Data Analytics. Data Science. Data Visualization. Geospatial Maps. Statistics. for more contact us at [email protected] www(dot)dataRecode(dot)com
- Tweets154
- Following27
- Followers20
- Likes35
![library(ggplot2) library(dplyr) library(magick) library(metR) # Read in image and convert to grayscale img_name <- "older_man23" img <- image_read("img/older_man_1.png") %>% image_convert(colorspace = "gray") # Get dimensions img_w <- image_info(img)$width img_h <- image_info(img)$height img_ratio <- img_w / img_h # Resize the longest dimension to 80 pixels if (img_w >= img_h) { img <- image_resize(img, "80") } else { img <- image_resize(img, ("x80")) } # Create array and number rows and columns img_array <- drop(as.integer(img[[1]])) rownames(img_array) <- 1:nrow(img_array) colnames(img_array) <- 1:ncol(img_array) # Create data frame from array and rename columns img_df <- as.data.frame.table(img_array) %>% `colnames<-`(c("y", "x", "b")) %>% mutate( across(everything(), as.numeric), # map b (0-255) to bf (1-0), so that "brighter" values become smaller numbers bf = 1 - b / 255, ) %>% arrange(y, x) ggplot(img_df) + geom_contour_filled(aes(x, y, z](https://pbs.twimg.com/media/HC89bRrWcAAWMfu.jpg?name=orig)
ALT library(ggplot2) library(dplyr) library(magick) library(metR) # Read in image and convert to grayscale img_name <- "older_man23" img <- image_read("img/older_man_1.png") %>% image_convert(colorspace = "gray") # Get dimensions img_w <- image_info(img)$width img_h <- image_info(img)$height img_ratio <- img_w / img_h # Resize the longest dimension to 80 pixels if (img_w >= img_h) { img <- image_resize(img, "80") } else { img <- image_resize(img, ("x80")) } # Create array and number rows and columns img_array <- drop(as.integer(img[[1]])) rownames(img_array) <- 1:nrow(img_array) colnames(img_array) <- 1:ncol(img_array) # Create data frame from array and rename columns img_df <- as.data.frame.table(img_array) %>% `colnames<-`(c("y", "x", "b")) %>% mutate( across(everything(), as.numeric), # map b (0-255) to bf (1-0), so that "brighter" values become smaller numbers bf = 1 - b / 255, ) %>% arrange(y, x) ggplot(img_df) + geom_contour_filled(aes(x, y, z
ALT Chart: R zip metadata extraction benchmark on 27 archives. Sequential (lapply/purrr) in ms; furrr parallel ~1s (100× slower). Shows parallelism overhead on small jobs."
ALT This visual illustrates how a user query (via R) is processed through a local document retriever powered by Ollama. The retriever accesses locally stored documents, which are then passed to a hosted Large Language Model (LLM) to generate a context-aware response. The flow emphasizes privacy, relevance, and control by keeping all data local. Graphics:dataRecode
ALT Visualization of IBM Plex Serif in both italic and plain styles across font weights 100–700. Each label shows the weight and style (e.g., w400_italic, w300_normal). Yellow text on a dark blue background. Generated in R using the fetch_google_font API, available on GitHub at
ALT This dual-panel visualization, using U.S. Energy Information Administration (EIA) data, maps global pumped storage hydropower capacity for 2020–2023 in a Craster Parabolic equal-area projection (+proj=crast), with color gradients and numeric labels showing the top two countries per continent. Asia, led by China, has seen exponential growth in pumped storage capacity compared to 1980–2020 trends, as depicted in the bar chart ranking countries. Europe boasts the most countries represented, while Africa is limited to only Morocco and South Africa. The plots highlight pumped storage’s role in renewable energy, driven by policy and innovation.
