韓国と日本のPCR検査実施人数等比較 (新型コロナウイルス:Coronavirus)
(使用するデータ)
日本 : PCR検査実施人数は、厚生労働省の報道発表資料から抜き出した。
韓国 : Coronavirus Disease-19, Republic of KoreaまたはKCDC「News Room」「Press Release」
人口は世界の人口 (世銀)直近データ2018年を使う。日本:126,529,000、韓国:51,635,000(日本の約41%)
新型コロナウイルスのPCR検査実施人数と感染状況(韓国)「累計」
日本と韓国の新型コロナウイルスによる死亡者数推移(累計で計算)
日本と韓国のPCR検査の検査陽性率(%)「累計」
日本と韓国のPCR検査の検査陽性率(%)の7日移動平均
- 直近の状況を知るために7日移動平均のグラフを作成しました。
日本と韓国のPCR検査の暫定致死率(%)「累計」
ここからは差分をとって、日別のデータをグラフにした。
週単位の陽性者増加比(日本、韓国)
韓国のPCR検査の結果(日別)
日本と韓国の検査陽性者数(日別)
日本と韓国の検査者数(韓国の場合は「結果が判明した数」)(日別)
日本のデータでありえない箇所(検査者数がマイナス!)がある。
韓国の(報告された)陽性者数 対数表示(日別)
日本の(報告された)感染者数 対数表示(日別)
主な地点の平年値(日本、韓国、オーストラリア)
ひと足早く冬を終えた南半球のオーストラリアと北半球の日本、韓国を比較するため気温のグラフを載せておきます。
回復された方、亡くなった方、療養中(入院、隔離、自宅療養)の方の推移
Rコードは、記事「東アジアの感染者の状況(新型コロナウイルス:Coronavirus)」にのせています。
日本
韓国
オーストラリア(南半球)
まず、注目していただきたいのは、感染率(Infection rates)です。たった0.3%くらいしかありません。徹底的に検査をしている証拠です。
(日本:4.9%弱、韓国:1.3%弱)
また、上の気温のグラフを見てもわかるとおり、冬の気温についてもオーストラリアは日本、韓国より有利です。
それでも、冬場は感染者数、死亡者数、致死率ともに急上昇しました。(下の図参照)
よって、冬の日本のコロナの感染者数、死者数が急上昇してもなんら不思議ではありません。(韓国ですらヤバイ)
ニュージーランド(南半球)
日本、韓国、オーストラリアの検査陽性者数の推移
日本、韓国、オーストラリアの新型コロナによる死亡者数の推移
日本、韓国、オーストラリアの新型コロナによる人口100万人あたり死亡者数の推移
日本、韓国、オーストラリアの致死率(%)の7日移動平均
北海道、埼玉、東京、神奈川、愛知、大阪、兵庫の致死率7日移動平均(データ:NHK)
北海道(約550万人)、埼玉(約719万人)、東京(約1316万人)、神奈川(約905万人)、愛知(約741万人)、大阪(約886万人)、兵庫(約559万人)
- 案の定、寒さの厳しい北海道の致死率が急上昇しています。
北海道、埼玉、東京、神奈川、愛知、大阪、兵庫の月別死者数と月別人口100万人あたりの死者数(データ:NHK)
北海道(約550万人)、埼玉(約719万人)、東京(約1316万人)、神奈川(約905万人)、愛知(約741万人)、大阪(約886万人)、兵庫(約559万人)
- 大阪の気温は他と比べて低いわけではないのに8月以降の死者数は比較的多い。 (注)「人口100万人あたり新型コロナウイルス月別死亡者」のグラフを直しました。(2020-12-12)
日本、韓国、台湾、シンガポール、香港の面積、人口、人口密度
country | area | pop | Population.density |
---|---|---|---|
Japan | 377,915 | 127,103,388 | 336 |
Korea, South | 99,720 | 49,039,986 | 492 |
Taiwan | 35,980 | 23,359,928 | 649 |
Hong Kong | 1,104 | 7,112,688 | 6,443 |
Singapore | 697 | 5,567,301 | 7,988 |
Confirmed、 Deaths、Deaths/Confirmed (%)の表(米ジョンズ・ホプキンス大学のデータを使った。)
人口100万人あたりの死亡者数を計算して表に入れた。
現在、日本の死亡者数にずれが生じている。
Confirmed | Deaths | Deaths/Confirmed (%) | Deaths/millionpeople | |
---|---|---|---|---|
Japan | 499,831 | 9325 | 1.87 | 73.37 |
Korea, South | 108,945 | 1765 | 1.62 | 35.99 |
Taiwan | 1,054 | 10 | 0.95 | 0.43 |
Hong Kong | 11,563 | 207 | 1.79 | 29.10 |
Singapore | 60,601 | 30 | 0.05 | 5.39 |
日本、韓国、台湾、シンガポール、香港の人口100万人あたりの新型コロナウイルスによる死者数
日本、韓国、台湾、シンガポール、香港のReported Confirmed
日本、韓国、台湾、シンガポール、香港のPCR検査の暫定致死率(%)
新型コロナウイルスによる死者数 in アジア
(注意)日本語名は手打ちしているのでもしかしたら間違いがあるかもしれません。
新型コロナウイルスによる人口100万人あたりの死者数 in アジア
韓国のPCR検査実施人数とその結果(陽性率、暫定致死率を計算し、表を作成した)
Row.names | 検査を受けた人 | 感染者数 | 死者 | 陰性 | 検査中 | 結果判明 | 陽性率(%) | 暫定致死率(%) |
---|---|---|---|---|---|---|---|---|
2020-02-01 | 371 | 12 | 0 | 289 | 70 | 301 | 3.99 | 0.000 |
2020-02-02 | 429 | 15 | 0 | 327 | 87 | 342 | 4.39 | 0.000 |
2020-02-03 | 429 | 15 | 0 | 414 | 0 | 429 | 3.50 | 0.000 |
2020-02-04 | 607 | 16 | 0 | 462 | 129 | 478 | 3.35 | 0.000 |
2020-02-05 | 714 | 18 | 0 | 522 | 174 | 540 | 3.33 | 0.000 |
2020-02-06 | 885 | 23 | 0 | 693 | 169 | 716 | 3.21 | 0.000 |
2020-02-07 | 1130 | 24 | 0 | 842 | 264 | 866 | 2.77 | 0.000 |
2020-02-08 | 1701 | 24 | 0 | 1057 | 620 | 1081 | 2.22 | 0.000 |
2020-02-09 | 2340 | 25 | 0 | 1355 | 960 | 1380 | 1.81 | 0.000 |
2020-02-10 | 2776 | 27 | 0 | 1940 | 809 | 1967 | 1.37 | 0.000 |
2020-02-11 | 3629 | 28 | 0 | 2736 | 865 | 2764 | 1.01 | 0.000 |
2020-02-12 | 5074 | 28 | 0 | 4054 | 992 | 4082 | 0.69 | 0.000 |
2020-02-13 | 5797 | 28 | 0 | 5099 | 670 | 5127 | 0.55 | 0.000 |
2020-02-14 | 6854 | 28 | 0 | 6134 | 692 | 6162 | 0.45 | 0.000 |
2020-02-15 | 7519 | 28 | 0 | 6853 | 638 | 6881 | 0.41 | 0.000 |
2020-02-16 | 7919 | 29 | 0 | 7313 | 577 | 7342 | 0.39 | 0.000 |
2020-02-17 | 8171 | 30 | 0 | 7733 | 408 | 7763 | 0.39 | 0.000 |
2020-02-18 | 9265 | 31 | 0 | 8277 | 957 | 8308 | 0.37 | 0.000 |
2020-02-19 | 10411 | 46 | 0 | 9335 | 1030 | 9381 | 0.49 | 0.000 |
2020-02-20 | 12161 | 82 | 0 | 10446 | 1633 | 10528 | 0.78 | 0.000 |
2020-02-21 | 14816 | 156 | 1 | 11953 | 2707 | 12109 | 1.29 | 0.641 |
2020-02-22 | 19621 | 346 | 2 | 13794 | 5481 | 14140 | 2.45 | 0.578 |
2020-02-23 | 22633 | 556 | 4 | 16038 | 6039 | 16594 | 3.35 | 0.719 |
2020-02-24 | 28615 | 763 | 7 | 19127 | 8725 | 19890 | 3.84 | 0.917 |
2020-02-25 | 36716 | 893 | 8 | 22550 | 13273 | 23443 | 3.81 | 0.896 |
2020-02-26 | 46127 | 1146 | 11 | 28247 | 16734 | 29393 | 3.90 | 0.960 |
2020-02-27 | 57990 | 1595 | 12 | 35298 | 21097 | 36893 | 4.32 | 0.752 |
2020-02-28 | 70940 | 2022 | 13 | 44167 | 24751 | 46189 | 4.38 | 0.643 |
2020-02-29 | 85693 | 2931 | 16 | 53608 | 29154 | 56539 | 5.18 | 0.546 |
2020-03-01 | 96985 | 3526 | 17 | 61037 | 32422 | 64563 | 5.46 | 0.482 |
2020-03-02 | 109591 | 4212 | 22 | 71580 | 33799 | 75792 | 5.56 | 0.522 |
2020-03-03 | 125851 | 4812 | 28 | 85484 | 35555 | 90296 | 5.33 | 0.582 |
2020-03-04 | 136707 | 5328 | 32 | 102965 | 28414 | 108293 | 4.92 | 0.601 |
2020-03-05 | 146541 | 5766 | 35 | 118965 | 21810 | 124731 | 4.62 | 0.607 |
2020-03-06 | 164740 | 6284 | 42 | 136624 | 21832 | 142908 | 4.40 | 0.668 |
2020-03-07 | 178189 | 6767 | 44 | 151802 | 19620 | 158569 | 4.27 | 0.650 |
2020-03-08 | 188518 | 7134 | 50 | 162008 | 19376 | 169142 | 4.22 | 0.701 |
2020-03-09 | 196618 | 7382 | 51 | 171778 | 17458 | 179160 | 4.12 | 0.691 |
2020-03-10 | 210144 | 7513 | 54 | 184179 | 18452 | 191692 | 3.92 | 0.719 |
2020-03-11 | 222395 | 7755 | 60 | 196100 | 18540 | 203855 | 3.80 | 0.774 |
2020-03-12 | 234998 | 7869 | 66 | 209402 | 17727 | 217271 | 3.62 | 0.839 |
2020-03-13 | 248647 | 7979 | 67 | 222728 | 17940 | 230707 | 3.46 | 0.840 |
2020-03-14 | 261335 | 8086 | 72 | 235615 | 17634 | 243701 | 3.32 | 0.890 |
2020-03-15 | 268212 | 8162 | 75 | 243778 | 16272 | 251940 | 3.24 | 0.919 |
2020-03-16 | 274504 | 8236 | 75 | 251297 | 14971 | 259533 | 3.17 | 0.911 |
2020-03-17 | 286716 | 8320 | 81 | 261105 | 17291 | 269425 | 3.09 | 0.974 |
2020-03-18 | 295647 | 8413 | 84 | 270888 | 16346 | 279301 | 3.01 | 0.998 |
2020-03-19 | 307024 | 8565 | 91 | 282555 | 15904 | 291120 | 2.94 | 1.062 |
2020-03-20 | 316664 | 8652 | 94 | 292487 | 15525 | 301139 | 2.87 | 1.086 |
2020-03-21 | 327509 | 8799 | 102 | 303006 | 15704 | 311805 | 2.82 | 1.159 |
2020-03-22 | 331780 | 8897 | 104 | 308343 | 14540 | 317240 | 2.80 | 1.169 |
2020-03-23 | 338036 | 8961 | 111 | 315447 | 13628 | 324408 | 2.76 | 1.239 |
2020-03-24 | 348582 | 9037 | 120 | 324105 | 15440 | 333142 | 2.71 | 1.328 |
2020-03-25 | 357896 | 9137 | 126 | 334481 | 14278 | 343618 | 2.66 | 1.379 |
2020-03-26 | 364942 | 9241 | 131 | 341332 | 14369 | 350573 | 2.64 | 1.418 |
2020-03-27 | 376961 | 9332 | 139 | 352410 | 15219 | 361742 | 2.58 | 1.489 |
2020-03-28 | 387925 | 9478 | 144 | 361883 | 16564 | 371361 | 2.55 | 1.519 |
2020-03-29 | 394141 | 9583 | 152 | 369530 | 15028 | 379113 | 2.53 | 1.586 |
2020-03-30 | 395194 | 9661 | 158 | 372002 | 13531 | 381663 | 2.53 | 1.635 |
2020-03-31 | 410564 | 9786 | 162 | 383886 | 16892 | 393672 | 2.49 | 1.655 |
2020-04-01 | 421547 | 9887 | 165 | 395075 | 16585 | 404962 | 2.44 | 1.669 |
2020-04-02 | 431743 | 9976 | 169 | 403882 | 17885 | 413858 | 2.41 | 1.694 |
2020-04-03 | 443273 | 10062 | 174 | 414303 | 18908 | 424365 | 2.37 | 1.729 |
2020-04-04 | 455032 | 10156 | 177 | 424732 | 20144 | 434888 | 2.34 | 1.743 |
2020-04-05 | 461233 | 10237 | 183 | 431425 | 19571 | 441662 | 2.32 | 1.788 |
2020-04-06 | 466804 | 10284 | 186 | 437225 | 19295 | 447509 | 2.30 | 1.809 |
2020-04-07 | 477304 | 10331 | 192 | 446323 | 20650 | 456654 | 2.26 | 1.858 |
2020-04-08 | 486003 | 10384 | 200 | 457761 | 17858 | 468145 | 2.22 | 1.926 |
2020-04-09 | 494711 | 10423 | 204 | 468779 | 15509 | 479202 | 2.18 | 1.957 |
2020-04-10 | 503051 | 10450 | 208 | 477303 | 15298 | 487753 | 2.14 | 1.990 |
2020-04-11 | 510479 | 10480 | 211 | 485929 | 14070 | 496409 | 2.11 | 2.013 |
2020-04-12 | 514621 | 10512 | 214 | 490321 | 13788 | 500833 | 2.10 | 2.036 |
2020-04-13 | 518743 | 10537 | 217 | 494815 | 13391 | 505352 | 2.09 | 2.059 |
2020-04-14 | 527438 | 10564 | 222 | 502223 | 14651 | 512787 | 2.06 | 2.101 |
2020-04-15 | 534552 | 10591 | 225 | 508935 | 15026 | 519526 | 2.04 | 2.124 |
2020-04-16 | 538775 | 10613 | 229 | 513894 | 14268 | 524507 | 2.02 | 2.158 |
2020-04-17 | 546463 | 10635 | 230 | 521642 | 14186 | 532277 | 2.00 | 2.163 |
2020-04-18 | 554834 | 10653 | 232 | 530631 | 13550 | 541284 | 1.97 | 2.178 |
2020-04-19 | 559109 | 10661 | 234 | 536205 | 12243 | 546866 | 1.95 | 2.195 |
2020-04-20 | 563035 | 10674 | 236 | 540380 | 11981 | 551054 | 1.94 | 2.211 |
2020-04-21 | 571014 | 10683 | 237 | 547610 | 12721 | 558293 | 1.91 | 2.218 |
2020-04-22 | 577959 | 10694 | 238 | 555144 | 12121 | 565838 | 1.89 | 2.226 |
2020-04-23 | 583971 | 10702 | 240 | 563130 | 10139 | 573832 | 1.87 | 2.243 |
2020-04-24 | 589520 | 10708 | 240 | 569212 | 9600 | 579920 | 1.85 | 2.241 |
2020-04-25 | 595161 | 10718 | 240 | 575184 | 9259 | 585902 | 1.83 | 2.239 |
2020-04-26 | 598285 | 10728 | 242 | 578558 | 8999 | 589286 | 1.82 | 2.256 |
2020-04-27 | 601660 | 10738 | 243 | 582027 | 8895 | 592765 | 1.81 | 2.263 |
2020-04-28 | 608514 | 10752 | 244 | 588559 | 9203 | 599311 | 1.79 | 2.269 |
2020-04-29 | 614197 | 10761 | 246 | 595129 | 8307 | 605890 | 1.78 | 2.286 |
2020-04-30 | 619881 | 10765 | 247 | 600482 | 8634 | 611247 | 1.76 | 2.294 |
2020-05-01 | 623069 | 10774 | 248 | 603610 | 8685 | 614384 | 1.75 | 2.302 |
2020-05-02 | 627562 | 10780 | 250 | 608286 | 8496 | 619066 | 1.74 | 2.319 |
2020-05-03 | 630973 | 10793 | 250 | 611592 | 8588 | 622385 | 1.73 | 2.316 |
2020-05-04 | 633921 | 10801 | 252 | 614944 | 8176 | 625745 | 1.73 | 2.333 |
2020-05-05 | 640237 | 10804 | 254 | 620575 | 8858 | 631379 | 1.71 | 2.351 |
2020-05-06 | 643095 | 10806 | 255 | 624280 | 8009 | 635086 | 1.70 | 2.360 |
2020-05-07 | 649388 | 10810 | 256 | 630149 | 8429 | 640959 | 1.69 | 2.368 |
2020-05-08 | 654863 | 10822 | 256 | 635174 | 8867 | 645996 | 1.68 | 2.366 |
2020-05-09 | 660030 | 10840 | 256 | 640037 | 9153 | 650877 | 1.67 | 2.362 |
2020-05-10 | 663886 | 10874 | 256 | 642884 | 10128 | 653758 | 1.66 | 2.354 |
2020-05-11 | 668492 | 10909 | 256 | 646661 | 10922 | 657570 | 1.66 | 2.347 |
2020-05-12 | 680890 | 10936 | 258 | 653624 | 16330 | 664560 | 1.65 | 2.359 |
2020-05-13 | 695920 | 10962 | 259 | 665379 | 19579 | 676341 | 1.62 | 2.363 |
2020-05-14 | 711484 | 10991 | 260 | 679771 | 20722 | 690762 | 1.59 | 2.366 |
2020-05-15 | 726747 | 11018 | 260 | 695854 | 19875 | 706872 | 1.56 | 2.360 |
2020-05-16 | 740645 | 11037 | 262 | 711265 | 18343 | 722302 | 1.53 | 2.374 |
2020-05-17 | 747653 | 11050 | 262 | 718943 | 17660 | 729993 | 1.51 | 2.371 |
2020-05-18 | 753211 | 11065 | 263 | 726053 | 16093 | 737118 | 1.50 | 2.377 |
2020-05-19 | 765574 | 11078 | 263 | 737571 | 16925 | 748649 | 1.48 | 2.374 |
2020-05-20 | 776433 | 11110 | 263 | 748972 | 16351 | 760082 | 1.46 | 2.367 |
2020-05-21 | 788684 | 11122 | 264 | 759473 | 18089 | 770595 | 1.44 | 2.374 |
2020-05-22 | 802418 | 11142 | 264 | 770990 | 20286 | 782132 | 1.42 | 2.369 |
2020-05-23 | 814420 | 11165 | 266 | 781686 | 21569 | 792851 | 1.41 | 2.382 |
2020-05-24 | 820289 | 11190 | 266 | 788766 | 20333 | 799956 | 1.40 | 2.377 |
2020-05-25 | 826437 | 11206 | 267 | 796142 | 19089 | 807348 | 1.39 | 2.383 |
2020-05-26 | 839475 | 11225 | 269 | 806206 | 22044 | 817431 | 1.37 | 2.396 |
2020-05-27 | 852876 | 11265 | 269 | 820550 | 21061 | 831815 | 1.35 | 2.388 |
2020-05-28 | 868666 | 11344 | 269 | 834952 | 22370 | 846296 | 1.34 | 2.371 |
2020-05-29 | 885120 | 11402 | 269 | 849161 | 24557 | 860563 | 1.32 | 2.359 |
2020-05-30 | 902901 | 11441 | 269 | 865162 | 26298 | 876603 | 1.31 | 2.351 |
2020-05-31 | 910822 | 11468 | 270 | 876060 | 23294 | 887528 | 1.29 | 2.354 |
2020-06-01 | 921391 | 11503 | 271 | 885830 | 24058 | 897333 | 1.28 | 2.356 |
2020-06-02 | 939851 | 11541 | 272 | 899388 | 28922 | 910929 | 1.27 | 2.357 |
2020-06-03 | 956852 | 11590 | 273 | 917397 | 27865 | 928987 | 1.25 | 2.355 |
