Append rows to data frames using base R rbind() or dplyr bind_rows(). Collect rows in a list first, then bind once for better performance with large data.
Cookbooks
Cookbooks
Practical recipes for real-world R programming tasks.
- How to Append Rows to Data Frames in R
- How to Add Trend Lines to Scatter Plots in R
Add trend lines to scatter plots using ggplot2 geom_smooth(). Use method = lm for linear fits or loess for smoothed curves, with optional confidence intervals.
- How to Apply Functions Row-Wise to Data Frames in R
Apply functions to each row of a data frame with base R apply(), dplyr rowwise(), or purrr pmap(). Prefer vectorized operations when possible for better speed.
- How to Calculate Correlation Between Columns in R
Calculate correlation between columns using Pearson, Spearman, or Kendall methods with cor(). Handle missing data and test significance with cor.test().
- How to Calculate Cumulative Sums in R with cumsum()
Calculate cumulative sums on vectors and data frames with cumsum() or dplyr mutate(). Group by categories to reset accumulation per group boundary.
- How to Calculate Rolling Means in R
Calculate rolling means in R with slider and zoo to smooth time series data. Choose centered or trailing windows to spot trends without lookahead bias.
- How to Add Calculated Columns to Data Frames in R
Add calculated columns to data frames with base R or dplyr mutate(). Chain dependent calculations and reference newly created columns in one step.
- How to Calculate Statistical Mode in R
Calculate statistical mode — the most frequent value in a vector — using base R custom functions. Handle ties, NAs, and data frame columns with practical code.
- How to Calculate Row-Wise Statistics in R
Calculate row-wise statistics across data frame columns using rowMeans, apply, and dplyr rowwise. Handle NA values and pick the fastest approach for your data.
- How to Check NA Values in R Data Frames
Check NA values in data frames using is.na, complete.cases, and dplyr. Find missing spots in your data and decide whether to impute or drop incomplete rows.
- How to Check Value Membership in R Vectors
Check value membership in R vectors with %in% and match(). Filter data frames by inclusion, exclude values, and write clean subsetting code.
- How to Combine Vectors in R with c() and append()
Combine vectors with c() and append() in R. Understand type coercion rules when mixing types and insert elements at any position in an existing vector.
- How to Compute Row Percentages of Total in R
Compute row percentages of total in R with dplyr mutate and sum(), grouped calculations, vector division, and na.rm for handling missing values.
- How to Compute Summary Statistics for Columns in R
Compute summary statistics for columns in R with dplyr summarise(), base R summary(), and skimr for exploratory analysis of mean, median, sd, min, and max.
- How to Convert Columns to Factors in R
Convert columns to factors in R using factor() with custom level ordering, as.factor() for quick conversion, and dplyr mutate for ordered factors with labels.
- How to Convert Lists to Data Frames in R
Convert lists to data frames in R with as.data.frame() for equal-length elements, bind_rows for mismatched lists, and rbindlist for large datasets.
- How to Convert Strings to Dates in R with lubridate
Convert strings to dates in R using lubridate ymd(), mdy(), dmy() for automatic format detection, and base R as.Date() with format codes for precise control.
- How to Count Rows by Group in R with dplyr
Count rows by group in R with dplyr count() for frequency summaries and base R table() for simpler cases, plus sorting, multiple groups, and weighted counts.
- How to Create Sequences of Numbers in R
Create sequences of numbers in R with seq() for step and length arguments, seq_along() for safe index generation, and rep() for repeating values in patterns.
- How to Convert Wide Data to Long Format in R
Convert wide data to long format in R with tidyr pivot_longer() for tidy workflows, data.table melt() for large datasets, and multiple value columns.
- How to Create Conditional Columns in R with dplyr
Create conditional columns in R with dplyr case_when() for multiple conditions and if_else() for single true/false splits, using vectorised row evaluation.
- How to Create Frequency Tables in R with table() and dplyr
Create frequency tables in R with base R table() and dplyr count() for proportional conversion, NA handling, and two-way cross-tabulations in tidy data frames.
- How to Create Bar Charts with ggplot2 in R
Create bar charts with ggplot2 in R using geom_bar() for automatic counts and geom_col() for pre-summarised values, with fill colours, stacking, and dodging.
- How to Create Scatter Plots with ggplot2 in R
Create scatter plots with ggplot2 in R using geom_point() with colour-by-group mapping, size aesthetics, trend lines, and faceting for multi-panel layouts.
- How to Detect Outliers in a Vector in R
Detect outliers in R using the IQR method and boxplot.stats(). Finding outliers is a key data cleaning step that belongs before any statistical analysis.
- How to Drop Column from Data Frame in R
Drop column from data frame using dplyr select(), base R NULL assignment, or data.table's := operator. Three ways to drop column in R for different pipelines.
- How to Encode Factor Variable as Numeric in R
Encode factor variable as numeric in R using as.numeric() for integer codes and as.numeric(as.character()) to recover original numbers.
- How to Extract Unique Values from a Vector in R
Extract unique values in R using unique() for vectors, distinct() for data frames, and n_distinct() to count them. Remove duplicates in both base R and dplyr.
- How to Filter Rows by a Condition in R
Filter rows by condition in R using dplyr::filter(). Combine conditions with & and |, handle NA values properly, and build readable transformation pipelines.
- How to Extract Coefficients from a Model in R
Extract coefficients from a fitted model in R using coef() for a named vector or broom::tidy() for a data frame with estimates, standard errors, and p-values.
