R/age_adjusted/sympt_age_country_world_pop.R
In gabrielburcea/cvindia: Analysis of symptoms
# Age adjusted Covid-19 symptoms
cohort_data_22092020 <- read_csv("/Users/gabrielburcea/rprojects/data/your.md/cleaned_data_22092020_2nd_dataset.csv")
cohort_data_22092020 <- cohort_data_22092020 %>%
dplyr::group_by(covid_tested) %>%
tidyr::drop_na()
# Select only the targeted countries
cohort_data_22092020_top_five <- cohort_data_22092020 %>%
dplyr::filter(country == "Brazil" | country == "United Kingdom" | country == "India" | country == "Mexico" | country == "Pakistan")
# get the positive tested only
age_std_data_no_sympt <- cohort_data_22092020_top_five %>%
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, loss_smell_taste, chest_pain, itchy_eyes, joint_pain, covid_tested) %>%
dplyr::filter(covid_tested == "positive") %>%
dplyr::group_by(country, age_band) %>%
dplyr::summarise(count_country_age_band_no_sympt = dplyr::n())
cohort_data_22092020_five_no_sympt_age <- left_join(cohort_data_22092020_top_five, age_std_data_no_sympt)
age_std_data_with_sympt <- cohort_data_22092020_top_five %>%
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, loss_smell_taste, chest_pain, itchy_eyes, joint_pain, covid_tested) %>%
dplyr::filter(covid_tested == "positive") %>%
tidyr::pivot_longer(cols = 4:20,
names_to = "symptoms",
values_to = "binary_sympt") %>%
dplyr::filter(binary_sympt == "Yes") %>%
dplyr::group_by(country, age_band, symptoms) %>%
dplyr::summarise(count_country_age_band_with_sympt = dplyr::n())
symptom_data <- left_join(age_std_data_no_sympt, age_std_data_with_sympt)
study_pop_symptom_data <- symptom_data %>%
dplyr::mutate(study_pop_sympt = count_country_age_band_with_sympt/count_country_age_band_no_sympt)
### Deal with world-wide population - get the worldwide population by age group
world_wide_pop <- read_excel("/Users/gabrielburcea/rprojects/cvindia/data/world_wide_pop.xlsx")
data_piv <- world_wide_pop %>%
dplyr::rename(year = "Reference date (as of 1 July)")
data_piv <- data_piv %>%
tidyr::pivot_longer(cols = 9:29,
names_to = "age_band",
values_to = "value") %>%
dplyr::filter(year == "2020")
data_piv <- data_piv %>% mutate_all(trimws)
data_piv$value <- as.numeric(data_piv$value)
data_piv$Value <- data_piv$value * 1000
count_world_pop <- data_piv %>%
dplyr::summarise(Total_wwpop = sum(Value))
data_piv$age_band <- as.factor(data_piv$age_band)
age_levels <- c(
"0-19" = "0-4" ,
"0-19" = "5-9",
"0-19" = "10-14",
"0-19" = "15-19",
"20-39" = "20-24",
"20-39" = "25-29",
"20-39" = "30-34",
"20-39" = "35-39",
"40-59" = "40-44",
"40-59" = "45-49",
"40-59" = "50-54",
"40-59" = "55-59",
"60+" = "60-64",
"60+" = "65-69",
"60+" = "70-74",
"60+" = "75-79",
"60+" = "80-84",
"60+" = "85-89",
"60+" = "90-94",
"60+" = "95-99",
"60+" = "100+"
)
data_rec <- data_piv %>%
dplyr::mutate(age_recoded_band = forcats::fct_recode(age_band,!!!age_levels)) %>%
dplyr::select(age_recoded_band, Value)
sum_world_wide_pop_age_group_total <- data_rec %>%
dplyr::group_by(age_recoded_band) %>%
dplyr::summarise(standard_pop = sum(Value)) %>%
dplyr::mutate(total_worldwide_pop = sum(standard_pop))
symptom_data_study_pop_world_wide_pop <- left_join(study_pop_symptom_data, sum_world_wide_pop_age_group_total, by = c("age_band" = "age_recoded_band"))
# get the expected symptoms in standard population
expected_symptom_age_group <- symptom_data_study_pop_world_wide_pop %>%
dplyr::mutate(expected_standard_pop_symptoms = study_pop_sympt * standard_pop)
# get the sum of expected symptoms in standard pop
sum_expected_sympt <- expected_symptom_age_group %>%
dplyr::group_by(symptoms, country) %>%
dplyr::mutate(sum_expected_symp = sum(expected_standard_pop_symptoms))
# get the age standardize rates for symptoms by dividing the sum of the expected symptoms in standard pop
age_standard_rate_sympt <- sum_expected_sympt %>%
dplyr::mutate(age_standardise_rate_in_sympt = sum_expected_symp / total_worldwide_pop *100)
adjusted_symptoms_rates_select <- age_standard_rate_sympt %>%
#dplyr::fil(age_recoded_band == "20-39") %>%
dplyr::select(country, symptoms, age_standardise_rate_in_sympt )
########### Standardisation for all countries ##############################
cohort_data_22092020 <- read_csv("/Users/gabrielburcea/rprojects/data/your.md/cleaned_data_22092020_2nd_dataset.csv")
cohort_data_22092020 <- cohort_data_22092020 %>%
dplyr::group_by(covid_tested) %>%
tidyr::drop_na() %>%
dplyr::filter(Country != "Brazil" ) %>%
dplyr::filter(Country != "India") %>%
dplyr::filter(Country != "Pakistan") %>%
dplyr::filter(Country != "Mexico") %>%
dplyr::filter(Country != "United Kingdom")
# age_std_data_covid_positive <- cohort_data_22092020 %>%
# dplyr::select(id, Country, age_band, count_country_age_band_comorbidities, standard_pop) %>%
# dplyr::rename(country = Country) %>%
# dplyr::filter(covid_tested == "positive") %>%
# tidyr::pivot_longer(cols = 4:12,
# names_to = "comorbidities",
# values_to = "binary_yes_no") %>%
# dplyr::filter(binary_yes_no =="Yes") %>%
# dplyr::select(id, country, age_band, comorbidities, standard_pop, count_country_age_band_comorbidities)
age_std_data_no_sympt_all_countries <- cohort_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, loss_smell_taste, chest_pain, itchy_eyes, joint_pain, covid_tested) %>%
dplyr::filter(covid_tested == "positive") %>%
dplyr::group_by(age_band) %>%
