Islam Asal Aug 31, 2024
knitr::opts_chunk$set(fig.path='Figs/')We load the necessary libraries for data manipulation (dplyr), date
handling (lubridate), spatial data handling (sf), mapping (ggmap),
data diagnosis (dlookr), and plotting (ggplot2, scales).
# Read the data
df <- read.csv('mpox.csv')
# Convert date column to Date type and adjust the date range
df$date <- as.Date(df$date, format="%m/%d/%Y")
# Diagnose the data
dlookr::diagnose(df)## # A tibble: 15 x 6
## variables types missing_count missing_percent unique_count unique_rate
## <chr> <chr> <int> <dbl> <int> <dbl>
## 1 location char~ 0 0 128 0.00150
## 2 date Date 0 0 848 0.00995
## 3 iso_code char~ 0 0 122 0.00143
## 4 total_cases inte~ 0 0 2799 0.0328
## 5 total_deaths inte~ 0 0 152 0.00178
## 6 new_cases inte~ 0 0 413 0.00484
## 7 new_deaths inte~ 0 0 10 0.000117
## 8 new_cases_smoot~ nume~ 0 0 764 0.00896
## 9 new_deaths_smoo~ nume~ 0 0 13 0.000152
## 10 new_cases_per_m~ nume~ 0 0 1182 0.0139
## 11 total_cases_per~ nume~ 0 0 21964 0.258
## 12 new_cases_smoot~ nume~ 0 0 740 0.00868
## 13 new_deaths_per_~ nume~ 0 0 151 0.00177
## 14 total_deaths_pe~ nume~ 0 0 5629 0.0660
## 15 new_deaths_smoo~ nume~ 0 0 107 0.00126
# Extract month name from the date
df$month <- month.name[month(df$date)]We read the data from a CSV file, convert the date column to Date
type, and extract month names from the date. We also perform data
diagnosis to identify potential issues.
# Top 10 countries by total cases
top_10_cases <- df %>%
group_by(location) %>%
summarise(total_cases = max(total_cases, na.rm = TRUE)) %>%
arrange(desc(total_cases)) %>%
slice(1:10)
# Plotting Top 10 Countries by Cases
ggplot(top_10_cases, aes(x = reorder(location, -total_cases), y = total_cases)) +
geom_bar(stat = "identity", fill = "#1f77b4") +
coord_flip() +
labs(title = "Top 10 Countries with the Highest Total Cases",
x = "Country", y = "Total Cases") +
theme_minimal(base_size = 15) +
theme(panel.grid = element_blank())
We identify and plot the top 10 countries with the highest total number of monkeypox cases, using a blue color for the bars and removing grid lines for a cleaner look.
# Top 10 countries by total deaths
top_10_deaths <- df %>%
group_by(location) %>%
summarise(total_deaths = max(total_deaths, na.rm = TRUE)) %>%
arrange(desc(total_deaths)) %>%
slice(1:10)
# Plotting Top 10 Countries by Deaths
ggplot(top_10_deaths, aes(x = reorder(location, -total_deaths), y = total_deaths)) +
geom_bar(stat = "identity", fill = "#d62728") +
coord_flip() +
labs(title = "Top 10 Countries with the Highest Total Deaths",
x = "Country", y = "Total Deaths") +
theme_minimal(base_size = 15) +
theme(panel.grid = element_blank())
Similarly, we plot the top 10 countries with the highest total deaths, using a red color for the bars.
# Convert date column to Date type and filter for 'World'
df$date <- as.Date(df$date)
global_cases <- df %>%
filter(location == "World") %>%
select(date, total_cases)
# Plot total cases over time
ggplot(global_cases, aes(x = date, y = total_cases)) +
geom_line(color = "#1f77b4") +
labs(title = "Change in Total Cases Worldwide Over Time",
x = "Date", y = "Total Cases") +
scale_x_date(labels = date_format("%b %Y")) +
theme_minimal(base_size = 15) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
This plot shows the total number of monkeypox cases worldwide over time. The line is colored dark blue, and the x-axis labels are angled for readability.
