Grafik Sederhana Covid-19
8 min readJun 21, 2020
Assalamu’alaikum Wr. Wb
Semoga kita semua masih diberi kesehatan ditengah pandemi Covid-19 yang sedang terjadi sekarang. Untuk kesempatan kali ini kita akan sama-sama belajar membuat grafik sederhana Covid-19 menggunakan RStudio.
Hasil akhir yang akan didapat ialah sebagai berikut:
Langkah pertama setelah membuka program Rstudio ialah file > New File > R Markdown..
Maka akan muncul seperti berikut,
Isilah Title, Author dan pilih HTML sebagai Default Output format lalu klik ‘Ok’
Maka setelahnya akan muncul tampilan sebagai berikut
Untuk sementara biarkan seperti itu, kemudian kita akan mengambil coding dari https://github.com/AntoineSoetewey/coronavirus_dashboard dimana yang akan kita ambil ialah file coronavirus-dashboard.Rmd
Dan setelah sedikit perubahan untuk penyesuaian negara Indonesia maka didapat hasil coding sebagai berikut,
---
title: "Grafik Covid-19"
author: "Venti Diah Intiari"
output:
flexdashboard::flex_dashboard:
orientation: rows
# social: ["facebook", "twitter", "linkedin"]
source_code: embed
vertical_layout: fill
---
```{r setup, include=FALSE}
library(flexdashboard)
# install.packages("devtools")
# devtools::install_github("RamiKrispin/coronavirus", force = TRUE)
library(coronavirus)
data(coronavirus)
update_dataset()
# View(coronavirus)
# max(coronavirus$date)
`%>%` <- magrittr::`%>%`
#------------------ Parameters ------------------
# Set colors
# https://www.w3.org/TR/css-color-3/#svg-color
confirmed_color <- "purple"
active_color <- "#1f77b4"
recovered_color <- "forestgreen"
death_color <- "red"
#------------------ Data ------------------
df <- coronavirus %>%
# dplyr::filter(date == max(date)) %>%
dplyr::filter(country == "Indonesia") %>%
dplyr::group_by(country, type) %>%
dplyr::summarise(total = sum(cases)) %>%
tidyr::pivot_wider(
names_from = type,
values_from = total
) %>%
# dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
dplyr::mutate(unrecovered = confirmed - ifelse(is.na(death), 0, death)) %>%
dplyr::arrange(-confirmed) %>%
dplyr::ungroup() %>%
dplyr::mutate(country = dplyr::if_else(country == "United Arab Emirates", "UAE", country)) %>%
dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
dplyr::mutate(country = trimws(country)) %>%
dplyr::mutate(country = factor(country, levels = country))
df_daily <- coronavirus %>%
dplyr::filter(country == "Indonesia") %>%
dplyr::group_by(date, type) %>%
dplyr::summarise(total = sum(cases, na.rm = TRUE)) %>%
tidyr::pivot_wider(
names_from = type,
values_from = total
) %>%
dplyr::arrange(date) %>%
dplyr::ungroup() %>%
#dplyr::mutate(active = confirmed - death - recovered) %>%
dplyr::mutate(active = confirmed - death) %>%
dplyr::mutate(
confirmed_cum = cumsum(confirmed),
death_cum = cumsum(death),
# recovered_cum = cumsum(recovered),
active_cum = cumsum(active)
)
df1 <- coronavirus %>% dplyr::filter(date == max(date))
```
Summary
=======================================================================
Row {data-width=400}
-----------------------------------------------------------------------
### confirmed {.value-box}
```{r}
valueBox(
value = paste(format(sum(df$confirmed), big.mark = ","), "", sep = " "),
caption = "Total confirmed cases",
icon = "fas fa-user-md",
color = confirmed_color
)
```
<!-- ### active {.value-box} -->
<!-- ```{r} -->
<!-- valueBox( -->
<!-- value = paste(format(sum(df$unrecovered, na.rm = TRUE), big.mark = ","), " (", -->
<!-- round(100 * sum(df$unrecovered, na.rm = TRUE) / sum(df$confirmed), 1), -->
<!-- "%)", -->
<!-- sep = "" -->
<!-- ), -->
<!-- caption = "Active cases (% of total cases)", icon = "fas fa-ambulance", -->
<!-- color = active_color -->
<!-- ) -->
<!-- ``` -->
### death {.value-box}
```{r}
valueBox(
value = paste(format(sum(df$death, na.rm = TRUE), big.mark = ","), " (",
round(100 * sum(df$death, na.rm = TRUE) / sum(df$confirmed), 1),
"%)",
sep = ""
),
caption = "Death cases (death rate)",
icon = "fas fa-heart-broken",
color = death_color
)
```
Row
-----------------------------------------------------------------------
