R match function with conditions-CodePudding

I have a dataframe (Df1) that needs to be updated with a 2nd dataframe (Df2).

If the "Bird.ID" is found in Df2, then the column "Status" takes the "Alive" status in Df1 (by matching IDs)

Df1$Status[match(Df2$Bird.ID, Df1$ID)] <- "Alive"

The problem is that in Df1, the "Status" column can have "Dead" value (and a dead bird should remain dead).

I want to find a way to fix the "dead" status and use the match function but if it is dead it remains dead. In other words, I want to apply the match function (or any similar function that can do the same job) on this dataframe on all rows where "Status" is not equal to "Dead".

CodePudding user response：

Well, it is easier to help if you share a small but reproducible example of your data. I created a sample dataset to demonstrate the solution.

Here's the data first.

Df1 <- data.frame(ID = 1:5, 
                  Status = c('Injured', 'Dead', 'Dead', 'Alive', 'Injured'))

Df2 <- data.frame(Bird.ID = c(1, 3, 5))

Df1

#  ID  Status
#1  1 Injured
#2  2    Dead
#3  3    Dead
#4  4   Alive
#5  5 Injured

Df2
#  Bird.ID
#1       1
#2       3
#3       5

Solution -

Df1$Status[Df1$ID %in% Df2$Bird.ID & Df1$Status != "Dead"] <- 'Alive'
Df1

#  ID Status
#1  1  Alive
#2  2   Dead
#3  3   Dead
#4  4  Alive
#5  5  Alive

CodePudding user response：

Using @RonakShah dataframes and Tidyverse()

library(tidyverse)

Df1 <- data.frame(ID = 1:5, 
                  Status = c('Injured', 'Dead', 'Dead', 'Alive', 'Injured'))

Df2 <- data.frame(Bird.ID = c(1, 3, 5))

Df1 <- Df1 %>% 
  mutate(Status = ifelse(ID %in% Df2$Bird.ID & Status != "Dead", "Alive", Status))