这是我的数据：
ID Day number.of.day
ID1 Day1 5
ID1 Day1 5
ID1 Day1 5
ID1 Day1 5
ID1 Day1 5
ID1 Day2 4
ID1 Day2 4
ID1 Day2 4
ID1 Day2 4
ID1 Day3 1
ID1 Day4 1
ID2 Day1 2
ID2 Day1 2
ID2 Day2 3
ID2 Day2 3
ID2 Day2 3
更新
我想逐日计算 number.of.day 每个ID 的进度，这是预期的结果：< / p>
ID Day number.of.day advance
ID1 Day1 5 NA
ID1 Day1 5 NA
ID1 Day1 5 NA
ID1 Day1 5 NA
ID1 Day1 5 NA
ID1 Day2 4 (4-5)/5
ID1 Day2 4 NA
ID1 Day2 4 NA
ID1 Day2 4 NA
ID1 Day3 1 (1-4)/4
ID1 Day4 1 (1-1)/1
ID2 Day1 2 NA
ID2 Day1 2 NA
ID2 Day2 3 (3-2)/2
ID2 Day2 3 NA
ID2 Day2 3 NA
希望得到你的回复！
答案 0 :(得分：2)
这是使用data.table
library(data.table)
setDT(df)[!duplicated(df), advance := c(NA, diff(number.of.day)/number.of.day[-.N])]
# ID Day number.of.day advance
# 1: ID1 Day1 5 NA
# 2: ID1 Day1 5 NA
# 3: ID1 Day1 5 NA
# 4: ID1 Day1 5 NA
# 5: ID1 Day1 5 NA
# 6: ID1 Day2 4 -0.20
# 7: ID1 Day2 4 NA
# 8: ID1 Day2 4 NA
# 9: ID1 Day2 4 NA
# 10: ID1 Day3 1 -0.75
# 11: ID1 Day4 1 0.00
# 12: ID2 Day1 2 1.00
# 13: ID2 Day1 2 NA
# 14: ID2 Day2 3 0.50
# 15: ID2 Day2 3 NA
# 16: ID2 Day2 3 NA
答案 1 :(得分：2)
library(dplyr)
newdf <- df %>% group_by(ID) %>%
mutate(advance = c(NA, head((lead(number.of.day)-number.of.day)/number.of.day, -1)),
diff = c(0, diff(as.numeric(Day))))
is.na(newdf$advance) <- newdf$diff == 0L
newdf[,-5]
# Source: local data frame [16 x 4]
# Groups: ID
#
# ID Day number.of.day advance
# 1 ID1 Day1 5 NA
# 2 ID1 Day1 5 NA
# 3 ID1 Day1 5 NA
# 4 ID1 Day1 5 NA
# 5 ID1 Day1 5 NA
# 6 ID1 Day2 4 -0.20
# 7 ID1 Day2 4 NA
# 8 ID1 Day2 4 NA
# 9 ID1 Day2 4 NA
# 10 ID1 Day3 1 -0.75
# 11 ID1 Day4 1 0.00
# 12 ID2 Day1 2 NA
# 13 ID2 Day1 2 NA
# 14 ID2 Day2 3 0.50
# 15 ID2 Day2 3 NA
# 16 ID2 Day2 3 NA
答案 2 :(得分：2)
这是另一个非常简单且只使用base R的建议：
new_day <- which(diff(as.numeric(df$Day))>0)
day_change <- c(diff(df$number.of.day),0)
res <- day_change/df$number.of.day
temp <- res[new_day]
res[res==0] <- NA
res[new_day] <- temp
res <- c(NA,res[-length(res)])
df <- cbind(df,res)
#> df
# ID Day number.of.day res
#1 ID1 Day1 5 NA
#2 ID1 Day1 5 NA
#3 ID1 Day1 5 NA
#4 ID1 Day1 5 NA
#5 ID1 Day1 5 NA
#6 ID1 Day2 4 -0.20
#7 ID1 Day2 4 NA
#8 ID1 Day2 4 NA
#9 ID1 Day2 4 NA
#10 ID1 Day3 1 -0.75
#11 ID1 Day4 1 0.00
#12 ID2 Day1 2 1.00
#13 ID2 Day1 2 NA
#14 ID2 Day2 3 0.50
#15 ID2 Day2 3 NA
#16 ID2 Day2 3 NA