### **Daily cumulative cases by type** (Indonesia only)
```{r}
plotly::plot_ly(data = df_daily) %>%
plotly::add_trace(
x = ~date,
# y = ~active_cum,
y = ~confirmed_cum,
type = "scatter",
mode = "lines+markers",
# name = "Active",
name = "Confirmed",
line = list(color = active_color),
marker = list(color = active_color)
) %>%
plotly::add_trace(
x = ~date,
y = ~death_cum,
type = "scatter",
mode = "lines+markers",
name = "Death",
line = list(color = death_color),
marker = list(color = death_color)
) %>%
plotly::add_annotations(
x = as.Date("2020-03-02"),
y = 1,
text = paste("First case"),
xref = "x",
yref = "y",
arrowhead = 5,
arrowhead = 3,
arrowsize = 1,
showarrow = TRUE,
ax = -10,
ay = -90
) %>%
plotly::add_annotations(
x = as.Date("2020-03-11"),
y = 1,
text = paste("First death"),
xref = "x",
yref = "y",
arrowhead = 5,
arrowhead = 3,
arrowsize = 1,
showarrow = TRUE,
ax = -10,
ay = -90
) %>%
plotly::layout(
title = "",
yaxis = list(title = "Cumulative number of cases"),
xaxis = list(title = "Date"),
legend = list(x = 0.1, y = 0.9),
hovermode = "compare"
)
```
Comparison
=======================================================================
Column {data-width=400}
-------------------------------------
### **Daily new confirmed cases**
```{r}
daily_confirmed <- coronavirus %>%
dplyr::filter(type == "confirmed") %>%
dplyr::filter(date >= "2020-02-29") %>%
dplyr::mutate(country = country) %>%
dplyr::group_by(date, country) %>%
dplyr::summarise(total = sum(cases)) %>%
dplyr::ungroup() %>%
tidyr::pivot_wider(names_from = country, values_from = total)
#----------------------------------------
# Plotting the data
daily_confirmed %>%
plotly::plot_ly() %>%
plotly::add_trace(
x = ~date,
y = ~Indonesia,
type = "scatter",
mode = "lines+markers",
name = "Indonesia"
) %>%
plotly::add_trace(
x = ~date,
y = ~Malaysia,
type = "scatter",
mode = "lines+markers",
name = "Malaysia"
) %>%
plotly::add_trace(
x = ~date,
y = ~Singapore,
type = "scatter",
mode = "lines+markers",
name = "Singapore"
) %>%
plotly::add_trace(
x = ~date,
y = ~Thailand,
type = "scatter",
mode = "lines+markers",
name = "Thailand"
) %>%
plotly::layout(
title = "",
legend = list(x = 0.1, y = 0.9),
yaxis = list(title = "Number of new confirmed cases"),
xaxis = list(title = "Date"),
# paper_bgcolor = "black",
# plot_bgcolor = "black",
# font = list(color = 'white'),
hovermode = "compare",
margin = list(
# l = 60,
# r = 40,
b = 10,
t = 10,
pad = 2
)
)
```
### **Cases distribution by type**
```{r daily_summary}
df_EU <- coronavirus %>%
# dplyr::filter(date == max(date)) %>%
dplyr::filter(country == "Indonesia" |
country == "Malaysia" |
country == "Singapore" |
country == "Thailand") %>%
dplyr::group_by(country, type) %>%
dplyr::summarise(total = sum(cases)) %>%
tidyr::pivot_wider(
names_from = type,
values_from = total
) %>%
# dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
dplyr::mutate(unrecovered = confirmed - ifelse(is.na(death), 0, death)) %>%
dplyr::arrange(confirmed) %>%
dplyr::ungroup() %>%
dplyr::mutate(country = dplyr::if_else(country == "United Arab Emirates", "UAE", country)) %>%
dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
dplyr::mutate(country = trimws(country)) %>%
dplyr::mutate(country = factor(country, levels = country))
plotly::plot_ly(
data = df_EU,
x = ~country,
# y = ~unrecovered,
y = ~ confirmed,
# text = ~ confirmed,
# textposition = 'auto',
type = "bar",
name = "Confirmed",
marker = list(color = active_color)
) %>%
plotly::add_trace(
y = ~death,
# text = ~ death,
# textposition = 'auto',
name = "Death",
marker = list(color = death_color)
) %>%
plotly::layout(
barmode = "stack",
yaxis = list(title = "Total cases"),
xaxis = list(title = ""),
hovermode = "compare",
margin = list(
# l = 60,
# r = 40,
b = 10,
t = 10,
pad = 2
)
)
```
Map
=======================================================================
### **World map of cases** (*use + and - icons to zoom in/out*)
```{r}
# map tab added by Art Steinmetz
library(leaflet)
library(leafpop)
library(purrr)
cv_data_for_plot <- coronavirus %>%
