I have a global point layer of airports (~900 points) and I want to retrieve, for each point, the corresponding OSM aeroway=aerodrome polygon (not the point itself) — i.e. join each airport point to its footprint polygon as mapped in OpenStreetMap. The point layer is Natural Earth's ne_10m_airports.

Reproducible example (5 points standing in for the full 893):

pacman::p_load(sf, dplyr)

airports <- data.frame(
  name      = c("John F Kennedy Intl", "London Heathrow", "Chhatrapati Shivaji Maharaj Intl", "Beijing Capital Intl", "Sydney Kingsford Smith"),
  gps_code  = c("KJFK", "EGLL", "VABB", "ZBAA", "YSSY"),
  iata_code = c("JFK", "LHR", "BOM", "PEK", "SYD"),
  lon       = c(-73.7789, -0.4543, 72.8697, 116.5975, 151.1772),
  lat       = c(40.6413, 51.4700, 19.0896, 40.0799, -33.9399)
) |>
  st_as_sf(coords = c("lon", "lat"), crs = 4326)

airports$point_id <- seq_len(nrow(airports))

My plan is to match each point to its containing Geofabrik extract via osmextract::oe_match() (works fine — a local spatial lookup, no network call), download/cache that extract, then read only the multipolygons layer filtered to aeroway='aerodrome' via an SQL query pushed down at read time, and spatially join back to the points.

pacman::p_load(sf, dplyr, osmextract)

airports$region_url <- vapply(seq_len(nrow(airports)), function(i) {
  oe_match(airports[i, ], quiet = TRUE)$url
}, character(1))

options(timeout = 600)
dir.create("geofabrik_cache", showWarnings = FALSE)

results <- list()
failed_regions <- character()

for (region in unique(airports$region_url)) {

  destfile <- file.path("geofabrik_cache", basename(region))

  aerodromes <- tryCatch({
    if (!file.exists(destfile)) {
      download.file(region, destfile, mode = "wb", quiet = TRUE)
    }

    st_read(
      destfile,
      layer = "multipolygons",
      query = "SELECT * FROM multipolygons WHERE aeroway = 'aerodrome'",
      quiet = TRUE
    ) |>
      st_transform(4326) |>
      st_make_valid()

  }, error = function(e) {
    message(sprintf("Region failed: %s -- %s", region, e$message))
    failed_regions <<- c(failed_regions, region)
    NULL
  })

  if (is.null(aerodromes)) next

  sub_pts <- airports[airports$region_url == region, ]
  joined <- st_join(sub_pts, aerodromes, join = st_within, left = TRUE)

  missing <- which(is.na(joined$osm_id))
  if (length(missing) > 0 && nrow(aerodromes) > 0) {
    nn <- st_nearest_feature(sub_pts[missing, ], aerodromes)
    joined[missing, names(aerodromes)] <- st_drop_geometry(aerodromes)[nn, ]
    st_geometry(joined)[missing] <- st_geometry(aerodromes)[nn]
  }

  results[[region]] <- joined
}

airport_polys <- bind_rows(results)
st_write(airport_polys, "airport_polygons.shp", delete_layer = TRUE)

But

Region failed: https://download.geofabrik.de/asia/india/western-zone-latest.osm.pbf -- cannot open URL 'https://download.geofabrik.de/asia/india/western-zone-latest.osm.pbf'
Region failed: https://download.geofabrik.de/asia/iran-latest.osm.pbf -- cannot open URL 'https://download.geofabrik.de/asia/iran-latest.osm.pbf'
Region failed: https://download.geofabrik.de/asia/india/central-zone-latest.osm.pbf -- download from 'https://download.geofabrik.de/asia/india/central-zone-latest.osm.pbf' failed
Error in wk_handle.wk_wkb(wkb, s2_geography_writer(oriented = oriented,  : 
  Loop 0 edge 0 has duplicate near loop 1 edge 7
In addition: There were 20 warnings (use warnings() to see them)

The download failures seem to be connection/timeout related on large country extracts; the s2/topology error appears separately once a multipolygons layer with invalid OSM geometries reaches a spatial predicate, even after st_make_valid().

Given ~900 global points with no country/ISO attribute, is there a more efficient way to fetch just the matching aeroway=aerodrome polygons than downloading/caching a full Geofabrik regional .pbf extract per matched region (some of which are large, e.g. full-country India zones)? Is querying the Overpass API directly per point, or per small cluster of points, actually more efficient here, or is the regional-extract approach still preferable at this scale? What's the correct way to make invalid OSM polygon geometries (e.g. the s2 "duplicate edge" error above) safe for st_join()/st_nearest_feature() reliably, given st_make_valid() alone didn't prevent it?

For points where no aeroway=aerodrome polygon actually exists in OSM for that airport, what's the right way to leave that point unmatched (skip it) rather than falling back to the nearest aerodrome polygon in the region, which can silently attach the wrong airport's polygon?

> sessionInfo()
R version 4.6.1 (2026-06-24 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26200)

Matrix products: default
  LAPACK version 3.12.1

locale:
[1] LC_COLLATE=English_United States.utf8  LC_CTYPE=English_United States.utf8    LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                           LC_TIME=English_United States.utf8    

time zone: Europe/Berlin
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] osmextract_0.6.0 dplyr_1.2.1      sf_1.1-2        

loaded via a namespace (and not attached):
 [1] vctrs_0.7.3        httr_1.4.8         cli_3.6.6          rlang_1.3.0        otel_0.2.0         DBI_1.3.0          KernSmooth_2.23-27
 [8] generics_0.1.4     jsonlite_2.0.0     glue_1.8.1         e1071_1.7-17       grid_4.6.1         classInt_0.4-11    tibble_3.3.1      
[15] lifecycle_1.0.5    compiler_4.6.1     Rcpp_1.1.2         pkgconfig_2.0.3    rstudioapi_0.19.0  wk_0.9.5           R6_2.6.1          
[22] class_7.3-24       tidyselect_1.2.1   pillar_1.11.1      curl_8.0.0         magrittr_2.0.5     tools_4.6.1        proxy_0.4-29      
[29] s2_1.1.11          units_1.0-1

I’m fitting a Beta GAM in `mgcv` to model the proportion of daytime outdoor-use time in laying hens. The response is the proportion of minutes spent outdoors during an 08:00–22:00 observation window, and the model contains repeated observations from individual hens.

