如何解决通过从总数中减去其他列的总和，有条件地替换一列中的 NA
更新：
dat1 <- dat %>%
mutate(across(starts_with("Number"),~case_when(is.na(.) ~
Total - sum(.[complete.cases(.)]),TRUE ~ .)))
进行了计算，但结果不正确。第一行，如果计算正确，应该是 2,2 从 Total 到 Number_end_u 。
head(dat1)
ID Total Number_end_f Number_end_m Number_end_u
1 14_E_3_6 2 -1029 -678 2
2 32_E_6_6 2 2 -678 -417
3 32_W_8_6 1 -1030 -679 1
4 32_E_8_4 12 -1019 2 10
5 32_E_7_4 4 4 -676 -415
6 32_E_6_4 1 1 -679 -418
此代码
dat1 <- dat %>%
mutate(across(starts_with("Number"),~case_when(is.na(.) ~
sum(.[complete.cases(.)]) - Total,TRUE ~ .)))
生产
head(dat1)
ID Total Number_end_f Number_end_m Number_end_u
1 14_E_3_6 2 1029 678 2
2 32_E_6_6 2 2 678 417
3 32_W_8_6 1 1030 679 1
4 32_E_8_4 12 1019 2 10
5 32_E_7_4 4 4 676 415
6 32_E_6_4 1 1 679 418
原问题：
Number_end_f + Number_end_m + Number_end_u = Total 在我的数据集中。我想通过从Total列中减去非NA的总和来替换一些的NA。这里有两种情况：
一类是 NA：通过从 Total 中减去其他两列的总和来填充 NA。
两个类别是不适用的：
-
如果
Total中的值等于非 NA 列中的值，则将其他 2 列中的 NA 替换为 0。 -
如果
Total中的值不等于非 NA 列中的值，则保留其他 2 列中的 NA。
这是我在代码中的粗略想法，基于此问题 Conditionally replace columns with NA 和此问题 R: selecting rows that contain a given number of NAs
dat2 <- dat %>%
mutate(is.na(.[,c(3:5)]) = Total - !is.na(.[,c(3:5)]))
Error: unexpected '=' in:
"dat2 <- dat %>%
mutate(is.na(.[,c(3:5)]) ="
或
dat2 <- dat %>%
mutate(across(starts_with("Number"),~replace(.,rowsum(is.na(.),Total - !is.na(.)))))
Error: Problem with `mutate()` input `..1`.
x argument "values" is missing,with no default
i Input `..1` is `(function (.cols = everything(),.fns = NULL,...,.names = NULL) ...`.
Run `rlang::last_error()` to see where the error occurred.
和数据
dput(dat)
structure(list(ID = c("14_E_3_6","32_E_6_6","32_W_8_6","32_E_8_4","32_E_7_4","32_E_6_4","39_E_6_3","22_W_2_3","22_E_4_3","22_E_2_3","35_E_4_2","22_E_6_3","19_E_6_2","19_E_5_2","19_E_3_2","35_E_3_2","35_E_5_2","17_W_6_3","41_W_5_3","22_E_3_3","41_W_6_3","17_E_4_3","17_W_7_3","19_E_7_2","35_E_2_2","19_E_1_2","22_E_1_3","17_W_3_3","45_E_7_3","41_E_1_3","19_W_8_2","19_W_5_2","45_W_6_2","17_W_5_3","22_W_3_3","27_E_1_3","23_E_4_4","45_E_8_3","45_E_5_3","48_E_3_4","17_E_3_3","17_W_2_3","27_W_2_3","38_W_2_4","42_W_7_3","11_E_5_4","16_E_2_3","16_E_4_3","16_E_6_3","17_E_1_3","21_W_2_3","38_E_1_4","38_W_8_4","11_W_5_4","16_E_5_3","21_W_6_3","21_W_7_3","23_W_5_4","27_E_7_3","27_W_5_3","45_E_1_3","45_W_1_3","35_E_6_2","14_E_6_1","14_E_5_1","14_E_3_1","14_E_4_1","14_E_7_1","17_W_2_4","32_E_6_1","17_W_5_4","45_E_6_6","33_E_3_6","33_W_3_6","42_W_2_6","49_W_8_3","32_E_8_3","49_E_3_3","49_E_6_3","32_W_3_3","29_W_6_3","49_E_4_3","49_W_4_3","