如何通过匹配来自另一个数据帧的整个列中的字符串来检索一个数据帧中的值？

Question

Say I have a data frame df1 like this below:

> df1
          probe                         OMIM
1  1565034_s_at                       601464
2     201000_at 601065 /// 613287 /// 616339
3     204565_at                       615652
4     205355_at            600301 /// 610006
5   205734_s_at                       601464
6   205735_s_at                       601464
7     206527_at            137150 /// 613163
8     209173_at                       606358
9   209459_s_at            137150 /// 613163
10    209460_at            137150 /// 613163
11    215465_at                             
12    223864_at                       610856
13    224742_at            612674 /// 613599

And a second data frame, df2 :

> df2
                                         platprobe   symbol
1   1565034_s_at,205734_s_at,242078_at,205735_s_at     AFF3
2                                        201000_at     AARS
3                                        201884_at   DNALI1
4                                      202779_s_at     PLK1
5                                        204565_at   ACOT13
6                              205355_at,226030_at   ACADSB
7      205808_at,207284_s_at,209135_at,210896_s_at   LIMCH1
8      206164_at,206165_s_at,206166_s_at,217528_at   SLC7A8
9                  206527_at,209459_s_at,209460_at     ABAT
10                             209173_at,228969_at     AGR2
11                                       215465_at   ABCA12
12                                     221024_s_at  TMEM144
13                                       223864_at ANKRD30A
14                 224742_at,228123_s_at,228124_at   ABHD12
15                           225421_at,225431_x_at   GALNT7
16                                       226120_at    PSAT1
17                                       228241_at     AGR3

I would like to add a new column to df1 , df1$symbol , based on matching df1$probe value with df2$platprobe . The result should be this:

> df1
          probe                         OMIM    symbol
1  1565034_s_at                       601464      AFF3
2     201000_at 601065 /// 613287 /// 616339      AARS
3     204565_at                       615652    ACOT13
4     205355_at            600301 /// 610006    ACADSB
5   205734_s_at                       601464      AFF3
6   205735_s_at                       601464      AFF3
7     206527_at            137150 /// 613163      ABAT
8     209173_at                       606358      AGR2
9   209459_s_at            137150 /// 613163      ABAT
10    209460_at            137150 /// 613163      ABAT
11    215465_at                                 ABCA12
12    223864_at                       610856  ANKRD30A
13    224742_at            612674 /// 613599    ABHD12

The challenging part for me is that df2$platprobe in many cases contains various annotation apart from that one found in df1$probe . So, if I try:

#This will retrieve only perfect matches (where df2$platprobe contains only one possible value, such as ABCA12):
df1$symbol <- df2$symbol[df2$probe %in% df1$platprobe]

#And if I use 'grepl', that won't work:
#(The reason for using 'unlist' and 'strsplit' is because I thought that maybe breaking all possible
#values from the entire df2$platprobe into a object that would work. But it doesn't)

df1$symbol <- df2$symbol[grepl(df1$probe, unlist(strsplit(paste(df2$platprobe, sep=",", collapse=","), ",")))]

Any help is much appreciated.

PS: also if you have a better idea for a more topic title, it is very welcome.

Update Thank you, @Anoushiravan R. And sorry for not putting the reproducible df's before. Now, here they are:

df1 <- data.frame(probe=c("1565034_s_at", "201000_at", "204565_at", 
"205355_at", "205734_s_at", "205735_s_at", "206527_at", "209173_at", 
"209459_s_at", "209460_at", "215465_at", "223864_at", "224742_at"
), OMIM = c("601464", "601065 /// 613287 /// 616339", "615652", 
"600301 /// 610006", "601464", "601464", "137150 /// 613163", 
"606358", "137150 /// 613163", "137150 /// 613163", "", "610856", 
"612674 /// 613599"))

df2 <- data.frame(platprobe = c("1565034_s_at, 205734_s_at, 205735_s_at, 
227198_at, 242078_at, 243967_at", "201000_at", "201884_at", "202779_s_at",
"204565_at", "205355_at,226030_at", "205808_at, 207284_s_at, 209135_at, 
210896_s_at, 224996_at, 225008_at, 242037_at", "206164_at, 206165_s_at, 
206166_s_at, 217528_at", "206527_at, 209459_s_at,209460_at", "209173_at, 
228969_at", "215465_at", "221024_s_at", "223864_at","224742_at, 228123_s_at, 
228124_at", "225421_at,225431_x_at", "226120_at", "228241_at"), symbol=c("AFF3", 
"AARS", "DNALI1", "PLK1", "ACOT13", "ACADSB", "LIMCH1", "SLC7A8", "ABAT", "AGR2", 
"ABCA12", "TMEM144", "ANKRD30A", "ABHD12", "GALNT7", "PSAT1", "AGR3"))

