从数据帧，是否有聚集（一个简单的方法sum，mean，max同时等c）中多个变量？
以下是一些示例数据：
library(lubridate)
days = 365*2
date = seq(as.Date("2000-01-01"), length = days, by = "day")
year = year(date)
month = month(date)
x1 = cumsum(rnorm(days, 0.05))
x2 = cumsum(rnorm(days, 0.05))
df1 = data.frame(date, year, month, x1, x2)我想同时按年份和月份汇总数据框中的x1和x2变量df2。以下代码汇总了x1变量，但是也可以同时汇总x2变量吗？
### aggregate variables by year month
df2=aggregate(x1 ~ year+month, data=df1, sum, na.rm=TRUE)
head(df2)任何建议将不胜感激。
Answers:
此year()功能来自哪里？
您也可以将reshape2包用于此任务：
require(reshape2)
df_melt <- melt(df1, id = c("date", "year", "month"))
dcast(df_melt, year + month ~ variable, sum)
# year month x1 x2
1 2000 1 -80.83405 -224.9540159
2 2000 2 -223.76331 -288.2418017
3 2000 3 -188.83930 -481.5601913
4 2000 4 -197.47797 -473.7137420
5 2000 5 -259.07928 -372.4563522是的，在中formula，您可以cbind聚合数字变量：
aggregate(cbind(x1, x2) ~ year + month, data = df1, sum, na.rm = TRUE)
year month x1 x2
1 2000 1 7.862002 -7.469298
2 2001 1 276.758209 474.384252
3 2000 2 13.122369 -128.122613
...
23 2000 12 63.436507 449.794454
24 2001 12 999.472226 922.726589请参阅?aggregate，formula参数和示例。
.在LHS上使用即可。aggregate(. ~ year + month, df1, sum, na.rm = TRUE)。在这个例子中，sum对于“日期”无厘头虽然....
使用该data.table软件包，速度很快（适用于较大的数据集）
https://github.com/Rdatatable/data.table/wiki
library(data.table)
df2 <- setDT(df1)[, lapply(.SD, sum), by=.(year, month), .SDcols=c("x1","x2")]
setDF(df2) # convert back to dataframe使用plyr包
require(plyr)
df2 <- ddply(df1, c("year", "month"), function(x) colSums(x[c("x1", "x2")]))使用Hmisc包中的summary（）（尽管在我的示例中列标题很乱）
# need to detach plyr because plyr and Hmisc both have a summarize()
detach(package:plyr)
require(Hmisc)
df2 <- with(df1, summarize( cbind(x1, x2), by=llist(year, month), FUN=colSums))dt[, .(x1.sum = sum(x1), x2.sum = sum(x2), by = c(year, month)？
随着dplyr包，您可以使用summarise_all，summarise_at或summarise_if功能，同时聚合多个变量。对于示例数据集，您可以执行以下操作：
library(dplyr)
# summarising all non-grouping variables
df2 <- df1 %>% group_by(year, month) %>% summarise_all(sum)
# summarising a specific set of non-grouping variables
df2 <- df1 %>% group_by(year, month) %>% summarise_at(vars(x1, x2), sum)
df2 <- df1 %>% group_by(year, month) %>% summarise_at(vars(-date), sum)
# summarising a specific set of non-grouping variables using select_helpers
# see ?select_helpers for more options
df2 <- df1 %>% group_by(year, month) %>% summarise_at(vars(starts_with('x')), sum)
df2 <- df1 %>% group_by(year, month) %>% summarise_at(vars(matches('.*[0-9]')), sum)
# summarising a specific set of non-grouping variables based on condition (class)
df2 <- df1 %>% group_by(year, month) %>% summarise_if(is.numeric, sum)后两个选项的结果：
year month x1 x2
<dbl> <dbl> <dbl> <dbl>
1 2000 1 -73.58134 -92.78595
