Let’s say I have

```
set.seed(1)
e <- data.frame(tti = log2(runif(200)),
corona = c(rep("Corona", 100), rep("Before", 100)),
type = rep(c("A", "B", "C", "D")))
```

I am comparing mean differences between `tti`

(time to treatment initiation on `log2`

-scale) before and during COVID-19 for each `e$type`

(here `n=4`

but many more in my dataset). I want to apply a `for loop`

for this repetitive task, but I am quite new to this and frankly stock at the moment.

My current attempt:

```
for(i in unique(e$type)){
m <- c(
round(1-2^(unique(t.test(e$tti[e$type == i] ~ e$corona[e$type == i])$estimate[2]) -
unique(t.test(e$tti[e$type == i] ~ e$corona[e$type == i])$estimate[1])),
digits = 3)*100
)
}
```

However, this attempt only return one value.

**Expected output**:

`m`

should be a vector containing the four estimates of mean differences

```
> m
24.7 10.5 1.5 28.7
```

How can this be done?

### >Solution :

You could do:

```
m <- c()
for(i in unique(e$type)){
m[i] <- c(
round(1-2^(unique(t.test(e$tti[e$type == i] ~ e$corona[e$type == i])$estimate[2]) -
unique(t.test(e$tti[e$type == i] ~ e$corona[e$type == i])$estimate[1])),
digits = 3)*100
)
}
```

This initializes m and then fills it during the loop. That should give you all 4 results in one vector.