Hi, I accidently left out few lines of code from the calclp function. Updated
function is pasted below.
I am still getting the same error ?Error: !(any(data1$norm_sd >= 1)) is not
TRUE?
I would appreciate any help.
Nilesh
dput(calclp)
function (dataset)
{
dat1 <- funlp1(dataset)
recursive_funlp <- function(dataset = dat1, func = funlp2) {
dat2 <- dataset %>% select(uniqueid, field_rep, lp) %>%
mutate(field_rep = paste(field_rep, "lp", sep =
".")) %>%
spread(key = field_rep, value = lp) %>% mutate_at(.vars =
grep("_",
names(.)), funs(norm = round(scale(.), 3)))
dat2$norm_sd <- round(apply(dat2[, grep("lp_norm",
names(dat2))],
1, sd, na.rm = TRUE), 3)
dat2$norm_max <- round(apply(dat2[, grep("lp_norm",
names(dat2))],
1, function(x) {
max(abs(x), na.rm = TRUE)
}), 3)
data1 <- dat2 %>% gather(key, value, -uniqueid, -norm_max,
-norm_sd) %>% separate(key, c("field_rep",
"treatment"),
"\\.") %>% spread(treatment, value) %>%
mutate(outlier = NA)
stopifnot(!(any(data1$norm_sd >= 1)))
if (!(any(data1$norm_sd >= 1))) {
df1 <- dat1
return(df1)
}
else {
df2 <- recursive_funlp()
return(df2)
}
}
df3 <- recursive_funlp(dataset = dat1, func = funlp2)
df3
}
From: DIGHE, NILESH [AG/2362]
Sent: Thursday, December 14, 2017 9:01 AM
To: 'Eric Berger' <ericjberger at gmail.com>
Cc: r-help <r-help at r-project.org>
Subject: RE: [R] help with recursive function
Eric: Thanks for taking time to look into my problem. Despite of making the
change you suggested, I am still getting the same error. I am wondering if the
logic I am using in the stopifnot and if functions is a problem.
I like the recursive function to stop whenever the norm_sd column has zero
values that are above or equal to 1. Below is the calclp function after the
changes you suggested.
Thanks. Nilesh
dput(calclp)
function (dataset)
{
dat1 <- funlp1(dataset)
recursive_funlp <- function(dataset = dat1, func = funlp2) {
dat2 <- dataset %>% select(uniqueid, field_rep, lp) %>%
mutate(field_rep = paste(field_rep, "lp", sep =
".")) %>%
spread(key = field_rep, value = lp) %>% mutate_at(.vars =
grep("_",
names(.)), funs(norm = round(scale(.), 3)))
dat2$norm_sd <- round(apply(dat2[, grep("lp_norm",
names(dat2))],
1, sd, na.rm = TRUE), 3)
dat2$norm_max <- round(apply(dat2[, grep("lp_norm",
names(dat2))],
1, function(x) {
max(abs(x), na.rm = TRUE)
}), 3)
stopifnot(!(any(dat2$norm_sd >= 1)))
if (!(any(dat2$norm_sd >= 1))) {
df1 <- dat1
return(df1)
}
else {
df2 <- recursive_funlp()
return(df2)
}
}
df3 <- recursive_funlp(dataset = dat1, func = funlp2)
df3
}
From: Eric Berger [mailto:ericjberger at gmail.com]
Sent: Thursday, December 14, 2017 8:17 AM
To: DIGHE, NILESH [AG/2362] <nilesh.dighe at
monsanto.com<mailto:nilesh.dighe at monsanto.com>>
Cc: r-help <r-help at r-project.org<mailto:r-help at r-project.org>>
Subject: Re: [R] help with recursive function
My own typo ... whoops ...
!( any(dat2$norm_sd >= 1 ))
On Thu, Dec 14, 2017 at 3:43 PM, Eric Berger <ericjberger at
gmail.com<mailto:ericjberger at gmail.com>> wrote:
You seem to have a typo at this expression (and some others like it)
Namely, you write
any(!dat2$norm_sd) >= 1
when you possibly meant to write
!( any(dat2$norm_sd) >= 1 )
i.e. I think your ! seems to be in the wrong place.
HTH,
Eric
On Thu, Dec 14, 2017 at 3:26 PM, DIGHE, NILESH [AG/2362] <nilesh.dighe at
monsanto.com<mailto:nilesh.dighe at monsanto.com>> wrote:
Hi, I need some help with running a recursive function. I like to run funlp2
recursively.
When I try to run recursive function in another function named
"calclp" I get this "Error: any(!dat2$norm_sd) >= 1 is not
TRUE".
I have never built a recursive function before so having trouble executing it in
this case. I would appreciate any help or guidance to resolve this issue.
Please see my data and the three functions that I am using below.
Please note that calclp is the function I am running and the other two functions
are within this calclp function.
# code:
Test<- calclp(dataset = dat)
# calclp function
calclp<- function (dataset)
{
dat1 <- funlp1(dataset)
recursive_funlp <- function(dataset = dat1, func = funlp2) {
dat2 <- dataset %>% select(uniqueid, field_rep, lp) %>%
mutate(field_rep = paste(field_rep, "lp", sep =
".")) %>%
spread(key = field_rep, value = lp) %>% mutate_at(.vars =
grep("_",
names(.)), funs(norm = round(scale(.), 3)))
dat2$norm_sd <- round(apply(dat2[, grep("lp_norm",
names(dat2))],
1, sd, na.rm = TRUE), 3)
dat2$norm_max <- round(apply(dat2[, grep("lp_norm",
names(dat2))],
1, function(x) {
max(abs(x), na.rm = TRUE)
}), 3)
stopifnot(any(!dat2$norm_sd) >= 1)
if (any(!dat2$norm_sd) >= 1) {
df1 <- dat1
return(df1)
}
else {
df2 <- recursive_funlp()
return(df2)
}
}
df3 <- recursive_funlp(dataset = dat1, func = funlp2)
df3
}
# funlp1 function
funlp1<- function (dataset)
{
dat2 <- dataset %>% select(field, set, ent_num, rep_num,
lp) %>% unite(uniqueid, set, ent_num, sep = ".") %>%
unite(field_rep, field, rep_num) %>% mutate(field_rep =
paste(field_rep,
"lp", sep = ".")) %>% spread(key = field_rep,
value = lp) %>%
mutate_at(.vars = grep("_", names(.)), funs(norm =
round(scale(.),
3)))
dat2$norm_sd <- round(apply(dat2[, grep("lp_norm",
names(dat2))],
1, sd, na.rm = TRUE), 3)
dat2$norm_max <- round(apply(dat2[, grep("lp_norm",
names(dat2))],
1, function(x) {
max(abs(x), na.rm = TRUE)
}), 3)
data1 <- dat2 %>% gather(key, value, -uniqueid, -norm_max,
-norm_sd) %>% separate(key, c("field_rep",
"treatment"),
"\\.<file://.>") %>% spread(treatment, value) %>%
mutate(outlier = NA)
df_clean <- with(data1, data1[norm_sd < 1, ])
datD <- with(data1, data1[norm_sd >= 1, ])
s <- split(datD, datD$uniqueid)
sdf <- lapply(s, function(x) {
data.frame(x, x$outlier <-
ifelse(is.na<http://is.na>(x$lp_norm), NA,
ifelse(abs(x$lp_norm) == x$norm_max, "yes",
"no")),
x$lp <- with(x, ifelse(outlier == "yes", NA, lp)))
x
})
sdf2 <- bind_rows(sdf)
all_dat <- bind_rows(df_clean, sdf2)
all_dat
}
# funlp2 function
funlp2<-function (dataset)
{
data1 <- dataset
df_clean <- with(data1, data1[norm_sd < 1, ])
datD <- with(data1, data1[norm_sd >= 1, ])
s <- split(datD, datD$uniqueid)
sdf <- lapply(s, function(x) {
data.frame(x, x$outlier <-
ifelse(is.na<http://is.na>(x$lp_norm), NA,
ifelse(abs(x$lp_norm) == x$norm_max, "yes",
"no")),
x$lp <- with(x, ifelse(outlier == "yes", NA, lp)))
x
})
sdf2 <- bind_rows(sdf)
all_dat <- bind_rows(df_clean, sdf2)
all_dat
}
# dataset
dput(dat)
structure(list(field = c("LM01", "LM01", "LM01",
"LM01", "LM01",
"LM01", "LM01", "LM01", "LM01",
"LM01", "LM01", "LM01", "LM01",
"LM01", "LM01", "LM01", "LM01",
"LM01", "LM01", "LM01", "LM01",
"LM01", "LM01", "LM01", "LM01",
"LM01", "LM01", "LM01", "LM01",
"LM01", "LM01", "LM01", "LM01",
"LM01", "LM01", "LM01", "LM01",
"LM01", "LM01", "LM01", "LM01",
"LM01", "LM01", "LM01", "LM01",
"LM01", "LM01", "LM01", "LM01",
"LM01", "LM01", "LM01", "LM01",
"LM01", "LM01", "LM01", "LM01",
"LM01", "LM01", "LM01", "LM01",
"LM01", "LM01", "LM01", "LM01",
"LM01", "LM01", "LM01", "OL01",
"OL01", "OL01", "OL01", "OL01",
"OL01", "OL01", "OL01", "OL01",
"OL01", "OL01", "OL01", "OL01",
"OL01", "OL01", "OL01", "OL01",
"OL01", "OL01", "OL01", "OL01",
"OL01", "OL01", "OL01", "OL01",
"OL01", "OL01", "OL01", "OL01",
"OL01", "OL01", "OL01", "OL01",
"OL01", "OL01", "OL01", "OL01",
"OL01", "OL01", "OL01", "OL01",
"OL01", "OL01", "OL01", "OL01",
"OL01", "OL01", "OL01", "OL01",
"OL01", "OL01", "OL01", "OL01",
"OL01", "OL01", "OL01", "OL01",
"OL01", "OL01", "OL01", "OL01",