dplyr::summarise(count_all_countries_age_band_no_sympt = dplyr::n())
age_std_data_with_sympt_all_countries <- cohort_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, loss_smell_taste, 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 = "binary_sympt") %>%
dplyr::filter(binary_sympt == "Yes") %>%
dplyr::group_by(age_band, symptoms) %>%
dplyr::summarise(count_all_countries_age_band_with_sympt = dplyr::n())
sympt_data_all_countries <- left_join(age_std_data_no_sympt_all_countries, age_std_data_with_sympt_all_countries)
study_pop_sympt_data_all_countries <- sympt_data_all_countries %>%
dplyr::mutate(study_pop_sympt_all_countries = count_all_countries_age_band_with_sympt/count_all_countries_age_band_no_sympt)
### Deal with world-wide population - get the worldwide population by age group
world_wide_pop <- read_excel("/Users/gabrielburcea/rprojects/cvindia/data/world_wide_pop.xlsx")
data_piv <- world_wide_pop %>%
dplyr::rename(year = "Reference date (as of 1 July)")
data_piv <- data_piv %>%
tidyr::pivot_longer(cols = 9:29,
names_to = "age_band",
values_to = "value") %>%
dplyr::filter(year == "2020")
data_piv <- data_piv %>% mutate_all(trimws)
data_piv$value <- as.numeric(data_piv$value)
data_piv$Value <- data_piv$value * 1000
count_world_pop <- data_piv %>%
dplyr::summarise(Total_wwpop = sum(Value))
data_piv$age_band <- as.factor(data_piv$age_band)
age_levels <- c(
"0-19" = "0-4" ,
"0-19" = "5-9",
"0-19" = "10-14",
"0-19" = "15-19",
"20-39" = "20-24",
"20-39" = "25-29",
"20-39" = "30-34",
"20-39" = "35-39",
"40-59" = "40-44",
"40-59" = "45-49",
"40-59" = "50-54",
"40-59" = "55-59",
"60+" = "60-64",
"60+" = "65-69",
"60+" = "70-74",
"60+" = "75-79",
"60+" = "80-84",
"60+" = "85-89",
"60+" = "90-94",
"60+" = "95-99",
"60+" = "100+"
)
data_rec <- data_piv %>%
dplyr::mutate(age_recoded_band = forcats::fct_recode(age_band,!!!age_levels)) %>%
dplyr::select(age_recoded_band, Value)
sum_world_wide_pop_age_group_total <- data_rec %>%
dplyr::group_by(age_recoded_band) %>%
dplyr::summarise(standard_pop = sum(Value)) %>%
dplyr::mutate(total_worldwide_pop = sum(standard_pop))
sympt_data_study_pop_world_wide_pop <- left_join(study_pop_sympt_data_all_countries, sum_world_wide_pop_age_group_total, by = c("age_band" = "age_recoded_band"))
# get the expected symptoms in standard population
expected_sympt_age_group <- sympt_data_study_pop_world_wide_pop %>%
dplyr::mutate(expected_standard_pop_sympt_all_countries = study_pop_sympt_all_countries * standard_pop)
# get the sum of expected symptoms in standard pop
sum_expected_sympt_all_countries <- expected_sympt_age_group %>%
dplyr::group_by(symptoms) %>%
dplyr::mutate(sum_expected_sympt_all_countries = sum(expected_standard_pop_sympt_all_countries))
# get the age standardize rates for symptoms by dividing the sum of the expected symptoms in standard pop
age_standard_rate_sympt_all_countries <- sum_expected_sympt_all_countries %>%
dplyr::mutate(age_standardise_rate_in_sympt = sum_expected_sympt_all_countries / total_worldwide_pop *100)
adjusted_sympt_rates_select_all_countries <- age_standard_rate_sympt_all_countries %>%
#dplyr::filter(age_recoded_band == "20-39") %>%
dplyr::select(symptoms, age_standardise_rate_in_sympt) %>%
dplyr::distinct() %>%
add_column(country = c("All other countries")) %>%
dplyr::select(country, symptoms, age_standardise_rate_in_sympt)
adjusted_sympt_rates_select <- age_standard_rate_sympt %>%
#dplyr::filter(age_recoded_band == "20-39") %>%
dplyr::select(country, symptoms, age_standardise_rate_in_sympt)
adjusted_sympt_rates_final <- bind_rows(adjusted_sympt_rates_select, adjusted_sympt_rates_select_all_countries)
adj_sympt_forcats <- adjusted_sympt_rates_final %>%
dplyr::mutate(age_standardise_rate_in_sympt = round(age_standardise_rate_in_sympt, 2)) %>%
dplyr::arrange(country)
symptom_levels <- c(
"muscle ache" = "muscle_ache",
"shortness of breath" = "shortness_breath",
"loss smell taste" = "loss_smell_taste",
"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"
)
adj_sympt_forcats <- adj_sympt_forcats %>%
dplyr::mutate(symptoms = forcats::fct_recode(symptoms, !!!symptom_levels))
#write.csv(adj_sympt_forcats, file = "/Users/gabrielburcea/rprojects/data/your.md/age_standard_rate_sympt_2910_2020.csv", row.names = FALSE)
cbbPalette <- c("#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7", "#CC6600")
title <- "Figure 4: Adjusted SARS-Covid-19 symptom rates for group with Covid-19 tested positive, for top 5 countries vs. all countries"
plot_adjusted_rates <- ggplot2::ggplot(adj_sympt_forcats,
ggplot2::aes(symptoms, age_standardise_rate_in_sympt, country)) +
ggplot2::coord_flip() +
ggplot2::geom_bar(ggplot2::aes(fill = country), width = 0.4,
position = position_dodge(width = 0.5), stat = "identity") +
ggplot2::scale_fill_manual(values = cbbPalette,
guide = guide_legend(reverse = TRUE), name = "Country" ) +
ggplot2::labs(x = "SARS-Covid-19 Symptoms", y = "Percentage") +
ggplot2::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)
)
plot_adjusted_rates
# DO the same for responders with showing symptoms
cohort_data_22092020 <- read_csv("/Users/gabrielburcea/rprojects/data/your.md/cleaned_data_22092020_2nd_dataset.csv")
cohort_data_22092020 <- cohort_data_22092020 %>%
dplyr::group_by(covid_tested) %>%
tidyr::drop_na()
