Dplyr drop_na

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R Remove Data Frame Rows with NA Values | na.omit, complete.cases, rowSums, is.na, drop_na & filter

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How to delete rows with some or all missing values in a data frame in the R programming language. More details: 🤍 R code of this video: data <- data.frame(x1 = c(4, 1, NA, 7, 8, 1), # Create example data x2 = c("A", NA, NA, "XX", "YO", "YA"), x3 = c(1, 0, NA, 1, 1, NA)) data1 <- na.omit(data) # Apply na.omit function data2 <- data[complete.cases(data), ] # Apply complete.cases function data3 <- data[rowSums(is.na(data)) 0, ] # Apply rowSums & is.na install.packages("tidyr") # Install & load tidyr package library("tidyr") data4 <- data %>% drop_na() # Apply drop_na function data5 <- data[!is.na(data$x1), ] # Apply is.na function install.packages("dplyr") # Install dplyr package library("dplyr") # Load dplyr package data6 <- filter(data, !is.na(data$x1)) # Apply filter function Follow me on Social Media: Facebook: 🤍 Patreon: 🤍 Pinterest: 🤍 Reddit: 🤍 Twitter: 🤍

Remove Rows with NA Using dplyr Package in R (3 Examples) | na.omit, filter, complete.cases Function

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24.04.2021

How to remove rows with NA using the dplyr package in the R programming language. More details: 🤍 R code of this video: data <- data.frame(x1 = c(1, 2, NA, 4, 5, 6), # Create example data x2 = c("X", NA, "Y", "AA", "X", "Z"), x3 = 4) data # Print example data install.packages("dplyr") # Install dplyr package library("dplyr") # Load dplyr package data %>% # Apply na.omit na.omit data %>% # Apply filter & complete.cases filter(complete.cases(.)) data %>% # Apply filter & is.na filter(!is.na(x1)) Follow me on Social Media: Facebook: 🤍 LinkedIn: 🤍 Patreon: 🤍 Pinterest: 🤍 Reddit: 🤍 Twitter: 🤍

dplyr summary count and base R na.rm and is.na

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In this video, I explain how na.rm can be used to deal with missing values and is.na can be used to filter() missing values Please follow link: 🤍

Using dplyr's filter function to pick rows from a data frame in R (CC161)

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R's filter function allows you to create queries to pick rows from a data frame that match your query. It's a tremendously powerful function that we get from the dplyr R package. In this episode we'll see how we can use it to search through a data frame with more than 100000 rows! You can make simple and more sophisticated queries by following a few simple concepts. We'll also see how you can remove NA values using these concepts and using the special drop_na function. I'll demonstrate how to use filter with a massive data frame that we downloaded from Our World in Data that describes COVID-19 vaccination rates by country and day. In this episode, Pat uses #filter and #drop_na from the #dplyr #R package in #Rstudio. The accompanying blog post can be found at 🤍 Want more practice on the concepts covered in Code Club? You can sign up for my weekly newsletter at 🤍 to get practice problems, tips, and insights. If you're interested in taking an upcoming 3 day R workshop be sure to check out our schedule at 🤍 You can also find complete tutorials for learning R with the tidyverse using... Microbial ecology data: 🤍 General data: 🤍 0:00 Filtering rows from a data frame 1:48 Filtering rows that satisfy a set of criteria 5:19 Combining filter statments with AND & OR 12:17 Filtering rows based on a logical column 15:09 Filtering out rows with NA values 16:47 Cleaning up script

Advanced Filtering in R (Or/And Conditions, Strings, Missing Values)

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03.03.2022

Get the code here: 🤍. Newsletter: 🤍 Twitter: 🤍 Buy me a coffee: 🤍 00:00 Filter data in RStudio 00:45 Install and Attach R packages 01:42 Filter rows with dplyr - 5 simple examples 05:29 Filter data using Multiple Columns 06:57 Filter character and words using stringr 08:30 Remove missing values 09:13 Advanced filtering using aggregators 09:44 Final remarks In this video I show you how to use the filter function from the dplyr R package in RStudio. We will see different ways of keeping or removing specific rows of a dataset using 5 simple examples. Then I will show you more advanced filtering methods, such as filtering data using logical conditions on multiple columns, how to remove missing values of a dataset or how to use specific words or strings to filter rows of character variables using the stringr R package.

