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server.R
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library(shiny)
library(eurostat)
library(dplyr)
library(stringr)
library(zoo)
library(lubridate)
library(plotly)
function(input, output, session) {
hikp_data <- eventReactive(input$update, {
req(input$countries, input$coicops)
tryCatch({
data <- get_eurostat("prc_hicp_midx", filters = list(unit = "I15",
geo = input$countries,
coicop = input$coicops))
clean_eurostat_cache()
data$time <- as.yearmon(data$time)
new_plot_data <<- TRUE
return(data)
}, error = function(e) {
#Message if no data
print(paste("No data available:", e$message))
remove("hikp_data", envir = .GlobalEnv)
})
})
#Weights data
hikp_w_data <- eventReactive(input$update_w, {
req(input$countries_w, input$coicop_w)
filtered_data <- coicop_set_hierarchy[coicop_set_hierarchy$parent_code == input$coicop_w, ]
result <- unique(filtered_data$coicop_code)
result <- result[!is.na(result)]
if (length(result) == 0) {
result <- input$coicop_w
}
data <- get_eurostat("prc_hicp_inw", filters = list(geo = input$countries_w,
#time = c(years),
coicop =result),update_cache = TRUE)
new_plot_w_data <<- TRUE
data_no_na <- data %>%
filter(!is.na(values))
# Group by "geo" & "coicop", find the first year with observations for every group
min_years <- data_no_na %>%
group_by(geo, coicop) %>%
summarise(min_year = min(time))
# Find the first common year
first_non_na_year <- min_years %>%
summarise(first_common_year = max(min_year)) %>%
pull(max(first_common_year))
data <- filter(data, time >= max(first_non_na_year))
label_set<-select(coicop_set_hierarchy, coicop_code, code_label)
# Merged datasets based on ID-column
merged_data <- merge(data, label_set, by.x = "coicop", by.y = "coicop_code", all.x = TRUE)
return(merged_data)
})
#Annual rates data
hikp_ar_data <- eventReactive(input$update_ar, {
req(input$countries_ar, input$coicop_ar)
filtered_data <- coicop_set_hierarchy[coicop_set_hierarchy$parent_code == input$coicop_ar, ]
result <- unique(filtered_data$coicop_code)
result <- result[!is.na(result)]
if (input$contribution_type_ar=="selected higher aggregate"){
full_coicop <- c(result, input$coicop_ar)
}
if (input$contribution_type_ar=="all-items HICP" && input$coicop_ar !="CP00"){
full_coicop <- c(result, input$coicop_ar,"CP00")
}
if (input$contribution_type_ar=="all-items HICP" && input$coicop_ar == "CP00"){
full_coicop <- c(result, input$coicop_ar)
}
if (length(result) == 0) {
result <- input$coicop_ar
full_coicop<-result
}
#Get the index data
data_I <- get_eurostat("prc_hicp_midx", filters = list(geo = input$countries_ar,
#time = c(years),
unit="I15",
coicop =full_coicop),update_cache = TRUE)
# Get weights
data_W <- get_eurostat("prc_hicp_inw", filters = list(geo = input$countries_ar,
#time = c(years),
coicop =full_coicop),update_cache = TRUE)
new_plot_ar_data <<- TRUE
# Index data for aggregates j
data_I_j <- filter(data_I, coicop %in% result)
data_I_j$year<-year(data_I_j$time)
data_I_j<-select(data_I_j,-freq, -unit)
data_I_j<-rename(data_I_j,IX_j=values)
# Weights for j
data_W_j <- filter(data_W, coicop %in% result)
data_W_j$year<-year(data_W_j$time)
data_W_j<-select(data_W_j, -freq, -time)
data_W_j<-rename(data_W_j,WT_j_pre=values)
# Weights for higher aggregate TOT
data_W_TOT <- filter(data_W, coicop %in% input$coicop_ar)
data_W_TOT$year<-year(data_W_TOT$time)
data_W_TOT<-select(data_W_TOT, -freq, -time, -coicop)
data_W_TOT<-rename(data_W_TOT,WT_TOT=values)
data_W_jTOT <- left_join(data_W_j, data_W_TOT, by = c("year", "geo"))
#Calculate weight for j in selected higher aggregate
#...depending on the type of contribution selected
if (input$contribution_type_ar == "selected higher aggregate") {
