CODEWITHSUNDEEP
rdplyr
Sharif
Sharif

Reputation: 391

Filtering data using dplyr function in R

I have a data set of company directors. For example, for company X in 2005 they have 3 directors. So for company x in 2005 have three observations. Each director has a unique ID. Now I want to filter only those observations in which this year directors and previous year directors are same (they are same in its entirety; if this year' member includes 1 new member and previous years' 2 old members; I do not want those observations). Each director has a unique ID. Also Each company has a unique ID such as ISIN.

The data set looks like this for only one company -

          ISIN year                    DirectorName   DirectorID
1  US9898171015 2006            Thomas (Tom) E Davin   2247441792
2  US9898171015 2006           Matthew (Matt) L Hyde   4842568996
3  US9898171015 2007             James (Jim) M Weber   3581636766
4  US9898171015 2007           Matthew (Matt) L Hyde   4842568996
5  US9898171015 2007         David (Dave) M DeMattei    759047198
6  US9898171015 2008             James (Jim) M Weber   3581636766
7  US9898171015 2008           Matthew (Matt) L Hyde   4842568996
8  US9898171015 2008         David (Dave) M DeMattei    759047198
9  US9898171015 2009 William (Bill) Milroy Barnum Jr  20462211719
10 US9898171015 2009             James (Jim) M Weber   3581636766
11 US9898171015 2009           Matthew (Matt) L Hyde   4842568996
12 US9898171015 2009         David (Dave) M DeMattei    759047198
13 US9898171015 2010 William (Bill) Milroy Barnum Jr  20462211719
14 US9898171015 2010             James (Jim) M Weber   3581636766
15 US9898171015 2010           Matthew (Matt) L Hyde   4842568996
16 US9898171015 2011      Sarah (Sally) Gaines McCoy  11434863691
17 US9898171015 2011 William (Bill) Milroy Barnum Jr  20462211719
18 US9898171015 2011             James (Jim) M Weber   3581636766
19 US9898171015 2011           Matthew (Matt) L Hyde   4842568996
20 US9898171015 2012      Sarah (Sally) Gaines McCoy  11434863691
21 US9898171015 2012                Ernest R Johnson  40425210975
22 US9898171015 2013      Sarah (Sally) Gaines McCoy  11434863691
23 US9898171015 2013                Ernest R Johnson  40425210975
24 US9898171015 2013                  Travis D Smith  53006212569
25 US9898171015 2014      Sarah (Sally) Gaines McCoy  11434863691
26 US9898171015 2014                Ernest R Johnson  40425210975
27 US9898171015 2014                  Travis D Smith  53006212569
28 US9898171015 2015                  Kalen F Holmes  11051172801
29 US9898171015 2015      Sarah (Sally) Gaines McCoy  11434863691
30 US9898171015 2015                Ernest R Johnson  40425210975
31 US9898171015 2015                  Travis D Smith  53006212569
32 US9898171015 2016      Sarah (Sally) Gaines McCoy  11434863691
33 US9898171015 2016                Ernest R Johnson  40425210975
34 US9898171015 2016                  Travis D Smith  53006212569
35 US9898171015 2017      Sarah (Sally) Gaines McCoy  11434863691
36 US9898171015 2017             Scott Andrew Bailey 174000000000
37 US9898171015 2017                Ernest R Johnson  40425210975
38 US9898171015 2017                  Travis D Smith  53006212569

I tried these codes 

endo <- ac %>% 
  group_by(ISIN) %>% 
  filter(DirectorID == lag (DirectorID, 1))

after using the above code, I got the following results.

          ISIN year                    DirectorName  DirectorID
1  US9898171015 2007           Matthew (Matt) L Hyde  4842568996
2  US9898171015 2008             James (Jim) M Weber  3581636766
3  US9898171015 2008           Matthew (Matt) L Hyde  4842568996
4  US9898171015 2008         David (Dave) M DeMattei   759047198
5  US9898171015 2009             James (Jim) M Weber  3581636766
6  US9898171015 2009           Matthew (Matt) L Hyde  4842568996
7  US9898171015 2009         David (Dave) M DeMattei   759047198
8  US9898171015 2010 William (Bill) Milroy Barnum Jr 20462211719
9  US9898171015 2010             James (Jim) M Weber  3581636766
10 US9898171015 2010           Matthew (Matt) L Hyde  4842568996
11 US9898171015 2011 William (Bill) Milroy Barnum Jr 20462211719
12 US9898171015 2011             James (Jim) M Weber  3581636766
13 US9898171015 2011           Matthew (Matt) L Hyde  4842568996
14 US9898171015 2012      Sarah (Sally) Gaines McCoy 11434863691
15 US9898171015 2013      Sarah (Sally) Gaines McCoy 11434863691
16 US9898171015 2013                Ernest R Johnson 40425210975
17 US9898171015 2014      Sarah (Sally) Gaines McCoy 11434863691
18 US9898171015 2014                Ernest R Johnson 40425210975
19 US9898171015 2014                  Travis D Smith 53006212569
20 US9898171015 2015      Sarah (Sally) Gaines McCoy 11434863691
21 US9898171015 2015                Ernest R Johnson 40425210975
22 US9898171015 2015                  Travis D Smith 53006212569
23 US9898171015 2016      Sarah (Sally) Gaines McCoy 11434863691
24 US9898171015 2016                Ernest R Johnson 40425210975
25 US9898171015 2016                  Travis D Smith 53006212569
26 US9898171015 2017      Sarah (Sally) Gaines McCoy 11434863691
27 US9898171015 2017                Ernest R Johnson 40425210975
28 US9898171015 2017                  Travis D Smith 53006212569

If the first data (data before using the code) is inspected manually, it is clear that only for years 2007 and 2008; and 2013 and 2014, the composition of boards were the same. So I want these observations ONLY.

but second data (Data After using the code) did not produce the expected results.