# Filtering data by continents
continents <- c('Africa', 'Asia', 'Europe', 'North America', 'South America', 'Oceania')
continent_cases <- df %>%
filter(location %in% continents) %>%
group_by(location) %>%
summarise(total_cases = max(total_cases, na.rm = TRUE))
# Plot total cases by continent
ggplot(continent_cases, aes(x = reorder(location, -total_cases), y = total_cases)) +
geom_bar(stat = "identity", fill = "#2ca02c") +
coord_flip() +
labs(title = "Total Cases by Continent",
x = "Continent", y = "Total Cases") +
theme_minimal(base_size = 15) +
theme(panel.grid = element_blank())
We plot total cases by continent, using a green color for the bars.
# Top 10 countries by case rate per million
cases_per_million <- df %>%
group_by(location) %>%
summarise(total_cases_per_million = max(total_cases_per_million, na.rm = TRUE)) %>%
arrange(desc(total_cases_per_million)) %>%
slice(1:10)
# Plot cases per million by country
ggplot(cases_per_million, aes(x = reorder(location, -total_cases_per_million), y = total_cases_per_million)) +
geom_bar(stat = "identity", fill = "#9467bd") +
coord_flip() +
labs(title = "Top 10 Countries by Case Rate per Million People",
x = "Country", y = "Cases per Million") +
theme_minimal(base_size = 15) +
theme(panel.grid = element_blank())
This plot highlights the top 10 countries based on the case rate per million people, with bars colored purple.
# Filter for Africa data
africa_data <- df %>%
filter(location == "Africa") %>%
select(date, total_cases, total_deaths) %>%
group_by(date) %>%
summarise(total_cases = sum(total_cases, na.rm = TRUE),
total_deaths = sum(total_deaths, na.rm = TRUE))
# Plot total cases and deaths over time for Africa
ggplot(africa_data, aes(x = date)) +
geom_line(aes(y = total_cases, color = "Total Cases"), size = 1) +
geom_line(aes(y = total_deaths, color = "Total Deaths"), size = 1) +
scale_color_manual(values = c("Total Cases" = "#1f77b4", "Total Deaths" = "#d62728")) +
labs(title = "Total Cases and Deaths in Africa Over Time",
x = "Date", y = "Count") +
scale_x_date(labels = date_format("%b %Y")) +
theme_minimal(base_size = 15) +
theme(legend.position = "bottom", axis.text.x = element_text(angle = 45, hjust = 1))
We plot the total number of cases and deaths in Africa over time, using blue for cases and red for deaths.
# Aggregating new cases and deaths per million
new_cases_deaths <- df %>%
group_by(date) %>%
summarise(new_cases_per_million = sum(new_cases_per_million, na.rm = TRUE),
new_deaths_per_million = sum(new_deaths_per_million, na.rm = TRUE))
# Plot new cases and deaths per million over time
ggplot(new_cases_deaths, aes(x = date)) +
geom_line(aes(y = new_cases_per_million, color = "New Cases per Million"), size = 1) +
geom_line(aes(y = new_deaths_per_million, color = "New Deaths per Million"), size = 1) +
scale_color_manual(values = c("New Cases per Million" = "#ff7f0e", "New Deaths per Million" = "#9467bd")) +
labs(title = "New Cases and Deaths per Million Population Over Time",
x = "Date", y = "Count per Million") +
scale_x_date(labels = date_format("%b %Y")) +
theme_minimal(base_size = 15) +
theme(legend.position = "bottom", axis.text.x = element_text(angle = 45, hjust = 1))
This plot visualizes new cases and deaths per million population over time, with distinct colors for each metric.
