Filter dataframe based on the values of two variables in R

Question

I am trying to create a dataframe where I transform and select variables based on the value rows take in two different columns. Let me explain.

This is what my data frame currently looks like:

ONSConstID is an id of each constituency. Each row is a different constituency.

All columns ending in vote15 are vote counts for each party in a constituency.

All columns ending in sex15 are the candidates' gender for each party.

The other columns aren't really relevant for what I am trying to do.

    dput(c[1:40,])
structure(list(ONSConstID = structure(c("W07000049", "W07000058", 
"S14000001", "S14000002", "S14000058", "S14000003", "E14000530", 
"E14000531", "E14000532", "W07000043", "E14000533", "S14000004", 
"W07000057", "S14000005", "E14000534", "E14000535", "E14000536", 
"E14000537", "E14000538", "S14000006", "S14000010", "S14000048", 
"E14000539", "S14000007", "E14000540", "E14000541", "E14000542", 
"E14000543", "E14000544", "E14000933", "E14000545", "E14000546", 
"E14000547", "E14000548", "E14000549", "E14000550", "E14000551", 
"E14000552", "E14000813", "E14000841"), label = "ONS Constituency ID", format.stata = "%9s"), 
    ConPPCsex19 = structure(c(1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 
    1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 
    1, 0, 1, 0, 1, 1, 0, 0, 1, 0), label = "Conservative 2019 candidate sex", format.stata = "%10.0g", labels = c(Male = 0, 
    Female = 1, `Non-binary` = 2), class = c("haven_labelled", 
    "vctrs_vctr", "double")), LabPPCsex19 = structure(c(0, 1, 
    0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 
    0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0), label = "Labour 2019 candidate sex", format.stata = "%10.0g", labels = c(Male = 0, 
    Female = 1, `Non-binary` = 2), class = c("haven_labelled", 
    "vctrs_vctr", "double")), LDPPCsex19 = structure(c(1, 0, 
    1, 0, 0, 0, 0, 0, 1, 1, 1, 0, NA, 0, 1, 1, 0, 0, 0, 1, 1, 
    1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, NA, 1, 0, 1, 0), label = " 2019 candidate sex", format.stata = "%10.0g", labels = c(Male = 0, 
    Female = 1, `Non-binary` = 2), class = c("haven_labelled", 
    "vctrs_vctr", "double")), SNPPPCsex19 = structure(c(NA, NA, 
    1, 0, 0, 0, NA, NA, NA, NA, NA, 0, NA, 0, NA, NA, NA, NA, 
    NA, 0, 1, 1, NA, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA), label = "Scottish National Party (SNP) 2019 candidate sex", format.stata = "%10.0g", labels = c(Male = 0, 
    Female = 1, `Non-binary` = 2), class = c("haven_labelled", 
    "vctrs_vctr", "double")), PCPPCsex19 = structure(c(0, 1, 
    NA, NA, NA, NA, NA, NA, NA, 1, NA, NA, 0, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA), label = " 2019 candidate sex", format.stata = "%10.0g", labels = c(Male = 0, 
    Female = 1, `Non-binary` = 2), class = c("haven_labelled", 
    "vctrs_vctr", "double")), UKIPPPCsex19 = structure(c(NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_), label = "United Kingdom Independence Party (UKIP) 2019 candidate sex", format.stata = "%10.0g", labels = c(Male = 0, 
    Female = 1, `Non-binary` = 2), class = c("haven_labelled", 
    "vctrs_vctr", "double")), GreenPPCsex19 = structure(c(1, 
    NA, 0, NA, NA, 1, 1, 0, 1, NA, 0, NA, NA, NA, 1, 1, 1, 0, 
    1, NA, NA, 0, 0, NA, 1, 0, 0, 0, 0, NA, 0, NA, NA, 0, 1, 
    1, 1, 0, 0, 1), label = "Green 2019 candidate sex", format.stata = "%10.0g", labels = c(Male = 0, 
    Female = 1, `Non-binary` = 2), class = c("haven_labelled", 
    "vctrs_vctr", "double")), BrexitPPCsex19 = structure(c(1, 
    NA, 0, NA, NA, NA, NA, NA, NA, 0, NA, NA, 0, NA, NA, 0, NA, 
    0, NA, NA, NA, NA, NA, NA, 1, 1, 0, 0, NA, NA, NA, 1, 0, 
    0, 0, NA, NA, 0, NA, NA), label = "Brexit 2019 candidate sex", format.stata = "%10.0g", labels = c(Male = 0, 
    Female = 1, `Non-binary` = 2), class = c("haven_labelled", 
    "vctrs_vctr", "double")), ConPPCsex17 = structure(c(1, 0, 
    1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 
    0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0), label = "Conservative 2017 candidate sex", format.stata = "%12.0g", labels = c(Male = 0, 
