R/world_map/world_map_bar_chart.R
In gabrielburcea/cvindia: Analysis of symptoms
library(tidyverse)
data <- read_csv("/Users/gabrielburcea/rprojects/data/your.md/cleaned_data_22092020_2nd_dataset.csv")
map_item <- ggplot2::map_data("world")
country_levels <- c(
"UK" = "United Kingdom",
"UK" = "Great Britain",
"USA" = "American Samoa",
"British Overseas Territory" = "Anguilla",
"Netherlands" = "Aruba",
"Cayman Islands" = "British Oversease Territory",
"British Overseas Territory" = "Bermuda",
"Netherlands" = "Bonaire",
"British Overseas Territory" = "British Indian Ocean Ter",
"Gibraltar" = "British Overseas Territory",
"Australia" = "Cocos Island",
"Canary Islands" = "Spain",
"Netherlands" = "Curaco",
"Denmark" = "Faroe Islands",
"British Overseas Territory" = "Isle of Man",
"France" = "Martinique",
"France" = "New Caledonia",
"USA" = "Guam",
"France" = "Mayotte",
"USA" = "Midway Islands",
"British Overseas Territory" = "Montserrat",
"Netherlands" = "Netherland Antilles",
"Australia" = "Norfolk Island",
"British Oversease Territory" = "Pitcairn Island",
"France" = "French Guiana",
"USA" = "United States of America",
"Congo" = "Zair",
"France" = "Wallis & Futana Is",
"USA" = "Virgin Islands (USA)",
"British Oversease Territory" = "Virgin Islands (Brit)",
"British Oversease Territory" = "Tuvalu",
"British Overseas Territory" = "Turks & Caicos Is",
"New Zeeland" = "Tokelau",
"Grenada" = "St Vincent & Grenadines",
"Netherlands" = "St Maarten",
"British Overseas Territory"= "St Lucia",
"British Overseas Territory" = "St Helena",
"Netherlands" = "St Eustatius",
"British Overseas Territory",
"USA" = "Saipan",
"USA" = "Palau Island",
"St Kitts-Nevis" = "Nevis",
"British Overseas Territory" = "Montserrat")
as.factor(data$country) %>% levels()
data <- data %>%
dplyr::mutate(country = forcats::fct_recode(country, !!!country_levels))
as.factor(map_item$region) %>% levels()
count_respondents <- data %>%
dplyr::select(id, country) %>%
dplyr::group_by(country) %>%
dplyr::summarise(count = n()) %>%
dplyr::mutate(percentage = count / sum(count)) %>%
dplyr::rename(Count = count) %>%
dplyr::arrange(Count)
philip_covid_count <- data %>%
dplyr::select(id, country, covid_tested) %>%
dplyr::filter(country == "Phillipines") %>%
dplyr::group_by(covid_tested, country) %>%
dplyr::tally()
brazil_covid_count <- data %>%
dplyr::select(id, country, covid_tested) %>%
dplyr::filter(country == "Brazil") %>%
dplyr::group_by(covid_tested, country) %>%
dplyr::tally()
map.world_joined <-
left_join(map_item, count_respondents, by = c('region' = 'country'))
# Set the title
#title_stub <- ": Count of respondent per country, SARS-COVID-19, "
# start_date_title <-
# format(as.Date(start_date), format = "%d %B %Y")
# end_date_title <- format(as.Date(end_date), format = "%d %B %Y")
#chart_title <- paste0(title, start_date_title, " to ", end_date_title)
# log the counts
map.world_joined$log <- log(map.world_joined$Count)
map <- ggplot2::ggplot(data = map.world_joined) +
ggplot2::geom_polygon(ggplot2::aes(
x = long,
y = lat,
group = group,
fill = log
)) +
#scale_fill_viridis_c(option = "C") +
#ggplot2::geom_sf(aes(fill = region, geometry = region)) +
#ggthemes::scale_fill_gradient2_tableau(palette = "Blue-Orange Diverging") +
scale_fill_gradient(low = "blue", high = "red") +
ggplot2::coord_equal() +
ggplot2::labs(y = "Latitude", x = "Longitude") +
theme_minimal() +
ggplot2::theme(
axis.text = element_text(size = 14),
axis.title = element_text(size = 14),