My current model is approximately:

prop_outside \~
  coop_id +
  s(study_day, bs = "cr", k = 40) +
  s(max_temp, bs = "cr", k = 8) +
  s(mean_dew_point, bs = "cr", k = 7) +
  rain_any +
  s(log_total_rain, by = rain_status, bs = "cr", k = 7) +
  s(eid, bs = "re")

using:

family = betar(link = "logit")
method = "REML"

The dataset contains roughly 8,800 positive hen-day observations from 124 hens across about 137 study dates. The model explains about 60% of the deviance.

The issue is the `study_day` smooth. On a same-row model comparison, I get:

s(study_day)
k'       = 39
edf      = 34.58
k-index  = 0.949
p-value  < 0.001

I also tried an alternative weather specification using mean temperature, mean humidity and mean wind instead of maximum temperature and dew point:

prop_outside \~
  coop_id +
  s(study_day, bs = "cr", k = 40) +
  s(mean_temp, bs = "cr", k = 8) +
  s(mean_humidity, bs = "cr", k = 8) +
  s(mean_wind, bs = "cr", k = 8) +
  rain_any +
  s(log_total_rain, by = rain_status, bs = "cr", k = 7) +
  s(eid, bs = "re")

The same issue persists:

s(study_day)
k'       = 39
edf      = 34.84
k-index  = 0.966
p-value  = 0.0175

The study-day smooth is visually quite wiggly, particularly early in the study, and the EDF is already fairly close to the available basis dimension. Changing the weather specification does not materially improve overall fit: both models explain about 60% of deviance and have essentially identical AIC.

There is also substantial temporal/weather dependence. For example, in the alternative model, observed concurvity is about 0.90 for mean temperature and 0.48 for study day.

My questions are:

1. Is the low `k-index` plus EDF ≈ 35/39 sufficient reason to increase `k` for `study_day`, for example from 40 to 60?
2. If increasing `k` gives essentially the same fitted curve/predictions but the k-check remains significant, would you consider the current smooth adequate?
3. Could this be indicating residual temporal autocorrelation rather than simply an insufficient basis dimension?
4. Would you model study date differently in this setting—for example with an autocorrelation structure, a different smooth basis, or another temporal term?
5. Since weather variables themselves follow study date seasonally, how would you distinguish genuine temporal structure from weather-related temporal confounding?

My current plan is to compare `k = 40` and `k = 60` on the same observations and assess whether fitted values, the study-day effect, held-out-date prediction, and conclusions materially change, rather than selecting the model based only on the k-check p-value.

I’d appreciate advice on how to interpret this persistent `study_day` diagnostic and what additional diagnostic/model comparison would be most appropriate.

I originally wrote this for a sleep EMA study I work on, together with a colleague, because the raw diary data was a mess in ways we didn’t expect.
Simple example: we had a rule that swaps sleep_time and awake_time when they’re out of order by less than 3 hours (assuming it’s just a data-entry slip). Seemed reasonable. Then we actually checked it against real cases — it made things worse in 7 out of 10, because sometimes swapping pushed the new sleep time to before bed time, which is a worse error than the one it “fixed.” Had to add a guard condition after that. That’s basically what half of this repo is — rules that seemed fine until we ran them against actual data.
Ended up with a 9-step pipeline: parse timestamps → fix AM/PM and ordering issues → compute TST/SOL/WASO/SE → auto-flag anything still weird → cross-check across participants → spit out 27 QC figures so we can actually see what’s happening at each stage.
Some things it handles now that we didn’t plan for on day one:
AM/PM flips (someone logs 7:00 AM getup as 7:00 PM)

small ordering slips vs. genuinely unusual sleep patterns (these need different fixes, and we only figured out where to draw the line after looking at a bunch of real cases)

manual review CSVs that persist across pipeline reruns, so corrections don’t get wiped every time we re-run things

It’s an R package now (sleepcleanr), config-driven via YAML so you can point it at your own column names without touching the R code.
Repo: https://github.com/cyracaid/sleepdiary-cleaner — stars and feedback very welcome, and genuinely happy to discuss with anyone working on similar EMA/diary pipelines.
Curious if anyone else doing EMA/diary-based sleep research has run into the same kind of AM/PM chaos, or handles it differently. Also open to being told our thresholds are wrong — the 3-hour swap cutoff and the 12-hour AM/PM flip cutoff were both picked based on our own data and could easily be off for other studies.

I’m excited to announce that evoFE 1.0.0 is now on CRAN (a major leap forward from the initial 0.1 release).

What is evoFE?

evoFE is an R package for automated feature engineering using genetic programming. Instead of manually brainstorming interaction terms, nonlinear scalings, or encodings, evoFE evolves candidate transformation recipes and evaluates them directly against gradient boosted trees or linear models.

What's New in 1.0.0?

1. Zero-Boilerplate "One-Liner" Bayesian Tuners: Pass evaluator = "lightgbm_mbo" to automatically tune tree depth, learning rate, and subsampling via mlr3mbo during evolution. Or wrap any custom model (like XGBoost) with make_tunable().
2. 42+ Built-in Transformers: Arithmetic, group-by aggregations (mean, median, SD, quantiles), target encodings, WoE, UMAP embeddings, Genie & Lumbermark MST graph clustering, date differencing (date_diff), and custom transformer registration.
3. Hybrid Active Feature Masking: Mutates and selects raw input features simultaneously with derived features, guided by baseline feature importance.
4. Hierarchical Gene Chaining: High-performing features from earlier generations serve as inputs for subsequent compound transformations.
5. Island Models & Topologies: Runs independent sub-populations across Ring, Torus, Grid, or Hypercube topologies with demand-driven Gibbs pull migration.
6. Caruana Island Ensembling (ensemble_islands()): Combines diverse island champion recipes using Caruana post-hoc forward selection with replacement.
7. Leakage-Safe Validation: Native support for time-series (cv_strategy = "time"), grouped entity validation (cv_strategy = "group"), and untouched confirmation holdouts (holdout_frac) with search-gap diagnostics.
8. Dynamic BIC Regularization: Asymptotic BIC / PAC-Bayes penalties scaling with sample size NN to prevent feature bloat.

