R 使用循环、ifelse 条件和子集 (dplyr) 将变量变异为来自另一个观察的变量值

Question

see my reproducible and desired output below.

I want to create a new variable, where I combine variable values from other observations (rows), which I want to identify in a loop using subset. The condition of the subset is to be defined by the loop. In example 1 subset(df, country == i) does not work, but doing it manually (in Ex.2) subset(df, country == 'US') works. I thought country == i and country == 'US' should be pretty much the same.

# create a df
country <- c('US', 'US', 'China', 'China')
Trump_virus <- c('Y', 'N' ,'Y', 'N')
cases <- c (1000, 2000, 4, 6)
df <- data.frame(country, Trump_virus, cases)
#################################################### Ex.1
for (i in df$country) {
 print(i)
 df <- df %>%
  mutate(cases_corected = ifelse(
   Trump_virus == 'Y'
   ,subset(df, Trump_virus == 'N' & country ==  i)$cases*1000
   ,'killer_virus'
  ))}
##
df$cases_corected
#################################################### Ex.2
    for (i in df$country) {
 print(i)
 df <- df %>%
  mutate(cases_corected = ifelse(
   Trump_virus == 'Y'
   ,subset(df, Trump_virus == 'N' & country ==  'US')$cases*1000
   ,'killer_virus'
  ))}
##
df$cases_corected
################################################### Desired output
> df$cases_corected
[1] "2e+06"       
[2] "killer_virus"
[3] "6000"        
[4] "killer_virus"

Answer 1

Here is a solution with dplyr . Updated based on the change in desired output

df <- df %>%
  mutate(country=toupper(country)) # to get same names for other variants of a country  #e.g. China and china

#genearting a dataset which have cases only for Trump_virus==N
df1<-df %>% 
  dplyr::filter(Trump_virus=="N") %>% 
  dplyr::mutate(ID= "Y",
                cases_corected=cases*1e3) %>%
  dplyr::select(-c(cases,Trump_virus))

# final merging
df<-df %>% 
  left_join(df1,by=c("country"="country","Trump_virus"="ID")) %>%
  mutate(cases_corected=ifelse(is.na(cases_corected),'killer_virus',cases_corected))

df

  country Trump_virus cases cases_corected
1      US           Y  1000          2e+06
2      US           N  2000   killer_virus
3   CHINA           Y     4           6000
4   CHINA           N     6   killer_virus