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I would like to obtain the estimation sample from a model object, i.e. the observations that were not dropped due to missing values. This seems to be simple for standard lm regressions (using case.names()) but less so for more recent packages such as fixest.

Is there any general way to access the estimation sample, irrespective of the package used for estimation?

My attempts for both lm and fixest objects are:

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I am a newbie in data analysis. I wish to know how to boxplot multiple columns (x-axis = Points, Score, Weigh) in a single graph and make the y-axis as a standardized scale for comparison. I have tried and couldn't understand the code (Python+Pandas+Seaborn) for this. Help me out guys. The dataset for the same is as follows:

Cars Points Score Weigh 0 Mazda RX4 3.90 2.620 16.46 1 Mazda RX4 Wag 3.90 2.875 17.02 2 Datsun 710 3.85 2.320 18.61 3 Hornet 4 Drive 3.08 3.215 19.44 4 Hornet Sportabout 3.15 3.440 17.02 5 Valiant 2.76 3.460 20.22 6 Duster 360 3.21 3.570 15.84 7 Merc 240D 3.69 3.190 20.00 8 Merc 230 3.92 3.150 22.90 9 Merc 280 3.92 3.440 18.30 10 Merc 280C 3.92 3.440 18.90 11 Merc 450SE 3.07 4.070 17.40 12 Merc 450SL 3.07 3.730 17.60 13 Merc 450SLC 3.07 3.780 18.00 14 Cadillac Fleetwood 2.93 5.250 17.98 15 Lincoln Continental 3.00 5.424 17.82 16 Chrysler Imperial 3.23 5.345 17.42 17 Fiat 128 4.08 2.200 19.47 18 Honda Civic 4.93 1.615 18.52 19 Toyota Corolla 4.22 1.835 19.90 20 Toyota Corona 3.70 2.465 20.01 21 Dodge Challenger 2.76 3.520 16.87 22 AMC Javelin 3.15 3.435 17.30 23 Camaro Z28 3.73 3.840 15.41 24 Pontiac Firebird 3.08 3.845 17.05 25 Fiat X1-9 4.08 1.935 18.90 26 Porsche 914-2 4.43 2.140 16.70 27 Lotus Europa 3.77 1.513 16.90 28 Ford Pantera L 4.22 3.170 14.50 29 Ferrari Dino 3.62 2.770 15.50 30 Maserati Bora 3.54 3.570 14.60 31 Volvo 142E 4.11 2.780 18.60

My output should look something like: Output Boxplot Graph

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I have a result DF that looks like this:

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I want to find a way to compare the values of hp of a car using Poisson distribution to see which one is more likely to have the lowest value of hp from all cars for example Mazda Rx4 has a horsepower of 110. I want to simulate this value following the Poisson distribution for each of the cars in the sample. I want to create a table that compares the probability of each car for all the cars in the data frame to have the lowest value in this indicator

I am using this example for simplicity, in reality, these are players names for golf player, and the horsepower is the number of strokes taken, that is why I want to have a list that has the probability of each observation in my sample to have the lowest score in this indicator

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I have created a reactive function in the server to get a list of elements. The idea is to show each element of the list as a individual choice in the ui, just like selectInput does in the ui.

I wrote an example with mtcars.

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I am relatively new to R and I am trying to find the top 3 elements in the data frame in a list. For example, I have a list like this

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I am trying to subsample a data.frame in a way that the sample would have observations that capture as much variation as possible among a set of columns of the original data.frame.

An example with the mtcars dataset: I'd like to find 3 cars that are the most different from each other by mpg, vs and carb. Looking at the data visually, it would probably be Toyota Corolla (high mpg, vs 1, low carb), Cadillac Fleetwood (low mpg, vs 0, medium carb) and either Maserati Bora (low-med mpg, vs 0, high carb) or Ferrari Dino (medium mpg, vs 0, med-high carb):

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As part of my answer to this post, I suggested a completely generic mechanism by which one data frame could be filtered by conditions stored in another. The OP has called me out (damn!) and asked me for an implementation.

My solution requires me to store functions in the filter dataframe. This is possible: this post shows how.

As a basic example, consider

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I have data that I need to clean up programmatically using a reference table. In the reference table, each row pertains to a different column in the data, and specifies the values by which to filter each data variable.

Example

Data

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