Data is the most important asset. It validates a research story and a conclusions; it provides a platform of confidence for other researchers who might continue your work; and it is a resource that can be used by researchers in other fields to undertake new work, perhaps completely unrelated to your own research interests. Well-organised data that is accessible to the research community can continue to provide extended benefit and value long after your projects have been completed.
Word clouds Word clouds visualize word frequencies of either single corpus or different corpora. Although word clouds are rarely used in academic publications, they are a common way to display language data and the topics of texts - which may be thought of as their semantic content. To exemplify how to use word clouds, we are going to have a look at the State of Environment issued in 2019 by the department of environment of the vice president’s office.
OpenStreetMaps is a great source of spatial data. Most common programming languages have packages for downloading data from OSM. In this tutorial we are going to see how to download hosptial features data using R’s osmdata (Padgham et al. 2017) package and plot it using ggplot (Wickham 2016), and interactively using tmap (Tennekes 2018). This requires some knowledge of spatial data structures.
The Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is a quasi-global rainfall data set. As its title suggests it combines data from real-time observing meteorological stations with infra-red satellite data to estimate precipitation. CHIRPS incorporates 0.05° resolution satellite imagery with in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring.
The global dataset covers the area from \(40^\circ\)N to \(4^\circ\)S and from \(20^\circ\)W to \(50^\circ\)E with a spatial resolution of 0.
In the post titled Access, Download, Process and VIsualize sea surface height and geostrophic current from AVISO in R posted in my blog on Monday, Apr 15, 2019, I explained how we can download the satellite data like sea surface height from AVISO in R. I illustrate in detail getting the data using xtractomatic package (Mendelssohn 2018). Though xtractomatic package provide functions that allows us to get access to the ERDDAP server and get the data, but one big challenge is that the data comes is array and need an expensive computation process, especially if you deal with gridded data for a long term time series.
