how to match to data based on several condition-CodePudding

I have a data like this

df<- structure(list(DES = c("AAAAA", "ABBBB", "BBDDD", "CHDDD", "NMHH", 
"YNNN", "UUUU", "IMMM", "TTSGG", "MHDDD", "IIKKKKK", "UDDDD", 
"AAAAA", "ABBBB"), Name = c("AB1", "TH1", "TH2", "HUA", "HUA1", 
"UMA", "YIN", "YIM", "IUMH", "YIMH", "YIH", "TH2", "AB1", "TH1"
), data = c(1.399260301, 1.435057306, 1.515715678, 1.157216398, 
1.452347714, 1.172466387, 1.16325675, 2.614138423, 0.895228818, 
1.571925745, 1.717242238, 1.572841664, 0.924094104, 1.59458932
), Exact = c(0.000536206, 0.000910058, 0.001145037, 0.00114666, 
0.001883335, 0.002365192, 0.002548559, 0.003406673, 0.004232483, 
0.005164572, 0.006269242, 0.006741774, 0.009565493, 0.009581586
), Class = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L)), class = "data.frame", row.names = c(NA, -14L))

now I have another data which looks like this

df2<- structure(list(DES = c("AAAAA", "ABBBB", "BBDDD", "CHDDD"), Name = c("AB1", 
"TH1", "TH2", "HUA")), class = "data.frame", row.names = c(NA, 
-4L))

I am trying to get where those ones in the df2 are situated in the df1 so I do this

match(df$DES,df2$DES)

but I am interested in those with Class 1

I do this

match(df$DES,df2$DES) && df$Class ==1

I basically dont know how to subset based on class 1 , I also tried several other ways but didn't work like subset(match(df$DES,df2$DES), Class==1)

CodePudding user response：

Try this to find the indices ,

match(df[df$Class == 1 , ]$DES,df2$DES)

then

df2[na.omit(match(df[df$Class == 1 , ]$DES,df2$DES)), ]
    DES Name

CodePudding user response：

Credit to r2evans--just noticed that this is the same as his comment, just wrapped in which as akrun suggested.

df2<- data.frame(DES = c("AAAAA", "ABBBB", "BBDDD", "CHDDD"), Name = c("AB1", "TH1", "TH2", "HUA"))
which((df$DES %in% df2$DES) & (df$Class == 1L))
#> [1] 1 2 3 4
which((df$DES %in% df2$DES) & (df$Class == 2L))
#> [1] 13 14

CodePudding user response：

Another possible solution, based on dplyr::inner_join:

library(tidyverse)

inner_join(df2, df %>% rownames_to_column) %>%
  filter(Class == 1) %>% pull(rowname) %>% as.numeric

#> Joining, by = c("DES", "Name")
#> [1] 1 2 3 4

To get Class == 2:

library(tidyverse)

inner_join(df2, df %>% rownames_to_column) %>%
  filter(Class == 2) %>% pull(rowname) %>% as.numeric

#> Joining, by = c("DES", "Name")
#> [1] 13 14