Here's a small handful of references I extracted from a combination of Google and Google Scholar searches on the value of developing a culture of open science.
LSE Impact Blog: The benefits of Open science are not inevitable: monitoring its development should be value-led
which is based on the draft paper, Monitoring Open Science as transformative change in research systems and has nice accompanying slides
PLOS Bio: Open science challenges, benefits and tips in early career and beyond
| # Functional Programming | |
| # ID529 | |
| # Topics to cover | |
| # | |
| # - sapply, apply family | |
| # - purrr::map | |
| # - summarize | |
| # - stratified models | |
| # |
| # Let's Review | |
| # We circulated a survey about what would be the most useful material for us to | |
| # review and revisit, as well as if there are any questions that would be useful | |
| # for us to go over together. | |
| # Overall topics to try to cover: | |
| # | |
| # - dplyr and data manipulation | |
| # - color in ggplot2 |
| # ID529 Core Concepts Script | |
| # This script covers the following skills: | |
| # - Installing packages | |
| # - Reading csv data in with readr::read_csv | |
| # - Using dplyr for group_by and summarize | |
| # - Creating factors, setting reference levels | |
| # - Creating plots with ggplot2 and saving them | |
| # - Fitting a model | |
| # - Reporting on a model with gtsummary |
| #' Create Encrypted Files | |
| create_encrypted_height_weight_medications <- function() { | |
| # load files to be encrypted | |
| dfs <- | |
| list( | |
| file1 = "sensitive_data/file1.xlsx", | |
| file2 = "sensitive_data/file2.xlsx", | |
| file3 = "sensitive_data/file3.xlsx" | |
| ) %>% purrr::map(readxl::read_excel) |
| library(raster) | |
| library(sf) | |
| library(mapview) | |
| library(janitor) | |
| library(tidyverse) | |
| library(RColorBrewer) | |
| # google: get shapefiles for states of india in r | |
| # results: | |
| # - https://rstudio-pubs-static.s3.amazonaws.com/435552_5656a7fe6636474fb0f8d4222d79db2c.html |
| msg | X1 | X2 | |
|---|---|---|---|
| msg1 | 0.0324862346463363 | 0.69194085852225 | |
| msg1 | 0.0394920482026479 | 0.677489436905826 | |
| msg1 | 0.0416296060991105 | 0.654030226700252 | |
| msg1 | 0.0438267683185091 | 0.638065001399384 | |
| msg1 | 0.0468689114781872 | 0.258300797649034 | |
| msg1 | 0.0479669631512071 | 0.682763302896725 | |
| msg1 | 0.0481195790287829 | 0.233654583287701 | |
| msg1 | 0.0498138500635324 | 0.62289798488665 | |
| msg1 | 0.0496950444726811 | 0.279729219143577 |
| 1e-04 | 0.593445948405704 | |
|---|---|---|
| 1e-04 | 0.580335382791219 | |
| 1e-04 | 0.56737126037494 | |
| 1e-04 | 0.516979202691876 | |
| 0.000181832713359549 | 0.593445948405704 | |
| 0.000181832713359549 | 0.56737126037494 | |
| 0.000181832713359549 | 0.554553581156866 | |
| 0.000181832713359549 | 0.516979202691876 | |
| 0.000287949410139229 | 0.593445948405704 | |
| 0.000287949410139229 | 0.554553581156866 |
| -0.1137452711223203 | 0.8618811881188119 | |
|---|---|---|
| -0.1137452711223203 | 0.849009900990099 | |
| -0.1137452711223203 | 0.8361386138613861 | |
| -0.1137452711223203 | 0.8232673267326732 | |
| -0.1137452711223203 | 0.8103960396039603 | |
| -0.1137452711223203 | 0.7975247524752475 | |
| -0.1137452711223203 | 0.7846534653465346 | |
| -0.1137452711223203 | 0.7717821782178218 | |
| -0.1137452711223203 | 0.7589108910891089 | |
| -0.1137452711223203 | 0.6559405940594059 |
| library(sf) | |
| library(ggplot2) | |
| nc <- st_read(system.file("shapes/sids.shp", package="spData")[1], quiet=TRUE) | |
| nc %>% | |
| ggplot(aes(fill = AREA)) + | |
| geom_sf(size = 0.05, color = 'white') + | |
| scale_fill_gradientn( | |
| colors = c("#DA8907", "#ECCD00", "white", "#62AEDC", "#203856")) |