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What we cover

Every part of R your course covers.

Wrangling

  • dplyr and tidyr
  • Joins and reshaping
  • Strings and dates
  • Data import

Statistics

  • Hypothesis tests
  • Linear and logistic regression
  • ANOVA
  • Time series

Visualization

  • ggplot2
  • Facets and themes
  • Interactive plots
  • Shiny dashboards

Reporting

  • R Markdown and Quarto
  • Reproducible reports
  • Tables with knitr
  • Simulation studies
Example order

One R order, start to finish.

What the student uploaded, what came back, and how the price was set.

01The brief
Applied StatisticsUndergradDue in 5 days

Does study time predict exam score?

Fit a linear regression of exam score on weekly study hours and prior GPA, check the assumptions, and present results in a knitted R Markdown report with one publication-quality plot.

Requirements
  • Tidy the raw survey CSV
  • Fit lm(score ~ hours + gpa)
  • Residual and Q–Q diagnostics
  • Knitted HTML report
02The solution
analysis.R14 lines
1library(tidyverse)
2
3survey <- read_csv("survey.csv") |>
4 filter(!is.na(score), hours <= 60) |> # drop impossible weeks
5 mutate(gpa = as.numeric(gpa))
6
7model <- lm(score ~ hours + gpa, data = survey)
8summary(model)
9
10ggplot(survey, aes(hours, score)) +
11 geom_point(alpha = 0.6) +
12 geom_smooth(method = "lm", se = TRUE) +
13 labs(x = "Study hours per week", y = "Exam score") +
14 theme_minimal()
knit: success · R 4.3 · 0 warnings
03The explanation doc
explanation.pdf
1.
Cleaning rules
Weeks over 60 hours are survey typos; dropping them changes the slope by less than 2%.
2.
Reading the coefficients
Each extra study hour adds about 0.87 points, holding GPA constant (p < 0.001).
3.
Assumptions
Residuals look normal on the Q–Q plot with no clear funnel, so the linear model holds.
04The price
AI predicted budget
$60–$110
Level · UndergradIncluded
Deadline · 5 daysStandard
Shown before any contact details were shared.
Final price from our team
$80
Delivered 8h early
How it works

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The AI does one job: it predicts your price. A person on our team reviews your files and sets the final number.

  1. 01
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Typical orders

Common R assignments and typical budgets.

Data wrangling with dplyr
$40–$90
Usually 1–2 days
typical range
Regression analysis report
$70–$160
Usually 2–3 days
typical range
ggplot2 visualization set
$50–$110
Usually 1–2 days
typical range
Shiny dashboard
$150–$320
Usually 4–6 days
typical range

Your budget depends on your files, deadline and level. Upload them for your own prediction.

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For 14 days after delivery, we revise until it matches your original instructions. No fees.
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team@domyprogramminghomework.io

Yes. The explanation doc covers what each test and coefficient means, at your course’s level.

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