axis.title.y = ggplot2::element_text(margin = ggplot2::margin(
t = 0,
r = 10,
b = 0,
l = 0
)),
plot.title = ggplot2::element_text(size = 9, face = "bold"),
plot.subtitle = ggplot2::element_text(size = 10),
legend.box = "horizontal",
legend.title = element_text(size = 14),
legend.text = element_text(size = 10)
)
map
world_map_numbers <- cvindia::count_map_world(data = data, item = "world", start_date = "2020-04-09", end_date = "2020-09-01", plot_chart = FALSE, title = "World Map")
world_map_n <- world_map_numbers %>%
arrange(desc(percentage)) %>% top_n(30) %>%
rename(Country = country, Count = count, Percentage = percentage) %>%
dplyr::arrange(Percentage)
plot_countries <- ggplot2::ggplot(world_map_n, aes(x = reorder(Country, -Percentage), y = Percentage)) +
ggplot2::geom_bar(stat = "identity", fill = "brown4", width = 0.9) +
coord_flip() +
theme_minimal() +
ggplot2::labs(#title = "% of responders across countries",
y = "Percentage", x = "Countries") +
ggplot2::theme(
axis.text = element_text(size = 14),
axis.title = element_text(size = 16),
axis.title.y = ggplot2::element_text(margin = ggplot2::margin(t = 0, r = 10, b = 0, l = 0)),
plot.title = ggplot2::element_text(size = 17, face = "bold"),
plot.subtitle = ggplot2::element_text(size = 17),
legend.box = "horizontal")
plot_countries
count_data_no_na <- data %>%
group_by(covid_tested) %>%
tidyr::drop_na()
count_country <- count_data_no_na %>%
dplyr::select(id, country) %>%
dplyr::group_by(country) %>%
tally() %>%
dplyr::mutate(Percent = n/sum(n) *100) %>%
top_n(30)
plot_countries <- ggplot2::ggplot(count_country, aes(x = reorder(country, -Percent), y = Percent)) +
ggplot2::geom_bar(stat = "identity", fill = "brown4", width = 0.9) +
coord_flip() +
theme_minimal() +
ggplot2::labs(#title = "% of responders across countries",
y = "Percentage", x = "Countries") +
ggplot2::theme(
axis.text = element_text(size = 14),
axis.title = element_text(size = 16),
axis.title.y = ggplot2::element_text(margin = ggplot2::margin(t = 0, r = 10, b = 0, l = 0)),
plot.title = ggplot2::element_text(size = 17, face = "bold"),
plot.subtitle = ggplot2::element_text(size = 17),
legend.box = "horizontal")
plot_countries
library(ggpubr)
bar_chart_plot <- ggpubr::ggarrange(map, plot_countries)
annotate_figure(bar_chart_plot,
top = text_grob("Figure 1: World map with responders across countries and the top 30 countries with highest number of responders in % ", face = "bold", size = 14))
library(tidyverse)
data <- read_csv("/Users/gabrielburcea/rprojects/data/your.md/cleaned_data_22092020_2nd_dataset.csv")
map_item <- ggplot2::map_data("world")
country_levels <- c(
"United Kingdom" = "Great Britain",
"USA" = "American Samoa",
"British Overseas Territory" = "Anguilla",
"Netherlands" = "Aruba",
"Cayman Islands" = "British Oversease Territory",
"British Overseas Territory" = "Bermuda",
"Netherlands" = "Bonaire",
"British Overseas Territory" = "British Indian Ocean Ter",
"Gibraltar" = "British Overseas Territory",
"Australia" = "Cocos Island",
"Canary Islands" = "Spain",
"Netherlands" = "Curaco",
"Denmark" = "Faroe Islands",
"British Overseas Territory" = "Isle of Man",
"France" = "Martinique",
"France" = "New Caledonia",
"USA" = "Guam",
"France" = "Mayotte",
"USA" = "Midway Islands",
"British Overseas Territory" = "Montserrat",
"Netherlands" = "Netherland Antilles",
"Australia" = "Norfolk Island",
"British Oversease Territory" = "Pitcairn Island",
"France" = "French Guiana",
