data[!duplicated(data$Seq), ]

应该可以。

library(dplyr)
data %>% distinct

应该是值得的，特别是如果你的数据对你的机器来说太大了。

最快的，你可以试试：

data[!kit::fduplicated(data$Seq), ]

以下是直接从文档中获取的一些基准：

x = sample(c(1:10,NA_integer_),1e8,TRUE) # 382 Mb
microbenchmark::microbenchmark(
  duplicated(x),
  fduplicated(x),
  times = 5L
)
# Unit: seconds
#           expr  min   lq  mean  median   uq   max neval
# duplicated(x)  2.21 2.21  2.48    2.21 2.22  3.55     5
# fduplicated(x) 0.38 0.39  0.45    0.48 0.49  0.50     5

kit也有funique功能。

kit::fduplicated似乎在具有许多唯一行（很少重复）的 Dataframe 中具有轻微的优势，而dplyr::distinct似乎在具有许多重复行（很少唯一行）的 Dataframe 中更有效：

# Make this example reproducible
set.seed(1)
n_samples <- 1e7

# Many unique rows case: Create a data frame with random integers between 1 and 100
df <- as.data.frame(matrix(round(runif(n=n_samples, min=1, max=1000), 0), nrow=n_samples/2))
names(df) <- c('A', 'B')

microbenchmark::microbenchmark(
  un_1 <- df[!base::duplicated(df), ],
  un_2 <- df[!kit::fduplicated(df), ],
  un_3 <- dplyr::distinct(df),
  times = 5L
)

# Unit: milliseconds
#                                expr       min         lq       mean     median         uq        max neval
# un_1 <- df[!base::duplicated(df), ] 9817.6096 10173.5799 10721.0293 10772.2749 11073.4896 11768.1927     5
# un_2 <- df[!kit::fduplicated(df), ]  558.9923   618.1214   673.6863   628.9305   671.2307   891.1565     5
#         un_3 <- dplyr::distinct(df)  596.9396   640.1986   680.0212   643.6371   674.5296   844.8010     5

# Many repeated rows case: Create a data frame with random integers between 1 and 10
df <- as.data.frame(matrix(round(runif(n=n_samples, min=1, max=10), 0), nrow=n_samples/2))
names(df) <- c('A', 'B')

microbenchmark::microbenchmark(
  un_1 <- df[!base::duplicated(df), ],
  un_2 <- df[!kit::fduplicated(df), ],
  un_3 <- dplyr::distinct(df),
  times = 5L
)

#Unit: milliseconds
#                                 expr       min        lq     mean    median        uq       max neval
#  un_1 <- df[!base::duplicated(df), ] 8282.4409 8439.2752 8550.715 8457.0352 8704.7729 8870.0511     5
#  un_2 <- df[!kit::fduplicated(df), ]  130.8126  136.0880  244.323  168.6322  221.6255  564.4568     5
#          un_3 <- dplyr::distinct(df)  148.4684  160.8196  162.815  165.0068  169.5027  170.2775     5