{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "\"COVID-19 Open Research Data Analysis\"\n", "---\n", "\n", "This is an analysis report of the Novel Coronavirus (COVID-19). It is focusing on data processing, visualisation and statstics for R Statistics Course.\n", "Data provided by Johns Hopkins University [github](https://github.com/CSSEGISandData/COVID-19)\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"package 'magrittr' was built under R version 3.6.3\"Warning message:\n", "\"package 'lubridate' was built under R version 3.6.3\"\n", "Attaching package: 'lubridate'\n", "\n", "The following object is masked from 'package:base':\n", "\n", " date\n", "\n", "Warning message:\n", "\"package 'tidyverse' was built under R version 3.6.3\"-- Attaching packages --------------------------------------- tidyverse 1.3.0 --\n", "v ggplot2 3.3.0 v purrr 0.3.3\n", "v tibble 2.1.3 v dplyr 0.8.5\n", "v tidyr 1.0.2 v stringr 1.4.0\n", "v readr 1.3.1 v forcats 0.5.0\n", "Warning message:\n", "\"package 'ggplot2' was built under R version 3.6.3\"Warning message:\n", "\"package 'tibble' was built under R version 3.6.3\"Warning message:\n", "\"package 'tidyr' was built under R version 3.6.3\"Warning message:\n", "\"package 'readr' was built under R version 3.6.3\"Warning message:\n", "\"package 'purrr' was built under R version 3.6.3\"Warning message:\n", "\"package 'dplyr' was built under R version 3.6.3\"Warning message:\n", "\"package 'stringr' was built under R version 3.6.3\"Warning message:\n", "\"package 'forcats' was built under R version 3.6.3\"-- Conflicts ------------------------------------------ tidyverse_conflicts() --\n", "x lubridate::as.difftime() masks base::as.difftime()\n", "x lubridate::date() masks base::date()\n", "x tidyr::extract() masks magrittr::extract()\n", "x dplyr::filter() masks stats::filter()\n", "x lubridate::intersect() masks base::intersect()\n", "x dplyr::lag() masks stats::lag()\n", "x purrr::set_names() masks magrittr::set_names()\n", "x lubridate::setdiff() masks base::setdiff()\n", "x lubridate::union() masks base::union()\n", "Warning message:\n", "\"package 'gridExtra' was built under R version 3.6.3\"\n", "Attaching package: 'gridExtra'\n", "\n", "The following object is masked from 'package:dplyr':\n", "\n", " combine\n", "\n", "Warning message:\n", "\"package 'leaflet' was built under R version 3.6.3\"Warning message:\n", "\"package 'ggforce' was built under R version 3.6.3\"Warning message:\n", "\"package 'kableExtra' was built under R version 3.6.3\"\n", "Attaching package: 'kableExtra'\n", "\n", "The following object is masked from 'package:dplyr':\n", "\n", " group_rows\n", "\n" ] }, { "data": { "text/html": [ "\n", "
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\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 258\n", "\\item 76\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 258\n", "2. 76\n", "\n", "\n" ], "text/plain": [ "[1] 258 76" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# This is an analysis report of the Novel Coronavirus (COVID-19)\n", "# Aim for data processing, visualisation and statstics\n", "# Source code: http://yanchang.rdatamining.com/\n", "# set directory\n", "# Data Source: 2019 Data Repository https://github.com/CSSEGISandData/COVID-19\n", "# R Packages:\n", "library(magrittr) # pipline operations\n", "library(lubridate) # date operation\n", "library(tidyverse) # data science pips\n", "library(gridExtra) # grid based plots\n", "library(dplyr)\n", "library(leaflet)\n", "library(ggforce)\n", "library(kableExtra)\n", "\n", "# Loading data\n", "# At first, three CSV files, are downloaded and saved as local files\n", "# and then loaded into R\n", "# source data files changes everytime\n", "filenames <- c('time_series_covid19_confirmed_global.csv',\n", " 'time_series_covid19_deaths_global.csv', \n", " 'time_series_covid19_recovered_global.csv')\n", "url.path <- paste0('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/',\n", " 'master/csse_covid_19_data/csse_covid_19_time_series/')\n", "\n", "#download files to local folder\n", "download <- function(filename) {\n", " url <- file.path(url.path, filename)\n", " dest <- file.path('./data', filename)\n", " download.file(url, dest)\n", "}\n", "bin <- lapply(filenames, download)\n", "\n", "\n", "# load data into R\n", "data.confirmed.original <- read.csv('./data/time_series_covid19_confirmed_global.csv')\n", "data.deaths.original <- read.csv('./data/time_series_covid19_deaths_global.csv')\n", "data.recovered.original <- read.csv('./data/time_series_covid19_recovered_global.csv')\n", "\n", "\n", "# check dimension of data confirmed\n", "dim(data.confirmed.original)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 10 × 10
Province.StateCountry.RegionLatLongX1.22.20X1.23.20X1.24.20X1.25.20X1.26.20X1.27.20
<fct><fct><dbl><dbl><int><int><int><int><int><int>
1 Afghanistan 33.0000 65.0000000000
2 Albania 41.1533 20.1683000000
3 Algeria 28.0339 1.6596000000
4 Andorra 42.5063 1.5218000000
5 Angola -11.2027 17.8739000000
6 Antigua and Barbuda 17.0608-61.7964000000
7 Argentina -38.4161-63.6167000000
8 Armenia 40.0691 45.0382000000
9Australian Capital TerritoryAustralia -35.4735149.0124000000
10New South Wales Australia -33.8688151.2093000034
\n" ], "text/latex": [ "A data.frame: 10 × 10\n", "\\begin{tabular}{r|llllllllll}\n", " & Province.State & Country.Region & Lat & Long & X1.22.20 & X1.23.20 & X1.24.20 & X1.25.20 & X1.26.20 & X1.27.20\\\\\n", " & & & & & & & & & & \\\\\n", "\\hline\n", "\t1 & & Afghanistan & 33.0000 & 65.0000 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t2 & & Albania & 41.1533 & 20.1683 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t3 & & Algeria & 28.0339 & 1.6596 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t4 & & Andorra & 42.5063 & 1.5218 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t5 & & Angola & -11.2027 & 17.8739 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t6 & & Antigua and Barbuda & 17.0608 & -61.7964 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t7 & & Argentina & -38.4161 & -63.6167 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t8 & & Armenia & 40.0691 & 45.0382 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t9 & Australian Capital Territory & Australia & -35.4735 & 149.0124 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t10 & New South Wales & Australia & -33.8688 & 151.2093 & 0 & 0 & 0 & 0 & 3 & 4\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 10 × 10\n", "\n", "| | Province.State <fct> | Country.Region <fct> | Lat <dbl> | Long <dbl> | X1.22.20 <int> | X1.23.20 <int> | X1.24.20 <int> | X1.25.20 <int> | X1.26.20 <int> | X1.27.20 <int> |\n", "|---|---|---|---|---|---|---|---|---|---|---|\n", "| 1 | | Afghanistan | 33.0000 | 65.0000 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| 2 | | Albania | 41.1533 | 20.1683 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| 3 | | Algeria | 28.0339 | 1.6596 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| 4 | | Andorra | 42.5063 | 1.5218 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| 5 | | Angola | -11.2027 | 17.8739 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| 6 | | Antigua and Barbuda | 17.0608 | -61.7964 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| 7 | | Argentina | -38.4161 | -63.6167 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| 8 | | Armenia | 40.0691 | 45.0382 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| 9 | Australian Capital Territory | Australia | -35.4735 | 149.0124 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| 10 | New South Wales | Australia | -33.8688 | 151.2093 | 0 | 0 | 0 | 0 | 3 | 4 |\n", "\n" ], "text/plain": [ " Province.State Country.Region Lat Long X1.22.20\n", "1 Afghanistan 33.0000 65.0000 0 \n", "2 Albania 41.1533 20.1683 0 \n", "3 Algeria 28.0339 1.6596 0 \n", "4 Andorra 42.5063 1.5218 0 \n", "5 Angola -11.2027 17.8739 0 \n", "6 Antigua and Barbuda 17.0608 -61.7964 0 \n", "7 Argentina -38.4161 -63.6167 0 \n", "8 Armenia 40.0691 45.0382 0 \n", "9 Australian Capital Territory Australia -35.4735 149.0124 0 \n", "10 New South Wales Australia -33.8688 151.2093 0 \n", " X1.23.20 X1.24.20 X1.25.20 X1.26.20 X1.27.20\n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "5 0 0 0 0 0 \n", "6 0 0 0 0 0 \n", "7 0 0 0 0 0 \n", "8 0 0 0 0 0 \n", "9 0 0 0 0 0 \n", "10 0 0 0 3 4 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Table:\n", "data.confirmed.original[1:10, 1:10]\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 2020-01-22\n", "\\item 2020-04-02\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 2020-01-22\n", "2. 2020-04-02\n", "\n", "\n" ], "text/plain": [ "[1] \"2020-01-22\" \"2020-04-02\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "# check time frame of the data\n", "n.col <- ncol(data.confirmed.original) # 58 variables\n", "# get dates from column names\n", "dates <- names(data.confirmed.original)[5:n.col] %>% substr(2,8) %>% mdy()\n", "range(dates)\n", "\n", "min.date <- min(dates)\n", "max.date <- max(dates)\n", "max.date.txt <- max.date %>% format('%d %b %Y')\n", "min.date.txt <- min.date %>% format('%d %b Y')\n", "# last update on 26 March 2020 max.date" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 10 × 5
countrydateconfirmeddeathsrecovered
<fct><date><int><int><int>
63Spain2020-03-24 39885 2808 3794
64Spain2020-03-25 49515 3647 5367
65Spain2020-03-26 57786 4365 7015
66Spain2020-03-27 65719 5138 9357
67Spain2020-03-28 73235 598212285
68Spain2020-03-29 80110 680314709
69Spain2020-03-30 87956 771616780
70Spain2020-03-31 95923 846419259
71Spain2020-04-01104118 938722647
72Spain2020-04-021120651034826743
\n" ], "text/latex": [ "A data.frame: 10 × 5\n", "\\begin{tabular}{r|lllll}\n", " & country & date & confirmed & deaths & recovered\\\\\n", " & & & & & \\\\\n", "\\hline\n", "\t63 & Spain & 2020-03-24 & 39885 & 2808 & 3794\\\\\n", "\t64 & Spain & 2020-03-25 & 49515 & 3647 & 5367\\\\\n", "\t65 & Spain & 2020-03-26 & 57786 & 4365 & 7015\\\\\n", "\t66 & Spain & 2020-03-27 & 65719 & 5138 & 9357\\\\\n", "\t67 & Spain & 2020-03-28 & 73235 & 5982 & 12285\\\\\n", "\t68 & Spain & 2020-03-29 & 80110 & 6803 & 14709\\\\\n", "\t69 & Spain & 2020-03-30 & 87956 & 7716 & 16780\\\\\n", "\t70 & Spain & 2020-03-31 & 95923 & 8464 & 19259\\\\\n", "\t71 & Spain & 2020-04-01 & 104118 & 9387 & 22647\\\\\n", "\t72 & Spain & 2020-04-02 & 112065 & 10348 & 26743\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 10 × 5\n", "\n", "| | country <fct> | date <date> | confirmed <int> | deaths <int> | recovered <int> |\n", "|---|---|---|---|---|---|\n", "| 63 | Spain | 2020-03-24 | 39885 | 2808 | 3794 |\n", "| 64 | Spain | 2020-03-25 | 49515 | 3647 | 5367 |\n", "| 65 | Spain | 2020-03-26 | 57786 | 4365 | 7015 |\n", "| 66 | Spain | 2020-03-27 | 65719 | 5138 | 9357 |\n", "| 67 | Spain | 2020-03-28 | 73235 | 5982 | 12285 |\n", "| 68 | Spain | 2020-03-29 | 80110 | 6803 | 14709 |\n", "| 69 | Spain | 2020-03-30 | 87956 | 7716 | 16780 |\n", "| 70 | Spain | 2020-03-31 | 95923 | 8464 | 19259 |\n", "| 71 | Spain | 2020-04-01 | 104118 | 9387 | 22647 |\n", "| 72 | Spain | 2020-04-02 | 112065 | 10348 | 26743 |\n", "\n" ], "text/plain": [ " country date confirmed deaths recovered\n", "63 Spain 2020-03-24 39885 2808 3794 \n", "64 Spain 2020-03-25 49515 3647 5367 \n", "65 Spain 2020-03-26 57786 4365 7015 \n", "66 Spain 2020-03-27 65719 5138 9357 \n", "67 Spain 2020-03-28 73235 5982 12285 \n", "68 Spain 2020-03-29 80110 6803 14709 \n", "69 Spain 2020-03-30 87956 7716 16780 \n", "70 Spain 2020-03-31 95923 8464 19259 \n", "71 Spain 2020-04-01 104118 9387 22647 \n", "72 Spain 2020-04-02 112065 10348 26743 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "# Data Preparation steps:\n", "# 1.From wide to long format\n", "# 2.Aggregate by country\n", "# 3. merge into a signe dataset\n", "# cleaning and transformation\n", "cleanData <- function(data) {\n", " ## remove some columns\n", " data %<>% select(-c(Province.State, Lat, Long)) %>% rename(country=Country.Region) \n", " ## convert from wide to long format\n", " data %<>% gather(key=date, value=count, -country) \n", " ## convert from character to date \n", " data %<>% mutate(date = date %>% substr(2,8) %>% mdy()) \n", " ## aggregate by country \n", " data %<>% group_by(country, date) %>% summarise(count=sum(count)) %>% as.data.frame()\n", " return(data)\n", "}\n", "# clean the three datasets\n", "data.confirmed <- data.confirmed.original %>% cleanData() %>% rename(confirmed=count) \n", "data.deaths <- data.deaths.original %>% cleanData() %>% rename(deaths=count)\n", "data.recovered <- data.recovered.original %>% cleanData() %>% rename(recovered=count)\n", "\n", "# merge above 3 datasets into one, by country and date\n", "data <- data.confirmed %>% merge(data.deaths, all = T) %>% merge(data.recovered, all = T)\n", "\n", "# countries/regions with confirmed cases (excl cruise ships)\n", "countries <- data %>% pull(country) %>% setdiff('Cruise Ship')\n", "\n", "# last 10 records when it first broke out in Spain\n", "data %>% filter(country =='Spain')%>% tail(10)\n", "\n", "# counts for worldwide\n", "data.world <- data %>% group_by(date) %>%\n", " summarise(country='World',\n", " confirmed=sum(confirmed, na.rm = T),\n", " deaths=sum(deaths, na.rm = T),\n", " recovered=sum(recovered, na.rm = T))\n", "\n", "data %<>% rbind(data.world)\n", "\n", "# current confirmed cases\n", "data %<>% mutate(remaining.confirmed = confirmed - deaths - recovered)\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 13104 × 9
countrydateconfirmeddeathsrecoveredremaining.confirmedconfirmed.newdeaths.newrecovered.new
<fct><date><int><int><int><int><dbl><dbl><dbl>
Afghanistan2020-01-220000NANANA
Afghanistan2020-01-230000 0 0 0
Afghanistan2020-01-240000 0 0 0
Afghanistan2020-01-250000 0 0 0
Afghanistan2020-01-260000 0 0 0
Afghanistan2020-01-270000 0 0 0
Afghanistan2020-01-280000 0 0 0
Afghanistan2020-01-290000 0 0 0
Afghanistan2020-01-300000 0 0 0
Afghanistan2020-01-310000 0 0 0
Afghanistan2020-02-010000 0 0 0
Afghanistan2020-02-020000 0 0 0
Afghanistan2020-02-030000 0 0 0
Afghanistan2020-02-040000 0 0 0
Afghanistan2020-02-050000 0 0 0
Afghanistan2020-02-060000 0 0 0
Afghanistan2020-02-070000 0 0 0
Afghanistan2020-02-080000 0 0 0
Afghanistan2020-02-090000 0 0 0
Afghanistan2020-02-100000 0 0 0
Afghanistan2020-02-110000 0 0 0
Afghanistan2020-02-120000 0 0 0
Afghanistan2020-02-130000 0 0 0
Afghanistan2020-02-140000 0 0 0
Afghanistan2020-02-150000 0 0 0
Afghanistan2020-02-160000 0 0 0
Afghanistan2020-02-170000 0 0 0
Afghanistan2020-02-180000 0 0 0
Afghanistan2020-02-190000 0 0 0
Afghanistan2020-02-200000 0 0 0
...........................
