我有一个数据框，其中包含一个整数类型的日期列。 我还想将价格除以 10,000 的范围，然后计算该月下降的频率
> df
date values price
11/25/18 a 10000
11/30/18 b 30500
12/4/18 a 20000
12/5/18 b 65000
12/5/18 a 50000
12/6/18 b 35000
12/6/18 c 40000
12/6/18 a 45000
12/6/18 a 30000
12/7/18 b 80000
12/7/18 c 85000
12/7/18 a 90000
12/9/18 b 20000
12/12/18 a 32500
12/12/18 c 40200
12/13/18 b 56000
1/9/19 a 82000
1/9/19 c 63000
1/9/19 b 20000
1/10/19 d 25000
1/10/19 d 34000
1/10/19 d 13020
1/10/19 a 50000
1/11/19 c 24300
1/11/19 d 40000
2/1/19 a 95000
2/10/19 a 20000
2/13/19 b 10000
3/14/19 d 30000
3/17/19 c 45000
5/4/19 d 18000
5/5/19 c 12000
5/6/19 d 90000
5/31/19 a 90000
我正在尝试此代码，但我无法在一个月内聚合
df %>%
group_by(date) %>%
count(values)
由此，我得到了每天的频率
group_by(month = month(date)) %>%
count(values)
当我尝试使用此代码以按月聚合日期时，我收到以下错误
(错误在 as 中。POSIXlt.character(as.character(x(， ...( ： 字符串不是标准的明确格式(
并按 10,000 步长(在价格列中(分组，我使用以下代码
tally(group_by(df, values,
price = cut(price, breaks = seq(10000, 200000, by = 10000)))) %>%
ungroup() %>%
spread(price, n, fill = 0)
问题：
我无法将其与代码相结合以按月聚合日期，然后按价格组传播数据。
预期产出：
date values 10k-20k 20k-30k 30k-40k 40k-50k 50k-60k 60k-70k 70k-80k 80k-90k
11/18 a 1
11/18 b 1
12/18 a 1 1 1 1 1
12/18 b 1 1 1 1
12/18 c 1 1 1
...
我们可以从日期列中提取月-年，使用cut将price分解为不同的存储桶，count频率，然后spread宽格式。
library(dplyr)
cut_group <- seq(10000,200000,by=10000)
df %>%
mutate(date = as.Date(date, "%m/%d/%y"),
month_year = format(date, "%m-%y"),
groups = cut(price, cut_group, include.lowest = TRUE,
labels = paste(cut_group[-length(cut_group)], cut_group[-1], sep = "-"))) %>%
count(values, month_year, groups) %>%
tidyr::spread(groups, n, fill = 0)
# values month_year `10000-20000` `20000-30000` `30000-40000` `40000-50000`
# <fct> <chr> <dbl> <dbl> <dbl> <dbl>
# 1 a 01-19 0 0 0 1
# 2 a 02-19 1 0 0 0
# 3 a 05-19 0 0 0 0
# 4 a 11-18 1 0 0 0
#.....
数据
df <- structure(list(date = structure(c(4L, 5L, 8L, 9L, 9L, 10L, 10L,
10L, 10L, 11L, 11L, 11L, 12L, 6L, 6L, 7L, 3L, 3L, 3L, 1L, 1L,
1L, 1L, 2L, 2L, 13L, 14L, 15L, 16L, 17L, 19L, 20L, 21L, 18L), .Label =
c("1/10/19", "1/11/19", "1/9/19", "11/25/18", "11/30/18", "12/12/18", "12/13/18",
"12/4/18", "12/5/18", "12/6/18", "12/7/18", "12/9/18", "2/1/19",
"2/10/19", "2/13/19", "3/14/19", "3/17/19", "5/31/19", "5/4/19",
"5/5/19", "5/6/19"), class = "factor"), values = structure(c(1L,
2L, 1L, 2L, 1L, 2L, 3L, 1L, 1L, 2L, 3L, 1L, 2L, 1L, 3L, 2L, 1L,
3L, 2L, 4L, 4L, 4L, 1L, 3L, 4L, 1L, 1L, 2L, 4L, 3L, 4L, 3L, 4L,
1L), .Label = c("a", "b", "c", "d"), class = "factor"), price = c(10000L,
30500L, 20000L, 65000L, 50000L, 35000L, 40000L, 45000L, 30000L,
80000L, 85000L, 90000L, 20000L, 32500L, 40200L, 56000L, 82000L,
63000L, 20000L, 25000L, 34000L, 13020L, 50000L, 24300L, 40000L,
95000L, 20000L, 10000L, 30000L, 45000L, 18000L, 12000L, 90000L,
90000L)), class = "data.frame", row.names = c(NA, -34L))
如果有帮助，我可以提供一个 data.table + 润滑解决方案：
library(data.table)
library(lubridate)
setDT(df)
df[, .N, by = floor_date(date, "month")]
编辑： 我错过了整个"10000 组"部分：
df2 <- df[, .N, by = .(date = floor_date(date, "month"), range = cut(price, seq(0, 100e3, 10e3))]
然后你可以使用 dcast 让它成为宽格式：
dcast(df2, date~range)