"USA" = "United States of America",
"Congo" = "Zair",
"France" = "Wallis & Futana Is",
"USA" = "Virgin Islands (USA)",
"British Oversease Territory" = "Virgin Islands (Brit)",
"British Oversease Territory" = "Tuvalu",
"British Overseas Territory" = "Turks & Caicos Is",
"New Zeeland" = "Tokelau",
"Grenada" = "St Vincent & Grenadines",
"Netherlands" = "St Maarten",
"British Overseas Territory"= "St Lucia",
"British Overseas Territory" = "St Helena",
"Netherlands" = "St Eustatius",
"British Overseas Territory",
"USA" = "Saipan",
"USA" = "Palau Island",
"St Kitts-Nevis" = "Nevis",
"British Overseas Territory" = "Montserrat")
as.factor(data$country) %>% levels()
data <- data %>%
dplyr::mutate(country = forcats::fct_recode(country, !!!country_levels))
as.factor(map_item$region) %>% levels()
count_respondents <- data %>%
dplyr::select(id, country, covid_tested) %>%
dplyr::filter(covid_tested == "positive") %>%
dplyr::group_by(country) %>%
dplyr::summarise(count = n()) %>%
dplyr::mutate(percentage = count / sum(count))%>%
dplyr::top_n(30)
plot_countries <- ggplot2::ggplot(count_respondents, aes(x = reorder(country, -count), y = count)) +
ggplot2::geom_bar(stat = "identity", fill = "brown4", width = 0.9) +
coord_flip() +
theme_minimal() +
ggplot2::labs(#title = "% of responders across countries",
y = "Percentage", x = "Countries") +
ggplot2::theme(
axis.text = element_text(size = 14),
axis.title = element_text(size = 16),
axis.title.y = ggplot2::element_text(margin = ggplot2::margin(t = 0, r = 10, b = 0, l = 0)),
plot.title = ggplot2::element_text(size = 17, face = "bold"),
plot.subtitle = ggplot2::element_text(size = 17),
legend.box = "horizontal")
plot_countries
gabrielburcea/cvindia documentation built on Feb. 17, 2021, 3:31 a.m.
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
library(tidyverse)
data <- read_csv("/Users/gabrielburcea/rprojects/data/your.md/cleaned_data_22092020_2nd_dataset.csv")
map_item <- ggplot2::map_data("world")
country_levels <- c(
"UK" = "United Kingdom",
"UK" = "Great Britain",
"USA" = "American Samoa",
"British Overseas Territory" = "Anguilla",
"Netherlands" = "Aruba",
"Cayman Islands" = "British Oversease Territory",
"British Overseas Territory" = "Bermuda",
"Netherlands" = "Bonaire",
"British Overseas Territory" = "British Indian Ocean Ter",
"Gibraltar" = "British Overseas Territory",
"Australia" = "Cocos Island",
"Canary Islands" = "Spain",
"Netherlands" = "Curaco",
"Denmark" = "Faroe Islands",
"British Overseas Territory" = "Isle of Man",
"France" = "Martinique",
"France" = "New Caledonia",
"USA" = "Guam",
"France" = "Mayotte",
"USA" = "Midway Islands",
"British Overseas Territory" = "Montserrat",
"Netherlands" = "Netherland Antilles",
"Australia" = "Norfolk Island",
"British Oversease Territory" = "Pitcairn Island",
"France" = "French Guiana",
"USA" = "United States of America",
"Congo" = "Zair",
"France" = "Wallis & Futana Is",
"USA" = "Virgin Islands (USA)",
"British Oversease Territory" = "Virgin Islands (Brit)",
"British Oversease Territory" = "Tuvalu",
"British Overseas Territory" = "Turks & Caicos Is",
"New Zeeland" = "Tokelau",
"Grenada" = "St Vincent & Grenadines",
"Netherlands" = "St Maarten",
"British Overseas Territory"= "St Lucia",
"British Overseas Territory" = "St Helena",
"Netherlands" = "St Eustatius",
"British Overseas Territory",
"USA" = "Saipan",
"USA" = "Palau Island",
"St Kitts-Nevis" = "Nevis",
"British Overseas Territory" = "Montserrat")
