#shorts Reordering dataframe columns in tidyverse can be done with relocate(). In Julia, you have more than one options for reordering columns: you can either use the Cols() column selector or the select!() function. Check out the #shorts video!
#shorts
R-Ladies used the arrange() function to sort all rows of ikea in various ways. Julia has the sort!() function to achieve the exact same effects. Check out the #shorts video.
Link to the ikea dataset:
https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-11-03/ikea.csv
Ladies Freiburg (Kyla McConnell and Julia Müller) - Tidy Data: Zero to sHero {Part 2}:
https://www.youtube.com/watch?v=ivSgLWKhNrw
R-Ladies material on github:
https://github.com/rladies/meetup-presentations_freiburg/tree/master/2021-02-17_tidydata_ZerotoShero
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https://www.youtube.com/watch?v=zcBrWRGG5yc
This is the introduction to a new playlist with short videos that will contain videos that follow youtube's format of #shorts that last 60 seconds or less. These videos will focus on how to manipulate dataframes in Julia for completing the exact same tasks that were completed with the R code presented by R-Ladies in their youtube channel.
Ladies Freiburg (Kyla McConnell and Julia Müller) - Tidy Data: Zero to sHero {Part 2}:
https://www.youtube.com/watch?v=ivSgLWKhNrw
R-Ladies material on github:
https://github.com/rladies/meetup-presentations_freiburg/tree/master/2021-01-20_IntrotoR_ZerotoShero
My last video on calculating association measures using Julia:
https://youtu.be/u2DI4zEoWL8
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https://www.youtube.com/watch?v=B7iUUrHWAh0
Welcome back to Categorical Data Analysis with Julia!
Today, we're going to break down the output of the logistic regression model we created in the previous short video.
Our model is predicting the probability of 'Survived' based on the 'Pclass' variable. Recall that 'Pclass 1' stands for first class, Pclass 2 for second class, and Pclass 3 for third class.
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https://www.youtube.com/watch?v=46G6YAWV6Es
In this short video I will present all the three arguments of the factor() function levels, exclude and labels, that offer flexibility while creating a factor and declaring its levels. Both arguments are related but used for slightly different purposes when creating a factor and their usage can be confusing to a beginner.
00:11 Learning outcomes
00:47 Inspecting the recorded_gender factor
03:37 The "levels" argument
04:39 The "exclude" argument
05:07 The "labels" argument
05:52 Tracing the differences between the 3 arguments
06:10 Code summary
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https://www.youtube.com/watch?v=DYz6Mlen74E
#shorts
Focusing on specific basic statistical indices, Julia and Kyla R-Ladies showed how to use the max() and min() functions to get back the maximum and minimum values of the height and width columns, respectively.
Julia outputs the same information in one step using describe() that accepts two additional arguments apart from ikea: the first of the two is the summary function that you want to use, max() and min() in our case, and the second additional argument is the named argument "cols" whose value or values are the column or columns for which you want to calculate the maximum and minimum values.
Link to the ikea dataset:
https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-11-03/ikea.csv
Ladies Freiburg (Kyla McConnell and Julia Müller) - Tidy Data: Zero to sHero {Part 2}:
https://www.youtube.com/watch?v=ivSgLWKhNrw
R-Ladies material on github:
https://github.com/rladies/meetup-presentations_freiburg/tree/master/2021-02-17_tidydata_ZerotoShero
...
https://www.youtube.com/watch?v=mLDuJiboVfI
#shorts
Welcome back to Categorical Data Analysis with Julia! In this video, we'll cover inferential statistics for categorical data.
Let's perform a chi-square test of independence using the 'ChisqTest' function from the HypothesisTests package for the two categorical variables, :Survived and :Pclass, to test whether different categories (outcomes) of Passenger Class are associated with different categories of the survived variable.
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https://www.youtube.com/watch?v=gSjHMynzSEw
We're going to create and use unnest_tokens(), a function in Julia that is used for 'unnesting tokens' This function is very similar to unnest_tokens() included in the 'tidytext' R package, but it's much faster. This function can be a game changer if you're dealing with large datasets.
We'll be using the Netflix Titles dataset for our demonstration, which is a popular dataset for text analysis exercises.
Kaggle dataset on neftlix titles:
https://www.kaggle.com/datasets/shivamb/netflix-shows
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https://www.youtube.com/watch?v=CwQLa2_ljpI