data_W_jTOT$WT_j<-data_W_jTOT$WT_j_pre/data_W_TOT$WT_TOT*1000
} else if (input$contribution_type_ar == "all-items HICP") {
data_W_jTOT$WT_j<-data_W_jTOT$WT_j_pre
}
data_W_jTOT<-select(data_W_jTOT,-WT_j_pre,-WT_TOT)
result_j <- left_join(data_I_j, data_W_jTOT, by = c("year", "coicop", "geo"))
# Annual rate for higher aggregate TOT
if (input$contribution_type_ar == "selected higher aggregate") {
data_AR <- filter(data_I, coicop %in% input$coicop_ar)
} else if (input$contribution_type_ar == "all-items HICP") {
data_AR <- filter(data_I, coicop %in% "CP00")
}
data_AR$year <- year(data_AR$time)
data_AR$month <- month(data_AR$time)
m_AR_ymin1 <- data_AR
m_AR_ymin1$year_plus1<-m_AR_ymin1$year+1
m_AR_ymin1<-select(m_AR_ymin1,-year, -unit,-time, -freq)
m_AR_ymin1<-rename(m_AR_ymin1,IX_j_m_ymin1=values,year=year_plus1)
data_AR <- left_join(data_AR, m_AR_ymin1, by = c("year","month","geo","coicop"))
data_AR$ann_rate_00<-data_AR$values/data_AR$IX_j_m_ymin1*100-100
data_AR<-select(data_AR, -freq,-unit,-values,-IX_j_m_ymin1)
# Indices for the higher aggregate TOT
data_I_TOT <- filter(data_I, coicop %in% input$coicop_ar)
data_I_TOT<-select(data_I_TOT,-freq, -unit, -coicop)
data_I_TOT<-rename(data_I_TOT,IX_TOT=values)
result_jTOT <- left_join(result_j, data_I_TOT, by = c("time", "geo"))
result_jTOT$year <- year(result_jTOT$time)
result_jTOT$month <- month(result_jTOT$time)
#December y-1 data for TOT
dec_data_ymin1 <- result_jTOT %>%
filter(month == 12)
dec_data_ymin1<-select(dec_data_ymin1,-month)
dec_data_ymin1$year_plus1<-dec_data_ymin1$year+1
dec_data_ymin1<-select(dec_data_ymin1,-year, -time)
dec_data_ymin1<-rename(dec_data_ymin1,IX_j_12_ymin1=IX_j,WT_j_12_ymin1=WT_j,IX_TOT_12_ymin1=IX_TOT,year=year_plus1)
result_jTOT2 <- left_join(result_jTOT, dec_data_ymin1, by = c("year", "geo","coicop"))
#December y-2 data for TOT
dec_data_ymin2 <- result_jTOT %>%
filter(month == 12)
dec_data_ymin2<-select(dec_data_ymin2,-month)
dec_data_ymin2$year_plus2<-dec_data_ymin2$year+2
dec_data_ymin2<-select(dec_data_ymin2,-year, -time)
dec_data_ymin2<-rename(dec_data_ymin2,IX_j_12_ymin2=IX_j,WT_j_12_ymin2=WT_j,IX_TOT_12_ymin2=IX_TOT,year=year_plus2)
result_jTOT2 <- left_join(result_jTOT2, dec_data_ymin2, by = c("year", "geo","coicop"))
#Data for current month m y-1 for TOT
m_data_ymin1 <- result_jTOT
m_data_ymin1$year_plus1<-m_data_ymin1$year+1
m_data_ymin1<-select(m_data_ymin1,-year, -time)
m_data_ymin1<-rename(m_data_ymin1,IX_j_m_ymin1=IX_j,WT_j_m_ymin1=WT_j,IX_TOT_m_ymin1=IX_TOT,year=year_plus1)
result_jTOT2 <- left_join(result_jTOT2, m_data_ymin1, by = c("year", "month","geo","coicop"))
numeric_vars <- c("IX_j", "WT_j", "IX_TOT", "IX_j_12_ymin1", "WT_j_12_ymin1", "IX_TOT_12_ymin1",
"IX_j_12_ymin2", "WT_j_12_ymin2", "IX_TOT_12_ymin2", "IX_j_m_ymin1",
"WT_j_m_ymin1", "IX_TOT_m_ymin1")
result_jTOT2 <- result_jTOT2 %>%
mutate(
Contr_j = if_else(
rowSums(is.na(across(all_of(numeric_vars)))) > 0,
NA,
(100 * (IX_TOT_12_ymin1 / IX_TOT_m_ymin1 * WT_j / 1000) * ((IX_j - IX_j_12_ymin1) / IX_j_12_ymin1)) +
100 * ((IX_TOT_12_ymin2 / IX_TOT_m_ymin1 * WT_j_12_ymin1 / 1000) * ((IX_j_12_ymin1 - IX_j_m_ymin1) / IX_j_12_ymin2))
)
)
result_jTOT2 <- full_join(result_jTOT2, data_AR, by = c("year","time", "month", "geo", "coicop"))
data<-select(result_jTOT2,year,month,time, geo, coicop, ann_rate_00, Contr_j)
data_no_na <- data %>%
filter(is.numeric(Contr_j) | is.numeric(ann_rate_00))
min_years <- data_no_na %>%
group_by(geo) %>%
summarise(min_year = min(time))
# Find the first common year
first_non_na_year <- min_years %>%
summarise(first_common_year = max(min_year)) %>%
pull(max(first_common_year))
data <- filter(data, time >= max(first_non_na_year))
label_set<-select(coicop_set_hierarchy, coicop_code, code_label)
