.Rhistory

}
stringbit = ""
}
i = i+1
}
if(nchar(stringbit) > 0){
new_string = paste0(new_string,"\n",stringbit)
}
return(new_string)
}
splitString(my_String)
splitString = function(string, max_chars = 15){
stringbits = strsplit(string, " ")[[1]]
new_string = ""
stringbit = ""
i = 1
while(i <= length(stringbits)){
if(nchar(stringbit) == 0){
stringbit = stringbits[i]
} else if(nchar(stringbit) < max_chars){
stringbit = paste(stringbit, stringbits[i])
} else {
if(nchar(new_string) == 0){
new_string = stringbit
} else {
new_string = paste0(new_string,"\n",stringbit)
}
stringbit = ""
}
i = i+1
}
if(nchar(stringbit) > 0){
new_string = paste0(new_string,"\n",stringbit)
}
return(new_string)
}
data.table(
characteristic = c("Role of host government", "Accessibility of pre-existing public health services", "Security environment", "Disease risk factors and burden") %>% sapply(splitString) %>% linebreak("l"),
scen_a = c("Relatively benign, non-oppressive entity (e.g. Somalia). Government has some control over the affected region and is perceived as legitimate (e.g. Burkina Faso)", "Moderate to good (e.g. the affected population has effective access to pre-existing health facilities offering routine vaccination; e.g. South Sudanese refugees in Uganda)", "Stable; populations have taken refuge in a secure region (e.g. most refugees). Low probability of further displacement", sprintf("Relatively low%s", footnote_marker_symbol(2))) %>% sapply(splitString(max_chars = 20)) %>% linebreak("l"),
scen_b = c("Involved in war crimes or otherwise hostile to parts/all of the affected population (e.g. South Sudan, Syria). Government is either not perceived as legitimate (e.g. Burma) or has limited/no control of the region (e.g. Afghanistan increasingly)", "Low or none (e.g. IDPs are in an unplanned site with no pre-existing health services, or the latter have minimal functionality/routine vaccination coverage (e.g. Tigray, Yemen)", "Volatile, with intermittent access to populations (e.g. Central African Republic, South Sudan, north-east Nigeria, or lake Chad region). High probability of further displacement", sprintf("Relatively high%s (particularly in the case of camp-based IDPs)", footnote_marker_symbol(2))) %>% sapply(splitString(max_chars = 20)) %>% linebreak("l")
) %>% kableExtra::kable(
caption="Relative best- and relative worst-case scenario for humanitarian PCV vaccination",
col.names=c("Characteristic", "Scenario A (best-case)", "Scenario B (worst-case)"),
booktabs=TRUE, escape=FALSE) %>%
kableExtra::kable_styling(latex_options = "HOLD_position") %>% footnote(symbol="Compared to the average crisis-affected population")
splitString = function(string, max_chars = 15){
stringbits = strsplit(string, " ")[[1]]
new_string = ""
stringbit = ""
i = 1
while(i <= length(stringbits)){
if(nchar(stringbit) == 0){
stringbit = stringbits[i]
} else if(nchar(stringbit) < max_chars){
stringbit = paste(stringbit, stringbits[i])
} else {
if(nchar(new_string) == 0){
new_string = stringbit
} else {
new_string = paste0(new_string,"\n",stringbit)
}
stringbit = ""
}
i = i+1
}
if(nchar(stringbit) > 0){
new_string = paste0(new_string,"\n",stringbit)
}
return(new_string)
}
data.table(
characteristic = c("Role of host government", "Accessibility of pre-existing public health services", "Security environment", "Disease risk factors and burden") %>% sapply(splitString) %>% linebreak("l"),
scen_a = c("Relatively benign, non-oppressive entity (e.g. Somalia). Government has some control over the affected region and is perceived as legitimate (e.g. Burkina Faso)", "Moderate to good (e.g. the affected population has effective access to pre-existing health facilities offering routine vaccination; e.g. South Sudanese refugees in Uganda)", "Stable; populations have taken refuge in a secure region (e.g. most refugees). Low probability of further displacement", sprintf("Relatively low%s", footnote_marker_symbol(2))) %>% sapply(function(x) splitString(x, max_chars = 20)) %>% linebreak("l"),
