Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/andrie/covid-19
Analysis of covid-19 cases using data provided by Johns Hopkins University
https://github.com/andrie/covid-19
covid-19 r-stats
Last synced: 22 days ago
JSON representation
Analysis of covid-19 cases using data provided by Johns Hopkins University
- Host: GitHub
- URL: https://github.com/andrie/covid-19
- Owner: andrie
- License: mit
- Created: 2020-03-19T03:39:15.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2021-02-22T09:15:13.000Z (almost 4 years ago)
- Last Synced: 2024-11-12T05:03:44.873Z (2 months ago)
- Topics: covid-19, r-stats
- Language: R
- Homepage:
- Size: 25.6 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
Awesome Lists containing this project
README
---
output: github_document
---
# covid-19
This is another analysis of the outbreak of [Coronavirus / Covid-19
disease](https://en.wikipedia.org/wiki/Coronavirus_disease_2019).
Many others have done excellent analysis, and my attempts are specifically trying to add flavour to the statistics in South Africa.
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
echo = FALSE,
warning = FALSE
)
```
```{r setup, warning=FALSE}
suppressPackageStartupMessages({
library(tidyverse)
library(ggplot2)
library(ggrepel)
library(DT)
library(maps)
library(pins)
library(mapproj)
})
to_include <- c(
"South Africa",
"Italy",
"India",
"Brazil",
"Russia",
"China",
"US",
"Singapore",
"United Kingdom",
"Spain",
"Germany",
"Sweden"
)
source("R/get_data.R")
source("R/prep_data.R")
source("R/plot_covid.R")
```
## Data source
The data source is [a repository maintained by Johns Hopkins
University](https://github.com/CSSEGISandData/COVID-19). The data is updated
once per day.
Last updated at `r Sys.time()`
## Total cases
```{r total-cases}
type <- "cases"
imported_threshold <- if (type == "cases") 30 else 10
dat <- get_covid19_data(type)
if (type == "cases"){
dat <-
dat %>%
bind_rows(
tribble(
~country, ~date, ~long, ~lat, ~n,
"China", as.Date("2020-01-16"), 112, 32.8, 28,
"China", as.Date("2020-01-17"), 112, 32.8, 41,
"China", as.Date("2020-01-20"), 112, 32.8, 260
# "South Africa", as.Date("2020-03-30"), 22.9, -30.6, 1326
)
) %>%
group_by(country, date) %>%
summarize(
long = mean(long),
lat = mean(lat),
n = max(n),
.groups = "drop_last"
)
attr(dat, "type") <- "cases"
}
```
```{r}
dat %>%
filter(country == "South Africa") %>%
select(country, date, lat, long, n) %>%
filter(n > 0) %>%
rename(cases = n) %>%
arrange(country, date) %>%
tail(10)
```
### Linear scale
Using a linear scale it's easiest to see the relative scale of the worst
affected countries, but it's hard to distinguish the countries that were
affected later.
```{r}
dat %>%
prep_data_timeseries(
threshold = imported_threshold,
highlighted_countries = to_include
) %>%
plot(scale = "linear")
```
### Logarithmic scale
Using a logarithmic scale it's easier to discern whether the pandemic is still
in the exponential growth phase. On this scale, a straight line indicates
exponential growth.
```{r}
dat %>%
prep_data_timeseries(
threshold = imported_threshold,
highlighted_countries = to_include
) %>%
plot(scale = "log10")
```
## New cases
```{r new-cases, warning=FALSE}
get_covid19_data(type, aggregate = TRUE) %>%
arrange(country, date) %>%
group_by(country) %>%
mutate(new = n - lag(n)) %>%
filter(
n > 0,
country %in% "South Africa",
new >= 0
) %>%
ggplot(aes(x = date, y = new, group = country)) +
geom_line() +
geom_smooth(method = "gam", formula = y ~ s(x, bs = "ts"),
method.args = list(family = poisson, gamma = 4)) +
scale_x_date(date_breaks = "1 month", guide = guide_axis(n.dodge = 2)) +
ggtitle("New cases reported") +
xlab(NULL) +
ylab(NULL) +
facet_wrap(facets = "country", scales = "free")
```
```{r new-cases-plot}
get_covid19_data(type, aggregate = TRUE) %>%
arrange(country, date) %>%
group_by(country) %>%
mutate(new = n - lag(n)) %>%
filter(
n > 100,
country %in% to_include,
new >= 0
) %>%
ggplot(aes(x = date, y = new, group = country)) +
geom_line() +
geom_smooth(method = "gam", formula = y ~ s(x, bs = "ts"),
method.args = list(family = poisson, gamma = 4)) +
scale_x_date(date_breaks = "3 month", guide = guide_axis(n.dodge = 2)) +
ggtitle("New cases reported") +
xlab(NULL) +
ylab(NULL) +
facet_wrap(facets = "country", scales = "free")
```
## Regional distribution of Covid-19 cases
```{r}
dat %>%
prep_data_map() %>%
plot()
```
```{r, message = FALSE, eval=FALSE}
dat %>%
prep_data_map() %>%
plot() +
coord_map(xlim = c(-15, 35), ylim = c(35, 65), clip = "on", projection = "conic", 50)
```
```{r, message = FALSE, eval=FALSE}
dat %>%
prep_data_map() %>%
plot() +
coord_map(xlim = c(-30, 60), ylim = c(-35, 35))
```
## Total number of deaths
```{r}
type <- "deaths"
imported_threshold <- if (type == "cases") 30 else 10
dat <- get_covid19_data(type)
```
```{r}
dat %>%
filter(country == "South Africa") %>%
select(country, date, n) %>%
filter(n > 0) %>%
rename(deaths = n) %>%
arrange(country, date) %>%
tail(10)
```
### Linear scale
```{r}
dat %>%
prep_data_timeseries(
threshold = imported_threshold,
highlighted_countries = to_include
) %>%
plot(scale = "linear")
```
### Logarithmic scale
```{r}
dat %>%
prep_data_timeseries(
threshold = imported_threshold,
highlighted_countries = to_include
) %>%
plot(scale = "log10")
```
## New deaths
```{r, warning=FALSE}
get_covid19_data(type, aggregate = TRUE) %>%
arrange(country, date) %>%
group_by(country) %>%
mutate(new = n - lag(n)) %>%
filter(
n > 10,
country %in% to_include,
new >= 0
) %>%
# filter(country == "South Africa") %>%
# filter(country == "Italy") %>%
ggplot(aes(x = date, y = new, group = country)) +
geom_line() +
geom_smooth(method = "gam", formula = y ~ s(x, bs = "ts"), method.args = list(family = poisson, gamma = 4)) +
scale_x_date(date_breaks = "1 month", guide = guide_axis(n.dodge = 2)) +
ggtitle("New deaths reported") +
xlab(NULL) +
ylab(NULL) +
facet_wrap(facets = "country", scales = "free")
```
## Regional distribution of Covid-19 deaths
```{r}
dat %>%
prep_data_map() %>%
plot()
```
```{r, message = FALSE, eval=FALSE}
dat %>%
prep_data_map() %>%
plot() +
coord_map(xlim = c(-15, 35), ylim = c(35, 65), clip = "on", projection = "conic", 50)
```
```{r, message = FALSE, eval=FALSE}
dat %>%
prep_data_map() %>%
plot() +
coord_map(xlim = c(-30, 60), ylim = c(-35, 35))
```