You can install the development version of careroll from GitHub with:
# install.packages("devtools")
devtools::install_github("andrewallenbruce/careroll", build_vignettes = TRUE)# install.packages("remotes")
remotes::install_github("andrewallenbruce/careroll", build_vignettes = TRUE)library(careroll)all <- careroll()
all |>
dplyr::filter(month == "Year", level == "National" | level == "State") |>
dplyr::select(year, state:state_name, dplyr::ends_with("_tot"), bene_orig, bene_ma_oth) |>
dplyr::group_by(year, state, state_name) |>
dplyr::summarise(
pct_orig = bene_orig / bene_tot,
pct_ma = bene_ma_oth / bene_tot,
pct_aged = bene_aged_tot / bene_tot,
pct_disabled = bene_dsb_tot / bene_tot,
pct_rx = bene_rx_tot / bene_tot
)#> # A tibble: 290 × 8
#> # Groups: year, state [290]
#> year state state_name pct_orig pct_ma pct_aged pct_disabled pct_rx
#> <int> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2017 AK Alaska 0.986 0.0145 0.863 0.137 0.401
#> 2 2017 AL Alabama 0.636 0.364 0.776 0.224 0.716
#> 3 2017 AR Arkansas 0.779 0.221 0.781 0.219 0.696
#> 4 2017 AS American Samoa 0.963 0.0369 0.691 0.309 0.0850
#> 5 2017 AZ Arizona 0.615 0.385 0.874 0.126 0.733
#> 6 2017 CA California 0.582 0.418 0.883 0.117 0.782
#> 7 2017 CO Colorado 0.625 0.375 0.879 0.121 0.722
#> 8 2017 CT Connecticut 0.718 0.282 0.875 0.125 0.772
#> 9 2017 DC District of Columbia 0.841 0.159 0.835 0.165 0.593
#> 10 2017 DE Delaware 0.884 0.116 0.861 0.139 0.741
#> # ℹ 280 more rows
total_yr <- levels(period = "year", level = "national", group = "total")
total_yr |>
dplyr::filter(year == 2017 | year == 2021) |>
change_year(bene_tot)#> # A tibble: 2 × 4
#> year bene_tot change_abs change_pct
#> <int> <dbl> <dbl> <dbl>
#> 1 2017 58457244 NA NA
#> 2 2021 63905513 5448269 0.0932
total_yr |>
dplyr::mutate(rolling_mean = slider::slide_mean(bene_tot, before = 1)) |>
change_year(bene_tot)#> # A tibble: 5 × 5
#> year bene_tot rolling_mean change_abs change_pct
#> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 2017 58457244 58457244 NA NA
#> 2 2018 59989883 59223564. 1532639 0.0262
#> 3 2019 61514510 60752196. 1524627 0.0254
#> 4 2020 62840267 62177388. 1325757 0.0216
#> 5 2021 63905513 63372890 1065246 0.0170
origMA_yr <- levels(period = "year", level = "national", group = "origMA")
origMA_yr |>
dplyr::mutate(pct_orig = bene_orig / (bene_orig + bene_ma_oth),
pct_ma = bene_ma_oth / (bene_orig + bene_ma_oth)) |>
change_year(bene_orig) |>
dplyr::rename(orig_change = change_abs, orig_pct_change = change_pct) |>
change_year(bene_ma_oth) |>
dplyr::rename(ma_change = change_abs, ma_pct_change = change_pct)#> # A tibble: 5 × 9
#> year bene_orig bene_ma_oth pct_orig pct_ma orig_change orig_pct_change
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2017 38667830 19789414 0.661 0.339 NA NA
#> 2 2018 38665082 21324800 0.645 0.355 -2748 -0.0000711
#> 3 2019 38577012 22937498 0.627 0.373 -88070 -0.00228
#> 4 2020 37776345 25063922 0.601 0.399 -800667 -0.0208
#> 5 2021 36369426 27536087 0.569 0.431 -1406919 -0.0372
#> # ℹ 2 more variables: ma_change <dbl>, ma_pct_change <dbl>
aged_yr <- levels(period = "year", level = "national", group = "aged")
aged_yr |>
dplyr::mutate(pct_aged = bene_aged_tot / bene_tot,
pct_esrd = bene_aged_esrd / bene_aged_tot,
pct_no_esrd = bene_aged_no_esrd / bene_aged_tot) |>
change_year(bene_aged_tot) |>
