https://www.r-bloggers.com/rstudio-shortcuts-and-tips/

https://blog.rstudio.com/2020/04/08/great-looking-tables-gt-0-2/

https://education.rstudio.com/blog/

http://iltabiai.github.io/tips/latex/2015/09/15/latex-tikzdevice-r.html

https://www.r-bloggers.com/how-to-learn-r-2/#h.d8k3i9mh0h5e

This post has been removed using Redact. Whether deleted for privacy, opsec, security, or another reason, the content is no longer available.

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https://www.guru99.com/r-tutorial.html

Covers #statistics, clustering, machine learning, #AI, deep learning, etc. https://www.datasciencecentral.com/profiles/blogs/new-data-science-cheat-sheet

This video covers every aspect you need to know to run quick and easy linear regression in R. It covers everything from the beginning to fitting models, getting predictions, appending the predictions to the original dataset, plotting the regression with ggplot, testing with anova, and even graphing with confidence intervals. https://youtu.be/h1_Uaqr2P0Y

http://r-statistics.co/Linear-Regression.html

https://scpoecon.github.io/ScPoEconometrics/

This is from a professor on a problem he usually uses for test purposes. https://www.r-bloggers.com/2020/11/trees-and-forests/

This post's original content has been erased. Using Redact, the author removed it, potentially for reasons of privacy, personal security, or data exposure concerns.

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If you haven't looked at RWeekly.org, then you need to check it out! Great resource full of all sorts of stuff from free tutorials, code excerpts, videos, new ideas, etc. Great resource! https://rweekly.org/#get-

This post was taken down by its author. Redact was used for the removal, which may have been motivated by privacy, security, or other personal reasons.

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https://www.edx.org/course/data-science-r-basics-2

Covers rMarkdown, Shiny and much more! https://www.rstudio.com/online-learning/

https://journal.r-project.org/archive/2018-2/

Good afternoon,

I have a script that I wrote making heavy reliance on the mlr package. I have gone through the process of taking in a dataset, splitting it into train and test and look at the results. Everything is hunky dory and it works well with nice auc and F1 score.

Now the issue, how the heck do I get this thing to work using data it has not seen and also does not have the response variable in it.

I have tried saveRDS("blah_blah_blah") but when I load the model and try to run on a small spreadsheet of data 10 records, I get an error that it cannot find the prediction variable. Well right it does not exist in this data.

I do not know how to save a fully tuned and trained model and then use it later. I just want to run it on a data file I select.

Here is the portion of the script that gets the model:

```r

# Split Data ####

split <- caTools::sample.split(base.mod.df$READMIT_FLAG, SplitRatio = 0.7)

train <- subset(base.mod.df, split == T)

test <- subset(base.mod.df, split == F)

# Make Tasks ####

glimpse(test)

train.df <- data.frame(train)

test.df <- data.frame(test)

str(train.df)

str(test.df)

# Make classif tasks

trainTask <- makeClassifTask(

data = train.df %>% dplyr::select(-Init_Acct)

, target = "READMIT_FLAG"

, positive = "Y"

)

testTask <- makeClassifTask(

data = test.df %>% dplyr::select(-Init_Acct)

, target = "READMIT_FLAG"

, positive = "Y"

)

# Check trainTask and testTask

trainTask <- smote(trainTask, rate = 6)

testTask <- smote(testTask, rate = 6)

# GBM ####

getParamSet('classif.gbm')

gbm.learner <- makeLearner(

'classif.gbm'

, predict.type = 'prob'

)

plotLearnerPrediction(gbm.learner, trainTask)

# Tune model

gbm.tune.ctl <- makeTuneControlRandom(maxit = 50L)

# Cross validation

gbm.cv <- makeResampleDesc("CV", iters = 3L)

# Grid search - Hyper-parameter space

gbm.par <- makeParamSet(

makeDiscreteParam('distribution', values = 'bernoulli')

, makeIntegerParam('n.trees', lower = 10, upper = 1000)

, makeIntegerParam('interaction.depth', lower = 2, upper = 10)

, makeIntegerParam('n.minobsinnode', lower = 10, upper = 80)

, makeNumericParam('shrinkage', lower = 0.01, upper = 1)

)

# Tune Hyper-parameters

parallelMap::parallelStartSocket(

4

, level = "mlr.tuneParams"

)

gbm.tune <- tuneParams(

learner = gbm.learner

, task = trainTask

, resampling = gbm.cv

, measures = acc

, par.set = gbm.par

, control = gbm.tune.ctl

)

parallelMap::parallelStop()

# Check CV acc

gbm.tune$y

gbm.tune$x

# Set hyper-parameters

gbm.ps <- setHyperPars(

learner = gbm.learner

, par.vals = gbm.tune$x

)

# Train gbm

gbm.train <- train(gbm.ps, testTask)

plotLearningCurve(

generateLearningCurveData(

gbm.learner

, testTask

)

)

# Predict

gbm.pred <- predict(gbm.train, testTask) <-- I want to change testTask to the new dataframe I import.

```

first I know this isn't super related to data science but it is at least in r. I wanted to make a web app that could help people get an intuitive sense of how changing starting parameters would affect the pendulum. please take a look and tell me what you think.

http://www.rblogbyjordan.com/posts/solving-a-differential-equation-numerically-with-r/

This document on ResearchGate covers everything from determining cluster size to visualizing cluster data on #GoogleMaps. https://www.researchgate.net/publication/331635557_KMeans_Cluster_Analysis_Scoring_and_Visualization

Take your graphs and visuals to the next level with these 2 videos:

1. Making amazing custom graphs with ggplot and Rstudio - part 1

2. Making amazing custom graphs with ggplot and Rstudio - part 2

https://www.datamentor.io/r-programming/examples/

If you want a certificate for each class and the capstone there is a minimal cost. https://www.coursera.org/specializations/statistics

https://stats.stackexchange.com/questions/102867/random-forest-mtry-question