I would like to do a column-wise ttest between two dataframes in R. That is, ttest(df1$col1,df2$col1)
,ttest(df1$col2,df2$col2)
and so on....The best option here is to use mapply
or Map
function. Something like:
mapply(t.test,tnav_DJF_histo.csv[,-1],tnav_DJF.csv[,-1])
works perfectly but if one of your df columns has NAs, it fails with this error:
Error in t.test.default(dots[[1L]][[1L]], dots[[2L]][[1L]]) :
not enough 'y' observations
Question: how can I use na.rm
to get the job done? For example, if a column in tnav_DJF.csv[,-1] has Nas but no NAs in tnav_DJF_histo.csv[,-1], how can I tell mapply
to to ignore or skip the analyses for these columns?
Many thanks.
aez.
You can do this with mapply
and an anonymous function as follows:
Example data:
df1 <- data.frame(a=runif(20), b=runif(20), c=rep(NA,20))
df2 <- data.frame(a=runif(20), b=runif(20), c=c(NA,1:18,NA))
#notice df1's third column is just NAs
Solution:
Use mapply
with an anonymous function as follows:
#anonumous function testing for NAs
mapply(function(x, y) {
if(all(is.na(x)) || all(is.na(y))) NULL else t.test(x, y, na.action=na.omit)
}, df1, df2)
Output:
$a
Welch Two Sample t-test
data: x and y
t = 1.4757, df = 37.337, p-value = 0.1484
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.0543192 0.3458648
sample estimates:
mean of x mean of y
0.5217619 0.3759890
$b
Welch Two Sample t-test
data: x and y
t = 1.1689, df = 37.7, p-value = 0.2498
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.0815067 0.3041051
sample estimates:
mean of x mean of y
0.5846343 0.4733351
$c
NULL
P.S. There is no na.rm
argument in the t.test
function to use. There is only a na.action
argument but even if you set that to na.omit
(which I have) you will still get an error if all the column elements are NA.
P.S.2 If some of the elements of either x or y are NA then the t.test
function will run properly by omitting those elements. If you want to ignore calculating the t.test if any of the columns contains even a single NA, then you need to change the all
in the above function to any
.