R/comorbiditiy_symptoms_patched/cmrbd_sympt_patched_plots.R
In gabrielburcea/cvindia: Analysis of symptoms
cleaned_data_22092020 <- readr::read_csv("/Users/gabrielburcea/rprojects/data/your.md/cleaned_data_22092020_2nd_dataset.csv")
cleaned_data_22092020 <- cleaned_data_22092020 %>%
dplyr::mutate(age_band = dplyr::case_when(
age == 0 | age <= 9 ~ '0-9',
age == 10 | age <= 19 ~ '10-19',
age == 20 | age <= 29 ~ '20-29',
age == 30 | age <= 39 ~ '30-39',
age == 40 | age <= 49 ~ '40-49',
age == 50 | age <= 59 ~ '50-59',
age >= 60 ~ "60+"))
cleaned_data_22092020 <- cleaned_data_22092020 %>%
dplyr::group_by(covid_tested) %>%
drop_na()
cleaned_data_22092020 %>%
dplyr::group_by(covid_tested) %>%
tally() %>%
dplyr::mutate(Percent = n/sum(n)*100)
country_covid_tested <- cleaned_data_22092020 %>%
dplyr::group_by(country) %>%
tally() %>%
dplyr::mutate(Percent = n/sum(n)*100)
count_symptoms_positive <- cleaned_data_22092020 %>%
dplyr::select(id, Country, age_band, chills, cough, diarrhoea, fatigue, headache, muscle_ache, nasal_congestion, nausea_vomiting, shortness_breath,
sore_throat, sputum, temperature, loss_appetite, sneezing, chest_pain, itchy_eyes, joint_pain, covid_tested) %>%
dplyr::filter(Country == "Brazil" | Country == "India" | Country == "Pakistan" | Country == "Mexico" | Country == "United Kingdom") %>%
dplyr::filter(covid_tested == "positive") %>%
tidyr::pivot_longer(cols = 4:20,
names_to = "Symptoms",
values_to = "bolean_yn_sympt") %>%
dplyr::filter(bolean_yn_sympt == "Yes") %>%
dplyr::rename(country = Country) %>%
dplyr::group_by(country, age_band, Symptoms) %>%
dplyr::tally() %>%
dplyr::rename(Count = n) %>%
dplyr::mutate(Percent = Count/sum(Count) *100)
count_symptoms_positive_all_countries <- cleaned_data_22092020 %>%
dplyr::select(id, Country, age_band, chills, cough, diarrhoea, fatigue, headache, muscle_ache, nasal_congestion, nausea_vomiting, shortness_breath,
sore_throat, sputum, temperature, loss_appetite, sneezing, chest_pain, itchy_eyes, joint_pain, covid_tested) %>%
dplyr::filter(Country != "Brazil" ) %>%
dplyr::filter(Country != "India") %>%
dplyr::filter(Country != "Pakistan") %>%
dplyr::filter(Country != "Mexico") %>%
dplyr::filter(Country != "United Kingdom") %>%
dplyr::filter(covid_tested == "positive") %>%
tidyr::pivot_longer(cols = 4:20,
names_to = "Symptoms",
values_to = "bolean_yn_sympt") %>%
dplyr::filter(bolean_yn_sympt == "Yes") %>%
add_column(country = c("All countries")) %>%
dplyr::group_by(country, age_band, Symptoms) %>%
dplyr::tally() %>%
dplyr::rename(Count = n) %>%
dplyr::mutate(Percent = Count/sum(Count) *100) %>%
dplyr::select(country, Symptoms, country, Count, Percent)
count_symptoms_positive_add <- full_join(count_symptoms_positive_all_countries, count_symptoms_positive)
write.csv(count_symptoms_positive_add, file = "/Users/gabrielburcea/rprojects/data/stack_overflow/count_symptoms_showing_symptoms_add.csv")
symptom_levels <- c(
"muscle ache" = "muscle_ache",
"nasal congestion" = "nasal_congestion",
"nausea and vomiting" = "nausea_vomiting",
"sore throat" = "sore_throat",
"loss of appetite" = "loss_appetite",
"chest pain" = "chest_pain",
"itchy eyes" = "itchy_eyes",
"joint pain" = "joint_pain"
)
count_symptoms_positive_add <- count_symptoms_positive_add %>%
dplyr::mutate(Symptoms = forcats::fct_recode(Symptoms, !!!symptom_levels))
sympt_count_plot <- ggplot2::ggplot(count_symptoms_positive_add, ggplot2::aes(x = age_band, y = Count, group = Symptoms, fill = Symptoms)) +
ggplot2::geom_area( color = "white") +
ggplot2::scale_x_discrete(limits = c( "0-9", "10-19", "20-29", "30-39", "40-49", "50-59",
"60+"), expand = c(0, 0)) +
ggplot2::scale_fill_viridis_d() +
#ggplot2::scale_y_continuous(breaks = c(seq(0,1000,by=100),seq(2100,3000,by=100))) +
ggplot2::labs(#title = "% of responders across countries",
y = "Count", x = "Age band") +
theme(
axis.text = element_text(size = 14),
axis.title = element_text(size = 16),
plot.title = ggplot2::element_text(size = 17, face = "bold"),
plot.subtitle = ggplot2::element_text(size = 17),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
strip.background = element_blank(),
strip.text = element_text(size = 10, face = "bold", hjust = 0),
legend.title = element_text(size = 16),
legend.text = element_text(size = 13)) +
ggplot2::facet_wrap(~country, ncol = 1, scale = "free_y")
sympt_count_plot
count_comorbidities_positive <- cleaned_data_22092020 %>%
dplyr::select(id, Country, age_band,asthma, diabetes_type_one, diabetes_type_two,