2 2000 2 -57.81334 -152.36983
3 2000 3 122.68758 153.55243
4 2000 4 450.24980 285.56374
5 2000 5 678.37867 384.42888
6 2000 6 792.68696 530.28694
7 2000 7 908.58795 452.31222
8 2000 8 710.69928 719.35225
9 2000 9 725.06079 914.93687
10 2000 10 770.60304 863.39337
# ... with 14 more rows注：summarise_each被弃用，取而代之的summarise_all，summarise_at和summarise_if。
如我上面的评论中所述，您还可以使用-package中的recast函数reshape2：
library(reshape2)
recast(df1, year + month ~ variable, sum, id.var = c("date", "year", "month"))这将给您相同的结果。
有趣的是，此处未展示base R aggregate的data.frame方法，而是在公式接口上方使用，因此出于完整性考虑：
aggregate(
x = df1[c("x1", "x2")],
by = df1[c("year", "month")],
FUN = sum, na.rm = TRUE
)聚合的data.frame方法的更通用用法：
由于我们提供了
data.frame作为x和list（data.frame也是a list）as by，如果我们需要以动态方式使用它（例如，使用其他列进行汇总和通过汇总非常简单），这将非常有用例如这样：
colsToAggregate <- c("x1")
aggregateBy <- c("year", "month")
dummyaggfun <- function(v, na.rm = TRUE) {
c(sum = sum(v, na.rm = na.rm), mean = mean(v, na.rm = na.rm))
}
aggregate(df1[colsToAggregate], by = df1[aggregateBy], FUN = dummyaggfun)随着devel版本dplyr（版本- ‘0.8.99.9000’），我们也可以使用summarise上多列应用功能across
library(dplyr)
df1 %>%
group_by(year, month) %>%
summarise(across(starts_with('x'), sum))
# A tibble: 24 x 4
# Groups: year [2]
# year month x1 x2
# <dbl> <dbl> <dbl> <dbl>
# 1 2000 1 11.7 52.9
# 2 2000 2 -74.1 126.
# 3 2000 3 -132. 149.
# 4 2000 4 -130. 4.12
# 5 2000 5 -91.6 -55.9
# 6 2000 6 179. 73.7
# 7 2000 7 95.0 409.
# 8 2000 8 255. 283.
# 9 2000 9 489. 331.
#10 2000 10 719. 305.
# … with 14 more rows为了更灵活，更快速地进行数据聚合，请查看CRAN上collap的collapse R包中的功能：
library(collapse)
# Simple aggregation with one function
head(collap(df1, x1 + x2 ~ year + month, fmean))
year month x1 x2
1 2000 1 -1.217984 4.008534
2 2000 2 -1.117777 11.460301
3 2000 3 5.552706 8.621904
4 2000 4 4.238889 22.382953
5 2000 5 3.124566 39.982799
6 2000 6 -1.415203 48.252283
# Customized: Aggregate columns with different functions
head(collap(df1, x1 + x2 ~ year + month,
custom = list(fmean = c("x1", "x2"), fmedian = "x2")))
year month fmean.x1 fmean.x2 fmedian.x2
1 2000 1 -1.217984 4.008534 3.266968
2 2000 2 -1.117777 11.460301 11.563387
3 2000 3 5.552706 8.621904 8.506329
4 2000 4 4.238889 22.382953 20.796205
5 2000 5 3.124566 39.982799 39.919145
6 2000 6 -1.415203 48.252283 48.653926
# You can also apply multiple functions to all columns
head(collap(df1, x1 + x2 ~ year + month, list(fmean, fmin, fmax)))
year month fmean.x1 fmin.x1 fmax.x1 fmean.x2 fmin.x2 fmax.x2
1 2000 1 -1.217984 -4.2460775 1.245649 4.008534 -1.720181 10.47825
2 2000 2 -1.117777 -5.0081858 3.330872 11.460301 9.111287 13.86184
3 2000 3 5.552706 0.1193369 9.464760 8.621904 6.807443 11.54485
4 2000 4 4.238889 0.8723805 8.627637 22.382953 11.515753 31.66365
