The R Working Group is WSU’s R community. It is a peer-to-peer learning network of graduate students, post-docs, and faculty to share R knowledge, techniques, and troubleshooting through weekly meetings and workshops. Come join us! https://cereo.wsu.edu/r-working-group/
Today Nicholas discussed his research on the impact of climate on agriculture. Specifically he explained how he was using the ‘matching’ package to create future variables for economic values for land parcels. The future predictions for climate variables came from model predictions Nicholas already had access to. His economic variables were more difficult as they […]
#### Issue 1#### #importing data using the “point and click” method in R studio leads to different data structures #how to make match data structure from read.csv()? # Input with point and click str(WeedAbundance) #input with read.csv() command dat <- read.csv(“WeedAbundance.csv”) str(dat) #lets make it match in type WA <- data.frame(WeedAbundance) str(WA) #lets make the […]
Submitted by our Working Group member Julia Piaskowski. We discussed this a bit in Trouble Shooting, but its relevant to anyone using Plotly. Thanks Julia! : Plotly is an enormously handy set of interactive plotting function that have been developed for R, Python and D3.js. One very useful feature of plotly is “event_data” where users are able […]
Today Stephanie Labou spoke about text mining and word clouds. Our script today used the “tm” and “wordcloud” packages. Some questions were asked at our session, primarily 1) can you mine PDFs? The answer is yes! You can! You can find more information about reading in text from PDFs here and here and here is the online book about text mining […]
This week was our introduction to R lecture. This lecture was a brief overview of what R is and how to use it, with the goal of making the newer R users in our community literate in R syntax. For this session users needed to have downloaded both R and R Studio. First we discussed […]
This week Dr. Steve Katz will discuss multivariate time series analysis using the MARSS package. There is some supplementary material for this talk: packages needed: MAR1 and MARSS An example of using MAR1 and MARSS on ecological data: R demo supplement 20130305 The package user guide to help orient you with the MARSS package https://cran.r-project.org/web/packages/MARSS/vignettes/UserGuide.pdf
Tomorrow PhD Candidate Zoe Hanley will discuss generalized linear models in R and making prediction maps for wolf distribution. Necessary packages are: library(glmmADMB) #Generalized Linear Mixed Modeling (GLMMs). Includes zero-inflated distributions. #Use download instructions from:http://glmmadmb.r-forge.r-project.org/ library(graphics) #temporal autocorrelation graphs library(lattice) #PACK vs. YEAR graphs library(bbmle) #AIC table library(plyr) #create cross-validation progress bar The data and […]
This week CEREO’s Stephanie Labou introduced us to the packrat package. Packrat is a relatively new package that assists collaboration and functionality of code by maintain and standardizing package versions used in a project. Depending on the level of experience, R users may not have ran into this issue before but it is a persistent problem […]