| digraph { | |
| comment="Example from:Tor Browser’s Latest Update Could Get You Fingerprinted... https://youtu.be/Ml99dXffRXk?t=131" | |
| size="50,50"; | |
| label="How secondary data narrows down possibilities"; | |
| fontsize=24; | |
| fontcolor="red"; | |
| splines=curved; | |
| node [fontname="Open Sans"]; | |
| edge [fontname="Open Sans"]; | |
| a[shape=plaintext, label="Human being: 8B people", fontsize="18"]; |
Get a better and more complete dataset from: https://tangled.sh/did:plc:hgyzg2hn6zxpqokmp5c2xrdo/47-watch/blob/batman/supplementary-files/2025-04-02-tariffs-with-metadata.csv
(Keeping the old info here)
Based on the work by https://mastodon.social/@hrbrmstr/114270384967300153 (fixing some minor OCR errors)
Caveat: The following "works for me"™, and it is kept simple because I implemented in on my 2016 refurbished Thinkpad P50. Also, I've done very minor testing of prompts, and the majority have worked, but YMMV.
This recipe assumes that you have already have ollama and aichat installed on your local machine. If you don't have them installed, you can follow the instructions in the following links:
There are different ways to install those two, so pick the one you are most comfortable with. I installed them on my laptop directly, but you might like to use docker instead.
When using rio::import() from a CSV data gets truncated at the first line with an error, in the example case described here, a different number of columns.
This does not happen when using readr::read_csv()
Dataset:
| library(tidyverse) | |
| v1 <- LETTERS[1:20] | |
| v1 | |
| for (i in 1:length(v1)) { | |
| if (i %% 2 == 0) { | |
| v1[i] <- tolower(v1[i]) | |
| } else { | |
| v1[i] <- "-" | |
| } |
| library(tidyverse) | |
| library(palmerpenguins) | |
| ggplot( | |
| penguins, | |
| aes(y = species, group = sex, fill = sex) | |
| ) + | |
| geom_bar(width = .4) + | |
| geom_text(aes(label = paste("Penguin species:", species), x = 0), nudge_y = .3, hjust = 0, size = 6) + | |
| theme_void() + |
| library(tidyverse) | |
| d <- tibble( | |
| id = c(1, 1, 2, 2), | |
| foo = c("oh", "no", "oh", "no"), | |
| bar = c("hmmm", "ok", "right", "sure?") | |
| ) | |
| d %>% | |
| pivot_wider( |
| # Data source: http://ghdx.healthdata.org/record/ihme-data/covid_19_excess_mortality | |
| library(tidyverse) | |
| library(sf) | |
| library(rnaturalearth) | |
| library(ggridges) | |
| library(patchwork) | |
| world_map <- ne_countries(type = "countries", returnclass = "sf") |
| # Using data from https://data.humdata.org/dataset/commuting-zones | |
| library(tidyverse) | |
| library(sf) | |
| library(geodata) | |
| library(patchwork) | |
| peru_map <- gadm("Peru", level = 2, path = ".") %>% | |
| st_as_sf() | |
| csv <- "~/Downloads/data-for-good-at-meta-commuting-zones-july-2021.csv" |
| library(tidyverse) | |
| library(arrow) | |
| # RDS de https://github.com/jmcastagnetto/covid-19-peru-limpiar-datos-minsa | |
| positivos <- readRDS("tmp/positivos_covid_aumentado.rds") %>% | |
| select(id_persona, fecha_resultado, metododx) %>% | |
| distinct() %>% | |
| mutate( | |
| id_persona = as.integer(id_persona) | |
| ) |