# dplyr::filter(country == "Indonesia") %>%
dplyr::filter(cases > 0) %>%
dplyr::group_by(country, province, lat, long, type) %>%
dplyr::summarise(cases = sum(cases)) %>%
dplyr::mutate(log_cases = 2 * log(cases)) %>%
dplyr::ungroup()
cv_data_for_plot.split <- cv_data_for_plot %>% split(cv_data_for_plot$type)
pal <- colorFactor(c("orange", "red", "green"), domain = c("confirmed", "death", "recovered"))
map_object <- leaflet() %>% addProviderTiles(providers$Stamen.Toner)
names(cv_data_for_plot.split) %>%
purrr::walk(function(df) {
map_object <<- map_object %>%
addCircleMarkers(
data = cv_data_for_plot.split[[df]],
lng = ~long, lat = ~lat,
# label=~as.character(cases),
color = ~ pal(type),
stroke = FALSE,
fillOpacity = 0.8,
radius = ~log_cases,
popup = leafpop::popupTable(cv_data_for_plot.split[[df]],
feature.id = FALSE,
row.numbers = FALSE,
zcol = c("type", "cases", "country", "province")
),
group = df,
# clusterOptions = markerClusterOptions(removeOutsideVisibleBounds = F),
labelOptions = labelOptions(
noHide = F,
direction = "auto"
)
)
})
map_object %>%
addLayersControl(
overlayGroups = names(cv_data_for_plot.split),
options = layersControlOptions(collapsed = FALSE)
)
```
About
=======================================================================
**The Coronavirus Dashboard: the case of Indonesia**
This [Coronavirus dashboard: the case of Indonesia](https://www.antoinesoetewey.com/files/coronavirus-dashboard.html) provides an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic for Indonesia. This dashboard is built with R using the R Makrdown framework and was adapted from this [dashboard](https://ramikrispin.github.io/coronavirus_dashboard/){target="_blank"} by Rami Krispin.
**Code**
The code behind this dashboard is available on [GitHub](https://github.com/AntoineSoetewey/coronavirus_dashboard){target="_blank"}.
**Data**
The input data for this dashboard is the dataset available from the [`{coronavirus}`](https://github.com/RamiKrispin/coronavirus){target="_blank"} R package. Make sure to download the development version of the package to have the latest data:
```
install.packages("devtools")
devtools::install_github("RamiKrispin/coronavirus")
```
The data and dashboard are refreshed on a daily basis.
The raw data is pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus [repository](https://github.com/RamiKrispin/coronavirus-csv){target="_blank"}.
**Information and contact**
More information about this dashboard and how to replicate it for your own country can be found in this [article](https://www.statsandr.com/blog/how-to-create-a-simple-coronavirus-dashboard-specific-to-your-country-in-r/).
For any question or feedback, you can [contact me](https://www.statsandr.com/contact/).
**Update**
The data is as of `r format(max(coronavirus$date), "%A %B %d, %Y")` and the dashboard has been updated on `r format(Sys.time(), "%A %B %d, %Y")`.
<br>Kemudian, kembali pada Rstudio lalu copylah coding tersebut menggantikan coding sebelumnya,
Langkah selanjutnya ialah menginstall package berikut,
install.packages(c("devtools", "flexdashboard", "leaflet", "leafpop")Tulislah syntax tersebut pada console,
Setelah terinstall maka akan menampilkan hasil seperti ini,
Selanjutnya, kita harus menginstall package ‘coronavirus’ yang telah dibuat oleh Rami Krispin pada https://github.com/RamiKrispin/coronavirus menggunakan syntax berikut,
devtools::install_github("RamiKrispin/coronavirus")
Jika telah berhasil diinstall maka tampilannya seperti berikut,
Selanjutnya, klik ‘knit’ yang berada pada kiri atas
Kemudian, kita diminta untuk menyimpan file terlebih dahulu simpanlah dengan nama yang sesuai
Tunggu proses pembuatan grafik sejenak, dan jika berhasil akan menghasilkan grafik seperti berikut,
Jadilah grafik jumlah pasien terkonfirmasi covid-19, jika ingin dibandingkan dengan negara lain maka bisa klik ‘comparison’
Dan juga terdapat peta penyebaran covid-19,
Maka itulah cara untuk membuat grafik sederhana penyebaran covid-19 yang ada di Indonesia maupun membandingkan dengan negara lain.
Semoga dapat bermanfaat dengan baik.
Referensi:
