christophsax

# Some speed comparison of fwrite and feather:

library(data.table)
library(feather)

# On an AWS EC2 m4.10xlarge with 40 cores:

# with 100 mio
size <- 100000000
iris2 <- data.table(iris[sample(1:nrow(iris), size, replace = TRUE), ])

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library(seasonal)

10 Year Forecast of AirPassengers seasonality

m <- seas(AirPassengers,  x11.appendfcst = "yes", forecast.maxlead = 120)
plot(series(m, "x11.adjustfac"))

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library(tsbox)  # remotes::install_github("christophsax/tsbox")
library(forecast)
library(seasonal)

(Short) Time Series with common pattern

Monthly time series, with 4 to 5 years of data. Here, Monthly Deaths from Lung Diseases in the UK

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``` r
library(seasonal)

m <- seas(AirPassengers, regression.variables = c("const", "seasonal"), regression.aictest = NULL)
monthplot(m)
```

![](https://i.imgur.com/v0jYXoO.png)

``` r

christophsax

### Preparing the Time Trip

library(timemachine)

# assuming EXP is available one period before
# (for demonstration only)
swiss_history2 <- swiss_history %>% 
  mutate(pub_date = if_else(var == "EXP", add_to_date(pub_date, "-1 quarter"), pub_date)) %>% 
  

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Life Expectancy

# devtools::install_github("christophsax/tsbox")
library(tsbox)
stopifnot(packageVersion("tsbox") >= "0.0.11.2")
suppressMessages(library(tidyverse))
library(wbstats)

# # Search

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#' Secondary Axis in ggplot2
#' 
#' To use a secondary axis in ggplot, the data shown on the right axis needs to
#' be transformed. Use `sec_scale` to calculate the scale adjutments. Use
#' `apply_sec_scale` to apply the scale adjutments within ggplot. See
#' examples.
#' 
#' @param df1 a ts-boxable object, plotted on the left axis
#' @param df2 a ts-boxable object, plotted on the right axis
#' @param x numeric vector

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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

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)