-
Notifications
You must be signed in to change notification settings - Fork 21
/
Copy pathREADME.md
409 lines (314 loc) · 11.5 KB
/
README.md
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
ncovr: Read and process nCoV data 新型冠状病毒数据获取和可视化
================
2020-03-16
这是一个 R 语言包，使用教程详见 <https://openr.pzhao.org/zh/tags/ncovr/>。这里是个简介。
ncovr 包是方便 R 用户获取新型冠状病毒（2019-nCoV）数据而开发的，后续增添了数据处理、建模、可视化等功能。
## 数据获取途径
ncovr 包获取数据的主要途径是
[BlankerL/DXY-2019-nCoV-Crawler](https://github.com/BlankerL/DXY-2019-nCoV-Crawler)。这个项目提供了
api 接口和 csv 文件。为了减轻 api 的流量压力， ncovr 每天将每天自动从这个 api 读一次数据，保存成 R 语言直接读取的
.RDS 格式，方便 R 语言用户调用。详见下面的示例。
## 安装
1. 安装 R。在 [CRAN](http://cran.r-project.org) 上选择适合你操作系统的安装包来安装。
2. 安装 remotes 包：`install.packages('remotes')`
3. 安装 ncovr 包：`remotes::install_github('pzhaonet/ncovr')`
## 获取数据
``` r
# Sys.setlocale('LC_CTYPE', 'Chinese') # windows 用户设置中文环境
require("ncovr")
```
## Loading required package: ncovr
``` r
require("leafletCN")
```
## Loading required package: leafletCN
``` r
ncov <- get_ncov() # 读取 RDS数据（推荐）国内从github读取数据不稳定!
# get_ncov(method = 'csv') # 从 csv 文件读取（推荐）
# get_ncov(method = 'api') # 从 api 接口读取
```
另外提供了下载数据的函数`get_ncov2()`，定义新的 ncov 类(实际也是data frame)，提供了`subset()`方法,
用于提取 ncov 子集，参数可以是省份、china或者world
``` r
ncov2 <- get_ncov2()
ncov2
```
## All COVID 2019 Data
## Updated at 2020-03-16 15:44:16
## From https://github.com/yiluheihei/nCoV-2019-Data
``` r
# 湖北 ncov, 按市统计
hubei_ncov <- ncov2["湖北"]
hubei_ncov
```
## Hubei COVID 2019 Data
## Updated at 2020-03-16 13:54:01
## From https://github.com/yiluheihei/nCoV-2019-Data
``` r
head(data.frame(hubei_ncov), 5)
```
## cityEnglishName cityName provinceName provinceShortName provinceEnglishName
## 1 Wuhan 武汉 湖北省 湖北 Hubei
## 2 Xiaogan 孝感 湖北省 湖北 Hubei
## 3 Ezhou 鄂州 湖北省 湖北 Hubei
## 4 Suizhou 随州 湖北省 湖北 Hubei
## 5 Jingzhou 荆州 湖北省 湖北 Hubei
## currentConfirmedCount confirmedCount suspectedCount curedCount deadCount
## 1 9102 50003 0 38432 2469
## 2 125 3518 0 3266 127
## 3 62 1394 0 1275 57
## 4 41 1307 0 1221 45
## 5 38 1580 0 1492 50
## updateTime
## 1 2020-03-16 13:54:01
## 2 2020-03-16 13:54:01
## 3 2020-03-16 13:54:01
## 4 2020-03-16 13:54:01
## 5 2020-03-16 13:54:01
``` r
# china，按省统计
china_ncov <- ncov2["china"]
head(data.frame(china_ncov), 5)
```
## provinceName provinceShortName provinceEnglishName currentConfirmedCount
## 1 中国 中国 China 9951
## 2 湖北省 湖北 Hubei 9557
## 3 辽宁省 辽宁 Liaoning 9
## 4 广西壮族自治区 广西 Guangxi 2
## 5 海南省 海南 Hainan 1
## confirmedCount suspectedCount curedCount deadCount updateTime
## 1 81099 0 67930 3218 2020-03-16 13:54:38
## 2 67798 0 55142 3099 2020-03-16 13:54:01
## 3 125 0 115 1 2020-03-16 13:54:01
## 4 252 0 248 2 2020-03-16 13:49:01
## 5 168 0 161 6 2020-03-16 13:49:01
``` r
# world， 按国家统计
world_ncov <- ncov2["world"]
