基于组中数据框中的数据框中的行数

发布于 2025-01-30 07:39:14 字数 620 浏览 2 评论 0原文

中有一个数据框

  ID   MONTH-YEAR   VALUE
  110   JAN. 2012     1000
  111   JAN. 2012     2000
         .         .
         .         .
  121   FEB. 2012     3000
  131   FEB. 2012     4000
         .           .
         .           .

我在r 连续性并处于休息状态）。我想计算每个一个月年度的行几行，即JAN有多少行。 2012年，2月有多少。 2012年，依此类推。这样的事情：

 MONTH-YEAR   NUMBER OF ROWS
 JAN. 2012     10
 FEB. 2012     13
 MAR. 2012     6
 APR. 2012     9

我试图这样做：

n_row <- nrow(dat1_frame %.% group_by(MONTH-YEAR))

但是它不会产生所需的输出。我该怎么做？

原文

I have a data frame in R like this:

  ID   MONTH-YEAR   VALUE
  110   JAN. 2012     1000
  111   JAN. 2012     2000
         .         .
         .         .
  121   FEB. 2012     3000
  131   FEB. 2012     4000
         .           .
         .           .

So, for each month of each year there are n rows and they can be in any order(mean they all are not in continuity and are at breaks). I want to calculate how many rows are there for each MONTH-YEAR i.e. how many rows are there for JAN. 2012, how many for FEB. 2012 and so on. Something like this:

 MONTH-YEAR   NUMBER OF ROWS
 JAN. 2012     10
 FEB. 2012     13
 MAR. 2012     6
 APR. 2012     9

I tried to do this:

n_row <- nrow(dat1_frame %.% group_by(MONTH-YEAR))

but it does not produce the desired output.How can I do that?

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羁客 2025-02-06 07:39:14

count（） plyr做您想要的工作：

library(plyr)

count(mydf, "MONTH-YEAR")

The count() function in plyr does what you want:

library(plyr)

count(mydf, "MONTH-YEAR")

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纸伞微斜 2025-02-06 07:39:14

这是一个示例，显示table（。）（或更紧密地匹配您所需的输出，data.frame（table（。））做听起来像您是的事注意

请

。

mydf <- structure(list(ID = c(110L, 111L, 121L, 131L, 141L), 
                       MONTH.YEAR = c("JAN. 2012", "JAN. 2012", 
                                      "FEB. 2012", "FEB. 2012", 
                                      "MAR. 2012"), 
                       VALUE = c(1000L, 2000L, 3000L, 4000L, 5000L)), 
                  .Names = c("ID", "MONTH.YEAR", "VALUE"), 
                  class = "data.frame", row.names = c(NA, -5L))

mydf
#    ID MONTH.YEAR VALUE
# 1 110  JAN. 2012  1000
# 2 111  JAN. 2012  2000
# 3 121  FEB. 2012  3000
# 4 131  FEB. 2012  4000
# 5 141  MAR. 2012  5000

table(mydf$MONTH.YEAR)
# 
# FEB. 2012 JAN. 2012 MAR. 2012 
#         2         2         1

data.frame(table(mydf$MONTH.YEAR))
#        Var1 Freq
# 1 FEB. 2012    2
# 2 JAN. 2012    2
# 3 MAR. 2012    1

Here's an example that shows how table(.) (or, more closely matching your desired output, data.frame(table(.)) does what it sounds like you are asking for.

Note also how to share reproducible sample data in a way that others can copy and paste into their session.

