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<!DOCTYPE html>
<html lang="en">
<head>
<title>API Calls Exercises in R: 17 Real-World Practice Problems</title>
<meta charset="utf-8">
<meta name="Description" content="R API calls exercises with httr2: 17 hands-on problems on GET/POST, headers, JSON parsing, auth, pagination, retries, and rate limit throttling.">
<meta name="Keywords" content="R API calls exercises, httr2 R exercises, jsonlite R, REST API R, R httr2 practice, R API pagination">
<meta name="Distribution" content="Global">
<meta name="Author" content="Selva Prabhakaran">
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<div id="sidebar-nav"><div class="continue-chip" data-continue-chip><span class="chip-label">Continue reading</span><a href="#" data-continue-link></a></div><div class="sidebar-tabs" role="tablist"><button class="sidebar-tab active" data-tab="posts" type="button" role="tab" onclick="var n=this.dataset.tab;document.querySelectorAll('.sidebar-tab').forEach(function(x){x.classList.toggle('active',x.dataset.tab===n)});document.querySelectorAll('.sidebar-panel').forEach(function(p){p.classList.toggle('active',p.dataset.panel===n)});try{localStorage.setItem('rstat_sidebar_tab',n)}catch(e){}">Posts</button><button class="sidebar-tab" data-tab="tools" type="button" role="tab" onclick="var n=this.dataset.tab;document.querySelectorAll('.sidebar-tab').forEach(function(x){x.classList.toggle('active',x.dataset.tab===n)});document.querySelectorAll('.sidebar-panel').forEach(function(p){p.classList.toggle('active',p.dataset.panel===n)});try{localStorage.setItem('rstat_sidebar_tab',n)}catch(e){}">Tools</button></div><div class="sidebar-panel active" data-panel="posts"><ul class="sidebar-menu list-unstyled"><li class="sidebar-section"><div class="sidebar-section-header"><span class="sidebar-chevron">▸</span> Learn R<span class="section-meta" data-section-meta></span></div><ul class="sidebar-section-items list-unstyled"><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec0sub1" data-collapsed="false"><span class="subsec-chevron">▼</span> Getting Started</li><li data-subkey="sec0sub1"><a href="/Is-R-Worth-Learning-in-2026.html"><span class="progress-dot"></span>Is R Worth Learning?</a></li><li data-subkey="sec0sub1"><a href="/Install-R-and-RStudio-2026.html"><span class="progress-dot"></span>Install R & RStudio</a></li><li data-subkey="sec0sub1"><a href="/RStudio-IDE-Tour.html"><span class="progress-dot"></span>RStudio IDE Tour</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec0sub2" data-collapsed="false"><span class="subsec-chevron">▼</span> R Fundamentals</li><li data-subkey="sec0sub2"><a href="/R-Syntax-101.html"><span class="progress-dot"></span>R Syntax 101</a></li><li data-subkey="sec0sub2"><a href="/R-Data-Types.html"><span class="progress-dot"></span>R Data Types</a></li><li data-subkey="sec0sub2"><a href="/R-Vectors.html"><span class="progress-dot"></span>R Vectors</a></li><li data-subkey="sec0sub2"><a href="/R-Matrices.html"><span class="progress-dot"></span>R Matrices</a></li><li data-subkey="sec0sub2"><a href="/R-Factors.html"><span class="progress-dot"></span>R Factors</a></li><li data-subkey="sec0sub2"><a href="/R-Data-Frames.html"><span class="progress-dot"></span>R Data Frames</a></li><li 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data-subkey="sec0sub4"><a href="/R-Cheat-Sheet.html"><span class="progress-dot"></span>R Cheat Sheet</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec0sub5" data-collapsed="false"><span class="subsec-chevron">▼</span> Professional R</li><li data-subkey="sec0sub5"><a href="/Data-Ethics-in-R.html"><span class="progress-dot"></span>Data Ethics</a></li><li data-subkey="sec0sub5"><a href="/Bias-in-Data-and-Models.html"><span class="progress-dot"></span>Bias in Data & Models</a></li><li data-subkey="sec0sub5"><a href="/Reproducibility-Crisis.html"><span class="progress-dot"></span>Reproducibility</a></li><li data-subkey="sec0sub5"><a href="/Data-Privacy-in-R.html"><span class="progress-dot"></span>Data Privacy</a></li><li data-subkey="sec0sub5"><a href="/Communicating-Uncertainty.html"><span class="progress-dot"></span>Communicating Uncertainty</a></li></ul></li><li class="sidebar-section"><div class="sidebar-section-header"><span class="sidebar-chevron">▸</span> Data Wrangling<span class="section-meta" data-section-meta></span></div><ul class="sidebar-section-items list-unstyled"><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec1sub1" data-collapsed="false"><span class="subsec-chevron">▼</span> Import & Setup</li><li data-subkey="sec1sub1"><a href="/Importing-Data-in-R.html"><span class="progress-dot"></span>Importing Data</a></li><li data-subkey="sec1sub1"><a href="/R-Pipe-Operator.html"><span class="progress-dot"></span>Pipe Operator</a></li><li data-subkey="sec1sub1"><a href="/Tidy-Data-in-R.html"><span class="progress-dot"></span>Tidy Data</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec1sub2" data-collapsed="false"><span class="subsec-chevron">▼</span> dplyr Essentials</li><li data-subkey="sec1sub2"><a href="/dplyr-filter-select.html"><span class="progress-dot"></span>dplyr filter & select</a></li><li data-subkey="sec1sub2"><a href="/dplyr-mutate-rename.html"><span class="progress-dot"></span>dplyr mutate & rename</a></li><li data-subkey="sec1sub2"><a href="/dplyr-group-by-summarise.html"><span class="progress-dot"></span>dplyr group_by & summarise</a></li><li data-subkey="sec1sub2"><a href="/dplyr-arrange-slice.html"><span class="progress-dot"></span>dplyr arrange & slice</a></li><li data-subkey="sec1sub2"><a href="/dplyr-across.html"><span class="progress-dot"></span>dplyr across()</a></li><li data-subkey="sec1sub2"><a href="/dplyr-case-when.html"><span class="progress-dot"></span>dplyr case_when()</a></li><li data-subkey="sec1sub2"><a href="/dplyr-Exercises-in-R-quiz.html"><span class="progress-dot"></span>dplyr Quiz</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec1sub3" data-collapsed="false"><span class="subsec-chevron">▼</span> Join & Reshape</li><li data-subkey="sec1sub3"><a href="/R-Joins.html"><span class="progress-dot"></span>R Joins</a></li><li data-subkey="sec1sub3"><a href="/pivot_longer-pivot_wider-Reshape-Data-in-R.html"><span class="progress-dot"></span>pivot_longer & pivot_wider</a></li><li data-subkey="sec1sub3"><a href="/tidyr-separate-unite-Split-Combine-Columns-in-R.html"><span class="progress-dot"></span>separate() & unite()</a></li><li data-subkey="sec1sub3"><a href="/tidyr-Exercises-in-R-quiz.html"><span class="progress-dot"></span>tidyr Quiz</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec1sub4" data-collapsed="false"><span class="subsec-chevron">▼</span> Clean & Quality</li><li data-subkey="sec1sub4"><a href="/Missing-Values-in-R-Detect-Count-Remove-Impute-NA.html"><span class="progress-dot"></span>Missing Values (NA)</a></li><li data-subkey="sec1sub4"><a href="/Data-Quality-Checking-in-R.html"><span class="progress-dot"></span>Data Quality Checking</a></li><li data-subkey="sec1sub4"><a href="/janitor-Package-in-R.html"><span class="progress-dot"></span>janitor Package</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec1sub5" data-collapsed="false"><span class="subsec-chevron">▼</span> Strings & Dates</li><li data-subkey="sec1sub5"><a href="/stringr-in-R.html"><span class="progress-dot"></span>stringr</a></li><li data-subkey="sec1sub5"><a href="/R-Regex-stringr-Pattern-Matching.html"><span class="progress-dot"></span>Regex Patterns</a></li><li data-subkey="sec1sub5"><a href="/lubridate-in-R.html"><span class="progress-dot"></span>lubridate</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec1sub6" data-collapsed="false"><span class="subsec-chevron">▼</span> Scale & Connect</li><li data-subkey="sec1sub6"><a href="/DBI-in-R.html"><span class="progress-dot"></span>DBI & Databases</a></li><li data-subkey="sec1sub6"><a href="/DuckDB-in-R.html"><span class="progress-dot"></span>DuckDB & duckplyr</a></li><li data-subkey="sec1sub6"><a href="/Web-Scraping-in-R-with-rvest.html"><span class="progress-dot"></span>Web Scraping (rvest)</a></li><li data-subkey="sec1sub6"><a href="/REST-APIs-in-R-with-httr2.html"><span class="progress-dot"></span>REST APIs (httr2)</a></li></ul></li><li class="sidebar-section"><div class="sidebar-section-header"><span class="sidebar-chevron">▸</span> Visualization<span class="section-meta" data-section-meta></span></div><ul class="sidebar-section-items list-unstyled"><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec2sub1" data-collapsed="false"><span class="subsec-chevron">▼</span> ggplot2 Foundations</li><li