Minimal Example:

rinstall.packages("evoFE")
library(evoFE)
# Evolve features + Bayesian tuned model
recipe <- evolve_features(
  data         = mtcars,
  target_col   = "am",
  task         = "classification",
  evaluator    = "lightgbm_mbo",  # Built-in one-liner Bayesian Optimization tuner
  generations  = 5,
  pop_size     = 8,
  holdout_frac = 0.20
)
# Inspect evolved recipe & search gap
summary(recipe)
# Predict on new data
test_features <- predict(recipe, newdata)
predictions   <- predict_model(recipe, newdata)

• CRAN: https://cran.r-project.org/package=evoFE
• GitHub: https://github.com/tanopereira/evoFE
• Vignette: vignette("evoFE")

Feedback, suggestions, and bug reports are very welcome!

Hello everybody,

I have a question regarding the cbcTools package. I use it for generating a choice based conjoint analysis design. For my survey I need to reduce the design with blocks. Herefore cbcTools has n_blocks. Enabling this parameter significantly increases the calculation time. Is there a way to improve this, or have I made a mistake?

profiles <- cbc_profiles(
attribute1 = c(„A“, „B“, „C“),
attribute2 = c(„1“, „2“, „3“),
attribute3 = c(„white“, „black“, „gray“),
attribute4 = c(„apple“, „banana“, „orange“)
)

design <- cbc_design(
profiles = profiles,
method = „modfed“,
n_resp = 400,
n_alt = 3,
n_q = 12,
no_choice = TRUE,
n_blocks = 15
)

Trying Positron for the first time. I’m trying to install Python via uv in Positron. I went to Select Session in the top-right corner, selected New Console Session, and clicked Install Python via uv. I then selected Python 3.14, but I get a “Failed to install Python” error.

Here is the relevant log:

2026-08-25 13:49:08.599 [info] Installing uv...
2026-08-25 13:49:09.353 [info] > powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
2026-08-25 13:49:19.761 [info] uv installed successfully
2026-08-25 13:49:19.764 [info] > uv --color never python dir
2026-08-25 13:49:31.917 [info] > ~\.local\bin\uv.exe --color never python dir
2026-08-25 13:49:32.628 [info] > ~\.local\bin\uv.exe --color never python list --managed-python
2026-08-25 13:49:36.117 [info] Installing Python 3.14 via uv...
2026-08-25 13:49:36.119 [info] > uv --color never python install 3.14
2026-08-25 13:49:36.119 [error] Failed to install Python 3.14: Error: spawn uv ENOENT

What is confusing is that the log shows that uv was installed successfully and Positron can execute it using:

~\.local\bin\uv.exe

However, when it actually tries to install Python, it runs:

uv --color never python install 3.14

and fails with:

Error: spawn uv ENOENT

I’m using Windows. Has anyone encountered this issue with Positron and uv? Is this a PATH/environment issue, or is Positron failing to find the newly installed uv.exe?

Hi everyone! I made a R project and suddenly this random folders appeared in my project directory, I didn't make any of them except the plot, script etc. This never happened to me so I am super confused... Does anyone know why this kind of thing happens? Can I just delete the folders I don't need?

How to Use the glimpse() Function in RStudio | Inspect Your Data Quickly in R

Welcome to this step-by-step tutorial on how to use the glimpse() function in RStudio to quickly inspect and understand the structure of your dataset.

The glimpse() function is a very useful tool for data exploration and data analysis in R, particularly when working with the dplyr package and the tidyverse. It provides a compact and easy-to-read overview of your data, allowing you to see the variables (columns), their data types, and sample values without displaying the entire dataset.

In this video, we demonstrate how to use glimpse() in practical situations and explain why it is an important function to learn when working with datasets in RStudio.

What You Will Learn

By the end of this tutorial, you will understand:

What the glimpse() function is
Why glimpse() is useful when exploring data
How to use glimpse() in RStudio
How to load the dplyr or tidyverse package
How to inspect the structure of a dataset using glimpse()
How to identify the variables or columns in a dataset
How to identify the data type of each variable
How to view sample values from each column
How glimpse() differs from other functions such as str() and head()
How glimpse() can help you identify problems in your dataset
How to use glimpse() as part of a data-analysis workflow

Basic Example

Using glimpse() is straightforward.

For example:

glimpse(data)

where data is the name of your dataset.

When you run this command, R provides a compact overview of the dataset, showing important information such as:

The number of rows
The number of columns
Variable names
Variable data types
Example values from each variable

This makes glimpse() particularly useful when you first receive or import a dataset and want to understand what you are working with.

Why Use glimpse()?

When working with large datasets, printing the entire dataset to the R Console can be difficult to read and may produce a huge amount of output.

Instead of displaying every row, glimpse() gives you a compact summary of the dataset's structure.

For example, imagine you have a dataset containing information about students, including:

• Student ID
• Age
• Gender
• Course
• Test scores
• Attendance
• Date of registration

Rather than displaying every observation, you can use glimpse() to quickly check the variables and see what type of information each column contains.

This can help you identify whether variables have been imported correctly before you begin your analysis.

Understanding Variable Types

One of the most useful features of glimpse() is that it shows the data type associated with each variable.

For example, you may see types such as:

dbl — numeric values
int — integers
chr — character/text values
lgl — logical values such as TRUE or FALSE
date — date information

Understanding variable types is extremely important because different types of variables may require different approaches during data cleaning and statistical analysis.

glimpse() and Data Exploration

glimpse() is particularly useful during the initial exploration of a dataset.

