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DBnomics R client"
In rdbnomics: Download DBnomics Data
DBnomics: the world's economic database
Explore all the economic data from different providers (national and international statistical institutes, central banks, etc.), for free, following the link db.nomics.world
(N.B.: in the examples, data have already been retrieved on april 6th 2020).
Fetch time series by ids
First, let's assume that we know which series we want to download. A series identifier (ids) is defined by three values, formatted like this: provider_code/dataset_code/series_code.
Fetch one series from dataset 'Unemployment rate' (ZUTN) of AMECO provider
library <- function(...) {
suppressWarnings(
suppressPackageStartupMessages(base::library(..., quietly = TRUE))
)
}
library(data.table)
library(rdbnomics)
reorder_cols <- function(x) {
data.table::setDT(x)
cols <- c(
"provider_code", "dataset_code", "dataset_name", "series_code",
"series_name", "original_period", "period", "original_value", "value",
"@frequency"
)
if ("unit" %in% colnames(x)) {
cols <- c(cols, "unit", "Unit")
}
if ("geo" %in% colnames(x)) {
cols <- c(cols, "geo", "Country")
}
if ("freq" %in% colnames(x)) {
cols <- c(cols, "freq", "Frequency")
}
cols_add <- setdiff(colnames(x), cols)
cols <- c(cols, cols_add)
cols <- cols[cols %in% colnames(x)]
cols <- match(cols, colnames(x))
x[, .SD, .SDcols = cols]
}
knitr::opts_chunk$set(dev.args = list(bg = "transparent"))
display_table <- function(DT) {
DT <- head(DT)
DT <- as.data.table(
lapply(DT, function(x) {
if (is.character(x)) {
ifelse(
nchar(x) > 16,
paste0(substr(x, 1, 16), "..."),
x
)
} else {
x
}
})
)
DT[]
}
df <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN")
df <- df[!is.na(value)]
df <- rdbnomics:::rdbnomics_df001
data.table::setDT(df)
In such data.table, you will always find at least ten columns:
provider_codedataset_codedataset_nameseries_codeseries_nameoriginal_period (character string)period (date of the first day of original_period)original_value (character string)value@frequency (harmonized frequency generated by DBnomics)
The other columns depend on the provider and on the dataset. They always come in pairs (for the code and the name). In the data.frame df, you have:
unit (code) and Unit (name)geo (code) and Country (name)freq (code) and Frequency (name)
df <- reorder_cols(df)
display_table(df)
# ggplot(df, aes(x = period, y = value, color = series_name)) +
# geom_line(size = 1.2) +
# geom_point(size = 2) +
# dbnomics()
i <- 1
x1 <- df[series_name == sort(unique(series_name))[i]]$period
y1 <- df[series_name == sort(unique(series_name))[i]]$value
cols <- c("red", "blue", "darkgreen")
PCH <- 18
plot(
x1, y1, col = cols[1], type = "l", xlab = "", ylab = "",
xlim = c(min(df$period), max(df$period)),
ylim = c(min(df$value) - 0.5, max(df$value)),
panel.first = grid(lty = 1)
)
points(x1, y1, col = cols[1], pch = PCH)
legend(
"bottomleft", inset = 0.005,
legend = sort(unique(df$series_name)),
col = cols,
lty = 1, pch = PCH, box.lty = 0, cex = 0.7
)
mtext(
text = "DBnomics <https://db.nomics.world>",
side = 3, col = "grey", font = 3, adj = 1
)
In the event that you only use the argument ids, you can drop it and run:
df <- rdb("AMECO/ZUTN/EA19.1.0.0.0.ZUTN")
Fetch two series from dataset 'Unemployment rate' (ZUTN) of AMECO provider
df <- rdb(ids = c("AMECO/ZUTN/EA19.1.0.0.0.ZUTN", "AMECO/ZUTN/DNK.1.0.0.0.ZUTN"))
df <- df[!is.na(value)]
df <- rdbnomics:::rdbnomics_df002
data.table::setDT(df)
df <- df[order(series_code, period)]
df <- reorder_cols(df)
display_table(df)
# ggplot(df, aes(x = period, y = value, color = series_name)) +
# geom_line(size = 1.2) +
# geom_point(size = 2) +
# dbnomics()
i <- 1
x1 <- df[series_name == sort(unique(series_name))[i]]$period
y1 <- df[series_name == sort(unique(series_name))[i]]$value
i <- 2
x2 <- df[series_name == sort(unique(series_name))[i]]$period
y2 <- df[series_name == sort(unique(series_name))[i]]$value
cols <- c("red", "blue", "darkgreen")
PCH <- 18
plot(
x1, y1, col = cols[1], type = "l", xlab = "", ylab = "",
xlim = c(min(df$period), max(df$period)),
ylim = c(min(df$value) - 1.7, max(df$value)),
panel.first = grid(lty = 1)
)
points(x1, y1, col = cols[1], pch = PCH)
lines(x2, y2, col = cols[2], type = "l")
points(x2, y2, col = cols[2], pch = PCH)
legend(
"bottomleft", inset = 0.005,
legend = sort(unique(df$series_name)),
col = cols,
lty = 1, pch = PCH, box.lty = 0, cex = 0.7
)
mtext(
text = "DBnomics <https://db.nomics.world>",
side = 3, col = "grey", font = 3, adj = 1
)
Fetch two series from different datasets of different providers
df <- rdb(ids = c("AMECO/ZUTN/EA19.1.0.0.0.ZUTN", "Eurostat/une_rt_q/Q.SA.Y15-24.PC_ACT.T.EA19"))
df <- df[!is.na(value)]
df <- rdbnomics:::rdbnomics_df003
data.table::setDT(df)
df <- df[order(series_code, period)]
df <- reorder_cols(df)
display_table(df)
# ggplot(df, aes(x = period, y = value, color = series_name)) +
# geom_line(size = 1.2) +
# geom_point(size = 2) +
# dbnomics(legend.text = element_text(size = 7))
i <- 1
x1 <- df[series_name == sort(unique(series_name))[i]]$period
y1 <- df[series_name == sort(unique(series_name))[i]]$value
i <- 2
x2 <- df[series_name == sort(unique(series_name))[i]]$period
y2 <- df[series_name == sort(unique(series_name))[i]]$value
cols <- c("red", "blue", "darkgreen")
PCH <- 18
legend_text <- sort(unique(df$series_name))
legend_text[2] <- sapply(
legend_text[2],
function(y) {
paste0(
paste0(
strsplit(y, "active ")[[1]], collapse = "active\n"
),
"\n"
)
}
)
plot(
x1, y1, col = cols[1], type = "l", xlab = "", ylab = "",
xlim = c(min(df$period), max(df$period)),
ylim = c(min(df$value) - 1.5, max(df$value)),
panel.first = grid(lty = 1)
)
points(x1, y1, col = cols[1], pch = PCH)
lines(x2, y2, col = cols[2], type = "l")
points(x2, y2, col = cols[2], pch = PCH)
legend(
"bottomleft", inset = 0.005,
legend = legend_text,
col = cols,
lty = 1, pch = PCH, box.lty = 0, cex = 0.7
)
mtext(
text = "DBnomics <https://db.nomics.world>",
side = 3, col = "grey", font = 3, adj = 1
)
Fetch time series by mask
The code mask notation is a very concise way to select one or many time series at once.
