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server.R
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# server functions' file
#source("scripts/read_data.R")
# source("scripts/compat_fxs.R")
# source("scripts/PT_fxs.R")
# source("scripts/ET_fxs.R")
# source("scripts/Lima_fxs.R")
# source("scripts/UK_fxs.R")
library(DT)
library(dplyr)
library(magrittr)
library(gtsummary)
library(histoc)
#############################################################
# function for multiple donors
res_mult <- function(df.donors = donors,
df.candidates = candidates,
df.abs = abs.d,
algorithm = pts,
n = 0,
check.validity = FALSE,
...){
all_pairs <- donor_recipient_pairs(df.donors = df.donors,
df.candidates = df.candidates,
df.abs = df.abs,
algorithm = algorithm,
n = 0,
check.validity = FALSE,
...)
# for loop to select 2 available candidates for a pool of donors
used.candidates <- NULL
result <- NULL
for(i in 1: length(all_pairs)){
tmp <- all_pairs[[i]][!ID %in% used.candidates][1:2,]
result <- data.table::rbindlist(list(result, tmp))
used.candidates <- c(used.candidates, tmp$ID)
}
result[!is.na(ID),] %>%
dplyr::rowwise() %>%
dplyr::mutate(txScore = histoc::txscore(recipient.age = age
, recipient.dialysis = dialysis
, donor.age = donor_age
, mmHLA_A = mmA
, mmHLA_B = mmB
, mmHLA_DR = mmDR)$prob5y
) %>% dplyr::ungroup()
}
## function for Utility / Justice matrix
# matrix columns (cos) and rows (ros)
co <- c('AgeDiff < 9 \n mmHLA 0-2',
'AgeDiff < 9 \n mmHLA 3-4',
'AgeDiff >= 9 \n mmHLA 0-2',
'AgeDiff < 9 \n mmHLA 5-6',
'AgeDiff >= 9 \n mmHLA 3-4',
'AgeDiff >= 9 \n mmHLA 5-6')
ro <- c('TmDial > q3 \n cPRA >50',
'TmDial > q3 \n cPRA <=50',
'TmDial > q2 \n cPRA >50',
'TmDial > q2 \n cPRA <=50',
'TmDial <= q2 \n cPRA >50',
'TmDial <= q2 \n cPRA <=50')
exg <- expand.grid(co,ro) %>%
rename(cos = Var1,
ros = Var2)
# data
mtx_data_uj <- function(u = 0.1, j = 0.1){
exg %>%
mutate(value = c(t(histoc::uj_matx(ratio.util = u, ratio.just = j))))
}
# plot
gg_matx <- function(data){
ggplot2::ggplot(data,
ggplot2::aes(x=cos, y=ros, col = value, fill = value, label = value)) +
ggplot2::geom_tile(ggplot2::aes(width=.90, height=.90)) +
ggplot2::geom_text(col = "black") +
ggplot2::theme_minimal() +
ggplot2::labs(x = 'Utility', y = 'Justice') +
ggplot2::scale_x_discrete(position = "top") +
ggplot2::scale_y_discrete(limits = rev(unique(sort(exg$ros)))) +
viridis::scale_fill_viridis(alpha = 0.7, option = 'G') +
# scale_fill_gradient2(low = "red", #mid = "yellow",
# high = "green") +
# scale_color_gradient2(low = "red", #mid = "yellow",
# high = "green")
ggplot2::theme(legend.position = 'none')
}
############################################
function(input, output, session) {
output$ex.cands<- renderDataTable({
datatable(histoc::candidates,
rownames = FALSE)
})
output$ex.abs<- renderDataTable({
datatable(histoc::cabs, #ex.abs
rownames = FALSE)
})
output$ex.donors<- renderDataTable({
datatable(histoc::donors, # ex.donors
rownames = FALSE)
})
# reactive to the input UK files
ukf<-reactive(input$ukfiles)
# read candidates' file
datasetCands <- reactive({
file_cands <- input$file_cand
if (is.null(file_cands))
return(NULL)
if (input$fileSepDF == 1) {
data<-read.csv(file_cands$datapath)
} else if (input$fileSepDF == 2) {
read.delim(file_cands$datapath)
} else if (input$fileSepDF == 3) {
data<-read.csv2(file_cands$datapath)
} else {data<-read.table(file_cands$datapath, encoding = "UTF-8")}
validate(
if(ukf() == 1){
need(histoc:::uk_candidate_dataframe_check(data) ,
"Candidates' file is not valid for UK algorithm!")
