更改DT（数据表）中的排序过滤器位置
问题描述 投票：0回答：1
我有以下代码：
library(shiny)
library(shinydashboard)
library(shinyWidgets)
library(dplyr)
library(DT)
library(tidyverse)
library(data.table)
#reproducible minimal data frame
YLMI <- structure(list(X = c(511L, 700L, 943L, 1402L, 1429L, 1483L, 1726L, 1834L, 1861L, 2266L),
name = c("Austria", "Belgium", "Bulgaria", "Cyprus", "Czech Republic", "Denmark",
"Estonia", "Finland", "France", "Iceland"),
year = c(2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L),
X1 = c(6.0948572, 5.1031427, 5.145143, 4.3162856, 5.9200001, 6.0751429, 5.8771429,
5.0911427, 4.8957143, 6.262857),
X2 = c(5.7982831, 5.1347985, 4.1193204,3.9259963, 5.9878144, 5.8885102, 5.5807657,
4.5704818, 4.8845162, 5.7285347),
X3 = c(5.8720002, 5.1729999, 4.1079998, 4.7049999, 5.8794999, 6.0700002, 5.3740001,
5.4159999, 5.2164998, 6.3175001),
X4 = c(6.0436354, 3.9714868, 6.0058327, 4.7928214, 4.636817, 6.1576967, 5.9891138,
3.3220425, 3.2921035, 4.1184382),
X5 = c(6.3000154, 5.7192054, 6.5671687, 3.4370663, 6.6064062, 5.8908257,
6.8782973, 4.7578831, 4.3325543, 6.2499504),
X6 = c(4.9257145, 5.5085716, 4.0457144, 3.737143, 2.817143, 5.0228572, 4.0057144,
3.0914288, 5.3942857, 1.7485714),
X7 = c(5.2685714, 5.8857141, 5.1657143, 4.4285712, 6.6914287, 3.7942855,
4.8914285, 5.7142859, 5.2857141, 5.0457144),
X8 = c(5.7268553, 5.3676248, 5.7317734, 5.1083288, 4.9277864, 6.2327962,
6.1439047, 5.5020885, 5.9025269, 5.6717625),
X9 = c(4.7919998, 5.428, 5.1039996, 4.7199998, 5.4880004, 6.2319999, 5.1399999,
5.3560004, 5.4160004, 5.3560004),
X10 = c(4.7384157, 3.7913544, 4.4407039, 5.8613172, 3.5934217, 5.534936,
4.0672798, 4.2066154, 4.3676648, 3.6402931),
X11 = c(5.7328, 5.1810961, 5.4579573, 5.5078635, 5.3274336, 5.7784905,
5.5863309, 5.2231383, 5.3318233, 5.2328768),
X12 = c(5.6389961, 3.9419262, 2.6277056, 4.8922715, 4.4109187, 6.3135815,
5.6100388, 6.3433652, 4.5896773, 6.6938777),
W1 = c(0.0833, 0.0833, 0.0833, 0.0833, 0.0833, 0.0833, 0.0833, 0.0833, 0.0833,
0.0833),
W2 = c(0.0833, 0.0833, 0.0833, 0.0833, 0.0833, 0.0833, 0.0833,
0.0833, 0.0833, 0.0833),
W3 = c(0.0833, 0.0833, 0.0833, 0.0833, 0.0833, 0.0833, 0.0833, 0.0833, 0.0833,
0.0833),
W4 = c(0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05),
W5 = c(0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05),
W6 = c(0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05),
W7 = c(0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05),
W8 = c(0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05),
W9 = c(0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125),
W10 = c(0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125),
W11 = c(0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125),
W12 = c(0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125),
indicators = c(12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L),
classes = c("A", "A", "A", "A", "A", "A", "A", "A", "A", "A"),
index_constant = c(5.51, 4.9, 4.69, 4.78, 5.12, 5.84, 5.35, 5.02, 4.92, 5.28),
ranking = c(18L, 48L, 59L, 53L, 31L, 7L, 25L, 36L, 45L, 27L)),
row.names = c(511L, 700L, 943L, 1402L, 1429L, 1483L, 1726L, 1834L, 1861L, 2266L),
class = "data.frame")
#helper function:
# ---- Index Calculation Based on User Weights ---- #
calculate_index_w_weights <- function(w1,w2,w3,w4) {
# Obtaining weights
weights <- array(rep(1,4))