World2020-03-04 95120 3254 51170 40696 2280 94 2942
World2020-03-05 97886 3348 53796 40742 2766 94 2626
World2020-03-06 101801 3460 55865 42476 3915 112 2069
World2020-03-07 105847 3558 58358 43931 4046 98 2493
World2020-03-08 109821 3802 60694 45325 3974 244 2336
World2020-03-09 113590 3988 62494 47108 3769 186 1800
World2020-03-10 118620 4262 64404 49954 5030 274 1910
World2020-03-11 125875 4615 67003 54257 7255 353 2599
World2020-03-12 128352 4720 68324 55308 2477 105 1321
World2020-03-13 145205 5404 70251 6955016853 684 1927
World2020-03-14 156101 5819 72624 7765810896 415 2373
World2020-03-15 167454 6440 76034 8498011353 621 3410
World2020-03-16 181574 7126 78088 9636014120 686 2054
World2020-03-17 197102 7905 8084010835715528 779 2752
World2020-03-18 214821 8733 8331212277617719 828 2472
World2020-03-19 242500 9867 84975147658276791134 1663
World2020-03-20 27203511299 87420173316295351432 2445
World2020-03-21 30439612973 91692199731323611674 4272
World2020-03-22 33695314651 97899224403325571678 6207
World2020-03-23 37823516505 98351263379412821854 452
World2020-03-24 41804518625108000291420398102120 9649
World2020-03-25 46765321181113787332685496082556 5787
World2020-03-26 52959123970122150383471619382789 8363
World2020-03-27 59329127198130915435178637003228 8765
World2020-03-28 66070630652139415490639674153454 8500
World2020-03-29 72011733925149082537110594113273 9667
World2020-03-30 7823653758216456658021762248365715484
World2020-03-31 8574874210717803463734675122452513468
World2020-04-01 9326054680919317769261975118470215143
World2020-04-0210131575298321026374991180552617417086
\n" ], "text/latex": [ "A data.frame: 13104 × 9\n", "\\begin{tabular}{lllllllll}\n", " country & date & confirmed & deaths & recovered & remaining.confirmed & confirmed.new & deaths.new & recovered.new\\\\\n", " & & & & & & & & \\\\\n", "\\hline\n", "\t Afghanistan & 2020-01-22 & 0 & 0 & 0 & 0 & NA & NA & NA\\\\\n", "\t Afghanistan & 2020-01-23 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t Afghanistan & 2020-01-24 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t Afghanistan & 2020-01-25 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t Afghanistan & 2020-01-26 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t Afghanistan & 2020-01-27 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t Afghanistan & 2020-01-28 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t Afghanistan & 2020-01-29 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t Afghanistan & 2020-01-30 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t Afghanistan & 2020-01-31 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t Afghanistan & 2020-02-01 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t Afghanistan & 2020-02-02 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t Afghanistan & 2020-02-03 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t Afghanistan & 2020-02-04 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t Afghanistan & 2020-02-05 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t Afghanistan & 2020-02-06 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t Afghanistan & 2020-02-07 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t Afghanistan & 2020-02-08 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t Afghanistan & 2020-02-09 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t Afghanistan & 2020-02-10 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t Afghanistan & 2020-02-11 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t Afghanistan & 2020-02-12 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t Afghanistan & 2020-02-13 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t Afghanistan & 2020-02-14 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t Afghanistan & 2020-02-15 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t Afghanistan & 2020-02-16 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t Afghanistan & 2020-02-17 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t Afghanistan & 2020-02-18 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t Afghanistan & 2020-02-19 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t Afghanistan & 2020-02-20 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t ... & ... & ... & ... & ... & ... & ... & ... & ...\\\\\n", "\t World & 2020-03-04 & 95120 & 3254 & 51170 & 40696 & 2280 & 94 & 2942\\\\\n", "\t World & 2020-03-05 & 97886 & 3348 & 53796 & 40742 & 2766 & 94 & 2626\\\\\n", "\t World & 2020-03-06 & 101801 & 3460 & 55865 & 42476 & 3915 & 112 & 2069\\\\\n", "\t World & 2020-03-07 & 105847 & 3558 & 58358 & 43931 & 4046 & 98 & 2493\\\\\n", "\t World & 2020-03-08 & 109821 & 3802 & 60694 & 45325 & 3974 & 244 & 2336\\\\\n", "\t World & 2020-03-09 & 113590 & 3988 & 62494 & 47108 & 3769 & 186 & 1800\\\\\n", "\t World & 2020-03-10 & 118620 & 4262 & 64404 & 49954 & 5030 & 274 & 1910\\\\\n", "\t World & 2020-03-11 & 125875 & 4615 & 67003 & 54257 & 7255 & 353 & 2599\\\\\n", "\t World & 2020-03-12 & 128352 & 4720 & 68324 & 55308 & 2477 & 105 & 1321\\\\\n", "\t World & 2020-03-13 & 145205 & 5404 & 70251 & 69550 & 16853 & 684 & 1927\\\\\n", "\t World & 2020-03-14 & 156101 & 5819 & 72624 & 77658 & 10896 & 415 & 2373\\\\\n", "\t World & 2020-03-15 & 167454 & 6440 & 76034 & 84980 & 11353 & 621 & 3410\\\\\n", "\t World & 2020-03-16 & 181574 & 7126 & 78088 & 96360 & 14120 & 686 & 2054\\\\\n", "\t World & 2020-03-17 & 197102 & 7905 & 80840 & 108357 & 15528 & 779 & 2752\\\\\n", "\t World & 2020-03-18 & 214821 & 8733 & 83312 & 122776 & 17719 & 828 & 2472\\\\\n", "\t World & 2020-03-19 & 242500 & 9867 & 84975 & 147658 & 27679 & 1134 & 1663\\\\\n", "\t World & 2020-03-20 & 272035 & 11299 & 87420 & 173316 & 29535 & 1432 & 2445\\\\\n", "\t World & 2020-03-21 & 304396 & 12973 & 91692 & 199731 & 32361 & 1674 & 4272\\\\\n", "\t World & 2020-03-22 & 336953 & 14651 & 97899 & 224403 & 32557 & 1678 & 6207\\\\\n", "\t World & 2020-03-23 & 378235 & 16505 & 98351 & 263379 & 41282 & 1854 & 452\\\\\n", "\t World & 2020-03-24 & 418045 & 18625 & 108000 & 291420 & 39810 & 2120 & 9649\\\\\n", "\t World & 2020-03-25 & 467653 & 21181 & 113787 & 332685 & 49608 & 2556 & 5787\\\\\n", "\t World & 2020-03-26 & 529591 & 23970 & 122150 & 383471 & 61938 & 2789 & 8363\\\\\n", "\t World & 2020-03-27 & 593291 & 27198 & 130915 & 435178 & 63700 & 3228 & 8765\\\\\n", "\t World & 2020-03-28 & 660706 & 30652 & 139415 & 490639 & 67415 & 3454 & 8500\\\\\n", "\t World & 2020-03-29 & 720117 & 33925 & 149082 & 537110 & 59411 & 3273 & 9667\\\\\n", "\t World & 2020-03-30 & 782365 & 37582 & 164566 & 580217 & 62248 & 3657 & 15484\\\\\n", "\t World & 2020-03-31 & 857487 & 42107 & 178034 & 637346 & 75122 & 4525 & 13468\\\\\n", "\t World & 2020-04-01 & 932605 & 46809 & 193177 & 692619 & 75118 & 4702 & 15143\\\\\n", "\t World & 2020-04-02 & 1013157 & 52983 & 210263 & 749911 & 80552 & 6174 & 17086\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 13104 × 9\n", "\n", "| country <fct> | date <date> | confirmed <int> | deaths <int> | recovered <int> | remaining.confirmed <int> | confirmed.new <dbl> | deaths.new <dbl> | recovered.new <dbl> |\n", "|---|---|---|---|---|---|---|---|---|\n", "| Afghanistan | 2020-01-22 | 0 | 0 | 0 | 0 | NA | NA | NA |\n", "| Afghanistan | 2020-01-23 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| Afghanistan | 2020-01-24 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| Afghanistan | 2020-01-25 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| Afghanistan | 2020-01-26 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| Afghanistan | 2020-01-27 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| Afghanistan | 2020-01-28 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| Afghanistan | 2020-01-29 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| Afghanistan | 2020-01-30 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| Afghanistan | 2020-01-31 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| Afghanistan | 2020-02-01 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| Afghanistan | 2020-02-02 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| Afghanistan | 2020-02-03 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| Afghanistan | 2020-02-04 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| Afghanistan | 2020-02-05 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| Afghanistan | 2020-02-06 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| Afghanistan | 2020-02-07 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| Afghanistan | 2020-02-08 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| Afghanistan | 2020-02-09 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| Afghanistan | 2020-02-10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| Afghanistan | 2020-02-11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| Afghanistan | 2020-02-12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| Afghanistan | 2020-02-13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| Afghanistan | 2020-02-14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| Afghanistan | 2020-02-15 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| Afghanistan | 2020-02-16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| Afghanistan | 2020-02-17 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| Afghanistan | 2020-02-18 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| Afghanistan | 2020-02-19 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| Afghanistan | 2020-02-20 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| ... | ... | ... | ... | ... | ... | ... | ... | ... |\n", "| World | 2020-03-04 | 95120 | 3254 | 51170 | 40696 | 2280 | 94 | 2942 |\n", "| World | 2020-03-05 | 97886 | 3348 | 53796 | 40742 | 2766 | 94 | 2626 |\n", "| World | 2020-03-06 | 101801 | 3460 | 55865 | 42476 | 3915 | 112 | 2069 |\n", "| World | 2020-03-07 | 105847 | 3558 | 58358 | 43931 | 4046 | 98 | 2493 |\n", "| World | 2020-03-08 | 109821 | 3802 | 60694 | 45325 | 3974 | 244 | 2336 |\n", "| World | 2020-03-09 | 113590 | 3988 | 62494 | 47108 | 3769 | 186 | 1800 |\n", "| World | 2020-03-10 | 118620 | 4262 | 64404 | 49954 | 5030 | 274 | 1910 |\n", "| World | 2020-03-11 | 125875 | 4615 | 67003 | 54257 | 7255 | 353 | 2599 |\n", "| World | 2020-03-12 | 128352 | 4720 | 68324 | 55308 | 2477 | 105 | 1321 |\n", "| World | 2020-03-13 | 145205 | 5404 | 70251 | 69550 | 16853 | 684 | 1927 |\n", "| World | 2020-03-14 | 156101 | 5819 | 72624 | 77658 | 10896 | 415 | 2373 |\n", "| World | 2020-03-15 | 167454 | 6440 | 76034 | 84980 | 11353 | 621 | 3410 |\n", "| World | 2020-03-16 | 181574 | 7126 | 78088 | 96360 | 14120 | 686 | 2054 |\n", "| World | 2020-03-17 | 197102 | 7905 | 80840 | 108357 | 15528 | 779 | 2752 |\n", "| World | 2020-03-18 | 214821 | 8733 | 83312 | 122776 | 17719 | 828 | 2472 |\n", "| World | 2020-03-19 | 242500 | 9867 | 84975 | 147658 | 27679 | 1134 | 1663 |\n", "| World | 2020-03-20 | 272035 | 11299 | 87420 | 173316 | 29535 | 1432 | 2445 |\n", "| World | 2020-03-21 | 304396 | 12973 | 91692 | 199731 | 32361 | 1674 | 4272 |\n", "| World | 2020-03-22 | 336953 | 14651 | 97899 | 224403 | 32557 | 1678 | 6207 |\n", "| World | 2020-03-23 | 378235 | 16505 | 98351 | 263379 | 41282 | 1854 | 452 |\n", "| World | 2020-03-24 | 418045 | 18625 | 108000 | 291420 | 39810 | 2120 | 9649 |\n", "| World | 2020-03-25 | 467653 | 21181 | 113787 | 332685 | 49608 | 2556 | 5787 |\n", "| World | 2020-03-26 | 529591 | 23970 | 122150 | 383471 | 61938 | 2789 | 8363 |\n", "| World | 2020-03-27 | 593291 | 27198 | 130915 | 435178 | 63700 | 3228 | 8765 |\n", "| World | 2020-03-28 | 660706 | 30652 | 139415 | 490639 | 67415 | 3454 | 8500 |\n", "| World | 2020-03-29 | 720117 | 33925 | 149082 | 537110 | 59411 | 3273 | 9667 |\n", "| World | 2020-03-30 | 782365 | 37582 | 164566 | 580217 | 62248 | 3657 | 15484 |\n", "| World | 2020-03-31 | 857487 | 42107 | 178034 | 637346 | 75122 | 4525 | 13468 |\n", "| World | 2020-04-01 | 932605 | 46809 | 193177 | 692619 | 75118 | 4702 | 15143 |\n", "| World | 2020-04-02 | 1013157 | 52983 | 210263 | 749911 | 80552 | 6174 | 17086 |\n", "\n" ], "text/plain": [ " country date confirmed deaths recovered remaining.confirmed\n", "1 Afghanistan 2020-01-22 0 0 0 0 \n", "2 Afghanistan 2020-01-23 0 0 0 0 \n", "3 Afghanistan 2020-01-24 0 0 0 0 \n", "4 Afghanistan 2020-01-25 0 0 0 0 \n", "5 Afghanistan 2020-01-26 0 0 0 0 \n", "6 Afghanistan 2020-01-27 0 0 0 0 \n", "7 Afghanistan 2020-01-28 0 0 0 0 \n", "8 Afghanistan 2020-01-29 0 0 0 0 \n", "9 Afghanistan 2020-01-30 0 0 0 0 \n", "10 Afghanistan 2020-01-31 0 0 0 0 \n", "11 Afghanistan 2020-02-01 0 0 0 0 \n", "12 Afghanistan 2020-02-02 0 0 0 0 \n", "13 Afghanistan 2020-02-03 0 0 0 0 \n", "14 Afghanistan 2020-02-04 0 0 0 0 \n", "15 Afghanistan 2020-02-05 0 0 0 0 \n", "16 Afghanistan 2020-02-06 0 0 0 0 \n", "17 Afghanistan 2020-02-07 0 0 0 0 \n", "18 Afghanistan 2020-02-08 0 0 0 0 \n", "19 Afghanistan 2020-02-09 0 0 0 0 \n", "20 Afghanistan 2020-02-10 0 0 0 0 \n", "21 Afghanistan 2020-02-11 0 0 0 0 \n", "22 Afghanistan 2020-02-12 0 0 0 0 \n", "23 Afghanistan 2020-02-13 0 0 0 0 \n", "24 Afghanistan 2020-02-14 0 0 0 0 \n", "25 Afghanistan 2020-02-15 0 0 0 0 \n", "26 Afghanistan 2020-02-16 0 0 0 0 \n", "27 Afghanistan 2020-02-17 0 0 0 0 \n", "28 Afghanistan 2020-02-18 0 0 0 0 \n", "29 Afghanistan 2020-02-19 0 0 0 0 \n", "30 Afghanistan 2020-02-20 0 0 0 0 \n", "... ... ... ... ... ... ... \n", "13075 World 2020-03-04 95120 3254 51170 40696 \n", "13076 World 2020-03-05 97886 3348 53796 40742 \n", "13077 World 2020-03-06 101801 3460 55865 42476 \n", "13078 World 2020-03-07 105847 3558 58358 43931 \n", "13079 World 2020-03-08 109821 3802 60694 45325 \n", "13080 World 2020-03-09 113590 3988 62494 47108 \n", "13081 World 2020-03-10 118620 4262 64404 49954 \n", "13082 World 2020-03-11 125875 4615 67003 54257 \n", "13083 World 2020-03-12 128352 4720 68324 55308 \n", "13084 World 2020-03-13 145205 5404 70251 69550 \n", "13085 World 2020-03-14 156101 5819 72624 77658 \n", "13086 World 2020-03-15 167454 6440 76034 84980 \n", "13087 World 2020-03-16 181574 7126 78088 96360 \n", "13088 World 2020-03-17 197102 7905 80840 108357 \n", "13089 World 2020-03-18 214821 8733 83312 122776 \n", "13090 World 2020-03-19 242500 9867 84975 147658 \n", "13091 World 2020-03-20 272035 11299 87420 173316 \n", "13092 World 2020-03-21 304396 12973 91692 199731 \n", "13093 World 2020-03-22 336953 14651 97899 224403 \n", "13094 World 2020-03-23 378235 16505 98351 263379 \n", "13095 World 2020-03-24 418045 18625 108000 291420 \n", "13096 World 2020-03-25 467653 21181 113787 332685 \n", "13097 World 2020-03-26 529591 23970 122150 383471 \n", "13098 World 2020-03-27 593291 27198 130915 435178 \n", "13099 World 2020-03-28 660706 30652 139415 490639 \n", "13100 World 2020-03-29 720117 33925 149082 537110 \n", "13101 World 2020-03-30 782365 37582 164566 580217 \n", "13102 World 2020-03-31 857487 42107 178034 637346 \n", "13103 World 2020-04-01 932605 46809 193177 692619 \n", "13104 World 2020-04-02 1013157 52983 210263 749911 \n", " confirmed.new deaths.new recovered.new\n", "1 NA NA NA \n", "2 0 0 0 \n", "3 0 0 0 \n", "4 0 0 0 \n", "5 0 0 0 \n", "6 0 0 0 \n", "7 0 0 0 \n", "8 0 0 0 \n", "9 0 0 0 \n", "10 0 0 0 \n", "11 0 0 0 \n", "12 0 0 0 \n", "13 0 0 0 \n", "14 0 0 0 \n", "15 0 0 0 \n", "16 0 0 0 \n", "17 0 0 0 \n", "18 0 0 0 \n", "19 0 0 0 \n", "20 0 0 0 \n", "21 0 0 0 \n", "22 0 0 0 \n", "23 0 0 0 \n", "24 0 0 0 \n", "25 0 0 0 \n", "26 0 0 0 \n", "27 0 0 0 \n", "28 0 0 0 \n", "29 0 0 0 \n", "30 0 0 0 \n", "... ... ... ... \n", "13075 2280 94 2942 \n", "13076 2766 94 2626 \n", "13077 3915 112 2069 \n", "13078 4046 98 2493 \n", "13079 3974 244 2336 \n", "13080 3769 186 1800 \n", "13081 5030 274 1910 \n", "13082 7255 353 2599 \n", "13083 2477 105 1321 \n", "13084 16853 684 1927 \n", "13085 10896 415 2373 \n", "13086 11353 621 3410 \n", "13087 14120 686 2054 \n", "13088 15528 779 2752 \n", "13089 17719 828 2472 \n", "13090 27679 1134 1663 \n", "13091 29535 1432 2445 \n", "13092 32361 1674 4272 \n", "13093 32557 1678 6207 \n", "13094 41282 1854 452 \n", "13095 39810 2120 9649 \n", "13096 49608 2556 5787 \n", "13097 61938 2789 8363 \n", "13098 63700 3228 8765 \n", "13099 67415 3454 8500 \n", "13100 59411 3273 9667 \n", "13101 62248 3657 15484 \n", "13102 75122 4525 13468 \n", "13103 75118 4702 15143 \n", "13104 80552 6174 17086 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "# Daily Increases and Death Rates\n", "\n", "# rate.upper = total deaths and recovered cases\n", "# rate.lower = total deaths and confirmed cases\n", "# expected death rate is to be between above rates\n", "# rate.daily =daily deaths and recovered cases\n", "\n", "## sort by country and date\n", "data %<>% arrange(country,date)\n", "# daily increases of deaths and recovered cases\n", "# set NA to increase on day1\n", "n <- nrow(data)\n", "day1 <- min(data$date) # set NA day1\n", "data %<>% mutate(confirmed.new=ifelse(date ==day1,NA, confirmed - lag(confirmed, n=1)),\n", " deaths.new=ifelse(date ==day1,NA,deaths - lag(deaths, n=1)),\n", " recovered.new=ifelse(date ==day1,NA,recovered - lag(recovered, n=1)))\n", "\n", "# change negative number of new cases to 0\n", "data %>% mutate(confirmed.new = ifelse(confirmed.new < 0,0, confirmed.new),\n", " deaths.new = ifelse(deaths.new < 0, 0, deaths.new),\n", " recovered.new= ifelse(recovered.new < 0, 0, recovered.new))\n", "\n", "# death rate base on total deaths and recovered cases\n", "data %<>% mutate(rate.upper = (100 *deaths / (deaths + recovered)) %>% round(1))\n", "# lower bound: death rate based on total confirmed cases\n", "data %<>% mutate(rate.lower = (100 * deaths / confirmed) %>% round(1))\n", "# death rate based on number f death/recovered on every single day\n", "data %<>% mutate(rate.daily = (100 * deaths.new / (deaths.new + recovered.new)) %>% round(1))\n", " \n", "# convert from wide to long format, for drawing area plot\n", "data.long <- data %>%\n", " select(c(country, date, confirmed, remaining.confirmed, recovered, deaths)) %>%\n", " gather(key = type, value = count, -c(country,date))\n", "# set for factor levels to show them in a desirable order\n", "data.long %<>% mutate(type =recode_factor(type, confirmed= 'Total Confirmed',\n", " remaining.confirmed = 'Remaining Confirmed',\n", " recovered= 'Recovered',\n", " deaths='Deaths'))\n", "\n", "# convert from wide to long format, for drawing area plots\n", "rates.long <- data %>%\n", " select(c(country, date, rate.upper, rate.lower, rate.daily)) %>% \n", " gather(key = type, value=count, -c(country,date))\n", "# set factor levels for desired order\n", "rates.long %<>% mutate(type =recode_factor(type, rate.daily = 'Daily',\n", " rate.lower = 'Lower bound',\n", " rate.upper = 'Upper bound'))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " [1] \"US\" \"Italy\" \"Spain\" \"Germany\" \n", " [5] \"China\" \"France\" \"Iran\" \"United Kingdom\"\n", " [9] \"Switzerland\" \"Turkey\" \"Belgium\" \"Netherlands\" \n", "[13] \"Canada\" \"Austria\" \"Korea, South\" \"Portugal\" \n", "[17] \"Brazil\" \"Israel\" \"Sweden\" \"Norway\" \n" ] } ], "source": [ "# Visualisation\n", "# After preparing the data, we portrait it in various graphs\n", "\n", "# TOP Ten Countries\n", "# ranking by confirmed cases\n", "data.latest.all <- data %>% filter(date == max(date)) %>%\n", " select(country, date,\n", " confirmed, confirmed.new, remaining.confirmed, recovered, deaths.new, deaths, death.rate = rate.lower) %>% mutate(ranking = dense_rank(desc(confirmed)))\n", "# top 20 countries incl 11 World\n", "k<- 20\n", "top.countries <- data.latest.all %>% filter(ranking <= k+1) %>%\n", " arrange(ranking) %>% pull(country) %>% as.character()\n", "top.countries %>% setdiff('World') %>% print()\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
  1. 'country'
  2. 'date'
  3. 'confirmed'
  4. 'confirmed.new'
  5. 'remaining.confirmed'
  6. 'recovered'
  7. 'deaths.new'
  8. 'deaths'
  9. 'death.rate'
  10. 'ranking'
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 'country'\n", "\\item 'date'\n", "\\item 'confirmed'\n", "\\item 'confirmed.new'\n", "\\item 'remaining.confirmed'\n", "\\item 'recovered'\n", "\\item 'deaths.new'\n", "\\item 'deaths'\n", "\\item 'death.rate'\n", "\\item 'ranking'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 'country'\n", "2. 'date'\n", "3. 'confirmed'\n", "4. 'confirmed.new'\n", "5. 'remaining.confirmed'\n", "6. 'recovered'\n", "7. 'deaths.new'\n", "8. 'deaths'\n", "9. 'death.rate'\n", "10. 'ranking'\n", "\n", "\n" ], "text/plain": [ " [1] \"country\" \"date\" \"confirmed\" \n", " [4] \"confirmed.new\" \"remaining.confirmed\" \"recovered\" \n", " [7] \"deaths.new\" \"deaths\" \"death.rate\" \n", "[10] \"ranking\" " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "\\begin{table}[!h]\n", "\n", "\\caption{\\label{tab:}Cases in Top 20 Countries - 02 Apr 2020.}\n", "\\centering\n", "\\fontsize{7}{9}\\selectfont\n", "\\begin{tabular}[t]{llrrrrrr}\n", "\\toprule\n", " & country & confirmed & deaths & death.rate & confirmed.new & deaths.new & remaining.confirmed\\\\\n", "\\midrule\n", "\\rowcolor{gray!6} 1 & World & 1,013,157 & 52,983 & 5.2\\% & 80,552 & 6,174 & 749,911\\\\\n", "2 & US & 243,453 & 5,926 & 2.4\\% & 30,081 & 1,169 & 228,526\\\\\n", "\\rowcolor{gray!6} 3 & Italy & 115,242 & 13,915 & 12.1\\% & 4,668 & 760 & 83,049\\\\\n", "4 & Spain & 112,065 & 10,348 & 9.2\\% & 7,947 & 961 & 74,974\\\\\n", "\\rowcolor{gray!6} 5 & Germany & 84,794 & 1,107 & 1.3\\% & 6,922 & 187 & 61,247\\\\\n", "\\addlinespace\n", "6 & China & 82,432 & 3,322 & 4.0\\% & 71 & 6 & 2,545\\\\\n", "\\rowcolor{gray!6} 7 & France & 59,929 & 5,398 & 9.0\\% & 2,180 & 1,355 & 41,983\\\\\n", "8 & Iran & 50,468 & 3,160 & 6.3\\% & 2,875 & 124 & 30,597\\\\\n", "\\rowcolor{gray!6} 9 & United Kingdom & 34,173 & 2,926 & 8.6\\% & 4,308 & 569 & 31,055\\\\\n", "10 & Switzerland & 18,827 & 536 & 2.8\\% & 1,059 & 48 & 14,278\\\\\n", "\\addlinespace\n", "\\rowcolor{gray!6} 11 & Turkey & 18,135 & 356 & 2.0\\% & 2,456 & 79 & 17,364\\\\\n", "12 & Belgium & 15,348 & 1,011 & 6.6\\% & 1,384 & 183 & 11,842\\\\\n", "\\rowcolor{gray!6} 13 & Netherlands & 14,788 & 1,341 & 9.1\\% & 1,092 & 166 & 13,187\\\\\n", "14 & Canada & 11,284 & 139 & 1.2\\% & 1,724 & 30 & 9,410\\\\\n", "\\rowcolor{gray!6} 15 & Austria & 11,129 & 158 & 1.4\\% & 418 & 12 & 9,222\\\\\n", "\\addlinespace\n", "16 & Korea, South & 9,976 & 169 & 1.7\\% & 89 & 4 & 3,979\\\\\n", "\\rowcolor{gray!6} 17 & Portugal & 9,034 & 209 & 2.3\\% & 783 & 22 & 8,757\\\\\n", "18 & Brazil & 8,044 & 324 & 4.0\\% & 1,208 & 84 & 7,593\\\\\n", "\\rowcolor{gray!6} 19 & Israel & 6,857 & 36 & 0.5\\% & 765 & 10 & 6,483\\\\\n", "20 & Sweden & 5,568 & 308 & 5.5\\% & 621 & 69 & 5,157\\\\\n", "\\addlinespace\n", "\\rowcolor{gray!6} 21 & Norway & 5,147 & 50 & 1.0\\% & 284 & 6 & 5,065\\\\\n", "22 & Others & 96,464 & 2,244 & 2.3\\% & 9,617 & 330 & 83,598\\\\\n", "\\bottomrule\n", "\\end{tabular}\n", "\\end{table}" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "names(data.latest.all)\n", "## add 'Others'\n", "top.countries %<>% c('Others')\n", "## put all others in a single group of 'Others'\n", "data.latest <- data.latest.all %>% filter(!is.na(country)) %>%\n", "mutate(country=ifelse(ranking <= k + 1, as.character(country), 'Others')) %>%\n", "mutate(country=country %>% factor(levels=c(top.countries)))\n", "\n", "data.latest %<>% group_by(country) %>%\n", " summarise(confirmed=sum(confirmed), confirmed.new=sum(confirmed.new), remaining.confirmed = sum(remaining.confirmed), recovered = sum(recovered),deaths=sum(deaths), deaths.new = sum(deaths.new)) %>%\n", " mutate(death.rate=(100*deaths/confirmed) %>% round(1))\n", "data.latest %<>% select(c(country, confirmed, deaths,death.rate, confirmed.new, deaths.new,remaining.confirmed))\n", "\n", "data.latest %>% mutate(death.rate=death.rate %>% format(nsmall=1) %>% paste0('%')) %>% kable('latex', booktabs=T, row.names=T, align=c('l', rep('r', 6)), caption=paste0('Cases in Top 20 Countries - ', max.date.txt,'.'), format.args=list(big.mark=',')) %>% kable_styling(font_size=7, latex_options=c('striped', 'hold_position', 'repeat_header'))\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 258 × 6
Country.RegionProvince.StateLatLongconfirmedtxt
<fct><fct><dbl><dbl><int><chr>
Afghanistan 33.0000 65.0000 273Afghanistan-:273
Albania 41.1533 20.1683 277Albania-:277
Algeria 28.0339 1.6596 986Algeria-:986
Andorra 42.5063 1.5218 428Andorra-:428
Angola -11.2027 17.8739 8Angola-:8
Antigua and Barbuda 17.0608-61.7964 9Antigua and Barbuda-:9
Argentina -38.4161-63.6167 1133Argentina-:1133
Armenia 40.0691 45.0382 663Armenia-:663
Australia Australian Capital Territory-35.4735149.0124 87Australia-Australian Capital Territory:87
Australia New South Wales -33.8688151.2093 2298Australia-New South Wales:2298
Australia Northern Territory -12.4634130.8456 21Australia-Northern Territory:21
Australia Queensland -28.0167153.4000 835Australia-Queensland:835
Australia South Australia -34.9285138.6007 367Australia-South Australia:367
Australia Tasmania -41.4545145.9707 72Australia-Tasmania:72
Australia Victoria -37.8136144.9631 1036Australia-Victoria:1036
Australia Western Australia -31.9505115.8605 400Australia-Western Australia:400
Austria 47.5162 14.550111129Austria-:11129
Azerbaijan 40.1431 47.5769 400Azerbaijan-:400
Bahamas 25.0343-77.3963 24Bahamas-:24
Bahrain 26.0275 50.5500 643Bahrain-:643
Bangladesh 23.6850 90.3563 56Bangladesh-:56
Barbados 13.1939-59.5432 46Barbados-:46
Belarus 53.7098 27.9534 304Belarus-:304
Belgium 50.8333 4.000015348Belgium-:15348
Benin 9.3077 2.3158 13Benin-:13
Bhutan 27.5142 90.4336 5Bhutan-:5
Bolivia -16.2902-63.5887 123Bolivia-:123
Bosnia and Herzegovina 43.9159 17.6791 533Bosnia and Herzegovina-:533
Brazil -14.2350-51.9253 8044Brazil-:8044
Brunei 4.5353114.7277 133Brunei-:133
..................
