Paperback , pages. Published November 1st by Spuyten Duyvil first published July 1st To see what your friends thought of this book, please sign up.
To ask other readers questions about The Number of Missing , please sign up. Be the first to ask a question about The Number of Missing. Lists with This Book. This book is not yet featured on Listopia. Nov 15, Haven Gordon rated it it was amazing. The loss, devastation, and the ability to cope come heavily into play as the characters try to move on past the deaths of their loved ones.
The Number of Missing
Their emotions seemed so real and so raw that it left me feeling gutted at times. This was truly a good book and I highly recommend it. I won this book from a Goodreads First Reads giveaway and while the contest did not shape my review, I am incredibly grateful for the chance to have read it! Aug 28, Erika Dreifus added it Shelves: Check out my interview with the author about this book.
Interview based on complimentary advance reading copy provided by the publisher. I really became engrossed in this book. I couldn't put it down. Read it in a matter of hours. Dec 13, Amy O'tinger rated it it was amazing Shelves: I received a free copy of this book from the Goodreads First Reads.
- The Number of Missing by Adam Berlin.
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Utterly devastating, and beautiful. Fantastic, but hard, read.
*Missing values
Roy Wano rated it it was amazing Nov 26, Krystal rated it it was amazing Feb 22, Kasia rated it it was amazing Dec 28, Ruth Yang rated it it was ok Nov 08, Different variables have different amounts of missing data and hence, changing the variables in a model changes the number of cases with complete data on all the variables in the model.
However, the presence of missing data can influence our results, especially when a dataset or even a single variable, has a high percentage of values missing. Thus it is always a good idea to check a dataset for missing data, and to think about how the missing data may influence our analyses.
This page shows a few methods of looking at missing values in a dataset, this information can be used to make better informed decisions about how to handle the missing values. Before we begin, we need some data with missing values, the code below inputs a small dataset into Stata, and then displays that data. In a small dataset, like the one below, it is very easy to look at the raw data and see where values are missing. However, when datasets are large, we need a more systematic way to examine our dataset for missing values. Below we show you some ways to do that, using the data below as an example.
The first thing we are going to do is determine which variables have a lot of missing values. We have created a small Stata program called mdesc that counts the number of missing values in both numeric and character variables. You can download mdesc from within Stata by typing search mdesc see How can I use the search command to search for programs and get additional help? Now we know the number of missing values in each variable.
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- 1. Number of missing values vs. number of non missing values;
- Gallery of Missing Data Visualisations!
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For instance, variable salepric has four missing values and saltoapr has two missing values. We can also look at the distribution of missing values across observations. The code below creates a variable called nmis that gives the number of missing values for each observation. The function rmiss2 used here is an extension to the egen function rmiss.
It counts the number of missing values in the varlist. You can download rmiss2 over the internet from within Stata by typing search rmiss2 see How can I use the search command to search for programs and get additional help?
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Below we tabulate the variable we just created. Looking at the frequency table we know that there are four observations with no missing values, nine observations with one missing values, one observation with two missing values and one observation with three missing values. It is powered by a dplyr:: This plot shows the number of missings in a given span, or breaksize, for a single selected variable.
- Missing Values: Order of Missing Values!
- 2. Obtaining the number of missing values per observation.
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- Exploring patterns with UpSetR.
This plot shows the cumulative sum of missing values, reading the rows of the dataset from the top to bottom. This plot shows the cumulative sum of missing values, reading columns from the left to the right of your dataframe. Exploring patterns with UpSetR An upset plot from the UpSetR package can be used to visualise the patterns of missingness, or rather the combinations of missingness across cases.
Exploring Missingness Mechanisms There are a few different ways to explore different missing data mechanisms and relationships.