Fetch one series from dataset 'Balance of Payments' (BOP) of IMF
df <- rdb("IMF", "BOP", mask = "A.FR.BCA_BP6_EUR")
df <- df[!is.na(value)]
df <- rdbnomics:::rdbnomics_df004
data.table::setDT(df)
df <- reorder_cols(df)
display_table(df)
# ggplot(df, aes(x = period, y = value, color = series_name)) +
# geom_step(size = 1.2) +
# geom_point(size = 2) +
# dbnomics()
i <- 1
x1 <- df[series_name == sort(unique(series_name))[i]]$period
y1 <- df[series_name == sort(unique(series_name))[i]]$value
cols <- c("red", "blue", "darkgreen")
PCH <- 18
legend_text <- sort(unique(df$series_name))
plot(
x1, y1, col = cols[1], type = "s", xlab = "", ylab = "",
xlim = c(min(df$period), max(df$period)),
ylim = c(min(df$value), max(df$value)),
panel.first = grid(lty = 1)
)
points(x1, y1, col = cols[1], pch = PCH)
legend(
"bottomleft", inset = 0.005,
legend = legend_text,
col = cols,
lty = 1, pch = PCH, box.lty = 0, cex = 0.7
)
mtext(
text = "DBnomics <https://db.nomics.world>",
side = 3, col = "grey", font = 3, adj = 1
)
In the event that you only use the arguments provider_code, dataset_code and mask, you can drop the name mask and run:
df <- rdb("IMF", "BOP", "A.FR.BCA_BP6_EUR")
Fetch two series from dataset 'Balance of Payments' (BOP) of IMF
You just have to add a + between two different values of a dimension.
df <- rdb("IMF", "BOP", mask = "A.FR+ES.BCA_BP6_EUR")
df <- df[!is.na(value)]
df <- rdbnomics:::rdbnomics_df005
data.table::setDT(df)
df <- df[order(series_code, period)]
df <- reorder_cols(df)
display_table(df)
# ggplot(df, aes(x = period, y = value, color = series_name)) +
# geom_step(size = 1.2) +
# geom_point(size = 2) +
# dbnomics()
i <- 1
x1 <- df[series_name == sort(unique(series_name))[i]]$period
y1 <- df[series_name == sort(unique(series_name))[i]]$value
i <- 2
x2 <- df[series_name == sort(unique(series_name))[i]]$period
y2 <- df[series_name == sort(unique(series_name))[i]]$value
cols <- c("red", "blue", "darkgreen")
PCH <- 18
legend_text <- sort(unique(df$series_name))
plot(
x1, y1, col = cols[1], type = "s", xlab = "", ylab = "",
xlim = c(min(df$period), max(df$period)),
ylim = c(min(df$value) - 2*10^4, max(df$value)),
panel.first = grid(lty = 1)
)
points(x1, y1, col = cols[1], pch = PCH)
lines(x2, y2, col = cols[2], type = "s")
points(x2, y2, col = cols[2], pch = PCH)
legend(
"bottomleft", inset = 0.005,
legend = legend_text,
col = cols,
lty = 1, pch = PCH, box.lty = 0, cex = 0.7
)
mtext(
text = "DBnomics <https://db.nomics.world>",
side = 3, col = "grey", font = 3, adj = 1
)
Fetch all series along one dimension from dataset 'Balance of Payments' (BOP) of IMF
df <- rdb("IMF", "BOP", mask = "A..BCA_BP6_EUR")
df <- df[!is.na(value)]
df <- df[order(-period, REF_AREA)]
df <- head(df, 100)
df <- rdbnomics:::rdbnomics_df006
data.table::setDT(df)
df <- reorder_cols(df)
display_table(df)
Fetch series along multiple dimensions from dataset 'Balance of Payments' (BOP) of IMF
df <- rdb("IMF", "BOP", mask = "A.FR.BCA_BP6_EUR+IA_BP6_EUR")
df <- df[!is.na(value)]
df <- df[order(period), head(.SD, 50), by = INDICATOR]
df <- rdbnomics:::rdbnomics_df007
data.table::setDT(df)
df <- reorder_cols(df)
display_table(df)
Fetch time series by dimensions
Searching by dimensions is a less concise way to select time series than using the code mask, but it works with all the different providers. You have a "Description of series code" at the bottom of each dataset page on the DBnomics website.
Fetch one value of one dimension from dataset 'Unemployment rate' (ZUTN) of AMECO provider
df <- rdb("AMECO", "ZUTN", dimensions = list(geo = "ea19"))
df <- df[!is.na(value))]
# or
# df <- rdb("AMECO", "ZUTN", dimensions = '{"geo": ["ea19"]}')
# df <- df[!is.na(value))]
df <- rdbnomics:::rdbnomics_df008
data.table::setDT(df)
df <- reorder_cols(df)
display_table(df)
# ggplot(df, aes(x = period, y = value, color = series_name)) +
# geom_line(size = 1.2) +
# geom_point(size = 2) +
# dbnomics()
i <- 1
x1 <- df[series_name == sort(unique(series_name))[i]]$period
y1 <- df[series_name == sort(unique(series_name))[i]]$value
cols <- c("red", "blue", "darkgreen")
PCH <- 18
legend_text <- sort(unique(df$series_name))
plot(
x1, y1, col = cols[1], type = "l", xlab = "", ylab = "",
xlim = c(min(df$period), max(df$period)),
ylim = c(min(df$value) - 0.2, max(df$value)),
panel.first = grid(lty = 1)
)
points(x1, y1, col = cols[1], pch = PCH)
legend(
"bottomleft", inset = 0.005,
legend = legend_text,
col = cols,
lty = 1, pch = PCH, box.lty = 0, cex = 0.7
)
mtext(
text = "DBnomics <https://db.nomics.world>",
side = 3, col = "grey", font = 3, adj = 1
)
Fetch two values of one dimension from dataset 'Unemployment rate' (ZUTN) of AMECO provider
df <- rdb("AMECO", "ZUTN", dimensions = list(geo = c("ea19", "dnk")))
df <- df[!is.na(value))]
# or
# df <- rdb("AMECO", "ZUTN", dimensions = '{"geo": ["ea19", "dnk"]}')
# df <- df[!is.na(value))]
df <- rdbnomics:::rdbnomics_df009
data.table::setDT(df)
df <- df[order(series_code, period)]
df <- reorder_cols(df)
display_table(df)
# ggplot(df, aes(x = period, y = value, color = series_name)) +
# geom_line(size = 1.2) +
# geom_point(size = 2) +
# dbnomics()
i <- 1
x1 <- df[series_name == sort(unique(series_name))[i]]$period
y1 <- df[series_name == sort(unique(series_name))[i]]$value
i <- 2
x2 <- df[series_name == sort(unique(series_name))[i]]$period
y2 <- df[series_name == sort(unique(series_name))[i]]$value
cols <- c("red", "blue", "darkgreen")
PCH <- 18
legend_text <- sort(unique(df$series_name))
plot(
x1, y1, col = cols[1], type = "l", xlab = "", ylab = "",
xlim = c(min(df$period), max(df$period)),
ylim = c(min(df$value) - 1.2, max(df$value)),
panel.first = grid(lty = 1)
)
points(x1, y1, col = cols[1], pch = PCH)
lines(x2, y2, col = cols[2], type = "l")
points(x2, y2, col = cols[2], pch = PCH)
legend(
"bottomleft", inset = 0.005,
legend = legend_text,
col = cols,
lty = 1, pch = PCH, box.lty = 0, cex = 0.7
)
mtext(
text = "DBnomics <https://db.nomics.world>",
side = 3, col = "grey", font = 3, adj = 1
)
Fetch several values of several dimensions from dataset 'Doing business' (DB) of World Bank
df <- rdb("WB", "DB", dimensions = list(country = c("DZ", "PE"), indicator = c("ENF.CONT.COEN.COST.ZS", "IC.REG.COST.PC.FE.ZS")))
df <- df[!is.na(value))]
# or
# df <- rdb("WB", "DB", dimensions = '{"country": ["DZ", "PE"], "indicator": ["ENF.CONT.COEN.COST.ZS", "IC.REG.COST.PC.FE.ZS"]}')
# df <- df[!is.na(value))]
df <- rdbnomics:::rdbnomics_df010
data.table::setDT(df)
df <- df[order(series_name, period)]
df <- reorder_cols(df)
display_table(df)
# ggplot(df, aes(x = period, y = value, color = series_name)) +
# geom_line(size = 1.2) +
# geom_point(size = 2) +
# dbnomics()
i <- 1
x1 <- df[series_name == sort(unique(series_name))[i]]$period
y1 <- df[series_name == sort(unique(series_name))[i]]$value
i <- 2
x2 <- df[series_name == sort(unique(series_name))[i]]$period
y2 <- df[series_name == sort(unique(series_name))[i]]$value
i <- 3
x3 <- df[series_name == sort(unique(series_name))[i]]$period
y3 <- df[series_name == sort(unique(series_name))[i]]$value
i <- 4
x4 <- df[series_name == sort(unique(series_name))[i]]$period
y4 <- df[series_name == sort(unique(series_name))[i]]$value
cols <- c("red", "blue", "darkgreen", "purple")
PCH <- 18
legend_text <- sort(unique(df$series_name))
plot(
x1, y1, col = cols[1], type = "l", xlab = "", ylab = "",
xlim = c(min(df$period), max(df$period)),
ylim = c(min(df$value) - 7, max(df$value)),
panel.first = grid(lty = 1)
)
points(x1, y1, col = cols[1], pch = PCH)
lines(x2, y2, col = cols[2], type = "l")
points(x2, y2, col = cols[2], pch = PCH)
lines(x3, y3, col = cols[3], type = "l")
points(x3, y3, col = cols[3], pch = PCH)
lines(x4, y4, col = cols[4], type = "l")
points(x4, y4, col = cols[4], pch = PCH)
legend(
"bottomleft", inset = 0.005,
legend = legend_text,
col = cols,
lty = 1, pch = PCH, box.lty = 0, cex = 0.7
)
mtext(
text = "DBnomics <https://db.nomics.world>",
side = 3, col = "grey", font = 3, adj = 1
)
Fetch time series with a query
The query is a Google-like search that will filter/select time series from a provider's dataset.
