![]() Hey Issac, thank you so much for all of the resources that you have provided for football analytics through your site. For info on how to share your scripts with the community, see here. We encourage people to use and improve our scripts, and to share them with the community so everyone benefits. If you’d rather calculate projections using our webapps without the R package, see here. To download and run our R scripts, see here. In addition to the ffanalytics R Package, we also have R scripts that accompany posts on the site. If you are able to find and fix an issue, please share the fix with the community (for more info how to share your scripts with the community, see here). Please note the version of R, RStudio, and the ffanalytics package that you’re using, along with the specific error you received and the code that caused the error. If you run into errors or issues, feel free to let us know in the comments or to open a GitHub issue on our GitHub repo. There is documentation included within the package that can be explored directly in R/RStudio, but we have also made that documentation available as a pkgdown site here. To save the projections to your current working directory, use: write.csv(my_projections, file=file.path(getwd(), "projections.csv"), row.names=FALSE) csv to be opened in Excel (you may have to change the path to a location where R has write privileges-R doesn’t have write privileges in the base c:/ directory): write.csv(my_projections, file="c:/projections.csv", row.names=FALSE) You can then save the projections by exporting them to. You can change the settings of tibbles to print more rows and columns (see here). Note that, by default, tibbles only print 10 rows and only as many of the first columns that fit on the screen. For example: my_scrape$RBĪnd you can view the projections by typing, into the R console, the name of the object storing the calculated projections. Then you can view the scraped data by typing, into the R console, the name of the object storing the scraped stats, along with position. If you have run the scrape and saved the scraped data into an object, i.e.: my_scrape <- scrape_data(src = c("CBS", "ESPN", "Yahoo"),Īnd if you have calculated the projections using the scraped data, i.e.: my_projections <- projections_table(my_scrape) my_projections % add_player_info() Viewing/Saving the Projections To add player data to the projections table use the add_player_info() function, which adds the player names, teams, positions, age, and experience to the data set. Player data is scraped from when the package loads and is stored in the player_table object. Here is how to install the packages manually (along with their dependencies): install.packages(c("tidyverse","rvest","httr","readxl","janitor","glue","Hmisc"), dependencies=TRUE, repos=c("", "")) Loading the PackageĪfter the ffanalytics package is installed, load the package via: library("ffanalytics") If you you receive errors that a package is missing or a non-zero exit status error, the ffanalytics package may not have installed the packages it depends on (or the dependencies of these packages). You can then install the ffanalytics R package via: devtools::install_github(repo = "FantasyFootballAnalytics/ffanalytics", build_vignettes = TRUE) You can install the devtools and rstudioapi packages in R/RStudio via: install.packages(c("devtools","rstudioapi"), dependencies=TRUE, repos=c("", "")) The package is available through our GitHub repository, and installation requires that you have already installed the devtools and rstudioapi packages. RStudio (best text editor for viewing, editing, and running R scripts).We recommend the latest versions of the following software: For documentation on tidyverse, please visit. The latest version of the package relies heavily on the vocabulary of tidyverse, so it would be beneficial to familiarize yourself with the concepts and notation from that envrionment. For troubleshooting the basics of R, please post to the R mailing list or forums if you have questions. We will not be troubleshooting R basics with users. For more info how to learn the basics of R, see here. Before trying to use the ffanalytics package, we highly recommend first learning the basics of R, so you know about loading packages and the different object types such as lists, vectors, data frames, etc. It is a package for R, a piece of software for statistical analysis that has a steep learning curve. We have released the ffanalytics package for fantasy football data analysis. To that aim, we are introducing the ffanalytics R package that includes a streamlined version of the scripts used to scrape projections from multiple sources and calculate projected points using the wisdom of the crowd. We are continuously looking to provide users ways to replicate our analyses and improve their performance in fantasy football. ![]()
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