} else {
need(histoc:::candidate_dataframe_check(data),
"Candidates' file is not valid!")}
)
data %>%
dplyr::mutate_at(vars(ID, A1,A2,B1,B2,DR1,DR2),as.character)
})
# read donors' file
datasetDonors <- reactive({
file_donors <- input$file_donor
if (is.null(file_donors))
return(NULL)
if (input$fileSepDF == 1) {
data<-read.csv(file_donors$datapath)
} else if (input$fileSepDF == 2) {
read.delim(file_donors$datapath)
} else if (input$fileSepDF == 3) {
data<-read.csv2(file_donors$datapath)
} else {data<-read.table(file_donors$datapath, encoding = "UTF-8")}
validate(
if(ukf() == 1){
need(histoc:::uk_donors_dataframe_check(data),
"Donors' file is not valid for UK algorithm!")
} else {
need(histoc:::donors_dataframe_check(data),
"Donors' file is not valid!")}
)
data %>%
dplyr::mutate_at(vars(ID,A1,A2,B1,B2,DR1,DR2),as.character) %>%
dplyr::mutate_at(vars(age), as.numeric)
})
# read candidates' antibodies' file
datasetAbs <- reactive({
file_abss <- input$file_abs
if (is.null(file_abss))
return(NULL)
if (input$fileSepDF == 1) {
data<-read.csv(file_abss$datapath)
} else if (input$fileSepDF == 2) {
read.delim(file_abss$datapath)
} else if (input$fileSepDF == 3) {
data<-read.csv2(file_abss$datapath)
} else {data<-read.table(file_abss$datapath, encoding = "UTF-8")}
validate(
need(identical(colnames(data),colnames(histoc::cabs)),
"HLA antibodies column names are not identical to example data!")
)
data %>%
dplyr::mutate_at(vars(ID), as.character)
})
output$sel.cands<- renderDataTable({
datasetCands() %>% datatable(rownames = FALSE)
})
output$sel.abs<- renderDataTable({
datasetAbs() %>% datatable(rownames = FALSE)
})
output$sel.donors<- renderDataTable({
datasetDonors() %>% datatable(rownames = FALSE)
})
############################
### PT algorithm
############################
## for 10 best candidates selected according to unique donor
output$res1 <- renderDataTable({
if (input$dataInput == 1) {candidates<-histoc::candidates} else {candidates<-datasetCands()}
if (input$dataInput == 1) {abs.d<-histoc::cabs} else {abs.d<-datasetAbs()}
validate(
need(candidates != "", "Please select a candidates data set!")
)
validate(
need(abs.d != "", "Please select candidates' HLA antibodies data set!")
)
dt<-histoc::pts(iso = input$iso, # isogroup compatibility
dABO = input$dabo, # donor's blood group
dA = c(input$a1,input$a2),
dB = c(input$b1,input$b2),
dDR = c(input$dr1,input$dr2),
donor.age = input$dage, # donor's age
data = candidates, # data file with candidates
points.80 = as.numeric(input$pra8), # points for a PRA equal or higher than 80%
points.50 = as.numeric(input$pra5), # points for a PRA equal or higher than 50%
points.dialysis = input$dialysis, # points for each month on dialysis
points.age = input$age_dif, # points for age difference in PT punctuation table
itemA = as.numeric(input$a), # points for A) on PT points table
itemB = as.numeric(input$b), # points for B) on PT points table
itemC = as.numeric(input$c), # points for C) on PT points table
itemD = as.numeric(input$d), # points for D) on PT points table
itemE = as.numeric(input$e), # points for E) on PT points table
df.abs = abs.d, # candidates' HLA antibodies
n = 10,
check.validity = FALSE)
dt <- dt %>%
dplyr::rowwise() %>%
dplyr::mutate(txScore = histoc::txscore(recipient.age = age
, recipient.dialysis = dialysis
, donor.age = donor_age
, mmHLA_A = mmA
, mmHLA_B = mmB
, mmHLA_DR = mmDR)$prob5y
) %>% dplyr::ungroup()
datatable(dt, options = list(pageLength = 5, dom = 'tip'))
})
#### to reset PT sidebarpanel
observeEvent(input$reset_inputPT, {
shinyjs::reset("side-panelPT")
})
N <- 1
compute_resm <- reactiveVal()
observeEvent(input$Go, {
compute_resm(NULL)
withProgress(message = 'Calculation in progress, be patient!', {
for(i in 1:N){
# Long Running Task
Sys.sleep(0.0001)
# Update progress
incProgress(1/N)
}
if (input$dataInput == 1) {candidates<-histoc::candidates} else {candidates<-datasetCands() %>%
select(ID, bg,A1,A2,B1,B2,DR1,DR2,age,dialysis,cPRA)}
if (input$dataInput == 1) {abs.d<-histoc::cabs} else {abs.d<-datasetAbs()}
if (input$dataInput == 1) {donors<-histoc::donors} else {donors<-datasetDonors()}
validate(
need(candidates != "", "Please select a candidates data set!")