data-subkey="sec2sub1"><a href="/ggplot2-Grammar-of-Graphics.html"><span class="progress-dot"></span>Grammar of Graphics</a></li><li data-subkey="sec2sub1"><a href="/ggplot2-Getting-Started.html"><span class="progress-dot"></span>ggplot2 Getting Started</a></li><li data-subkey="sec2sub1"><a href="/ggplot2-Aesthetics-aes-Map-Data.html"><span class="progress-dot"></span>ggplot2 Aesthetics (aes)</a></li><li data-subkey="sec2sub1"><a href="/ggplot2-Colours.html"><span class="progress-dot"></span>ggplot2 Colours</a></li><li data-subkey="sec2sub1"><a href="/ggplot2-Scales.html"><span class="progress-dot"></span>ggplot2 Scales</a></li><li data-subkey="sec2sub1"><a href="/ggplot2-Themes-in-R.html"><span class="progress-dot"></span>ggplot2 Themes</a></li><li data-subkey="sec2sub1"><a href="/ggplot2-Labels-and-Annotations.html"><span class="progress-dot"></span>Labels & Annotations</a></li><li data-subkey="sec2sub1"><a href="/ggplot2-Facets.html"><span class="progress-dot"></span>ggplot2 Facets</a></li><li data-subkey="sec2sub1"><a href="/ggplot2-Exercises-in-R-quiz.html"><span class="progress-dot"></span>ggplot2 Quiz</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec2sub2" data-collapsed="false"><span class="subsec-chevron">▼</span> Core Charts</li><li data-subkey="sec2sub2"><a href="/ggplot2-Scatter-Plots.html"><span class="progress-dot"></span>Scatter Plots</a></li><li data-subkey="sec2sub2"><a href="/ggplot2-Line-Charts.html"><span class="progress-dot"></span>Line Charts</a></li><li data-subkey="sec2sub2"><a href="/ggplot2-Bar-Charts.html"><span class="progress-dot"></span>Bar Charts</a></li><li data-subkey="sec2sub2"><a href="/ggplot2-Distribution-Charts.html"><span class="progress-dot"></span>Distribution Charts</a></li><li data-subkey="sec2sub2"><a href="/Error-Bars-in-R.html"><span class="progress-dot"></span>Error Bars</a></li><li data-subkey="sec2sub2"><a href="/geom_smooth-in-R.html"><span class="progress-dot"></span>geom_smooth()</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec2sub3" data-collapsed="false"><span class="subsec-chevron">▼</span> Distributions & Groups</li><li data-subkey="sec2sub3"><a href="/Violin-Plot-in-R.html"><span class="progress-dot"></span>Violin Plot</a></li><li data-subkey="sec2sub3"><a href="/Ridgeline-Plot-in-R.html"><span class="progress-dot"></span>Ridgeline Plot</a></li><li data-subkey="sec2sub3"><a href="/Lollipop-Chart-in-R.html"><span class="progress-dot"></span>Lollipop Chart</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec2sub4" data-collapsed="false"><span class="subsec-chevron">▼</span> Relationships</li><li data-subkey="sec2sub4"><a href="/Bubble-Chart-in-R.html"><span class="progress-dot"></span>Bubble Chart</a></li><li data-subkey="sec2sub4"><a href="/Heatmap-in-R.html"><span class="progress-dot"></span>Heatmap in R</a></li><li data-subkey="sec2sub4"><a href="/Correlation-Matrix-Plot-in-R.html"><span class="progress-dot"></span>Correlation Matrix</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec2sub5" data-collapsed="false"><span class="subsec-chevron">▼</span> Advanced Charts</li><li data-subkey="sec2sub5"><a href="/Pie-Donut-Chart-in-R.html"><span class="progress-dot"></span>Pie & Donut Chart</a></li><li data-subkey="sec2sub5"><a href="/Treemap-in-R.html"><span class="progress-dot"></span>Treemap</a></li><li data-subkey="sec2sub5"><a href="/Waffle-Chart-in-R.html"><span class="progress-dot"></span>Waffle Chart</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec2sub6" data-collapsed="false"><span class="subsec-chevron">▼</span> Exploratory Analysis</li><li data-subkey="sec2sub6"><a href="/Exploratory-Data-Analysis-in-R.html"><span class="progress-dot"></span>EDA (7-Step Framework)</a></li><li data-subkey="sec2sub6"><a href="/Univariate-EDA-in-R.html"><span class="progress-dot"></span>Univariate EDA</a></li><li data-subkey="sec2sub6"><a href="/Bivariate-EDA-in-R.html"><span class="progress-dot"></span>Bivariate EDA</a></li><li data-subkey="sec2sub6"><a href="/Descriptive-Statistics-in-R.html"><span class="progress-dot"></span>Descriptive Statistics</a></li><li data-subkey="sec2sub6"><a href="/Correlation-Analysis-in-R.html"><span class="progress-dot"></span>Correlation Analysis</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec2sub7" data-collapsed="false"><span class="subsec-chevron">▼</span> Interactive & Maps</li><li data-subkey="sec2sub7"><a href="/Combining-ggplot2-with-plotly.html"><span class="progress-dot"></span>ggplot2 + plotly Interactive</a></li><li data-subkey="sec2sub7"><a href="/Interactive-Maps-in-R-with-leaflet.html"><span class="progress-dot"></span>Leaflet Interactive Maps</a></li><li data-subkey="sec2sub7"><a href="/Spatial-Data-in-R-with-sf.html"><span class="progress-dot"></span>Spatial Data (sf)</a></li><li data-subkey="sec2sub7"><a href="/Choropleth-Maps-in-R.html"><span class="progress-dot"></span>Choropleth Maps (sf)</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec2sub8" data-collapsed="false"><span class="subsec-chevron">▼</span> Customization & Reference</li><li data-subkey="sec2sub8"><a href="/ggplot2-Legends-in-R.html"><span class="progress-dot"></span>ggplot2 Legends</a></li><li data-subkey="sec2sub8"><a href="/ggplot2-Secondary-Axis.html"><span class="progress-dot"></span>Secondary Axis</a></li><li data-subkey="sec2sub8"><a href="/ggplot2-Log-Scale.html"><span class="progress-dot"></span>Log Scale</a></li><li data-subkey="sec2sub8"><a href="/patchwork-Package.html"><span class="progress-dot"></span>patchwork (Combine Plots)</a></li><li data-subkey="sec2sub8"><a href="/Publication-Quality-Figures-in-R.html"><span class="progress-dot"></span>Publication-Ready Figures</a></li><li data-subkey="sec2sub8"><a href="/ggplot2-cheatsheet.html"><span class="progress-dot"></span>ggplot2 Quickref</a></li></ul></li><li class="sidebar-section"><div class="sidebar-section-header"><span class="sidebar-chevron">▸</span> Statistics<span class="section-meta" data-section-meta></span></div><ul class="sidebar-section-items list-unstyled"><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec3sub1" data-collapsed="false"><span class="subsec-chevron">▼</span> EDA & Data Quality</li><li data-subkey="sec3sub1"><a href="/Automated-EDA-in-R.html"><span class="progress-dot"></span>Automated EDA</a></li><li data-subkey="sec3sub1"><a href="/Missing-Data-Visualization-in-R-naniar.html"><span class="progress-dot"></span>Missing Data Viz (naniar)</a></li><li data-subkey="sec3sub1"><a href="/Outlier-Detection-in-R.html"><span class="progress-dot"></span>Outlier Detection</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec3sub2" data-collapsed="false"><span class="subsec-chevron">▼</span> Probability</li><li data-subkey="sec3sub2"><a href="/Sample-Spaces-Events-and-Probability-Axioms-in-R-With-Monte-Carlo-Proof.html"><span class="progress-dot"></span>Probability Axioms</a></li><li data-subkey="sec3sub2"><a href="/Conditional-Probability-in-R.html"><span class="progress-dot"></span>Conditional Probability</a></li><li data-subkey="sec3sub2"><a href="/Random-Variables-in-R.html"><span class="progress-dot"></span>Random Variables</a></li><li data-subkey="sec3sub2"><a href="/Binomial-and-Poisson-Distributions-in-R.html"><span class="progress-dot"></span>Binomial vs Poisson</a></li><li data-subkey="sec3sub2"><a href="/Normal-t-F-and-Chi-Squared-Distributions-in-R.html"><span class="progress-dot"></span>Normal, t, F, Chi-Squared</a></li><li data-subkey="sec3sub2"><a href="/Central-Limit-Theorem-in-R.html"><span class="progress-dot"></span>Central Limit Theorem</a></li><li data-subkey="sec3sub2"><a href="/Sampling-Distributions-in-R.html"><span class="progress-dot"></span>Sampling Distributions</a></li><li data-subkey="sec3sub2"><a href="/Law-of-Large-Numbers-vs-CLT-in-R.html"><span class="progress-dot"></span>LLN vs CLT</a></li><li data-subkey="sec3sub2"><a href="/What-Is-Probability-Simulation-First-Intuition-in-R-Before-the-Formulas.html"><span class="progress-dot"></span>Probability (Simulation-First)</a></li><li data-subkey="sec3sub2"><a href="/Expected-Value-and-Variance-in-R.html"><span class="progress-dot"></span>Expected Value and Variance</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec3sub3" data-collapsed="false"><span class="subsec-chevron">▼</span> Inference & Estimation</li><li data-subkey="sec3sub3"><a href="/Maximum-Likelihood-Estimation-in-R.html"><span class="progress-dot"></span>Maximum Likelihood Estimation</a></li><li data-subkey="sec3sub3"><a href="/Hypothesis-Testing-in-R.html"><span class="progress-dot"></span>Hypothesis Testing</a></li><li data-subkey="sec3sub3"><a