Here's the (reproducible) sample data:

mydf <- structure(list(ID = c(110L, 111L, 121L, 131L, 141L), 
                       MONTH.YEAR = c("JAN. 2012", "JAN. 2012", 
                                      "FEB. 2012", "FEB. 2012", 
                                      "MAR. 2012"), 
                       VALUE = c(1000L, 2000L, 3000L, 4000L, 5000L)), 
                  .Names = c("ID", "MONTH.YEAR", "VALUE"), 
                  class = "data.frame", row.names = c(NA, -5L))

mydf
#    ID MONTH.YEAR VALUE
# 1 110  JAN. 2012  1000
# 2 111  JAN. 2012  2000
# 3 121  FEB. 2012  3000
# 4 131  FEB. 2012  4000
# 5 141  MAR. 2012  5000

Here's the calculation of the number of rows per group, in two output display formats:

table(mydf$MONTH.YEAR)
# 
# FEB. 2012 JAN. 2012 MAR. 2012 
#         2         2         1

data.frame(table(mydf$MONTH.YEAR))
#        Var1 Freq
# 1 FEB. 2012    2
# 2 JAN. 2012    2
# 3 MAR. 2012    1

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毁梦 2025-02-06 07:39:14

尝试在dplyr中使用计数函数：

library(dplyr)
dat1_frame %>% 
    count(MONTH.YEAR)

我不确定您是如何作为变量名称的一个月。我的R版本不允许这样的变量名称，因此我用一个月代替了它。

附带说明，您的代码中的错误是dat1_frame％。％group_by（一个月年）没有总结函数将返回原始数据框架而没有任何修改。所以，你想使用

dat1_frame %>%
    group_by(MONTH.YEAR) %>%
    summarise(count=n())

Try using the count function in dplyr:

library(dplyr)
dat1_frame %>% 
    count(MONTH.YEAR)

I am not sure how you got MONTH-YEAR as a variable name. My R version does not allow for such a variable name, so I replaced it with MONTH.YEAR.

As a side note, the mistake in your code was that dat1_frame %.% group_by(MONTH-YEAR) without a summarise function returns the original data frame without any modifications. So, you want to use

dat1_frame %>%
    group_by(MONTH.YEAR) %>%
    summarise(count=n())

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浅笑依然 2025-02-06 07:39:14

使用Ananda虚拟升级的示例数据集，这是一个示例，使用gengregate（），它是core R. core R. gengregate（）仅需要一些东西来计数为月份的不同值。在这种情况下，我使用value作为计数的东西：

aggregate(cbind(count = VALUE) ~ MONTH.YEAR, 
          data = mydf, 
          FUN = function(x){NROW(x)})

它为您提供了。

  MONTH.YEAR count
1  FEB. 2012     2
2  JAN. 2012     2
3  MAR. 2012     1

Using the example data set that Ananda dummied up, here's an example using aggregate(), which is part of core R. aggregate() just needs something to count as function of the different values of MONTH-YEAR. In this case, I used VALUE as the thing to count:

aggregate(cbind(count = VALUE) ~ MONTH.YEAR, 
          data = mydf, 
          FUN = function(x){NROW(x)})

which gives you..

  MONTH.YEAR count
1  FEB. 2012     2
2  JAN. 2012     2
3  MAR. 2012     1

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神经暖 2025-02-06 07:39:14

只是为了完成数据。表解决方案：

library(data.table)

mydf <- structure(list(ID = c(110L, 111L, 121L, 131L, 141L), 
                       MONTH.YEAR = c("JAN. 2012", "JAN. 2012", 
                                      "FEB. 2012", "FEB. 2012", 
                                      "MAR. 2012"), 
                       VALUE = c(1000L, 2000L, 3000L, 4000L, 5000L)), 
                  .Names = c("ID", "MONTH.YEAR", "VALUE"), 
                  class = "data.frame", row.names = c(NA, -5L))

setDT(mydf)
mydf[, .(`Number of rows` = .N), by = MONTH.YEAR]

   MONTH.YEAR Number of rows
1:  JAN. 2012              2
2:  FEB. 2012              2
3:  MAR. 2012              1

Just for completion the data.table solution:

library(data.table)

mydf <- structure(list(ID = c(110L, 111L, 121L, 131L, 141L), 
                       MONTH.YEAR = c("JAN. 2012", "JAN. 2012", 
                                      "FEB. 2012", "FEB. 2012", 
                                      "MAR. 2012"), 
                       VALUE = c(1000L, 2000L, 3000L, 4000L, 5000L)), 
                  .Names = c("ID", "MONTH.YEAR", "VALUE"), 
                  class = "data.frame", row.names = c(NA, -5L))

setDT(mydf)
mydf[, .(`Number of rows` = .N), by = MONTH.YEAR]

