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# Plot regional or national Rt for COVID-19 based on FHM data, to reproduce the | |
# results published on https://www.folkhalsomyndigheten.se/smittskydd-beredskap/utbrott/aktuella-utbrott/covid-19/statistik-och-analyser/analys-och-prognoser/ | |
# Adapted from: | |
# Churches (2020, Feb. 18). Tim Churches Health Data Science Blog: Analysing COVID-19 (2019-nCoV) outbreak data with R - part 1. Retrieved from https://timchurches.github.io/blog/posts/2020-02-18-analysing-covid-19-2019-ncov-outbreak-data-with-r-part-1/, CC BY-SA | |
library(readxl) | |
library(EpiEstim) | |
# path to latest FHM Excel file | |
secov <- read_excel("data/FHM/FHM_latest.xlsx") | |
secov$Statistikdatum <- as.Date(secov$Statistikdatum) |
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*** default.beamer 2020-02-13 11:50:17.541486784 +0100 | |
--- tagged-pres.beamer 2020-02-13 11:32:29.142235033 +0100 | |
*************** | |
*** 10,15 **** | |
--- 10,20 ---- | |
$endif$ | |
$endif$ | |
% | |
+ \begin{filecontents*}{\jobname.xmpdata} | |
+ \Title{$title$} |
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*** default.context 2019-06-22 08:40:19.015364224 +0200 | |
--- default-pdfa2a.context 2019-06-22 08:43:07.763916183 +0200 | |
*************** | |
*** 27,33 **** | |
% make chapter, section bookmarks visible when opening document | |
\placebookmarks[chapter, section, subsection, subsubsection, subsubsubsection, subsubsubsubsection][chapter, section] | |
\setupinteractionscreen[option=bookmark] | |
- \setuptagging[state=start] | |
$if(papersize)$ |
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function ConvertTo-WordPDFA | |
{ | |
Param( | |
[Parameter(Mandatory=$true,Position=0)][String]$dpath | |
) | |
$word_app = New-Object -ComObject Word.Application | |
$doc = $word_app.Documents.Open($dpath) | |
$dpath_dir = [System.IO.Path]::GetDirectoryName($dpath) | |
$dpath_base = [System.IO.Path]::GetFileNameWithoutExtension($dpath) | |
$pdf_fname = $dpath_base + ".pdf" |
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using DataFrames | |
deaths = readtable(normpath("data", "2017-9-10-4Amod.csv")) | |
ages = DataFrame(age = collect(0:20), | |
agest = [0;0;1;collect(5:5:90)], | |
age2 =[0;1;fill(2,3);fill(3,6);fill(4,4);fill(5,2);fill(6,4)]) | |
ages2 = by(ages[:,[:agest, :age2]], :age2, | |
df -> DataFrame(agest=minimum(df[:agest]))) | |
pop = DataFrame() | |
pop1 = readtable(normpath("data", "mfsv16.csv")) |
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using DataFrames, PyPlot | |
m = readtable("meni9815.csv") | |
magg = aggregate(m, :Ålder, sum) | |
bar(magg[:Ålder], magg[:Kvinnor_sum], -2, align="edge", label = "Kvinnor") | |
bar(magg[:Ålder], magg[:Män_sum], 2, align="edge", label = "Män") | |
grid(1) | |
legend() | |
xlabel("Ålder") | |
ylabel("Antal (summa för ålder över år)") | |
title("Sjukhusvårdade meningokockinfektion Sverige 1998\u20132015") |
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age | fall | mall | fskin | mskin | |
---|---|---|---|---|---|
0 | 0.0002006 | 0.0002459 | 0.0 | 0.0 | |
5 | 9.22e-5 | 0.0001006 | 0.0 | 0.0 | |
10 | 0.0001424 | 0.0001568 | 0.0 | 0.0 | |
15 | 0.00022940000000000002 | 0.0002389 | 0.0 | 0.0 | |
20 | 0.0003875 | 0.00036030000000000003 | 9.3e-6 | 0.0 | |
25 | 0.0007676 | 0.0004501 | 9.2e-6 | 0.0 | |
30 | 0.0011704 | 0.0007615 | 1.34e-5 | 0.0 | |
35 | 0.0023091 | 0.0009373000000000001 | 2.02e-5 | 9.7e-6 | |
40 | 0.0032355 | 0.0013991 | 3.74e-5 | 3.0299999999999998e-5 |
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using LifeTable, DataFrames | |
function AmpLt(inframe, sex, rate = "inc") | |
age = inframe[1] | |
pop = inframe[2] | |
acd = inframe[3] | |
cd = inframe[4] | |
cc = inframe[5] | |
if rate == "inc" | |
ncol = cc |
We can make this file beautiful and searchable if this error is corrected: It looks like row 3 should actually have 36 columns, instead of 22 in line 2.
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Year,Pop1,Pop2,Pop3,Pop4,Pop5,Pop6,Pop7,Pop8,Pop9,Pop10,Pop11,Pop12,Pop13,Pop14,Pop15,Pop16,Pop17,Pop18,Pop19,Pop20,Pop21,Pop22,Pop23,Pop24,Pop25,Pop26,Pop36sum,Pop222sum,Pop2325sum,Pop38mean,Pop914mean,Pop1518mean,Pop1920mean,Pop2122mean,List | |
1951,3.268342161613885e-05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.923816852635629e-05,2.2123893805309735e-05,2.982848620432513e-05,5.200594353640416e-05,8.46091861402095e-05,4.908522980812137e-05,8.060453400503778e-05,6.872852233676975e-05,6.93000693000693e-05,3.5842293906810036e-05,5.46448087431694e-05,0.00010416666666666667,4.464285714285714e-05,,,,0.0,3.260653831977091e-05,4.464285714285714e-05,0.0,2.053274867873255e-05,7.07568680725346e-05,5.2571181603439665e-05,7.940573770491804e-05,07A | |
1952,3.8315247790580604e-05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.42282176028306e-06,0.0,3.637686431429611e-05,4.514672686230248e-05,5.167958656330749e-05,5.966587112171838e-05,0.00010048055919615553,8.91089108910891e-05,7.369614512471656e-05,7.407407407407407e-05,9.70873786407767e-05,6. |
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ctryname | country | totfat | satfat | suc | m55mort | f55mort | |
---|---|---|---|---|---|---|---|
Jordan | 3170 | 18.5 | 3.7 | 11 | 49 | 19 | |
El Salvador | 2190 | 19.5 | 5.5 | 51 | 28 | ||
Taiwan | 3070 | 15.3 | 4.8 | 4.2 | 58 | 53 | |
Philippines | 3300 | 9.6 | 3.7 | 5.5 | 89 | 40 | |
Guatemala | 2250 | 15.1 | 4.1 | 115 | 84 | ||
Mexico | 2310 | 24.8 | 7.9 | 122 | 65 | ||
Greece | 4140 | 26.5 | 5.4 | 4.9 | 162 | 61 | |
Japan | 3160 | 14.5 | 3.4 | 7.3 | 165 | 92 | |
Panama | 2350 | 22.6 | 6.8 | 8.7 | 170 | 76 |
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