[英]Add column that sums all previous rows that meet condition
說我有一個以下列的大表
subject stim1 stim2 Chosen
1: 1 2 1 2
2: 1 3 2 2
3: 1 3 1 1
4: 1 2 3 3
5: 1 1 3 1
我正在尋找一種有效的方法(因為完整的數據集很大)來改變另外兩個列(按主題)
所需 output
subject stim1 stim2 Chosen stim1_chosen stim2_chosen
1: 1 2 1 2 0 0
2: 1 3 2 2 0 1
3: 1 3 1 1 0 0
4: 1 2 3 3 2 0
5: 1 1 3 1 1 1
6: 1 2 1 1 2 2
理想情況下,它會使用 data.table 或 dplyr。
這是輸入
structure(list(subject = c(1021, 1021, 1021, 1021, 1021, 1021
), stim1 = c(51L, 48L, 49L, 48L, 49L, 46L), stim2 = c(50L, 50L,
47L, 46L, 51L, 47L), Chosen = c(50L, 50L, 49L, 48L, 49L, 46L)), row.names = c(NA,
-6L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x7fc9ce8158e0>)
好的,這適用於示例數據。 最好在我們有更多主題並且列中的值大於 1 的地方運行它。 我假設它的data.table
object 稱為dt
1. 索引
使用merge
操作更改行排序真的很容易,所以不要依賴行號,而是通過subject
創建一個rowid
。 .N
是用於長度/行數的 data.table 語法。
# order matters, so make a rowid
dt[, rowid := 1:.N, by=subject]
# sets orders and indexing to make it quicker
setkey(dt, subject, rowid)
2. 見過的cols
需要將stim1
和stim2
合並為一列。 通過使用melt
從寬格式到長格式來做到這一點。 seen:=0:(.N-1)
然后按這些值分組以按行查找累積出現次數。 但是當我們查看先前的值時,我們減去 1。
然后我們進行兩次合並,因為我們有興趣將其與兩個 stim cols 進行比較
# for seen, melt wide to long
dt_seen <- melt(dt,
id.vars = c("subject", "rowid"),
measure.vars = c("stim1", "stim2"))
# interested in finding occurences
dt_seen <- unique(dt_seen[, .(subject, rowid, value)])
setorder(dt_seen, rowid)
dt_seen[, seen:=0:(.N-1), by=.(subject, value)]
# merge across twice
dt <- merge(dt, dt_seen,
by.x=c("subject", "rowid", "stim1"),
by.y=c("subject", "rowid", "value"),
all.x=TRUE, sort=FALSE)
setnames(dt, "seen", "stim1_seen")
dt <- merge(dt, dt_seen,
by.x=c("subject", "rowid", "stim2"),
by.y=c("subject", "rowid", "value"),
all.x=TRUE, sort=FALSE)
setnames(dt, "seen", "stim2_seen")
dt[]
3. 選擇
我一直很懶惰並且有效地完成了與第 (2) 節相同的操作,但首先過濾到 Chosen 與 stim 值匹配的行。 並且一個一個地做而不是一起做,因為這些cols是獨立的。 stim1 和 stim2 的過程是相同的,所以可以稍微整理一下。
# turn Chosen from wide to long
dt_chosen <- melt(dt,
id.vars = c("subject", "rowid"),
measure.vars = c("Chosen"))
# interested in finding occurences
# need to expand
dt_chosen[, variable := NULL]
# going to expand the grid, so can look at e.g. value 50 for all rowids
library(tidyr)
dt_chosen[, chosen_row := 1]
dt_chosen_full <- expand(dt_chosen, nesting(subject, rowid), value) %>% setDT
# pull in the actual data and fill rest with 0's
dt_chosen_full <- merge(dt_chosen_full, dt_chosen, by=c("subject", "rowid", "value"),
all.x=TRUE)
dt_chosen_full[is.na(chosen_row), chosen_row := 0]
# use cumsum to identify now the cumulative count of these across the full row set
dt_chosen_full[, chosen := cumsum(chosen_row), by=.(subject, value)]
# as its prior, on the row itself, subtract one so the update happens after the row
dt_chosen_full[chosen_row==1, chosen := chosen-1]
# merge across twice
dt <- merge(dt, dt_chosen_full[, -"chosen_row"],
by.x=c("subject", "rowid", "stim1"),
by.y=c("subject", "rowid", "value"),
all.x=TRUE, sort=FALSE)
setnames(dt, "chosen", "stim1_chosen")
dt[is.na(stim1_chosen), stim1_chosen := 0]