   MONTH.YEAR Number of rows
1:  JAN. 2012              2
2:  FEB. 2012              2
3:  MAR. 2012              1

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苦行僧 2025-02-06 07:39:14

library(plyr)
ddply(data, .(MONTH-YEAR), nrow)

如果“一个月年度”是一个变量，这将为您提供答案。
首先，尝试唯一（数据$月），看看它是否返回唯一值（无重复）。

然后，在简单的拆分竞争中，将返回您要寻找的内容。

library(plyr)
ddply(data, .(MONTH-YEAR), nrow)

This will give you the answer, if "MONTH-YEAR" is a variable.
First, try unique(data$MONTH-YEAR) and see if it returns unique values (no duplicates).

Then above simple split-apply-combine will return what you are looking for.

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甜味拾荒者 2025-02-06 07:39:14

这是使用汇总按组计数行的另一种方法：

my.data <- read.table(text = '
    month.year    my.cov
      Jan.2000     apple
      Jan.2000      pear
      Jan.2000     peach
      Jan.2001     apple
      Jan.2001     peach
      Feb.2002      pear
', header = TRUE, stringsAsFactors = FALSE, na.strings = NA)

rows.per.group  <- aggregate(rep(1, length(my.data$month.year)),
                             by=list(my.data$month.year), sum)
rows.per.group

#    Group.1 x
# 1 Feb.2002 1
# 2 Jan.2000 3
# 3 Jan.2001 2

Here is another way of using aggregate to count rows by group:

my.data <- read.table(text = '
    month.year    my.cov
      Jan.2000     apple
      Jan.2000      pear
      Jan.2000     peach
      Jan.2001     apple
      Jan.2001     peach
      Feb.2002      pear
', header = TRUE, stringsAsFactors = FALSE, na.strings = NA)

rows.per.group  <- aggregate(rep(1, length(my.data$month.year)),
                             by=list(my.data$month.year), sum)
rows.per.group

#    Group.1 x
# 1 Feb.2002 1
# 2 Jan.2000 3
# 3 Jan.2001 2

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阪姬 2025-02-06 07:39:14

假设我们有以下DF_DATA

> df_data
   ID MONTH-YEAR VALUE
1 110   JAN.2012  1000
2 111   JAN.2012  2000
3 121   FEB.2012  3000
4 131   FEB.2012  4000
5 141   MAR.2012  5000

框

> summary(df_data假设我们有以下DF_DATA
> df_data
   ID MONTH-YEAR VALUE
1 110   JAN.2012  1000
2 111   JAN.2012  2000
3 121   FEB.2012  3000
4 131   FEB.2012  4000
5 141   MAR.2012  5000

框
MONTH-YEAR`)

FEB.2012 JAN.2012 MAR.2012 
   2        2        1

数据 noreferrer“>
摘要函数将从因子参数创建一个表，然后为结果创建一个向量（第7行＆amp; 8）

Suppose we have a df_data data frame as below

> df_data
   ID MONTH-YEAR VALUE
1 110   JAN.2012  1000
2 111   JAN.2012  2000
3 121   FEB.2012  3000
4 131   FEB.2012  4000
5 141   MAR.2012  5000

To count number of rows in df_data grouped by MONTH-YEAR column, you can use:

> summary(df_dataSuppose we have a df_data data frame as below
> df_data
   ID MONTH-YEAR VALUE
1 110   JAN.2012  1000
2 111   JAN.2012  2000
3 121   FEB.2012  3000
4 131   FEB.2012  4000
5 141   MAR.2012  5000

To count number of rows in df_data grouped by MONTH-YEAR column, you can use:
MONTH-YEAR`)

FEB.2012 JAN.2012 MAR.2012 
   2        2        1

summary function will create a table from the factor argument, then create a vector for the result (line 7 & 8)