if (input$contribution_type_ar == "selected higher aggregate") {
# Merged datasets based on ID-column
merged_data <- merge(data, label_set, by.x = "coicop", by.y = "coicop_code", all.x = TRUE)
merged_data <- merged_data %>%
mutate(code_label = ifelse(coicop == input$coicop_ar, NA, code_label))
} else if (input$contribution_type_ar == "all-items HICP") {
# Merged datasets based on ID-column
merged_data <- merge(data, label_set, by.x = "coicop", by.y = "coicop_code", all.x = TRUE)
}
return(merged_data)
})
#Monthly rates data
hikp_mr_data <- eventReactive(input$update_mr, {
req(input$countries_mr, input$coicop_mr)
filtered_data <- coicop_set_hierarchy[coicop_set_hierarchy$parent_code == input$coicop_mr, ]
result <- unique(filtered_data$coicop_code)
result <- result[!is.na(result)]
result <- unique(filtered_data$coicop_code)
result <- result[!is.na(result)]
if (input$contribution_type=="selected higher aggregate"){
full_coicop <- c(result, input$coicop_mr)
}
if (input$contribution_type=="all-items HICP" && input$coicop_mr !="CP00"){
full_coicop <- c(result, input$coicop_mr,"CP00")
}
if (input$contribution_type=="all-items HICP" && input$coicop_mr == "CP00"){
full_coicop <- c(result, input$coicop_mr)
}
if (length(result) == 0) {
result <- input$coicop_mr
full_coicop<-result
}
#Get the index data
data_I <- get_eurostat("prc_hicp_midx", filters = list(geo = input$countries_mr,
#time = c(years),
unit="I15",
coicop =full_coicop),update_cache = TRUE)
# Get weights
data_W <- get_eurostat("prc_hicp_inw", filters = list(geo = input$countries_mr,
#time = c(years),
coicop =full_coicop),update_cache = TRUE)
new_plot_mr_data <<- TRUE
# Index data for aggregates j -OK
data_I_j <- filter(data_I, coicop %in% result)
data_I_j$year<-year(data_I_j$time)
data_I_j<-select(data_I_j,-freq, -unit)
data_I_j<-rename(data_I_j,IX_j=values)
# Weights for j -OK
data_W_j <- filter(data_W, coicop %in% result)
data_W_j$year<-year(data_W_j$time)
data_W_j<-select(data_W_j, -freq, -time)
data_W_j<-rename(data_W_j,WT_j_pre=values)
# Weights for higher aggregate TOT -OK
data_W_TOT <- filter(data_W, coicop %in% input$coicop_mr)
data_W_TOT$year<-year(data_W_TOT$time)
data_W_TOT<-select(data_W_TOT, -freq, -time, -coicop)
data_W_TOT<-rename(data_W_TOT,WT_TOT=values)
#Calculate weight for j in selected higher aggregate
data_W_jTOT <- left_join(data_W_j, data_W_TOT, by = c("year", "geo"))
#...depending on the type of contribution selected
if (input$contribution_type == "selected higher aggregate") {
data_W_jTOT$WT_j<-data_W_jTOT$WT_j_pre/data_W_TOT$WT_TOT*1000
} else if (input$contribution_type == "all-items HICP") {
data_W_jTOT$WT_j<-data_W_jTOT$WT_j_pre
}
data_W_jTOT<-select(data_W_jTOT,-WT_j_pre,-WT_TOT)
result_j <- left_join(data_I_j, data_W_jTOT, by = c("year", "coicop", "geo"))
# Monthly rate for higher aggregate TOT
if (input$contribution_type == "selected higher aggregate") {
data_MR <- filter(data_I, coicop %in% input$coicop_mr)
} else if (input$contribution_type == "all-items HICP") {
data_MR <- filter(data_I, coicop %in% "CP00")
}
data_MR$year <- year(data_MR$time)
data_MR$month <- month(data_MR$time)
#Find the monthly rate of change for total index
m_MR_mmin1 <- data_MR
m_MR_mmin1$new_year <- ifelse(m_MR_mmin1$month == 12, m_MR_mmin1$year + 1, m_MR_mmin1$year)
m_MR_mmin1$new_month <- ifelse(m_MR_mmin1$month == 12, 1, m_MR_mmin1$month %% 12 + 1)
m_MR_mmin1<-select(m_MR_mmin1,-year,-month, -unit,-time, -freq)
m_MR_mmin1<-rename(m_MR_mmin1,IX_j_m_mmin1=values,year=new_year,month=new_month)
data_MR <- left_join(data_MR, m_MR_mmin1, by = c("year","month","geo","coicop"))
data_MR$m_rate_00<-data_MR$values/data_MR$IX_j_m_mmin1*100-100
data_MR<-select(data_MR, -freq,-unit,-values,-IX_j_m_mmin1)
