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r glue mutate uses

  • Data Wrangling - A foundation for wrangling in R

    Summarise uses summary functions, functions that take a vector of values and return a single value, such as: Mutate uses window functions, functions that take a vector of values and return another vector of values, such as: window function summary function dplyr::first First value of a vector. dplyr::last Last value of a vector. dplyr::nth

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  • Package ‘textclean’ - R

    2018-7-23 · Package ‘textclean’ July 23, 2018 Title Text Cleaning Tools Version 0.9.3 Maintainer Tyler Rinker Description Tools to clean and process text.

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  • In packages - cran.r-project.org

    2021-3-3 · unnest() uses the emerging tidyverse standard to disambiguate duplicated names. Use names_repair = tidyr_legacy to request the previous approach..id has been deprecated because it can be easily replaced by creating the column of names prior to unnest(), e.g. with an upstream call to mutate().

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  • textclean package - RDocumentation

    textclean. textclean is a collection of tools to clean and normalize text. Many of these tools have been taken from the qdap package and revamped to be more intuitive, better named, and faster. Tools are geared at checking for substrings that are not optimal for analysis and replacing or removing them (normalizing) with more analysis friendly substrings (see Sproat, Black, Chen, Kumar ...

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  • Spelunking macOS ‘ScreenTime’ App Usage with R | R

    2019-10-28 · That visual schema was created in OmniGraffle via a small R script that uses the OmniGraffle automation framework. The OmniGraffle source files are also available upon request. Most of the interesting bits (for any tracking-related spelunking) are in the ZOBJECT table and to get a full picture of usage we’ll need to join it with some other tables that are connected via a few foreign keys:

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  • Analysing the R Twitterverse · Perry Stephenson

    2018-8-11 · In a previous blog post I wrote about scraping Twitter to get 400,000 tweets about R as part of my capstone project at the University of Technology, Sydney. I’ve got some big plans for network analysis with this dataset, but before I start untangling hairballs I thought I might as well take a look for any interesting stories that can help me understand the structure of the the R Twitter ...

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  • From base R • stringr

    2021-5-3 · To avoid dependence between arguments, stringr instead uses helper functions (like fixed(), regexp(), and coll()). Next we’ll walk through each of the functions, noting the similarities and important differences. These examples come from the stringr documentation and here, they are contrasted with the analogous base R operation(s).

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  • R Vector - How to Create, Combine and Index Vectors

    2021-5-10 · In this TechVidvan tutorial, you’ll learn about vector in R programming. You’ll learn to create, combine, and index vectors in R. Vectors are the simplest data structures in R. They are sequences of elements of the same basic type. These types can be numeric, integer, complex, character, and logical. In R, the more complicated data ...

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  • 5 Transform Lists and Vectors | The Tidyverse Cookbook

    2020-7-20 · A vector is a one dimensional array of elements. Vectors are the basic building blocks of R. Almost all data in R is stored in a vector, or even a vector of vectors. A list is a recursive vector: a vector that can contain another vector or list in each of its elements. Lists are one of the most flexible data structures in R.

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  • Data Wrangling - A foundation for wrangling in R

    Summarise uses summary functions, functions that take a vector of values and return a single value, such as: Mutate uses window functions, functions that take a vector of values and return another vector of values, such as: window function summary function dplyr::first First value of a vector. dplyr::last Last value of a vector. dplyr::nth

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  • How to Use Lightgbm with Tidymodels - Roel's R-tefacts

    2020-8-27 · It is a unified machine learning framework that uses sane defaults, keeps model definitions andimplementation separate and allows you to easily swap models or change parts of the processing. In this howto I signify r packages by using the {packagename} convention, f.e.: {ggplot2} Tidymodels already works with XGBoost and many many other machine ...

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  • Chapter 3 The Tidyverse | R for Data Engineers

    2021-1-11 · The answer is that R uses lazy evaluation: function arguments aren’t evaluated until they’re needed, so the function filter actually gets the expression lo > 0.5, which allows it to check that there’s a column called lo and then use it appropriately.

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  • Spelunking macOS ‘ScreenTime’ App Usage with R | R

    2019-10-28 · That visual schema was created in OmniGraffle via a small R script that uses the OmniGraffle automation framework. The OmniGraffle source files are also available upon request. Most of the interesting bits (for any tracking-related spelunking) are in the ZOBJECT table and to get a full picture of usage we’ll need to join it with some other tables that are connected via a few foreign keys:

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  • rqog-package for R • rqog

    2021-1-30 · The data rqoq imports to R is in .csv-format without the labels and names shipped together with spss or Stata formats. As such it is the desired format to work with in R, especially with numeric indicators. However, many of the indicators in QoG are factors meaning that they have discrete values with a corresponding label.

