immigrant | Foreign key migration generator for Rails | Application Framework library

I found a work around to this issue by simply transforming the dataset to a Numpy ndarray.

The problem is that your read_json(....).text line returns a series, not a dataframe.

Adding a .to_frame() and referencing the column in the following line should fix it:

The Excel file appears to contain a pivot table based on some other categorical data source. If you have access to that data source, import it instead.

Suppose the Excel file is a download from some government website and the original data is unavailable.

As you found out a single simple IMPORT will make all the columns character due to mixed types in the header cells and the values in the column headers pollute the flat import.

A pivot table has three parts

• The column headers (A crossing of class variables)
  • The table in your file crosses three variables
    Year, Quarter, and Month
• The row headers (A crossing of class variables)
  • The table in your file crosses one variable
    Country
• The data portion (the cells where the dimensions intersect)
  • Your table contains count totals

Proc IMPORT can read ranges of cells from an Excel file and this gives rise to the following strategy for unpivoting the pivot table:

• IMPORT the column header part,
• IMPORT the row header and data portion together. This can be done because the row header is one dimensional.
• TRANSPOSE the column header part and the row header + data part and merge into a final categorical form.
  • The merging will associate a categorical (or class) values (country, year, quarter, and month) to each data cell.

IMPORTING with GETNAMES=NO will produce a raw starting point where each column from Excel will be named F. When the raw data is transposed you will have a new column named _NAME_ whose values will be "F". The column names have just become data.

Looking across the cells in the data portion, each row has 98 columns that would become 98 rows when transposed. However, the totals columns can be discarded, and you will be left with 72 rows (in transpose) corresponding to 6 years * 12 months/year.

The original report can be reproduced using Proc TABULATE once you have transformed the IMPORTs into categorical time.

Example:

The values of the _NAME_ variable are mapped to a corresponding month using a custom informat built from the column header transpose.

You could do

Pass logit$fit as the first argument to stargazer().

The logitmfx() operation returns a bunch of stuff, but stargazer() expects a fitted model object (or a data frame) as its first argument.

You could use the tables package :

Strange, somehow can't recognize the functions in the formula any more. You can try this, if you remove scale it works fine. Also no other function like log() seems to work.

You could try calculating scale(AGE) before, then you don't need to put it in the formula.

Could look like this:

The margins package takes care of this automatically if you declare a variable to be a factor. See the subsetting section of the vignette or you can inspect the source code to see that marginal effects are computed as differences for factor variables.

Note that the default setting for margins is to compute the "average marginal effect", and not the "marginal effect at the mean". IMO, the default setting is best in most cases, but if you insist on considering a "synthetic" average observation, it is easy to do with the at argument of the margins function.

Code example. In the first case, vs is treated as a continuous variable. In the second, vs is treated as a binary variable.

Try using a cdn.

Replace :