dplyr::rename(aged_change = change_abs, aged_pct_change = change_pct)#> # A tibble: 5 × 10
#> year bene_tot bene_aged_tot bene_aged_esrd bene_aged_no_esrd pct_aged
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2017 58457244 49678033 278642 49399391 0.850
#> 2 2018 59989883 51303898 290243 51013655 0.855
#> 3 2019 61514510 52991455 301798 52689658 0.861
#> 4 2020 62840267 54531919 305080 54226839 0.868
#> 5 2021 63905513 55858782 305788 55552994 0.874
#> # ℹ 4 more variables: pct_esrd <dbl>, pct_no_esrd <dbl>, aged_change <dbl>,
#> # aged_pct_change <dbl>
dsb_yr <- levels(period = "year", level = "national", group = "disabled")
dsb_yr |>
dplyr::mutate(pct_dsb = bene_dsb_tot / bene_tot,
pct_dsb_esrd = bene_dsb_esrd_and_only_esrd / bene_dsb_tot,
pct_dsb_no_esrd = bene_dsb_no_esrd / bene_dsb_tot) |>
change_year(bene_dsb_tot) |>
dplyr::rename(dsb_change = change_abs, dsb_pct_change = change_pct)#> # A tibble: 5 × 10
#> year bene_tot bene_dsb_tot bene_dsb_esrd_and_only_…¹ bene_dsb_no_esrd pct_dsb
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2017 58457244 8779211 258366 8520845 0.150
#> 2 2018 59989883 8685985 261246 8424739 0.145
#> 3 2019 61514510 8523055 262179 8260876 0.139
#> 4 2020 62840267 8308348 255949 8052399 0.132
#> 5 2021 63905513 8046731 249670 7797061 0.126
#> # ℹ abbreviated name: ¹bene_dsb_esrd_and_only_esrd
#> # ℹ 4 more variables: pct_dsb_esrd <dbl>, pct_dsb_no_esrd <dbl>,
#> # dsb_change <dbl>, dsb_pct_change <dbl>
# Beneficiaries with Hospital (Part A) and Supplementary (Part B)
partAB_yr <- levels(period = "year", level = "national", group = "partAB")
partAB_yr |>
dplyr::mutate(pct_ab = bene_ab_total / bene_tot,
pct_ab_orig = bene_ab_orig / bene_ab_total,
pct_ab_ma_oth = bene_ab_ma_oth / bene_ab_total) |>
change_year(bene_ab_total) |>
dplyr::rename(ab_change = change_abs, ab_pct_change = change_pct)#> # A tibble: 5 × 10
#> year bene_tot bene_ab_total bene_ab_orig bene_ab_ma_oth pct_ab pct_ab_orig
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2017 58457244 53008234 33242085 19766149 0.907 0.627
#> 2 2018 59989883 54349822 33052639 21297184 0.906 0.608
#> 3 2019 61514510 55653848 32758741 22895108 0.905 0.589
#> 4 2020 62840267 56966865 31934411 25032454 0.907 0.561
#> 5 2021 63905513 58043480 30540086 27503394 0.908 0.526
#> # ℹ 3 more variables: pct_ab_ma_oth <dbl>, ab_change <dbl>, ab_pct_change <dbl>
# Prescription Drug (Part D) Beneficiaries
partD_yr <- levels(period = "year", level = "national", group = "partD")
partD_yr |>
dplyr::mutate(pct_rx = bene_rx_tot / bene_tot,
pct_rx_pdp = bene_rx_pdp / bene_rx_tot,
pct_rx_mapd = bene_rx_mapd / bene_rx_tot) |>
change_year(bene_rx_tot) |>
dplyr::rename(rx_change = change_abs, rx_pct_change = change_pct)#> # A tibble: 5 × 10
#> year bene_tot bene_rx_tot bene_rx_pdp bene_rx_mapd pct_rx pct_rx_pdp
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2017 58457244 42728443 25243684 17484759 0.731 0.591
#> 2 2018 59989883 44249461 25563945 18685516 0.738 0.578
#> 3 2019 61514510 45827091 25583137 20243954 0.745 0.558
#> 4 2020 62840267 47413121 25171949 22241173 0.755 0.531
#> 5 2021 63905513 48823714 24169759 24653955 0.764 0.495
#> # ℹ 3 more variables: pct_rx_mapd <dbl>, rx_change <dbl>, rx_pct_change <dbl>
# Medicare Beneficiaries
total_state_yr <- levels(period = "year", level = "state", group = "total")# Aged Beneficiaries
