Note that when the site and user profile files are sourced only the base package is loaded, so objects in other packages need to be referred to by e.g. utils::dump.frames or after explicitly loading the package concerned. Which implies that when 3rd party packages are loaded via files lik...
boots<-bootstraps(fish_df,times=250,apparent=TRUE)fit_lm_on_bootstrap<-function(split){lm(Height~sqrt_Weight,analysis(split))}boot_models<-boots%>%dplyr::mutate(model=map(splits,fit_lm_on_bootstrap),coef_info=map(model,tidy))boot_coefs<-boot_models%>%unnest(coef_info)percentile_interva...
R, with packages likedplyrandtidyr, is another strong option, particularly favored in the statistical and academic communities for its robust data analysis capabilities. Both languages offer a high degree of flexibility, allowing users to script custom workflows and automate repetitive tasks, making th...
We often get asked “why bother withrquery, givendplyrwas first.” I’d say: if you are happy withdplyrdon’t worry aboutrquery. Though I would add: you really owe it to yourself to check outdata.table, it is by far the best data manipulation system inR. However, let’s take a ...
Coded data were sorted and compiled into an interrogable database using the packages ‘dplyr’ and ‘tidyr’ in R version 3.1.0 (R Development Core Team). A structural matrix of linkages between interventions and outcomes was visualized as a heat map using the package ggplot2 [59]. ...
Springer, New York Wickham H, François R, Henry L, Müller K (2021) dplyr: a grammar of data manipulation. R Package Ver‑ sion 1:4 Wise SB (2010) Climate change in the classroom: patterns, motivations, and barriers to instruction among Colorado science teachers. J Geosci Educ 58:...
The evidence base is growing on conservation-human well-being linkages, but biases in the extent and robustness of articles on key linkages persist. Priorities for systematic review, include linkages between marine resource management and economic/material well-being outcomes; and protected areas and ...
But of course I don’t want to have to actually click around on GitHub to remember what I was doing. What I want is some rectangular data that I can work on in R. Enter theghpackage, a package that lets you interact with the GitHub API. We can useghto run the created and closed...
#install.packages("readxl") # install package if required library("readxl") nhefs <- read_excel("F:/Homework/Textbooks/Hernan - Causal Inferences/nhefs.xls") nhefs$cens <- ifelse(is.na(nhefs$wt82), 1, 0) # regression on covariates, allowing for some effect modification ...
Why reprex? Getting unstuck is hard. Your first step here is usually to create a reprex, or reproducible example. The goal of a reprex is to package your code, and information about your problem so that others can run it…