R/ff_panel_expand.R
ff_panel_expand_longandwide.RdThere is a variable recorded for a panel, and a variable with date info, and another with obs info Compute averages over sub-groups of dates for each variable, with different ways of specifying date subgroups. average(svr_data) over svr Reshape data so each date is a variable for selected subset of key variables Merge results from 2 back to main, so that each indi/date observation has as variables all lagged and forward information as additional variables. Append not n lag m forward, but full history as additional variables Doing this allows for lagged intereaction that are time specific in an arbitrary way.
ff_panel_expand_longandwide(df, svr_id_t, svr_id_i, svr_data)string time variable name
string individual ID name
string variable name
a long panel frame with wide expansion of group mean lagged vars
https://fanwangecon.github.io/REconTools/reference/ff_panel_expand_longandwide.html https://fanwangecon.github.io/REconTools/articles/fv_panel_expand_longandwide.html https://github.com/FanWangEcon/REconTools/blob/master/R/ff_panel_expand.R
library(dplyr)
df_hw_cebu <- df_hgt_wgt %>% filter(S.country == 'Cebu' & svymthRound <= 24 & svymthRound > 0)
df_hw_cebu <- df_hw_cebu %>% mutate(mth6 = recode(svymthRound,
`0`="m00t06", `2`="m00t06", `4`="m00t06", `6`="m00t06",
`8`="m08t12", `10`="m08t12", `12`="m08t12",
`14`="m14t18", `16`="m14t18", `18`="m14t18",
`20`="m20t24", `22`="m20t24", `24`="m20t24"))
df_longandwide <- ff_panel_expand_longandwide(df_hw_cebu,
svr_id_t = 'mth6',
svr_id_i = 'indi.id',
svr_data = 'cal')
#> Joining, by = "indi.id"
print(df_longandwide %>% select(indi.id, svymthRound, mth6, cal, matches('mth6'), everything()), n=100)
#> # A tibble: 16,188 x 20
#> indi.id svymthRound mth6 cal mth6_cal_m00t06 mth6_cal_m08t12
#> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 1 2 m00t06 4.7 65.2 239.
#> 2 1 4 m00t06 10.3 65.2 239.
#> 3 1 6 m00t06 181. 65.2 239.
#> 4 1 8 m08t12 85.3 65.2 239.
#> 5 1 10 m08t12 288 65.2 239.
#> 6 1 12 m08t12 343. 65.2 239.
#> 7 1 14 m14t18 NA 65.2 239.
#> 8 1 16 m14t18 NA 65.2 239.
#> 9 1 18 m14t18 NA 65.2 239.
#> 10 1 20 m20t24 NA 65.2 239.
#> 11 1 22 m20t24 NA 65.2 239.
#> 12 1 24 m20t24 NA 65.2 239.
#> 13 2 2 m00t06 166. 63.6 148.
#> 14 2 4 m00t06 0.5 63.6 148.
#> 15 2 6 m00t06 24.1 63.6 148.
#> 16 2 8 m08t12 155. 63.6 148.
#> 17 2 10 m08t12 142. 63.6 148.
#> 18 2 12 m08t12 146. 63.6 148.
#> 19 2 14 m14t18 228 63.6 148.
#> 20 2 16 m14t18 194. 63.6 148.
#> 21 2 18 m14t18 111. 63.6 148.
#> 22 2 20 m20t24 305 63.6 148.
#> 23 2 22 m20t24 332. 63.6 148.
#> 24 2 24 m20t24 363. 63.6 148.
#> 25 3 2 m00t06 0.5 19.4 206.
#> 26 3 4 m00t06 12.9 19.4 206.
#> 27 3 6 m00t06 44.8 19.4 206.
#> 28 3 8 m08t12 57 19.4 206.
#> 29 3 10 m08t12 73.7 19.4 206.
#> 30 3 12 m08t12 488. 19.4 206.
#> 31 3 14 m14t18 459. 19.4 206.
#> 32 3 16 m14t18 408 19.4 206.
#> 33 3 18 m14t18 692. 19.4 206.
#> 34 3 20 m20t24 612 19.4 206.
#> 35 3 22 m20t24 1226. 19.4 206.
#> 36 3 24 m20t24 555. 19.4 206.