# Select only the targeted countries
cohort_data_22092020_top_five <- cohort_data_22092020 %>%
dplyr::filter(country == "Brazil" | country == "United Kingdom" | country == "India" | country == "Mexico" | country == "Pakistan")
# get the positive tested only
age_std_data_no_sympt <- cohort_data_22092020_top_five %>%
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, loss_smell_taste, chest_pain, itchy_eyes, joint_pain, covid_tested) %>%
dplyr::filter(covid_tested == "showing symptoms") %>%
dplyr::group_by(country, age_band) %>%
dplyr::summarise(count_country_age_band_no_sympt = dplyr::n())
cohort_data_22092020_five_no_sympt_age <- left_join(cohort_data_22092020_top_five, age_std_data_no_sympt)
age_std_data_with_sympt <- cohort_data_22092020_top_five %>%
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, loss_smell_taste, chest_pain, itchy_eyes, joint_pain, covid_tested) %>%
dplyr::filter(covid_tested == "showing symptoms") %>%
tidyr::pivot_longer(cols = 4:20,
names_to = "symptoms",
values_to = "binary_sympt") %>%
dplyr::filter(binary_sympt == "Yes") %>%
dplyr::group_by(country, age_band, symptoms) %>%
dplyr::summarise(count_country_age_band_with_sympt = dplyr::n())
symptom_data <- left_join(age_std_data_no_sympt, age_std_data_with_sympt)
study_pop_symptom_data <- symptom_data %>%
dplyr::mutate(study_pop_sympt = count_country_age_band_with_sympt/count_country_age_band_no_sympt)
### Deal with world-wide population - get the worldwide population by age group
world_wide_pop <- read_excel("/Users/gabrielburcea/rprojects/cvindia/data/world_wide_pop.xlsx")
data_piv <- world_wide_pop %>%
dplyr::rename(year = "Reference date (as of 1 July)")
data_piv <- data_piv %>%
tidyr::pivot_longer(cols = 9:29,
names_to = "age_band",
values_to = "value") %>%
dplyr::filter(year == "2020")
data_piv <- data_piv %>% mutate_all(trimws)
data_piv$value <- as.numeric(data_piv$value)
data_piv$Value <- data_piv$value * 1000
count_world_pop <- data_piv %>%
dplyr::summarise(Total_wwpop = sum(Value))
data_piv$age_band <- as.factor(data_piv$age_band)
age_levels <- c(
"0-19" = "0-4" ,
"0-19" = "5-9",
"0-19" = "10-14",
"0-19" = "15-19",
"20-39" = "20-24",
"20-39" = "25-29",
"20-39" = "30-34",
"20-39" = "35-39",
"40-59" = "40-44",
"40-59" = "45-49",
"40-59" = "50-54",
"40-59" = "55-59",
"60+" = "60-64",
"60+" = "65-69",
"60+" = "70-74",
"60+" = "75-79",
"60+" = "80-84",
"60+" = "85-89",
"60+" = "90-94",
"60+" = "95-99",
"60+" = "100+"
)
data_rec <- data_piv %>%
dplyr::mutate(age_recoded_band = forcats::fct_recode(age_band,!!!age_levels)) %>%
dplyr::select(age_recoded_band, Value)
sum_world_wide_pop_age_group_total <- data_rec %>%
dplyr::group_by(age_recoded_band) %>%
dplyr::summarise(standard_pop = sum(Value)) %>%
dplyr::mutate(total_worldwide_pop = sum(standard_pop))
symptom_data_study_pop_world_wide_pop <- left_join(study_pop_symptom_data, sum_world_wide_pop_age_group_total, by = c("age_band" = "age_recoded_band"))
# get the expected symptoms in standard population
expected_symptom_age_group <- symptom_data_study_pop_world_wide_pop %>%
dplyr::mutate(expected_standard_pop_symptoms = study_pop_sympt * standard_pop)
# get the sum of expected symptoms in standard pop
sum_expected_sympt <- expected_symptom_age_group %>%
dplyr::group_by(symptoms, country) %>%
dplyr::mutate(sum_expected_symp = sum(expected_standard_pop_symptoms))
# get the age standardize rates for symptoms by dividing the sum of the expected symptoms in standard pop
age_standard_rate_sympt <- sum_expected_sympt %>%
dplyr::mutate(age_standardise_rate_in_sympt = sum_expected_symp / total_worldwide_pop *100)
adjusted_symptoms_rates_select <- age_standard_rate_sympt %>%
#dplyr::fil(age_recoded_band == "20-39") %>%
dplyr::select(country, symptoms, age_standardise_rate_in_sympt )
########### Standardisation for all countries ##############################
cohort_data_22092020 <- read_csv("/Users/gabrielburcea/rprojects/data/your.md/cleaned_data_22092020_2nd_dataset.csv")
cohort_data_22092020 <- cohort_data_22092020 %>%
dplyr::group_by(covid_tested) %>%
tidyr::drop_na() %>%
dplyr::filter(Country != "Brazil" ) %>%
dplyr::filter(Country != "India") %>%
dplyr::filter(Country != "Pakistan") %>%
dplyr::filter(Country != "Mexico") %>%
dplyr::filter(Country != "United Kingdom")
# age_std_data_covid_positive <- cohort_data_22092020 %>%
# dplyr::select(id, Country, age_band, count_country_age_band_comorbidities, standard_pop) %>%
# dplyr::rename(country = Country) %>%
# dplyr::filter(covid_tested == "positive") %>%
# tidyr::pivot_longer(cols = 4:12,
# names_to = "comorbidities",
# values_to = "binary_yes_no") %>%
# dplyr::filter(binary_yes_no =="Yes") %>%
# dplyr::select(id, country, age_band, comorbidities, standard_pop, count_country_age_band_comorbidities)
age_std_data_no_sympt_all_countries <- cohort_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, loss_smell_taste, chest_pain, itchy_eyes, joint_pain, covid_tested) %>%
dplyr::filter(covid_tested == "showing symptoms") %>%
dplyr::group_by(age_band) %>%
dplyr::summarise(count_all_countries_age_band_no_sympt = dplyr::n())
age_std_data_with_sympt_all_countries <- cohort_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, loss_smell_taste, chest_pain, itchy_eyes, joint_pain, covid_tested) %>%
dplyr::filter(covid_tested == "showing symptoms") %>%
tidyr::pivot_longer(cols = 4:20,
names_to = "symptoms",