)
validate(
need(abs.d != "", "Please select candidates' HLA antibodies data set!")
)
validate(
need(donors != "", "Please select donors' data set!")
)
dt <- res_mult(df.donors = donors,
df.candidates = candidates,
df.abs = abs.d,
algorithm = pts,
n = 0,
check.validity = FALSE,
iso = input$iso,
points.80 = as.numeric(input$pra8),
points.50 = as.numeric(input$pra5),
points.dialysis = input$dialysis,
points.age = input$age_dif,
itemA = as.numeric(input$a),
itemB = as.numeric(input$b),
itemC = as.numeric(input$c),
itemD = as.numeric(input$d),
itemE = as.numeric(input$e)
)
compute_resm(dt)
})
})
output$resm <- renderDataTable({
compute_resm()
})
# Downloadable csv of selected dataset ----
output$downloadData <- downloadHandler(
filename = function() {
paste("PT_results", ".csv", sep = "")
},
content = function(file) {
write.csv2(compute_resm(), file, row.names = FALSE, fileEncoding="latin1")
}
)
## Resume dataset results from PT algorithm
output$resumePT <-
render_gt({
validate(
need(compute_resm() != "", "Results will be presented after the run!")
)
tabsum<-compute_resm() %>%
dplyr::select(bg, age, dialysis, cPRA, HI, mmHLA, txScore) %>%
dplyr::rename(`Blood group` = bg,
`receptores' age (years)` = age,
`time on dialysis (months)` = dialysis,
`Hiper Immunized` = HI,
`HLA miss matchs` = mmHLA,
TxScore = txScore)
gtsummary::tbl_summary(tabsum) %>% gtsummary::as_gt()
})
############################
### ET algorithm
############################
## for 10 first candidates selected according to unique donor
output$res1ET <- renderDataTable({
if (input$hlafreqs == 1) {
hlaA <- histoc::hlaApt
hlaB <- histoc::hlaBpt
hlaDR <- histoc::hlaDRpt
ABOfreq <- histoc::ABOpt
} else {
hlaA <- histoc::hlaAet
hlaB <- histoc::hlaBet
hlaDR <- histoc::hlaDRet
ABOfreq <- histoc::ABOpt
}
if (input$dataInput == 1) {candidates<-histoc::candidates} else {candidates<-datasetCands()}
if (input$dataInput == 1) {abs.d<-histoc::cabs} else {abs.d<-datasetAbs()}
validate(
need(candidates != "", "Please select a candidates data set!")
)
validate(
need(abs.d != "", "Please select candidates' HLA antibodies data set!")