Fetch one series from dataset 'WEO by countries (2019-10 release)' (WEO:2019-10) of IMF
df <- rdb("IMF", "WEO:2019-10", query = "France current account balance percent")
df <- df[!is.na(value))]
df <- rdbnomics:::rdbnomics_df014
data.table::setDT(df)
df <- reorder_cols(df)
display_table(df)
# ggplot(df, aes(x = period, y = value, color = series_name)) +
# geom_line(size = 1.2) +
# geom_point(size = 2) +
# dbnomics()
i <- 1
x1 <- df[series_name == sort(unique(series_name))[i]]$period
y1 <- df[series_name == sort(unique(series_name))[i]]$value
cols <- c("red", "blue", "darkgreen", "purple")
PCH <- 18
legend_text <- sort(unique(df$series_name))
plot(
x1, y1, col = cols[1], type = "l", xlab = "", ylab = "",
xlim = c(min(df$period), max(df$period)),
ylim = c(min(df$value) - 0.5, max(df$value)),
panel.first = grid(lty = 1)
)
points(x1, y1, col = cols[1], pch = PCH)
legend(
"bottomleft", inset = 0.005,
legend = legend_text,
col = cols,
lty = 1, pch = PCH, box.lty = 0, cex = 0.7
)
mtext(
text = "DBnomics <https://db.nomics.world>",
side = 3, col = "grey", font = 3, adj = 1
)
Fetch series from dataset 'WEO by countries (2019-10 release)' (WEO:2019-10) of IMF
df <- rdb("IMF", "WEO:2019-10", query = "current account balance percent")
df <- df[!is.na(value))]
df <- rdbnomics:::rdbnomics_df015
data.table::setDT(df)
df <- reorder_cols(df)
display_table(df)
# ggplot(df, aes(x = period, y = value, color = `WEO Country`)) +
# geom_line(size = 1.2) +
# geom_point(size = 2) +
# ggtitle("Current account balance (% GDP)") +
# dbnomics(legend.direction = "horizontal")
i <- 1
x1 <- df[series_name == sort(unique(series_name))[i]]$period
y1 <- df[series_name == sort(unique(series_name))[i]]$value
i <- 2
x2 <- df[series_name == sort(unique(series_name))[i]]$period
y2 <- df[series_name == sort(unique(series_name))[i]]$value
i <- 3
x3 <- df[series_name == sort(unique(series_name))[i]]$period
y3 <- df[series_name == sort(unique(series_name))[i]]$value
i <- 4
x4 <- df[series_name == sort(unique(series_name))[i]]$period
y4 <- df[series_name == sort(unique(series_name))[i]]$value
cols <- c("red", "blue", "darkgreen", "purple")
PCH <- 18
legend_text <- sort(unique(df$series_name))
plot(
x1, y1, col = cols[1], type = "l", xlab = "", ylab = "",
xlim = c(min(df$period), max(df$period)),
ylim = c(min(df$value), max(df$value)),
panel.first = grid(lty = 1)
)
points(x1, y1, col = cols[1], pch = PCH)
lines(x2, y2, col = cols[2], type = "l")
points(x2, y2, col = cols[2], pch = PCH)
lines(x3, y3, col = cols[3], type = "l")
points(x3, y3, col = cols[3], pch = PCH)
lines(x4, y4, col = cols[4], type = "l")
points(x4, y4, col = cols[4], pch = PCH)
legend(
"bottomleft", inset = 0.005,
legend = legend_text,
col = cols,
lty = 1, pch = PCH, box.lty = 0, cex = 0.7
)
mtext(
text = "DBnomics <https://db.nomics.world>",
side = 3, col = "grey", font = 3, adj = 1
)
Fetch time series found on the web site
When you don't know the codes of the dimensions, provider, dataset or series, you can:
-
go to the page of a dataset on DBnomics website, for example Doing Business,
-
select some dimensions by using the input widgets of the left column,
-
click on "Copy API link" in the menu of the "Download" button,
-
use the rdb(api_link = ...) function such as below.
df <- rdb(api_link = "https://api.db.nomics.world/v22/series/WB/DB?dimensions=%7B%22country%22%3A%5B%22FR%22%2C%22IT%22%2C%22ES%22%5D%7D&q=IC.REG.PROC.FE.NO&observations=1&format=json&align_periods=1&offset=0&facets=0")
df <- df[!is.na(value))]
df <- rdbnomics:::rdbnomics_df011
data.table::setDT(df)
df <- df[order(period, series_name)]
df <- reorder_cols(df)
display_table(df)
# ggplot(df, aes(x = period, y = value, color = series_name)) +
# geom_step(size = 1.2) +
# geom_point(size = 2) +
# dbnomics()
i <- 1
x1 <- df[series_name == sort(unique(series_name))[i]]$period
y1 <- df[series_name == sort(unique(series_name))[i]]$value
i <- 2
x2 <- df[series_name == sort(unique(series_name))[i]]$period
y2 <- df[series_name == sort(unique(series_name))[i]]$value
i <- 3
x3 <- df[series_name == sort(unique(series_name))[i]]$period
y3 <- df[series_name == sort(unique(series_name))[i]]$value
cols <- c("red", "blue", "darkgreen")
PCH <- 18
plot(
x1, y1, col = cols[1], type = "s", xlab = "", ylab = "",
xlim = c(min(df$period), max(df$period)),
ylim = c(min(df$value) - 1.2, max(df$value)),
panel.first = grid(lty = 1)
)
points(x1, y1, col = cols[1], pch = PCH)
lines(x2, y2, col = cols[2], type = "s")
points(x2, y2, col = cols[2], pch = PCH)
lines(x3, y3, col = cols[3], type = "s")
points(x3, y3, col = cols[3], pch = PCH)
legend(
"bottomleft", inset = 0.005,
legend = sort(unique(df$series_name)),
col = cols,
lty = 1, pch = PCH, box.lty = 0, cex = 0.7
)
mtext(
text = "DBnomics <https://db.nomics.world>",
side = 3, col = "grey", font = 3, adj = 1
)
In the event that you only use the argument api_link, you can drop the name and run:
df <- rdb("https://api.db.nomics.world/v22/series/WB/DB?dimensions=%7B%22country%22%3A%5B%22FR%22%2C%22IT%22%2C%22ES%22%5D%7D&q=IC.REG.PROC.FE.NO&observations=1&format=json&align_periods=1&offset=0&facets=0")
Fetch time series from the cart
On the cart page of the DBnomics website, click on "Copy API link" and copy-paste it as an argument of the rdb(api_link = ...) function. Please note that when you update your cart, you have to copy this link again, because the link itself contains the ids of the series in the cart.