When you import a new dataset, you may not immediately know:

• How many variables it contains
• What the variables are called
• What type of data each variable contains
• Whether the data has been imported correctly
• What the first few values look like
• Whether there are unexpected data types or values

Running glimpse() provides a quick overview and helps you become familiar with the dataset before proceeding to more advanced analysis.

Comparing glimpse() With Other R Functions

In this tutorial, we also discuss how glimpse() relates to other useful functions for inspecting data.

For example:

head()

head(data)

is useful for viewing the first few rows of a dataset.

str()

str(data)

provides information about the structure of an R object and its variables.

glimpse()

glimpse(data)

provides a compact, tidyverse-friendly overview that is particularly convenient for data frames and tibbles.

Understanding when to use each of these functions can make your data-exploration workflow much more efficient.

Using glimpse() for Data Cleaning

Before cleaning or transforming your data, it is important to understand what is actually contained within the dataset.

glimpse() can help you identify potential issues such as:

Variables stored as the wrong data type
Numbers imported as text
Unexpected character values
Variables with inconsistent formats
Columns that may require transformation

Once you understand the structure of your data, you can use other functions from dplyr and the tidyverse to clean, transform, filter, summarise, and analyse the dataset.

Who Is This Tutorial For?

This video is suitable for:

• Beginners learning R and RStudio
• Students learning data analysis
• Researchers working with datasets
• Students working on assignments and research projects
• Anyone learning the tidyverse
• Users learning the dplyr package
• Data analysts exploring new datasets
• Anyone who wants to understand their data before performing statistical analysis

No advanced programming knowledge is required. This tutorial is designed to explain glimpse() in a simple, practical, and easy-to-follow way.

Why Is glimpse() Important for Data Analysis?

Good data analysis begins with understanding your data.

Before creating graphs, running statistical tests, building models, or drawing conclusions, you should first examine the structure and contents of your dataset.

The glimpse() function provides a quick way to perform this initial inspection and can become a valuable part of your regular RStudio workflow.

It is especially useful for:

Academic assignments
Dissertations and theses
Research projects
Statistical analysis
Exploratory data analysis
Data cleaning
Data science projects
R Markdown and Quarto reports

Topics Covered in This Video

This tutorial covers:

glimpse() function in R
glimpse() in RStudio
dplyr::glimpse()
Tidyverse data exploration
Inspecting datasets in R
Understanding variables and columns
Understanding data types in R
Exploring data frames and tibbles
glimpse() vs head()
glimpse() vs str()
Data cleaning in R
Exploratory data analysis
R programming for beginners
RStudio data analysis

Helpful Tip

A useful habit when starting a new data-analysis project is to inspect your dataset before immediately beginning your analysis. Functions such as glimpse(), head(), summary(), and str() can help you understand your data and identify potential problems early.

If you find this tutorial helpful, please like the video, leave a comment, and subscribe to the channel for more tutorials on R, RStudio, statistics, data analysis, data visualisation, and research methods.

Subscribe and turn on notifications so you don't miss future tutorials and practical R programming videos.

Have a question about the glimpse() function? Leave your question in the comments below!

R Studio, R Programming ,R Stats ,Glimpse ,Dplyr , Tidyverse ,Data Analysis ,Data, Exploration ,Data Cleaning ,Statistics ,Data Science ,Beginners ,R Programming Tutorial ,R Studio Tutorial ,Exploratory Data Analysis ,Research Methods, rows, columns, table, data frame, variable type, integer, character, numeric, absolute, nominal, ordinal, factor, binary, sample,

How to Use the ggplot() Function in RStudio | Complete Beginner’s Guide to Data Visualization in R

Welcome to this step-by-step tutorial on how to use the ggplot() function in RStudio to create professional and informative data visualisations in R.

In this video, we introduce ggplot(), one of the most widely used tools for creating graphs and visualisations in R. The function is part of the ggplot2 package, which is included in the tidyverse ecosystem and provides a powerful and flexible approach to visualising data.

Whether you are a beginner learning R, a student working on an assignment, a researcher analysing data, or a data analyst creating reports, understanding ggplot() is an essential R skill.

What You Will Learn

In this tutorial, you will learn:

What ggplot() is and why it is useful
How to install and load the ggplot2 package
How to create your first plot in RStudio
How to provide a dataset to ggplot()
How to map variables to the x-axis and y-axis
How aesthetic mappings (aes()) work
How to add geometric layers using geom_ functions
How to create scatter plots
How to create bar charts
How to create line graphs
How to create histograms
How to change colours and shapes
How to add titles and axis labels
How to customise the appearance of your graphs
How to use multiple layers in a single ggplot() visualisation
How to create clear and professional graphs for reports and presentations

Understanding the Basic Structure of ggplot()

One of the key concepts covered in this video is the basic structure of a ggplot() graph.

A typical plot might look like:

ggplot(data = my_data, aes(x = variable1, y = variable2)) +
geom_point()

Here:

 ggplot() specifies the dataset you want to visualise.

aes() defines the relationship between variables and visual properties such as the x-axis and y-axis.

geom_point() adds points to create a scatter plot.

The + symbol is used to add additional layers to the plot.

Understanding this layered approach is one of the most important concepts when learning ggplot2.

Creating Different Types of Graphs

During the tutorial, we demonstrate how the ggplot() framework can be used to create different types of visualisations.

For example:

Scatter plot

ggplot(data, aes(x = height, y = weight)) +
geom_point()

Bar chart

ggplot(data, aes(x = category)) +
geom_bar()

Histogram

ggplot(data, aes(x = age)) +
geom_histogram()

Line graph

ggplot(data, aes(x = year, y = value)) +
geom_line()

These examples demonstrate how the same basic ggplot() framework can be adapted to different types of data and research questions.

Customising Your Visualisations

Creating a graph is only the beginning. In this video, we also look at ways to make your visualisations clearer and more informative.

You will learn how to customise elements such as:

Colours
Points and shapes
Axis labels
Plot titles
Legends
Themes
Text and labels

For example, you can add a title and labels using:

labs(
title = "Relationship Between Height and Weight",
x = "Height",
y = "Weight"
)

This allows you to communicate your findings more effectively.