)
dt<-et(iso = input$isoET,
dABO = input$daboET,
dA = c(input$a1ET,input$a2ET),
dB = c(input$b1ET,input$b2ET),
dDR = c(input$dr1ET,input$dr2ET),
donor.age = input$dageET,
data = candidates,
month = as.numeric(input$tdET),
mm0 = as.numeric(input$mm0),
mm1 = as.numeric(input$mm1),
mm2 = as.numeric(input$mm2),
mm3 = as.numeric(input$mm3),
mm4 = as.numeric(input$mm4),
mm5 = as.numeric(input$mm5),
mm6 = as.numeric(input$mm6),
df.abs = abs.d,
hlaA = hlaA,
hlaB = hlaB,
hlaDR = hlaDR,
abo.freq = ABOfreq,
n = 10)
dt <- dt %>%
dplyr::rowwise() %>%
dplyr::mutate(txScore = histoc::txscore(recipient.age = age
, recipient.dialysis = dialysis
, donor.age = donor_age
, mmHLA_A = mmA
, mmHLA_B = mmB
, mmHLA_DR = mmDR)$prob5y
) %>% dplyr::ungroup()
datatable(dt, options = list(pageLength = 5, dom = 'tip'))
})
observeEvent(input$reset_inputET, {
shinyjs::reset("side-panelET")
})
## compute nultiple results for ET algorithm
compute_resmET <- reactiveVal()
observeEvent(input$GoET, {
compute_resmET(NULL)
withProgress(message = 'Calculation in progress, be patient!', {
for(i in 1:N){
# Long Running Task
Sys.sleep(0.01)
# Update progress
incProgress(1/N)
}
if (input$hlafreqs == 1) {
hlaA <- histoc::hlaApt
hlaB <- histoc::hlaBpt
hlaDR <- histoc::hlaDRpt
ABOfreq <- histoc::ABOpt
} else {
hlaA <- histoc::hlaAet
hlaB <- histoc::hlaBet
hlaDR <- histoc::hlaDRet
ABOfreq <- histoc::ABOpt
}
if (input$dataInput == 1) {candidates<-histoc::candidates} else {candidates<-datasetCands()}
if (input$dataInput == 1) {abs.d<-histoc::cabs} else {abs.d<-datasetAbs()}
if (input$dataInput == 1) {donors<-histoc::donors} else {donors<-datasetDonors()}
validate(
need(candidates != "", "Please select a candidates data set!")
)
validate(
need(abs.d != "", "Please select candidates' HLA antibodies data set!")
)
validate(
need(donors != "", "Please select donors' data set!")
)
dt <- res_mult(df.donors = donors,
df.candidates = candidates,
df.abs = abs.d,
algorithm = et,
n = 0,
check.validity = FALSE,
iso = input$isoET,
month = as.numeric(input$tdET),
mm0 = as.numeric(input$mm0),
mm1 = as.numeric(input$mm1),
mm2 = as.numeric(input$mm2),
mm3 = as.numeric(input$mm3),
mm4 = as.numeric(input$mm4),
mm5 = as.numeric(input$mm5),
mm6 = as.numeric(input$mm6),
hlaA = hlaA,
hlaB = hlaB,
hlaDR = hlaDR,
abo.freq = ABOfreq,
)
compute_resmET(dt)
})
})
output$resmET <- renderDataTable({
compute_resmET()
})
# Downloadable csv of selected dataset ----
output$downloadDataET <- downloadHandler(
filename = function() {
paste("ET_results", ".csv", sep = "")
},
content = function(file) {
write.csv2(compute_resmET(), file, row.names = FALSE, fileEncoding="latin1")
}
)
## Resume dataset results from ET algorithm
output$resumeET <-
render_gt({
validate(
need(compute_resmET() != "", "Results will be presented after the run!")
)
tabsum<-compute_resmET() %>%
dplyr::select(bg, age, dialysis, cPRA, HI, mmHLA, txScore) %>%
dplyr::rename(`Blood group` = bg,
`receptores' age (years)` = age,
`time on dialysis (months)` = dialysis,
`Hiper Immunized` = HI,
`HLA miss matchs` = mmHLA,
TxScore = txScore)
gtsummary::tbl_summary(tabsum) %>% gtsummary::as_gt()
})
############################
### Lima algorithm
############################
#### to reset LIMA sidebarpanel
observeEvent(input$reset_inputLIMA, {
shinyjs::reset("side-panelLima")
})
output$res1LIMA <- renderDataTable({
if (input$dataInput == 1) {candidates<-histoc::candidates} else {candidates<-datasetCands()}
if (input$dataInput == 1) {abs.d<-histoc::cabs} else {abs.d<-datasetAbs()}
validate(
need(candidates != "", "Please select a candidates data set!")
)
validate(
need(abs.d != "", "Please select candidates' HLA antibodies data set!")