- How to Find Duplicate Rows in R and Remove Them
Find duplicate rows in R using duplicated() and dplyr::distinct(), then remove them in one pipeline. Covers base R, dplyr, and data.table approaches.
- How to Format Dates in R with format() and lubridate
Format dates in R with format() strptime codes and lubridate helpers like stamp(). Convert Date objects to readable strings in ISO, US, and European formats.
- How to Fit Linear Model and Interpret Results in R
Fit linear model in R with lm() and interpret coefficients, R-squared, fitted values, and diagnostic plots. Covers simple and multiple regression.
- How to Format Numbers with Commas in R Using format() and scales
Format numbers with commas in R using format() big.mark and scales::comma(). Add thousand separators to console output, tables, and ggplot2 axis labels.
- How to Group Data and Summarise with dplyr in R
Group data in R with dplyr group_by() and summarise(), base R aggregate(), and data.table. Compute mean, count, and multiple stats per group in one pipeline.
- How to Join Two Data Frames in R with dplyr and merge()
Join two data frames in R by common columns with dplyr left_join(), base R merge(), and data.table. Covers left, inner, right, and full joins with examples.
- How to Make HTTP Requests in R with httr2
Make HTTP requests in R with httr2 — a pipe-friendly package covering GET, POST, authentication, JSON parsing, rate limiting, and error handling.
- How to Pivot Data from Wide to Long Format in R
Learn how to pivot data from wide to long format in R using tidyr::pivot_longer(), with examples for basic reshaping and selecting columns.
- How to Merge Data Frames with Different Key Names in R
Learn how to merge data frames with different key names in R using dplyr left_join and base R merge, with examples for mismatched join columns.
- How to Plot Histograms in R Using ggplot2 and Base R
Plot histograms in R with ggplot2 geom_histogram() and base R hist(), covering bin width control, colors, density overlays, and grouped panels.
- How to Read CSV Files in R with readr and Base R
Read CSV files in R with readr read_csv() and base R read.csv(), covering column types, delimiter handling, and fast alternatives for large datasets.
- How to Remove NA Values from Vectors in R
Remove NA values from vectors and data frames using base R, dplyr, and tidyr. NA values represent missing data in R and can bias your analysis.
- How to Rename Columns in Data Frames in R
Rename columns in a data frame using dplyr, base R, and data.table. A frequent data cleaning task that makes datasets easier to work with.
- How to Read Excel Files in R with readxl
Read Excel files in R using readxl::read_excel(), covering sheet selection, column type specification, and common import patterns for .xls and .xlsx workbooks.
- How to Repeat Values in R with rep()
Repeat values in R using rep() to create vectors by cycling through elements, repeating each element in place, or filling to a fixed length.
- How to Replace NA Values with Specific Values in R
Replace NA values in vectors and data frames using base R, dplyr, and data.table. A common data cleaning step that replaces missing data with a chosen default.
- How to Replace Values in Data Frame Columns in R
Replace values in a data frame column using base R, dplyr, and data.table. Covers conditional replacement, bulk find-and-replace, and NA handling.
- How to Run t-Tests in R with t.test()
Run t-tests in R with t.test() for one-sample, two-sample, and paired comparisons. Returns p-values and confidence intervals in a single call.
- How to Round Numbers in R
Round numbers using base R functions like round(), floor(), ceiling(), and trunc(). R uses banker's rounding where halves round to the nearest even digit.
- How to Sample Random Rows from Data Frames in R
Sample random rows from data frames with dplyr slice_sample(), base R sample(), and data.table. For train/test splits, bootstrap, and randomising data.
- How to Sample Without Replacement in R with sample()
Sample without replacement from a population using sample() and dplyr. Each element appears once at most, ideal for subsets and random assignments.
- How to Save ggplot Charts to Files in R with ggsave()
Save ggplot charts to PNG, PDF, and SVG files with ggsave(). Control resolution, dimensions, and background colour for publication-quality output in R.
- How to Select Columns by Name in R with dplyr::select()
Select columns from data frames by name with dplyr select(), base R, and data.table. Use helpers like starts_with() and where() for flexible extraction.
- How to Sort Data Frames by Column in R with arrange()
Sort data frames by column with dplyr arrange(), base R order(), and data.table setorder(). Stable sorts keep tied rows in their original relative order.
- How to Sort Vectors in Descending Order in R
Sort vectors from highest to lowest with base R sort(), order(), and data.table. Use decreasing = TRUE for descending order in a single function call.
- How to Split String Columns into Multiple Columns in R
Split string columns into multiple fields with strsplit(), stringr str_split(), and tidyr separate(). A common data cleaning task in R.
- How to Subset Data Frames by Multiple Conditions in R
Subset data frames by several criteria with AND, OR, and NOT operators. Works across base R, dplyr, and data.table with only minor syntactic differences.
- How to Write Data Frames to CSV Files in R
Write data frames to CSV with readr write_csv(), base R write.csv(), and data.table fwrite(). A routine export task in R data analysis pipelines.
- How to Read CSV Files and Summarise with dplyr in R
Read CSV files and produce grouped summaries with readr and dplyr group_by() and summarise(). A fast pattern for initial data exploration in R.
- How to Write Data Frames to Excel Files in R
Write data frames to Excel with openxlsx for formatting and writexl for quick exports. Handles multi-sheet and single-sheet xlsx files with R.