    Female = 1, `Non-binary` = 2), class = c("haven_labelled", 
    "vctrs_vctr", "double")), LabPPCsex17 = structure(c(0, 1, 
    0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 
    0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0), label = "Labour 2017 candidate sex", format.stata = "%12.0g", labels = c(Male = 0, 
    Female = 1, `Non-binary` = 2), class = c("haven_labelled", 
    "vctrs_vctr", "double")), LDPPCsex17 = structure(c(0, 1, 
    1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 
    0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0), label = "Liberal Democrat 2017 candidate sex", format.stata = "%12.0g", labels = c(Male = 0, 
    Female = 1, `Non-binary` = 2), class = c("haven_labelled", 
    "vctrs_vctr", "double")), SNPPPCsex17 = structure(c(NA, NA, 
    1, 0, 0, 0, NA, NA, NA, NA, NA, 0, NA, 0, NA, NA, NA, NA, 
    NA, 1, 1, 1, NA, 1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA), label = "SNP 2017 candidate sex", format.stata = "%12.0g", labels = c(Male = 0, 
    Female = 1, `Non-binary` = 2), class = c("haven_labelled", 
    "vctrs_vctr", "double")), PCPPCsex17 = structure(c(0, 0, 
    NA, NA, NA, NA, NA, NA, NA, 1, NA, NA, 0, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA), label = "Plaid Cymru 2017 candidate sex", format.stata = "%12.0g", labels = c(Male = 0, 
    Female = 1, `Non-binary` = 2), class = c("haven_labelled", 
    "vctrs_vctr", "double")), UKIPPPCsex17 = structure(c(1, NA, 
    NA, NA, NA, NA, 0, NA, NA, 0, NA, NA, NA, NA, 0, 0, 0, 0, 
    0, NA, NA, NA, 0, NA, 0, 0, 0, 0, 1, 0, 0, NA, NA, NA, 0, 
    0, NA, NA, NA, 0), label = "UKIP 2017 candidate sex", format.stata = "%12.0g", labels = c(Male = 0, 
    Female = 1, `Non-binary` = 2), class = c("haven_labelled", 
    "vctrs_vctr", "double")), GreenPPCsex17 = structure(c(NA, 
    NA, NA, NA, NA, NA, 1, NA, 1, NA, 0, NA, NA, NA, 1, 0, 1, 
    0, 1, NA, NA, NA, 0, NA, 1, 0, NA, 0, NA, 0, 0, NA, 1, 0, 
    1, 0, 1, 1, 0, 1), label = "Green Party 2017 candidate sex", format.stata = "%12.0g", labels = c(Male = 0, 
    Female = 1, `Non-binary` = 2), class = c("haven_labelled", 
    "vctrs_vctr", "double")), ConPPCsex15 = structure(c(0, 0, 
    0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 
    0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0), label = "Conservative 2015 candidate sex", format.stata = "%12.0g", labels = c(Male = 0, 
    Female = 1, `Non-binary` = 2), class = c("haven_labelled", 
    "vctrs_vctr", "double")), LabPPCsex15 = structure(c(0, 1, 
    0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 
    1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0), label = "Labour 2015 candidate sex", format.stata = "%12.0g", labels = c(Male = 0, 
    Female = 1, `Non-binary` = 2), class = c("haven_labelled", 
    "vctrs_vctr", "double")), LDPPCsex15 = structure(c(1, 0, 
    0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 
    1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0), label = "Liberal Democrat 2015 candidate sex", format.stata = "%12.0g", labels = c(Male = 0, 
    Female = 1, `Non-binary` = 2), class = c("haven_labelled", 
    "vctrs_vctr", "double")), SNPPPCsex15 = structure(c(NA, NA, 
    1, 0, 0, 0, NA, NA, NA, NA, NA, 0, NA, 0, NA, NA, NA, NA, 
    NA, 1, 1, 1, NA, 1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA), label = "SNP 2015 candidate sex", format.stata = "%12.0g", labels = c(Male = 0, 
    Female = 1, `Non-binary` = 2), class = c("haven_labelled", 
    "vctrs_vctr", "double")), PCPPCsex15 = structure(c(0, 0, 
    NA, NA, NA, NA, NA, NA, NA, 1, NA, NA, 0, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA), label = "Plaid Cymru 2015 candidate sex", format.stata = "%12.0g", labels = c(Male = 0, 
    Female = 1, `Non-binary` = 2), class = c("haven_labelled", 
    "vctrs_vctr", "double")), UKIPPPCsex15 = structure(c(0, 0, 
    NA, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, NA, 
    1, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1), label = "UKIP 2015 candidate sex", format.stata = "%12.0g", labels = c(Male = 0, 
    Female = 1, `Non-binary` = 2), class = c("haven_labelled", 
    "vctrs_vctr", "double")), GreenPPCsex15 = structure(c(0, 