obesity, hypertension, heart_disease, lung_condition, liver_disease, kidney_disease, covid_tested) %>%
dplyr::filter(Country == "Brazil" | Country == "India" | Country == "Pakistan" | Country == "Mexico" | Country == "United Kingdom") %>%
tidyr::pivot_longer(cols = 4:12,
names_to = "Comorbidities",
values_to = "bolean_yn_comorb") %>%
dplyr::filter(bolean_yn_comorb == "Yes") %>%
dplyr::group_by(Country, age_band, Comorbidities) %>%
dplyr::tally() %>%
dplyr::rename(Count = n) %>%
dplyr::mutate(Percent = Count/sum(Count) *100) %>%
dplyr::rename(country = Country)
count_comorbidities_positive_all_countries <- cleaned_data_22092020 %>%
dplyr::select(id, Country, age_band, asthma, diabetes_type_one, diabetes_type_two,
obesity, hypertension, heart_disease, lung_condition, liver_disease, kidney_disease, covid_tested) %>%
dplyr::filter(Country != "Brazil" ) %>%
dplyr::filter(Country != "India") %>%
dplyr::filter(Country != "Pakistan") %>%
dplyr::filter(Country != "Mexico") %>%
dplyr::filter(Country != "United Kingdom") %>%
dplyr::filter(covid_tested == "positive") %>%
tidyr::pivot_longer(cols = 4:12,
names_to = "Comorbidities",
values_to = "bolean_yn_comorb") %>%
dplyr::filter(bolean_yn_comorb == "Yes") %>%
add_column(country = c("All countries")) %>%
dplyr::group_by(country, age_band, Comorbidities) %>%
dplyr::tally() %>%
dplyr::rename(Count = n) %>%
dplyr::mutate(Percent = Count/sum(Count) *100) %>%
dplyr::select(country, Comorbidities, country, Count, Percent)
count_comorbidities_positive_add <- full_join(count_comorbidities_positive, count_comorbidities_positive_all_countries)
comorbidities_levels <- c(
"heart disease" = "heart_disease",
"lung disease" = "lung_disease",
"liver disease" = "liver_disease",
"kidney disease" = "kidney_disease"
)
count_comorbidities_positive_add <- count_comorbidities_positive_add %>%
dplyr::mutate(Comorbidities = forcats::fct_recode(Comorbidities, !!!comorbidities_levels))
# sympt_percent_plot <- ggplot(count_symptoms_positive) +
# geom_area(aes(x = age_band, y = Percent, group = Symptoms, fill = Symptoms),
# color = "white") +
# scale_x_discrete(limits = c( "0-19" ,"20-39", "40-59","60+"), expand = c(0, 0)) +
# scale_y_continuous(expand = expansion(mult = c(0, 0.1))) +
# scale_fill_viridis_d() +
# theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
# strip.background = element_blank(),
# strip.text = element_text(size = 10, face = "bold", color = "white")) +
# facet_wrap(~Country, ncol = 1)
# sympt_percent_plot
comorb_count_plot <- ggplot2::ggplot(count_comorbidities_positive_add, ggplot2::aes(age_band, Count, group = Comorbidities, fill = Comorbidities)) +
ggplot2::geom_area( color = "white") +
scale_x_discrete(limits = c("0-9", "10-19", "20-29", "30-39", "40-49", "50-59",
"60+"), expand = c(0, 0)) +
#ggplot2::scale_y_continuous(breaks = c(0, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000)) +
scale_fill_brewer(palette = "Set1") +
ggplot2::labs(#title = "% of responders across countries",
y = "Count", x = "Age band") +
theme(
axis.text = element_text(size = 14),
axis.title = element_text(size = 16),
plot.title = ggplot2::element_text(size = 17, face = "bold"),
plot.subtitle = ggplot2::element_text(size = 17),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
strip.background = element_blank(),
strip.text = element_text(size = 10, face = "bold", hjust = 0),
legend.title = element_text(size = 16),
legend.text = element_text(size = 13)) +
ggplot2::facet_wrap(~country, ncol = 1, scale = "free_y")
comorb_count_plot
`# comorb_percent_plot <- ggplot(count_comorbidities_positive) +
# geom_area(aes(age_band, Percent, group = Comorbidities, fill = Comorbidities),
# color = "white") +
# scale_x_discrete(limits = c( "0-19" ,"20-39", "40-59","60+"),
# expand = c(0, 0)) +
# scale_y_continuous(expand = expansion(mult = c(0, 0.1))) +
# scale_fill_brewer(palette = "Oranges") +
# theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
# strip.background = element_blank(),
# strip.text = element_text(size = 10, face = "bold", color = "white")) +
# facet_wrap(~Country, ncol = 1)
#
# comorb_percent_plot
plot_sympt_comorb <- sympt_count_plot + theme(legend.position = "none") +
comorb_count_plot + theme(legend.position = "none") + plot_legend
plot_sympt_comorb
plot_comorb <- sympt_count_plot + theme(legend.position = "none") +
comorb_count_plot + theme(legend.position = "none")
plot_legend <- wrap_plots(
cowplot::get_legend(sympt_count_plot),
cowplot::get_legend(comorb_count_plot),
ncol = 1)
