#dplyr — Public Fediverse posts

{duckplyr} is a drop-in replacement for dplyr powered by DuckDB. Keep the familiar dplyr syntax while speeding up work with large datasets: https://duckplyr.tidyverse.org/ #rstats #dplyr #duckdb #datascience

{duckplyr} is a drop-in replacement for dplyr powered by DuckDB. Keep the familiar dplyr syntax while speeding up work with large datasets: https://duckplyr.tidyverse.org/ #rstats #dplyr #duckdb #datascience

{duckplyr} is a drop-in replacement for dplyr powered by DuckDB. Keep the familiar dplyr syntax while speeding up work with large datasets: https://duckplyr.tidyverse.org/ #rstats #dplyr #duckdb #datascience

{duckplyr} is a drop-in replacement for dplyr powered by DuckDB. Keep the familiar dplyr syntax while speeding up work with large datasets: https://duckplyr.tidyverse.org/ #rstats #dplyr #duckdb #datascience

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

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

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

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.

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

#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.

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

#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

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

#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.

#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.

#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.

#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.

#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.

#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.

#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.

#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.

#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.

#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.

#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.

#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.

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

#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.

#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.

#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.

#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.

#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.

#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.

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

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

#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.

#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.

#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.

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

#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.

#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.

#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.