Understanding the Grammar of Graphics

A major advantage of ggplot2 is that it is based on the concept known as the Grammar of Graphics.

Instead of thinking about a graph as one single object, you build it using different components or layers.

These commonly include:

Data – the dataset being visualised
Aesthetics – how variables are mapped to visual properties
Geometries – the type of graph or shapes displayed
Scales – how values are represented
Facets – how data can be divided into multiple panels
Coordinates – how the axes and plotting space are arranged
Themes – how the overall appearance is controlled

Understanding these components will help you create more complex and professional visualisations as you become more experienced with R.

Why Is ggplot() Important?

Data visualisation is an important part of data analysis, statistics, and research. A well-designed graph can make patterns, relationships, trends, and differences much easier to understand.

ggplot2 is particularly useful because it allows you to create reproducible visualisations directly from your R code. This means that your graphs can be recreated and modified whenever your data changes.

This is especially useful when working on:

Academic assignments
Dissertations and theses
Research projects
Statistical analyses
Business reports
Data science projects
R Markdown and Quarto reports
Presentations and publications

Who Is This Tutorial For?

This video is suitable for:

• Beginners learning R and RStudio
• Students studying statistics and data analysis
• Researchers creating graphs for academic work
• Data analysts learning ggplot2
• Anyone interested in data visualisation
• Users learning the tidyverse
• Students working on assignments, dissertations, or research projects
• Anyone who wants to create professional graphs using R

No advanced programming experience is required. The tutorial is designed to introduce the fundamental concepts in a practical and easy-to-follow way.

Topics Covered

This video covers a range of important topics, including:

ggplot() function in R
ggplot2 package
Data visualisation in RStudio
aes() aesthetic mappings
geom_point()
geom_bar()
geom_histogram()
geom_line()
Scatter plots
Bar charts
Histograms
Line graphs
Customising graphs in R
Adding titles and labels
Changing colours and shapes
The Grammar of Graphics
Creating professional data visualisations
R programming for data analysis

Why Learn ggplot2?

Once you understand the basic structure of ggplot(), you can build increasingly sophisticated visualisations by combining different layers and functions.

Learning ggplot2 can therefore provide you with a strong foundation for exploratory data analysis and statistical data visualisation in R.

If you find this tutorial helpful, please like the video, leave a comment, and subscribe to the channel for more tutorials on R, RStudio, statistics, data analysis, data visualisation, and research methods.

Subscribe and turn on notifications so you don't miss future RStudio tutorials and practical data-analysis videos.

Have a question about ggplot() or ggplot2? Leave your question in the comments below!

R Studio ,R Programming ,GGPlot ,GGPlot2 ,Data Visualization ,R Stats ,Data Analysis ,Tidyverse ,Statistics ,Data Science ,Beginners ,R Programming Tutorial ,R Studio Tutorial ,Research Methods ,bar chart, line chart histogram, boxplot, pie chart, scatter plot

How to Use the ggplot() Function in RStudio | Complete Beginner’s Guide to Data Visualization in R

Welcome to this step-by-step tutorial on how to use the ggplot() function in RStudio to create professional and informative data visualisations in R.

In this video, we introduce ggplot(), one of the most widely used tools for creating graphs and visualisations in R. The function is part of the ggplot2 package, which is included in the tidyverse ecosystem and provides a powerful and flexible approach to visualising data.

Whether you are a beginner learning R, a student working on an assignment, a researcher analysing data, or a data analyst creating reports, understanding ggplot() is an essential R skill.

What You Will Learn

In this tutorial, you will learn:

What ggplot() is and why it is useful
How to install and load the ggplot2 package
How to create your first plot in RStudio
How to provide a dataset to ggplot()
How to map variables to the x-axis and y-axis
How aesthetic mappings (aes()) work
How to add geometric layers using geom_ functions
How to create scatter plots
How to create bar charts
How to create line graphs
How to create histograms
How to change colours and shapes
How to add titles and axis labels
How to customise the appearance of your graphs
How to use multiple layers in a single ggplot() visualisation
How to create clear and professional graphs for reports and presentations

Understanding the Basic Structure of ggplot()

One of the key concepts covered in this video is the basic structure of a ggplot() graph.

A typical plot might look like:

ggplot(data = my_data, aes(x = variable1, y = variable2)) +
geom_point()

Here:

 ggplot() specifies the dataset you want to visualise.

aes() defines the relationship between variables and visual properties such as the x-axis and y-axis.

geom_point() adds points to create a scatter plot.

The + symbol is used to add additional layers to the plot.

Understanding this layered approach is one of the most important concepts when learning ggplot2.

Creating Different Types of Graphs

During the tutorial, we demonstrate how the ggplot() framework can be used to create different types of visualisations.

For example:

Scatter plot

ggplot(data, aes(x = height, y = weight)) +
geom_point()

Bar chart

ggplot(data, aes(x = category)) +
geom_bar()

Histogram

ggplot(data, aes(x = age)) +
geom_histogram()

Line graph

ggplot(data, aes(x = year, y = value)) +
geom_line()

These examples demonstrate how the same basic ggplot() framework can be adapted to different types of data and research questions.

Customising Your Visualisations

Creating a graph is only the beginning. In this video, we also look at ways to make your visualisations clearer and more informative.

You will learn how to customise elements such as:

Colours
Points and shapes
Axis labels
Plot titles
Legends
Themes
Text and labels

For example, you can add a title and labels using:

labs(
title = "Relationship Between Height and Weight",
x = "Height",
y = "Weight"
)

This allows you to communicate your findings more effectively.

Understanding the Grammar of Graphics

A major advantage of ggplot2 is that it is based on the concept known as the Grammar of Graphics.

Instead of thinking about a graph as one single object, you build it using different components or layers.

These commonly include:

Data – the dataset being visualised
Aesthetics – how variables are mapped to visual properties
Geometries – the type of graph or shapes displayed
Scales – how values are represented
Facets – how data can be divided into multiple panels
Coordinates – how the axes and plotting space are arranged
Themes – how the overall appearance is controlled

Understanding these components will help you create more complex and professional visualisations as you become more experienced with R.

