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Dplyr Package смотреть последние обновления за сегодня на .
Dplyr Essentials (easy data manipulation in R): select, mutate, filter, group_by, summarise, & more Timeline 0:00 Intro 1:01 Piping 2:15 select() 4:36 mutate() 5:54 filter() 7:19 distinct() 8:39 group_by() 8:53 summarise() 9:40 arrange() 10:26 count() Links: 🤍 🤍 🤍
1.Link to Download Dplyr package : 🤍 Google Drive link for Dplyr 🤍 2.Link to download Data table package 🤍 Google Drive Link for Data table package 🤍 please subscribe for more videos if you face any problem please mail me bdurgababu2000🤍gmail.com
Details about dplyr package in R. how to load and from where to load. what all impartant functions in it
This video is about how to recode data and manipulate data using R programming. It is really an R programming for beginners videos. It provides a demonstration of how to recode data using the tidyverse package (specifically the dplyr package in the tidyverse set of packages). The demonstration is in R Studio. This channel is for people who are interested in quantitative and statistical analysis using R. Everything to do with data science. This video is part of the "cleaning data" series.
dplyr is a new R package for data manipulation. Using a series of examples on a dataset you can download, this tutorial covers the five basic dplyr "verbs" as well as a dozen other dplyr functions. Watch the follow-up tutorial: 🤍 View the R Markdown document: 🤍 Download the source document: 🤍 Read about why I love dplyr: 🤍 Tutorial contents: 1. Introduction to dplyr (starts at 0:00) 2. Loading dplyr and the example dataset (starts at 2:29) 3. Understanding "local data frames" (starts at 3:23) 4. Verb #1: `filter` (starts at 5:17) 5. Verb #2: `select`, plus `contains`, `starts_with`, `ends_with`, `matches` (starts at 7:54) 6. Using chaining syntax for more readable code (starts at 9:34) 7. Verb #3: `arrange` (starts at 12:53) 8. Verb #4: `mutate` (starts at 13:55) 9. Verb #5: `summarise`, plus `group_by`, `summarise_each`, `n`, `n_distinct`, `tally` (starts at 15:31) 10. Window functions: `min_rank`, `top_n`, `lag` (starts at 26:47) 11. Convenience functions: `sample_n`, `sample_frac`, `glimpse` (starts at 32:44) 12. Connecting to databases (starts at 34:21) RESOURCES Reference manual and vignettes: 🤍 July 2014 webinar: 🤍 July 2014 webinar code: 🤍 Tutorial by Hadley Wickham: 🤍 GitHub repo: 🤍 List of releases: 🤍 LET'S CONNECT! Newsletter: 🤍 Twitter: 🤍 Facebook: 🤍 LinkedIn: 🤍
🔥Data Analyst Program (Discount Coupon: YTBE15) : 🤍 🔥 Professional Certificate Program In Data Analytics: 🤍 This video on Data Manipulation in R will help you learn how to transform and summarize your data using different packages and functions. Here, you will see data manipulation in R with the dplyr package to select, filter, arrange, and mutate data. You will use the tidyr library to create tidy data. You will also look at functions such as gather, spread, separate, and unite. Let's begin this R Programming For Beginners! 🔥Enroll for Free DataScience Course & Get Your Completion Certificate: 🤍 The topics covered in this video on Data Manipulation in R are: Introduction 00:00:00 Data Manipulation in R - dplyr 00:00:12 Data Manipulation in R - tidyr 00:25:55 ✅Subscribe to our Channel to learn more about the top Technologies: 🤍 ⏩ Check out the Machine Learning tutorial videos: 🤍 #DataManipulationInR #DataManipulationInRWithDPLYR #RProgramming #RProgrammingForBeginners #RForBeginners #DataScienceTutorial #DataScienceTraining #DataScienceCareers #DataScience #Simplilearn What Is R Programming? R is an open-source programming language used for statistical computing. It is one of the most popular programming languages today. R was inspired by S+, which is similar to the S programming language. R has various data structures and operators. It can be integrated with other programming languages like C, C, Java, and Python. Data