df <- rdb(api_link = "https://api.db.nomics.world/v22/series?observations=1&series_ids=BOE/6008/RPMTDDC,BOE/6231/RPMTBVE")
df <- df[!is.na(value))]
df <- rdbnomics:::rdbnomics_df012
data.table::setDT(df)
df[
,
series_name := sapply(
series_name,
function(y) {
paste0(
paste0(
strsplit(y, "institutions' ")[[1]], collapse = "institutions'\n"
),
"\n"
)
}
)
]
df <- df[order(period, series_name)]
df <- reorder_cols(df)
display_table(df)
# ggplot(df, aes(x = period, y = value, color = series_name)) +
# geom_line(size = 1.2) +
# geom_point(size = 2) +
# dbnomics()
i <- 1
x1 <- df[series_name == sort(unique(series_name))[i]]$period
y1 <- df[series_name == sort(unique(series_name))[i]]$value
i <- 2
x2 <- df[series_name == sort(unique(series_name))[i]]$period
y2 <- df[series_name == sort(unique(series_name))[i]]$value
cols <- c("red", "blue", "darkgreen")
PCH <- 18
plot(
x1, y1, col = cols[1], type = "l", xlab = "", ylab = "",
xlim = c(min(df$period), max(df$period)),
ylim = c(min(df$value) - 4*10^3, max(df$value)),
panel.first = grid(lty = 1)
)
points(x1, y1, col = cols[1], pch = PCH)
lines(x2, y2, col = cols[2], type = "l")
points(x2, y2, col = cols[2], pch = PCH)
legend(
"bottomleft", inset = 0.005,
legend = sort(unique(df$series_name)),
col = cols,
lty = 1, pch = PCH, box.lty = 0, cex = 0.7
)
mtext(
text = "DBnomics <https://db.nomics.world>",
side = 3, col = "grey", font = 3, adj = 1
)
Fetch the available datasets of a provider
When fetching series from DBnomics, you need
to give a provider and a dataset before specifying correct dimensions. With
the function rdb_datasets, you can download the list of the available datasets
for a provider.
For example, to fetch the IMF datasets, you have to use:
rdb_datasets(provider_code = "IMF")
The result is a named list (its name is IMF) with one element which is a
data.table:
str(rdbnomics:::rdbnomics_df016)
With the same function, if you want to fetch the available datasets for multiple
providers, you can give a vector of providers and get a named list.
rdb_datasets(provider_code = c("IMF", "BDF"))
str(rdbnomics:::rdbnomics_df018)
DT <- rdbnomics:::rdbnomics_df018
DT <- sapply(DT, function(y) { paste0(": ", nrow(y)) })
DT <- paste0("Number of datasets for ", names(DT), " ", unname(DT))
cat(DT, sep = "\n")
In the event that you only request the datasets for one provider, if you define
simplify = TRUE, then the result will be a data.table not a named list.
rdb_datasets(provider_code = "IMF", simplify = TRUE)
DT <- rdbnomics:::rdbnomics_df017
data.table::setDT(DT)
display_table(DT)
The extent of datasets gathered by DBnomics can be
appreciate by using the function with the argument provider_code set to
NULL:
options(rdbnomics.progress_bar_datasets = TRUE)
rdb_datasets()
options(rdbnomics.progress_bar_datasets = FALSE)
DT <- rdbnomics:::rdbnomics_df019
DT <- data.table(Provider = names(DT), `Number of datasets` = sapply(DT, nrow))
DT <- DT[order(Provider)]
display_table(DT)
Fetch the possible dimensions of available datasets of a provider
When fetching series from DBnomics, it can be
interesting and especially useful to specify dimensions for a particular
dataset to download only the series you want to analyse. With
the function rdb_dimensions, you can download these dimensions and their
meanings.
For example, for the dataset WEO:2019-10 of the IMF, you may use:
rdb_dimensions(provider_code = "IMF", dataset_code = "WEO:2019-10")
The result is a nested named list (its names are IMF, WEO:2019-10 and the
dimensions names) with a data.table at the end of each branch:
DT <- rdbnomics:::rdbnomics_df020
DT <- DT$IMF$`WEO:2019-10`
DT <- paste0("Number of dimensions for IMF/WEO:2019-10 : ", length(DT))
cat(DT, sep = "\n")
DT <- rdbnomics:::rdbnomics_df020
DT <- DT$IMF$`WEO:2019-10`[[1]]
display_table(DT)
DT <- rdbnomics:::rdbnomics_df020
DT <- DT$IMF$`WEO:2019-10`[[2]]
display_table(DT)
DT <- rdbnomics:::rdbnomics_df020
DT <- DT$IMF$`WEO:2019-10`[[3]]
display_table(DT)
In the event that you only request the dimensions for one dataset for one
provider, if you define simplify = TRUE, then the result will be a named list
data.table not a nested named list.
rdb_dimensions(provider_code = "IMF", dataset_code = "WEO:2019-10", simplify = TRUE)
str(rdbnomics:::rdbnomics_df021)
You can measure the vast extent of datasets gathered by
DBnomics by downloading all the possible
dimensions. To do this, you have to set the arguments
provider_code and dataset_code to NULL.
⚠ It's relatively long to run and heavy to show so we display
the first 100.
options(rdbnomics.progress_bar_datasets = TRUE)
rdb_dimensions()
options(rdbnomics.progress_bar_datasets = FALSE)
DT <- rdbnomics:::rdbnomics_df022
DT <- DT[order(Provider, Dataset)]
DT <- head(DT, 100)
display_table(DT)
# rdbnomics_df022 %>%
# sapply(function(u) {
# sapply(
# u,
# function(x) {
# sapply(
# x,
# function(y) {
# nrow(y)
# },
# simplify = FALSE
# ) %>%
# {
# data.table(Dimension = names(.), `Number of codes` = unname(.))
# }
# },
# simplify = FALSE
# ) %>%
# rbindlist(idcol = "Dataset")
# },
# simplify = FALSE
# ) %>%
# rbindlist(idcol = "Provider")
Fetch the series codes and names of available datasets of a provider
You can download the list of series, and especially their codes, of a dataset's
provider by using the function rdb_series. The result is a nested named list
with a data.table at the end of each branch. If you define simplify = TRUE,
then the result will be a data.table not a nested named list.
For example, for the IMF provider and the dataset WEO:2019-10, the command is (only first 100):
rdb_series(provider_code = "IMF", dataset_code = "WEO:2019-10", simplify = TRUE)
DT <- rdbnomics:::rdbnomics_df023
DT <- head(DT, 100)
display_table(DT)
Like the function rdb(), you can add features to rdb_series(). You can ask for
the series with specific dimensions:
rdb_series(provider_code = "IMF", dataset_code = "WEO:2019-10", dimensions = list(`weo-subject` = "NGDP_RPCH"), simplify = TRUE)
or with a query:
rdb_series(provider_code = "IMF", dataset_code = c("WEO:2019-10", "WEOAGG:2019-10"), query = "NGDP_RPCH")
⚠ We ask the user to use this function parsimoniously because there are a huge amount
of series per dataset. Please only fetch for one dataset if you need it or
visit the website https://db.nomics.world.
For example, for the IMF provider, the number of series is (only first 5):
DT <- rdbnomics:::rdbnomics_df024
DT <- DT[order(-`Number of series`)]
DT <- head(DT, 5)
display_table(DT)
Proxy configuration or connection error Could not resolve host
When using the function rdb, you may come across the following error:
Error in open.connection(con, "rb") :
Could not resolve host: api.db.nomics.world
To get round this situation, you have two options:
-
configure curl to use a specific and authorized proxy.
-
use the default R internet connection i.e. the Internet Explorer proxy defined in internet2.dll.
Configure curl to use a specific and authorized proxy
In rdbnomics, by default the function curl_fetch_memory (of the package curl) is used to fetch the data. If a specific proxy must be used, it is possible to define it permanently with the package option rdbnomics.curl_config or on the fly through the argument curl_config. Because the object is a named list, its elements are passed to the connection (the curl_handle object created internally with new_handle()) with handle_setopt() before using curl_fetch_memory.
To see the available parameters, run names(curl_options()) in R or visit the website https://curl.haxx.se/libcurl/c/curl_easy_setopt.html. Once they are chosen, you define the curl object as follows:
h <- list(
proxy = "<proxy>",
proxyport = <port>,
proxyusername = "<username>",
proxypassword = "<password>"
)
Set the connection up for a session
The curl connection can be set up for a session by modifying the following package option:
options(rdbnomics.curl_config = h)
When fetching the data, the following command is executed:
hndl <- curl::new_handle()
curl::handle_setopt(hndl, .list = getOption("rdbnomics.curl_config"))
curl::curl_fetch_memory(url = <...>, handle = hndl)
After configuration, just use the standard functions of rdbnomics e.g.:
df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN")
This option of the package can be disabled with:
options(rdbnomics.curl = NULL)
Use the connection only for a function call
If a complete configuration is not needed but just an "on the fly" execution, then use the argument curl_config of the function rdb:
df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN", curl_config = h)
Use the default R internet connection
To retrieve the data with the default R internet connection, rdbnomics will use the base function readLines.