```
## Parsed with column specification:
## cols(
## countryName = col_character(),
## countryEnglishName = col_character()
## )
``` r
head(data.frame(world_ncov), 5)
```
## countryEnglishName countryName currentConfirmedCount confirmedCount
## 1 Afghanistan 阿富汗 16 16
## 2 Albania 阿尔巴尼亚 41 42
## 3 Algeria 阿尔及利亚 41 54
## 4 Andorra 安道尔 1 2
## 5 Antigua and Barbuda 安提瓜和巴布达 1 1
## suspectedCount curedCount deadCount updateTime
## 1 0 0 0 2020-03-16 06:53:45
## 2 0 0 1 2020-03-16 06:53:45
## 3 0 10 3 2020-03-16 06:53:45
## 4 0 1 0 2020-03-16 06:53:45
## 5 0 0 0 2020-03-16 06:53:45
## 国家地图
按省级显示
``` r
plot_map(ncov$area)
```
<!-- -->
``` r
# log scale
plot_map(ncov$area, scale = "log")
```
<!-- -->
更进一步使用`plot_china_map()`可通过设置`bins`参数控制如何分组以填充不同的颜色，
自动把ncov为0的地区（包括南海驻岛）填充为白色
``` r
plot_china_map(
china_ncov,
bins = c(1, 100, 500, 1000, 10000),
legend_position = "bottomleft"
)
```
<!-- -->
按城市显示
``` r
plot_map(ncov$area, method = "city", scale = "log")
```
<!-- -->
ggplot
``` r
library(ggplot2)
ncov$area$date <- as.Date(ncovr:::conv_time(ncov$area$updateTime))
choose_date <- "2020-02-10"
x <- ncov$area[ncov$area$date <= as.Date(choose_date), ]
x <- x[!duplicated(x$provinceName), ]
plot_ggmap(x)
```
## Parsed with column specification:
## cols(
## province = col_character(),
## city = col_character(),
## long = col_double(),
## lat = col_double()
## )
<!-- -->
## 省疫情图
湖北省疫情图
``` r
# plot_province_map(ncov2, "湖北省")，或
plot_province_map(hubei_ncov, "湖北省", bins = c(1, 100, 200, 500, 1000, 10000))
```
<!-- -->
或直接基于ncov作图，无需提前取各省ncov数据，天津疫情图
``` r
plot_province_map(ncov2, "天津市", bins = c(1, 10, 20, 50))
```
<!-- -->
## 世界地图:各国疫情图
``` r
ncov$area$date <- as.Date(ncovr:::conv_time(ncov$area$updateTime))
ncov$area <- ncov$area[rev(order(ncov$area$date)), ]
ncov_date <- as.character(Sys.Date())
y <- ncov$area[ncov$area$date <= as.Date(ncov_date), ]
y <- y[!duplicated(y$provinceName), ]
countryname <- data.frame(
ncovr = c("United Kiongdom", "United States of America",
"New Zealand", "Kampuchea (Cambodia )"
),
leafletNC = c("UnitedKingdom", "UnitedStates",
"NewZealand", "Cambodia"
),
stringsAsFactors = FALSE
)
x <- data.frame(
countryEnglishName = y$countryEnglishName,
countryName = y$countryName,
confirmedCount = y$confirmedCount,
stringsAsFactors = FALSE
)
loc <- which(x$countryEnglishName %in% countryname$ncovr)
x$countryEnglishName[loc] <- countryname$leafletNC[
match(x$countryEnglishName[loc], countryname$ncovr)
]
x$countryEnglishName2 = x$countryEnglishName # for taiwan
x_other <- x[!is.na(x$countryEnglishName) & x$countryEnglishName != 'China', ]
x_china <- data.frame(
countryEnglishName = 'China',
countryName = unique(x[!is.na(x$countryEnglishName) & x$countryEnglishName == 'China', 'countryName']),
