Use R to turn planning and policy data into a reproducible analysis. The skill starts with an RStudio project and a raw CSV, then teaches the data-frame workflow needed to inspect, clean, transform, join, reshape, summarise and visualise ward, census or survey data. It finishes with basic inference and regression in R and a report that can be rebuilt from code. This is an R skill, not a general statistics course: the emphasis is executing an appropriate analysis and reading its output without overstating the result.
Needed across
What you will be able to answer
A planning team gives you a ward-level CSV containing population, households, settlement type and water-service coverage, plus a separate ward lookup table. What can you produce in R before the findings meeting?
A self-contained R project that imports the untouched source files, records the cleaning decisions in code, converts variables to the right types, preserves missing values explicitly and joins the lookup table on a checked ward key. The script creates grouped summaries and proportions, reshapes repeated-year columns when needed and produces labelled plots that reveal service gaps. It runs a t-test or chi-square test only where the variable types and question support it, fits a simple linear model when a relationship needs to be quantified, and interprets estimates, intervals and p-values without turning association into causation. A rendered Quarto report contains the tables, plots, model output and caveats and can be rebuilt from the raw files without manual spreadsheet steps.
One payment
₹99
The videos are free
Course outline
Concept 1
Concept 1 · Start an R project and bring the data in
Concept 2
After: project-import-and-inspect
The inspection that catches a ward code treated as a number, a date treated as text or a category that will not group correctly.
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Concept 3
After: data-frames-and-types
A cleaning script that turns blanks, dashes and inconsistent spellings into explicit, reviewable decisions.
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Concept 4
After: clean-missing-and-categories
The small set of verbs that turns a cleaned table into a result by ward, settlement type or service category.
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Concept 5
After: clean-missing-and-categories
A ward key that combines tables—and the checks that prove the join did not lose or duplicate places.
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Concept 6
After: clean-missing-and-categories
Turning population_2011 and population_2021 into a year variable that summaries and plots can actually use.
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Concept 7
After: transform-and-summarise, reshape-wide-and-long
A chart whose geometry, mappings, labels and scale follow the variables rather than a decorative template.
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Concept 8
After: transform-and-summarise, visualise-with-ggplot
A two-group question carried from descriptive difference to estimate, interval and p-value—with assumptions and practical size still visible.
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Concept 9
After: transform-and-summarise
A contingency table tested for association while expected counts, effect direction and the causation boundary remain visible.
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Concept 10
After: transform-and-summarise, visualise-with-ggplot
A scatter plot, correlation and fitted line translated into units—without turning the slope into a causal claim.
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Concept 11
After: join-by-key, reshape-wide-and-long, visualise-with-ggplot, t-test-in-r, chi-square-in-r, correlation-and-regression
One project that rebuilds its cleaned data, tables, plots and model output from the original files.
Video title and channel appear once unlocked.
12 candidates did not meet the course criteria.
Teaching and research at planning schools (SPAs, CEPT, planning departments) or research institutes (NIUA, WRI, CPR), entered via a master's + NET and/or PhD. Rare for a fresh B.Plan — it requires a master's minimum and is a multi-year route.
9 mapped employers
Explore path →A planner in the central Town and Country Planning Organisation (TCPO) or Ministry of Housing & Urban Affairs (MoHUA), working on national urban policy, guidelines (URDPFI) and central missions. Rare for a fresh B.Plan — central posts can accept B.Plan / B.Arch plus experience or AITP membership through UPSC routes, but an M.Plan is highly preferred; freshers more often enter as project/research associates.
8 mapped employers
Explore path →