calculate descriptives for a nested variable - r

Question

I want to calculate the M, min, and max of a variable. Data were collected at different visits. My data look like this:
id visit V1
1 1 18
1 2 24
2 2 NA
2 3 5
2 4 6
I want it to look like this, where I have columns for the M, SD, min, and max for V1 for each participant.
id visit V1 M MIN MAX
1 1 18 21 18 24
2 2 3 4.67 3 6
In calculating the M, I want to take into account the # of visits (e.g., 18 + 24/2 visits). I tried this as a first step:
df %>%
group_by(id) %>%
mutate(M = mean(V1), MIN = min(V1), MAX = max(V1), na.rm = T)
When I try to handle the NAs by making sure they are not included, the na.rm = T results in a new column entitled "na.rm" with every value being true, which isn't what I want. Any thoughts on making this work?

Answer 1

The dplyr package makes this easy. You can group_by() a variable, and whatever you do after that only applies within the group. In dplyr notation, the %>% is a special operator that feeds the outcome of the function on the left into the first argument of the function on the right.
There are two ways to do it. The first way keeps all of the data, but your summary statistics are repeated in each row.
library(dplyr)
df %>%
group_by(id) %>%
mutate(M = mean(V1), MIN = min(V1), MAX = max(V1)
id visit V1 M MIN MAX
1 1 18 21 18 24
1 2 24 21 18 24
2 2 3 4.67 3 6
2 3 5 4.67 3 6
2 4 6 4.67 3 6
The second way provides only the summary statistics by the group.
library(dplyr)
df %>%
group_by(id) %>%
summarize(M = mean(V1), MIN = min(V1), MAX = max(V1)
id M MIN MAX
1 21 18 24
2 4.67 3 6

Answer 2

You can try this dplyr approach similar to #ThomasIsCoding that produces something similar to what you want:
library(dplyr)
#Data
df <- structure(list(id = c(1L, 1L, 2L, 2L, 2L), visit = c(1L, 2L,
2L, 3L, 4L), V1 = c(18L, 24L, 3L, 5L, 6L)), class = "data.frame", row.names = c(NA,
-5L))
The code:
df %>% group_by(id) %>% mutate(M=mean(V1),Min=min(V1),Max=max(V1),SD=sd(V1))
Output:
# A tibble: 5 x 7
# Groups: id [2]
id visit V1 M Min Max SD
<int> <int> <int> <dbl> <int> <int> <dbl>
1 1 1 18 21 18 24 4.24
2 1 2 24 21 18 24 4.24
3 2 2 3 4.67 3 6 1.53
4 2 3 5 4.67 3 6 1.53
5 2 4 6 4.67 3 6 1.53

Answer 3

Maybe you want something like below
transform(df,
M = ave(V1, id, FUN = mean),
MIN = ave(V1, id, FUN = min),
MAX = ave(V1, id, FUN = max)
)
which gives
id visit V1 M MIN MAX
1 1 1 18 21.000000 18 24
2 1 2 24 21.000000 18 24
3 2 2 3 4.666667 3 6
4 2 3 5 4.666667 3 6
5 2 4 6 4.666667 3 6
Data
> dput(df)
structure(list(id = c(1L, 1L, 2L, 2L, 2L), visit = c(1L, 2L,
2L, 3L, 4L), V1 = c(18L, 24L, 3L, 5L, 6L)), class = "data.frame", row.names = c(NA,
-5L))