scen_b = c("Involved in war crimes or otherwise hostile to parts/all of the affected population (e.g. South Sudan, Syria). Government is either not perceived as legitimate (e.g. Burma) or has limited/no control of the region (e.g. Afghanistan increasingly)", "Low or none (e.g. IDPs are in an unplanned site with no pre-existing health services, or the latter have minimal functionality/routine vaccination coverage (e.g. Tigray, Yemen)", "Volatile, with intermittent access to populations (e.g. Central African Republic, South Sudan, north-east Nigeria, or lake Chad region). High probability of further displacement", sprintf("Relatively high%s (particularly in the case of camp-based IDPs)", footnote_marker_symbol(2))) %>% sapply(function(x) splitString(x, max_chars = 20)) %>% linebreak("l")
) %>% kableExtra::kable(
caption="Relative best- and relative worst-case scenario for humanitarian PCV vaccination",
col.names=c("Characteristic", "Scenario A (best-case)", "Scenario B (worst-case)"),
booktabs=TRUE, escape=FALSE) %>%
kableExtra::kable_styling(latex_options = "HOLD_position") %>% footnote(symbol="Compared to the average crisis-affected population")
splitString = function(string, max_chars = 15){
stringbits = strsplit(string, " ")[[1]]
new_string = ""
stringbit = ""
i = 1
while(i <= length(stringbits)){
if(nchar(stringbit) == 0){
stringbit = stringbits[i]
} else if(nchar(stringbit) < max_chars){
stringbit = paste(stringbit, stringbits[i])
} else {
if(nchar(new_string) == 0){
new_string = stringbit
} else {
new_string = paste0(new_string,"\n",stringbit)
}
stringbit = ""
}
i = i+1
}
if(nchar(stringbit) > 0){
new_string = paste0(new_string,"\n",stringbit)
}
return(new_string)
}
data.table(
characteristic = c("Role of host government", "Accessibility of pre-existing public health services", "Security environment", "Disease risk factors and burden") %>% sapply(function(x) splitString(x, max_chars = 12)) %>% linebreak("l"),
scen_a = c("Relatively benign, non-oppressive entity (e.g. Somalia). Government has some control over the affected region and is perceived as legitimate (e.g. Burkina Faso)", "Moderate to good (e.g. the affected population has effective access to pre-existing health facilities offering routine vaccination; e.g. South Sudanese refugees in Uganda)", "Stable; populations have taken refuge in a secure region (e.g. most refugees). Low probability of further displacement", sprintf("Relatively low%s", footnote_marker_symbol(2))) %>% sapply(function(x) splitString(x, max_chars = 16)) %>% linebreak("l"),
scen_b = c("Involved in war crimes or otherwise hostile to parts/all of the affected population (e.g. South Sudan, Syria). Government is either not perceived as legitimate (e.g. Burma) or has limited/no control of the region (e.g. Afghanistan increasingly)", "Low or none (e.g. IDPs are in an unplanned site with no pre-existing health services, or the latter have minimal functionality/routine vaccination coverage (e.g. Tigray, Yemen)", "Volatile, with intermittent access to populations (e.g. Central African Republic, South Sudan, north-east Nigeria, or lake Chad region). High probability of further displacement", sprintf("Relatively high%s (particularly in the case of camp-based IDPs)", footnote_marker_symbol(2))) %>% sapply(function(x) splitString(x, max_chars = 16)) %>% linebreak("l")
) %>% kableExtra::kable(
caption="Relative best- and relative worst-case scenario for humanitarian PCV vaccination",
col.names=c("Characteristic", "Scenario A (best-case)", "Scenario B (worst-case)"),
booktabs=TRUE, escape=FALSE) %>%
kableExtra::kable_styling(latex_options = "HOLD_position") %>% footnote(symbol="Compared to the average crisis-affected population")
c("Involved in war crimes or otherwise hostile to parts/all of the affected population (e.g. South Sudan, Syria). Government is either not perceived as legitimate (e.g. Burma) or has limited/no control of the region (e.g. Afghanistan increasingly)", "Low or none (e.g. IDPs are in an unplanned site with no pre-existing health services, or the latter have minimal functionality/routine vaccination coverage (e.g. Tigray, Yemen)", "Volatile, with intermittent access to populations (e.g. Central African Republic, South Sudan, north-east Nigeria, or lake Chad region). High probability of further displacement", sprintf("Relatively high%s (particularly in the case of camp-based IDPs)", footnote_marker_symbol(2))) %>% sapply(function(x) splitString(x, max_chars = 16))