values_to = "binary_sympt") %>%
dplyr::filter(binary_sympt == "Yes") %>%
dplyr::group_by(age_band, symptoms) %>%
dplyr::summarise(count_all_countries_age_band_with_sympt = dplyr::n())
sympt_data_all_countries <- left_join(age_std_data_no_sympt_all_countries, age_std_data_with_sympt_all_countries)
study_pop_sympt_data_all_countries <- sympt_data_all_countries %>%
dplyr::mutate(study_pop_sympt_all_countries = count_all_countries_age_band_with_sympt/count_all_countries_age_band_no_sympt)
### Deal with world-wide population - get the worldwide population by age group
world_wide_pop <- read_excel("/Users/gabrielburcea/rprojects/cvindia/data/world_wide_pop.xlsx")
data_piv <- world_wide_pop %>%
dplyr::rename(year = "Reference date (as of 1 July)")
data_piv <- data_piv %>%
tidyr::pivot_longer(cols = 9:29,
names_to = "age_band",
values_to = "value") %>%
dplyr::filter(year == "2020")
data_piv <- data_piv %>% mutate_all(trimws)
data_piv$value <- as.numeric(data_piv$value)
data_piv$Value <- data_piv$value * 1000
count_world_pop <- data_piv %>%
dplyr::summarise(Total_wwpop = sum(Value))
data_piv$age_band <- as.factor(data_piv$age_band)
age_levels <- c(
"0-19" = "0-4" ,
"0-19" = "5-9",
"0-19" = "10-14",
"0-19" = "15-19",
"20-39" = "20-24",
"20-39" = "25-29",
"20-39" = "30-34",
"20-39" = "35-39",
"40-59" = "40-44",
"40-59" = "45-49",
"40-59" = "50-54",
"40-59" = "55-59",
"60+" = "60-64",
"60+" = "65-69",
"60+" = "70-74",
"60+" = "75-79",
"60+" = "80-84",
"60+" = "85-89",
"60+" = "90-94",
"60+" = "95-99",
"60+" = "100+"
)
data_rec <- data_piv %>%
dplyr::mutate(age_recoded_band = forcats::fct_recode(age_band,!!!age_levels)) %>%
dplyr::select(age_recoded_band, Value)
sum_world_wide_pop_age_group_total <- data_rec %>%
dplyr::group_by(age_recoded_band) %>%
dplyr::summarise(standard_pop = sum(Value)) %>%
dplyr::mutate(total_worldwide_pop = sum(standard_pop))
sympt_data_study_pop_world_wide_pop <- left_join(study_pop_sympt_data_all_countries, sum_world_wide_pop_age_group_total, by = c("age_band" = "age_recoded_band"))
# get the expected symptoms in standard population
expected_sympt_age_group <- sympt_data_study_pop_world_wide_pop %>%
dplyr::mutate(expected_standard_pop_sympt_all_countries = study_pop_sympt_all_countries * standard_pop)
# get the sum of expected symptoms in standard pop
sum_expected_sympt_all_countries <- expected_sympt_age_group %>%
dplyr::group_by(symptoms) %>%
dplyr::mutate(sum_expected_sympt_all_countries = sum(expected_standard_pop_sympt_all_countries))
# get the age standardize rates for symptoms by dividing the sum of the expected symptoms in standard pop
age_standard_rate_sympt_all_countries <- sum_expected_sympt_all_countries %>%
dplyr::mutate(age_standardise_rate_in_sympt = sum_expected_sympt_all_countries / total_worldwide_pop *100)
adjusted_sympt_rates_select_all_countries <- age_standard_rate_sympt_all_countries %>%
#dplyr::filter(age_recoded_band == "20-39") %>%
dplyr::select(symptoms, age_standardise_rate_in_sympt) %>%
dplyr::distinct() %>%
add_column(country = c("All other countries")) %>%
dplyr::select(country, symptoms, age_standardise_rate_in_sympt)
adjusted_sympt_rates_select <- age_standard_rate_sympt %>%
#dplyr::filter(age_recoded_band == "20-39") %>%
dplyr::select(country, symptoms, age_standardise_rate_in_sympt)
adjusted_sympt_rates_final <- bind_rows(adjusted_sympt_rates_select, adjusted_sympt_rates_select_all_countries)
adj_sympt_forcats <- adjusted_sympt_rates_final %>%
dplyr::mutate(age_standardise_rate_in_sympt = round(age_standardise_rate_in_sympt, 2)) %>%
dplyr::arrange(country)
symptom_levels <- c(
"muscle ache" = "muscle_ache",
"shortness of breath" = "shortness_breath",
"loss smell taste" = "loss_smell_taste",
"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"
)
adj_sympt_forcats <- adj_sympt_forcats %>%
dplyr::mutate(symptoms = forcats::fct_recode(symptoms, !!!symptom_levels))
#write.csv(adj_sympt_forcats, file = "/Users/gabrielburcea/rprojects/data/your.md/age_standard_rate_sympt_2910_2020.csv", row.names = FALSE)
cbbPalette <- c("#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7", "#CC6600")
title <- "Figure 4: Adjusted SARS-Covid-19 symptom rates for group with Covid-19 tested positive, for top 5 countries vs. all countries"
plot_adjusted_rates <- ggplot2::ggplot(adj_sympt_forcats,
ggplot2::aes(symptoms, age_standardise_rate_in_sympt, country)) +
ggplot2::coord_flip() +
ggplot2::geom_bar(ggplot2::aes(fill = country), width = 0.4,
position = position_dodge(width = 0.5), stat = "identity") +
ggplot2::scale_fill_manual(values = cbbPalette,
guide = guide_legend(reverse = TRUE), name = "Country" ) +
ggplot2::labs(x = "SARS-Covid-19 Symptoms", y = "Percentage") +
ggplot2::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)
)
plot_adjusted_rates
# unique_age_adjusted_sympt <- adj_sympt_forcats %>%
# distinct(country, symptoms, age_standardise_rate_in_sympt
# )
#
# pivlong_sympt <- unique_age_adjusted_sympt %>%
# dplyr::select(country, symptoms, age_standardise_rate_in_sympt) %>%
# tidyr::pivot_wider(names_from = "country",
# values_from = "age_standardise_rate_in_sympt")
#
#
# write.csv(pivlong_sympt, file = "/Users/gabrielburcea/rprojects/data/your.md/age_standardised/adjusted_rates_symptoms_tables.csv", row.names = FALSE)
gabrielburcea/cvindia documentation built on Feb. 17, 2021, 3:31 a.m.