Answer 1

You can use the following solution:

library(dplyr)
library(stringr)
library(purrr)

df1 %>%
  mutate(symbol = map_chr(probe, ~ df2$symbol[which(str_detect(df2$platprobe, .x))]))


          probe                         OMIM   symbol
1  1565034_s_at                       601464     AFF3
2     201000_at 601065 /// 613287 /// 616339     AARS
3     204565_at                       615652   ACOT13
4     205355_at            600301 /// 610006   ACADSB
5   205734_s_at                       601464     AFF3
6   205735_s_at                       601464     AFF3
7     206527_at            137150 /// 613163     ABAT
8     209173_at                       606358     AGR2
9   209459_s_at            137150 /// 613163     ABAT
10    209460_at            137150 /// 613163     ABAT
11    215465_at                                ABCA12
12    223864_at                       610856 ANKRD30A
13    224742_at            612674 /// 613599   ABHD12

Answer 2

Though the answer above serves the purpose, yet to show that it can be done without purrr also

library(dplyr)
library(tidyr)
library(stringr)

df1 %>% left_join(df2 %>% separate_rows(platprobe, sep = ',') %>%
                    mutate(platprobe = str_trim(platprobe)), by = c('probe' = 'platprobe'))

          probe                         OMIM   symbol
1  1565034_s_at                       601464     AFF3
2     201000_at 601065 /// 613287 /// 616339     AARS
3     204565_at                       615652   ACOT13
4     205355_at            600301 /// 610006   ACADSB
5   205734_s_at                       601464     AFF3
6   205735_s_at                       601464     AFF3
7     206527_at            137150 /// 613163     ABAT
8     209173_at                       606358     AGR2
9   209459_s_at            137150 /// 613163     ABAT
10    209460_at            137150 /// 613163     ABAT
11    215465_at                                ABCA12
12    223864_at                       610856 ANKRD30A
13    224742_at            612674 /// 613599   ABHD12

Answer 3

Another way to deal with your problem is based on your view / observation that your matching key is "collapsed" in the 2nd dataframe.

{tidyr} has a great function to split nested values in new rows, ie tidyr()::separate_rows() . This will turn your 2nd df in a long format.

Note: separate_rows() allows to split over multiple columns if needed. But here we use only your key platprobe .

library(dplyr)   # data crunching 
library(tidyr)   # data manipulation for generating tidy df

# how to separate the nested column values to rows
df2 %>% separate_rows(platprobe, sep = ",")

Checking the row-spread:

# A tibble: 33 x 2
   platprobe    symbol
   <chr>        <chr> 
 1 1565034_s_at AFF3  
 2 205734_s_at  AFF3  
 3 242078_at    AFF3  
 4 205735_s_at  AFF3  
 5 201000_at    AARS  
...

You now have a proper alignment of the matching keys and do a left_join() to merge both data frames.

# merging the "long" lookup df2 with df1
df1 %>% left_join(
     df2 %>% separate_rows(platprobe, sep = ",")
   , by = c("probe" = "platprobe")    # define matching keys in df1 and df2
)

This delivers

          probe   symbol
1  1565034_s_at     AFF3
2     201000_at     AARS
3     204565_at   ACOT13
4     205355_at   ACADSB
...

Answer 4

In case you want to use grep for matching you can do this via sapply or lapply .

df1$symbol <- df2$symbol[sapply(df1$probe, grep, df2$platprobe)]

df1
#          probe                         OMIM   symbol
#1  1565034_s_at                       601464     AFF3
#2     201000_at 601065 /// 613287 /// 616339     AARS
#3     204565_at                       615652   ACOT13
#4     205355_at            600301 /// 610006   ACADSB
#5   205734_s_at                       601464     AFF3
#6   205735_s_at                       601464     AFF3
#7     206527_at            137150 /// 613163     ABAT
#8     209173_at                       606358     AGR2
#9   209459_s_at            137150 /// 613163     ABAT
#10    209460_at            137150 /// 613163     ABAT
#11    215465_at                                ABCA12
#12    223864_at                       610856 ANKRD30A
#13    224742_at            612674 /// 613599   ABHD12