2020-06-04 | 973858 | 11629 | 273 | 934030 | 28199 | 945659 | 1.23 | 2.348 |
2020-06-05 | 990960 | 11668 | 273 | 950526 | 28766 | 962194 | 1.21 | 2.340 |
2020-06-06 | 1005305 | 11719 | 273 | 965632 | 27954 | 977351 | 1.20 | 2.330 |
2020-06-07 | 1012769 | 11776 | 273 | 974512 | 26481 | 986288 | 1.19 | 2.318 |
2020-06-08 | 1018214 | 11814 | 273 | 982026 | 24374 | 993840 | 1.19 | 2.311 |
2020-06-09 | 1035997 | 11852 | 274 | 996686 | 27459 | 1008538 | 1.18 | 2.312 |
2020-06-10 | 1051972 | 11902 | 276 | 1013847 | 26223 | 1025749 | 1.16 | 2.319 |
2020-06-11 | 1066888 | 11947 | 276 | 1029447 | 25494 | 1041394 | 1.15 | 2.310 |
2020-06-12 | 1081486 | 12002 | 277 | 1045240 | 24244 | 1057242 | 1.14 | 2.308 |
2020-06-13 | 1094704 | 12051 | 277 | 1059301 | 23352 | 1071352 | 1.12 | 2.299 |
2020-06-14 | 1100327 | 12084 | 277 | 1066887 | 21356 | 1078971 | 1.12 | 2.292 |
2020-06-15 | 1105719 | 12121 | 277 | 1072805 | 20793 | 1084926 | 1.12 | 2.285 |
2020-06-16 | 1119767 | 12155 | 278 | 1084980 | 22632 | 1097135 | 1.11 | 2.287 |
2020-06-17 | 1132823 | 12198 | 279 | 1099136 | 21489 | 1111334 | 1.10 | 2.287 |
2020-06-18 | 1145712 | 12257 | 280 | 1111741 | 21714 | 1123998 | 1.09 | 2.284 |
2020-06-19 | 1158063 | 12306 | 280 | 1124567 | 21190 | 1136873 | 1.08 | 2.275 |
2020-06-20 | 1170901 | 12373 | 280 | 1137058 | 21470 | 1149431 | 1.08 | 2.263 |
2020-06-21 | 1176463 | 12421 | 280 | 1143971 | 20071 | 1156392 | 1.07 | 2.254 |
2020-06-22 | 1182066 | 12438 | 280 | 1150225 | 19403 | 1162663 | 1.07 | 2.251 |
2020-06-23 | 1196012 | 12484 | 281 | 1161250 | 22278 | 1173734 | 1.06 | 2.251 |
2020-06-24 | 1208597 | 12535 | 281 | 1175817 | 20245 | 1188352 | 1.05 | 2.242 |
2020-06-25 | 1220478 | 12563 | 282 | 1189015 | 18900 | 1201578 | 1.05 | 2.245 |
2020-06-26 | 1232315 | 12602 | 282 | 1200885 | 18828 | 1213487 | 1.04 | 2.238 |
2020-06-27 | 1243780 | 12653 | 282 | 1211261 | 19866 | 1223914 | 1.03 | 2.229 |
2020-06-28 | 1251695 | 12715 | 282 | 1219975 | 19005 | 1232690 | 1.03 | 2.218 |
2020-06-29 | 1259954 | 12757 | 282 | 1228698 | 18499 | 1241455 | 1.03 | 2.211 |
2020-06-30 | 1273766 | 12799 | 282 | 1240157 | 20810 | 1252956 | 1.02 | 2.203 |
2020-07-01 | 1285231 | 12850 | 282 | 1252855 | 19526 | 1265705 | 1.02 | 2.195 |
2020-07-02 | 1295962 | 12904 | 282 | 1263276 | 19782 | 1276180 | 1.01 | 2.185 |
2020-07-03 | 1307761 | 12967 | 282 | 1273234 | 21560 | 1286201 | 1.01 | 2.175 |
2020-07-04 | 1319523 | 13030 | 283 | 1284172 | 22321 | 1297202 | 1.00 | 2.172 |
2020-07-05 | 1326055 | 13089 | 283 | 1291315 | 21651 | 1304404 | 1.00 | 2.162 |
2020-07-06 | 1331796 | 13137 | 284 | 1297367 | 21292 | 1310504 | 1.00 | 2.162 |
2020-07-07 | 1346194 | 13181 | 285 | 1309338 | 23675 | 1322519 | 1.00 | 2.162 |
2020-07-08 | 1359735 | 13243 | 285 | 1322479 | 24013 | 1335722 | 0.99 | 2.152 |
2020-07-09 | 1371771 | 13293 | 287 | 1334566 | 23912 | 1347859 | 0.99 | 2.159 |
2020-07-10 | 1384890 | 13338 | 288 | 1348025 | 23527 | 1361363 | 0.98 | 2.159 |
2020-07-11 | 1396941 | 13373 | 288 | 1360618 | 22950 | 1373991 | 0.97 | 2.154 |
2020-07-12 | 1402144 | 13417 | 289 | 1366897 | 21830 | 1380314 | 0.97 | 2.154 |
2020-07-13 | 1408312 | 13479 | 289 | 1372988 | 21845 | 1386467 | 0.97 | 2.144 |
2020-07-14 | 1420616 | 13512 | 289 | 1382815 | 24289 | 1396327 | 0.97 | 2.139 |
2020-07-15 | 1431316 | 13551 | 289 | 1394468 | 23297 | 1408019 | 0.96 | 2.133 |
2020-07-16 | 1441348 | 13612 | 291 | 1404332 | 23404 | 1417944 | 0.96 | 2.138 |
2020-07-17 | 1451017 | 13672 | 293 | 1414235 | 23110 | 1427907 | 0.96 | 2.143 |
2020-07-18 | 1460204 | 13711 | 294 | 1423570 | 22923 | 1437281 | 0.95 | 2.144 |
2020-07-19 | 1465299 | 13745 | 295 | 1429601 | 21953 | 1443346 | 0.95 | 2.146 |
2020-07-20 | 1470193 | 13771 | 296 | 1435120 | 21302 | 1448891 | 0.95 | 2.149 |
2020-07-21 | 1482390 | 13816 | 296 | 1444710 | 23864 | 1458526 | 0.95 | 2.142 |
2020-07-22 | 1492071 | 13879 | 297 | 1456441 | 21751 | 1470320 | 0.94 | 2.140 |
2020-07-23 | 1500854 | 13938 | 297 | 1465498 | 21418 | 1479436 | 0.94 | 2.131 |
2020-07-24 | 1510327 | 13979 | 298 | 1475789 | 20559 | 1489768 | 0.94 | 2.132 |
2020-07-25 | 1518634 | 14092 | 298 | 1484861 | 19681 | 1498953 | 0.94 | 2.115 |
2020-07-26 | 1522926 | 14150 | 298 | 1489562 | 19214 | 1503712 | 0.94 | 2.106 |
2020-07-27 | 1526974 | 14175 | 299 | 1494029 | 18770 | 1508204 | 0.94 | 2.109 |
2020-07-28 | 1537704 | 14203 | 300 | 1503057 | 20444 | 1517260 | 0.94 | 2.112 |
2020-07-29 | 1547307 | 14251 | 300 | 1513730 | 19326 | 1527981 | 0.93 | 2.105 |
2020-07-30 | 1556215 | 14269 | 300 | 1522928 | 19018 | 1537197 | 0.93 | 2.102 |
2020-07-31 | 1563796 | 14305 | 301 | 1531161 | 18330 | 1545466 | 0.93 | 2.104 |
2020-08-01 | 1571830 | 14336 | 301 | 1539216 | 18278 | 1553552 | 0.92 | 2.100 |
2020-08-02 | 1576246 | 14366 | 301 | 1544112 | 17768 | 1558478 | 0.92 | 2.095 |
2020-08-03 | 1579757 | 14389 | 301 | 1547967 | 17401 | 1562356 | 0.92 | 2.092 |
2020-08-04 | 1589780 | 14423 | 301 | 1556633 | 18724 | 1571056 | 0.92 | 2.087 |
2020-08-05 | 1598187 | 14456 | 302 | 1565241 | 18490 | 1579697 | 0.92 | 2.089 |
2020-08-06 | 1606487 | 14499 | 302 | 1573957 | 18031 | 1588456 | 0.91 | 2.083 |
2020-08-07 | 1613652 | 14519 | 303 | 1582065 | 17068 | 1596584 | 0.91 | 2.087 |
2020-08-08 | 1620514 | 14562 | 304 | 1589847 | 16105 | 1604409 | 0.91 | 2.088 |
2020-08-09 | 1624650 | 14598 | 305 | 1593249 | 16803 | 1607847 | 0.91 | 2.089 |
2020-08-10 | 1628303 | 14626 | 305 | 1597281 | 16396 | 1611907 | 0.91 | 2.085 |
2020-08-11 | 1637844 | 14660 | 305 | 1605695 | 17489 | 1620355 | 0.90 | 2.080 |
2020-08-12 | 1646652 | 14714 | 305 | 1614563 | 17375 | 1629277 | 0.90 | 2.073 |
2020-08-13 | 1654898 | 14770 | 305 | 1622330 | 17798 | 1637100 | 0.90 | 2.065 |
2020-08-14 | 1665084 | 14873 | 305 | 1630079 | 20132 | 1644952 | 0.90 | 2.051 |
2020-08-15 | 1675296 | 15039 | 305 | 1638639 | 21618 | 1653678 | 0.91 | 2.028 |
2020-08-16 | 1681787 | 15318 | 305 | 1644464 | 22005 | 1659782 | 0.92 | 1.991 |
2020-08-17 | 1688470 | 15515 | 305 | 1649991 | 22964 | 1665506 | 0.93 | 1.966 |
2020-08-18 | 1697042 | 15761 | 306 | 1656062 | 25219 | 1671823 | 0.94 | 1.942 |
2020-08-19 | 1715064 | 16058 | 306 | 1667984 | 31022 | 1684042 | 0.95 | 1.906 |
2020-08-20 | 1734083 | 16346 | 307 | 1682739 | 34998 | 1699085 | 0.96 | 1.878 |
2020-08-21 | 1754123 | 16670 | 309 | 1699408 | 38045 | 1716078 | 0.97 | 1.854 |
2020-08-22 | 1775800 | 17002 | 309 | 1716371 | 42427 | 1733373 | 0.98 | 1.817 |
2020-08-23 | 1791186 | 17399 | 309 | 1726223 | 47564 | 1743622 | 1.00 | 1.776 |
2020-08-24 | 1804422 | 17665 | 309 | 1738762 | 47995 | 1756427 | 1.01 | 1.749 |
2020-08-25 | 1825837 | 17945 | 310 | 1757530 | 50362 | 1775475 | 1.01 | 1.728 |
2020-08-26 | 1849506 | 18265 | 312 | 1778446 | 52795 | 1796711 | 1.02 | 1.708 |
2020-08-27 | 1869579 | 18706 | 313 | 1798832 | 52041 | 1817538 | 1.03 | 1.673 |
2020-08-28 | 1887717 | 19077 | 316 | 1817929 | 50711 | 1837006 | 1.04 | 1.656 |
2020-08-29 | 1909329 | 19400 | 321 | 1835883 | 54046 | 1855283 | 1.05 | 1.655 |
2020-08-30 | 1924170 | 19699 | 323 | 1846450 | 58021 | 1866149 | 1.06 | 1.640 |
2020-08-31 | 1937689 | 19947 | 324 | 1859866 | 57876 | 1879813 | 1.06 | 1.624 |
2020-09-01 | 1959080 | 20182 | 324 | 1882155 | 56743 | 1902337 | 1.06 | 1.605 |
2020-09-02 | 1980295 | 20449 | 326 | 1903098 | 56748 | 1923547 | 1.06 | 1.594 |
2020-09-03 | 2000552 | 20644 | 329 | 1924384 | 55524 | 1945028 | 1.06 | 1.594 |
2020-09-04 | 2018906 | 20842 | 331 | 1945798 | 52266 | 1966640 | 1.06 | 1.588 |
2020-09-05 | 2037045 | 21010 | 333 | 1963958 | 52077 | 1984968 | 1.06 | 1.585 |
2020-09-06 | 2045935 | 21177 | 334 | 1975137 | 49621 | 1996314 | 1.06 | 1.577 |
2020-09-07 | 2051297 | 21296 | 336 | 1982892 | 47109 | 2004188 | 1.06 | 1.578 |
2020-09-08 | 2066078 | 21432 | 341 | 2001276 | 43370 | 2022708 | 1.06 | 1.591 |
2020-09-09 | 2082234 | 21588 | 344 | 2024113 | 36533 | 2045701 | 1.06 | 1.593 |
2020-09-10 | 2099591 | 21743 | 346 | 2044830 | 33018 | 2066573 | 1.05 | 1.591 |
2020-09-11 | 2119211 | 21919 | 350 | 2067869 | 29423 | 2089788 | 1.05 | 1.597 |
2020-09-12 | 2135457 | 22055 | 355 | 2085576 | 27826 | 2107631 | 1.05 | 1.610 |
2020-09-13 | 2143270 | 22176 | 358 | 2093389 | 27705 | 2115565 | 1.05 | 1.614 |
2020-09-14 | 2151002 | 22285 | 363 | 2101241 | 27476 | 2123526 | 1.05 | 1.629 |
2020-09-15 | 2164578 | 22391 | 367 | 2114877 | 27310 | 2137268 | 1.05 | 1.639 |
2020-09-16 | 2178832 | 22504 | 367 | 2130486 | 25842 | 2152990 | 1.05 | 1.631 |
2020-09-17 | 2191892 | 22657 | 372 | 2143727 | 25508 | 2166384 | 1.05 | 1.642 |
2020-09-18 | 2206365 | 22783 | 377 | 2158179 | 25403 | 2180962 | 1.04 | 1.655 |
2020-09-19 | 2219162 | 22893 | 378 | 2171564 | 24705 | 2194457 | 1.04 | 1.651 |
2020-09-20 | 2226701 | 22975 | 383 | 2179452 | 24274 | 2202427 | 1.04 | 1.667 |
2020-09-21 | 2231589 | 23045 | 385 | 2186008 | 22536 | 2209053 | 1.04 | 1.671 |
2020-09-22 | 2245112 | 23106 | 388 | 2198784 | 23222 | 2221890 | 1.04 | 1.679 |
2020-09-23 | 2256899 | 23216 | 388 | 2213156 | 20527 | 2236372 | 1.04 | 1.671 |
2020-09-24 | 2268999 | 23341 | 393 | 2224876 | 20782 | 2248217 | 1.04 | 1.684 |
2020-09-25 | 2280276 | 23455 | 395 | 2237246 | 19575 | 2260701 | 1.04 | 1.684 |
2020-09-26 | 2290345 | 23516 | 399 | 2248321 | 18508 | 2271837 | 1.04 | 1.697 |
2020-09-27 | 2296517 | 23611 | 401 | 2254028 | 18878 | 2277639 | 1.04 | 1.698 |
2020-09-28 | 2301303 | 23661 | 406 | 2259055 | 18587 | 2282716 | 1.04 | 1.716 |
2020-09-29 | 2313044 | 23699 | 407 | 2269289 | 20056 | 2292988 | 1.03 | 1.717 |
2020-09-30 | 2322999 | 23812 | 413 | 2278591 | 20596 | 2302403 | 1.03 | 1.734 |
2020-10-01 | 2328435 | 23889 | 415 | 2284517 | 20029 | 2308406 | 1.03 | 1.737 |
2020-10-02 | 2333777 | 23952 | 416 | 2289830 | 19995 | 2313782 | 1.04 | 1.737 |
2020-10-03 | 2339859 | 24027 | 420 | 2294545 | 21287 | 2318572 | 1.04 | 1.748 |
2020-10-04 | 2346345 | 24091 | 421 | 2300138 | 22116 | 2324229 | 1.04 | 1.748 |
2020-10-05 | 2352378 | 24164 | 422 | 2305767 | 22447 | 2329931 | 1.04 | 1.746 |
2020-10-06 | 2365433 | 24239 | 422 | 2318457 | 22737 | 2342696 | 1.03 | 1.741 |
2020-10-07 | 2378073 | 24353 | 425 | 2331582 | 22138 | 2355935 | 1.03 | 1.745 |
2020-10-08 | 2388844 | 24422 | 427 | 2343444 | 20978 | 2367866 | 1.03 | 1.748 |
2020-10-09 | 2400233 | 24476 | 428 | 2355058 | 20699 | 2379534 | 1.03 | 1.749 |
2020-10-10 | 2404684 | 24548 | 430 | 2361154 | 18982 | 2385702 | 1.03 | 1.752 |
2020-10-11 | 2410483 | 24605 | 432 | 2366574 | 19304 | 2391179 | 1.03 | 1.756 |
2020-10-12 | 2415610 | 24703 | 433 | 2371715 | 19192 | 2396418 | 1.03 | 1.753 |
2020-10-13 | 2428771 | 24805 | 434 | 2383842 | 20124 | 2408647 | 1.03 | 1.750 |
2020-10-14 | 2441443 | 24878 | 438 | 2398169 | 18396 | 2423047 | 1.03 | 1.761 |
2020-10-15 | 2450739 | 24988 | 439 | 2407489 | 18262 | 2432477 | 1.03 | 1.757 |
2020-10-16 | 2459426 | 25035 | 441 | 2417036 | 17355 | 2442071 | 1.03 | 1.762 |
2020-10-17 | 2468527 | 25108 | 443 | 2426101 | 17318 | 2451209 | 1.02 | 1.764 |
2020-10-18 | 2474529 | 25199 | 444 | 2431549 | 17781 | 2456748 | 1.03 | 1.762 |
2020-10-19 | 2479226 | 25275 | 444 | 2437188 | 16763 | 2462463 | 1.03 | 1.757 |
2020-10-20 | 2491311 | 25333 | 447 | 2446599 | 19379 | 2471932 | 1.02 | 1.764 |
2020-10-21 | 2503489 | 25422 | 450 | 2458574 | 19493 | 2483996 | 1.02 | 1.770 |
2020-10-22 | 2515325 | 25543 | 453 | 2469969 | 19813 | 2495512 | 1.02 | 1.773 |
2020-10-23 | 2528621 | 25698 | 455 | 2482493 | 20430 | 2508191 | 1.02 | 1.771 |
2020-10-24 | 2540679 | 25775 | 457 | 2493016 | 21888 | 2518791 | 1.02 | 1.773 |
2020-10-25 | 2546146 | 25836 | 457 | 2498628 | 21682 | 2524464 | 1.02 | 1.769 |
2020-10-26 | 2552264 | 25955 | 457 | 2505546 | 20763 | 2531501 | 1.03 | 1.761 |
2020-10-27 | 2567587 | 26043 | 460 | 2518541 | 23003 | 2544584 | 1.02 | 1.766 |