)
dt<-histoc::lima(iso = input$isoLIMA, # isogroup compatibility
dABO = input$daboLIMA, # donor's blood group
dA = c(input$a1LIMA,input$a2LIMA),
dB = c(input$b1LIMA,input$b2LIMA),
dDR = c(input$dr1LIMA,input$dr2LIMA),
donor.age = input$dageLIMA, # donor's age
data = candidates, # data file with candidates
df.abs = abs.d, # candidates' HLA antibodies
n = 10,
q2 = input$td2q,
q3 = input$td3q,
cPRA1 = 50,
cPRA2 = 85,
check.validity = FALSE)
dt <- dt %>%
dplyr::rowwise() %>%
dplyr::mutate(txScore = histoc::txscore(recipient.age = age
, recipient.dialysis = dialysis
, donor.age = donor_age
, mmHLA_A = mmA
, mmHLA_B = mmB
, mmHLA_DR = mmDR)$prob5y
) %>% dplyr::ungroup()
datatable(dt, options = list(pageLength = 5, dom = 'tip'))
})
## compute multiple results for LIMA algorithm
compute_resmLIMA <- reactiveVal()
observeEvent(input$GoLIMA, {
compute_resmLIMA(NULL)
withProgress(message = 'Calculation in progress, be patient!', {
for(i in 1:N){
# Long Running Task
Sys.sleep(1)
# Update progress
incProgress(1/N)
}
if (input$dataInput == 1) {candidates<-histoc::candidates} else {candidates<-datasetCands()}
if (input$dataInput == 1) {abs.d<-histoc::cabs} else {abs.d<-datasetAbs()}
if (input$dataInput == 1) {donors<-histoc::donors} else {donors<-datasetDonors()}
validate(
need(candidates != "", "Please select a candidates data set!")
)
validate(
need(abs.d != "", "Please select candidates' HLA antibodies data set!")
)
validate(
need(donors != "", "Please select donors' data set!")
)
dt <- res_mult(df.donors = donors,
df.candidates = candidates,
df.abs = abs.d,
algorithm = lima,
n = 0,
check.validity = FALSE,
iso = input$isoLIMA,
q2 = input$td2q,
q3 = input$td3q,
cPRA1 = 50,
cPRA2 = 85
)
compute_resmLIMA(dt)
})
})
output$resmLIMA <- renderDataTable({
compute_resmLIMA()
})
# Downloadable csv of selected dataset ----
output$downloadDataLIMA <- downloadHandler(
filename = function() {
paste("LIMA_results", ".csv", sep = "")
},
content = function(file) {
write.csv2(compute_resmLIMA(), file, row.names = FALSE, fileEncoding="latin1")
}
)
## Resume dataset results from LIMA algorithm
output$resumeLIMA <-
render_gt({
validate(
need(compute_resmLIMA() != "", "Results will be presented after the run!")
)
tabsum<-compute_resmLIMA() %>%
dplyr::select(bg, age, dialysis, cPRA, HI, mmHLA, txScore) %>%
dplyr::rename(`Blood group` = bg,
`receptores' age (years)` = age,
`time on dialysis (months)` = dialysis,
`Hiper Immunized` = HI,
`HLA miss matchs` = mmHLA,
TxScore = txScore)
gtsummary::tbl_summary(tabsum) %>% gtsummary::as_gt()
})
############################
### UK Transplant
############################
# Donor-recipient risk index combinations
# multiplyer<-reactive({input$multipleUK})
output$tableDRriskUK <- renderDT({
dt<-data.frame(R1=c(1000,700,350,0),
R2=c(700,1000,500,350),
R3=c(350,500,1000,700),
R4=c(0,350,700,1000))
rownames(dt)<-c("D1","D2","D3","D4")
datatable(dt * 1, # multiplyer()
selection = 'single', escape=FALSE,
options = list(searching = FALSE, dom = 't'))
})
# Matchability - illustrative plot
pointsM<-reactive({
input$mUK * (1 + ((1:9) / input$nUK)^input$oUK)
})
output$matchability<-renderPlot({
ggplot2::ggplot(data.frame(Points = pointsM(), MatchScore = 1:9)) +
ggplot2::geom_line(ggplot2::aes(MatchScore,Points)) +
ggplot2::ggtitle("Points scored illustration")
})
#### to reset UK sidebarpanel
observeEvent(input$reset_inputUK, {
shinyjs::reset("side-panelUK")
})
############################ UK algorithm ###################
## compute DRI for one donor
driv<-reactive({
exp(0.023 * (input$dageUK-50) +
-0.152 * ((input$dheightUK - 170) / 10) +
0.149 * ifelse(input$dhtUK == 'Yes', 1, 0) +
-0.184 * ifelse(input$dsexUK == 'Female', 1, 0) +
0.19 * ifelse(input$dcmvUK == 'Yes', 1, 0) +
-0.023 * (input$dgfrUK-90)/10 +
0.015 * input$dhospUK
)
})
output$dri<-renderText({
paste("DRI is:", round(driv(),2), "; the donor belong to",
ifelse(driv() <= 0.79, "D1",
ifelse(driv() <= 1.12,"D2",
ifelse(driv() <= 1.5,"D3","D4"))))
})
output$res1UK <- renderDataTable({
if (input$dataInput == 1) {candidates<-histoc::candidates.uk} else {candidates<-datasetCands()}
if (input$dataInput == 1) {abs.d<-histoc::cabs} else {abs.d<-datasetAbs()}
validate(
need(candidates != "", "Please select a candidates data set!")