Set the connection up for a session
To activate this feature for a session, you need to enable an option of the package:
options(rdbnomics.use_readLines = TRUE)
And then use the standard function as follows:
df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN")
This configuration can be disabled with:
options(rdbnomics.use_readLines = FALSE)
Use the connection only for a function call
If you just want to do it once, you may use the argument use_readLines of the function rdb:
df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN", use_readLines = TRUE)
Transform time series with filters
The rdbnomics package can interact with the Time Series Editor of DBnomics to transform time series by applying filters to them.
Available filters are listed on the filters page https://editor.nomics.world/filters.
Here is an example of how to proceed to interpolate two annual time series with a monthly frequency, using a spline interpolation:
filters <- list(
code = "interpolate",
parameters = list(frequency = "monthly", method = "spline")
)
The request is then:
df <- rdb(
ids = c("AMECO/ZUTN/EA19.1.0.0.0.ZUTN", "AMECO/ZUTN/DNK.1.0.0.0.ZUTN"),
filters = filters
)
If you want to apply more than one filter, the filters argument will be a list of valid filters:
filters <- list(
list(
code = "interpolate",
parameters = list(frequency = "monthly", method = "spline")
),
list(
code = "aggregate",
parameters = list(frequency = "bi-annual", method = "end_of_period")
)
)
df <- rdb(
ids = c("AMECO/ZUTN/EA19.1.0.0.0.ZUTN", "AMECO/ZUTN/DNK.1.0.0.0.ZUTN"),
filters = filters
)
The data.table columns change a little bit when filters are used. There are two new columns:
period_middle_day: the middle day of original_period (can be useful when you compare graphically interpolated series and original ones).filtered (boolean): TRUE if the series is filtered, FALSE otherwise.
The content of two columns are modified:
series_code: same as before for original series, but the suffix _filtered is added for filtered series.series_name: same as before for original series, but the suffix (filtered) is added for filtered series.
df <- rdbnomics:::rdbnomics_df013
data.table::setDT(df)
df <- df[order(filtered, series_name, period)]
df <- reorder_cols(df)
display_table(df)
# ggplot(df[!is.na(value)], aes(x = period, y = value, color = series_name)) +
# geom_line(size = 1.2) +
# geom_point(size = 2) +
# dbnomics()
df <- df[!is.na(value)]
i <- 1
x1 <- df[series_name == sort(unique(series_name))[i]]$period
y1 <- df[series_name == sort(unique(series_name))[i]]$value
i <- 2
x2 <- df[series_name == sort(unique(series_name))[i]]$period
y2 <- df[series_name == sort(unique(series_name))[i]]$value
i <- 3
x3 <- df[series_name == sort(unique(series_name))[i]]$period
y3 <- df[series_name == sort(unique(series_name))[i]]$value
i <- 4
x4 <- df[series_name == sort(unique(series_name))[i]]$period
y4 <- df[series_name == sort(unique(series_name))[i]]$value
cols <- c("red", "blue", "darkgreen", "purple")
PCH <- 18
legend_text <- sort(unique(df$series_name))
plot(
x1, y1, col = cols[1], type = "l", xlab = "", ylab = "",
xlim = c(min(df$period), max(df$period)),
ylim = c(min(df$value) - 4, max(df$value)),
panel.first = grid(lty = 1)
)
points(x1, y1, col = cols[1], pch = PCH)
lines(x2, y2, col = cols[2], type = "l")
points(x2, y2, col = cols[2], pch = PCH)
lines(x3, y3, col = cols[3], type = "l")
points(x3, y3, col = cols[3], pch = PCH)
lines(x4, y4, col = cols[4], type = "l")
points(x4, y4, col = cols[4], pch = PCH)
legend(
"bottomleft", inset = 0.005,
legend = legend_text,
col = cols,
lty = 1, pch = PCH, box.lty = 0, cex = 0.7
)
mtext(
text = "DBnomics <https://db.nomics.world>",
side = 3, col = "grey", font = 3, adj = 1
)
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rdbnomics documentation built on Oct. 26, 2020, 1:06 a.m.
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DBnomics: the world's economic database
Explore all the economic data from different providers (national and international statistical institutes, central banks, etc.), for free, following the link db.nomics.world
(N.B.: in the examples, data have already been retrieved on april 6th 2020).
Fetch time series by ids
First, let's assume that we know which series we want to download. A series identifier (ids) is defined by three values, formatted like this: provider_code/dataset_code/series_code.
Fetch one series from dataset 'Unemployment rate' (ZUTN) of AMECO provider
library <- function(...) { suppressWarnings( suppressPackageStartupMessages(base::library(..., quietly = TRUE)) ) }
library(data.table) library(rdbnomics)
reorder_cols <- function(x) { data.table::setDT(x) cols <- c( "provider_code", "dataset_code", "dataset_name", "series_code", "series_name", "original_period", "period", "original_value", "value", "@frequency" ) if ("unit" %in% colnames(x)) { cols <- c(cols, "unit", "Unit") } if ("geo" %in% colnames(x)) { cols <- c(cols, "geo", "Country") } if ("freq" %in% colnames(x)) { cols <- c(cols, "freq", "Frequency") } cols_add <- setdiff(colnames(x), cols) cols <- c(cols, cols_add) cols <- cols[cols %in% colnames(x)] cols <- match(cols, colnames(x)) x[, .SD, .SDcols = cols] } knitr::opts_chunk$set(dev.args = list(bg = "transparent")) display_table <- function(DT) { DT <- head(DT) DT <- as.data.table( lapply(DT, function(x) { if (is.character(x)) { ifelse( nchar(x) > 16, paste0(substr(x, 1, 16), "..."), x ) } else { x } }) ) DT[] }
df <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN") df <- df[!is.na(value)]
df <- rdbnomics:::rdbnomics_df001 data.table::setDT(df)
In such data.table, you will always find at least ten columns:
provider_codedataset_codedataset_nameseries_codeseries_nameoriginal_period(character string)period(date of the first day oforiginal_period)original_value(character string)value@frequency(harmonized frequency generated by DBnomics)
The other columns depend on the provider and on the dataset. They always come in pairs (for the code and the name). In the data.frame df, you have:
unit(code) andUnit(name)geo(code) andCountry(name)freq(code) andFrequency(name)
df <- reorder_cols(df) display_table(df)
# ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_line(size = 1.2) + # geom_point(size = 2) + # dbnomics() i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen") PCH <- 18 plot( x1, y1, col = cols[1], type = "l", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 0.5, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) legend( "bottomleft", inset = 0.005, legend = sort(unique(df$series_name)), col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics <https://db.nomics.world>", side = 3, col = "grey", font = 3, adj = 1 )
In the event that you only use the argument ids, you can drop it and run:
df <- rdb("AMECO/ZUTN/EA19.1.0.0.0.ZUTN")
Fetch two series from dataset 'Unemployment rate' (ZUTN) of AMECO provider
df <- rdb(ids = c("AMECO/ZUTN/EA19.1.0.0.0.ZUTN", "AMECO/ZUTN/DNK.1.0.0.0.ZUTN")) df <- df[!is.na(value)]
df <- rdbnomics:::rdbnomics_df002 data.table::setDT(df)
df <- df[order(series_code, period)] df <- reorder_cols(df) display_table(df)
# ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_line(size = 1.2) + # geom_point(size = 2) + # dbnomics() i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value i <- 2 x2 <- df[series_name == sort(unique(series_name))[i]]$period y2 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen") PCH <- 18 plot( x1, y1, col = cols[1], type = "l", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 1.7, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) lines(x2, y2, col = cols[2], type = "l") points(x2, y2, col = cols[2], pch = PCH) legend( "bottomleft", inset = 0.005, legend = sort(unique(df$series_name)), col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics <https://db.nomics.world>", side = 3, col = "grey", font = 3, adj = 1 )
Fetch two series from different datasets of different providers
df <- rdb(ids = c("AMECO/ZUTN/EA19.1.0.0.0.ZUTN", "Eurostat/une_rt_q/Q.SA.Y15-24.PC_ACT.T.EA19")) df <- df[!is.na(value)]
df <- rdbnomics:::rdbnomics_df003 data.table::setDT(df)
df <- df[order(series_code, period)] df <- reorder_cols(df) display_table(df)
# ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_line(size = 1.2) + # geom_point(size = 2) + # dbnomics(legend.text = element_text(size = 7)) i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value i <- 2 x2 <- df[series_name == sort(unique(series_name))[i]]$period y2 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen") PCH <- 18 legend_text <- sort(unique(df$series_name)) legend_text[2] <- sapply( legend_text[2], function(y) { paste0( paste0( strsplit(y, "active ")[[1]], collapse = "active\n" ), "\n" ) } ) plot( x1, y1, col = cols[1], type = "l", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 1.5, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) lines(x2, y2, col = cols[2], type = "l") points(x2, y2, col = cols[2], pch = PCH) legend( "bottomleft", inset = 0.005, legend = legend_text, col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics <https://db.nomics.world>", side = 3, col = "grey", font = 3, adj = 1 )
Fetch time series by mask
The code mask notation is a very concise way to select one or many time series at once.
