Analyses de l’activité

pour description et pilotage

Guillaume Pressiat

01/02/2020

1 - Chirurgie ambulatoire : 55 gestes marqueurs

library(dplyr, warn.conflicts = FALSE)
library(pmeasyr)

p <- noyau_pmeasyr(finess = '290000017',
                   annee  = 2018,
                   mois   = 12,
                   path   = '~/Documents/data/mco', 
                   tolower_names = TRUE,
                   n_max = Inf)


library(nomensland)

dicts <- get_dictionnaire_listes()
lgm <- get_all_listes('Chir ambu : 55 GM')

periodes <- list(
  list(an = 2013, moi = 12),
  list(an = 2014, moi = 12),
  list(an = 2015, moi = 12),
  list(an = 2016, moi = 12),
  list(an = 2017, moi = 12),
  list(an = 2018, moi = 12),
  list(an = 2019, moi = 11))

result <- periodes %>% purrr::map_dfr(ana_r_ca_gestes_marqueurs, p = p, gestes_marqueurs = lgm)
result <- result %>% arrange(`Geste marqueur`, `Période`)

knitr::kable(head(result))

pivot_result <- result %>% 
  select(`Geste marqueur`, nofiness, taux_ambu, `Nb total`, `Période`) %>% 
  mutate(stat = paste0(scales::percent(taux_ambu), ' (', `Nb total`, ')')) %>% 
  select(-taux_ambu, - `Nb total`) %>% 
  tidyr::spread(`Période`, stat, '')

knitr::kable(head(pivot_result))

2 - Chirurgie ambulatoire : GHM C et 7 racines

library(dplyr, warn.conflicts = FALSE)
library(pmeasyr)

p <- noyau_pmeasyr(finess = '290000017',
                   annee  = 2018,
                   mois   = 12,
                   path   = '~/Documents/data/mco', 
                   tolower_names = TRUE,
                   n_max = Inf)


library(nomensland)

ghmc_7r <- get_liste('chir_ambu_ghm_C_7_racines')

periodes <- list(
  list(an = 2013, moi = 12),
  list(an = 2014, moi = 12),
  list(an = 2015, moi = 12),
  list(an = 2016, moi = 12),
  list(an = 2017, moi = 12),
  list(an = 2018, moi = 12),
  list(an = 2019, moi = 11))

result <- periodes %>% purrr::map_dfr(ana_r_ghm_ambu_dms, p = p, requete = ghmc_7r)
result <- result %>% arrange(niveau, Requete, `Période`)


knitr::kable(head(result))


pivot_result <- result %>% 
  select(niveau, Requete, nofiness, taux_ambu, `Nb total`, `Période`) %>% 
  mutate(stat = paste0(scales::percent(taux_ambu), ' (', `Nb total`, ')')) %>% 
  select(-taux_ambu, - `Nb total`) %>% 
  tidyr::spread(`Période`, stat, '')

knitr::kable(head(pivot_result))