我有以下数据框
x <- data.frame("treatment"= c(1, 1, 1, 1, 2, 2, 2, 2),
"Time" = c(0, 30, 60, 180, 0, 30, 60, 180),
"cells_alive" = c(500, 470, 100, 20, 476, 310, 99, 2))在这个实验中,我有两个处理,我测量了随着时间的推移活细胞的数量。时间0的细胞数量是该处理的初始细胞数量。我需要计算新列中每个时间的细胞存活百分比。因此,在处理1的情况下,它将是500/500,470/500,100/500等等。你知道怎么计算这个吗?
谢谢
发布于 2020-10-14 02:59:22
require(tidyverse)
x %>%
left_join(x %>% select(treatment, cells_alive) %>%
group_by(treatment) %>%
top_n(1) %>%
ungroup(), by = "treatment") %>%
mutate(cells_alive_per = cells_alive.x/cells_alive.y)发布于 2020-10-14 03:08:07
使用data.table
library(data.table)
setDT(x) #converting x to data.table
x[,.(Time, value = cells_alive / cells_alive[which(Time == 0)]),treatment]
#output
treatment Time value
1: 1 0 1.000000000
2: 1 30 0.940000000
3: 1 60 0.200000000
4: 1 180 0.040000000
5: 2 0 1.000000000
6: 2 30 0.651260504
7: 2 60 0.207983193
8: 2 180 0.004201681发布于 2020-10-14 17:40:54
在发布了dplyr和data.table版本之后,为了完整起见,这里有一个不需要安装包的版本:
stack(tapply(x$cells_alive, x$treatment, function(ca) ca / ca[1] ))这给了我们
> stack(tapply(x$cells_alive, x$treatment, function(ca) ca / ca[1] ))
values ind
1 1.000000000 1
2 0.940000000 1
3 0.200000000 1
4 0.040000000 1
5 1.000000000 2
6 0.651260504 2
7 0.207983193 2
8 0.004201681 2https://stackoverflow.com/questions/64341464
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