查找 R 中多天变量变化的次数
Finding how many times variable changes over multiple days in R
我在执行此任务时遇到了问题。基本上我有价格数据以及购买商品的时间和日期。有了这个，我想找出一件商品的价格在一天内变化了多少次。例如，早上的价格是 7000，但晚上的价格可能是 4000。所以我想在几天内为多个项目执行此操作。还有一个与购买相关联的订单 ID，但它不必是唯一的。
我发现这篇文章真的很有帮助，我取得了一些进步，但无法得到我需要的东西
我已经为数据放置了 dput 输出，以便可以重新创建它。结果应该是这样的
Item Price_changed_over_all_days Price_changed_in_one_day
x 10 3
y 4 1
z 5 2
感谢任何advice/help！如果我能让问题更清楚，请告诉我。
PS：如果可能的话，我也希望能够说出任何给定日期的最高价和最低价，如果这也能算出来那就太好了。我知道如何在特定的一天执行此操作，但是
structure(list(item = c("x", "x", "x", "x", "x", "x", "x", "x",
"x", "x", "x", "x", "x", "x", "x", "x", "x", "x", "x", "x", "x",
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"y", "y", "y", "y", "y", "y", "y", "z", "z", "z", "z", "z", "z",
"z", "z", "z", "z", "z", "z", "z", "z", "z", "z", "z", "z", "z",
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"z", "z", "z", "z", "z", "z", "z", "z", "z", "z", "z", "z", "z",
"z", "z", "z", "z", "z", "z", "z", "z", "z", "z", "z", "z"),
bought_date = structure(c(1600646400, 1600646400, 1600646400,
1600646400, 1600646400, 1600646400, 1600732800, 1600732800,
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1600732800, 1600732800, 1600732800, 1600732800, 1600732800,
1600732800, 1600732800, 1600732800, 1600732800), class = c("POSIXct",
"POSIXt"), tzone = "UTC"), bought_time = structure(c(-2209016101,
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-2209005139, -2209004957, -2208998695, -2208998233, -2208990008,
-2208989978), class = c("POSIXct", "POSIXt"), tzone = "UTC"),
ID = c(540273, 540333, 540568, 540734, 540766, 540771, 540808,
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542745, 542835, 542911, 542917, 542920, 543019, 543031, 543112,
543117, 543178, 543179), price = c(7190, 9200, 7170, 7170,
7170, 9170, 7170, 7170, 7170, 9170, 7170, 9170, 8330, 7170,
9170, 7170, 9170, 7170, 7170, 7170, 7170, 7170, 7170, 7170,
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7160, 7160, 8330, 8330, 8330, 8330, 7190, 7190, 7190, 7190,
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"tbl", "data.frame"), row.names = c(NA, -202L))
首先，我假设您的数据在变量 df 中。
您必须仔细考虑要计算的内容。
如果你想要不分天数的价格变化，你可以这样做：
df %>% group_by(item) %>%
summarise(Price_changed_over_all_days =
sum((lead(price) - price)!=0, na.rm = TRUE))
# A tibble: 3 x 2
# item Price_changed_over_all_days
# <chr> <int>
#1 x 24
#2 y 30
#3 z 24
然而，如果你想计算特定日期的价格变化次数，你会得到这样的结果：
df %>% group_by(item, bought_date) %>%
summarise(Price_changed_in_one_day =
sum((lead(price) - price)!=0, na.rm = TRUE))
# A tibble: 6 x 3
# Groups: item [3]
# item bought_date Price_changed_in_one_day
# <chr> <dttm> <int>
#1 x 2020-09-21 00:00:00 3
#2 x 2020-09-22 00:00:00 20
#3 y 2020-09-21 00:00:00 4
#4 y 2020-09-22 00:00:00 26
#5 z 2020-09-21 00:00:00 5
#6 z 2020-09-22 00:00:00 18
只是在这种情况下，您在摘要中有更多的行 table。
如果你只想要一个 table，你必须以某种方式 assemble 它并根据当天的值决定一些统计数据。也许平均值在这里合适？我不知道。
df %>% group_by(item) %>%
summarise(Price_changed_over_all_days =
sum((lead(price) - price)!=0, na.rm = TRUE)) %>%
left_join(
df %>% group_by(item, bought_date) %>%
summarise(Price_changed_in_one_day =