"OL01", "OL01", "OL01", "OL01",
"OL01", "OL01", "OL01", "OL01",
"OL01", "OL01", "OL01", "SGI1",
"SGI1", "SGI1", "SGI1", "SGI1",
"SGI1", "SGI1", "SGI1", "SGI1",
"SGI1", "SGI1", "SGI1", "SGI1",
"SGI1", "SGI1", "SGI1", "SGI1",
"SGI1", "SGI1", "SGI1", "SGI1",
"SGI1", "SGI1", "SGI1", "SGI1",
"SGI1", "SGI1", "SGI1", "SGI1",
"SGI1", "SGI1", "SGI1", "SGI1",
"SGI1", "SGI1", "SGI1", "SGI1",
"SGI1", "SGI1", "SGI1", "SGI1",
"SGI1", "SGI1", "SGI1", "SGI1",
"SGI1", "SGI1", "SGI1", "SGI1",
"SGI1", "SGI1", "SGI1", "SGI1",
"SGI1", "SGI1", "SGI1", "SGI1",
"SGI1", "SGI1", "SGI1", "SGI1",
"SGI1", "SGI1", "SGI1", "SGI1",
"SGI1", "SGI1", "SGI1", "SGI1",
"SGI1", "SGI1", "SGI1", "B2NW",
"B2NW", "B2NW", "B2NW", "B2NW",
"B2NW", "B2NW", "B2NW", "B2NW",
"B2NW", "B2NW", "B2NW", "B2NW",
"B2NW", "B2NW", "B2NW", "B2NW",
"B2NW", "B2NW", "B2NW", "B2NW",
"B2NW", "B2NW", "B2NW", "B2NW",
"B2NW", "B2NW", "B2NW", "B2NW",
"B2NW", "B2NW", "B2NW", "B2NW",
"B2NW", "B2NW", "B2NW", "B2NW",
"B2NW", "B2NW", "B2NW", "B2NW",
"B2NW", "B2NW", "B2NW", "B2NW",
"B2NW", "B2NW", "B2NW", "B2NW",
"B2NW", "B2NW", "B2NW", "B2NW",
"B2NW", "B2NW", "B2NW", "B2NW",
"B2NW", "B2NW", "B2NW", "B2NW",
"B2NW", "B2NW", "O2LS", "O2LS",
"O2LS", "O2LS", "O2LS", "O2LS",
"O2LS", "O2LS", "O2LS", "O2LS",
"O2LS", "O2LS", "O2LS", "O2LS",
"O2LS", "O2LS", "O2LS", "O2LS",
"O2LS", "O2LS", "O2LS", "O2LS",
"O2LS", "O2LS", "O2LS", "O2LS",
"O2LS", "O2LS", "O2LS", "O2LS",
"O2LS", "O2LS", "O2LS", "O2LS",
"O2LS", "O2LS", "O2LS", "O2LS",
"O2LS", "O2LS", "O2LS", "O2LS",
"O2LS", "O2LS", "O2LS", "O2LS",
"O2LS", "O2LS", "O2LS", "O2LS",
"O2LS", "O2LS", "O2LS", "O2LS",
"O2LS", "O2LS", "O2LS", "O2LS",
"O2LS", "O2LS", "O2LS", "O2LS",
"O2LS", "O2LS", "O2LS", "O2LS",
"O2LS", "O2LS", "O2LS", "O2LS",
"O2LS", "O2LS", "B2NW", "B2NW",
"B2NW", "B2NW", "B2NW", "B2NW",
"B2NW", "B2NW", "B2NW", "17C3",
"17C3", "17C3", "17C3", "17C3",
"17C3", "17C3", "17C3", "17C3",
"17C3", "17C3", "17C3", "17C3",
"17C3", "17C3", "17C3", "17C3",
"17C3", "17C3", "17C3", "17C3",
"17C3", "17C3", "17C3", "17C3",
"17C3", "17C3", "17C3", "17C3",
"17C3", "17C3", "17C3", "17C3",
"17C3", "17C3", "17C3", "17C3",
"17C3", "17C3", "17C3", "17C3",
"17C3", "17C3", "17C3", "17C3",
"17C3", "17C3", "17C3", "17C3",
"17C3", "17C3", "17C3", "17C3",
"17C3", "17C3", "17C3", "17C3",
"17C3", "17C3", "17C3", "17C3",
"17C3", "17C3", "17C3", "17C3",
"17C3", "17C3", "17C3", "17C3",
"17C3", "17C3", "17C3", "WCI1",
"WCI1", "WCI1", "WCI1", "WCI1",
"WCI1", "WCI1", "WCI1", "WCI1",
"WCI1", "WCI1", "WCI1", "WCI1",
"WCI1", "WCI1", "WCI1", "WCI1",
"WCI1", "WCI1", "WCI1", "WCI1",
"WCI1", "WCI1", "WCI1", "WCI1",
"WCI1", "WCI1", "WCI1", "WCI1",
"WCI1", "WCI1", "WCI1", "WCI1",
"WCI1", "WCI1", "WCI1", "WCI1",
"WCI1", "WCI1", "WCI1", "WCI1",
"WCI1", "WCI1", "WCI1", "WCI1",
"WCI1", "WCI1", "WCI1", "WCI1",
"WCI1", "WCI1", "WCI1", "WCI1",
"WCI1", "WCI1", "WCI1", "WCI1",
"WCI1", "WCI1", "WCI1", "WCI1",
"WCI1", "WCI1", "WCI1", "WCI1",
"WCI1", "WCI1", "WCI1", "WCI1",
"WCI1", "WCI1", "WCI1", "NY01",
"NY01", "NY01", "NY01", "NY01",
"NY01", "NY01", "NY01", "NY01",
"NY01", "NY01", "NY01", "NY01",
"NY01", "NY01", "NY01", "NY01",
"NY01", "NY01", "NY01", "NY01",
"NY01", "NY01", "NY01", "NY01",
"NY01", "NY01", "NY01", "NY01",
"NY01", "NY01", "NY01", "NY01",
"NY01", "NY01", "NY01", "NY01",
"NY01", "NY01", "NY01", "NY01",
"NY01", "NY01", "NY01", "NY01",
"NY01", "NY01", "NY01", "NY01",
"NY01", "NY01", "NY01", "NY01",
"NY01", "NY01", "NY01", "NY01",
"NY01", "NY01", "NY01", "NY01",
"NY01", "NY01", "NY01", "NY01",
"NY01", "NY01", "NY01", "NY01",
"NY01", "NY01", "EX01", "EX01",
"EX01", "EX01", "EX01", "EX01",
"EX01", "EX01", "EX01", "EX01",
"EX01", "EX01", "EX01", "EX01",
"EX01", "EX01", "EX01", "EX01",
"EX01", "EX01", "EX01", "EX01",
"EX01", "EX01", "EX01", "EX01",
"EX01", "EX01", "EX01", "EX01",
"EX01", "EX01", "EX01", "EX01",
"EX01", "MAND", "MAND", "MAND",
"MAND", "MAND", "MAND", "MAND",
"MAND", "MAND", "MAND", "MAND",
"MAND", "MAND", "MAND", "MAND",
"MAND", "MAND", "MAND", "MAND",
"MAND", "MAND", "MAND", "MAND",
"MAND", "MAND", "MAND", "MAND",
"MAND", "MAND", "MAND", "MAND",
"MAND", "MAND", "MAND", "MAND",
"MAND", "MAND", "MAND", "MAND",
"MAND", "MAND", "MAND", "MAND",
"MAND", "MAND", "MAND", "MAND",
"MAND", "MAND", "MAND", "MAND",
"MAND", "MAND", "MAND", "MAND",
"MAND", "MAND", "MAND", "MAND",
"MAND", "MAND", "MAND", "MAND",
"MAND", "MAND", "MAND", "MAND",
"MAND", "MAND", "MAND", "MAND",
"MAND", "28EP", "28EP", "28EP",
"28EP", "28EP", "28EP", "28EP",
"28EP", "28EP", "28EP", "28EP",
"28EP", "28EP", "28EP", "28EP",
"28EP", "28EP", "28EP", "28EP",
"28EP", "28EP", "28EP", "28EP",
"28EP", "28EP", "28EP", "28EP",
"28EP", "28EP", "28EP", "28EP",
"28EP", "28EP", "28EP", "28EP",
"28EP", "28EP", "28EP", "28EP",
"28EP", "28EP", "28EP", "28EP",
"28EP", "28EP", "28EP", "28EP",
"28EP", "28EP", "28EP", "28EP",
"28EP", "28EP", "28EP", "28EP",
"28EP", "28EP", "28EP", "28EP",
"28EP", "28EP", "28EP", "28EP",
"28EP", "28EP", "28EP", "28EP",
"28EP", "28EP", "28EP", "28EP",
"28EP", "EX01", "EX01", "EX01",
"EX01", "EX01", "EX01", "EX01",
"EX01", "EX01", "EX01", "EX01",
"EX01", "EX01", "EX01", "EX01",
"EX01", "EX01", "EX01", "EX01",
"EX01", "EX01", "EX01", "EX01",
"EX01", "EX01", "EX01", "EX01",
"EX01", "EX01", "EX01", "EX01",
"EX01", "EX01", "EX01", "EX01",
"EX01", "EX01"), set = c("seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
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"seta", "seta", "seta", "seta",
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"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
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"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
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"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
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"seta", "seta", "seta", "seta",
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"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta", "seta", "seta",
"seta", "seta"), ent_num = c(23L, 14L, 43L, 30L, 44L, 60L,
17L,
34L, 41L, 40L, 9L, 36L, 38L, 19L, 61L, 51L, 45L, 42L, 3L, 39L,
21L, 11L, 12L, 7L, 35L, 5L, 70L, 47L, 28L, 16L, 72L, 13L, 49L,
67L, 56L, 32L, 27L, 46L, 24L, 63L, 15L, 66L, 26L, 29L, 48L, 1L,
54L, 37L, 2L, 50L, 52L, 31L, 33L, 25L, 6L, 69L, 53L, 10L, 18L,
55L, 59L, 4L, 58L, 22L, 20L, 64L, 71L, 57L, 11L, 17L, 43L, 13L,
1L, 16L, 34L, 47L, 52L, 72L, 59L, 22L, 54L, 18L, 25L, 61L, 56L,
41L, 27L, 14L, 49L, 19L, 29L, 31L, 64L, 6L, 53L, 35L, 37L, 67L,
39L, 51L, 40L, 15L, 69L, 60L, 38L, 4L, 23L, 3L, 48L, 32L, 42L,
24L, 28L, 33L, 57L, 8L, 21L, 46L, 7L, 30L, 45L, 2L, 63L, 36L,
68L, 20L, 66L, 70L, 58L, 5L, 10L, 12L, 62L, 50L, 71L, 9L, 55L,
26L, 44L, 65L, 14L, 63L, 46L, 58L, 62L, 19L, 59L, 2L, 5L, 6L,
40L, 21L, 44L, 37L, 55L, 35L, 71L, 56L, 10L, 36L, 53L, 25L, 61L,
12L, 26L, 23L, 4L, 13L, 28L, 38L, 57L, 54L, 72L, 48L, 66L, 9L,
70L, 15L, 39L, 60L, 17L, 34L, 51L, 67L, 42L, 49L, 31L, 30L, 3L,
18L, 65L, 32L, 27L, 52L, 22L, 11L, 47L, 64L, 8L, 43L, 41L, 16L,
20L, 33L, 7L, 50L, 68L, 24L, 1L, 69L, 45L, 29L, 37L, 30L, 55L,
54L, 43L, 32L, 21L, 27L, 33L, 40L, 67L, 57L, 68L, 31L, 17L, 13L,
6L, 62L, 19L, 22L, 3L, 10L, 44L, 34L, 69L, 70L, 4L, 1L, 25L,
11L, 51L, 5L, 63L, 71L, 12L, 38L, 58L, 39L, 49L, 59L, 56L, 65L,
2L, 64L, 8L, 35L, 46L, 45L, 29L, 53L, 36L, 42L, 23L, 18L, 50L,
26L, 14L, 48L, 66L, 20L, 24L, 7L, 15L, 53L, 22L, 39L, 20L, 60L,
59L, 43L, 19L, 41L, 6L, 62L, 1L, 55L, 34L, 50L, 38L, 40L, 44L,
