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基于使用 case_when 预先确定的向量创建新列

[英]Create a new column based on vectors pre-determined using case_when

 原文  2023-02-01 14:23:24  1   1    r/ dplyr/ case/ grouping/ mutate

问题描述

我有一个名为“ sim ”的大数据框，它有一个名为“ CAUSABAS ”的字符列，如下所示：

CAUSABAS OBITOGRAV OBITOPARTO   OBITOPUERP  ANO idade      idade_dias 
1     I110      <NA>       <NA>       <NA> 2013 95.58      34909  
2     C349      <NA>       <NA>       <NA> 2013 80.70      29474 
3     C490      <NA>       <NA>       <NA> 2013 97.90      35757 
4     I219      <NA>       <NA>       <NA> 2013 87.60      31995 
5     I259      <NA>       <NA>       <NA> 2013 62.57      22853 
6     I678      <NA>       <NA>       <NA> 2013 51.99      18988

此 data.frame 是使用 package microdatasus创建的，如下所示：

sim_mg <- fetch_datasus(year_start = 2013, year_end = 2018,
                        information_system = "SIM-DO",
                        uf = "MG")
sim_es <- fetch_datasus(year_start = 2013, year_end = 2018,
                        information_system = "SIM-DO",
                        uf = "ES")
sim <- bind_rows(sim_es, sim_mg) %>%
  select(TIPOBITO, DTOBITO, DTNASC, CODMUNOCOR, CODMUNRES, TPMORTEOCO, TPOBITOCOR, LINHAA, LINHAB, LINHAC, LINHAD, LINHAII, CAUSABAS, OBITOGRAV, OBITOPARTO, OBITOPUERP) %>%
  mutate(ANO = substr(DTOBITO, 5, 8))
sim$CAUSABAS <- unfactor(sim$CAUSABAS)

我想创建以下列： grupo_causa_basica ，对这些CAUSABAS进行分类。

一些观察有 3 个字符串，如B50 ，但另一个有 4 个字符串，如B501 。

例如， B50组有 3 个其他CAUSABAS 、 B500 、 B508和B509 。 有时输入是B50 ，但并非总是如此。 所有的观察都遵循这种组织。

我创建了一些向量来指定什么是什么：

doen_cardio <- c("F01","G45","G46","I10","I11","I13","I15","I21","I22","I23","I24","I25",
                 "I26","I27","I28","I37","I49","I50","I51","I52","I63","I64","I67","I68",
                 "I69","I71","I72","I73","I80","K55","O10","O16","P29","P60","R931","R943")
diabetes <- c("E10","E11","E12","E13","E14","O24","P70")
doencas_respiratorias <- c("R91","R942","J06","J16","J20","J21","J22","J30","J31","J34",
                           "J40","J41","J42","J45","J46","J60","J61","J62","J63","J64",
                           "J66","J67","J68","J69","J70","J80","J81","J82","J84","J96",
                           "J98","J99","P22","P26","P27","P28","R04","R06","R09","R84",
                           "T17","W77","W83","W84")
doen_renais <- c("R934","R944","E27","I12","N07","N17","N18","N19","N25","N27","Q60")
intox_exogenas <- c("T45","T46","T47","T48","T57","T61","T62","T65","T97","X47","X49",
                    "Y13","Y17","Y19")
doen_infec_veic_hidrica <- c("A01","A02","A03","A04","A05","A06","A07","A08","A09","A27",
                             "B15","B58","B65","B77","R10","A00","B68","B69","B76","B80",
                             "B82","B89","N220")
arbov <- c("A92","P354","A90","A91","A95","B50","B51","B52","B53","B54")
doen_pele <- c("R23","L08","L98","L99")