Fetch one series from dataset 'Balance of Payments' (BOP) of IMF
df <- rdb("IMF", "BOP", mask = "A.FR.BCA_BP6_EUR") df <- df[!is.na(value)]
df <- rdbnomics:::rdbnomics_df004 data.table::setDT(df)
df <- reorder_cols(df) display_table(df)
# ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_step(size = 1.2) + # geom_point(size = 2) + # dbnomics() i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen") PCH <- 18 legend_text <- sort(unique(df$series_name)) plot( x1, y1, col = cols[1], type = "s", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value), max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) legend( "bottomleft", inset = 0.005, legend = legend_text, col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics <https://db.nomics.world>", side = 3, col = "grey", font = 3, adj = 1 )
In the event that you only use the arguments provider_code, dataset_code and mask, you can drop the name mask and run:
df <- rdb("IMF", "BOP", "A.FR.BCA_BP6_EUR")
Fetch two series from dataset 'Balance of Payments' (BOP) of IMF
You just have to add a + between two different values of a dimension.
df <- rdb("IMF", "BOP", mask = "A.FR+ES.BCA_BP6_EUR") df <- df[!is.na(value)]
df <- rdbnomics:::rdbnomics_df005 data.table::setDT(df)
df <- df[order(series_code, period)] df <- reorder_cols(df) display_table(df)
# ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_step(size = 1.2) + # geom_point(size = 2) + # dbnomics() i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value i <- 2 x2 <- df[series_name == sort(unique(series_name))[i]]$period y2 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen") PCH <- 18 legend_text <- sort(unique(df$series_name)) plot( x1, y1, col = cols[1], type = "s", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 2*10^4, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) lines(x2, y2, col = cols[2], type = "s") points(x2, y2, col = cols[2], pch = PCH) legend( "bottomleft", inset = 0.005, legend = legend_text, col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics <https://db.nomics.world>", side = 3, col = "grey", font = 3, adj = 1 )
Fetch all series along one dimension from dataset 'Balance of Payments' (BOP) of IMF
df <- rdb("IMF", "BOP", mask = "A..BCA_BP6_EUR") df <- df[!is.na(value)] df <- df[order(-period, REF_AREA)] df <- head(df, 100)
df <- rdbnomics:::rdbnomics_df006 data.table::setDT(df)
df <- reorder_cols(df) display_table(df)
Fetch series along multiple dimensions from dataset 'Balance of Payments' (BOP) of IMF
df <- rdb("IMF", "BOP", mask = "A.FR.BCA_BP6_EUR+IA_BP6_EUR") df <- df[!is.na(value)] df <- df[order(period), head(.SD, 50), by = INDICATOR]
df <- rdbnomics:::rdbnomics_df007 data.table::setDT(df)
df <- reorder_cols(df) display_table(df)
Fetch time series by dimensions
Searching by dimensions is a less concise way to select time series than using the code mask, but it works with all the different providers. You have a "Description of series code" at the bottom of each dataset page on the DBnomics website.
Fetch one value of one dimension from dataset 'Unemployment rate' (ZUTN) of AMECO provider
df <- rdb("AMECO", "ZUTN", dimensions = list(geo = "ea19")) df <- df[!is.na(value))] # or # df <- rdb("AMECO", "ZUTN", dimensions = '{"geo": ["ea19"]}') # df <- df[!is.na(value))]
df <- rdbnomics:::rdbnomics_df008 data.table::setDT(df)
df <- reorder_cols(df) display_table(df)
# ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_line(size = 1.2) + # geom_point(size = 2) + # dbnomics() i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen") PCH <- 18 legend_text <- sort(unique(df$series_name)) plot( x1, y1, col = cols[1], type = "l", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 0.2, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) legend( "bottomleft", inset = 0.005, legend = legend_text, col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics <https://db.nomics.world>", side = 3, col = "grey", font = 3, adj = 1 )
Fetch two values of one dimension from dataset 'Unemployment rate' (ZUTN) of AMECO provider
df <- rdb("AMECO", "ZUTN", dimensions = list(geo = c("ea19", "dnk"))) df <- df[!is.na(value))] # or # df <- rdb("AMECO", "ZUTN", dimensions = '{"geo": ["ea19", "dnk"]}') # df <- df[!is.na(value))]
df <- rdbnomics:::rdbnomics_df009 data.table::setDT(df)
df <- df[order(series_code, period)] df <- reorder_cols(df) display_table(df)
# ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_line(size = 1.2) + # geom_point(size = 2) + # dbnomics() i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value i <- 2 x2 <- df[series_name == sort(unique(series_name))[i]]$period y2 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen") PCH <- 18 legend_text <- sort(unique(df$series_name)) plot( x1, y1, col = cols[1], type = "l", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 1.2, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) lines(x2, y2, col = cols[2], type = "l") points(x2, y2, col = cols[2], pch = PCH) legend( "bottomleft", inset = 0.005, legend = legend_text, col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics <https://db.nomics.world>", side = 3, col = "grey", font = 3, adj = 1 )
Fetch several values of several dimensions from dataset 'Doing business' (DB) of World Bank
df <- rdb("WB", "DB", dimensions = list(country = c("DZ", "PE"), indicator = c("ENF.CONT.COEN.COST.ZS", "IC.REG.COST.PC.FE.ZS"))) df <- df[!is.na(value))] # or # df <- rdb("WB", "DB", dimensions = '{"country": ["DZ", "PE"], "indicator": ["ENF.CONT.COEN.COST.ZS", "IC.REG.COST.PC.FE.ZS"]}') # df <- df[!is.na(value))]
df <- rdbnomics:::rdbnomics_df010 data.table::setDT(df)
df <- df[order(series_name, period)] df <- reorder_cols(df) display_table(df)
# ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_line(size = 1.2) + # geom_point(size = 2) + # dbnomics() i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value i <- 2 x2 <- df[series_name == sort(unique(series_name))[i]]$period y2 <- df[series_name == sort(unique(series_name))[i]]$value i <- 3 x3 <- df[series_name == sort(unique(series_name))[i]]$period y3 <- df[series_name == sort(unique(series_name))[i]]$value i <- 4 x4 <- df[series_name == sort(unique(series_name))[i]]$period y4 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen", "purple") PCH <- 18 legend_text <- sort(unique(df$series_name)) plot( x1, y1, col = cols[1], type = "l", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 7, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) lines(x2, y2, col = cols[2], type = "l") points(x2, y2, col = cols[2], pch = PCH) lines(x3, y3, col = cols[3], type = "l") points(x3, y3, col = cols[3], pch = PCH) lines(x4, y4, col = cols[4], type = "l") points(x4, y4, col = cols[4], pch = PCH) legend( "bottomleft", inset = 0.005, legend = legend_text, col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics <https://db.nomics.world>", side = 3, col = "grey", font = 3, adj = 1 )
Fetch time series with a query
The query is a Google-like search that will filter/select time series from a provider's dataset.