4L, 46L, 29L, 65L, 57L, 48L, 33L, 69L, 14L, 35L, 67L, 72L, 54L,
3L, 49L, 2L, 12L, 18L, 30L, 10L, 70L, 31L, 15L, 63L, 71L, 21L,
45L, 28L, 56L, 27L, 64L, 61L, 51L, 5L, 24L, 68L, 25L, 66L, 16L,
36L, 58L, 37L, 52L, 26L, 9L, 42L, 7L, 11L, 8L, 32L, 23L, 13L,
47L, 17L, 61L, 72L, 47L, 60L, 16L, 9L, 28L, 52L, 41L, 1L, 61L,
6L, 23L, 58L, 63L, 25L, 28L, 30L, 36L, 62L, 9L, 32L, 19L, 31L,
56L, 45L, 2L, 22L, 27L, 40L, 14L, 11L, 50L, 13L, 70L, 20L, 64L,
39L, 26L, 21L, 43L, 29L, 35L, 54L, 52L, 37L, 17L, 16L, 72L, 48L,
12L, 18L, 44L, 42L, 49L, 68L, 5L, 55L, 69L, 51L, 66L, 59L, 53L,
15L, 71L, 41L, 57L, 4L, 60L, 8L, 7L, 33L, 34L, 24L, 10L, 67L,
47L, 38L, 3L, 65L, 46L, 20L, 34L, 71L, 1L, 33L, 57L, 13L, 21L,
66L, 29L, 3L, 61L, 69L, 24L, 62L, 39L, 49L, 47L, 31L, 53L, 52L,
43L, 17L, 7L, 8L, 12L, 60L, 63L, 50L, 2L, 51L, 46L, 10L, 23L,
48L, 11L, 26L, 40L, 70L, 42L, 59L, 15L, 56L, 58L, 27L, 6L, 35L,
4L, 37L, 5L, 65L, 44L, 28L, 14L, 32L, 36L, 45L, 9L, 18L, 55L,
68L, 30L, 54L, 41L, 25L, 22L, 38L, 16L, 67L, 64L, 19L, 72L, 68L,
28L, 33L, 15L, 51L, 4L, 47L, 36L, 8L, 57L, 48L, 1L, 52L, 39L,
32L, 50L, 13L, 30L, 63L, 2L, 9L, 62L, 22L, 6L, 61L, 16L, 53L,
38L, 37L, 20L, 69L, 44L, 56L, 29L, 26L, 14L, 17L, 46L, 66L, 58L,
42L, 60L, 19L, 45L, 3L, 59L, 70L, 31L, 24L, 55L, 40L, 43L, 25L,
65L, 12L, 67L, 21L, 7L, 27L, 49L, 72L, 54L, 41L, 23L, 34L, 5L,
64L, 35L, 18L, 71L, 11L, 24L, 19L, 38L, 14L, 4L, 56L, 5L, 54L,
34L, 64L, 55L, 33L, 69L, 71L, 52L, 61L, 48L, 23L, 43L, 41L, 20L,
39L, 11L, 63L, 36L, 22L, 9L, 25L, 27L, 51L, 53L, 37L, 57L, 13L,
18L, 64L, 22L, 53L, 16L, 5L, 28L, 60L, 31L, 11L, 29L, 45L, 59L,
72L, 49L, 67L, 13L, 20L, 3L, 42L, 44L, 69L, 33L, 38L, 15L, 70L,
35L, 48L, 26L, 56L, 19L, 39L, 43L, 40L, 14L, 2L, 68L, 51L, 12L,
47L, 10L, 55L, 23L, 4L, 71L, 41L, 50L, 7L, 24L, 61L, 27L, 54L,
46L, 58L, 37L, 66L, 57L, 1L, 36L, 32L, 18L, 62L, 9L, 30L, 21L,
6L, 52L, 8L, 65L, 17L, 25L, 63L, 34L, 65L, 22L, 56L, 9L, 7L,
11L, 31L, 4L, 63L, 29L, 61L, 54L, 12L, 62L, 59L, 5L, 23L, 53L,
36L, 24L, 35L, 66L, 49L, 72L, 18L, 70L, 32L, 43L, 20L, 45L, 34L,
46L, 28L, 6L, 44L, 71L, 39L, 13L, 27L, 1L, 58L, 30L, 68L, 17L,
33L, 26L, 57L, 15L, 21L, 52L, 48L, 42L, 16L, 40L, 38L, 8L, 69L,
2L, 51L, 67L, 55L, 64L, 47L, 60L, 19L, 41L, 50L, 3L, 14L, 25L,
10L, 37L, 6L, 15L, 45L, 49L, 8L, 17L, 50L, 16L, 58L, 72L, 26L,
60L, 7L, 32L, 1L, 46L, 66L, 68L, 62L, 47L, 35L, 70L, 10L, 31L,
65L, 2L, 3L, 21L, 12L, 30L, 40L, 28L, 59L, 42L, 67L, 44L, 29L
), rep_num = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L), lp = c(NA, 41.64, 38.8, 44.45, 40.54,
38.54, 41.94, 39.6, 37.39, 40.95, 38.45, 43.47, 41.66, 40.91,
42.68, 43.12, 38.22, 40.95, 46.24, 42.95, 38.95, 39.88, 40.57,
40.13, 38.57, 45.45, 40.78, 43.52, 39.75, 39.93, 40.35, 37.6,
37.7, 43.05, 43.32, 41.31, 39.03, 42.5, 43.18, 41.32, 39.58,
40.62, 39.64, 39.85, 38.75, 40.18, 41.44, 40.5, 40.87, 40.75,
38.37, 40.26, 35.11, 40.89, 41.67, 38.87, 37.32, 35.85, 38.25,
42.21, 43.15, 38.69, 38.8, 38.77, 37.98, 39.8, 33.37, 40.09,
42.87, 44.07, 43.78, 42.47, 42.8, 41.1, 41.17, 44.07, 44.24,
43.16, 46.12, 42.64, 43.92, 42.16, 43.69, 42.89, 42.63, 43.58,
44.4, 43.17, 43.3, 42.5, 42.6, 44.05, 44.63, 43.09, 45.17, 45.17,
42.78, 42.38, 45.76, 42.16, 44.22, 43.67, 40.45, 41.95, 42.49,
41.98, 45.15, 46.01, 38.51, 43.08, 45.19, 44.53, 43.14, 39.93,
46.84, 43.59, 41.68, 43.7, 44.63, 44.02, 42.07, 43.88, 43.2,
46.2, 40.84, 39.14, 43.89, 42.58, 41.53, 45.32, 39.56, 43.77,
45.45, 45.16, 43.4, 40.08, 42.27, 43.74, 43.77, 41.31, 41.38,
45.01, 45.76, 40.4, 48.39, 46.27, 15.44, 44.76, 47.45, 44.87,
46.8, 41.83, 43.03, 43.96, 42.51, 45.06, 45.55, 44.9, 42.47,
44.9, 45, 44.77, 43.79, 44.26, 44.57, 44.4, 43.42, 42.31, 47.18,
43.83, 45.72, 44.83, 44.96, 40.28, 42.85, 41.23, 45.23, 47.09,
43.61, 42.69, 46.27, 45.16, 44.14, 43.34, 45.97, 43.81, 43.01,
39.82, 45.91, 45.97, 43.61, 45.12, 46.37, 42.67, 42.47, 45.86,
44.19, 44.46, 42.64, 43.95, 44.93, 40.33, 42.75, 39.92, 44.17,
44.49, 41.51, 42.43, 44.14, 40.5, 41.29, 44.89, 37.98, 39.02,
39.62, 42.13, 39.03, 44.16, 39.15, 41.49, 42.63, 40.11, 39.97,
42.85, 35.98, 39.45, 40.99, 42.44, 42.11, 37.36, 40.63, 40.69,
43.57, 39.04, 39.3, 42.19, 36.88, 40.39, 37.78, 38.6, 40.2, 40.98,
36.58, 43.59, 42.49, 39.96, 39.84, 40.43, 38.94, 42.72, 39.43,
42.13, 40.36, 40.58, 40.01, 42.17, 42.17, 41.35, 43.27, 40.15,
39.76, 40.94, 40.87, 42.32, 41.81, 41.97, 43.72, 41.32, 40.83,
37.64, 41.03, 38.98, 40.61, 41.17, 41.96, 40.07, 38.48, 42.85,
38.68, 39.09, 42.16, 38.14, 37.99, 41.06, 37.4, 41.88, 39.35,
39.73, 38.38, 41.34, 40.67, 40.89, 39.28, 37.59, 39.3, 39.72,
40.79, 39.42, 34.5, 37.61, 37.76, 40.24, 41.17, 41.24, 42.14,
42.53, 39.71, 39.44, 40.19, 42.51, 40.15, 36.26, 37.48, 40.43,
37.5, 42.11, 41.44, 40.29, 39.75, 37.58, 41.25, 40.39, 41.63,
42.18, 43.71, 38.69, 43.47, 41.98, 35.51, 42.22, 38.51, 40.17,
39.4, 39.54, 41.7, 40.93, 40.01, 35.97, 41.44, 41.89, 40.9, 39.95,
40.69, 43.18, 37.03, 39.91, 36.75, 42.75, 41.61, 42.6, 38.55,
42.84, 38.41, 42.55, 41.44, 41.03, 40, 41.35, 42.18, 42.66, 40.06,
42.48, 41.33, 41.92, 40.88, 40.99, 42.78, 38.26, 40.81, 39.95,
37.89, 40.81, 38.33, 39.33, 39.67, 40.12, 41.75, 39.81, 41.92,
44.59, 37.8, 40.54, 40.49, 41.82, 42.13, 39.93, 39.94, 42.54,
41.74, 43.06, 41.72, 41.27, 39.42, 42.07, 40.06, 42.1, 38.91,
39.28, 42.94, 39.94, 41.86, 38.56, 38.15, 41.47, 42.34, 38.14,
40.19, 40.2, 43.01, 42.35, 40.89, 41.44, 42.89, 44.49, 39.1,
38.42, 37.44, 43.28, 37.96, 40.56, 41.53, 41.59, 41.45, 42.55,
40.6, 44.04, 39.31, 41.08, 37.14, 40.03, 41.49, 40.82, 38.47,
43.43, 38.82, 40.4, 41.09, 42.26, 41.85, 41.13, 37.27, 41.97,
45.24, 43.35, 40.53, 43.14, 39.71, 42.77, 42.2, 42.13, 41.91,
42.59, 41.99, 42.11, 40.43, 40.02, 43.53, 43.01, 39.55, 43.2,
39.95, 42.51, 42, 42.26, 42.39, 42.64, 39.9, 41.89, 43.05, 41.02,
41.95, 39.92, 43.42, 44.01, 42.6, 40.84, 42.86, 44.31, 42.24,
41.25, 42.38, 44.99, 41.24, 43.97, 41.12, 37.88, 41.53, 41.56,
39.18, 40.83, 42.96, 41.92, 43.86, 42.48, 42.65, 43.3, 41.99,
42.51, 40.65, 42.77, 34.71, 40.97, 38.15, 39.76, 36.74, 37.95,
39.17, 38.22, 39.31, 43.57, 37.21, 39.35, 42.37, 42.01, 42.39,
43.21, 36.91, 36.69, 41.07, 41.91, 34.63, 40.61, 36.23, 38.12,
40.76, 39.14, 41.81, 39.14, 41.04, 37.32, 40.83, 40.81, 38.29,
39.61, 40.96, 40.71, 40.47, 38.64, 39.76, 38.19, 39.05, 38.04,
41.14, 38.35, 42.3, 34.44, 40.93, 39.3, 41.44, 38.3, 42.74, 36.66,
40.02, 36.62, 40.48, 41.72, 41.23, 41.81, 42.07, 40.22, 37.83,
36.54, 37.77, 40.49, 38.65, 43.2, 43.32, 40.67, 41.95, 36.11,
39.28, 42.38, 40.35, 40.3, 43.48, 40.55, 40.54, 44.03, 41.94,
37.97, 41.98, 41.53, 38.19, 38.66, 41.18, 41.95, 42.53, 38.7,
44.55, 42.39, 41.55, 38.46, 42.27, 42.19, 41.95, 41.81, 39.81,
38.12, 42.94, 42.99, 38.85, 41.26, 43.13, 44.21, 38.54, 44.02,
43.46, 41.64, 42.06, 42.11, 42.34, 41.86, 37.91, 40.89, 40.5,
39.54, 37.87, 40.86, 41.36, 41.77, 42.03, 39.15, 40.04, 44.11,
41.34, 42.97, 38.42, 37.28, 41.04, 41.48, 38.82, 41.94, 37.95,
40.9, 40.39, 40.31, 43.19, 41.22, 41.49, 41.25, 40.07, 36.7,
39.97, 39.99, 41.7, 37.09, 42.58, 43.01, 37.7, 41.81, 39.99,
42.95, 43.19, 42.69, 41.5, 40.64, 43.24, 41.14, 41.21, 41.29,
41.43, 44.21, 38.52, 42.54, 40.54, 42.49, 43.2, 38.12, 40.08,
39.02, 41.45, 42.33, 41.11, 38.93, 41.63, 44.22, 41.41, 39.08,
40.9, 41.1, 43.88, 40.96, 46.75, 47.54, 40.35, 41.97, 44.94,
44.91, 44.66, 44.5, 44.4, 46.4, 47.97, 46.05, 45.57, 42.83, 41.48,
47.48, 45.43, 41.98, 43.14, 45.6, 44.78, 45.45, 45.69, 44.82,
44.24, 41.14, 43.14, 46.61, 43.92, 43.56, 43.68, 45.37, 45.15,
