CODEWITHSUNDEEP
rconditional-statementsmatch
Andre230
Andre230

Reputation: 145

R match function with conditions

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".

Upvotes: 0

Views: 1565

Answers (2)

Tech Commodities
Tech Commodities

Reputation: 1959

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))

Upvotes: 1

Ronak Shah
Ronak Shah

Reputation: 388817

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

Upvotes: 2

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