Most liked R-bloggers’ posts from last week (2018-10-07 till 2018-10-13 – based on twitter)

I’ve recently added new bloggers to the site, leading to an influx of posts (142 new posts on the site, just last week). And while I love R, I do not have the time to read them all. Luckily, these posts are also published on R-bloggers twitter page, where over 62K followers see the new posts and decide if to give them a “like” or not. So I thought it might be helpful to sort all of last weeks posts (that got at least 15 likes), just to make sure I will not miss out on reading a particular popular post. I’m posting it here in the hopes that this would be helpful to others as well. Here is the list:  

Title	Number of likes
R and Python: How to Integrate the Best of Both into Your Data Science Workflow	223
How to build your own Neural Network from scratch in R	209
Introduction to ggplot2	110
Making Calendar with ggplot + Moon Phase Calendar for fun	105
How to import a directory of csvs at once with base R and data.table. Can you guess which way	99
Geocoding with ggmap and the Google API	99
neuralnet: Train and Test Neural Networks Using R	86
Some R Guides: tidyverse and data.table Versions	80
Optimize your R Code using Memoization	79
The Economist’s Big Mac Index is calculated with R	78
Running the Same Task in Python and R	76
Working with panel data in R: Fixed vs. Random Effects (plm)	76
New Course: Mixture Models in R	69
In regression, we assume noise is independent of all measured predictors. What happens if it	68
The “Gold Standard” of Data Science Project Management	62
First steps of data exploration and visualization with Tidyverse	61
Monte Carlo techniques to create counterfactuals	52
Announcing MCHT: An R Package for Bootstrap and Monte Carlo Hypothesis Testing	51
Modeling Airbnb prices	49
RStudio 1.2 Preview: Reticulated Python	48
Applications of DAGs in Causal Inference	45
Scraping Tables from Wikipedia for Visualizing Climate Data	43
Machine Learning as a Service	42
Mining Sent Email for Self-Knowledge	39
Data Science With R Course Series – Week 4	38
New Course: Developing R Packages	37
Partially additive (generalized) linear model trees	36
Open Workshop: Deep Learning in R and Keras, November 14th in Frankfurt	34
Project Euler in R	34
Reading in an epub (ebook) file with the pubcrawl package	33
Deep Learning with Keras – using R (talk)	33
How R gets built on Windows	32
Prettify your Shiny Tables with DT: Exercises	32
Experiments with count(), tally(), and summarise(): how to count and sum and list elements of	32
RStudio 1.2 Preview: C/C++ and Rcpp	31
Dashboard 2.0	30
Add value to your visualizations in R	29
Big Data-2: Move into the big league:Graduate from R to SparkR	28
Analyzing the Greatest Strikers in Football II: Visualizing Data	28
<U+653C><U+3E64><U+613C><U+3E30><U+623C><U+3E64><U+653C><U+3E64><U+623C><U+3E30><U+383C><U+3E64> Custom set up of keras and TensorFlow for R and Python	28
Using R to help plan the future of transport. Join MünsteR for our next meetup!	27
Parsing Metadata with R – A Package Story	27
The myth of interpretability of econometric models	26
Combining automatically factor levels in R	26
Fast Random Numbers for R with dqrng	25
Review: Excel TV’s Data Science with Power BI and R	24
Bayesian Experimental Design through An Drug Study Example	24
Open Workshop: Data Visualization in R and ggplot2, November 8th in Frankfurt	24
Testing Entry with R Rmarkdown File	24
Drilling into non-rectangular data with purrr	24
dqsample: A bias-free alternative to base::sample()	23
Andrew Gelman discusses election forecasting and polling. (Transcript)	23
Using UMAP in R with rPython	23
Are you buying an apartment? How to hack competition in the real estate market with data	22
New package in CRAN: PkgsFromFiles	18
A question and an answer about recoding several factors simultaneously in R	18
Acquiring data for language research (3/3): web scraping	17
Make more useless packages!	17
Subsetting in the presence of NAs	16
daqana’s R style guide is online	15
The world (population) is changing	15
Understanding the limitations of group-level inequality data by @ellis2013nz	15