R Programming for Environmental Analysis Training Course

Duration 10 days 20 modules · 65 contact hours
Level Intermediate Certificate on completion
Who attends Environmental Managers, Environmental Scientists and others
Fee from USD 2,200 KSh 190,000 for Kenyan organisations
Where 52 cities + online See dates by city
Next start 16 Nov 2026 Course code SD-EM-158

Before you join: Participants should understand basic environmental monitoring concepts, such as sampling locations, laboratory results, detection limits, permit conditions or environmental indicators.

Dates & prices

Two start dates a month in every city, and live online on weekdays or weekends. Fees are per participant; classroom fees include tuition, materials, lunch and refreshments, and the certificate.

Suggest a date or city

Showing the next six months. Later dates, other cities and private deliveries for your team are available on request.

Course overview

Environmental teams collect large volumes of monitoring, laboratory, spatial and operational data, yet reporting often depends on spreadsheets, manual chart production and difficult-to-reproduce calculations. R provides a practical way to clean field records, analyse environmental trends, map sampling results, test regulatory hypotheses and generate repeatable reports. Used well, it helps environmental managers distinguish signal from seasonal variation, identify potential compliance issues earlier and explain findings clearly to regulators, communities and internal decision-makers.

This course develops applied R programming skills for environmental analysis. Participants work with common environmental datasets, including water-quality results, air-emissions records, biodiversity surveys, meteorological observations, contaminated-land samples and GIS layers. They learn to structure projects, import and validate data, transform monitoring records, calculate environmental indicators, conduct statistical tests, build regression and time-series models, create maps and publish reproducible reports. The course also addresses data-quality checks, censored laboratory results, missing values, spatial reference systems and defensible visual communication.

Teaching combines short technical briefings with guided case work, group analysis and individual application planning. Participants progressively develop an R-based environmental analysis workflow during the first week, then extend it with spatial analysis, modelling and automated reporting during the second. They leave with a reusable R project template, coded analysis scripts, environmental visualisation patterns and a completed capstone analysis based on a realistic monitoring scenario.

The course is suited to environmental professionals who already work with data and need stronger analytical capability without becoming full-time software developers. It is particularly relevant for staff responsible for environmental monitoring, compliance reporting, ESG data, remediation assessment, natural-resource management and health, safety and environment performance reporting.

What you will be able to do

  • Build reproducible R projects for environmental monitoring and compliance datasets
  • Import, clean and validate laboratory, field and operational environmental records
  • Apply descriptive statistics and visual diagnostics to environmental monitoring data
  • Model environmental relationships using regression, generalised additive and mixed-effects models
  • Analyse trends, seasonality and exceedances in environmental time-series data
  • Produce spatial analyses and thematic maps from vector and raster environmental datasets
  • Create automated environmental reports using Quarto, R Markdown and parameterised outputs
  • Present a defensible environmental analysis with documented assumptions, code and findings

Benefits of attending

For you

  • Produce repeatable analyses instead of manually rebuilding monthly environmental reports
  • Interpret monitoring trends with stronger statistical evidence and documented assumptions
  • Create publication-quality maps and charts for regulators, management and stakeholder meetings
  • Handle larger environmental datasets than spreadsheet-based workflows can reliably support
  • Build a portfolio-quality R analysis project relevant to environmental management roles

For your organisation

  • Reduce manual effort in recurring environmental monitoring and compliance reporting
  • Improve traceability of calculations, transformations and reported environmental indicators
  • Identify adverse trends and potential permit exceedances earlier through systematic analysis
  • Standardise data-quality checks across sites, laboratories and environmental programmes
  • Strengthen technical evidence supporting remediation, investment and regulatory decisions

Course modules

20 modules over 10 days

Participants can establish an R workflow and work confidently with environmental datasets.

  • RStudio projects, scripts, packages and reproducible folder structures for environmental work
  • Objects, vectors, data frames and factors used in monitoring datasets
  • Reading CSV, Excel and delimited laboratory data into R
  • Inspecting variable classes, units, codes and missing-value conventions
  • Base R indexing for selecting environmental records and sampling attributes
  • Writing clear comments and script sections for reviewable analytical code

Participants can structure environmental records using clear metadata and identifiers.