2020-10-28 | 2582960 | 26146 | 461 | 2533910 | 22904 | 2560056 | 1.02 | 1.763 |
2020-10-29 | 2597978 | 26271 | 462 | 2545559 | 26148 | 2571830 | 1.02 | 1.759 |
2020-10-30 | 2612231 | 26384 | 463 | 2559474 | 26373 | 2585858 | 1.02 | 1.755 |
2020-10-31 | 2624492 | 26511 | 464 | 2572303 | 25678 | 2598814 | 1.02 | 1.750 |
2020-11-01 | 2630630 | 26635 | 466 | 2579157 | 24838 | 2605792 | 1.02 | 1.750 |
2020-11-02 | 2636650 | 26732 | 468 | 2584394 | 25524 | 2611126 | 1.02 | 1.751 |
2020-11-03 | 2649859 | 26807 | 472 | 2597237 | 25815 | 2624044 | 1.02 | 1.761 |
2020-11-04 | 2662260 | 26925 | 474 | 2611080 | 24255 | 2638005 | 1.02 | 1.760 |
2020-11-05 | 2673706 | 27050 | 475 | 2621594 | 25062 | 2648644 | 1.02 | 1.756 |
2020-11-06 | 2686314 | 27195 | 476 | 2633490 | 25629 | 2660685 | 1.02 | 1.750 |
2020-11-07 | 2697249 | 27284 | 477 | 2643748 | 26217 | 2671032 | 1.02 | 1.748 |
2020-11-08 | 2702880 | 27427 | 478 | 2649805 | 25648 | 2677232 | 1.02 | 1.743 |
2020-11-09 | 2709199 | 27553 | 480 | 2655844 | 25802 | 2683397 | 1.03 | 1.742 |
2020-11-10 | 2723960 | 27653 | 485 | 2668452 | 27855 | 2696105 | 1.03 | 1.754 |
2020-11-11 | 2736534 | 27799 | 487 | 2680047 | 28688 | 2707846 | 1.03 | 1.752 |
2020-11-12 | 2749772 | 27942 | 487 | 2692546 | 29284 | 2720488 | 1.03 | 1.743 |
2020-11-13 | 2761411 | 28133 | 488 | 2703159 | 30119 | 2731292 | 1.03 | 1.735 |
2020-11-14 | 2777289 | 28338 | 492 | 2714259 | 34692 | 2742597 | 1.03 | 1.736 |
2020-11-15 | 2786878 | 28546 | 493 | 2721954 | 36378 | 2750500 | 1.04 | 1.727 |
2020-11-16 | 2797691 | 28768 | 494 | 2730609 | 38314 | 2759377 | 1.04 | 1.717 |
2020-11-17 | 2815755 | 28998 | 494 | 2745555 | 41202 | 2774553 | 1.05 | 1.704 |
2020-11-18 | 2834362 | 29311 | 496 | 2762363 | 42688 | 2791674 | 1.05 | 1.692 |
2020-11-19 | 2853843 | 29654 | 498 | 2778664 | 45525 | 2808318 | 1.06 | 1.679 |
2020-11-20 | 2873443 | 30017 | 501 | 2795283 | 48143 | 2825300 | 1.06 | 1.669 |
2020-11-21 | 2896746 | 30403 | 503 | 2814998 | 51345 | 2845401 | 1.07 | 1.654 |
2020-11-22 | 2908890 | 30733 | 505 | 2824128 | 54029 | 2854861 | 1.08 | 1.643 |
2020-11-23 | 2922135 | 31004 | 509 | 2834676 | 56455 | 2865680 | 1.08 | 1.642 |
2020-11-24 | 2946399 | 31353 | 510 | 2857277 | 57769 | 2888630 | 1.09 | 1.627 |
2020-11-25 | 2966405 | 31735 | 513 | 2878832 | 55838 | 2910567 | 1.09 | 1.617 |
2020-11-26 | 2988046 | 32318 | 515 | 2900367 | 55361 | 2932685 | 1.10 | 1.594 |
2020-11-27 | 3009562 | 32871 | 516 | 2920054 | 56637 | 2952925 | 1.11 | 1.570 |
2020-11-28 | 3032003 | 33374 | 522 | 2939835 | 58794 | 2973209 | 1.12 | 1.564 |
2020-11-29 | 3046910 | 33763 | 523 | 2950318 | 62829 | 2984081 | 1.13 | 1.549 |
2020-11-30 | 3061172 | 34201 | 526 | 2963606 | 63365 | 2997807 | 1.14 | 1.538 |
2020-12-01 | 3083997 | 34652 | 526 | 2984453 | 64892 | 3019105 | 1.15 | 1.518 |
2020-12-02 | 3106970 | 35163 | 526 | 3006653 | 65154 | 3041816 | 1.16 | 1.496 |
2020-12-03 | 3131886 | 35703 | 529 | 3030395 | 65788 | 3066098 | 1.16 | 1.482 |
2020-12-04 | 3157410 | 36332 | 536 | 3057061 | 64017 | 3093393 | 1.17 | 1.475 |
2020-12-05 | 3180496 | 36915 | 540 | 3077314 | 66267 | 3114229 | 1.19 | 1.463 |
2020-12-06 | 3194867 | 37546 | 545 | 3089605 | 67716 | 3127151 | 1.20 | 1.452 |
2020-12-07 | 3209376 | 38161 | 549 | 3103205 | 68010 | 3141366 | 1.21 | 1.439 |
2020-12-08 | 3221317 | 38746 | 552 | 3111297 | 71274 | 3150043 | 1.23 | 1.425 |
2020-12-09 | 3253220 | 39416 | 556 | 3138724 | 75080 | 3178140 | 1.24 | 1.411 |
2020-12-10 | 3277948 | 40097 | 564 | 3165087 | 72764 | 3205184 | 1.25 | 1.407 |
2020-12-11 | 3311213 | 40786 | 572 | 3192739 | 77688 | 3233525 | 1.26 | 1.402 |
2020-12-12 | 3349864 | 41736 | 578 | 3221386 | 86742 | 3263122 | 1.28 | 1.385 |
2020-12-13 | 3374595 | 42766 | 580 | 3241700 | 90129 | 3284466 | 1.30 | 1.356 |
2020-12-14 | 3397039 | 43484 | 587 | 3264308 | 89247 | 3307792 | 1.31 | 1.350 |
2020-12-15 | 3441220 | 44364 | 600 | 3303383 | 93473 | 3347747 | 1.33 | 1.352 |
2020-12-16 | 3488769 | 45439 | 612 | 3348546 | 94784 | 3393985 | 1.34 | 1.347 |
2020-12-17 | 3538840 | 46453 | 634 | 3393129 | 99258 | 3439582 | 1.35 | 1.365 |
2020-12-18 | 3589797 | 47517 | 645 | 3431662 | 110618 | 3479179 | 1.37 | 1.357 |
2020-12-19 | 3646247 | 48568 | 659 | 3471446 | 126233 | 3520014 | 1.38 | 1.357 |
2020-12-20 | 3683094 | 49665 | 674 | 3493954 | 139475 | 3543619 | 1.40 | 1.357 |
2020-12-21 | 3713861 | 50591 | 698 | 3516832 | 146438 | 3567423 | 1.42 | 1.380 |
2020-12-22 | 3772430 | 51458 | 722 | 3569843 | 151129 | 3621301 | 1.42 | 1.403 |
2020-12-23 | 3826570 | 52548 | 739 | 3620913 | 153109 | 3673461 | 1.43 | 1.406 |
2020-12-24 | 3882210 | 53529 | 756 | 3678148 | 150533 | 3731677 | 1.43 | 1.412 |
2020-12-25 | 3939357 | 54770 | 773 | 3727798 | 156789 | 3782568 | 1.45 | 1.411 |
2020-12-26 | 3969415 | 55902 | 793 | 3756501 | 157012 | 3812403 | 1.47 | 1.419 |
2020-12-27 | 4006412 | 56872 | 808 | 3782564 | 166976 | 3839436 | 1.48 | 1.421 |
2020-12-28 | 4038307 | 57679 | 819 | 3810749 | 169879 | 3868428 | 1.49 | 1.420 |
2020-12-29 | 4098179 | 58723 | 859 | 3868494 | 170962 | 3927217 | 1.50 | 1.463 |
2020-12-30 | 4159522 | 59773 | 879 | 3927978 | 171771 | 3987751 | 1.50 | 1.471 |
2020-12-31 | 4213880 | 60740 | 900 | 3982802 | 170338 | 4043542 | 1.50 | 1.482 |
2021-01-01 | 4269318 | 61769 | 917 | 4030622 | 176927 | 4092391 | 1.51 | 1.485 |
2021-01-02 | 4302799 | 62593 | 942 | 4060819 | 179387 | 4123412 | 1.52 | 1.505 |
2021-01-03 | 4340838 | 63244 | 962 | 4091122 | 186472 | 4154366 | 1.52 | 1.521 |
2021-01-04 | 4376608 | 64264 | 981 | 4121612 | 190732 | 4185876 | 1.54 | 1.527 |
2021-01-05 | 4439360 | 64978 | 1007 | 4180631 | 193751 | 4245609 | 1.53 | 1.550 |
2021-01-06 | 4504866 | 65816 | 1027 | 4246968 | 192082 | 4312784 | 1.53 | 1.560 |
2021-01-07 | 4569808 | 66684 | 1046 | 4311362 | 191762 | 4378046 | 1.52 | 1.569 |
2021-01-08 | 4630004 | 67358 | 1081 | 4372194 | 190452 | 4439552 | 1.52 | 1.605 |
2021-01-09 | 4689616 | 67999 | 1100 | 4435694 | 185923 | 4503693 | 1.51 | 1.618 |
2021-01-10 | 4723464 | 68664 | 1125 | 4463346 | 191454 | 4532010 | 1.52 | 1.638 |
2021-01-11 | 4751685 | 69114 | 1140 | 4492808 | 189763 | 4561922 | 1.52 | 1.649 |
2021-01-12 | 4814085 | 69650 | 1165 | 4557665 | 186770 | 4627315 | 1.51 | 1.673 |
2021-01-13 | 4872312 | 70204 | 1185 | 4619194 | 182914 | 4689398 | 1.50 | 1.688 |
2021-01-14 | 4925359 | 70728 | 1195 | 4684889 | 169742 | 4755617 | 1.49 | 1.690 |
2021-01-15 | 4978074 | 71240 | 1217 | 4744511 | 162323 | 4815751 | 1.48 | 1.708 |
2021-01-16 | 5032270 | 71820 | 1236 | 4804811 | 155639 | 4876631 | 1.47 | 1.721 |
2021-01-17 | 5061290 | 72340 | 1249 | 4829565 | 159385 | 4901905 | 1.48 | 1.727 |
2021-01-18 | 5087220 | 72729 | 1264 | 4856456 | 158035 | 4929185 | 1.48 | 1.738 |
2021-01-19 | 5140325 | 73114 | 1283 | 4912866 | 154345 | 4985980 | 1.47 | 1.755 |
2021-01-20 | 5192129 | 73518 | 1300 | 4970470 | 148141 | 5043988 | 1.46 | 1.768 |
2021-01-21 | 5237606 | 73916 | 1316 | 5022620 | 141070 | 5096536 | 1.45 | 1.780 |
2021-01-22 | 5282223 | 74261 | 1328 | 5074830 | 133132 | 5149091 | 1.44 | 1.788 |
2021-01-23 | 5329707 | 74692 | 1337 | 5118386 | 136629 | 5193078 | 1.44 | 1.790 |
2021-01-24 | 5354349 | 75084 | 1349 | 5143500 | 135765 | 5218584 | 1.44 | 1.797 |
2021-01-25 | 5376086 | 75521 | 1360 | 5166016 | 134549 | 5241537 | 1.44 | 1.801 |
2021-01-26 | 5422763 | 75870 | 1371 | 5208269 | 138624 | 5284139 | 1.44 | 1.807 |
2021-01-27 | 5469247 | 76429 | 1378 | 5254391 | 138427 | 5330820 | 1.43 | 1.803 |
2021-01-28 | 5522189 | 76926 | 1386 | 5305839 | 139424 | 5382765 | 1.43 | 1.802 |
2021-01-29 | 5569264 | 77395 | 1399 | 5349116 | 142753 | 5426511 | 1.43 | 1.808 |
2021-01-30 | 5616530 | 77850 | 1414 | 5392885 | 145795 | 5470735 | 1.42 | 1.816 |
2021-01-31 | 5640818 | 78203 | 1420 | 5413065 | 149550 | 5491268 | 1.42 | 1.816 |
2021-02-01 | 5661842 | 78508 | 1425 | 5433878 | 149456 | 5512386 | 1.42 | 1.815 |
2021-02-02 | 5711413 | 78844 | 1435 | 5478304 | 154265 | 5557148 | 1.42 | 1.820 |
2021-02-03 | 5756714 | 79311 | 1441 | 5538554 | 138849 | 5617865 | 1.41 | 1.817 |
2021-02-04 | 5803095 | 79761 | 1448 | 5602796 | 120538 | 5682557 | 1.40 | 1.815 |
2021-02-05 | 5847178 | 80131 | 1459 | 5659590 | 107457 | 5739721 | 1.40 | 1.821 |
2021-02-06 | 5893353 | 80524 | 1464 | 5715152 | 97677 | 5795676 | 1.39 | 1.818 |
2021-02-07 | 5916975 | 80896 | 1471 | 5745328 | 90751 | 5826224 | 1.39 | 1.818 |
2021-02-08 | 5938197 | 81184 | 1474 | 5772170 | 84843 | 5853354 | 1.39 | 1.816 |
2021-02-09 | 5987405 | 81486 | 1482 | 5823289 | 82630 | 5904775 | 1.38 | 1.819 |
2021-02-10 | 6030023 | 81930 | 1486 | 5868017 | 80076 | 5949947 | 1.38 | 1.814 |
2021-02-11 | 6070008 | 82434 | 1496 | 5905960 | 81614 | 5988394 | 1.38 | 1.815 |
2021-02-12 | 6093369 | 82837 | 1507 | 5929671 | 80861 | 6012508 | 1.38 | 1.819 |
2021-02-13 | 6115337 | 83199 | 1514 | 5961887 | 70251 | 6045086 | 1.38 | 1.820 |
2021-02-14 | 6140086 | 83525 | 1522 | 5978361 | 78200 | 6061886 | 1.38 | 1.822 |
2021-02-15 | 6162860 | 83868 | 1527 | 5998846 | 80146 | 6082714 | 1.38 | 1.821 |
2021-02-16 | 6213490 | 84325 | 1534 | 6052268 | 76897 | 6136593 | 1.37 | 1.819 |
2021-02-17 | 6260567 | 84946 | 1538 | 6103211 | 72410 | 6188157 | 1.37 | 1.811 |
2021-02-18 | 6303214 | 85567 | 1544 | 6146927 | 70720 | 6232494 | 1.37 | 1.804 |
2021-02-19 | 6345992 | 86128 | 1550 | 6188748 | 71116 | 6274876 | 1.37 | 1.800 |
2021-02-20 | 6390631 | 86576 | 1553 | 6227918 | 76137 | 6314494 | 1.37 | 1.794 |
2021-02-21 | 6411340 | 86992 | 1557 | 6250992 | 73356 | 6337984 | 1.37 | 1.790 |
2021-02-22 | 6429144 | 87324 | 1562 | 6269359 | 72461 | 6356683 | 1.37 | 1.789 |
2021-02-23 | 6472679 | 87680 | 1573 | 6310934 | 74065 | 6398614 | 1.37 | 1.794 |
2021-02-24 | 6510988 | 88120 | 1576 | 6347880 | 74988 | 6436000 | 1.37 | 1.788 |
2021-02-25 | 6551214 | 88516 | 1581 | 6394026 | 68672 | 6482542 | 1.37 | 1.786 |
2021-02-26 | 6590066 | 88906 | 1585 | 6432218 | 68942 | 6521124 | 1.36 | 1.783 |
2021-02-27 | 6627215 | 89320 | 1595 | 6468906 | 68989 | 6558226 | 1.36 | 1.786 |
2021-02-28 | 6649006 | 89674 | 1603 | 6486443 | 72889 | 6576117 | 1.36 | 1.788 |
2021-03-01 | 6665755 | 90029 | 1605 | 6501981 | 73745 | 6592010 | 1.37 | 1.783 |
2021-03-01 | 6665755 | 90028 | 1605 | 6501982 | 73745 | 6592010 | 1.37 | 1.783 |
2021-03-02 | 6681976 | 90372 | 1606 | 6518172 | 73432 | 6608544 | 1.37 | 1.777 |
2021-03-03 | 6716203 | 90816 | 1612 | 6559520 | 65867 | 6650336 | 1.37 | 1.775 |
2021-03-04 | 6751900 | 91240 | 1619 | 6600571 | 60089 | 6691811 | 1.36 | 1.774 |
2021-03-05 | 6789011 | 91637 | 1627 | 6633667 | 63707 | 6725304 | 1.36 | 1.775 |
2021-03-06 | 6821943 | 92055 | 1632 | 6664717 | 65171 | 6756772 | 1.36 | 1.773 |
2021-03-07 | 6843126 | 92471 | 1634 | 6684259 | 66396 | 6776730 | 1.36 | 1.767 |
2021-03-08 | 6861809 | 92817 | 1642 | 6701598 | 67394 | 6794415 | 1.37 | 1.769 |
2021-03-09 | 6902984 | 93263 | 1645 | 6739220 | 70501 | 6832483 | 1.36 | 1.764 |
2021-03-10 | 6938884 | 93733 | 1648 | 6774873 | 70278 | 6868606 | 1.36 | 1.758 |
2021-03-11 | 6976985 | 94198 | 1652 | 6814539 | 68248 | 6908737 | 1.36 | 1.754 |
2021-03-12 | 7012664 | 94686 | 1662 | 6849240 | 68738 | 6943926 | 1.36 | 1.755 |
2021-03-13 | 7046782 | 95176 | 1667 | 6883732 | 67874 | 6978908 | 1.36 | 1.751 |
2021-03-14 | 7066401 | 95635 | 1669 | 6900923 | 69843 | 6996558 | 1.37 | 1.745 |
2021-03-15 | 7084940 | 96017 | 1675 | 6917333 | 71590 | 7013350 | 1.37 | 1.744 |
2021-03-16 | 7126077 | 96380 | 1678 | 6951653 | 78044 | 7048033 | 1.37 | 1.741 |
2021-03-17 | 7171510 | 96849 | 1686 | 6991199 | 83462 | 7088048 | 1.37 | 1.741 |
2021-03-18 | 7218087 | 97294 | 1688 | 7031071 | 89722 | 7128365 | 1.36 | 1.735 |
2021-03-19 | 7264941 | 97757 | 1690 | 7078843 | 88341 | 7176600 | 1.36 | 1.729 |
2021-03-20 | 7308950 | 98209 | 1693 | 7117723 | 93018 | 7215932 | 1.36 | 1.724 |
