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First of all, I must say I'm completely noob in R. So I apologize in advance for asking for help with such a simple task. My task is to form a graph of COVID-19 cases for a certain period using data from the CSV file. Unfortunately, at the moment I cannot contact the person from the World Health Organization who provided the data and the script for launching. But I was left with an error that I cannot fix either myself, not with the help of Google.
script.R
library(EpiEstim)
library(ggplot2)
COVID<-read.csv("dataset.csv")
res_parametric_si<-estimate_R(COVID$I,method="parametric_si",config=make_config(list(mean_si=4,std_si=3)))
plot(res_parametric_si)
dataset.csv
Date,Suspected per day,Total suspected,Discarded/pending,Confirmed per day,Total confirmed,Deaths per day,Deaths Total,Case fatality rate,Daily confirmed,Recovered per day,Recovered total,Active cases,Tested with PCR,# of PCR tests total,average tests/ 7 days,Inf HCW,Inf HCW/d,Vent HCW,Susp per day
01-Jul-20,1239,91172,45285,889,45887,12,1185,2.58%,889,505,20053,24649,11109,676684,10073,6828,63,,1239
02-Jul-20,1249,92421,45658,876,46763,27,1212,2.59%,876,505,20558,24993,13167,689851,9966,6874,46,,1249
03-Jul-20,1288,93709,46032,914,47677,15,1227,2.57%,914,597,21155,25295,11825,701676,9915.7,6937,63,,1288
04-Jul-20,926,94635,46135,823,48500,22,1249,2.58%,823,221,21376,25875,9934,711610,9957,6990,53,,926
05-Jul-20,680,95315,46272,543,49043,13,1262,2.57%,543,327,21703,26078,6696,718306,9963.7,7030,40,,680
06-Jul-20,871,96186,46579,564,49607,21,1283,2.59%,564,490,22193,26131,9343,727649,10303.9,7046,16,,871
07-Jul-20,1170,97356,46942,807,50414,23,1306,2.59%,807,926,23119,25989,13568,741217,10806,7092,46,,1170
Error
Error in process_I(incid) (script.R#4): incid must be a vector or a dataframe with either i) a column called 'I', or ii) 2 columns called 'local' and 'imported'.
For the example data the issue seems to be that it does only cover 7 data points, and the configurator assumes that there it can window over more than 7 days. What worked for me was the following code (working in the sense that it does not throw an error).
config <- make_config(incid = COVID$Daily.confirmed,
method="parametric_si",
list(mean_si=4,std_si=3, t_start = c(2,3),t_end = c(6,7)))
res_parametric_si<-estimate_R(COVID$Daily.confirmed,method="parametric_si",config=config)
plot(res_parametric_si)
I have 200,000 links that I am trying to download, I have tried downloading it all in one go but I ran into memory issues.
I am trying to create a function which will download 1000 links at a time and save them in a folder.
Packages:
library(dplyr)
library(purrr)
library(edgarWebR)
A small sample of the data is as follows:
Data 1:
urls_to_parse <- c("https://www.sec.gov/Archives/edgar/data/1750/000104746918004978/a2236183z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746917004528/a2232622z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746916014299/a2228768z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746915006136/a2225345z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746914006243/a2220733z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746913007797/a2216052z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746912007300/a2210166z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746911006302/a2204709z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746910006500/a2199382z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746909006783/a2193700z10-k.htm"
)
I then apply the following function to download these 10 links
parsed_files <- map(urls_to_parse, possibly(parse_filing, otherwise = NA))
Which stores it as a nice list, I can then apply names(parsed_files) <- urls_to_parse to name the lists as the links from where they were downloading them from. I can also use output <- plyr::ldply(parsed_files, data.frame) to store everything in a nice data frame.
Using the below data, how could I create batches to download the data in say batches of 10?
What I have currently:
start = 1
end = 100
output <- NULL
output_fin <- NULL
for(i in start:end){
output[[i]] <- map(urls_to_parse[[i]], possibly(parse_filing, otherwise = NA))
names(output) <- urls_to_parse[start:end]
save(output_fin, file = paste0("C:/Users/Downloads/data/",i, "output.RData"))
}
I am sure there is a better way using a function, since this code breaks for some of the results.