5 2000 5 3.124566 -1.5985090 7.341478 39.982799 31.957653 46.13732
6 2000 6 -1.415203 -4.6072295 2.655084 48.252283 42.809211 52.31309
# When you do that, you can also return the data in a long format
head(collap(df1, x1 + x2 ~ year + month, list(fmean, fmin, fmax), return = "long"))
Function year month x1 x2
1 fmean 2000 1 -1.217984 4.008534
2 fmean 2000 2 -1.117777 11.460301
3 fmean 2000 3 5.552706 8.621904
4 fmean 2000 4 4.238889 22.382953
5 fmean 2000 5 3.124566 39.982799
6 fmean 2000 6 -1.415203 48.252283
注意：可以将诸如mean, maxetc之类的基本函数与配合使用collap，但是fmean, fmaxetc等是折叠包中提供的基于C ++的分组函数，它们的运行速度显着提高（即大型数据聚合的性能与data.table相同，同时提供了更大的灵活性，并且这些快速分组的函数也可以不带collap）使用。
注意2：collap还支持灵活的多类型数据聚合，您当然可以使用custom参数来完成，但也可以半自动方式将函数应用于数字和非数字列：
# wlddev is a data set of World Bank Indicators provided in the collapse package
head(wlddev)
country iso3c date year decade region income OECD PCGDP LIFEEX GINI ODA
1 Afghanistan AFG 1961-01-01 1960 1960 South Asia Low income FALSE NA 32.292 NA 114440000
2 Afghanistan AFG 1962-01-01 1961 1960 South Asia Low income FALSE NA 32.742 NA 233350000
3 Afghanistan AFG 1963-01-01 1962 1960 South Asia Low income FALSE NA 33.185 NA 114880000
4 Afghanistan AFG 1964-01-01 1963 1960 South Asia Low income FALSE NA 33.624 NA 236450000
5 Afghanistan AFG 1965-01-01 1964 1960 South Asia Low income FALSE NA 34.060 NA 302480000
6 Afghanistan AFG 1966-01-01 1965 1960 South Asia Low income FALSE NA 34.495 NA 370250000
# This aggregates the data, applying the mean to numeric and the statistical mode to categorical columns
head(collap(wlddev, ~ iso3c + decade, FUN = fmean, catFUN = fmode))
country iso3c date year decade region income OECD PCGDP LIFEEX GINI ODA
1 Aruba ABW 1961-01-01 1962.5 1960 Latin America & Caribbean High income FALSE NA 66.58583 NA NA
2 Aruba ABW 1967-01-01 1970.0 1970 Latin America & Caribbean High income FALSE NA 69.14178 NA NA
3 Aruba ABW 1976-01-01 1980.0 1980 Latin America & Caribbean High income FALSE NA 72.17600 NA 33630000
4 Aruba ABW 1987-01-01 1990.0 1990 Latin America & Caribbean High income FALSE 23677.09 73.45356 NA 41563333
5 Aruba ABW 1996-01-01 2000.0 2000 Latin America & Caribbean High income FALSE 26766.93 73.85773 NA 19857000
6 Aruba ABW 2007-01-01 2010.0 2010 Latin America & Caribbean High income FALSE 25238.80 75.01078 NA NA
# Note that by default (argument keep.col.order = TRUE) the column order is also preserved晚会晚了，但是最近发现了另一种获取汇总统计数据的方法。
library(psych)
describe(data)
将输出：每个变量的平均值，最小值，最大值，标准偏差，n，标准误差，峰度，偏度，中位数和范围。
describe不按组进行任何操作……
describe.by(column, group = grouped_column)将值分组
recast功能（也reshape2）集成melt和dcast：功能一气呵成像这样的任务recast(df1, year + month ~ variable, sum, id.var = c("date", "year", "month"))