2021-03-21 | 7332714 | 98665 | 1696 | 7135741 | 98308 | 7234406 | 1.36 | 1.719 |
2021-03-22 | 7355964 | 99075 | 1697 | 7153757 | 103132 | 7252832 | 1.37 | 1.713 |
2021-03-23 | 7400990 | 99418 | 1704 | 7201400 | 100172 | 7300818 | 1.36 | 1.714 |
2021-03-24 | 7441210 | 99846 | 1707 | 7247038 | 94326 | 7346884 | 1.36 | 1.710 |
2021-03-25 | 7485859 | 100276 | 1709 | 7298600 | 86983 | 7398876 | 1.36 | 1.704 |
2021-03-26 | 7529403 | 100770 | 1716 | 7351224 | 77409 | 7451994 | 1.35 | 1.703 |
2021-03-27 | 7572568 | 101275 | 1721 | 7396119 | 75174 | 7497394 | 1.35 | 1.699 |
2021-03-28 | 7595596 | 101757 | 1722 | 7415234 | 78605 | 7516991 | 1.35 | 1.692 |
2021-03-29 | 7616330 | 102135 | 1726 | 7435257 | 78938 | 7537392 | 1.36 | 1.690 |
2021-03-30 | 7663999 | 102582 | 1729 | 7482993 | 78424 | 7585575 | 1.35 | 1.685 |
2021-03-31 | 7707800 | 103088 | 1731 | 7533410 | 71302 | 7636498 | 1.35 | 1.679 |
2021-04-01 | 7747303 | 103636 | 1735 | 7575008 | 68659 | 7678644 | 1.35 | 1.674 |
2021-04-02 | 7788295 | 104193 | 1737 | 7613350 | 70752 | 7717543 | 1.35 | 1.667 |
2021-04-03 | 7829601 | 104736 | 1740 | 7649577 | 75288 | 7754313 | 1.35 | 1.661 |
2021-04-04 | 7849476 | 105279 | 1744 | 7664398 | 79799 | 7769677 | 1.35 | 1.657 |
2021-04-05 | 7868820 | 105752 | 1748 | 7682571 | 80497 | 7788323 | 1.36 | 1.653 |
2021-04-06 | 7921290 | 106230 | 1752 | 7735905 | 79155 | 7842135 | 1.35 | 1.649 |
2021-04-07 | 7966167 | 106898 | 1756 | 7784627 | 74642 | 7891525 | 1.35 | 1.643 |
2021-04-08 | 8012421 | 107598 | 1758 | 7826829 | 77994 | 7934427 | 1.36 | 1.634 |
2021-04-09 | 8059113 | 108268 | 1764 | 7868933 | 81912 | 7977201 | 1.36 | 1.629 |
2021-04-10 | 8106630 | 108945 | 1765 | 7907671 | 90014 | 8016616 | 1.36 | 1.620 |
Rコード
データから検査陽性率、暫定致死率を計算し、表を作成
library(knitr)
date<- seq(as.Date("2020-02-01"), as.Date("2021-04-10"), by = "day")
検査を受けた人<-c(371,429,429,607,714,885,1130,1701,2340,2776,3629,5074,5797,6854,7519,7919,8171,
9265,10411,12161,14816,19621,22633,28615,36716,46127,57990,70940,85693,96985,109591,125851,
136707,146541,164740,178189,188518,196618,210144,222395,234998, 248647,261335,268212,274504,
286716,295647,307024,316664,327509,331780,338036,348582,357896,364942,376961,387925,394141,
395194,410564,421547,431743,443273,455032,461233,466804,477304,486003,494711,503051,510479,
514621,518743,527438,534552,538775,546463,554834,559109,563035,571014,577959,583971,589520,
595161,598285,601660,608514,614197,619881,623069,627562,630973,633921,640237,643095,649388,
654863,660030,663886,668492,680890,695920,711484,726747,740645,747653,753211,765574,776433,
788684,802418,814420,820289,826437,839475,852876,868666,885120,902901,910822,921391,939851,
956852,973858,990960,1005305,1012769,1018214,1035997,1051972,1066888,1081486,1094704,1100327,
1105719,1119767,1132823,1145712,1158063,1170901,1176463,1182066,1196012,1208597,1220478,1232315,
1243780,1251695,1259954,1273766,1285231,1295962,1307761,1319523,1326055,1331796,1346194,1359735,
1371771,1384890,1396941,1402144,1408312,1420616,1431316,1441348,1451017,1460204,1465299,1470193,
1482390,1492071,1500854,1510327,1518634,1522926,1526974,1537704,1547307,1556215,1563796,1571830,
1576246,1579757,1589780,1598187,1606487,1613652,1620514,1624650,1628303,1637844,1646652,1654898,
1665084,1675296,1681787,1688470,1697042,1715064,1734083,1754123,1775800,1791186,1804422,1825837,
1849506,1869579,1887717,1909329,1924170,1937689,1959080,1980295,2000552,2018906,2037045,2045935,
2051297,2066078,2082234,2099591,2119211,2135457,2143270,2151002,2164578,2178832,2191892,2206365,
2219162,2226701,2231589,2245112,2256899,2268999,2280276,2290345,2296517,2301303,2313044,2322999,
2328435,2333777,2339859,2346345,2352378,2365433,2378073,2388844,2400233,2404684,2410483,2415610,
2428771,2441443,2450739,2459426,2468527,2474529,2479226,2491311,2503489,2515325,2528621,2540679,
2546146,2552264,2567587,2582960,2597978,2612231,2624492,2630630,2636650,2649859,2662260,2673706,
2686314,2697249,2702880,2709199,2723960,2736534,2749772,2761411,2777289,2786878,2797691,2815755,
2834362,2853843,2873443,2896746,2908890,2922135,2946399,2966405,2988046,3009562,3032003,3046910,
3061172,3083997,3106970,3131886,3157410,3180496,3194867,3209376,3221317,3253220,3277948,3311213,
3349864,3374595,3397039,3441220,3488769,3538840,3589797,3646247,3683094,3713861,3772430,3826570,
3882210,3939357,3969415,4006412,4038307,4098179,4159522,4213880,4269318,4302799,4340838,4376608,
4439360,4504866,4569808,4630004,4689616,4723464,4751685,4814085,4872312,4925359,4978074,5032270,
5061290,5087220,5140325,5192128,5237606,5282223,5329707,5354349,5376086,5422763,5469247,5522189,
5569264,5616530,5640818,5661842,5711413,5756714,5803095,5847178,5893353,5916975,5938197,5987405,
6030023,6070008,6093369,6115337,6140086,6162860,6213490,6260567,6303214,6345992,6390631,6411340,
6429144,6472679,6510988,6551214,6590066,6627215,6649006,6665755,6681976,6716203,6751900,6789011,
6821943,6843126,6861809,6902984,6938884,6976985,7012664,7046782,7066401,7084940,7126077,7171510,
7218087,7264941,7308950,7332714,7355964,7400990,7441210,7485859,7529403,7572568,7595596,7616330,
7663999,7707800,7747303,7788295,7829601,7849476,7868820,7921290,7966167,8012421,8059113,8106630)
感染者数<-c(12,15,15,16,18,23,24,24,25,27,28,28,28,28,28,29,30,31,46,82,156,346,556,763,893,1146,
1595,2022,2931,3526,4212,4812,5328,5766,6284,6767,7134,7382,7513,7755,7869,7979,8086,8162,
8236,8320,8413,8565,8652,8799,8897,8961,9037,9137,9241,9332,9478,9583,9661,9786,9887,9976,
10062,10156,10237,10284,10331,10384,10423,10450,10480,10512,10537,10564,10591,10613,10635,
10653,10661,10674,10683,10694,10702,10708,10718,10728,10738,10752,10761,10765,10774,10780,
10793,10801,10804,10806,10810,10822,10840,10874,10909,10936,10962,10991,11018,11037,11050,
11065,11078,11110,11122,11142,11165,11190,11206,11225,11265,11344,11402,11441,11468,11503,
11541,11590,11629,11668,11719,11776,11814,11852,11902,11947,12002,12051,12084,12121,12155,
12198,12257,12306,12373,12421,12438,12484,12535,12563,12602,12653,12715,12757,12799,12850,
12904,12967,13030,13089,13137,13181,13243,13293,13338,13373,13417,13479,13512,13551,13612,
13672,13711,13745,13771,13816,13879,13938,13979,14092,14150,14175,14203,14251,14269,14305,
14336,14366,14389,14423,14456,14499,14519,14562,14598,14626,14660,14714,14770,14873,15039,
15318,15515,15761,16058,16346,16670,17002,17399,17665,17945,18265,18706,19077,19400,19699,
19947,20182,20449,20644,20842,21010,21177,21296,21432,21588,21743,21919,22055,22176,22285,
22391,22504,22657,22783,22893,22975,23045,23106,23216,23341,23455,23516,23611,23661,23699,
23812,23889,23952,24027,24091,24164,24239,24353,24422,24476,24548,24605,24703,24805,24878,
24988,25035,25108,25199,25275,25333,25422,25543,25698,25775,25836,25955,26043,26146,26271,
26384,26511,26635,26732,26807,26925,27050,27195,27284,27427,27553,27653,27799,27942,28133,
28338,28546,28768,28998,29311,29654,30017,30403,30733,31004,31353,31735,32318,32871,33374,
33763,34201,34652,35163,35703,36332,36915,37546,38161,38746,39416,40097,40786,41736,42766,
43484,44364,45439,46453,47517,48568,49665,50591,51458,52548,53529,54770,55902,56872,57679,
58723,59773,60740,61769,62593,63244,64264,64978,65816,66684,67358,67999,68664,69114,69650,
70204,70728,71240,71820,72340,72729,73114,73517,73916,74261,74692,75084,75521,75870,76429,
76926,77395,77850,78203,78508,78844,79311,79761,80131,80524,80896,81184,81486,81930,82434,
82837,83199,83525,83868,84325,84946,85567,86128,86576,86992,87324,87680,88120,88516,88906,
89320,89674,90028,90372,90816,91240,91637,92055,92471,92817,93263,93733,94198,94686,95176,
95635,96017,96380,96849,97294,97757,98209,98665,99075,99418,99846,100276,100770,101275,101757,
102135,102582,103088,103636,104193,104736,105279,105752,106230,106898,107598,108268,108945)
死者<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,4,7,8,11,12,13,16,17,22,28,32,35,42,44,50,51,
54,60,66,67,72,75,75,81,84,91,94,102,104,111,120,126,131,139,144,152,158,162,165,169,174,
177,183,186,192,200,204,208,211,214,217,222,225,229,230,232,234,236,237,238,240,240,240,242,
243,244,246,247,248,250,250,252,254,255,256,256,256,256,256,258,259,260,260,262,262,263,263,
263,264,264,266,266,267,269,269,269,269,269,270,271,272,273,273,273,273,273,273,274,276,276,
277,277,277,277,278,279,280,280,280,280,280,281,281,282,282,282,282,282,282,282,282,282,283,
283,284,285,285,287,288,288,289,289,289,289,291,293,294,295,296,296,297,297,298,298,298,299,
300,300,300,301,301,301,301,301,302,302,303,304,305,305,305,305,305,305,305,305,305,306,306,
307,309,309,309,309,310,312,313,316,321,323,324,324,326,329,331,333,334,336,341,344,346,350,
355,358,363,367,367,372,377,378,383,385,388,388,393,395,399,401,406,407,413,415,416,420,421,
422,422,425,427,428,430,432,433,434,438,439,441,443,444,444,447,450,453,455,457,457,457,460,
461,462,463,464,466,468,472,474,475,476,477,478,480,485,487,487,488,492,493,494,494,496,498,
501,503,505,509,510,513,515,516,522,523,526,526,526,529,536,540,545,549,552,556,564,572,578,
580,587,600,612,634,645,659,674,698,722,739,756,773,793,808,819,859,879,900,917,942,962,981,
1007,1027,1046,1081,1100,1125,1140,1165,1185,1195,1217,1236,1249,1264,1283,1300,1316,1328,1337,
1349,1360,1371,1378,1386,1399,1414,1420,1425,1435,1441,1448,1459,1464,1471,1474,1482,1486,1496,
1507,1514,1522,1527,1534,1538,1544,1550,1553,1557,1562,1573,1576,1581,1585,1595,1603,1605,1606,
1612,1619,1627,1632,1634,1642,1645,1648,1652,1662,1667,1669,1675,1678,1686,1688,1690,1693,1696,
1697,1704,1707,1709,1716,1721,1722,1726,1729,1731,1735,1737,1740,1744,1748,1752,1756,1758,1764,
1765)
陰性<-c(289,327,414,462,522,693,842,1057,1355,1940,2736,4054,5099,6134,6853,7313,7733,8277,9335,10446,
11953,13794,16038,19127,22550,28247,35298,44167,53608,61037,71580,85484,102965,118965,136624,
151802,162008,171778,184179,196100,209402,222728,235615,243778,251297,261105,270888,282555,
292487,303006,308343,315447,324105,334481,341332,352410,361883,369530,372002,383886,395075,
403882,414303,424732,431425,437225,446323,457761,468779,477303,485929,490321,494815,502223,
508935,513894,521642,530631,536205,540380,547610,555144,563130,569212,575184,578558,582027,
588559,595129,600482,603610,608286,611592,614944,620575,624280,630149,635174,640037,642884,
646661,653624,665379,679771,695854,711265,718943,726053,737571,748972,759473,770990,781686,
788766,796142,806206,820550,834952,849161,865162,876060,885830,899388,917397,934030,950526,
965632,974512,982026,996686,1013847,1029447,1045240,1059301,1066887,1072805,1084980,1099136,
1111741,1124567,1137058,1143971,1150225,1161250,1175817,1189015,1200885,1211261,1219975,1228698,
1240157,1252855,1263276,1273234,1284172,1291315,1297367,1309338,1322479,1334566,1348025,1360618,
1366897,1372988,1382815,1394468,1404332,1414235,1423570,1429601,1435120,1444710,1456441,1465498,
1475789,1484861,1489562,1494029,1503057,1513730,1522928,1531161,1539216,1544112,1547967,1556633,
1565241,1573957,1582065,1589847,1593249,1597281,1605695,1614563,1622330,1630079,1638639,1644464,
1649991,1656062,1667984,1682739,1699408,1716371,1726223,1738762,1757530,1778446,1798832,1817929,
1835883,1846450,1859866,1882155,1903098,1924384,1945798,1963958,1975137,1982892,2001276,2024113,
2044830,2067869,2085576,2093389,2101241,2114877,2130486,2143727,2158179,2171564,2179452,2186008,
2198784,2213156,2224876,2237246,2248321,2254028,2259055,2269289,2278591,2284517,2289830,2294545,
2300138,2305767,2318457,2331582,2343444,2355058,2361154,2366574,2371715,2383842,2398169,2407489,
2417036,2426101,2431549,2437188,2446599,2458574,2469969,2482493,2493016,2498628,2505546,2518541,
2533910,2545559,2559474,2572303,2579157,2584394,2597237,2611080,2621594,2633490,2643748,2649805,
2655844,2668452,2680047,2692546,2703159,2714259,2721954,2730609,2745555,2762363,2778664,2795283,