Fetch one series from dataset 'WEO by countries (2019-10 release)' (WEO:2019-10) of IMF
df <- rdb("IMF", "WEO:2019-10", query = "France current account balance percent") df <- df[!is.na(value))]
df <- rdbnomics:::rdbnomics_df014 data.table::setDT(df)
df <- reorder_cols(df) display_table(df)
# ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_line(size = 1.2) + # geom_point(size = 2) + # dbnomics() i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen", "purple") PCH <- 18 legend_text <- sort(unique(df$series_name)) plot( x1, y1, col = cols[1], type = "l", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 0.5, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) legend( "bottomleft", inset = 0.005, legend = legend_text, col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics <https://db.nomics.world>", side = 3, col = "grey", font = 3, adj = 1 )
Fetch series from dataset 'WEO by countries (2019-10 release)' (WEO:2019-10) of IMF
df <- rdb("IMF", "WEO:2019-10", query = "current account balance percent") df <- df[!is.na(value))]
df <- rdbnomics:::rdbnomics_df015 data.table::setDT(df)
df <- reorder_cols(df) display_table(df)
# ggplot(df, aes(x = period, y = value, color = `WEO Country`)) + # geom_line(size = 1.2) + # geom_point(size = 2) + # ggtitle("Current account balance (% GDP)") + # dbnomics(legend.direction = "horizontal") i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value i <- 2 x2 <- df[series_name == sort(unique(series_name))[i]]$period y2 <- df[series_name == sort(unique(series_name))[i]]$value i <- 3 x3 <- df[series_name == sort(unique(series_name))[i]]$period y3 <- df[series_name == sort(unique(series_name))[i]]$value i <- 4 x4 <- df[series_name == sort(unique(series_name))[i]]$period y4 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen", "purple") PCH <- 18 legend_text <- sort(unique(df$series_name)) plot( x1, y1, col = cols[1], type = "l", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value), max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) lines(x2, y2, col = cols[2], type = "l") points(x2, y2, col = cols[2], pch = PCH) lines(x3, y3, col = cols[3], type = "l") points(x3, y3, col = cols[3], pch = PCH) lines(x4, y4, col = cols[4], type = "l") points(x4, y4, col = cols[4], pch = PCH) legend( "bottomleft", inset = 0.005, legend = legend_text, col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics <https://db.nomics.world>", side = 3, col = "grey", font = 3, adj = 1 )
Fetch time series found on the web site
When you don't know the codes of the dimensions, provider, dataset or series, you can:
-
go to the page of a dataset on DBnomics website, for example Doing Business,
-
select some dimensions by using the input widgets of the left column,
-
click on "Copy API link" in the menu of the "Download" button,
-
use the
rdb(api_link = ...)function such as below.
df <- rdb(api_link = "https://api.db.nomics.world/v22/series/WB/DB?dimensions=%7B%22country%22%3A%5B%22FR%22%2C%22IT%22%2C%22ES%22%5D%7D&q=IC.REG.PROC.FE.NO&observations=1&format=json&align_periods=1&offset=0&facets=0") df <- df[!is.na(value))]
df <- rdbnomics:::rdbnomics_df011 data.table::setDT(df)
df <- df[order(period, series_name)] df <- reorder_cols(df) display_table(df)
# ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_step(size = 1.2) + # geom_point(size = 2) + # dbnomics() i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value i <- 2 x2 <- df[series_name == sort(unique(series_name))[i]]$period y2 <- df[series_name == sort(unique(series_name))[i]]$value i <- 3 x3 <- df[series_name == sort(unique(series_name))[i]]$period y3 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen") PCH <- 18 plot( x1, y1, col = cols[1], type = "s", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 1.2, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) lines(x2, y2, col = cols[2], type = "s") points(x2, y2, col = cols[2], pch = PCH) lines(x3, y3, col = cols[3], type = "s") points(x3, y3, col = cols[3], pch = PCH) legend( "bottomleft", inset = 0.005, legend = sort(unique(df$series_name)), col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics <https://db.nomics.world>", side = 3, col = "grey", font = 3, adj = 1 )
In the event that you only use the argument api_link, you can drop the name and run:
df <- rdb("https://api.db.nomics.world/v22/series/WB/DB?dimensions=%7B%22country%22%3A%5B%22FR%22%2C%22IT%22%2C%22ES%22%5D%7D&q=IC.REG.PROC.FE.NO&observations=1&format=json&align_periods=1&offset=0&facets=0")
Fetch time series from the cart
On the cart page of the DBnomics website, click on "Copy API link" and copy-paste it as an argument of the rdb(api_link = ...) function. Please note that when you update your cart, you have to copy this link again, because the link itself contains the ids of the series in the cart.
df <- rdb(api_link = "https://api.db.nomics.world/v22/series?observations=1&series_ids=BOE/6008/RPMTDDC,BOE/6231/RPMTBVE") df <- df[!is.na(value))]
df <- rdbnomics:::rdbnomics_df012 data.table::setDT(df)
df[
,
series_name := sapply(
series_name,
function(y) {
paste0(
paste0(
strsplit(y, "institutions' ")[[1]], collapse = "institutions'\n"
),
"\n"
)
}
)
]
df <- df[order(period, series_name)] df <- reorder_cols(df) display_table(df)
# ggplot(df, aes(x = period, y = value, color = series_name)) + # geom_line(size = 1.2) + # geom_point(size = 2) + # dbnomics() i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value i <- 2 x2 <- df[series_name == sort(unique(series_name))[i]]$period y2 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen") PCH <- 18 plot( x1, y1, col = cols[1], type = "l", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 4*10^3, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) lines(x2, y2, col = cols[2], type = "l") points(x2, y2, col = cols[2], pch = PCH) legend( "bottomleft", inset = 0.005, legend = sort(unique(df$series_name)), col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics <https://db.nomics.world>", side = 3, col = "grey", font = 3, adj = 1 )
Fetch the available datasets of a provider
When fetching series from DBnomics, you need
to give a provider and a dataset before specifying correct dimensions. With
the function rdb_datasets, you can download the list of the available datasets
for a provider.
For example, to fetch the IMF datasets, you have to use:
rdb_datasets(provider_code = "IMF")
The result is a named list (its name is IMF) with one element which is a
data.table:
str(rdbnomics:::rdbnomics_df016)
With the same function, if you want to fetch the available datasets for multiple providers, you can give a vector of providers and get a named list.
rdb_datasets(provider_code = c("IMF", "BDF"))
str(rdbnomics:::rdbnomics_df018)
DT <- rdbnomics:::rdbnomics_df018 DT <- sapply(DT, function(y) { paste0(": ", nrow(y)) }) DT <- paste0("Number of datasets for ", names(DT), " ", unname(DT)) cat(DT, sep = "\n")
In the event that you only request the datasets for one provider, if you define
simplify = TRUE, then the result will be a data.table not a named list.
rdb_datasets(provider_code = "IMF", simplify = TRUE)
DT <- rdbnomics:::rdbnomics_df017 data.table::setDT(DT) display_table(DT)
The extent of datasets gathered by DBnomics can be
appreciate by using the function with the argument provider_code set to
NULL:
options(rdbnomics.progress_bar_datasets = TRUE) rdb_datasets() options(rdbnomics.progress_bar_datasets = FALSE)
DT <- rdbnomics:::rdbnomics_df019 DT <- data.table(Provider = names(DT), `Number of datasets` = sapply(DT, nrow)) DT <- DT[order(Provider)] display_table(DT)
Fetch the possible dimensions of available datasets of a provider
When fetching series from DBnomics, it can be
interesting and especially useful to specify dimensions for a particular
dataset to download only the series you want to analyse. With
the function rdb_dimensions, you can download these dimensions and their
meanings.
For example, for the dataset WEO:2019-10 of the IMF, you may use:
rdb_dimensions(provider_code = "IMF", dataset_code = "WEO:2019-10")
The result is a nested named list (its names are IMF, WEO:2019-10 and the
dimensions names) with a data.table at the end of each branch:
DT <- rdbnomics:::rdbnomics_df020 DT <- DT$IMF$`WEO:2019-10` DT <- paste0("Number of dimensions for IMF/WEO:2019-10 : ", length(DT)) cat(DT, sep = "\n")
DT <- rdbnomics:::rdbnomics_df020 DT <- DT$IMF$`WEO:2019-10`[[1]] display_table(DT)
DT <- rdbnomics:::rdbnomics_df020 DT <- DT$IMF$`WEO:2019-10`[[2]] display_table(DT)
DT <- rdbnomics:::rdbnomics_df020 DT <- DT$IMF$`WEO:2019-10`[[3]] display_table(DT)
In the event that you only request the dimensions for one dataset for one
provider, if you define simplify = TRUE, then the result will be a named list
data.table not a nested named list.
rdb_dimensions(provider_code = "IMF", dataset_code = "WEO:2019-10", simplify = TRUE)
str(rdbnomics:::rdbnomics_df021)
You can measure the vast extent of datasets gathered by
DBnomics by downloading all the possible
dimensions. To do this, you have to set the arguments
provider_code and dataset_code to NULL.