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# Age adjusted Covid-19 symptoms
cohort_data_22092020 <- read_csv("/Users/gabrielburcea/rprojects/data/your.md/cleaned_data_22092020_2nd_dataset.csv")
cohort_data_22092020 <- cohort_data_22092020 %>%
dplyr::group_by(covid_tested) %>%
tidyr::drop_na()
# Select only the targeted countries
cohort_data_22092020_top_five <- cohort_data_22092020 %>%
dplyr::filter(country == "Brazil" | country == "United Kingdom" | country == "India" | country == "Mexico" | country == "Pakistan")
# get the positive tested only
age_std_data_no_sympt <- cohort_data_22092020_top_five %>%
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, loss_smell_taste, chest_pain, itchy_eyes, joint_pain, covid_tested) %>%
dplyr::filter(covid_tested == "positive") %>%
dplyr::group_by(country, age_band) %>%
dplyr::summarise(count_country_age_band_no_sympt = dplyr::n())
cohort_data_22092020_five_no_sympt_age <- left_join(cohort_data_22092020_top_five, age_std_data_no_sympt)
age_std_data_with_sympt <- cohort_data_22092020_top_five %>%
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, loss_smell_taste, chest_pain, itchy_eyes, joint_pain, covid_tested) %>%
dplyr::filter(covid_tested == "positive") %>%
tidyr::pivot_longer(cols = 4:20,
names_to = "symptoms",
values_to = "binary_sympt") %>%
dplyr::filter(binary_sympt == "Yes") %>%
dplyr::group_by(country, age_band, symptoms) %>%
dplyr::summarise(count_country_age_band_with_sympt = dplyr::n())
symptom_data <- left_join(age_std_data_no_sympt, age_std_data_with_sympt)
study_pop_symptom_data <- symptom_data %>%
dplyr::mutate(study_pop_sympt = count_country_age_band_with_sympt/count_country_age_band_no_sympt)
### Deal with world-wide population - get the worldwide population by age group
world_wide_pop <- read_excel("/Users/gabrielburcea/rprojects/cvindia/data/world_wide_pop.xlsx")
data_piv <- world_wide_pop %>%
dplyr::rename(year = "Reference date (as of 1 July)")
data_piv <- data_piv %>%
tidyr::pivot_longer(cols = 9:29,
names_to = "age_band",
values_to = "value") %>%
dplyr::filter(year == "2020")
data_piv <- data_piv %>% mutate_all(trimws)
data_piv$value <- as.numeric(data_piv$value)
data_piv$Value <- data_piv$value * 1000
count_world_pop <- data_piv %>%
dplyr::summarise(Total_wwpop = sum(Value))
data_piv$age_band <- as.factor(data_piv$age_band)
age_levels <- c(
"0-19" = "0-4" ,
"0-19" = "5-9",
"0-19" = "10-14",
"0-19" = "15-19",
"20-39" = "20-24",
"20-39" = "25-29",
"20-39" = "30-34",
"20-39" = "35-39",
"40-59" = "40-44",
"40-59" = "45-49",
"40-59" = "50-54",
"40-59" = "55-59",
"60+" = "60-64",
"60+" = "65-69",
"60+" = "70-74",
"60+" = "75-79",
"60+" = "80-84",
"60+" = "85-89",
"60+" = "90-94",
"60+" = "95-99",
"60+" = "100+"
)
data_rec <- data_piv %>%
dplyr::mutate(age_recoded_band = forcats::fct_recode(age_band,!!!age_levels)) %>%
dplyr::select(age_recoded_band, Value)
sum_world_wide_pop_age_group_total <- data_rec %>%
dplyr::group_by(age_recoded_band) %>%
dplyr::summarise(standard_pop = sum(Value)) %>%
dplyr::mutate(total_worldwide_pop = sum(standard_pop))
symptom_data_study_pop_world_wide_pop <- left_join(study_pop_symptom_data, sum_world_wide_pop_age_group_total, by = c("age_band" = "age_recoded_band"))
# get the expected symptoms in standard population
expected_symptom_age_group <- symptom_data_study_pop_world_wide_pop %>%
dplyr::mutate(expected_standard_pop_symptoms = study_pop_sympt * standard_pop)
# get the sum of expected symptoms in standard pop
sum_expected_sympt <- expected_symptom_age_group %>%
dplyr::group_by(symptoms, country) %>%
dplyr::mutate(sum_expected_symp = sum(expected_standard_pop_symptoms))
# get the age standardize rates for symptoms by dividing the sum of the expected symptoms in standard pop
age_standard_rate_sympt <- sum_expected_sympt %>%
dplyr::mutate(age_standardise_rate_in_sympt = sum_expected_symp / total_worldwide_pop *100)
adjusted_symptoms_rates_select <- age_standard_rate_sympt %>%
#dplyr::fil(age_recoded_band == "20-39") %>%
dplyr::select(country, symptoms, age_standardise_rate_in_sympt )
########### Standardisation for all countries ##############################
cohort_data_22092020 <- read_csv("/Users/gabrielburcea/rprojects/data/your.md/cleaned_data_22092020_2nd_dataset.csv")
cohort_data_22092020 <- cohort_data_22092020 %>%
dplyr::group_by(covid_tested) %>%
tidyr::drop_na() %>%
dplyr::filter(Country != "Brazil" ) %>%
dplyr::filter(Country != "India") %>%
dplyr::filter(Country != "Pakistan") %>%
dplyr::filter(Country != "Mexico") %>%
dplyr::filter(Country != "United Kingdom")
# age_std_data_covid_positive <- cohort_data_22092020 %>%
# dplyr::select(id, Country, age_band, count_country_age_band_comorbidities, standard_pop) %>%
# dplyr::rename(country = Country) %>%
# dplyr::filter(covid_tested == "positive") %>%
# tidyr::pivot_longer(cols = 4:12,
# names_to = "comorbidities",
# values_to = "binary_yes_no") %>%
# dplyr::filter(binary_yes_no =="Yes") %>%
# dplyr::select(id, country, age_band, comorbidities, standard_pop, count_country_age_band_comorbidities)
age_std_data_no_sympt_all_countries <- cohort_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, loss_smell_taste, chest_pain, itchy_eyes, joint_pain, covid_tested) %>%
dplyr::filter(covid_tested == "positive") %>%
dplyr::group_by(age_band) %>%