Removing outliers in R with tools from dplyr and ggplot2 (CC232)

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21.07.2022

If you know you have outliers in your dataset how would you go about removing them in R? In this episode, Pat will show you how to identify outliers using graphical approaches using ggplot2 with geom_histogram and geom_line. If they are truly anomalous we remove them using functions from dplyr like filter, mutate, if_else, and drop_na. We'll do all this using local weather data from the NOAA website in RStudio You can find my blog post for this episode at 🤍 #ggplot2 #dplyr #R #Rstudio #Rstats Want more practice on the concepts covered in Code Club? You can sign up for my weekly newsletter at 🤍 to get practice problems, tips, and insights. If you're interested in taking an upcoming 3 day R workshop be sure to check out our schedule at 🤍 You can also find complete tutorials for learning R with the tidyverse using... Microbial ecology data: 🤍 General data: 🤍 0:00 Introduction 2:40 Identifying problematic data with line plots 6:16 Identifying problematic data with histograms 7:25 Identifying problematic data with slice_max 8:52 Rinse, repeat 11:36 Removing anomalous data 15:50 How you would remove categorical data 18:49 Removing rows with NA values

Handling NA in R | is.na, na.omit & na.rm Functions for Missing Values

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11.01.2019

How to handle NA in R programming - 4 examples for is.na, na.omit & na.rm. Find more R tutorials here: 🤍 Find missing values in R: 🤍 How to use the is.na function in R: 🤍 Handling NAs with na.omit: 🤍 What does NA actually mean? The NA value explained: 🤍 Listwise deletion (including an R example): 🤍 The R code of this tutorial: ### Load data data("airquality") ### Find missing values is.na(airquality) ### Count missing values sum(is.na(airquality)) ### Omit missing values na.omit(airquality) ### Remove missing values mean(airquality$Ozone, na.rm = TRUE)

Drop Multiple Columns from Data Frame Using dplyr Package in R (Example) | select & one_of Functions

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02.11.2020

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: 🤍

Using the dplyr lag and lead function to find the length of drought (CC245)

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06.09.2022

The lag and lead functions from dplyr allow you to create columns that are offset from the reference column by one or more rows. In this Code Club, Pat responds to a commenter's question to calculate the number of days between precipitation events to see if the length of drought is increasing or decreasing over time. Pat will address these questions using local weather data downloaded from NOAA in RStudio with a lot of help from the tidyverse and the bench R package You can find my blog post for this episode at 🤍 #lag #lead #dplyr #tidyverse #R #Rstudio #Rstats Support Riffomonas by becoming a Patreon member! 🤍 Want more practice on the concepts covered in Code Club? You can sign up for my weekly newsletter at 🤍 to get practice problems, tips, and insights. If you're interested in taking an upcoming 3 day R workshop be sure to check out our schedule at 🤍 You can also find complete tutorials for learning R with the tidyverse using... Microbial ecology data: 🤍 General data: 🤍 0:00 Introduction 1:37 Plan for attacking problem 3:19 Replacing NA values with 0 4:50 Filtering out no or low precipitation days 6:07 Lag vs. lead 8:05 Calculating the length of drought 9:38 Visualizing the length of drought for one year 11:28 Calculating and visualizing summary statistics 17:08 Cleaning up appearance of visualization

#8 dplyr package in R : rename() & rename_with()

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12.11.2022

Using rename() and rename_with() to change column names, convert column names to UPPER case or lower case. This is one of the most important functions which will be used a lot to maintain formats in our data. Chapters: 0:00 Introduction 0:57 Importing data set 1:30 Using rename() 3:07 Using rename_with() Dataset used in video : 🤍 dplyr in R Playlist : 🤍 #rprogramming #dplyr #data #datanalytics #rstudio #rename #column

R Order Data Frame Rows According to Vector (Example) | Sort & Rearrange | match & left_join [dplyr]

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How to sort data frame rows based on the values of a vector with a specific order in the R programming language. More details: 🤍 R code of this video: data <- data.frame(x1 = 11:15, # Create example data x2 = letters[1:5], x3 = 6:10) vec <- c("b", "e", "a", "c", "d") # Create example vector vec # Print example vector data_new1 <- data[match(vec, data$x2), ] # Reorder data frame install.packages("dplyr") # Install & load dplyr package library("dplyr") data_new2 <- left_join(data.frame(x2 = vec), # Reorder data frame data, by = "x2") Follow me on Social Media: Facebook: 🤍 LinkedIn: 🤍 Patreon: 🤍 Pinterest: 🤍 Reddit: 🤍 Twitter: 🤍

dplyr: Data Wrangling (part of the Rfun Introduction to R series)

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Materials available at 🤍 Code: 🤍 YouTube Playlist: 🤍 More Rfun at 🤍 Part of the DVS Workshop Series: 🤍

