根据R中列中的特定值删除行

时间：2014-10-30 08:45:44

标签： r dataframe delete-row

如果列Conf_mat_Modis_2000中的值等于0或NA，我想删除Driver_90_00_Visual表中的行。有人可以帮我解决这个问题。 

dput(Conf_mat_Modis_2000)
structure(list(Driver_90_00_Modis500 = c(100, 200, 200, 100, 
100, 200, 200, 200, 100, 200, 500, 100, 100, 100, 100, 100, 100, 
100, 100, 500, 500, 100, 200, 200, 100, 500, 100, 100, 500, 100, 
100, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 200, 
100, 300, 100, 200, 100, 200, 100, 100, 100, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 200, 500, 
500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 500, 200, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, 200, 200, 200, 200, 200, 500, 200, 
200, 500, 500, 500, 500, 200, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, 500, 200, 500, 500, 200, 200, 200, 
200, 200, 100, 200, 200, 100, 200, 500, 500, 500, 200, 500, 500, 
500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 
100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 
100, 100, 100, 100, 100, 100, 100, 200, 100, 100, 100, 100, 100, 
200, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 
200, 100, 100, 200, 100, 100, 100, 100, 100, 100, 100, 100, 100, 
100, 100, 100, 100, 100, 100, 200, 100, 200, 100, 100, 100, 100, 
100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 200, 100, 100, 
200, 100, 100, 100, 100, 100, 100, 100, 100, 500, 100, 100, 100, 
100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 500, 100, 100, 
500, 100, 500, 200, 100, 100, 100, 100, 100, 100, 100, 100, 100, 
100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 500, 
500, 100, 200, 100, 500, 500, 500, 500, 200, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
200, 200, 200, 100, 500, 500, 500, 100, 200, 100, 100, 100, 200, 
100, 200, 200, 200, 100, 100, 100, 100, 500, 100, 100, 200, 100, 
100, 100, 100, 500, 500, 100, 200, 200, 200, 200, 200, 500, 200, 
200, 200, 200, 200, 200, 200, 200, 200, 500, 100, 200, 200, 500, 
200, 200, 500, 200, 200, 200, 200, 200, 200, 200, 500, 200, 500, 
200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 500, 200, 200, 
200, 200, 100, 200, 200, 200, 200, 200, 200, 200, 200, 100, 200, 
200, 100, 200, 100, 100, 100, 200, 200, 200, 200, 100, 200, 200, 
100, 200, 200, 100, 200, 200, 200, 200, 200, 200, 200, 200, 200, 
200, 200, 200, 200, 200, 200, 100, 200, 200, 200, 200, 200, 200, 
200, 200, 100, 200, 200, 100, 200, 200, 200, 200, 100, 200, 200, 
200, 200, 200, 200, 100, 200, 200, 100, 200, 100, 100, 100, 200, 
100, 100, 100, 100, 200, 100, 500, 100, 100, 100, 500, 100, 200, 
100, 100, 200, 200, 200, 200, 100, 100, 200, 100, 200, 100, 200, 
200, 200, 200, 200, 100, 100, 500, 100, 200, 100, 200, 200, 200, 
100, 100, 200, 100, 100, 200, 100, 200, 200, 200, 100, 200, 100, 
200, 200, 100, 100, 100, 100, 100, 200, 100, 100, 100, 100, 200, 
200, 100, 200, 100, 100, 100, 100, 100, 200, 200, 100, 100, 200, 
200, 100, 200, 100, 200, 200, 200, 200, 200, 100, 200, 200, 200, 
200, 100, 200, 200, 200, 100, 100, 500, 200, 100, 100, 100, 200, 
200, 200, 200, 100, 100, 100, 200, 200, 200, 100, 200, 200, 200, 
