2018-01-21            4          0            0           4   0    4    0        0         0        0             0 2018   Jan   Mi
3 2018-01-21 2018-01-24            3          0            0           3   0    0    3        0         0        0             0 2018   Jan   So
4 2018-02-09 2018-02-11            2          0            2           0   0    0    0        2         0        0             0 2018   Feb   Fr
5 2018-02-09 2018-02-13            4          0            4           0   0    0    4        0         0        0             0 2018   Feb   Fr
6 2018-02-16 2018-02-18            2          0            0           2   0    0    0        0         0        0             2 2018   Feb   Fr


Rows: 323
Columns: 16
$ arr_date      <date> 2018-01-12, 2018-01-17, 2018-01-21, 2018-02-09, 2018-02-09, 2018-02-16, 2018-02-23, 2018-02-22, 2018-03-19, 2018-03-27, 2018-03-29, 2018-04-19, 2018-04-27, 201…
$ dep_date      <date> 2018-01-14, 2018-01-21, 2018-01-24, 2018-02-11, 2018-02-13, 2018-02-18, 2018-02-25, 2018-02-25, 2018-03-24, 2018-04-03, 2018-04-04, 2018-04-24, 2018-05-01, 201…
$ total_nights  <dbl> 2, 4, 3, 2, 4, 2, 2, 3, 5, 7, 5, 5, 4, 3, 6, 5, 17, 7, 7, 15, 13, 9, 19, 7, 7, 14, 8, 13, 10, 11, 6, 7, 14, 6, 5, 5, 13, 7, 7, 10, 3, 6, 5, 7, 12, 12, 12, 2, 2,…
$ hochsaison    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 5, 0, 3, 3, 6, 5, 17, 7, 7, 15, 13, 9, 19, 7, 7, 14, 8, 13, 10, 11, 6, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ mittelsaison  <dbl> 0, 0, 0, 2, 4, 0, 0, 0, 0, 0, 0, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 14, 6, 5, 5, 13, 7, 7, 10, 3, 6, 5, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ nebensaison   <dbl> 2, 4, 3, 0, 0, 2, 2, 3, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 12, 12, 12, 2, 2, 4, 2, 4, 1…
$ ost           <dbl> 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 17, 0, 0, 15, 0, 0, 19, 0, 0, 0, 0, 13, 0, 0, 0, 0, 0, 0, 5, 5, 0, 7, 0, 0, 0, 6, 0, 7, 12, 0, 0, 0, 0, 0, 0, 0,…
$ west          <dbl> 0, 4, 0, 0, 0, 0, 0, 0, 5, 0, 5, 5, 0, 0, 6, 0, 0, 0, 7, 0, 13, 0, 0, 7, 0, 14, 0, 0, 0, 11, 0, 7, 14, 0, 0, 0, 0, 0, 0, 10, 0, 0, 5, 0, 0, 12, 0, 0, 0, 0, 2, 0…
$ sued          <dbl> 0, 0, 3, 0, 4, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 7, 0, 8, 0, 10, 0, 6, 0, 0, 6, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 12, 0, 0, 4, 0, 0, 0,…
$ ost.west      <dbl> 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ sued.west     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 13, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ sued.ost      <dbl> 2, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0…
$ gesamtes_haus <dbl> 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 4, 3, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0…
$ year          <dbl> 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018…
$ month         <chr> "Jan", "Jan", "Jan", "Feb", "Feb", "Feb", "Feb", "Feb", "Mär", "Mär", "Mär", "Apr", "Apr", "Mai", "Mai", "Jun", "Jun", "Jun", "Jun", "Jul", "Jul", "Jul", "Jul",…
$ wday          <chr> "Fr", "Mi", "So", "Fr", "Fr", "Fr", "Fr", "Do", "Mo", "Di", "Do", "Do", "Fr", "Fr", "Sa", "Fr", "Do", "Sa", "Sa", "Sa", "So", "Do", "So", "Sa", "Sa", "Sa", "Sa"…
> 


structure(list(arr_date = structure(c(17543, 17548, 17552, 17571, 
17571, 17578), class = "Date"), dep_date = structure(c(17545, 
17552, 17555, 17573, 17575, 17580), class = "Date"), total_nights = c(2, 
4, 3, 2, 4, 2), hochsaison = c(0, 0, 0, 0, 0, 0), mittelsaison = c(0, 
0, 0, 2, 4, 0), nebensaison = c(2, 4, 3, 0, 0, 2), ost = c(0, 
0, 0, 0, 0, 0), west = c(0, 4, 0, 0, 0, 0), sued = c(0, 0, 3, 
0, 4, 0), ost.west = c(0, 0, 0, 2, 0, 0), sued.west = c(0, 0, 
0, 0, 0, 0), sued.ost = c(2, 0, 0, 0, 0, 0), gesamtes_haus = c(0, 
0, 0, 0, 0, 2), year = c(2018, 2018, 2018, 2018, 2018, 2018), 
    month = c("Jan", "Jan", "Jan", "Feb", "Feb", "Feb"), wday = c("Fr", 
    "Mi", "So", "Fr", "Fr", "Fr"), wohnung = c("kombi", "west", 
    "sued", "kombi", "sued", "gesamtes haus"), saison = c("nebensaison", 
    "nebensaison", "nebensaison", "mittelsaison", "mittelsaison", 
    "nebensaison")), row.names = c(NA, 6L), class = "data.frame")

Answer 1

這里有一些東西可以幫助您入門。 我建議你玩這個，完善你的想法，當你對自己的目標有更清晰的想法時提出一個新問題。

library(dplyr)
library(tidyr)
library(lubridate)
library(purrr)
library(ggplot2)

apt = c("ost", "west", "sued", "ost.west", "sued.west", "sued.ost")

df %>%
  ## get the days in each booking interval
  mutate(days = map2(arr_date, dep_date, .f = seq.Date, by = "1 day")) %>%
  ## get rid of unneeded columns
  select(all_of(apt), days) %>%
  ## convert the night counts in each apartment to 1 or 0
  mutate(across(all_of(apt), pmin, 1)) %>%
  ## pivot to long format
  pivot_longer(cols = all_of(apt), names_to = "apt") %>%
  ## discard apartments with no booking
  filter(value != 0) %>%
  select(-value) %>%
  ## convert days to its own column
  unnest(days) -> df_long

## count days for plotting
count(df_long, days) %>%
  ggplot(aes(x = days, y = n)) +
  geom_line() +
  labs(
    x = "date",
    y = "separate bookings"
  ) +
  expand_limits(y = 0)

要了解代碼的作用，請逐行運行以查看每次發生的變化（首先是df %>% mutate(...) ，然后是df %>% mutate(...) %>% select(...) ，然后df %>% mutate(...) %>% select(...) %>% mutate(...) )。