scen_b = c("Involved in war crimes or otherwise hostile to parts/all of the affected population (e.g. South Sudan, Syria). Government is either not perceived as legitimate (e.g. Burma) or has limited/no control of the region (e.g. Afghanistan increasingly)", "Low or none (e.g. IDPs are in an unplanned site with no pre-existing health services, or the latter have minimal functionality/routine vaccination coverage (e.g. Tigray, Yemen)", "Volatile, with intermittent access to populations (e.g. Central African Republic, South Sudan, north-east Nigeria, or lake Chad region). High probability of further displacement", sprintf("Relatively high%s (particularly in the case of camp-based IDPs)", footnote_marker_symbol(2))) %>% sapply(function(x) splitString(x, max_chars = 10))
c("Involved in war crimes or otherwise hostile to parts/all of the affected population (e.g. South Sudan, Syria). Government is either not perceived as legitimate (e.g. Burma) or has limited/no control of the region (e.g. Afghanistan increasingly)", "Low or none (e.g. IDPs are in an unplanned site with no pre-existing health services, or the latter have minimal functionality/routine vaccination coverage (e.g. Tigray, Yemen)", "Volatile, with intermittent access to populations (e.g. Central African Republic, South Sudan, north-east Nigeria, or lake Chad region). High probability of further displacement", sprintf("Relatively high%s (particularly in the case of camp-based IDPs)", footnote_marker_symbol(2))) %>% sapply(function(x) splitString(x, max_chars = 10))
c("Involved in war crimes or otherwise hostile to parts/all of the affected population (e.g. South Sudan, Syria). Government is either not perceived as legitimate (e.g. Burma) or has limited/no control of the region (e.g. Afghanistan increasingly)", "Low or none (e.g. IDPs are in an unplanned site with no pre-existing health services, or the latter have minimal functionality/routine vaccination coverage (e.g. Tigray, Yemen)", "Volatile, with intermittent access to populations (e.g. Central African Republic, South Sudan, north-east Nigeria, or lake Chad region). High probability of further displacement", sprintf("Relatively high%s (particularly in the case of camp-based IDPs)", footnote_marker_symbol(2))) %>% sapply(function(x) splitString(x, max_chars = 10))
c("Involved in war crimes or otherwise hostile to parts/all of the affected population (e.g. South Sudan, Syria). Government is either not perceived as legitimate (e.g. Burma) or has limited/no control of the region (e.g. Afghanistan increasingly)", "Low or none (e.g. IDPs are in an unplanned site with no pre-existing health services, or the latter have minimal functionality/routine vaccination coverage (e.g. Tigray, Yemen)", "Volatile, with intermittent access to populations (e.g. Central African Republic, South Sudan, north-east Nigeria, or lake Chad region). High probability of further displacement", sprintf("Relatively high%s (particularly in the case of camp-based IDPs)", footnote_marker_symbol(2))) %>% sapply(function(x) splitString(x, max_chars = 10))
data.table(
characteristic = c("Role of host government", "Accessibility of pre-existing public health services", "Security environment", "Disease risk factors and burden") %>% sapply(function(x) splitString(x, max_chars = 10)) %>% linebreak("l"),
scen_a = c("Relatively benign, non-oppressive entity (e.g. Somalia). Government has some control over the affected region and is perceived as legitimate (e.g. Burkina Faso)", "Moderate to good (e.g. the affected population has effective access to pre-existing health facilities offering routine vaccination; e.g. South Sudanese refugees in Uganda)", "Stable; populations have taken refuge in a secure region (e.g. most refugees). Low probability of further displacement", sprintf("Relatively low%s", footnote_marker_symbol(2))) %>% sapply(function(x) splitString(x, max_chars = 10)) %>% linebreak("l"),