href="/Sample-Size-Planning-in-R.html"><span class="progress-dot"></span>Sample Size Planning</a></li><li data-subkey="sec3sub3"><a href="/Which-Statistical-Test-in-R.html"><span class="progress-dot"></span>Choosing the Right Test</a></li><li data-subkey="sec3sub3"><a href="/Statistical-Tests-in-R.html"><span class="progress-dot"></span>Statistical Tests</a></li><li data-subkey="sec3sub3"><a href="/Measures-of-Association-in-R.html"><span class="progress-dot"></span>Measures of Association</a></li><li data-subkey="sec3sub3"><a href="/Point-Estimation-in-R.html"><span class="progress-dot"></span>Point Estimation</a></li><li data-subkey="sec3sub3"><a href="/Confidence-Intervals-in-R.html"><span class="progress-dot"></span>Confidence Intervals</a></li><li data-subkey="sec3sub3"><a href="/Type-I-and-Type-II-Errors-in-R.html"><span class="progress-dot"></span>Type I and II Errors</a></li><li data-subkey="sec3sub3"><a href="/Statistical-Power-Analysis-in-R.html"><span class="progress-dot"></span>Power Analysis</a></li><li data-subkey="sec3sub3"><a href="/Effect-Size-in-R.html"><span class="progress-dot"></span>Effect Size</a></li><li data-subkey="sec3sub3"><a href="/t-Tests-in-R.html"><span class="progress-dot"></span>t-Tests</a></li><li data-subkey="sec3sub3"><a href="/Proportion-Tests-in-R.html"><span class="progress-dot"></span>Proportion Tests</a></li><li data-subkey="sec3sub3"><a href="/Normality-and-Variance-Tests-in-R.html"><span class="progress-dot"></span>Normality & Variance Tests</a></li><li data-subkey="sec3sub3"><a href="/Chi-Square-Tests-in-R.html"><span class="progress-dot"></span>Chi-Square Tests</a></li><li data-subkey="sec3sub3"><a href="/Wilcoxon-Mann-Whitney-and-Kruskal-Wallis-in-R.html"><span class="progress-dot"></span>Wilcoxon, Mann-Whitney & Kruskal-Wallis</a></li><li data-subkey="sec3sub3"><a href="/Multiple-Comparisons-in-R.html"><span class="progress-dot"></span>Multiple Testing Correction</a></li><li data-subkey="sec3sub3"><a href="/Hypothesis-Testing-Exercises-in-R-quiz.html"><span class="progress-dot"></span>Hypothesis Testing Quiz</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec3sub4" data-collapsed="false"><span class="subsec-chevron">▼</span> Regression</li><li data-subkey="sec3sub4"><a href="/Linear-Regression.html"><span class="progress-dot"></span>Linear Regression</a></li><li data-subkey="sec3sub4"><a href="/Logistic-Regression-With-R.html"><span class="progress-dot"></span>Logistic Regression</a></li><li data-subkey="sec3sub4"><a href="/Variable-Selection-and-Importance-With-R.html"><span class="progress-dot"></span>Feature Selection</a></li><li data-subkey="sec3sub4"><a href="/Model-Selection-in-R.html"><span class="progress-dot"></span>Model Selection</a></li><li data-subkey="sec3sub4"><a href="/Missing-Value-Treatment-With-R.html"><span class="progress-dot"></span>Missing Value Treatment</a></li><li data-subkey="sec3sub4"><a href="/Outlier-Treatment-With-R.html"><span class="progress-dot"></span>Outlier Analysis</a></li><li data-subkey="sec3sub4"><a href="/adv-regression-models.html"><span class="progress-dot"></span>Advanced Regression Models</a></li><li data-subkey="sec3sub4"><a href="/Linear-Regression-Exercises-in-R-quiz.html"><span class="progress-dot"></span>Linear Regression Quiz</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec3sub5" data-collapsed="false"><span class="subsec-chevron">▼</span> Reporting</li><li data-subkey="sec3sub5"><a href="/Statistical-Consulting-in-R.html"><span class="progress-dot"></span>Statistical Consulting</a></li><li data-subkey="sec3sub5"><a href="/Statistical-Report-Writing-in-R.html"><span class="progress-dot"></span>Statistical Report Writing</a></li><li data-subkey="sec3sub5"><a href="/Bootstrap-Confidence-Intervals-in-R.html"><span class="progress-dot"></span>Bootstrap Confidence Intervals</a></li><li data-subkey="sec3sub5"><a href="/Reporting-Statistics-in-R.html"><span class="progress-dot"></span>Reporting Statistics</a></li><li data-subkey="sec3sub5"><a href="/Correlation-in-R.html"><span class="progress-dot"></span>Correlation (Pearson, Spearman, Kendall)</a></li><li data-subkey="sec3sub5"><a href="/Linear-Regression-Assumptions-in-R.html"><span class="progress-dot"></span>Linear Regression Assumptions</a></li><li data-subkey="sec3sub5"><a href="/Dummy-Variables-in-R.html"><span class="progress-dot"></span>Dummy Variables in R</a></li><li data-subkey="sec3sub5"><a href="/Interaction-Effects-in-R.html"><span class="progress-dot"></span>Interaction Effects</a></li><li data-subkey="sec3sub5"><a href="/Regression-Diagnostics-in-R.html"><span class="progress-dot"></span>Regression Diagnostics</a></li><li data-subkey="sec3sub5"><a href="/Logistic-Regression-in-R.html"><span class="progress-dot"></span>Logistic Regression (glm + ROC)</a></li><li data-subkey="sec3sub5"><a href="/Variable-Selection-in-R.html"><span class="progress-dot"></span>Variable Selection</a></li><li data-subkey="sec3sub5"><a href="/Poisson-Regression-in-R.html"><span class="progress-dot"></span>Poisson Regression</a></li><li data-subkey="sec3sub5"><a href="/Ridge-and-Lasso-Regression-in-R.html"><span class="progress-dot"></span>Ridge & Lasso Regression</a></li><li data-subkey="sec3sub5"><a href="/Polynomial-and-Spline-Regression-in-R.html"><span class="progress-dot"></span>Polynomial & Splines</a></li><li data-subkey="sec3sub5"><a href="/Regression-Tables-in-R.html"><span class="progress-dot"></span>Regression Tables (3 packages)</a></li><li data-subkey="sec3sub5"><a href="/One-Way-ANOVA-in-R.html"><span class="progress-dot"></span>One-Way ANOVA</a></li><li data-subkey="sec3sub5"><a href="/Post-Hoc-Tests-After-ANOVA.html"><span class="progress-dot"></span>Post-Hoc Tests After ANOVA</a></li><li data-subkey="sec3sub5"><a href="/Two-Way-ANOVA-in-R.html"><span class="progress-dot"></span>Two-Way ANOVA</a></li><li data-subkey="sec3sub5"><a href="/Repeated-Measures-ANOVA-in-R.html"><span class="progress-dot"></span>Repeated Measures ANOVA</a></li><li data-subkey="sec3sub5"><a href="/ANCOVA-in-R.html"><span class="progress-dot"></span>ANCOVA</a></li><li data-subkey="sec3sub5"><a href="/Experimental-Design-Principles-in-R.html"><span class="progress-dot"></span>Experimental Design in R</a></li><li data-subkey="sec3sub5"><a href="/Factorial-Experiments-in-R.html"><span class="progress-dot"></span>Factorial Designs (2^k)</a></li><li data-subkey="sec3sub5"><a href="/AB-Testing-in-R.html"><span class="progress-dot"></span>A/B Testing</a></li><li data-subkey="sec3sub5"><a href="/MANOVA-in-R.html"><span class="progress-dot"></span>MANOVA</a></li><li data-subkey="sec3sub5"><a href="/Mixed-ANOVA-in-R.html"><span class="progress-dot"></span>Mixed ANOVA</a></li><li data-subkey="sec3sub5"><a href="/Multivariate-Statistics-in-R.html"><span class="progress-dot"></span>Multivariate Distances & Hotelling's T²</a></li><li data-subkey="sec3sub5"><a href="/PCA-in-R.html"><span class="progress-dot"></span>PCA with prcomp()</a></li><li data-subkey="sec3sub5"><a href="/Interpreting-PCA-Results-in-R.html"><span class="progress-dot"></span>Interpreting PCA Output</a></li><li data-subkey="sec3sub5"><a href="/Exploratory-Factor-Analysis-in-R.html"><span class="progress-dot"></span>Exploratory Factor Analysis</a></li><li 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href="/Robust-Regression-in-R.html"><span class="progress-dot"></span>Robust Regression (rlm)</a></li><li data-subkey="sec3sub5"><a href="/factoextra-and-FactoMineR.html"><span class="progress-dot"></span>factoextra (PCA + Clusters)</a></li><li data-subkey="sec3sub5"><a href="/Categorical-Data-in-R.html"><span class="progress-dot"></span>Categorical Data (Tables & Mosaic)</a></li><li data-subkey="sec3sub5"><a href="/Chi-Square-Test-of-Independence-in-R.html"><span class="progress-dot"></span>Chi-Square Test of Independence</a></li><li data-subkey="sec3sub5"><a href="/Chi-Square-Goodness-of-Fit-Test-in-R.html"><span class="progress-dot"></span>Chi-Square Goodness-of-Fit</a></li><li data-subkey="sec3sub5"><a href="/Fishers-Exact-Test-in-R.html"><span class="progress-dot"></span>Fisher's Exact Test</a></li><li data-subkey="sec3sub5"><a href="/Odds-Ratios-and-Relative-Risk-in-R.html"><span class="progress-dot"></span>Odds Ratios & Relative Risk</a></li><li data-subkey="sec3sub5"><a href="/Logistic-Regression-in-R-2.html"><span class="progress-dot"></span>Logistic Regression (Diagnostics)</a></li><li data-subkey="sec3sub5"><a href="/Poisson-and-Negative-Binomial-Regression.html"><span class="progress-dot"></span>Poisson & Negative Binomial Regression</a></li><li data-subkey="sec3sub5"><a