Color palette inspired by this gorgeous image of Mars landscape, captured by HiRISE (NASA) and processed by Seán Doran. I created this color scale with the help of Chroma.js Color Palette Helper.
Image credit: HiRISE & Seán Doran| ``` | |
| ################# 4) Build a Balanced Panel data set ------------------------------ | |
| # data with one observation for each area each day, even if there was no notification on that day/area | |
| # get all dates and munis | |
| all_munis <- unique(df$code_muni) | |
| all_dates <- seq(min(df$DT_NOTIFIC), | |
| max(df$DT_NOTIFIC), | |
| by = "day") |
In this gist we show with a reproducible example how to create an animation of public transport networks using GTFS data in R. We use a few packages to do this. One of the core packages here is the new gtfs2gps. The gtfs2gps package converts public transport data in GTFS format to GPS-like records in a data.frame/data.table, which we will be using to create a .gif with the gganimate package.
The first step is to process a GTFS.zip file. The function gtfs2gps{gtfs2gps} interpolates the space-time position of each vehicle in each trip considering the network distance and average speed between stops. The output is a data.table where each row represents the timestamp of each vehicle at a given spatial resolution. In this example, we use a sample of the public transport network of Sao Paulo (Brazil) mapped e
Using R and the geobr package to plot Brazilian metropolitan areas in different years.
library(geobr)
library(dplyr)
library(ggplot2)
library(sf)
| ### how to use R to find your most popular tweet of 2019 | |
| # code by @nnstats | |
| library(rtweet) | |
| library(tidyverse) | |
| library(lubridate) | |
| show_most_popular_tweet <- function(user, year){ | |
| y <- year |
| # Libraries | |
| library(geobr) | |
| library(leafgl) | |
| library(leaflet) | |
| library(sf) | |
| library(colourvalues) | |
| # get data of disaster risk areas in Brazil using the geobr package |
The data.table package has the operator %like%, which is super handy for partial string matching:
"system with blue screen" %in% "blue"
> FALSE
"system with blue screen" %like% "blue"
> TRUE