    1, NA, 0, 0, NA, 0, 0, 0, 0, 0, 0, NA, NA, 1, NA, 1, 1, 0, 
    NA, 1, NA, 0, NA, 0, 0, NA, 0, NA, NA, NA, 0, 0, 0, 0, 0, 
    1, 0, 0, 0), label = "Green Party 2015 candidate sex", format.stata = "%12.0g", labels = c(Male = 0, 
    Female = 1, `Non-binary` = 2), class = c("haven_labelled", 
    "vctrs_vctr", "double")), ConVote15 = structure(c(3742, 12513, 
    5304, 11087, 15916, 3389, 23369, 20558, 26771, 13197, 20106, 
    12900, 3521, 7733, 34331, 10628, 30094, 8610, 28083, 10355, 
    8803, 7968, 30749, 13148, 7019, 5485, 5622, 17525, 22668, 
    19788, 25769, 15122, 17833, 15769, 26730, 33621, 27955, 19625, 
    32544, 34891), label = "2015 Conservative number of votes", format.stata = "%12.0g"), 
    LabVote15 = structure(c(15416, 8514, 11397, 12991, 2487, 
    15108, 8468, 8835, 13481, 16540, 15901, 3919, 8122, 5394, 
    6324, 19448, 10580, 19366, 8391, 14227, 13410, 15068, 12354, 
    2647, 24826, 20376, 21079, 18320, 10186, 11493, 14706, 23965, 
    6216, 21826, 18792, 6074, 9484, 18528, 9217, 9247), label = "2015 Labour number of votes", format.stata = "%12.0g"), 
    LDVote15 = structure(c(1397, 1391, 2050, 2252, 11812, 678, 
    4076, 1330, 4235, 1733, 1360, 1216, 718, 14486, 4062, 7030, 
    3433, 943, 5885, 855, 917, 896, 3440, 2347, 562, 770, 1217, 
    1169, 1636, 1356, 3919, 1331, 14000, 2396, 2241, 3927, 3378, 
    1958, 4193, 3418), label = "2015 Liberal Democrat number of votes", format.stata = "%12.0g"), 
    SNPVote15 = structure(c(NA, NA, 24793, 20221, 22949, 23887, 
    NA, NA, NA, NA, NA, 24130, NA, 22959, NA, NA, NA, NA, NA, 
    25492, 26999, 28641, NA, 27487, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA), label = "2015 Scottish National Party (SNP) number of votes", format.stata = "%12.0g"), 
    PCVote15 = structure(c(3663, 3536, NA, NA, NA, NA, NA, NA, 
    NA, 1608, NA, NA, 11790, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA), label = "2015 Plaid Cymru number of votes", format.stata = "%12.0g"), 
    UKIPVote15 = structure(c(4971, 3467, NA, 897, 1006, 1088, 
    8253, 7751, 4047, 7260, 7263, 1355, 2277, 1311, 8154, 10150, 
    10798, 8468, 10925, 1280, NA, 1296, 8050, NA, 9554, 7941, 
    9045, 5070, 8538, 12097, 8290, 7865, 2922, 9080, 1586, 7310, 
    6108, 4434, 8966, 8579), label = "2015 United Kingdom Independence Party (UKIP) number of votes", format.stata = "%12.0g"), 
    GreenVote15 = structure(c(711, 727, NA, 964, 885, NA, 2025, 
    826, 1983, 976, 1087, 965, NA, NA, 3606, NA, 2467, 1531, 
    2135, NA, 645, NA, 2686, NA, 897, 938, NA, 1061, NA, NA, 
    NA, 1006, 5634, 1232, 1682, 2231, 1878, 1412, 2462, 2537), label = "2015 Green Party number of votes", format.stata = "%12.0g"), 
    BNPVote15 = structure(c(NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_
    ), label = "2015 British National Party (BNP) number of votes", format.stata = "%12.0g"), 
    Winner15 = structure(c(2, 1, 4, 4, 4, 4, 1, 1, 1, 2, 1, 4, 
    5, 4, 1, 2, 1, 2, 1, 4, 4, 4, 1, 4, 2, 2, 2, 2, 1, 1, 1, 
    2, 1, 2, 1, 1, 1, 1, 1, 1), label = "2015 Winning party", format.stata = "%12.0g", labels = c(Conservative = 1, 
    Labour = 2, `Liberal Democrat` = 3, `Scottish National Party` = 4, 
    `Plaid Cymru` = 5, UKIP = 6, Green = 7, Speaker. = 9), class = c("haven_labelled", 
    "vctrs_vctr", "double")), Majority15 = structure(c(33.134536687498, 
    13.2645614966167, 30.4898033503278, 14.8915573314659, 12.7418653525618, 
    19.8234204940613, 32.2595310774826, 29.6807352457149, 26.3079755329889, 
    8.09168804763518, 9.1978913751996, 25.2444644262111, 13.6676975816969, 
    16.3309754640248, 46.3498415284098, 18.6040625197747, 33.6331311441121, 
    27.637596998818, 30.9605009112398, 21.5767396425904, 26.7636979556466, 
    25.1963095657985, 31.7111432905806, 31.4251901203182, 35.4824469691689, 
    34.0125820568928, 31.2433470934912, 1.8370883882149, 29.009017384029, 
    16.8688175816463, 20.8436958323913, 17.9411227657287, 8.12644433608243, 
    11.9990491095307, 15.5552507299485, 49.4911874800143, 37.8480831096449, 
    2.38033242199366, 40.1774026868756, 43.7073902372512), label = "2015 Majority", format.stata = "%12.0g")), row.names = c(NA, 
-40L), class = c("tbl_df", "tbl", "data.frame"))