manipulation is the process of modifying data in order to make it simpler to read. Data is manipulated for analysis and visualization. The dplyr package is used to transform and summarize tabular data with rows and columns. We can get the dplyr package by calling the library function. Meanwhile, the tidyr package helps you create tidy data. A tidy data is easy to visualize and model. Post Graduate Program in Data Science Ranked #1 Data Science program by Economic Times Accelerate your career with this acclaimed Post Graduate Program in Data Science, in partnership with Purdue University, & in collaboration with IBM - that features the perfect mix of theory, case studies, & extensive hands-on practicum. This is a #1 ranked Data Science certification program by ET. This Post Graduate Program in Data Science gives you broad exposure to key concepts and tools from Python, R, to Machine Learning, and more. Hands-on labs and project work in this data science certification program bring the concepts to life with our trainers and teaching assistants to guide you along the way. Key Features: ✅ Purdue Alumni Association Membership ✅ Industry-recognized IBM certificates for IBM courses ✅ Enrollment in Simplilearn’s JobAssist ✅ 25+ hands-on Projects on GPU enabled Labs ✅ 450+ hours of Applied learning ✅ Capstone Project in 3 Domains ✅ Purdue Post Graduate Program Certification ✅ Masterclasses from Purdue ✅Get noticed by the top hiring companies 👉Learn more at: 🤍 For more updates on courses and tips follow us on: - Facebook: 🤍 - Twitter: 🤍 - LinkedIn: 🤍 - Website: 🤍 Get the Android app: 🤍 Get the iOS app: 🤍 🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688
Introduction to the dplyr package. Learn the philosophy that guides dplyr, and discover some useful applications of the dplyr package. Start the interactive dplyr course by DataCamp and RStudio for free at 🤍 Learn how to to perform sophisticated data manipulation tasks using dplyr. Master the five verbs of data manipulation, and complementing techniques to chain your operations, perform group-wise calculations and access data stored in a database outside R.
In this video, We are explaining about Manipulate Data in R with dplyr Package. Please do watch the complete video for in-depth information. JOIN: 🤍 Link to our "English Youtube Channel": 🤍 WsCubeTech – Digital Marketing Agency & Institute. ✔ We can help you to create a Digital Marketing plan to take your business to new heights. ✔ Offering Job Oriented Most Latest, Updated, and advanced Digital Marketing Courses with Practical, Hands-on Live Projects Training & Exposure. For More information : Call us at : +91- 92696-98122 Or visit at 🤍 There is a complete playlist of Digital Marketing Interview Tips & Tricks available - 🤍 There is a complete playlist of Facebook Ads available - Link: 🤍 There is a complete playlist of Twitter Ads available. Link: 🤍 ✅ CONNECT WITH THE FOUNDER (Mr. Kushagra Bhatia) - 👉 Instagram - 🤍 👉 LinkedIn - 🤍 👉 Facebook - 🤍 Please don’t Forget to Like, Share & Subscribe ►Subscribe: 🤍 ► Facebook: 🤍 ► Twitter: 🤍 ► Instagram: 🤍 ► LinkedIn : 🤍 ► Youtube: 🤍 ► Website: 🤍 | Thanks |- #RProgramming #ManipulateDatainR #dplyrPackage
Learn dplyr by example with lions! This dplyr tutorial gives you a complete workflow of data wrangling using the R programming package dplyr. Great starting course for beginners to understand the basics! We go over and integrate the 6 verbs: group_by, arrange, filter, select, mutate and summarize. Dplyr is an essential tool in RStats for data science! Ask us questions in the comments and subscribe for more like this! 📰 Article: Davies, A. B., Tambling, C. J., Kerley, G. I., & Asner, G. P. (2016). Effects of vegetation structure on the location of lion kill sites in African thicket. PloS one, 11(2). 🤍 🦁 Dataset: 🤍 00:00 Introduction 01:03 Loading data 03:47 filter() 05:47 pipe operator 06:20 mutate() 06:47 group_by() 07:06 summarise() 07:55 arrange() 08:25 select() 10:34 Conclusion 🏎️ R performance playlist 🤍 🧮 dplyr playlist 🤍 #R #dplyr #Rtutorial #Rprogramming #tidyverse #RStats #RStudio #datascience #DDS #DDSR