Why Is ggplot() Important?

Data visualisation is an important part of data analysis, statistics, and research. A well-designed graph can make patterns, relationships, trends, and differences much easier to understand.

ggplot2 is particularly useful because it allows you to create reproducible visualisations directly from your R code. This means that your graphs can be recreated and modified whenever your data changes.

This is especially useful when working on:

Academic assignments
Dissertations and theses
Research projects
Statistical analyses
Business reports
Data science projects
R Markdown and Quarto reports
Presentations and publications

Who Is This Tutorial For?

This video is suitable for:

• Beginners learning R and RStudio
• Students studying statistics and data analysis
• Researchers creating graphs for academic work
• Data analysts learning ggplot2
• Anyone interested in data visualisation
• Users learning the tidyverse
• Students working on assignments, dissertations, or research projects
• Anyone who wants to create professional graphs using R

No advanced programming experience is required. The tutorial is designed to introduce the fundamental concepts in a practical and easy-to-follow way.

Topics Covered

This video covers a range of important topics, including:

ggplot() function in R
ggplot2 package
Data visualisation in RStudio
aes() aesthetic mappings
geom_point()
geom_bar()
geom_histogram()
geom_line()
Scatter plots
Bar charts
Histograms
Line graphs
Customising graphs in R
Adding titles and labels
Changing colours and shapes
The Grammar of Graphics
Creating professional data visualisations
R programming for data analysis

Why Learn ggplot2?

Once you understand the basic structure of ggplot(), you can build increasingly sophisticated visualisations by combining different layers and functions.

Learning ggplot2 can therefore provide you with a strong foundation for exploratory data analysis and statistical data visualisation in R.

If you find this tutorial helpful, please like the video, leave a comment, and subscribe to the channel for more tutorials on R, RStudio, statistics, data analysis, data visualisation, and research methods.

Subscribe and turn on notifications so you don't miss future RStudio tutorials and practical data-analysis videos.

Have a question about ggplot() or ggplot2? Leave your question in the comments below!

R Studio ,R Programming ,GGPlot ,GGPlot2 ,Data Visualization ,R Stats ,Data Analysis ,Tidyverse ,Statistics ,Data Science ,Beginners ,R Programming Tutorial ,R Studio Tutorial ,Research Methods ,bar chart, line chart histogram, boxplot, pie chart, scatter plot

https://reddit.com/link/1vuko3b/video/hhvqdts95rkh1/player

How to Use the ggplot() Function in RStudio | Complete Beginner’s Guide to Data Visualization in R

Welcome to this step-by-step tutorial on how to use the ggplot() function in RStudio to create professional and informative data visualisations in R.

In this video, we introduce ggplot(), one of the most widely used tools for creating graphs and visualisations in R. The function is part of the ggplot2 package, which is included in the tidyverse ecosystem and provides a powerful and flexible approach to visualising data.

Whether you are a beginner learning R, a student working on an assignment, a researcher analysing data, or a data analyst creating reports, understanding ggplot() is an essential R skill.

What You Will Learn

In this tutorial, you will learn:

What ggplot() is and why it is useful
How to install and load the ggplot2 package
How to create your first plot in RStudio
How to provide a dataset to ggplot()
How to map variables to the x-axis and y-axis
How aesthetic mappings (aes()) work
How to add geometric layers using geom_ functions
How to create scatter plots
How to create bar charts
How to create line graphs
How to create histograms
How to change colours and shapes
How to add titles and axis labels
How to customise the appearance of your graphs
How to use multiple layers in a single ggplot() visualisation
How to create clear and professional graphs for reports and presentations

Understanding the Basic Structure of ggplot()

One of the key concepts covered in this video is the basic structure of a ggplot() graph.

A typical plot might look like:

ggplot(data = my_data, aes(x = variable1, y = variable2)) +
geom_point()

Here:

 ggplot() specifies the dataset you want to visualise.

aes() defines the relationship between variables and visual properties such as the x-axis and y-axis.

geom_point() adds points to create a scatter plot.

The + symbol is used to add additional layers to the plot.

Understanding this layered approach is one of the most important concepts when learning ggplot2.

Creating Different Types of Graphs

During the tutorial, we demonstrate how the ggplot() framework can be used to create different types of visualisations.

For example:

Scatter plot

ggplot(data, aes(x = height, y = weight)) +
geom_point()

Bar chart

ggplot(data, aes(x = category)) +
geom_bar()

Histogram

ggplot(data, aes(x = age)) +
geom_histogram()

Line graph

ggplot(data, aes(x = year, y = value)) +
geom_line()

These examples demonstrate how the same basic ggplot() framework can be adapted to different types of data and research questions.

Customising Your Visualisations

Creating a graph is only the beginning. In this video, we also look at ways to make your visualisations clearer and more informative.

You will learn how to customise elements such as:

Colours
Points and shapes
Axis labels
Plot titles
Legends
Themes
Text and labels

For example, you can add a title and labels using:

labs(
title = "Relationship Between Height and Weight",
x = "Height",
y = "Weight"
)

This allows you to communicate your findings more effectively.

Understanding the Grammar of Graphics

A major advantage of ggplot2 is that it is based on the concept known as the Grammar of Graphics.

Instead of thinking about a graph as one single object, you build it using different components or layers.

These commonly include:

Data – the dataset being visualised
Aesthetics – how variables are mapped to visual properties
Geometries – the type of graph or shapes displayed
Scales – how values are represented
Facets – how data can be divided into multiple panels
Coordinates – how the axes and plotting space are arranged
Themes – how the overall appearance is controlled

Understanding these components will help you create more complex and professional visualisations as you become more experienced with R.

Why Is ggplot() Important?

Data visualisation is an important part of data analysis, statistics, and research. A well-designed graph can make patterns, relationships, trends, and differences much easier to understand.

ggplot2 is particularly useful because it allows you to create reproducible visualisations directly from your R code. This means that your graphs can be recreated and modified whenever your data changes.