More data: - 100 links
urls_to_parse <- c("https://www.sec.gov/Archives/edgar/data/1750/000104746918004978/a2236183z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746917004528/a2232622z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746916014299/a2228768z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746915006136/a2225345z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746914006243/a2220733z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746913007797/a2216052z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746912007300/a2210166z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746911006302/a2204709z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746910006500/a2199382z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746909006783/a2193700z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746908008126/a2186742z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000110465907055173/a07-18543_110k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000110465906047248/a06-15961_110k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000110465905033688/a05-12324_110k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746904023905/a2140220z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000104746903028005/a2116671z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/1750/000091205702033450/a2087919z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/61478/000095012310108231/c61492e10vk.htm",
"https://www.sec.gov/Archives/edgar/data/61478/000095015208010514/n48172e10vk.htm",
"https://www.sec.gov/Archives/edgar/data/61478/000095013707018659/c22309e10vk.htm",
"https://www.sec.gov/Archives/edgar/data/61478/000095013707000193/c11187e10vk.htm",
"https://www.sec.gov/Archives/edgar/data/61478/000095013406000594/c01109e10vk.htm",
"https://www.sec.gov/Archives/edgar/data/61478/000120677405000032/d16006.htm",
"https://www.sec.gov/Archives/edgar/data/61478/000120677404000013/d13773.htm",
"https://www.sec.gov/Archives/edgar/data/61478/000104746903001075/a2097401z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/61478/000091205702001614/a2067550z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/319126/000115752308008030/a5800571.htm",
"https://www.sec.gov/Archives/edgar/data/319126/000115752307009801/a5515869.htm",
"https://www.sec.gov/Archives/edgar/data/319126/000115752306009238/a5227919.htm",
"https://www.sec.gov/Archives/edgar/data/730469/000073046908000102/alpharmainc_10k.htm",
"https://www.sec.gov/Archives/edgar/data/730469/000073046907000017/alo10k2006.htm",
"https://www.sec.gov/Archives/edgar/data/730469/000073046906000027/alo10k2005.htm",
"https://www.sec.gov/Archives/edgar/data/730469/000073046905000021/alo10k2004final.htm",
"https://www.sec.gov/Archives/edgar/data/730469/000073046904000058/alo10k2003master.htm",
"https://www.sec.gov/Archives/edgar/data/730469/000073046903000001/alo10k.htm",
"https://www.sec.gov/Archives/edgar/data/730469/000073046902000004/alo10k2001.htm",
"https://www.sec.gov/Archives/edgar/data/730469/000073046901500003/alo.htm",
"https://www.sec.gov/Archives/edgar/data/4515/000000620118000009/a10k123117.htm",
"https://www.sec.gov/Archives/edgar/data/4515/000119312517051216/d286458d10k.htm",
"https://www.sec.gov/Archives/edgar/data/4515/000119312516474605/d78287d10k.htm",
"https://www.sec.gov/Archives/edgar/data/4515/000119312515061145/d829913d10k.htm",
"https://www.sec.gov/Archives/edgar/data/4515/000000620114000004/aagaa10k-20131231.htm",
"https://www.sec.gov/Archives/edgar/data/6201/000000620113000023/amr-10kx20121231.htm",
"https://www.sec.gov/Archives/edgar/data/6201/000119312512063516/d259681d10k.htm",
"https://www.sec.gov/Archives/edgar/data/6201/000095012311014726/d78201e10vk.htm",
"https://www.sec.gov/Archives/edgar/data/6201/000000620110000006/ar123109.htm",
"https://www.sec.gov/Archives/edgar/data/6201/000000620109000009/ar120810k.htm",
"https://www.sec.gov/Archives/edgar/data/6201/000000451508000014/ar022010k.htm",
"https://www.sec.gov/Archives/edgar/data/6201/000095013407003888/d43815e10vk.htm",
"https://www.sec.gov/Archives/edgar/data/6201/000095013406003715/d33303e10vk.htm",
"https://www.sec.gov/Archives/edgar/data/6201/000095013405003726/d22731e10vk.htm",
"https://www.sec.gov/Archives/edgar/data/6201/000095013404002668/d12953e10vk.htm",
"https://www.sec.gov/Archives/edgar/data/6201/000104746903013301/a2108197z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/65695/000095013407003823/h42902e10vk.htm",
"https://www.sec.gov/Archives/edgar/data/65695/000095012906002343/h31028e10vk.htm",
"https://www.sec.gov/Archives/edgar/data/65695/000095012905002955/h22337e10vk.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000156459018005085/cece-10k_20171231.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000156459017004264/cece-10k_20161231.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000156459016015157/cece-10k_20151231.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000119312515095828/d864880d10k.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000119312514098407/d661608d10k.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000119312513109153/d444138d10k.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000119312512119293/d293768d10k.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000119312511067373/d10k.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000119312510069639/d10k.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000119312509055504/d10k.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000119312508058939/d10k.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000119312507071909/d10k.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000119312506068031/d10k.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000119312505077739/d10k.htm",