scen_b = c("Involved in war crimes or otherwise hostile to parts/all of the affected population (e.g. South Sudan, Syria). Government is either not perceived as legitimate (e.g. Burma) or has limited/no control of the region (e.g. Afghanistan increasingly)", "Low or none (e.g. IDPs are in an unplanned site with no pre-existing health services, or the latter have minimal functionality/routine vaccination coverage (e.g. Tigray, Yemen)", "Volatile, with intermittent access to populations (e.g. Central African Republic, South Sudan, north-east Nigeria, or lake Chad region). High probability of further displacement", sprintf("Relatively high%s (particularly in the case of camp-based IDPs)", footnote_marker_symbol(2))) %>% sapply(function(x) splitString(x, max_chars = 10)) %>% linebreak("l")
)
data.table(
characteristic = c("Role of host government", "Accessibility of pre-existing public health services", "Security environment", "Disease risk factors and burden") %>% sapply(function(x) splitString(x, max_chars = 10)) %>% linebreak("l"),
scen_a = c("Relatively benign, non-oppressive entity (e.g. Somalia). Government has some control over the affected region and is perceived as legitimate (e.g. Burkina Faso)", "Moderate to good (e.g. the affected population has effective access to pre-existing health facilities offering routine vaccination; e.g. South Sudanese refugees in Uganda)", "Stable; populations have taken refuge in a secure region (e.g. most refugees). Low probability of further displacement", sprintf("Relatively low%s", footnote_marker_symbol(2))) %>% sapply(function(x) splitString(x, max_chars = 10)) %>% linebreak("l"),
scen_b = c("Involved in war crimes or otherwise hostile to parts/all of the affected population (e.g. South Sudan, Syria). Government is either not perceived as legitimate (e.g. Burma) or has limited/no control of the region (e.g. Afghanistan increasingly)", "Low or none (e.g. IDPs are in an unplanned site with no pre-existing health services, or the latter have minimal functionality/routine vaccination coverage (e.g. Tigray, Yemen)", "Volatile, with intermittent access to populations (e.g. Central African Republic, South Sudan, north-east Nigeria, or lake Chad region). High probability of further displacement", sprintf("Relatively high%s (particularly in the case of camp-based IDPs)", footnote_marker_symbol(2))) %>% sapply(function(x) splitString(x, max_chars = 10)) %>% linebreak("l")
)
splitString("Involved in war crimes or otherwise")
splitString("Involved in war crimes or otherwise") %>% linebreak("l")
?linebreak
splitString("Involved in war crimes or otherwise") %>% linebreak("l", TRUE)
splitString("Involved in war crimes or otherwise") %>% linebreak("l", linebreaker="\n")
splitString("Involved in war crimes or otherwise") %>% linebreak("l", linebreaker="<br>")
splitString("Involved in war crimes or otherwise")
(string = "Involved in war crimes or otherwise")
string
stringbits = strsplit(string, " ")[[1]]
stringbits
new_string = ""
stringbit = ""
i = 1
if(nchar(stringbit) == 0){
stringbit = stringbits[i]
} else if(nchar(stringbit) < max_chars){
stringbit = paste(stringbit, stringbits[i])
} else {
if(nchar(new_string) == 0){
new_string = stringbit
} else {
new_string = paste0(new_string,"\n",stringbit)
}
stringbit = ""
}
i = i+1
if(nchar(stringbit) == 0){
stringbit = stringbits[i]
} else if(nchar(stringbit) < max_chars){
stringbit = paste(stringbit, stringbits[i])
} else {
if(nchar(new_string) == 0){
new_string = stringbit
} else {
new_string = paste0(new_string,"\n",stringbit)
}
stringbit = ""
}
if(nchar(stringbit) == 0){
stringbit = stringbits[i]
} else if(nchar(stringbit) < max_chars){
stringbit = paste(stringbit, stringbits[i])
} else {
if(nchar(new_string) == 0){
new_string = stringbit
} else {
new_string = paste0(new_string,"\n",stringbit)
}
stringbit = ""
}
i = i+1
stringbit
i
i <= length(stringbits)
nchar(stringbit) == 0
