汇总/汇总每组的多个变量（例如总和、平均值）

是的，在你的 中，你可以对要聚合的数值变量：formulacbind

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

---

请参见 ，参数和示例。?aggregateformula

这个功能从何而来？year()

您还可以将该包用于此任务：reshape2

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

使用快速的包（对较大的数据集有用）data.table

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 软件包

require(plyr)
df2 <- ddply(df1, c("year", "month"), function(x) colSums(x[c("x1", "x2")]))

使用 Hmisc 包中的 summarize（） （不过，在我的示例中，列标题很混乱）

# 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))

使用 dplyr 包，您可以使用 across（） 使用 tidyselect 语言聚合多个变量。对于示例数据集，您可以按如下方式执行此操作：

library(dplyr)
set.seed(13)

# summarising all non-grouping variables
df1 %>% group_by(year, month) %>% summarise(across(everything(), n_distinct))

# summarising a specific set of non-grouping variables
df1 %>% group_by(year, month) %>% summarise(across(x1:x2, sum))
df1 %>% group_by(year, month) %>% summarise(across(c(x1, x2), sum))
df1 %>% group_by(year, month) %>% summarise(across(-date, sum))

# summarising a specific set of non-grouping variables using selection helpers:
df1 %>% group_by(year, month) %>% summarise(across(starts_with('x'), sum))
df1 %>% group_by(year, month) %>% summarise(across(matches('.*[0-9]'), sum))

# summarising a specific set of non-grouping variables based on condition (class)
df1 %>% group_by(year, month) %>% summarise(across(where(is.numeric), sum))

除第一个结果外，所有结果都为：

# A tibble: 24 × 4
# Groups:   year [2]
    year month     x1    x2
   <dbl> <dbl>  <dbl> <dbl>
 1  2000     1  131.   27.4
 2  2000     2   44.8 155. 
 3  2000     3   60.7 207. 
 4  2000     4  -11.5 379. 
 5  2000     5   64.0 441. 
 6  2000     6  -16.5 517. 
 7  2000     7  210.  530. 
 8  2000     8  112.  573. 
 9  2000     9 -129.  347. 
10  2000    10 -165.  444. 
# … with 14 more rows

您还可以将多个函数应用于所选列：

df1 %>% 
  group_by(year, month) %>% 
  summarise(across(x1:x2, list(sum = sum, avg = mean)))

# A tibble: 24 × 6
# Groups:   year [2]
    year month x1_sum x1_avg x2_sum x2_avg
   <dbl> <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
 1  2000     1  131.   4.24    27.4  0.884
 2  2000     2   44.8  1.54   155.   5.34 
 3  2000     3   60.7  1.96   207.   6.69 
 4  2000     4  -11.5 -0.385  379.  12.6  
 5  2000     5   64.0  2.06   441.  14.2  
 6  2000     6  -16.5 -0.550  517.  17.2  
 7  2000     7  210.   6.76   530.  17.1  
 8  2000     8  112.   3.60   573.  18.5  
 9  2000     9 -129.  -4.30   347.  11.6  
10  2000    10 -165.  -5.33   444.  14.3  
# … with 14 more rows

最后的几点说明：

• 默认情况下，删除最后一个分组级别，因此上述所有示例仍将按 分组。要删除所有分组，您可以添加呼叫，或在呼叫中设置。summarise()yearungroup().groups = "drop"summarise()
• 从 dplyr 1.1.0 开始，您也可以使用 .by 参数为汇总操作指定分组，例如，df1 %>% summarise(across(c(x1, x2), sum), .by = c(year, month))
• across()也适用于其他 dplyr 动词，例如 和 .mutate()reframe()
• 在引入 dplyr 1.0.0 之前，这些类型的操作是通过 、 、 和（甚至更早）完成的。这些现在已被取代或弃用，取而代之的是 .across()summarise_all()summarise_at()summarise_if()summarise_each()across()

晚会，但最近找到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、标准差、n、标准误差、峰度、偏度、中位数和范围。

有趣的是，这里没有展示基础 R 的方法，上面使用了公式接口，所以为了完整起见：aggregatedata.frame

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：

由于我们提供了一个

• data.frameas 和x
• a（也是 a ）作为，如果我们需要以动态方式使用它，这将非常有用，例如，使用其他列进行聚合和聚合依据非常简单listdata.framelistby
• 还具有定制的聚合功能

例如，像这样：

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)

使用版本 >= ，我们还可以使用dplyr1.0.0summariseacross

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

有关更灵活、更快速的数据聚合方法，请查看 CRAN 上提供的折叠 R 包中的函数：collap

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：您可以将 etc. 与 一起使用基本函数，但 etc. 是基于 C++ 的分组函数，在折叠包中提供，它们的速度要快得多（即在大型数据聚合上的性能与 data.table 相同，同时提供了更大的灵活性，并且这些快速分组函数也可以在没有 的情况下使用）。mean, maxcollapfmean, fmaxcollap

注意 2：还支持灵活的多类型数据聚合，您当然可以使用参数来完成，但您也可以以半自动的方式将函数应用于数值和非数值列：collapcustom

# 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

下面是总结多列的另一种方法，当函数需要更多参数时特别有用。您可以通过以下方式选择所有列，也可以选择列的子集，如 。everything()any_of(c("a", "b"))

library(dplyr)
# toy data
df <- tibble(a = sample(c(NA, 5:7), 30, replace = TRUE), 
             b = sample(c(NA, 1:5), 30, replace = TRUE), 
             c = sample(1:5, 30, replace = TRUE), 
             grp = sample(1:3, 30, replace = TRUE))
df
#> # A tibble: 30 × 4
#>        a     b     c   grp
#>    <int> <int> <int> <int>
#>  1     7     1     3     1
#>  2     7     4     4     2
#>  3     5     1     3     3
#>  4     7    NA     3     2
#>  5     7     2     5     2
#>  6     7     4     4     2
#>  7     7    NA     3     3
#>  8    NA     5     4     1
#>  9     5     1     1     2
#> 10    NA     3     1     2
#> # … with 20 more rows
df %>% 
  group_by(grp) %>%
  summarise(across(everything(), 
                   list(mean = ~mean(., na.rm = TRUE),
                        q75 = ~quantile(., probs = .75, na.rm = TRUE))))
#> # A tibble: 3 × 7
#>     grp a_mean a_q75 b_mean b_q75 c_mean c_q75
#>   <int>  <dbl> <dbl>  <dbl> <dbl>  <dbl> <dbl>
#> 1     1   6.6      7   2.88  4.25   3        4
#> 2     2   6.33     7   2.62  3.25   2.9      4
#> 3     3   5.78     6   3.33  4      3.09     4

更新的解决方案：由于 ，您可以使用 .by in 进行内联临时分组（在计算后自动进行分组）。dplyrdplyr 1.1.0summariseungroup

使用 （available from ） 允许同时对多个列使用相同的函数。acrossdplyr 1.0.0

library(dplyr)
df1 %>%
  summarise(across(starts_with('x'), sum), .by = c(year, month))

# A tibble: 24 x 4
#    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