29_E_5_3","29_W_1_3","29_E_7_3","32_W_7_3","49_W_7_3","32_W_5_3","32_W_6_3","14_E_1_3","49_W_2_3","14_E_8_3","32_W_4_3","49_E_7_3","49_W_6_3","49_E_1_3","29_W_5_3","49_W_5_3","45_E_7_4","14_E_4_3","45_W_8_2","32_E_2_3","29_W_4_3","29_W_7_3","32_E_4_3","14_W_3_3","29_W_8_3","32_E_1_3","32_E_3_3","49_E_2_3","49_E_5_3","14_E_6_3","14_E_7_3","33_E_2_4","35_E_2_4","45_W_1_4","45_W_5_4","35_E_1_4","35_E_5_4","19_E_3_4","21_W_2_4","25_E_1_4","25_E_5_4","25_W_2_4","25_W_3_4","25_W_4_4","31_W_3_4","33_W_7_4","35_W_1_4","42_W_3_4","45_E_8_4","45_W_6_4","19_E_1_4","19_W_1_2","25_E_2_4","25_E_4_4","25_E_8_4","25_W_8_4","31_E_3_4","33_E_7_4","33_E_8_4","33_W_3_4","35_E_3_4","42_W_6_4","45_E_2_4","29_W_7_1","29_W_5_1","39_W_3_4","29_W_8_1","19_E_1_1","33_E_1_2","33_E_7_2","17_W_4_1","33_E_8_2","33_W_2_2","42_W_3_2","33_E_2_2","16_E_4_2","16_E_1_2","16_E_2_2","42_E_7_2","42_W_5_2","42_W_7_2","35_W_1_2","25_E_1_2","35_W_2_2","42_E_8_2","17_W_5_1","42_W_4_2","25_E_4_2","42_E_6_2","25_W_1_2","42_E_4_2","42_E_3_2","42_E_5_2","42_W_1_2","41_E_8_4","41_W_5_4","42_W_2_2","42_W_8_2","25_W_3_2","33_E_4_2","25_E_5_2","16_W_5_2","31_E_1_2","33_E_6_2","33_W_1_2","42_E_1_2","12_E_8_2","16_E_5_2","16_W_1_2","16_W_3_2","25_W_4_2","33_E_5_2","39_W_2_4","45_W_8_4","12_E_4_2","16_E_6_2","16_W_4_2","25_E_8_2","42_E_2_2","12_W_2_2","16_W_2_2","17_W_2_1","21_W_2_2","27_E_1_4","27_W_3_4","27_W_7_4","17_W_6_1","38_W_7_1","38_W_1_1","38_E_8_1","19_E_5_1","17_E_8_2","19_E_4_1","17_E_5_2","48_E_3_1","27_E_8_1","38_W_2_1","17_W_4_2","48_W_7_1","38_E_7_1","48_E_2_1","17_E_3_2","16_E_4_1","19_E_6_1","14_E_1_2","38_E_1_1","41_E_2_2","38_W_3_1","14_E_2_2","25_W_4_1","48_W_6_1","11_W_8_1","29_W_7_2","39_W_4_2","25_W_5_1","11_W_5_1","29_W_5_2","14_W_3_2","11_E_3_1","16_E_3_1","17_W_7_2","11_W_2_1","49_W_8_2","23_W_4_1","29_W_4_2","23_W_7_1","38_W_4_1","29_E_6_2","48_W_2_1","38_E_2_1","39_E_6_2","32_E_7_2","35_W_5_1","23_W_5_1","32_W_5_2","38_E_5_1","11_W_1_1","11_W_4_1","16_E_2_1","33_E_2_1","14_E_8_2","38_W_8_1","49_E_3_2","11_E_1_1","41_E_1_2","19_E_8_1","32_W_8_2","32_E_6_2","22_E_4_2","23_W_1_1","29_E_8_2","32_W_3_2","23_W_8_1","39_W_1_2","48_W_1_1","23_W_2_1","29_E_5_2","11_E_5_1","14_E_3_2","14_W_2_2","33_W_2_1","49_E_6_2","22_E_2_2","23_W_3_1","27_W_6_1","41_E_8_2","27_W_3_1","22_E_6_2","29_E_7_2","41_W_2_2","19_W_3_1","22_W_2_2","11_E_2_1","49_W_5_2","25_E_3_1","32_E_5_2","35_E_2_1","41_E_4_2","29_W_8_2","11_E_8_1","17_W_3_2","19_W_1_1","11_E_4_1","33_W_6_1","14_W_4_2","35_E_1_1","17_E_4_2","33_E_7_1","25_W_1_1","35_E_6_1","16_