christophsax

id,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021,2022,2023,2024,2025
Einnahmen,64626987042060,70879681716000,64283526391450,66805566752980,65001776209490,67749345457040,65413356862180,68383475359200,71059585910600,73414202114620,76115434968500,76953145837330,73429902178500,76664986400000,79717180500000,81804672200000,83027897400000,84659430600000
Einnahmen > Ertrag,64375327966740,65205100668200,63950881889520,65921999311200,64779337153410,66216599079490,65072728118170,68017644513290,70183445361950,72687751733340,74845228658230,76201274068400,72723482580310,75988717300000,78993402500000,81063777900000,82232655900000,83858480500000
Einnahmen > Ertrag > Fiskalertrag,58052128664830,56789874552120,57756523349950,60096024353080,58288097285280,60337880047620,60188442169370,62689212719070,63098518215700,66196789451970,68597756927080,69891809439560,67236991404630,71066062200000,73092762500000,75220748200000,76518928800000,78133148300000
Einnahmen > Ertrag > Fiskalertrag > Direkte Bundessteuer,1

christophsax

Function with multiple warnings

fun_w_multiple_warnings <- function() {
  log(-1)
  log(-1)
  warning("one more")
  TRUE
}
fun_w_multiple_warnings()

christophsax

library(seasonal)

# basic use
m <- seas(AirPassengers)
summary(m)

# understanding the defaults
?seas

# some methods

christophsax

trendecon: daily economic indicators based on Google searches

Project

During the Covid-19 pandemic, information about the economic and social situation has changed rapidly. Traditional indicators are not sufficiently frequent to monitor and forecast economic and social activity at high frequency. We use Google search trends to overcome this data gap and create meaningful indicators. We extract daily search data on keywords reflecting consumers' perception of the economic situation. The indicators are available at www.trendecon.org.

An accompanying R package contains the code to construct long daily time series from Google Trends for any keyword. Robustness of the series is achieved by querying Google multiple times. The queries are sampled at daily, weekly and monthly frequencies and then harmonized such that the long term trend is preserved. A more detailed methodological description is given on the website. We are currently summarizin

christophsax

library(tidyverse)

# download all province data from
# https://github.com/pcm-dpc/COVID-19/tree/master/dati-province
url <- "https://raw.githubusercontent.com/pcm-dpc/COVID-19/master/dati-province/dpc-covid19-ita-province.csv"
dta <- read_csv(url, col_types = cols()) %>%
  transmute(time = data, region = denominazione_regione, province = denominazione_provincia, value = totale_casi)

dta <-

christophsax

library(tidyverse)
library(anytime)

# Data Repo Johns Hopkins CSSE
# https://github.com/CSSEGISandData/COVID-19
url <- "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv"
dta_raw <- read_csv(url, col_types = cols()) %>%
  select(-Lat, -Long)

christophsax

library(tidyverse)
library(readxl)
library(geofacet)

url <- "http://www.data.efv.admin.ch/Finanzstatistik/d/fs_ktn/ktn_schuld.xlsx"
tf <- tempfile(fileext = ".xlsx")
download.file(url, tf)
dta <- read_excel(tf, sheet = "schuld_per_capita", skip = 6) %>% 
 select(-X__1) %&gt;% 

christophsax

It's relatively straigtforward to read the Gapminder data directly from the Systema Globalis repo into R. Here's the code to produce an extract with the Gapminder core variables.

The result is stored below and can be accessed by:

gapminder <- read.csv("https://gist.githubusercontent.com/christophsax/8d2814a7d3e464d020e38a9cb468930b/raw/361889b5ea7830e2bf8f28f40239a2a08765432d/gapminder.csv", stringsAsFactors = FALSE)

library(ggplot2)

christophsax

library(tsbox)
suppressMessages(library(quantmod))
suppressMessages(library(ggplot2))

ts_fred <- function(..., class = "data.frame") {
  symb <- c(...)
  dta.env <- new.env()
  suppressMessages(getSymbols(symb, env = dta.env, src = "FRED"))
 z &lt;- data.table::rbindlist(lapply(as.list(dta.env), ts_dt), idcol = "id")

christophsax