2814998,2824128,2834676,2857277,2878832,2900367,2920054,2939835,2950318,2963606,2984453,3006653,
3030395,3057061,3077314,3089605,3103205,3111297,3138724,3165087,3192739,3221386,3241700,3264308,
3303383,3348546,3393129,3431662,3471446,3493954,3516832,3569843,3620913,3678148,3727798,3756501,
3782564,3810749,3868494,3927978,3982802,4030622,4060819,4091122,4121612,4180631,4246968,4311362,
4372194,4435694,4463346,4492808,4557665,4619194,4684889,4744511,4804811,4829565,4856456,4912866,
4970470,5022620,5074830,5118386,5143500,5166016,5208269,5254391,5305839,5349116,5392885,5413065,
5433878,5478304,5538554,5602796,5659590,5715152,5745328,5772170,5823289,5868017,5905960,5929671,
5961887,5978361,5998846,6052268,6103211,6146927,6188748,6227918,6250992,6269359,6310934,6347880,
6394026,6432218,6468906,6486443,6501982,6518172,6559520,6600571,6633667,6664717,6684259,6701598,
6739220,6774873,6814539,6849240,6883732,6900923,6917333,6951653,6991199,7031071,7078843,7117723,
7135741,7153757,7201400,7247038,7298600,7351224,7396119,7415234,7435257,7482993,7533410,7575008,
7613350,7649577,7664398,7682571,7735905,7784627,7826829,7868933,7907671)
検査中<- 検査を受けた人- (陰性+感染者数)
#df<- data.frame(date,感染者数,死者,検査を受けた人_感染者除く,陰性,検査中)
#kable(df,row.names=F)
df<- data.frame(検査を受けた人,感染者数,死者,陰性,検査中)
rownames(df)<- date
#
# 陽性率、暫定致死率を計算
結果判明 <- 感染者数 + 陰性
陽性率 <- round(感染者数/結果判明*100,2)
暫定致死率 <- round(死者/感染者数*100,3)
#
df2 <- data.frame(結果判明,感染者数,陰性,陽性率,死者,暫定致死率)
colnames(df2)<- c("結果判明","感染者数","陰性","陽性率(%)","死者","暫定致死率(%)")
rownames(df2)<- date
#kable(df2)
kable(merge(df,df2[,c(1,4,6)],by=0))
新型コロナウイルスのPCR検査実施人数と感染状況(韓国)
#日本のPCR検査実施人数と結果(結果判明した数)
Jpcr1<- c(rep(NA,6),151,NA,NA,174,NA,190,200,214,NA,NA,487,523,532)+c(rep(NA,6),566,NA,NA,764,NA,764,764,764,NA,NA,764,764,764)
# 3/19 : PCR検査実施人数が減少したのは、千葉県が人数でなく件数でカウントしていたことが判明したため、千葉県の件数を引いたことによる
Jpcr2<- c(603,693,778,874,913,1017,1061,1229,1380,1510,1688,1784,1855,5690,5948,6647,7200,7347,7457,8771,9195,9376,11231,
12090,12197,12239,14322,14525,14072,18015,18134,18226+1173,18322+1189,22184+1417,21266+1426,22858+1484,24663+1513,
26105+1530,26401+1530,26607+1530,30088+1580,32002+1677,32002+1679,36687+1930,39992+2061,40263+3547,40481+4862,48357+6125,
52901+7768,54284+9274,57125+10817,61991+12071,63132+13420,63132+14741,72801+15921,76425+16982,81825+18049,86800+18743,
91050+19446,91695+20292,94826+21070,101818+21903,107430+22328,112108+23046,117367+23404,122700+23925,123633+24612,
124456+25407,133578+26139,136695+26731,137338+27442,145243+28078,152029+28669,153047+29375,153581+30176,154646+30868,156866+31232,
157563+31638,169546+31638,179043+32125,180478+32949,183845+33530,188646+34174,188031+34807,196816+35499,194323+35730,
203284+36255,206790+36833,211832+37490,216624+38222,218744+38704,221397+39346,224972+39701,227445+40203,229669+40703,
230562+41297,233399+41942,235422+42391,237367+43097,240334+43600,242734+44191,244824+44783,246100+45640,248662+46892,
251808+47640,254229+48575,257330+49303,260551+50067,262642+51012,263962+52057,267069+53155,269976+53870,273204+54697,
276032+55346,279184+56148,280720+57209,281697+58392,284092+59605,286739+60483,334250+61354,340426+62125,345249+62944,
347723+64117,351850+65272,357226+66712,360948+67704,365927+68739,370382+69528,375140+70620,378673+72270,380186+73594,
385696+74921,390347+76268,396911+77568,402371+78677,408968+80490,412102+82233,414720+84349,422948+86216,430047+87334,
438166+88551,449671+89911,459538+91291,464900+92893,466738+94538,477290+96313,488444+97433,499787+98623,516470+99750,
524327+100993,531493+102675,534755+104237,550714+105992,562828+106962,579185+108022,587495+109188,593190+110381,
602720+111734,606505+113167,630687+114713,648773+115744,667442+116953,687129+118109,704483+119436,714089+121486,
724688+123598,750482+125823,769222+127289,808945+128965,830960+130126,853658+131614,867334+133118,880670+134698,
887110+136296,920049+137286,945831+138662,966893+139815,1022133+141067,1033883+142601,1041321+144318,1067156+146160,
1088286+147065,1110223+148370,1132112+149752,1153978+151321,1167326+152924,1178279+154776,1200330+156469,1218568+157386,
1237921+158650,1259467+160293,1278064+161933,1290979+163802,1300556+165747,1322085+168061,1340216+169417,1364339+171053,
1387330+172346,1402787+173828,1414717+175349,1418892+176840,1442904+178416,1457541+179353,1480613+180649,1502919+181963,
1519398+183507,1529943+185188,1530415+186811,1555861+188621,1576024+189625,1594338+191175,1617651+193036,1635256+195161,
1644786+196891,1650939+198893,1653717+200694,1657103+201761,1684028+203299,1703049+204829,1731575+206423,1742097+208323,
1746647+210497,1766397+212703,1870073+213992,1889535+215599,1907206+217727,1929066+220189,1942750+222552,1947678+224528,
1969543+226459,1989978+227846,2011379+229421,2032776+231078,2052473+232768,2063276+234940,2068353+236851,2091588+238594,
2111416+239986,2133253+241845,2153990+243589,2174691+245523,2184587+248249,2189710+250611,2211630+253054,2227116+254540,
2249260+256307,2272678+258464,2295558+260827,2308769+263897,2314196+266431,2336798+268572,2362512+270262,2385576+272181,
2406080+274758,2427862+277477,2439260+281216,2445474+284221,2467009+287608,2474486+288777,2496326+290388,2525859+291995,
2554096+291443,2567556+293163,2575560+294737,2601036+296556,2626215+297511,2659896+298849,2689808+300427,2713846+301670,
2730497+303998,2739624+305471,2769733+307137,2797367+308363,2831997+309838,2869807+311568,2911577+313261,2936501+315193,
2956890+316935,2966450+319044,3010223+320340,3051275+321959,3093909+323782,3137260+325583,3159567+328270,3171542+330165,
3212877+332210,3257166+333602,3300402+335281,3337225+337190,3375370+338931,3397683+341634,3415295+344181,3453032+346828,
3493092+348603,3547649+350491,3592261+352740,3646095+354630,3674298+357689,3687442+360439,3741200+363619,3787017+365743,
3841320+367647,3903759+370045,3960180+372008,3987229+374828,3999511+378727,4055161+383283,4086271+386156,4158529+388421,
4222041+391069,4282302+393373,4314979+396043,4326942+398195,4401325+400085,4426175+402065,4447244+403864,4486819+405670,
4511891+407177,4520637+408569,4540928+411247,4625266+413737,4713712+415071,4787679+416785,4869119+418607,4932492+420307,
4968222+421935,4992724+425533,5012172+429314,5098723+431753,5179978+434449,5281233+436832,5351843+439975,5396505+443680,
5412543+447118,5493316+451547,5596629+456022,5683433+460947,5769202+466023,5870475+467292,5907591+469079,5926375+470336,
6008316+472374,6074651+473253,6165475+474662,6229807+475757,6312103+476999,6352528+478790,6370300+480301,6437569+482725,
6496679+483761,6565486+485170,6627388+486304,6698532+487519,6731575+489260,6749766+490619,6808265+492515,6886418+493341,
6951770+494671,6984635+495913,7041285+497289,7071505+498771,7086221+499986,7144642+501660,7204491+502466,7265629+503748,
7320133+504849,7395088+506005,7426667+507867,7441350+509398,7495530+511480,7513854+512316,7568556+513845,7627790+515305,
7685654+516802,7714850+519303,7734625+521148,7794715+524148,7877039+525508,7907207+527272,7976063+528983,8021216+530768,
8052274+533048,8066683+535057,8134600+537672,8174837+538949,8234989+540423,8291840+542130,8347402+544014,8378697+546770,
8395963+549455,8445833+553431,8493609+555715,8578528+557940,8633427+560131,8704728+562309,8735386+565106,8752383+567416,
8803852+570164,8856567+571717,8916541+573331,8979186+575034,9046482+576766,9086073+579131,9104960+581260,9177139+584163,
9218386+585610,9285821+587263,9351198+589611,9412344+591834,9453447+594837,9470636+596581,9530910+598885,9583929+600256,
9642353+601670,9713312+603621,9780921+605382)+829
Jpcr<- c(Jpcr1,Jpcr2)
Confirmed<- c(rep(NA,6),25,NA,NA,26,NA,28,29,33,NA,NA,59,66,73,84,93,105,132,144,156,164,186,210,230,239,254,268,284,317,348,
407,454,487,513,567,619,674,714,777,809,824,868,907,943,996,1046,1089,1128,1193,1291,1387,1499,1693,1866,1953,2178,
2381,2617,2935,3271,3654,3906,4257,4768,5347,6005,6748,7255,7645,8100,8582,9167,9795,10361,10751,11119,11496,11919,
12388,12829,13182,13385,13576,13852,14088,14281,14544,14839,15057,15231,15354,15463,15547,15649,15747,15798,15874,16024,
16079,16193,16237,16285,16305,16365,16385,16424,16513,16536,16550,16581,16623,16651,16683,16719,16804,16851,16884,16930,
16986,17018,17064,17103,17141,17174,17210,17251,17292,17332,17382,17429,17502,17587,17628,17668,17740,17799,17864,17916,
17968,18024,18110,18197,18297,18390,18476,18593,18723,18874,19068,19282,19522,19775,19981,20174,20371,20719,21129,21502,
21868,22220,22508,22890,23473,24132,24642,25096,25736,26303,27029,27956,28786,29382,29989,30961,31901,33049,34372,35836,
36689,38687,39858,41129,42263,43815,45439,46783,47990,48928,50210,51147,52217,53577,54714,55667,56685,57550,58501,59721,
60733,61747,62507,63121,63822,64668,65573,66423,67264,67865,68392,69001,69599,70268,70876,71419,71856,72234,72726,73221,
73901,74544,75218,75657,75958,76448,77009,77494,78073,78657,79140,79438,79768,80041,80497,81055,81690,82131,82494,83010,
83563,84215,84768,85339,85739,86047,86543,87020,87639,88233,88912,89347,89673,90140,90710,91431,92063,92656,93127,93480,
93933,94524,95138,95835,96534,97074,97498,98116,98852,99622,100392,101146,101813,102281,102900,103838,104782,105914,107086,
108084,108983,110156,111711,113298,114983,116677,118136,119326,120815,122966,125267,127665,130179,132358,133929,135400,
137261,139491,142068,144653,146760,148694,150386,152827,155232,157674,160098,162067,163929,165840,168573,171542,174299,
177287,179653,181870,184042,187103,190138,193031,195880,198523,200658,203113,206139,209980,213547,217312,220236,223120,
226596,230304,234395,238012,240954,243847,247960,252317,258393,265299,273154,280775,286752,292212,297315,302623,309214,
315910,322296,328294,334328,339774,345221,351020,356074,360661,364813,368143,371680,375607,379516,383083,386742,389518,
391626,393836,396429,399048,401355,403435,404990,406766,408186,410012,411751,413154,414472,415782,417765,419015,420408,
421967,423311,424507,425597,426456,427467,428553,429472,430539,431740,432773,433504,434356,435548,436728,437892,438956,
439992,440671,441729,443001,444289,445585,446873,447906,448688,449713,451186,452702,454158,455638,456781,457754,459043,
460897,462840,464866,466849,468614,470175,472112,474773,477458,480165,482867,485325,487545,489576,492875,496206,499793)
Deaths<- c(rep(NA,6),0,NA,NA,0,NA,0,0,1,NA,NA,1,1,1,1,1,1,1,1,1,1,3,4,5,5,6,6,6,6,6,6,6,7,9,12,15,19,21,22,24,28,29,31,33,35,36,
41,42,43,45,46,49,52,54,56,57,60,63,69,70,73,80,81,85,88,94,98,102,109,119,136,148,154,161,171,186,277,287,317,334,348,
351,376,389,415,432,458,492,510,521,543,551,557,600,613,621,643,668,687,710,725,744,749,763,771,777,796,808,820,830,846,
858,867,874,886,891,892,894,900,903,907,914,916,916,916,919,920,922,924,925,925,927,931,935,935,952,953,953,955,963,968,
969,971,971,972,972,974,975,976,977,977,977,978,980,981,982,982,982,982,982,984,985,985,985,985,985,988,989,990,992,993,
996,996,998,1001,1004,1006,1011,1011,1012,1016,1022,1026,1033,1039,1040,1047,1052,1059,1063,1073,1085,1088,1099,1115,1128,
1144,1155,1169,1176,1181,1196,1209,1226,1238,1255,1264,1279,1296,1307,1319,1330,1349,1357,1363,1377,1393,1406,1412,1423,1439,
1442,1451,1461,1473,1482,1495,1500,1500,1508,1512,1520,1532,1540,1545,1548,1557,1564,1571,1578,1590,1597,1599,1602,1605,1613,
1616,1624,1627,1629,1634,1638,1646,1650,1661,1670,1674,1676,1679,1685,1694,1706,1711,1718,1725,1730,1733,1744,1755,1766,1774,
1780,1786,1794,1806,1809,1812,1818,1829,1841,1851,1867,1880,1883,1885,1903,1913,1922,1943,1963,1974,1981,1989,2001,2022,2051,
2074,2106,2119,2139,2172,2213,2240,2283,2315,2335,2382,2420,2465,2502,2534,2562,2585,2643,2688,2739,2783,2828,2873,2900,2944,
2994,3050,3105,3155,3213,3252,3306,3349,3414,3460,3514,3548,3599,3655,3719,3791,3857,3932,3996,4044,4094,4145,4233,4315,4380,