nchar(stringbit) < max_chars
stringbits[i]
stringbit = paste(stringbit, stringbits[i])
stringbit
i = i+1
nchar(stringbit) < max_chars
new_string = ""
stringbit = ""
i = 1
while(i <= length(stringbits)){
if(nchar(stringbit) == 0){
stringbit = stringbits[i]
} else if(nchar(stringbit) < max_chars){
stringbit = paste(stringbit, stringbits[i])
} else {
if(nchar(new_string) == 0){
new_string = stringbit
} else {
new_string = paste0(new_string,"\n",stringbit)
}
stringbit = ""
}
i = i+1
}
new_string
stringbit
stringbits = strsplit(string, " ")[[1]]
stringbits
new_string = ""
stringbit = ""
i = 1
if(nchar(stringbit) == 0){
stringbit = stringbits[i]
} else if(nchar(stringbit) < max_chars){
stringbit = paste(stringbit, stringbits[i])
} else {
if(nchar(new_string) == 0){
new_string = stringbit
} else {
new_string = paste0(new_string,"\n",stringbit)
}
stringbit = ""
}
i = i+1
if(nchar(stringbit) == 0){
stringbit = stringbits[i]
} else if(nchar(stringbit) < max_chars){
stringbit = paste(stringbit, stringbits[i])
} else {
if(nchar(new_string) == 0){
new_string = stringbit
} else {
new_string = paste0(new_string,"\n",stringbit)
}
stringbit = ""
}
i = i+1
i
nchar(stringbit) == 0
nchar(stringbit) < max_chars
stringbit
new_string = ""
stringbit = ""
i = 1
if(nchar(stringbit) == 0){
stringbit = stringbits[i]
} else if(nchar(stringbit) < max_chars){
stringbit = paste(stringbit, stringbits[i])
} else {
if(nchar(new_string) == 0){
new_string = stringbit
} else {
new_string = paste0(new_string,"\n",stringbit)
}
stringbit = ""
}
i = i+1
i
if(nchar(stringbit) == 0){
stringbit = stringbits[i]
} else if(nchar(stringbit) < max_chars){
stringbit = paste(stringbit, stringbits[i])
} else {
if(nchar(new_string) == 0){
new_string = stringbit
} else {
new_string = paste0(new_string,"\n",stringbit)
}
stringbit = ""
}
i = i+1
i
stringbit
if(nchar(stringbit) == 0){
stringbit = stringbits[i]
} else if(nchar(stringbit) < max_chars){
stringbit = paste(stringbit, stringbits[i])
} else {
if(nchar(new_string) == 0){
new_string = stringbit
} else {
new_string = paste0(new_string,"\n",stringbit)
}
stringbit = ""
}
i = i+1
i
stringbit
nchar(stringbit) < max_chars
nchar(new_string) == 0
stringbit
new_string = ""
stringbit = ""
i = 1
while(i <= length(stringbits)){
if(nchar(stringbit) == 0){
stringbit = stringbits[i]
i = i+1
} else if(nchar(stringbit) < max_chars){
stringbit = paste(stringbit, stringbits[i])
i = i+1
} else {
if(nchar(new_string) == 0){
new_string = stringbit
} else {
new_string = paste0(new_string,"\n",stringbit)
}
stringbit = ""
}
}
new_string
stringbit
caption="Relative best- and relative worst-case scenario for humanitarian PCV vaccination",
human_responses = fread("./data/humanitarian_responses.csv")
human_responses %>% setorder(type, position) %>% .[, -c("type", "position")]
human_responses[type == "idp"]
human_responses[type == "idp", .N]
?footnote
#company_id = 'CIK0001318605'
company_id = 'CIK0001618835'
#company_id = 'CIK0001411690'
company_facts_url = sprintf('https://data.sec.gov/api/xbrl/companyfacts/%s.json', company_id)
request = httr::GET(url=company_facts_url)
#' We check the return code passed by the server. If the server is configured
#'  correctly, we should receive a 200 code (success)
if(request$status_code != 200){
stop("Something went wrong")
}
request = httr::GET(url=company_facts_url)
#' We check the return code passed by the server. If the server is configured
#'  correctly, we should receive a 200 code (success)
if(request$status_code != 200){
stop("Something went wrong")
}
request = httr::GET(url=company_facts_url)
#' We check the return code passed by the server. If the server is configured
#'  correctly, we should receive a 200 code (success)
if(request$status_code != 200){
stop("Something went wrong")
}
#' If the request was succesful, we can use the content function to retrieve the
#'  response from our request. If all went well, we should receive a HTML
#'  document.
#' The text on the webpage is encoded as 'Windows-1252'. We need to specify this
#'  to the content function to allow it to properly read the page.