I am trying to get 2 things I am having huge issues with:

is to get only the two most voted candidates for each row (ONSConstID), meaning those with higher values in the variables ending with Vote15 (UKIPVote15, LabVote15, etc.)
Is to keep those rows only if one of those two with most votes has sex15 == 1, and to have the same variables for candidate 1 (most voted) and 2 (second most voted).

Ideally, the final data frame would look something like this. I did it for the 3 first rows:

ONSConstID <- c( "W07000058", "S14000001")
votes1 <- c(12513, 20221)
party1 <- c( "Con", "SNP")
sex1 <- c(0, 1)
votes2 <- c(8514, 11397)
party2 <- c("Lab", "Lab" )
sex2 <- c(1, 0)
idealdf <- data.frame(c(ONSConstID, votes1, party1, sex1,
                        votes2, party2, sex2))
idealdf
  ONSConstID votes1 party1 sex1 votes2 party2 sex2
1  W07000058  12513    Con    0   8514    Lab    1
2  S14000001  20221    SNP    1  11397    Lab    0

I excluded the 1st row "W07000049" because no candidate there had Sex15 == 1, and then I limited the information for just the 2 most voted candidates.

I have tried many different things with pivoting data back and forth from wide to longer in order to order candidates by vote and select just the 2 higher ones, but I am not able to get to the idealdf I am showing here.