40.76, 43.78, 44.67, 41.36, 41.4, 40.97, 41.87, 39.83, 43.8,
48.36, 44.28, 43.29, 44.69, 43.26, 43.35, 44.34, 45.08, 42.26,
39.7, 42.4, 44.03, 43.22, 42.71, 45.89, 44.89, 44.81, 42.59,
40.85, 43.82, 44.85, 47.47, 43.64, 42.65, 45.67, 43.24, 42.33,
40.61, 38.3, 39.84, 41.08, 42.33, 44.44, 40.85, 39.58, 42.55,
41.75, 39.44, 41.79, 39.31, 41.34, 42.76, 40.79, 37.31, 42.85,
42.88, 42.01, 44.63, 38.85, 41.13, 40.43, 41.34, 43.14, 40.58,
42.21, 38.94, 44.88, 42.33, 42.61, 41.88, 41.13, 41.83, 42.8)), .Names =
c("field",
"set", "ent_num", "rep_num", "lp"),
class = "data.frame", row.names = c(NA,
-787L))
# session info
R version 3.4.1 (2017-06-30)
Platform: i386-w64-mingw32/i386 (32-bit)
Running under: Windows 7 x64 (build 7601) Service Pack 1
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] bindrcpp_0.2 tidyr_0.6.3 dplyr_0.7.4
loaded via a namespace (and not attached):
[1] compiler_3.4.1 magrittr_1.5 assertthat_0.2.0 R6_2.2.2
tools_3.4.1
[6] glue_1.1.1 tibble_1.3.3 Rcpp_0.12.11 stringi_1.1.5
pkgconfig_2.0.1
[11] rlang_0.1.2 bindr_0.1
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The message is coming from your stopifnot() condition being met. On Thu, Dec 14, 2017 at 5:31 PM, DIGHE, NILESH [AG/2362] < nilesh.dighe at monsanto.com> wrote:> Hi, I accidently left out few lines of code from the calclp function. > Updated function is pasted below. > > I am still getting the same error ?Error: !(any(data1$norm_sd >= 1)) is > not TRUE? > > > > I would appreciate any help. > > Nilesh > > dput(calclp) > > function (dataset) > > { > > dat1 <- funlp1(dataset) > > recursive_funlp <- function(dataset = dat1, func = funlp2) { > > dat2 <- dataset %>% select(uniqueid, field_rep, lp) %>% > > mutate(field_rep = paste(field_rep, "lp", sep = ".")) %>% > > spread(key = field_rep, value = lp) %>% mutate_at(.vars > grep("_", > > names(.)), funs(norm = round(scale(.), 3))) > > dat2$norm_sd <- round(apply(dat2[, grep("lp_norm", names(dat2))], > > 1, sd, na.rm = TRUE), 3) > > dat2$norm_max <- round(apply(dat2[, grep("lp_norm", names(dat2))], > > 1, function(x) { > > max(abs(x), na.rm = TRUE) > > }), 3) > > data1 <- dat2 %>% gather(key, value, -uniqueid, -norm_max, > > -norm_sd) %>% separate(key, c("field_rep", "treatment"), > > "\\.") %>% spread(treatment, value) %>% mutate(outlier = NA) > > stopifnot(!(any(data1$norm_sd >= 1))) > > if (!(any(data1$norm_sd >= 1))) { > > df1 <- dat1 > > return(df1) > > } > > else { > > df2 <- recursive_funlp() > > return(df2) > > } > > } > > df3 <- recursive_funlp(dataset = dat1, func = funlp2) > > df3 > > } > > > > > > *From:* DIGHE, NILESH [AG/2362] > *Sent:* Thursday, December 14, 2017 9:01 AM > *To:* 'Eric Berger' <ericjberger at gmail.com> > *Cc:* r-help <r-help at r-project.org> > *Subject:* RE: [R] help with recursive function > > > > Eric: Thanks for taking time to look into my problem. Despite of making > the change you suggested, I am still getting the same error. I am > wondering if the logic I am using in the stopifnot and if functions is a > problem. > > I like the recursive function to stop whenever the norm_sd column has zero > values that are above or equal to 1. Below is the calclp function after the > changes you suggested. > > Thanks. Nilesh > > > > dput(calclp) > > function (dataset) > > { > > dat1 <- funlp1(dataset) > > recursive_funlp <- function(dataset = dat1, func = funlp2) { > > dat2 <- dataset %>% select(uniqueid, field_rep, lp) %>% > > mutate(field_rep = paste(field_rep, "lp", sep = ".")) %>% > > spread(key = field_rep, value = lp) %>% mutate_at(.vars > grep("_", > > names(.)), funs(norm = round(scale(.), 3))) > > dat2$norm_sd <- round(apply(dat2[, grep("lp_norm", names(dat2))], > > 1, sd, na.rm = TRUE), 3) > > dat2$norm_max <- round(apply(dat2[, grep("lp_norm", names(dat2))], > > 1, function(x) { > > max(abs(x), na.rm = TRUE) > > }), 3) > > stopifnot(!(any(dat2$norm_sd >= 1))) > > if (!(any(dat2$norm_sd >= 1))) { > > df1 <- dat1 > > return(df1) > > } > > else { > > df2 <- recursive_funlp() > > return(df2) > > } > > } > > df3 <- recursive_funlp(dataset = dat1, func = funlp2) > > df3 > > } > > > > > > *From:* Eric Berger [mailto:ericjberger at gmail.com <ericjberger at gmail.com>] > > *Sent:* Thursday, December 14, 2017 8:17 AM > *To:* DIGHE, NILESH [AG/2362] <nilesh.dighe at monsanto.com> > *Cc:* r-help <r-help at r-project.org> > *Subject:* Re: [R] help with recursive function > > > > My own typo ... whoops ... > > > > !( any(dat2$norm_sd >= 1 )) > > > > > > > > On Thu, Dec 14, 2017 at 3:43 PM, Eric Berger <ericjberger at gmail.com> > wrote: > > You seem to have a typo at this expression (and some others like it) > > > > Namely, you write > > > > any(!dat2$norm_sd) >= 1 > > > > when you possibly meant to write > > > > !( any(dat2$norm_sd) >= 1 ) > > > > i.e. I think your ! seems to be in the wrong place. > > > > HTH, > Eric > > > > > > On Thu, Dec 14, 2017 at 3:26 PM, DIGHE, NILESH [AG/2362] < > nilesh.dighe at monsanto.com> wrote: > > Hi, I need some help with running a recursive function. I like to run > funlp2 recursively. > When I try to run recursive function in another function named "calclp" I > get this "Error: any(!dat2$norm_sd) >= 1 is not TRUE". > > I have never built a recursive function before so having trouble executing > it in this case. I would appreciate any help or guidance to resolve this > issue. Please see my data and the three functions that I am using below. > Please note that calclp is the function I am running and the other two > functions are within this calclp function. > > # code: > Test<- calclp(dataset = dat) > > # calclp function > > calclp<- function (dataset) > > { > > dat1 <- funlp1(dataset) > > recursive_funlp <- function(dataset = dat1, func = funlp2) { > > dat2 <- dataset %>% select(uniqueid, field_rep, lp) %>% > > mutate(field_rep = paste(field_rep, "lp", sep = ".")) %>% > > spread(key = field_rep, value = lp) %>% mutate_at(.vars > grep("_", > > names(.)), funs(norm = round(scale(.), 3))) > > dat2$norm_sd <- round(apply(dat2[, grep("lp_norm", names(dat2))], > > 1, sd, na.rm = TRUE), 3) > > dat2$norm_max <- round(apply(dat2[, grep("lp_norm", names(dat2))], > > 1, function(x) { > > max(abs(x), na.rm = TRUE) > > }), 3) > > stopifnot(any(!dat2$norm_sd) >= 1) > > if (any(!dat2$norm_sd) >= 1) { > > df1 <- dat1 > > return(df1) > > } > > else { > > df2 <- recursive_funlp() > > return(df2) > > } > > } > > df3 <- recursive_funlp(dataset = dat1, func = funlp2) > > df3 > > } > > > # funlp1 function > > funlp1<- function (dataset) > > { > > dat2 <- dataset %>% select(field, set, ent_num, rep_num, > > lp) %>% unite(uniqueid, set, ent_num, sep = ".") %>% > > unite(field_rep, field, rep_num) %>% mutate(field_rep > paste(field_rep, > > "lp", sep = ".")) %>% spread(key = field_rep, value = lp) %>% > > mutate_at(.vars = grep("_", names(.)), funs(norm = round(scale(.), > > 3))) > > dat2$norm_sd <- round(apply(dat2[, grep("lp_norm", names(dat2))], > > 1, sd, na.rm = TRUE), 3) > > dat2$norm_max <- round(apply(dat2[, grep("lp_norm", names(dat2))], > > 1, function(x) { > > max(abs(x), na.rm = TRUE) > > }), 3) > > data1 <- dat2 %>% gather(key, value, -uniqueid, -norm_max, > > -norm_sd) %>% separate(key, c("field_rep", "treatment"), > > "\\.") %>% spread(treatment, value) %>% mutate(outlier = NA) > > df_clean <- with(data1, data1[norm_sd < 1, ]) > > datD <- with(data1, data1[norm_sd >= 1, ]) > > s <- split(datD, datD$uniqueid) > > sdf <- lapply(s, function(x) { > > data.frame(x, x$outlier <- ifelse(is.na(x$lp_norm), NA, > > ifelse(abs(x$lp_norm) == x$norm_max, "yes", "no")), > > x$lp <- with(x, ifelse(outlier == "yes", NA, lp))) > > x > > }) > > sdf2 <- bind_rows(sdf) > > all_dat <- bind_rows(df_clean, sdf2) > > all_dat > > } > > > # funlp2 function > > funlp2<-function (dataset) > > { > > data1 <- dataset > > df_clean <- with(data1, data1[norm_sd < 1, ]) > > datD <- with(data1, data1[norm_sd >= 1, ]) > > s <- split(datD, datD$uniqueid) > > sdf <- lapply(s, function(x) { > > data.frame(x, x$outlier <- ifelse(is.na(x$lp_norm), NA, > > ifelse(abs(x$lp_norm) == x$norm_max, "yes", "no")), > > x$lp <- with(x, ifelse(outlier == "yes", NA, lp))) > > x > > }) > > sdf2 <- bind_rows(sdf) > > all_dat <- bind_rows(df_clean, sdf2) > > all_dat > > } > > > # dataset > dput(dat) > structure(list(field = c("LM01", "LM01", "LM01", "LM01", "LM01", > "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", > "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", > "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", > "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", > "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", > "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", > "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", > "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", "OL01", > "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", > "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", > "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", > "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", > "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", > "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", > "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", > "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", > "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", "SGI1", > "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", > "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", > "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", > "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", > "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", > "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", > "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", > "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", > "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "B2NW", > "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", > "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", > "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", > "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", > "B2NW", "B2NW", 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16L, > 20L, 33L, 7L, 50L, 68L, 24L, 1L, 69L, 45L, 29L, 37L, 30L, 55L, > 54L, 43L, 32L, 21L, 27L, 33L, 40L, 67L, 57L, 68L, 31L, 17L, 13L, > 6L, 62L, 19L, 22L, 3L, 10L, 44L, 34L, 69L, 70L, 4L, 1L, 25L, > 11L, 51L, 5L, 63L, 71L, 12L, 38L, 58L, 39L, 49L, 59L, 56L, 65L, > 2L, 64L, 8L, 35L, 46L, 45L, 29L, 53L, 36L, 42L, 23L, 18L, 50L, > 26L, 14L, 48L, 66L, 20L, 24L, 7L, 15L, 53L, 22L, 39L, 20L, 60L, > 59L, 43L, 19L, 41L, 6L, 62L, 1L, 55L, 34L, 50L, 38L, 40L, 44L, > 4L, 46L, 29L, 65L, 57L, 48L, 33L, 69L, 14L, 35L, 67L, 72L, 54L, > 3L, 49L, 2L, 12L, 18L, 30L, 10L, 70L, 31L, 15L, 63L, 71L, 21L, > 45L, 28L, 56L, 27L, 64L, 61L, 51L, 5L, 24L, 68L, 25L, 66L, 16L, > 36L, 58L, 37L, 52L, 26L, 9L, 42L, 7L, 11L, 8L, 32L, 23L, 13L, > 47L, 17L, 61L, 72L, 47L, 60L, 16L, 9L, 28L, 52L, 41L, 1L, 61L, > 6L, 23L, 58L, 63L, 25L, 28L, 30L, 36L, 62L, 9L, 32L, 19L, 31L, > 56L, 45L, 2L, 22L, 27L, 40L, 14L, 11L, 50L, 13L, 70L, 20L, 64L, > 39L, 26L, 21L, 43L, 29L, 35L, 54L, 52L, 37L, 17L, 16L, 72L, 48L, > 12L, 18L, 44L, 42L, 49L, 68L, 5L, 55L, 69L, 51L, 66L, 59L, 53L, > 15L, 71L, 41L, 57L, 4L, 60L, 8L, 7L, 33L, 34L, 24L, 10L, 67L, > 47L, 38L, 3L, 65L, 46L, 20L, 34L, 71L, 1L, 33L, 57L, 13L, 21L, > 66L, 29L, 3L, 61L, 69L, 24L, 62L, 39L, 49L, 47L, 31L, 53L, 52L, > 43L, 17L, 7L, 8L, 12L, 60L, 63L, 50L, 2L, 51L, 46L, 10L, 23L, > 48L, 11L, 26L, 40L, 70L, 42L, 59L, 15L, 56L, 58L, 27L, 6L, 35L, > 4L, 37L, 5L, 65L, 44L, 28L, 14L, 32L, 36L, 45L, 9L, 18L, 55L, > 68L, 30L, 54L, 41L, 25L, 22L, 38L, 16L, 67L, 64L, 19L, 72L, 68L, > 28L, 33L, 15L, 51L, 4L, 47L, 36L, 8L, 57L, 48L, 1L, 52L, 39L, > 32L, 50L, 13L, 30L, 63L, 2L, 9L, 62L, 22L, 6L, 61L, 16L, 53L, > 38L, 37L, 20L, 69L, 44L, 56L, 29L, 26L, 14L, 17L, 46L, 66L, 58L, > 42L, 60L, 19L, 45L, 3L, 59L, 70L, 31L, 24L, 55L, 40L, 43L, 25L, > 65L, 12L, 67L, 21L, 7L, 27L, 49L, 72L, 54L, 41L, 23L, 34L, 5L, > 64L, 35L, 18L, 71L, 11L, 24L, 19L, 38L, 14L, 4L, 56L, 5L, 54L, > 34L, 64L, 55L, 33L, 69L, 71L, 52L, 61L, 48L, 23L, 43L, 41L, 20L, > 39L, 11L, 63L, 36L, 22L, 9L, 25L, 27L, 51L, 53L, 37L, 57L, 13L, > 18L, 64L, 22L, 53L, 16L, 5L, 28L, 60L, 31L, 11L, 29L, 45L, 59L, > 72L, 49L, 67L, 13L, 20L, 3L, 42L, 44L, 69L, 33L, 38L, 15L, 70L, > 35L, 48L, 26L, 56L, 19L, 39L, 43L, 40L, 14L, 2L, 68L, 51L, 12L, > 47L, 10L, 55L, 23L, 4L, 71L, 41L, 50L, 7L, 24L, 61L, 27L, 54L, > 46L, 58L, 37L, 66L, 57L, 1L, 36L, 32L, 18L, 62L, 9L, 30L, 21L, > 6L, 52L, 8L, 65L, 17L, 25L, 63L, 34L, 65L, 22L, 56L, 9L, 7L, > 11L, 31L, 4L, 63L, 29L, 61L, 54L, 12L, 62L, 59L, 5L, 23L, 53L, > 36L, 24L, 35L, 66L, 49L, 72L, 18L, 70L, 32L, 43L, 20L, 45L, 34L, > 46L, 28L, 6L, 44L, 71L, 39L, 13L, 27L, 1L, 58L, 30L, 68L, 17L, > 33L, 26L, 57L, 15L, 21L, 52L, 48L, 42L, 16L, 40L, 38L, 8L, 69L, > 2L, 51L, 67L, 55L, 64L, 47L, 60L, 19L, 41L, 50L, 3L, 14L, 25L, > 10L, 37L, 6L, 15L, 45L, 49L, 8L, 17L, 50L, 16L, 58L, 72L, 26L, > 60L, 7L, 32L, 1L, 46L, 66L, 68L, 62L, 47L, 35L, 70L, 10L, 31L, > 65L, 2L, 3L, 21L, 12L, 30L, 40L, 28L, 59L, 42L, 67L, 44L, 29L > ), rep_num = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L), lp = c(NA, 41.64, 38.8, 44.45, 40.54, > 38.54, 41.94, 39.6, 37.39, 40.95, 38.45, 43.47, 41.66, 40.91, > 42.68, 43.12, 38.22, 40.95, 46.24, 42.95, 38.95, 39.88, 40.57, > 40.13, 38.57, 45.45, 40.78, 43.52, 39.75, 39.93, 40.35, 37.6, > 37.7, 43.05, 43.32, 41.31, 39.03, 42.5, 43.18, 41.32, 39.58, > 40.62, 39.64, 39.85, 38.75, 40.18, 41.44, 40.5, 40.87, 40.75, > 38.37, 40.26, 35.11, 40.89, 41.67, 38.87, 37.32, 35.85, 38.25, > 42.21, 43.15, 38.69, 38.8, 38.77, 37.98, 39.8, 33.37, 40.09, > 42.87, 44.07, 43.78, 42.47, 42.8, 41.1, 41.17, 44.07, 44.24, > 43.16, 46.12, 42.64, 43.92, 42.16, 43.69, 42.89, 42.63, 43.58, > 44.4, 43.17, 43.3, 42.5, 42.6, 44.05, 44.63, 43.09, 45.17, 45.17, > 42.78, 42.38, 45.76, 42.16, 44.22, 43.67, 40.45, 41.95, 42.49, > 41.98, 45.15, 46.01, 38.51, 43.08, 45.19, 44.53, 43.14, 39.93, > 46.84, 43.59, 41.68, 43.7, 44.63, 44.02, 42.07, 43.88, 43.2, > 46.2, 40.84, 39.14, 43.89, 42.58, 41.53, 45.32, 39.56, 43.77, > 45.45, 45.16, 43.4, 40.08, 42.27, 43.74, 43.77, 41.31, 41.38, > 45.01, 45.76, 40.4, 48.39, 46.27, 15.44, 44.76, 47.45, 44.87, > 46.8, 41.83, 43.03, 43.96, 42.51, 45.06, 45.55, 44.9, 42.47, > 44.9, 45, 44.77, 43.79, 44.26, 44.57, 44.4, 43.42, 42.31, 47.18, > 43.83, 45.72, 44.83, 44.96, 40.28, 42.85, 41.23, 45.23, 47.09, > 43.61, 42.69, 46.27, 45.16, 44.14, 43.34, 45.97, 43.81, 43.01, > 39.82, 45.91, 45.97, 43.61, 45.12, 46.37, 42.67, 42.47, 45.86, > 44.19, 44.46, 42.64, 43.95, 44.93, 40.33, 42.75, 39.92, 44.17, > 44.49, 41.51, 42.43, 44.14, 40.5, 41.29, 44.89, 37.98, 39.02, > 39.62, 42.13, 39.03, 44.16, 39.15, 41.49, 42.63, 40.11, 39.97, > 42.85, 35.98, 39.45, 40.99, 42.44, 42.11, 37.36, 40.63, 