How to Handle Datasets in R for Statistics Assignments using dslabs
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- What Is the dslabs Package?
- 1. Climate Change Datasets – Visualizing Our Planet’s Urgency
- 2. Astronomy Data – Explore the Universe with the H-R Diagram
- 3. Life Tables – Introducing Survival Analysis with death_prob
- 4. Brexit Polling Data – Practice Inference, Forecasting, and Confidence Intervals
- 5. Breast Cancer Diagnostic Data (brca) – Classic for Classification and PCA
- Why We Recommend dslabs to Our Students
- Ready to Learn? Here’s Your Action Plan
- Final Thoughts
We don’t just help students solve statistics assignments—we help them build lasting statistical thinking and practical data skills. One of the most effective ways to master data science is by working with real, relevant, and relatable datasets. That’s why we’re excited about the expanded dslabs package—an R package that provides high-quality, real-world data perfect for academic use.
The latest update to dslabs includes new datasets on climate change, astronomy, life expectancy, and even breast cancer diagnostics. Whether you're a beginner exploring your first data visualization or an advanced student working on machine learning and principal component analysis, these datasets offer an excellent foundation for learning by doing.
By engaging with these realistic datasets, students gain the confidence to handle complex analytical tasks in R, improving both theoretical understanding and coding ability. This hands-on experience is exactly what our statistics homework help service supports—ensuring students not only complete their assignments but also understand the underlying concepts.
If you're struggling to analyze data, build visualizations, or write clean, efficient R code, our team also offers expert guidance in r programming homework help. Dive into this blog as we explore how each of these new datasets can enhance your academic journey.
What Is the dslabs Package?
The dslabs package is a treasure chest of well-curated, engaging datasets specifically designed for teaching and learning data science. It was originally created to support the HarvardX Data Science Professional Certificate Program and has gained popularity among students and educators alike.
To support visualization and analysis, we also recommend installing these packages:
if(!require("tidyverse")) install.packages("tidyverse")if(!require("ggrepel")) install.packages("ggrepel")if(!require("matrixStats")) install.packages("matrixStats")library(tidyverse)library(ggrepel)library(matrixStats)Now, let’s explore the new additions to the package.
1. Climate Change Datasets – Visualizing Our Planet’s Urgency
Three datasets have been added to bring climate data to life in your R assignments. These are powerful examples of how data visualization and wrangling can tell compelling stories.
temp_carbon – Global Temperature and Carbon Emissions
This dataset combines global temperature anomalies from 1880 to 2018 with carbon emissions data from 1751 to 2014. It’s a must-use for time-series analysis and ggplot2-based visualizations.
Example Plot:
temp_carbon %>%select(Year = year, Global = temp_anomaly, Land = land_anomaly, Ocean = ocean_anomaly) %>%gather(Region, Temp_anomaly, Global:Ocean) %>%ggplot(aes(Year, Temp_anomaly, col = Region)) +geom_line(size = 1) +geom_hline(aes(yintercept = 0), col = "red", lty = 2) +geom_label(aes(x = 2005, y = -.08), label = "20th century mean", size = 4) +ggtitle("Temperature anomaly relative to 20th century mean, 1880-2018")Use this to discuss trends, seasonal shifts, or even compare human vs. natural impacts on climate.
greenhouse_gases – GHG Concentrations Since 0 CE
Spanning 2000 years, this dataset allows students to trace the rise of CO₂, CH₄, and N₂O concentrations using ice core data.
greenhouse_gases %>%ggplot(aes(year, concentration)) +geom_line() +facet_grid(gas ~ ., scales = "free") +ggtitle("Atmospheric greenhouse gas concentration by year, 0-2000 CE")This is great for creating multi-panel plots, faceting, and exploring historical time series.
historic_co2 – CO₂ Levels Over 800,000 Years
Go back nearly a million years with this dataset, combining ancient ice core data and modern Mauna Loa readings.
historic_co2 %>%ggplot(aes(year, co2, col = source)) +geom_line() +ggtitle("Atmospheric CO2 concentration, -800,000 BCE to today")This dataset offers the chance to build striking visual comparisons and develop hypothesis-driven assignments on climate patterns.
2. Astronomy Data – Explore the Universe with the H-R Diagram
The stars dataset provides a rich playground for visualizing astronomical data, including temperature, magnitude, and spectral class of over 100 stars.