href="/Multinomial-and-Ordinal-Logistic-Regression-in-R.html"><span class="progress-dot"></span>Multinomial & Ordinal Logistic Regression</a></li><li data-subkey="sec3sub5"><a href="/When-to-Use-Nonparametric-Tests-in-R.html"><span class="progress-dot"></span>When to Use Nonparametric Tests</a></li><li data-subkey="sec3sub5"><a href="/Wilcoxon-Signed-Rank-Test-in-R.html"><span class="progress-dot"></span>Wilcoxon Signed-Rank Test</a></li><li data-subkey="sec3sub5"><a href="/Mann-Whitney-U-Test-in-R.html"><span class="progress-dot"></span>Mann-Whitney U Test</a></li><li data-subkey="sec3sub5"><a href="/Kruskal-Wallis-Test-in-R-2.html"><span class="progress-dot"></span>Kruskal-Wallis Test</a></li><li data-subkey="sec3sub5"><a href="/Friedman-Test-in-R.html"><span class="progress-dot"></span>Friedman Test</a></li><li data-subkey="sec3sub5"><a href="/Spearman-and-Kendall-Correlation-in-R.html"><span class="progress-dot"></span>Spearman & Kendall Correlation</a></li><li data-subkey="sec3sub5"><a href="/Bootstrap-in-R.html"><span class="progress-dot"></span>Bootstrap (boot package)</a></li><li data-subkey="sec3sub5"><a href="/Quantile-Regression-in-R-2.html"><span class="progress-dot"></span>Quantile Regression</a></li><li data-subkey="sec3sub5"><a href="/Matrix-Operations-in-R.html"><span class="progress-dot"></span>Matrix Operations in R</a></li><li data-subkey="sec3sub5"><a href="/Solving-Linear-Systems-in-R.html"><span class="progress-dot"></span>Solving Linear Systems in R</a></li><li data-subkey="sec3sub5"><a href="/Eigenvalues-and-Eigenvectors-in-R.html"><span class="progress-dot"></span>Eigenvalues & Eigenvectors in R</a></li><li data-subkey="sec3sub5"><a href="/Singular-Value-Decomposition-in-R.html"><span class="progress-dot"></span>Singular Value Decomposition in R</a></li><li data-subkey="sec3sub5"><a href="/Projections-and-the-Hat-Matrix-in-R.html"><span class="progress-dot"></span>Projections & the Hat Matrix</a></li><li data-subkey="sec3sub5"><a href="/QR-Decomposition-in-R.html"><span class="progress-dot"></span>QR Decomposition in R</a></li><li data-subkey="sec3sub5"><a href="/Quadratic-Forms-in-R.html"><span class="progress-dot"></span>Quadratic Forms</a></li><li data-subkey="sec3sub5"><a href="/Matrix-Derivatives-and-the-Hessian-in-R.html"><span class="progress-dot"></span>Matrix Derivatives & Hessian</a></li><li data-subkey="sec3sub5"><a href="/Exponential-Family-Distributions-in-R.html"><span class="progress-dot"></span>Exponential Family Distributions</a></li><li data-subkey="sec3sub5"><a href="/Sufficient-Statistics-in-R.html"><span class="progress-dot"></span>Sufficient Statistics</a></li><li data-subkey="sec3sub5"><a href="/Complete-and-Ancillary-Statistics-in-R.html"><span class="progress-dot"></span>Complete & Ancillary Statistics</a></li><li data-subkey="sec3sub5"><a href="/UMVUE-in-R-2.html"><span class="progress-dot"></span>UMVUE (Rao-Blackwell & Lehmann-Scheffé)</a></li><li data-subkey="sec3sub5"><a href="/Cramer-Rao-Lower-Bound-in-R-2.html"><span class="progress-dot"></span>Cramér-Rao Lower Bound</a></li><li data-subkey="sec3sub5"><a href="/Asymptotic-Theory-in-R-2.html"><span class="progress-dot"></span>Asymptotic Theory</a></li><li data-subkey="sec3sub5"><a href="/Neyman-Pearson-Lemma-in-R-2.html"><span class="progress-dot"></span>Neyman-Pearson Lemma</a></li><li data-subkey="sec3sub5"><a href="/Likelihood-Ratio-Tests-and-Pivotal-Methods.html"><span class="progress-dot"></span>Likelihood Ratio & Pivotal Methods</a></li><li data-subkey="sec3sub5"><a href="/Decision-Theory-in-R.html"><span class="progress-dot"></span>Decision Theory</a></li><li data-subkey="sec3sub5"><a href="/Asymptotic-Relative-Efficiency-in-R.html"><span class="progress-dot"></span>Asymptotic Relative Efficiency</a></li><li data-subkey="sec3sub5"><a href="/Bayes-Theorem-in-R.html"><span class="progress-dot"></span>Bayes' Theorem</a></li><li data-subkey="sec3sub5"><a href="/Bayesian-Statistics-in-R.html"><span class="progress-dot"></span>Bayesian Statistics</a></li><li data-subkey="sec3sub5"><a href="/Conjugate-Priors-in-R.html"><span class="progress-dot"></span>Conjugate Priors</a></li><li data-subkey="sec3sub5"><a href="/Grid-Approximation-in-R.html"><span class="progress-dot"></span>Grid Approximation</a></li><li data-subkey="sec3sub5"><a href="/MCMC-in-R.html"><span class="progress-dot"></span>MCMC in R</a></li><li data-subkey="sec3sub5"><a href="/Gibbs-Sampling-in-R.html"><span class="progress-dot"></span>Gibbs Sampling</a></li><li data-subkey="sec3sub5"><a href="/Hamiltonian-Monte-Carlo-in-R.html"><span class="progress-dot"></span>Hamiltonian Monte Carlo</a></li><li data-subkey="sec3sub5"><a href="/Stan-in-R.html"><span class="progress-dot"></span>Stan</a></li><li data-subkey="sec3sub5"><a href="/brms-in-R.html"><span class="progress-dot"></span>brms</a></li><li data-subkey="sec3sub5"><a href="/Choosing-Priors-in-R.html"><span class="progress-dot"></span>Choosing Priors</a></li><li data-subkey="sec3sub5"><a href="/Prior-Predictive-Checks-in-R.html"><span class="progress-dot"></span>Prior Predictive Checks</a></li><li data-subkey="sec3sub5"><a href="/Compare-Bayesian-Models-in-R.html"><span class="progress-dot"></span>Compare Bayesian Models</a></li><li data-subkey="sec3sub5"><a href="/Posterior-Predictive-Checks-in-R.html"><span class="progress-dot"></span>Posterior Predictive Checks</a></li><li data-subkey="sec3sub5"><a href="/Bayesian-Linear-Regression-in-R.html"><span class="progress-dot"></span>Bayesian Linear Regression</a></li><li data-subkey="sec3sub5"><a href="/Bayesian-Logistic-Regression-in-R.html"><span class="progress-dot"></span>Bayesian Logistic Regression</a></li><li data-subkey="sec3sub5"><a href="/Bayesian-Hierarchical-Models-in-R.html"><span class="progress-dot"></span>Bayesian Hierarchical Models</a></li><li data-subkey="sec3sub5"><a href="/Multilevel-Models-in-R.html"><span class="progress-dot"></span>Multilevel Models</a></li><li data-subkey="sec3sub5"><a href="/Bayesian-ANOVA-in-R.html"><span class="progress-dot"></span>Bayesian ANOVA</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec3sub6" data-collapsed="false"><span class="subsec-chevron">▼</span> Machine Learning</li><li data-subkey="sec3sub6"><a href="/Machine-Learning-Exercises-in-R-quiz.html"><span class="progress-dot"></span>Machine Learning Quiz</a></li></ul></li><li class="sidebar-section"><div class="sidebar-section-header"><span class="sidebar-chevron">▸</span> Time Series<span class="section-meta" data-section-meta></span></div><ul class="sidebar-section-items list-unstyled"><li data-subkey="sec4sub0"><a href="/Time-Series-Analysis-With-R.html"><span class="progress-dot"></span>Time Series Analysis</a></li><li data-subkey="sec4sub0"><a href="/Time-Series-Forecasting-With-R.html"><span class="progress-dot"></span>Time Series Forecasting</a></li><li data-subkey="sec4sub0"><a href="/Time-Series-Forecasting-With-R-part2.html"><span class="progress-dot"></span>More Time Series Forecasting</a></li><li data-subkey="sec4sub0"><a href="/Time-Series-Exercises-in-R-quiz.html"><span class="progress-dot"></span>Time Series Quiz</a></li></ul></li><li class="sidebar-section"><div class="sidebar-section-header"><span class="sidebar-chevron">▸</span> Advanced R<span class="section-meta" data-section-meta></span></div><ul class="sidebar-section-items list-unstyled"><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec5sub1" data-collapsed="false"><span class="subsec-chevron">▼</span> Functional Programming</li><li data-subkey="sec5sub1"><a href="/Functional-Programming-in-R.html"><span class="progress-dot"></span>Functional Programming</a></li><li data-subkey="sec5sub1"><a href="/R-Functional-Programming-Exercises-quiz.html"><span class="progress-dot"></span>Functional Programming Quiz</a></li><li data-subkey="sec5sub1"><a href="/purrr-map-Variants.html"><span class="progress-dot"></span>purrr map() Variants</a></li><li data-subkey="sec5sub1"><a href="/R-Anonymous-Functions.html"><span class="progress-dot"></span>R Anonymous Functions</a></li><li data-subkey="sec5sub1"><a href="/R-Function-Factories.html"><span class="progress-dot"></span>R Function Factories</a></li><li data-subkey="sec5sub1"><a href="/R-Function-Operators.html"><span class="progress-dot"></span>R Function Operators</a></li><li data-subkey="sec5sub1"><a href="/Reduce-Filter-Map-in-R.html"><span class="progress-dot"></span>Reduce, Filter, Map</a></li><li data-subkey="sec5sub1"><a href="/Memoization-in-R.html"><span class="progress-dot"></span>Memoization in R</a></li><li data-subkey="sec5sub1"><a href="/Writing-Composable-R-Code.html"><span