R · Statistics-first · From $20
Do My R Homework
R homework is graded on a report that reproduces, not a test suite that passes. A statistician writes the dplyr and ggplot2 code, knits your .Rmd clean from a blank session, and matches your R version so it renders the same on your machine.
Knits clean from blank · Pinned with renv · Pay 50% after it reproduces
What we cover in R
The R stack your course actually grades
R is the academic-statistics default, so the work is data analysis written as a report, not a program compiled into a binary. We cover the tidyverse verb grammar, the ggplot2 grammar of graphics, the R Markdown and Quarto engine that renders it, and the modelling stack that grades the statistics underneath.
The eight named dplyr verbs (filter, select, mutate, arrange, group_by, summarise, left_join, and a slice for good measure) carry most weekly problem sets. When a professor mandates base R instead, we drop tidyverse and write with the apply family and data.table DT[i, j, by] syntax. Packages come from CRAN, or Bioconductor through BiocManager for the bioinformatics courses.
R assignments we do
Four R deliverables, start to knitted submission
Knitted EDA report in R Markdown
Import a messy CSV with readr::read_csv(), clean it with dplyr and tidyr (filter, mutate, pivot_longer, handle NA), and produce 3 to 5 ggplot2 figures with correct aes() mappings and faceting. We submit both the .Rmd source and the knitted HTML or PDF, and the report knits clean from a blank environment.
Regression modelling assignment
Fit lm(), or glm(family = binomial) for a logistic model, interpret the coefficients and p-values, and check all 5 linear-regression assumptions with diagnostic plots, residuals, and vif() from the car package. The write-up reports model output through broom::tidy(). Standard in econometrics, biostatistics, and psychology courses.
Shiny reactive web app
Build a ui and server app with input widgets, reactive() expressions, and renderPlot or renderTable. We often wrap a caret or randomForest model so you tune sample size or tree count and watch the predictions update live, then deploy the final to your shinyapps.io account.
Base-R function and Monte-Carlo simulation
When the professor bans tidyverse, we write vectorized functions with the apply family and [ indexing, run a Monte-Carlo simulation seeded with set.seed() for reproducibility, and return a tidy result table. This is the assignment that tests whether you understand R itself, not just dplyr verbs.
R problems we fix
Six R errors we trace and fix every week
Most R bugs are not logic bugs. They are scope, type, and environment bugs that the console hides until the grader re-knits. Here is what breaks, and the one move that fixes each.
Error: object "x" not found
Assign the object in the same chunk and match its case exactly. R is case-sensitive, so Mean and mean are two different names, and a variable defined in another scope never reaches the one that calls it.
Error: could not find function "..."
Add the library() call, or the pkg::fn() namespace, at the top of the script. The package was installed but never loaded, so the function exists on disk yet not in the session.
Warning: NAs introduced by coercion
Clean the column before coercing and pass na.rm = TRUE. as.numeric() on an unparseable string returns NA, then mean() or sum() quietly carries the NA through the whole calculation.
A left_join() that returns all-NA rows
Coerce both keys to character with as.character() before the join. The join silently fails when one key is a factor and the other a character, or when stale stringsAsFactors behavior changes the type underneath you.
Vector recycling corruption
Check length() and nrow() alignment and use an explicit dplyr join instead of column-binding. Operate on unequal-length vectors and R recycles the shorter one, producing wrong numbers with no error at all.
It knits on my machine, but not on the grader
Knit in a clean session, declare every dependency in the first chunk, and pin versions with renv. The report breaks because the .Rmd leans on objects living in your global Environment that do not exist on a fresh knit.
Do my R assignment
Do My R Assignment: from brief to knitted report
An R assignment is the larger graded project, the regression write-up or the Shiny app, not a weekly problem set. We scope it before quoting: the dataset you send, the model the rubric asks for, the output format on Canvas, and the deadline. You get the .Rmd source, the knitted HTML or PDF, and a deployed shinyapps.io link where the work calls for one.
Help with an R assignment you can defend
Every delivery ships ready for the question your grader asks in office hours. We comment each chunk in plain English, then add two or three viva-defense questions with answers: why this geom, why a logistic model over a linear one, why faceting instead of a single panel. You walk in able to explain the choice, not just read the code.
R assignment help
R assignment help for modelling and Shiny projects
Modelling assignments turn on the diagnostics, not the one-line fit. We fit the model, then check linearity, independence, homoscedasticity, normal residuals, and multicollinearity, and report the result through broom::tidy() so the marker reads a statistics argument. For Shiny, the guidance covers reactive flow: what belongs in a reactive() expression, why an isolate() stops a render loop, and how a caret model hands predictions back to renderPlot.
This is the guided, informational side of the work. Bring the half-built model that throws a rank-deficiency warning, or the Shiny app that re-renders on every keystroke, and we diagnose the mechanism before we touch the code.
R homework help
R homework help for tidyverse and ggplot2 problem sets
Weekly R homework is the tidyverse problem set: read a CSV, reshape it with pivot_longer, group and summarise, then plot. We write the dplyr pipeline and the ggplot2 figures, match the aes() mappings to the rubric, and knit the result so it reproduces. These sets feed the data-science and intro-statistics courses where the marker reads your pipeline line by line.
Need help with R homework tonight?
Short deadlines are normal here. The most common 2am request is the rescue knit: the report ran fine in the console an hour ago, and now it will not render. We move every dependency into the chunks, knit from a clean session, and turn a 48 to 72 hour problem set around well inside the deadline. Send the .Rmd and the dataset, and we quote a delivery time before you pay anything.
# before: "Error in eval(...): object 'cleaned_df' not found" on knit
# after: knits clean from a blank session, renv.lock pinned, all 5
# regression assumptions checked, delivered in 8h
#
# "It rendered on the grader's machine on the first try. I could
# explain why I logged the response in office hours."
# - statistics student, DATA 100 style course, 2026How it works
From your CSV to a report that reproduces
Send the brief and the data
Upload the assignment PDF, the rubric, your CSV, your R version, and the deadline. Tell us if the course bans tidyverse so we write in the right style from the start.
Get a fixed quote in 15 minutes
A statistician who works in R reads the brief and sends one price. No hourly meter, no surprise fees for a second figure or an extra diagnostic.
We write, knit, and verify
We build the pipeline, fit the model, render the .Rmd from a blank session, and pin packages with renv so the renv.lock reproduces our environment on yours.
Pay the rest after it knits
Knit it yourself and confirm it reproduces. Pay the second 50% only once it renders clean, with 7 days of free revisions either way.
The full process, the privacy terms, and the revision window are on the how it works page.
Pricing
One fixed price per R assignment, from $20
Priced by complexity, not by a meter. A weekly tidyverse problem set sits at the low end. A semester Shiny app with a tuned model sits at the high end. You see the full number before you pay, you pay half to start, and there are no rush fees.
R homework help
R questions, answered
The questions R students actually ask: version matching, base R, autograders, Shiny deployment, and whether the report reproduces.
Will the .Rmd knit on my version of R? +
Yes. We match your R version across 4.2, 4.3, and 4.4, pin every package with renv so the renv.lock reproduces our environment, and knit from a fresh session before delivery so it renders clean on yours.
My professor bans tidyverse. Can you do it in base R? +
Yes. We write it with base-R [ indexing and the apply family, or with data.table, matched to whatever style the course mandates. The deliverable reads the way your rubric expects, not the way that is easiest for us.
Can you make it pass the Gradescope autograder? +
Yes. We write to the hidden-test format the gradeR package checks, then confirm the results.json passes before sending it back. The Data 100 and GOV 50 autograder setups are familiar ground.
My code runs in the console but will not knit. Can you fix it? +
Yes. That is a clean-environment bug, not a logic bug. We move every dependency into the chunks, declare libraries in the first block, and re-knit from blank so the report reproduces on its own.
Can you build and deploy a Shiny app to shinyapps.io? +
Yes. We build the reactive ui and server, wire the input widgets and reactive() expressions, and deploy the final to your shinyapps.io account so the link works when you submit it.
Will you check the regression assumptions, not just fit lm()? +
Yes. We check all 5 assumptions with diagnostic plots, run vif() for multicollinearity, and summarize the fit through broom::tidy() so the write-up reads like a statistics submission, not a code dump.
Can you comment each line so I can explain it to my grader? +
Yes. Many statistics rubrics require a plain-English comment per chunk, so we write one, then add two or three viva-defense questions and answers for office hours.
My ggplot2 figures look nothing like the rubric examples. Can you match them? +
Yes. We match the geoms, the aes() mappings, the theme, and the faceting to the reference figures in your rubric so the marker sees the chart they expect.
Related help
Where R meets your other coursework
Do My MATLAB Homework the numerical-computing sibling, for the proprietary statistics and engineering work R competes with.
Do My SQL Homework for the data-pipeline pairing when you pull rows in SQL and analyze them in R.
Machine Learning Assignment Help the right hub for caret, tidymodels, and randomForest coursework.
Send your R brief and the CSV
A statistician quotes a fixed price in 15 minutes, knits the report clean from a blank session, and you pay the second half only after it reproduces on your machine.