R 4.4 Modern Features
Native Pipe Operator:
Create result by piping data through filter removing NA values, mutate adding log_value as log of value, and summarise computing mean_log. For non-first argument position, use underscore placeholder in lm formula call with data parameter.
Lambda Syntax with Backslash:
Use map with data and backslash x syntax for x squared. Use map2 with two lists and backslash x y for x plus y. In dplyr contexts, use mutate with across on numeric columns applying backslash x for scale function extracting first column.
tidyverse Data Manipulation
dplyr Core Verbs:
Load dplyr library. Create processed by piping raw_data through filter for active status and positive amount, select for specific columns, mutate adding month using floor_date and amount_scaled dividing by max, then arrange descending by date. For grouped summaries, pipe through group_by, summarise with n for count, sum and mean for aggregations, and groups drop.
tidyr Reshaping Pattern:
Load tidyr library. For wide to long transformation, use pivot_longer with cols starting with year prefix, names_to for column name, names_prefix to strip, and values_to for values. For long to wide transformation, use pivot_wider with names_from and values_from, adding values_fill for missing value handling.
purrr Functional Programming:
Load purrr library. Use map with files and lambda to read_csv each file. Use map_dfr for row-binding with id parameter for source column. Use map_dbl for extracting numeric results with mean and na.rm TRUE. For error handling, create safe_read using safely wrapper on read_csv. Map files through safe_read, extract results, and use compact to filter successes.
ggplot2 Visualization Patterns
Complete Plot Structure:
Load ggplot2 and scales libraries. Create p using ggplot with data and aesthetics for x, y, and color by group. Add geom_point with alpha and size, geom_smooth with lm method and standard error. Apply scale_x_continuous with comma labels, scale_y_log10 with dollar labels, and scale_color_brewer with Set2 palette. Add facet_wrap by category with free_y scales. Add labs for title, subtitle, and axis labels. Apply theme_minimal with base_size and theme for legend position. Save with ggsave specifying filename, plot, dimensions, and dpi.
Multiple Plots with patchwork:
Load patchwork library. Create p1 with histogram, p2 with scatter plot, and p3 with boxplot. Combine using pipe and parentheses for layout with p1 beside p2 over p3. Add plot_annotation for title and tag_levels.
Shiny Application Patterns
Modular Shiny App:
Create dataFilterUI function taking id parameter. Use NS function for namespace. Return tagList with selectInput for category with NULL initial choices and sliderInput for range. Create dataFilterServer function taking id and data reactive. Use moduleServer with inner function. In observe block, extract unique categories and updateSelectInput. Return reactive filtering data by category and range inputs using req for input validation.
Reactive Patterns:
In server function, create processed_data as reactive caching filtered data by input year. Create counter as reactiveVal initialized to 0. Use observeEvent on input increment to update counter. Create analysis as eventReactive on input run_analysis for expensive computation. Apply debounce with 300 milliseconds on search input reactive for rapid input handling.
testthat Testing Framework
Test Structure Pattern:
Load testthat library. Create test_that block for calculate_growth with tibble of years and values. Call function and store result. Use expect_equal for row count, expect_equal for growth value with tolerance, and expect_true for NA check.
renv Dependency Management
Project Setup:
Call renv::init for initialization. Call renv::install for tidyverse and shiny packages. Call renv::snapshot to record state. Call renv::restore to restore from lockfile.
---
|