使用ave, ddply, dplyr或data.table:

df$num <- ave(df$val, df$cat, FUN = seq_along)

or:

library(plyr)
ddply(df, .(cat), mutate, id = seq_along(val))

or:

library(dplyr)
df %>% group_by(cat) %>% mutate(id = row_number())

or(最有效的内存，因为它在DT中通过引用分配):

library(data.table)
DT <- data.table(df)

DT[, id := seq_len(.N), by = cat]
DT[, id := rowid(cat)]

非常简单，整洁的解决方案。

整个data.frame的行号

library(tidyverse)

iris %>%
  mutate(row_num = seq_along(Sepal.Length)) %>%
  head

    Sepal.Length Sepal.Width Petal.Length Petal.Width    Species row_num
1            5.1         3.5          1.4         0.2     setosa       1
2            4.9         3.0          1.4         0.2     setosa       2
3            4.7         3.2          1.3         0.2     setosa       3
..           ...         ...          ...         ...     ......     ...
148          6.5         3.0          5.2         2.0  virginica     148
149          6.2         3.4          5.4         2.3  virginica     149
150          5.9         3.0          5.1         1.8  virginica     150

在data.frame中按组进行行号

iris %>% 
  group_by(Species) %>% 
  mutate(num_in_group=seq_along(Species)) %>% 
  as.data.frame


    Sepal.Length Sepal.Width Petal.Length Petal.Width    Species num_in_group
1            5.1         3.5          1.4         0.2     setosa            1
2            4.9         3.0          1.4         0.2     setosa            2
3            4.7         3.2          1.3         0.2     setosa            3
..           ...         ...          ...         ...     ......           ..
48           4.6         3.2          1.4         0.2     setosa           48
49           5.3         3.7          1.5         0.2     setosa           49
50           5.0         3.3          1.4         0.2     setosa           50
51           7.0         3.2          4.7         1.4 versicolor            1
52           6.4         3.2          4.5         1.5 versicolor            2
53           6.9         3.1          4.9         1.5 versicolor            3
..           ...         ...          ...         ...     ......           ..
98           6.2         2.9          4.3         1.3 versicolor           48
99           5.1         2.5          3.0         1.1 versicolor           49
100          5.7         2.8          4.1         1.3 versicolor           50
101          6.3         3.3          6.0         2.5  virginica            1
102          5.8         2.7          5.1         1.9  virginica            2
103          7.1         3.0          5.9         2.1  virginica            3
..           ...         ...          ...         ...     ......           ..
148          6.5         3.0          5.2         2.0  virginica           48
149          6.2         3.4          5.4         2.3  virginica           49
150          5.9         3.0          5.1         1.8  virginica           50

下面是一个按组而不是按行使用for循环的选项(就像OP那样)

for (i in unique(df$cat)) df$num[df$cat == i] <- seq_len(sum(df$cat == i))

为了使这个R -faq问题更完整，一个带sequence和rle的base R替代方案:

df$num <- sequence(rle(df$cat)$lengths)

给出了预期的结果:

> df 猫val num 4是0.05638315 2是0.25767250 2 1是0.30776611 结果是0.46854928 3是0.55232243 5 10 BBB 0.17026205 8 BBB 0.37032054 6 BBB 0.48377074 9 bb 0.54655860 7 Bob 0.81240262 13 c 0.28035384 14 CCC 0.39848790 11 cc 0.62499648 15 cc 0.76255108 12 c 0.88216552

如果df$cat是一个因子变量，则需要将其包装为。性格:

df$num <- sequence(rle(as.character(df$cat))$lengths)

我想增加一个数据。使用rank()函数的表变体，它提供了额外的可能性来改变排序，从而使它比seq_len()解决方案更灵活，并且非常类似于RDBMS中的row_number函数。

# Variant with ascending ordering
library(data.table)
dt <- data.table(df)
dt[, .( val
   , num = rank(val))
    , by = list(cat)][order(cat, num),]

    cat        val num
 1: aaa 0.05638315   1
 2: aaa 0.25767250   2
 3: aaa 0.30776611   3
 4: aaa 0.46854928   4
 5: aaa 0.55232243   5
 6: bbb 0.17026205   1
 7: bbb 0.37032054   2
 8: bbb 0.48377074   3
 9: bbb 0.54655860   4
10: bbb 0.81240262   5
11: ccc 0.28035384   1
12: ccc 0.39848790   2
13: ccc 0.62499648   3
14: ccc 0.76255108   4

# Variant with descending ordering
dt[, .( val
   , num = rank(desc(val)))
    , by = list(cat)][order(cat, num),]

在2021-04-16进行编辑，使降序和升序之间的切换更加安全

使用ave, ddply, dplyr或data.table:

df$num <- ave(df$val, df$cat, FUN = seq_along)

or:

library(plyr)
ddply(df, .(cat), mutate, id = seq_along(val))

or:

library(dplyr)
df %>% group_by(cat) %>% mutate(id = row_number())

or(最有效的内存，因为它在DT中通过引用分配):

library(data.table)
DT <- data.table(df)

DT[, id := seq_len(.N), by = cat]
DT[, id := rowid(cat)]