"https://www.sec.gov/Archives/edgar/data/3197/000119312504052176/d10k.htm",
"https://www.sec.gov/Archives/edgar/data/2601/000110465910047121/a10-16705_110k.htm",
"https://www.sec.gov/Archives/edgar/data/2601/000114420409046933/v159572_10k.htm",
"https://www.sec.gov/Archives/edgar/data/2601/000110465906060737/a06-19311_110k.htm",
"https://www.sec.gov/Archives/edgar/data/2601/000104746905022854/a2162888z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/2601/000104746904028585/a2143353z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/2601/000104746903031974/a2119476z10-k.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000143774918010388/avx20180331_10k.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916317000028/avx-20170331x10k.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916316000079/avx-20160331x10k.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916315000024/avx-20150331x10k.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916314000035/avx-20140331x10k.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916313000022/avx-20130331x10k.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916312000024/avxform10kfy12.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916311000013/avxform10kfy11.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916310000020/avxform10kfy10.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916309000117/form10kfy09.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916308000192/form10qq1fy09.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916308000101/form10kfy08.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916307000122/form10kfy07.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916306000102/avxfy06form10-k.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916305000094/fy0510k.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916304000091/fy0410k.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916303000020/fy0310k.htm",
"https://www.sec.gov/Archives/edgar/data/859163/000085916302000007/r10k-0302.htm",
"https://www.sec.gov/Archives/edgar/data/7286/000076462218000018/pnw2017123110-k.htm",
"https://www.sec.gov/Archives/edgar/data/7286/000076462217000010/pnw2016123110-k.htm",
"https://www.sec.gov/Archives/edgar/data/7286/000076462216000087/pnw2015123110-k.htm",
"https://www.sec.gov/Archives/edgar/data/7286/000076462215000013/pnw12311410-k.htm",
"https://www.sec.gov/Archives/edgar/data/7286/000110465914012068/a13-25897_110k.htm"
)
Looping over to do batch job as you showed is a bad idea. If you have a 1000s of files to be downloaded, how do you recover from errors?
The performance is not solely depend on your computer's configuration, but the network performance is crucial.
Here are couple of suggestions.
Option 1
partition all URLs in to batches to be able to download them parallelly. The number of files to be downloaded could be equal to number of cores in your computer. Look at this question; reading multiple files quickly in R
store these batches in a queue objects - For ex: using a package like https://cran.r-project.org/web/packages/dequer/dequer.pdf
pop the queue and use the batch of URLs in your parallel file download function.
Use a retryable file download function like in -- HTTP error 400 in R, error handling, How to retry instead of forcing to stop?
Once the queue is completed, move to the next partition.
wrap the whole operation in a retryable loop. For example; How to retry a statement on error?
Why do I use a queue? Because you could retry on error easily.
A pseudo code
file_url_partitions <- partion_as_batches(all_urls, batch_size)
attempts = 3
while( file_url_partitions is not empty && attempt <= 3 ) {
batch = file_url_partitions.pop()
tryCatch({
download_parallel(batch)
}, some_exception = function(se) {
file_url_partitions.push(batch)
attemp = attempt+1
})
}
Note: I don't have access to R studio/environment now hence no way to try.
Option 2
Download files separately using a download manager/similar and use downloaded files.
Some useful resources:
https://www.r-bloggers.com/r-with-parallel-computing-from-user-perspectives/
http://adv-r.had.co.nz/beyond-exception-handling.html
I have an R data frame with a list of 500ish URLs. It looks a bit like this:
websites <- data.frame(rbind("www.nytimes.com", "www.google.com", "www.facebook.com"))
I want to go through these URLs and open them (maybe 10 at a time) in Google Chrome.
How would I go about this automatically with R?
I used this to get all 3 of them to open.
websites <- data.frame(rbind("www.nytimes.com", "www.google.com", "www.facebook.com"))
websites <- as.data.frame(t(websites))
websites[] <- lapply(websites, as.character)
webVec <- unname(unlist(websites[1,]))
for(i in 1:length(webVec)){
shell.exec(paste(webVec[i]))
}
This opens all of them however, and I'm not sure how to open only a certain amount at a time. I took a stab at it though:
setTen <- 1
for(i in (1 * (10 * (setTen - 1))):(10 * setTen )){
shell.exec(paste(webVec[i]))
}
the setTen variable asks if you want the first ten websites, second ten, ect.
I couldn't test it though since there is only 3 sites in this data frame.
If it doesn't work let me know and I'll try to figure out a different method.