# Indices for the higher aggregate TOT
data_I_TOT <- filter(data_I, coicop %in% input$coicop_mr)
data_I_TOT<-select(data_I_TOT,-freq, -unit, -coicop)
data_I_TOT<-rename(data_I_TOT,IX_TOT=values)
result_jTOT <- left_join(result_j, data_I_TOT, by = c("time", "geo"))
result_jTOT$year <- year(result_jTOT$time)
result_jTOT$month <- month(result_jTOT$time)
#Previous month indices
data_mmin1 <- result_jTOT
data_mmin1$new_year <- ifelse(data_mmin1$month == 12, data_mmin1$year + 1, data_mmin1$year)
data_mmin1$new_month <- ifelse(data_mmin1$month == 12, 1, data_mmin1$month %% 12 + 1)
data_mmin1<-select(data_mmin1,-year,-month, -time, -WT_j)
data_mmin1<-rename(data_mmin1,IX_TOT_m_mmin1=IX_TOT, IX_j_m_mmin1=IX_j,year=new_year,month=new_month)
result_jTOT2 <- left_join(result_jTOT, data_mmin1, by = c("year","month", "geo","coicop"))
#Contribution to monthly rate of change
numeric_vars <- c("IX_j", "WT_j", "IX_TOT_m_mmin1", "IX_j_m_mmin1")
result_jTOT2 <- result_jTOT2 %>%
mutate(
Contr_j = if_else(
rowSums(is.na(across(all_of(numeric_vars)))) > 0,
NA,
(100 * (WT_j/1000*(IX_j - IX_j_m_mmin1))/IX_TOT_m_mmin1)
)
)
result_jTOT2 <- full_join(result_jTOT2, data_MR, by = c("year","time", "month", "geo", "coicop"))
data<-select(result_jTOT2,year,month,time, geo, coicop, m_rate_00, Contr_j)
data_no_na <- data %>%
filter(is.numeric(Contr_j) | is.numeric(m_rate_00))
min_years <- data_no_na %>%
group_by(geo) %>%
summarise(min_year = min(time))
#Find the first common year
first_non_na_year <- min_years %>%
summarise(first_common_year = max(min_year)) %>%
pull(max(first_common_year))
data <- filter(data, time >= max(first_non_na_year))
#data <- data %>%
# filter(!is.na(m_rate_00) | !is.na(Contr_j))
label_set<-select(coicop_set_hierarchy, coicop_code, code_label)
# Merged datasets based on ID-column
#...depending on contribution type selected
if (input$contribution_type == "selected higher aggregate") {
# Merged datasets based on ID-column
merged_data <- merge(data, label_set, by.x = "coicop", by.y = "coicop_code", all.x = TRUE)
merged_data <- merged_data %>%
mutate(code_label = ifelse(coicop == input$coicop_mr, NA, code_label))
} else if (input$contribution_type == "all-items HICP") {
# Merged datasets based on ID-column
merged_data <- merge(data, label_set, by.x = "coicop", by.y = "coicop_code", all.x = TRUE)
}
return(merged_data)
})
#Seasonality data
hikp_se_data <- eventReactive(input$update_se, {
req(input$countries_se, input$coicop_se)
#Get the index data
data_I <- get_eurostat("prc_hicp_midx", filters = list(geo = input$countries_se,
#time = c(years),
unit="I15",
coicop =input$coicop_se),update_cache = TRUE)
new_plot_se_data <<- TRUE
# Index data for aggregates j
data_I_j <- data_I
data_I_j$year<-year(data_I_j$time)
data_I_j$month<-month(data_I_j$time)
data_I_j<-select(data_I_j,-freq, -unit)
data_I_j<-rename(data_I_j,IX_j=values)
result_j <- data_I_j
#December y-1 data for TOT
dec_data_ymin1 <- result_j %>%
filter(month == 12)
dec_data_ymin1<-select(dec_data_ymin1,-month)
dec_data_ymin1$year_plus1<-dec_data_ymin1$year+1
dec_data_ymin1<-select(dec_data_ymin1,-year, -time)
dec_data_ymin1<-rename(dec_data_ymin1,IX_j_12_ymin1=IX_j,year=year_plus1)
result_j2 <- left_join(result_j, dec_data_ymin1, by = c("year", "geo","coicop"))
result_j2$index_dec_prv_yr<-result_j2$IX_j/result_j2$IX_j_12_ymin1*100
data<-result_j2
data_no_na <- data %>%
filter(is.numeric(index_dec_prv_yr))
min_years <- data_no_na %>%
group_by(geo) %>%
summarise(min_year = min(time))
# Find the first common year
first_non_na_year <- min_years %>%
summarise(first_common_year = max(min_year)) %>%
pull(max(first_common_year))
data <- filter(data, time >= max(first_non_na_year))
data <- data %>%
filter(!is.na(index_dec_prv_yr))