#' The content function will automatically parse this as an xml_document (which
#' is an R data type from the xml2 package)
response = httr::content(request, type='application/json', as='parsed')
common_stock_outstanding = response$facts$dei$EntityCommonStockSharesOutstanding$units$shares %>% lapply(as.data.table) %>% rbindlist(fill=T) %>%
.[, date_end := as.Date(end)]
common_stock_outstanding %>% ggplot(aes(x=date_end, y=val))+geom_point()
public_float = response$facts$dei$EntityPublicFloat$units$USD %>% lapply(as.data.table) %>% rbindlist(fill=T)  %>%
.[, date_end := as.Date(end)]
public_float %>% ggplot(aes(x=date_end, y=val))+geom_point()
common_stock_outstanding %>% merge(public_float, by="frame") %>% .[!is.na(frame)] %>% .[, float_per_share := val.y/val.x] %>% ggplot(aes(x=date_end.x, xend=date_end.y, y=float_per_share, yend=float_per_share))+geom_segment()
response$facts$`us-gaap`$Assets$units$USD %>% lapply(as.data.table) %>% rbindlist(fill=T) %>% .[, date_end := as.Date(end)] %>% ggplot(aes(x=date_end, y=val))+geom_point()
response$facts$`us-gaap`$DebtCurrent$units$USD %>% lapply(as.data.table) %>% rbindlist(fill=T) %>% .[, date_end := as.Date(end)] %>% ggplot(aes(x=date_end, y=val))+geom_point()
response$facts$`us-gaap`$LongTermDebtCurrent$units$USD %>% lapply(as.data.table) %>% rbindlist(fill=T) %>% .[, date_end := as.Date(end)] %>% ggplot(aes(x=date_end, y=val))+geom_point()
response$facts$`us-gaap`$Cash$units$USD %>% lapply(as.data.table) %>% rbindlist(fill=T) %>% .[, date_end := as.Date(end)] %>% ggplot(aes(x=date_end, y=val))+geom_point()
response$facts$`us-gaap`$DebtCurrent$units$USD %>% lapply(as.data.table) %>% rbindlist(fill=T) %>% .[, date_end := as.Date(end)] %>% ggplot(aes(x=date_end, y=val))+geom_point()
response$facts$`us-gaap`$LongTermDebtCurrent$units$USD %>% lapply(as.data.table) %>% rbindlist(fill=T) %>% .[, date_end := as.Date(end)] %>% ggplot(aes(x=date_end, y=val))+geom_point()
response$facts$`us-gaap`$Cash$units$USD %>% lapply(as.data.table) %>% rbindlist(fill=T) %>% .[, date_end := as.Date(end)] %>% ggplot(aes(x=date_end, y=val))+geom_point()
1/0.1331
1/0.3935
48444/12
library("socialmixr")
socialmixr::list_surveys()
socialmixr::get_survey("https://zenodo.org/record/5226281#.YR-TzlvTVH6")
pacman::p_load(magrittr, socialmixr)
digaale_contact_data = socialmixr::get_survey("https://zenodo.org/record/5226281#.YR-TzlvTVH6")
digaale_contact_data %>% attributes()
digaale_contact_data %>% attr()
ls))
ls()
digaale_survey_population = read.csv("https://zenodo.org/record/5226281/files/espicc_somaliland_digaale_survey_population.csv")
digaale_survey_population
digaale_contact_data %>%
socialmixr::contact_matrix(survey.pop = digaale_survey_population, age.limits = digaale_survey_population$lower.age.limit, symmetric = TRUE, weights = "survey_weight", weigh.dayofweek = TRUE)
digaale_contact_data %>%
socialmixr::contact_matrix(survey.pop = digaale_survey_population, age.limits = digaale_survey_population$lower.age.limit, symmetric = TRUE, weights = "survey_weight", weigh.dayofweek = TRUE) %>%
image()
digaale_contact_data %>%
socialmixr::contact_matrix(survey.pop = digaale_survey_population, age.limits = digaale_survey_population$lower.age.limit, symmetric = TRUE, weights = "survey_weight", weigh.dayofweek = TRUE) %>%
.[["matrix"]] %>% image()
digaale_contact_data$participants
digaale_contact_data$participants[, c("dayofweek", "weekend")]
digaale_contact_data$participants[, c("dayofweek", "weekend")] %>% unique
digaale_contact_data$participants[, c("dayofweek", "dayofweek_name", "weekend")] %>% unique
digaale_contact_data$participants[, c("dayofweek", "dayofweek_name", "weekend")] %>% unique %>% setorder(dayofweek) %>% .[]
pacman::p_load(magrittr, socialmixr, data.table)
digaale_contact_data$participants[, c("dayofweek", "dayofweek_name", "weekend")] %>% unique %>% setorder(dayofweek) %>% .[]
digaale_contact_data$participants[, c("dayofweek", "dayofweek_name", "weekend")] %>% unique %>% setorder(dayofweek) %>%
