Have a set of longitudinal data in which measures were repeatedly collected at various waves (see example of set up below. As this sort of data goes however, there was attrition, with some waves stopping before the study ended. However, my analysis has the assumption that each participant have at least 3 observations

How would I subset only those IDs (subjects) that have at least 3 observations? I've looked into similar questions on stackoverflow but they do not seem to fit this specific issue.

CodePudding user response：

Method 1

# set as data table
setDT(df)

# calculate no. of waves per ID
df[, no_of_waves := .N, ID]

# subset
df[no_of_waves >= 3]

Method 2

# calculate no. of waves per ID
df[, no_of_waves := max(Wave), ID]

# subset
df[no_of_waves >= 3]

CodePudding user response：

Let me contribute a tidyverse approach.

If one row = one observation

library(tidyverse)

# filter ID with at least 3 rows
df %>% group_by(ID) %>% filter(n() >= 3)

If value in wave = observation

library(tidyverse)

# filter ID with sum of Wave equals to or greater than 3
df %>% group_by(ID) %>% filter(sum(Wave) >= 3)

CodePudding user response：

Using base R, you could try this one-liner.

out <- with(df, df[ID %in% names(which(sapply(split(df, ID), nrow) > 2)), ])

Output

> out
    ID Wave Score
3 1001    0     6
4 1001    1     6
5 1001    2     7

Data

df <- data.frame(
  ID = unlist(mapply(rep, 1000:1001, 2:3)),
  Wave = c(0,1,0,1,2),
  Score = c(5,4,6,6,7)
)