Match same rows from 2 data frames

[zorro]

I have 2 data frames below.
By the way, I tried to use datapasta but I got error:

> Error in if ((nchar(joined_s) + indent_context) > defn_width) { : 
>   argument is of length zero

So I have to paste the tables here

January data frame:

	Plugin ID	CVE	CVSS	Risk	Host	Protocol	Port	Name
1	80101	CVE-2013-4786	7.8	High	10.10.1.254	udp	623	IPMI v2.0 Password Hash Disclosure
2	10815	CVE-2002-1060	4.3	Medium	10.10.1.79	tcp	80	Web Server Generic XSS
3	18405	CVE-2005-1794	5.1	Medium	10.92.102.11	tcp	3389	Microsoft Windows Remote Desktop Protocol Server Man-in-the-Middle Weakness
4	35291	CVE-2004-2761	5.0	Medium	10.92.102.11	tcp	3389	SSL Certificate Signed Using Weak Hashing Algorithm
5	42873	CVE-2016-2183	5.0	Medium	10.92.102.11	tcp	3389	SSL Medium Strength Cipher Suites Supported (SWEET32)
6	45411	NA	5.0	Medium	10.92.102.11	tcp	3389	SSL Certificate with Wrong Hostname
7	51192	NA	6.4	Medium	10.92.102.11	tcp	3389	SSL Certificate Cannot Be Trusted
8	57582	NA	6.4	Medium	10.92.102.11	tcp	3389	SSL Self-Signed Certificate
9	57608	NA	5.0	Medium	10.92.102.11	tcp	445	SMB Signing not required
10	57690	NA	4.3	Medium	10.92.102.11	tcp	3389	Terminal Services Encryption Level is Medium or Low

July data frame:

	Plugin ID	CVE	CVSS	Risk	Host	Protocol	Port	Name
1	57608	NA	5.0	Medium	10.92.102.11	tcp	445	SMB Signing not required
2	57690	NA	4.3	Medium	10.92.102.11	tcp	3389	Terminal Services Encryption Level is Medium or Low
3	58453	NA	4.3	Medium	10.92.102.11	tcp	3389	Terminal Services Doesn't Use Network Level Authentication (NLA) Only
4	79638	CVE-2014-6321	10.0	Critical	10.92.102.11	tcp	3389	MS14-066: Vulnerability in Schannel Could Allow Remote Code Execution (2992611) (uncredentialed check)
5	82828	CVE-2015-1635	10.0	Critical	10.92.102.11	tcp	80	MS15-034: Vulnerability in HTTP.sys Could Allow Remote Code Execution (3042553) (uncredentialed check)
6	90510	CVE-2016-0128	6.8	Medium	10.92.102.11	tcp	49155	MS16-047: Security Update for SAM and LSAD Remote Protocols (3148527) (Badlock) (uncredentialed check)
7	97833	CVE-2017-0143	10.0	Critical	10.92.102.11	tcp	445	MS17-010: Security Update for Microsoft Windows SMB Server (4013389) (ETERNALBLUE) (ETERNALCHAMPION) (ETERNALROMANCE) (ETERNALSYNERGY) (WannaCry) (EternalRocks) (Petya) (uncredentialed check)
8	99439	NA	10.0	Critical	10.92.102.11	tcp	445	SMB Server DOUBLEPULSAR Backdoor / Implant Detection (EternalRocks)
9	108797	NA	10.0	Critical	10.92.102.11	tcp	0	Unsupported Windows OS
10	18405	CVE-2005-1794	5.1	Medium	10.92.102.12	tcp	3389	Microsoft Windows Remote Desktop Protocol Server Man-in-the-Middle Weakness
11	35291	CVE-2004-2761	5.0	Medium	10.92.102.12	tcp	3389	SSL Certificate Signed Using Weak Hashing Algorithm
12	42873	CVE-2016-2183	5.0	Medium	10.92.102.12	tcp	3389	SSL Medium Strength Cipher Suites Supported (SWEET32

My actual question is how do I match common rows on both data frames but only for certain columns? For example I want rows with values that match in column Risk, Host, Port and Name only?

I have tried using setdiff() and compare() but they seem to compare only by one column and do not have extra flags to do an advanced comparison.

Thanks

[EconomiCurtis]

@zorro

My actual question is how do I match common rows on both data frames but only for certain columns?

I quite like dplyr's set operations for tables, where you can definitely select specific and multiple columns using dplyr::select to narrow down your tables to only those columns you are interested in comparing.

library(dplyr)
a <- tibble(
  c1 = 1:10,
  c2 = 5:14,
  c3 = 11:20
)
b <- tibble(
  c1 = 1:5,
  c2 = c(5,6,1,1,1),
  c3 = c(1,12,1:3)
)

dplyr::intersect(a,b)
#> # A tibble: 1 x 3
#>      c1    c2    c3
#>   <int> <dbl> <dbl>
#> 1     2     6    12

a %>% 
  select(c1,c2) %>% 
  dplyr::intersect(
    b %>% select(c1,c2)
  )
#> # A tibble: 2 x 2
#>      c1    c2
#>   <int> <dbl>
#> 1     1     5
#> 2     2     6

^{Created on 2021-02-24 by the reprex package (v1.0.0)}

[zorro]

Hi Sir,

Thank you for the solution, it works very well.

My next question is how can I get

1. the count of intersecting rows
2. the rows and the count of non-intersecting rows which belong to data frame 1 only
3. the rows and the count of non-intersecting rows which belong to data frame 2 only