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  • textclean package - RDocumentation

    textclean. textclean is a collection of tools to clean and normalize text. Many of these tools have been taken from the qdap package and revamped to be more intuitive, better named, and faster. Tools are geared at checking for substrings that are not optimal for analysis and replacing or removing them (normalizing) with more analysis friendly substrings (see Sproat, Black, Chen, Kumar ...

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  • rstatix package - RDocumentation

    Provides a simple and intuitive pipe-friendly framework, coherent with the 'tidyverse' design philosophy, for performing basic statistical tests, including t-test, Wilcoxon test, ANOVA, Kruskal-Wallis and correlation analyses. The output of each test is automatically transformed into a tidy data frame to facilitate visualization. Additional functions are available for reshaping, reordering ...

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  • Column-wise operations - The Comprehensive R

    2021-5-5 · Basic usage. across() has two primary arguments: The first argument, .cols, selects the columns you want to operate on.It uses tidy selection (like select()) so you can pick variables by position, name, and type.. The second argument, .fns, is a function or list of functions to apply to each column.This can also be a purrr style formula (or list of formulas) like ~ .x 2.

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  • Validation of Bioequivalence Test Performed by BE R

    2018-10-29 · C max. Comparison of 90% confidence interval for the ratio of the geometric means of AUC last between the T and R products is shown in Table 2.. Cmax_R_BE <- tab_r_be_results('Cmax') Cmax_proc_glm <- tab_sas_proc_results('SAS: PROC GLM', skip = 294) Cmax_proc_mixed <- tab_sas_proc_results('SAS: PROC MIXED', skip = 366) # Combine all analyses of Cmax Cmax_all_analyses <- bind_rows(Cmax_R…

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  • Recreate - Sankey flow chart | Emil Hvitfeldt

    2020-5-1 · The goal - A flowing sankey chart from nytimes. In this excellent article Extensive Data Shows Punishing Reach of Racism for Black Boys by NYTimes includes a lot of very nice charts, both in motion and still. The chart that got biggest reception is the following: (see article for moving picture) We see a animated flow chart that follow the style of the classical Sankey chart.

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  • How to Use Lightgbm with Tidymodels - Roel's R-tefacts

    2020-8-27 · It is a unified machine learning framework that uses sane defaults, keeps model definitions andimplementation separate and allows you to easily swap models or change parts of the processing. In this howto I signify r packages by using the {packagename} convention, f.e.: {ggplot2} Tidymodels already works with XGBoost and many many other machine ...

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  • rqog-package for R • rqog

    2021-1-30 · The data rqoq imports to R is in .csv-format without the labels and names shipped together with spss or Stata formats. As such it is the desired format to work with in R, especially with numeric indicators. However, many of the indicators in QoG are factors meaning that they have discrete values with a corresponding label.

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  • Chapter 3 Licenses in the R World | Licensing R

    2019-12-19 · The R Core team uses a quite similar term (“add-on”) to describe R packages: packages are named “add-on” packages in the R Installation and Administration manuals.. As said before, this book is not legal advice but aims at providing elements to understand how lincensing works.

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  • R Formula Tutorial For Beginners - DataCamp

    2017-11-23 · Generic R functions such as print(), summary(), plot(), anova(), etc. will have methods defined for specific object classes to return information that is appropriate for that kind of object. Probably one of the well known modeling functions is lm(), which uses all of the arguments described above.

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  • rOpenSci | vitae: Dynamic CVs with R Markdown

    2019-1-10 · Having seen several CVs put together into an R Markdown document (including my own, featuring a few quick and dirty hacks to make it work), the need for an R package was obvious. With many attendees of the 2018 rOpenSci OzUnconf having converted their CV to use R Markdown, the conference was the perfect space to develop and formalise the ideas ...

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  • Interpreting Machine Learning Models ... - UC R

    2020-3-4 · Once we have these three components we can create a predictor object. Similar to DALEX and lime, the predictor object holds the model, the data, and the class labels to be applied to downstream functions.A unique characteristic of the iml package is that it uses R6 classes, which is rather rare.To main differences between R6 classes and the normal S3 and S4 classes we typically work with are:

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  • Exceptions and debugging · Advanced R.

    2019-8-8 · In R, the “fail fast” principle is implemented in three ways: Be strict about what you accept. For example, if your function is not vectorised in its inputs, but uses functions that are, make sure to check that the inputs are scalars. You can use stopifnot(), the assertthat package, or …

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  • Calculating rowwise totals and proportions using

    2018-2-4 · I haven't delved too deep into tidyeval and quasiquotation yet, but I have a case where it seems like it makes sense to use and I need some help to make it work. Say I have a tibble in wide format where each row is an election district and each column is the number of votes a candidate received. I want to calculate to total votes per district and the proportion of votes each candidate …

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  • Validation of Bioequivalence Test Performed by BE R

    2020-5-10 · Technically it uses nest() + mutate() + map() to apply arbitrary computation to a grouped data frame. sample_n_by() : sample n rows by group from a table convert_as_factor(), set_ref_level(), reorder_levels() : Provides pipe-friendly functions to convert simultaneously multiple variables into a …

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  • How to Use Lightgbm with Tidymodels - Roel's R-tefacts

    2020-8-27 · It is a unified machine learning framework that uses sane defaults, keeps model definitions andimplementation separate and allows you to easily swap models or change parts of the processing. In this howto I signify r packages by using the {packagename} convention, f.e.: {ggplot2} Tidymodels already works with XGBoost and many many other machine ...