之后我尝试使用mutate plus case_when ：

sim <- sim %>%
   mutate(grupo_causa_basica = case_when(CAUSABAS %in% doen_cardio ~ "cardio",
                                         CAUSABAS %in% diabetes ~ "diabetes",
                                         CAUSABAS %in% doencas_respiratorias ~ "doen_resp",
                                         CAUSABAS %in% doen_renais ~ "doen_renais",
                                         CAUSABAS %in% intox_exogenas ~ "intox_exogenas",
                                         CAUSABAS %in% doen_infec_veic_hidrica ~ "doen_infec_veic_hidrica",
                                         CAUSABAS %in% arbov ~ "arbov",
                                         CAUSABAS %in% doen_pele ~ "doen_pele"))

这些有效，但是，我的代码忘记了B500 、 B508 、 B509等情况。 正如我在矢量“ arbov ”中指定的只有B50 ，它只分类B50 ，而不是B500或B508 。

这样，我想知道我必须对向量做些什么：获取向量中以这些规范开头的所有字符串。 所以我意识到我可以使用startsWith ：

sim <- sim %>%
  mutate(grupo_causa_basica = case_when(startsWith(CAUSABAS, doen_cardio) ~ "cardio",
                                        startsWith(CAUSABAS, diabetes) ~ "diabetes",
                                        startsWith(CAUSABAS, doencas_respiratorias) ~ "doen_resp",
                                        startsWith(CAUSABAS, doen_renais) ~ "doen_renais",
                                        startsWith(CAUSABAS, intox_exogenas) ~ "intox_exogenas",
                                        startsWith(CAUSABAS, doen_infec_veic_hidrica) ~ "doen_infec_veic_hidrica",
                                        startsWith(CAUSABAS, arbov) ~ "arbov",
                                        startsWith(CAUSABAS, doen_pele) ~ "doen_pele"))

但是，这不是理想的解决方案。 当我第一次尝试时，它给了我类似的东西：

table(sim$grupo_causa_basica)

                  arbov                  cardio doen_infec_veic_hidrica             doen_renais               doen_resp 
                    638                   51087                    2514                    2614                    2895

当我第二次尝试时，它给我带来了：

 table(sim$grupo_causa_basica)

                  arbov                  cardio                diabetes doen_infec_veic_hidrica               doen_pele 
                    103                    5650                    5445                     173                     254 
            doen_renais               doen_resp          intox_exogenas 
                   1371                     656                      17

考虑到所有以向量中指定的字符串开头的字符串，我该怎么做？

1 个解决方案

解决方案1
 0  2023-02-01 20:33:58

API在我尝试使用数据下载功能时超时，但是像这样：

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(stringr)
doen_cardio <- c("F01","G45","G46","I10","I11","I13","I15","I21","I22","I23","I24","I25",
                 "I26","I27","I28","I37","I49","I50","I51","I52","I63","I64","I67","I68",
                 "I69","I71","I72","I73","I80","K55","O10","O16","P29","P60","R931","R943")
diabetes <- c("E10","E11","E12","E13","E14","O24","P70")
doencas_respiratorias <- c("R91","R942","J06","J16","J20","J21","J22","J30","J31","J34",
                           "J40","J41","J42","J45","J46","J60","J61","J62","J63","J64",
                           "J66","J67","J68","J69","J70","J80","J81","J82","J84","J96",
                           "J98","J99","P22","P26","P27","P28","R04","R06","R09","R84",
                           "T17","W77","W83","W84")
doen_renais <- c("R934","R944","E27","I12","N07","N17","N18","N19","N25","N27","Q60")
intox_exogenas <- c("T45","T46","T47","T48","T57","T61","T62","T65","T97","X47","X49",
                    "Y13","Y17","Y19")
doen_infec_veic_hidrica <- c("A01","A02","A03","A04","A05","A06","A07","A08","A09","A27",
                             "B15","B58","B65","B77","R10","A00","B68","B69","B76","B80",
                             "B82","B89","N220")
arbov <- c("A92","P354","A90","A91","A95","B50","B51","B52","B53","B54")
doen_pele <- c("R23","L08","L98","L99")

