[英]Conditional replacement of values in dataframe
我有兩個具有七個描述性數據列和可變數量的其他分析列的數據框(基於代碼中的較早步驟)。 我想更換一些值的分析列dataframe1
與相應的值dataframe2
基於在第一列一個布爾值dataframe1
。
dataframe1
:
structure(list(compare = c(1, 1, 0, 1, 1, 1, 0, 1), ID_TREE = 29338:29345,
ID_PLOT = c(1068L, 1068L, 1068L, 1068L, 1068L, 1068L, 1068L,
1068L), ID_CATEGORY = c(3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L),
ID_WOOD_SPGR_GREENVOL_DRYWT = c(28L, 28L, 28L, 7L, 28L, 28L,
28L, 28L), ID_BARK_SPGR_GREENVOL_DRYWT = c(25L, 25L, 25L,
18L, 25L, 25L, 25L, 25L), ID_BARK_VOL_PCT = c(2L, 2L, 2L,
10L, 2L, 2L, 2L, 2L), VOLCFGRS = c(3.21875, 6.576453125,
12.2406407654729, 0.863593268246, 1.15809306543472, 0.755301358016,
13.6662694477056, 4.549483421824)), row.names = c(NA, -8L
), class = c("data.table", "data.frame"), .internal.selfref = <pointer: (nil)>)
dataframe2
:
structure(list(compare = c(1, 1, 0, 1, 1, 1, 0, 1), ID_TREE = 29338:29345,
ID_PLOT = c(1068L, 1068L, 1068L, 1068L, 1068L, 1068L, 1068L,
1068L), ID_CATEGORY = c(3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L),
ID_WOOD_SPGR_GREENVOL_DRYWT = c(28L, 28L, 28L, 7L, 28L, 28L,
28L, 28L), ID_BARK_SPGR_GREENVOL_DRYWT = c(25L, 25L, 25L,
18L, 25L, 25L, 25L, 25L), ID_BARK_VOL_PCT = c(2L, 2L, 2L,
10L, 2L, 2L, 2L, 2L), VOLCFGRS = c(-2.32258333333333, 5.81718680555556,
12.2406407654729, -32.9676545519935, -27.9506018960536, -38.5047101237694,
13.6662694477056, 1.9138577595677)), row.names = c(NA, -8L
), class = c("data.table", "data.frame"), .internal.selfref = <pointer: (nil)>)
到目前為止,我已經獲得了下面的代碼行可用於1列:
df1[df1$compare==0,8]<- df2[df1$compare==0,8]
但是當我嘗試對其進行抽象以使其可用於任意數量的列時,出現錯誤:
df1[df1$compare==0,-(1:7)]<- df2[df1$compare==0,-(1:7)]
我也這樣,並得到了類似的錯誤:
df1[,-(1:7)]<- ifelse(df1$compare==0, df2[,-(1:7)], df1[,-(1:7)])
這兩個數據框將始終具有相同的列數。
最簡單的是,您可以“反轉”您的子集:
df1[df1$compare==0,8:ncol(df1)] <- df2[df1$compare==0,8:ncol(df1)]
另一種選擇是rbind
的dijointed排在一起。
rbind(df1[df1$compare!=0], df2[df1$compare==0])
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