E_6_1","22_E_1_2","25_W_2_1","23_W_6_1","42_W_7_1","19_E_7_1","32_W_4_2","38_W_6_1","49_E_8_2","27_W_5_1","41_E_3_2","25_W_3_1","33_E_1_1","35_E_4_1","39_E_1_2","16_W_3_1","25_E_2_1","42_W_4_1","42_W_5_1","41_W_4_2","16_W_2_1","32_E_3_2","39_W_3_2","16_E_1_1","25_E_1_1","25_E_6_1","33_W_3_1","27_W_2_1","38_W_5_1","14_E_5_2","22_W_3_2","16_W_7_1","35_E_5_1","41_W_5_2","42_W_3_1","33_E_3_1","33_W_1_1","49_E_5_2","12_E_8_1","17_E_1_2","21_E_7_1","25_E_8_1","42_W_2_1","45_E_5_1","45_E_8_1","16_W_1_1","45_E_6_1","45_E_7_1","16_W_8_1","17_W_6_2","21_E_5_1","12_E_1_1","21_E_4_1","31_W_2_1","39_E_7_1","39_W_2_1","39_E_8_1","39_E_6_1","29_E_8_5","29_E_6_5","29_E_7_5","29_W_5_5","29_W_6_5","49_E_1_5","29_E_4_5","49_E_7_5","29_W_7_5","49_W_1_5","49_W_4_5","35_E_2_5","14_W_2_5","14_E_2_5","14_W_4_5","35_E_8_2","23_W_5_2","38_W_8_2","11_E_3_2","48_W_4_2","35_W_6_2","48_E_3_2","41_E_6_4","48_E_1_2","45_E_8_2","35_W_3_2","38_E_8_2","35_W_7_2","11_W_5_2","11_W_8_2","48_W_2_2","35_E_7_2","45_W_1_2","35_W_5_2","41_E_5_4","22_E_3_4","22_E_6_4","41_E_3_4","22_E_2_4","19_W_6_2","23_W_1_2","17_W_6_5","23_W_4_2","38_W_3_2","41_E_1_4","41_E_2_4","17_W_2_5","45_E_5_2","49_E_3_1","49_E_7_1","49_E_6_1","49_W_7_1","49_W_5_1","49_E_8_1","49_W_6_1","49_E_2_1","49_W_8_1","27_E_3_2","27_W_6_2","23_W_5_3","27_E_4_2","49_W_1_1","49_W_4_1","49_W_3_1","11_E_3_3","48_W_6_3","27_E_7_2","33_E_8_3","49_E_5_1","14_W_1_4","49_E_1_1","27_W_4_2","27_W_7_2","27_W_8_2","33_E_7_3","45_W_4_3","45_W_7_3","11_E_2_3","45_W_3_3","29_W_7_4","48_W_2_3","14_E_2_3","29_E_1_4","32_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= c(2L,2L,1L,12L,4L,17L,11L,10L,6L,23L,3L,7L,5L,8L,20L,15L,9L,14L,13L,22L,16L,25L,24L,19L,1L),Number_end_f = c(NA,NA,NA),Number_end_m = c(NA,18L,Number_end_u = c(2L,1L)),row.names = c(1L,21L,26L,27L,28L,29L,30L,31L,32L,33L,34L,36L,38L,39L,40L,41L,42L,43L,44L,45L,46L,47L,48L,49L,50L,51L,52L,53L,54L,55L,56L,57L,58L,59L,60L,61L,62L,63L,64L,65L,66L,67L,68L,69L,70L,71L,74L,75L,76L,77L,78L,79L,80L,81L,82L,83L,84L,85L,86L,87L,88L,89L,90L,93L,94L,95L,96L,97L,98L,100L,101L,102L,103L,104L,105L,106L,107L,108L,109L,110L,111L,112L,113L,114L,115L,116L,117L,118L,119L,120L,121L,122L,123L,124L,125L,127L,128L,129L,130L,131L,132L,133L,134L,135L,136L,137L,138L,139L,140L,141L,142L,143L,144L,145L,146L,147L,148L,149L,150L,151L,152L,153L,154L,155L,156L,157L,158L,159L,160L,161L,162L,163L,164L,165L,166L,167L,168L,169L,170L,171L,172L,173L,174L,175L,176L,177L,178L,179L,180L,181L,182L,183L,184L,185L,187L,188L,189L,191L,192L,193L,194L,195L,196L,197