嗨，假設我有一張這樣的桌子。我想要的是按“百分比”排名，但我只希望它基于 cat 列何時為“HIGH”組并忽略“LOW”。
name cat Freq percent
1 berry HIGH 259 0.583
2 jack HIGH 45 0.634
3 steve HIGH 331 0.943
4 nadia HIGH 304 0.580
5 jacob HIGH 179 0.844
6 susan HIGH 15 0.833
7 luthered HIGH 14 0.264
8 jane HIGH 99 0.513
9 berry LOW 185 0.417
10 jack LOW 26 0.366
11 steve LOW 20 0.057
12 nadia LOW 220 0.420
13 jacob LOW 33 0.156
14 susan LOW 3 0.167
15 luthered LOW 39 0.736
16 jane LOW 94 0.487
我試過這樣做，但我似乎無法讓條件發揮作用。
temp = structure(list(name = c("berry", "jack", "steve", "nadia", "jacob",
"susan", "luthered", "jane", "berry", "jack", "steve", "nadia",
"jacob", "susan", "luthered", "jane"), cat = c("HIGH", "HIGH",
"HIGH", "HIGH", "HIGH", "HIGH", "HIGH", "HIGH", "LOW", "LOW",
"LOW", "LOW", "LOW", "LOW", "LOW", "LOW"), Freq = c(259L, 45L,
331L, 304L, 179L, 15L, 14L, 99L, 185L, 26L, 20L, 220L, 33L, 3L,
39L, 94L), percent = c(0.583, 0.634, 0.943, 0.58, 0.844, 0.833,
0.264, 0.513, 0.417, 0.366, 0.057, 0.42, 0.156, 0.167, 0.736,
0.487)), class = "data.frame", row.names = c(NA, -16L))
我試過這樣做，但順序不正確。
temp %>% arrange(desc ( percent), cat =="HIGH" )
如果正確排序，則名稱應按如下順序排序：steve jacob susan jack berry nadia jane luthered
提前致謝。
uj5u.com熱心網友回復：
我們可能會使用
temp %>%
arrange(replace(rep(n() 1, n()), cat == "HIGH",
dense_rank(-percent[cat == "HIGH"])))
或者也可以使用
temp %>%
group_by(cat) %>%
group_modify(~ .x %>%
arrange(if(.y$cat == "HIGH") desc(percent) else n() 1 )) %>%
ungroup
-輸出
# A tibble: 16 × 4
cat name Freq percent
<chr> <chr> <int> <dbl>
1 HIGH steve 331 0.943
2 HIGH jacob 179 0.844
3 HIGH susan 15 0.833
4 HIGH jack 45 0.634
5 HIGH berry 259 0.583
6 HIGH nadia 304 0.58
7 HIGH jane 99 0.513
8 HIGH luthered 14 0.264
9 LOW berry 185 0.417
10 LOW jack 26 0.366
11 LOW steve 20 0.057
12 LOW nadia 220 0.42
13 LOW jacob 33 0.156
14 LOW susan 3 0.167
15 LOW luthered 39 0.736
16 LOW jane 94 0.487
或者，如果“貓”應該根據對應于“HIGH”的“百分比”值排序
temp %>%
arrange(factor(name, levels = unique(name[cat == "HIGH"
][order(dense_rank(-percent[cat == "HIGH"]))])))
-輸出
name cat Freq percent
1 steve HIGH 331 0.943
2 steve LOW 20 0.057
3 jacob HIGH 179 0.844
4 jacob LOW 33 0.156
5 susan HIGH 15 0.833
6 susan LOW 3 0.167
7 jack HIGH 45 0.634
8 jack LOW 26 0.366
9 berry HIGH 259 0.583
10 berry LOW 185 0.417
11 nadia HIGH 304 0.580
12 nadia LOW 220 0.420
13 jane HIGH 99 0.513
14 jane LOW 94 0.487
15 luthered HIGH 14 0.264
16 luthered LOW 39 0.736
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