I wandered if you can help me in measuring the p-value from this simple data.frame. My data frame is called (my_data). By viewing it, you can see similar values I have that I am comparing:
my_data <- read.csv("densityleftOK.csv", stringsAsFactors = FALSE [c(1,2,3),]
P1 P2 P3 P4 P5 T1 T2 T3 T4 T5 T6
A 1008 1425 869 1205 954 797 722 471 435 628 925
B 550 443 317 477 337 383 54 111 27 239 379
C 483 574 597 375 593 553 249 325 238 354 411
Thus, I would like to get a single pvalue for each row by comparing placebo vs treated samples. If you don't mind, I'd like to get also the standard deviation between either placebo (P) and treated (T).
I appreciate any help. Thanks
You can try something like below, where you pivot the data into long format,group by the ids, introduce a grouping vector("P" or "T") and use tidy on t.test to wrap it up in a table format:
library(broom)
library(tidyr)
library(dplyr)
library(tibble)
data = read.table(text="P1 P2 P3 P4 P5 T1 T2 T3 T4 T5 T6
A 1008 1425 869 1205 954 797 722 471 435 628 925
B 550 443 317 477 337 383 54 111 27 239 379
C 483 574 597 375 593 553 249 325 238 354 411",header=TRUE,row.names=1)
res = data %>%
rownames_to_column("id") %>%
pivot_longer(-id) %>%
mutate(grp=sub("[0-9]","",name)) %>%
group_by(id) %>%
do(tidy(t.test(value ~ grp,data=.))) %>%
select(c(id,estimate,estimate1,estimate2,statistic,p.value)) %>%
mutate(stderr = estimate/statistic)
# A tibble: 3 x 7
# Groups: id [3]
id estimate estimate1 estimate2 statistic p.value stderr
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 A 429. 1092. 663 3.40 0.00950 126.
2 B 226. 425. 199. 2.89 0.0192 78.2
3 C 169. 524. 355 2.65 0.0266 64.0
If you don't use packages.. then it's a matter of using apply, and I guess easier to declare the groups up front:
grp = gsub("[0-9]","",colnames(data))
res = apply(data,1,function(i){
data.frame(t.test(i~grp)[c("statistic","p.value","stderr")])
})
res = do.call(rbind,res)
statistic p.value stderr
A 3.395303 0.009498631 126.40994
B 2.890838 0.019173060 78.16650
C 2.646953 0.026608838 63.99812