  • Monitoring programme metadata: stations, parameters, methods, laboratories and reporting periods
  • Tidy data principles applied to environmental sampling result tables
  • Unique identifiers for samples, locations, field events and analytical batches
  • Controlled vocabularies for units, media, species names and compliance categories
  • Data dictionaries documenting source systems, transformations and analytical limitations
  • Relational joins linking sampling results, site registers and permit limits

Participants can clean environmental data and document defensible quality-assurance decisions.

  • dplyr verbs for filtering, selecting, mutating and summarising monitoring records
  • Standardising parameter names, units and date formats before analysis
  • Duplicate-record checks using sample identifiers, timestamps and laboratory references
  • Range checks against physical limits, method ranges and permit thresholds
  • Flagging suspect values, transcription errors and inconsistent location codes
  • Audit trails recording cleaning rules, exclusions and corrected values

Participants can prepare laboratory datasets containing detection limits and incomplete observations.

  • Distinguishing reporting limits, detection limits and quantitation limits
  • Left-censored results represented consistently within laboratory result datasets
  • Simple substitution rules and their limitations for non-detect values
  • Nondetects package methods for censored environmental concentration data
  • Missing-data patterns assessed using summaries, plots and sampling records
  • Separating absent samples from true zero measurements during data preparation

Participants can calculate and interpret core environmental monitoring indicators.

  • Arithmetic mean, median, percentile and geometric mean for concentration data
  • Minimum, maximum and interquartile range for monitoring result summaries
  • Grouped summaries by site, parameter, season and reporting period
  • Exceedance counts against permit limits, guidelines and action levels
  • Load calculations from flow, concentration and unit conversion factors
  • Frequency tables for incident categories, waste streams and inspection findings

Participants can produce clear graphics that reveal patterns and data-quality concerns.

  • ggplot2 grammar for constructing consistent environmental monitoring graphics
  • Time-series plots showing observations, thresholds and sampling gaps
  • Boxplots comparing concentrations across sites, seasons and operational units
  • Histogram and density plots for skewed concentration distributions
  • Faceting charts by parameter, catchment, facility or monitoring programme
  • Colour scales and annotations designed for accessible technical communication

Participants can assess distributions and choose suitable transformations for environmental data.

  • Skewness, outliers and zero inflation in environmental measurement datasets
  • Q-Q plots and histograms for distributional diagnostic checks
  • Log transformation for positively skewed concentration and load data
  • Back-transformation and interpretation of modelled environmental concentrations
  • Normality tests used cautiously alongside graphical diagnostic evidence
  • Selecting summary measures appropriate to distribution shape and decision purpose

Participants can apply statistical tests to common environmental monitoring questions.

  • Confidence intervals for mean concentrations, proportions and exceedance rates
  • Two-sample t-tests comparing upstream and downstream monitoring results
  • Wilcoxon tests for non-normal or ordinal environmental observations
  • Analysis of variance across multiple sites, treatments or seasons
  • Kruskal-Wallis tests for non-parametric multi-group comparisons
  • P-values, effect sizes and confidence intervals interpreted for decisions

Participants can build and evaluate linear regression models for environmental variables.

  • Linear regression linking environmental outcomes with operational predictor variables
  • Model formulas, coefficients and fitted values interpreted in context
  • Residual plots checking linearity, variance patterns and influential observations
  • Categorical predictors representing sites, process units and monitoring seasons
  • Interaction terms testing whether environmental relationships differ between locations
  • Prediction intervals distinguishing expected observations from mean response estimates

Participants can select flexible models for non-linear and grouped environmental data.

  • Generalised additive models for non-linear environmental response patterns
  • Smooth terms representing seasonal cycles and meteorological effects
  • Mixed-effects models accounting for repeated sampling at monitoring stations
  • Random intercepts for sites, catchments, facilities and sampling campaigns
  • Model comparison using residual diagnostics, AIC and ecological plausibility
  • Communicating model uncertainty without overstating causal conclusions

Participants can analyse environmental trends, seasonal patterns and unusual observations over time.