L,198L,199L,200L,201L,202L,203L,204L,205L,206L,207L,208L,209L,210L,212L,213L,214L,215L,216L,217L,218L,219L,220L,221L,222L,223L,224L,225L,226L,227L,228L,229L,231L,232L,233L,234L,236L,237L,238L,239L,240L,241L,242L,244L,246L,248L,249L,250L,251L,252L,253L,254L,255L,256L,257L,258L,260L,261L,262L,263L,264L,265L,266L,267L,268L,269L,270L,271L,272L,273L,274L,275L,276L,277L,278L,279L,280L,281L,282L,284L,285L,286L,287L,289L,290L,291L,292L,294L,296L,297L,298L,300L,301L,302L,303L,304L,305L,306L,307L,308L,309L,310L,311L,312L,313L,314L,315L,316L,317L,318L,319L,320L,321L,322L,323L,324L,325L,326L,327L,328L,329L,330L,331L,332L,333L,334L,336L,337L,338L,339L,340L,341L,342L,343L,344L,346L,347L,348L,349L,351L,352L,353L,354L,355L,356L,357L,358L,359L,361L,362L,363L,364L,365L,366L,367L,368L,369L,370L,371L,372L,373L,374L,375L,376L,377L,378L,379L,380L,381L,382L,383L,384L,385L,386L,387L,388L,389L,390L,391L,392L,393L,394L,395L,396L,397L,398L,399L,400L,401L,402L,403L,404L,405L,406L,407L,408L,409L,410L,411L,412L,413L,414L,415L,416L,417L,418L,419L,420L,421L,422L,423L,424L,425L,426L,427L,428L,429L,431L,432L,433L,434L,435L,436L,437L,438L,439L,440L,441L,442L,443L,444L,445L,446L,447L,448L,449L,450L,451L,452L,455L,456L,457L,458L,459L,461L,462L,463L,465L,466L,467L,468L,469L,470L,471L,472L,473L,474L,475L,476L,478L,480L,481L,482L,483L,484L,485L,486L,487L,488L,489L,490L,491L,492L,493L,494L,495L,496L,497L,498L,499L,500L,501L,502L,503L,504L,505L,506L,507L,508L,509L,510L,511L,513L,514L,515L,516L,517L,518L,519L,520L,521L,522L,524L,525L,526L,527L,528L,529L,530L,531L,532L,533L,534L,535L,536L,537L,538L,539L,540L,541L,542L,543L,544L,545L,546L,547L,548L,549L,550L,551L,552L,553L,554L,555L,556L,557L,558L,559L,560L,561L,562L,563L,564L,565L,566L,567L,569L,570L,572L,573L,574L,575L,576L,577L,578L,579L,580L,581L,582L,583L,584L,585L,586L,587L,588L,589L,590L,591L,592L,593L,594L,595L,596L,597L,598L,599L,600L,601L,602L,603L,604L,605L,606L,607L,608L
),class = "data.frame")
如何修复我的代码？提前致谢。
解决方法
这在 base R 中更容易。对名称中以 'Number' 前缀开头的列进行子集化，将该子组的每一行 (rowSums) 的总和与 'Total' 列进行比较（如果有一些 NA，将那些 NA 替换为 FALSE，然后在该子集 ('i2') 上获得 NA 的逻辑矩阵。使用选定列的第一个和第二个索引将这些 NA 分配给 0
nm1 <- grep("^Number",names(dat),value = TRUE)
i1 <- rowSums(dat[nm1],na.rm = TRUE) == dat$Total
i1[is.na(i1)] <- FALSE
i2 <- is.na(dat[i1,nm1])
dat[i1,nm1][i2] <- 0
-输出
head(dat)
# ID Total Number_end_f Number_end_m Number_end_u
#1 14_E_3_6 2 0 0 2
#2 32_E_6_6 2 2 0 0
#3 32_W_8_6 1 0 0 1
#4 32_E_8_4 12 0 2 10
#5 32_E_7_4 4 4 0 0
#6 32_E_6_4 1 1 0 0
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