doen_cardio <- ifelse(nchar(doen_cardio) == 3, paste0(doen_cardio, ".*"), doen_cardio)
diabetes <- ifelse(nchar(diabetes) == 3, paste0(diabetes, ".*"), diabetes)
doencas_respiratorias <- ifelse(nchar(doencas_respiratorias) == 3, paste0(doencas_respiratorias, ".*"), doencas_respiratorias)
doen_renais <- ifelse(nchar(doen_renais) == 3, paste0(doen_renais, ".*"), doen_renais)
intox_exogenas <- ifelse(nchar(intox_exogenas) == 3, paste0(intox_exogenas, ".*"), intox_exogenas)
doen_infec_veic_hidrica <- ifelse(nchar(doen_infec_veic_hidrica) == 3, paste0(doen_infec_veic_hidrica, ".*"), doen_infec_veic_hidrica)
arbov <- ifelse(nchar(arbov) == 3, paste0(arbov, ".*"), arbov)
doen_pele <- ifelse(nchar(doen_pele) == 3, paste0(doen_pele, ".*"), doen_pele)

doen_cardio <- paste(doen_cardio, collapse="|")
diabetes <- paste(diabetes, collapse="|")
doencas_respiratorias <- paste(doencas_respiratorias, collapse="|")
doen_renais <- paste(doen_renais, collapse="|")
intox_exogenas <- paste(intox_exogenas, collapse="|")
doen_infec_veic_hidrica <- paste(doen_infec_veic_hidrica, collapse="|")
arbov <- paste(arbov, collapse="|")
doen_pele <- paste(doen_pele, collapse="|")

tib <- tibble::tribble(
  ~CAUSABAS, ~OBITOGRAV, ~OBITOPARTO,   ~OBITOPUERP,  ~ANO, ~idade,      ~idade_dias, 
"I110",      NA,       NA,       NA, 2013, 95.58,      34909,  
"C349",      NA,       NA,       NA, 2013, 80.70,      29474, 
"C490",      NA,       NA,       NA, 2013, 97.90,      35757, 
"I219",      NA,       NA,       NA, 2013, 87.60,      31995, 
"I259",      NA,       NA,       NA, 2013, 62.57,      22853, 
"I678",      NA,       NA,       NA, 2013, 51.99,      18988)


tib %>% 
  mutate(grupo_causa_basica = case_when(str_detect(CAUSABAS, doen_cardio) ~ "cardio",
                                        str_detect(CAUSABAS, diabetes) ~ "diabetes",
                                        str_detect(CAUSABAS, doencas_respiratorias) ~ "doen_resp",
                                        str_detect(CAUSABAS, doen_renais) ~ "doen_renais",
                                        str_detect(CAUSABAS, intox_exogenas) ~ "intox_exogenas",
                                        str_detect(CAUSABAS, doen_infec_veic_hidrica) ~ "doen_infec_veic_hidrica",
                                        str_detect(CAUSABAS, arbov) ~ "arbov",
                                        str_detect(CAUSABAS, doen_pele) ~ "doen_pele"))
#> # A tibble: 6 × 8
#>   CAUSABAS OBITOGRAV OBITOPARTO OBITOPUERP   ANO idade idade_dias grupo_causa_…¹
#>   <chr>    <lgl>     <lgl>      <lgl>      <dbl> <dbl>      <dbl> <chr>         
#> 1 I110     NA        NA         NA          2013  95.6      34909 cardio        
#> 2 C349     NA        NA         NA          2013  80.7      29474 <NA>          
#> 3 C490     NA        NA         NA          2013  97.9      35757 <NA>          
#> 4 I219     NA        NA         NA          2013  87.6      31995 cardio        
#> 5 I259     NA        NA         NA          2013  62.6      22853 cardio        
#> 6 I678     NA        NA         NA          2013  52.0      18988 cardio        
#> # … with abbreviated variable name ¹​grupo_causa_basica

由reprex package (v2.0.1) 创建于 2023-02-01

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