Using this dataset, students can recreate the classic Hertzsprung-Russell (H-R) diagram:
stars %>%mutate(type = factor(type, levels = c("O", "B", "A", "F", "G", "K", "M")),star = ifelse(star %in% c("Sun", "Betelgeuse", "Sirius"), as.character(star), NA)) %>%ggplot(aes(log10(temp), magnitude, col = type)) +geom_point() +geom_label_repel(aes(label = star)) +scale_x_reverse() +scale_y_reverse() +ggtitle("H-R diagram of selected stars")Perfect for practicing multi-variable plotting and color-coding by category.
3. Life Tables – Introducing Survival Analysis with death_prob
The death_prob dataset offers real-world applications of probability and statistical modeling, containing the 2015 period life tables from the U.S. Social Security Administration.
Use this to simulate mortality, build basic actuarial models, and study age-specific probabilities—a great introduction to survival analysis concepts and random variables.
4. Brexit Polling Data – Practice Inference, Forecasting, and Confidence Intervals
brexit_polls provides polling data from six months before the UK's Brexit referendum, ideal for practicing:
- Polling data visualization
- Confidence intervals
- Hypothesis testing
- Smoothing and trend lines
brexit_polls %>%ggplot(aes(enddate, spread, color = poll_type)) +geom_hline(aes(yintercept = -.038, color = "Actual spread")) +geom_smooth(method = "loess", span = 0.4) +geom_point() +ggtitle("Spread of Brexit referendum online and telephone polls")This real-world dataset brings political science, data journalism, and statistical inference together in one hands-on exercise.
5. Breast Cancer Diagnostic Data (brca) – Classic for Classification and PCA
The brca dataset is a go-to classic in machine learning education. It contains fine-grain measurements of cell nuclei extracted from biopsy images to classify breast cancer tumors as benign or malignant.
Here’s how to prepare data for Principal Component Analysis (PCA):
x_centered <- sweep(brca$x, 2, colMeans(brca$x))x_scaled <- sweep(x_centered, 2, colSds(brca$x), FUN = "/")pca <- prcomp(x_scaled)data.frame(pca$x[,1:2], type = ifelse(brca$y == "B", "Benign", "Malignant")) %>%ggplot(aes(PC1, PC2, color = type)) +geom_point() +stat_ellipse() +ggtitle("PCA separates breast biopsies into benign and malignant clusters")Great for training models with over 95% accuracy. You can also use logistic regression, decision trees, and support vector machines on this dataset—making it perfect for advanced assignments.
Why We Recommend dslabs to Our Students
At StatisticsHomeworkHelper.com, we’ve assisted thousands of students with R assignments, projects, and data analysis coursework. When students use synthetic or irrelevant data, the learning curve becomes steep and disconnected. That’s why we recommend the dslabs package—it bridges theory and practice using meaningful, research-grade data.
Here’s what makes dslabs stand out:
- 📊 Diverse Topics: From climate science to cancer detection and political polling, there's a dataset for every type of learner.
- 🧠 Skill-Building: Practice all major R workflows—data wrangling, visualization, inference, modeling, and PCA.
- 🎯 Assignment-Ready: Each dataset lends itself to structured academic tasks that match real university curricula.
- ✅ Beginner-Friendly: Clean, well-documented data that's ready to be used with tidyverse functions.
Ready to Learn? Here’s Your Action Plan
If you're new to R or data science, we highly recommend the HarvardX Data Science Professional Certificate on edX, designed by Rafael Irizarry. It’s beginner-friendly, teaches everything from scratch, and uses the dslabs package extensively.
Meanwhile, if you need help with your data science assignments—be it plotting, modeling, inference, or interpretation—our expert tutors at StatisticsHomeworkHelper.com are just a click away. We specialize in:
- R programming assignments
- Data visualization with ggplot2
- Statistical inference
- Machine learning using real datasets
- PCA, regression, classification, and clustering
Final Thoughts
The expanded dslabs package is more than a teaching tool—it’s a gateway to mastering data science through real-world exploration. With new datasets on climate, health, and astronomy, students now have access to the kind of data that fuels global research and policy decisions.
So, whether you’re plotting atmospheric CO₂ trends or building predictive models for healthcare, make dslabs your companion—and let StatisticsHomeworkHelper.com be your guide.