I am trying to run a model in R using PVA and transition matrices. Whenever I run a line and the text editor spreadsheet pops up, I am able to edit the data but it won't close and just freezes so I have to shut everything down. I've tried restarting several times but I can't get it to work and I need to be able to run this line of code. Specifically it happens when I run "supplement_matrix <- make.supplement.matrix(num.stages)" Here is my code:
library(popbio)
library(reshape2)
library(expm)
multiyear.stages <- read.csv('multiyear_stages_longform.csv')
head(multiyear.stages)
unique(multiyear.stages$Survey)
nrow(multiyear.stages[multiyear.stages$Survey==1,])
nrow(multiyear.stages[multiyear.stages$Survey==1&multiyear.stages$Stage==1,])
nrow(multiyear.stages[multiyear.stages$Survey==2,])
nrow(multiyear.stages[multiyear.stages$Survey==2&multiyear.stages$Stage==2,])
nrow(multiyear.stages[multiyear.stages$Survey==1&multiyear.stages$Stage<=2,])
nrow(multiyear.stages[multiyear.stages$Survey<=2&multiyear.stages$Stage==1,])
nrow(multiyear.stages[multiyear.stages$Survey==2&multiyear.stages$Stage==2,])
plants<-dcast(multiyear.stages, ID~Survey, value.var="Stage", fill=-1,
fun.aggregate=mean)
head(plants)
nrow(plants[plants$"1"==1&plants$"2"==2,])
nrow(plants[plants$"1"==1&plants$"2"==0,])
nrow(plants[plants$"1"==1&plants$"2"==1,])
nrow(plants[plants$"1"==1&plants$"2"==3,])
nrow(plants[plants$"1"==2&plants$"2"==0,])
nrow(plants[plants$"1"==2&plants$"2"==1,])
nrow(plants[plants$"1"==2&plants$"2"==2,])
nrow(plants[plants$"1"==2&plants$"2"==3,])
nrow(plants[plants$"1"==3&plants$"2"==1,])
nrow(plants[plants$"1"==3&plants$"2"==2,])
nrow(plants[plants$"1"==3&plants$"2"==3,])
nrow(plants[plants$"2"==-1&plants$"3"==1,])
nrow(plants[plants$"2"==1&plants$"3"==0,])
nrow(plants[plants$"2"==1&plants$"3"==1,])
nrow(plants[plants$"2"==1&plants$"3"==2,])
nrow(plants[plants$"2"==1&plants$"3"==3,])
nrow(plants[plants$"2"==2&plants$"3"==0,])
nrow(plants[plants$"2"==2&plants$"3"==1,])
nrow(plants[plants$"2"==2&plants$"3"==2,])
nrow(plants[plants$"2"==3&plants$"3"==3,])
nrow(plants[plants$"2"==3&plants$"3"==0,])
nrow(plants[plants$"2"==3&plants$"3"==1,])
nrow(plants[plants$"2"==3&plants$"3"==2,])
nrow(plants[plants$"2"==3&plants$"3"==3,])
nrow(plants[plants$"1"==3,])
A<-cbind(c(0.317,0.384,0.017), c(0.071,0.592,0.195), c(0.02,0.76,0.1))
N0<-c(40,136,38)
N0 %*% A
source('PVA_source.R')
num.stages<-3
num.iter<-50
num.years<-50
num.iter<-10
num.years<-10
quasi_extinction_threshold<-10
carrying_capacity<-1000
number_of_plants_in_year_1_Stage1 <-cbind(c(19), c(12), c(1))
transition_matrix<-cbind(c(0.317,0.384,0.017), c(0.071,0.592,0.195),
c(0.02,0.76,0.1))
fertility <- c(2.475)
supplement_matrix <- make.supplement.matrix(num.stages)
Here are links to the data I'm using
https://drive.google.com/file/d/0BwRwgnqxDOlxUWNyS25FYVpOZFk/view?usp=sharing
https://drive.google.com/file/d/0BwRwgnqxDOlxZXUzX2EwM2NOZEU/view?usp=sharing
It's hard to diagnose the problem, because I can't replicate it (it runs fine for me). However, you can work around it.
Here's the function from that PVA script that's freezing your system:
make.supplement.matrix<-function(num.stages){
supplement <- matrix(0,nrow=num.stages,,ncol=1,
dimnames=list(paste('stage',1:num.stages),'Number of extra plants to plant each year')
)
supplement <- edit(supplement)
supplement
}
Instead of using it, why not just run:
supplement <- matrix(0,nrow=num.stages,,ncol=1,
dimnames=list(paste('stage',1:num.stages),'Number of extra plants to plant each year')
)
then manually edit (in your script or interactive console) the matrix called supplement any way you please?
Suppose we have files in one folder file1.bin, file2.bin, ... , and file1460.bin in directory C:\R\Data and we want to read them and make a loop to go from 1 to 4 and take the average then from 4 to 8 average and so on till 1460.in the end will get 360 files
I tried to have them in a list,but did not know how to make the loop.
How do I read multiple files and manupulat them? in R language
I have been wasting countless hourse to figuer it out.any help
results <- array(dim=360)
for (i in 1:360){
results <- mean(yourlist[[(i*4):(i*4+3)]])
}
YMMV with the mean(yourList) call, but that structure would be how you could loop through the data once it's loaded.