How To... Remove Records with Missing Data in R #74

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Learn how to deal with missing values in datasets and to recognise where missing values occur in R with 🤍EugeneOLoughlin. The R script (74_How_To_Code.R) and data file (74_How_To_Code.csv) for this video are available to download from Github at: 🤍

Dealing with NA in R

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This video is about identifying where missing values (represented as NAs) are in your data using R. I also talk about extracting rows and columns that have missing values, replacing missing values and using the dplyr package to deal with missing values. Some of the functions I use include: na.omit(), is.na(), complete.cases(), anyNA(), table(). Timestamp 0:00 Intro 0:59 Identifying missing values 7:43 Extracting rows or columns that have NAs 14:15 Replacing NAs or any values that meet a certain condition 23:05 Using dplyr to filter out rows that have NAs

How to clean and join data from mothur with the dplyr R package (CC101)

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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

Removing NAs in R dataframes

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Sort Table in R (Examples) | Increasing & Decreasing Order | Base R & dplyr Package | order Function

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How to order a table by frequency in the R programming language. More details: 🤍 R code of this video: x <- c(letters[1:4], letters[2:5], "d") # Create example vector x # Print example vector my_tab <- table(x) # Create example table my_tab # Print example table my_tab_sort1 <- my_tab[order(my_tab)] # Order table my_tab_sort1 # Print ordered table my_tab_sort2 <- my_tab[order(my_tab, # Decreasing order of table decreasing = TRUE)] my_tab_sort2 # Print ordered table install.packages("dplyr") # Install & load dplyr package library("dplyr") my_tab_sort3 <- my_tab %>% # Order table with dplyr as.data.frame() %>% arrange(desc(Freq)) my_tab_sort3 # Print ordered table as data frame Follow me on Social Media: Facebook – Statistics Globe Page: 🤍 Facebook – R Programming Group for Discussions & Questions: 🤍 Facebook – Python Programming Group for Discussions & Questions: 🤍 LinkedIn – Statistics Globe Page: 🤍 LinkedIn – R Programming Group for Discussions & Questions: 🤍 LinkedIn – Python Programming Group for Discussions & Questions: 🤍 Twitter: 🤍 Music by bensound.com

[R Beginners] Dplyr Essentials for easy data manipulation in R - Series Video - 6 Slice command

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04.08.2022

[R Beginners] DPLYR series SLICE 06 🤍 #dplyr #ggplot2 #Rstudio #R #Rstats #DataVisualisation #piechart #sankey We mainly create videos on R, Python and other related technologies which compliment the data science needs. If you are beginner then watch this video to get started How to install R and R Studio 🤍 Watch our playlists GGPLOT charting galore 🤍 DPLYR series - DPLYR is one of the most important tool in data handling. Learn all about it in 🤍 Geo analytics mapping techniques 🤍 Statistics in R 🤍 Python - statistics, automation and visualisation 🤍 HighCharter interactive and static charting 🤍 Some amazing stuff that Excel can do 🤍 We Our everygrowing playlist of sharp and short videos in the #shorts format for one minute learning 🤍

Use dplyr filter to remove rows with missing values

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Previous video on basic use of filter: 🤍 Previous video on importing the data used: 🤍 Download the data from: 🤍

Remove Empty Rows of Data Frame in R (2 Examples) | apply, all, rowSums, is.na & ncol Functions

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How to delete empty rows in a data frame in the R programming language. More details: 🤍 R code of this video: data1 <- data.frame(x1 = c("1", "", "2", "", "3"), # Create data with empty cells x2 = c("a", "", "b", "c", "d")) data1[!apply(data1 "", 1, all), ] # Remove rows with only empty cells data2 <- data.frame(x1 = c(1, NA, 2, NA, 3), # Create data with NAs x2 = c("a", NA, "b", "c", "d")) data2[rowSums(is.na(data2)) != ncol(data2), ] # Remove rows with only NAs Follow me on Social Media: Twitter: 🤍 Facebook: 🤍 Reddit: 🤍 Pinterest: 🤍

R: sum if | group by | case when | dplyr || 12

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In this video we learn how to do operations such as sum if in R with the dplyr package

Remove Last N Rows from Data Frame in R (Example) | Delete Bottom | head, slice & n of dplyr Package

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How to drop the last N rows of a data frame in the R programming language. More details: 🤍 R code of this video: data <- data.frame(x1 = LETTERS[1:10], # Create example data frame x2 = 1:10, x3 = 20:11) data_new1 <- head(data, - 3) # Apply head function install.packages("dplyr") # Install dplyr package library("dplyr") # Load dplyr package data_new2 <- slice(data, 1:(n() - 3)) # Apply slice & n functions Follow me on Social Media: Facebook: 🤍 LinkedIn: 🤍 Twitter: 🤍