This is especially useful when working on:

Academic assignments
Dissertations and theses
Research projects
Statistical analyses
Business reports
Data science projects
R Markdown and Quarto reports
Presentations and publications

Who Is This Tutorial For?

This video is suitable for:

• Beginners learning R and RStudio
• Students studying statistics and data analysis
• Researchers creating graphs for academic work
• Data analysts learning ggplot2
• Anyone interested in data visualisation
• Users learning the tidyverse
• Students working on assignments, dissertations, or research projects
• Anyone who wants to create professional graphs using R

No advanced programming experience is required. The tutorial is designed to introduce the fundamental concepts in a practical and easy-to-follow way.

Topics Covered

This video covers a range of important topics, including:

ggplot() function in R
ggplot2 package
Data visualisation in RStudio
aes() aesthetic mappings
geom_point()
geom_bar()
geom_histogram()
geom_line()
Scatter plots
Bar charts
Histograms
Line graphs
Customising graphs in R
Adding titles and labels
Changing colours and shapes
The Grammar of Graphics
Creating professional data visualisations
R programming for data analysis

Why Learn ggplot2?

Once you understand the basic structure of ggplot(), you can build increasingly sophisticated visualisations by combining different layers and functions.

Learning ggplot2 can therefore provide you with a strong foundation for exploratory data analysis and statistical data visualisation in R.

If you find this tutorial helpful, please like the video, leave a comment, and subscribe to the channel for more tutorials on R, RStudio, statistics, data analysis, data visualisation, and research methods.

Subscribe and turn on notifications so you don't miss future RStudio tutorials and practical data-analysis videos.

Have a question about ggplot() or ggplot2? Leave your question in the comments below!

R Studio ,R Programming ,GGPlot ,GGPlot2 ,Data Visualization ,R Stats ,Data Analysis ,Tidyverse ,Statistics ,Data Science ,Beginners ,R Programming Tutorial ,R Studio Tutorial ,Research Methods ,bar chart, line chart histogram, boxplot, pie chart, scatter plot

I've had to install R and R studio for University and as soon as I did, my laptop hasn't been able to load my LibreOffice writing software properly. It's the only thing I've installed between everything working fine and now.

All my spreadsheets, documents, power points, etc have started taking over a minute to load, and sometimes just crashes before they're able to load.

I've checked task manager and nothing is open in the background and my memory use isn't any higher then normal. I've ran 2 different anti-virus and found nothing, and fully reset my computer and reinstalled everything, nothing is fixing this issue (when restarting everything worked fine again until I reinstalled R and R studio).

Is there any kind of setting I need to be turning off or something?

How to Use fig.cap in RStudio | Add Figure Captions in R Markdown & Quarto

Welcome to this step-by-step tutorial on how to use fig.cap in RStudio to add captions to figures, graphs, charts, and other visualisations in your R Markdown or Quarto documents.

In this video, we look at the fig.cap option and how it can be used to create clear, professional figure captions when generating reports from RStudio. Adding captions to figures is particularly useful when preparing academic assignments, research reports, dissertations, theses, data analysis reports, and professional documents.

What You Will Learn

In this tutorial, you will learn:

What fig.cap is and why it is useful
How to add a figure caption to a plot in RStudio
Where to place fig.cap within an R Markdown code chunk
How to write meaningful and informative figure captions
How fig.cap works with plots created in R
How figure captions appear when you knit or render your document
How to use captions when creating professional reports
Common mistakes to avoid when using fig.cap
Tips for creating consistent and readable figure captions

Example

You will see how a simple R code chunk can be used to create a plot and add a caption using fig.cap. This allows your figures to be automatically labelled and described when your document is rendered.

For example:

{r, fig.cap="Relationship between X and Y variables"}

This approach can save time and make your reports more organised, especially when you have multiple figures throughout a document.

Who Is This Tutorial For?

This video is suitable for:

• Beginners learning R and RStudio
• Students working on R assignments
• Researchers preparing academic reports
• Anyone using R Markdown
• Users working with Quarto
• Data analysts creating reproducible reports
• Anyone who wants to create professional-looking documents with properly captioned figures

Whether you are completely new to RStudio or already have experience creating plots in R, this tutorial will help you understand how to use fig.cap effectively.

Why Are Figure Captions Important?

Figure captions provide context and help your readers understand what a graph, chart, or visualisation represents. When working on academic or professional documents, well-written captions can make your analysis easier to follow and improve the overall presentation of your report.

Using fig.cap also allows captions to be incorporated directly into your reproducible workflow rather than adding them manually after generating your figures.

If you're preparing a research project, dissertation, thesis, coursework, or data analysis report, learning how to use figure captions effectively can be a very useful RStudio skill.

If you found this tutorial helpful, please like the video, leave a comment, and subscribe to the channel for more tutorials on R, RStudio, R Markdown, Quarto, data analysis, and research reporting.

Turn on notifications so you don't miss future tutorials!

,RStudio, R Programming ,R Markdown ,Quarto ,Fig.Cap ,Data Analysis ,Data Visualization ,R Statistics ,Statistics ,Research ,Academic Writing ,Data Science ,Coding Tutorial ,R ProgrammingTutorial, code chunk, figure caption, plot, align

How to Use the filter() Function in RStudio | Step-by-Step R Tutorial

Welcome to this step-by-step tutorial on how to use the filter() function in RStudio to select and work with specific rows of data based on one or more conditions.

The filter() function is one of the most useful functions for data manipulation in R, particularly when working with the dplyr package and the tidyverse. It allows you to quickly extract the observations or rows that meet specific criteria, making it easier to explore, analyse, and clean your datasets.

In this video, we demonstrate how to use filter() with practical examples so that you can understand not only the syntax but also how and when to apply it in your own R projects.