dplyr::summarise(count_all_countries_age_band_no_sympt = dplyr::n())
age_std_data_with_sympt_all_countries <- cohort_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, loss_smell_taste, 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 = "binary_sympt") %>%
dplyr::filter(binary_sympt == "Yes") %>%
dplyr::group_by(age_band, symptoms) %>%
dplyr::summarise(count_all_countries_age_band_with_sympt = dplyr::n())
sympt_data_all_countries <- left_join(age_std_data_no_sympt_all_countries, age_std_data_with_sympt_all_countries)
study_pop_sympt_data_all_countries <- sympt_data_all_countries %>%
dplyr::mutate(study_pop_sympt_all_countries = count_all_countries_age_band_with_sympt/count_all_countries_age_band_no_sympt)
### Deal with world-wide population - get the worldwide population by age group
world_wide_pop <- read_excel("/Users/gabrielburcea/rprojects/cvindia/data/world_wide_pop.xlsx")
data_piv <- world_wide_pop %>%
dplyr::rename(year = "Reference date (as of 1 July)")
data_piv <- data_piv %>%
tidyr::pivot_longer(cols = 9:29,
names_to = "age_band",
values_to = "value") %>%
dplyr::filter(year == "2020")
data_piv <- data_piv %>% mutate_all(trimws)
data_piv$value <- as.numeric(data_piv$value)
data_piv$Value <- data_piv$value * 1000
count_world_pop <- data_piv %>%
dplyr::summarise(Total_wwpop = sum(Value))
data_piv$age_band <- as.factor(data_piv$age_band)
age_levels <- c(
"0-19" = "0-4" ,
"0-19" = "5-9",
"0-19" = "10-14",
"0-19" = "15-19",
"20-39" = "20-24",
"20-39" = "25-29",
"20-39" = "30-34",
"20-39" = "35-39",
"40-59" = "40-44",
"40-59" = "45-49",
"40-59" = "50-54",
"40-59" = "55-59",
"60+" = "60-64",
"60+" = "65-69",
"60+" = "70-74",
"60+" = "75-79",
"60+" = "80-84",
"60+" = "85-89",
"60+" = "90-94",
"60+" = "95-99",
"60+" = "100+"
)
data_rec <- data_piv %>%
dplyr::mutate(age_recoded_band = forcats::fct_recode(age_band,!!!age_levels)) %>%
dplyr::select(age_recoded_band, Value)
sum_world_wide_pop_age_group_total <- data_rec %>%
dplyr::group_by(age_recoded_band) %>%
dplyr::summarise(standard_pop = sum(Value)) %>%
dplyr::mutate(total_worldwide_pop = sum(standard_pop))
sympt_data_study_pop_world_wide_pop <- left_join(study_pop_sympt_data_all_countries, sum_world_wide_pop_age_group_total, by = c("age_band" = "age_recoded_band"))
# get the expected symptoms in standard population
expected_sympt_age_group <- sympt_data_study_pop_world_wide_pop %>%
dplyr::mutate(expected_standard_pop_sympt_all_countries = study_pop_sympt_all_countries * standard_pop)
# get the sum of expected symptoms in standard pop
sum_expected_sympt_all_countries <- expected_sympt_age_group %>%
dplyr::group_by(symptoms) %>%
dplyr::mutate(sum_expected_sympt_all_countries = sum(expected_standard_pop_sympt_all_countries))
# get the age standardize rates for symptoms by dividing the sum of the expected symptoms in standard pop
age_standard_rate_sympt_all_countries <- sum_expected_sympt_all_countries %>%
dplyr::mutate(age_standardise_rate_in_sympt = sum_expected_sympt_all_countries / total_worldwide_pop *100)
adjusted_sympt_rates_select_all_countries <- age_standard_rate_sympt_all_countries %>%
#dplyr::filter(age_recoded_band == "20-39") %>%
dplyr::select(symptoms, age_standardise_rate_in_sympt) %>%
dplyr::distinct() %>%
add_column(country = c("All other countries")) %>%
dplyr::select(country, symptoms, age_standardise_rate_in_sympt)
adjusted_sympt_rates_select <- age_standard_rate_sympt %>%
#dplyr::filter(age_recoded_band == "20-39") %>%
dplyr::select(country, symptoms, age_standardise_rate_in_sympt)
adjusted_sympt_rates_final <- bind_rows(adjusted_sympt_rates_select, adjusted_sympt_rates_select_all_countries)
adj_sympt_forcats <- adjusted_sympt_rates_final %>%
dplyr::mutate(age_standardise_rate_in_sympt = round(age_standardise_rate_in_sympt, 2)) %>%
dplyr::arrange(country)
symptom_levels <- c(
"muscle ache" = "muscle_ache",
"shortness of breath" = "shortness_breath",
"loss smell taste" = "loss_smell_taste",
"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"
)
adj_sympt_forcats <- adj_sympt_forcats %>%
dplyr::mutate(symptoms = forcats::fct_recode(symptoms, !!!symptom_levels))
#write.csv(adj_sympt_forcats, file = "/Users/gabrielburcea/rprojects/data/your.md/age_standard_rate_sympt_2910_2020.csv", row.names = FALSE)
cbbPalette <- c("#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7", "#CC6600")
title <- "Figure 4: Adjusted SARS-Covid-19 symptom rates for group with Covid-19 tested positive, for top 5 countries vs. all countries"
plot_adjusted_rates <- ggplot2::ggplot(adj_sympt_forcats,
ggplot2::aes(symptoms, age_standardise_rate_in_sympt, country)) +
ggplot2::coord_flip() +
ggplot2::geom_bar(ggplot2::aes(fill = country), width = 0.4,
position = position_dodge(width = 0.5), stat = "identity") +
ggplot2::scale_fill_manual(values = cbbPalette,
guide = guide_legend(reverse = TRUE), name = "Country" ) +
ggplot2::labs(x = "SARS-Covid-19 Symptoms", y = "Percentage") +
ggplot2::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)
)
plot_adjusted_rates
# DO the same for responders with showing symptoms
cohort_data_22092020 <- read_csv("/Users/gabrielburcea/rprojects/data/your.md/cleaned_data_22092020_2nd_dataset.csv")
cohort_data_22092020 <- cohort_data_22092020 %>%
dplyr::group_by(covid_tested) %>%
tidyr::drop_na()
# Select only the targeted countries
cohort_data_22092020_top_five <- cohort_data_22092020 %>%