class="progress-dot"></span>Composable R Code</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec5sub2" data-collapsed="false"><span class="subsec-chevron">▼</span> OOP in R</li><li data-subkey="sec5sub2"><a href="/OOP-in-R.html"><span class="progress-dot"></span>OOP in R: S3/S4/R6</a></li><li data-subkey="sec5sub2"><a href="/S3-Classes-in-R.html"><span class="progress-dot"></span>S3 Classes</a></li><li data-subkey="sec5sub2"><a href="/S3-Method-Dispatch-in-R.html"><span class="progress-dot"></span>S3 Method Dispatch</a></li><li data-subkey="sec5sub2"><a href="/S4-Classes-in-R.html"><span class="progress-dot"></span>S4 Classes</a></li><li data-subkey="sec5sub2"><a href="/S4-Methods-in-R.html"><span class="progress-dot"></span>S4 Methods & Dispatch</a></li><li data-subkey="sec5sub2"><a href="/R6-Classes-in-R.html"><span class="progress-dot"></span>R6 Classes</a></li><li data-subkey="sec5sub2"><a href="/R6-Advanced.html"><span class="progress-dot"></span>R6 Advanced</a></li><li data-subkey="sec5sub2"><a href="/Operator-Overloading-in-R.html"><span class="progress-dot"></span>Operator Overloading</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec5sub3" data-collapsed="false"><span class="subsec-chevron">▼</span> How R Works</li><li data-subkey="sec5sub3"><a href="/R-Names-and-Values.html"><span class="progress-dot"></span>R Names & Values</a></li><li data-subkey="sec5sub3"><a href="/R-Assignment-Deep-Dive.html"><span class="progress-dot"></span>R Assignment Deep Dive</a></li><li data-subkey="sec5sub3"><a href="/R-Memory-lobstr.html"><span class="progress-dot"></span>R Memory & lobstr</a></li><li data-subkey="sec5sub3"><a href="/R-Environments.html"><span class="progress-dot"></span>R Environments</a></li><li data-subkey="sec5sub3"><a href="/R-Lexical-Scoping.html"><span class="progress-dot"></span>Lexical Scoping</a></li><li data-subkey="sec5sub3"><a href="/R-Closures.html"><span class="progress-dot"></span>R Closures</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec5sub4" data-collapsed="false"><span class="subsec-chevron">▼</span> Debugging & Performance</li><li data-subkey="sec5sub4"><a href="/R-Conditions-System.html"><span class="progress-dot"></span>Conditions System</a></li><li data-subkey="sec5sub4"><a href="/R-Debugging.html"><span class="progress-dot"></span>Debugging R Code</a></li><li data-subkey="sec5sub4"><a href="/R-Common-Errors.html"><span class="progress-dot"></span>50 Common R Errors</a></li><li data-subkey="sec5sub4"><a href="/Parallel-Computing-With-R.html"><span class="progress-dot"></span>Parallel Computing</a></li><li data-subkey="sec5sub4"><a href="/Strategies-To-Improve-And-Speedup-R-Code.html"><span class="progress-dot"></span>Speedup R Code</a></li><li data-subkey="sec5sub4"><a href="/Shiny-Exercises-in-R-quiz.html"><span class="progress-dot"></span>Shiny Quiz</a></li></ul></li><li class="sidebar-section"><div class="sidebar-section-header"><span class="sidebar-chevron">▸</span> Classic Tutorials<span class="section-meta" data-section-meta></span></div><ul class="sidebar-section-items list-unstyled"><li data-subkey="sec6sub0"><a href="/R-Tutorial.html"><span class="progress-dot"></span>R Tutorial (Classic)</a></li><li data-subkey="sec6sub0"><a href="/ggplot2-Tutorial-With-R.html"><span class="progress-dot"></span>ggplot2 Short Tutorial</a></li><li data-subkey="sec6sub0"><a href="/Complete-Ggplot2-Tutorial-Part1-With-R-Code.html"><span class="progress-dot"></span>ggplot2 Tutorial 1 - Intro</a></li><li data-subkey="sec6sub0"><a href="/Complete-Ggplot2-Tutorial-Part2-Customizing-Theme-With-R-Code.html"><span class="progress-dot"></span>ggplot2 Tutorial 2 - Theme</a></li><li data-subkey="sec6sub0"><a href="/Top50-Ggplot2-Visualizations-MasterList-R-Code.html"><span class="progress-dot"></span>ggplot2 Tutorial 3 - Masterlist</a></li><li data-subkey="sec6sub0"><a href="/Association-Mining-With-R.html"><span class="progress-dot"></span>Association Mining</a></li><li data-subkey="sec6sub0"><a href="/Multi-Dimensional-Scaling-With-R.html"><span class="progress-dot"></span>Multi Dimensional Scaling</a></li><li data-subkey="sec6sub0"><a href="/Optimization-With-R.html"><span class="progress-dot"></span>Optimization</a></li><li data-subkey="sec6sub0"><a href="/Information-Value-With-R.html"><span class="progress-dot"></span>InformationValue Package</a></li></ul></li><li class="sidebar-section expanded"><div class="sidebar-section-header"><span class="sidebar-chevron">▸</span> Practice Exercises<span class="section-meta" data-section-meta></span></div><ul class="sidebar-section-items list-unstyled"><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec7sub1" data-collapsed="false"><span class="subsec-chevron">▼</span> Mastery Quizzes (Certificate)</li><li data-subkey="sec7sub1"><a href="/R-Beginner-Exercises-quiz.html"><span class="progress-dot"></span>R Fundamentals Quiz</a></li><li data-subkey="sec7sub1"><a href="/dplyr-Exercises-in-R-quiz.html"><span class="progress-dot"></span>dplyr Quiz</a></li><li data-subkey="sec7sub1"><a href="/ggplot2-Exercises-in-R-quiz.html"><span class="progress-dot"></span>ggplot2 Quiz</a></li><li data-subkey="sec7sub1"><a href="/Hypothesis-Testing-Exercises-in-R-quiz.html"><span class="progress-dot"></span>Hypothesis Testing Quiz</a></li><li data-subkey="sec7sub1"><a href="/Linear-Regression-Exercises-in-R-quiz.html"><span class="progress-dot"></span>Linear Regression Quiz</a></li><li data-subkey="sec7sub1"><a href="/Machine-Learning-Exercises-in-R-quiz.html"><span class="progress-dot"></span>Machine Learning Quiz</a></li><li data-subkey="sec7sub1"><a href="/tidyr-Exercises-in-R-quiz.html"><span 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<h1>API Calls Exercises in R: 17 Real-World Practice Problems</h1>
<p class="lead">Seventeen practice problems on calling REST APIs from R with httr2 and <a class="auto-link" href="Importing-Data-in-R.html" title="Importing Data into R: read_csv(), read_excel(), read_json() — Complete Guide">jsonlite</a>: building requests, parsing JSON, handling auth, retries, rate limits, and pagination. Solutions are hidden so you can attempt each one cold and reveal only when stuck.</p>
<div class="post-byline" style="color:#6b7280;font-size:14px;margin:2px 0 18px 0;line-height:1.5;">By <strong>Selva Prabhakaran</strong> · Published May 17, 2026 · Last updated May 17, 2026</div>
<div class="engagement-header" data-difficulty="Mixed" data-time="40" data-exercises="17" data-xp="255"></div>
<pre><code class="language-r">library(httr2)
library(jsonlite)
library(dplyr)
library(tibble)
library(purrr)</code></pre>
<blockquote><p>The network exercises target public stable endpoints (<code>jsonplaceholder.typicode.com</code>, <code>httpbin.org</code>, <code>api.agify.io</code>). They are read-only sandboxes and safe to hit repeatedly. The auth and retry exercises use <code>req_dry_run()</code> so you can verify the wire request without sending it.</p></blockquote>
<h2>Section 1. Building and inspecting httr2 requests (4 problems)</h2>
<section class="exercise" data-exercise-id="API-Calls-Exercises-in-R-ex-1-1" data-grade-mode="output-compare" data-difficulty="beginner">
<h3 class="exercise-title">Exercise 1.1: Construct a basic GET request and inspect its URL</h3>
<p class="exercise-task"><strong>Task:</strong> A junior analyst onboarding to the data team is exploring <code>httr2</code> for the first time. Build a request object pointing at <code>https://jsonplaceholder.typicode.com/posts/1</code> using <code>request()</code>, do NOT perform it, and save the request to <code>ex_1_1</code> so the analyst can examine the URL, method, and body before any traffic is sent.</p>
<div class="exercise-expected">
<p><strong>Expected result:</strong></p>
<pre><code>#> <httr2_request>
#> GET https://jsonplaceholder.typicode.com/posts/1
#> Body: empty</code></pre>
</div>
<p><strong>Difficulty:</strong> Beginner</p>
<div class="exercise-hints" hidden><p>A request in httr2 is a lazy description of a call - you state where to point it without sending anything over the network yet.</p><p>Pass the URL string to request() and assign the result to ex_1_1; do not add any perform step.</p></div>
<pre><code class="language-r">ex_1_1 <- # your code here
ex_1_1</code></pre>
<details class="exercise-solution">
<summary>Click to reveal solution</summary>
<pre><code class="language-r">ex_1_1 <- request("https://jsonplaceholder.typicode.com/posts/1")
ex_1_1
#> <httr2_request>
#> GET https://jsonplaceholder.typicode.com/posts/1
#> Body: empty</code></pre>
