Removing duplicate rows from one column without losing data from another column - r

Question

I apologize if this has already been asked, but the solutions I came across didn't seem to work for me.
I have a data set that was initially multiple excel sheets containing different variables for the same subjects. I was able to import the data into r and combine into a single data frame using:
x1_data <- "/data.xlsx"
excel_sheets(path = x1_data)
tab_names <- excel_sheets(path = x1_data)
list_all <- lapply(tab_names, function(x)
read_excel(path = x1_data, sheet = x))
str(list_all)
df <- rbind.fill(list_all)
df <- as_tibble(df)
However, I now have many duplicate rows for each subject, as each sheet was essentially added beneath the preceding sheet. Something like this:
Sheet 1
ID: 1,2
Age: 32, 29
Sex: M, F
Sheet 2
ID: 1, 2
Weight: 75, 89
Height: 157, 146
Combined
ID: 1, 2, 1, 2
Age: 32, 29, NA, NA
Sex: M, F, NA, NA
Weight: NA, NA, 75, 89
Height: NA, NA, 157, 146
I can't seem to figure out how to delete the duplicate ID rows without losing the data in the columns that belong to those rows. I tried aggregate and group_by without success. What I am after is this:
Combined
ID: 1, 2
Age: 32, 29
Sex: M, F
Weight: 75, 89
Height: 157, 146
Any help would be appreciated. Thanks.

Answer 1

Here's a possible solution:
library(tidyverse)
df <- tibble(ID = c(1, 2, 1, 2),
Age = c(32, 29, NA, NA),
Sex = c("M", "F", NA, NA),
Weight = c(NA, NA, 75, 89),
Height = c(NA, NA, 157, 146))
df1 <- df %>% filter(is.na(Age)) %>% select(ID, Weight, Height)
df2 <- df %>% filter(!is.na(Age)) %>% select(ID, Age, Sex)
df.merged <- df2 %>% left_join(df1, by = "ID")
For future questions, please provide a already formatted sample of your data which makes it much more easier to work with.