    Get Price
  • rqog-package for R • rqog

    2021-1-30 · The data rqoq imports to R is in .csv-format without the labels and names shipped together with spss or Stata formats. As such it is the desired format to work with in R, especially with numeric indicators. However, many of the indicators in QoG are factors meaning that they have discrete values with a corresponding label.

    Get Price
  • R Formula Tutorial For Beginners - DataCamp

    2017-11-23 · Generic R functions such as print(), summary(), plot(), anova(), etc. will have methods defined for specific object classes to return information that is appropriate for that kind of object. Probably one of the well known modeling functions is lm(), which uses all of the arguments described above.

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  • An Introduction to Text Processing and Analysis with R

    2018-9-9 · Base R. A lot of folks new to R are not aware of just how much basic text processing R comes with out of the box. Here are examples of note. paste: glue text/numeric values together; substr: extract or replace substrings in a character vector; grep family: use regular expressions to deal with patterns of text; strsplit: split strings

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  • Chapter 3 The Tidyverse | R for Data Engineers

    2021-1-11 · The answer is that R uses lazy evaluation: function arguments aren’t evaluated until they’re needed, so the function filter actually gets the expression lo > 0.5, which allows it to check that there’s a column called lo and then use it appropriately.

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  • Introduction to Fama French · R Views

    2018-4-11 · An R community blog edited by RStudio. In two previous posts, we calculated and then visualized the CAPM beta of a portfolio by fitting a simple linear model.. Today, we move beyond CAPM’s simple linear regression and explore the Fama French (FF) multi-factor model of equity risk/return.

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  • Best R packages for data import, data wrangling &

    2019-10-30 · Useful R packages in a handy searchable table. The table below shows my favorite go-to R packages for data import, wrangling, visualization and analysis -- …

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  • 11 Map with multiple inputs | Functional Programming

    11.2 pmap(). There are no map3() or map4() functions. Instead, you can use a pmap() (p for parallel) function to map over more than two vectors.. The pmap() functions work slightly differently than the map() and map2() functions. In map() and map2() functions, you specify the vector(s) to supply to the function. In pmap() functions, you specify a single list that contains all the vectors (or ...

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  • Recreate - Sankey flow chart | Emil Hvitfeldt

    2020-5-1 · The goal - A flowing sankey chart from nytimes. In this excellent article Extensive Data Shows Punishing Reach of Racism for Black Boys by NYTimes includes a lot of very nice charts, both in motion and still. The chart that got biggest reception is the following: (see article for moving picture) We see a animated flow chart that follow the style of the classical Sankey chart.

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  • Using R and Python to Predict Housing Prices -

    2020-4-17 · Using R and Python to Predict Housing Prices. 50 minute read, more or less. Created: April 17, 2020 Some folks work in R. Some work in Python. Some work in both. I’m more on the R side, which has served my needs as a Phd student, but I also use Python on occasion.

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  • Live Free or Dichotomize - Building a data-driven CV

    2019-9-4 · Unfortunately, printing an na value in R returns 'na', so I used mutate_all() to turn every missing value in the dataframe into the string 'N/A'. positions_no_na <- positions_collapsed_bullets %>% mutate_all(~ifelse(is.na(.), 'N/A', .)) After all that, we just plop our glue template into glue_data and pipe in our newly modified positions dataframe.

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  • Chapter 3 The Tidyverse | R for Data Engineers

    2021-1-11 · The answer is that R uses lazy evaluation: function arguments aren’t evaluated until they’re needed, so the function filter actually gets the expression lo > 0.5, which allows it to check that there’s a column called lo and then use it appropriately.

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  • Counting Named Users in RStudio Connect and

    2021-4-1 · glue('Thanks for using RStudio! This server has had {count} named users since {today() - dyears(1)}.') If you prefer to have the audit logs continuously available as a CSV or JSON file, set the Server.AuditLogFormat configuration option to either CSV or JSON .