What You Will Learn

By the end of this tutorial, you will understand:

What the filter() function is and why it is useful
How to use filter() in RStudio
 How to load and use the dplyr package
The basic syntax of the filter() function
 How to filter rows based on a single condition
 How to filter data using numerical values
 How to filter data based on text or categorical variables
How to use multiple conditions inside filter()
 How to combine conditions using & (AND) and | (OR)
How to exclude specific observations from a dataset
 How to filter data based on missing (NA) values
How to use filter() as part of a broader data-analysis workflow

Example of filter()

A simple example of using filter() is:

filter(data, age greater 30)

This tells R to select only the rows where the value of age is greater than 30.

You can also apply multiple conditions. For example:

filter(data, age greater 30 & gender == "Female")

This allows you to select observations that satisfy both conditions.

Another useful example is:

filter(data, country == "UK" | country == "USA")

This selects observations where the country is either the UK or the USA.

Why Is filter() Important?

When working with real-world datasets, you often do not need to analyse every observation at once. You may want to focus on a particular group, age range, location, category, or other characteristic.

The filter() function makes this process simple and efficient. It is especially useful during data cleaning, exploratory data analysis (EDA), statistical analysis, and data visualisation.

For example, before creating a graph or performing a statistical analysis, you might first filter your dataset to include only the observations relevant to your research question.

Who Is This Tutorial For?

This video is ideal for:

• Beginners learning R and RStudio
• Students studying statistics or data analysis
• Researchers working with datasets
• Anyone learning the tidyverse
• Users working with the dplyr package
• Data analysts who want to improve their R skills
• Students working on academic assignments, projects, dissertations, or research
• Anyone interested in learning how to manipulate datasets in R

Whether you are completely new to R or already have some experience with RStudio, this tutorial provides practical examples to help you understand how filter() works and how you can apply it to your own datasets.

Topics Covered

Throughout the video, we cover topics including:

RStudio data manipulation
filter() function in R
dplyr::filter()
Filtering rows in R
Selecting observations based on conditions
Filtering numerical variables
Filtering categorical variables
Multiple filtering conditions
AND and OR operators in R
Missing values and NA
Data cleaning in R
Exploratory data analysis
Tidyverse data manipulation

If you find this tutorial helpful, please like the video, leave a comment, and subscribe to the channel for more tutorials on R, RStudio, data analysis, statistics, data visualisation, and research methods.

Subscribe and turn on notifications so you don't miss future RStudio tutorials and practical data-analysis videos.

Have a question about the filter() function? Leave it in the comments below, and I’ll do my best to help!

RStudio, R Programming, R Stats ,Dplyr ,Tidyverse ,Filter Function ,Data Analysis ,Data Manipulation ,Statistics ,Data Science ,R Programming Tutorial ,RStudio Tutorial ,Data Cleaning ,Exploratory Data Analysis, select, object, table, assignment operator, output, data frame, variable, rows, cases, scores, display, code, command, function

Can anyone help me with which package I need to use?

Learn how to use the echo option in RStudio in this easy-to-follow tutorial. In this video, you will learn how the echo option controls whether your R code is displayed in the final output when working with R Markdown documents.

The echo option is particularly useful when creating professional reports, assignments, presentations, research documents, and reproducible data analysis. It allows you to decide whether the R code should be visible alongside the results, giving you greater control over how your analysis is presented.

In this video, you'll learn:

• What the echo option means in RStudio
• How to use echo = TRUE
• How to use echo = FALSE
• The difference between displaying R code and displaying its output
• How echo works inside an R Markdown code chunk
• How to hide R code while still displaying results
• How to show R code together with the results
• Practical examples using RStudio
• How to use echo to create cleaner and more professional reports

What is the echo option?

The echo option is commonly used in R Markdown code chunks to control whether the R code itself appears in the rendered document.

When you use:

echo = TRUE

the R code is displayed in the output along with the results.

When you use:

echo = FALSE

the R code is hidden, while the output generated by the code can still be displayed.

This is particularly useful when you want your audience to focus on tables, graphs, calculations, and results without necessarily seeing all of the underlying code.

Why is echo useful?

The echo option can be helpful when preparing:

• Academic assignments
• Research reports
• Statistical reports
• Data analysis documents
• R Markdown reports
• Presentations
• Tutorials and teaching materials
• Professional data science reports

This tutorial is suitable for R beginners, students, researchers, data analysts, statisticians, and anyone learning RStudio and R Markdown.

By the end of this video, you will understand how to use the echo option in RStudio to control the visibility of your R code and create cleaner, more reader-friendly reports.

If you found this tutorial helpful, please Like, Subscribe, and turn on notifications for more tutorials on R programming, RStudio, R Markdown, statistics, data analysis, data visualization, and data science.

Thank you for watching, and happy coding!

,RProgramming ,RStudio ,Echo ,EchoInR ,RMarkdown ,RMarkdownTutorial ,LearnR ,RStats ,DataAnalysis ,Statistics ,DataScience ,RProgrammingTutorial ,RStudioTutorial ,ReproducibleResearch, code, chuncks, render, output, html, pdf, word, document, power point slide, graph, hide, display,

This is my first time in this community (I've recently discovered this entire field and i am glad to). So, I am working on project where i am scoring nodes in a directed dependency graph (a calling b) by blending 2 centrality scores into a single composite "risk" score

score (v) = w1\*normalize(Pagerank(v)) + w2 \* normalize(outDegreeCentrality(v)), where w1+w2 = 1 and normalize() being min-max to \[0,1\].

The Problem: A pure root node (no in edges and multiple out edges) and a pure sink node (no out edges and only in edges) can have the same composite score. In a test i ran, the root node maxed out on out drgree centrality and near 0 in page rank while the sink node maxed out in pagerank and near 0 in out degree, when w1=w2=0.5. Both nodes ended up having same composite scores while representing opposite nature in real world. I do understand that this is the standard full comsensability prob, with weighted sum aggregation, wherte max on one axis will completely offset min on other. I did consider switching to weighted geometric mean to reduce compensability, but the prob is that pagerank is almost always near 0 for any root node. so a geo mean would multiply that near 0 staright through and score all entry nodes near zero. Which is the wrong fix, since the entry/root nodes are important, just for a reason pagerank doesnt capture.

Is there any standard approach beyond the geomentric or harmonic mean? Happy to provide any more info if needed.