wrap_plots(sympt_count_plot,comorb_count_plot,
nrow = 1, widths = c(2, 2, 1))
# plot_annotation(
# title = "Figure 2: SARS-Covid-19 Symptoms and Comorbidities in responders with Covid tested positive, across top 5 countries with the highest prevalence of responders",
# subtitle = "Symptoms of SARS-Covid-19, first two columns, on the left in counts and percentages and comorbidities on the right counts and percentages \nNote: i) excludes the responders who show symptoms of SARS-Covid-19; ii) period chosen - between 04/09/2020 - 22/09/2020",
# caption = "Data source: Your.md")
######################################################
# Doing the same for showing symptoms responders
#####################################################
count_symptoms_shwsympt <- cleaned_data_22092020 %>%
dplyr::select(id, Country, age_band, chills, cough, diarrhoea, fatigue, headache, muscle_ache, nasal_congestion, nausea_vomiting, shortness_breath,
sore_throat, sputum, temperature, loss_appetite, sneezing, chest_pain, itchy_eyes, joint_pain, covid_tested) %>%
dplyr::filter(Country == "Brazil" | Country == "India" | Country == "Pakistan" | Country == "Mexico" | Country == "United Kingdom") %>%
dplyr::filter(covid_tested == "showing symptoms") %>%
tidyr::pivot_longer(cols = 4:20,
names_to = "Symptoms",
values_to = "bolean_yn_sympt") %>%
dplyr::filter(bolean_yn_sympt == "Yes") %>%
dplyr::group_by(Country, age_band, Symptoms) %>%
dplyr::tally() %>%
dplyr::rename(Count = n) %>%
dplyr::mutate(Percent = Count/sum(Count) *100) %>%
dplyr::rename(country = Country)
count_symptoms_showing_symptoms_all_countries <- cleaned_data_22092020 %>%
dplyr::select(id, Country, age_band, chills, cough, diarrhoea, fatigue, headache, muscle_ache, nasal_congestion, nausea_vomiting, shortness_breath,
sore_throat, sputum, temperature, loss_appetite, sneezing, chest_pain, itchy_eyes, joint_pain, covid_tested) %>%
dplyr::filter(Country != "Brazil" ) %>%
dplyr::filter(Country != "India") %>%
dplyr::filter(Country != "Pakistan") %>%
dplyr::filter(Country != "Mexico") %>%
dplyr::filter(Country != "United Kingdom") %>%
dplyr::filter(covid_tested == "showing symptoms") %>%
tidyr::pivot_longer(cols = 4:20,
names_to = "Symptoms",
values_to = "bolean_yn_sympt") %>%
dplyr::filter(bolean_yn_sympt == "Yes") %>%
add_column(country = c("All countries")) %>%
dplyr::group_by(country, age_band, Symptoms) %>%
dplyr::tally() %>%
dplyr::rename(Count = n) %>%
dplyr::mutate(Percent = Count/sum(Count) *100) %>%
dplyr::select(country, Symptoms, country, Count, Percent)
count_symptoms_showing_symptoms_add <- full_join(count_symptoms_showing_symptoms_all_countries, count_symptoms_shwsympt)
symptom_levels <- c(
"muscle ache" = "muscle_ache",
"nasal congestion" = "nasal_congestion",
"nausea and vomiting" = "nausea_vomiting",
"sore throat" = "sore_throat",
"loss of appetite" = "loss_appetite",
"chest pain" = "chest_pain",
"itchy eyes" = "itchy_eyes",
"joint pain" = "joint_pain"
)
count_symptoms_showing_symptoms_add <- count_symptoms_showing_symptoms_add %>%
dplyr::mutate(Symptoms = forcats::fct_recode(Symptoms, !!!symptom_levels))
sympt_count_plot_shwsympt <- ggplot2::ggplot(count_symptoms_showing_symptoms_add, ggplot2::aes(x = age_band, y = Count, group = Symptoms, fill = Symptoms)) +
ggplot2::geom_area( color = "white") +
ggplot2::scale_x_discrete(limits = c( "0-19" ,"20-39", "40-59","60+"), expand = c(0, 0)) +
ggplot2::scale_fill_viridis_d() +
ggplot2::scale_y_continuous(expand = expansion(mult = c(0, 0.1))) +
ggplot2::labs(#title = "% of responders across countries",
y = "Count", x = "Age band") +
theme(
axis.text = element_text(size = 14),
axis.title = element_text(size = 16),
plot.title = ggplot2::element_text(size = 17, face = "bold"),
plot.subtitle = ggplot2::element_text(size = 17),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
strip.background = element_blank(),
strip.text = element_text(size = 10, face = "bold", hjust = 0),
legend.title = element_text(size = 16),
legend.text = element_text(size = 13)) +
ggplot2::facet_wrap(~country, ncol = 1)
sympt_count_plot_shwsympt
count_comorbidities_shwsympt <- cleaned_data_22092020 %>%
dplyr::select(id, Country, age_band,asthma, diabetes_type_one, diabetes_type_two,
obesity, hypertension, heart_disease, lung_condition, liver_disease, kidney_disease, covid_tested) %>%
dplyr::filter(covid_tested == "showing symptoms") %>%
dplyr::filter(Country == "Brazil" | Country == "India" | Country == "Pakistan" | Country == "Mexico" | Country == "United Kingdom") %>%