label_set<-select(coicop_set_hierarchy, coicop_code, code_label)
# Merged datasets based on ID-column
merged_data <- merge(data, label_set, by.x = "coicop", by.y = "coicop_code", all.x = TRUE)
merged_data <- merged_data %>%
mutate(code_label = ifelse(coicop == input$coicop_se, NA, code_label))
return(merged_data)
})
selected_data <- reactive({
req(hikp_data())
data <- hikp_data()
data <- data[data$geo %in% input$countries, ]
# Remove NAs in column values
data_no_na <- data %>%
filter(!is.na(values), substr(time, 1, 3) == "Jan" | substr(time, 1, 3) == "Dec")
# Group by "geo" and "coicop" and find the earliest common year with index values
max_years <- data_no_na %>%
group_by(geo, coicop) %>%
summarise(max_year = max(time))
# Group by "geo" & "coicop", find the first year with observations for every group
min_years <- data_no_na %>%
group_by(geo, coicop) %>%
summarise(min_year = min(time))
# Find the first common year
first_non_na_year <- min_years %>%
summarise(first_common_year = max(min_year)) %>%
pull(max(first_common_year))
# Find the last common year
last_non_na_year <- max_years %>%
summarise(last_common_year = min(max_year)) %>%
pull(min(last_common_year))
# Common filtering based on the first and last common years
if (input$period_type == "Full year") {
data <- filter(data, time > max(first_non_na_year) & time < min(last_non_na_year))
} else if (input$period_type == "Month") {
data <- filter(data, time >= max(first_non_na_year) & time < min(last_non_na_year))
}
if (input$period_type == "Full year") {
year_list<-unique(format(data$time, "%Y"))
return(year_list)
} else if (input$period_type == "Month") {
# Create a list of Year + Month combinations
year_month_list <- unique(paste(format(data$time, "%Y"), format(data$time, "%B"), sep = " "))
return(year_month_list)
}
})
# Update selectInput to show the list of available years (index)
observe({
updateSelectInput(session, "select_years", choices = selected_data())
req(input$countries, input$coicops)
})
selected_w_data <- reactive({
req(hikp_w_data())
data <- hikp_w_data()
data$time <- as.yearmon(data$time)
data <- data[data$geo %in% input$countries_w, ]
# Remove NAs in column values
data_no_na <- data %>%
filter(is.numeric(values))
max_years <- data_no_na %>%
group_by(geo) %>%
summarise(max_year = max(time,na.rm=TRUE))
min_years <- data_no_na %>%
group_by(geo) %>%
summarise(min_year = min(time))
# Find the first common year
first_non_na_year <- min_years %>%
summarise(first_common_year = max(min_year)) %>%
pull(max(first_common_year))
# Find the last common year
last_non_na_year <- max_years %>%
summarise(last_common_year = min(max_year)) %>%
pull(min(last_common_year))
# Common filtering based on the first and last common years
data <- filter(data, time >= max(first_non_na_year) & time <= min(last_non_na_year))
year_list<-unique(format(data$time, "%Y"))
return(year_list)
})
observe({
req(hikp_w_data())
year_choices <- selected_w_data()
max_year<-as.numeric(max(year_choices))
min_year<-as.numeric(min(year_choices))
updateSliderInput(session, "range_slider_w", min = min_year, max = max_year,step=1, value = c(max(c(max_year-5, min_year)), max_year))
req(hikp_w_data, input$update_w, input$countries_w, input$coicop_w)
})
selected_ar_data <- reactive({
req(hikp_ar_data())
data <- hikp_ar_data()
data$time <- as.yearmon(data$time)
data <- data[data$geo %in% input$countries_ar, ]
# Remove NAs in column values
data_no_na <- data %>%
filter(is.numeric(Contr_j) | is.numeric(ann_rate_00), month == 1 | month == 12)
max_years <- data_no_na %>%
group_by(geo) %>%
summarise(max_year = max(time,na.rm=TRUE))
min_years <- data_no_na %>%
group_by(geo) %>%
summarise(min_year = min(time))
# Find the first common year
first_non_na_year <- min_years %>%
