#RStats #OpenSource #purrr or #lapply #r
A) lapply()
B) purrr
#r #lapply #purrr #OpenSource #RStats
A time series is a set of data points collected at regular intervals of time. Sometimes, the data points in a time series change over time in a predictable way. This is called a stationary time series. Other times, the data points change in an unpredictable way. This is called a non-stationary time series.
Post: https://www.spsanderson.com/steveondata/posts/rtip-2023-01-23/
#timeseries #timeseriesanalysis #stationarity #dataanalytics #adf #phillipsperron #r #lists #lapply #data
#Data #lapply #lists #r #phillipsperron #adf #dataanalytics #stationarity #timeseriesanalysis #timeseries
In #r many times we will work with a #list #object it is a very common type to work with. I won't go into the mechanics of a list but rather a brief snippet of what you can do.
In my post I make use of #lapply and #map_if from #purrr out of the #tidyverse
Post: https://www.spsanderson.com/steveondata/posts/rtip-2022-11-29/
#tidyverse #purrr #map_if #lapply #object #list #r
There might be times when you may want to get some sort of #summary #statistic like a #quantile or #IRQ on your #distribution data.
With my #r #package {TidyDensity} this is possible given the data comes from a tidy_ distribution function. If you have a vector of data you can use tidy_empirical() as a cheat.
With this function you can get output as #sapply #lapply #tibble or a #tibble where #datatable is doing the work.
Post: https://www.spsanderson.com/steveondata/posts/weekly-rtip-tidydensity-2022-11-23/
See attached!
#datatable #tibble #lapply #sapply #package #r #distribution #irq #quantile #statistic #summary