tidyr::pivot_longer(cols = 4:12,
names_to = "Comorbidities",
values_to = "bolean_yn_comorb") %>%
dplyr::filter(bolean_yn_comorb == "Yes") %>%
dplyr::group_by(Country, age_band, Comorbidities) %>%
dplyr::tally() %>%
dplyr::rename(Count = n) %>%
dplyr::mutate(Percent = Count/sum(Count) *100) %>%
dplyr::rename(country = Country)
count_comorbidities_showing_symptoms_all_countries <- cleaned_data_22092020 %>%
dplyr::select(id, Country, age_band, asthma, diabetes_type_one, diabetes_type_two,
obesity, hypertension, heart_disease, lung_condition, liver_disease, kidney_disease, covid_tested) %>%
dplyr::filter(Country != "Brazil" ) %>%
dplyr::filter(Country != "India") %>%
dplyr::filter(Country != "Pakistan") %>%
dplyr::filter(Country != "Mexico") %>%
dplyr::filter(Country != "United Kingdom") %>%
dplyr::filter(covid_tested == "showing symptoms") %>%
tidyr::pivot_longer(cols = 4:12,
names_to = "Comorbidities",
values_to = "bolean_yn_comorb") %>%
dplyr::filter(bolean_yn_comorb == "Yes") %>%
add_column(country = c("All countries")) %>%
dplyr::group_by(country, age_band, Comorbidities) %>%
dplyr::tally() %>%
dplyr::rename(Count = n) %>%
dplyr::mutate(Percent = Count/sum(Count) *100) %>%
dplyr::select(country, Comorbidities, country, Count, Percent)
count_comorbidities_showing_symptoms_add <- full_join(count_comorbidities_showing_symptoms_all_countries, count_comorbidities_shwsympt)
comorbidities_levels <- c(
"heart disease" = "heart_disease",
"lung disease" = "lung_disease",
"liver disease" = "liver_disease",
"kidney disease" = "kidney_disease"
)
count_comorbidities_showing_symptoms_add <- count_comorbidities_showing_symptoms_add %>%
dplyr::mutate(Comorbidities = forcats::fct_recode(Comorbidities, !!!comorbidities_levels))
# sympt_percent_plot <- ggplot(count_symptoms_shwsympt) +
# geom_area(aes(x = age_band, y = Percent, group = Symptoms, fill = Symptoms),
# color = "white") +
# scale_x_discrete(limits = c( "0-19" ,"20-39", "40-59","60+"), expand = c(0, 0)) +
# scale_y_continuous(expand = expansion(mult = c(0, 0.1))) +
# scale_fill_viridis_d() +
# theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
# strip.background = element_blank(),
# strip.text = element_text(size = 10, face = "bold", color = "white")) +
# facet_wrap(~Country, ncol = 1)
# sympt_percent_plot
comorb_count_plot_shwsympt <- ggplot(count_comorbidities_showing_symptoms_add) +
geom_area(aes(age_band, Count, group = Comorbidities, fill = Comorbidities),
color = "white") +
scale_x_discrete(limits = c("0-19", "20-39", "40-59","60+"),
expand = c(0, 0)) +
scale_y_continuous(expand = expansion(mult = c(0, 0.1))) +
scale_fill_brewer(palette = "Reds") +
ggplot2::labs(#title = "% of responders across countries",
y = "Count", x = "Age band") +
theme(
axis.text = element_text(size = 14),
axis.title = element_text(size = 16),
plot.title = ggplot2::element_text(size = 17, face = "bold"),
plot.subtitle = ggplot2::element_text(size = 17),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
strip.background = element_blank(),
strip.text = element_text(size = 10, face = "bold", hjust = 0),
legend.title = element_text(size = 16),
legend.text = element_text(size = 13)) +
ggplot2::facet_wrap(~country, ncol = 1)
comorb_count_plot_shwsympt
# comorb_percent_plot <- ggplot(count_comorbidities_shwsympt) +
# geom_area(aes(age_band, Percent, group = Comorbidities, fill = Comorbidities),
# color = "white") +
# scale_x_discrete(limits = c( "0-19" ,"20-39", "40-59","60+"),
# expand = c(0, 0)) +
# scale_y_continuous(expand = expansion(mult = c(0, 0.1))) +
# scale_fill_brewer(palette = "Oranges") +
# theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
# strip.background = element_blank(),
# strip.text = element_text(size = 10, face = "bold", color = "white")) +
# facet_wrap(~Country, ncol = 1)
#
# comorb_percent_plot
plot_sympt_comorb_showsympt <- sympt_count_plot_shwsympt + theme(legend.position = "none") +
comorb_count_plot_shwsympt + theme(legend.position = "none") + plot_legend
plot_sympt_comorb_showsympt
# plot_comorb <- comorb_count_plot + theme(legend.position = "none") +
# comorb_percent_plot + theme(legend.position = "none")
plot_legend_shwsympt <- wrap_plots(
cowplot::get_legend(sympt_count_plot_shwsympt),
cowplot::get_legend(comorb_count_plot_shwsympt),
ncol = 1)
# wrap_plots(sympt_count_plot_shwsympt,comorb_count_plot_shwsympt,
# nrow = 1, widths = c(2, 2, 1))
gabrielburcea/cvindia documentation built on Feb. 17, 2021, 3:31 a.m.
