COVID-19 datasets...
On Thu, May 7, 2020 at 4:16 PM Thomas Petzoldt <thpe at simecol.de> wrote:
On 07.05.2020 at 11:19 Deepayan Sarkar wrote:
On Thu, May 7, 2020 at 12:58 AM Thomas Petzoldt <thpe at simecol.de> wrote:
Sorry if I'm joining a little bit late. I've put some related links and scripts together a few weeks ago. Then I stopped with this, because there is so much. The data format employed by John Hopkins CSSE was sort of a big surprise to me.
Why? I find it quite convenient to drop the first few columns and extract the data as a matrix (using data.matrix()). -Deepayan
Many thanks for the hint to use data.matrix My aim was not to say that it is difficult, especially as R has all the tools for data mangling. My surprise was that "wide tables" and non-ISO dates as column names are not the "data base way" that we in general teach to our students
Well, I am all for long format data when it makes sense, but I would disagree that that is always the "right approach". In the case of regular multiple time series, as in this context, a matrix-like structure seems much more natural (and nicely handled by ts() in R), and I wouldn't even bother reshaping the data in the first place. See, for example, https://github.com/deepayan/deepayan.github.io/blob/master/covid-19/deaths.rmd and https://deepayan.github.io/covid-19/deaths.html -Deepayan
With reshape2::melt or tidyr::gather resp. pivot_longer, conversion is
quite easy, regardless if one wants to use tidyverse or not, see example
below.
Again, thanks, Thomas
library("dplyr")
library("readr")
library("tidyr")
file <-
"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv"
dat <- read_delim(file, delim=",")
names(dat)[1:2] <- c("Province_State", "Country_Region")
dat2 <-
dat %>%
## summarize Country/Region duplicates
group_by(Country_Region) %>% summarise_at(vars(-(1:4)), sum) %>%
## make it a long table
pivot_longer(cols = -Country_Region, names_to = "time") %>%
## convert to ISO 8601 date
mutate(time = as.POSIXct(time, format="%m/%e/%y"))
An opposite approach was taken in Germany, that organized it as a big JSON trees. Fortunately, both can be "tidied" with R, and represent good didactic examples for our students. Here yet another repo linking to the data: https://github.com/tpetzoldt/covid Thomas On 04.05.2020 at 20:48 James Spottiswoode wrote:
Sure. COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University is available here: https://github.com/CSSEGISandData/COVID-19 All in csv fiormat.
On May 4, 2020, at 11:31 AM, Bernard McGarvey <mcgarvey.bernard at comcast.net> wrote: Just curious does anyone know of a website that has data available in a format that R can download and analyze? Thanks Bernard McGarvey Director, Fort Myers Beach Lions Foundation, Inc. Retired (Lilly Engineering Fellow).
______________________________________________ R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
James Spottiswoode Applied Mathematics & Statistics (310) 270 6220 jamesspottiswoode Skype james at jsasoc.com
-- Dr. Thomas Petzoldt senior scientist Technische Universitaet Dresden Faculty of Environmental Sciences Institute of Hydrobiology 01062 Dresden, Germany https://tu-dresden.de/Members/thomas.petzoldt