Vietnam 16.000000 108.000000233Vietnam-:233
Zambia -15.416700 28.283300 39Zambia-:39
Zimbabwe -20.000000 30.000000 9Zimbabwe-:9
Canada Diamond Princess 0.000000 0.000000 0Canada-Diamond Princess:0
Dominica 15.415000 -61.371000 12Dominica-:12
Grenada 12.116500 -61.679000 10Grenada-:10
Mozambique -18.665695 35.529562 10Mozambique-:10
Syria 34.802075 38.996815 16Syria-:16
Timor-Leste -8.874217 125.727539 1Timor-Leste-:1
Belize 13.193900 -59.543200 3Belize-:3
Canada Recovered 0.000000 0.000000 0Canada-Recovered:0
Laos 19.856270 102.495496 10Laos-:10
Libya 26.335100 17.228331 11Libya-:11
West Bank and Gaza 31.952200 35.233200161West Bank and Gaza-:161
Guinea-Bissau 11.803700 -15.180400 9Guinea-Bissau-:9
Mali 17.570692 -3.996166 36Mali-:36
Saint Kitts and Nevis 17.357822 -62.782998 9Saint Kitts and Nevis-:9
Canada Northwest Territories 64.825500-124.845700 2Canada-Northwest Territories:2
Canada Yukon 64.282300-135.000000 6Canada-Yukon:6
Kosovo 42.602636 20.902977125Kosovo-:125
Burma 21.916200 95.956000 20Burma-:20
United Kingdom Anguilla 18.220600 -63.068600 3United Kingdom-Anguilla:3
United Kingdom British Virgin Islands 18.420700 -64.640000 3United Kingdom-British Virgin Islands:3
United Kingdom Turks and Caicos Islands 21.694000 -71.797900 5United Kingdom-Turks and Caicos Islands:5
MS Zaandam 0.000000 0.000000 9MS Zaandam-:9
Botswana -22.328500 24.684900 4Botswana-:4
Burundi -3.373100 29.918900 3Burundi-:3
Sierra Leone 8.460555 -11.779889 2Sierra Leone-:2
Netherlands Bonaire, Sint Eustatius and Saba 12.178400 -68.238500 2Netherlands-Bonaire, Sint Eustatius and Saba:2
Malawi -13.254308 34.301525 3Malawi-:3
\n" ], "text/latex": [ "A data.frame: 258 × 6\n", "\\begin{tabular}{llllll}\n", " Country.Region & Province.State & Lat & Long & confirmed & txt\\\\\n", " & & & & & \\\\\n", "\\hline\n", "\t Afghanistan & & 33.0000 & 65.0000 & 273 & Afghanistan-:273 \\\\\n", "\t Albania & & 41.1533 & 20.1683 & 277 & Albania-:277 \\\\\n", "\t Algeria & & 28.0339 & 1.6596 & 986 & Algeria-:986 \\\\\n", "\t Andorra & & 42.5063 & 1.5218 & 428 & Andorra-:428 \\\\\n", "\t Angola & & -11.2027 & 17.8739 & 8 & Angola-:8 \\\\\n", "\t Antigua and Barbuda & & 17.0608 & -61.7964 & 9 & Antigua and Barbuda-:9 \\\\\n", "\t Argentina & & -38.4161 & -63.6167 & 1133 & Argentina-:1133 \\\\\n", "\t Armenia & & 40.0691 & 45.0382 & 663 & Armenia-:663 \\\\\n", "\t Australia & Australian Capital Territory & -35.4735 & 149.0124 & 87 & Australia-Australian Capital Territory:87\\\\\n", "\t Australia & New South Wales & -33.8688 & 151.2093 & 2298 & Australia-New South Wales:2298 \\\\\n", "\t Australia & Northern Territory & -12.4634 & 130.8456 & 21 & Australia-Northern Territory:21 \\\\\n", "\t Australia & Queensland & -28.0167 & 153.4000 & 835 & Australia-Queensland:835 \\\\\n", "\t Australia & South Australia & -34.9285 & 138.6007 & 367 & Australia-South Australia:367 \\\\\n", "\t Australia & Tasmania & -41.4545 & 145.9707 & 72 & Australia-Tasmania:72 \\\\\n", "\t Australia & Victoria & -37.8136 & 144.9631 & 1036 & Australia-Victoria:1036 \\\\\n", "\t Australia & Western Australia & -31.9505 & 115.8605 & 400 & Australia-Western Australia:400 \\\\\n", "\t Austria & & 47.5162 & 14.5501 & 11129 & Austria-:11129 \\\\\n", "\t Azerbaijan & & 40.1431 & 47.5769 & 400 & Azerbaijan-:400 \\\\\n", "\t Bahamas & & 25.0343 & -77.3963 & 24 & Bahamas-:24 \\\\\n", "\t Bahrain & & 26.0275 & 50.5500 & 643 & Bahrain-:643 \\\\\n", "\t Bangladesh & & 23.6850 & 90.3563 & 56 & Bangladesh-:56 \\\\\n", "\t Barbados & & 13.1939 & -59.5432 & 46 & Barbados-:46 \\\\\n", "\t Belarus & & 53.7098 & 27.9534 & 304 & Belarus-:304 \\\\\n", "\t Belgium & & 50.8333 & 4.0000 & 15348 & Belgium-:15348 \\\\\n", "\t Benin & & 9.3077 & 2.3158 & 13 & Benin-:13 \\\\\n", "\t Bhutan & & 27.5142 & 90.4336 & 5 & Bhutan-:5 \\\\\n", "\t Bolivia & & -16.2902 & -63.5887 & 123 & Bolivia-:123 \\\\\n", "\t Bosnia and Herzegovina & & 43.9159 & 17.6791 & 533 & Bosnia and Herzegovina-:533 \\\\\n", "\t Brazil & & -14.2350 & -51.9253 & 8044 & Brazil-:8044 \\\\\n", "\t Brunei & & 4.5353 & 114.7277 & 133 & Brunei-:133 \\\\\n", "\t ... & ... & ... & ... & ... & ...\\\\\n", "\t Vietnam & & 16.000000 & 108.000000 & 233 & Vietnam-:233 \\\\\n", "\t Zambia & & -15.416700 & 28.283300 & 39 & Zambia-:39 \\\\\n", "\t Zimbabwe & & -20.000000 & 30.000000 & 9 & Zimbabwe-:9 \\\\\n", "\t Canada & Diamond Princess & 0.000000 & 0.000000 & 0 & Canada-Diamond Princess:0 \\\\\n", "\t Dominica & & 15.415000 & -61.371000 & 12 & Dominica-:12 \\\\\n", "\t Grenada & & 12.116500 & -61.679000 & 10 & Grenada-:10 \\\\\n", "\t Mozambique & & -18.665695 & 35.529562 & 10 & Mozambique-:10 \\\\\n", "\t Syria & & 34.802075 & 38.996815 & 16 & Syria-:16 \\\\\n", "\t Timor-Leste & & -8.874217 & 125.727539 & 1 & Timor-Leste-:1 \\\\\n", "\t Belize & & 13.193900 & -59.543200 & 3 & Belize-:3 \\\\\n", "\t Canada & Recovered & 0.000000 & 0.000000 & 0 & Canada-Recovered:0 \\\\\n", "\t Laos & & 19.856270 & 102.495496 & 10 & Laos-:10 \\\\\n", "\t Libya & & 26.335100 & 17.228331 & 11 & Libya-:11 \\\\\n", "\t West Bank and Gaza & & 31.952200 & 35.233200 & 161 & West Bank and Gaza-:161 \\\\\n", "\t Guinea-Bissau & & 11.803700 & -15.180400 & 9 & Guinea-Bissau-:9 \\\\\n", "\t Mali & & 17.570692 & -3.996166 & 36 & Mali-:36 \\\\\n", "\t Saint Kitts and Nevis & & 17.357822 & -62.782998 & 9 & Saint Kitts and Nevis-:9 \\\\\n", "\t Canada & Northwest Territories & 64.825500 & -124.845700 & 2 & Canada-Northwest Territories:2 \\\\\n", "\t Canada & Yukon & 64.282300 & -135.000000 & 6 & Canada-Yukon:6 \\\\\n", "\t Kosovo & & 42.602636 & 20.902977 & 125 & Kosovo-:125 \\\\\n", "\t Burma & & 21.916200 & 95.956000 & 20 & Burma-:20 \\\\\n", "\t United Kingdom & Anguilla & 18.220600 & -63.068600 & 3 & United Kingdom-Anguilla:3 \\\\\n", "\t United Kingdom & British Virgin Islands & 18.420700 & -64.640000 & 3 & United Kingdom-British Virgin Islands:3 \\\\\n", "\t United Kingdom & Turks and Caicos Islands & 21.694000 & -71.797900 & 5 & United Kingdom-Turks and Caicos Islands:5 \\\\\n", "\t MS Zaandam & & 0.000000 & 0.000000 & 9 & MS Zaandam-:9 \\\\\n", "\t Botswana & & -22.328500 & 24.684900 & 4 & Botswana-:4 \\\\\n", "\t Burundi & & -3.373100 & 29.918900 & 3 & Burundi-:3 \\\\\n", "\t Sierra Leone & & 8.460555 & -11.779889 & 2 & Sierra Leone-:2 \\\\\n", "\t Netherlands & Bonaire, Sint Eustatius and Saba & 12.178400 & -68.238500 & 2 & Netherlands-Bonaire, Sint Eustatius and Saba:2\\\\\n", "\t Malawi & & -13.254308 & 34.301525 & 3 & Malawi-:3 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 258 × 6\n", "\n", "| Country.Region <fct> | Province.State <fct> | Lat <dbl> | Long <dbl> | confirmed <int> | txt <chr> |\n", "|---|---|---|---|---|---|\n", "| Afghanistan | | 33.0000 | 65.0000 | 273 | Afghanistan-:273 |\n", "| Albania | | 41.1533 | 20.1683 | 277 | Albania-:277 |\n", "| Algeria | | 28.0339 | 1.6596 | 986 | Algeria-:986 |\n", "| Andorra | | 42.5063 | 1.5218 | 428 | Andorra-:428 |\n", "| Angola | | -11.2027 | 17.8739 | 8 | Angola-:8 |\n", "| Antigua and Barbuda | | 17.0608 | -61.7964 | 9 | Antigua and Barbuda-:9 |\n", "| Argentina | | -38.4161 | -63.6167 | 1133 | Argentina-:1133 |\n", "| Armenia | | 40.0691 | 45.0382 | 663 | Armenia-:663 |\n", "| Australia | Australian Capital Territory | -35.4735 | 149.0124 | 87 | Australia-Australian Capital Territory:87 |\n", "| Australia | New South Wales | -33.8688 | 151.2093 | 2298 | Australia-New South Wales:2298 |\n", "| Australia | Northern Territory | -12.4634 | 130.8456 | 21 | Australia-Northern Territory:21 |\n", "| Australia | Queensland | -28.0167 | 153.4000 | 835 | Australia-Queensland:835 |\n", "| Australia | South Australia | -34.9285 | 138.6007 | 367 | Australia-South Australia:367 |\n", "| Australia | Tasmania | -41.4545 | 145.9707 | 72 | Australia-Tasmania:72 |\n", "| Australia | Victoria | -37.8136 | 144.9631 | 1036 | Australia-Victoria:1036 |\n", "| Australia | Western Australia | -31.9505 | 115.8605 | 400 | Australia-Western Australia:400 |\n", "| Austria | | 47.5162 | 14.5501 | 11129 | Austria-:11129 |\n", "| Azerbaijan | | 40.1431 | 47.5769 | 400 | Azerbaijan-:400 |\n", "| Bahamas | | 25.0343 | -77.3963 | 24 | Bahamas-:24 |\n", "| Bahrain | | 26.0275 | 50.5500 | 643 | Bahrain-:643 |\n", "| Bangladesh | | 23.6850 | 90.3563 | 56 | Bangladesh-:56 |\n", "| Barbados | | 13.1939 | -59.5432 | 46 | Barbados-:46 |\n", "| Belarus | | 53.7098 | 27.9534 | 304 | Belarus-:304 |\n", "| Belgium | | 50.8333 | 4.0000 | 15348 | Belgium-:15348 |\n", "| Benin | | 9.3077 | 2.3158 | 13 | Benin-:13 |\n", "| Bhutan | | 27.5142 | 90.4336 | 5 | Bhutan-:5 |\n", "| Bolivia | | -16.2902 | -63.5887 | 123 | Bolivia-:123 |\n", "| Bosnia and Herzegovina | | 43.9159 | 17.6791 | 533 | Bosnia and Herzegovina-:533 |\n", "| Brazil | | -14.2350 | -51.9253 | 8044 | Brazil-:8044 |\n", "| Brunei | | 4.5353 | 114.7277 | 133 | Brunei-:133 |\n", "| ... | ... | ... | ... | ... | ... |\n", "| Vietnam | | 16.000000 | 108.000000 | 233 | Vietnam-:233 |\n", "| Zambia | | -15.416700 | 28.283300 | 39 | Zambia-:39 |\n", "| Zimbabwe | | -20.000000 | 30.000000 | 9 | Zimbabwe-:9 |\n", "| Canada | Diamond Princess | 0.000000 | 0.000000 | 0 | Canada-Diamond Princess:0 |\n", "| Dominica | | 15.415000 | -61.371000 | 12 | Dominica-:12 |\n", "| Grenada | | 12.116500 | -61.679000 | 10 | Grenada-:10 |\n", "| Mozambique | | -18.665695 | 35.529562 | 10 | Mozambique-:10 |\n", "| Syria | | 34.802075 | 38.996815 | 16 | Syria-:16 |\n", "| Timor-Leste | | -8.874217 | 125.727539 | 1 | Timor-Leste-:1 |\n", "| Belize | | 13.193900 | -59.543200 | 3 | Belize-:3 |\n", "| Canada | Recovered | 0.000000 | 0.000000 | 0 | Canada-Recovered:0 |\n", "| Laos | | 19.856270 | 102.495496 | 10 | Laos-:10 |\n", "| Libya | | 26.335100 | 17.228331 | 11 | Libya-:11 |\n", "| West Bank and Gaza | | 31.952200 | 35.233200 | 161 | West Bank and Gaza-:161 |\n", "| Guinea-Bissau | | 11.803700 | -15.180400 | 9 | Guinea-Bissau-:9 |\n", "| Mali | | 17.570692 | -3.996166 | 36 | Mali-:36 |\n", "| Saint Kitts and Nevis | | 17.357822 | -62.782998 | 9 | Saint Kitts and Nevis-:9 |\n", "| Canada | Northwest Territories | 64.825500 | -124.845700 | 2 | Canada-Northwest Territories:2 |\n", "| Canada | Yukon | 64.282300 | -135.000000 | 6 | Canada-Yukon:6 |\n", "| Kosovo | | 42.602636 | 20.902977 | 125 | Kosovo-:125 |\n", "| Burma | | 21.916200 | 95.956000 | 20 | Burma-:20 |\n", "| United Kingdom | Anguilla | 18.220600 | -63.068600 | 3 | United Kingdom-Anguilla:3 |\n", "| United Kingdom | British Virgin Islands | 18.420700 | -64.640000 | 3 | United Kingdom-British Virgin Islands:3 |\n", "| United Kingdom | Turks and Caicos Islands | 21.694000 | -71.797900 | 5 | United Kingdom-Turks and Caicos Islands:5 |\n", "| MS Zaandam | | 0.000000 | 0.000000 | 9 | MS Zaandam-:9 |\n", "| Botswana | | -22.328500 | 24.684900 | 4 | Botswana-:4 |\n", "| Burundi | | -3.373100 | 29.918900 | 3 | Burundi-:3 |\n", "| Sierra Leone | | 8.460555 | -11.779889 | 2 | Sierra Leone-:2 |\n", "| Netherlands | Bonaire, Sint Eustatius and Saba | 12.178400 | -68.238500 | 2 | Netherlands-Bonaire, Sint Eustatius and Saba:2 |\n", "| Malawi | | -13.254308 | 34.301525 | 3 | Malawi-:3 |\n", "\n" ], "text/plain": [ " Country.Region Province.State Lat \n", "1 Afghanistan 33.0000 \n", "2 Albania 41.1533 \n", "3 Algeria 28.0339 \n", "4 Andorra 42.5063 \n", "5 Angola -11.2027 \n", "6 Antigua and Barbuda 17.0608 \n", "7 Argentina -38.4161 \n", "8 Armenia 40.0691 \n", "9 Australia Australian Capital Territory -35.4735 \n", "10 Australia New South Wales -33.8688 \n", "11 Australia Northern Territory -12.4634 \n", "12 Australia Queensland -28.0167 \n", "13 Australia South Australia -34.9285 \n", "14 Australia Tasmania -41.4545 \n", "15 Australia Victoria -37.8136 \n", "16 Australia Western Australia -31.9505 \n", "17 Austria 47.5162 \n", "18 Azerbaijan 40.1431 \n", "19 