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cleaned_data_22092020 <- readr::read_csv("/Users/gabrielburcea/rprojects/data/your.md/cleaned_data_22092020_2nd_dataset.csv")
cleaned_data_22092020 <- cleaned_data_22092020 %>%
dplyr::mutate(age_band = dplyr::case_when(
age == 0 | age <= 9 ~ '0-9',
age == 10 | age <= 19 ~ '10-19',
age == 20 | age <= 29 ~ '20-29',
age == 30 | age <= 39 ~ '30-39',
age == 40 | age <= 49 ~ '40-49',
age == 50 | age <= 59 ~ '50-59',
age >= 60 ~ "60+"))
cleaned_data_22092020 <- cleaned_data_22092020 %>%
dplyr::group_by(covid_tested) %>%
drop_na()
cleaned_data_22092020 %>%
dplyr::group_by(covid_tested) %>%
tally() %>%
dplyr::mutate(Percent = n/sum(n)*100)
country_covid_tested <- cleaned_data_22092020 %>%
dplyr::group_by(country) %>%
tally() %>%
dplyr::mutate(Percent = n/sum(n)*100)
count_symptoms_positive <- cleaned_data_22092020 %>%
dplyr::select(id, Country, age_band, chills, cough, diarrhoea, fatigue, headache, muscle_ache, nasal_congestion, nausea_vomiting, shortness_breath,
sore_throat, sputum, temperature, loss_appetite, sneezing, chest_pain, itchy_eyes, joint_pain, covid_tested) %>%
dplyr::filter(Country == "Brazil" | Country == "India" | Country == "Pakistan" | Country == "Mexico" | Country == "United Kingdom") %>%
dplyr::filter(covid_tested == "positive") %>%
tidyr::pivot_longer(cols = 4:20,
names_to = "Symptoms",
values_to = "bolean_yn_sympt") %>%
dplyr::filter(bolean_yn_sympt == "Yes") %>%
dplyr::rename(country = Country) %>%
dplyr::group_by(country, age_band, Symptoms) %>%
dplyr::tally() %>%
dplyr::rename(Count = n) %>%
dplyr::mutate(Percent = Count/sum(Count) *100)
count_symptoms_positive_all_countries <- cleaned_data_22092020 %>%
dplyr::select(id, Country, age_band, chills, cough, diarrhoea, fatigue, headache, muscle_ache, nasal_congestion, nausea_vomiting, shortness_breath,
sore_throat, sputum, temperature, loss_appetite, sneezing, chest_pain, itchy_eyes, joint_pain, covid_tested) %>%
dplyr::filter(Country != "Brazil" ) %>%
dplyr::filter(Country != "India") %>%
dplyr::filter(Country != "Pakistan") %>%
dplyr::filter(Country != "Mexico") %>%
dplyr::filter(Country != "United Kingdom") %>%
dplyr::filter(covid_tested == "positive") %>%
tidyr::pivot_longer(cols = 4:20,
names_to = "Symptoms",
values_to = "bolean_yn_sympt") %>%
dplyr::filter(bolean_yn_sympt == "Yes") %>%
add_column(country = c("All countries")) %>%
dplyr::group_by(country, age_band, Symptoms) %>%
dplyr::tally() %>%
dplyr::rename(Count = n) %>%
dplyr::mutate(Percent = Count/sum(Count) *100) %>%
dplyr::select(country, Symptoms, country, Count, Percent)
count_symptoms_positive_add <- full_join(count_symptoms_positive_all_countries, count_symptoms_positive)
write.csv(count_symptoms_positive_add, file = "/Users/gabrielburcea/rprojects/data/stack_overflow/count_symptoms_showing_symptoms_add.csv")
symptom_levels <- c(
"muscle ache" = "muscle_ache",
"nasal congestion" = "nasal_congestion",
"nausea and vomiting" = "nausea_vomiting",
"sore throat" = "sore_throat",
"loss of appetite" = "loss_appetite",
"chest pain" = "chest_pain",
"itchy eyes" = "itchy_eyes",
"joint pain" = "joint_pain"
)
count_symptoms_positive_add <- count_symptoms_positive_add %>%
dplyr::mutate(Symptoms = forcats::fct_recode(Symptoms, !!!symptom_levels))
sympt_count_plot <- ggplot2::ggplot(count_symptoms_positive_add, ggplot2::aes(x = age_band, y = Count, group = Symptoms, fill = Symptoms)) +
ggplot2::geom_area( color = "white") +
ggplot2::scale_x_discrete(limits = c( "0-9", "10-19", "20-29", "30-39", "40-49", "50-59",
"60+"), expand = c(0, 0)) +
ggplot2::scale_fill_viridis_d() +
#ggplot2::scale_y_continuous(breaks = c(seq(0,1000,by=100),seq(2100,3000,by=100))) +
ggplot2::labs(#title = "% of responders across countries",
y = "Count", x = "Age band") +
theme(
axis.text = element_text(size = 14),
axis.title = element_text(size = 16),
plot.title = ggplot2::element_text(size = 17, face = "bold"),
plot.subtitle = ggplot2::element_text(size = 17),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
strip.background = element_blank(),
strip.text = element_text(size = 10, face = "bold", hjust = 0),
legend.title = element_text(size = 16),
legend.text = element_text(size = 13)) +
ggplot2::facet_wrap(~country, ncol = 1, scale = "free_y")
sympt_count_plot
count_comorbidities_positive <- cleaned_data_22092020 %>%
dplyr::select(id, Country, age_band,asthma, diabetes_type_one, diabetes_type_two,