summarise(first_common_year = max(min_year)) %>%
pull(max(first_common_year))
# Find the last common year
last_non_na_year <- max_years %>%
summarise(last_common_year = min(max_year)) %>%
pull(min(last_common_year))
# Common filtering based on the first and last common years
data <- filter(data, time >= max(first_non_na_year) & time <= min(last_non_na_year))
year_list<-unique(format(data$time, "%Y"))
return(year_list)
})
observe({
req(hikp_ar_data())
year_choices <- selected_ar_data()
max_year<-as.numeric(max(year_choices))
min_year<-as.numeric(min(year_choices))
updateSliderInput(session, "range_slider", min = min_year, max = max_year,step=1, value = c(max(c(max_year-2, min_year)), max_year))
req(hikp_ar_data, input$update_ar, input$countries_ar, input$coicop_ar)
})
selected_mr_data <- reactive({
req(hikp_mr_data())
data <- hikp_mr_data()
data$time <- as.yearmon(data$time)
data <- data[data$geo %in% input$countries_mr, ]
# Remove NAs in column values
data_no_na <- data %>%
filter(!is.na(Contr_j) | !is.na(m_rate_00))
max_years <- data_no_na %>%
group_by(geo) %>%
summarise(max_year = max(time,na.rm=TRUE))
min_years <- data_no_na %>%
group_by(geo) %>%
summarise(min_year = min(time))
# Find the first common year
first_non_na_year <- min_years %>%
summarise(first_common_year = max(min_year)) %>%
pull(max(first_common_year))
# Find the last common year
last_non_na_year <- max_years %>%
summarise(last_common_year = min(max_year)) %>%
pull(min(last_common_year))
# Common filtering based on the first and last common years
data <- filter(data, time >= max(first_non_na_year) & time <= min(last_non_na_year))
period_list<-unique(format(rev(data$time), "%Y %B"))
return(period_list)
})
# Update selectInput to show the list of available periods (monthly rate)
observe({
updateSelectInput(session, "select_period_mr", choices = selected_mr_data(),selected = selected_mr_data()[1])
req(input$countries_mr, input$coicop_mr)
})
selected_se_data <- reactive({
req(hikp_se_data())
data <- hikp_se_data()
data$time <- as.yearmon(data$time)
data <- data[data$geo %in% input$countries_se, ]
# Remove NAs in column values
data_no_na <- data %>%
filter(is.numeric(index_dec_prv_yr))
max_years <- data_no_na %>%
group_by(geo) %>%
summarise(max_year = max(time,na.rm=TRUE))
min_years <- data_no_na %>%
group_by(geo) %>%
summarise(min_year = min(time))
# Find the first common year
first_non_na_year <- min_years %>%
summarise(first_common_year = max(min_year)) %>%
pull(max(first_common_year))
# Find the last common year
last_non_na_year <- max_years %>%
summarise(last_common_year = min(max_year)) %>%
pull(min(last_common_year))
# Common filtering based on the first and last common years
data <- filter(data, time >= max(first_non_na_year) & time <= min(last_non_na_year))
year_list<-unique(format(rev(data$time), "%Y"))
return(year_list)
})
# Update selectInput to show the list of available periods (monthly rate)
observe({
updateSelectInput(session, "select_years_se", choices = selected_se_data(),selected = selected_se_data()[1])
req(input$countries_se, input$coicop_se)
})
#Create index data for plot
plot_data <- eventReactive(input$rebase,{
req(hikp_data())
data<-hikp_data()
coicop<-input$coicops
geo<-input$countries
# Adjust filtering depending on choice of period type
if (input$period_type == "Full year") {
avg_refyear <- data %>%
filter(as.numeric(format(time, "%Y")) == as.numeric(input$select_years)) %>%
group_by(coicop, geo) %>%
summarise(mean_values = mean(values))
} else if (input$period_type == "Month") {
avg_refyear <- data %>%
filter(as.character(format(time, "%Y %B")) == input$select_years) %>%
group_by(coicop, geo) %>%
summarise(mean_values = mean(values))
}