⚠ It's relatively long to run and heavy to show so we display
the first 100.
options(rdbnomics.progress_bar_datasets = TRUE) rdb_dimensions() options(rdbnomics.progress_bar_datasets = FALSE)
DT <- rdbnomics:::rdbnomics_df022 DT <- DT[order(Provider, Dataset)] DT <- head(DT, 100) display_table(DT) # rdbnomics_df022 %>% # sapply(function(u) { # sapply( # u, # function(x) { # sapply( # x, # function(y) { # nrow(y) # }, # simplify = FALSE # ) %>% # { # data.table(Dimension = names(.), `Number of codes` = unname(.)) # } # }, # simplify = FALSE # ) %>% # rbindlist(idcol = "Dataset") # }, # simplify = FALSE # ) %>% # rbindlist(idcol = "Provider")
Fetch the series codes and names of available datasets of a provider
You can download the list of series, and especially their codes, of a dataset's
provider by using the function rdb_series. The result is a nested named list
with a data.table at the end of each branch. If you define simplify = TRUE,
then the result will be a data.table not a nested named list.
For example, for the IMF provider and the dataset WEO:2019-10, the command is (only first 100):
rdb_series(provider_code = "IMF", dataset_code = "WEO:2019-10", simplify = TRUE)
DT <- rdbnomics:::rdbnomics_df023 DT <- head(DT, 100) display_table(DT)
Like the function rdb(), you can add features to rdb_series(). You can ask for
the series with specific dimensions:
rdb_series(provider_code = "IMF", dataset_code = "WEO:2019-10", dimensions = list(`weo-subject` = "NGDP_RPCH"), simplify = TRUE)
or with a query:
rdb_series(provider_code = "IMF", dataset_code = c("WEO:2019-10", "WEOAGG:2019-10"), query = "NGDP_RPCH")
⚠ We ask the user to use this function parsimoniously because there are a huge amount
of series per dataset. Please only fetch for one dataset if you need it or
visit the website https://db.nomics.world.
For example, for the IMF provider, the number of series is (only first 5):
DT <- rdbnomics:::rdbnomics_df024 DT <- DT[order(-`Number of series`)] DT <- head(DT, 5) display_table(DT)
Proxy configuration or connection error Could not resolve host
When using the function rdb, you may come across the following error:
Error in open.connection(con, "rb") : Could not resolve host: api.db.nomics.world
To get round this situation, you have two options:
-
configure curl to use a specific and authorized proxy.
-
use the default R internet connection i.e. the Internet Explorer proxy defined in internet2.dll.
Configure curl to use a specific and authorized proxy
In rdbnomics, by default the function curl_fetch_memory (of the package curl) is used to fetch the data. If a specific proxy must be used, it is possible to define it permanently with the package option rdbnomics.curl_config or on the fly through the argument curl_config. Because the object is a named list, its elements are passed to the connection (the curl_handle object created internally with new_handle()) with handle_setopt() before using curl_fetch_memory.
To see the available parameters, run names(curl_options()) in R or visit the website https://curl.haxx.se/libcurl/c/curl_easy_setopt.html. Once they are chosen, you define the curl object as follows:
h <- list( proxy = "<proxy>", proxyport = <port>, proxyusername = "<username>", proxypassword = "<password>" )
Set the connection up for a session
The curl connection can be set up for a session by modifying the following package option:
options(rdbnomics.curl_config = h)
When fetching the data, the following command is executed:
hndl <- curl::new_handle() curl::handle_setopt(hndl, .list = getOption("rdbnomics.curl_config")) curl::curl_fetch_memory(url = <...>, handle = hndl)
After configuration, just use the standard functions of rdbnomics e.g.:
df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN")
This option of the package can be disabled with:
options(rdbnomics.curl = NULL)
Use the connection only for a function call
If a complete configuration is not needed but just an "on the fly" execution, then use the argument curl_config of the function rdb:
df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN", curl_config = h)
Use the default R internet connection
To retrieve the data with the default R internet connection, rdbnomics will use the base function readLines.
Set the connection up for a session
To activate this feature for a session, you need to enable an option of the package:
options(rdbnomics.use_readLines = TRUE)
And then use the standard function as follows:
df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN")
This configuration can be disabled with:
options(rdbnomics.use_readLines = FALSE)
Use the connection only for a function call
If you just want to do it once, you may use the argument use_readLines of the function rdb:
df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN", use_readLines = TRUE)
Transform time series with filters
The rdbnomics package can interact with the Time Series Editor of DBnomics to transform time series by applying filters to them.
Available filters are listed on the filters page https://editor.nomics.world/filters.
Here is an example of how to proceed to interpolate two annual time series with a monthly frequency, using a spline interpolation:
filters <- list( code = "interpolate", parameters = list(frequency = "monthly", method = "spline") )
The request is then:
df <- rdb( ids = c("AMECO/ZUTN/EA19.1.0.0.0.ZUTN", "AMECO/ZUTN/DNK.1.0.0.0.ZUTN"), filters = filters )
If you want to apply more than one filter, the filters argument will be a list of valid filters:
filters <- list( list( code = "interpolate", parameters = list(frequency = "monthly", method = "spline") ), list( code = "aggregate", parameters = list(frequency = "bi-annual", method = "end_of_period") ) ) df <- rdb( ids = c("AMECO/ZUTN/EA19.1.0.0.0.ZUTN", "AMECO/ZUTN/DNK.1.0.0.0.ZUTN"), filters = filters )
The data.table columns change a little bit when filters are used. There are two new columns:
period_middle_day: the middle day oforiginal_period(can be useful when you compare graphically interpolated series and original ones).filtered(boolean):TRUEif the series is filtered,FALSEotherwise.
The content of two columns are modified:
series_code: same as before for original series, but the suffix_filteredis added for filtered series.series_name: same as before for original series, but the suffix(filtered)is added for filtered series.
df <- rdbnomics:::rdbnomics_df013 data.table::setDT(df)
df <- df[order(filtered, series_name, period)] df <- reorder_cols(df) display_table(df)
# ggplot(df[!is.na(value)], aes(x = period, y = value, color = series_name)) + # geom_line(size = 1.2) + # geom_point(size = 2) + # dbnomics() df <- df[!is.na(value)] i <- 1 x1 <- df[series_name == sort(unique(series_name))[i]]$period y1 <- df[series_name == sort(unique(series_name))[i]]$value i <- 2 x2 <- df[series_name == sort(unique(series_name))[i]]$period y2 <- df[series_name == sort(unique(series_name))[i]]$value i <- 3 x3 <- df[series_name == sort(unique(series_name))[i]]$period y3 <- df[series_name == sort(unique(series_name))[i]]$value i <- 4 x4 <- df[series_name == sort(unique(series_name))[i]]$period y4 <- df[series_name == sort(unique(series_name))[i]]$value cols <- c("red", "blue", "darkgreen", "purple") PCH <- 18 legend_text <- sort(unique(df$series_name)) plot( x1, y1, col = cols[1], type = "l", xlab = "", ylab = "", xlim = c(min(df$period), max(df$period)), ylim = c(min(df$value) - 4, max(df$value)), panel.first = grid(lty = 1) ) points(x1, y1, col = cols[1], pch = PCH) lines(x2, y2, col = cols[2], type = "l") points(x2, y2, col = cols[2], pch = PCH) lines(x3, y3, col = cols[3], type = "l") points(x3, y3, col = cols[3], pch = PCH) lines(x4, y4, col = cols[4], type = "l") points(x4, y4, col = cols[4], pch = PCH) legend( "bottomleft", inset = 0.005, legend = legend_text, col = cols, lty = 1, pch = PCH, box.lty = 0, cex = 0.7 ) mtext( text = "DBnomics <https://db.nomics.world>", side = 3, col = "grey", font = 3, adj = 1 )
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