Introduction This post offers some technique on how to analyse data from a surveys and questionnaires in R, provides tips on visualizing survey data, and exemplifies how survey and questionnaire data can be analyzed. Questionnaires and surveys are widely used in research and thus one of the most common research designs. Questionnaires elicit three types of data: Factual Behavioral Attitudinal While factual and behavioral questions are about what the respondent is and does, attitudinal questions tap into what the respondent thinks or feels.

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.

Hydrographic data collected over the past three decades has provided a relatively coherent view of mean seasonal dynamics in the pelagic environment on the Pemba Channel. Surface waters are cold and nutrient-rich during NE months. Prevailing winds are from the north, resulting in general upwelling and causing the Columbia River effluent to lie to the south and along the Pemba Channel coast. 5 pM nitrate) rarely break the surface during upwelling favorable conditions past mid-summer -- Distributional features of dissolved nutrients (nitrate and phosphate) are displayed in figure 1 and 2.