Calculating means based on conditionals for another column - r

Question

I have a dataframe like
df <- structure(list(DATE = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L,
4L), .Label = c("04/23/90", "04/28/90", "05/03/95", "05/07/95"
), class = "factor"), JULIAN = c(113L, 113L, 113L, 113L, 113L,
113L, 118L, 118L, 118L, 118L, 118L, 118L, 123L, 123L, 123L, 123L,
123L, 123L, 127L, 127L, 127L, 127L, 127L, 127L), ID = structure(c(1L,
2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L,
6L, 1L, 2L, 3L, 4L, 5L, 6L), .Label = c("AHFG-01", "AHFG-02",
"AHFG-03", "OIUR-01", "OIUR-02", "OIUR-03"), class = "factor"),
PERCENT = c(0L, 0L, 0L, 80L, 55L, 0L, 25L, 50L, 75L, 100L,
75L, 45L, 10L, 20L, 30L, 50L, 50L, 50L, 50L, 60L, 70L, 75L,
90L, 95L)), .Names = c("DATE", "JULIAN", "ID", "PERCENT"), class = "data.frame", row.names = c(NA,
-24L))
DATE JULIAN ID PERCENT
1 04/23/90 113 AHFG-01 0
2 04/23/90 113 AHFG-02 0
3 04/23/90 113 AHFG-03 0
4 04/23/90 113 OIUR-01 80
5 04/23/90 113 OIUR-02 55
6 04/23/90 113 OIUR-03 0
7 04/28/90 118 AHFG-01 25
8 04/28/90 118 AHFG-02 50
9 04/28/90 118 AHFG-03 75
10 04/28/90 118 OIUR-01 100
11 04/28/90 118 OIUR-02 75
12 04/28/90 118 OIUR-03 45
13 05/03/95 123 AHFG-01 10
14 05/03/95 123 AHFG-02 20
15 05/03/95 123 AHFG-03 30
16 05/03/95 123 OIUR-01 50
17 05/03/95 123 OIUR-02 50
18 05/03/95 123 OIUR-03 50
19 05/07/95 127 AHFG-01 50
20 05/07/95 127 AHFG-02 60
21 05/07/95 127 AHFG-03 70
22 05/07/95 127 OIUR-01 75
23 05/07/95 127 OIUR-02 90
24 05/07/95 127 OIUR-03 95
In this dataframe, ID gives replicates at different sites. For example, AHFG-01 is replicate 1 and AHFG-02 is replicate 2, both at site AHFG. PERCENT refers to percent completion.
I need to calculate two things:
1) Mean JULIAN when PERCENT first exceeds 50 for each site, across years
2) Mean JULIAN when PERCENT first exceeds 50 for all sites, across years
I am a bit baffled about the best way to proceed here. My approach is to:
1) Calculate mean PERCENT for each site (from ID) at each DATE/JULIAN
2) Identify JULIAN when mean PERCENT first exceeds 50, for each site for each YEAR
3) Calculate mean JULIAN from 2) for each site across years
4) Calculate mean JULIAN from 2) for all sites across years
For the datamrame above, the end results I need by site and for sites together would look something like this:
SITE JULIAN
AHFG 122.5
OIUR 120.5
JULIAN, all sites combined = 121.5
What I have done so far is first create columns YEAR and SITE to use for operations:
df$DATE <- as.POSIXct(df$DATE, format='%m/%d/%y')
df$YEAR <- format(df$DATE, format='%Y')
df$SITE <- gsub("[^aA-zZ]", " ", df$ID)
Then I can use aggregate to calculate SITE means for step 1 above:
df2 <- aggregate(PERCENT ~ SITE + JULIAN + YEAR,FUN=mean,data=df)
However, I am getting stuck at step 2 and beyond. Can anyone suggest a way to calculate the mean JULIAN when PERCENT first exceeds 50, for each SITE across years, and all combined SITEs across years?
Solution:
Here is a modified form of Hekrik's excellent solution that is working for me. Note that Henkik's original solution did work but my question was a bit unclear on what I wanted (see comments below).
# make year column
df$DATE <- as.POSIXct(df$DATE, format='%m/%d/%y')
df$YEAR <- format(df$DATE, format='%Y')
# make new ID column (remove numbers for individuals)
df$SITE <- gsub("[^aA-zZ]", " ", df$ID)
# Calculate average PERCENT for each SITE
df2 <- aggregate(PERCENT ~ SITE + JULIAN + YEAR,FUN=mean,data=df)
# order by SITE and JULIAN
df2 <- df2[order(df2$SITE, df2$JULIAN), ]
# within each YEAR and SITE, select first registration where PERCENT is 50 or more
df2 <- do.call(rbind,
by(df2, list(df2$YEAR, df2$SITE), function(x){
x[x$PERCENT >= 50, ][1, ]
}))
# calculate mean JULIAN per SITE
aggregate(JULIAN ~ SITE, data = df2, mean)
# overall mean
mean(df2$JULIAN)

Answer 1

Here's one possibility:
# order by SITE and DATE
df <- df[order(df$SITE, df$DATE), ]
# within each YEAR and SITE, select first registration where PERCENT exceeds 50
df2 <- do.call(rbind,
by(df, list(df$YEAR, df$SITE), function(x){
x[x$PERCENT > 50, ][1, ]
}))
df2
# DATE JULIAN ID PERCENT YEAR SITE
# 6 1990-04-28 118 AHFG-03 75 1990 AHFG
# 11 1995-05-07 127 AHFG-02 60 1995 AHFG
# 13 1990-04-23 113 OIUR-01 80 1990 OIUR
# 22 1995-05-07 127 OIUR-01 75 1995 OIUR
# calculate mean JULIAN per SITE
aggregate(JULIAN ~ SITE, data = df2, mean)
# SITE JULIAN
# 1 AHFG 122.5
# 2 OIUR 120.0
# overall mean
mean(df2$JULIAN)
# [1] 121.25
Please note that I don't get the same mean for OIUR as in your example.