The expected results are here - 

          ISIN year               DirectorName  DirectorID
1  US9898171015 2007        James (Jim) M Weber  3581636766
2  US9898171015 2007      Matthew (Matt) L Hyde  4842568996
3  US9898171015 2007    David (Dave) M DeMattei   759047198
4  US9898171015 2008        James (Jim) M Weber  3581636766
5  US9898171015 2008      Matthew (Matt) L Hyde  4842568996
6  US9898171015 2008    David (Dave) M DeMattei   759047198
7  US9898171015 2013 Sarah (Sally) Gaines McCoy 11434863691
8  US9898171015 2013           Ernest R Johnson 40425210975
9  US9898171015 2013             Travis D Smith 53006212569
10 US9898171015 2014 Sarah (Sally) Gaines McCoy 11434863691
11 US9898171015 2014           Ernest R Johnson 40425210975
12 US9898171015 2014             Travis D Smith 53006212569

I appreciate your help.

Upvotes: 0

Views: 184

Answers (2)

the-mad-statter
the-mad-statter

Reputation: 8936

This is verbose and likely inefficient, but it gets the job done using nested data frames.

library(dplyr)
library(purrr)
library(readr)
library(tidyr)

"ROW,ISIN,YEAR,DIRECTOR_NAME,DIRECTOR_ID
1,US9898171015,2006,Thomas (Tom) E Davin,2247441792
2,US9898171015,2006,Matthew (Matt) L Hyde,4842568996
3,US9898171015,2007,James (Jim) M Weber,3581636766
4,US9898171015,2007,Matthew (Matt) L Hyde,4842568996
5,US9898171015,2007,David (Dave) M DeMattei,759047198
6,US9898171015,2008,James (Jim) M Weber,3581636766
7,US9898171015,2008,Matthew (Matt) L Hyde,4842568996
8,US9898171015,2008,David (Dave) M DeMattei,759047198
9,US9898171015,2009,William (Bill) Milroy Barnum Jr,20462211719
10,US9898171015,2009,James (Jim) M Weber,3581636766
11,US9898171015,2009,Matthew (Matt) L Hyde,4842568996
12,US9898171015,2009,David (Dave) M DeMattei,759047198
13,US9898171015,2010,William (Bill) Milroy Barnum Jr,20462211719
14,US9898171015,2010,James (Jim) M Weber,3581636766
15,US9898171015,2010,Matthew (Matt) L Hyde,4842568996
16,US9898171015,2011,Sarah (Sally) Gaines McCoy,11434863691
17,US9898171015,2011,William (Bill) Milroy Barnum Jr,20462211719
18,US9898171015,2011,James (Jim) M Weber,3581636766
19,US9898171015,2011,Matthew (Matt) L Hyde,4842568996
20,US9898171015,2012,Sarah (Sally) Gaines McCoy,11434863691
21,US9898171015,2012,Ernest R Johnson,40425210975
22,US9898171015,2013,Sarah (Sally) Gaines McCoy,11434863691
23,US9898171015,2013,Ernest R Johnson,40425210975
24,US9898171015,2013,Travis D Smith,53006212569
25,US9898171015,2014,Sarah (Sally) Gaines McCoy,11434863691
26,US9898171015,2014,Ernest R Johnson,40425210975
27,US9898171015,2014,Travis D Smith,53006212569
28,US9898171015,2015,Kalen F Holmes,11051172801
29,US9898171015,2015,Sarah (Sally) Gaines McCoy,11434863691
30,US9898171015,2015,Ernest R Johnson,40425210975
31,US9898171015,2015,Travis D Smith,53006212569
32,US9898171015,2016,Sarah (Sally) Gaines McCoy,11434863691
33,US9898171015,2016,Ernest R Johnson,40425210975
34,US9898171015,2016,Travis D Smith,53006212569
35,US9898171015,2017,Sarah (Sally) Gaines McCoy,11434863691
36,US9898171015,2017,Scott Andrew Bailey,174000000000
37,US9898171015,2017,Ernest R Johnson,40425210975
38,US9898171015,2017,Travis D Smith,53006212569
" %>% 
  read_csv() %>% 
  group_by(ISIN, YEAR) %>% 
  nest(.key = "OTHER_DATA") %>% 
  group_by(ISIN) %>% 
  mutate(OTHER_DATA_LAG = lag(OTHER_DATA, 1), 
         OTHER_DATA_LEAD = lead(OTHER_DATA, 1), 
         KEEP = pmap(list(OTHER_DATA_LAG, OTHER_DATA, OTHER_DATA_LEAD), function(x, y, z) {
           isTRUE(all_equal(x["DIRECTOR_ID"], y["DIRECTOR_ID"])) || 
           isTRUE(all_equal(y["DIRECTOR_ID"], z["DIRECTOR_ID"]))
         })) %>% 
  filter(unlist(KEEP)) %>% 
  select(-OTHER_DATA_LAG, -OTHER_DATA_LEAD, -KEEP) %>% 
  unnest() %>% 
  ungroup()

Upvotes: 1

James Brusey
James Brusey

Reputation: 365

It seems like what you are trying to do is to identify when a repeat occurs. You might want 

a <- c(1,2,2,3)
a == lag(a)

to yield TRUE for 3 and FALSE elsewhere. But it doesn't, so what's going on?

The issue with lag is discussed more in this blogpost https://heuristically.wordpress.com/2012/10/29/lag-function-for-data-frames/

The blogpost has a more sophisticated version, but for your needs, the following might be sufficient:

mylag <- function(v) { c(NA, head(v, -1)) }
a == mylag(a)

Upvotes: 0

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