Extract rows for the first occurrence of a variable in a data frame

I have a data frame with two variables, Date and Taxa and want to get the date for the first time each taxa occurs. There are 9 different dates and 40 different taxa in the data frame consisting of 172 rows, but my answer should only have 40 rows.

Taxa is a factor and Date is a date.

For example, my data frame (called 'species') is set up like this:

Date          Taxa
2013-07-12    A
2011-08-31    B
2012-09-06    C
2012-05-17    A
2013-07-12    C
2012-09-07    B

and I would be looking for an answer like this:

Date          Taxa
2012-05-17    A
2011-08-31    B
2012-09-06    C

I tried using:

t.first <-  species[unique(species$Taxa),]

and it gave me the correct number of rows but there were Taxa repeated. If I just use unique(species$Taxa) it appears to give me the right answer, but then I don't know the date when it first occurred.

Thanks for any help.

Solution 1:

t.first <- species[match(unique(species$Taxa), species$Taxa),]

should give you what you're looking for. match returns indices of the first match in the compared vectors, which give you the rows you need.

Solution 2:

In the following command, duplicated creates a logical index for duplicated data$Taxa values. A subset of the data frame without the corresponding rows is created with:

data[!duplicated(data$Taxa), ]

The result:

        Date Taxa
1 2012-05-17    A
2 2011-08-31    B
3 2012-09-06    C

Solution 3:

Here is a dplyr option that is not dependent on the data being sorted in date order and accounts for ties:

library(dplyr)
df %>% 
  mutate(Date = as.Date(Date)) %>% 
  group_by(Taxa) %>% 
  filter(Date == min(Date)) %>% 
  slice(1) %>% # takes the first occurrence if there is a tie
  ungroup()

# A tibble: 3 x 2
  Date       Taxa 
  <date>     <chr>
1 2012-05-17 A    
2 2011-08-31 B    
3 2012-09-06 C 

# sample data:
df <- read.table(text = 'Date          Taxa
                         2013-07-12    A
                         2011-08-31    B
                         2012-09-06    C
                         2012-05-17    A
                         2013-07-12    C
                         2012-09-07    B', header = TRUE, stringsAsFactors = FALSE)

And you could get the same by sorting by date as well:

df %>% 
  mutate(Date = as.Date(Date)) %>% 
  group_by(Taxa) %>% 
  arrange(Date) %>% 
  slice(1) %>% 
  ungroup()