rebased_data <-left_join(hikp_data(),avg_refyear, by = c("coicop", "geo"))
rebased_data$newbase <- ifelse(!is.na(rebased_data$values) & !is.na(rebased_data$mean_values),
rebased_data$values/rebased_data$mean_values*100,NA)
# Adjust filtering based on if Full year or Month is selected as reference period
if (input$period_type == "Full year") {
rebased_data <- rebased_data %>% filter(as.numeric(format(time, "%Y")) >= as.numeric(input$select_years))
} else if (input$period_type == "Month") {
# Extract year and month
selected_year <- as.numeric(str_extract(input$select_years, "\\d{4}"))
selected_month <- match(tolower(str_extract(input$select_years, "[A-Za-z]+")), tolower(month.name))
# Filter data
rebased_data <- rebased_data %>%
filter(year(time) > selected_year | (year(time) == selected_year & month(time) >= selected_month))
}
new_rebased_data<<-TRUE
new_plot_data<<-TRUE
return(rebased_data)
})
# Create the plot (weights)
observeEvent(input$update_w, {
output$plot_w <- renderPlotly({
req(hikp_w_data())
data <- hikp_w_data()
# Filter data based on selected years
time_filtered_data <- data[as.numeric(format(data$time, "%Y")) >= input$range_slider_w[1] & as.numeric(format(data$time, "%Y")) <= input$range_slider_w[2], ]
# Determine common max and min on y-axis
weight_by_geo <- time_filtered_data %>%
group_by(geo, time) %>%
summarise(sum = sum(values, na.rm = TRUE))
max_weight <-max(weight_by_geo$sum)
if(is.null(input$coicop_w)==FALSE && is.null(input$countries_w)==FALSE){
#Create list to store subplots
subplots <- list()
# Loop through each unique value in geo "geo"
count <- 0
for (geo_value in unique(time_filtered_data$geo)) {
# Filter dataset for the current geo value
count <- count + 1
filtered_data <- time_filtered_data[time_filtered_data$geo == geo_value, ]
filtered_data$values <- ifelse(is.na(filtered_data$values), 0, filtered_data$values)
# Check if all values in "values" are 0
if(all(filtered_data$values == 0)) {
# If all values are 0, add text "Data unavailable" in the middle
text_annotation <- list(
x = 0.25,
y = 0.75,
xref = "paper",
yref = "paper",
text = "Data unavailable",
showarrow = FALSE,
font = list(size = 14, color = "black")
)
subpl <- plot_ly(filtered_data,x = ~time, y = ~values, showlegend = FALSE,color = ~code_label,legendgroup = ~code_label) # Empty subplot
subpl <- subpl %>% layout(barmode = 'stack', annotations = list(text = geo_value, xref = "paper", yref = "paper", yanchor = "bottom", xanchor = "center", align = "center", x = 0.5, y = 0.95, showarrow = FALSE))
# Add axis labels
subpl <- subpl %>% layout(xaxis = list(title = ""), yaxis = list(title = "Weight, per mille"))
subpl <- subpl %>% layout(annotations = list(text_annotation))
} else {
# Create a stacked bar chart for current country
if(count == 1) {
subpl <- plot_ly(filtered_data, x = ~time, y = ~values, color = ~code_label, type = "bar", legendgroup = ~code_label)
} else {
subpl <- plot_ly(filtered_data, x = ~time, y = ~values, color = ~code_label, type = "bar", legendgroup = ~code_label, showlegend = FALSE)
}
subpl <- subpl %>% layout(yaxis = list(range = c(0, max_weight*1.05)),barmode = 'stack', annotations = list(text = geo_value, xref = "paper", yref = "paper", yanchor = "bottom", xanchor = "center", align = "center", x = 0.5, y = 0.95, showarrow = FALSE))
# Add axis labels
subpl <- subpl %>% layout(xaxis = list(title = ""), yaxis = list(title = "Weight, per mille"))
}
# Add the subplot in the list
subplots[[geo_value]] <- subpl
}
plot_rows=1
if(length(subplots)>2){
plot_rows=length(subplots)-1}
if((length(subplots) %% 2) == 0) {
#Even number of subplots
plot_rows=length(subplots)/2
} else {
#Odd number of subplots
plot_rows=(length(subplots)+1)/2
}
