Find the percentage that two columns are equal but for each grouping variable-CodePudding

I'm trying to figure out the number of times ordre_ou and Rank are both equal to 3. I would then want to put that number over the number of times ordre_ou and rank aren't equal to eachother (one or the other equals 3, but the other value is a 1 or 2). But, what makes this difficult is that sometimes ordre_ou = 9. When it equals nine, this basically means "NA". So, if for example ordre_ou = 9 but rank = 3, that doesn't tell me anything. I would want to count that ID in the equation.

I know this is a confusing situation so I'll explain more if needed. Basically, ordre_ou is what is correct in the field and rank is what the computer calculated for the size of the eggs. I'm trying to see the percentage that the field work matches up with the computer. Everything should be grouped by ID.

dput(head(test, n= 100))


structure(list(ordre_ou = c("9", "9", "9", "9", "9", "9", "9", 
"9", "9", "9", "9", "9", "3", "9", "9", "9", "9", "9", "1", "2", 
"9", "9", "9", "9", "9", "9", "9", "9", "9", "9", "9", "9", "9", 
"9", "9", "9", "9", "9", "9", "9", "9", "9", "9", "9", "9", "9", 
"9", "9", "9", "1", "2", "9", "9", "9", "9", "9", "9", "1", "9", 
"9", "9", "9", "9", "9", "9", "9", "9", "9", "9", "9", "9", "9", 
"3", "9", "9", "9", "9", "9", "3", "9", "9", "9", "9", "9", "9", 
"9", "9", "9", "9", "9", "9", "9", "9", "9", "9", "9", "3", "9", 
"9", "9"), ID = c(65L, 65L, 65L, 65L, 88L, 88L, 88L, 201L, 201L, 
201L, 245L, 245L, 245L, 492L, 492L, 492L, 566L, 566L, 670L, 670L, 
704L, 704L, 704L, 753L, 753L, 753L, 784L, 784L, 784L, 819L, 819L, 
819L, 899L, 899L, 978L, 978L, 978L, 1060L, 1060L, 1060L, 1085L, 
1085L, 1085L, 1101L, 1101L, 1101L, 1235L, 1235L, 1235L, 1245L, 
1245L, 1269L, 1269L, 1269L, 1355L, 1355L, 1355L, 1356L, 1398L, 
1398L, 1398L, 1432L, 1432L, 1432L, 1458L, 1458L, 1458L, 1485L, 
1485L, 1485L, 1495L, 1495L, 1495L, 1505L, 1505L, 1505L, 1547L, 
1547L, 1547L, 1647L, 1647L, 1647L, 1657L, 1657L, 1688L, 1688L, 
1689L, 1689L, 1689L, 1698L, 1698L, 1708L, 1708L, 1708L, 1788L, 
1788L, 1788L, 1818L, 1818L, 1818L), PVC = c("2JA", "2JA", "2JA", 
"2JA", "378", "378", "378", "6180113", "6180113", "6180113", 
"6C9", "6C9", "6C9", "ABBM", "ABBM", "ABBM", "ACAD", "ACAD", 
"ADHY", "ADHY", "AFA2", "AFA2", "AFA2", "AFWY", "AFWY", "AFWY", 
"AH62", "AH62", "AH62", "AHJZ", "AHJZ", "AHJZ", "ALX7", "ALX7", 
"AMNH", "AMNH", "AMNH", "AP1D", "AP1D", "AP1D", "APLV", "APLV", 
"APLV", "APWU", "APWU", "APWU", "AT0F", "AT0F", "AT0F", "AT4Y", 
"AT4Y", "AT9F", "AT9F", "AT9F", "AV09", "AV09", "AV09", "AV1H", 
"AVH3", "AVH3", "AVH3", "AVZ1", "AVZ1", "AVZ1", "AWJR", "AWJR", 
"AWJR", "AXWA", "AXWA", "AXWA", "AXZ8", "AXZ8", "AXZ8", "AY45", 
"AY45", "AY45", "AZTN", "AZTN", "AZTN", "BABH", "BABH", "BABH", 
"BAHF", "BAHF", "BBHW", "BBHW", "BBJ2", "BBJ2", "BBJ2", "BBR7", 
"BBR7", "BC48", "BC48", "BC48", "BLU4", "BLU4", "BLU4", "BMWZ", 
"BMWZ", "BMWZ"), volume = c(59.23784990144, 57.67430439496, 55.941075465885, 
48.404429520525, 67.157961538635, 64.8180845235, 63.97980996672, 
68.91794748218, 58.15209427632, 57.52472936967, 66.667141436785, 
64.58676156675, 64.023665822545, 65.69135053824, 64.95949243106, 
63.717349423605, 57.7816829604, 57.75826384494, 67.7353109265, 
67.722914861455, 59.107361578275, 53.6827574912, 52.437236559625, 
64.64865510559, 60.092046898125, 55.65314064794, 65.9105613504, 
62.9811246456, 61.480030107375, 68.98102287872, 64.97415691434, 
62.0186388864, 63.249484535685, 62.914807201085, 60.94741873068, 
59.17492867088, 56.411384122335, 63.2272234135, 62.956485644075, 
59.157746675305, 57.32597558688, 55.434329712945, 54.3981319965, 
62.651453886945, 62.577372119625, 62.2632710628, 68.695014093745, 
64.7600712, 62.976206042835, 58.64841651305, 52.945914427435, 