#> 37 4 2 m00t06 0.5 17.5 43.8
#> 38 4 4 m00t06 0.5 17.5 43.8
#> 39 4 6 m00t06 51.4 17.5 43.8
#> 40 4 8 m08t12 39.4 17.5 43.8
#> 41 4 10 m08t12 47 17.5 43.8
#> 42 4 12 m08t12 44.9 17.5 43.8
#> 43 4 14 m14t18 221. 17.5 43.8
#> 44 4 16 m14t18 242. 17.5 43.8
#> 45 4 18 m14t18 300. 17.5 43.8
#> 46 4 20 m20t24 581. 17.5 43.8
#> 47 4 22 m20t24 420. 17.5 43.8
#> 48 4 24 m20t24 466. 17.5 43.8
#> 49 5 2 m00t06 12.1 99.9 131.
#> 50 5 4 m00t06 25.3 99.9 131.
#> 51 5 6 m00t06 262. 99.9 131.
#> 52 5 8 m08t12 71.3 99.9 131.
#> 53 5 10 m08t12 87.8 99.9 131.
#> 54 5 12 m08t12 234. 99.9 131.
#> 55 5 14 m14t18 453. 99.9 131.
#> 56 5 16 m14t18 1484. 99.9 131.
#> 57 5 18 m14t18 1206. 99.9 131.
#> 58 5 20 m20t24 1178. 99.9 131.
#> 59 5 22 m20t24 1221. 99.9 131.
#> 60 5 24 m20t24 943. 99.9 131.
#> 61 6 2 m00t06 34.9 135. 293.
#> 62 6 4 m00t06 267. 135. 293.
#> 63 6 6 m00t06 102. 135. 293.
#> 64 6 8 m08t12 169. 135. 293.
#> 65 6 10 m08t12 387. 135. 293.
#> 66 6 12 m08t12 324. 135. 293.
#> 67 6 14 m14t18 653. 135. 293.
#> 68 6 16 m14t18 534. 135. 293.
#> 69 6 18 m14t18 479. 135. 293.
#> 70 6 20 m20t24 417. 135. 293.
#> 71 6 22 m20t24 346. 135. 293.
#> 72 6 24 m20t24 700. 135. 293.
#> 73 7 2 m00t06 3.6 93.1 270.
#> 74 7 4 m00t06 153. 93.1 270.
#> 75 7 6 m00t06 122. 93.1 270.
#> 76 7 8 m08t12 408. 93.1 270.
#> 77 7 10 m08t12 173 93.1 270.
#> 78 7 12 m08t12 228. 93.1 270.
#> 79 7 14 m14t18 391. 93.1 270.
#> 80 7 16 m14t18 NA 93.1 270.
#> 81 7 18 m14t18 637. 93.1 270.
#> 82 7 20 m20t24 688. 93.1 270.
#> 83 7 22 m20t24 629. 93.1 270.
#> 84 7 24 m20t24 510 93.1 270.
#> 85 8 2 m00t06 307. 384. 713.
#> 86 8 4 m00t06 363. 384. 713.
#> 87 8 6 m00t06 483. 384. 713.
#> 88 8 8 m08t12 418. 384. 713.
#> 89 8 10 m08t12 694. 384. 713.
#> 90 8 12 m08t12 1028. 384. 713.
#> 91 8 14 m14t18 691. 384. 713.
#> 92 8 16 m14t18 880. 384. 713.
#> 93 8 18 m14t18 916. 384. 713.
#> 94 8 20 m20t24 637. 384. 713.
#> 95 8 22 m20t24 1169. 384. 713.
#> 96 8 24 m20t24 775. 384. 713.
#> 97 9 2 m00t06 0.5 7.1 65
#> 98 9 4 m00t06 0.5 7.1 65
#> 99 9 6 m00t06 20.3 7.1 65
#> 100 9 8 m08t12 15.1 7.1 65
#> # ... with 16,088 more rows, and 14 more variables: mth6_cal_m14t18 <dbl>,
#> # mth6_cal_m20t24 <dbl>, S.country <chr>, vil.id <dbl>, sex <chr>,
#> # momEdu <dbl>, wealthIdx <dbl>, hgt <dbl>, wgt <dbl>, hgt0 <dbl>,
#> # wgt0 <dbl>, prot <dbl>, p.A.prot <dbl>, p.A.nProt <dbl>