Bahamas 25.0343 \n", "20 Bahrain 26.0275 \n", "21 Bangladesh 23.6850 \n", "22 Barbados 13.1939 \n", "23 Belarus 53.7098 \n", "24 Belgium 50.8333 \n", "25 Benin 9.3077 \n", "26 Bhutan 27.5142 \n", "27 Bolivia -16.2902 \n", "28 Bosnia and Herzegovina 43.9159 \n", "29 Brazil -14.2350 \n", "30 Brunei 4.5353 \n", "... ... ... ... \n", "229 Vietnam 16.000000\n", "230 Zambia -15.416700\n", "231 Zimbabwe -20.000000\n", "232 Canada Diamond Princess 0.000000\n", "233 Dominica 15.415000\n", "234 Grenada 12.116500\n", "235 Mozambique -18.665695\n", "236 Syria 34.802075\n", "237 Timor-Leste -8.874217\n", "238 Belize 13.193900\n", "239 Canada Recovered 0.000000\n", "240 Laos 19.856270\n", "241 Libya 26.335100\n", "242 West Bank and Gaza 31.952200\n", "243 Guinea-Bissau 11.803700\n", "244 Mali 17.570692\n", "245 Saint Kitts and Nevis 17.357822\n", "246 Canada Northwest Territories 64.825500\n", "247 Canada Yukon 64.282300\n", "248 Kosovo 42.602636\n", "249 Burma 21.916200\n", "250 United Kingdom Anguilla 18.220600\n", "251 United Kingdom British Virgin Islands 18.420700\n", "252 United Kingdom Turks and Caicos Islands 21.694000\n", "253 MS Zaandam 0.000000\n", "254 Botswana -22.328500\n", "255 Burundi -3.373100\n", "256 Sierra Leone 8.460555\n", "257 Netherlands Bonaire, Sint Eustatius and Saba 12.178400\n", "258 Malawi -13.254308\n", " Long confirmed txt \n", "1 65.0000 273 Afghanistan-:273 \n", "2 20.1683 277 Albania-:277 \n", "3 1.6596 986 Algeria-:986 \n", "4 1.5218 428 Andorra-:428 \n", "5 17.8739 8 Angola-:8 \n", "6 -61.7964 9 Antigua and Barbuda-:9 \n", "7 -63.6167 1133 Argentina-:1133 \n", "8 45.0382 663 Armenia-:663 \n", "9 149.0124 87 Australia-Australian Capital Territory:87 \n", "10 151.2093 2298 Australia-New South Wales:2298 \n", "11 130.8456 21 Australia-Northern Territory:21 \n", "12 153.4000 835 Australia-Queensland:835 \n", "13 138.6007 367 Australia-South Australia:367 \n", "14 145.9707 72 Australia-Tasmania:72 \n", "15 144.9631 1036 Australia-Victoria:1036 \n", "16 115.8605 400 Australia-Western Australia:400 \n", "17 14.5501 11129 Austria-:11129 \n", "18 47.5769 400 Azerbaijan-:400 \n", "19 -77.3963 24 Bahamas-:24 \n", "20 50.5500 643 Bahrain-:643 \n", "21 90.3563 56 Bangladesh-:56 \n", "22 -59.5432 46 Barbados-:46 \n", "23 27.9534 304 Belarus-:304 \n", "24 4.0000 15348 Belgium-:15348 \n", "25 2.3158 13 Benin-:13 \n", "26 90.4336 5 Bhutan-:5 \n", "27 -63.5887 123 Bolivia-:123 \n", "28 17.6791 533 Bosnia and Herzegovina-:533 \n", "29 -51.9253 8044 Brazil-:8044 \n", "30 114.7277 133 Brunei-:133 \n", "... ... ... ... \n", "229 108.000000 233 Vietnam-:233 \n", "230 28.283300 39 Zambia-:39 \n", "231 30.000000 9 Zimbabwe-:9 \n", "232 0.000000 0 Canada-Diamond Princess:0 \n", "233 -61.371000 12 Dominica-:12 \n", "234 -61.679000 10 Grenada-:10 \n", "235 35.529562 10 Mozambique-:10 \n", "236 38.996815 16 Syria-:16 \n", "237 125.727539 1 Timor-Leste-:1 \n", "238 -59.543200 3 Belize-:3 \n", "239 0.000000 0 Canada-Recovered:0 \n", "240 102.495496 10 Laos-:10 \n", "241 17.228331 11 Libya-:11 \n", "242 35.233200 161 West Bank and Gaza-:161 \n", "243 -15.180400 9 Guinea-Bissau-:9 \n", "244 -3.996166 36 Mali-:36 \n", "245 -62.782998 9 Saint Kitts and Nevis-:9 \n", "246 -124.845700 2 Canada-Northwest Territories:2 \n", "247 -135.000000 6 Canada-Yukon:6 \n", "248 20.902977 125 Kosovo-:125 \n", "249 95.956000 20 Burma-:20 \n", "250 -63.068600 3 United Kingdom-Anguilla:3 \n", "251 -64.640000 3 United Kingdom-British Virgin Islands:3 \n", "252 -71.797900 5 United Kingdom-Turks and Caicos Islands:5 \n", "253 0.000000 9 MS Zaandam-:9 \n", "254 24.684900 4 Botswana-:4 \n", "255 29.918900 3 Burundi-:3 \n", "256 -11.779889 2 Sierra Leone-:2 \n", "257 -68.238500 2 Netherlands-Bonaire, Sint Eustatius and Saba:2\n", "258 34.301525 3 Malawi-:3 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\t\n", "\t\t\n", "\t\t\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\t\n", "\t\n", "\t\t
\n", "\n", "\t\n", "\n" ], "text/plain": [ "HTML widgets cannot be represented in plain text (need html)" ] }, "metadata": { "text/html": { "isolated": true } }, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\t\n", "\t\t\n", "\t\t\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\t\n", "\t\n", "\t\t
\n", "\n", "\t\n", "\n" ], "text/plain": [ "HTML widgets cannot be represented in plain text (need html)" ] }, "metadata": { "text/html": { "isolated": true } }, "output_type": "display_data" } ], "source": [ "x <- data.confirmed.original\n", "x$confirmed <- x[, ncol(x)]\n", "x %>% select(c(Country.Region, Province.State, Lat, Long, confirmed)) %>%\n", " mutate(txt=paste0(Country.Region, '-', Province.State, ':', confirmed))\n", "map <- leaflet() %>% addTiles()\n", "#marker\n", "map %<>% addCircleMarkers(x$Long, x$Lat, radius = 2+log2(x$confirmed), stroke = F,\n", " color = 'red', fillOpacity = 0.3, popup = x$txt)\n", "map\n", "\n", "map %>% setView(5, 52,zoom = 6)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "world.long <- data.long %>% filter(country =='World') # can be also filtered for different countries\n", " " ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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KqhpG+WkEinF2lbI8LuB6LKKbl79yTS3iIVe7RPn5RoRNj9QFQxJXvvnkTaWaTy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\nRHp7lDtdt03B1LIzhBTFRLFnHk2kbY2oRpiu+4EoJoo982AilXj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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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6F7HfUKKe+cDEASpoGkXRUbSGFHdSCtX38FklEaSNpVOR6kYXtuEyBZpYGkXZVj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\nEpCEZW6+gRTPxqQcH1LIEZCEZW6+gRSJhJR4emEESIoSIMlH2TW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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ " # area plot \n", "plot1 <- world.long %>% filter(type != 'Total Confirmed') %>%\n", " ggplot(aes(x=date, y=count)) +\n", " geom_area(aes(fill=type), alpha=0.5) + \n", " labs(title=paste0('Cases Worldwide - ', max.date.txt)) +\n", " scale_fill_manual(values=c('red', 'green', 'black')) +\n", " theme(legend.title=element_blank(), legend.position='bottom',\n", " plot.title = element_text(size=8),\n", " axis.title.x=element_blank(),\n", " axis.title.y=element_blank(),\n", " legend.key.size=unit(0.2, 'cm'),\n", " legend.text=element_text(size=6),\n", " axis.text=element_text(size=7),\n", " axis.text.x=element_text(angle=45, hjust=1))\n", "\n", "\n", "plot2 <- world.long %>%\n", " ggplot(aes(x=date,y=count)) +\n", " geom_line(aes(color=type)) + \n", " labs(title = paste0('Cases Worldwide - (log scale) - ', max.date.txt)) +\n", " scale_color_manual(values=c('purple', 'red', 'green', 'black')) +\n", " theme(legend.title=element_blank(), legend.position='bottom',\n", " plot.title = element_text(size =8),\n", " axis.title.x=element_blank(),\n", " axis.title.y = element_blank(),\n", " legend.key.size = unit(0.2, 'cm'),\n", " legend.text = element_text(size =6),\n", " axis.text = element_text(size = 7),\n", " axis.text.x =element_text(angle = 45, hjust = 1)) + \n", " scale_y_continuous(trans = 'log10')\n", "\n", "plot2\n", "grid.arrange(plot1, plot2, ncol=2)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "List of 1\n", " $ axis.text.x:List of 11\n", " ..$ family : NULL\n", " ..$ face : NULL\n", " ..$ colour : NULL\n", " ..$ size : NULL\n", " ..$ hjust : num 1\n", " ..$ vjust : NULL\n", " ..$ angle : num 45\n", " ..$ lineheight : NULL\n", " ..$ margin : NULL\n", " ..$ debug : NULL\n", " ..$ inherit.blank: logi FALSE\n", " ..- attr(*, \"class\")= chr [1:2] \"element_text\" \"element\"\n", " - attr(*, \"class\")= chr [1:2] \"theme\" \"gg\"\n", " - attr(*, \"complete\")= logi FALSE\n", " - attr(*, \"validate\")= logi TRUE" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "`geom_smooth()` using method = 'loess' and formula 'y ~ x'\n", "`geom_smooth()` using method = 'loess' and formula 'y ~ x'\n", "Warning message:\n", "\"Removed 1 rows containing non-finite values (stat_smooth).\"Warning message:\n", "\"Removed 1 rows containing missing values (geom_point).\"" ] }, { "data": { "image/png": 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yKtq3R6kFZIigCJ5W/l1LIn7eJAyrfkAQGSu6mUFRrsc4O0GQNIgCSXCJDi+jFF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\n9wKJayQtLyDFqHQYkHqJXm4MsWqn4wWkGJWOBdJrfzaqIlI9iQ4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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data.world <- data %>% filter(country == 'World')\n", "n <- nrow(data.world)\n", "\n", "##current confirmed and daily new confirmed\n", "plot1 <- ggplot(data.world, aes(x=date, y=remaining.confirmed)) +\n", " geom_point()+geom_smooth()+\n", " xlab('') + ylab('Count') + labs(title = 'Current Confirmed Cases') +\n", " theme(axis.text.x = element_text(angle = 45, hjust = 1))\n", "\n", "plot2 <- ggplot(data.world, aes(x=date, y=confirmed.new))+ geom_point() + geom_smooth() + xlab('') + ylab('Count') + labs(title = 'Daily New Cases Confirmed')\n", " theme(axis.text.x = element_text(angle =45, hjust=1))\n", "\n", "grid.arrange(plot1, plot2, ncol=2)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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GzTgishkJ6va2dSwA0g7W7TgisJkB7m6e97MlLkeVSAJGzTgqttICXEdv8QIJ2Z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\nYCA9ftfu54KMHUX4oHl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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data.latest.long <- data.latest %>% filter(country!='World') %>% gather(key=type, value=count, -country)\n", "\n", "\n", "data.latest.long %<>% mutate(type=recode_factor(type, confirmed='Total Confirmed', deaths='Total Deaths', death.rate='Death Rate (%)', confirmed.new='New Confirmed (compared with one day before)', deaths.new='New Deaths (compared with one day before)', remaining.confirmed='Current Confirmed'))\n", "\n", "## bar chart\n", "data.latest.long %>% ggplot(aes(x=country, y=count, fill=country, group=country)) +\n", " geom_bar(stat='identity') + \n", " geom_text(aes(label=count, y=count),size=2, vjust=0) +\n", " xlab('') + ylab('') +\n", " labs(title=paste0('Top 20 Countries with Most Confirmed Cases - ', max.date.txt))+ scale_fill_discrete(name='Country', labels=aes(count)) +\n", " theme(legend.title=element_blank(),\n", " legend.position='none',\n", " plot.title=element_text(size=11),axis.text=element_text(size=7), axis.text.x=element_text(angle=45, hjust=1)) + facet_wrap(~type, ncol=1, scales='free_y')\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"Transformation introduced infinite values in continuous x-axis\"Warning message:\n", "\"Transformation introduced infinite values in continuous y-axis\"" ] }, { "data": { "image/png": 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QTJCp9X0bVGkPA2NEjdaV2u4+t1XS70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\nO7tz57rQe6QeINYazKa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HukJaSWQNI7Ny6jSIk7PLg97l3+yG7x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\natxLG+AFyXfyzsaHzEd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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Confirmed versus Deaths\n", "linetypes <- rep(c(\"solid\", \"dashed\", \"dotted\"), each=8)\n", "colors <- rep(c('black', 'blue', 'red', 'green', 'orange', 'purple', 'yellow', 'grey'), 3)\n", "df <- data %>% filter(country %in% setdiff(top.countries, c('World', 'Others'))) %>%\n", "mutate(country=country %>% factor(levels=c(top.countries)))\n", "vs <- df %>% ggplot(aes(x=confirmed, y=deaths, group=country)) +\n", " geom_line(aes(color=country, linetype=country)) +\n", " xlab('Total Confirmed') + ylab('Total Deaths') +\n", " scale_linetype_manual(values=linetypes) +\n", " scale_color_manual(values=colors) +\n", " theme(legend.title=element_blank(),\n", " legend.text=element_text(size=8),\n", " legend.key.size=unit(0.5, 