as.factor(data$country) %>% levels()
data <- data %>%
dplyr::mutate(country = forcats::fct_recode(country, !!!country_levels))
as.factor(map_item$region) %>% levels()
count_respondents <- data %>%
dplyr::select(id, country) %>%
dplyr::group_by(country) %>%
dplyr::summarise(count = n()) %>%
dplyr::mutate(percentage = count / sum(count)) %>%
dplyr::rename(Count = count) %>%
dplyr::arrange(Count)
philip_covid_count <- data %>%
dplyr::select(id, country, covid_tested) %>%
dplyr::filter(country == "Phillipines") %>%
dplyr::group_by(covid_tested, country) %>%
dplyr::tally()
brazil_covid_count <- data %>%
dplyr::select(id, country, covid_tested) %>%
dplyr::filter(country == "Brazil") %>%
dplyr::group_by(covid_tested, country) %>%
dplyr::tally()
map.world_joined <-
left_join(map_item, count_respondents, by = c('region' = 'country'))
# Set the title
#title_stub <- ": Count of respondent per country, SARS-COVID-19, "
# start_date_title <-
# format(as.Date(start_date), format = "%d %B %Y")
# end_date_title <- format(as.Date(end_date), format = "%d %B %Y")
#chart_title <- paste0(title, start_date_title, " to ", end_date_title)
# log the counts
map.world_joined$log <- log(map.world_joined$Count)
map <- ggplot2::ggplot(data = map.world_joined) +
ggplot2::geom_polygon(ggplot2::aes(
x = long,
y = lat,
group = group,
fill = log
)) +
#scale_fill_viridis_c(option = "C") +
#ggplot2::geom_sf(aes(fill = region, geometry = region)) +
#ggthemes::scale_fill_gradient2_tableau(palette = "Blue-Orange Diverging") +
scale_fill_gradient(low = "blue", high = "red") +
ggplot2::coord_equal() +
ggplot2::labs(y = "Latitude", x = "Longitude") +
theme_minimal() +
ggplot2::theme(
axis.text = element_text(size = 14),
axis.title = element_text(size = 14),
axis.title.y = ggplot2::element_text(margin = ggplot2::margin(
t = 0,
r = 10,
b = 0,
l = 0
)),
plot.title = ggplot2::element_text(size = 9, face = "bold"),
plot.subtitle = ggplot2::element_text(size = 10),
legend.box = "horizontal",
legend.title = element_text(size = 14),
legend.text = element_text(size = 10)
)
map
world_map_numbers <- cvindia::count_map_world(data = data, item = "world", start_date = "2020-04-09", end_date = "2020-09-01", plot_chart = FALSE, title = "World Map")
world_map_n <- world_map_numbers %>%
arrange(desc(percentage)) %>% top_n(30) %>%
rename(Country = country, Count = count, Percentage = percentage) %>%
dplyr::arrange(Percentage)
plot_countries <- ggplot2::ggplot(world_map_n, aes(x = reorder(Country, -Percentage), y = Percentage)) +
ggplot2::geom_bar(stat = "identity", fill = "brown4", width = 0.9) +
coord_flip() +
theme_minimal() +
ggplot2::labs(#title = "% of responders across countries",
y = "Percentage", x = "Countries") +
ggplot2::theme(
axis.text = element_text(size = 14),
axis.title = element_text(size = 16),
axis.title.y = ggplot2::element_text(margin = ggplot2::margin(t = 0, r = 10, b = 0, l = 0)),
plot.title = ggplot2::element_text(size = 17, face = "bold"),
plot.subtitle = ggplot2::element_text(size = 17),
legend.box = "horizontal")
plot_countries
count_data_no_na <- data %>%
group_by(covid_tested) %>%
tidyr::drop_na()
count_country <- count_data_no_na %>%
dplyr::select(id, country) %>%
dplyr::group_by(country) %>%
tally() %>%
dplyr::mutate(Percent = n/sum(n) *100) %>%
top_n(30)
plot_countries <- ggplot2::ggplot(count_country, aes(x = reorder(country, -Percent), y = Percent)) +
ggplot2::geom_bar(stat = "identity", fill = "brown4", width = 0.9) +
coord_flip() +