aged_state_yr <- levels(period = "year", level = "state", group = "aged")# Disabled Beneficiaries
dsb_state_yr <- levels(period = "year", level = "state", group = "disabled")# Beneficiaries with Hospital (Part A) and Supplementary (Part B)
partAB_state_yr <- levels(period = "year", level = "state", group = "partAB")# Prescription Drug (Part D) Beneficiaries
partD_state_yr <- levels(period = "year", level = "state", group = "partD")# Medicare Beneficiaries
total_county_yr <- levels(period = "year", level = "county", group = "total")# Aged Beneficiaries
aged_county_yr <- levels(period = "year", level = "county", group = "aged")# Disabled Beneficiaries
dsb_county_yr <- levels(period = "year", level = "county", group = "disabled")# Beneficiaries with Hospital (Part A) and Supplementary (Part B)
partAB_county_yr <- levels(period = "year", level = "county", group = "partAB")# Prescription Drug (Part D) Beneficiaries
partD_county_yr <- levels(period = "year", level = "county", group = "partD")# Medicare Beneficiaries
levels(period = "month", level = "national", group = "total")#> # A tibble: 12 × 5
#> year month bene_tot bene_orig bene_ma_oth
#> <int> <chr> <dbl> <dbl> <dbl>
#> 1 2021 August 64092708 36362083 27730625
#> 2 2021 September 64205181 36367940 27837241
#> 3 2021 October 64301080 36379987 27921093
#> 4 2021 November 64381367 36420411 27960956
#> 5 2021 December 64473547 36487945 27985602
#> 6 2022 January 64522277 35173593 29348684
#> 7 2022 February 64492482 35098135 29394347
#> 8 2022 March 64543413 35079288 29464125
#> 9 2022 April 64598907 35034846 29564061
#> 10 2022 May 64673459 35015438 29658021
#> 11 2022 June 64744497 34993661 29750836
#> 12 2022 July 64831706 34919687 29912019
# Aged Beneficiaries
levels(period = "month", level = "national", group = "aged")#> # A tibble: 12 × 6
#> year month bene_tot bene_aged_tot bene_aged_esrd bene_aged_no_esrd
#> <int> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 2021 August 64092708 56012739 297799 55714940
#> 2 2021 September 64205181 56100172 291991 55808181
#> 3 2021 October 64301080 56180436 285857 55894579
#> 4 2021 November 64381367 56241208 279835 55961373
#> 5 2021 December 64473547 56301890 273823 56028067
#> 6 2022 January 64522277 56723513 308349 56415164
#> 7 2022 February 64492482 56683511 300452 56383059
#> 8 2022 March 64543413 56715692 294317 56421375
#> 9 2022 April 64598907 56754505 288444 56466061
#> 10 2022 May 64673459 56815359 283239 56532120
#> 11 2022 June 64744497 56864729 277732 56586997
#> 12 2022 July 64831706 56936772 272490 56664282
# Disabled Beneficiaries
levels(period = "month", level = "national", group = "disabled")#> # A tibble: 12 × 6
#> year month bene_tot bene_dsb_tot bene_dsb_esrd_and_on…¹ bene_dsb_no_esrd
#> <int> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 2021 August 64092708 8079969 249523 7830446
#> 2 2021 September 64205181 8105009 249214 7855795
#> 3 2021 October 64301080 8120644 248105 7872539
#> 4 2021 November 64381367 8140159 247153 7893006
#> 5 2021 December 64473547 8171657 246130 7925527
#> 6 2022 January 64522277 7798764 241340 7557424
#> 7 2022 February 64492482 7808971 239101 7569870
#> 8 2022 March 64543413 7827721 237900 7589821
#> 9 2022 April 64598907 7844402 236881 7607521
#> 10 2022 May 64673459 7858100 236028 7622072
#> 11 2022 June 64744497 7879768 234518 7645250
#> 12 2022 July 64831706 7894934 232535 7662399
#> # ℹ abbreviated name: ¹bene_dsb_esrd_and_only_esrd
# Beneficiaries with Hospital (Part A) and Supplementary (Part B)
levels(period = "month", level = "national", group = "partAB")#> # A tibble: 12 × 6
#> year month bene_tot bene_ab_total bene_ab_orig bene_ab_ma_oth
#> <int> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 2021 August 64092708 58278351 30580561 27697790
#> 2 2021 September 64205181 58369224 30564716 27804508
#> 3 2021 October 64301080 58435733 30547392 27888341
#> 4 2021 November 64381367 58492520 30564326 27928194
#> 5 2021 December 64473547 58563216 30610174 27953042
#> 6 2022 January 64522277 58697772 29381915 29315857
#> 7 2022 February 64492482 58672279 29310704 29361575
#> 8 2022 March 64543413 58715155 29283810 29431345
#> 9 2022 April 64598907 58779906 29248613 29531293
#> 10 2022 May 64673459 58855192 29229980 29625212
#> 11 2022 June 64744497 58935892 29217909 29717983
#> 12 2022 July 64831706 59220817 29341694 29879123
# Prescription Drug (Part D) Beneficiaries
levels(period = "month", level = "national", group = "partD")#> # A tibble: 12 × 6
#> year month bene_tot bene_rx_tot bene_rx_pdp bene_rx_mapd
#> <int> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 2021 August 64092708 48997548 24156115 24841433
#> 2 2021 September 64205181 49083048 24142036 24941012
#> 3 2021 October 64301080 49153500 24131966 25021534
#> 4 2021 November 64381367 49203219 24141304 25061915
#> 5 2021 December 64473547 49241505 24154044 25087461
#> 6 2022 January 64522277 49889544 23463857 26425687
#> 7 2022 February 64492482 49907031 23428283 26478748
#> 8 2022 March 64543413 49960110 23410551 26549559
#> 9 2022 April 64598907 50013873 23367036 26646837
#> 10 2022 May 64673459 50097946 23361943 26736003
#> 11 2022 June 64744497 50190877 23365123 26825754
#> 12 2022 July 64831706 50343344 23365035 26978309
# Medicare Beneficiaries
levels(period = "month", level = "state", group = "total")#> # A tibble: 684 × 7
#> year month state state_name bene_tot bene_orig bene_ma_oth
#> <int> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 2021 August AL Alabama 1072505 527115 545390
#> 2 2021 August AK Alaska 108856 106598 2258
#> 3 2021 August AZ Arizona 1404230 767775 636455
#> 4 2021 August AR Arkansas 654414 430169 224245
#> 5 2021 August CA California 6523853 3442959 3080894
#> 6 2021 August CO Colorado 965609 519995 445614
#> 7 2021 August CT Connecticut 704756 362700 342056
#> 8 2021 August DE Delaware 223611 171416 52195
#> 9 2021 August DC District of Columbia 94288 70143 24145
#> 10 2021 August FL Florida 4818048 2358735 2459313
#> # ℹ 674 more rows
# Aged Beneficiaries
levels(period = "month", level = "state", group = "aged")#> # A tibble: 684 × 8
#> year month state state_name bene_tot bene_aged_tot bene_aged_esrd
#> <int> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 2021 August AL Alabama 1072505 867759 5344
#> 2 2021 August AK Alaska 108856 97029 365
#> 3 2021 August AZ Arizona 1404230 1260141 5733
#> 4 2021 August AR Arkansas 654414 528256 2671
#> 5 2021 August CA California 6523853 5918013 39105
#> 6 2021 August CO Colorado 965609 875251 2894
#> 7 2021 August CT Connecticut 704756 628541 2721
#> 8 2021 August DE Delaware 223611 198195 1072
#> 9 2021 August DC District of Columbia 94288 81190 923
#> 10 2021 August FL Florida 4818048 4302487 20296
