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r glue mutate uses

  • r - tidyr use glue strings later in function - Stack Overflow

    2021-4-8 · 3. Use across: my_summarise5 <- function (data, mean_var ) { data %>% mutate ( 'mean_ { {mean_var}}' := mean ( { { mean_var }}), across (last_col (), ~.+1, .names = ' {col}_plusone') ) } mtcars %>% my_summarise5 (cyl) %>% head. giving:

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  • dataframe - dplyr mutate in R - add column as concat of ...

    2016-9-29 · dplyr mutate in R - add column as concat of columns. Ask Question Asked 7 years, 3 months ago. Active 4 years, 7 months ago. Viewed 64k times 30. 7. I have a problem with using mutate{dplyr} function with the aim of adding a new column to data frame. I want a new column to be of character type and to consist of 'concat' of sorted words from ...

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  • Programming with dplyr - The Comprehensive R Archive

    2021-5-5 · The following function uses embracing to create a wrapper around summarise() that computes the minimum and maximum values of a variable, as well as the number of observations that were summarised: var_summary <- function (data, var) { data %>% summarise ( n = n (), min = min ({{ var }}), max = max ({{ var }})) } mtcars %>% group_by (cyl) %>% var_summary (mpg)

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  • Tidy evaluation, most common actions – That's so Random

    Welcome. This is the website for “R for Data Science”.This book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it.

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  • Welcome | R for Data Science

    2019-8-13 · 使用原因最近项目中用到ndk camera,因此,学习了一下google所提供的ndk sample。sample中有两个module,分别是basic和texture-view。texture-view module将textureview作为传参,传入ndk camera。而basic module通过android_native_app_glue框架

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  • Real Knife | Glue Piece Roblox Wiki | Fandom

    2021-5-21 · The Real Knife is the newest weapon added to glue piece. It was added in the Chara Update, along with the Glue fruit, sand fruit and light awakening. It has a 10% chance of being dropped and looks like a knife with a glowing red blade in appearance 1 Moveset 1.1 Pros 1.2 Cons 1.3 Trivia Changed Move set skill T is very good for stunning This Knife is very good for fighting …

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  • Article - The 5 verbs of dplyr - Getting started | teachR

    2020-9-19 · Mutate with groups. Sometimes its useful to define new variables based on a group. Remember groups tell R to operate on the data frame one group at a time as opposed to using all of the rows in the data frame. For example, examine the following – …

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  • dplyr/across.R at master · tidyverse/dplyr · GitHub

    2021-3-10 · This can use ` {.col}` to stand for the selected column name, and. #' ` {.fn}` to stand for the name of the function being applied. The default. #' `' {.col}_ {.fn}'` for the case where a …

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  • From base R • stringr

    2021-5-3 · str_glue(): Interpolate strings It’s often useful to interpolate varying values into a fixed string. In base R, you can use sprintf() for this purpose; stringr provides a wrapper for the more general purpose glue package.

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  • Fast and Readable 'If Else' in R · TysonBarrett.com

    2019-10-16 · Fast and Readable 'If Else' in R 16 Oct 2019. As I’ve spent time learning about different approaches to working with data, I’ve seen several subtle, but important, differences in how to do things. This very short post is presenting how one can perform vectorized “if else” functions in R. The idea of “if else” basically is:

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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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  • Tidyverse packages

    glue provides an alternative to paste() that makes it easier to combine data and strings. Model Modeling with the tidyverse uses the collection of tidymodels packages, which largely replace the modelr package used in R4DS. These packages provide a comprehensive foundation for creating and using models of all types.

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  • Data Wrangling in R with the Tidyverse (Part 2)

    mutate_at(), rename_at(): uses vars() to select specic variables to apply a function to i.e. mutate_at(vars(SELECT), FUNCTION, FUNCTION_ARGUMENTS) # mutate_at changes the data in specified columns demo_data %>% mutate_at(vars(contains('race'), sex), as.factor) %>% head(2) # A tibble: 2 x 8 record age sex grade race4 race7 bmi stweight

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  • R Vector - How to Create, Combine and Index Vectors in 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 factor variable.

