Question

以下问题让我头痛不已。

我有一个看起来像这样的大数据集。

Name   Date         C1   C2    C3    C4    C5    C6   C7
 A     2008-01-03   100
 A     2008-01-05   NA
 A     2008-01-07   120
 A     2008-02-03   NA
 A     2008-03-10   50
 A     2008-07-14   70
 A     2008-07-15   NA
 A     2009-01-03   40
 A     2009-01-05   NA
 A     2010-01-07   NA
 A     2010-03-03   30
 A     2010-03-10   20
 A     2011-07-14   10
 A     2011-07-15   NA
 B     2008-01-03   NA
 B     2008-01-05   5
 B     2008-01-07   3
 B     2008-02-03   11
 B     2008-03-10   13
 B     2008-07-14   ....

如您所见，我的观察中有很多NA。其他列看起来相似，数据集有+100.000行。所以它的巨大。

我想要做的是，我希望以下列方式汇总我的数据。例如C1：我想在2000-01到2012-12之间的时间范围内为每个姓名以及每个月和每个月建立月平均值。

月平均值应使用每个月的日期计算得出。

完成计算后，我的数据集应如下所示。

Name   Date         C1          C2    C3    C4    C5    C6   C7
 A     2008-01  monthly average
 A     2008-02  monthly average
 A     2008-03  monthly average
 A     2008-04  monthly average
 A     2008-05  monthly average
 A     2008-06  monthly average
 A     2008-07  monthly average
 A     2008-08  monthly average
 A     2008-09  monthly average
 A     2008-10  monthly average
 A     2008-11  monthly average
 A     2008-12  monthly average
 A     2009-01  monthly average

 B     2008-01  monthly average
 B     2008-02  monthly average
 B     2008-03  monthly average
 B     2008-04  monthly average
 B     2008-05  monthly average
 B     2008-06   ....

因此，我的输出数据应显示每年每个月的每个名称。如果月份只有NA值，或者它们是某个月的月平均值，则值为NA。

例如：

   Name    Date       C1
   A     2008-01-03   100
   A     2008-01-05   NA
   A     2008-01-07   120

我们期待：

   Name    Date       C1
   A     2008-01   (100+120)/2 = 110

例如：

   Name    Date       C1
   A     2008-01-03   NA
   A     2008-01-05   NA
   A     2008-01-07   NA

我们期待：

    Name    Date       C1
    A     2008-01   NA

例如：

   Name    Date       C1
   A     2008-01-03   100
   A     2008-01-05   50
   A     2008-01-07   120

我们期待：

    Name    Date       C1
    A     2008-01    (100+50+120)/3 = 90

由于我相对较新，我不知道如何解决这个问题，我希望找到一个可以解决这个问题的人，并告诉我这样的事情是如何解决的。我非常感谢您的支持：）

Answer 1

library(dplyr)

#generating sample data 

data <- data.frame(Name = c(rep("A",25), rep("B",50)),
                   Date = seq(as.Date("2018-01-01"), as.Date("2020-01-12"), by = 10),
                   C1 = rep(c(100,NA,NA,NA,NA,500,320,102,412,NA,200,NA,145,800,230),5))

#grouping by Name and Month and summarizing mean of values  

data %>% 
group_by(Name, month = cut(Date, "month")) %>% 
summarise(C1 = mean(C1, na.rm = TRUE)) %>% mutate(C1 = ifelse(is.nan(C1),NA,C1))

Answer 2

您可以dplyr::summarise_all计算所有列C1，C2等的平均值。

group_by和Name上的第一个YearMon并取消选择Date列，然后使用summarise_all

library(dplyr)
library(lubridate)

#Added C2 to demonstrate calculation for multiple columns in one go.
df %>% mutate(Date = ymd(Date), C2 = C1*2) %>%  
  group_by(Name, YearMon = format(Date, "%Y-%m")) %>%
  select(-Date) %>%
  summarise_all("mean", na.rm=TRUE)


#OR - Use summarise_at and calculate mean for all columns starting with 'C'
df %>% mutate(Date = ymd(Date), C2 = C1*2) %>% 
  group_by(Name, YearMon = format(Date, "%Y-%m")) %>%
  summarise_at(vars(starts_with("C")), mean, na.rm=TRUE)

# A tibble: 12 x 4
# Groups: Name [?]
   Name  YearMon     C1     C2
   <chr> <chr>    <dbl>  <dbl>
 1 A     2008-01 110    220   
 2 A     2008-02 NaN    NaN   
 3 A     2008-03  50.0  100   
 4 A     2008-07  70.0  140   
 5 A     2009-01  40.0   80.0 
 6 A     2010-01 NaN    NaN   
 7 A     2010-03  25.0   50.0 
 8 A     2011-07  10.0   20.0 
 9 B     2008-01   4.00   8.00
10 B     2008-02  11.0   22.0 
11 B     2008-03  13.0   26.0 
12 B     2008-07 NaN    NaN

数据：

df <- read.table(text = "Name Date C1 A 2008-01-03 100 A 2008-01-05 NA A 2008-01-07 120 A 2008-02-03 NA A 2008-03-10 50 A 2008-07-14 70 A 2008-07-15 NA A 2009-01-03 40 A 2009-01-05 NA A 2010-01-07 NA A 2010-03-03 30 A 2010-03-10 20 A 2011-07-14 10 A 2011-07-15 NA B 2008-01-03 NA B 2008-01-05 5 B 2008-01-07 3 B 2008-02-03 11 B 2008-03-10 13 B 2008-07-14 NA", header = TRUE, stringsAsFactors = FALSE)

建立特定时间段的平均值

2 个答案: