#dplyr — Public Fediverse posts

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: https://statisticsglobe.com/hub

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

#Day29 | Uncertainties – Monochrome | #30DayChartChallenge | . Coffee Price Forecast — Holt-Winters (HW) Built with #RStats using #forecast, #ggplot2, #dplyr, #lubridate, #scales and #tidyr.

#Día27 | Incertidumbre – Animación | #30DayChartChallenge | Tendencia de la temperatura global. Creado con #RStats usando #dplyr, #ggplot2 y #gganimate.

#Día26 | Incertidumbre – Tendencias | #30DayChartChallenge | Tendencia de la temperatura global. Creado con #RStats usando #dplyr y #ggplot2

#Día24 | Series de Tiempo – Día Temático - South China Morning Post | #30DayChartChallenge | Producción de Café en Centroamérica: Tendencias 2021-2025. Creada usando #Rstats con #ggplot2, #patchwork, #dplyr, #grid, #gridExtra, #scales, #sf, #rnaturalearth y #rnaturalearthdata.

#Día23 | Series de Tiempo – Seasons (Temporadas) | #30DayChartChallenge | Malcolm in the Middle. Creada usando #Rstats con #ggplot2, #dplyr, #ggtext, #showtext, #patchwork, #scales y #glue.

#Día21 | Series de Tiempo – Histórico | #30DayChartChallenge | Precio histórico del café y cacao. Creada usando #Rstats con #quantmod, #ggplot2, #dplyr, #scales, #ggtext, #patchwork, #magick y #cowplot.

#Día20 | Series de Tiempo – Cambio Global | #30DayChartChallenge | Anomalía anual de temperatura superficial global respecto al promedio 1951–1980. Creada usando #Rstats con #ggplot2, #dplyr, #readr, #scales y #ggtext.

#Día19 | Series de Tiempo – Evolución | #30DayChartChallenge | Nuevas especies de mamíferos descritas por la ciencia · 1900–2050. Creada usando #Rstats con #ggplot2, #dplyr, #scales y #patchwork.

#Day18 | Relationships – UNICEF – Data Day | #30DayChartChallenge | UNICEF Children's Climate and Environment Risk Index (CCRI). Built with #RStats using #ggplot2, #dplyr, #ggrepel, and #showtext.

#Day17 | Relationships – Remake | #30DayChartChallenge | Are we making more or fewer remakes as the years go by?. Built with #RStats using #rvest, #dplyr, #stringr, #ggplot2 and #showtext.

#Día14 | Relaciones – Comercio | #30DayChartChallenge | Precios del Café. Creada usando #Rstats con #quantmod, #ggplot2, #dplyr y #scales.

#Day12 | Distributions – FlowingData – ThemeDay | #30DayChartChallenge | Heat Spots in Central America 2020-2024, source: NASA Firms . Built with #RStats using #ggplot2, #dplyr, #readr, #stringr and scales.

#Day9 | Distributions – Wealth | #30DayChartChallenge | Income Distribution in Central America, source World Bank. Built with #RStats using #ggplot2, #dplyr, #tidyr, #patchwork, #ggtext, #scales, #wbstats and #purrr.

#Day8 | Distributions – Circular | #30DayChartChallenge | Elevation distribution in the most circular department of Honduras. Built with #RStats using #sf, #raster, #exactextractr, #ggplot2, #ggnewscale, #ggtext, #dplyr, #terra, #showtext, #scales, #patchwork and #ggspatial.

#Day7 | Distributions – Multiscale | #30DayChartChallenge | Comparison of NDVI distributions across two spatial scales. Built with #RStats using #ggplot2, #dplyr, #terra, #tidyterra, #patchwork, #ggtext, and #scales.

#Día5 | Comparaciones – Experimental | #30DayChartChallenge. Experimenté agregando una sumatoria horizontal de observaciones en un boxplot sobre la capacidad endocraneana en especies del género Homo. Creada usando R con #ggplot2, #ggdist, #dplyr, #scales, #ggtext, #patchwork, #tibble y #tidyr.

#Día4 | Comparaciones – Slope | #30DayChartChallenge. Comportamiento de los focos de calor detectados para los paises de América Central. Creada usando R con #ggplot2, #dplyr, #scales, #readr, #stringr y #ggtext.

#Día3 | Comparación– Mosaico | #30DayChartChallenge. Focos de calor detectados para los paises de América Central. Un gráfico con valores absolutos y otro con valores relativos. Creada usando R con #ggplot2, #treemapify, #dplyr, #scales, #readr y #stringr.

#Día 2 | Comparaciones – Pictograma | #30DayChartChallenge. Centroamérica suma más de 51 millones de habitantes. El gráfico fue creada usando R con #ggplot2, #dplyr, #tidyr#, #scales, #ggflags, #sf, #rnaturalearth, #rnaturalearthdata, #patchwork.

I 💚 `filter_out()` in #dplyr 1.2.0

#rstats

#Rstats #dplyr #ducdkb community extension by ChanYub Park Use dplyr synthax in #duckdb duckdb.org/community_ex...

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

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

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

Na #PythonCerrado2025, tivemos ontem um excelente tutorial do Lucas Marcondes Pavelski https://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

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: https://gist.github.com/Kudusch/577b6f07c686a64a3aace685fd9f3bee

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.

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

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