Introduction to the dplyr package of the R programming language. More information: 🤍 R code of this video: data ＜- data.frame(x1 = 1:6, # Create example data x2 = c(1, 2, 2, 3, 1, 2), x3 = c("F", "B", "C", "E", "A", "D")) data # Print example data install.packages("dplyr") # Install dplyr package library("dplyr") # Load dplyr package arrange(data, x3) # Apply arrange function filter(data, x2 2) # Apply filter function mutate(data, x4 = x1 + x2) # Apply mutate function pull(data, x2) # Apply pull function rename(data, new_name = x3) # Apply rename function set.seed(765) # Set seed for reproducibility sample_n(data, 3) # Apply sample_n function select(data, c(x2, x3)) # Apply select function
This video explains how to install and load the dplyr package in R. This video is based on Ch.4 of "Easy R: Access, Prepare, Visualize, Explore Data, and Write Papers" by Elizabeth Gohmert, Quan Li (Texas A&M University, USA), and Douglas Wise, 🤍
How to create and transform variables of data frames and tibbles with the mutate and transmute functions of the dplyr package in the R programming language. More info: 🤍 R code of this video: data ＜- data.frame(x1 = 1:5, # Create example data x2 = 5:9) data # Print example data install.packages("dplyr") # Install dplyr add-on package library("dplyr") # Load dplyr add-on package mutate(data, x3 = x1 + x2) # Apply mutate function transmute(data, x3 = x1 + x2) # Apply transmute function
Subscribe to RichardOnData here: 🤍 GitHub: 🤍 In this video I show you how to manipulate data with R, using the package "dplyr" from the tidyverse. Manipulating data with R is typically an important first step before other things like visualization and modeling can be done. And "dplyr" is an extremely useful package for beginners, which will quickly help you filter rows, arrange rows, select and rename columns, mutate the dataset to add new columns, created grouped summaries, and join to other datasets. Please note that code in this tutorial was adapted from Chapters 5 and 13 of the book "R for Data Science" by Hadley Wickham and Garrett Grolemund. The full book can be found at: 🤍 Amazon link: 🤍 A good cheat sheet for dplyr functions can be found at: 🤍 PayPal: richardondata🤍gmail.com Patreon: 🤍 BTC: 3LM5d1vibhp1F7pcxAFX8Ys1DM6XLUoNVL ETH: 0x3CfC599C4c1040963B644780a0E62d45999bE9D8 LTC: MH8yPjvSmKvpmRRmufofjRB9hnRAFHfx32
R is a FREE software and the most popular programming language for STATISTICAL COMPUTING AND ANALYSIS. This video lesson covers in detail two of the most important packages of the tidyverse collection, namely, tidyr (for reshaping and tidying data) and dplyr (grammar of data manipulation). Please SUBSCRIBE to the channel and click on the "Notification Button" to get alerted anytime I post a video!
dplyr package is an exciting way to manipulate the data. It is an R package that provides you with a fast and intuitive way to transform data sets with R. dplyr is the successor of plyr and is mainly authored by Hadley Wickham and Romain Francois. It is designed to be intuitive and easy to learn, thereby making "doing things" in R more user-friendly. Basics dplyr tutorial introduces six key functions to you: summarize, group_by, select, mutate, filter, arrange. You will also learn how to use pipe operator to chain the functions - %＞% To stay up to date with our latest videos make sure to subscribe to this YouTube channel!
dplyr provides a consistent set of verbs that help you solve the most common data manipulation challenges: 1. mutate() adds new variables that are functions of existing variables 2. select() picks variables based on their names. 3. filter() picks cases based on their values. 4. summarise() reduces multiple values down to a single summary. 5. arrange() changes the ordering of the rows.
