R datasets through statsmodel

R datasets

R datasets is a project that makes easily available a number of datasets in the R programming language. The list of packages is visible at https://vincentarelbundock.github.io/Rdatasets/articles/data.html

In python

In Python, datasets from this R datasets collection are easily accessible through the statsmodel package, as explained at https://www.statsmodels.org/dev/datasets/index.html

import statsmodels.api as sm

doctor_visits = sm.datasets.get_rdataset("DoctorVisits", "AER")
print(doctor_visits.__doc__)

.. container::

   ============ ===============
   DoctorVisits R Documentation
   ============ ===============

   .. rubric:: Australian Health Service Utilization Data
      :name: DoctorVisits

   .. rubric:: Description
      :name: description

   Cross-section data originating from the 1977–1978 Australian Health
   Survey.

   .. rubric:: Usage
      :name: usage

   .. code:: R

      data("DoctorVisits")

   .. rubric:: Format
      :name: format

   A data frame containing 5,190 observations on 12 variables.

   visits
      Number of doctor visits in past 2 weeks.

   gender
      Factor indicating gender.

   age
      Age in years divided by 100.

   income
      Annual income in tens of thousands of dollars.

   illness
      Number of illnesses in past 2 weeks.

   reduced
      Number of days of reduced activity in past 2 weeks due to illness
      or injury.

   health
      General health questionnaire score using Goldberg's method.

   private
      Factor. Does the individual have private health insurance?

   freepoor
      Factor. Does the individual have free government health insurance
      due to low income?

   freerepat
      Factor. Does the individual have free government health insurance
      due to old age, disability or veteran status?

   nchronic
      Factor. Is there a chronic condition not limiting activity?

   lchronic
      Factor. Is there a chronic condition limiting activity?

   .. rubric:: Source
      :name: source

   Journal of Applied Econometrics Data Archive.

   http://qed.econ.queensu.ca/jae/1997-v12.3/mullahy/

   .. rubric:: References
      :name: references

   Cameron, A.C. and Trivedi, P.K. (1986). Econometric Models Based on
   Count Data: Comparisons and Applications of Some Estimators and
   Tests. *Journal of Applied Econometrics*, **1**, 29–53.

   Cameron, A.C. and Trivedi, P.K. (1998). *Regression Analysis of Count
   Data*. Cambridge: Cambridge University Press.

   Mullahy, J. (1997). Heterogeneity, Excess Zeros, and the Structure of
   Count Data Models. *Journal of Applied Econometrics*, **12**,
   337–350.

   .. rubric:: See Also
      :name: see-also

   ``CameronTrivedi1998``

   .. rubric:: Examples
      :name: examples

   .. code:: R

      data("DoctorVisits", package = "AER")
      library("MASS")

      ## Cameron and Trivedi (1986), Table III, col. (1)
      dv_lm <- lm(visits ~ . + I(age^2), data = DoctorVisits)
      summary(dv_lm)

      ## Cameron and Trivedi (1998), Table 3.3 
      dv_pois <- glm(visits ~ . + I(age^2), data = DoctorVisits, family = poisson)
      summary(dv_pois)                  ## MLH standard errors
      coeftest(dv_pois, vcov = vcovOPG) ## MLOP standard errors
      logLik(dv_pois)
      ## standard errors denoted RS ("unspecified omega robust sandwich estimate")
      coeftest(dv_pois, vcov = sandwich)

      ## Cameron and Trivedi (1986), Table III, col. (4)
      dv_nb <- glm.nb(visits ~ . + I(age^2), data = DoctorVisits)
      summary(dv_nb)
      logLik(dv_nb)

The datsets can be loaded directly into pandas dataframes:

df = doctor_visits.data
df.head()

	visits	gender	age	income	illness	reduced	health	private	freepoor	freerepat	nchronic	lchronic
0	1	female	0.19	0.55	1	4	1	yes	no	no	no	no
1	1	female	0.19	0.45	1	2	1	yes	no	no	no	no
2	1	male	0.19	0.90	3	0	0	no	no	no	no	no
3	1	male	0.19	0.15	1	0	0	no	no	no	no	no
4	1	male	0.19	0.45	2	5	1	no	no	no	yes	no

df.tail()

	visits	gender	age	income	illness	reduced	health	private	freepoor	freerepat	nchronic	lchronic
5185	0	female	0.22	0.55	0	0	0	no	no	no	no	no
5186	0	male	0.27	1.30	0	0	1	no	no	no	no	no
5187	0	female	0.37	0.25	1	0	1	no	no	yes	no	no
5188	0	female	0.52	0.65	0	0	0	no	no	no	no	no
5189	0	male	0.72	0.25	0	0	0	no	no	yes	no	no