4446,4501,4548,4647,4743,4830,4935,5019,5084,5158,5252,5361,5452,5546,5654,5722,5794,5912,6020,6135,6243,6338,6395,6476,6557,
6678,6774,6849,6912,6952,7015,7102,7196,7274,7333,7417,7474,7529,7584,7647,7722,7807,7860,7887,7933,7984,8052,8119,8178,8227,
8253,8299,8353,8402,8451,8509,8560,8590,8622,8678,8717,8758,8790,8812,8835,8861,8908,8938,8967,8998,9031,9061,9086,9113,9162,
9185,9213,9221,9231,9249,9279,9301,9334,9353)
Jdf<- data.frame(Tested=Jpcr,Confirmed,Deaths)
kj<-paste0(round(結果判明[length(結果判明)]/max(Jpcr,na.rm=T),2),"倍")
#
Jpop<- 127103388
Kpop<- 49039986
#
jp<- round(max(Jpcr2,na.rm=T)*1000000/Jpop,0)
kr<- round(max(結果判明,na.rm=T)*1000000/Kpop,0)
#
# 指数表示を抑制
options(scipen=2)
#png("pcr04.png",width=800,height=600)
par(mar=c(4,6,4,2),family="serif")
b<- barplot(t(df[,c(2,4,5)]),col=c("red","lightblue","gray80"),
ylim= c(0,max(Jpcr2,na.rm=T)*1.1),yaxt ="n",
legend=T,args.legend = list(x="topleft",inset=c(0.03,0.03)),axisnames=F)
labels<- sub("-","/",sub("-0","-",sub("^0","",sub("^....-","",rownames(df)))))
labelpos<- paste0(1:12,"/",1)
for (i in labelpos){
at<- which(labels== i)
axis(1,at=b[at],labels = rep(paste0(sub("/1","",i),"月"),length(at)),tck= -0.02)
}
mtext(text="2020年",at=b[1],side=1,line=2.5,cex=1.2)
mtext(text="2021年",at=b[336],side=1,line=2.5,cex=1.2)
# Add comma separator to axis labels
axis(side=2, at=axTicks(2), labels=formatC(axTicks(2), format="d", big.mark=','),las=1)
#text(x=b,y=検査を受けた人,labels= 検査を受けた人,pos=3,col="blue")
#points(x=b,y=Jpcr,pch=16)
lines(x=b,y=Jpcr,pch=16,lwd=2)
legend(x="topleft",inset=c(0.03,0.2),bty="n",legend="日本のPCR検査実施人数(データ:厚生労働省HP)\n(注意)一部自治体について件数を計上しているため、実際の人数より過大である。",lwd=2)
legend(x="topleft",inset=c(0.01,0.28),bty="n",legend="* 韓国の人口は日本の約41%",cex=1.2)
legend(x="topleft",inset=c(0.01,0.33),bty="n",legend=paste("* PCR検査で結果判明した数は日本の",kj),cex=1.2)
legend(x="topleft",inset=c(0.01,0.38),bty="n",legend="(日本:チャーター便帰国者及び空港検疫も含む)",cex=1.2)
legend("topleft",inset=c(0.27,-0.05),cex=1.5,bty="n",
legend=paste("PCR検査数(人口100万あたり)\n ・日本:",formatC(jp, format="d", big.mark=','),"人\n ・韓国:",formatC(kr, format="d", big.mark=','),"人(結果判明した数)"),text.col="blue")
title("韓国と日本のPCR検査実施人数の推移",cex.main=2)
#dev.off()
日本と韓国の新型コロナウイルスによる死亡者数推移(累計で計算)
# 日本、韓国の人口
# DataComputingパッケージの"CountryData"より
Jpop<- 127103388
Kpop<- 49039986
#
Jpos <- Deaths
#
jp<- round(max(Jpos,na.rm=T)*1000000/Jpop,2)
kr<- round(max(死者,na.rm=T)*1000000/Kpop,2)
#
ylim<- c(0,max(c(Jpos,死者),na.rm=T)*1.1)
#png("pcr04_2.png",width=800,height=600)
par(mar=c(5,4,4,2),family="serif")
plot(死者,type="l",col="blue",lwd=2,xaxt="n",xlab="",yaxt="n",ylab="",las=1,ylim=ylim,bty="n")
axis(side=2, at=axTicks(2), labels=formatC(axTicks(2), format="d", big.mark=','),las=1)
box(bty="l",lwd=2)
text(x=par("usr")[1],y=par("usr")[4]*1.02,labels="(人)",pos=2,xpd=T)
lines(Jpos,col="red",lwd=2)
#points(Jpos,col="red",pch=16)
#表示するx軸ラベルを指定
labels<- sub("-","/",sub("-0","-",sub("^0","",sub("^....-","",date))))
labelpos<- paste0(rep(1:12,each=1),"/",1)
#axis(1,at=1,labels =labels[1],tick=F)
for (i in labelpos){
at<- match(i,labels)
if (!is.na(at)){ axis(1,at=at,labels = i,tck= -0.02)}
}
legend("topleft",inset=0.03,pch=16,lwd=2,cex=1.5,col=c("red","blue"),legend=c("日本","韓国"),bty="n")
legend("topleft",inset=c(0,0.15),cex=1.5,bty="n",
legend=paste("新型コロナウイルスによる死亡者数(人口100万あたり)\n ・日本:",jp,"(5/14 韓国を追い抜く)\n ・韓国:",kr))
points(x=c(180,299,328,346,359,370,382,398,422),y=c(1001,2001,3050,4044,5019,6020,7015,8052,9031),pch=16,col="red")
text(x=c(180,299,328,346,359,370,382,398,422),y=c(1001,2001,3050,4044,5019,6020,7015,8052,9031),labels=c("7/29\n1001人","11/25\n2001人","12/24\n3050人",
"1/11\n4044人","1/24\n5019人","2/4\n6020人","2/16\n7015人","3/4\n8052人","3/28\n9031人"),pos=3)
points(x=c(180,299,328,346,359,370,382,398,422),y=c(300,513,756,1140,1349,1448,1534,1619,1722),pch=16,col="blue")
text(x=c(180,299,328,346,359,370,382,398,422),y=c(300,513,756,1140,1349,1448,1534,1619,1722),labels=c("7/29\n300人","11/25\n513人","12/24\n756人",
"1/11\n1140人","1/24\n1349人","2/4\n1448人","2/16\n1534人","3/4\n1619人","3/28\n1722人"),pos=c(rep(3,4),1,3,1))
mtext(text="2020年",at=1,side=1,line=2.5,cex=1.2)
mtext(text="2021年",at=336,side=1,line=2.5,cex=1.2)
title("日本と韓国の新型コロナウイルスによる死亡者数推移","Data : 日本(厚生労働省の報道発表資料) 韓国(KCDC)",cex.main=2)
#dev.off()
日本と韓国のPCR検査の検査陽性率(%)推移(累計で計算)
Jpos <- round(Confirmed/Jpcr*100,2)
ylim<- c(0,max(c(Jpos,df2$"陽性率(%)"),na.rm=T)*1.1)
#png("pcr05.png",width=800,height=600)
par(mar=c(5,6,4,4),family="serif")
plot(df2$"陽性率(%)",type="l",col="blue",lwd=2,xaxt="n",xlab="",ylab="陽性率(%)",las=1,ylim=ylim,bty="n")
box(bty="l",lwd=2)
lines(Jpos,col="red",lwd=2)
#points(Jpos,col="red",pch=16)
#表示するx軸ラベルを指定
labels<- sub("-","/",sub("-0","-",sub("^0","",sub("^....-","",date))))
labelpos<- paste0(1:12,"/",1)
#axis(1,at=1,labels =labels[1],tick=F)
for (i in labelpos){
at<- match(i,labels)
if (!is.na(at)){ axis(1,at=at,labels = paste0(sub("/1","",i),"月"),tck= -0.02)} #,hadj =0
}
legend("topright",inset=0.03,lwd=2,cex=1.5,col=c("red","blue"),legend=c("日本","韓国"),bty="n")
text(x=par("usr")[2],y=c(tail(df2$"陽性率(%)",1),tail(Jpos,1)),labels=paste0(c(tail(df2$"陽性率(%)",1),tail(Jpos,1)),"%"),xpd=T)
mtext(text="2020年",at=1,side=1,line=2.5,cex=1.2)
mtext(text="2021年",at=336,side=1,line=2.5,cex=1.2)
title("日本と韓国のPCR検査の検査陽性率(%)の推移","Data : 日本(厚生労働省の報道発表資料) 韓国(KCDC)",cex.main=2)
#dev.off()
日本と韓国のPCR検査の暫定致死率(%)推移(累計で計算)
Jpos <- round(Deaths/Confirmed*100,2)
ylim<- c(0,max(c(Jpos,df2$"暫定致死率(%)"),na.rm=T)*1.1)
#png("pcr06.png",width=800,height=600)
par(mar=c(5,6,4,2),family="serif")
plot(df2$"暫定致死率(%)",type="l",col="blue",lwd=2,xaxt="n",xlab="",ylab="暫定致死率(%)",las=1,ylim=ylim,bty="n")
box(bty="l",lwd=2)
lines(Jpos,col="red",lwd=2)
#points(Jpos,col="red",pch=16)
#表示するx軸ラベルを指定
labels<- sub("-","/",sub("-0","-",sub("^0","",sub("^....-","",date))))
labelpos<- paste0(rep(1:12,each=1),"/",1)
#axis(1,at=1,labels =labels[1],tick=F)
for (i in labelpos){
at<- match(i,labels)
if (!is.na(at)){ axis(1,at=at,labels = i,tck= -0.02)}
}
mtext(text="2020年",at=1,side=1,line=2.5,cex=1.2)
mtext(text="2021年",at=336,side=1,line=2.5,cex=1.2)
legend("topleft",inset=0.03,pch=16,lwd=2,cex=1.5,col=c("red","blue"),legend=c("日本","韓国"),bty="n")
title("日本と韓国の暫定致死率(%)の推移","Data : 日本(厚生労働省の報道発表資料) 韓国(KCDC)",cex.main=2)
#dev.off()
週単位の陽性者増加比(日本、韓国)
library(TTR)
jpkr<- data.frame(Japan=Confirmed,"South_Korea"=感染者数)
rownames(jpkr)<- sub("-","/",sub("-0","-",sub("^0","",sub("2020-","",date))))
#(日本のデータ)欠損値が多い箇所は外す
jpkr<- jpkr[17:nrow(jpkr),]
#
#grep("TRUE",is.na(jpkr$Japan))
#jpkr$Japan[grep("TRUE",is.na(jpkr$Japan))]<- (jpkr$Japan[grep("TRUE",is.na(jpkr$Japan))-1]+jpkr$Japan[grep("TRUE",is.na(jpkr$Japan))+1])/2
#
x<- apply(jpkr,2,diff)
fun<- function(x){round(runSum(x,n=7)/(runSum(x,n=14) -runSum(x,n=7)),2)}
jpkr2<- apply(x,2,fun)
# InfにNAを入れる
#jpkr2[jpkr2==Inf]<- NA
jpkr2<- data.frame(jpkr2)
rownames(jpkr2)<- rownames(jpkr)[-1]
#
pdat<- jpkr2[14:nrow(jpkr2),]
#png("pcr06_2.png",width=800,height=600)
par(mar=c(5,6,4,7),family="serif")
matplot(pdat,type="l",lwd=2,las=1,lty=1,ylim=c(0,max(pdat)),col=c("red","blue"),xlab="",ylab="",xaxt="n",bty="n")
box(bty="l",lwd=2.5)
abline(h=1,lty=2,col="darkgreen",lwd=1.5)
labels<-gsub("^.*/","",rownames(pdat))
pos<-gsub("/.*$","",rownames(pdat))
pos<- factor(pos,levels=min(as.numeric(pos)):max(as.numeric(pos)))
#axis(1,at=1:nrow(pdat),labels =NA)
#月の区切り
#axis(1,at=cumsum(as.vector(table(pos)))+0.5, labels =NA,tck=-0.1,lty=2 ,lwd=1)
for (i in c("1","10","20")){
at<- grep("TRUE",is.element(labels,i))
axis(1,at=at,labels = rep(i,length(at)))
}
#Month<-c("January","February","March","April","May","June","July","August","September","October","November","December")
Month<-c("Jan.","Feb.","Mar.","Apr.","May","Jun.","Jul.","Aug.","Sep.","Oct.","Nov.","Dec.")
#cut(1:12,breaks = seq(0,12),right=T, labels =Month)
mon<-cut(as.numeric(names(table(pos))),breaks = seq(0,12),right=T, labels =Month)
# 月の中央
#mtext(text=mon,at=cumsum(as.vector(table(pos)))-as.vector(table(pos)/2),side=1,line=2)
# 月のはじめ
mtext(text=mon,at=1+cumsum(as.vector(table(pos)))-as.vector(table(pos)),side=1,line=2)
text(x=par("usr")[2],y=pdat[nrow(pdat),],labels=colnames(pdat),xpd=T,pos=1)
arrows(par("usr")[2]*1.08, 1.1,par("usr")[2]*1.08,1.68,length = 0.2,lwd=2.5,xpd=T)
text(x=par("usr")[2]*1.08,y=1.9,labels="増加\n傾向",xpd=T)
arrows(par("usr")[2]*1.08, 0.9,par("usr")[2]*1.08,0.32,length = 0.2,lwd=2.5,xpd=T)
text(x=par("usr")[2]*1.08,y=0.1,labels="減少\n傾向",xpd=T)
title("週単位の陽性者増加比(日本、韓国)",cex.main=1.5)
#dev.off()
韓国のPCR検査の結果(日別)
dat<-rbind(diff(df2$感染者数),diff(df2$陰性))
rownames(dat)<- c("陽性","陰性")
colnames(dat)<- rownames(df2[-1,])
#png("pcr08.png",width=800,height=600)
par(mar=c(6,6,4,2),family="serif")
b<- barplot(dat,col=c("red","lightblue"),las=1,legend=T,ylim=c(0,max(dat,na.rm=T)*1.1),
args.legend=list(x="topleft",inset=c(0.03,0.03)),axisnames=F)
box(bty="l",lwd=2)
labels<- sub("-","/",sub("-0","-",sub("^0","",sub("^....-","",colnames(dat)))))
labelpos<- paste0(1:12,"/",1)
for (i in labelpos){
at<- match(i,labels)
if (!is.na(at)){ axis(1,at=b[at],labels = paste0(sub("/1","",i),"月"),tck= -0.02)}
}
mtext(text="2020年",at=b[1],side=1,line=2.5,cex=1.2)
mtext(text="2021年",at=b[335],side=1,line=2.5,cex=1.2)
title("韓国のPCR検査の結果(日別)",cex.main=2)
#dev.off()
日本と韓国の検査陽性者数(日別)
ylim<- max(max(diff(Jdf$Confirmed),na.rm=T),max(diff(df$感染者数),na.rm=T))*1.2
#png("pcr07.png",width=800,height=600)
par(mar=c(6,6,4,2),family="serif")
b<- barplot(diff(Jdf$Confirmed),col=rgb(1,0,0,alpha=0.5),axes=F,ylim=c(0,ylim))
b<- barplot(diff(df$感染者数),col=rgb(0,1,0,alpha=0.5),las=1,add=T,ylim=c(0,ylim))
labels<- sub("-","/",sub("-0","-",sub("^0","",sub("^....-","",date[-1]))))
labelpos<- paste0(1:12,"/",1)
for (i in labelpos){
at<- match(i,labels)
if (!is.na(at)){ axis(1,at=b[at],labels = paste0(sub("/1","",i),"月"),tck= -0.02)}
}
mtext(text="2020年",at=b[1],side=1,line=2.5,cex=1.2)
mtext(text="2021年",at=b[335],side=1,line=2.5,cex=1.2)
legend("topleft",inset=c(0.03,0.08),pch=15,col=c(rgb(1,0,0,alpha=0.5),rgb(0,1,0,alpha=0.5)),legend=c("日本","韓国"),bty="n",cex=1.5)
title("日本と韓国の検査陽性者数(日別)",cex.main=1.5)
#dev.off()
日本と韓国の検査者数(韓国の場合は「結果が判明した数」)(日別)
kdf<- diff(結果判明)
jdf<- diff(Jpcr)
ymin<- min(min(kdf,na.rm=T),min(jdf,na.rm=T))
ymax<- max(max(kdf,na.rm=T),max(jdf,na.rm=T))
#png("pcr07_2.png",width=800,height=600)
par(mar=c(4,6,4,2),family="serif")
plot(jdf,type="l",lwd=2,col="red",xlab="",ylab="",xaxt="n",yaxt="n",bty="n",ylim=c(ymin,ymax),
panel.first=grid(NA,NULL,lty=2,col="darkgray"))
box(bty="l",lwd=2)
lines(kdf,lwd=2,col="blue")
#points(kdf,pch=16,cex=1,col="blue")
axis(side=2, at=axTicks(2), labels=formatC(axTicks(2), format="d", big.mark=','),las=1)
#表示するx軸ラベルを指定
labels<- sub("-","/",sub("-0","-",sub("^0","",sub("^....-","",date[-1]))))
labelpos<- paste0(1:12,"/",1)
#axis(1,at=1,labels =labels[1],tick=F)
for (i in labelpos){
at<- match(i,labels)
if (!is.na(at)){ axis(1,at=at,labels = paste0(sub("/1","",i),"月"),tck= -0.02)}
}
for(i in 1:(length(jdf)-1)){
polygon(c(i,i,i+1,i+1), c(0,jdf[i],jdf[i+1], 0), col = rgb(1,0,0,alpha=0.5), lty=0)
}
for(i in 1:(length(kdf)-1)){
polygon(c(i,i,i+1,i+1), c(0,kdf[i],kdf[i+1], 0), col = rgb(0,0,1,alpha=0.5), lty=0)
}
mtext(text="2020年",at=1,side=1,line=2.5,cex=1.2)
mtext(text="2021年",at=335,side=1,line=2.5,cex=1.2)
legend("topleft",inset=0.03,pch=16,lwd=2,cex=1.5,col=c("red","blue"),legend=c("日本","韓国"),bty="n")
legend("topleft",inset=c(0.03,0.15),cex=1.2,
legend="日本の6月18日の検査人数が極端に多いのは\n東京都が医療機関等の行った検査の検査人数を過去に遡って計上しているため",bty="n")
title("日本と韓国の検査者数(韓国の場合は「結果が判明した数」)(日別)",cex.main=1.5)
#dev.off()
日本と韓国のPCR検査の検査陽性率(%)の7日移動平均
library(TTR)
x0<- data.frame(日本のPCR検査数=Jpcr,日本の検査陽性者数=Confirmed,韓国のPCR検査数=結果判明,韓国の検査陽性者数=感染者数)
#欠損値のなくなる17番目のデータ以降
x0<- x0[-c(1:16),]
#差分
x1<- apply(x0,2,diff)
#7日間の合計
x2<- data.frame(apply(x1,2,runSum,n=7))