# Creating weight matrices to re-calculate the indicator scores.
w1_matrix <- matrix(weights[1], nrow= 10, ncol=3)
w2_matrix <- matrix(weights[2], nrow= 10, ncol=5)
w3_matrix <- matrix(weights[3], nrow= 10, ncol=2)
w4_matrix <- matrix(weights[4], nrow= 10, ncol=2)
# Unnecessary for now
YLMI[,c("W1","W2","W3")]<-YLMI[,c("W1","W2","W3")] * w1_matrix
YLMI[,c("W4","W5","W6","W7", "W8")]<-YLMI[,c("W4","W5","W6", "W7","W8")] * w2_matrix
YLMI[,c("W9","W10")]<-YLMI[,c("W9","W10")] * w3_matrix
YLMI[,c("W11","W12")]<-YLMI[,c("W11","W12")] * w4_matrix
ActivityState = YLMI[,c("X1", "X2", "X3")] * YLMI[,c("W1","W2","W3")] #5454x3
WorkingConditions= YLMI[,c("X4", "X5", "X6", "X7", "X8")] * YLMI[,c("W4","W5","W6", "W7", "W8")] #5454x5
Education= YLMI[,c("X9", "X10")] * YLMI[,c("W9","W10")] #5454x2
TransitionSmoothness= YLMI[,c("X11", "X12")] * YLMI[,c("W11","W12")] #5454x2
c1 <- rowSums(ActivityState) #5454 x 1 sum(x1*w1....)
c2 <- rowSums(WorkingConditions)
c3 <- rowSums(Education)
c4 <- rowSums(TransitionSmoothness)
w1_i <-rowSums(YLMI[,c("W1","W2","W3")])
w2_i <-rowSums(YLMI[,c("W4","W5","W6","W7", "W8")])
w3_i <-rowSums(YLMI[,c("W9","W10")])
w4_i <-rowSums(YLMI[,c("W11","W12")])
# weighted_index = YLMI_Nominator / sum_weights
ActivityState = c1 / w1_i
WorkingConditions = c2 / w2_i
Education = c3 / w3_i
TransitionSmoothness = c4 / w4_i
# Category weighting
weights_category <- array(rep(0.25,4))
# User input on weights
w_unit <- 1 / (w1+w2+w3+w4)
weights_category[1] <- w_unit * w1
weights_category[2] <- w_unit * w2
weights_category[3] <- w_unit * w3
weights_category[4] <- w_unit * w4
w1_cat_matrix <- matrix(weights_category[1], nrow= 10, ncol=1)
w2_cat_matrix <- matrix(weights_category[2], nrow= 10, ncol=1)
w3_cat_matrix <- matrix(weights_category[3], nrow= 10, ncol=1)
w4_cat_matrix <- matrix(weights_category[4], nrow= 10, ncol=1)
categories <- data.frame(ActivityState, WorkingConditions, Education, TransitionSmoothness,
W1_C=w1_cat_matrix, W2_C=w2_cat_matrix, W3_C= w3_cat_matrix, W4_C=w4_cat_matrix)
