Nothing
Cancer RADAR report
In cancerradarr: Cancer RADAR Project Tool
knitr::opts_chunk$set(echo = TRUE)
library('tidyverse')
library('plotly')
library('cancerradarr')
filename.out <- params$filename.out
# filename.out <- '~/Documents/gitlab/cancerradarr/inst/extdata/ex_cancerRADAR_output.xlsx'
Scatter plot
dat.01.iso3 <- read_cancerradar_output_01(filename.out, aggr.level = 'cob_iso3')
dat.01.unreg <- read_cancerradar_output_01(filename.out, aggr.level = 'un_region')
dat.01.unsubreg <- read_cancerradar_output_01(filename.out, aggr.level = 'un_subregion')
dat.01.hdi <- read_cancerradar_output_01(filename.out, aggr.level = 'hdi_cat')
dat.01.burden <- read_cancerradar_output_01(filename.out, aggr.level = 'asr_rank_cat')
dat.01.any <- read_cancerradar_output_01(filename.out, aggr.level = 'any_migr')
## check migrant population at risk availability
has.py.migrant <-
any(!is.na(dat.01.any |> filter(index == 'sir', reg_label %in% c('migrant'), ref == 'registry', can != 'allC') |> pull('est')))
index.names.01 <-
if(has.py.migrant){
c('cir', 'cirr', 'cird', 'asir', 'asirr', 'asird', 'sir', 'pir', 'py', 'n')
} else {
c('pir', 'n')
}
index.x100000.01 <- c('cir', 'cird', 'asir', 'asird')
aggr.names.01 <-
c(
'country', 'UN region', 'UN subregion', 'HDI category', 'burden in country of origin', 'any migration background'
)
inputPanel(
radioButtons('aggr_', label = 'Aggregation level', choices = aggr.names.01, selected = 'UN region'),
checkboxInput('ref_', label = 'Add general population', value = TRUE)
)
inputPanel(
selectInput("x_sex_", label = "X sex",
choices = c('female', 'male'), selected = 'female'),
selectInput("x_can_", label = "X cancer",
choices = c('allC', 'cx', 'liv', 'stm', 'brea', 'colo', 'lun'), selected = ifelse(has.py.migrant, 'allC', 'cx') ),
selectInput("x_ref_", label = "X reference data",
choices = c('registry', 'globocan national', 'globocan eu27', 'globocan euUN'), selected = 'registry'),
selectInput("x_index_", label = "X index",
choices = index.names.01, selected = ifelse(has.py.migrant, 'sir', 'pir')),
selectInput("x_trans_", label = "X transformation",
choices = c('identity', 'log', 'log10', 'log2'), selected = 'identity'),
checkboxInput("x_ci_", label = 'X confidence interval', value = FALSE)
)
inputPanel(
selectInput("y_sex_", label = "Y sex",
choices = c('female', 'male'), selected = 'female'),
selectInput("y_can_", label = "Y cancer",
choices = c('allC', 'cx', 'liv', 'stm', 'brea', 'colo', 'lun'), selected = ifelse(has.py.migrant, 'allC', 'brea')),
selectInput("y_ref_", label = "Y reference data",
choices = c('registry', 'globocan national', 'globocan eu27', 'globocan euUN'), selected = 'registry'),
selectInput("y_index_", label = "Y index",
choices = index.names.01, selected = ifelse(has.py.migrant, 'sir', 'pir')),
selectInput("y_trans_", label = "Y transformation",
choices = c('identity', 'log', 'log10', 'log2'), selected = 'identity'),
checkboxInput("y_ci_", label = 'Y confidence interval', value = FALSE)
)
renderPlotly({
dat.01 <-
switch(
input$aggr_,
'country' = dat.01.iso3,
'UN region' = dat.01.unreg,
'UN subregion' = dat.01.unsubreg,
'HDI category' = dat.01.hdi,
'burden in country of origin' = dat.01.burden,
'any migration background' = dat.01.any
)
gg.dat.01 <-
left_join(
dat.01 %>%
dplyr::filter(
sex == as.character(input$x_sex_),
can == as.character(input$x_can_),
ref == as.character(input$x_ref_),
index == as.character(input$x_index_)
),
dat.01 %>%