sum((lead(price) - price)!=0, na.rm = TRUE)) %>%
group_by(item) %>%
summarise(Price_changed_in_one_day =
mean(Price_changed_in_one_day)
), by= "item")
# A tibble: 3 x 3
# item Price_changed_over_all_days Price_changed_in_one_day
# <chr> <int> <dbl>
#1 x 24 11.5
#2 y 30 15
#3 z 24 11.5
另请注意，价格变化可能会在一天之内发生，因此给定产品几天内的变化总和不一定等于该产品所有价格变化的总和。在您的情况下，产品“x”就是这种情况。
对于 data.table 你可以使用 rleid:
library(data.table)
setDT(data)
data[,.(times=max(rleid(price))-1),by=.(item)]
# item times
#1: x 24
#2: y 30
#3: z 24
data[,.(timesday=max(rleid(price))-1),by=.(item,bought_date)]
# item bought_date timesday
#1: x 2020-09-21 3
#2: x 2020-09-22 20
#3: y 2020-09-21 4
#4: y 2020-09-22 26
#5: z 2020-09-21 5
#6: z 2020-09-22 18
我在执行此任务时遇到了问题。基本上我有价格数据以及购买商品的时间和日期。有了这个，我想找出一件商品的价格在一天内变化了多少次。例如，早上的价格是 7000，但晚上的价格可能是 4000。所以我想在几天内为多个项目执行此操作。还有一个与购买相关联的订单 ID，但它不必是唯一的。
我发现这篇文章真的很有帮助，我取得了一些进步，但无法得到我需要的东西
我已经为数据放置了 dput 输出，以便可以重新创建它。结果应该是这样的
Item Price_changed_over_all_days Price_changed_in_one_day
x 10 3
y 4 1
z 5 2
感谢任何advice/help！如果我能让问题更清楚，请告诉我。
PS：如果可能的话，我也希望能够说出任何给定日期的最高价和最低价，如果这也能算出来那就太好了。我知道如何在特定的一天执行此操作，但是
structure(list(item = c("x", "x", "x", "x", "x", "x", "x", "x",
"x", "x", "x", "x", "x", "x", "x", "x", "x", "x", "x", "x", "x",
"x", "x", "x", "x", "x", "x", "x", "x", "x", "x", "x", "x", "x",
"x", "x", "x", "x", "x", "x", "x", "x", "x", "x", "x", "x", "x",
"x", "x", "x", "x", "x", "x", "x", "x", "x", "x", "x", "x", "x",
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"y", "y", "y", "y", "y", "y", "y", "y", "y", "y", "y", "y", "y",
"y", "y", "y", "y", "y", "y", "y", "z", "z", "z", "z", "z", "z",
"z", "z", "z", "z", "z", "z", "z", "z", "z", "z", "z", "z", "z",
"z", "z", "z", "z", "z", "z", "z", "z", "z", "z", "z", "z", "z",
"z", "z", "z", "z", "z", "z", "z", "z", "z", "z", "z", "z", "z",
"z", "z", "z", "z", "z", "z", "z", "z", "z", "z", "z", "z"),
bought_date = structure(c(1600646400, 1600646400, 1600646400,
1600646400, 1600646400, 1600646400, 1600732800, 1600732800,
1600732800, 1600732800, 1600732800, 1600732800, 1600732800,
1600732800, 1600732800, 1600732800, 1600732800, 1600732800,
1600732800, 1600732800, 1600732800, 1600732800, 1600732800,
1600732800, 1600732800, 1600732800, 1600732800, 1600732800,
1600732800, 1600732800, 1600732800, 1600732800, 1600732800,
1600732800, 1600732800, 1600732800, 1600732800, 1600732800,
1600732800, 1600732800, 1600732800, 1600732800, 1600732800,
1600732800, 1600732800, 1600732800, 1600732800, 1600732800,
1600732800, 1600732800, 1600732800, 1600732800, 1600732800,
1600732800, 1600732800, 1600732800, 1600732800, 1600732800,
1600732800, 1600732800, 1600732800, 1600732800, 1600732800,
1600732800, 1600732800, 1600732800, 1600732800, 1600732800,
1600732800, 1600732800, 1600646400, 1600646400, 1600646400,
1600646400, 1600646400, 1600646400, 1600646400, 1600646400,
1600646400, 1600646400, 1600646400, 1600646400, 1600646400,
1600646400, 1600646400, 1600646400, 1600646400, 1600646400,
1600646400, 1600646400, 1600732800, 1600732800, 1600732800,
1600732800, 1600732800, 1600732800, 1600732800, 1600732800,