categories[is.na(categories) == TRUE] = 0
# If category value is zero, then no weight assigned to that category for the index calculation.
categories <- within(categories, W1_C[ActivityState == 0] <- 0)
categories <- within(categories, W2_C[WorkingConditions == 0] <- 0)
categories <- within(categories, W3_C[Education == 0] <- 0)
categories <- within(categories, W4_C[TransitionSmoothness == 0] <- 0)
weights_category_sum <-rowSums(categories[,c("W1_C","W2_C","W3_C","W4_C")])
YLMI_Nominator1=categories[,c("ActivityState")] * categories[,c("W1_C")]
YLMI_Nominator2=categories[,c("WorkingConditions")] * categories[,c("W2_C")]
YLMI_Nominator3=categories[,c("Education")] * categories[,c("W3_C")]
YLMI_Nominator4=categories[,c("TransitionSmoothness")] * categories[,c("W4_C")]
YLMI_Nominator = YLMI_Nominator1 + YLMI_Nominator2 + YLMI_Nominator3 + YLMI_Nominator4
index = YLMI_Nominator / weights_category_sum
YLMI["weighted_index"]<-index
YLMI["ActivityState"]<-ActivityState
YLMI["WorkingConditions"]<-WorkingConditions
YLMI["Education"]<-Education
YLMI["TransitionSmoothness"]<-TransitionSmoothness
#creating subset for single indicator scores
YLMI_IScores <- data.frame(
Country = YLMI[, c("name")],
Year = YLMI[, c("year")],
Classes = YLMI[, c("classes")],
Index = YLMI[, c("index_constant")],
Weighted_Index = YLMI[, c("weighted_index")],
ActivityState=YLMI[, c("ActivityState")],
WorkingConditions=YLMI[, c("WorkingConditions")],
Education=YLMI[, c("Education")],
TransitionSmoothness=YLMI[, c("TransitionSmoothness")],
UnemploymentRate = YLMI[, c("X1")],
RelaxedUnemploymentRate = YLMI[, c("X2")],
NEETRate = YLMI[, c("X3")],
TemporaryWorkersRate = YLMI[, c("X4")],
InvoluntaryPartTimeWorkersRate = YLMI[, c("X5")],
AtypicalWorkingHoursRate = YLMI[, c("X6")],
InWorkatRiskofPovertyRate = YLMI[, c("X7")],
VulnerableEmploymentRate = YLMI[, c("X8")],
FormalEducationandTrainingRate = YLMI[, c("X9")],
SkillsMismatchRate = YLMI[, c("X10")],
RelativeUnemploymentRatio = YLMI[, c("X11")],
LongTermUnemploymentRate = YLMI[, c("X12")])
# Deleting rows if calculated index is NaN
YLMI_IScores <- YLMI_IScores[!is.na(YLMI_IScores$Index), ]
YLMI_IScores[is.na(YLMI_IScores) == TRUE] = "-"
return(YLMI_IScores)
}
##server##
server <- function(input, output, session) {
#scoreboard
#table layout for scoreboard
sketch <- htmltools:: withTags(
table(
class = "display",
thead(
tr(
th(colspan = 3, "Selection", style = "border-right: solid 2px;"),
th(colspan = 2, "Aggregate Index", style = "border-right: solid 2px;"),
th(colspan = 4, "Sub-Index Values by Dimension", style = "border-right: solid 2px;"),
th(colspan = 3, "Dimension: Activity State", style = "border-right: solid 2px;"),
th(colspan = 5, "Dimension: Working Conditions", style = "border-right: solid 2px;"),
th(colspan = 2, "Dimension: Education", style = "border-right: solid 2px;"),
th(colspan = 2, "Dimension: Transition Smoothness", style = "border-right: solid 2px;")
),
tr(
th("Country"),
th("Year"),
th("Classes", style = "border-right: solid 2px;"),
th("Index"),
th("Weighted Index", style = "border-right: solid 2px;"),
th("Activity State"),
th("Working Conditions"),
th("Education"),
th("Transition Smoothness", style = "border-right: solid 2px;"),
th("Unemployment Rate"),
th("Relaxed Unemployment Rate"),
th("NEET Rate", style = "border-right: solid 2px;"),
th("Temporary Workers Rate"),
th("Involuntary Part Time Workers Rate"),
th("Atypical Working Hours Rate"),
th("In Work at Risk of Poverty Rate"),
th("Vulnerable Employment Rate", style = "border-right: solid 2px;"),