obesity, hypertension, heart_disease, lung_condition, liver_disease, kidney_disease, covid_tested) %>%
dplyr::filter(Country == "Brazil" | Country == "India" | Country == "Pakistan" | Country == "Mexico" | Country == "United Kingdom") %>%
tidyr::pivot_longer(cols = 4:12,
names_to = "Comorbidities",
values_to = "bolean_yn_comorb") %>%
dplyr::filter(bolean_yn_comorb == "Yes") %>%
dplyr::group_by(Country, age_band, Comorbidities) %>%
dplyr::tally() %>%
dplyr::rename(Count = n) %>%
dplyr::mutate(Percent = Count/sum(Count) *100) %>%
dplyr::rename(country = Country)
count_comorbidities_positive_all_countries <- cleaned_data_22092020 %>%
dplyr::select(id, Country, age_band, asthma, diabetes_type_one, diabetes_type_two,
obesity, hypertension, heart_disease, lung_condition, liver_disease, kidney_disease, covid_tested) %>%
dplyr::filter(Country != "Brazil" ) %>%
dplyr::filter(Country != "India") %>%
dplyr::filter(Country != "Pakistan") %>%
dplyr::filter(Country != "Mexico") %>%
dplyr::filter(Country != "United Kingdom") %>%
dplyr::filter(covid_tested == "positive") %>%
tidyr::pivot_longer(cols = 4:12,
names_to = "Comorbidities",
values_to = "bolean_yn_comorb") %>%
dplyr::filter(bolean_yn_comorb == "Yes") %>%
add_column(country = c("All countries")) %>%
dplyr::group_by(country, age_band, Comorbidities) %>%
dplyr::tally() %>%
dplyr::rename(Count = n) %>%
dplyr::mutate(Percent = Count/sum(Count) *100) %>%
dplyr::select(country, Comorbidities, country, Count, Percent)
count_comorbidities_positive_add <- full_join(count_comorbidities_positive, count_comorbidities_positive_all_countries)
comorbidities_levels <- c(
"heart disease" = "heart_disease",
"lung disease" = "lung_disease",
"liver disease" = "liver_disease",
"kidney disease" = "kidney_disease"
)
count_comorbidities_positive_add <- count_comorbidities_positive_add %>%
dplyr::mutate(Comorbidities = forcats::fct_recode(Comorbidities, !!!comorbidities_levels))
# sympt_percent_plot <- ggplot(count_symptoms_positive) +
# geom_area(aes(x = age_band, y = Percent, group = Symptoms, fill = Symptoms),
# color = "white") +
# scale_x_discrete(limits = c( "0-19" ,"20-39", "40-59","60+"), expand = c(0, 0)) +
# scale_y_continuous(expand = expansion(mult = c(0, 0.1))) +
# scale_fill_viridis_d() +
# theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
# strip.background = element_blank(),
# strip.text = element_text(size = 10, face = "bold", color = "white")) +
# facet_wrap(~Country, ncol = 1)
# sympt_percent_plot
comorb_count_plot <- ggplot2::ggplot(count_comorbidities_positive_add, ggplot2::aes(age_band, Count, group = Comorbidities, fill = Comorbidities)) +
ggplot2::geom_area( color = "white") +
scale_x_discrete(limits = c("0-9", "10-19", "20-29", "30-39", "40-49", "50-59",
"60+"), expand = c(0, 0)) +
#ggplot2::scale_y_continuous(breaks = c(0, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000)) +
scale_fill_brewer(palette = "Set1") +
ggplot2::labs(#title = "% of responders across countries",
y = "Count", x = "Age band") +
theme(
axis.text = element_text(size = 14),
axis.title = element_text(size = 16),
plot.title = ggplot2::element_text(size = 17, face = "bold"),
plot.subtitle = ggplot2::element_text(size = 17),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
strip.background = element_blank(),
strip.text = element_text(size = 10, face = "bold", hjust = 0),
legend.title = element_text(size = 16),
legend.text = element_text(size = 13)) +
ggplot2::facet_wrap(~country, ncol = 1, scale = "free_y")
comorb_count_plot
`# comorb_percent_plot <- ggplot(count_comorbidities_positive) +
# geom_area(aes(age_band, Percent, group = Comorbidities, fill = Comorbidities),
# color = "white") +
# scale_x_discrete(limits = c( "0-19" ,"20-39", "40-59","60+"),
# expand = c(0, 0)) +
# scale_y_continuous(expand = expansion(mult = c(0, 0.1))) +
# scale_fill_brewer(palette = "Oranges") +
# theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
# strip.background = element_blank(),
# strip.text = element_text(size = 10, face = "bold", color = "white")) +
# facet_wrap(~Country, ncol = 1)
#
# comorb_percent_plot
plot_sympt_comorb <- sympt_count_plot + theme(legend.position = "none") +
comorb_count_plot + theme(legend.position = "none") + plot_legend
plot_sympt_comorb
plot_comorb <- sympt_count_plot + theme(legend.position = "none") +
comorb_count_plot + theme(legend.position = "none")
plot_legend <- wrap_plots(
cowplot::get_legend(sympt_count_plot),
cowplot::get_legend(comorb_count_plot),
ncol = 1)