dplyr::filter(country == "Brazil" | country == "United Kingdom" | country == "India" | country == "Mexico" | country == "Pakistan")
# get the positive tested only
age_std_data_no_sympt <- cohort_data_22092020_top_five %>%
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, loss_smell_taste, chest_pain, itchy_eyes, joint_pain, covid_tested) %>%
dplyr::filter(covid_tested == "showing symptoms") %>%
dplyr::group_by(country, age_band) %>%
dplyr::summarise(count_country_age_band_no_sympt = dplyr::n())
cohort_data_22092020_five_no_sympt_age <- left_join(cohort_data_22092020_top_five, age_std_data_no_sympt)
age_std_data_with_sympt <- cohort_data_22092020_top_five %>%
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, loss_smell_taste, chest_pain, itchy_eyes, joint_pain, covid_tested) %>%
dplyr::filter(covid_tested == "showing symptoms") %>%
tidyr::pivot_longer(cols = 4:20,
names_to = "symptoms",
values_to = "binary_sympt") %>%
dplyr::filter(binary_sympt == "Yes") %>%
dplyr::group_by(country, age_band, symptoms) %>%
dplyr::summarise(count_country_age_band_with_sympt = dplyr::n())
symptom_data <- left_join(age_std_data_no_sympt, age_std_data_with_sympt)
study_pop_symptom_data <- symptom_data %>%
dplyr::mutate(study_pop_sympt = count_country_age_band_with_sympt/count_country_age_band_no_sympt)
### Deal with world-wide population - get the worldwide population by age group
world_wide_pop <- read_excel("/Users/gabrielburcea/rprojects/cvindia/data/world_wide_pop.xlsx")
data_piv <- world_wide_pop %>%
dplyr::rename(year = "Reference date (as of 1 July)")
data_piv <- data_piv %>%
tidyr::pivot_longer(cols = 9:29,
names_to = "age_band",
values_to = "value") %>%
dplyr::filter(year == "2020")
data_piv <- data_piv %>% mutate_all(trimws)
data_piv$value <- as.numeric(data_piv$value)
data_piv$Value <- data_piv$value * 1000
count_world_pop <- data_piv %>%
dplyr::summarise(Total_wwpop = sum(Value))
data_piv$age_band <- as.factor(data_piv$age_band)
age_levels <- c(
"0-19" = "0-4" ,
"0-19" = "5-9",
"0-19" = "10-14",
"0-19" = "15-19",
"20-39" = "20-24",
"20-39" = "25-29",
"20-39" = "30-34",
"20-39" = "35-39",
"40-59" = "40-44",
"40-59" = "45-49",
"40-59" = "50-54",
"40-59" = "55-59",
"60+" = "60-64",
"60+" = "65-69",
"60+" = "70-74",
"60+" = "75-79",
"60+" = "80-84",
"60+" = "85-89",
"60+" = "90-94",
"60+" = "95-99",
"60+" = "100+"
)
data_rec <- data_piv %>%
dplyr::mutate(age_recoded_band = forcats::fct_recode(age_band,!!!age_levels)) %>%
dplyr::select(age_recoded_band, Value)
sum_world_wide_pop_age_group_total <- data_rec %>%
dplyr::group_by(age_recoded_band) %>%
dplyr::summarise(standard_pop = sum(Value)) %>%
dplyr::mutate(total_worldwide_pop = sum(standard_pop))
symptom_data_study_pop_world_wide_pop <- left_join(study_pop_symptom_data, sum_world_wide_pop_age_group_total, by = c("age_band" = "age_recoded_band"))
# get the expected symptoms in standard population
expected_symptom_age_group <- symptom_data_study_pop_world_wide_pop %>%
dplyr::mutate(expected_standard_pop_symptoms = study_pop_sympt * standard_pop)
# get the sum of expected symptoms in standard pop
sum_expected_sympt <- expected_symptom_age_group %>%
dplyr::group_by(symptoms, country) %>%
dplyr::mutate(sum_expected_symp = sum(expected_standard_pop_symptoms))
# get the age standardize rates for symptoms by dividing the sum of the expected symptoms in standard pop
age_standard_rate_sympt <- sum_expected_sympt %>%
dplyr::mutate(age_standardise_rate_in_sympt = sum_expected_symp / total_worldwide_pop *100)
adjusted_symptoms_rates_select <- age_standard_rate_sympt %>%
#dplyr::fil(age_recoded_band == "20-39") %>%
dplyr::select(country, symptoms, age_standardise_rate_in_sympt )
########### Standardisation for all countries ##############################
cohort_data_22092020 <- read_csv("/Users/gabrielburcea/rprojects/data/your.md/cleaned_data_22092020_2nd_dataset.csv")
cohort_data_22092020 <- cohort_data_22092020 %>%
dplyr::group_by(covid_tested) %>%
tidyr::drop_na() %>%
dplyr::filter(Country != "Brazil" ) %>%
dplyr::filter(Country != "India") %>%
dplyr::filter(Country != "Pakistan") %>%
dplyr::filter(Country != "Mexico") %>%
dplyr::filter(Country != "United Kingdom")
# age_std_data_covid_positive <- cohort_data_22092020 %>%
# dplyr::select(id, Country, age_band, count_country_age_band_comorbidities, standard_pop) %>%
# dplyr::rename(country = Country) %>%
# dplyr::filter(covid_tested == "positive") %>%
# tidyr::pivot_longer(cols = 4:12,
# names_to = "comorbidities",
# values_to = "binary_yes_no") %>%
# dplyr::filter(binary_yes_no =="Yes") %>%
# dplyr::select(id, country, age_band, comorbidities, standard_pop, count_country_age_band_comorbidities)
age_std_data_no_sympt_all_countries <- cohort_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, loss_smell_taste, chest_pain, itchy_eyes, joint_pain, covid_tested) %>%
dplyr::filter(covid_tested == "showing symptoms") %>%
dplyr::group_by(age_band) %>%
dplyr::summarise(count_all_countries_age_band_no_sympt = dplyr::n())
age_std_data_with_sympt_all_countries <- cohort_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, loss_smell_taste, chest_pain, itchy_eyes, joint_pain, covid_tested) %>%
dplyr::filter(covid_tested == "showing symptoms") %>%
tidyr::pivot_longer(cols = 4:20,
names_to = "symptoms",