th("Formal Educationand Training Rate"),
th("Skills Mismatch Rate", style = "border-right: solid 2px;"),
th("Relative Unemployment Ratio"),
th("Long Term Unemployment Rate")
),
)
)
)
#data filtering based on user input
filterData <- reactive({
w1 <- input$w_1
w2 <- input$w_2
w3 <- input$w_3
w4 <- input$w_4
YLMI_IScores <- calculate_index_w_weights(w1,w2,w3,w4)
rows <- (YLMI_IScores$Country %in% input$country_scb) & (YLMI_IScores$Classes %in% input$country_classes_scb)
data <- YLMI_IScores[rows,, drop = FALSE]
data2 <- datatable(data, rownames = FALSE, container = sketch,
options = list(info = TRUE, order= list(3,"dsc"), pageLength = 50,
columnDefs = list(list(targets = "_all", className = "dt-center")))) %>%
formatStyle(c(3,5,9,12,17,19,21), `border-right` = "solid 2px") %>%
formatStyle(columns = "Index", backgroundColor = "#fdb9c4") %>%
formatStyle(columns = "Weighted_Index", backgroundColor = "#f72a66") %>%
formatStyle(columns = "ActivityState", backgroundColor = "#fff9ee") %>%
formatStyle(columns = "WorkingConditions", backgroundColor = "#fff9ee") %>%
formatStyle(columns = "Education", backgroundColor = "#fff9ee") %>%
formatStyle(columns = "TransitionSmoothness", backgroundColor = "#fff9ee") %>%
formatRound(columns = c(4:21), digits = 2)
data2
})
output$scb_table <- DT::renderDT({
filterData()
})
}
##ui ##
ui <- fluidPage(
sidebarLayout(
#scoreboard
sidebarPanel(
pickerInput(
inputId = "country_scb",
label = "Select country/countries",
selected = unique(sort(YLMI$name)), # Default selecting all the countries here! TODO
choices = unique(sort(YLMI$name)),
multiple = TRUE,
options = list(`actions-box` = TRUE)
),
awesomeCheckboxGroup(
inputId = "country_classes_scb",
label = "Filter countries by data availability:",
choices = unique(sort(YLMI$classes)),
selected = unique(sort(YLMI$classes)),
),
###### ----- Weight Buttons ---- #####
# Weight Arangements 1
sliderInput("w_1",
label = "Select weight of Dimension Activity State:",
min = 0,
max = 3,
value = 1,
step=1,
sep = ""
),
# Weight Arangements 2
sliderInput("w_2",
label = "Select weight of Dimension Working Conditions:",
min = 0,
max = 3,
value = 1,
step=1,
sep = ""
),
# Weight Arangements 3
sliderInput("w_3",
label = "Select weight of Dimension Education:",
min = 0,
max = 3,
value = 1,
step=1,
sep = ""
),
# Weight Arangements 4
sliderInput("w_4",
label = "Select weight of Dimension Transitional Smoothness:",
min = 0,
max = 3,
value = 1,
step=1,
sep = ""
)
),
mainPanel(
# Show data table
DT::dataTableOutput("scb_table")
)
)
)
shinyApp(ui = ui, server = server)
产生以下输出：
但是，我想添加对我的值进行排序的可能性，但修改参数
ordering = FALSE
我该怎么做？
1个回答
0
投票
投票
您可以将以下
csstable.dataTable thead > tr > th.sorting::after {
top: 90% !important;
}
table.dataTable thead > tr > th.sorting::before {
bottom: 10% !important;
}
完整示例：
library(shiny)
library(shinydashboard)
library(shinyWidgets)
library(dplyr)
library(DT)
library(tidyverse)
library(data.table)
#reproducible minimal data frame
YLMI <- structure(list(X = c(511L, 700L, 943L, 1402L, 1429L, 1483L, 1726L, 1834L, 1861L, 2266L),
name = c("Austria", "Belgium", "Bulgaria", "Cyprus", "Czech Republic", "Denmark",
"Estonia", "Finland", "France", "Iceland"),
year = c(2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L),
X1 = c(6.0948572, 5.1031427, 5.145143, 4.3162856, 5.9200001, 6.0751429, 5.8771429,
5.0911427, 4.8957143, 6.262857),
X2 = c(5.7982831, 5.1347985, 4.1193204,3.9259963, 5.9878144, 5.8885102, 5.5807657,
4.5704818, 4.8845162, 5.7285347),