#> # ℹ 674 more rows
#> # ℹ 1 more variable: bene_aged_no_esrd <dbl>
# Disabled Beneficiaries
levels(period = "month", level = "state", group = "disabled")#> # A tibble: 684 × 8
#> year month state state_name bene_tot bene_dsb_tot bene_dsb_esrd_and_on…¹
#> <int> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 2021 August AL Alabama 1072505 204746 5319
#> 2 2021 August AK Alaska 108856 11827 395
#> 3 2021 August AZ Arizona 1404230 144089 4933
#> 4 2021 August AR Arkansas 654414 126158 2778
#> 5 2021 August CA California 6523853 605840 26935
#> 6 2021 August CO Colorado 965609 90358 2347
#> 7 2021 August CT Connecticut 704756 76215 1944
#> 8 2021 August DE Delaware 223611 25416 828
#> 9 2021 August DC District of … 94288 13098 753
#> 10 2021 August FL Florida 4818048 515561 15521
#> # ℹ 674 more rows
#> # ℹ abbreviated name: ¹bene_dsb_esrd_and_only_esrd
#> # ℹ 1 more variable: bene_dsb_no_esrd <dbl>
# Beneficiaries with Hospital (Part A) and Supplementary (Part B)
levels(period = "month", level = "state", group = "partAB")#> # A tibble: 684 × 8
#> year month state state_name bene_tot bene_ab_total bene_ab_orig
#> <int> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 2021 August AL Alabama 1072505 994211 448914
#> 2 2021 August AK Alaska 108856 95922 93665
#> 3 2021 August AZ Arizona 1404230 1290697 654471
#> 4 2021 August AR Arkansas 654414 607064 382855
#> 5 2021 August CA California 6523853 5839500 2763487
#> 6 2021 August CO Colorado 965609 876124 433972
#> 7 2021 August CT Connecticut 704756 637618 295639
#> 8 2021 August DE Delaware 223611 206672 154494
#> 9 2021 August DC District of Columbia 94288 78484 54378
#> 10 2021 August FL Florida 4818048 4517960 2059475
#> # ℹ 674 more rows
#> # ℹ 1 more variable: bene_ab_ma_oth <dbl>
# Prescription Drug (Part D) Beneficiaries
levels(period = "month", level = "state", group = "partD")#> # A tibble: 684 × 8
#> year month state state_name bene_tot bene_rx_tot bene_rx_pdp bene_rx_mapd
#> <int> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 2021 August AL Alabama 1072505 807899 288725 519174
#> 2 2021 August AK Alaska 108856 70147 68960 1187
#> 3 2021 August AZ Arizona 1404230 1072451 467916 604535
#> 4 2021 August AR Arkansas 654414 483197 273471 209726
#> 5 2021 August CA California 6523853 5255973 2277980 2977993
#> 6 2021 August CO Colorado 965609 730984 343406 387578
#> 7 2021 August CT Connecticut 704756 570887 265543 305344
#> 8 2021 August DE Delaware 223611 171440 129099 42341
#> 9 2021 August DC District of… 94288 59714 36399 23315
#> 10 2021 August FL Florida 4818048 3799169 1465730 2333439
#> # ℹ 674 more rows
# Medicare Beneficiaries
levels(period = "month", level = "county", group = "total")#> # A tibble: 38,688 × 8
#> year month state state_name county bene_tot bene_orig bene_ma_oth
#> <int> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 2021 August AL Alabama Autauga 11417 5323 6094
#> 2 2021 August AL Alabama Baldwin 57663 28236 29427
#> 3 2021 August AL Alabama Barbour 6203 2639 3564
#> 4 2021 August AL Alabama Bibb 4716 1846 2870
#> 5 2021 August AL Alabama Blount 13252 5243 8009
#> 6 2021 August AL Alabama Bullock 2302 931 1371
#> 7 2021 August AL Alabama Butler 4959 2709 2250
#> 8 2021 August AL Alabama Calhoun 27080 15531 11549
#> 9 2021 August AL Alabama Chambers 8777 4421 4356
