在R数据帧中没有值的情况下输入0 [英] Input 0 where there is no value in R data frame

问题描述

我想获得一个data.frame，如下所示，但包括每个主题的所有年份。我所做的这个操作按年份计算每个主题的项目数，但是当某年中没有任何项目时，它只是不会为该特定主题创建该行，并且在最终图形中为空白。谁能告诉我如何为没有价值的主题添加计数== 0的缺失年份？

I want to get a data.frame like the one below, but including all years per topic. This one I made counts the number of items by year for each topic but when there is no item in some year, it just doesn't create that row for that particular topic, and it's blank in the final graph. Could anyone please tell me how to add the missing year with Count == 0 for the topics that have no value?

dtd2 <- structure(list(Topic = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 
11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L), .Label = c("Topic 1", 
"Topic 10", "Topic 11", "Topic 12", "Topic 2", "Topic 3", "Topic 4", 
"Topic 5", "Topic 6", "Topic 7", "Topic 8", "Topic 9"), class = "factor"), 
    Year = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 2L, 
    3L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 
    3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 
    9L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 
    8L, 9L, 1L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 6L, 7L, 8L, 
    9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 
    6L, 7L, 8L, 9L, 2L, 3L, 4L, 5L, 6L, 7L, 8L), .Label = c("2011", 
    "2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019"
    ), class = "factor"), Count = c(3L, 3L, 3L, 5L, 5L, 11L, 
    17L, 14L, 4L, 1L, 1L, 4L, 2L, 3L, 9L, 4L, 2L, 1L, 3L, 4L, 
    5L, 18L, 23L, 19L, 15L, 1L, 5L, 6L, 8L, 11L, 17L, 7L, 1L, 
    3L, 6L, 4L, 20L, 21L, 18L, 12L, 3L, 1L, 1L, 2L, 5L, 5L, 11L, 
    5L, 2L, 1L, 1L, 2L, 2L, 5L, 7L, 23L, 9L, 1L, 1L, 2L, 3L, 
    6L, 4L, 9L, 8L, 1L, 1L, 6L, 2L, 3L, 3L, 1L, 3L, 2L, 5L, 7L, 
    11L, 11L, 28L, 11L, 2L, 1L, 2L, 2L, 5L, 6L, 5L, 16L, 3L, 
    4L, 2L, 2L, 7L, 6L, 8L, 6L)), row.names = c(NA, -96L), class = "data.frame")

ggplot(dtd2, aes(x = Year, y = Count, colour = Topic, group = Topic)) + geom_point() + geom_line() + labs(x = "Year", y = NULL, title = "Timeline")

推荐答案

时间序列方法可能是

library(tidyverse)
library(lubridate)
#> 
#> Attaching package: 'lubridate'
#> The following object is masked from 'package:base':
#> 
#>     date
library(tsibble)
#> 
#> Attaching package: 'tsibble'
#> The following objects are masked from 'package:lubridate':
#> 
#>     interval, new_interval
#> The following object is masked from 'package:dplyr':
#> 
#>     id


dtd2 <- structure(list(Topic = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 
  1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
  3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
  5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
  7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 
  10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 
  11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L), .Label = c("Topic 1", 
    "Topic 10", "Topic 11", "Topic 12", "Topic 2", "Topic 3", "Topic 4", 
    "Topic 5", "Topic 6", "Topic 7", "Topic 8", "Topic 9"), class = "factor"), 
  Year = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 2L, 
    3L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 
    3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 
    9L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 
    8L, 9L, 1L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 6L, 7L, 8L, 
    9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 
    6L, 7L, 8L, 9L, 2L, 3L, 4L, 5L, 6L, 7L, 8L), .Label = c("2011", 
      "2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019"
    ), class = "factor"), Count = c(3L, 3L, 3L, 5L, 5L, 11L, 
      17L, 14L, 4L, 1L, 1L, 4L, 2L, 3L, 9L, 4L, 2L, 1L, 3L, 4L, 
      5L, 18L, 23L, 19L, 15L, 1L, 5L, 6L, 8L, 11L, 17L, 7L, 1L, 
      3L, 6L, 4L, 20L, 21L, 18L, 12L, 3L, 1L, 1L, 2L, 5L, 5L, 11L, 
      5L, 2L, 1L, 1L, 2L, 2L, 5L, 7L, 23L, 9L, 1L, 1L, 2L, 3L, 
      6L, 4L, 9L, 8L, 1L, 1L, 6L, 2L, 3L, 3L, 1L, 3L, 2L, 5L, 7L, 
      11L, 11L, 28L, 11L, 2L, 1L, 2L, 2L, 5L, 6L, 5L, 16L, 3L, 
      4L, 2L, 2L, 7L, 6L, 8L, 6L)), row.names = c(NA, -96L), class = "data.frame")
tsibble2 <- dtd2 %>%
  mutate(Year = as_date(str_c(Year,"01",'01'))) %>% 
  as_tsibble(index = Year,key = Topic) %>%
  tsibble::fill_gaps(.full = TRUE) %>%
  group_by_key() %>% 
  index_by(year = Year %>% year) %>% 
  summarise(Count = Count %>% sum(na.rm = T)) %>% 
  as_tibble() %>% 
  mutate(year = year %>% as_factor())

tsibble2 %>% 
  ggplot() +
  aes(x = year,y = Count,color = Topic,group = Topic) +
  geom_line() +
  geom_point()

^{由 reprex软件包（v0.3.0）}

^{Created on 2020-01-08 by the reprex package (v0.3.0)}

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