Improving the appearance of a stacked barchart with ggplot2, dplyr, and forcats (CC103)

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Sometimes we don't have a choice in the type of data visualization format we use. Although a stacked barchart is not ideal, there are methods for improving the appearance of stacked barcharts. In this episode of Code Club, Pat uses ggplot2, dplyr, and forcats to show how to pool rare taxa and order the more abundant taxa to give a common anchor point on the y-axis to make comparisons. Pat will use RStudio and functions from the tidyverse including #ggplot2, #dplyr, and #forcats packages. 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 Introduction 4:16 Pooling rare phyla 8:36 Specifying order of stacked bars w/ help of forcats 14:41 Changing colors and appearance of legend 17:33 Making bars sit on x-axis 18:15 Critique of figure 21:28 Recap

Using dplyr's slice functions to pick specific and random rows from a data frame in R (CC042)

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26.10.2020

In this screencast tutorial, Pat Schloss shows how you can use dplyr's slice functions including slice, slice_head, slice_tail, and slice_sample to pick specific and random rows from a data frame in R. We'll then modify code from a previous episode to recalculate the specificity of 16S rRNA genes for each taxonomic group at each taxonomic rank using slice_sample to incorporate randomness into the analysis. This episode is part of a larger arc of episodes investigating the sensitivity and specificity of amplicon sequence variants (ASVs), also known as exact sequence variants (ESVs). ASVs are growing in popularity for analyzing microbial communities using 16S rRNA gene sequences. Pat demonstrates these concepts by live coding at the command line interface using RStudio, GitHub Flow, and make. 0:00 Introduction 2:13 Today's issue 5:46 Slice commands 9:50 Outlining approach to downsample species with pseudocode 14:10 Filling in code to address uneven sampling of species 21:41 Trying different number of genomes per species 23:07 Comparing results using git diff 25:18 Conclusion The accompanying blog post contains the exercises and solutions can be found at 🤍

Data Manipulation in R using dplyr arrange function - 1(c) | arrange in R

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23.04.2020

arrange in ascending or descending order | sort in R dplyr package provides a general framework for the manipulation of data frames in R. arrange() function reorder the rows in ascending or descending order filter function: 🤍 select function: 🤍

Using the drop argument in count and group_by with factors to include missing data (CC240)

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18.08.2022

You want to know about the drop argument for count and group_by, if you have categories that you want to be sure to include in your analysis, but that might be missing. Pat will demonstrate how to use .drop with a toy dataset and snow data for the past 130 years that he obtained from local weather data downloaded from NOAA. He'll also show how to convert a factor into a continuous variable for plotting on the x-axis. All this will be done in RStudio with a lot of help from the tidyverse You can find my blog post for this episode at 🤍 #count #group_by #ggplot2 #dplyr #R #Rstudio #Rstats Want more practice on the concepts covered in Code Club? You can sign up for my weekly newsletter at 🤍 to get practice problems, tips, and insights. If you're interested in taking an upcoming 3 day R workshop be sure to check out our schedule at 🤍 You can also find complete tutorials for learning R with the tidyverse using... Microbial ecology data: 🤍 General data: 🤍 0:00 Introduction 2:15 Reviewing code from previous episode 6:13 Using count on a factor 8:28 Using group_by with a factor 9:30 Using count and group_by with two variables 11:49 Applying to snow precipitation data 13:29 Plotting with factors on x-axis

Remove Data Frame Columns by Name in R (6 Examples) | Drop Variable | subset, within, select & setDT

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How to drop the variable of a data frame by its name in the R programming language. More details: 🤍 R code of this video: data <- data.frame(x1 = 1:5, # Create example data x2 = 6:10, x3 = letters[1:5], x4 = letters[6:10]) data1 <- data[ , ! names(data) %in% c("x1", "x3")] # Apply %in%-operator data2 <- data[ , names(data) %in% c("x2", "x4")] # Keep certain variables data3 <- subset(data, select = - c(x1, x3)) # Apply subset function data4 <- within(data, rm(x1, x3)) # Apply within function install.packages("dplyr") # Install dplyr package library("dplyr") # Load dplyr data5 <- select(data, - c(x1, x3)) # Apply select function install.packages("data.table") # Install & load data.table package library("data.table") data6 <- data setDT(data6)[ , c("x1", "x3") := NULL] # Using := NULL class(data6) # Check class of data Follow me on Social Media: Facebook: 🤍 Patreon: 🤍 Pinterest: 🤍 Reddit: 🤍 Twitter: 🤍

R data wrangling with dplyr, tidyr, readr and more, part 2 of 3 (tidyverse approach 2020)