  • Date-time handling for irregular sampling schedules and continuous sensor records
  • Seasonal decomposition separating trend, seasonal and residual components
  • Rolling averages smoothing short-term variability in monitoring series
  • Mann-Kendall trend test for monotonic environmental trend detection
  • Sen's slope estimator for reporting trend magnitude and direction
  • Control charts identifying unusual changes in routine monitoring results

Participants can assess environmental results against permit conditions and action thresholds.

  • Permit limits, guideline values and trigger levels represented as data
  • Automated exceedance classification using parameter-specific compliance rules
  • Rolling-period calculations for monthly, annual and discharge-limit conditions
  • Percentile compliance calculations for permits with statistical conditions
  • Compliance dashboards summarising status by site, parameter and reporting period
  • Narrative findings distinguishing exceedances, trends and data-quality exceptions

Participants can prepare and inspect environmental spatial data using R.

  • sf objects for points, lines and polygons in environmental mapping
  • Coordinate reference systems, datum selection and projection transformation
  • Reading shapefiles, GeoJSON and geopackage files into spatial workflows
  • Spatial joins linking monitoring locations with catchments and administrative areas
  • Geometry validity checks before area, distance and overlay calculations
  • Attribute tables connecting mapped features with environmental measurement records

Participants can create spatial analyses and maps for environmental interpretation.

  • Buffer analysis around emissions sources, watercourses and sensitive receptors
  • Point-in-polygon analysis assigning sampling points to management areas
  • Distance calculations between contamination locations and sensitive land uses
  • Choropleth maps displaying emissions, waste and compliance indicators
  • Proportional-symbol maps comparing monitoring concentrations across sampling locations
  • Map layouts including scale bars, north arrows and source attribution

Participants can process raster data for land, habitat and environmental exposure analysis.

  • terra workflows for raster reading, inspection and coordinate alignment
  • Raster extraction of elevation, land cover and rainfall values
  • Cropping and masking rasters to project boundaries and catchments
  • Raster algebra for environmental indices and exposure screening
  • Cell resolution, extent and resampling choices affecting analytical results
  • Combining raster layers with field observations for environmental interpretation

Participants can analyse ecological survey data and communicate biodiversity findings.

  • Species occurrence tables structured by survey, location and observation effort
  • Species richness, Shannon diversity and evenness index calculations
  • Rarefaction concepts for comparing surveys with unequal sampling effort
  • Community composition visualised using non-metric multidimensional scaling
  • Ordination plots interpreted alongside habitat, season and survey limitations
  • Detection bias and survey effort considered before ecological conclusions

Participants can analyse operational environmental performance data across major media.

  • Emissions intensity calculated against production, throughput and operating hours
  • Water balance analysis linking abstraction, consumption, discharge and losses
  • Waste generation rates compared by facility, process and production volume
  • Greenhouse gas activity data checked for completeness and unit consistency
  • Weather normalisation concepts for interpreting air-emissions monitoring variation
  • Performance scorecards combining environmental indicators with operational context

Participants can publish reviewable environmental reports from documented R analysis workflows.

  • Quarto documents combining narrative, code, tables and environmental figures
  • Parameterised reports for sites, reporting periods and monitoring programmes
  • Automated tables formatted for compliance reports and management briefings
  • Version control concepts for tracking scripts, data changes and review comments
  • Code review checklists covering assumptions, units, thresholds and reproducibility
  • File paths and package dependencies managed through portable project settings

Participants can translate analytical results into concise environmental management recommendations.

  • Problem statements linking monitoring evidence to environmental management decisions
  • Material findings separated from statistical noise and data limitations
  • Risk-based prioritisation using consequence, likelihood and exposure evidence
  • Technical charts adapted for regulators, executives and community stakeholders
  • Methods sections documenting datasets, assumptions, transformations and model choices
  • Recommendations tied to monitoring changes, controls and investigation priorities

Participants can build, review and present a complete reproducible environmental monitoring assessment.