)
validate(
need(abs.d != "", "Please select candidates' HLA antibodies data set!")
)
dt<-histoc::uk(DRI = ifelse(driv() <= 0.79, "D1",
ifelse(driv() <= 1.12,"D2",
ifelse(driv() <= 1.5,"D3","D4"))), # Donor RisK Index group
dA = c(input$a1UK,input$a2UK), # donor's HLA typing'
dB = c(input$b1UK,input$b2UK),
dDR = c(input$dr1UK,input$dr2UK),
dABO = input$daboUK, # donors' blood group
donor.age = input$dageUK, # donors' age
data = candidates, # data file with candidates
D1R1 = 1000,
D1R2 = 700,
D1R3 = 350,
D1R4 = 0,
D2R1 = 700,
D2R2 = 1000,
D2R3 = 500,
D2R4 = 350,
D3R1 = 350,
D3R2 = 500,
D3R3 = 1000,
D3R4 = 700,
D4R1 = 0,
D4R2 = 350,
D4R3 = 700,
D4R4 = 1000,
ptsDial = input$tdUK,
a1 = input$aa1UK,
a2 = input$aa2UK,
b1 = input$bb1UK,
b2 = input$bb2UK,
b3 = input$bb3UK,
m = input$mUK,
nn = input$nUK,
o = input$oUK,
mm1 = as.numeric(input$mm1UK),
mm23 = as.numeric(input$mm23UK),
mm46 = as.numeric(input$mm46UK),
pts = input$bloodUK,
df.abs = abs.d,
n = 10,
check.validity = FALSE
)
dt <- dt %>%
dplyr::rowwise() %>%
dplyr::mutate(txScore = histoc::txscore(recipient.age = age
, recipient.dialysis = dialysis
, donor.age = donor_age
, mmHLA_A = mmA
, mmHLA_B = mmB
, mmHLA_DR = mmDR)$prob5y
) %>% dplyr::ungroup()
datatable(dt, options = list(pageLength = 5, dom = 'tip'))
})
## compute nultiple results for UK algorithm
compute_resmUK <- reactiveVal()
observeEvent(input$GoUK, {
compute_resmUK(NULL)
withProgress(message = 'Calculation in progress, be patient!', {
for(i in 1:N){
# Long Running Task
Sys.sleep(1)
# Update progress
incProgress(1/N)
}
if (input$dataInput == 1) {candidates<-histoc::candidates.uk} else {candidates<-datasetCands()}
if (input$dataInput == 1) {abs.d<-histoc::cabs} else {abs.d<-datasetAbs()}
if (input$dataInput == 1) {donors<-histoc::donors.uk %>%
select(ID, bg, A1, A2, B1, B2, DR1, DR2, age, DRI)} else {donors<-datasetDonors()}
validate(
need(candidates != "", "Please select a candidates data set!")
)
validate(
need(abs.d != "", "Please select candidates' HLA antibodies data set!")
)
validate(
need(donors != "", "Please select donors' data set!")