'cm')) + ggtitle('Confirmed vs Deaths')\n", "vs\n", "vs + scale_x_log10() + scale_y_log10()\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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JMuRyTvXyQ989oowD7mf3Ro4TBCe/6IFK+0FElti1y2SB/Lj9me6WP238fdLttR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\nAAAASUVORK5CYII=", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df <- data.latest %>% filter(country %in% setdiff(top.countries, 'World'))\n", "\n", "plot1 <- df %>% ggplot(aes(x=confirmed, y=deaths, col=death.rate, size=remaining.confirmed)) + ggtitle('Number of confirmed cases and deaths in top 20 countries.') +\n", "scale_size(name='Remaining Confirmed', trans='log2', breaks=c(1e3, 2e3, 5e3, 1e4, 2e4, 4e4)) +\n", "geom_text(aes(label=country), size=2.5, check_overlap=T, vjust=-1.6) +\n", "geom_point() +\n", "xlab('Total Confirmed') + ylab('Total Deaths') +\n", "labs(col=\"Death Rate (%)\") +\n", "scale_color_gradient(low='#f75656', high='#132B43') +\n", "scale_x_log10() + scale_y_log10()\n", "plot1" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"Transformation introduced infinite values in continuous y-axis\"" ] }, { "data": { "image/png": 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h6kfvS9fghSSSbegv/T4igMkjNXgx2w\nr7dRjHK0ApBGhSAVZJLGURAkohaWVIAZ2PbHLA7SOEcIUsMSWLD4EznKAYmYAWZg0x+zOEgJ\nHCU3klcOkt3g2PkTsqkcxYufGIYRvSRObdXyx8QSmu9q0+UIYvT5DzOpsNYNEpELYn4OrYUa\nl8Bixe8dyOLBaBwkYBhYEniJtPmpNuPDfpmlf/zxR2pqCNL9gBTjyMAofh5VfpJ+z2+j8I+v\nAxj9+QeVmVRY6waJieiSJfCzZy3UtgQWKv4MjOLFT33Ct1GMcvTHH3kgrXrKYiYCLpHk58B6\nww2SZI5Y8UcSImoBPu8OpFSONgQS6XFeO6s+GsMoVvykN73ydTZs2kmmMY6kj1sDSf5BRtZC\nDUsgmlC74k/E6PNo8ROwIvZ24vSAN/kxCQnNcLWZxtHvZlJh/f3PoNYBEoF/kZG1ULsSiCfU\nrPjd3iUvRuPnUXVttOu3UYxxpDH6fTMgEb0kuwUpsToqOI+OqcGPSUposW4bC6PNgET0Sv7b\nHUipGH0uOI+OqfqPSUyoJUgjd+MMjLYCktMS2eHIhgKMEKTI9uhohs9/WBxtBKRCtSmB8YQa\nFH/ixVHpeXRHTgp5ZzlxOBI2/vLLL2ZSYSFI6y1+cO6MU1R+Ht2Jk1rRy6PPDkYIUvUSSEmo\nbvFbDZAQRG51hCAFt0cx+uxihCDVLoGkhGoWv1ngEYrc6mg4CMws9u0k2B59tNhTHSFItUsg\nLaFaxf+XQVEMIj9G6cW/dSfN7XZLeaRVR/VPM6mwEKS1FX96VfTZPovmN0h2pQhG0Egwbnug\nIDVxBGllIFlVUYigOEe/mFns00lre7CrzmrVAYgG/WYmFVYqSPZkww4FLhbLTX5yn8ohKIpR\nxnl0THWP5fSEqvd/BrsYJEaOhQyj32qDJOalMzf00Q0LglSvBPISKix+s4gTCPJiZBwBZha7\ncTKw3d//KU5XTmWkIfqtDUh6isdOTIivptKX8xXreSPNKsyrnYMkijat8hnBqPg8ugknRwLs\nDm+nMoIYmRT9+uuvZhZhZTXtOvWfd8Z875aw9ggSLc4SbhIqo6Lz6P06mRbwl03Rf+2q6A+n\nQtcQ/doApB5i1Om2njGVPvgbQaKazswoROwQ0OfRzOK/GydLMo9AFKzRtYVU//jHP8wswsq6\nRjJBkpVSPzpdvl/3DlIDSrIp+sVHUUbxL+Vkwm/NzSPW/zleFf3iWChkZhFWXq8dmAtfXyN1\ncJs1h35M9wlSa1aCilVFxcW/kJN5v5zSkJ55rB5ybDSqot8MC/+HK9VJvI9U9zzaSLGqyKAo\ns/iXcXKSFX+NSxMUc9GtzQ0L/82V6iSClFL8k0p+kiL3iszi/0dR8c/k5HL+BVy0anOfhVT/\n+c9/Up3cCEhNHuxbWdnr4tcE/Y9d9v/hChZ/0tsoKjm5qH9cXhe9BDkW/i9X0ElL2wCJqAXT\nXRe/U+6AIPPkaRZ8UvFbRrkBtZxc2kXXRqcO8p6B/lfp/3AhSNnFv2SZu+yEyv0/jpyyjxS/\nZZT+JAMmO7kiC81aJ8FC7d//VUKQEop/mUI32xhD+TrVSkxmmf9fQ/9PaRSk6Nso7sFJcbJJ\n9s210bTw/wW0V5A2O2VxHRGxIOpDECR0MktbA4kp9Tw6ErD0mOWwooFOqMcotd7v2ygSAmNO\n2lr/lMUpImrBlOnZjopfGhV8GwU6GQuNHILrAUmOhAh0z0ZF1IIp07MdFb80ilibEKSk0Mgh\n+PdvQc0JEumA8pMiasGU6dmOil8aRaxNCFJSaOQQXAlIXwFHX/OTImqBioqIBVEfxgJQaVoJ\nSL1u2hUpeMMeZco/gCEWgErSekBCoe5YKwLphRRfI6FQC2s9IL1M6GxAoRbWekAiJb0MKNQ6\ntB6QsCZC3bHWA9Kn7r1xhihUM60HpB/k6UfjHFGoVloPSFNGNqBQCysRJOsAr3KstwPpDQWF\nTtZSxKnkGik+83eJ2tU8S/u9MqGTtRRxKg8kNX+dWHtm108XgjST0MlaijhVBBKcbLUrBgKb\ndjMJnayliFOFIHVgYlUEaeVCJ2sp4tS0GqmvCBLXj6cvhckBLe33yoRO1lLEqczOBl0JqTch\nFVcg/ojv3XSSlvZ7ZUInayni1PT7SJVBqjFUaGm/VyZ0spYiTk0Gqfyw98f81pHiFKWW9ntl\nQidrKeLUCkc2vExOeWm/VyZ0spYiTq0OJDKdIyx+U+hkLUWcWg9IFTWHpw9Ck9Ko9WXiWrmT\nUCOmzmRYUBGnEKRCPVjrKWk01sqdhHowVk4QguSTDdL7y2PXPb5UeCppDk8RpBaKuIogheQ8\njyQukqY/lTSHp7DIZWvEXD/IcLH54e3hwdgHQXIUdvWBG2Y5PbMiTq1nyuLnjj7Y9+Ope56c\n8hyegiJ/8K8lSGqzOqc+yCNlji96ryD5XJUWPsB9Z1TEqfWAJG/E3skNWdDZYBS59dlzRMx+\nKKzcSSgPSPCz7fTcijj1969BIUgxgXKWRIVAgsHmhzm+6N2C9GC17d6Aa4s07O4EpLts2tmn\nTXPjg3GF7O6LINmyayS5zQZpIZQiTq0HpLvsbECQ6soDklW/u3bPqIhT6wHpPru/nZa7fTEU\nuUbCzgZHAVftpt0aOxvWA1I9zeEpPC/6u7/VTrBPAru/o4IjGyynlGHQ8ZkVcQpB2r7QyVqK\nOLUakL4Sunx7/l4h5aX9XpnQyVqKOLUWkJ66jiFEKjxFgcVvCp2spYhTKwHpW0de2R9vpPs2\nOeWl/V6Z0Mlaiji1EpCeulfx12v3NDnlpf1emdDJWoo4tRKQwHCGOxnZcEdCJ2sp4lQiSMZ0\nc53zR5FCIJFJqVIt7ffKhE7WUsSpVJD6avwYaUo9dXI8w487GSJ0R0InayniVCZInf4DTFrc\nF82QCvf/qvB5VldL5Vra75UJnayliFMZTTuDnMD/OTJ2J90n+jXfPnWPean4dPEqsDkckLu9\nKCQ/k7FAJxSdDKuak3k10ihOOTJ2l0NWrTGrZFDOGovfE5pXLIYys9+xk5kgyQ/VQer7b58G\njD6ZN5GIWKSuhTI921XxE3HOwVPSeGALkMQlUKfeiWT/MRkkn4hYpK6FMj3bVfGTNGfRSV9o\n5FBNBMlWPjbeNJJEEKSsQAQpNamxwBlAqnHbtAlI/6Ka/NW2KwLXBEFaHKQKSgSJ9FgjZQVG\ni19dIo06iaekLCFIOwMJnUwP3FSNROBibC2U6dn2iv+n+dnjKTqZELglkIheEiz+pEAEKTWp\nscBxJ7VWDhIBK4LFnxQYLX50MiNwOyCR1NuHeBsRhv6MgyT/IUg1QVrNlMUV1bgEVl/8EZDw\nlJQRiCC1LYG1F//Pn3+Ze6OTBfmw0NgpyRSCtL3iR5ASkxoLzALpH0EhSNnmr6L4B44QpKSk\nxgIRpLYlsO7i/1kTpL3r58/UPTcJ0q5FOfqrVmKNzwrrPiWxk5K5d1ibBKlxCay6+FmFhE27\npKTGAhGktiWw5uLnHCFISUmNBf5EkJqWwJqLn3OEICUlNRb4M6P/E0HaVvEjSBlJjQT+RJDa\nlsCKi19w9F9zb0uELXBkw1gggtS4BFZc/JyjOEiJD/bt3EnBEYLUsATWW/yyQoqBRHDQalog\ngrTf4pcVUgQkgqO/0wJ/NgHJnvvbgsBlIoGSjCdkcTa2pEBVIdUAae9zNnCOkm9tp4LULQSS\n4IP0ekXwPBoC6a9RkEifCtKunbzIll31GklNB8mnhFTzRfKFnikSziM5MmVX2hOypAdFaxe3\nvRZqXAKrLX7FURAkZRQ6ORL4M/PWdnLTrhufPt/cMspJ7uQnPRZ/PFA17CIgyWeO0cmRwGYg\nycrIfpuLAsmdX3+ElByQcDa2BGmO/hvdj/QI0mjgz8xb25kgqQ8Jc+qPoYLTcZVlEgr8qTka\nvyGLTsYDf7YDCV4I+Wohu3YaffNYTq9doLjttVDjElhp8f+VAxL2f8YDJUfbuY9E4F9kZC3U\nuATWWfywQoqDlKHGP2adTlJJjjYGklyMrYUal8A6ix9WSH+ae5er8Y9Zp5MXUCFtECTSI0iR\nQMgRgpSWVDhwqyDhbGxjgSZH1UDaqTRHtUc2tBCOtSvLxBf4l9GwqwdS4x+zQieZAEjm3mEh\nSFsofpsjBCkpqVAg4AhBalgC6yt+CNKfCFJqUqFABEmocQmsrvgdjhCkpKQCgZCjdJBwyuK7\nL36XIwQpKSl/4E8ESapxCays+D0cRUDC/s+xQIOjdJD+J6j7BWlX8nH0Z3BvIhZja6HGx/Ia\nQTI52jdIjUtgVcVv99eN1EhELMbWO3RSCEHSalwCayp+p+Ob6bO5ty2CIAUDLY4QpIYlsKLi\nD3BUA6SdPtllcZQ+sgFBunuQXI5iIKU+IrkzJ4XsCglrpIYlsJ7i93H0+XMcpB6bdpFAm6Pk\nB1IQpDsufg9Hnz8jSMlJeQIRJKjGJbCW4g9zFAaJiMXYel9OKjkcbQckwpd4G9EJdO8fff6M\nIOUk5QZaHG0IJMGJWIythRqXwDqKX3H0558OR5GmHZ6SIoFuhbQVkAhghCBIOvAvh6PPn5NA\nylPjH7MGJ6HcCqk6SJE5gUqvdXDOhrJMLgokb6sOQUpOyg70VEi1QTIm0be0KpB2chtRlrMf\no8+fa+XT+FheG0guRy1AYmt7gm9jmSeskcoyufDrI1UdORRVrJF2JV+FFJ+zFigTpMAcq31J\nvYQglWXCOFKddT6MsGmXlpQZ6OWobY3kTLOKNVJxSG4moJSDGCFIaUldYiBlTrWZd400+kKK\nHCFIBZnIUg5XRghSclJGoL9CatRr1zmTfaurJgSpLCQvE8BRBCMEKS0pGBjgaCv3kXBkgyF9\ncRTFCEFKSwoGem4hbQukEjUugaWKP6VNNwoSnpK8gSGOEKSGJbBM8Ys23ThEUZCIWIytt+yk\nLzDQsEOQWpbAAsWvro3SOEKQkpLSgUGOEKSGJTBz8YPCTcQIn0dKS0oF+nu+EaTGJTBn8eui\nTYaoEkg7GWzFFOao9siGFkKQRjIpISgFJNJjjWQERjhKnrN2kyBtQaJMSyj6PDJolfQIEgwM\nXyDlgIRTFq+x+IurooQaicBFaL0ZJ8dBinKUfEcOQVph8U+oisZBInpJEKQ+ylEOSP8OCkHK\nLpkaxT8U5jSI4iARsCIIknzWOMQRgtSwBBoWfxWKYiARIoYu4MgGHhht2CFITUugUfFXIWgM\npF04mZrUZeQCaWTOWkMI0jqKvyZFCFJiUgkcIUgNS6B28VeGCEFKTSqBIwSpYQlULf4GFFUE\naduKd3wz/ZGa1p2AtM1L5DYQ1QQp82feV42UUB99/sNMKqx7AQmsyDY6bdtRhCAlbU/iCEFq\nWALTi78pRAhS0vYUjjYHEoFrcucgVbpVhCBNyiSRo62BJC+R+v7eB//PgRHOtDq6PcKRfpT/\njxYgWYd96ayQ4RRjImIxthZqWQKxhMZD5oCIyszbY+bGum1yM4lxpEz8owVInXXcV+i7zk2C\n3DtIc1EUB4los+7WyamZhDkyq6NmIOkZ7bphQ1c8yapIMk/kvot/RoxinbYEH6MITWHnVkf1\nQYIzrXY9+CADC7Srpt2sGMWLn8DF/Tk5OfOU6khxVB0kc45VCNIcNRIB/+6w+OekaPwSmcBF\n2MnVd9sUKsgRnC9QcfR7arIFNVIvqqUJ036rNFN1z9MazovR+CUygYvQepVO1sg8BSPA0e9m\nUmGlgaTf4sKujXitZEwBXqB9jLVbAiMEKbQ94eLI5KgySE20A5BmpijtEpnARWi9NierZJ5y\ncWRihCDVLYHEhKyQxTD6I1r8BC5C63U5WSfzpFbdZwMjBKlqCaQmZIQsRtFYg4TARWi9Jifr\nZB7CKODi7wjSGop/bopyzqOEL++w26Y487/+CmA0YuPvv/9iJhUWgtSi+JfCKPs8unonq2Tu\nxyhs4++ao1/MpMLaJEgLam6EPlttOqFaP6fasZyZUE2Q0ijyuThw9IuZRVibBKlSCWQX/wIU\nuZUR1khAiVWRrzJiGCFIU0ugYPvcCIHy/91WavFvWskUec9Gv3ClZoZTFtcAaWZ8zOJ3KNp9\njeQQlOSjpzrKqZEQpInFPw80gcL3QZTT17QqJyeGOL1zE5wUFP3zn/80swgLQZpU/G1pSS53\nUxnFvx4nJ4RkcTPupobon7/99puZd1h//ycoBCm+vTYiaSUfI4hDlFP863AyPaSqlZGqaOBo\ncPG3X3/91fxWYSFI+cVcszTzNIbQP//5K1dq8S/s5DL9n8FKXVH0C4cox8mNgDTf/fimRRzU\nOEECIfqqxX//+9+pxb+kk5dF6vZQs1g36ARB/+BKdXIbIAd5sMMAACAASURBVBG1YGpV/HMW\n+Oc4PQAijY8uu9Ti34mTVLELS3FJZBCUCdKCuheQZi7wEXTEmTN8/lspSDO7mGAo6FRw8Bn0\nb35mKnZyPq0XpKWLGGgEmHWCNJd/Xo10aQp8THSGCl17979c/4drryBtdaaBuUTUAp28F623\nRircXnnMckTRQCcUnQyrnZPzCUEqy2QsEEFKTWosEEHK8wyLH51MC51+pLYQglSWyVgggpSa\n1Fjg7kHKVO4ldckleHacCdf5U7oIiFrMkfOWnZxP7UY2ZAqLHwidrBN1Rq3mUXMs/lpCJ5cQ\nglQ7k+lRJwqdXEIIUu1MpkedKHRyCa0GJBTqnoUgoVAVhCChUBWEIKFQFYQgoVAVhCChtqNN\nTsfVWmQjeSwvspE8EKS+wOiSMTS5USaM0ymPOVXZOW/ISQQp2+miksmMNKH0J8Wdpj07iSD1\nmaM0y0omL5NJLEwZdTpNe3YSQRoq8OyiIdljpPMyKcujKKua2rOTCBK3OicC373gmGmbR2FW\n9bRrJxEkwhYkI4LYOSNKdiYleRRmVU/ZOW/JyV2DBGr8NL8I3DcpRn4mJXkUZlVP6OSeQSLQ\nsLSiISIeWNXOpCCP0qyqKT/nzTm5Y5CEz0R+TIpC1M6JZ97sTPLzKM6qltDJfYMkViQnivQ4\ntR8oP5P8PIqzqqWCnDfn5J5BImKdFSPzOM3OpCCP0qyqCZ1MBqmT/7p6h//SIKnLUJIcIW/3\nokxK8ijMqp7QyUyQJE9VtDhIRdeh+eVfkklh+ZdkVUVlOW/JyYGXzq8QSNW0ApAIX5VFa5pJ\nUfkX/p4KQif33LQT/Tr5TudFKMskd/8JWdXQ7p3MBKnfVNOu5z06DRPPy2TCV8nNqr527uSu\nr5FaizTev07UexBpvP/0qIkgyTbd/TftCFtknG2yI6iI2b1S2VlUiFouInJOzjo7gop4B06m\ngtRAC4FEMu8wZEcwYrbcv07USXmik1K7A4k6RcS6UQQYs+n+daJOyZNk5b1lJ/cHkqq9SbMI\nKk5GsydzfzMqWYoknmly1ht2cocg5d8kyC9+eu4lfXqh5O4Po/alDabpQieV9giScIq0i8CL\ng/SphZK7vxFXJ5AbdbrQSandgcSq7xy3MiMQuSBpEXL3t6MXNJYqCZ2E2htIohwJSS/9rAjq\nbEh6XTYV9/d+v34JkNBJQ/sBibAF6XVbuHYEHouY8WrvryOqBOCnOURUtqRHJ4V2A5J2iaSW\nS2YEEU1f6yZFyt1fxVNn9tJWfanQSZ/2ApK2iv5BGkRQEeUFb2KR5O6v4+lz8PwcoZOWdgKS\n8ie1+s6OoGPKM1tinNz9VTzSy5JfgCN00tI+QJInReHyuF3ZEXQcWZ7V93fi9+L0S2YkCZ0M\naBcg6cJMrPmzI8D9SJ9Unrn7exKQ59L56iR0MqRdgCRsahmhF8Uh4yXEzd3fk0BJrGlCJwPa\nB0jM6rYRet1AT42Yu78v/uxCJ/3aCUj5VhU2EeRVa5P9fdFnFzrp1V5AmrH8M2Lm7m9HL4w3\nTeikT7sBac7yb5tHrdjzZbsHJ/cD0ibLf0rkCdnm5rsDJ3cE0hbLfyGhk472BBIK1UyJIMFZ\nVmsBgCChtiMECYWqoIGXg18WSGxGO76oNE8kgoTajnJrpE78X4ECBAm1HeWBJGokBAmFMpUF\nUoc1EgrlVQZI7Dqp4kz6CBJqO0oEyRaChEJBlYFUhQEECbUdFdZINYQgobYjBAmFqiAECYWq\nIAQJhaogBAmFqiAECYWqIAQJhaogBAmFqiAECYWqIAQJhaogBAmFqiAECYWqIAQJhaogBAmF\nqiAECYWqIAQJhaogBAmFqiAECYWqIAQJhaogBAmFqqBUkNhEXIYmc4AgobajRJBqzWUHhSCh\ntqOBl6tfDki9qpfUzMVONZUjBAm1HeU17Zy59KfQgCChtqOMzgZNjp5LH0FCoajyrpG8/5cK\nQUJtR5lNO7lEkFAoqIymXV/52EeQUNsRgoRCVVAeSFWFIKG2IwQJhaogBAmFqiAECYWqIAQJ\nhaogBAmFqiAECYWqIAQJhaqgzYH07VPXdZ9em6Q9Lv1cyfuz+XlqeuG0svJ4fSZd9/jynpin\nR29PXfck/P06/P341dqBdGT8e8CYoRTh9tVrYyB9Jx3XU4PEE6QPQvnkVq306oD0SdjTRY7P\neHpvPP4X+veTz+vXeOpcMGYoRbh9/doWSN+HyuitZ+eyT/VTT9DKQfraEXqM//jadd+T8nT1\n2H0bqn22z0v39GPwmpjH+qfuZdR8I2YoRbD9DrQtkB472c54Gj8rttDKQSLdD/7H1+45KU9H\nb5yaF3qQE77jd6Mp9949DsUQazr2ZsxQinD7HWhTIL3qU+H37oWuXh6HdsIb3/LcdeSZ/w1a\n33Azl47Tde+PLMnvw5UFef7Ot/VyNfx7Id0jT+f1qSNf9EHYiWdNuv4rkd/AbPO/8WP5rWOB\nz3RlZMOy5ulZaVsytoMkeLyvMNzmfNihe353frX7ZbU5vCr7DkE0vsCX4Vz2VdRR4te/Gl7a\nXz2Uom/7irUpkD7Z1RDRFwRv4OLgG//zxdrsxBkaimyv1w5u41+eUfKkNr+wv55dkJ7ZX29m\nriKjnkVkG+g52MyGZS0aPGbaluB2mMRwTLP8QPiTzr1XCRPnV3u+rE6BrWi9o78A+HtI6H0I\nJeKbfVGpSC/Nr/4YTtG3fcXaFEjEalJ8YWfGL+zalbW4h+PjkZ7k6Kn3O2v9gc1unIEUmuBw\n5fVlODqGw+6HBRJ5698/0V2HXYZ0XonbtBsuSuQuOleR02sv+7heh1ytbFjWNBU3bVNgu5GE\nJ94PWl19+672pl+OwWX+as+XtTMDib7CayTeKHjuRC1E+Df4rn6QIRYzlKJn+5q1KZBsz0Vb\nXTzWqza/8M3vtMydYjLjvPH9+Zn0ma4NkF5ZMqwdwi7O3okDEthF58r0gzZafnSP9KB/HhZW\nNqqZ5aYd/NVGEiKecbn+g3fb8dsDz2wHdsq3nXK+rJ0ZSPSRgHDeKBBt7E5ckhH9gwyxmKEU\nEaRUtQdp0PfXL09s8yd6Lubl+ij7gDtjsxtHpPcojocf9JAzr5Hkn/Ly+pO/s4HvonPloo2X\nL903dvp9Cmfjph381UYSjxIGI947+3GikoZ1hPGr3S9rZaYDPnXAPtmo420D2TH+6dFbNjxm\nKEUEKVX1DXJ6i74SdTT8YH+ye30dOErAZjcOOJrVH36Q5C5PYZA659j8Npy9Hzt6QfBKG2Ej\n2ei0rSPc9yeM5zkS355g48nzq50vKz84ib4Yrb8vKh5t7skG82Pn+xYvsv3nTxFBSlV9g55h\nof6gnbzd08u3H6IoXp9FAZtFozZzwTj5ILnXSM4uQN0zbTt96t6f6RlgDCTnoO6t7aMg6b9Y\nzQEi2r/aUxPJGlx8FpWNyZHssxBdGBKkJ1+KMmYoRXv7ypUMUv0TQ32QQPf3D8IaS6A/l+r7\nMy1gYt+N5Ju5YJz0pp34890+aMEuTq7sKoZ1FvO7OiPZvIdKIL1pB/oO+Dd61xHNX+35svIr\nswDRKf3+RIwrH10A7FpJfvI07XTMUIob7f7u6h/4DapsIu/eDS22V1l4r7AM6d/PvGy+g3Od\nDcArBOlFlKXoBXiX4eBg/8RL/WsEJE+ub93TEO979yh60I1sImmbAtuNJERnA4inOye/0W/x\nLHpCiPurPV9WfmVWd/Mexx+EmJeXn1SPwhvvyeEokxcbJBAzlKKxff0aeDn65Zv7W5+zdPNC\nNJ0FaRl0NADpjQ8Reh9a/PRkyAY6iP5f3s/NRq98Zzc0vhPd/Q0GtcA4ovCHFs8L71T+Ts/q\nn95FODjYv7KO3m8dBAlcMdMVzFVnxrMhTjbRtE2B7UYSvPvbiPc0JDUc2z9eWG/ia0e+i+5v\n+1f7vqz8yixR+o3fCTHD4D0fWh+yzn+a7LsFkhEzlCLYfgdKrJEAK9b/vfExB44GIKkbkhyM\nr/LTm7rzyk54Yi9wQ1afWWEcWfjwNieP8GKBJEZbftGHy2Nn7wJyleI3bj6JbYH7vm7apmC/\ngOeGLATpxxPsCxA3ZJ98v9rzZbm+8wB6+nnurAxg7UHvBbGbsOYP4jJihlIE2+9A6SBJmmyQ\njC1Lg9S/s87dZzkon96AfOMt9zc2FkhcQ9ChPbynDm6246jChwNv3h7leB2jL+KbNYzn+yO4\nnucrkKv8tuyA/io7kP0jkdy0TRmHsz1EaKihjXivnwh9jELs8HX4ki/+X+1+WSE9dqgzch5k\n1FDDhyFgYNX4QdZX7mIpbvIxCqPl5q2a1gESypZ7U3U21e+gWq9SQdKnSBsesCWPjR25vIh4\nq2g4rfvqlZm+wVI5z69kkJKEIK1I8hJpufswCFIhSHm+7cjlZfRKL/WfFrxYR5BKa6Qs7chl\n1OaFIKFQFYQgoVAVhCChUBWEIKFQFYQgoVAVhCChUBWEIKFQFYQgoVAVtCWQ3lCO0M6airiF\nIG1baGdNRdxCkLYttLOmIm4hSNsW2llTEbcQpG0L7aypiFsI0raFdtZUxC0EadtCO2sq4haC\ntG2hnTUVcQtB2rbQzpqKuIUgbVtoZ01F3EoFiU81pmbjqiEEaQahnTUVcSsRJEFR1WP/TkB6\nkPKHGvsFEnjzB/jTqatl7YxaB/fz/ulJLBAwl8ERtwZePvzyzf2t6qUOzPtdqjsBieoh+En+\nzcvYV1ppJbhRkKz1lKTWYHDErYwaqQ/M+12qbYHk2zG0aTSHelrWzpogBVPaAkg7q5F44wIu\nfeWrQvR+D/wj2AZ2fHtIapuUaVk7IUiOLW9wo9wO/4TGrMPgiFvZ10ju/6W6O5Bk4+LB+vvN\nKGcV8mDszT/qhZXc5kEybXEtAJsM51ZlcMStRJDA1ZEz73ep7hqkN/NvXzmDNfjo32GrNZLu\nbHB/rUnZg/fPlRkccSsVpAa6c5BECw/u4O4VLecHkMRGQVILz699kJubgVTd4IhbCFKKPCDB\n4ptwwnyrWc4eLWun6Zv14eENmtOqRrLzn6SIWwhSilyQvIXlPQwQpDBIfnp8IK3D4IhbCFKK\n/CA9wMIyTrLp5fygk2qiZe0E1YlLx4PhYgAkZcwqDI64hSClSB4Q4rpZ9rACkFSg3ktEDJbz\nXru/30wLwjWSZcwKDI64hSBtW3dvZ6szTJEibiFI29Y921nv2qaWIm4hSNvWXduZMt51VkXc\nQpC2LbSzpiJuIUjbFtpZUxG3EKRtC+2sqYhbCNK2hXbWVMQtBGnbQjtrKuIWgrRtoZ01FXFr\nSyBd/Aptzw8IRogF1cu+JHQ+O2v+zjt0E0HKCECQIvkjSAhSagCCFMkfQUKQUgMQpEj+CBKC\nlBqAIEXyR5AQpNSAuwCpMz/PZuc2QUp2E0HKCFg9SHQ2Gmvv2ezcHkhZbiJIGQGrBokXOzbt\n0tOKhua6mQjStIm3/EpJkAQ2D5JrvUtz71cMkjx3Ros+y80dg6SqogYgRZIoVUKasFzhZrkw\ng5t7v1qQdBMkVvR5bu4WJNCeywPp5JcHpE5On6/m0JeLEo3HI845lOgl6REkJqMlHyn6TDd3\nCpJxWdSkadeDefPB/+XTFqc37XSTg+glmVrymwDJvh7uL1fzc7GbewTJddPaO6yspp2aNx/M\n+11+9ZQMkl7YRa+PiX8NKvsadyxt/YHrOiiyPzEXcTf3aOeEyLkgOf+XZ54NEiGirNUGFcrU\n/CS2rhqJnT0BQYNSOhtS3dxbjWR3dfPQg/k5rGyQQI0kr5Ga10j6XAmWBO7C1Nz7EZACb2yb\nW+Giz3JzVyCBNt3BUKShbCoRpBbKq5HA56VAyjqC62cvJEpYFDyrhpiOg5JrpAQ39wOSoEgY\nKwJUHW/sHdZdg2SFtvSegbJ4044WdK9KXhT0kcsft9zN1iDFTkJzgtQdQgQBW+XeYd0NSMSz\nlfRTSj4jQBT0giDpsu4VRXRlFjW7b2GmVexmQ5AEOr29aeb63QRIEnRUatFr10Il3d9yM7gX\nr7dWs9gKUIW6GEi6tPk1sT5j6n0oQzcqM61iN5uBlGCnecVZlMtYACeo0y1k19EtgZSlShaL\nAE9BLgSSiVF/kWUOdhEQZRV9ZTuTT0vjMawQWAxVQJL1EHMzULlfxLgEM62wECRvgL9psQBI\nsO0hayN44pTVkKyIVglSoMmW6Vn8MjXjtCQMZW7q05J5NcQJwhqpyGKqsgZ6I5AgRIM6dvI0\nIDo5TbnL2fw8m53ZbpZ4Fk4w+7TEG3XmaUlIVUQIUrbFVKPthzlBOlgU0ZJnFIloRj2kdD6b\nHC0I0oTW2GiIB6bM0xKtjozuTiazOTfYe04+LSFIQinXQrOB5EA0qKMY8WiUoJMbb8BoLU27\npCvLcpBEFpCnvNNSJ6+IQDSDoBujyDorIUgJhZISYxaQ3KrownuVhpIXJWxfAzPxYl8DSJM7\n2nJCJE85pyVQD4loZj1EV8xObNollYoMSG4mzAASLXM7YIBINuQ8PUlM6uS5PEgFja4KIaEr\nMc9p6XgE4xV6GyJK0VnaiSAllYpsbyfHaA6SuOUKthiXQqzAnWhn3QTpuqVBynCzKki6bxBs\nO3hOSxAj87QEqyKVaO1Bqy20CpCyeoAagyTPnSxAEHSlRc83iyI3o4liZ11Qy8/ZUN6fNjlE\nBSiYhJ1GjAEjNTpVnpZu+mR1ti6L7Nos4ta+QQq0BxYBSRYarINY3ywteXDmBNGGYlcEiTTs\ncZbz2ZntZiOQqAK3cIdTEnNK1ER2x6eGSLjqPFkRcWvXIOXe4GgFkh7yZYxO4N103cW8KlLR\nhnKHJc1HiS1YI2XfLmoH0oU3yawRfcxM6aYgSIcyjPR5iSZwjD5vbGrHIAVvFM4MEqyKdACt\nWmibzulbYKHm2ZISdJWhyYNa6tqZ72YrkIyRqPqqSXbYmPWQcpNhJDw9HumDSMNZbDuPUTTT\n4O+S2SvR5yKohqKFm9kT48e+P52cGNS2oU0nPh35vqxPnK1uToQZtBY3e2qos0mgE3ST+gk2\nHYWnedoSSOknMX6Wyj5VVq+R+JnTHqHQi+FfQ23U8RC5BzsehkYI/Hy9nA48Oj37LtG0K3Sz\nRY1k9g7Iyud6pdYMbtqRWC8n6GCgTemrvB713GiIuLVLkD6SCiV9ezQonAstdY4EBIO2J+Rd\nV2cI0AVeEsvyhmELgFTqZm2QDjdtqLEeWmkUCrN7XE6oCbrp6E66MUd78PA+Usz78edh2oNE\ni/lw8Q2XG0qyF3RYnUb0ky7coyh0fgpmP4keExn9THXsLHezEkjCwgM0UrtKLzVv1E0TMGmt\nxOho1D38N31kubk3kCYNAqsAkizPg2fE6eXCr277ow+jThQ7S09eCPOmDLj9Yd/Gb2vntCF1\nk0HSBrIn8MVGYCwf2+24SSsmPphOuMnOW7IuKnMzEaSFpizO07j3EweBTQMJgGP0cmvJB8xO\n3smhRPEeQdv9cDFu5Hu+R0s7Gz27mhgCeNEDGIyzE8Po0ttu8r8HWtiFkxwAPOw8pAJux3I3\na8/ZsA2QUqfYaAESKHbYyw0lMKJFb2Ak2nQXHtxfVK/s4eMDnjuP4p+Rezs7p7pZDpJZlctx\nVXYdTweF8LsHBkbQzdOplz3kooYSabMkj9ZdpDGQbn7ZINkTf0/WbkAyivdg3sKAuqp77jZH\nrNzZXSXZp8f0Ieni/66eRBvauRRIVpP4oDrnTPEuBhrDro6Umzw96ufA0uHj/EE33w70xu3x\ndh1SrN/ZoGb4BpMVT9XsIE0ellwE0s3iKBjlqkbTORxdeM/s6aTmUuW/Zdh2EASpG7LW92pm\n5/RB3mUg2dVRsH73uck4uvB7CzwUnJaODKdCN/NAgtOsTtY+QLrBEKNryQFJXPk4l0ey+Xa6\nymhDW+RMT6ndxW59bBok58JyaNWF6vcRNylleornoWrn44cGO2MdKBG3skDq7rpGcsawzNzZ\ncLDvu5r7iZIXM9vwv9nSfnj8OPyS4xmeOCPfo5md093MBulmhbDbRqEojpuyw1uG8qpocHOo\nis7MSjU/sbrj1AykO66RajwokwmS0eY4OI14K8qJtdo7HTAcBLQn9swbdWKvIw+9HpyuWf/3\naGRnlceOMkGyBpkeDkYf3Yib/O6BcPNquPkRctOdZyXiViJILTQrSFUelMkCyZgg63Bwbxrp\nKKyHgTXVOhjAuxI0Rvz6+KxvwyZ8jzZ21nnsqLjXjg1MDdbvlptqwd08yT7Pym7uBCTv0OSm\nIME2B2jKe6KwLgT2wIRoffCh3WfVCGESvUxXNrIhWPLzgFTHzXKQWGUUiEIHfFyBm+qZ4TMP\nNfo4K7q5C5BqPXFWdo1kNOXdKKcrn9HGfA8sxYgNW73Ccv84d7Ev4fkeTexMynlCgBMCW3AH\nv5/KzavhpvRLtJCv8nxEFx/ny6GamysHiQQ2Z839Xe2JsyKQ+ADvUBRKimrO6wBxp+PImyHi\nIrijP2RK0We5mWtnO5Bg7XMI2cmj+N0UGDluDontBKSR93AT9YEp4Ea9J85KQGIHvo8jFkUV\nvCx5fnksL45gz9yZ/pBJRZ/nZq6dzUCC5onTSAgkUePYL1SGlbrYSt30DamKf8PIobpqkMbe\nw030Jiq/Gc4jOkGbRgMKQKIHvm90KosyXBNbGLE/aNuE3zDU3dt8dgZW9OEmved7lLuZa2cr\nkAyOeIi/fj8JO607R0fauc2uQ7WdYq6LywQ3ba0apLH3cPdwHSj5GvOuj0aAQXZDpPdjdDo5\nGNHq6Hhm9wbpbXlwk0gNqbtMaoxkuZlrZ/vOBjk21Wcoc5M+b2+7aXR3c1Vy09Q9gKQX4aIP\nvoZ77megwaPe/Kln77Pfp1NPy53+qW3ojv2Zbhr+mc9E89/gPkOdK2Iu4m567ZzZTeidfIbc\n9zA9s5MZZ7t5bOemqXsCyXkPN9ukd/adUzy3p4Pnm9GAsRrJHk3Jtvkindggb1blGO0QVkVd\nPTE6mpxnnrbRr1jupi+fj0j+9Wukm9mq4yGe6uh0EoPioZvd5co7vJu5aeouQCp+q/lHrOSr\ng2SOThUc+SLxNh0DyeCIteNPRgzzQYmRL+EJLXfTZ2cs/7ZNO/nrfRyJt1TadvI5mNq5aeou\nQLI+J4NU8SVvY673Nkdi7QOJD1+BBd913ZH3efOB/1J0cKrYR10UV6mREtzMtbMpSMrOoJvg\ntNTxWwcXfje7mZum7hokK9T61fI27AK9dgdwArUjiekBrkMLTt01ZH2zjCM7Lfeh52Yg2aFm\nQh9jdtYFyVe7X3wgyX4E1U7uuJvqCcdmbpq6G5CIZ6sVYPzm1Dfn5ASkgqTL3Y0kObrYE+LC\nqdutrUYXba1rpDE3jZSmvDyqACTz6kj/fLd+N05LjCPB1FkiFqzdRr6DLzRypN4DSEu/1TzB\ndfOpI7XVjSTuD13B+P7j5cyHtcj+bh7AD1136oaK3d9RN3NNqAiS382LB6Qjd1N3e3fi1UeK\nI+mmftdA2nfwhkaO1JWDlKVEN8oDwiUPg0yOzEhH8SSMGt+vR6fq+0Y0wL0sHv0S3tD57Kxn\ns9/Ni6ehTO1k4370+2yO7GGJs753xNwMzwsU+nLe0IhbCFJGQLjkQZDFkQXSSffSssaIfl2P\nvmtIYxivF2nTGKlsZ5vOBvDTb05D+ShvW/P32dBOhqt9VuJuwli7bNplKdGN8oCUaySbIyPS\nCXBEA466/w5Mft+Lklcva0n9Ep7Q+exsAhLkyIkj3OStuh48emKnNYebCFJGwDhIB4cjGOl0\nVJ3e9Bwqiv1qceScj/cFEqh3Dg5HtpvwtMQG1V3cN0iov5q6iSBlBIyCBPuXnEj0sc0r762T\nZ8/ovAuB4ZT7Acm6PLLiMDf1YIazmHv2ejFfbPMxk5sIUkbAGEg+jlQkidGF9zAc9VuszJce\nfNCnNiNjkjcOku7p9HEETkvHq2zWsbNSL94nZbr5cZ7LTQQpIyB8CmVBXo5EpBPvXpINdTbC\nW5a82pX2eNPHNn39tOPf2hs6n521QTo4W3UcOtZb9jKwOWJ0xW6dlC5zuZkIUteDA9+aRqjT\ny85DR5CXjYCkLoLdBr2KxFp1F90MoY356/Fiib9SIrdw46Hz2VnHZp+bdkOZDVLV1ZGaI4bK\nfqPRbG6WgOT9297Du2taQKlyvaoLUoCjC3t+DwxhYaz4Zhe+fHT8+DFOoE6zZNsgyZAAR8xN\nipE+LZ0v9DFIT2LzujnwcvbLBxKf9ruTc4CzkA7OZax3kvuKJf/TmDZ8IyCJoBBHl/7Em3Fy\nTBBrhxxFau4J1MrGbd7vAiTzZ0OQFEaaIzUBLZR3hFhDN/NqpND/vWrVRXay55bcKkjWSH/x\nyLh6tdVFlDw/uRqSz9w4WxK/hCd0PjvbgQQNpU+gcIxMjmhiRi+DL5eWbk4AyZx2tevGaNs0\nSCGO6ItDZGcdVXcRg8HYyVVKFPvZyWZnIEnvghXS6XhRnXVU4LR0sjg6zOvmpBqpN/9Ioqgh\nSEtKPblsPgh9Yg9AqwZt113Zs8+m2CvBD313tgP2JuXdwbuV2Undk34OdgbTmtvNRJDU1ZGq\njsyLINhrJ6+WnGuk7dVI+uVhvb2F6URfVmVOx+GmJlshZzsg78v5Q+ezs4LNqkLqfVt5fWRO\nXabtVFHoEx+06jnbAXlfzhsacSsVpGx1o6RsESQfR+qjdTtDTFl6EP8QJPe0ZL6Fb7Dz2uvT\n0lm/Uw88VvwxbXx3PDTiVjOQ+tH3+t0/SLrkRZDFUXekjQ/58axmKeSXxYGJlHU2ae+biIfO\nZ2c9m9UkWeaEgNZpiXd+KjvZxNQ+x2Zysx1Io9oeSPxzJ3TizzzLSPwmLBigqqoiHsvNZu8g\n2d2fx+4K4vARqiyALkBNdF7CTQQpIyAYgRf9jQMktp0ufHSyiCRuv19ZiDkdjz34v5eJ5n0J\nb+h8dlYHyTwtdaxCknE6/sZC67EjpoXcRJAyAsIlVvFJrwAAIABJREFUT4Nu8Dx45FNuyZcr\nCrx4hfRhTsfjPEOzc5DkfKrGaYk37ICbukIyJ11byk0EKSMgCpLJkZiagUVSM00f1U3D6Pzt\nvdol50v4Q+ezc7LN4CaSfVoSHAmQwDMop4u8NIIVvyeX5m4iSBkBEZDodbEueTgFvtHUgDcN\nQWpmf95Oe+0MkCyOWI+diiMtPXmfhlzGTQQpIwBsh/2yBzHvjSpAg6NebrtcP05id5Xa2TfG\nf58gwVEN/c30RTSUL7x+5y+PGkyWt4yo+Obl3ESQggGsZCUwbN1bNzaEDnzeG8DR5aQ4Ypud\nt/qyXM6+KQZi36sgdD47a4JE/7YayvL1YNKy68ARiH3Oz74gNOIWgmSJksKudSNvfbV04LMI\nyZI/XmCFxOaJEjPomk+V977zp8wm/b2m8dD57IzbLE9H4zGc+p2/QZn/Jdz8+Dh63PTbOZOb\nCJKWKua8lA5ilgGzR4EW81DlfIg5A6zO2tHvtQ2QdD2utqjNgZQOF6t+hxVSxyqeoXY338IW\n+1oIUr5yvbKaFp75SpJSCoFEG24fAqEjfWOc9Sb62Pe69zkbbglums1mqcPFrN8vsEI6g2kZ\nrnxvqeDj5DO5iSBlBPi3i1kG1FsrOUdnOGjyZJ1BaVg3P0jE75v9Cr8yO+H2hBZcKMA6LV2M\nKyT2Ht3LyaqMRnNBkPKKPtcrEeD2H6gYxxTdwN8XDo3qRhDPQIuCVz1MSd+rSqjpWthj4u6Q\nmT+/sPRcWo66CXc+XHwgievNI2so23NdcK+xsyGmvKLP9aq3R3Nd7MIWG7uI4Ayg6jF92U9H\njwb37HmWUxbnfuGCUMM1201iukmq1UhSYWBADBMkWL+L4NOVX3B2H0ZtJE5LYn4zBCmizKLP\n9EqePd3CVjE6Y5jKyaub+ut8kq9skdFpJy1P7JoxXUCzohfuhV/GbNidl794g4QHndHfCUny\ngMQnRR/OPh+Xq/siy9HHjhAkKsKXaUWf6xULOB7dWsZPzMmb0k29i5QXKbhl1LHrJRYJVkvq\nzfSpxTcl1HXTMc/jZvBlzCHxNyQP6PQjbva9uwcsgZv8QyV9ZO9SPtOXK+u3Ktd+lfJ03QNI\nehEv+vyvcKQYOVuNl2AH5ljSGsARK/HKb/X4c8eSomV+CD8S7VOrw4TAhedlzNOadrQC6tzO\ns5N6Fs+g6uKtkQ7OVPnHK+284SNATpcj7zoA56Xx01Kkq2FnNZJcjBV9jle0wFhl5Aapmkdc\n7jgpsRpH1jzOCyGta6Jjb2wD6c1f9Ny3yMuYoZuZIPF6xd3O3KRBnnOQ2CUCEnWZMnfmNh8P\nTv0+8rUQJC7CFolFn+EVHct1DESQIAUGEpuDfW7OFFri1qH462hmD8fWBcswWvJ1mnbgcwWQ\nbvTFrd79WT198Y+RoteUgz7YggJjXHCexAUnfzXbB7f1eDFPS/GvxdXMTVN3AZL1uQZI9JW9\nSUUP12y7lZAN0lWt6In2YObiq99c3RtIg5n2CUNJzaYgriBhGN3EzNVVkq7feU1EnyY/scdQ\njhd1M8FIIPy1hBAkKqIW4PN0kG4de92bv0tB9mWnpHQ72EG6QvJESgNpts4Gx2O4OQOkS7qb\n8PTUnwU4CqTDjYf3H/zGK6uQzjE3x+v3lfXaddZkww4FLhY1Jj8h5sLZmgqSrw/Jsgm0PmqB\ndPJHCqZ2OByiT/wFosVCXTfDL2NOBsl081boJgWJS4FkDHbUV1LCTcsbM8n53TSVCJKYl87c\n0Ec31AMptejDbugHlkXD+xg1kQZ4S97Z4oAE2++yY9zX9z2WfVjVin5M4Rz0jTW2KnRTg6RI\nMkE68ZadzEyGeC+RFndz4IWeWf72rG2Q9BSPnboXIOd+7PSckJ05uX5YM461UyVPx7DIG4X2\nVZDpXxJIt4sfJN7NIIveX/KLF32xndJNYWapmxAkOdoK/oozHcYg3l4N3AR2wk7Cpd3Matp1\n6j/vjPneLWHNBxI1/Hbjr1dJ9N5f8vYGVSHJIF3IB1ry7KUTctvZ6hx2UnOaiaEvlxHayM7b\nZbqbACShm0xRsHe6GBUSf4WHtHNlbmZ0NnQ2SKISsmYpXh1IaiAPvGEBJ54xJFvmhwTdDmGQ\nmCBIoelttO4DJGpmJTftzTZIl9NRzcpwsECy7VzazaxrJBMkWSn1o9Pl+zUTSKcePgMuZE4s\nBxQdSWxvd94UK/u+9aNJOtIoSDaUiV8iHtrCTvGzp7o5BFie3GSaCiR2E8nn5ihIc7uZ1bSD\nr5zQ10gd3GbNoR/TPCCdLh6QzInlgOIPOKSCJNshsOjtht3yRT/Vzqlu+kC66HGuZ/ZE3+ni\ndXN9p6WMpl1tzQVSp473yiA5r1z2vhA4/yp4xSCdzDleuGqBdLsAkPgU+YIj9/eNnpbGAwpC\nI25tHaQTeNTseAE9Sv4RqMq/a4Juzhae5cEoZfG8TEYZrhekwcxOV0iT3Lz4QVIdeGf2ar4T\nd9N8onyVbm4cpA688ONyNOZvinqfdK/CqZG4eFx2ah2K/Bru/Q1nH9OCIN2MMduT3AyCJH/F\nWexkusm6blbp5rZB6mDJGw+QnePep4DknZjAfrvyNdL7G84+pgVBMkZqTHTzmgKS+67qAaR1\nuokg+fxLuoUaAEk3Q4y3UYylBuKtFaSuECSvm9eLfYoxum74HPm2m6FhDW72s7u5aZDiJT8R\npJsvCJ5C4dsoRlNLmqerILSZnZPddOpqB6Sr7eYlGaT53dwSSIbYjKnqWVc6cOUIQtmbesPd\nCD1f9Elr+vjrAazp7zpej8ec33c4rO/haSjqZtfCzfN5+B/+O6h//eAov4ly7Nfv5pZAcs8o\noVPo5XwV3QBZp6nxGsl633IsscVb9dl2nkAO093kjV5Qw3juyt2Rm5sGKVzydDENJDjneyTK\nxR1R5qS20OuDp9hZw007BnXUcNP7RPla3VwPSHIkRGAuuwQ5PkRBSu8G8G4PzSnpRhoreucR\nznsEqcBNC6Tg7W0YZb1urgQkAqdnKk3QtkFcq/bm08wX9dpEGcEpsQSQ7KklwwUSKvnFi77U\nzkslN609E0AK3UOCuewcpK+Ao69VUj8O167Bf/RBmhprmY+1pvmf1Zpf+R4OoXWVn9tYTd28\nXq+x9ZX1SbD1it1cCUi9btqhUHeo9YCEQt2xVgTSC5l4jYRCLab1gPQyubMBhVpM6wGJVOpl\nQKEW0HpAwpoIdcdaD0ifuvfGGaJQzbQekH6Qpx+Nc0ShWmk9IE0f2YBCLaZEkKwDvMqxXh2k\nN5SjCeWz9FdfoSJuJddI8Zm/S1S95lna5jUK7aypiFsDL3Sc0t+etQckNX+dWHtm108XgjSD\n0M6airiVVyOBWfThhzJh024GlXqJdvoUcasQpA5MrIogrVilXqKdPkXcmlYj9RVB4vrx9KUw\nOSx5n4rNRDs9iriV2dmgKyF5rVR+qeOP+N4Vk7S0zWtUqZdop08Rt5JBCqoySBOGCi1t8xpV\n6iXa6VPErckglfe9+WN+60hpgkvbvEaVeol2+hRxa3qNVKxQZ8NLaYJL27xGTSifpb/6ChVx\na3UgkWKOsOQ9mlA+S3/1FSri1npAmq6Z3HwQmpRGrS8zpvXbCTRi7GymBRVxC0HK1oO1npJG\nc63fTqAHY+UEIUh+2SC9vzx23eNL+VNJM7mJILVSxFkEKSzneSRxkVT8VNJMbsLili0Rc/0g\nw8Xmh7eHB2MfBMmrsLMP3DTL7dkVcWs9ID139MG+H0/dc2mCM7kJivvBv5Ygqc3qfPogj5KZ\nvuvdguRzVtr4APedVRG31gOSvBG7+huyoLPBKG7rs+doWOAwmFA+c31FLQ9I8LPt9vyKuIUg\nZQuUsSQqBBIMNj/M9F3vF6QHq233BpxbqGF3JyDdWdPOPmWaGx+Mq2N3XwTJJ7tGkttskBZD\nKeLWekC6s84GBKm+PCBZdbxr+ayKuLUekO6t+9tptdsXQ5FrJOxs8CrgrN20w84GU1u4Ievv\n/lY7wT4J7P4eFRzZYLmlTIOuz66IWwjStoV21lTErdWA9JXQ5dvz9/IEl7Z5jZpQPkt/9RUq\n4tZaQHrqOoYQKX+KAkveownls/RXX6Eibq0EpG8deWV/vJHuW2mCS9u8Rk0on6W/+goVcWsl\nID11r+Kv1+6pNMGlbV6jJpTP0l99hYq4tRKQwHCG1Y9suCtNKJ+lv/oKFXErESRjurnO+aNI\nIZBIaYJL27xGTSifpb/6ChVxKxWkvho/RppST50cz/Bj9UOE7koTymfpr75CRdwaeLkO+tuz\n9oHU6T/ApMV90QypcP+vCp9ndbVERfxRySC51rssbfMalVkmUEt/9RUq4lZG084gJ/B/jozd\nSfeJfs23T90j3Eq8MYlcmMEXv0Lb8wOCEWJB9bIvCc0rkil21vydd+hmXtNuFKccGbvLIavG\nmFXi1EhEL0mPII2H5hXJFDsRpByQ5IfqIPX9t08DRp+sm0iEL1UDjuglcRp+1SxGkArsRJBS\nm3ZibV4jWRdLOUrZnZgLGyRN2L8G5WWPGlHmoYcgpYBkKx8bbxqjInBBiCBHbVChTNUs3myN\nxL2CXTW+dZmdCFIJSBNum+okEvYhbKFrHrAkcBemahavE6SPRJlpGW4S6F5oXWjnNkE6mJ/D\nKgWpgvJqJPB5wyAlAFJ+DiVWBR9YF9q5MZAOXNbeYd01SFZoc+9rgxSApXHTznYv4OaOQVIE\nzdC0q6CSzgZzK+mnlPwSIEUaYUnZZIdG7Ayte951s8e+G1ENgS3X1Kj3AJJ9FRy6PM498uYE\nyYPOUp0NchFaF9p5/zWS3ZCjQ3ysvcNaOUhZau59CUjhygdBSv4VbXIxQy2MGETbadplqbn3\nuSAxgtbUYUvgIrQutPN+QXK7FWRVhCC18T4LpNGuAwQp+Ve0yYWH2g06ilEobuTgQ5AyAtJA\nshpzCFL+L5kPpBhGCFIr78dB8lwOrQ4kHNkgxDrozE1W9wKC1Mb7EZBq9SlMAsnqaJrPzjsD\nSVwVGaF2J92l7zrzc1gIUkbASNdc6+zHQ53+WgTJG6D7FkCo493l0l1OZlphIUgZAYHtFKF5\n+5m84h22x6O592x23g9I8LpIhrqnoAGjrjc5QpAqBTjbdU20OEjsQDgOGCUPs6xs592AdHBD\nPRQNHJ1OFkcIUqUAY7vZmFsYpOFIOAwU3ajMvWez805AsrrpWKgPowulCDsbqnrv2e5cES0I\nEh3JcrsJhjKKvrKd6wUp9fkTqNPJNyQl4haClBEgtvv6FZYC6cAhuhxVaPLl8R0rDwoZyxiN\nquQblno60SU7N6V+JQQpI4Bt9/fOLQGSqoYuiiOnWT+fnQ1qpCAaJbm4t15ZqKdZdwL1+9nc\nO6wtgTSDwPltYfGTpTidHvmKn0hraeTAzD6SUwOm8RII8GNk9zKAUxOPa3K0F5DKLE4P8Bft\nSFrVayRQ2PwwOPIeb14Z9ZfkW4iV7Zz+OzU+td0MYHS5ymjS1MFE6J/FEYJUIyA2kHsWkERh\nq5uJEiO6FE06OtmTldZsdk77nWYFVNnNEEb8QQldBw0mGhid6/fa2XN/WxC4TCRQclcgjZwo\nW4PECls8vik2aYw0RXfYa+dtxVV1M1wdXZix8jPFyKqOGoDU7Ruk0ckUWoLEC1seDyL0Ktt0\nontBHAP3A1L4OqiqmyGMRP2uolEbjdr87Ek04tbAy1Aex789ax9IHZ+3uFfzRfKFnikSziM5\nMmXX/YAki3sBkFS742CGXmlldDqpXrrOE5d9ns3O7IbyHPeRBowC9fvViMZsNJt1vkQjbiU3\n7brx6fPNLaOc3AdI8Kw5J0hmBxI4r7LQgaOL7umWOzrjGkJFT/hysccoiodV5bnJXPPX77K3\njofyprHe53z2Jxo5+NKvkTrPJPrgo/2il/E5JO8BpMTpSuqC5A70gc2Tnl8lH08XQE4HQs1M\nfIpPFFloZ/rvHK/gq7jpm1LL6O6UoSeLo/MZhhqZhJUJkvqQMKf+GCrrByn9GrgmSLrdLmcp\ntIZZ0qPgZJKWBdLIRJGFdqb+zpQKvoabngclxFnHfAZWXmOqTQqj47Fdr524+PHVQnbtNPrm\nsZWD5LkQngWkmw4I3Ys/nW4Bji5p95EIXNjrQjvHf6fdu9AUpIMTID0zn4E1umouoDqiPTn1\nQWqhVYOUd1u9Ikhg4Km/v+l2Oh2th44gSEn3kQhc2Ou+xQSRcMzbLLLG1elRc8bYOjEWRB+K\nZzPa8ZiaH4LkC8julZ0E0g1KBjjzTstdT0dna+f9k2fiFYELe11oZ+R3Bnq5W9ZIZucMqMCN\n+ujEoznVkbDYPl9hjZQZUDCXcGnRezraLr2foht9XuJ66qMgpTVGCFzY60I7A9uD94ragWRe\nVfYQI4Mj8cQRaNbxleDneLxi0y7Xe6ihDZKbUmHReyC6hOqii7jz4QEJ1kL9JWXyEwIX9rrQ\nTu/26O2iNiBZ7hkeG2NUTzya08sgq6PrNfG0xIQg2YoUfVWQ/BQFbiEOx8BN3H+NVkjOQ9N+\npwhc2OtCOz3bR24XNQHJxgjGsJp1LJqnt45qqI48uUUOPgTJUmxMXUWQzBLW8t1CvFA+brLo\nIxVSd0lsjBC4sNeFdtrbP0ZvF7UAyeLIiGE36y6wWcc4OloXRwhSjvdQ8XNoPZBugQB3rjUm\n0apjyxBIV3ZQZIDUcmQDvDCaEySDI17lyxiwrtYDq2Qo7WawMdoSSCSwudFrXUbOodVAugUC\nrHvx1rgfFySKDnt0gs0mxSrT9FkGxpT5S/X2j8TxIPVBghxJ10QMH0ZqtPwZNuuO9gAikElY\n6wbJOEOCzWJB1AemIu+BRt8u2Rokc3y3uoaSxRoASX0avv7JmTC5XBm/B26vMGdzKUiAI30B\nymNAjsS6E6FnE6Ojrru6rYBEnBqJ6CWpDdJ4Y6QWSDdvgG/2Typ1DDggMY5U8Ac91WYU/ZjS\nfw/cnjEvTGWQYHfdDQZcrMsjvhKPnQiKQKtOYdTluLlqkOzGvA1SD9dTQUoYu9IUJOMWohwU\nZrw0zneNdLVOtQuD5LtnNBNI3upIxAhyJDE6wlU4t4hb9wCSXoRBmjymZcYBLO4UT9aLS/ku\nV3uiKD6cxRi0cm04g0zmoUy35w1gqAqS5sieItPb7c04GjBi6cnqyJkqcqMgES5ja63OhqTL\n4zo1knUV7D7E2V/gEzNKokIC6dkTNHhyq1dc41pyhiV1HnJnojNPSHpw3dkeV6f2LDk73QVI\nRLXtwJLAXZhSj2Q3wO7qyk0pAyT7KtgzNtW5POayQfJgtGjTLnskUMUaSd3Fdm9yWz6yR2Gp\nc2crvaPYE84fs8UaCXyuDVJyP1MFkIyrYPdhIx3DnbjQAqkzhzSc/N9jQvkk/R6t4FRlM4Lk\nGSxi3aKWj5SrZ2DlZZE7YGRIdS8gWaF53hf0M00GCRS0FyEdwzcBKFtKkOgOAKRT4BbYhPJJ\n+D1a4QGKM4BEney9GF3s8XJiihP40JFc21OZDalmvG3qbkAinq1WQG6p8ICMfqapIKmCPoii\nD8cYAel67bwcLQVS0djUSiCJV+95hy5erRh8xi398J4Ipc06u6HsG6wVceseQLLHrlQd2ZDz\nAN9EkERJH3yTCah96AQ3vrdeWTWSeStW/4xFQIpP+tcYJFGve0cA2wNPT0Z1dJHPwB59fqcP\nuGJaOUhZSvYeBGR12NYA6eA+Aw3lG3MsJEFiha4gGhmPM4edI5P+tQVJcuSLcrVjnAyORCgf\n6O2J3GKm1RZaA0h5T8JOA4lzFI6i+rzjIFkcXS7Gi1wWAKl4kHcNkFR95IlytWOwCsl6v4S/\nl6G73Wj7z24vRtzaN0iZ70+eBJLNkdMvThejILFOWvOy2HiRSx5IhC+njP4eHaDYFCTup+8N\naxcA0kkIcKTeJ2VxxGdXFx9ODWqkyJxApUDsECTP60svIDgJJPuV9RNAEvyIhf6QaKc5KdAS\nIDE/b4EhwGouVWWQyxHvreOpyNqnE1HzRi4mgmRMom/pfkHKvYVYDJIspQhITE5jBEqApDDq\nwGahQ86dDzkmWC70hzQ7Uyf9awcS5ygUxQeSzdGl90yawTv1MkcuDrzQ88rfnrUNElvbE3wb\nyzwtDlL2LcRSkNTEw/4o9hQ3/rRoXx6dh8sG6cKPCzpjO+1TtwZexh0jcKE/pNm5OEjUTkWB\nE0U9U6QemzAvkDhBdj9DB7a498YiVmbUSOovd47VvgSLhUEquIVYCJKfIxnFnePGl9aAiRht\nqUK7jw9xjBx1+vZN3rhjBC70h6R57RZ/deEBTFbnkRphx0bX8TO9GF3HY3W98wLZ60FHyRw5\nmAlSYLLvO6yRSm4hTgPJOcbZErYrzDckAPH3t7A/O1AjyHMtqIQKOhvkQn9IsnP683vTaqSD\n2SSzo4jLHP66DnlZSSsk+dbdoxOtEyWk307Q7Bpp9IUUOVoUpKJbiGUgBSokGOV6hY8eOWnR\nMr/KVyawUH4UQ5DU07XJrw/uJ4GUPp9zG5DsKxsPSCcx4ZZoBHOCrCePehhBXxwFEo1YmQiS\nvhSyJvtWV033BVLZPFFFIIU4MkCKpsV6lq7dqWPTM9BQk6NB58NZysokKgIX+kOCnTUehJ0A\nkm9WTVNXaY965w2rkBhA7DnYq9lDalxAFYwTSQWpgRYEqfBefAlI7JZ7eJA308jjZEc6u4nC\nRoB0hgoPl4g7RuBCfxi3s8qDsMUg3W4xN5mYX7xfHE6kyjm6ONObXA+Ao/D7MiJW7hGk4gnX\nCkBit9ytYr/Zdz7iIB27izgweI10PrvvXJ4ZpKw3DNQGib5NdyTK7XI9iVpLOQVGqjpTPKs7\nsPRsFZlzIGLl7kBKeQFfPZDYLffIW0z5XxGQBmREc17WSPYr69WeF34FlfJaFynCl7kjGyo9\nUV4EEvXMZyiIMuwiK3DeFDaavAZHLNpVbjiNdelHrNwZSMYIz/Yg8XkgAyAl1Ej05pC8LA6D\nRPeQx0LGEzTFdma+qqMqSM74EBBFnpnYuwboH3LKTHPWus6KpjX6hGfErS2BNK6Z733w+xWH\nkb3smxlaR2jRiRt21p/VbgVfLUGhgzxzXFVNkNhbyL2vu4GnJd7n3an3vJsTbtk5wedlx75H\nxK0tgTRaKlPvxefWSO6wIG8UpxYRa/PkeRIz38gDWdRQB/PYyLtGKrJzOZBCDWVj0CqojERa\njBLAEfSbRTtbAxaV8P1I/lKZGSTOkR3iTH4SBglwdOXP0gxl3oO+bz6WARR3zht9Cu2sN8dJ\nJki3UEPZnOIb3oDlAWx6fP7B5oh1d/o5Onq+R8StPYE0+V58HkicmDKQ+MVRpzu4r+cz/yR/\nxUkcUHyV91bzKXbmvzyqEkhiVk1jHkhwO0n2Y5/s6ZXgLWrRAap0uPDHJJ1ehsD3iLiFICX4\nNxrBFyQ4skOconenZ5APm8GWu76PZP6KyHAJ/rmynR/55tQBSdg50lC+npxZynrgosWRiuZM\nmY6vdQlanPOS8hogqQF2BSAJji5m0w5ECx5ONUHyaqmhqqzf5jDWb3M9eY4qPQ+kEXZgn1lg\njR+1D5D87zFtBdJNtznGJguiSgJJN+PDELmTT1WvkUpeZ1ijRlLdNjTE+65DdkNIXEiaAbxG\n6uyZvZmP597fyxD4HhG39gBS6G3AzUC6XMTcj6OzblFpkI5C7MP54gdJpyduMx6HVSc/twWp\n6HWGFUDS3Z99YL4gGuDliIJEByvaE+Sz1K5np1FndNVlPJSyfZCCL9VuC5IvxJndnQpwZARY\nFZL6JKLpM6lxhDQFqex1hpNBgk8XRxK7dp2vejkf4Q1rJX7qcWZliH/DiFsbB4m26eqV8DSQ\nAgP/FUjmCxftCok/LXNQnQ1nlclsIMWH+jYDSfbaxON03dXtxKZ1tBqnCnUdfsyVemilZ9VG\nCBJX6Vu1p4PkmXXLM/DffP+RByQVeD0orPjhrDkChc/nFTXBqmln1IRWIJmzAQbiDJWReIbP\n0FlMqKkskX6Oz33kDY24tVmQPia8VbsmSOBxGCDrFWJU+vinaVFQwPwBV1U/fciGX3DImfm5\njp0f43a2AcmaNMY7DyTt8T75Jnk8i7RcjuhCPvYn5ZngDkGKuVEeMKlGMmRNqArGpl4gSHJX\neBtJ1ldQRS8iqWxnE5CMZh2lyhOH3TjyXuwIkCyO2OsHDsJQTzQw9QWC5C2VyQEpIPEa6KBv\nu4+DZHco9eIQ8ILEu3PHB/DJz0kifFkyQeTo9gkg3UyO/HGYSydnbBQVOxs5z27xNM3X4Pim\nW/XkFrEQQcoISKuRrJH+oyA5hdjzMQ2d3vVwSQGJJWpDmSJzoshCO+uDpO/F6RtzdhxBiTuX\n40W9Ssw7J7F6P1IQIl9uEQ8RpIyAKEiqsBNAOp7jYjuZjxgpkFggSN/s3C2pkayJIgvtrA6S\nd/IlI46qbE7unegLuwd78YEEp2eA0dzpzLYDEglstl5zzhUrlSoB/u2MILNGOo7oeh7Jns9z\nIlp1bK3+nfkMOerzUYd5Ek0rCmIuiuysDZI4K4UnC9JNNtpd52nzHfnEmcbGqzlW8Rg5N3q+\nYcTCZJCsw750Vshwij5Zr0VSm/UC7pDoRnmAs53N72TrxqY8jSZ2DgWIGLJVJxv4uqEvb4sF\n7sdWAillgsjGuoGlV+rwOZ28j0Z2x3PfH9VDj1fxAOVHo+dJU0HqrOO+wtcZT4I4NRLRS+Ls\nEDwuawXY22/6hGfWSBNBcp8bV50NAiRwx+QI/3DOsGlFQcxFkZ2Va6TAQ5E6jp5owf9SqeMZ\nPlNkdHzDK87gd/CFRiwceKF3sv72rD0g6RntumFDVzzJqkhyXIQvrZYcsf4USnSjPEBvv4mq\n5+gGAZBOAZ1DAZbYQ9OnA/+LJ+mCRDW1+5uYiyI764IUerhYxQEc2SCpK82jjkF3UY26s3wh\nAYg29a5cYo0EZ1rtevBBBhao5B2ypPdu4I1HGjxsAAAMkUlEQVSRsq9RoNstPE/C6XRTIIT2\nObtNEY8Zh2HjYWiP0J2vH3wqajEhtfM4wcRpG4i54Aoe5Hnbi0C60fNRdLIg8C4Oc6ZHOF+Q\nEaA5AkMg5r5G6sw5ViFI7WskuSBcC9ZIuhvWeZpf/nEab9qd3dcg2TPb8IEE3eVDDHyR68uJ\nHQW1n0ci5qLIzpogBTkScfS9IQck8DiskYvoZWDtXzDPxewg9UYl1E2c9lulOSbCFkS17cAS\nbuBKdKM8gG4f6bhOEi1VMeSGrulhAT/L7MGNJC32oMT2QQrMXsfigDusiiOZmJoFsgMgsT0+\n5Ibz6F2+YGjEwjSQ9Ftc2LURr5WMKcALlFcjgc/LgsT+8txE5To551JPYrrovXfdNUgnvbfQ\n+WKBZD0jMAGk9YxsuIVPFsw09Qn4wxNzOTJzUVPGmN8hdGJq0f1dX/cD0g1MMhwHiRaUAKkL\n66r+CmdP07ienOdnz/aXO5rfpgwkr/zezADSzT5ZWHH0/SO6MG6gidI5GiCJPY6+yYLmbtq1\nUElng7119hrJeneiHYW+MeLWHboOtNTcdeh9YuJCqJcPBHgfg+4FPB39xw6Z2FefUD5hE7K2\nZwfceGdDECRfw04kpi6QNEcapIv3QfRLpYbyPYBktznmHdlwM7bHQaIlxY6ByETsl5FXxbIA\nfq5VIBlTGh7938Cf24TyCX+7rO25AWz2ukBXgwmS0fA1QYJPd2nzPCemvdRIWUp0Iy/gZm73\ngATabLxlN+2dyzpAHQFn+pccg9c7XyCW23x2VgJJPrw1ApLsxbyCAC9HdA84Ksh+f1Toy3lD\nI24hSCMBoyDJGaYv4oxnn0yLQDrIhBlC9iS7B3MW1lhu89lZByQ17jcK0smtq/mQ+aPsvzmq\nwB5BylWiG1kBxpjJ0UEJPEYeSKdhfzp8oeOTMnR8DXoihvrIBSn1N9X2uLXk4LrIDHYdeH8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FbKDpzIflQysBwgtVJ24ET2o5L+xS/s\nSgKkE9kPkHipwkGA1EnZgWLtB0i8VOEgQOqk7ECx9gMkXqpwECB1UnagWPsBEi9VOAiQOik7\nUKz9AImXKhg0KLDXDgsgncf+jOMo5JVBAXUTgASQUElX6JF6KTtQqv0AiZkqGNQDqRr/r+R2\nf4CU10g4CJB4qUJBkwJ77bB4II08iQgg5TUSDgIkXqpQcBuQxASQ8hoJBwESL1UoqA/SXkO7\n+q5ooLbWUHagUPsBEjdVKLgBSLddhnb19BAK2EFlBwq1P+cUOSXlF1NmJS0K7LXDOsQ5kgek\n2g4ApAYgsVMFgpogjWO6fYZ2EybzAM4GyemtlB0o036AxE4VCKqCpCAOSD1ARs/kgDQR9nsr\n0T/zODK8k0qpvC8DJAkxQBoosripa6dHuvzFBvRIzFT+oE2BvXZYxwBpeKjnnsc5RzJ/XtP+\nBiCxU/mDVwDJWQSQTOXYn5LyiymyklcGCUO7BiABpKQCvNgBY7myA0XaD5AAUlrmDQyEgLID\nRdoPkACSuJQdKNJ+gCRUSYcCe+2wMGXxOexvrD3g2V4blWRkA0izlB0o0X57BwBIpFTeYC5I\nz0EBJOZygHSGSgKkWcoOlGg/QAJI8lJ2oET7ARJAkpeyAyXab19rAEikVL7gC0CapexAgfY7\nHRJAIqXyBQGSIWUHCrQfIAEkBSk7UKD9AAkgKUjZgQLtd06RABIplS8IkAwpO1Cg/QAJIClI\n2YEC7XdGdgCJlMoTdDkCSIoOlGc/QMpqxBPUBsmcZVUKAICU14g3aHMEkEipPEGAZErZgfLs\nB0hZjXiCLkfkSpJB6ma06x+E5okESHmNeIMA6SggDft9NfwToAAg5TXiC74ApEOBNPRIAEku\nIg7SM0CipvIEXY50QKrQI0UTASSh5ecGqTtPEpxJHyDlNeILAiSZSi5GdtIguQJIUhHx4yhA\nIqdaBhccKYMkwgBAymvEE3Q5AkikVMvg1iCJCCDlNeIJskDCdx+GgwDJkrIDxdnPAsn/JaL4\nyrZOC44AkqIDxdnfuBxF7K/dHgnffWgIIFlSdqA4+xkgTTOo47sPfUGAZEnZgeLs54OE7z70\nasnRM3XTU4J0NS1OkYL21zcbJHz3ofXMA5K9dliYsvj49i+vNYTsn8+E8N2HviBAsqXsQGn2\nL0d2QZCsDmhYNj8CpAVHAEnRgdLsp4PUybk2h+8+nLUGpK9BASTm8qOBhO8+BEhxKTtQmv0Z\nIOG7D73BJUcASdGB0uxngoRKhoKeDgkgKTpQmP0ejgASKZUbBEiOlB0ozH6AlNuIGwRIjpQd\nKMx+gJTbiBv0cASQFB0ozH6AlNuIGwRIjpQdKMz+ZskRQCKlcoK+kR1AUnSgLPsBUnYjDUCK\nS9mBsuz3gfTVXhuVJGXzcQSQFB0oy37fKRJAIqVqdgCpm4jL0moOAFJeI24QIOU20mwPktRc\ndqYAUl4jbtBzigSQSKnsoPcUSQOk29QvTTMXL7opjgBSXiNu0NMhASRSqiYIEv+yDW9ot5hL\nfw0NACmvETcIkHIbaXYA6WaSM8+lD5DWRgRH9gCJmarZHqS5O1r+yxVAymukCYD0DJCYqZoQ\nSM96PdIwtBsfAZJURAGkrwCJlsoK+jskpfeRRPd9gJTXSAOQhBppAiA9A6Reyg6UZD9AWtFI\nkwaJXEnc2XBs+/2nSACJlKrxg/QMkAYpO1CS/f4OCSCRUjUAKS5lB0qyHyCtaMQMBjgCSIoO\nFGT/vAM8AyR2qgYgxaXsQEH2e0D6CpCoqZoUSIxKAqRD2x8Y2QEkUqpGEiRMWXxk+wMjO4BE\nStX4QHo+OUj45lNv0N8hASRSqiYBEqeS/3wKqiiQ8M2n/iBAWtFIcz2Q8M2ngaB/ZAeQSKmM\nYGhkdzKQ8M2ngWCgQwJIpFRNHCRWJQ8FEr751FWgQ/oqlV95XwZIEqKDhG8+DQVDINlr50v5\nxZRTydDI7lQg4ZtPg0GAtKaRJgoSr5KHAAnffBoKAqQ1jTQXA6kTvvnUFwxcawBIpFRNDCRm\nJY8HEr75dAHSwn2AREo1Bz3XGs4MEr751AmG3AdIpFTNAqTFyO58IHGk7EAx9gOkdY00e4C0\nalJVrwBSXiNNGKSv0iBdRcGRHfkdOSpI62be8gog5TXSbAiS8ospppJhkOy1wyKCNO30w8R2\n3dx25kTg1TgJK90kgJTXSOOCtHQfIJFSNbuAZM8KaU4PaTzlwAGQ8hppAJJQI40L0vIUSaVH\nqhaTrC4mAgdI3IgkSF8BEivVFAx3SOIXG6yRW2gWcIDEj0geRwESL1WzA0jjVbvlAM9YwmMD\nIOU10gAkoUaaPUAiCSCxI+tB8rgPkEipGgekNZUUBYmHBkDKa6QJgTS7/8leG5WMZ4t0SLiz\nQdGBQuwHSGsbaUIgfQVInZQdKMR+gLS2kQYgxaXsQCH2h0+RPgEkUqrGBsnHEUBSdKAQ+yMd\nEkAipRqCsQ4JICk6UIb9AGl1I40fJJMjciUB0lHtH3YA78gOIJFSNRZI3g6JDtJRpizmSNmB\nMuyPdUgAiZSq8YJkcQSQFB0ow36AtLqRPhgd2QEkRQeKsD8M0ieAREzVmCD5RnaMSv7zPiiA\nxHZme5D8HRJAIqVqfCB9BUiTlB0owv7oyA4gkVI1ACkuZQeKsL8J2f8JIFFTdcH4yA4gKTpQ\ngv3xkR1AIqVqPCB9BUizlB0owX6AJNBIEweJVUmAdEz74yM7gERK1QCkuJQdKMF+gCTQSBtM\ncASQFB0owH4HJNd9gERK1QCkuJQdKMD+RIcEkEipmhhIzEqeEqQLKGG/VDPK+/L1QBqnVZUU\neqS8Rhr0SEKNNLF3Y5VAWmFGSAApr5FmAdLCfYBEStVEQOJWkgXSPMv3OO33+JAjgJTXSDOB\ntN7+y1dyc5Cq0Dyr+V9TAZDyGmmmkT1AWtdI7CMUuj3SzZ1qdc3ZE0DKa6QJg/QJIHFSETok\nNZBWzfZtCyDlNdK4IK2w/+qV3BEka6rvqYvKEUDKa6QZQRKw/+qV3BwkDQGkvEaa4P1BnwAS\nK1X4IxT8SgKk49kPkIQaIXRIAEnRgb3tb1IXvz+9t1OhkgGFQPoEkDopO7C3/SGQPgEkXqrQ\nx/U/AaReyg7sbX96ZAeQSKkIHRK5kgDpcPYTRnZh++u7vDUbA7W1hvKLKROkTwBpkLIDhYL0\niQBSPT04mgJ2UPnFFAGSO7KzOAJIig6UDtJ7Fki1HbguSN4OiQ4SZlo9mv2EDiluf8fLNICz\nQXJ6K+UXs28l05caUpU0BJCOZj+lQ0qCZPRMDkgTYb+3UrOoBFFGdu+pyQDSMUGKHEdTIE0D\nuJGbunZ6pItcbEiO7JKHJEP/vAkKIHGd2XBkH++Q0iDNPY9zjmT+PHklKR0SQFJ0oESQPpFB\ncq83XBYkUocEkBQdKACk8MjufcJ+sxtyF1xraEfqkACSogMFDUgCHVLkDVnjR+0urxdXwJVf\nTNEgpQ5JtgDSwez3guS6H34faTw3WtzhYN7ZcIlK2hyxK+kKIB3Lfv/ILtv+C1cy9SYSQDq1\n/akBCUCiLqeN7ACSngMFgRS41ACQ0suJIzuApOZAASDJ2X/dShJHdgBJzQGAdIpKEkd2AEnN\ngZJH9u8BEnE5dWQnDRLm/qYk2ggkQfsvW0nqyO6NnSqsY4CEz3WOonVIZPtT0n0xpYHkOSIp\ngOTOoL9a9Cz4XOeo+IAEIFGXU0d2bzRAMn4RIWkVSBf9XCftOEq3PyXVF1MMSMF3EdRAMucr\nXi1mEnyusyGP7ABSfHmiQzIKqQNStVOP1Amf6+w/05n2/+6dVIN6+3I80aYgxTokJZB27JHw\nuc6G3iGhR4ovp3dI0iBpKAOkq3+uk9ohAaTo8vjIzuLodCDhc50NsUNi2Z+S4ouJJtIHidgh\nnQ0kfK6zFUASaZzTIZ0MJHyusxWdI4AUW06+ZHM6kPC5zk4xkDLtT0nvxcQT7QfS+zODxJWW\nA6lE+iBJH0cvWUkfR6EjEkDScCCVaMNTZCH7r1hJVocEkBQcSCZSBynkf7b9V6wkq0N6ZacK\nC1MWH8R+VocEkCLLfTc1ACSvlBxIJtp8ZA+QMhphdUh0kF4FBZCYy7cDyfF/wRFACi83OUqB\n9AogKTiQTrT1yD7YIZHtv2AlYx3Sp0WHBJDkHUgn2npkH+yQAFJ4OWFkZ3AEkOQdSCfaeGQf\n7pAAUnA5oUMyOQJI4g4QEu1zHPV0SAApuDzdIVkcASRxBwiJtgUpMB5hjeyvV8n0B/osjgCS\nuAOERNuAlBrYAaTIcm6HBJCkHaAk2nRAEuMIIIWWRzqkxZVvgKThACXRlgOS2MAOIIWWp+/7\nfQOQZik4QEq0IUhxjgBSYDmbI4Ak7AAp0YYDkjhHACmwPHhACgzsXv1spwqLCFJ1M3Z8Zxqh\nan6sPHQEeQFI5Ea4HRJA8i8Pc+S9YnfnSBUk7+/uGt5VaYHVkneAlmjzAUmII4DkXx7+8l2r\nkgZHSiD1035X4xzgXaQy5zKeVxrXHR77X61pwwEStRF2h0S2/1qVJHZIJkdaIAX+3aZRXWQl\nd25JgERshN0h0e2/lmgdksXRz9TcK0Cyp12tqhRtACmzEfKlb/6A5FKVDHPk7ZA6jrbpkW72\nLySKABK3EXKHlDEguVQlk99h7eHoVztVWNTL3+PZ0dQd2SdB5lW78WxpcY4EkLIaoXZI5g5g\np0IlW00HpAVHn8IciYPEVpUkBSCRGsnhCCB5lpMGdi5H+4N0S36vH0AiNUK8Ypc3ILlQJd0O\nKXqCNHFUAEhJASRKI1kcAaTl8hd/HRMcASQ5B+iJNI+j8QsNuQOS61TS6ZBcjiyQDI4AkpgD\njESax9Foh5Q9ILlMJZ0DUoyjn02Org3SeeR2SNGBnbEDSLUvuS9zEkmDlOLofYij3+xUYZ0S\nJEEHWIkUj6PPLI7QI9nLnToyOAJIMg7wEukdR6McvVlwRLb/IpUMcbR8J9bliA4Spiwu1/5s\njgCStTxQR98dDS5HAEnEAWYitePoM5MjgGQtTwzsYhwBJBEHmIm07E9yZA5IWPZfopKhDilw\nwc7EiAHSz0EBJObyTQYkif6Iaf8VKrmKo1/sJsICSIXaHziO0jgi23+BSgY6ds87cfbouKvj\nLwBpvQNFHUe/sjgCSJPiA2Q/RzNGAGm9A0UdRwMcBXcAu4nrVtKdWDPMkedoBJAEHMhZrncc\nTXFkjkdY9p+9ks9Ujl4FOAJIKx3IWa52HCX1R1nH0ZNX0t8fvY9w5GD0y//sJsICSOXZ7x5H\nv/oxsgYk9g5Atv/clYxztDjNdI9G9zr+DyCtciBvuRRI3vFIlCMHI7r9p65k9LpnmCMTI4C0\nyoHM5UIgRTh6Q+CIZf+pK0l4/+CVOzr+xcYIIK1yIHO5sP2L0yMHI+94hGn/mSvp++SE06+/\nckfHC45+spsICyAVZr/vOLrA6E3oOMq1/8yVTJ9mvnJ79UUdfxIHyZ1seEHBEgtMfpLTeMj+\nwKjOOo7y7T9xJV/o3ZHN0VzFn+RBGualsxfcogsAUlbj6bMj9zhqUcS0/8SVnDFKD+vcKs6F\n1ABpnuKxGibEn6bSH+crnueNtLswrwCSJ+ByFH7vyLsDcO0/byVf6FcZFkejuY5KQ7tq+s87\nY753SVgAaRl48WDkPTmKYvTTH3YTV6vkfDQi3Oob6tS7Ov5BriTjYkPlgjR0Qs4sxQAps3HO\nyZF5HF0cRBn2n7KS82mmnyNvp+6r4l1/2k2ExTpHskEaO6Vbcrp8vwCSFXi2j6PLN2AXl7zD\n9l8ZpPmqp3k3kN0dLTt1ty/qC/nnn9IgzZPoT7PoT+dIlbnMmUM/JoBkBHz2W2O6N/ag3mv/\nH5PsJi5UyWcvR3Ydh/3bHtMtqvhnL7uJsPA+0v72W/eoes+MjOOo1/4/LDHsP1kln61Ceqo4\ncfSbNTL2HIv+BEhHs9+aSzdo/6vlkN5HEdP+M1XSviXkXkhfFV9FuqKfbITu+vvvv+22wwJI\npdg/nhmF7DcwinRFFwXp2aKor2TwWGRcowsN6HqI/gZILG9KsH8Y0YW6ovk4GuqK/sg6jp6i\nkvZ8xItKvnplvmk0UPSTpRBFAInlzT722wfQ5WjONxLxErTmOHqCSk4Y+Y5Fr6wDUWgwZ5Tw\nb0v//vuv3XZYAGkP+wfzJQn6M8v+w1dyKKOvim9Cp0OLKi4p+neS3XZYAGlz+4cDqPeiQm//\ncvix9F7G/uNWcu6GlhQNHVCobssq+kp4SpDqu6IBewWmNxvbfx/MBfGhez/bv/D+v//+s9s+\nXSUXBLU9D6FwoSq65VCRelkAACAASURBVDNktx3WIaYsrqeHQMBZgenNFvYPB1Bj7Mb1ffD+\n78XR8z+P7L/qdJVcUcI/rD4oUsKLgFRbgYLtf7lrGG2s8z1m+P+5sv+qc1SyJWjDEiYq6eqf\nX4MqEKR53FGy/XfDcw6XaVPpCtl/sErmQDPDsr6MkUq6OhRIhssh+39vJfhHnkyopJYOCVLd\nq9TjqGrj6eAiikqGlV1JV8cCqZ5GJCUPSFQbTwdJIKGShOB5QXIWwX5SFJUMCyAtA7A/EEUl\nw7owSHU6wKzZhexHJTnB04EUeNs9GGDW7Er2o5KM4PlAYopZswvbj0rGggCJV7ML249KxoIA\niVezC9uPSsaCCiANUwZNT1d4I5vEK2bNLmw/KhkLqvRI8Zm/cwSQ8hpJBQESNVUqqAfSNH/d\n8NMzuz5dzmZjjxf4vAxHzJpd2H5UMhZUB8mcbLXK7lnM7erKUGa+WcyaXdh+VDIW3ACkyphY\nVQKkzwZHnzPzQdB+Wtcj3aRAus1DOwg6oJgXG+ZOaPompOz9H+BA59H695HEQPpQS50jQdDW\nWg1S/m7vbPlB7mIDBG2tcu5sqHGVATquygEJPRF0YJUD0rvqh3KDEKSmckD6Xr/9rtwiBGmp\nHJAE72yAoK11SpCeIFOopJQilTrElMVc7V3vwoRKSilSKYB0fqGSUopU6p/fgsLQ7iRCJaUU\nqRRAOr9QSSlFKlUOSL2+v/24PvPe9S5MqKSUIpUqDaTbj2o9SXvXuzChklKKVKo4kCRuFdq7\n3oUJlZRSpFLFgfRXtX7Ohr3rXZhQSSlFKlUOSNO1hg+rM+9d78KESkopUqniQKrXcwT7baGS\nUopUqhyQBLVFTR8Grcoh9cfEVXglTSWKulHBgopUCiBl6sH5uSaHsgqvpKkH68ciBJB8ckH6\n8eF1Vb3+IPCppC1qCpA0FKnqOUCybjioFr9kafF5pOEkaf2nkraoqWn5OBqxfz6M8WHxw9PD\ng7UOQFooXNWHvmBOpTdWpFJUkG5i/Fg5DT1W7Qf7vr+tHldn3qKmhuUP/p8jSNPi6Zj6MO4p\nW/yhRwXJV9WxhA/muhsqUikmSNX8izFp8S3rHrnAnA0HeUPWuNhgWe489+wRm+8KhVfSlAck\n87lb6a0VqRRjaGeRE/jH0cFBmh6eRqJCIJlh+8kWf+hhQXpwxnZPRtV2GdgJ9khJnDg6w9DO\nPWzaCx+sM+TlugDJldsjjctckHZCKVIpJkjjEwWQDnmxASDJygOS078vy72hIpViXrWrpu9E\ncn9ZDdIxL38vRu7uyVDkHAkXGxYKVNUd2h33YoMrPjbeHEraoqbmcdF/+XtaybwmgcvfUZl3\nNjiVmgpmVnxjRSqV+4aswCWBY4N0IKGSUopUqpg7Gz53n554evwmkHnvehcmVFJKkUqVAtLb\nquoQqgU+RQH7baGSUopUqhCQ/qrqL90vT3X11+rMe9e7MKGSUopUqhCQ3lZfht++VG9XZ967\n3oUJlZRSpFKFgGTeEnuMOxsOJFRSSpFKFQgS5mwQFioppUilCpmy+G013s/w/SC3CB1IqKSU\nIpUqBKTPEz6P09lSvvaud2FCJaUUqVQhIN3q6l37Zz69q16vz9x4FVgcDnCXZ0X4jaSCiygq\nGZZYJf/5JahNQRpvWZW4ZxX2O89RyYx2fNFIpUoB6Xb7690do3fr30S6wX73OSqZ0Y4vGqlU\nOSAJilmzC9uPSsaCAIlXswvbj0rGggCJV7ML249KxoKnAql/e7a+a/xZO8/Nn72YNbuw/Skx\nm79wJUsHaeBkfBhoGZ+7Pwcxa3Zh+1NiNn/hShYOUn0DSNwgQKKmSgVPBJKJRz0/AUic6AqP\nmM1fuJIHA2k8RRqfOz9vt99bCf+hFxZz7wNIBwApBhB6pFh0hUfM5i9cSSJI7tzf7iypi7wE\nSjJAGn8BSJwoqc5+MZu/cCWpIFV7g7Q8VwJIxCipzn4xm79wJZkgVf28xbdpvsj+YZ4p0pxH\nMvFZVxZI9fwIkACSTCOpoMrQrkpPn28vSXLCekN2fhK/2AD7ARI5VSqoc45UeSbRN566X/SS\nnnyBAVKduqMBdzZEoqQ6+8Vs/sKVZII0PSHMqZ9CBffa5TWSCgIkaqpUUPGq3XDy4+uF3N4p\n+c1jACmvkVQQIFFTpYIKIGkIIOU1kgoCJGqqVBAg8Wp2YftRyVgQIPFqdmH7UclYECDxanZh\n+1HJWBAg8Wp2YftRyVgQIPFqdmH7UclYECDxanZh+1HJWBAg8Wp2Pvtf7OeoZEY7fZRcyVKm\nLBaVsgMnsh+VjAUBkq4DJ7IflYwFWSD9LyiAxC7+4eyHEnqhrgiQABIqGY6iR1J04ET2o5Kx\nIEDSdeBE9qOSsSBA0nXgRPajkrEgQNJ14ET2o5KxIEDSdeBE9qOSsSBA0nXgRPajkrEgQNJ1\n4ET2o5KxIEDSdaB4+18AEilVKsipJEC6tP2oZCyoAVJkTqBcIPRAurpeyDe2pKS8L18PJGsS\nfUflgaTswInsRyVjQSWQup/uBN/WI08AKa+RVBAgUVOlgoogBeZYveVgAZDyGkkFARI1VSqo\n3iMtpllFj5QdAUhSkcOAFP82CoCUFwFIUpFDgDSfCjmTfU9nTQApLwKQpCLHAElBACmvkVQQ\nIFFTpYIASdeB8u1/tp+jkhntdFGApOlA+fYDJFKqVBAg6TpQuv0vAImWKhW8VxIgKTpQvv0A\niZQqFQRIug6Ubz9AIqVKBQGSrgPl2w+QSKlSQYCk60D59gMkUqpUkAUSpiw+of0AiZQqFQRI\nug6Ub38cpLp7uCv2E5VseCD9FBRAYhe/FPujIHWg9NCEf6KSTccRQFJ0oHz7YyDVN4BEDAIk\nXQfKtz8C0gALQCIEAZKuA8Xb/ywB0u+t1Cw6hFqQqOsCpGuBVN/QI5GDiUGyJYB0QvvDIE2c\nACRCECDpOlC8/RGQegEkUhAg6TpQvP2xc6QbeiRq8OVUIPWmpt4+xNuIswASNVUieCqQ6tlZ\nDEhIwRcSSDgkpYNnAqm+ASRmMA0SQ8ovpvhKKoDk7Pa5s0KGMwYEkJhBgEROlQiqgFQ5+73A\nlQIVkPA2YgeSVDLlffmyIM0z2lX3BVX2JKtDSorQIzGD6JHIqRJBDZDMmVarm/FkDGYIIOU1\nkgi2HAEkUqp48IV1SKKCZM+xaoKEHmlVBCBJRY4B0s3qhKqV035POdMCSMwgQCKnigc1QJq/\nxaU7N+p7JWsK8AwBpLxG4sEXgEROFQ+q9Egawp0NeY3EgwCJnioePBlIOVJ2oHT7ARIxVTwI\nkK5uP0AipooHAdLF7X8GSMRU0eALQLqy/QCJkSoaBEiXth8gMVJFgy+8QTJAOp39AImaKhoE\nSFe3HyBRU0WDXJAwZfHZ7AdI1FTRIEC6uv0AiZoqGgRIF7f/GSB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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "df <- data.long %>% filter(country %in% top.countries) %<>% mutate(country=country %>% factor(levels=c(top.countries)))\n", "\n", "### CASES AROUND WORLD\n", "p <- df%>% filter(country !='World') %>%\n", " ggplot(aes(x=date, y=count)) + xlab('') + ylab('Count') +\n", " theme(legend.title=element_blank(),\n", " legend.text = element_text(size=6),\n", " legend.key.size=unit(0.6, 'cm'),\n", " axis.text.x=element_text(angle = 45, hjust=1)) +\n", " facet_wrap(~type, ncol = 2, scale='free_y')\n", "# area plot\n", "plot1 <- p + geom_area(aes(fill=country)) +\n", " labs(title='Cases around the World - ', max.date.txt)\n", "\n", "# line plot and in log scale\n", "#linetypes <- rep(c('solid','dashed','dotted'), each=8)\n", "#colors <- rep(c('black','blue','red','green','orange', 'purple', 'yellow', 'grey'), 3)\n", "plot2 <- p + geom_line(aes(color=country, linetype=country)) +\n", " scale_linetype_manual(values = linetypes) +\n", " scale_color_manual(values = colors) +\n", " labs(title =paste0('Cases around the world - Log Scale -', max.date.txt)) +\n", " scale_y_continuous(trans = 'log10')\n", "grid.arrange(plot1, plot2, ncol=1)\n", "\n", "\n", "# Plot: excluding China\n", "p <- df%>% filter(!(country %in% c('World', 'China'))) %>%\n", " ggplot(aes(x=date, y=count)) + xlab('') + ylab('Count') +\n", " theme(legend.title=element_blank(),\n", " legend.text = element_text(size=6),\n", " legend.key.size=unit(0.6, 'cm'),\n", " axis.text.x=element_text(angle = 45, hjust=1)) +\n", " facet_wrap(~type, ncol = 2, scale='free_y')\n", "p + geom_area(aes(fill=country)) +\n", " labs(title=paste0('Cases worlwide (excl. China) - ', max.date.txt))\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"Removed 36 row(s) containing missing values (geom_path).\"" ] }, { "data": { "image/png": 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0ju5LtJXGydgqNdCUjuncS7QJJX6A+AZA6QdEAyj4HaLmrcULry0L0gRbP5UpMD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\nbReApLc+CiT14g79aVR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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rate.max <- rates.long$count %>% max(na.rm=T)\n", "\n", "df <- rates.long %>% filter(country %in% setdiff(top.countries, 'World')) %>%\n", " mutate(country=factor(country, levels=top.countries))\n", "\n", "df %>% ggplot(aes(x=date, y=count, color=type)) +\n", " geom_line() +\n", " xlab('') + ylab('Death Rate (%)') +\n", " theme(legend.position='bottom', legend.title=element_blank(),\n", " legend.text=element_text(size=8),\n", " legend.key.size=unit(0.5, 'cm'),\n", " axis.text.x=element_text(angle=45, hjust=1)) +\n", "ylim(c(0, 100)) +\n", "facet_wrap(~country, ncol=4)\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\\begin{table}[!h]\n", "\n", "\\caption{\\label{tab:}Top 20 Countries with Highest Death Rates - 02 Apr 2020}\n", "\\centering\n", "\\fontsize{7}{9}\\selectfont\n", "\\begin{tabular}[t]{llrrrrrrr}\n", "\\toprule\n", " & country & confirmed & confirmed.new & remaining.confirmed & recovered & deaths & deaths.new & death.rate\\\\\n", "\\midrule\n", "\\rowcolor{gray!6} 1 & San Marino & 245 & 9 & 194 & 21 & 30 & 4 & 12.2\\%\\\\\n", "2 & Italy & 115,242 & 4,668 & 83,049 & 18,278 & 13,915 & 760 & 12.1\\%\\\\\n", "\\rowcolor{gray!6} 3 & Congo (Kinshasa) & 134 & 25 & 118 & 3 & 13 & 4 & 9.7\\%\\\\\n", "4 & Indonesia & 1,790 & 113 & 1,508 & 112 & 170 & 13 & 9.5\\%\\\\\n", "\\rowcolor{gray!6} 5 & Spain & 112,065 & 7,947 & 74,974 & 26,743 & 10,348 & 961 & 9.2\\%\\\\\n", "\\addlinespace\n", "6 & Netherlands & 14,788 & 1,092 & 13,187 & 260 & 1,341 & 166 & 9.1\\%\\\\\n", "\\rowcolor{gray!6} 7 & France & 59,929 & 2,180 & 41,983 & 12,548 & 5,398 & 1,355 & 9.0\\%\\\\\n", "8 & Algeria & 986 & 139 & 839 & 61 & 86 & 28 & 8.7\\%\\\\\n", "\\rowcolor{gray!6} 9 & United Kingdom & 34,173 & 4,308 & 31,055 & 192 & 2,926 & 569 & 8.6\\%\\\\\n", "10 & Iraq & 772 & 44 & 516 & 202 & 54 & 2 & 7.0\\%\\\\\n", "\\addlinespace\n", "\\rowcolor{gray!6} 11 & Egypt & 865 & 86 & 606 & 201 & 58 & 6 & 6.7\\%\\\\\n", "12 & Belgium & 15,348 & 1,384 & 11,842 & 2,495 & 1,011 & 183 & 6.6\\%\\\\\n", "\\rowcolor{gray!6} 13 & Bolivia & 123 & 8 & 114 & 1 & 8 & 1 & 6.5\\%\\\\\n", "14 & Honduras & 219 & 47 & 202 & 3 & 14 & 4 & 6.4\\%\\\\\n", "\\rowcolor{gray!6} 15 & Iran & 50,468 & 2,875 & 30,597 & 16,711 & 3,160 & 124 & 6.3\\%\\\\\n", "\\addlinespace\n", "16 & Morocco & 708 & 54 & 633 & 31 & 44 & 5 & 6.2\\%\\\\\n", "\\rowcolor{gray!6} 17 & Albania & 277 & 18 & 185 & 76 & 16 & 1 & 5.8\\%\\\\\n", "18 & Burkina Faso & 288 & 6 & 222 & 50 & 16 & 0 & 5.6\\%\\\\\n", "\\rowcolor{gray!6} 19 & Sweden & 5,568 & 621 & 5,157 & 103 & 308 & 69 & 5.5\\%\\\\\n", "20 & Dominican Republic & 1,380 & 96 & 1,304 & 16 & 60 & 3 & 4.3\\%\\\\\n", "\\bottomrule\n", "\\end{tabular}\n", "\\end{table}" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Latest Cases by Country - TOP confirmed cases\n", "## sort the latest data by death rate, and if tie, by confirmed\n", "df <- data %>% filter(date == max(date) & country != 'World' & confirmed >= 100) %>% \n", " select(country, confirmed, confirmed.new, remaining.confirmed,\n", " recovered, deaths, deaths.new, death.rate=rate.lower) %>%\n", " arrange(desc(death.rate, confirmed))\n", "\n", "df %>% head(20) %>%\n", "mutate(death.rate=death.rate %>% format(nsmall=1) %>% paste0('%')) %>%\n", " kable('latex', booktabs=T, row.names=T, align=c('l', rep('r', 7)),\n", " caption=paste0('Top 20 Countries with Highest Death Rates - ', max.date.txt), format.args=list(big.mark=',')) %>%\n", "kable_styling(font_size=7, latex_options=c('striped', 'hold_position', 'repeat_header'))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that this is an developing story. Check back for updates." ] } ], "metadata": { "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 4 }