X3 = c(5.8720002, 5.1729999, 4.1079998, 4.7049999, 5.8794999, 6.0700002, 5.3740001,
5.4159999, 5.2164998, 6.3175001),
X4 = c(6.0436354, 3.9714868, 6.0058327, 4.7928214, 4.636817, 6.1576967, 5.9891138,
3.3220425, 3.2921035, 4.1184382),
X5 = c(6.3000154, 5.7192054, 6.5671687, 3.4370663, 6.6064062, 5.8908257,
6.8782973, 4.7578831, 4.3325543, 6.2499504),
X6 = c(4.9257145, 5.5085716, 4.0457144, 3.737143, 2.817143, 5.0228572, 4.0057144,
3.0914288, 5.3942857, 1.7485714),
X7 = c(5.2685714, 5.8857141, 5.1657143, 4.4285712, 6.6914287, 3.7942855,
4.8914285, 5.7142859, 5.2857141, 5.0457144),
X8 = c(5.7268553, 5.3676248, 5.7317734, 5.1083288, 4.9277864, 6.2327962,
6.1439047, 5.5020885, 5.9025269, 5.6717625),
X9 = c(4.7919998, 5.428, 5.1039996, 4.7199998, 5.4880004, 6.2319999, 5.1399999,
5.3560004, 5.4160004, 5.3560004),
X10 = c(4.7384157, 3.7913544, 4.4407039, 5.8613172, 3.5934217, 5.534936,
4.0672798, 4.2066154, 4.3676648, 3.6402931),
X11 = c(5.7328, 5.1810961, 5.4579573, 5.5078635, 5.3274336, 5.7784905,
5.5863309, 5.2231383, 5.3318233, 5.2328768),
X12 = c(5.6389961, 3.9419262, 2.6277056, 4.8922715, 4.4109187, 6.3135815,
5.6100388, 6.3433652, 4.5896773, 6.6938777),
W1 = c(0.0833, 0.0833, 0.0833, 0.0833, 0.0833, 0.0833, 0.0833, 0.0833, 0.0833,
0.0833),
W2 = c(0.0833, 0.0833, 0.0833, 0.0833, 0.0833, 0.0833, 0.0833,
0.0833, 0.0833, 0.0833),
W3 = c(0.0833, 0.0833, 0.0833, 0.0833, 0.0833, 0.0833, 0.0833, 0.0833, 0.0833,
0.0833),
W4 = c(0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05),
W5 = c(0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05),
W6 = c(0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05),
W7 = c(0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05),
W8 = c(0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05),
W9 = c(0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125),
W10 = c(0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125),
W11 = c(0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125),
W12 = c(0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125),
indicators = c(12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L),
classes = c("A", "A", "A", "A", "A", "A", "A", "A", "A", "A"),
index_constant = c(5.51, 4.9, 4.69, 4.78, 5.12, 5.84, 5.35, 5.02, 4.92, 5.28),
ranking = c(18L, 48L, 59L, 53L, 31L, 7L, 25L, 36L, 45L, 27L)),
row.names = c(511L, 700L, 943L, 1402L, 1429L, 1483L, 1726L, 1834L, 1861L, 2266L),
class = "data.frame")
#helper function:
# ---- Index Calculation Based on User Weights ---- #
calculate_index_w_weights <- function(w1,w2,w3,w4) {
# Obtaining weights
weights <- array(rep(1,4))
# Creating weight matrices to re-calculate the indicator scores.
w1_matrix <- matrix(weights[1], nrow= 10, ncol=3)
w2_matrix <- matrix(weights[2], nrow= 10, ncol=5)
w3_matrix <- matrix(weights[3], nrow= 10, ncol=2)
w4_matrix <- matrix(weights[4], nrow= 10, ncol=2)
# Unnecessary for now
YLMI[,c("W1","W2","W3")]<-YLMI[,c("W1","W2","W3")] * w1_matrix
YLMI[,c("W4","W5","W6","W7", "W8")]<-YLMI[,c("W4","W5","W6", "W7","W8")] * w2_matrix
YLMI[,c("W9","W10")]<-YLMI[,c("W9","W10")] * w3_matrix
YLMI[,c("W11","W12")]<-YLMI[,c("W11","W12")] * w4_matrix
ActivityState = YLMI[,c("X1", "X2", "X3")] * YLMI[,c("W1","W2","W3")] #5454x3
WorkingConditions= YLMI[,c("X4", "X5", "X6", "X7", "X8")] * YLMI[,c("W4","W5","W6", "W7", "W8")] #5454x5
Education= YLMI[,c("X9", "X10")] * YLMI[,c("W9","W10")] #5454x2
TransitionSmoothness= YLMI[,c("X11", "X12")] * YLMI[,c("W11","W12")] #5454x2
c1 <- rowSums(ActivityState) #5454 x 1 sum(x1*w1....)