How to execute a SQL query in the R programming language. More details: 🤍 Create & Connect to Databases in R: 🤍 Profile of Kirby White: 🤍 R code of this video: # install.packages("odbc") # install.packages("DBI") # install.packages("tidyverse") library(odbc) library(DBI) library(tidyverse) #Load the sample data data("population") data("who") #Create database con ＜- dbConnect(drv = RSQLite::SQLite(), dbname = ":memory:") #store sample data in database dbWriteTable(conn = con, name = "population", value = population) dbWriteTable(conn = con, name = "who", value = who) #remove the local data from the environment rm(who, population) tbl(src = con, #the source if the database connection profile "who") #the name of the table to preview select ＜- "SELECT who.country, who.year, who.new_sp_m2534, population.population" from ＜- "FROM who" ljoin ＜- "LEFT JOIN population ON population.country = who.country AND population.year = who.year" where ＜- "WHERE who.country IN ('Brazil', 'Germany') AND who.year ＞= 2000 AND who.year ＜= 2010" query ＜- paste(select, from, ljoin, where) M2_results ＜- DBI::dbGetQuery(conn = con, statement = query) head(M2_results) M3_results ＜- tbl(src = con, "who") %＞% filter(country %in% c("Brazil", "Germany"), year ＞= 2000, year ＜= 2010) %＞% dplyr::select(country, year, new_sp_m014) %＞% left_join(y = tbl(src = con, "population"), by = c("country", "year")) %＞% collect() #this tells dplyr to execute and store the query Follow me on Social Media: Facebook – Statistics Globe Page: 🤍 Facebook – Group for Discussions & Questions: 🤍 LinkedIn – Statistics Globe Page: 🤍 LinkedIn – Group for Discussions & Questions: 🤍 Twitter: 🤍 Music by bensound.com
In this video we will look at the summarize and arrange functions within dplyr package in R. These functions can be used for calculating mean, average, maximum values, etc. of groups and to arrange column values in ascending or descending orders. For similar videos on data analysis and visualization, check out the links attached: Data Visualization Basics tutorial series: 🤍 Data Analysis Basics in R tutorial series: 🤍 Intermediate Data Analysis in R tutorial series: 🤍 Other related videos/links: # Intermediate Data Analysis in R FOR Loop: 🤍 WHILE Loop: 🤍 REPEAT Loop: 🤍 NESTED FOR Loop: 🤍 loop through dataframes: 🤍 Sequence with increments: 🤍 Adding columns using mutate and transmute (dplyr): 🤍 Subsetting rows and columns using filter and select (dplyr): 🤍 # Data Visualization Basics in R Intro to plotting using qplot in R: 🤍 Histogram and boxplot in R: 🤍 Violin plots in R: 🤍 Add or change legend names while using qplot: 🤍 Change scatter plot colors in R manually: 🤍 Stacking area plots: 🤍 Saving plots as .JPEG / .PNG: 🤍 Creating density plots using ggplot2: 🤍 Dot plots: 🤍 Stacked histograms: 🤍 Frequency plots: 🤍 Plotting labels and text: 🤍 Error bars for bar plots: 🤍 Adjusting geom positions while using ggplot2 package: 🤍 Adding plot labels: 🤍 Remove background color in plots: 🤍 Remove legend and legend titles: 🤍 Create custom color palette: 🤍 Plot multiple plots: 🤍 Export multiple plots as pdf. 🤍 # Data Analysis Basics in R Setting a working directory in R Studio: 🤍 Data structures in R: 🤍 Create a dataframe in R: 🤍 Install packages in R: 🤍 How to import .csv file in R: 🤍 How to import .txt file in R: 🤍 How to add a column in R: 🤍 How to remove a column in R: 🤍 How to add a row in R: 🤍 How to remove a row in R: 🤍 Count the number of rows and columns in R: 🤍 Sum of rows in R: 🤍 Sum of columns in R: 🤍 Finding names of rows and columns in R: 🤍 How to change column names in R dataframes: 🤍 How to remove rows with 0 values in R dataframes: 🤍 How to find unique column values in R dataframes: 🤍 How to find duplicate values in R: 🤍 Clear console in R Studio: 🤍 # GIS/Remote Sensing Intro to GIS and Remote Sensing: 🤍 What is LiDAR Remote Sensing: 🤍 Intro to spaceborne lidar and NASA GEDI: 🤍 LiDAR Remote Sensing/NASA GEDI Tutorial Series: 🤍
In this video I've talked about how you use the dplyr pipe operator. This is one the very useful dplyr operator because it allows you the nest the functions and enable to do complex operations in a very simple manner.
How to combine columns and rows with the bind_rows and bind_cols functions of the dplyr package in the R programming language. More details: 🤍 R code of this video: data1 ＜- data.frame(x1 = 1:5, # Create three data frames x2 = letters[1:5]) data2 ＜- data.frame(x1 = 0, x3 = 5:9) data3 ＜- data.frame(x3 = 5:9, x4 = letters[5:9]) install.packages("dplyr") # Install dplyr package library("dplyr") # Load dplyr package bind_rows(data1, data2) # Apply bind_rows function bind_cols(data1, data3) # Apply bind_cols function Also, check out my video on the rbind function of Base R: 🤍
#DataScience #DataAnalytics #BusinessAnalytics This video shows how powerful dplyr package functions, helps in data manipulation in R for data analytics.
