将年度数据转换为 r 中的月度数据

Convert yearly to monthly data in r

我有一个年度数据,在年中有值变化。我想把它变成一个显示价值变化的月度数据。这是我的数据片段。

year    value   Start date  End date
1985    35451   7/1/1985            3/20/1986
1986    45600   3/21/1986   12/23/1986
1987    46089   1/1/1987            10/31/1989

我希望所有的飞蛾都在列中,年份在行中(类似于下面,但在 Jun 之后没有中断):

Jan Feb Mar Apr May Jun
1985    0   0   0   0   0   0
1986    35451   35451   38725   45600   45600   45600

Jul Aug Sep Oct Nov Dec
1985    35451   35451   35451   35451   35451   35451
1986    45600   45600   45600   45600   45600   45726

1986 年 3 月和 12 月具有加权平均值,因为值的变化发生在该月。

感谢并感谢。

实际上,您在这里只需要 seq.Datextabs(或您最喜欢的变体),但需要进行大量修改才能使其正常工作。在 Hadleyverse 包中,但如果您愿意,可以在基础或 data.table 中重写:

library(dplyr)
library(tidyr)
library(lubridate)

       # Format dates as dates, then,
df %>% mutate_each(funs(mdy), ends_with('date')) %>% 
    # evaluating each row separately,
    rowwise() %>% 
    # create a list column with a month-wise sequence of dates for each.
    mutate(month = list(seq.Date(Start.date, End.date, by = 'month'))) %>%
    # Expand list column to long form,
    unnest() %>% 
    # change year column to year of sequence, not label, and reduce month column to month.abb.
    mutate(year = year(month), month = month(month, label = TRUE)) %>% 
    # For each year-month combination,
    group_by(year, month) %>% 
    # take the mean of values, so each has only one row, then
    summarise(value = mean(value)) %>% 
    # spread the result to wide form.
    spread(month, value, fill = 0)    # or xtabs(value ~ year + month, data = .)

# Source: local data frame [5 x 13]
# Groups: year [5]
# 
#    year   Jan   Feb     Mar   Apr   May   Jun   Jul   Aug   Sep   Oct   Nov   Dec
#   (dbl) (dbl) (dbl)   (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl)
# 1  1985     0     0     0.0     0     0     0 35451 35451 35451 35451 35451 35451
# 2  1986 35451 35451 40525.5 45600 45600 45600 45600 45600 45600 45600 45600 45600
# 3  1987 46089 46089 46089.0 46089 46089 46089 46089 46089 46089 46089 46089 46089
# 4  1988 46089 46089 46089.0 46089 46089 46089 46089 46089 46089 46089 46089 46089
# 5  1989 46089 46089 46089.0 46089 46089 46089 46089 46089 46089 46089     0     0