<p class="exercise-explanation"><strong>Explanation:</strong> <code>request()</code> builds a lazy request object: no network traffic happens until you call <code>req_perform()</code> on it. This separation lets you <a class="auto-link" href="R-Function-Operators.html" title="R Function Operators: Compose, Negate & Partial Application">compose</a>, inspect, and unit-test requests safely. The printed representation shows method (default <code>GET</code>), URL, and body status, which is the fastest way to confirm you assembled the request correctly before sending it. The same object can be modified later with <code>req_url_path_append()</code> or <code>req_method()</code> rather than building a new request from scratch.</p>
</details>
</section>
<section class="exercise" data-exercise-id="API-Calls-Exercises-in-R-ex-1-2" data-grade-mode="output-compare" data-difficulty="beginner">
<h3 class="exercise-title">Exercise 1.2: Attach a custom User-Agent header</h3>
<p class="exercise-task"><strong>Task:</strong> Many APIs reject requests with a generic agent string. Take the request from Exercise 1.1, add a <code>User-Agent</code> header reading <code>r-statistics-tutorial/1.0 (selva@example.com)</code> with <code>req_user_agent()</code>, and save the modified request to <code>ex_1_2</code> so it identifies itself politely to the server.</p>
<div class="exercise-expected">
<p><strong>Expected result:</strong></p>
<pre><code>#> <httr2_request>
#> GET https://jsonplaceholder.typicode.com/posts/1
#> Headers:
#> * User-Agent: 'r-statistics-tutorial/1.0 (selva@example.com)'
#> Body: empty</code></pre>
</div>
<p><strong>Difficulty:</strong> Beginner</p>
<div class="exercise-hints" hidden><p>Identifying who is calling is just one more piece of metadata layered onto an existing request before it is sent.</p><p>Pipe the request from Exercise 1.1 into req_user_agent() with the agent string as its only argument.</p></div>
<pre><code class="language-r">ex_1_2 <- # your code here
ex_1_2</code></pre>
<details class="exercise-solution">
<summary>Click to reveal solution</summary>
<pre><code class="language-r">ex_1_2 <- request("https://jsonplaceholder.typicode.com/posts/1") |>
req_user_agent("r-statistics-tutorial/1.0 (selva@example.com)")
ex_1_2
#> <httr2_request>
#> GET https://jsonplaceholder.typicode.com/posts/1
#> Headers:
#> * User-Agent: 'r-statistics-tutorial/1.0 (selva@example.com)'
#> Body: empty</code></pre>
<p class="exercise-explanation"><strong>Explanation:</strong> A descriptive User-Agent is the polite-internet default: it tells the server who is calling so the operator can contact you if your script misbehaves. <code>req_user_agent()</code> is a thin wrapper around <code>req_headers()</code> that targets the User-Agent slot specifically, so subsequent <code>req_headers()</code> calls do not overwrite it accidentally. Generic agents like the default <code>httr2/x.y.z libcurl/...</code> are commonly throttled or 403'd by anti-bot middleware, so set this once at the top of your script.</p>
</details>
</section>
<section class="exercise" data-exercise-id="API-Calls-Exercises-in-R-ex-1-3" data-grade-mode="output-compare" data-difficulty="intermediate">
<h3 class="exercise-title">Exercise 1.3: Attach query parameters and dry-run the wire URL</h3>
<p class="exercise-task"><strong>Task:</strong> A data engineer needs to confirm that query parameters serialize correctly before pointing a job at production. Build a request to <code>https://api.agify.io</code>, add query parameters <code>name = "selva"</code> and <code>country_id = "IN"</code> with <code>req_url_query()</code>, then use <code>req_dry_run()</code> to print the wire-level request and save the unsent request to <code>ex_1_3</code>.</p>
<div class="exercise-expected">
<p><strong>Expected result:</strong></p>
<pre><code>#> GET /?name=selva&country_id=IN HTTP/1.1
#> host: api.agify.io
#> user-agent: httr2/1.0.0 r-curl/5.2.0 libcurl/8.4.0
#> accept: */*
#> accept-encoding: deflate, gzip</code></pre>
</div>
<p><strong>Difficulty:</strong> Intermediate</p>
<div class="exercise-hints" hidden><p>Query parameters are key-value pairs appended to the URL, and the library can encode them for you instead of you hand-building the string.</p><p>Add req_url_query(name = "selva", country_id = "IN") to a request() for the base URL.</p></div>
<pre><code class="language-r">ex_1_3 <- # your code here
ex_1_3 |> req_dry_run()</code></pre>
<details class="exercise-solution">
<summary>Click to reveal solution</summary>
<pre><code class="language-r">ex_1_3 <- request("https://api.agify.io") |>
req_url_query(name = "selva", country_id = "IN")
ex_1_3 |> req_dry_run()
#> GET /?name=selva&country_id=IN HTTP/1.1
#> host: api.agify.io
#> user-agent: httr2/1.0.0 r-curl/5.2.0 libcurl/8.4.0
#> accept: */*
#> accept-encoding: deflate, gzip</code></pre>
<p class="exercise-explanation"><strong>Explanation:</strong> <code>req_url_query()</code> is preferable to hand-building a query string because it URL-encodes values for you (spaces, ampersands, unicode) and de-duplicates keys. <code>req_dry_run()</code> prints the exact HTTP message that would be sent without performing the request, which is invaluable when debugging mysterious 400 responses or auth failures. The header user-agent in your output may differ depending on installed <code>httr2</code> and <code>curl</code> versions; only the request line and host need to match.</p>
</details>
</section>
<section class="exercise" data-exercise-id="API-Calls-Exercises-in-R-ex-1-4" data-grade-mode="output-compare" data-difficulty="beginner">
<h3 class="exercise-title">Exercise 1.4: Switch a GET request to POST and inspect the body</h3>
<p class="exercise-task"><strong>Task:</strong> A code reviewer wants to see how the same request URL can be reused across HTTP methods. Take a request to <code>https://httpbin.org/anything</code>, change the method to <code>POST</code> with <code>req_method()</code>, attach a JSON body <code>list(team = "data", priority = 1)</code> via <code>req_body_json()</code>, and save the unsent request to <code>ex_1_4</code> so the reviewer can inspect it before run-time.</p>
<div class="exercise-expected">
<p><strong>Expected result:</strong></p>
<pre><code>#> <httr2_request>
#> POST https://httpbin.org/anything
#> Body: json encoded data</code></pre>
</div>
<p><strong>Difficulty:</strong> Beginner</p>
<div class="exercise-hints" hidden><p>The same URL can carry a different verb and a payload - you reshape an existing request rather than build a new one.</p><p>Chain req_method("POST") and req_body_json(list(team = "data", priority = 1)) onto the request().</p></div>
<pre><code class="language-r">ex_1_4 <- # your code here
ex_1_4</code></pre>
<details class="exercise-solution">
<summary>Click to reveal solution</summary>
<pre><code class="language-r">ex_1_4 <- request("https://httpbin.org/anything") |>
req_method("POST") |>
req_body_json(list(team = "data", priority = 1))
ex_1_4
#> <httr2_request>
#> POST https://httpbin.org/anything
#> Body: json encoded data</code></pre>
<p class="exercise-explanation"><strong>Explanation:</strong> <code>req_body_json()</code> does two jobs at once: it serializes the R list to JSON with <code>jsonlite::toJSON()</code> AND sets the <code>Content-Type: application/json</code> header. If you skip the latter, many APIs will silently reject the body or treat it as form data. Note that calling <code>req_body_json()</code> also implicitly sets the method to <code>POST</code>, so the explicit <code>req_method("POST")</code> here is redundant but documentary. Use <code>req_dry_run()</code> to see the serialized JSON in the body.</p>
</details>
</section>
<h2>Section 2. Sending GET requests and parsing JSON (4 problems)</h2>
<section class="exercise" data-exercise-id="API-Calls-Exercises-in-R-ex-2-1" data-grade-mode="output-compare" data-difficulty="intermediate">
<h3 class="exercise-title">Exercise 2.1: Fetch a JSON post and extract a single field</h3>
<p class="exercise-task"><strong>Task:</strong> A content team wants to pull the title of post 1 from a CMS-like sandbox. Perform a GET to <code>https://jsonplaceholder.typicode.com/posts/1</code>, parse the response with <code>resp_body_json()</code>, and save the value of the <code>title</code> element (a character scalar) to <code>ex_2_1</code> so it can be used in a downstream report.</p>
<div class="exercise-expected">
<p><strong>Expected result:</strong></p>
<pre><code>#> [1] "sunt aut facere repellat provident occaecati excepturi optio reprehenderit"</code></pre>
</div>
<p><strong>Difficulty:</strong> Intermediate</p>