  • Define an environmental monitoring question, decision context and analytical scope
  • Prepare source data using documented validation and transformation rules
  • Analyse trends, thresholds, spatial patterns or environmental drivers appropriately
  • Develop charts, maps and tables supporting defensible technical findings
  • Publish a parameterised report with code, assumptions and limitations
  • Present recommendations and respond to technical review questions clearly

Exercises and examples are adapted to each cohort's sectors and roles.

What you take back to work

  1. Environmental R project template
  2. Monitoring data-quality checklist
  3. Environmental visualisation code library
  4. Compliance threshold analysis model
  5. Spatial monitoring map template
  6. Parameterised environmental reporting template

Who should attend

  • Environmental Managers — need defensible analysis for monitoring, compliance and investment decisions
  • Environmental Scientists — need repeatable workflows for field, laboratory and spatial datasets
  • HSE Analysts — need to convert operational and emissions data into usable evidence
  • ESG and Sustainability Analysts — need stronger methods for environmental performance reporting
  • Water and Wastewater Specialists — need to analyse sampling results, trends and permit limits
  • GIS and Natural Resource Officers — need R methods for spatial monitoring and habitat data

Skills and tools covered

Environmental data wranglingStatistical environmental modellingSpatial data analysisTime-series analysisReproducible reportingEnvironmental data visualisation

Tools and standards

R, tidyverse, sf, terra

How the course runs

Training methodology

The course uses short instructor briefings followed by guided coding, environmental case work, group interpretation tasks and structured review of analysis outputs. Cases draw on water quality, emissions, biodiversity, contaminated land and operational monitoring records. Participants build code progressively, test analytical choices and discuss how assumptions affect environmental decisions. Trainer demonstrations show efficient R workflows, while application planning helps participants adapt scripts, project structures and reporting patterns to their own monitoring programmes after the course.

Requirements and prerequisites

Participants should understand basic environmental monitoring concepts, such as sampling locations, laboratory results, detection limits, permit conditions or environmental indicators. They should be comfortable working with spreadsheets and interpreting tables, charts and simple summary statistics. Prior programming experience is helpful but not required; the course introduces the R workflow from first principles before moving to intermediate analysis. Participants do not need advanced mathematics, prior GIS coding, database administration or software-development experience. Familiarity with their own environmental datasets will help them apply the methods after the course.

A typical training day

08:30 – 10:30First session
10:30 – 10:45Refreshment break
10:45 – 12:30Second session
12:30 – 13:30Lunch and networking
13:30 – 15:00Third session
15:00 – 15:15Refreshment break
15:15 – 16:30Workshop and daily review

Live online deliveries follow the same structure in East Africa Time, with shorter screen blocks and longer breaks.

What the fee includes

  • Instruction by a practitioner facilitator
  • Full course workbook and materials
  • Exercise files, templates and case studies
  • Refreshments and lunch on classroom deliveries
  • Certificate of completion
  • Post-course application plan

Frequently asked questions

No prior R programming is required, although participants should be comfortable working with spreadsheets and environmental datasets. The course begins with R projects, data structures and importing files before moving into statistical and spatial analysis.

Participants should bring a laptop capable of running R and RStudio, plus any non-confidential sample datasets they may wish to consider during application planning. Installation guidance can be provided before the course.

It is designed for environmental managers, scientists, HSE analysts, sustainability analysts, water specialists and GIS practitioners. It suits professionals who need to analyse environmental evidence rather than develop production software applications.

General R courses commonly focus on programming syntax and generic datasets. This course uses environmental monitoring, laboratory, spatial, ecological, emissions and compliance-reporting scenarios throughout.

Participants can use the project structure, data checks, chart patterns and reporting templates for recurring monitoring and compliance work. The methods also support investigation of trends, exceedances, site differences and environmental performance drivers.

Each training day runs from 08:30 to 16:30 and provides approximately 6.5 contact hours. The first week establishes data handling, visualisation and statistical analysis, while the second develops time-series, spatial analysis, reporting and the capstone assessment.

Questions about this course?

Ask about dates, fees, group bookings or running it for your team. Our training team replies by email.

Ask on WhatsApp info@skillsetdevelopment.com

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