只是想知道是否有人知道在分割后如何使用数据。这就是我现在所拥有的。
combined_cost_freq_2_inp <- run_sql("m5c_comb_out_name.sql", cond_str)
checker <- subset(combined_cost_freq_2_inp, is.na(combined_cost_freq_2_inp$inp_allowed))
holders <- split(combined_cost_freq_2_inp, list(combined_cost_freq_1$cd1,combined_cost_freq_1$cd2))
if( is.na(checker$inp_allowed) == TRUE )
{
sub1 <- subset(holders, !is.na(inp_allowed) & svc_code_category == "Facility - Inpatient")
sub2 <- subset(holders, is.na(inp_allowed)& svc_code_category == "Facility - Inpatient")
sum_freq_0 <- sum(sub2$svcc_pos_freq)
sum_freq_div <- sum_freq_0 / length(sub1$svcc_pos_freq)
sum_freq_added <- (sub1$svcc_pos_freq + sum_freq_div)
if( sum_freq_added > 1)
{
sub1$svcc_pos_freq <- 1
}
else
{
sub1$svcc_pos_freq <- sum_freq_added
}
holder <- rbind(sub1, sub2)
combined_cost_freq_2_inp <- holder
拆分下方的代码在拆分之前工作得很好,但现在我意识到我需要拆分独特的值,这确实让事情变得比我想要的复杂得多,所以任何帮助都会非常感激!
示例数据: 注意:dput(head(holder,5))只是为了发布
Browse[2]> str(holders)
List of 1
$ Surgical Treatment.Laparoscopic Gallbladder Removal (Cholecystectomy):'data.frame': 1392 obs. of 26 variables:
..$ state : chr [1:1392] "MO" "MO" "MO" "MO" ...
..$ hrrcity : chr [1:1392] "Cape Girardeau" "Cape Girardeau" "Cape Girardeau" "Cape Girardeau" ...
..$ mcp_category : chr [1:1392] "Digestive Conditions" "Digestive Conditions" "Digestive Conditions" "Digestive Conditions" ...
..$ diagnosis_group : chr [1:1392] "Gallstones" "Gallstones" "Gallstones" "Gallstones" ...
..$ cd1 : chr [1:1392] "Surgical Treatment" "Surgical Treatment" "Surgical Treatment" "Surgical Treatment" ...
..$ cd2 : chr [1:1392] "Laparoscopic Gallbladder Removal (Cholecystectomy)" "Laparoscopic Gallbladder Removal (Cholecystectomy)" "Laparoscopic Gallbladder Removal (Cholecystectomy)" "Laparoscopic Gallbladder Removal (Cholecystectomy)" ...
..$ cd3 : chr [1:1392] "Inpatient Hospital" "Inpatient Hospital" "Inpatient Hospital" "Inpatient Hospital" ...
..$ timeline_ind : chr [1:1392] "Evaluation" "Evaluation" "Evaluation" "Evaluation" ...
..$ svc_lvl_code : chr [1:1392] "" "Consultation and Management" "Consultation and Management" "Consultation and Management" ...
..$ svc_code_category: chr [1:1392] "74174" "Initial hospital care, per day (70 minutes)" "Initial observation care visit, high complexity" "Office visit, 40 minutes" ...
..$ svcc_pos : chr [1:1392] "" "" "" "" ...
..$ claim_type : chr [1:1392] "" "" "" "" ...
..$ ep_count : int [1:1392] 14 14 14 14 14 14 14 14 14 14 ...
..$ svcc_freq : num [1:1392] 0.0714 0.0714 0.0714 0.2857 0.0714 ...
..$ svcc_pos_freq : num [1:1392] 0.0714 0.0714 0.0714 0.2857 0 ...
..$ avg_services : num [1:1392] 1 1 1 2 1 1 1 1 1 1 ...
..$ pos_indicator : chr [1:1392] NA "" "" "" ...
..$ average_billed : num [1:1392] NA 389 440 266 651 ...
..$ average_allowed : num [1:1392] NA 215.8 196.2 151.7 51.6 ...
..$ rep_code : chr [1:1392] NA NA NA NA ...
..$ rx_brand_name : chr [1:1392] NA NA NA NA ...
..$ rx_generic_name : chr [1:1392] NA NA NA NA ...
..$ rx_avg_cost : num [1:1392] NA NA NA NA NA NA NA NA NA NA ...
..$ drg_id : chr [1:1392] NA NA NA NA ...
..$ inp_billed : num [1:1392] NA NA NA NA NA NA NA NA NA NA ...
..$ inp_allowed : num [1:1392] NA NA NA NA NA NA NA NA NA NA ...