# Aggregating smoothed averages of new cases and deaths per million
smoothed_cases_deaths <- df %>%
group_by(date) %>%
summarise(new_cases_smoothed_per_million = sum(new_cases_smoothed_per_million, na.rm = TRUE),
new_deaths_smoothed_per_million = sum(new_deaths_smoothed_per_million, na.rm = TRUE))
# Plot smoothed averages of new cases and deaths per million over time
ggplot(smoothed_cases_deaths, aes(x = date)) +
geom_line(aes(y = new_cases_smoothed_per_million, color = "Smoothed New Cases per Million"), size = 1) +
geom_line(aes(y = new_deaths_smoothed_per_million, color = "Smoothed New Deaths per Million"), size = 1) +
scale_color_manual(values = c("Smoothed New Cases per Million" = "#2ca02c", "Smoothed New Deaths per Million" = "#8c564b")) +
labs(title = "Smoothed New Cases and Deaths per Million Population Over Time",
x = "Date", y = "Smoothed Count per Million") +
scale_x_date(labels = date_format("%b %Y")) +
theme_minimal(base_size = 15) +
theme(legend.position = "bottom", axis.text.x = element_text(angle = 45, hjust = 1))
This plot shows smoothed averages of new cases and deaths per million population, with green and brown colors.
# Read world map shapefile
world <- st_read("TM_WORLD_BORDERS-0.3.shp")## Reading layer `TM_WORLD_BORDERS-0.3' from data source
## `C:\Users\Esso\Desktop\R_Projects\Mpox\TM_WORLD_BORDERS-0.3.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 246 features and 11 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -180 ymin: -90 xmax: 180 ymax: 83.6236
## Geodetic CRS: WGS 84
# Join world map with data
world_data <- world %>%
left_join(df %>%
select(iso_code, total_cases_per_million) %>%
group_by(iso_code) %>%
summarise(total_cases_per_million = max(total_cases_per_million, na.rm = TRUE)),
by = c("iso3" = "iso_code"))
# Plot world map
ggplot(data = world_data) +
geom_sf(aes(fill = total_cases_per_million), color = "gray80", size = 0.3) +
scale_fill_viridis_c(option = "C", name = "Total Cases per Million",
breaks = scales::pretty_breaks(n = 5),
labels = scales::label_comma()) +
labs(title = "Distribution of Monkeypox Cases by Country",
subtitle = "Visualizing total monkeypox cases per million population across different countries.",
caption = "Data source: OWID Monkeypox Dataset\nDate range: 5 Jan 2022 to 25 Aug 2024\nAuthor: Islam Asal") +
theme_minimal(base_size = 14) +
theme(
legend.position = "bottom",
legend.title = element_text(size = 12),
legend.text = element_text(size = 10),
plot.title = element_text(size = 16, face = "bold"),
plot.subtitle = element_text(size = 14, face = "italic"),
plot.caption = element_text(size = 10, face = "italic"),
axis.text = element_blank(),
axis.title = element_blank(),
panel.grid = element_blank(),
panel.border = element_blank()
) +
coord_sf(expand = FALSE)
We create a world map showing the distribution of monkeypox cases by country. The map uses a color scale to represent cases per million, with a minimalist theme.
# Scatter plot: New Cases vs. Total Cases
ggplot(df, aes(x = total_cases, y = new_cases)) +
geom_point(alpha = 0.5, color = "#1f77b4") +
labs(title = "New Monkeypox Cases vs. Total Cases", x = "Total Cases", y = "New Cases") +
theme_minimal() +
theme(panel.grid = element_blank())
This scatter plot compares new monkeypox cases against total cases, with points colored blue and no grid lines.
# Scatter plot: New Cases Smoothed vs. New Deaths Smoothed
ggplot(df, aes(x = new_cases_smoothed_per_million, y = new_deaths_smoothed_per_million, color = new_cases_smoothed_per_million)) +
geom_point() +
scale_color_viridis_c() +
labs(title = "New Monkeypox Cases vs New Deaths per Million Population",
x = "New Cases per Million Population",
y = "New Deaths per Million Population",
color = "New Cases per Million Population") +
theme_minimal() +
theme(panel.grid = element_blank())
The final scatter plot shows the relationship between smoothed new monkeypox cases and smoothed new deaths per million population, using a color gradient and a minimalist theme.