在 2 種條件下變異 3 列鹼基

Question

有人知道一種更有效的方法來運行此代碼以根據與特定列相關的條件從 NA 中的 3 列轉換值。 例如用 mutate_at 代替 mutate。

Data = DATA %>%  
  mutate(Temperature1 = ifelse(Temperature1 < 19 & Cyclon1== "f","NA",Temperature1 )) %>% 
  mutate(Temperature2 = ifelse(Temperature2 < 19 & Cyclon2== "f","NA",Temperature2 )) %>%
  mutate(Temperature3 = ifelse(Temperature3 < 19 & Cyclon3== "f","NA",Temperature3 ))

提前致謝

Answer 1

這不是那么簡單，因為您需要將 Temperature1 與 Cyclon1 匹配，如果您想堅持 dplyr，那么出路是先到 pivot 更長的時間，變異和 pivot 回來。 例如，如果您的數據是這樣的：

set.seed(111)
DATA = data.frame(Temperature1=runif(100,min=0,max=100),
Temperature2=runif(100,min=0,max=100),
Temperature3=runif(100,min=0,max=100),
Cyclon1 = sample(c("t","f"),100,replace=TRUE),
Cyclon2 = sample(c("t","f"),100,replace=TRUE),
Cyclon3 = sample(c("t","f"),100,replace=TRUE))

然后我們這樣做：

DATA %>%  rownames_to_column("id") %>% 
pivot_longer(-id,names_to=c(".value","set"),names_pattern="([^0-9]*)([0-9])")

# A tibble: 300 x 4
   id    set   Temperature Cyclon
   <chr> <chr>       <dbl> <fct> 
 1 1     1            59.3 t     
 2 1     2            57.6 f     
 3 1     3            72.6 t     
 4 2     1            72.6 t     
 5 2     2            13.6 t     
 6 2     3            92.0 f  

在此步驟中，對於每個組 (1-3)，您都有一個相應的 Cyclon 和溫度，剩下的就是讓您再次變異和 pivot 寬：

data1 = DATA %>%  rownames_to_column("id") %>% 
pivot_longer(-id,names_to=c(".value","set"),names_pattern="([^0-9]*)([0-9])") %>%   
mutate(Temperature=replace(Temperature,Temperature < 19 & Cyclon== "f",NA)) %>%
pivot_wider(values_from=c(Temperature,Cyclon),names_from=set)

我們可以檢查這些值：

head(DATA[DATA$Temperature1 < 19 & DATA$Cyclon1=="f",])
   Temperature1 Temperature2 Temperature3 Cyclon1 Cyclon2 Cyclon3
7      1.065785     64.00623     58.11568       f       t       t
10     9.368152     96.53025     53.62925       f       t       t
14     4.754785     90.39043     47.44193       f       f       f
15    15.620252     96.45305     72.74062       f       t       f
17    17.144369     54.89127     95.85764       f       t       f
31     5.859646     35.14933     44.92498       f       f       t

head(data1[DATA$Temperature1 < 19 & DATA$Cyclon1=="f",])
# A tibble: 6 x 7
  id    Temperature_1 Temperature_2 Temperature_3 Cyclon_1 Cyclon_2 Cyclon_3
  <chr>         <dbl>         <dbl>         <dbl> <fct>    <fct>    <fct>   
1 7                NA          64.0          58.1 f        t        t       
2 10               NA          96.5          53.6 f        t        t       
3 14               NA          90.4          47.4 f        f        f       
4 15               NA          96.5          72.7 f        t        f       
5 17               NA          54.9          95.9 f        t        f       
6 31               NA          35.1          44.9 f        f        t       

Answer 2

我假設了一些數據：

DATA <- tibble(Record = LETTERS[1:6],
                Temperature1 = c(17:22),
                Cyclon1 = rep(c("f", "g"), 3),
                Temperature2 = c(17:22),
                Cyclon2 = rep(c("f", "g"), 3),
                Temperature3 = c(17:22),
                Cyclon3 = rep(c("f", "g"), 3))

我gather ed，然后mutate d （因為我的 R 安裝還沒有pivot long ）

LONGDATA <- DATA %>% 
  gather("Cyclon", "cValue", starts_with("Cyclon")) %>% 
  gather("Temperature", "tValue", starts_with("Temperature")) %>% 
    # Here's where the logic is.
  mutate(tValue = ifelse(tValue < 19 & cValue == "f", "NA", tValue ))

LONGDATA
# A tibble: 54 x 5
   Record Cyclon  cValue Temperature  tValue
   <chr>  <chr>   <chr>  <chr>        <chr> 
 1 A      Cyclon1 f      Temperature1 NA    
 2 B      Cyclon1 g      Temperature1 18    
 3 C      Cyclon1 f      Temperature1 19    
 4 D      Cyclon1 g      Temperature1 20    
 5 E      Cyclon1 f      Temperature1 21    
 6 F      Cyclon1 g      Temperature1 22    
 7 A      Cyclon2 f      Temperature1 NA    
 8 B      Cyclon2 g      Temperature1 18    
 9 C      Cyclon2 f      Temperature1 19    
10 D      Cyclon2 g      Temperature1 20   

我個人會將其保留為 LONGDATA 形式。 但如果你真的想要你的寬風格回來......

NEWDATA <- LONGDATA %>% 
  spread(key = Cyclon, value = cValue) %>% 
  spread(key = Temperature, value = tValue)

 NEWDATA
# A tibble: 6 x 7
  Record Cyclon1 Cyclon2 Cyclon3 Temperature1 Temperature2 Temperature3
  <chr>  <chr>   <chr>   <chr>   <chr>        <chr>        <chr>       
1 A      f       f       f       NA           NA           NA          
2 B      g       g       g       18           18           18          
3 C      f       f       f       19           19           19          
4 D      g       g       g       20           20           20          
5 E      f       f       f       21           21           21          
6 F      g       g       g       22           22           22