我使用来自大阿尔伯克基地区的几个气象站的月度气候数据，我已经以机场数据的这个子集为例，我最终会将这个相同的流程应用到所有地点。有近500个月的数据可用，但我已经包括了前30个。
> head(ABQ, 30)
STATION_NAME DATE CLDD
9698 ALBUQUERQUE INTERNATIONAL AIRPORT NM US 1945-05-01 449
9699 ALBUQUERQUE INTERNATIONAL AIRPORT NM US 1945-06-01 1335
9700 ALBUQUERQUE INTERNATIONAL AIRPORT NM US 1945-07-01 2330
9701 ALBUQUERQUE INTERNATIONAL AIRPORT NM US 1945-08-01 2269
9702 ALBUQUERQUE INTERNATIONAL AIRPORT NM US 1945-09-01 1247
9703 ALBUQUERQUE INTERNATIONAL AIRPORT NM US 1945-10-01 13
9709 ALBUQUERQUE INTERNATIONAL AIRPORT NM US 1946-04-01 62
9710 ALBUQUERQUE INTERNATIONAL AIRPORT NM US 1946-05-01 251
9711 ALBUQUERQUE INTERNATIONAL AIRPORT NM US 1946-06-01 2097
9712 ALBUQUERQUE INTERNATIONAL AIRPORT NM US 1946-07-01 2303
9713 ALBUQUERQUE INTERNATIONAL AIRPORT NM US 1946-08-01 1889
9714 ALBUQUERQUE INTERNATIONAL AIRPORT NM US 1946-09-01 1111
9715 ALBUQUERQUE INTERNATIONAL AIRPORT NM US 1946-10-01 23
9721 ALBUQUERQUE INTERNATIONAL AIRPORT NM US 1947-04-01 1
9722 ALBUQUERQUE INTERNATIONAL AIRPORT NM US 1947-05-01 611
9723 ALBUQUERQUE INTERNATIONAL AIRPORT NM US 1947-06-01 1273
9724 ALBUQUERQUE INTERNATIONAL AIRPORT NM US 1947-07-01 2636
9725 ALBUQUERQUE INTERNATIONAL AIRPORT NM US 1947-08-01 1892
9726 ALBUQUERQUE INTERNATIONAL AIRPORT NM US 1947-09-01 1265
9727 ALBUQUERQUE INTERNATIONAL AIRPORT NM US 1947-10-01 171
9733 ALBUQUERQUE INTERNATIONAL AIRPORT NM US 1948-04-01 91
9734 ALBUQUERQUE INTERNATIONAL AIRPORT NM US 1948-05-01 642
9735 ALBUQUERQUE INTERNATIONAL AIRPORT NM US 1948-06-01 1506
9736 ALBUQUERQUE INTERNATIONAL AIRPORT NM US 1948-07-01 2529
9737 ALBUQUERQUE INTERNATIONAL AIRPORT NM US 1948-08-01 2186
9738 ALBUQUERQUE INTERNATIONAL AIRPORT NM US 1948-09-01 1130
9739 ALBUQUERQUE INTERNATIONAL AIRPORT NM US 1948-10-01 13
9745 ALBUQUERQUE INTERNATIONAL AIRPORT NM US 1949-04-01 88
9746 ALBUQUERQUE INTERNATIONAL AIRPORT NM US 1949-05-01 304
9747 ALBUQUERQUE INTERNATIONAL AIRPORT NM US 1949-06-01 1477
我想调用ABQ $ CLDD的年度总和并将该值应用于ggplot（）...这样的事情
CLDD_yr <- apply.yearly(ABQ$DATE, sum(CLDD))
p <- ggplot(CLDD_yr, aes(YEAR, CLDD_yr)),
+ stat_smooth(method = "lm", formula = y~x + I(x^2), size = 1)
我知道我在调用我认为的数据的某个地方犯了一个错误，但我似乎无法解决这个问题。
DATE列是POSIX时间，如此处所示
> class(ABQ$DATE)
[1] "POSIXlt" "POSIXt"
编辑： 每个coffienjunkies评论
也许新的df是解决这个问题的最好方法，因为我需要以相同的方式查看多个位置的数据
> stations
unique(Bernalillo_data$STATION_NAME)
1 ALBUQUERQUE INTERNATIONAL AIRPORT NM US
2 PETROGLYPH NATIONAL MON NM US
3 SANDIA PARK NM US
4 ALBUQUERQUE VLY NM US
5 ALBUQUERQUE FOOTHILLS NE NM US
6 SANDIA RANGER STATION NM US
7 SANDIA CREST NM US
8 LA MADERA SKI AREA NM US
9 NETHERWOOD PARK NM US
10 EXPERIMENT FARM NM US
11 KIRTLAND AFB NM US
也许新的DF应该像
header <- station_name Year CLDD_sum
答案 0 :(得分：2)
试试这个，
from django.conf.urls import url, import
请注意，您必须安装require(data.table)
setDT(ABQ)
ABQ[, CLDD_yr := sum(CLDD), by = year(DATE)]
# Required because data.table and ggplot don't play nice.
setDF(ABQ)
p <- ggplot(ABQ, aes(YEAR, CLDD_yr)),
+ stat_smooth(method = "lm", formula = y~x + I(x^2), size = 1)
。请注意，这将为每行创建摘要统计信息，因此您可能会在ggplot中重叠几个点。如果您不想要，可以尝试，
data.table希望这有帮助。
答案 1 :(得分：1)
我认为您可以使用许多方法，但某些聚合必须在某些时候发生。以下是两条建议：
library(dplyr)
library(ggplot2)
df$year <- df$DATE$year
df$DATE <- as.POSIXct(df$DATE) # dplyr doesn't play well with POSIXlt
df_yr <- df %>% group_by(year) %>% summarise(cldd_yr = sum(CLDD))
这会产生：
Source: local data frame [5 x 2]
year cldd_yr
(chr) (int)
1 1945 7643
2 1946 7736
3 1947 7849
4 1948 8097
5 1949 1869
您可以与ggplot结合使用。对于多个站，只需将站添加为分组变量。例如，df_yr <- df %>% group_by(year, station) %>% summarise(cldd_yr = sum(CLDD))将为您提供所有年份和电台的摘要，前提是station是您的标识符的调用方式。
如果您真的不想使用新的数据框，但可以添加列，请尝试
df <- group_by(df, year) %>% mutate(yr.sum = sum(CLDD))
在yr.sum中，您有每年的总和。请注意，此值正在重复，您必须确保ggplot正确使用它。我建议使用第一种方法，因为它可能更有效，更透明。