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.

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.

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

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()

Here is a solution using data.table:

library(data.table)
setDT(species)
species[, .SD[which.min(Date)], by = Taxa]
#    Taxa       Date
# 1:    A 2012-05-17
# 2:    B 2011-08-31
# 3:    C 2012-09-06

Data:

species <- data.frame(
  Date = as.Date(c("2013-07-12", "2011-08-31", "2012-09-06", 
                   "2012-05-17", "2013-07-12", "2012-09-07")), 
  Taxa = c("A", "B", "C", "A", "C", "B")
)

This should do the trick:

# Create some dummy data:

# Create some dates 
Date=as.POSIXct(c("2013-07-12","2011-08-31","2012-09-06","2009-01-01",
                  "2012-05-17","2013-07-12","2012-09-07","2013-02-02"))

# Create unique taxa
Taxa=rep(c("A","B","C","D"),2)

# Combine the two into a dataframe
data=as.data.frame(list(Date=Date,Taxa=Taxa))

# this returns a numeric vector of the minimum dates
xx=tapply(data$Date,list(data$Taxa),min)

# And this will return a dataframe with the first occurence
# of your taxa (or variables)
as.data.frame(list(Date=as.POSIXct(xx,origin="1970-01-01"),
                   Taxa=names(xx)))

Note: You can add simplify=T in tapply to return a POSIXt object but it returns a list. More info can be found here: Unexpected behaviour of min, tapply and POSIXct/POSIXlt classes?

This is a good question. First of all, I want to highlight that the output you've mentioned is not correct as per your requirement. It should be:

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

i.e., the first entry was not correct. Now talking about the code for this, all these are good answers but the solution I propose is more robust. To demonstrate better, I have used a new dataframe.

d <- data.frame(a = c(rep("A", 4), c(rep("B",4)), rep("C",4)), b=c(0,0,1,1,0,1,1,1,0,0,0,1))
d %>% group_by(a) %>% mutate(c = detect_index(.x = b, .f = p), d = row_number()) %>% mutate(e = ifelse(c==d,1,0)) %>% ungroup()

Keep Coding!