Please watch this video before our class in Week 2.
How to apply the if_else function of the dplyr package in the R programming language. More details: 🤍 R code of this tutorial: install.packages("dplyr") # Install dplyr library("dplyr") # Load dplyr x ＜- -3:3 # Example vector x if_else(x ＜ 0, "neg", "pos") # Basic application of if_else x_NA ＜- c(-3:3, NA) # Example vector with NA x_NA if_else(x_NA ＜ 0, "neg", "pos", "xxx") # Apply if_else with NA condition
This package goes through a basic introduction to manipulating data in R using the dplyr package. I cover the pipe, pull(), filter(), select(), and mutate().
Data Manipulation with dplyr package in R in Urdu | Urdu & Hindi R Language Complete Course Outline What is R Language Difference between R and Python (When to choose R Language) Basics of R Language Installation Packages Introduction (Importing packages) Data Types, Vector, Lists, Matrices, Arrays, Factors, Data Frames, Variables, Operators If-else, Functions and Strings Importing, Exporting, Cleaning, Merging, Transforming, Analyzing and Extracting insights from data Data reshaping in R Basics of data analysis and data visualizations Uses of which(), gsub(), sub(), apply(), lspply(), sapply() and data frames. Create, use, append, modify, rename, access and subset lists in R Language. Family of functions Uses of R Language In Ecommerce Industry In Education Field In Statistics In Finance In Sports Advance topics related to Data Science/AI and Machine Learning Understand the Law of Large Numbers Understand the Normal distribution Handle CSV, Excel, SQL files or web scraping Machine Learning, AI and data science algorithms Identifying and locating missing records in data frames Median, Factual Analysis etc Some topics in Data Visualization Scatterplots Line Charts Histograms Box Plots Bar Charts Mosaic Plots How to Export charts from R Language to share with friends Final Project from Freelance Find R Language project from Upwork ►► Best Videos to Learning Programming and Freelancing in Urdu/Hindi ► Upwork Tutorials in Urdu/Hindi - 🤍 ► R Language for beginners 🤍 ► Angular for beginners 🤍 ► Learn ASO in Urdu & Hindi - 🤍 ► Learn Swift in Urdu/Hindi - 🤍 ► Learn React-Native in Urdu/Hindi - 🤍 ► Learn ReactJS in Urdu/Hindi - 🤍 Follow us on ► Company Website: 🤍 ► Twitter: 🤍 ► Facebook: 🤍 ► Instagram: 🤍 ► Linked In: 🤍 ► Android Apps: 🤍 ► iOS Apps: 🤍 Developer Survey Report: 🤍 #datamanipulation #rstudio #lettech Instructor Name Akhzar Nazir Co-Instructor Tahir Hameed
How to remove multiple variables from data frame using the dplyr package in the R programming language. More details: 🤍 R code of this video: data ＜- data.frame(x1 = 1:5, # Create data x2 = letters[1:5], x3 = 5, x4 = c(3, 1, 6, 3, 7)) install.packages("dplyr") # Install dplyr package library("dplyr") # Load dplyr col_remove ＜- c("x1", "x3") # Define columns that should be dropped data_new ＜- data %＞% # Apply select & one_of functions select(- one_of(col_remove)) Follow me on Social Media: Twitter: 🤍 Facebook: 🤍 Reddit: 🤍 Pinterest: 🤍
How to use a function for every row of a data frame or tibble with the dplyr package in the R programming language. More information: 🤍 R code: data ＜- as.tbl(data.frame(x1 = 1:5, # Create example data x2 = 2:6, x3 = 3:7)) data # Print data to console install.packages("dplyr") # Install and load dplyr library("dplyr") data %＞% # Apply rowwise function rowwise() %＞% mutate(row_sum = sum(x1, x2, x3))
How to extract a variable from a data frame or tibble with the pull function of the dplyr package in the R programming language. More Details: 🤍 R code of this video: ##### Example data data ＜- data.frame(x1 = 1:5, # Create example data x2 = LETTERS[1:5]) data # Print example data ##### Install & load dplyr install.packages("dplyr") # Install dplyr library("dplyr") # Load dplyr ##### Example 1 pull(data, x1) # Apply pull function ##### Example 2 pull(data, 1) # pull function with index
