#tidyr — Public Fediverse posts

#Day30| Uncertainties – Data Day – Global Health Data Exchange | #30DayChartChallenge | Life Expectancy at Birth — Latin America & Caribbean. Built with #RStats using #ggplot2, #patchwork, #scales, #grid, #gridExtra and #tidyr.

#Day30| Uncertainties – Data Day – Global Health Data Exchange | #30DayChartChallenge | Life Expectancy at Birth — Latin America & Caribbean. Built with #RStats using #ggplot2, #patchwork, #scales, #grid, #gridExtra and #tidyr.

#Day30| Uncertainties – Data Day – Global Health Data Exchange | #30DayChartChallenge | Life Expectancy at Birth — Latin America & Caribbean. Built with #RStats using #ggplot2, #patchwork, #scales, #grid, #gridExtra and #tidyr.

#Day30| Uncertainties – Data Day – Global Health Data Exchange | #30DayChartChallenge | Life Expectancy at Birth — Latin America & Caribbean. Built with #RStats using #ggplot2, #patchwork, #scales, #grid, #gridExtra 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.

#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.

#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.

#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.

#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.

#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.

#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.

Finally sat down to try to get my head around `reshape()` after being told somewhere that `melt`/`cast` are old hat, `reshape` is the new hotness...the result?

Friendship ended with `reshape`, now me and #tidyr are besties, `pivot_longer`/`pivot_wider` ilu :blobcat_hearthug: #rlang #dataviz

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

It's a 72nd day of my #100DaysOfCoding with #R and #RStats. I met #tidyr and #purrr.

Any #rstats users here that use #tidyr et al. for clustering and ordination?

I'm trying to wrap my head around how I should manage a workflow like:

data -> distance matrix -> clustering or ordination

*without* the benefit of row names to link the original data to the resulting cluster leaf or ordination point.

I also want to acknowledge that gcplyr is built on foundations laid by the #tidyr package for data tidying and the #dplyr package for data manipulation, the latter of which is the inspiration for its name: a grammar of data manipulation for growth curve data
11/12

Great new features in #tidyr 1.2.0

https://www.tidyverse.org/blog/2022/02/tidyr-1-2-0/

#RStats @rstats

@rstats

@jorge posted a quite interesting #webinar #shortcourse on how to handle data efficiently with #rstats

• data management plans
• version control
• R for reproducible data manipulation
• working on clusters
• data publication

#shateEGU20 #FAIRprinciples #tidyverse #dplyr #broom #tidyr #purrr #readr #ggplot2 #markdown #git #spatialdata