Any idea? Thank you so much in advance!

dput data is too long and isn't working either. Can you revise your Q by posting a small sample of your dataset. Besides, have you seen similar Q in [1](https://stackoverflow.com/questions/20084462/how-to-filter-data-frame-with-conditions-of-two-columns), [2](https://stackoverflow.com/questions/30037199/how-to-filter-dataframe-with-multiple-conditions),[3](https://stackoverflow.com/questions/34425847/r-subset-data-frame-based-on-multiple-values-for-multiple-variables)? — mnm, Oct 06 '20 at 12:07
Can you clarify what "Is to keep those rows only if one of those two with most votes has sex15 == 1, and to have the same variables for candidate 1 (most voted) and 2 (second most voted)." means? It's not possible that party1 == party2, so you must mean something else. Please try to explain what you want further or differently. — BrianLang, Oct 06 '20 at 12:33
@py_b you're right, sorry. I filter those out. The dput is now updated — AntVal, Oct 06 '20 at 13:53
@BrianLang sorry for not being clear. I meant keeping only those rows (ONSConstID) where at least one of the two most voted parties has a value of sex15 == 1. Party 1 is not == party 2, true. What I meant is to have the variables you can see in the "idealdf" for each candidate: votes, party, and sex, i.e. to have a column for the sex, votes and party of each of the most voted candidates for each row/constituency. Let me know if I was any clearer or not. Thanks for the help!!! — AntVal, Oct 06 '20 at 13:59

BrianLang · Accepted Answer · 2020-10-06T14:19:17.647

You were probably on the right path with pivoting and spreading and so on, but it's a bit complicated stuff. I commented my code so you see what each line is doing.

voting_data %>% 
  ## remove the haven labels
  mutate_all(haven::zap_labels) %>%
  ## turn everything to character so that you can pivot
  mutate_all(as.character)  %>%  
  ## pivot anything with a party name
  pivot_longer(cols = matches("^Lab|^Green|^Con|^LDP|^SNP|^PCP|^UKIP|^Brexit|^PC|^LD|^BNP")) %>%
  ## strip out features of variables
  mutate(date = str_extract(name,pattern = "[0-9]{2}"),
         vartype = str_extract(name, pattern = "sex|Vote"),
         party = str_extract(name, pattern = "^Lab|^Green|^Con|^LDP|^SNP|^PCP|^UKIP|^Brexit|^PC|^LD|^BNP")) %>%
  ## remove the variable "name"
  select(-name) %>%
  ## spread the variable with the "sex" and "vote" values
  spread(vartype, value)  %>%
  ## we only want year 15 and rows with votes!
  filter(date == 15 & !is.na(Vote))  %>%
  ## group by candidate
  group_by(ONSConstID) %>%
  ## arrange by candidate and votes as decreasing
  arrange(ONSConstID, desc(as.numeric(Vote))) %>%
  ## take just the first two rows for each candidate (highest two votes)
  slice(1:2) %>% 
  ## do we only want sex == 1?
  filter(any(sex == 1)) %>%
  ## add place to each row
  mutate(place = 1:n()) %>%
  ## spread to have the format requested.
  pivot_wider(values_from = c(party, sex, Vote), names_from = place)

Here are the final results:

# A tibble: 6 x 12
# Groups:   ONSConstID [6]
  ONSConstID TotalVote15 RejectedVote15 Winner15 Majority15       date  party_1 party_2 sex_1 sex_2 Vote_1 Vote_2
  <chr>      <chr>       <chr>          <chr>    <chr>            <chr> <chr>   <chr>   <chr> <chr> <chr>  <chr> 
1 E14000531  39497       105            1        29.6807352457149 15    Con     Lab     1     0     20558  8835  
2 E14000535  47409       209            2        18.6040625197747 15    Lab     Con     1     1     19448  10628 
3 E14000537  38918       198            2        27.637596998818  15    Lab     Con     1     1     19366  8610  
4 E14000539  58008       183            1        31.7111432905806 15    Con     Lab     1     0     30749  12354 
5 E14000540  43041       111            2        35.4824469691689 15    Lab     UKIP    1     0     24826  9554  
6 E14000545  53076       219            1        20.8436958323913 15    Con     Lab     1     0     25769  14706

Ahhhh! Damn it, I see what I was doing wrong now. But wouldn't get there alone. Thank you so much for taking the time to help. Super useful — AntVal, Oct 06 '20 at 14:48

Filter dataframe based on the values of two variables in R

1 Answers1