#
# Create layout for the frame graph, size depending on number of selected countries
layout <- plotly::subplot(subplots, nrows = plot_rows, titleX=TRUE, shareX=TRUE,titleY=TRUE,shareY=TRUE)
if(length(subplots)>2){
layout <- layout %>% layout(height = plot_rows*300,width=1100)
}
if(length(subplots)<=2){
layout <- layout %>% layout(height = plot_rows*475,width=1100)
}
# Create the frame graph
plotly_plot_w <- plotly_build(layout)
# Add the class "plot-container" to the labels box
plotly_plot_w$x$attrs$legend$x$class <- "plot-container"
return(plotly_plot_w)
}
})
})
# Create the plot (annual rate of change)
observeEvent(input$update_ar, {
output$plot_ar <- renderPlotly({
req(hikp_ar_data(), input$coicop_ar)
data <- hikp_ar_data()
# Filter data based on selected year range
time_filtered_data <- data[as.numeric(format(data$time, "%Y")) >= input$range_slider[1] & as.numeric(format(data$time, "%Y")) <= input$range_slider[2], ]
time_filtered_data$sign <- ifelse(time_filtered_data$Contr_j > 0, 1,
ifelse(time_filtered_data$Contr_j < 0, -1, 1))
# Determine common max and min on y-axis
contr_by_geo <- time_filtered_data %>%
group_by(geo, time, sign) %>%
summarise(sum_value = sum(Contr_j, na.rm = TRUE))
max_contr <-max(contr_by_geo$sum_value)
min_contr <-min(contr_by_geo$sum_value)
ar_00_by_geo <- time_filtered_data %>%
group_by(geo, time) %>%
summarise(max_value = max(ann_rate_00, na.rm = TRUE),min_value = min(ann_rate_00, na.rm = TRUE))
max_ar_00 <-max(ar_00_by_geo$max_value)
min_ar_00 <-min(ar_00_by_geo$min_value)
#...depending on the type of contribution selected
if (input$contribution_type_ar == "selected higher aggregate") {
max_ar<-max(max_ar_00,max_contr)
min_ar<-min(min_ar_00,min_contr,0)
} else if (input$contribution_type_ar == "all-items HICP") {
max_ar<-max(max_contr)
min_ar<-min(min_contr,0)
}
if(is.null(input$coicop_ar)==FALSE && is.null(input$countries_ar)==FALSE){
#Create list to store subplots
subplots <- list()
# Loop through each unique value in geo "geo"
count <- 0
for (geo_value in unique(time_filtered_data$geo)) {
# Filter dataset for the current geo value
count <- count + 1
filtered_data <- time_filtered_data[time_filtered_data$geo == geo_value, ]
filtered_data$Contr_j <- ifelse(is.na(filtered_data$Contr_j), 0, filtered_data$Contr_j)
# Check if all values in "Contr_j" are 0
if(all(filtered_data$Contr_j == 0 & is.na(filtered_data$ann_rate_00))) {
# If all values are 0, add text "Data unavailable" in the middle
text_annotation <- list(
x = 0.25,
y = 0.75,
xref = "paper",
yref = "paper",
text = "Data unavailable",
showarrow = FALSE,
font = list(size = 14, color = "black")
)
subpl <- plot_ly(filtered_data,x = ~time, y = ~Contr_j, showlegend = FALSE,color = ~code_label,legendgroup = ~code_label) # Empty subplot
subpl <- subpl %>% layout(barmode = 'relative', annotations = list(text = geo_value, xref = "paper", yref = "paper", yanchor = "bottom", xanchor = "center", align = "center", x = 0.5, y = 0.95, showarrow = FALSE),xaxis = list(tickformat = "%Y %B"))
#Add axis labels
if (input$contribution_type_ar == "selected higher aggregate") {
subpl <- subpl %>% layout(yaxis = list(range = c(min_ar-2, max_ar+2)),barmode = 'relative', annotations = list(text = geo_value, xref = "paper", yref = "paper", yanchor = "bottom", xanchor = "center", align = "center", x = 0.5, y = 0.95, showarrow = FALSE),xaxis = list(tickformat = "%Y %B",tickangle = 45))
# Add a line for annual rate
subpl <- subpl %>% add_trace(filtered_data ,x = ~time, y = ~ann_rate_00, type = 'scatter', mode = 'lines', name = 'Annual rate M/M-12', line = list(color = 'black', dash = 'dash',width = 1.5))
# Add axis labels
subpl <- subpl %>% layout(xaxis = list(title = ""), yaxis = list(title = "Annual rate of change, %"))
}