<div class="exercise-hints" hidden><p>Sending the request returns a response, and the single value you want lives inside its parsed body.</p><p>After <a class="auto-link" href="REST-APIs-in-R-with-httr2.html" title="REST APIs in R with httr2: GET, POST, OAuth, and Paginated Results">req_perform()</a>, call resp_body_json() and pull the title element with $title.</p></div>
<pre><code class="language-r">ex_2_1 <- # your code here
ex_2_1</code></pre>
<details class="exercise-solution">
<summary>Click to reveal solution</summary>
<pre><code class="language-r">resp <- request("https://jsonplaceholder.typicode.com/posts/1") |>
req_perform()
parsed <- resp |> resp_body_json()
ex_2_1 <- parsed$title
ex_2_1
#> [1] "sunt aut facere repellat provident occaecati excepturi optio reprehenderit"</code></pre>
<p class="exercise-explanation"><strong>Explanation:</strong> <code>req_perform()</code> is the verb that finally sends bytes over the network and returns an <code>httr2_response</code> object. <code>resp_body_json()</code> parses the body as JSON using <code>jsonlite</code> under the hood and returns a nested R list (not a tibble) by default, because JSON is fundamentally heterogeneous. To get a data frame for arrays of records, pass <code>simplifyVector = TRUE</code> or use <code>jsonlite::fromJSON()</code> on <code>resp_body_string()</code>. Pulling a single field is just list extraction.</p>
</details>
</section>
<section class="exercise" data-exercise-id="API-Calls-Exercises-in-R-ex-2-2" data-grade-mode="self-check" data-difficulty="intermediate">
<h3 class="exercise-title">Exercise 2.2: Convert a list of JSON posts to a tidy tibble</h3>
<p class="exercise-task"><strong>Task:</strong> A reporting analyst needs every post by user 1 as a tibble for joining against an internal users table. Perform a GET to <code>https://jsonplaceholder.typicode.com/posts?userId=1</code>, parse the JSON array, and convert it to a tibble with columns <code>userId</code>, <code>id</code>, <code>title</code>, <code>body</code>. Save the result to <code>ex_2_2</code>.</p>
<div class="exercise-expected">
<p><strong>Expected result:</strong></p>
<pre><code>#> # A tibble: 10 x 4
#> userId id title body
#> <int> <int> <chr> <chr>
#> 1 1 1 sunt aut facere repellat provident occaecati excepturi opt~ quia~
#> 2 1 2 qui est esse est ~
#> 3 1 3 ea molestias quasi exercitationem repellat qui ipsa sit au~ et i~
#> ...
#> # 7 more rows hidden</code></pre>
</div>
<p><strong>Difficulty:</strong> Intermediate</p>
<div class="exercise-hints" hidden><p>An array of flat JSON objects is already table-shaped - the work is getting it into a rectangular structure.</p><p>Take resp_body_string(), pass it to fromJSON(), then coerce with as_tibble().</p></div>
<pre><code class="language-r">ex_2_2 <- # your code here
ex_2_2</code></pre>
<details class="exercise-solution">
<summary>Click to reveal solution</summary>
<pre><code class="language-r">resp <- request("https://jsonplaceholder.typicode.com/posts") |>
req_url_query(userId = 1) |>
req_perform()
ex_2_2 <- resp |>
resp_body_string() |>
fromJSON() |>
as_tibble()
ex_2_2
#> # A tibble: 10 x 4
#> userId id title body
#> <int> <int> <chr> <chr>
#> 1 1 1 sunt aut facere repellat provident occaecati excepturi opt~ quia~
#> 2 1 2 qui est esse est ~
#> ...</code></pre>
<p class="exercise-explanation"><strong>Explanation:</strong> <code>resp_body_json()</code> returns a list-of-lists for an array; <code>jsonlite::fromJSON()</code> on the raw string auto-simplifies a JSON array of flat objects into a data frame in one shot, which is usually what you want for "table-shaped" responses. If the records had nested objects (e.g., <code>address</code> with sub-fields), you would call <code>fromJSON(..., flatten = TRUE)</code> or use <code>tidyr::unnest_wider()</code> on the list-column. Always inspect with <code>glimpse()</code> before assuming column types.</p>
</details>
</section>
<section class="exercise" data-exercise-id="API-Calls-Exercises-in-R-ex-2-3" data-grade-mode="output-compare" data-difficulty="intermediate">
<h3 class="exercise-title">Exercise 2.3: Flatten a nested JSON response into a data frame</h3>
<p class="exercise-task"><strong>Task:</strong> A data team is reading a payload that contains user profiles with nested <code>address</code> and <code>company</code> objects. Parse the JSON string <code>nested_json</code> (defined below) with <code>fromJSON(flatten = TRUE)</code>, coerce to a tibble, and save only the columns <code>id</code>, <code>name</code>, <code>address.city</code>, <code>company.name</code> to <code>ex_2_3</code> for a quick directory view.</p>
<pre><code class="language-r">nested_json <- '[
{"id":1,"name":"Leanne","address":{"city":"Gwenborough"},"company":{"name":"Romaguera-Crona"}},
{"id":2,"name":"Ervin", "address":{"city":"Wisokyburgh"}, "company":{"name":"Deckow-Crist"}},
{"id":3,"name":"Clementine","address":{"city":"McKenziehaven"},"company":{"name":"Romaguera-Jacobson"}}
]'</code></pre>
<div class="exercise-expected">
<p><strong>Expected result:</strong></p>
<pre><code>#> # A tibble: 3 x 4
#> id name address.city company.name
#> <int> <chr> <chr> <chr>
#> 1 1 Leanne Gwenborough Romaguera-Crona
#> 2 2 Ervin Wisokyburgh Deckow-Crist
#> 3 3 Clementine McKenziehaven Romaguera-Jacobson</code></pre>
</div>
<p><strong>Difficulty:</strong> Intermediate</p>
<div class="exercise-hints" hidden><p>Nested objects break the rectangle; collapsing them turns each sub-field into its own dotted column.</p><p>Call fromJSON() with flatten = TRUE, coerce with as_tibble(), then <a class="auto-link" href="dplyr-filter-select.html" title="dplyr filter() and select(): Subset Rows & Columns with Precision">select()</a> the four columns.</p></div>
<pre><code class="language-r">ex_2_3 <- # your code here
ex_2_3</code></pre>
<details class="exercise-solution">
<summary>Click to reveal solution</summary>
<pre><code class="language-r">ex_2_3 <- fromJSON(nested_json, flatten = TRUE) |>
as_tibble() |>
select(id, name, address.city, company.name)
ex_2_3
#> # A tibble: 3 x 4
#> id name address.city company.name
#> <int> <chr> <chr> <chr>
#> 1 1 Leanne Gwenborough Romaguera-Crona
#> 2 2 Ervin Wisokyburgh Deckow-Crist
#> 3 3 Clementine McKenziehaven Romaguera-Jacobson</code></pre>
<p class="exercise-explanation"><strong>Explanation:</strong> <code>flatten = TRUE</code> collapses nested objects into dotted column names (<code>address.city</code>), which keeps the result rectangular and dplyr-friendly. Without <code>flatten</code>, you would get list-columns for <code>address</code> and <code>company</code> that need a second pass with <code>tidyr::unnest_wider()</code>. The dotted names contain periods, so wrap them in backticks if you need to reference them in <code>mutate()</code> or <code>filter()</code>. For deeply nested or irregular JSON, prefer <code>tibblify</code> or stepwise <code>unnest_*</code> over a single flat dump.</p>
</details>
</section>
<section class="exercise" data-exercise-id="API-Calls-Exercises-in-R-ex-2-4" data-grade-mode="output-compare" data-difficulty="beginner">
<h3 class="exercise-title">Exercise 2.4: Inspect status code, content type, and headers of a response</h3>
<p class="exercise-task"><strong>Task:</strong> A platform engineer reviewing log output wants a one-shot health check on an endpoint. Perform a GET to <code>https://jsonplaceholder.typicode.com/posts/1</code>, then build a <a class="auto-link" href="R-Lists.html" title="R Lists: When Data Frames Aren't Flexible Enough (Complete Guide)">named list</a> with elements <code>status</code> (from <code>resp_status()</code>), <code>content_type</code> (from <code>resp_content_type()</code>), and <code>server</code> (the <code>server</code> response header via <code>resp_header()</code>). Save this list to <code>ex_2_4</code>.</p>
<div class="exercise-expected">
<p><strong>Expected result:</strong></p>
<pre><code>#> $status
#> [1] 200
#>
#> $content_type
#> [1] "application/json"
#>
#> $server
#> [1] "cloudflare"</code></pre>
</div>
<p><strong>Difficulty:</strong> Beginner</p>
<div class="exercise-hints" hidden><p>A response carries metadata alongside its body - status, content type, and headers are all data you can read directly.</p><p>Build a named list() combining resp_status(), resp_content_type(), and resp_header(resp, "server").</p></div>
<pre><code class="language-r">ex_2_4 <- # your code here
ex_2_4</code></pre>
<details class="exercise-solution">
<summary>Click to reveal solution</summary>
<pre><code class="language-r">resp <- request("https://jsonplaceholder.typicode.com/posts/1") |>
req_perform()
ex_2_4 <- list(
status = resp_status(resp),
content_type = resp_content_type(resp),