confirmedCount = sum(x[!is.na(x$countryEnglishName) & x$countryEnglishName == 'China', 'confirmedCount']),
countryEnglishName2 = 'China'
)
x_taiwan <- x_china
x_taiwan$countryEnglishName2 = "Taiwan"
x <- rbind(x_other, x_china, x_taiwan)
plot_map(
x = x,
key = "confirmedCount",
scale = "log",
method = 'country',
legend_title = paste0("Cnfrm 确诊"),
filter = '待明确地区'
)
```
## New names:
## * `` -> ...76
<!-- -->
更进一步
``` r
plot_world_map(world_ncov)
```
## Parsed with column specification:
## cols(
## name = col_character(),
## name_zh = col_character()
## )
<!-- -->
## ts
``` r
x_ts <- ncov$area[, c('countryEnglishName', 'countryName', 'date', 'confirmedCount', 'curedCount', 'deadCount')] %>%
dplyr::group_by(countryEnglishName, date) %>%
dplyr::summarise(
confirmed = max(confirmedCount),
cured = max(curedCount),
dead = max(deadCount)) %>%
dplyr::ungroup() %>%
dplyr::filter(!is.na(countryEnglishName) & !countryEnglishName == 'China') %>%
as.data.frame()
loc <- which(x_ts$countryEnglishName %in% countryname$ncovr)
x_ts$countryEnglishName[loc] <-
countryname$leafletNC[
match(x_ts$countryEnglishName[loc], countryname$ncovr)
]
plot_ts(
x_ts,
area = "Italy",
area_col = "countryEnglishName",
date_col = "date",
ts_col = c("confirmed", "cured", "dead")
)
```
<!-- -->
## 国外疫情图
### 韩国疫情图
``` r
korea_ncov <- get_foreign_ncov("韩国")
plot_foreign_map(korea_ncov, "korea")
```
## Parsed with column specification:
## cols(
## name = col_character(),
## name_zh = col_character(),
## provinceName = col_character(),
## provinceEnglishName = col_character()
## )
<!-- -->
### 日本疫情图
``` r
jp_ncov <- get_foreign_ncov("日本")
plot_foreign_map(jp_ncov, "japan")
```
## Parsed with column specification:
## cols(
## name = col_character(),
## name_zh = col_character(),
## provinceName = col_character(),
## provinceEnglishName = col_character()
## )
<!-- -->
### 伊朗疫情图
``` r
iran_ncov <- get_foreign_ncov("伊朗")
plot_foreign_map(iran_ncov, "iran")
```
## Parsed with column specification:
## cols(
## name = col_character(),
## name_zh = col_character(),
## provinceName = col_character(),
## provinceEnglishName = col_character()
## )
<!-- -->
### 意大利疫情图
``` r
italy_ncov <- get_foreign_ncov("意大利")
plot_foreign_map(italy_ncov, "italy")
```
## Parsed with column specification:
## cols(
## name = col_character(),
## name_zh = col_character(),
## provinceName = col_character(),
## provinceEnglishName = col_character()
## )
<!-- -->
### 直接画这四个国家的疫情图
``` r
foreign_countries <- c("韩国", "伊朗", "日本", "意大利")
names(foreign_countries) <- c("korea", "iran", "japan", "italy")
htmltools::tagList(purrr::imap(
foreign_countries,
~ get_foreign_ncov(.x) %>%
plot_foreign_map(.y)
))
```
更多功能请参看函数的帮助信息
# License
Copyright [Peng Zhao](http://pzhao.org).
Released under the GPL-3 license.