values_to = "binary_sympt") %>%
dplyr::filter(binary_sympt == "Yes") %>%
dplyr::group_by(age_band, symptoms) %>%
dplyr::summarise(count_all_countries_age_band_with_sympt = dplyr::n())
sympt_data_all_countries <- left_join(age_std_data_no_sympt_all_countries, age_std_data_with_sympt_all_countries)
study_pop_sympt_data_all_countries <- sympt_data_all_countries %>%
dplyr::mutate(study_pop_sympt_all_countries = count_all_countries_age_band_with_sympt/count_all_countries_age_band_no_sympt)
### Deal with world-wide population - get the worldwide population by age group
world_wide_pop <- read_excel("/Users/gabrielburcea/rprojects/cvindia/data/world_wide_pop.xlsx")
data_piv <- world_wide_pop %>%
dplyr::rename(year = "Reference date (as of 1 July)")
data_piv <- data_piv %>%
tidyr::pivot_longer(cols = 9:29,
names_to = "age_band",
values_to = "value") %>%
dplyr::filter(year == "2020")
data_piv <- data_piv %>% mutate_all(trimws)
data_piv$value <- as.numeric(data_piv$value)
data_piv$Value <- data_piv$value * 1000
count_world_pop <- data_piv %>%
dplyr::summarise(Total_wwpop = sum(Value))
data_piv$age_band <- as.factor(data_piv$age_band)
age_levels <- c(
"0-19" = "0-4" ,
"0-19" = "5-9",
"0-19" = "10-14",
"0-19" = "15-19",
"20-39" = "20-24",
"20-39" = "25-29",
"20-39" = "30-34",
"20-39" = "35-39",
"40-59" = "40-44",
"40-59" = "45-49",
"40-59" = "50-54",
"40-59" = "55-59",
"60+" = "60-64",
"60+" = "65-69",
"60+" = "70-74",
"60+" = "75-79",
"60+" = "80-84",
"60+" = "85-89",
"60+" = "90-94",
"60+" = "95-99",
"60+" = "100+"
)
data_rec <- data_piv %>%
dplyr::mutate(age_recoded_band = forcats::fct_recode(age_band,!!!age_levels)) %>%
dplyr::select(age_recoded_band, Value)
sum_world_wide_pop_age_group_total <- data_rec %>%
dplyr::group_by(age_recoded_band) %>%
dplyr::summarise(standard_pop = sum(Value)) %>%
dplyr::mutate(total_worldwide_pop = sum(standard_pop))
sympt_data_study_pop_world_wide_pop <- left_join(study_pop_sympt_data_all_countries, sum_world_wide_pop_age_group_total, by = c("age_band" = "age_recoded_band"))
# get the expected symptoms in standard population
expected_sympt_age_group <- sympt_data_study_pop_world_wide_pop %>%
dplyr::mutate(expected_standard_pop_sympt_all_countries = study_pop_sympt_all_countries * standard_pop)
# get the sum of expected symptoms in standard pop
sum_expected_sympt_all_countries <- expected_sympt_age_group %>%
dplyr::group_by(symptoms) %>%
dplyr::mutate(sum_expected_sympt_all_countries = sum(expected_standard_pop_sympt_all_countries))
# get the age standardize rates for symptoms by dividing the sum of the expected symptoms in standard pop
age_standard_rate_sympt_all_countries <- sum_expected_sympt_all_countries %>%
dplyr::mutate(age_standardise_rate_in_sympt = sum_expected_sympt_all_countries / total_worldwide_pop *100)
adjusted_sympt_rates_select_all_countries <- age_standard_rate_sympt_all_countries %>%
#dplyr::filter(age_recoded_band == "20-39") %>%
dplyr::select(symptoms, age_standardise_rate_in_sympt) %>%
dplyr::distinct() %>%
add_column(country = c("All other countries")) %>%
dplyr::select(country, symptoms, age_standardise_rate_in_sympt)
adjusted_sympt_rates_select <- age_standard_rate_sympt %>%
#dplyr::filter(age_recoded_band == "20-39") %>%
dplyr::select(country, symptoms, age_standardise_rate_in_sympt)
adjusted_sympt_rates_final <- bind_rows(adjusted_sympt_rates_select, adjusted_sympt_rates_select_all_countries)
adj_sympt_forcats <- adjusted_sympt_rates_final %>%
dplyr::mutate(age_standardise_rate_in_sympt = round(age_standardise_rate_in_sympt, 2)) %>%
dplyr::arrange(country)
symptom_levels <- c(
"muscle ache" = "muscle_ache",
"shortness of breath" = "shortness_breath",
"loss smell taste" = "loss_smell_taste",
"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"
)
adj_sympt_forcats <- adj_sympt_forcats %>%
dplyr::mutate(symptoms = forcats::fct_recode(symptoms, !!!symptom_levels))
#write.csv(adj_sympt_forcats, file = "/Users/gabrielburcea/rprojects/data/your.md/age_standard_rate_sympt_2910_2020.csv", row.names = FALSE)
cbbPalette <- c("#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7", "#CC6600")
title <- "Figure 4: Adjusted SARS-Covid-19 symptom rates for group with Covid-19 tested positive, for top 5 countries vs. all countries"
plot_adjusted_rates <- ggplot2::ggplot(adj_sympt_forcats,
ggplot2::aes(symptoms, age_standardise_rate_in_sympt, country)) +
ggplot2::coord_flip() +
ggplot2::geom_bar(ggplot2::aes(fill = country), width = 0.4,
position = position_dodge(width = 0.5), stat = "identity") +
ggplot2::scale_fill_manual(values = cbbPalette,
guide = guide_legend(reverse = TRUE), name = "Country" ) +
ggplot2::labs(x = "SARS-Covid-19 Symptoms", y = "Percentage") +
ggplot2::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)
)
plot_adjusted_rates
# unique_age_adjusted_sympt <- adj_sympt_forcats %>%
# distinct(country, symptoms, age_standardise_rate_in_sympt
# )
#
# pivlong_sympt <- unique_age_adjusted_sympt %>%
# dplyr::select(country, symptoms, age_standardise_rate_in_sympt) %>%
# tidyr::pivot_wider(names_from = "country",
# values_from = "age_standardise_rate_in_sympt")
#
#
# write.csv(pivlong_sympt, file = "/Users/gabrielburcea/rprojects/data/your.md/age_standardised/adjusted_rates_symptoms_tables.csv", row.names = FALSE)
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