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  • Tidyverse

    2020-3-20 · (This isn’t very useful when used directly, but as you’ll see shortly, it’s really useful inside of functions.) To put this another way, before dplyr 1.0.0, each summary had to be a single value (one row, one column), but now we’ve lifted that restriction so each summary can …

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  • 2 A tidyverse primer | Tidy Modeling with R

    2 A tidyverse primer. The tidyverse is a collection of R packages for data analysis that are developed with common ideas and norms. From Wickham et al. (): “At a high level, the tidyverse is a language for solving data science challenges with R code.

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  • Best R packages for data import, data wrangling &

    2019-10-30 · Useful R packages in a handy searchable table. The table below shows my favorite go-to R packages for data import, wrangling, visualization and analysis -- …

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  • Presence of mismatches between diagnostic PCR

    2020-6-10 · The methodology outlined here uses MSA of publicly available viral sequences and is prone to certain biases despite its general utility in diagnostic PCR assay design. One of the biases is the compositional bias, which may arise as a result of sampling from certain geographical locations due to access to better facilities for viral genome ...

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  • Argument type: data-masking — dplyr_data_masking

    2021-5-5 · Key terms. The primary motivation for tidy evaluation in dplyr is that it provides data masking, which blurs the distinction between two types of variables: env-variables are 'programming' variables and live in an environment. They are usually created with <-.Env-variables can be any type of R object.

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  • Pivot data from long to wide — pivot_wider • tidyr

    2021-3-3 · data: A data frame to pivot. id_cols A set of columns that uniquely identifies each observation. Defaults to all columns in data except for the columns specified in names_from and values_from.Typically used when you have redundant variables, i.e. variables whose values are perfectly correlated with existing variables.

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  • Launch an edge node for Amazon EMR to run RStudio

    2018-9-13 · RStudio Server provides a browser-based interface for R and a popular tool among data scientists. Data scientist use Apache Spark cluster running on Amazon EMR to perform distributed training. In a previous blog post, the author showed how you can install RStudio Server on Amazon EMR cluster.However, in certain scenarios you might want to install it on a standalone Amazon EC2 …

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  • Tutorial on tidymodels for Machine Learning

    2020-2-9 · caret is a well known R package for machine learning, which includes almost everything from data pre-processing to cross-validation. The unofficial successor of caret is tidymodels, which has a modular approach meaning that specific, smaller packages are designed to work hand in hand.

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  • Presence of mismatches between diagnostic PCR

    2020-6-10 · The methodology outlined here uses MSA of publicly available viral sequences and is prone to certain biases despite its general utility in diagnostic PCR assay design. One of the biases is the compositional bias, which may arise as a result of sampling from certain geographical locations due to access to better facilities for viral genome ...

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  • How to do Optical Character Recognition (OCR) of

    2017-7-17 · One of the many great packages of rOpenSci has implemented the open source engine Tesseract.. Optical character recognition (OCR) is used to digitize written or typed documents, i.e. photos or scans of text documents are “translated” into a digital text on your computer.

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  • Model Interpretability with DALEX - UC R Programming

    2020-3-4 · ↩ Model Interpretability with DALEX. As advanced machine learning algorithms are gaining acceptance across many organizations and domains, machine learning interpretability is growing in importance to help extract insight and clarity regarding how these algorithms are performing and why one prediction is made over another.

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  • Accessing the RESTful API • rOpenSci: ruODK

    2021-5-8 · Three ways to happiness. ODK Central offers no less than three different ways to access data: viewing ODK Central data in MS PowerBI, MS Excel, Tableau, or ruODK through the OData service endpoints, or; downloading all submissions including attachments as one (possibly gigantic) zip archive either through the “Export Submissions” button in the ODK Central form submissions page or through ...

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  • Supervised classification with text data | Computing

    2021-4-5 · By default this uses tokenizers::tokenize_words(). Next we remove stop words with step_stopwords(); the default choice is the Snowball stop word list, but custom lists can be provided too. Before we calculate tf-idf we use step_tokenfilter() to only keep the 500 most frequent tokens, to avoid creating too many variables in our first model.

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  • Ch. 14: Strings | Yet another ‘R for Data Science’ study

    2019-8-15 · 14.5: Other types of patterns. regex args to know:. ignore_case = TRUE allows characters to match either their uppercase or lowercase forms. This always uses the current locale. multiline = TRUE allows ^ and to match the start and end of each line rather than the start and end of the complete string.; comments = TRUE allows you to use comments and white space to make complex regular ...

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  • 14 Strings | R for Data Science: Exercise Solutions

    2020-7-19 · This is in line with how the numeric R functions, e.g. sum(), mean(), handle missing values. However, the paste functions, convert NA to the string 'NA' and …

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  • Kallisto - GitHub Pages

    2020-12-4 · Since Kalisto uses transcripts estimates and many programs, like DEseq and EdgeR, works better with gene counts there are programs to get gene counts from transctipt estimates in our case we will use txImport. TxImport can import estimates from a lot of different sources. You can read more about it in the txImport vignette

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