I have a data.frame (df1) and I want to include a single, most recent age for each of my samples from another data.frame (df2):
df1$age <- df2$age_9[match(df1$Sample_ID, df2$Sample_ID)]
The problem is that in df2 there are 9 columns for age, as each one indicates the age at a specific check-up date (age_1 is from the first visit, age_9 is the age at the 9th visit) and patients dont make all their visits.
How do I add the most recently obtained age from a non empty check up date?
aka, if age_9 == "." replace "." with age_8 then if age_8 == "." replace "." with age_7... etc
From this:
View(df1)
Sample Age
1 50
2 .
3 .
To:
View(df1)
Sample Age
1 50
2 49
3 30
From the data df2
View(df2)
Sample Age_1 Age_2 Age_3
1 40 42 44
2 35 49 .
3 30 . .
This is my attempt:
df1$age[which(df1$age == ".")] <- df2$age_8[match(df1$Sample_ID, df2$Sample_ID)]
With base R
, we can use max.col
to return the last
column index for each row, where the 'Age' columns are not .
, cbind
with sequence of rows to return a row/column index, extract the elements and change the 'Age' column in 'df1', where the 'Age' is .
df1$Age <- ifelse(df1$Age == ".", df2[-1][cbind(seq_len(nrow(df2)),
max.col(df2[-1] != ".", "last"))], df1$Age)
df1 <- type.convert(df1, as.is = TRUE)
-output
df1
# Sample Age
#1 1 50
#2 2 49
#3 3 30
or using tidyverse
by reshaping into 'long' format and then do a join after slice
ing the last row grouped by 'Sample'
library(dplyr)
library(tidyr)
df2 %>%
mutate(across(starts_with('Age'), as.integer)) %>%
pivot_longer(cols = starts_with('Age'), values_drop_na = TRUE) %>%
group_by(Sample) %>%
slice_tail(n = 1) %>%
ungroup %>%
select(-name) %>%
right_join(df1) %>%
transmute(Sample, Age = coalesce(as.integer(Age), value))
-output
# A tibble: 3 x 2
# Sample Age
# <int> <int>
#1 1 50
#2 2 49
#3 3 30
df1 <- structure(list(Sample = 1:3, Age = c("50", ".", ".")),
class = "data.frame",
row.names = c(NA,
-3L))
df2 <- structure(list(Sample = 1:3, Age_1 = c(40L, 35L, 30L), Age_2 = c("42",
"49", "."), Age_3 = c("44", ".", ".")), class = "data.frame",
row.names = c(NA,
-3L))
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