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  • Tree-Based Models in R - GitHub Pages

    2020-5-15 · 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 factor variable.

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  • Regular expressions • stringr

    2021-5-22 · 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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  • 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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  • To mutate, or immutate, that is the question - LogRocket Blog

    2019-6-13 · It’s worth mentioning that the only TypeScript specific syntax is the type definition in line 26 and the as XXX in lines 28 and 32, the rest is plain old JavaScript that is validated by the compiler.. Being able to mark a value as read-only is really helpful when working with libraries such as Redux that relies on the reducers being immutable to properly work.

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  • dplyr package - R Documentation and manuals | R

    mutate() adds new variables that are functions of existing variables; select() picks variables based on their names. filter() picks cases based on their values. summarise() reduces multiple values down to a single summary. arrange() changes the ordering of the rows.

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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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  • R Formula Tutorial For Beginners - DataCamp

    2020-5-15 · 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 factor variable.

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  • 14 Strings | R for Data Science

    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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  • Chapter 7 Intellectual Debt | R for Data Engineers

    14.1 Introduction. This chapter introduces you to string manipulation in R. You’ll learn the basics of how strings work and how to create them by hand, but the focus of this chapter will be on regular expressions, or regexps for short.

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  • Why is purrr/possibly's otherwise not triggered ...

    2021-1-11 · 7.1 Learning Objectives. Explain what the formula operator ~ was created for and what other uses it has.; Describe and use ., .x, .y,..1,..2`, and other convenience parameters.; Define copy-on-modify and explain its use in R.

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  • Finding and replacing values in a dataframe - tidyverse ...

    2021-3-27 · This topic was automatically closed 21 days after the last reply. New replies are no longer allowed. If you have a query related to it or one of …

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  • Data Wrangling in R with the Tidyverse (Part 2)

    mutate_at(), rename_at(): uses vars() to select specic variables to apply a function to i.e. mutate_at(vars(SELECT), FUNCTION, FUNCTION_ARGUMENTS) # mutate_at changes the data in specified columns demo_data %>% mutate_at(vars(contains('race'), sex), as.factor) %>% head(2) # A tibble: 2 x 8 record age sex grade race4 race7 bmi stweight

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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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  • 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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  • 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, …

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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 …

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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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  • Interpreting Machine Learning Models with the iml Package ...

    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 …

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  • Validation of Bioequivalence Test Performed by BE R package

    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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  • Finding and replacing values in a dataframe - tidyverse ...

    2019-1-2 · Can someone help with the following please? In the code below, I want to do the following: Filter on ID 3 and then replace the NA value in the 'Code' column with a value, lets say in this case 'N3'. And also filter on …

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  • Data Wrangling in R with the Tidyverse (Part 2)

    mutate_at(), rename_at(): uses vars() to select specic variables to apply a function to i.e. mutate_at(vars(SELECT), FUNCTION, FUNCTION_ARGUMENTS) # mutate_at changes the data in specified columns demo_data %>% mutate_at(vars(contains('race'), sex), as.factor) %>% head(2) # A tibble: 2 x 8 record age sex grade race4 race7 bmi stweight

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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.

    Get Price
  • R Formula Tutorial For Beginners - DataCamp

    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 factor variable.

    Get Price
  • Chapter 3 Licenses in the R World | Licensing R

    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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  • survivoR | Data from the TV series in R - Daniel Oehm ...

    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 …

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  • 11 Map with multiple inputs | Functional Programming

    2021-1-18 · The scale_fill_survivor() scales uses a colour palette extracted from the season logo and scale_fill_tribes() scales uses the tribal colours of the specified season as a colour palette. Simply input the desired season number. If no season is provided it will default to season 40.

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  • Recreate - Sankey flow chart | Emil Hvitfeldt

    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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  • rOpenSci | vitae: Dynamic CVs with R Markdown

    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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  • Supervised classification with text data | Computing for ...

    2019-1-10 · The vitae package leverages the dynamic nature of R Markdown to quickly produce and update CV entries from a variety of data sources. With use of the included templates, examples and helper functions, it should be possible to produce a reasonable looking and data-driven CV in …

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