64.2293729965, 60.22368881416, 60.125075029525, 67.72683495592, 
67.22997541, 60.662151860875, 60.95034911232, 67.22828506375, 
66.915609213375, 60.36643261348, 62.80147116288, 61.349764975875, 
56.381856553125, 65.138343264, 63.83578172832, 63.70508488183, 
65.374179521625, 64.57772389536, 61.54970130264, 68.11689275854, 
67.19495722272, 62.6687671468, 52.671580238965, 52.6496500866, 
49.0571970612, 65.35484747352, 62.99067352736, 56.28804189684, 
62.95487917906, 62.3909914575, 60.54756491358, 65.748093606, 
54.28226780316, 62.28287596851, 58.394627784, 58.54877398376, 
58.236602452875, 55.7946013138, 62.92579837536, 52.510981991085, 
69.18252370636, 67.45773857636, 56.322131567625, 60.486013583465, 
57.9529663461, 56.943494213215, 58.03166940876, 52.74560792151, 
51.3303436), rank = c(1, 2, 3, 4, 1, 2, 3, 1, 2, 3, 1, 2, 3, 
1, 2, 3, 1, 2, 1, 2, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 
1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 1, 2, 3, 1, 
2, 3, 1, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 
1, 2, 3, 1, 2, 3, 1, 2, 1, 2, 1, 2, 3, 1, 2, 1, 2, 3, 1, 2, 3, 
1, 2, 3), Year = c("2016", "2016", "2016", "2016", "2016", "2016", 
"2016", "2016", "2016", "2016", "2016", "2016", "2016", "2016", 
"2016", "2016", "2016", "2016", "2016", "2016", "2016", "2016", 
"2016", "2016", "2016", "2016", "2016", "2016", "2016", "2016", 
"2016", "2016", "2016", "2016", "2016", "2016", "2016", "2016", 
"2016", "2016", "2016", "2016", "2016", "2016", "2016", "2016", 
"2016", "2016", "2016", "2016", "2016", "2016", "2016", "2016", 
"2016", "2016", "2016", "2016", "2016", "2016", "2016", "2016", 
"2016", "2016", "2016", "2016", "2016", "2016", "2016", "2016", 
"2016", "2016", "2016", "2016", "2016", "2016", "2016", "2016", 
"2016", "2016", "2016", "2016", "2016", "2016", "2016", "2016", 
"2016", "2016", "2016", "2016", "2016", "2016", "2016", "2016", 
"2016", "2016", "2016", "2016", "2016", "2016")), class = c("grouped_df", 
"tbl_df", "tbl", "data.frame"), row.names = c(NA, -100L), groups = structure(list(
    PVC = c("2JA", "378", "6180113", "6C9", "ABBM", "ACAD", "ADHY", 
    "AFA2", "AFWY", "AH62", "AHJZ", "ALX7", "AMNH", "AP1D", "APLV", 
    "APWU", "AT0F", "AT4Y", "AT9F", "AV09", "AV1H", "AVH3", "AVZ1", 
    "AWJR", "AXWA", "AXZ8", "AY45", "AZTN", "BABH", "BAHF", "BBHW", 
    "BBJ2", "BBR7", "BC48", "BLU4", "BMWZ"), Year = c("2016", 
    "2016", "2016", "2016", "2016", "2016", "2016", "2016", "2016", 
    "2016", "2016", "2016", "2016", "2016", "2016", "2016", "2016", 
    "2016", "2016", "2016", "2016", "2016", "2016", "2016", "2016", 
    "2016", "2016", "2016", "2016", "2016", "2016", "2016", "2016", 
    "2016", "2016", "2016"), .rows = structure(list(1:4, 5:7, 
        8:10, 11:13, 14:16, 17:18, 19:20, 21:23, 24:26, 27:29, 
        30:32, 33:34, 35:37, 38:40, 41:43, 44:46, 47:49, 50:51, 
        52:54, 55:57, 58L, 59:61, 62:64, 65:67, 68:70, 71:73, 
        74:76, 77:79, 80:82, 83:84, 85:86, 87:89, 90:91, 92:94, 
        95:97, 98:100), ptype = integer(0), class = c("vctrs_list_of", 
    "vctrs_vctr", "list"))), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -36L), .drop = TRUE))

CodePudding user response：

With the following code, you will get the count of matching, not matching and total with a percentage column saying the correct percentage rate. I've added a commented line for filtering out rows where ordre_ou is equal to 9 as i was not completely sure what you meant by this being like NA.

library(magrittr)
library(dplyr)

test %>% 
  #filter(ordre_ou != 9) %>% 
  group_by(ID) %>% 
  summarise(
    correct = sum(ordre_ou == 3 & rank == 3),
    wrong = sum(!(ordre_ou == 3 & rank == 3)),
    total = n()
  ) %>% 
  mutate(
    perc = correct/total
  )