dplyr::filter(
sex == as.character(input$y_sex_),
can == as.character(input$y_can_),
ref == as.character(input$y_ref_),
index == as.character(input$y_index_)
),
by = c('reg_label')
) |>
filter(
!is.na(est.x),
!is.na(est.y)
)
gg.01 <-
ggplot(aes(x = est.x, y = est.y, label = reg_label, colour = reg_label), data = gg.dat.01 %>% filter(reg_label != 'general population')) +
(if(input$ref_){geom_hline(aes(yintercept = est.y), data = gg.dat.01 %>% filter(reg_label == 'general population'), show.legend = FALSE, linetype = 2)}) +
(if(input$ref_){geom_vline(aes(xintercept = est.x), data = gg.dat.01 %>% filter(reg_label == 'general population'), show.legend = FALSE, linetype = 2)}) +
(if(input$x_ci_){geom_segment(aes(y = est.y, yend = est.y, x = lci.x, xend = uci.x))}) +
(if(input$y_ci_){geom_segment(aes(y = lci.y, yend = uci.y, x = est.x, xend = est.x))}) +
geom_point() +
scale_x_continuous(
paste0(as.character(input$x_index_), ' - ', as.character(input$x_can_), ', ', as.character(input$x_sex_), if(as.character(input$x_index_) %in% index.x100000.01){' (per 100.000 person.year)'}),
trans = as.character(input$x_trans_),
labels = if(as.character(input$x_index_) %in% index.x100000.01) {function(x) scales::comma(x, scale = 100000)} else {function(x) scales::comma(x)}
) +
scale_y_continuous(
paste0(as.character(input$y_index_), ' - ', as.character(input$y_can_), ', ', as.character(input$y_sex_), if(as.character(input$y_index_) %in% index.x100000.01){' (per 100.000 person.year)'}),
trans = as.character(input$y_trans_),
labels = if(as.character(input$y_index_) %in% index.x100000.01) {function(x) scales::comma(x, scale = 100000)} else {function(x) scales::comma(x)}
) +
labs(
colour = as.character(input$aggr_)
) +
theme_light()
plotly::ggplotly(gg.01)
})
Age specific incidence / proportion rates
dat.02.iso3 <- read_cancerradar_output_02(filename.out, aggr.level = 'cob_iso3')
dat.02.unreg <- read_cancerradar_output_02(filename.out, aggr.level = 'un_region')
dat.02.unsubreg <- read_cancerradar_output_02(filename.out, aggr.level = 'un_subregion')
dat.02.hdi <- read_cancerradar_output_02(filename.out, aggr.level = 'hdi_cat')
dat.02.burden <- read_cancerradar_output_02(filename.out, aggr.level = 'asr_rank_cat')
dat.02.any <- read_cancerradar_output_02(filename.out, aggr.level = 'any_migr')
index.names.02 <-
if(has.py.migrant){
c('ir', 'pr')
} else {
c('pr')
}
index.x100000.02 <- c('ir')
inputPanel(
radioButtons('aggr.02_', label = 'Aggregation level', choices = aggr.names.01, selected = 'any migration background'),
checkboxInput('ref.02_', label = 'Add general population', value = TRUE)
)
inputPanel(
selectInput("sex_", label = "sex",
choices = c('female', 'male'), selected = 'female'),
selectInput("can_", label = "cancer",
choices = c('allC', 'cx', 'liv', 'stm', 'brea', 'colo', 'lun'), selected = ifelse(has.py.migrant, 'allC', 'brea')),
selectInput("index_", label = "index",
choices = index.names.02, selected = ifelse(has.py.migrant, 'ir', 'pr')),
selectInput("trans_", label = "transformation",
choices = c('identity', 'log', 'log10', 'log2'), selected = 'identity'),
checkboxInput("ci_", label = 'confidence interval', value = FALSE)
)
renderPlotly({
dat.02 <-
switch(
input$aggr.02_,
'country' = dat.02.iso3,
'UN region' = dat.02.unreg,
'UN subregion' = dat.02.unsubreg,
'HDI category' = dat.02.hdi,
'burden in country of origin' = dat.02.burden,
'any migration background' = dat.02.any
)
gg.dat.02 <-
dat.02 %>%
