109Results for "tidyverse"
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The tidyverse3m
The tidyverse
From: Complete Guide to R: Wrangling, Visualizing, and Modeling Data
Video
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Duplicate and redundant data4m
Duplicate and redundant data
From: CompTIA Data+ (DA0-002) Cert Prep
Video
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What are %>% and |> for in the tidyverse3m
What are %>% and |> for in the tidyverse
From: Learning the R Tidyverse
Video
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Work with sf datasets and the tidyverse3m
Work with sf datasets and the tidyverse
From: Creating Maps with R
Video
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Piping commands with %>%5m
Piping commands with %>%
From: Complete Guide to R: Wrangling, Visualizing, and Modeling Data
Video
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Using color palettes8m
Using color palettes
From: Complete Guide to R: Wrangling, Visualizing, and Modeling Data
Video
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Loading data with the tidyverse9m
Loading data with the tidyverse
From: R Tidyverse Applications
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Handling NAs in the tidyverse with drop_na() and replace_na()4m
Handling NAs in the tidyverse with drop_na() and replace_na()
From: Learning the R Tidyverse
Video
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Comparing two means: Independent samples t-test8m
Comparing two means: Independent samples t-test
From: Complete Guide to R: Wrangling, Visualizing, and Modeling Data
Video
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Importing data from a spreadsheet5m
Importing data from a spreadsheet
From: R for Data Science: Analysis and Visualization
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Reading data from databases and the web2m
Reading data from databases and the web
From: Data Wrangling in R
Video
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Working with dates and times6m
Working with dates and times
From: Complete Guide to R: Wrangling, Visualizing, and Modeling Data
Video
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Working with list data5m
Working with list data
From: Complete Guide to R: Wrangling, Visualizing, and Modeling Data
Video
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Importing the social security disability dataset1m
Importing the social security disability dataset
From: Data Wrangling in R
Video
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Installing, loading, and working with the tidyverse packages5m
Installing, loading, and working with the tidyverse packages
From: Learning the R Tidyverse
Video
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Understanding how the tidyverse evolves and deprecates3m
Understanding how the tidyverse evolves and deprecates
From: Learning the R Tidyverse
Video
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Reading in the water quality dataset1m
Reading in the water quality dataset
From: Data Wrangling in R
Video
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Piping commands with |4m
Piping commands with |
From: R for Data Science: Analysis and Visualization
Video
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Using tibbles4m
Using tibbles
From: Complete Guide to R: Wrangling, Visualizing, and Modeling Data
Video
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Breaking apart columns with separate()3m
Breaking apart columns with separate()
From: Data Wrangling in R
Video
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Using colors in R5m
Using colors in R
From: Complete Guide to R: Wrangling, Visualizing, and Modeling Data
Video
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Datasets built into the tidyverse packages3m
Datasets built into the tidyverse packages
From: Learning the R Tidyverse
Video
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Recoding categorical data9m
Recoding categorical data
From: Complete Guide to R: Wrangling, Visualizing, and Modeling Data
Video
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Importing data from a spreadsheet5m
Importing data from a spreadsheet
From: Complete Guide to R: Wrangling, Visualizing, and Modeling Data
Video
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Saving data in native R formats6m
Saving data in native R formats
From: Complete Guide to R: Wrangling, Visualizing, and Modeling Data
Video
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Using the mutate() function to modify and add columns4m
Using the mutate() function to modify and add columns
From: Learning the R Tidyverse
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Using the select() function to obtain columns from data4m
Using the select() function to obtain columns from data
From: Learning the R Tidyverse
Video
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Conducting an item analysis17m
Conducting an item analysis
From: Complete Guide to R: Wrangling, Visualizing, and Modeling Data
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Using data.table4m
Using data.table
From: Complete Guide to R: Wrangling, Visualizing, and Modeling Data
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Exploring sample datasets with pacman6m
Exploring sample datasets with pacman
From: Complete Guide to R: Wrangling, Visualizing, and Modeling Data
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Getting started in the R tidyverse56s
Getting started in the R tidyverse
From: Learning the R Tidyverse
Video
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Converting data from wide to tall and from tall to wide4m
Converting data from wide to tall and from tall to wide
From: Complete Guide to R: Wrangling, Visualizing, and Modeling Data
Video