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So, I´m working with ENIGH - Database, which stands for ¨National Survey of Household Income and Expenses¨ in Spanish, this is an exercise conducted by the Mexican government and like most surveys of its kind, it works with Weights.

What I´m trying to do is to calculate the mean, maximum and minimum household income by Decile. In other words What´s the income of each 10%, grouping household base on their income. To be honest, I haven’t gone that far but this is what I got until now:

  1. I need my svydesign object
  2. Convert that into a table using svytable
  3. Arrange using desc() on my income variable
ENIGH_design <-svydesign(id=~upm, strata=~est_dis, weights=~factor_hog, data = ENIGH)
ENIGH_table <- svytable(ing_cor, ENIGH_design)

Here is where it gets tricky, supposing I have 100 rows, I can’t take the first 10 of them because in reality, when taking weights in mind, the might be 9% or 20% (I´m just throwing numbers) of the actual population.

I could use cut() on my income variable but I would be forgetting about weights and results will only be representative of the sample, not total population.

I think that the best approach would be to use a combination of:

  • mutate() to create a new variable base
  • if() in conjugation with mutate to define on which decile each row falls to
  • group_by() and mean() to calculate what I´m aiming for

This way I will have an extra variable which I could use to calculate whatever I want with whatever other variable I wish to. But again, I haven´t define my groups so it´s pretty much useless.

Thank you for reading. Thank you for your help.

Database available: https://www.inegi.org.mx/programas/enigh/nc/2016/default.html#Datos_abiertos

Here is a glimpse of how my DB looks:

folioviv    foliohog    ubica_geo   est_dis  upm  factor    ing_cor
100587003      1        10010000       2     610    180     22,723
100587004      1        10010000       2     610    180     17,920
100587005      1        10010000       2     610    180     27,506
100587006      1        10010000       2     610    180     56,236
100605201      1        10010000       2     620    178     41,587
100605202      1        10010000       2     620    178     135,437
100605203      1        10010000       2     620    178     62,386
100605205      1        10010000       2     620    178     103,502
100605206      1        10010000       2     620    178     27,323
100606301      1        10010000       3     630    223     68,042
100606302      1        10010000       3     630    223     98,537
100606305      1        10010000       3     630    223     53,237
100606306      1        10010000       3     630    223     132,861
100609801      1        10010000       3     640    232     190,033
100609802      1        10010000       3     640    232     28,654
100609805      1        10010000       3     640    232     74,408
100631401      1        10010000       1     650    171     80,761
100711503      1        10010000       1     770    184     38,640
100711504      1        10010000       1     770    184     81,672

There are many more columns but they aren´t necessary for this exercise.

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In the Hmisc and reldist packages, you have the function wdt.quantile(). You can calculate your quantiles with this function, and then use cut() to make your groups and then your calculations.

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