rownames(x2)<- date[-c(1:17)]
Jpos<- round((x2$日本の検査陽性者数/x2$日本のPCR検査数)*100,2)
Kpos<- round((x2$韓国の検査陽性者数/x2$韓国のPCR検査数)*100,2)
#
#png("pcr07_3.png",width=800,height=600)
par(mar=c(5,6,4,6),family="serif")
plot(Jpos,type="l",col="red",lwd=2,xaxt="n",xlab="",ylab="陽性率(%)",las=1,bty="n",ylim=c(0,12))
box(bty="l",lwd=2)
lines(Kpos,col="blue",lwd=2)
#points(Kpos,col="blue",pch=16)
#表示するx軸ラベルを指定
labels<- sub("-","/",sub("-0","-",sub("^0","",sub("^....-","",date[-1]))))
labelpos<- paste0(1:12,"/",1)
#axis(1,at=1,labels =labels[1],tick=F)
for (i in labelpos){
at<- match(i,labels)
if (!is.na(at)){ axis(1,at=at,labels = paste0(sub("/1","",i),"月"),tck= -0.02)}
}
legend("topright",inset=0.03,lwd=2,cex=1.5,col=c("red","blue"),legend=c("日本","韓国"),bty="n")
text(x=par("usr")[2],y=c(tail(Jpos,1),tail(Kpos,1)),labels=paste0(c(tail(Jpos,1),tail(Kpos,1)),"%"),xpd=T)
title("日本と韓国のPCR検査の検査陽性率(%)の7日移動平均","Data : 日本(厚生労働省の報道発表資料) 韓国(KCDC)",cex.main=2)
#dev.off()
韓国の(報告された)感染者数 対数表示(日別)
dat<- diff(df2$感染者数)
dat[dat==0]<- NA
dat2<- diff(df2$結果判明)
ylim<- c(0.9,60000)
#png("pcr08_2.png",width=800,height=600)
par(mar=c(4,5,4,3),family="serif")
b<- barplot(rep(NA,length(dat)),las=1,log="y",ylim=ylim)
abline(h=10^(0:4),col="darkgray",lwd=1.2,lty=3)
for (i in 1:9){
abline(h=i*10^(0:4),col="darkgray",lwd=0.8,lty=3)
}
barplot(dat,col="red",las=1,log="y",ylim=ylim,axes=F,add=T)
labels<- sub("-","/",sub("-0","-",sub("^0","",sub("^....-","",date[-1]))))
labelpos<- paste0(1:12,"/",1)
for (i in labelpos){
at<- match(i,labels)
if (!is.na(at)){ axis(1,at=b[at],labels = paste0(sub("/1","",i),"月"),tck= -0.02)}
}
mtext(text="2020年",at=b[1],side=1,line=2.5,cex=1.2)
lines(x=b,y=dat2,lwd=2,col="darkgreen")
points(x=b,y=dat2,pch=16,col="darkgreen")
#text(x=par("usr")[2],y=dat2[length(dat2)],labels="結果判明",col="darkgreen",xpd=T)
mtext(text="2020年",at=b[1],side=1,line=2.5,cex=1.2)
mtext(text="2021年",at=b[335],side=1,line=2.5,cex=1.2)
legend("topleft",inset=0.03,bty="n",legend="PCR検査結果判明\nConfirmed+Tested negative",lwd=2,lty=1,pch=16,col="darkgreen")
title("韓国の検査陽性者数 対数表示(日別)",cex.main=1.5)
#dev.off()
日本の(報告された)感染者数 対数表示(日別)
dat<- diff(Jdf$Confirmed)
dat[dat==0]<- NA
dat2<- diff(Jdf$Tested)
# 韓国のグラフにyの範囲を揃える
ylim<- c(0.9,60000)
#png("pcr08_3.png",width=800,height=600)
par(mar=c(4,5,4,3),family="serif")
b<- barplot(rep(NA,length(dat)),las=1,log="y",ylim=ylim)
abline(h=10^(0:4),col="darkgray",lwd=1.2,lty=3)
for (i in 1:9){
abline(h=i*10^(0:4),col="darkgray",lwd=0.8,lty=3)
}
barplot(dat,col="red",las=1,log="y",ylim=ylim,axes=F,add=T)
labels<- sub("-","/",sub("-0","-",sub("^0","",sub("^....-","",date[-1]))))
labelpos<- paste0(1:12,"/",1)
for (i in labelpos){
at<- match(i,labels)
if (!is.na(at)){ axis(1,at=b[at],labels = paste0(sub("/1","",i),"月"),tck= -0.02)}
}
mtext(text="2020年",at=b[1],side=1,line=2.5,cex=1.2)
mtext(text="2021年",at=b[335],side=1,line=2.5,cex=1.2)
lines(x=b,y=dat2,lwd=2,col="darkgreen")
points(x=b,y=dat2,pch=16,col="darkgreen")
legend("topleft",inset=0.03,bty="n",legend="PCR検査実施人数",lwd=2,lty=1,pch=16,col="darkgreen")
title("日本の検査陽性者数 対数表示(日別)",cex.main=1.5)
#dev.off()
日本、韓国、台湾、シンガポール、香港のReported Confirmed(報告された感染者)を計算、プロット
# read.csvの際には、check.names=Fをつける
url<- "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv"
Confirmed<- read.csv(url,check.names=F)
# Country/Regionごとに集計
#Confirmed
Ctl<- aggregate(Confirmed[,5:ncol(Confirmed)], sum, by=list(Confirmed$"Country/Region"))
rownames(Ctl)<-Ctl[,1]
Ctl<- Ctl[,-1]
#Japan,South Korea,Taiwan,Singapore
datC<-Ctl[grep("(Japan|Korea, South|Taiwan*|Singapore)",rownames(Ctl)),]
#Hong Kong
HK<- Confirmed[Confirmed$"Province/State"=="Hong Kong",5:ncol(Confirmed)]
rownames(HK)<- "Hong Kong"
datC<- rbind(datC,HK)
datC[datC<0]<- NA
#png("pcr11.png",width=800,height=600)
par(mar=c(4,5,4,10),family="serif")
matplot(t(datC),type="l",lty=1,lwd=3,xaxt="n",yaxt="n",bty="n",ylab="",xaxs="i")
box(bty="l",lwd=2)
#y軸ラベル
axis(side=2, at=axTicks(2), labels=formatC(axTicks(2), format="d", big.mark=','),las=1)
#表示するx軸ラベルを指定
labelpos<- paste0(1:12,"/",1)
for (i in labelpos){
at<- match(i,sub("/..$","",colnames(datC)))
if (!is.na(at)){ axis(1,at=at,labels = paste0(sub("/1","",i),"月"),tck= -0.02)}
}
mtext(text="2020年",at=1,side=1,line=2.5,cex=1.2)
mtext(text="2021年",at=346,side=1,line=2.5,cex=1.2)
text(x=par("usr")[2],y=apply(datC,1,max,na.rm=T),labels=paste(rownames(datC),":",formatC(apply(datC,1,max,na.rm=T), format="d", big.mark=',')),pos=4,xpd=T)
title("Reported Confirmed : Japan , South Korea , Taiwan , Singapore , Hong Kong")
#dev.off()
日本、韓国、台湾、シンガポール、香港の致死率を計算、プロット
url<- "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv"
Deaths<- read.csv(url,check.names=F)
#Deaths
Dtl<- aggregate(Deaths[,5:ncol(Deaths)], sum, by=list(Deaths$"Country/Region"))
rownames(Dtl)<-Dtl[,1]
Dtl<- Dtl[,-1]
#
datD<-Dtl[grep("(Japan|Korea, South|Taiwan*|Singapore)",rownames(Dtl)),]
#Hong Kong
HK<- Deaths[Deaths$"Province/State"=="Hong Kong",5:ncol(Deaths)]
rownames(HK)<- "Hong Kong"
datD<- rbind(datD,HK)
datD[datD<0]<- NA
datD<- datD[order(apply(datD,1,max,na.rm=T),decreasing=T),]
# 亡くなった人の数
knitr::kable(apply(datD,1,max,na.rm=T))
#
# 致死率(%)計算
#DpC<- matrix(NA,nrow=nrow(datD),ncol=ncol(datD))
DpC<- NULL
for (i in rownames(datD)){
temp<- round(datD[rownames(datD)== i,] / datC[rownames(datC)== i,]*100,2)
DpC<- rbind(DpC,temp)
}
#
DpC<- DpC[order(DpC[,ncol(DpC)],decreasing=T),]
n<-nrow(DpC)
col<- rainbow(n)
#pch<-rep(c(0,1,2,4,5,6,15,16,17,18),3)
#png("Coronavirus01_1_2.png",width=800,height=600)
par(mar=c(4,5,4,10),family="serif")
#40日めから
matplot(t(DpC)[40:ncol(DpC),],type="l",lty=1,lwd=3,las=1,col=col,ylab="Reported Deaths/Reported Confirmed(%)",xaxt="n",bty="n")
box(bty="l",lwd=2)
#axis(1,at=1:nrow(t(DpC)[40:ncol(DpC),]),labels=sub("/20","",rownames(t(DpC)[40:ncol(DpC),])))
#表示するx軸ラベルを指定
labelpos<- paste0(1:12,"/",1)
for (i in labelpos){
at<- match(i,sub("/..$","",rownames(t(DpC)[40:ncol(DpC),])))
if (!is.na(at)){ axis(1,at=at,labels = paste0(sub("/1","",i),"月"),tck= -0.02)}
}
mtext(text="2020年",at=1,side=1,line=2.5,cex=1.2)
mtext(text="2021年",at=307,side=1,line=2.5,cex=1.2)
legend(x=par("usr")[2],y=par("usr")[4],legend=rownames(DpC),lty=1,lwd=3,col=col,bty="n",title="Country/Region",xpd=T)
title("Reported Deaths / Reported Confirmed (%) ")
#dev.off()
日本、韓国、台湾、シンガポール、香港の面積、人口、人口密度
library(DataComputing)
library(knitr)
data("CountryData")
adata<- CountryData[grep("(Japan|Korea, South|Taiwan|Singapore|Hong Kong)",CountryData$country),1:3]
# 人口密度計算
adata$"Population density"<- round(adata$pop/adata$area,0)
#人口で並べ替える(降順)
adata<- adata[order(adata$pop,decreasing=T),]
kable(data.frame(lapply(adata,function(x)formatC(x, format="f", big.mark=",",digits=0))),
row.names=F,align=c("c",rep("r",3)))
表
library(knitr)
x<- as.data.frame(apply(datC,1,max,na.rm=T))
colnames(x)<- "Confirmed"
y<- as.data.frame(apply(datD,1,max,na.rm=T))
colnames(y)<- "Deaths"
x<- merge(x,y,by =0)
rownames(x)<- x[,1]
x<- x[,-1]
x$"Deaths/Confirmed (%)"<- round(x[,2]/x[,1]*100,2)
rownames(x)<- sub("\\*","",rownames(x))
x<- merge(x,adata[,c(1,3)],by.x=0,by.y="country")
x$"Deaths/millionpeople"<- round(x$Deaths/x$pop*1000000,2)
x[,1]<-factor(x[,1],levels=c("Japan","Korea, South","Taiwan" ,"Hong Kong", "Singapore"))
x<- x[order(x[,1],decreasing=F),]
kable(data.frame(Confirmed=formatC(x[,2], format="f", big.mark=",",digits=0),x[,c(3:4,6)],check.names=F,row.names=x[,1]),
row.names=T,align=rep("r",3))
日本、韓国、台湾、シンガポール、香港の100万人あたりの新型コロナウイルスによる死者数
# Deaths/millionpeopleで並べ替え
dat<- x[order(x[,"Deaths/millionpeople"]),]
#png("DperMil01.png",width=800,height=600)
par(mar=c(7,7,3,2),family="serif")
b<- barplot(dat[,"Deaths/millionpeople"],horiz=T,col="pink",xaxt="n",names=dat[,1],xlim=c(0,max(dat[,"Deaths/millionpeople"])*1.2),las=1)
axis(side=1, at=axTicks(1), labels=formatC(axTicks(1), format="d", big.mark=','))
text(x=dat[,"Deaths/millionpeople"],y=b,labels= dat[,"Deaths/millionpeople"],pos=4)
title("日本、韓国、台湾、シンガポール、香港の人口100万人あたりの新型コロナウイルスによる死者数")
#dev.off()
新型コロナウイルスによる死者数 in アジア
アジアの国のデータ:How many Countries in Asia?:https://www.worldometers.info/geography/how-many-countries-in-asia/
に台湾のデータを加えた。
(注意)日本語名は手打ちしているのでもしかしたら間違いがあるかもしれません。
text<- "country,Population(2020),Subregion,Jname
Turkey,84339067,Western Asia,トルコ
Iraq,40222493,Western Asia,イラク
Saudi Arabia,34813871,Western Asia,サウジアラビア
Yemen,29825964,Western Asia,イエメン
Syria,17500658,Western Asia,シリア
Jordan,10203134,Western Asia,ヨルダン
Azerbaijan,10139177,Western Asia,アゼルバイジャン
United Arab Emirates,9890402,Western Asia,アラブ首長国連邦
Israel,8655535,Western Asia,イスラエル
Lebanon,6825445,Western Asia,レバノン
Oman,5106626,Western Asia,オマーン
State of Palestine,5101414,Western Asia,パレスチナ
Kuwait,4270571,Western Asia,クエート
Georgia,3989167,Western Asia,グルジア
Armenia,2963243,Western Asia,アルメニア
Qatar,2881053,Western Asia,カタール
Bahrain,1701575,Western Asia,バーレーン
Cyprus,1207359,Western Asia,キプロス
India,1380004385,Southern Asia,インド
Pakistan,220892340,Southern Asia,パキスタン
Bangladesh,164689383,Southern Asia,バングラデシュ
Iran,83992949,Southern Asia,イラン
Afghanistan,38928346,Southern Asia,アフガニスタン
Nepal,29136808,Southern Asia,ネパール
Sri Lanka,21413249,Southern Asia,スリランカ
Bhutan,771608,Southern Asia,ブータン
Maldives,540544,Southern Asia,モルジブ
Indonesia,273523615,South-Eastern Asia,インドネシア
Philippines,109581078,South-Eastern Asia,フィリピン
Vietnam,97338579,South-Eastern Asia,ベトナム
Thailand,69799978,South-Eastern Asia,タイ
Myanmar,54409800,South-Eastern Asia,ミャンマー
Malaysia,32365999,South-Eastern Asia,マレーシア
Cambodia,16718965,South-Eastern Asia,カンボジア
Laos,7275560,South-Eastern Asia,ラオス
Singapore,5850342,South-Eastern Asia,シンガポール
Timor-Leste,1318445,South-Eastern Asia,東ティモール
Brunei,437479,South-Eastern Asia,ブルネイ
China,1439323776,Eastern Asia,中国
Japan,126476461,Eastern Asia,日本
South Korea,51269185,Eastern Asia,韓国
North Korea,25778816,Eastern Asia,北朝鮮
Mongolia,3278290,Eastern Asia,モンゴル
Uzbekistan,33469203,Central Asia,ウズベキスタン
Kazakhstan,18776707,Central Asia,カザクスタン
Tajikistan,9537645,Central Asia,タジキスタン
Kyrgyzstan,6524195,Central Asia,キルギスタン
Turkmenistan,6031200,Central Asia,トルクメニスタン
Taiwan,23816775,Eastern Asia,台湾"
#
asia<- read.csv(text=text,check.names=F)
# 米ジョンズ・ホプキンス大学のデータは、「Taiwan*」 , 「Korea, South」となっているので上のデータと一致させる。
rownames(Dtl)<- gsub("\\*","",rownames(Dtl))
rownames(Dtl)<- gsub("Korea, South","South Korea",rownames(Dtl))
asia$country[!is.element(asia$country,rownames(Dtl))]
#[1] State of Palestine Myanmar North Korea Turkmenistan
#
dat<-Dtl[is.element(rownames(Dtl),asia$country),]
#
# 一番新しい日のデータのみ取り出す。
df<- dat[,ncol(dat),drop=F]
names(df)<- "Deaths"
nrow(df)
df<- merge(df,asia,by.x=0,by.y="country")
nrow(df)
df<- df[order(df$Deaths,decreasing=F),]
knitr::kable(df,row.names=F)
df$Subregion<- factor(df$Subregion,
levels=c("Central Asia","Eastern Asia","South-Eastern Asia","Southern Asia","Western Asia"))
新型コロナウイルスによる死者数 in アジア
# 東アジアの国の名前を赤
col<- ifelse(is.element(df$Subregion,c("Eastern Asia")),"red","black")
#png("CdeathsA01.png",width=800,height=800)
par(mar=c(3,8,4,2),family="serif")
b<- barplot(df$Deaths,las=1,col=as.numeric(unclass(df$Subregion)),horiz=T,xlim=c(0,max(df$Deaths,na.rm=T)*1.2),xaxt="n")
axis(side=1, at=axTicks(1), labels=formatC(axTicks(1), format="d", big.mark=','))
axis(2, at = b,labels=NA,tck= -0.008)
text(x=par("usr")[1],y=b, labels = df$Jname, col=col,pos=2,xpd=T,font=3)
title("新型コロナウイルスによる死者数 in アジア\n(データのない国 : Myanmar,North Korea,Turkmenistan,State of Palestine)",
cex.main=1.5)
legend("bottomright",inset=c(0.15,0.05),legend=c("Central Asia","Eastern Asia","South-Eastern Asia","Southern Asia","Western Asia"),
pch=15,col=1:5,cex=1.5,bty="n",title="Subregion")
#dev.off()
新型コロナウイルスによる人口100万人あたりの死者数 in アジア
df$DpP <- round(1000000*df$Deaths/df$"Population(2020)",2)
df<- df[order(df$DpP,decreasing=F),]
#
# 東アジアの国の名前を赤
col<- ifelse(is.element(df$Subregion,c("Eastern Asia")),"red","black")