.[, newdayofweek := dayofweek - 1]
digaale_contact_data$participants[, c("dayofweek", "dayofweek_name", "weekend")] %>% unique %>% setorder(dayofweek) %>%
.[, newdayofweek := dayofweek - 1] %>% .[]
digaale_contact_data$participants[, c("dayofweek", "dayofweek_name", "weekend")] %>% unique %>% setorder(dayofweek) %>%
.[, newdayofweek := dayofweek + 1] %>% .[]
digaale_contact_data$participants[, c("dayofweek", "dayofweek_name", "weekend")] %>% unique %>% setorder(dayofweek) %>%
.[, newdayofweek := ifelse(dayofweek == 6, 0, dayofweek + 1)] %>% .[]
digaale_contact_data$participants[, c("dayofweek", "dayofweek_name", "weekend")] %>% unique %>% setorder(dayofweek) .[]
digaale_contact_data$participants[, c("dayofweek", "dayofweek_name", "weekend")] %>% unique %>% setorder(dayofweek) %>% .[]
digaale_contact_data$participants[, dayofweek := ifelse(dayofweek == 6, 0, dayofweek + 1)]
digaale_contact_data$participants[, c("dayofweek", "dayofweek_name", "weekend")] %>% unique %>% setorder(dayofweek) %>% .[]
digaale_contact_data %>%
socialmixr::contact_matrix(survey.pop = digaale_survey_population, age.limits = digaale_survey_population$lower.age.limit, symmetric = TRUE, weights = "survey_weight", weigh.dayofweek = TRUE) %>%
.[["matrix"]] %>% image()
digaale_contact_matrix = digaale_contact_data %>%
socialmixr::contact_matrix(survey.pop = digaale_survey_population,
age.limits = digaale_survey_population$lower.age.limit,
symmetric = TRUE, weights = "survey_weight", weigh.dayofweek = TRUE)
digaale_contact_data %>%
socialmixr::contact_matrix(survey.pop = digaale_survey_population,
age.limits = digaale_survey_population$lower.age.limit,
symmetric = TRUE, weights = "survey_weight", weigh.dayofweek = TRUE) %>% cite()
digaale_contact_data %>% cite()
#' The estimated population size in Digaale (for provided age groups) can manually be downloaded
digaale_survey_population = data.table::fread("https://zenodo.org/record/5226281/files/espicc_somaliland_digaale_survey_population.csv")
#' The estimated population size in Digaale (for provided age groups) can manually be downloaded
digaale_survey_population =
data.table::fread("https://zenodo.org/record/5226281/files/espicc_somaliland_digaale_survey_population.csv")
digaale_contact_data$participants[, c("dayofweek", "dayofweek_name", "weekend")] %>% unique %>% setorder(dayofweek) %>% .[]
#' Get data from Zenodo
digaale_contact_data = socialmixr::get_survey("https://zenodo.org/record/5226281#.YR-TzlvTVH6")
digaale_contact_data$participants[, c("dayofweek", "dayofweek_name", "weekend")] %>% unique %>% setorder(dayofweek) %>% .[]
digaale_contact_data$participants[, c("dayofweek", "dayofweek_name", "weekend")] %>% unique %>% setorder(dayofweek) %>% .[]
pacman::p_load(magrittr, socialmixr, data.table)
#' Get data from Zenodo
digaale_contact_data = socialmixr::get_survey("https://zenodo.org/record/5226281#.YR-TzlvTVH6")
#' The estimated population size in Digaale (for provided age groups) can manually be downloaded
digaale_survey_population =
data.table::fread("https://zenodo.org/record/5226281/files/espicc_somaliland_digaale_survey_population.csv")
#' Note that weekends fall on Fridays and Saturdays in Somaliland.
#' - The dayofweek variable provided in the dataset has been kept consistent with R defaults (0: Sunday to 6: Saturday)
#' - socialmixr currently assumes dayofweek 5
digaale_contact_data$participants[, c("dayofweek", "dayofweek_name", "weekend")] %>% unique %>% setorder(dayofweek) %>% .[]
digaale_contact_matrix
pacman::p_load(magrittr, socialmixr, data.table)
#' Get data from Zenodo
digaale_contact_data = socialmixr::get_survey("https://zenodo.org/record/5226281#.YR-TzlvTVH6")
#' The estimated population size in Digaale (for provided age groups) can manually be downloaded
digaale_survey_population =
data.table::fread("https://zenodo.org/record/5226281/files/espicc_somaliland_digaale_survey_population.csv")
#' Note that weekends fall on Fridays and Saturdays in Somaliland.