dplyr::filter(
sex == as.character(input$sex_),
can == as.character(input$can_),
index == as.character(input$index_)
) |>
filter(
!is.na(est)
)
gg.02 <-
ggplot(aes(x = ageg_mid, y = est, label = reg_label, group = reg_label, colour = reg_label), data = gg.dat.02 %>% dplyr::filter(reg_label != 'general population')) +
(if(input$ref.02_ & input$ci_){geom_ribbon(aes(x = ageg_mid, ymin = lci, ymax = uci, group = reg_label), alpha = .2, colour = NA, fill = 'black', data = gg.dat.02 %>% filter(reg_label == 'general population'), inherit.aes = FALSE, show.legend = FALSE)}) +
(if(input$ref.02_){geom_line(aes(x = ageg_mid, y = est, group = reg_label), linetype = 2, color = 'black', data = gg.dat.02 %>% filter(reg_label == 'general population'), inherit.aes = FALSE, show.legend = FALSE)}) +
(if(input$ci_){geom_ribbon(aes(ymin = lci, ymax = uci, fill = reg_label), alpha = .2, colour = NA)}) +
geom_line() +
scale_y_continuous(
paste0(as.character(input$index_), ' - ', as.character(input$can_), ', ', as.character(input$sex_), if(as.character(input$index_) %in% index.x100000.02){' (per 100.000 person.year)'}),
trans = as.character(input$trans_),
labels = if(as.character(input$index_) %in% index.x100000.02) {function(x) scales::comma(x, scale = 100000)} else {function(x) scales::comma(x)}
) +
labs(
colour = as.character(input$aggr.02_),
fill = as.character(input$aggr.02_),
x = 'Age (years)'
) +
theme_light()
plotly::ggplotly(gg.02)
})
ASIR for different cancer types stratified by migration background
aggr.names.03 <-
c(
'country', 'UN region', 'UN subregion', 'HDI category', 'burden in country of origin', 'any migration background'
)
inputPanel(
radioButtons('aggr.03_', label = 'Aggregation level', choices = aggr.names.03, selected = 'any migration background'),
checkboxInput('ref.03_', label = 'Add general population', value = TRUE)
)
inputPanel(
selectInput("sex.03_", label = "sex",
choices = c('female', 'male'), selected = 'female'),
checkboxGroupInput("can.03_", "cancer",
choices = c('allC', 'cx', 'liv', 'stm', 'brea', 'colo', 'lun'), selected = c('cx', 'brea')),
selectInput("trans.03_", label = "transformation",
choices = c('identity', 'log', 'log10', 'log2'), selected = 'log10'),
checkboxInput("ci.03_", label = 'confidence interval', value = FALSE)
)
renderPlotly({
dat.03 <-
switch(
input$aggr.03_,
'country' = dat.01.iso3,
'UN region' = dat.01.unreg,
'UN subregion' = dat.01.unsubreg,
'HDI category' = dat.01.hdi,
'burden in country of origin' = dat.01.burden,
'any migration background' = dat.01.any
)
gg.dat.03 <-
dat.03 |>
dplyr::filter(
sex == as.character(input$sex.03_),
can %in% as.character(input$can.03_),
index == 'asir'
) |>
filter(
!is.na(est)
)
gg.03 <-
gg.dat.03 |>
ggplot(aes(x = can, y = est, color = reg_label)) +
geom_point(aes(shape = (reg_label == 'general population')), size = 3, show.legend = FALSE) +
(if(input$ci.03_){geom_segment(aes(y = lci, yend = uci, x = can, xend = can))}) +
scale_y_continuous(trans = as.character(input$trans.03_), labels = function(.x) scales::comma(.x, scale = 100000)) +
scale_shape_manual(values = c(6, 19)) +
labs(x = 'Cancer type', y = 'ASIR per 100.000 person.year', color = 'Population') +
theme_light() +
theme(
legend.background = element_rect(fill = 'white', colour = 'lightgrey')
) +
guides(shape = FALSE)
plotly::ggplotly(gg.03)
})
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cancerradarr documentation built on Aug. 8, 2025, 7:28 p.m.
