使用cumsum在ggplot中绘图 | 使用cumsum在ggplot中绘图

本文介绍了使用cumsum在ggplot中绘图的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！

问题描述

我正在尝试使用ggplot2绘制日期列与数字列。

我有一个数据框，我正在尝试与作为中国或不属于中国的国家/地区进行操作，并成功创建了以下与以下链接的数据框：

  is_china<-Confirmed_cases_worldwide％>％
过滤器（国家==中国，类型==已确认）％>％ 
 group_by（国家/地区）％&％;％
变异（cumu_cases =累计（cases））
 
 is_not_china<-Confirmed_cases_worldwide％>％
 filter（country！ =中国，类型==已确认）％&％;％
变异（cumu_cases =累计（例））
 
 is_not_china $ country<-非中国 
 
 china_vs_world<-rbind（is_china，is_not_china）

现在基本上我正在尝试在中国和非中国 $ b $之间绘制带有 cumu_cases 和 date 的折线图b我正在尝试执行以下代码：

  plt_china_vs_world<-ggplot（china_vs_world）+ 
 geom_line（aes（ x = date，y = cumu_cases，group = country， color = country））+ 
 ylab（累计确诊病例）

现在我继续得到的图形如下所示：

不知道为什么会这样，正在尝试转换数据类型并其他方法。
感谢您的帮助，我在下面同时链接了两个csv

注意：此比例尺显示的中国数字很小。

@Edward提到对数刻度会更易于理解

  china_vs_world％>％
 group_by（国家/地区，日期）％&％;％
 summarise（cumu_cases = sum（case））％&％;％ 
 ungroup％&％;％
 mutate（cumu_cases = cumsum（cumu_cases））％>％
 ggplot（）+ 
 geom_line（aes（x = date，y = cumu_cases， group = country，color = country））+ 
 ylab（累计确诊病例）+ 
 scale_y_continuous（trans ='log'）

或者使用 facet_wrap

  china_vs_world％>％
 group_by（国家/日期）％>％
 summarise（cumu_cases = sum （cases））％>％
 ungroup％&％;％
 mutate（cumu_cases = cumsum（cumu_cases））％>％
 ggplot（）+ 
 geom_line（aes（ x = date，y = cumu_cases，group = country，color = country））+ 
 ylab（累计确诊病例）+ 
 facet_wrap（〜country，scales ='free_y'）

数据

  china_vs_world<-read.csv（" https：//raw.githubusercontent.com/ king-sules / Covid / master / china_vs_world.csv，stringsAsFactors = FALSE）
 china_vs_world $ date<-as.Date（china_vs_world $ date）

I am trying to use ggplot2 to plot a date column vs. a numeric column.

I have a dataframe that I am trying to manipulate with country as either china or not china, and successfully created the dataframe linked below with:

is_china <- confirmed_cases_worldwide %>%
  filter(country == "China", type=='confirmed') %>%
  group_by(country) %>%
  mutate(cumu_cases = cumsum(cases))

is_not_china <- confirmed_cases_worldwide %>%
  filter(country != "China", type=='confirmed') %>%
  mutate(cumu_cases = cumsum(cases))

is_not_china$country <- "Not China"

china_vs_world <- rbind(is_china,is_not_china)

Now essentially I am trying to plot a line graph with cumu_cases and date between "china" and "not china"I am trying to execute this code:

plt_china_vs_world <- ggplot(china_vs_world) +
  geom_line(aes(x=date,y=cumu_cases,group=country,color=country)) +
  ylab("Cumulative confirmed cases")

Now I keep getting a graph looking like this:

Don't understand why this is happening, been trying to convert data types and other methods.Any help is appreciated, I linked both csv below

https://github.com/king-sules/Covid

解决方案

The 'date' for other 'country' are repeated because the 'country' is now changed to 'Not China'. It would be either changed in the OP's 'is_not_china' step or do this in 'china_vs_world'

library(ggplot2)
library(dplyr)
china_vs_world %>%
   group_by(country, date) %>%
   summarise(cumu_cases = sum(cases)) %>%
   ungroup %>%
   mutate(cumu_cases = cumsum(cumu_cases)) %>%
   ggplot() +
    geom_line(aes(x=date,y=cumu_cases,group=country,color=country)) +
       ylab("Cumulative confirmed cases")

-output

NOTE: It is the scale that shows the China numbers to be small.

As @Edward mentioned a log scale would make it more easier to understand

china_vs_world %>%
   group_by(country, date) %>%
   summarise(cumu_cases = sum(cases)) %>%
   ungroup %>%
   mutate(cumu_cases = cumsum(cumu_cases)) %>%
   ggplot() +
    geom_line(aes(x=date,y=cumu_cases,group=country,color=country)) +
       ylab("Cumulative confirmed cases") +
    scale_y_continuous(trans='log')

Or with a facet_wrap

china_vs_world %>%
   group_by(country, date) %>%
   summarise(cumu_cases = sum(cases)) %>%
   ungroup %>%
   mutate(cumu_cases = cumsum(cumu_cases)) %>%
  ggplot() +
    geom_line(aes(x=date,y=cumu_cases,group=country,color=country)) +
      ylab("Cumulative confirmed cases") +
    facet_wrap(~ country, scales = 'free_y')

data

china_vs_world <- read.csv("https://raw.githubusercontent.com/king-sules/Covid/master/china_vs_world.csv", stringsAsFactors = FALSE)
china_vs_world$date <- as.Date(china_vs_world$date)

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