如何绘制单词频率与时间的关系，时间变量在 R 中分为月/年和年答案

【问题标题】：How to plot frequencies of words vs. time, with time variable grouped into month/year and year in R如何绘制单词频率与时间的关系，时间变量在 R 中分为月/年和年
【发布时间】：2016-01-19 06:12:46
【问题描述】：

我有一个文档术语矩阵，频率超过 600 个单词，每个频率值都有一个对应的日期 (mm/dd/yyyy)：

     > head(mydata3,3)
  Claim.Number  Note.Date  LOSSDATE DATEREPORTED
1       106810  7/10/1998 12/9/1997   12/29/1997
2       106810  7/21/1998 12/9/1997   12/29/1997
3       106810 10/21/1999 12/9/1997   12/29/1997
  DATEENTERED Row Topic absenc abus academ access
1    1/5/1998   3     4      0    0      0      0
2    1/5/1998   4     2      0    0      0      0
3    1/5/1998   8    11      0    0      0      0
  accid accommod account accus act action activ add
1     0        0       0     0   0      0     0   0
2     0        0       0     0   0      0     0   0
3     0        0       0     0   0      0     0   0
  addit addl adequ adjust administr admiss advanc
1     0    0     0      0         0      0      0
2     0    0     0      0         0      0      0
3     0    0     0      0         0      0      0
  advers advic african age agenc agreement aid ambul
1      0     0       0   0     0         0   0     0
2      0     0       0   0     0         0   0     0
3      0     0       0   0     0         0   0     0
  amount analysi ankl answer anticip appeal appel
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2      0       0    0      0       0      2     0
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  appli applic appoint appropri approv approxim arbitr
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3    0        0    1   0      0     0    0       0
  assert assess assist athlet attach attent audit auto
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  discharg disciplin disciplinari discoveri discrimin
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  discuss dismiss disput distress district doc docket
1       0       0      0        0        0   0      0
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1      0        0    0    0    0     0     0     0
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1      0    0   0       0    0    0    0      0   0
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  els email emot employ employe encourag end endors
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3   0     0    0      1       2        0   1      0
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  form formal former forward fractur free fund futur
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  group hand happi harass head health hear held higher
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  ice identifi immedi immun impact import impress
1   0        0      0     0      0      0       0
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3   0        0      0     0      0      0       0
  improv inappropri inclin incur indemn individu injur
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  injuri inquir inquiri inspect instruct intent
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  interest intern invoic job joint judg judgment juri
1        0      0      0   0     0    0        0    0
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3        0      1      0   0     0    0        0    0
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1         0   0    0        0     0    0    0      0
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3         0   0    0        0     0    0    0      0
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1   0      0     0     0    0   0     0      1     0
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3   0      0     0     0    0   0     0      0     0
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1      0    0    0     0     0    0     0     0    0
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3      0    0    0     0     0    0     0     0    0
  loss lost low mail mainten major male manag materi
1    0    0   0    0       0     0    0     0      0
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  open oper opinion opportun oppos opposit oral order
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  partner pass pay payment pend perman personnel petit
1       0    1   0       0    0      0         0     0
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  phone photo physic physician pictur plan player
1     0     0      0         0      0    0      0
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  plead poa polic polici poor postpon potenti practic
1     0   0     0      0    0       0       0       0
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3     0   0     0      0    0       0       0       0
  preliminari premis prepar pres presid press pressur
1           0      0      0    0      0     0       0
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  prevail prevent primari privat proceed product
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  proper properti propos protect provis provost pull
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  punit pursu push qualifi quick quiet quit race rais
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  rang rate reach recal receipt recov recoveri rediari
1    0    0     0     0       0     0        0       0
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  wrote xbocx xdolx ximex xmsjx xnpcx xoopx xprosex
1     0     0     0     0     0     0     0       0
2     0     0     0     0     0     0     0       0
3     1     0     0     0     0     0     0       0
  xsolx
1     0
2     0
3     0

我正在尝试按月/年和年对频率值进行分组。例如，对于“上诉”一词，不是在 1998 年 1 月 5 日出现 2 次，在 1998 年 1 月 5 日出现另一次，我想在 1998 年出现 3 次，然后也出现 3 次（假设在 1998 年的剩余时间里没有更多的命中）。然后我想绘制每月/每年与每月/每年的频率，以及每年与每年的频率。

我尝试使用以下代码按月/年分组：

df %>%
      mutate(month_year = format(date, "%Y/%m")) %>%
      group_by(month_year) %>%
      summarise(total = sum(vocabfreq))

其中 value 是原始数据集中具有单词频率的所有列。另一个问题是我的数据集非常大，我很难在一个显示独特特征的图表上绘制多个系列。

【问题讨论】：

标签： r date plot grouping

【解决方案1】：

您应该使用summarise_each 而不是summarise。顺便说一句，我正在使用@DunderChief 的代码来生成数据。谢谢你。

dat <- data.frame(date=c('7/10/2014', '7/10/2014', '7/11/2014', '8/05/2015', '9/21/2015'),
              word1= c(1,2,1, 4, 3), word2=c(3, 10, 1, 2, 4))
library(dplyr)

dat %>%
  mutate(date = as.Date(date, format='%m/%d/%Y')) %>%
  group_by(date) %>%
  summarise_each(funs(sum(.)))

【讨论】：

当我在我的数据集上尝试这个时，我得到一个错误：sum not defined for "Date" objects
@Learning_R 哦，原因是您的数据文件中有其他日期列，而. 表示除分组列之外的每一列。你不能在那些 Date 对象上使用sum
@Learning_R 您应该将所有日期列放入 group_by 或想一个好方法来组织您的日期列。

【解决方案2】：

xts 方法：

library(xts)
dat <- data.frame(date=c('7/10/2014', '7/10/2014', '7/11/2014', '8/05/2015', '9/21/2015'),
                  word1= c(1,2,1, 4, 3), word2=c(3, 10, 1, 2, 4))
dates <- as.POSIXct(dat$date, format='%m/%d/%Y')
dat.xts <- xts(subset(dat, select= -date), order.by=dates)
apply.daily(dat.xts, colSums)
apply.monthly(dat.xts, colSums)

【讨论】：

当我尝试这个时，日期列不包含在数据框中？
它不是一个data.frame，它是一个xts对象。可以通过index(dat.xts) 检索日期。这是处理日期数据的一种更有效的方式。
另外，在您的问题中放置一个可重现的示例会很好，这样我们就可以使用您的数据为您提供示例。见?dput