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  1. Choosing the wrong join can silently remove observations, duplicate rows, or introduce missing values into your data.

    More info in the latest Statistics Globe Hub module: statisticsglobe.com/hub

    #rstats #datascience #statistics #programming #tidyverse #dplyr #analytics #statisticsglobehub

  2. | Uncertainties – Monochrome | | . Coffee Price Forecast — Holt-Winters (HW) Built with using , , , , and .

  3. | Incertidumbre – Animación | | Tendencia de la temperatura global. Creado con usando , y .

  4. | Incertidumbre – Tendencias | | Tendencia de la temperatura global. Creado con usando y

  5. | Series de Tiempo – Día Temático - South China Morning Post | | Producción de Café en Centroamérica: Tendencias 2021-2025. Creada usando con , , , , , , , y .

  6. | Series de Tiempo – Seasons (Temporadas) | | Malcolm in the Middle. Creada usando con , , , , , y .

  7. | Series de Tiempo – Histórico | | Precio histórico del café y cacao. Creada usando con , , , , , , y .

  8. | Series de Tiempo – Cambio Global | | Anomalía anual de temperatura superficial global respecto al promedio 1951–1980. Creada usando con , , , y .

  9. | Series de Tiempo – Evolución | | Nuevas especies de mamíferos descritas por la ciencia · 1900–2050. Creada usando con , , y .

  10. | Relationships – UNICEF – Data Day | | UNICEF Children's Climate and Environment Risk Index (CCRI). Built with using , , , and .

  11. | Relationships – Remake | | Are we making more or fewer remakes as the years go by?. Built with using , , , and .

  12. | Distributions – FlowingData – ThemeDay | | Heat Spots in Central America 2020-2024, source: NASA Firms . Built with using , , , and scales.

  13. | Distributions – Wealth | | Income Distribution in Central America, source World Bank. Built with using , , , , , , and .

  14. | Distributions – Circular | | Elevation distribution in the most circular department of Honduras. Built with using , , , , , , , , , , and .

  15. | Distributions – Multiscale | | Comparison of NDVI distributions across two spatial scales. Built with using , , , , , , and .

  16. | Comparaciones – Experimental | . Experimenté agregando una sumatoria horizontal de observaciones en un boxplot sobre la capacidad endocraneana en especies del género Homo. Creada usando R con , , , , , , y .

  17. | Comparaciones – Slope | . Comportamiento de los focos de calor detectados para los paises de América Central. Creada usando R con , , , , y .

  18. | Comparación– Mosaico | . Focos de calor detectados para los paises de América Central. Un gráfico con valores absolutos y otro con valores relativos. Creada usando R con , , , , y .

  19. 2 | Comparaciones – Pictograma | . Centroamérica suma más de 51 millones de habitantes. El gráfico fue creada usando R con , , #, , , , , , .

  20. I'm choice 2, using group_nest and map as it retains the original column types and there is no need to rename, although if I put some effort into it I'm sure I could remove the need to enframe and rename. I just don't feel like doing that this morning. #tidyr #purrr #dplyr #imap #map #RandomWalker

  21. Here is a simple script to show what different distribution densities look like. You can easily achieve this using my TidyDensity package. #dplyr #ggplot2 #TidyDensity #R #RStats

  22. It's not going to accept it, but in a good world, it would. #dplyr #RStats

  23. Na #PythonCerrado2025, tivemos ontem um excelente tutorial do Lucas Marcondes Pavelski github.com/lucasmpavelski.

    Aprendemos sobre #R, #tidyverse, #reticulate, várias ferramentas essenciais como #ggplot2 e #dplyr, vendo na prática como aplicá-las. Foco na ponte #Python <-> R.

    Tudo novidade pra mim, vieram várias ideias interessantes de análises e plots.

    #PythonCerrado

  24. I recently saw a kind of stacked donut/pie chart that visualized nested count data (e.g. a sample description with two relevant categories, like favorite ice cream and gender) and wondered how I'd do that in #rstats.

    So, if you ever want to make a plot like this, here's the #ggplot2 and #dplyr code: gist.github.com/Kudusch/577b6f

    This wouldn't work well with too many categories and pie charts in general aren't optimal, but for this specific kind/shape of data, I think it works well enough.

  25. Last one for today, I just added random_hypergeometric_walk() to the #dev version of my #R #package #RandomWalker this package relies on #dplyr #ggplot2 #stats #purrr #R #RStats #tech #Coding

  26. Hey #RStats hivemind, can someone sanity check me?

    for some reason filtering with a value assigned to 'x' isn't working here (I was trying to make a dummy dataset for an lapply + ggplot problem I'm having, and instrad have bumped into this weird inconcistency).

    df <- data.frame(name = c("delta^13*C", "delta^13*C", "delta^18*O", "delta^18*O"),
    x = c(1,1, 2, 2),
    y = c(1,3, 5, 4))

    x <- "delta^13*C"

    df %>%
    filter(name == x)

    # [1] name x y
    # <0 rows> (or 0-length row.names)

    df %>%
    filter(name == "delta^13*C")

    # name x y
    # delta^13*C 1 1
    # delta^13*C 1 3

    #dplyr