c2 <- rowSums(WorkingConditions)
c3 <- rowSums(Education)
c4 <- rowSums(TransitionSmoothness)
w1_i <-rowSums(YLMI[,c("W1","W2","W3")])
w2_i <-rowSums(YLMI[,c("W4","W5","W6","W7", "W8")])
w3_i <-rowSums(YLMI[,c("W9","W10")])
w4_i <-rowSums(YLMI[,c("W11","W12")])
# weighted_index = YLMI_Nominator / sum_weights
ActivityState = c1 / w1_i
WorkingConditions = c2 / w2_i
Education = c3 / w3_i
TransitionSmoothness = c4 / w4_i
# Category weighting
weights_category <- array(rep(0.25,4))
# User input on weights
w_unit <- 1 / (w1+w2+w3+w4)
weights_category[1] <- w_unit * w1
weights_category[2] <- w_unit * w2
weights_category[3] <- w_unit * w3
weights_category[4] <- w_unit * w4
w1_cat_matrix <- matrix(weights_category[1], nrow= 10, ncol=1)
w2_cat_matrix <- matrix(weights_category[2], nrow= 10, ncol=1)
w3_cat_matrix <- matrix(weights_category[3], nrow= 10, ncol=1)
w4_cat_matrix <- matrix(weights_category[4], nrow= 10, ncol=1)
categories <- data.frame(ActivityState, WorkingConditions, Education, TransitionSmoothness,
W1_C=w1_cat_matrix, W2_C=w2_cat_matrix, W3_C= w3_cat_matrix, W4_C=w4_cat_matrix)
categories[is.na(categories) == TRUE] = 0
# If category value is zero, then no weight assigned to that category for the index calculation.
categories <- within(categories, W1_C[ActivityState == 0] <- 0)
categories <- within(categories, W2_C[WorkingConditions == 0] <- 0)
categories <- within(categories, W3_C[Education == 0] <- 0)
categories <- within(categories, W4_C[TransitionSmoothness == 0] <- 0)
weights_category_sum <-rowSums(categories[,c("W1_C","W2_C","W3_C","W4_C")])
YLMI_Nominator1=categories[,c("ActivityState")] * categories[,c("W1_C")]
YLMI_Nominator2=categories[,c("WorkingConditions")] * categories[,c("W2_C")]
YLMI_Nominator3=categories[,c("Education")] * categories[,c("W3_C")]
YLMI_Nominator4=categories[,c("TransitionSmoothness")] * categories[,c("W4_C")]
YLMI_Nominator = YLMI_Nominator1 + YLMI_Nominator2 + YLMI_Nominator3 + YLMI_Nominator4
index = YLMI_Nominator / weights_category_sum
YLMI["weighted_index"]<-index
YLMI["ActivityState"]<-ActivityState
YLMI["WorkingConditions"]<-WorkingConditions
YLMI["Education"]<-Education
YLMI["TransitionSmoothness"]<-TransitionSmoothness
#creating subset for single indicator scores
YLMI_IScores <- data.frame(
Country = YLMI[, c("name")],
Year = YLMI[, c("year")],
Classes = YLMI[, c("classes")],
Index = YLMI[, c("index_constant")],
Weighted_Index = YLMI[, c("weighted_index")],
ActivityState=YLMI[, c("ActivityState")],
WorkingConditions=YLMI[, c("WorkingConditions")],
Education=YLMI[, c("Education")],
TransitionSmoothness=YLMI[, c("TransitionSmoothness")],
UnemploymentRate = YLMI[, c("X1")],
RelaxedUnemploymentRate = YLMI[, c("X2")],
NEETRate = YLMI[, c("X3")],
TemporaryWorkersRate = YLMI[, c("X4")],
InvoluntaryPartTimeWorkersRate = YLMI[, c("X5")],
AtypicalWorkingHoursRate = YLMI[, c("X6")],
InWorkatRiskofPovertyRate = YLMI[, c("X7")],
VulnerableEmploymentRate = YLMI[, c("X8")],
FormalEducationandTrainingRate = YLMI[, c("X9")],
SkillsMismatchRate = YLMI[, c("X10")],
RelativeUnemploymentRatio = YLMI[, c("X11")],
LongTermUnemploymentRate = YLMI[, c("X12")])
# Deleting rows if calculated index is NaN
YLMI_IScores <- YLMI_IScores[!is.na(YLMI_IScores$Index), ]
YLMI_IScores[is.na(YLMI_IScores) == TRUE] = "-"
return(YLMI_IScores)
}
##server##
server <- function(input, output, session) {
#scoreboard
#table layout for scoreboard
sketch <- htmltools:: withTags(
table(
class = "display",
thead(
tr(
th(colspan = 3, "Selection", style = "border-right: solid 2px;"),
th(colspan = 2, "Aggregate Index", style = "border-right: solid 2px;"),
th(colspan = 4, "Sub-Index Values by Dimension", style = "border-right: solid 2px;"),