200, 200, 200, 200, 200, 200, 200, 200, 200, 100, 500), Driver_90_00_Visual = c(600, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, 500, 200, 200, 200, 200, 
200, 200, 200, 200, 200, NA, 200, NA, 200, 200, 200, NA, NA, 
NA, NA, NA, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, 500, 500, 500, 500, 500, NA, 500, 500, 500, 500, 500, 500, 
500, 200, 200, 300, 200, 200, NA, 300, 200, 200, NA, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 500, 
500, 500, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
NA, NA, NA, NA, NA, NA, 200, 200, NA, 200, 200, NA, NA, 200, 
NA, NA, NA, 200, NA, 200, 500, NA, 200, 200, NA, 500, NA, 200, 
NA, NA, NA, NA, NA, NA, 200, 200, 500, NA, NA, 200, NA, NA, NA, 
200, NA, NA, 200, 200, 200, NA, NA, NA, 200, NA, 200, 200, NA, 
200, NA, NA, NA, NA, 200, 200, 200, NA, NA, NA, NA, NA, NA, 200, 
NA, NA, 500, NA, NA, NA, NA, NA, 500, NA, NA, NA, NA, NA, 200, 
200, NA, 500, NA, NA, NA, NA, 200, 500, NA, 500, 200, 500, NA, 
NA, NA, 200, NA, 500, 500, NA, NA, NA, NA, 500, 200, 500, NA, 
NA, 200, 200, 200, 200, 200, 200, 500, 500, 500, 500, NA, 500, 
200, 200, 200, 500, 500, 200, 500, 500, 200, 200, NA, NA, 200, 
200, 200, 200, 200, 200, 200, 200, 200, 200, NA, 500, 500, NA, 
NA, NA, 500, NA, NA, NA, NA, NA, NA, NA, NA, 500, 500, NA, NA, 
NA, NA, 500, 500, NA, NA, NA, NA, NA, 500, NA, 200, NA, NA, NA, 
NA, NA, 500, NA, 500, NA, NA, NA, NA, 500, NA, NA, 600, 600, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
500, 500, NA, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
500, NA, NA, NA, 500, 500, NA, NA, 500, 500, 500, 500, 500, 500, 
500, 600, 500, 500, 500, 200, 200, 200, NA, 200, NA, 500, 500, 
200, 200, 200, 200, NA, 200, 200, 200, 200, 200, 200, 200, 200, 
200, NA, 999, 999, NA, 500, 999, 200, 200, 200, 500, 200, 500, 
200, 200, 200, 200, 200, 200, 500, 200, 200, 200, 200, 200, 200, 
200, 200, 200, 200, 200, 500, 500, 500, 500, 500, 500, NA, 500, 
500, 500, 500, 500, 500, 200, 500, 500, 500, 500, 200, 500, 500, 
200, 500, 200, 500, 200, 200, 200, 500, NA, 500, 200, 200, 200, 
500, 500, 500, 200, 500, 500, 200, 500, 200, 200, 500, 200, 200, 
200, 500, 200, 200, NA, NA, NA, 200, 200, 500, 200, 200, 200, 
500, 200, 200, 200, 200, 500, 200, 200, 200, 200, 200, 200, 500, 
200, 200, 500, 200, 200, 500, 200, 500, 200, 200, 500, 200, 500, 
200, 200, 200, 200, 200, 200, 200, 500, 200, 200, 200, 200, 200, 
200, NA, 200, NA, 200, 200, 200, 200, 200, 200, 200, NA, NA, 
200, 200, 200, 500, 200, 500, 200, 500, 200, 200, 300, 300, 300, 
500, 200, 500, 200, 500, 500, 500, NA, 500, 200, 500, NA, 500, 
500, 200, 200, 500, 200, 200, 200, 200, 500, 500, 200, 500, 200, 
NA, 500)), .Names = c("Driver_90_00_Modis500", "Driver_90_00_Visual"
), class = "data.frame", row.names = c(NA, -1081L))

1 个答案:

答案 0 :(得分：0)

假设df是你的数据框，可以做

df[!(df$Driver_90_00_Visual %in% c(0, NA)),]

参考评论：

df_is_na <- df[df$Driver_90_00_Visual != 0 & !is.na(df$Driver_90_00_Visual),]
df_NA <- df[!(df$Driver_90_00_Visual %in% c(0, NA)),]
identical(df_is_na, df_NA)
# [1] TRUE
相关问题
最新问题