#png("CdeathsA02.png",width=800,height=800)
par(mar=c(3,8,4,2),family="serif")
b<- barplot(df$DpP,las=1,col=as.numeric(unclass(df$Subregion)),horiz=T,xlim=c(0,max(df$DpP,na.rm=T)*1.2))
axis(2, at = b,labels=NA,tck= -0.008)
text(x=par("usr")[1],y=b,labels = df$Jname,col=col,pos=2,xpd=T,font=3)
title("新型コロナウイルスによる人口100万人あたりの死者数 in アジア\n(データのない国 : Myanmar,North Korea,Turkmenistan,State of Palestine)",
cex.main=1.5)
legend("bottomright",inset=c(0.15,0.05),legend=c("Central Asia","Eastern Asia","South-Eastern Asia","Southern Asia","Western Asia"),
pch=15,col=1:5,cex=1.5,bty="n",title="Subregion")
#dev.off()
「人口あたりの死者数」で評価した「日本モデル」より優秀なモデル in アジア
jdeath<- df$DpP[is.element(df$Row.names,c("Japan"))]
paste(df$Jname[df$DpP< jdeath],"モデル")
日本、韓国、台湾、シンガポール、香港のTotal Tests for COVID-19
library("rvest")
# "COVID-19 testing"のデータ取得
html <- read_html("https://en.wikipedia.org/wiki/COVID-19_testing")
tbl<- html_table(html,fill = T)
# "covid19-testing"のtableが何番目か見つける
nodes<- html_nodes(html, "table")
class<-html_attr(nodes,"class")
#num<-grep("plainrowheaders",class)
num<- 3
#
Wtest<- tbl[[num]][,c(1:3,5:8)]
str(Wtest)
#
for (i in c(3:7)){
Wtest[,i]<- as.numeric(gsub(",","",Wtest[,i]))
}
str(Wtest)
save("Wtest",file="Wtest.Rdata")
#load("Wtest.Rdata")
#asia5<- Wtest[grep("(Japan|South Korea|Singapore|Taiwan|Hong Kong)",Wtest[,1]),]
(注)国と地域の表が別になっていた
num<- 5
#
Wtest2<- tbl[[num]][,1:8]
str(Wtest2)
#
for (i in 4:8){
Wtest2[,i]<- as.numeric(gsub(",","",Wtest2[,i]))
}
str(Wtest2)
save("Wtest2",file="Wtest2.Rdata")
(asia5<- Wtest[grep("(Japan|South Korea|Singapore|Taiwan)",Wtest[,1]),])
colnames(asia5)[1]<- "Country or Subdivision"
colnames(asia5)[2]<- "Date"
colnames(asia5)[3]<- "Tested"
colnames(asia5)[6]<- "Tested /millionpeople"
(asia5 <- asia5[!is.na(asia5[,4]),])
temp<- Wtest2[grep("(Taiwan|Hong Kong)",Wtest2[,"Subdivision"]),]
(temp <- temp[!is.na(temp[,4]),2:8])
colnames(temp)[1]<- "Country or Subdivision"
colnames(temp)[2]<- "Date"
( asia5<- rbind(asia5,temp) )
日本、韓国、台湾、シンガポール、香港のTotal Tests for COVID-19
# Testedで並べ替え
dat<- asia5[order(asia5[,"Tested"]),]
#png("pcr09.png",width=800,height=600)
par(mar=c(7,7,3,2),family="serif")
b<- barplot(dat[,"Tested"],horiz=T,col="pink",xaxt="n",names=dat[,1],xlim=c(0,max(dat[,"Tested"])*1.2),las=1)
axis(side=1, at=axTicks(1), labels=formatC(axTicks(1), format="d", big.mark=','))
text(x=dat[,"Tested"],y=b,labels= paste("As of",dat[,"Date"],"\n",formatC(dat[,"Tested"],format="d",big.mark=',')),pos=4)
title("Total Tests for COVID-19(Japan,South Korea,Singapore,Taiwan,Hong Kong)",
"Data : [Wikipedia:COVID-19 testing](https://en.wikipedia.org/wiki/COVID-19_testing)")
#dev.off()
日本、韓国、台湾、シンガポール、香港の検査陽性率(%) Positive/Tests*100
# %で並べ替え
dat<- asia5[order(asia5[,"%"]),]
#png("pcr12.png",width=800,height=600)
par(mar=c(7,7,3,2),family="serif")
b<- barplot(dat[,"%"],horiz=T,col="pink",xaxt="n",names=dat[,1],xlim=c(0,max(dat[,"%"])*1.2),las=1)
axis(side=1, at=axTicks(1), labels=axTicks(1))
text(x=dat[,"%"],y=b,labels= paste("As of",dat[,"Date"],"\n",dat[,"%"],"%"),pos=4)
title("Positive/Tests*100 for COVID-19(Japan,South Korea,Singapore,Taiwan,Hong Kong)",
"Data : [Wikipedia:COVID-19 testing](https://en.wikipedia.org/wiki/COVID-19_testing)")
#dev.off()
日本、韓国、台湾、シンガポール、香港のTests /million people for COVID-19
# 人口100万人あたり
# Tested /millionpeopleで並べ替え
dat<- asia5[order(asia5[,"Tested /millionpeople"]),]
#png("pcr10.png",width=800,height=600)
par(mar=c(7,7,3,2),family="serif")
b<- barplot(dat[,"Tested /millionpeople"],horiz=T,col="pink",xaxt="n",names=dat[,1],xlim=c(0,max(dat[,"Tested /millionpeople"])*1.2),las=1)
axis(side=1, at=axTicks(1), labels=formatC(axTicks(1), format="d", big.mark=','))
text(x=dat[,"Tested /millionpeople"],y=b,labels= paste("As of",dat[,"Date"],"\n",formatC(dat[,"Tested /millionpeople"],format="d",big.mark=',')),pos=4)
title("Tested /million people for COVID-19(Japan,South Korea,Singapore,Taiwan,Hong Kong)",
"Data : [Wikipedia:COVID-19 testing](https://en.wikipedia.org/wiki/COVID-19_testing)")
#dev.off()
主な地点の平年値(日本、韓国、オーストラリア)
ダーウィン<- c(28.2,28.0,28.1,28.2,27.0,25.1,24.7,25.6,27.7,29.0,29.2,28.8)
ブリズベン<- c(25,24.8,23.5,21.1,18.3,15.6,14.8,15.6,18.2,20.5,22.3,24)
パース<- c(24.4,24.6,22.8,19.5,16.3,13.7,12.7,13.2,14.6,16.6,19.7,22.1)
キャンベラ<- c(20.8,20.2,17.8,13.5,9.8,6.7,5.8,7.2,10.2,13,16.2,18.8)
ソウル<- c(-2.4,0.6,5.7,12.5,17.8,22.2,24.9,25.7,21.2,14.8,7.2,0.4)
プサン<- c(3.2,4.9,8.7,13.6,17.5,20.7,24.1,26,22.3,17.6,11.6,5.8)
札幌<- c(-3.6,-3.1,0.6,7.1,12.4,16.7,20.5,22.3,18.1,11.8,4.9,-0.9)
東京<- c(5.2,5.7,8.7,13.9,18.2,21.4,25,26.4,22.8,17.5,12.1,7.6)
大阪<- c(6.0,6.3,9.4,15.1,19.7,23.5,27.4,28.8,25.0,19.0,13.6,8.6)
那覇<- c(17,17.1,18.9,21.4,24,26.8,28.9,28.7,27.6,25.2,22.1,18.7)
#
temp<- data.frame(ダーウィン,ブリズベン,パース,キャンベラ,ソウル,プサン,札幌,東京,大阪,那覇)
rownames(temp)<- c("1月","2月","3月","4月","5月","6月","7月","8月","9月","10月","11月","12月")
#
col=c("chartreuse","chartreuse2","chartreuse3","darkgreen","blue","blue4","deeppink","red","red1","red2")
#png("Ctemp01.png",width=800,height=600)
par(mar=c(6,4,4,8),family="serif")
matplot(temp,type="o",pch=c(0,1,2,4,5,6,15,16,17,18),lty=c(3,3,3,3,2,2,1,1,1,1),col=col,lwd=2,xaxt="n",bty="n",las=1,ylab="気温(℃)")
box(bty="l",lwd=3)
axis(1,at=1:12,labels=rownames(temp))
text(x=par("usr")[2],y=temp[nrow(temp),],labels=colnames(temp),xpd=T,pos=c(rep(3,4),rep(1,6)),col=col)
abline(h=10,lty=4,lwd=0.5)
title("主な地点の平年値(日本、韓国、オーストラリア)",
"[Data:http://www.data.jma.go.jp/gmd/cpd/monitor/mainstn/nrmlist.php#reg0 & https://www.stat.go.jp/data/nihon/01.html]")
#dev.off()
致死率 7日移動平均(Hokkaido|Tokyo|Kanagawa|Aichi|Osaka)
library(xts)
library(TTR)
#[NHK](https://www3.nhk.or.jp/n-data/opendata/coronavirus/nhk_news_covid19_prefectures_daily_data.csv)
nhkC<- read.csv("https://www3.nhk.or.jp/n-data/opendata/coronavirus/nhk_news_covid19_prefectures_daily_data.csv")
save(nhkC,file="nhkC.Rdata")
#load("nhkC.Rdata")
code<- c(1, 11, 13, 14, 23, 27)
#感染者数
#
Cdata<- nhkC[nhkC$都道府県コード==code[1],c(1,4)]
Cdata.xts<- as.xts(read.zoo(Cdata, format="%Y/%m/%d"))
#
for (i in code[-1]){
Cdata<- nhkC[nhkC$都道府県コード== i,c(1,4)]
tmp.xts<- as.xts(read.zoo(Cdata, format="%Y/%m/%d"))
Cdata.xts<- merge(Cdata.xts,tmp.xts)
}
# NA<- 0
coredata(Cdata.xts)[is.na(Cdata.xts)]<- 0
colnames(Cdata.xts)<- paste0(unique(nhkC[nhkC$都道府県コード==code,"都道府県名"]),"C")
#
#死者数
#
Ddata<- nhkC[nhkC$都道府県コード==code[1],c(1,6)]
Ddata.xts<- as.xts(read.zoo(Ddata, format="%Y/%m/%d"))
#
for (i in code[-1]){
Ddata<- nhkC[nhkC$都道府県コード== i,c(1,6)]
tmp.xts<- as.xts(read.zoo(Ddata, format="%Y/%m/%d"))
Ddata.xts<- merge(Ddata.xts,tmp.xts)
}
# NA<- 0
coredata(Ddata.xts)[is.na(Ddata.xts)]<- 0
colnames(Ddata.xts)<- paste0(unique(nhkC[nhkC$都道府県コード==code,"都道府県名"]),"D")
data.xts<- merge(Cdata.xts,Ddata.xts)
# 致死率(%)7日移動平均
#死者数(7日間合計)/陽性者数(7日間合計)*100
data<- data.frame(apply(data.xts,2,runSum,7))
rownames(data)<- as.character(index(data.xts))
data<- data[-c(1:6),]
for (i in 1:length(code)){
data<- cbind(data, round(data[,(i+length(code))]/data[,i]*100,2))
}
data2<- data[,(length(code)*2+1):(length(code)*3)]
colnames(data2)<- unique(nhkC[nhkC$都道府県コード==code,"都道府県名"])
#2020-07-20から
data2<- data2[-c(1:180),]
#
labels<- sub("-","/",sub("-0","-",sub("^0","",sub("^....-","",rownames(data2)))))
# 毎月1日
labelpos<- paste0(1:12,"/",1)
#png("covOsaka11.png",width=800,height=600)
par(mar=c(5,4,5,9),family="serif")
matplot(data2,type="l",lty=1,col=rainbow(length(code),alpha=0.8),lwd=1.5,las=1,bty="n",xlab="",ylab="",xaxt="n",xaxs="i")
box(bty="l",lwd=2.5)
for (i in labelpos){
at<- match(i,labels)
if (!is.na(at)){ axis(1,at=at,labels = paste0(sub("/1","",i),"月"),tck= -0.02)}
}
text(x=par("usr")[1],y=par("usr")[4],labels="(%)",pos=2,xpd=T)
text(x=par("usr")[2],y=tail(data2,1),labels=paste0(colnames(data2),":",tail(data2,1),"%"),xpd=T,pos=4)
mtext(text="2020年",at=1,side=1,line=2.5,cex=1.2)
mtext(text="2021年",at=166,side=1,line=2.5,cex=1.2)
legend("topleft",inset=c(0.03,0.03),legend=colnames(data2),lty=1,col=rainbow(length(code),alpha=0.8),lwd=1.5,xpd=T)
title("致死率(%)7日移動平均","[データ:NHK https://www3.nhk.or.jp/n-data/opendata/coronavirus/nhk_news_covid19_prefectures_daily_data.csv]")
#dev.off()
北海道、埼玉、東京、神奈川、愛知、大阪の月別死者数と月別人口100万人あたりの死者数(データ:NHK)
#都道府県別人口はNipponMapパッケージのデータを使う
shp <- system.file("shapes/jpn.shp", package = "NipponMap")[1]
m <- sf::read_sf(shp)
#
data.xts<- Ddata.xts
colnames(data.xts)<- unique(nhkC[nhkC$都道府県コード==code,"都道府県名"])
#
monthsum<- NULL
for (i in 1:ncol(data.xts)){
#各月ごとの死亡者の合計
m.xts<- apply.monthly(data.xts[,i],sum)
monthsum<- cbind(monthsum,m.xts)
}
#
monthsum<- data.frame(monthsum)
#if (rownames(monthsum)[nrow(monthsum)]!="11"){
# monthsum= rbind(monthsum,0)
#}
# 最初の月の死亡者がすべての県で0なら削除
if ( all(monthsum[1,]==0) ) {monthsum<- monthsum[-1,]}
#plot
png("covOsaka12.png",width=800,height=600)
par(mar=c(4,5,3,2),family="serif",mfrow=c(2,1))
b<- barplot(t(monthsum),beside=T,names=paste0(sub("^0","",substring(rownames(monthsum),6,7)),"月"),las=1,col=rainbow(ncol(monthsum)),ylim=c(0,max(monthsum)*1.2),
legend=T,args.legend=list(x="topleft",inset=0.02))
box(bty="l",lwd=2.5)
abline(h=c(100,200),lwd=0.8,lty=2,col="gray")
#for (i in 1:ncol(monthsum)){
# text(x=b[i,],y=monthsum[,i],labels=monthsum[,i],pos=3)
#}
mtext(text="2020年",at=b[1],side=1,line=2.5,cex=1.2)
mtext(text="2021年",at=b[12],side=1,line=2.5,cex=1.2)
title("新型コロナウイルス月別死亡者")
title("\n\n\nデータ:[NHK](https://www3.nhk.or.jp/n-data/opendata/coronavirus/nhk_news_covid19_prefectures_daily_data.csv)",cex.main=0.8)
#
CperPop<- monthsum
for (i in 1:ncol(CperPop)){
CperPop[,i]<- round(CperPop[,i]/m$population[code[i]]*10^6,0)
}
#
#plot
b<- barplot(t(CperPop),beside=T,names=paste0(sub("^0","",substring(rownames(CperPop),6,7)),"月"),las=1,col=rainbow(ncol(CperPop)),ylim=c(0,max(CperPop)*1.2),
legend=T,args.legend=list(x="topleft",inset=0.02))
box(bty="l",lwd=2.5)
abline(h=c(10,20,30,40,50),lwd=0.8,lty=2,col="gray")
#for (i in 1:ncol(CperPop)){
# text(x=b[i,],y=CperPop[,i],labels=CperPop[,i],pos=3)
#}
mtext(text="2020年",at=b[1],side=1,line=2.5,cex=1.2)
mtext(text="2021年",at=b[12],side=1,line=2.5,cex=1.2)
title("人口100万人あたり新型コロナウイルス月別死亡者")
title("\n\n\nデータ:[NHK](https://www3.nhk.or.jp/n-data/opendata/coronavirus/nhk_news_covid19_prefectures_daily_data.csv) & 都道府県別人口:NipponMapパッケージ",cex.main=0.8)
#
#dev.off()
(注意)
新型コロナウイルス感染症の現在の状況と厚生労働省の対応について(令和2年4月21日版)
- 都道府県から公表された死亡者数の合計は244(+21)名であるが、うち58名については個々の陽性者との突合作業中のため、計上するに至っていない。
新型コロナウイルス感染症の現在の状況と厚生労働省の対応について(令和2年4月22日版)
- 退院した者のうち616名、死亡者のうち74名については、個々の陽性者との突合作業中。従って、入退院等の状況の合計とPCR検査陽性者数は一致しない。
※3:日本のPCR検査実施人数は、一部自治体について件数を計上しているため、実際の人数より過大である。
また、人数を公表していない自治体の数は計上しておらず、更新がなかった自治体については、前日の数値を使用している。
また、ありえないデータ(日本)
- PCR検査実施人数 国内事例 5月12日: 188646 5月13日: 188031 5月12日の方が多い!
- PCR検査実施人数 国内事例 5月14日: 196816 5月15日: 194323 5月14日の方が多い!
※6:東京都は、医療機関による保険適用での検査人数等を除いた検査人数をウェブサイトに掲載していたが、 6月15日以降、医療機関等が行った検査を含む検査人数を過去に遡って計上しているため、検査実施人数が大幅に(4万人以上)増加している。
日本と韓国の新型コロナウイルスに関する情報開示の比較 「データの信頼性」という観点からみてみると、(参考)COVID-19
(注)日本の6月18日の検査人数の上昇は以下の理由によるものです。
- 東京都は、医療機関による保険適用での検査人数等を除いた検査人数をウェブサイトに掲載していたが、 6月15日以降、医療機関等が行った検査を含む検査人数を過去に遡って計上しているため、検査実施人数が大幅に(4万人以上)増加している。
<span style="color: red; ">6月9日に韓国のPCR検査の結果判明数が100万人超えました。</span>
(注意)日本のPCR検査実施人数は、一部自治体について件数を計上しているため、実際の人数より過大である。
日本の新型コロナウイルスによる死亡者数はもっと多いのではないか。
根拠:日本法医病理学会HP:法医解剖、検案からの検体に対する新型コロナウイルス検査状況(pdf) 2020/04/26
法医解剖、検案からの検体に対する新型コロナウイルス検査状況
○ 回答機関: 26 機関
- 実施件数
- 保健所: 9 件 他の検査機関: 2 件
- 拒否件数
- 保健所: 12 件 他の検査機関: 0 件
(注)日本の6月18日のPCR検査の陽性率の下降は以下の理由によるものです。
- 東京都は、医療機関による保険適用での検査人数等を除いた検査人数をウェブサイトに掲載していたが、 6月15日以降、医療機関等が行った検査を含む検査人数を過去に遡って計上しているため、検査実施人数が大幅に(4万人以上)増加している。
(注)日本の6月18日の検査人数の上昇は以下の理由によるものです。
- 東京都は、医療機関による保険適用での検査人数等を除いた検査人数をウェブサイトに掲載していたが、 6月15日以降、医療機関等が行った検査を含む検査人数を過去に遡って計上しているため、検査実施人数が大幅に(4万人以上)増加している。
データの信頼性(データのとり方の一貫性)が劣るのでおかしな箇所(検査人数より陽性者が多い!)が見受けられます。