In this video we will look at the select and filter functions within dplyr package in R. These functions can be used for subsetting rows and columns in a dataframe. For similar videos on data analysis and visualization, check out the links attached: Data Visualization Basics tutorial series: 🤍 Data Analysis Basics in R tutorial series: 🤍 Intermediate Data Analysis in R tutorial series: 🤍 Other related videos/links: # Intermediate Data Analysis in R FOR Loop: 🤍 WHILE Loop: 🤍 REPEAT Loop: 🤍 NESTED FOR Loop: 🤍 loop through dataframes: 🤍 Sequence with increments: 🤍 Adding columns using mutate and transmute (dplyr): 🤍 # Data Visualization Basics in R Intro to plotting using qplot in R: 🤍 Histogram and boxplot in R: 🤍 Violin plots in R: 🤍 Add or change legend names while using qplot: 🤍 Change scatter plot colors in R manually: 🤍 Area plots using geom_area() function: 🤍 Stacking area plots: 🤍 Saving plots as .JPEG / .PNG: 🤍 Creating density plots using ggplot2: 🤍 Stacked density plots: 🤍 Dot plots: 🤍 Stacked histograms: 🤍 Frequency plots: 🤍 Ribbon plots: 🤍 Plotting labels and text: 🤍 Error bars for bar plots: 🤍 Adjusting geom positions while using ggplot2 package: 🤍 Adding plot labels: 🤍 Remove background color in plots: 🤍 Remove legend and legend titles: 🤍 Create custom color palette: 🤍 Plot multiple plots: 🤍 Export multiple plots as pdf. 🤍 # Data Analysis Basics in R Setting a working directory in R Studio: 🤍 Data structures in R: 🤍 Create a dataframe in R: 🤍 Install packages in R: 🤍 How to import .csv file in R: 🤍 How to import .txt file in R: 🤍 How to add a column in R: 🤍 How to remove a column in R: 🤍 How to add a row in R: 🤍 How to remove a row in R: 🤍 Count the number of rows and columns in R: 🤍 Sum of rows in R: 🤍 Sum of columns in R: 🤍 Finding names of rows and columns in R: 🤍 How to change column names in R dataframes: 🤍 How to remove rows with 0 values in R dataframes: 🤍 How to find unique column values in R dataframes: 🤍 How to find duplicate values in R: 🤍 Clear console in R Studio: 🤍 # GIS/Remote Sensing Intro to GIS and Remote Sensing: 🤍 What is LiDAR Remote Sensing: 🤍 Intro to spaceborne lidar and NASA GEDI: 🤍 LiDAR Remote Sensing/NASA GEDI Tutorial Series: 🤍
This tutorial video shows, how we can manipulate or prepare data very easily with dplyr package. This further illustrates the following functions and their use with hflights data set. This tutorial includes select function, filter function, arrange function, mutate function, group_by function and summarise function.
With ggplot2, the dplyr R package is the foundation of the tidyverse. In this episode of Code Club, Pat shows how to use dplyr to clean and join data generated from the #mothur software package. He will cover select, rename, rename_all, mutate, separate, pivot_longer, str_replace, str_replace_all, group_by, summarize, inner_join, anti_join, and more. In this overview, you'll get a sense of how powerful dplyr is for working with data. Pat will use RStudio and functions from #dplyr and the rest of the tidyverse further demonstratin the power of #R. The accompanying blog post can be found at 🤍 Do you have a figure that you would like to receive a critique or help improving? Let me know and I'd be happy to arrange a guest appearance! If you're interested in taking an upcoming 3 day R workshop, email me at riffomonas🤍gmail.com! R: 🤍 RStudio: 🤍 Raw data: 🤍 Workshops: 🤍 You can also find complete tutorials for learning R with the tidyverse using... Microbial ecology data: 🤍 General data: 🤍 0:00 Overview 6:02 Cleaning up metadata 8:26 Cleaning up OTU counts table 11:39 Cleaning up taxonomy data 17:54 Joining data frames 21:05 Calculating relative abundances 23:17 Tidying by taxonomy 24:53 Conclusion
This video is a tutorial on the arrange, filter, mutate, rename, select and slice from the Dplyr package in R
