knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "tools/README-", message = FALSE ) library(ggplot2) theme_set(theme_minimal())
Author: Julia Silge
License: MIT
There are now more than 11,000 packages on CRAN, and R users must approach this abundance of packages with effective strategies to find what they need and choose which packages to invest time in learning how to use. At useR!2017, I contributed to an organized session focused on discovering, learning about, and evaluating R packages. In preparation for that session, I ran a brief online survey in the spring of 2017 to ask R users how they currently discover and learn about R packages. This package contains the results of that survey.
This package can be install from GitHub using devtools.
devtools::install_github("juliasilge/packagesurvey")
The survey results are available in this package in the package_survey
data object.
library(packagesurvey) data("package_survey")
There were r n_distinct(package_survey$respondent)
respondents to the survey. You can easily explore how many respondents chose each answer.
library(dplyr) package_survey %>% mutate(total = n_distinct(respondent)) %>% count(answer, total) %>% arrange(desc(n)) %>% mutate(proportion = scales::percent(n / total)) %>% select(-total, -n) %>% kable(col.names = c("How do you currently discover and learn about R packages", "% of respondents who chose each answer"))
You might also be interested in when R users responded to the survey.
package_survey %>% distinct(respondent, .keep_all = TRUE) %>% ggplot(aes(response_time)) + geom_histogram() + labs(x = NULL, y = "Number of R users", title = "Responses to survey on package discovery over time")
This project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
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