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Ryan Womack, Data Librarian Rutgers University - New Brunswick Libraries 🤍 🤍 Screencast version of a workshop at Rutgers University. This series is an introduction to R with an emphasis on the tidyverse. Some of the most powerful features of the tidyverse relate to its abilities to import, filter, and otherwise manipulate data. This session reviews major packages within the tidyverse that relate to the essential data handling steps require before (and during) data analysis. Part 2 discuss core functionality of dplyr. Related R materials at 🤍 🤍 Code at 🤍

Group by and Summarise functions in R programming - use the tidyverse package to wrangle your data

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If you are learning to work with data then being able to structure, manipulate and summarize your data is extremely important. This forms part of what we call descriptive statistics. the group_by() and summarise() functions are part of the tidyverse set of packages. So if you're an aspiring data scientist or statistician, or you're doing a PHD or masters degree and need to be able to do quantitative analysis - then this video is for you. This channel is supported by Nested Knowledge - an online platform that supports the entire literature review process. Please do check them out at this link: 🤍

Data Manipulation with R using [dplyr] Package| select remove specific columns| remove duplicate row

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08.11.2020

🤍Data/ Fun - IN This lesson we are see 1 . How to Select N random rows by using dplyr package in R 2. How to select random samples from data frame with specific size 3. How remove Duplicate Rows based 4. How to select specific Variables (or Columns) 5. How to drop specific Variables (or Columns) 6. How to select Columns/Variables contain specific letter 7. How reorder Variables Columns Subscribe 🤍DataFunAnalyticaltricks YouTube channel for more tricks in Data Science and R programming

dplyr rename() select()

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In this video I use R for Data Science to explain the dplyr verbs rename() and select() Please follow link: 🤍 Please follow link: 🤍

Chapter 5.01 Tidy up your data in R (using dplyr / tidyverse package)

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14.11.2021

This is one of several videos that accompany my courses in statistics for psychologists at the University of Lausanne. Chapter 5 is about data manipulation with the tidyverse (dplyr) package. Video 1 of chapter 5 is about how to tidy up your data in R. Chapter 5 5.01 Tidy up your data in R (using dplyr / tidyverse package) 5.02 Data handling 1: Use select, filter, pipe, and rename in R 5.03 Data handling 2: Creating subgroups in R (using dplyr / tidyverse package) 5.04 Data handling 3 in R: Recode items 5.05 Data handling 4 in R: Compute a scale (mean scores and sum scores). 5.06 Data handling 5 in R: Center and standardize variables (using dplyr / tidyverse package) 5.07 Data handling 6 in R: Combine all the functions to deal with a more complex longitudinal data set (using dplyr / tidyverse package) 5.07 Data handling 7 in R: Merge data sets 5.08 Data handling 7 in R: From wide to long data sets and back (using dplyr / tidyverse package) 5.09 Data handling 7 in R: Reading in and merging lots of data sets into one simultaneously using a for loop

Basics of dplyr

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An overview of the pipe operator and the core functions of dplyr.

R Replace Values in Data Frame Conditionally (4 Examples) | Exchange Value in Column & Entire Matrix

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How to replace values in data frame variables in the R programming language. More details: 🤍 R code of this video: data <- data.frame(num1 = 1:5, # Example data num2 = 3:7, char = letters[1:5], fac = c("gr1", "gr2", "gr1", "gr3", "gr2")) data$char <- as.character(data$char) # Convert to character data$num1[data$num1 1] <- 99 # Replace 1 by 99 data$char[data$char "b"] <- "XXX" # Replace b by XXX data$fac <- as.character(data$fac) # Convert factor to character data$fac[data$fac "gr1"] <- "new_group" # Replace gr1 by new_group data$fac <- as.factor(data$fac) # Convert character to factor data[data 3] <- 777 # Replace all values Follow me on Social Media: Twitter: 🤍 Facebook: 🤍 Reddit: 🤍 Pinterest: 🤍

Find Common Rows Between Two Data Frames in R | Identify Duplicates | intersect & inner_join [dplyr]

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How to return all rows that exist in two data frames in the R programming language. More details: 🤍 R code of this video: data1 <- data.frame(x1 = 1:5, # Create first example data x2 = letters[1:5], x3 = "x") data2 <- data.frame(x1 = 3:6, # Create second example data x2 = letters[3:6], x3 = c("x", "x", "y", "y")) data_common1 <- generics::intersect(data1, data2) # Apply intersect function install.packages("dplyr") # Install & load dplyr package library("dplyr") data_common2 <- inner_join(data1, data2) # Apply inner_join function 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

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