In mkearney/datacenter: Create, Add, and Update Centralized Data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
library(datacenter)

datacenter

The goal of datacenter is to assist in the consolidation of information spread across multiple data sets.

Installation

install.packages("datacenter")

You can install the development version of datacenter from Github with:

remotes::install_github("mkearney/datacenter")

Example

Let's say you want to consolidate and maximize information (variables) for obversations spread across multiple different data sets.

## data set with variables v1, v2
data1 <- tibble::tibble(
  id = c("a", "b", "c"),
  v1 = c(TRUE, FALSE, FALSE),
  v2 = c(1, NA_real_, NA_real_)
)

## data set with variables v2, v3
data2 <- tibble::tibble(
  id = c("b", "c"),
  v2 = c(2, 3),
  v3 = c("z", "x")
)

Create a data center with a fixed set of variables

## create data center with all variables
dc <- new_datacenter(c(names(data1), names(data2)))

## print datacenter
dc

## add data1 to datacenter
dc$add(data1)
dc

## update datacenter with data2
dc$update(data2)
dc

mkearney/datacenter documentation built on May 9, 2019, 1:10 a.m.