1600732800, 1600732800, 1600732800, 1600732800, 1600732800,
1600732800, 1600732800, 1600732800, 1600732800, 1600732800,
1600732800, 1600732800, 1600732800, 1600732800, 1600732800,
1600732800, 1600732800, 1600732800, 1600732800, 1600732800,
1600732800, 1600732800, 1600732800, 1600732800, 1600732800,
1600732800, 1600732800, 1600732800, 1600732800, 1600732800,
1600732800, 1600732800, 1600732800, 1600732800, 1600732800,
1600732800, 1600732800, 1600732800, 1600732800, 1600732800,
1600732800, 1600732800, 1600732800, 1600732800, 1600732800,
1600732800, 1600732800, 1600646400, 1600646400, 1600646400,
1600646400, 1600646400, 1600646400, 1600646400, 1600646400,
1600646400, 1600732800, 1600732800, 1600732800, 1600732800,
1600732800, 1600732800, 1600732800, 1600732800, 1600732800,
1600732800, 1600732800, 1600732800, 1600732800, 1600732800,
1600732800, 1600732800, 1600732800, 1600732800, 1600732800,
1600732800, 1600732800, 1600732800, 1600732800, 1600732800,
1600732800, 1600732800, 1600732800, 1600732800, 1600732800,
1600732800, 1600732800, 1600732800, 1600732800, 1600732800,
1600732800, 1600732800, 1600732800, 1600732800, 1600732800,
1600732800, 1600732800, 1600732800, 1600732800, 1600732800,
1600732800, 1600732800, 1600732800, 1600732800), class = c("POSIXct",
"POSIXt"), tzone = "UTC"), bought_time = structure(c(-2209016101,
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首先，我假设您的数据在变量 df 中。
您必须仔细考虑要计算的内容。
如果你想要不分天数的价格变化，你可以这样做：
df %>% group_by(item) %>%
summarise(Price_changed_over_all_days =
sum((lead(price) - price)!=0, na.rm = TRUE))
# A tibble: 3 x 2
# item Price_changed_over_all_days
# <chr> <int>
#1 x 24
#2 y 30
#3 z 24
然而，如果你想计算特定日期的价格变化次数，你会得到这样的结果：
df %>% group_by(item, bought_date) %>%
summarise(Price_changed_in_one_day =
sum((lead(price) - price)!=0, na.rm = TRUE))
# A tibble: 6 x 3
# Groups: item [3]
# item bought_date Price_changed_in_one_day
# <chr> <dttm> <int>
#1 x 2020-09-21 00:00:00 3
#2 x 2020-09-22 00:00:00 20
#3 y 2020-09-21 00:00:00 4
#4 y 2020-09-22 00:00:00 26
#5 z 2020-09-21 00:00:00 5
#6 z 2020-09-22 00:00:00 18
只是在这种情况下，您在摘要中有更多的行 table。 如果你只想要一个 table，你必须以某种方式 assemble 它并根据当天的值决定一些统计数据。也许平均值在这里合适？我不知道。
df %>% group_by(item) %>%
summarise(Price_changed_over_all_days =
sum((lead(price) - price)!=0, na.rm = TRUE)) %>%
left_join(
df %>% group_by(item, bought_date) %>%
summarise(Price_changed_in_one_day =
sum((lead(price) - price)!=0, na.rm = TRUE)) %>%
group_by(item) %>%
summarise(Price_changed_in_one_day =
mean(Price_changed_in_one_day)
), by= "item")
# A tibble: 3 x 3
# item Price_changed_over_all_days Price_changed_in_one_day
# <chr> <int> <dbl>
#1 x 24 11.5
#2 y 30 15
#3 z 24 11.5
另请注意，价格变化可能会在一天之内发生，因此给定产品几天内的变化总和不一定等于该产品所有价格变化的总和。在您的情况下，产品“x”就是这种情况。
对于 data.table 你可以使用 rleid:
library(data.table)
setDT(data)
data[,.(times=max(rleid(price))-1),by=.(item)]
# item times
#1: x 24
#2: y 30
#3: z 24
data[,.(timesday=max(rleid(price))-1),by=.(item,bought_date)]
# item bought_date timesday
#1: x 2020-09-21 3
#2: x 2020-09-22 20
#3: y 2020-09-21 4
#4: y 2020-09-22 26
#5: z 2020-09-21 5
#6: z 2020-09-22 18