#> 10 2021 August AL Alabama Cherokee 7467 3899 3568
#> # ℹ 38,678 more rows
# Aged Beneficiaries
levels(period = "month", level = "county", group = "aged")#> # A tibble: 38,688 × 9
#> year month state state_name county bene_tot bene_aged_tot bene_aged_esrd
#> <int> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 2021 August AL Alabama Autauga 11417 9039 54
#> 2 2021 August AL Alabama Baldwin 57663 50646 191
#> 3 2021 August AL Alabama Barbour 6203 4812 53
#> 4 2021 August AL Alabama Bibb 4716 3499 30
#> 5 2021 August AL Alabama Blount 13252 10715 40
#> 6 2021 August AL Alabama Bullock 2302 1738 18
#> 7 2021 August AL Alabama Butler 4959 3898 28
#> 8 2021 August AL Alabama Calhoun 27080 21097 123
#> 9 2021 August AL Alabama Chambers 8777 6680 75
#> 10 2021 August AL Alabama Cherokee 7467 5863 37
#> # ℹ 38,678 more rows
#> # ℹ 1 more variable: bene_aged_no_esrd <dbl>
# Disabled Beneficiaries
levels(period = "month", level = "county", group = "disabled")#> # A tibble: 38,688 × 9
#> year month state state_name county bene_tot bene_dsb_tot
#> <int> <chr> <chr> <chr> <chr> <dbl> <dbl>
#> 1 2021 August AL Alabama Autauga 11417 2378
#> 2 2021 August AL Alabama Baldwin 57663 7017
#> 3 2021 August AL Alabama Barbour 6203 1391
#> 4 2021 August AL Alabama Bibb 4716 1217
#> 5 2021 August AL Alabama Blount 13252 2537
#> 6 2021 August AL Alabama Bullock 2302 564
#> 7 2021 August AL Alabama Butler 4959 1061
#> 8 2021 August AL Alabama Calhoun 27080 5983
#> 9 2021 August AL Alabama Chambers 8777 2097
#> 10 2021 August AL Alabama Cherokee 7467 1604
#> # ℹ 38,678 more rows
#> # ℹ 2 more variables: bene_dsb_esrd_and_only_esrd <dbl>, bene_dsb_no_esrd <dbl>
# Beneficiaries with Hospital (Part A) and Supplementary (Part B)
levels(period = "month", level = "county", group = "partAB")#> # A tibble: 38,688 × 9
#> year month state state_name county bene_tot bene_ab_total bene_ab_orig
#> <int> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 2021 August AL Alabama Autauga 11417 10583 4491
#> 2 2021 August AL Alabama Baldwin 57663 53990 24574
#> 3 2021 August AL Alabama Barbour 6203 5868 2305
#> 4 2021 August AL Alabama Bibb 4716 4417 1547
#> 5 2021 August AL Alabama Blount 13252 12466 4457
#> 6 2021 August AL Alabama Bullock 2302 2127 756
#> 7 2021 August AL Alabama Butler 4959 4647 2397
#> 8 2021 August AL Alabama Calhoun 27080 24984 13435
#> 9 2021 August AL Alabama Chambers 8777 8197 3841
#> 10 2021 August AL Alabama Cherokee 7467 7106 3538
#> # ℹ 38,678 more rows
#> # ℹ 1 more variable: bene_ab_ma_oth <dbl>
# Prescription Drug (Part D) Beneficiaries
levels(period = "month", level = "county", group = "partD")#> # A tibble: 38,688 × 9
#> year month state state_name county bene_tot bene_rx_tot bene_rx_pdp
#> <int> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 2021 August AL Alabama Autauga 11417 7871 1928
#> 2 2021 August AL Alabama Baldwin 57663 43444 15409
#> 3 2021 August AL Alabama Barbour 6203 4949 1526
#> 4 2021 August AL Alabama Bibb 4716 3640 978
#> 5 2021 August AL Alabama Blount 13252 10695 2891
#> 6 2021 August AL Alabama Bullock 2302 1868 545
#> 7 2021 August AL Alabama Butler 4959 3897 1761
#> 8 2021 August AL Alabama Calhoun 27080 17909 7039
#> 9 2021 August AL Alabama Chambers 8777 6842 2614
#> 10 2021 August AL Alabama Cherokee 7467 5913 2619
#> # ℹ 38,678 more rows
#> # ℹ 1 more variable: bene_rx_mapd <dbl>