How to replace a value of a data frame variable using the dplyr package in the R programming language. More details: 🤍 R code of this video: data ＜- data.frame(x1 = c(1, 2, 3, 2, 1, 1, 2), # Create example data x2 = "XX", x3 = 66) install.packages("dplyr") # Install dplyr package library("dplyr") # Load dplyr package data_new ＜- data %＞% # Replacing values mutate(x1 = replace(x1, x1 2, 99)) Follow me on Social Media: Facebook: 🤍 LinkedIn: 🤍 Patreon: 🤍 Pinterest: 🤍 Reddit: 🤍 Twitter: 🤍
In this video we cover some basic functions of the dplyr package. The bike buyers dataset used in this video can be found here: 🤍
Created by: Dr Lyndon Walker A demonstration of data frame manipulation functions in the dplyr package, using the R statistical software programme. Video originally created for STA80006 Using R for Statistical Analysis. Copyright owned by Swinburne University of Technology. This video is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 License. More like this: 🤍
Data cleaning is one of the more undervalued steps in a data anlaysis. In this episode we'll use a variety of functions from the tidyverse to get three data frames into the right format and then we'll join them all together. This will help us get ready for downstream analyses looking for microbiome-based biomarkers associated with colorectal cancer. In this episode, Pat will use the #tidyverse in #RStudio. The accompanying blog post can be found at 🤍 If you're interested in taking an upcoming 3 day R workshop, email me at riffomonas🤍gmail.com! R: 🤍 RStudio: 🤍 Raw data: 🤍 Workshops: 🤍 You can also find complete tutorials for learning R with the tidyverse using... Microbial ecology data: 🤍 General data: 🤍 0:00 Introduction 2:29 Tidying a mothur shared file 6:21 Formatting a taxonomy file 15:14 Calculating genus relative abundances 17:39 Formatting metadata and joining to relative abundances 23:31 Committing changes 24:26 Recap
In this video we will try to calculate the number and size of groups within dplyr package in R. For similar videos on data analysis and visualization, check out the links attached: Data Visualization Basics tutorial series: 🤍 Data Analysis Basics in R tutorial series: 🤍 Intermediate Data Analysis in R tutorial series: 🤍 Other related videos/links: # Intermediate Data Analysis in R FOR Loop: 🤍 WHILE Loop: 🤍 REPEAT Loop: 🤍 NESTED FOR Loop: 🤍 loop through dataframes: 🤍 Sequence with increments: 🤍 Adding columns using mutate and transmute (dplyr): 🤍 Subsetting rows and columns using filter and select (dplyr): 🤍 Subsetting rows using slice function (dplyr): 🤍 Summarize and arrange: 🤍 # Data Visualization Basics in R Intro to plotting using qplot in R: 🤍 Histogram and boxplot in R: 🤍 Add or change legend names while using qplot: 🤍 Change scatter plot colors in R manually: 🤍 Saving plots as .JPEG / .PNG: 🤍 Error bars for bar plots: 🤍 Adjusting geom positions while using ggplot2 package: 🤍 Adding plot labels: 🤍 Remove legend and legend titles: 🤍 Create custom color palette: 🤍 Plot multiple plots: 🤍 # Data Analysis Basics in R Setting a working directory in R Studio: 🤍 Data structures in R: 🤍 Create a dataframe in R: 🤍 Install packages in R: 🤍 How to import .csv file in R: 🤍 How to add a column in R: 🤍 How to remove a row in R: 🤍 Count the number of rows and columns in R: 🤍 Sum of rows in R: 🤍 Sum of columns in R: 🤍 Finding names of rows and columns in R: 🤍 How to change column names in R dataframes: 🤍 How to remove rows with 0 values in R dataframes: 🤍 How to find unique column values in R dataframes: 🤍 # GIS/Remote Sensing Intro to GIS and Remote Sensing: 🤍 What is LiDAR Remote Sensing: 🤍 Intro to spaceborne lidar and NASA GEDI: 🤍 LiDAR Remote Sensing/NASA GEDI Tutorial Series: 🤍
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![Data Manipulation Using dplyr Package [R Data Science Tutorial 5.0]](https://i.ytimg.com/vi/L1OHcrDOlUc/mqdefault.jpg "Data Manipulation Using dplyr Package [R Data Science Tutorial 5.0]")