dt <- merge(dt, dt_chosen_full[, -"chosen_row"],
by.x=c("subject", "rowid", "stim2"),
by.y=c("subject", "rowid", "value"),
all.x=TRUE, sort=FALSE)
setnames(dt, "chosen", "stim2_chosen")
dt[is.na(stim2_chosen), stim2_chosen := 0]
Output
dt[]
subject rowid stim2 stim1 Chosen stim1_seen stim2_seen stim1_chosen stim2_chosen
1: 1021 1 50 51 50 0 0 0 0
2: 1021 2 50 48 50 0 1 0 1
3: 1021 3 47 49 49 0 0 0 0
4: 1021 4 46 48 48 1 0 0 0
5: 1021 5 51 49 49 1 1 1 0
6: 1021 6 47 46 46 1 1 0 0
這是一個 pipe,在兩個框架上都進行了演示。
dat1
是您顯示一些預期的 output 的地方
dat1[, c("stim1_seen", "stim2_seen") :=
lapply(.SD, function(z) mapply(function(x, S) {
sum(stim1[S] %in% x | stim2[S] %in% x)
}, z, lapply(seq_len(.N)-1, seq_len))),
.SDcols = c("stim1", "stim2"), by = .(subject)
][, c("stim1_chosen", "stim2_chosen") :=
lapply(.SD, function(z) mapply(function(x, S) {
sum(Chosen[S] %in% x)
}, z, lapply(seq_len(.N)-1, seq_len))),
.SDcols = c("stim1", "stim2"), by = .(subject)]
# subject stim1 stim2 Chosen stim1_seen stim2_seen stim1_chosen stim2_chosen
# <int> <int> <int> <int> <int> <int> <int> <int>
# 1: 1 2 1 2 0 0 0 0
# 2: 1 3 2 2 0 1 0 1
# 3: 1 3 1 1 1 1 0 0
# 4: 1 2 3 3 2 2 2 0
# 5: 1 1 3 1 2 3 1 1
# 6: 1 2 1 1 3 3 2 2
dat2
是您的輸入 output (不同的數據)
dat2[, c("stim1_seen", "stim2_seen") :=
lapply(.SD, function(z) mapply(function(x, S) {
sum(stim1[S] %in% x | stim2[S] %in% x)
}, z, lapply(seq_len(.N)-1, seq_len))),
.SDcols = c("stim1", "stim2"), by = .(subject)
][, c("stim1_chosen", "stim2_chosen") :=
lapply(.SD, function(z) mapply(function(x, S) {
sum(Chosen[S] %in% x)
}, z, lapply(seq_len(.N)-1, seq_len))),
.SDcols = c("stim1", "stim2"), by = .(subject)]
# subject stim1 stim2 Chosen stim1_seen stim2_seen stim1_chosen stim2_chosen
# <num> <int> <int> <int> <int> <int> <int> <int>
# 1: 1021 51 50 50 0 0 0 0
# 2: 1021 48 50 50 0 1 0 1
# 3: 1021 49 47 49 0 0 0 0
# 4: 1021 48 46 48 1 0 0 0
# 5: 1021 49 51 49 1 1 1 0
# 6: 1021 46 47 46 1 1 0 0
這里的努力是試圖做一個“累積%in%
”。 實際上,這就是mapply
正在做的事情。
知道data.table
的.N
特殊符號提供了組中的行數,那么這很有用:
lapply(seq_len(.N)-1, seq_len) # [[1]] # integer(0) # [[2]] # [1] 1 # [[3]] # [1] 1 2 # [[4]] # [1] 1 2 3 # [[5]] # [1] 1 2 3 4 # [[6]] # [1] 1 2 3 4 5
這用於索引每行之前的所有行; 也就是說,在第 1 行中,沒有前面的行,因此我們在integer(0)
上進行索引; 在第 5 行,我們對1 2 3 4
進行索引; 等等
我們將它們與每個stim1
(然后stim2
值)一起“壓縮”(使用mapply
),以查找在S
上索引的原始stim1
和stim2
列(來自上一個項目符號)中的存在,並對出現的次數求和
最后,我們通過迭代.SD
(使用.SDcols
)對兩個stim*
列執行此操作
在Chosen
列上重復此過程(盡管更簡單)
數據
dat1 <- setDT(structure(list(subject = c(1L, 1L, 1L, 1L, 1L, 1L), stim1 = c(2L, 3L, 3L, 2L, 1L, 2L), stim2 = c(1L, 2L, 1L, 3L, 3L, 1L), Chosen = c(2L, 2L, 1L, 3L, 1L, 1L)), class = c("data.table", "data.frame"), row.names = c(NA, -6L)))
dat2 <- setDT(structure(list(subject = c(1021, 1021, 1021, 1021, 1021, 1021), stim1 = c(51L, 48L, 49L, 48L, 49L, 46L), stim2 = c(50L, 50L, 47L, 46L, 51L, 47L), Chosen = c(50L, 50L, 49L, 48L, 49L, 46L)), row.names = c(NA, -6L), class = c("data.table", "data.frame")))
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