)
dt <- res_mult(df.donors = donors,
df.candidates = candidates,
df.abs = abs.d,
algorithm = uk,
n = 0,
check.validity = FALSE,
DRI = ifelse(driv() <= 0.79, "D1",
ifelse(driv() <= 1.12,"D2",
ifelse(driv() <= 1.5,"D3","D4"))),
D1R1 = 1000,
D1R2 = 700,
D1R3 = 350,
D1R4 = 0,
D2R1 = 700,
D2R2 = 1000,
D2R3 = 500,
D2R4 = 350,
D3R1 = 350,
D3R2 = 500,
D3R3 = 1000,
D3R4 = 700,
D4R1 = 0,
D4R2 = 350,
D4R3 = 700,
D4R4 = 1000,
ptsDial = input$tdUK,
a1 = input$aa1UK,
a2 = input$aa2UK,
b1 = input$bb1UK,
b2 = input$bb2UK,
b3 = input$bb3UK,
m = input$mUK,
nn = input$nUK,
o = input$oUK,
mm1 = as.numeric(input$mm1UK),
mm23 = as.numeric(input$mm23UK),
mm46 = as.numeric(input$mm46UK),
pts = input$bloodUK
)
# add to reactiveval
compute_resmUK(dt)
})
})
output$resmUK <- renderDataTable({
compute_resmUK()
})
# Downloadable csv of selected dataset ----
output$downloadDataUK <- downloadHandler(
filename = function() {
paste("UK_results", ".csv", sep = "")
},
content = function(file) {
write.csv2(compute_resmUK(), file, row.names = FALSE, fileEncoding="latin1")
}
)
## Resume dataset results from UK algorithm
output$resumeUK <-
render_gt({
validate(
need(compute_resmUK() != "", "Results will be presented after the run!")
)
tabsum<-compute_resmUK() %>%
dplyr::select(bg, age, dialysis, cPRA, Tier, mmHLA, txScore) %>%
dplyr::rename(`Blood group` = bg,
`receptores' age (years)` = age,
`time on dialysis (months)` = dialysis,
`Tier` = Tier,
`HLA miss matchs` = mmHLA,
TxScore = txScore)
gtsummary::tbl_summary(tabsum) %>% gtsummary::as_gt()
})
############################
### EQM algorithm
############################
# Utility/Justice matrix - illustrative plot
ratio.u<-reactive({
input$ratio.utilEQM
})
ratio.j<-reactive({
input$ratio.justEQM
})
output$uj_matx_plot<-renderPlot({
matx_data <- mtx_data_uj(u = ratio.u(), j = ratio.j())
gg_matx(data = matx_data)
})
#### to reset EQM sidebarpanel
observeEvent(input$reset_inputEQM, {
shinyjs::reset("side-panelEQM")
})
output$res1EQM <- renderDataTable({
if (input$dataInput == 1) {candidates<-histoc::candidates} else {candidates<-datasetCands()}
if (input$dataInput == 1) {abs.d<-histoc::cabs} else {abs.d<-datasetAbs()}
validate(
need(candidates != "", "Please select a candidates data set!")
)
validate(
need(abs.d != "", "Please select candidates' HLA antibodies data set!")
)
dt<-histoc::eqm(iso = input$isoEQM, # isogroup compatibility
dABO = input$daboEQM, # donor's blood group
dA = c(input$a1EQM,input$a2EQM),
dB = c(input$b1EQM,input$b2EQM),
dDR = c(input$dr1EQM,input$dr2EQM),
donor.age = input$dageEQM, # donor's age
data = candidates, # data file with candidates
df.abs = abs.d, # candidates' HLA antibodies
n = 10,
uj_matx(max.val = 100, ratio.util = input$ratio.utilEQM, ratio.just = input$ratio.justEQM),
q2 = input$td2q_eqm,
q3 = input$td3q_eqm,
SP = input$spEQM,
AM = input$amEQM,
mm000 = input$mm000EQM,
check.validity = FALSE)
dt <- dt %>%
dplyr::rowwise() %>%
dplyr::mutate(txScore = histoc::txscore(recipient.age = age
, recipient.dialysis = dialysis
, donor.age = donor_age
, mmHLA_A = mmA
, mmHLA_B = mmB
, mmHLA_DR = mmDR)$prob5y
) %>% dplyr::ungroup()
datatable(dt, options = list(pageLength = 5, dom = 'tip'))
})
## compute multiple results for EQM algorithm
compute_resmEQM <- reactiveVal()
observeEvent(input$GoEQM, {
compute_resmEQM(NULL)