wrap_plots(sympt_count_plot,comorb_count_plot,
nrow = 1, widths = c(2, 2, 1))
# plot_annotation(
# title = "Figure 2: SARS-Covid-19 Symptoms and Comorbidities in responders with Covid tested positive, across top 5 countries with the highest prevalence of responders",
# subtitle = "Symptoms of SARS-Covid-19, first two columns, on the left in counts and percentages and comorbidities on the right counts and percentages \nNote: i) excludes the responders who show symptoms of SARS-Covid-19; ii) period chosen - between 04/09/2020 - 22/09/2020",
# caption = "Data source: Your.md")
######################################################
# Doing the same for showing symptoms responders
#####################################################
count_symptoms_shwsympt <- cleaned_data_22092020 %>%
dplyr::select(id, Country, age_band, chills, cough, diarrhoea, fatigue, headache, muscle_ache, nasal_congestion, nausea_vomiting, shortness_breath,
sore_throat, sputum, temperature, loss_appetite, sneezing, chest_pain, itchy_eyes, joint_pain, covid_tested) %>%
dplyr::filter(Country == "Brazil" | Country == "India" | Country == "Pakistan" | Country == "Mexico" | Country == "United Kingdom") %>%
dplyr::filter(covid_tested == "showing symptoms") %>%
tidyr::pivot_longer(cols = 4:20,
names_to = "Symptoms",
values_to = "bolean_yn_sympt") %>%
dplyr::filter(bolean_yn_sympt == "Yes") %>%
dplyr::group_by(Country, age_band, Symptoms) %>%
dplyr::tally() %>%
dplyr::rename(Count = n) %>%
dplyr::mutate(Percent = Count/sum(Count) *100) %>%
dplyr::rename(country = Country)
count_symptoms_showing_symptoms_all_countries <- cleaned_data_22092020 %>%
dplyr::select(id, Country, age_band, chills, cough, diarrhoea, fatigue, headache, muscle_ache, nasal_congestion, nausea_vomiting, shortness_breath,
sore_throat, sputum, temperature, loss_appetite, sneezing, chest_pain, itchy_eyes, joint_pain, covid_tested) %>%
dplyr::filter(Country != "Brazil" ) %>%
dplyr::filter(Country != "India") %>%
dplyr::filter(Country != "Pakistan") %>%
dplyr::filter(Country != "Mexico") %>%
dplyr::filter(Country != "United Kingdom") %>%
dplyr::filter(covid_tested == "showing symptoms") %>%
tidyr::pivot_longer(cols = 4:20,
names_to = "Symptoms",
values_to = "bolean_yn_sympt") %>%
dplyr::filter(bolean_yn_sympt == "Yes") %>%
add_column(country = c("All countries")) %>%
dplyr::group_by(country, age_band, Symptoms) %>%
dplyr::tally() %>%
dplyr::rename(Count = n) %>%
dplyr::mutate(Percent = Count/sum(Count) *100) %>%
dplyr::select(country, Symptoms, country, Count, Percent)
count_symptoms_showing_symptoms_add <- full_join(count_symptoms_showing_symptoms_all_countries, count_symptoms_shwsympt)
symptom_levels <- c(
"muscle ache" = "muscle_ache",
"nasal congestion" = "nasal_congestion",
"nausea and vomiting" = "nausea_vomiting",
"sore throat" = "sore_throat",
"loss of appetite" = "loss_appetite",
"chest pain" = "chest_pain",
"itchy eyes" = "itchy_eyes",
"joint pain" = "joint_pain"
)
count_symptoms_showing_symptoms_add <- count_symptoms_showing_symptoms_add %>%
dplyr::mutate(Symptoms = forcats::fct_recode(Symptoms, !!!symptom_levels))
sympt_count_plot_shwsympt <- ggplot2::ggplot(count_symptoms_showing_symptoms_add, ggplot2::aes(x = age_band, y = Count, group = Symptoms, fill = Symptoms)) +
ggplot2::geom_area( color = "white") +
ggplot2::scale_x_discrete(limits = c( "0-19" ,"20-39", "40-59","60+"), expand = c(0, 0)) +
ggplot2::scale_fill_viridis_d() +
ggplot2::scale_y_continuous(expand = expansion(mult = c(0, 0.1))) +
ggplot2::labs(#title = "% of responders across countries",
y = "Count", x = "Age band") +
theme(
axis.text = element_text(size = 14),
axis.title = element_text(size = 16),
plot.title = ggplot2::element_text(size = 17, face = "bold"),
plot.subtitle = ggplot2::element_text(size = 17),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
strip.background = element_blank(),
strip.text = element_text(size = 10, face = "bold", hjust = 0),
legend.title = element_text(size = 16),
legend.text = element_text(size = 13)) +
ggplot2::facet_wrap(~country, ncol = 1)
sympt_count_plot_shwsympt
count_comorbidities_shwsympt <- cleaned_data_22092020 %>%
dplyr::select(id, Country, age_band,asthma, diabetes_type_one, diabetes_type_two,
obesity, hypertension, heart_disease, lung_condition, liver_disease, kidney_disease, covid_tested) %>%
dplyr::filter(covid_tested == "showing symptoms") %>%
dplyr::filter(Country == "Brazil" | Country == "India" | Country == "Pakistan" | Country == "Mexico" | Country == "United Kingdom") %>%
