Interesting packages taken from R/Pharma

Additionally also things that did not change are mentioned, with can be really helpful, in case you do not check for full equality of the data frames you are comparing.Differences found between the objects!A summary is given below.There are columns in BASE and COMPARE with different classes !!All rows are shown in table below ================================== VARIABLE CLASS.BASE CLASS.COMP ———————————- Species factor some class ———————————-There are columns in COMPARE that are not in BASE !!All rows are shown in table below ========= COLUMNS ——— new_var ———Not all Values Compared EqualAll rows are shown in table below ================================= Variable No of Differences ——————————— Sepal.Length 1 Sepal.Width 1 Petal.Length 1 ———————————All rows are shown in table below ============================================ VARIABLE ..ROWNUMBER…BASE COMPARE ——————————————– Sepal.Length 1 5.1 1 ——————————————–All rows are shown in table below =========================================== VARIABLE ..ROWNUMBER…BASE COMPARE ——————————————- Sepal.Width 2 3 4 ——————————————-All rows are shown in table below ============================================ VARIABLE ..ROWNUMBER…BASE COMPARE ——————————————– Petal.Length 3 1.3 9 ——————————————–The output is easily readable and covers all the information you need to do the expected: comparing two data frames..What I really like is the quick feedback on how many differences were observed..In case you have a lot of differences, expect you added +1 to every value of a column, you can immediately see this in the summary.Additionally the detailed information, given not only the value difference, but also the position of the value in the table, is a huge advantage..Sometimes analyzing large cohorts of patients, can reveal a difference in measurement 99,880 and you do not want to scroll through a table of “matches” until you find this one difference..Therefore this detail view is a huge advantage against other packages.ArchivistAn R package designed to improve the management of results of data analysis..Key functionalities of this package include:(i) management of local and remote repositories which contain R objects and their meta-data (objects’ properties and relations between them);(ii) archiving R objects to repositories;(iii) sharing and retrieving objects (and it’s pedigree) by their unique hooks;(iv) searching for objects with specific properties or relations to other objects;(v) verification of object’s identity and context of it’s creation.This can be really important in reproducible data analytics..In pharmacological projects you often have to reproduce cases after really long time..The archivist package allows to store models, data sets and whole R objects, which can also be functions or expressions, in files..Now you can store the file in a long-term data storage and even after 10 years, using packrat + archivist you’ll be able to reproduce your study.Example for task (ii) — restore modelsThis example gives a list of models stored inside the packagelibrary(archivist)models <- asearch("pbiecek/graphGallery", patterns = "class:lm")modelsBIC <- sapply(models, BIC)sort(modelsBIC)Example for task (i) — store objects locallyA data.frame comes with in my repository at in the arepo folder.. More details

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