#' - The dayofweek variable provided in the dataset has been kept consistent with R defaults (0: Sunday to 6: Saturday)
digaale_contact_data$participants[, c("dayofweek", "dayofweek_name", "weekend")] %>%
unique %>% setorder(dayofweek) %>% .[]
#' socialmixr currently assumes the weekend to fall on dayofweek 6 (Saturday) and 0 (Sunday)
#' - dayofweek can be manually edited so that Fridays and Saturdays are taken as the weekend, if you wish to weight
#'   contacts by weekday
digaale_contact_data$participants[, dayofweek := ifelse(dayofweek == 6, 0, dayofweek + 1)]
#' The contact matrix can then be constructed as follows
#' - The provided survey_population can be used to construct a population representative matrix for Digaale IDP camp
#' - As the sample is not self-weighing (oversampling of young age groups), it is recommended to apply the survey_weight
#'   as weights
digaale_contact_matrix = digaale_contact_data %>%
socialmixr::contact_matrix(survey.pop = digaale_survey_population,
age.limits = digaale_survey_population$lower.age.limit,
symmetric = TRUE, weights = "survey_weight", weigh.dayofweek = TRUE)
digaale_contact_matrix
digaale_contact_matrix$matrix
digaale_contact_matrix %>% round(1)
digaale_contact_matrix$matrix %>% round(1)
digaale_contact_matrix$matrix %>% rowSums()
digaale_contact_data$participants$sample_weight
digaale_contact_data$participants$sample_weight %>% summary()
digaale_contact_matrix$matrix %>% round(1)
getwd()
#' Install and load dependencies
#' - make sure you have pacman installed: install.packages("pacman")
#' - or install and load manually
pacman::p_load(data.table, survey, ggplot2, patchwork, magrittr, kableExtra, stringr, lmtest, zscorer)
setwd("~/workspace/espicc-somaliland-digaale-survey-2019")
getwd()
#' Set the analysis directory (usually the root directory of repo)
analysis_dir = getwd()
#' Set global options, create output folders, load helper functions, and load data
source(sprintf("%s/scripts/0_setup.R", analysis_dir))
#' This estimates the total population size in Digaale, and FPC to use in the models
#' - creates table_sB1_vacant_households
#' - creates table_sB2_households_visited
source(sprintf("%s/scripts/1_fpc_correction.R", analysis_dir))
#' This does some additional cleaning of the data, assigns age-groups, creates new variables, and creates the survey
#' objects to use when correcting for FPC and to do the poststratification
source(sprintf("%s/scripts/2_prepare_data.R", analysis_dir))
#' Create table 1: Characteristics of participating households and prevalence of risk factors in Digaale IDP camp
#' - creates table1_characteristics_and_risk_factors
source(sprintf("%s/scripts/3_table1.R", analysis_dir))
#' Create tables 2, 3, and supplemental table C1
#' - creates table2_travel
#' - creates table3_contacts
#' - creates table_sC1_school_work_sens
source(sprintf("%s/scripts/4_tables_other.R", analysis_dir))
#' Regression analysis assessing collected variables with self-reported pneumonia incidence
#' - creates table_sD1_regression_pneumonia
#' - creates table_sD2_regression_pneumonia_6m
source(sprintf("%s/scripts/5_tables_regression.R", analysis_dir))
#' Create figure 1: Demographic distributions in Digaale IDP camp
#' - creates figure1_demographics
source(sprintf("%s/scripts/6_figure1.R", analysis_dir))
#' Create figure 2: Contact frequencies, types, and matrices
#' - creates figure2_contacts
source(sprintf("%s/scripts/7_figure2.R", analysis_dir))
#' Additional analyses contact estimates
#' - creates figure_sC1_bootstrapped_matrices
#' - creates figure_sC2_intra_extra_household_contacts
#' - creates figure_sC3_contact_bysex
#' - creates figure_sC4_household_contacts_expected_reported
source(sprintf("%s/scripts/8_figures_supp.R", analysis_dir))