theme_minimal() +
ggplot2::labs(#title = "% of responders across countries",
y = "Percentage", x = "Countries") +
ggplot2::theme(
axis.text = element_text(size = 14),
axis.title = element_text(size = 16),
axis.title.y = ggplot2::element_text(margin = ggplot2::margin(t = 0, r = 10, b = 0, l = 0)),
plot.title = ggplot2::element_text(size = 17, face = "bold"),
plot.subtitle = ggplot2::element_text(size = 17),
legend.box = "horizontal")
plot_countries
library(ggpubr)
bar_chart_plot <- ggpubr::ggarrange(map, plot_countries)
annotate_figure(bar_chart_plot,
top = text_grob("Figure 1: World map with responders across countries and the top 30 countries with highest number of responders in % ", face = "bold", size = 14))
library(tidyverse)
data <- read_csv("/Users/gabrielburcea/rprojects/data/your.md/cleaned_data_22092020_2nd_dataset.csv")
map_item <- ggplot2::map_data("world")
country_levels <- c(
"United Kingdom" = "Great Britain",
"USA" = "American Samoa",
"British Overseas Territory" = "Anguilla",
"Netherlands" = "Aruba",
"Cayman Islands" = "British Oversease Territory",
"British Overseas Territory" = "Bermuda",
"Netherlands" = "Bonaire",
"British Overseas Territory" = "British Indian Ocean Ter",
"Gibraltar" = "British Overseas Territory",
"Australia" = "Cocos Island",
"Canary Islands" = "Spain",
"Netherlands" = "Curaco",
"Denmark" = "Faroe Islands",
"British Overseas Territory" = "Isle of Man",
"France" = "Martinique",
"France" = "New Caledonia",
"USA" = "Guam",
"France" = "Mayotte",
"USA" = "Midway Islands",
"British Overseas Territory" = "Montserrat",
"Netherlands" = "Netherland Antilles",
"Australia" = "Norfolk Island",
"British Oversease Territory" = "Pitcairn Island",
"France" = "French Guiana",
"USA" = "United States of America",
"Congo" = "Zair",
"France" = "Wallis & Futana Is",
"USA" = "Virgin Islands (USA)",
"British Oversease Territory" = "Virgin Islands (Brit)",
"British Oversease Territory" = "Tuvalu",
"British Overseas Territory" = "Turks & Caicos Is",
"New Zeeland" = "Tokelau",
"Grenada" = "St Vincent & Grenadines",
"Netherlands" = "St Maarten",
"British Overseas Territory"= "St Lucia",
"British Overseas Territory" = "St Helena",
"Netherlands" = "St Eustatius",
"British Overseas Territory",
"USA" = "Saipan",
"USA" = "Palau Island",
"St Kitts-Nevis" = "Nevis",
"British Overseas Territory" = "Montserrat")
as.factor(data$country) %>% levels()
data <- data %>%
dplyr::mutate(country = forcats::fct_recode(country, !!!country_levels))
as.factor(map_item$region) %>% levels()
count_respondents <- data %>%
dplyr::select(id, country, covid_tested) %>%
dplyr::filter(covid_tested == "positive") %>%
dplyr::group_by(country) %>%
dplyr::summarise(count = n()) %>%
dplyr::mutate(percentage = count / sum(count))%>%
dplyr::top_n(30)
plot_countries <- ggplot2::ggplot(count_respondents, aes(x = reorder(country, -count), y = count)) +
ggplot2::geom_bar(stat = "identity", fill = "brown4", width = 0.9) +
coord_flip() +
theme_minimal() +
ggplot2::labs(#title = "% of responders across countries",
y = "Percentage", x = "Countries") +
ggplot2::theme(
axis.text = element_text(size = 14),
axis.title = element_text(size = 16),
axis.title.y = ggplot2::element_text(margin = ggplot2::margin(t = 0, r = 10, b = 0, l = 0)),
plot.title = ggplot2::element_text(size = 17, face = "bold"),
plot.subtitle = ggplot2::element_text(size = 17),
legend.box = "horizontal")
plot_countries
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