server = resp_header(resp, "server")
)
ex_2_4
#> $status
#> [1] 200
#> $content_type
#> [1] "application/json"
#> $server
#> [1] "cloudflare"</code></pre>
<p class="exercise-explanation"><strong>Explanation:</strong> Treat the response metadata as first-class data: status code drives retry logic, content type tells you which parser to call (<code>resp_body_json()</code> vs <code>resp_body_html()</code> vs <code>resp_body_raw()</code>), and headers carry rate-limit, caching, and pagination hints. <code>resp_header()</code> is case-insensitive on header names per RFC 7230, so <code>"Server"</code> and <code>"server"</code> both work. By default <code>httr2</code> raises an error on 4xx/5xx; if you need to inspect failures without throwing, wrap <code>req_perform()</code> in <code>req_error(is_error = ~ FALSE)</code>.</p>
</details>
</section>
<h2>Section 3. POST, PUT, DELETE, and request bodies (3 problems)</h2>
<section class="exercise" data-exercise-id="API-Calls-Exercises-in-R-ex-3-1" data-grade-mode="output-compare" data-difficulty="intermediate">
<h3 class="exercise-title">Exercise 3.1: Send a JSON POST and read the echoed payload back</h3>
<p class="exercise-task"><strong>Task:</strong> A QA engineer is verifying that an upstream service round-trips arbitrary JSON correctly. POST the body <code>list(name = "Selva", role = "instructor", years = 12)</code> to <code>https://httpbin.org/post</code>, parse the response, and save the echoed <code>json</code> element (which httpbin returns verbatim) to <code>ex_3_1</code> so the engineer can diff inputs against outputs.</p>
<div class="exercise-expected">
<p><strong>Expected result:</strong></p>
<pre><code>#> $name
#> [1] "Selva"
#> $role
#> [1] "instructor"
#> $years
#> [1] 12</code></pre>
</div>
<p><strong>Difficulty:</strong> Intermediate</p>
<div class="exercise-hints" hidden><p>A reflection endpoint hands your payload back inside its response, so you send a body and then read the same thing returned.</p><p>POST with req_body_json(payload), parse via resp_body_json(), and extract the json element.</p></div>
<pre><code class="language-r">ex_3_1 <- # your code here
ex_3_1</code></pre>
<details class="exercise-solution">
<summary>Click to reveal solution</summary>
<pre><code class="language-r">payload <- list(name = "Selva", role = "instructor", years = 12)
resp <- request("https://httpbin.org/post") |>
req_body_json(payload) |>
req_perform()
ex_3_1 <- resp |> resp_body_json() |> _$json
ex_3_1
#> $name
#> [1] "Selva"
#> $role
#> [1] "instructor"
#> $years
#> [1] 12</code></pre>
<p class="exercise-explanation"><strong>Explanation:</strong> <code>httpbin.org/post</code> is a reflection endpoint: it returns a JSON object containing what you sent. The <code>json</code> element is parsed from your raw body, which is the cleanest way to confirm <code>req_body_json()</code> serialized your list as intended (numeric stayed numeric, strings stayed strings, no accidental array-wrapping of scalars). The new pipe placeholder <code>_$json</code> is a 4.2+ idiom; on older R use <code>[["json"]]</code> after the pipe or store the parsed body first.</p>
</details>
</section>
<section class="exercise" data-exercise-id="API-Calls-Exercises-in-R-ex-3-2" data-grade-mode="output-compare" data-difficulty="intermediate">
<h3 class="exercise-title">Exercise 3.2: Send a form-urlencoded POST and parse the echoed form</h3>
<p class="exercise-task"><strong>Task:</strong> A reporting tool wants to talk to a legacy endpoint that expects <code>application/x-www-form-urlencoded</code> bodies, not JSON. POST the fields <code>username = "selva"</code> and <code>topic = "httr2"</code> to <code>https://httpbin.org/post</code> using <code>req_body_form()</code>, then extract the echoed <code>form</code> element as a tibble with one row and two columns. Save the tibble to <code>ex_3_2</code>.</p>
<div class="exercise-expected">
<p><strong>Expected result:</strong></p>
<pre><code>#> # A tibble: 1 x 2
#> topic username
#> <chr> <chr>
#> 1 httr2 selva</code></pre>
</div>
<p><strong>Difficulty:</strong> Intermediate</p>
<div class="exercise-hints" hidden><p>Legacy endpoints expect form-encoded fields rather than JSON, so the body builder you pick has to match what the server parses.</p><p>Use req_body_form() for the two fields, then pluck("form") from the parsed response and pass it to as_tibble().</p></div>
<pre><code class="language-r">ex_3_2 <- # your code here
ex_3_2</code></pre>
<details class="exercise-solution">
<summary>Click to reveal solution</summary>
<pre><code class="language-r">resp <- request("https://httpbin.org/post") |>
req_body_form(username = "selva", topic = "httr2") |>
req_perform()
ex_3_2 <- resp |>
resp_body_json() |>
pluck("form") |>
as_tibble()
ex_3_2
#> # A tibble: 1 x 2
#> topic username
#> <chr> <chr>
#> 1 httr2 selva</code></pre>
<p class="exercise-explanation"><strong>Explanation:</strong> <code>req_body_form()</code> sets <code>Content-Type: application/x-www-form-urlencoded</code> and URL-encodes the values for you. This is the body format that traditional HTML form submissions and many SOAP-era REST endpoints expect; if you send JSON instead, the server will not parse the fields and they will appear in <code>data</code> rather than <code>form</code>. <code>purrr::pluck()</code> is a safe deep-extract that returns <code>NULL</code> rather than erroring on a missing element, which makes it a safer pick than <code>$</code> chains.</p>
</details>
</section>
<section class="exercise" data-exercise-id="API-Calls-Exercises-in-R-ex-3-3" data-grade-mode="output-compare" data-difficulty="intermediate">
<h3 class="exercise-title">Exercise 3.3: Issue a DELETE request and confirm the success status</h3>
<p class="exercise-task"><strong>Task:</strong> A cleanup script needs to delete resource 7 and assert it returned 200 before logging success. Issue a DELETE to <code>https://jsonplaceholder.typicode.com/posts/7</code> using <code>req_method("DELETE")</code>, capture the response, and save the integer status code to <code>ex_3_3</code> so the wrapper can branch on it.</p>
<div class="exercise-expected">
<p><strong>Expected result:</strong></p>
<pre><code>#> [1] 200</code></pre>
</div>
<p><strong>Difficulty:</strong> Intermediate</p>
<div class="exercise-hints" hidden><p>Deleting a resource is just another HTTP verb, and success is signalled by the status code rather than the body.</p><p>Set req_method("DELETE"), perform the request, and read the integer code with resp_status().</p></div>
<pre><code class="language-r">ex_3_3 <- # your code here
ex_3_3</code></pre>
<details class="exercise-solution">
<summary>Click to reveal solution</summary>
<pre><code class="language-r">resp <- request("https://jsonplaceholder.typicode.com/posts/7") |>
req_method("DELETE") |>
req_perform()
ex_3_3 <- resp_status(resp)
ex_3_3
#> [1] 200</code></pre>
<p class="exercise-explanation"><strong>Explanation:</strong> REST conventions allow DELETE to return <code>200 OK</code> (with a body), <code>202 Accepted</code> (queued), or <code>204 No Content</code> (success, empty body); a robust client treats any 2xx as success. <code>jsonplaceholder</code> returns <code>200</code> with an empty JSON body for DELETE, which is convenient for sandboxes but uncommon in production. Always assert on the status range rather than equality: <code>status >= 200 && status < 300</code>. If the server returns 4xx, <code>httr2</code> raises by default so this code never reaches the status check on failure.</p>
</details>
</section>
<h2>Section 4. Authentication, tokens, and secrets (3 problems)</h2>
<section class="exercise" data-exercise-id="API-Calls-Exercises-in-R-ex-4-1" data-grade-mode="self-check" data-difficulty="advanced">
<h3 class="exercise-title">Exercise 4.1: Attach a Bearer token from an environment variable</h3>
<p class="exercise-task"><strong>Task:</strong> A compliance officer wants tokens to live in <code>~/.Renviron</code>, never in source code. Read the env var <code>GITHUB_PAT</code> with <code>Sys.getenv("GITHUB_PAT", unset = "demo-token")</code>, attach it as a Bearer token to a request for <code>https://api.github.com/user</code> using <code>req_auth_bearer_token()</code>, dry-run the request, and save the unsent request object to <code>ex_4_1</code>.</p>
<div class="exercise-expected">
<p><strong>Expected result:</strong></p>
<pre><code>#> GET /user HTTP/1.1
#> host: api.github.com
#> user-agent: httr2/1.0.0 r-curl/5.2.0 libcurl/8.4.0
#> accept: */*
#> accept-encoding: deflate, gzip
#> authorization: Bearer demo-token</code></pre>
</div>
<p><strong>Difficulty:</strong> Advanced</p>