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knitr::opts_chunk$set(echo = TRUE)
library('tidyverse') library('plotly') library('cancerradarr')
filename.out <- params$filename.out # filename.out <- '~/Documents/gitlab/cancerradarr/inst/extdata/ex_cancerRADAR_output.xlsx'
Scatter plot
dat.01.iso3 <- read_cancerradar_output_01(filename.out, aggr.level = 'cob_iso3') dat.01.unreg <- read_cancerradar_output_01(filename.out, aggr.level = 'un_region') dat.01.unsubreg <- read_cancerradar_output_01(filename.out, aggr.level = 'un_subregion') dat.01.hdi <- read_cancerradar_output_01(filename.out, aggr.level = 'hdi_cat') dat.01.burden <- read_cancerradar_output_01(filename.out, aggr.level = 'asr_rank_cat') dat.01.any <- read_cancerradar_output_01(filename.out, aggr.level = 'any_migr') ## check migrant population at risk availability has.py.migrant <- any(!is.na(dat.01.any |> filter(index == 'sir', reg_label %in% c('migrant'), ref == 'registry', can != 'allC') |> pull('est')))
index.names.01 <- if(has.py.migrant){ c('cir', 'cirr', 'cird', 'asir', 'asirr', 'asird', 'sir', 'pir', 'py', 'n') } else { c('pir', 'n') } index.x100000.01 <- c('cir', 'cird', 'asir', 'asird') aggr.names.01 <- c( 'country', 'UN region', 'UN subregion', 'HDI category', 'burden in country of origin', 'any migration background' ) inputPanel( radioButtons('aggr_', label = 'Aggregation level', choices = aggr.names.01, selected = 'UN region'), checkboxInput('ref_', label = 'Add general population', value = TRUE) ) inputPanel( selectInput("x_sex_", label = "X sex", choices = c('female', 'male'), selected = 'female'), selectInput("x_can_", label = "X cancer", choices = c('allC', 'cx', 'liv', 'stm', 'brea', 'colo', 'lun'), selected = ifelse(has.py.migrant, 'allC', 'cx') ), selectInput("x_ref_", label = "X reference data", choices = c('registry', 'globocan national', 'globocan eu27', 'globocan euUN'), selected = 'registry'), selectInput("x_index_", label = "X index", choices = index.names.01, selected = ifelse(has.py.migrant, 'sir', 'pir')), selectInput("x_trans_", label = "X transformation", choices = c('identity', 'log', 'log10', 'log2'), selected = 'identity'), checkboxInput("x_ci_", label = 'X confidence interval', value = FALSE) ) inputPanel( selectInput("y_sex_", label = "Y sex", choices = c('female', 'male'), selected = 'female'), selectInput("y_can_", label = "Y cancer", choices = c('allC', 'cx', 'liv', 'stm', 'brea', 'colo', 'lun'), selected = ifelse(has.py.migrant, 'allC', 'brea')), selectInput("y_ref_", label = "Y reference data", choices = c('registry', 'globocan national', 'globocan eu27', 'globocan euUN'), selected = 'registry'), selectInput("y_index_", label = "Y index", choices = index.names.01, selected = ifelse(has.py.migrant, 'sir', 'pir')), selectInput("y_trans_", label = "Y transformation", choices = c('identity', 'log', 'log10', 'log2'), selected = 'identity'), checkboxInput("y_ci_", label = 'Y confidence interval', value = FALSE) ) renderPlotly({ dat.01 <- switch( input$aggr_, 'country' = dat.01.iso3, 'UN region' = dat.01.unreg, 'UN subregion' = dat.01.unsubreg, 'HDI category' = dat.01.hdi, 'burden in country of origin' = dat.01.burden, 'any migration background' = dat.01.any ) gg.dat.01 <- left_join( dat.01 %>% dplyr::filter( sex == as.character(input$x_sex_), can == as.character(input$x_can_), ref == as.character(input$x_ref_), index == as.character(input$x_index_) ), dat.01 %>% dplyr::filter( sex == as.character(input$y_sex_), can == as.character(input$y_can_), ref == as.character(input$y_ref_), index == as.character(input$y_index_) ), by = c('reg_label') ) |> filter( !is.na(est.x), !is.na(est.y) ) gg.01 <- ggplot(aes(x = est.x, y = est.y, label = reg_label, colour = reg_label), data = gg.dat.01 %>% filter(reg_label != 'general population')) + (if(input$ref_){geom_hline(aes(yintercept = est.y), data = gg.dat.01 %>% filter(reg_label == 'general population'), show.legend = FALSE, linetype = 2)}) + (if(input$ref_){geom_vline(aes(xintercept = est.x), data = gg.dat.01 %>% filter(reg_label == 'general population'), show.legend = FALSE, linetype = 2)}) + (if(input$x_ci_){geom_segment(aes(y = est.y, yend = est.y, x = lci.x, xend = uci.x))}) + (if(input$y_ci_){geom_segment(aes(y = lci.y, yend = uci.y, x = est.x, xend = est.x))}) + geom_point() + scale_x_continuous( paste0(as.character(input$x_index_), ' - ', as.character(input$x_can_), ', ', as.character(input$x_sex_), if(as.character(input$x_index_) %in% index.x100000.01){' (per 100.000 person.year)'}), trans = as.character(input$x_trans_), labels = if(as.character(input$x_index_) %in% index.x100000.01) {function(x) scales::comma(x, scale = 100000)} else {function(x) scales::comma(x)} ) + scale_y_continuous( paste0(as.character(input$y_index_), ' - ', as.character(input$y_can_), ', ', as.character(input$y_sex_), if(as.character(input$y_index_) %in% index.x100000.01){' (per 100.000 person.year)'}), trans = as.character(input$y_trans_), labels = if(as.character(input$y_index_) %in% index.x100000.01) {function(x) scales::comma(x, scale = 100000)} else {function(x) scales::comma(x)} ) + labs( colour = as.character(input$aggr_) ) + theme_light() plotly::ggplotly(gg.01) })
Age specific incidence / proportion rates
dat.02.iso3 <- read_cancerradar_output_02(filename.out, aggr.level = 'cob_iso3') dat.02.unreg <- read_cancerradar_output_02(filename.out, aggr.level = 'un_region') dat.02.unsubreg <- read_cancerradar_output_02(filename.out, aggr.level = 'un_subregion') dat.02.hdi <- read_cancerradar_output_02(filename.out, aggr.level = 'hdi_cat') dat.02.burden <- read_cancerradar_output_02(filename.out, aggr.level = 'asr_rank_cat') dat.02.any <- read_cancerradar_output_02(filename.out, aggr.level = 'any_migr')
index.names.02 <- if(has.py.migrant){ c('ir', 'pr') } else { c('pr') } index.x100000.02 <- c('ir') inputPanel( radioButtons('aggr.02_', label = 'Aggregation level', choices = aggr.names.01, selected = 'any migration background'), checkboxInput('ref.02_', label = 'Add general population', value = TRUE) ) inputPanel( selectInput("sex_", label = "sex", choices = c('female', 'male'), selected = 'female'), selectInput("can_", label = "cancer", choices = c('allC', 'cx', 'liv', 'stm', 'brea', 'colo', 'lun'), selected = ifelse(has.py.migrant, 'allC', 'brea')), selectInput("index_", label = "index", choices = index.names.02, selected = ifelse(has.py.migrant, 'ir', 'pr')), selectInput("trans_", label = "transformation", choices = c('identity', 'log', 'log10', 'log2'), selected = 'identity'), checkboxInput("ci_", label = 'confidence interval', value = FALSE) ) renderPlotly({ dat.02 <- switch( input$aggr.02_, 'country' = dat.02.iso3, 'UN region' = dat.02.unreg, 'UN subregion' = dat.02.unsubreg, 'HDI category' = dat.02.hdi, 'burden in country of origin' = dat.02.burden, 'any migration background' = dat.02.any ) gg.dat.02 <- dat.02 %>% dplyr::filter( sex == as.character(input$sex_), can == as.character(input$can_), index == as.character(input$index_) ) |> filter( !is.na(est) ) gg.02 <- ggplot(aes(x = ageg_mid, y = est, label = reg_label, group = reg_label, colour = reg_label), data = gg.dat.02 %>% dplyr::filter(reg_label != 'general population')) + (if(input$ref.02_ & input$ci_){geom_ribbon(aes(x = ageg_mid, ymin = lci, ymax = uci, group = reg_label), alpha = .2, colour = NA, fill = 'black', data = gg.dat.02 %>% filter(reg_label == 'general population'), inherit.aes = FALSE, show.legend = FALSE)}) + (if(input$ref.02_){geom_line(aes(x = ageg_mid, y = est, group = reg_label), linetype = 2, color = 'black', data = gg.dat.02 %>% filter(reg_label == 'general population'), inherit.aes = FALSE, show.legend = FALSE)}) + (if(input$ci_){geom_ribbon(aes(ymin = lci, ymax = uci, fill = reg_label), alpha = .2, colour = NA)}) + geom_line() + scale_y_continuous( paste0(as.character(input$index_), ' - ', as.character(input$can_), ', ', as.character(input$sex_), if(as.character(input$index_) %in% index.x100000.02){' (per 100.000 person.year)'}), trans = as.character(input$trans_), labels = if(as.character(input$index_) %in% index.x100000.02) {function(x) scales::comma(x, scale = 100000)} else {function(x) scales::comma(x)} ) + labs( colour = as.character(input$aggr.02_), fill = as.character(input$aggr.02_), x = 'Age (years)' ) + theme_light() plotly::ggplotly(gg.02) })
ASIR for different cancer types stratified by migration background
aggr.names.03 <- c( 'country', 'UN region', 'UN subregion', 'HDI category', 'burden in country of origin', 'any migration background' ) inputPanel( radioButtons('aggr.03_', label = 'Aggregation level', choices = aggr.names.03, selected = 'any migration background'), checkboxInput('ref.03_', label = 'Add general population', value = TRUE) ) inputPanel( selectInput("sex.03_", label = "sex", choices = c('female', 'male'), selected = 'female'), checkboxGroupInput("can.03_", "cancer", choices = c('allC', 'cx', 'liv', 'stm', 'brea', 'colo', 'lun'), selected = c('cx', 'brea')), selectInput("trans.03_", label = "transformation", choices = c('identity', 'log', 'log10', 'log2'), selected = 'log10'), checkboxInput("ci.03_", label = 'confidence interval', value = FALSE) ) renderPlotly({ dat.03 <- switch( input$aggr.03_, 'country' = dat.01.iso3, 'UN region' = dat.01.unreg, 'UN subregion' = dat.01.unsubreg, 'HDI category' = dat.01.hdi, 'burden in country of origin' = dat.01.burden, 'any migration background' = dat.01.any ) gg.dat.03 <- dat.03 |> dplyr::filter( sex == as.character(input$sex.03_), can %in% as.character(input$can.03_), index == 'asir' ) |> filter( !is.na(est) ) gg.03 <- gg.dat.03 |> ggplot(aes(x = can, y = est, color = reg_label)) + geom_point(aes(shape = (reg_label == 'general population')), size = 3, show.legend = FALSE) + (if(input$ci.03_){geom_segment(aes(y = lci, yend = uci, x = can, xend = can))}) + scale_y_continuous(trans = as.character(input$trans.03_), labels = function(.x) scales::comma(.x, scale = 100000)) + scale_shape_manual(values = c(6, 19)) + labs(x = 'Cancer type', y = 'ASIR per 100.000 person.year', color = 'Population') + theme_light() + theme( legend.background = element_rect(fill = 'white', colour = 'lightgrey') ) + guides(shape = FALSE) plotly::ggplotly(gg.03) })
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