th(colspan = 3, "Dimension: Activity State", style = "border-right: solid 2px;"),
th(colspan = 5, "Dimension: Working Conditions", style = "border-right: solid 2px;"),
th(colspan = 2, "Dimension: Education", style = "border-right: solid 2px;"),
th(colspan = 2, "Dimension: Transition Smoothness", style = "border-right: solid 2px;")
),
tr(
th("Country"),
th("Year"),
th("Classes", style = "border-right: solid 2px;"),
th("Index"),
th("Weighted Index", style = "border-right: solid 2px;"),
th("Activity State"),
th("Working Conditions"),
th("Education"),
th("Transition Smoothness", style = "border-right: solid 2px;"),
th("Unemployment Rate"),
th("Relaxed Unemployment Rate"),
th("NEET Rate", style = "border-right: solid 2px;"),
th("Temporary Workers Rate"),
th("Involuntary Part Time Workers Rate"),
th("Atypical Working Hours Rate"),
th("In Work at Risk of Poverty Rate"),
th("Vulnerable Employment Rate", style = "border-right: solid 2px;"),
th("Formal Educationand Training Rate"),
th("Skills Mismatch Rate", style = "border-right: solid 2px;"),
th("Relative Unemployment Ratio"),
th("Long Term Unemployment Rate")
),
)
)
)
#data filtering based on user input
filterData <- reactive({
w1 <- input$w_1
w2 <- input$w_2
w3 <- input$w_3
w4 <- input$w_4
YLMI_IScores <- calculate_index_w_weights(w1,w2,w3,w4)
rows <- (YLMI_IScores$Country %in% input$country_scb) & (YLMI_IScores$Classes %in% input$country_classes_scb)
data <- YLMI_IScores[rows,, drop = FALSE]
data2 <- datatable(data, rownames = FALSE, container = sketch,
options = list(info = TRUE, order= list(3,"dsc"), pageLength = 50,
columnDefs = list(list(targets = "_all", className = "dt-center")))) %>%
formatStyle(c(3,5,9,12,17,19,21), `border-right` = "solid 2px") %>%
formatStyle(columns = "Index", backgroundColor = "#fdb9c4") %>%
formatStyle(columns = "Weighted_Index", backgroundColor = "#f72a66") %>%
formatStyle(columns = "ActivityState", backgroundColor = "#fff9ee") %>%
formatStyle(columns = "WorkingConditions", backgroundColor = "#fff9ee") %>%
formatStyle(columns = "Education", backgroundColor = "#fff9ee") %>%
formatStyle(columns = "TransitionSmoothness", backgroundColor = "#fff9ee") %>%
formatRound(columns = c(4:21), digits = 2)
data2
})
output$scb_table <- DT::renderDT({
filterData()
})
}
##ui ##
ui <- fluidPage(
tags$head(tags$style(HTML(
c(
"table.dataTable thead > tr > th.sorting::after {",
" top: 90% !important;",
"}",
"table.dataTable thead > tr > th.sorting::before {",
" bottom: 10% !important;",
"}"
)
))),
sidebarLayout(
#scoreboard
sidebarPanel(
pickerInput(
inputId = "country_scb",
label = "Select country/countries",
selected = unique(sort(YLMI$name)), # Default selecting all the countries here! TODO
choices = unique(sort(YLMI$name)),
multiple = TRUE,
options = list(`actions-box` = TRUE)
),
awesomeCheckboxGroup(
inputId = "country_classes_scb",
label = "Filter countries by data availability:",
choices = unique(sort(YLMI$classes)),
selected = unique(sort(YLMI$classes)),
),
###### ----- Weight Buttons ---- #####
# Weight Arangements 1
sliderInput("w_1",
label = "Select weight of Dimension Activity State:",
min = 0,
max = 3,
value = 1,
step=1,
sep = ""
),
# Weight Arangements 2
sliderInput("w_2",
label = "Select weight of Dimension Working Conditions:",
min = 0,
max = 3,
value = 1,
step=1,
sep = ""
),
# Weight Arangements 3
sliderInput("w_3",
label = "Select weight of Dimension Education:",
min = 0,
max = 3,
value = 1,
step=1,
sep = ""
),
# Weight Arangements 4
sliderInput("w_4",
label = "Select weight of Dimension Transitional Smoothness:",
min = 0,
max = 3,
value = 1,
step=1,
sep = ""
)
),
mainPanel(
# Show data table
DT::dataTableOutput("scb_table")
)
)
)
shinyApp(ui = ui, server = server)
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