tidyr::pivot_longer(cols = 4:12,
names_to = "Comorbidities",
values_to = "bolean_yn_comorb") %>%
dplyr::filter(bolean_yn_comorb == "Yes") %>%
dplyr::group_by(Country, age_band, Comorbidities) %>%
dplyr::tally() %>%
dplyr::rename(Count = n) %>%
dplyr::mutate(Percent = Count/sum(Count) *100) %>%
dplyr::rename(country = Country)
count_comorbidities_showing_symptoms_all_countries <- cleaned_data_22092020 %>%
dplyr::select(id, Country, age_band, asthma, diabetes_type_one, diabetes_type_two,
obesity, hypertension, heart_disease, lung_condition, liver_disease, kidney_disease, covid_tested) %>%
dplyr::filter(Country != "Brazil" ) %>%
dplyr::filter(Country != "India") %>%
dplyr::filter(Country != "Pakistan") %>%
dplyr::filter(Country != "Mexico") %>%
dplyr::filter(Country != "United Kingdom") %>%
dplyr::filter(covid_tested == "showing symptoms") %>%
tidyr::pivot_longer(cols = 4:12,
names_to = "Comorbidities",
values_to = "bolean_yn_comorb") %>%
dplyr::filter(bolean_yn_comorb == "Yes") %>%
add_column(country = c("All countries")) %>%
dplyr::group_by(country, age_band, Comorbidities) %>%
dplyr::tally() %>%
dplyr::rename(Count = n) %>%
dplyr::mutate(Percent = Count/sum(Count) *100) %>%
dplyr::select(country, Comorbidities, country, Count, Percent)
count_comorbidities_showing_symptoms_add <- full_join(count_comorbidities_showing_symptoms_all_countries, count_comorbidities_shwsympt)
comorbidities_levels <- c(
"heart disease" = "heart_disease",
"lung disease" = "lung_disease",
"liver disease" = "liver_disease",
"kidney disease" = "kidney_disease"
)
count_comorbidities_showing_symptoms_add <- count_comorbidities_showing_symptoms_add %>%
dplyr::mutate(Comorbidities = forcats::fct_recode(Comorbidities, !!!comorbidities_levels))
# sympt_percent_plot <- ggplot(count_symptoms_shwsympt) +
# geom_area(aes(x = age_band, y = Percent, group = Symptoms, fill = Symptoms),
# color = "white") +
# scale_x_discrete(limits = c( "0-19" ,"20-39", "40-59","60+"), expand = c(0, 0)) +
# scale_y_continuous(expand = expansion(mult = c(0, 0.1))) +
# scale_fill_viridis_d() +
# theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
# strip.background = element_blank(),
# strip.text = element_text(size = 10, face = "bold", color = "white")) +
# facet_wrap(~Country, ncol = 1)
# sympt_percent_plot
comorb_count_plot_shwsympt <- ggplot(count_comorbidities_showing_symptoms_add) +
geom_area(aes(age_band, Count, group = Comorbidities, fill = Comorbidities),
color = "white") +
scale_x_discrete(limits = c("0-19", "20-39", "40-59","60+"),
expand = c(0, 0)) +
scale_y_continuous(expand = expansion(mult = c(0, 0.1))) +
scale_fill_brewer(palette = "Reds") +
ggplot2::labs(#title = "% of responders across countries",
y = "Count", x = "Age band") +
theme(
axis.text = element_text(size = 14),
axis.title = element_text(size = 16),
plot.title = ggplot2::element_text(size = 17, face = "bold"),
plot.subtitle = ggplot2::element_text(size = 17),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
strip.background = element_blank(),
strip.text = element_text(size = 10, face = "bold", hjust = 0),
legend.title = element_text(size = 16),
legend.text = element_text(size = 13)) +
ggplot2::facet_wrap(~country, ncol = 1)
comorb_count_plot_shwsympt
# comorb_percent_plot <- ggplot(count_comorbidities_shwsympt) +
# geom_area(aes(age_band, Percent, group = Comorbidities, fill = Comorbidities),
# color = "white") +
# scale_x_discrete(limits = c( "0-19" ,"20-39", "40-59","60+"),
# expand = c(0, 0)) +
# scale_y_continuous(expand = expansion(mult = c(0, 0.1))) +
# scale_fill_brewer(palette = "Oranges") +
# theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
# strip.background = element_blank(),
# strip.text = element_text(size = 10, face = "bold", color = "white")) +
# facet_wrap(~Country, ncol = 1)
#
# comorb_percent_plot
plot_sympt_comorb_showsympt <- sympt_count_plot_shwsympt + theme(legend.position = "none") +
comorb_count_plot_shwsympt + theme(legend.position = "none") + plot_legend
plot_sympt_comorb_showsympt
# plot_comorb <- comorb_count_plot + theme(legend.position = "none") +
# comorb_percent_plot + theme(legend.position = "none")
plot_legend_shwsympt <- wrap_plots(
cowplot::get_legend(sympt_count_plot_shwsympt),
cowplot::get_legend(comorb_count_plot_shwsympt),
ncol = 1)
# wrap_plots(sympt_count_plot_shwsympt,comorb_count_plot_shwsympt,
# nrow = 1, widths = c(2, 2, 1))
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