40.69, > 43.57, 39.04, 39.3, 42.19, 36.88, 40.39, 37.78, 38.6, 40.2, 40.98, > 36.58, 43.59, 42.49, 39.96, 39.84, 40.43, 38.94, 42.72, 39.43, > 42.13, 40.36, 40.58, 40.01, 42.17, 42.17, 41.35, 43.27, 40.15, > 39.76, 40.94, 40.87, 42.32, 41.81, 41.97, 43.72, 41.32, 40.83, > 37.64, 41.03, 38.98, 40.61, 41.17, 41.96, 40.07, 38.48, 42.85, > 38.68, 39.09, 42.16, 38.14, 37.99, 41.06, 37.4, 41.88, 39.35, > 39.73, 38.38, 41.34, 40.67, 40.89, 39.28, 37.59, 39.3, 39.72, > 40.79, 39.42, 34.5, 37.61, 37.76, 40.24, 41.17, 41.24, 42.14, > 42.53, 39.71, 39.44, 40.19, 42.51, 40.15, 36.26, 37.48, 40.43, > 37.5, 42.11, 41.44, 40.29, 39.75, 37.58, 41.25, 40.39, 41.63, > 42.18, 43.71, 38.69, 43.47, 41.98, 35.51, 42.22, 38.51, 40.17, > 39.4, 39.54, 41.7, 40.93, 40.01, 35.97, 41.44, 41.89, 40.9, 39.95, > 40.69, 43.18, 37.03, 39.91, 36.75, 42.75, 41.61, 42.6, 38.55, > 42.84, 38.41, 42.55, 41.44, 41.03, 40, 41.35, 42.18, 42.66, 40.06, > 42.48, 41.33, 41.92, 40.88, 40.99, 42.78, 38.26, 40.81, 39.95, > 37.89, 40.81, 38.33, 39.33, 39.67, 40.12, 41.75, 39.81, 41.92, > 44.59, 37.8, 40.54, 40.49, 41.82, 42.13, 39.93, 39.94, 42.54, > 41.74, 43.06, 41.72, 41.27, 39.42, 42.07, 40.06, 42.1, 38.91, > 39.28, 42.94, 39.94, 41.86, 38.56, 38.15, 41.47, 42.34, 38.14, > 40.19, 40.2, 43.01, 42.35, 40.89, 41.44, 42.89, 44.49, 39.1, > 38.42, 37.44, 43.28, 37.96, 40.56, 41.53, 41.59, 41.45, 42.55, > 40.6, 44.04, 39.31, 41.08, 37.14, 40.03, 41.49, 40.82, 38.47, > 43.43, 38.82, 40.4, 41.09, 42.26, 41.85, 41.13, 37.27, 41.97, > 45.24, 43.35, 40.53, 43.14, 39.71, 42.77, 42.2, 42.13, 41.91, > 42.59, 41.99, 42.11, 40.43, 40.02, 43.53, 43.01, 39.55, 43.2, > 39.95, 42.51, 42, 42.26, 42.39, 42.64, 39.9, 41.89, 43.05, 41.02, > 41.95, 39.92, 43.42, 44.01, 42.6, 40.84, 42.86, 44.31, 42.24, > 41.25, 42.38, 44.99, 41.24, 43.97, 41.12, 37.88, 41.53, 41.56, > 39.18, 40.83, 42.96, 41.92, 43.86, 42.48, 42.65, 43.3, 41.99, > 42.51, 40.65, 42.77, 34.71, 40.97, 38.15, 39.76, 36.74, 37.95, > 39.17, 38.22, 39.31, 43.57, 37.21, 39.35, 42.37, 42.01, 42.39, > 43.21, 36.91, 36.69, 41.07, 41.91, 34.63, 40.61, 36.23, 38.12, > 40.76, 39.14, 41.81, 39.14, 41.04, 37.32, 40.83, 40.81, 38.29, > 39.61, 40.96, 40.71, 40.47, 38.64, 39.76, 38.19, 39.05, 38.04, > 41.14, 38.35, 42.3, 34.44, 40.93, 39.3, 41.44, 38.3, 42.74, 36.66, > 40.02, 36.62, 40.48, 41.72, 41.23, 41.81, 42.07, 40.22, 37.83, > 36.54, 37.77, 40.49, 38.65, 43.2, 43.32, 40.67, 41.95, 36.11, > 39.28, 42.38, 40.35, 40.3, 43.48, 40.55, 40.54, 44.03, 41.94, > 37.97, 41.98, 41.53, 38.19, 38.66, 41.18, 41.95, 42.53, 38.7, > 44.55, 42.39, 41.55, 38.46, 42.27, 42.19, 41.95, 41.81, 39.81, > 38.12, 42.94, 42.99, 38.85, 41.26, 43.13, 44.21, 38.54, 44.02, > 43.46, 41.64, 42.06, 42.11, 42.34, 41.86, 37.91, 40.89, 40.5, > 39.54, 37.87, 40.86, 41.36, 41.77, 42.03, 39.15, 40.04, 44.11, > 41.34, 42.97, 38.42, 37.28, 41.04, 41.48, 38.82, 41.94, 37.95, > 40.9, 40.39, 40.31, 43.19, 41.22, 41.49, 41.25, 40.07, 36.7, > 39.97, 39.99, 41.7, 37.09, 42.58, 43.01, 37.7, 41.81, 39.99, > 42.95, 43.19, 42.69, 41.5, 40.64, 43.24, 41.14, 41.21, 41.29, > 41.43, 44.21, 38.52, 42.54, 40.54, 42.49, 43.2, 38.12, 40.08, > 39.02, 41.45, 42.33, 41.11, 38.93, 41.63, 44.22, 41.41, 39.08, > 40.9, 41.1, 43.88, 40.96, 46.75, 47.54, 40.35, 41.97, 44.94, > 44.91, 44.66, 44.5, 44.4, 46.4, 47.97, 46.05, 45.57, 42.83, 41.48, > 47.48, 45.43, 41.98, 43.14, 45.6, 44.78, 45.45, 45.69, 44.82, > 44.24, 41.14, 43.14, 46.61, 43.92, 43.56, 43.68, 45.37, 45.15, > 40.76, 43.78, 44.67, 41.36, 41.4, 40.97, 41.87, 39.83, 43.8, > 48.36, 44.28, 43.29, 44.69, 43.26, 43.35, 44.34, 45.08, 42.26, > 39.7, 42.4, 44.03, 43.22, 42.71, 45.89, 44.89, 44.81, 42.59, > 40.85, 43.82, 44.85, 47.47, 43.64, 42.65, 45.67, 43.24, 42.33, > 40.61, 38.3, 39.84, 41.08, 42.33, 44.44, 40.85, 39.58, 42.55, > 41.75, 39.44, 41.79, 39.31, 41.34, 42.76, 40.79, 37.31, 42.85, > 42.88, 42.01, 44.63, 38.85, 41.13, 40.43, 41.34, 43.14, 40.58, > 42.21, 38.94, 44.88, 42.33, 42.61, 41.88, 41.13, 41.83, 42.8)), .Names > c("field", > "set", "ent_num", "rep_num", "lp"), class = "data.frame", row.names = c(NA, > -787L)) > > # session info > > R version 3.4.1 (2017-06-30) > > Platform: 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If you are trying to understand why the "stopifnot" condition is met you can replace it by something like: if ( any(dat2$norm_sd >= 1) ) browser() This will put you in a debugging session where you can examine your variables, e.g.> dat$norm_sdHTH, Eric On Thu, Dec 14, 2017 at 5:33 PM, Eric Berger <ericjberger at gmail.com> wrote:> The message is coming from your stopifnot() condition being met. > > > On Thu, Dec 14, 2017 at 5:31 PM, DIGHE, NILESH [AG/2362] < > nilesh.dighe at monsanto.com> wrote: > >> Hi, I accidently left out few lines of code from the calclp function. >> Updated function is pasted below. >> >> I am still getting the same error ?Error: !(any(data1$norm_sd >= 1)) is >> not TRUE? >> >> >> >> I would appreciate any help. >> >> Nilesh >> >> dput(calclp) >> >> function (dataset) >> >> { >> >> dat1 <- funlp1(dataset) >> >> recursive_funlp <- function(dataset = dat1, func = funlp2) { >> >> dat2 <- dataset %>% select(uniqueid, field_rep, lp) %>% >> >> mutate(field_rep = paste(field_rep, "lp", sep = ".")) %>% >> >> spread(key = field_rep, value = lp) %>% mutate_at(.vars >> grep("_", >> >> names(.)), funs(norm = round(scale(.), 3))) >> >> dat2$norm_sd <- round(apply(dat2[, grep("lp_norm", names(dat2))], >> >> 1, sd, na.rm = TRUE), 3) >> >> dat2$norm_max <- round(apply(dat2[, grep("lp_norm", >> names(dat2))], >> >> 1, function(x) { >> >> max(abs(x), na.rm = TRUE) >> >> }), 3) >> >> data1 <- dat2 %>% gather(key, value, -uniqueid, -norm_max, >> >> -norm_sd) %>% separate(key, c("field_rep", "treatment"), >> >> "\\.") %>% spread(treatment, value) %>% mutate(outlier = NA) >> >> stopifnot(!(any(data1$norm_sd >= 1))) >> >> if (!(any(data1$norm_sd >= 1))) { >> >> df1 <- dat1 >> >> return(df1) >> >> } >> >> else { >> >> df2 <- recursive_funlp() >> >> return(df2) >> >> } >> >> } >> >> df3 <- recursive_funlp(dataset = dat1, func = funlp2) >> >> df3 >> >> } >> >> >> >> >> >> *From:* DIGHE, NILESH [AG/2362] >> *Sent:* Thursday, December 14, 2017 9:01 AM >> *To:* 'Eric Berger' <ericjberger at gmail.com> >> *Cc:* r-help <r-help at r-project.org> >> *Subject:* RE: [R] help with recursive function >> >> >> >> Eric: Thanks for taking time to look into my problem. Despite of making >> the change you suggested, I am still getting the same error. I am >> wondering if the logic I am using in the stopifnot and if functions is a >> problem. >> >> I like the recursive function to stop whenever the norm_sd column has >> zero values that are above or equal to 1. Below is the calclp function >> after the changes you suggested. >> >> Thanks. Nilesh >> >> >> >> dput(calclp) >> >> function (dataset) >> >> { >> >> dat1 <- funlp1(dataset) >> >> recursive_funlp <- function(dataset = dat1, func = funlp2) { >> >> dat2 <- dataset %>% select(uniqueid, field_rep, lp) %>% >> >> mutate(field_rep = paste(field_rep, "lp", sep = ".")) %>% >> >> spread(key = field_rep, value = lp) %>% mutate_at(.vars >> grep("_", >> >> names(.)), funs(norm = round(scale(.), 3))) >> >> dat2$norm_sd <- round(apply(dat2[, grep("lp_norm", names(dat2))], >> >> 1, sd, na.rm = TRUE), 3) >> >> dat2$norm_max <- round(apply(dat2[, grep("lp_norm", >> names(dat2))], >> >> 1, function(x) { >> >> max(abs(x), na.rm = TRUE) >> >> }), 3) >> >> stopifnot(!(any(dat2$norm_sd >= 1))) >> >> if (!(any(dat2$norm_sd >= 1))) { >> >> df1 <- dat1 >> >> return(df1) >> >> } >> >> else { >> >> df2 <- recursive_funlp() >> >> return(df2) >> >> } >> >> } >> >> df3 <- recursive_funlp(dataset = dat1, func = funlp2) >> >> df3 >> >> } >> >> >> >> >> >> *From:* Eric Berger [mailto:ericjberger at gmail.com <ericjberger at gmail.com>] >> >> *Sent:* Thursday, December 14, 2017 8:17 AM >> *To:* DIGHE, NILESH [AG/2362] <nilesh.dighe at monsanto.com> >> *Cc:* r-help <r-help at r-project.org> >> *Subject:* Re: [R] help with recursive function >> >> >> >> My own typo ... whoops ... >> >> >> >> !( any(dat2$norm_sd >= 1 )) >> >> >> >> >> >> >> >> On Thu, Dec 14, 2017 at 3:43 PM, Eric Berger <ericjberger at gmail.com> >> wrote: >> >> You seem to have a typo at this expression (and some others like it) >> >> >> >> Namely, you write >> >> >> >> any(!dat2$norm_sd) >= 1 >> >> >> >> when you possibly meant to write >> >> >> >> !( any(dat2$norm_sd) >= 1 ) >> >> >> >> i.e. I think your ! seems to be in the wrong place. >> >> >> >> HTH, >> Eric >> >> >> >> >> >> On Thu, Dec 14, 2017 at 3:26 PM, DIGHE, NILESH [AG/2362] < >> nilesh.dighe at monsanto.com> wrote: >> >> Hi, I need some help with running a recursive function. I like to run >> funlp2 recursively. >> When I try to run recursive function in another function named "calclp" I >> get this "Error: any(!dat2$norm_sd) >= 1 is not TRUE". >> >> I have never built a recursive function before so having trouble >> executing it in this case. I would appreciate any help or guidance to >> resolve this issue. Please see my data and the three functions that I am >> using below. >> Please note that calclp is the function I am running and the other two >> functions are within this calclp function. >> >> # code: >> Test<- calclp(dataset = dat) >> >> # calclp function >> >> calclp<- function (dataset) >> >> { >> >> dat1 <- funlp1(dataset) >> >> recursive_funlp <- function(dataset = dat1, func = funlp2) { >> >> dat2 <- dataset %>% select(uniqueid, field_rep, lp) %>% >> >> mutate(field_rep = paste(field_rep, "lp", sep = ".")) %>% >> >> spread(key = field_rep, value = lp) %>% mutate_at(.vars >> grep("_", >> >> names(.)), funs(norm = round(scale(.), 3))) >> >> dat2$norm_sd <- round(apply(dat2[, grep("lp_norm", names(dat2))], >> >> 1, sd, na.rm = TRUE), 3) >> >> dat2$norm_max <- round(apply(dat2[, grep("lp_norm", names(dat2))], >> >> 1, function(x) { >> >> max(abs(x), na.rm = TRUE) >> >> }), 3) >> >> stopifnot(any(!dat2$norm_sd) >= 1) >> >> if (any(!dat2$norm_sd) >= 1) { >> >> df1 <- dat1 >> >> return(df1) >> >> } >> >> else { >> >> df2 <- recursive_funlp() >> >> return(df2) >> >> } >> >> } >> >> df3 <- recursive_funlp(dataset = dat1, func = funlp2) >> >> df3 >> >> } >> >> >> # funlp1 function >> >> funlp1<- function (dataset) >> >> { >> >> dat2 <- dataset %>% select(field, set, ent_num, rep_num, >> >> lp) %>% unite(uniqueid, set, ent_num, sep = ".") %>% >> >> unite(field_rep, field, rep_num) %>% mutate(field_rep >> paste(field_rep, >> >> "lp", sep = ".")) %>% spread(key = field_rep, value = lp) %>% >> >> mutate_at(.vars = grep("_", names(.)), funs(norm = round(scale(.), >> >> 3))) >> >> dat2$norm_sd <- round(apply(dat2[, grep("lp_norm", names(dat2))], >> >> 1, sd, na.rm = TRUE), 3) >> >> dat2$norm_max <- round(apply(dat2[, grep("lp_norm", names(dat2))], >> >> 1, function(x) { >> >> max(abs(x), na.rm = TRUE) >> >> }), 3) >> >> data1 <- dat2 %>% gather(key, value, -uniqueid, -norm_max, >> >> -norm_sd) %>% separate(key, c("field_rep", "treatment"), >> >> "\\.") %>% spread(treatment, value) %>% mutate(outlier = NA) >> >> df_clean <- with(data1, data1[norm_sd < 1, ]) >> >> datD <- with(data1, data1[norm_sd >= 1, ]) >> >> s <- split(datD, datD$uniqueid) >> >> sdf <- lapply(s, function(x) { >> >> data.frame(x, x$outlier <- ifelse(is.na(x$lp_norm), NA, >> >> ifelse(abs(x$lp_norm) == x$norm_max, "yes", "no")), >> >> x$lp <- with(x, ifelse(outlier == "yes", NA, lp))) >> >> x >> >> }) >> >> sdf2 <- bind_rows(sdf) >> >> all_dat <- bind_rows(df_clean, sdf2) >> >> all_dat >> >> } >> >> >> # funlp2 function >> >> funlp2<-function (dataset) >> >> { >> >> data1 <- dataset >> >> df_clean <- with(data1, data1[norm_sd < 1, ]) >> >> datD <- with(data1, data1[norm_sd >= 1, ]) >> >> s <- split(datD, datD$uniqueid) >> >> sdf <- lapply(s, function(x) { >> >> data.frame(x, x$outlier <- ifelse(is.na(x$lp_norm), NA, >> >> ifelse(abs(x$lp_norm) == x$norm_max, "yes", "no")), >> >> x$lp <- with(x, ifelse(outlier == "yes", NA, lp))) >> >> x >> >> }) >> >> sdf2 <- bind_rows(sdf) >> >> all_dat <- bind_rows(df_clean, sdf2) >> >> all_dat >> >> } >> >> >> # dataset >> dput(dat) >> structure(list(field = c("LM01", "LM01", "LM01", "LM01", "LM01", >> "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", >> "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", >> "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", >> "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", >> "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", >> "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", >> "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", >> "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", "LM01", "OL01", >> "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", >> "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", >> "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", >> "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", >> "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", >> "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", >> "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", >> "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", >> "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", "OL01", "SGI1", >> "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", >> "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", >> "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", >> "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", >> "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", >> "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", >> "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", >> "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", >> "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "SGI1", "B2NW", >> "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", >> "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", >> "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", >> "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", >> "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", >> "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", >> "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", >> "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", "O2LS", "O2LS", >> "O2LS", "O2LS", "O2LS", "O2LS", "O2LS", "O2LS", "O2LS", "O2LS", >> "O2LS", "O2LS", "O2LS", "O2LS", "O2LS", "O2LS", "O2LS", "O2LS", >> "O2LS", "O2LS", "O2LS", "O2LS", "O2LS", "O2LS", "O2LS", "O2LS", >> "O2LS", "O2LS", "O2LS", "O2LS", "O2LS", "O2LS", "O2LS", "O2LS", >> "O2LS", "O2LS", "O2LS", "O2LS", "O2LS", "O2LS", "O2LS", "O2LS", >> "O2LS", "O2LS", "O2LS", "O2LS", "O2LS", "O2LS", "O2LS", "O2LS", >> "O2LS", "O2LS", "O2LS", "O2LS", "O2LS", "O2LS", "O2LS", "O2LS", >> "O2LS", "O2LS", "O2LS", "O2LS", "O2LS", "O2LS", "O2LS", "O2LS", >> "O2LS", "O2LS", "O2LS", "O2LS", "O2LS", "O2LS", "B2NW", "B2NW", >> "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", "B2NW", "17C3", >> "17C3", "17C3", "17C3", "17C3", "17C3", "17C3", "17C3", "17C3", >> "17C3", "17C3", "17C3", "17C3", "17C3", "17C3", "17C3", "17C3", >> "17C3", "17C3", "17C3", "17C3", "17C3", "17C3", "17C3", "17C3", >> "17C3", "17C3", "17C3", "17C3", "17C3", "17C3", "17C3", "17C3", >> "17C3", "17C3", "17C3", "17C3", "17C3", "17C3", "17C3", "17C3", >> "17C3", "17C3", "17C3", "17C3", "17C3", "17C3", "17C3", "17C3", >> "17C3", "17C3", "17C3", "17C3", "17C3", "17C3", "17C3", "17C3", >> "17C3", "17C3", "17C3", "17C3", "17C3", "17C3", "17C3", "17C3", >> "17C3", "17C3", "17C3", "17C3", "17C3", "17C3", "17C3", "WCI1", >> "WCI1", "WCI1", "WCI1", "WCI1", "WCI1", "WCI1", "WCI1", "WCI1", >> "WCI1", "WCI1", "WCI1", "WCI1", "WCI1", "WCI1", "WCI1", "WCI1", >> "WCI1", "WCI1", "WCI1", "WCI1", "WCI1", "WCI1", "WCI1", "WCI1", >> "WCI1", "WCI1", "WCI1", "WCI1", "WCI1", "WCI1", "WCI1", "WCI1", >> "WCI1", "WCI1", "WCI1", "WCI1", "WCI1", "WCI1", "WCI1", "WCI1", >> "WCI1", "WCI1", "WCI1", "WCI1", "WCI1", "WCI1", "WCI1", "WCI1", >> "WCI1", "WCI1", "WCI1", "WCI1", "WCI1", "WCI1", "WCI1", "WCI1", >> "WCI1", "WCI1", "WCI1", "WCI1", "WCI1", "WCI1", "WCI1", "WCI1", >> "WCI1", "WCI1", "WCI1", "WCI1", "WCI1", "WCI1", "WCI1", "NY01", >> "NY01", "NY01", "NY01", "NY01", "NY01", "NY01", "NY01", "NY01", >> "NY01", "NY01", "NY01", "NY01", "NY01", "NY01", "NY01", "NY01", >> "NY01", "NY01", "NY01", "NY01", "NY01", "NY01", "NY01", "NY01", >> "NY01", "NY01", "NY01", "NY01", "NY01", "NY01", "NY01", "NY01", >> "NY01", "NY01", "NY01", "NY01", "NY01", "NY01", "NY01", "NY01", >> "NY01", "NY01", "NY01", "NY01", "NY01", "NY01", "NY01", "NY01", >> "NY01", "NY01", "NY01", "NY01", "NY01", "NY01", "NY01", "NY01", >> "NY01", "NY01", "NY01", "NY01", "NY01", "NY01", "NY01", "NY01", >> "NY01", "NY01", "NY01", "NY01", "NY01", "NY01", "EX01", "EX01", >> "EX01", "EX01", "EX01", "EX01", "EX01", "EX01", "EX01", "EX01", >> "EX01", "EX01", "EX01", "EX01", "EX01", "EX01", "EX01", "EX01", >> "EX01", "EX01", "EX01", "EX01", "EX01", "EX01", "EX01", "EX01", >> "EX01", "EX01", "EX01", "EX01", "EX01", "EX01", "EX01", "EX01", >> "EX01", "MAND", "MAND", "MAND", "MAND", "MAND", "MAND", "MAND", >> "MAND", "MAND", "MAND", "MAND", "MAND", "MAND", "MAND", "MAND", >> "MAND", "MAND", "MAND", "MAND", "MAND", "MAND", "MAND", "MAND", >> "MAND", "MAND", "MAND", "MAND", "MAND", "MAND", "MAND", "MAND", >> "MAND", "MAND", "MAND", "MAND", "MAND", "MAND", "MAND", "MAND", >> "MAND", "MAND", "MAND", "MAND", "MAND", "MAND", "MAND", "MAND", >> "MAND", "MAND", "MAND", "MAND", "MAND", "MAND", "MAND", "MAND", >> "MAND", "MAND", "MAND", "MAND", "MAND", "MAND", "MAND", "MAND", >> "MAND", "MAND", "MAND", "MAND", "MAND", "MAND", "MAND", "MAND", >> "MAND", "28EP", "28EP", "28EP", "28EP", "28EP", "28EP", "28EP", >> "28EP", "28EP", "28EP", "28EP", "28EP", "28EP", "28EP", "28EP", >> "28EP", "28EP", "28EP", "28EP", "28EP", "28EP", "28EP", "28EP", >> "28EP", "28EP", "28EP", "28EP", "28EP", "28EP", "28EP", "28EP", >> "28EP", "28EP", "28EP", "28EP", "28EP", "28EP", "28EP", "28EP", >> "28EP", "28EP", "28EP", "28EP", "28EP", "28EP", "28EP", "28EP", >> "28EP", "28EP", "28EP", "28EP", "28EP", "28EP", "28EP", "28EP", >> "28EP", "28EP", "28EP", "28EP", "28EP", "28EP", "28EP", "28EP", >> "28EP", "28EP", "28EP", "28EP", "28EP", "28EP", "28EP", "28EP", >> "28EP", "EX01", "EX01", "EX01", "EX01", "EX01", "EX01", "EX01", >> "EX01", "EX01", "EX01", "EX01", "EX01", "EX01", "EX01", "EX01", >> "EX01", "EX01", "EX01", "EX01", "EX01", "EX01", "EX01", "EX01", >> "EX01", "EX01", "EX01", "EX01", "EX01", "EX01", "EX01", "EX01", >> "EX01", "EX01", "EX01", "EX01", "EX01", "EX01"), set = c("seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta", "seta", "seta", "seta", "seta", "seta", "seta", >> "seta", "seta"), ent_num = c(23L, 14L, 43L, 30L, 44L, 60L, 17L, >> 34L, 41L, 40L, 9L, 36L, 38L, 19L, 61L, 51L, 45L, 42L, 3L, 39L, >> 21L, 11L, 12L, 7L, 35L, 5L, 70L, 47L, 28L, 16L, 72L, 13L, 49L, >> 67L, 56L, 32L, 27L, 46L, 24L, 63L, 15L, 66L, 26L, 29L, 48L, 1L, >> 54L, 37L, 2L, 50L, 52L, 31L, 33L, 25L, 6L, 69L, 53L, 10L, 18L, >> 55L, 59L, 4L, 58L, 22L, 20L, 64L, 71L, 57L, 11L, 17L, 43L, 13L, >> 1L, 16L, 34L, 47L, 52L, 72L, 59L, 22L, 54L, 18L, 25L, 61L, 56L, >> 41L, 27L, 14L, 49L, 19L, 29L, 31L, 64L, 6L, 53L, 35L, 37L, 67L, >> 39L, 51L, 40L, 15L, 69L, 60L, 38L, 4L, 23L, 3L, 48L, 32L, 42L, >> 24L, 28L, 33L, 57L, 8L, 21L, 46L, 7L, 30L, 45L, 2L, 63L, 36L, >> 68L, 20L, 66L, 70L, 58L, 5L, 10L, 12L, 62L, 50L, 71L, 9L, 55L, >> 26L, 44L, 65L, 14L, 63L, 46L, 58L, 62L, 19L, 59L, 2L, 5L, 6L, >> 40L, 21L, 44L, 37L, 55L, 35L, 71L, 56L, 10L, 36L, 53L, 25L, 61L, >> 12L, 26L, 23L, 4L, 13L, 28L, 38L, 57L, 54L, 72L, 48L, 66L, 9L, >> 70L, 15L, 39L, 60L, 17L, 34L, 51L, 67L, 42L, 49L, 31L, 30L, 3L, >> 18L, 65L, 32L, 27L, 52L, 22L, 11L, 47L, 64L, 8L, 43L, 41L, 16L, >> 20L, 33L, 7L, 50L, 68L, 24L, 1L, 69L, 45L, 29L, 37L, 30L, 55L, >> 54L, 43L, 32L, 21L, 27L, 33L, 40L, 67L, 57L, 68L, 31L, 17L, 13L, >> 6L, 62L, 19L, 22L, 3L, 10L, 44L, 34L, 69L, 70L, 4L, 1L, 25L, >> 11L, 51L, 5L, 63L, 71L, 12L, 38L, 58L, 39L, 49L, 59L, 56L, 65L, >> 2L, 64L, 8L, 35L, 46L, 45L, 29L, 53L, 36L, 42L, 23L, 18L, 50L, >> 26L, 14L, 48L, 66L, 20L, 24L, 7L, 15L, 53L, 22L, 39L, 20L, 60L, >> 59L, 43L, 19L, 41L, 6L, 62L, 1L, 55L, 34L, 50L, 38L, 40L, 44L, >> 4L, 46L, 29L, 65L, 57L, 48L, 33L, 69L, 14L, 35L, 67L, 72L, 54L, >> 3L, 49L, 2L, 12L, 18L, 30L, 10L, 70L, 31L, 15L, 63L, 71L, 21L, >> 45L, 28L, 56L, 27L, 64L, 61L, 51L, 5L, 24L, 68L, 25L, 66L, 16L, >> 36L, 58L, 37L, 52L, 26L, 9L, 42L, 7L, 11L, 8L, 32L, 23L, 13L, >> 47L, 17L, 61L, 72L, 47L, 60L, 16L, 9L, 28L, 52L, 41L, 1L, 61L, >> 6L, 23L, 58L, 63L, 25L, 28L, 30L, 36L, 62L, 9L, 32L, 19L, 31L, >> 56L, 45L, 2L, 22L, 27L, 40L, 14L, 11L, 50L, 13L, 70L, 20L, 64L, >> 39L, 26L, 21L, 43L, 29L, 35L, 54L, 52L, 37L, 17L, 16L, 72L, 48L, >> 12L, 18L, 44L, 42L, 49L, 68L, 5L, 55L, 69L, 51L, 66L, 59L, 53L, >> 15L, 71L, 41L, 57L, 4L, 60L, 8L, 7L, 33L, 34L, 24L, 10L, 67L, >> 47L, 38L, 3L, 65L, 46L, 20L, 34L, 71L, 1L, 33L, 57L, 13L, 21L, >> 66L, 29L, 3L, 61L, 69L, 24L, 62L, 39L, 49L, 47L, 31L, 53L, 52L, >> 43L, 17L, 7L, 8L, 12L, 60L, 63L, 50L, 2L, 51L, 46L, 10L, 23L, >> 48L, 11L, 26L, 40L, 70L, 42L, 59L, 15L, 56L, 58L, 27L, 6L, 35L, >> 4L, 37L, 5L, 65L, 44L, 28L, 14L, 32L, 36L, 45L, 9L, 18L, 55L, >> 68L, 30L, 54L, 41L, 25L, 22L, 38L, 16L, 67L, 64L, 19L, 72L, 68L, >> 28L, 33L, 15L, 51L, 4L, 47L, 36L, 8L, 57L, 48L, 1L, 52L, 39L, >> 32L, 50L, 13L, 30L, 63L, 2L, 9L, 62L, 22L, 6L, 61L, 16L, 53L, >> 38L, 37L, 20L, 69L, 44L, 56L, 29L, 26L, 14L, 17L, 46L, 66L, 58L, >> 42L, 60L, 19L, 45L, 3L, 59L, 70L, 31L, 24L, 55L, 40L, 43L, 25L, >> 65L, 12L, 67L, 21L, 7L, 27L, 49L, 72L, 54L, 41L, 23L, 34L, 5L, >> 64L, 35L, 18L, 71L, 11L, 24L, 19L, 38L, 14L, 4L, 56L, 5L, 54L, >> 34L, 64L, 55L, 33L, 69L, 71L, 52L, 61L, 48L, 23L, 43L, 41L, 20L, >> 39L, 11L, 63L, 36L, 22L, 9L, 25L, 27L, 51L, 53L, 37L, 57L, 13L, >> 18L, 64L, 22L, 53L, 16L, 5L, 28L, 60L, 31L, 11L, 29L, 45L, 59L, >> 72L, 49L, 67L, 13L, 20L, 3L, 42L, 44L, 69L, 33L, 38L, 15L, 70L, >> 35L, 48L, 26L, 56L, 19L, 39L, 43L, 40L, 14L, 2L, 68L, 51L, 12L, >> 47L, 10L, 55L, 23L, 4L, 71L, 41L, 50L, 7L, 24L, 61L, 27L, 54L, >> 46L, 58L, 37L, 66L, 57L, 1L, 36L, 32L, 18L, 62L, 9L, 30L, 21L, >> 6L, 52L, 8L, 65L, 17L, 25L, 63L, 34L, 65L, 22L, 56L, 9L, 7L, >> 11L, 31L, 4L, 63L, 29L, 61L, 54L, 12L, 62L, 59L, 5L, 23L, 53L, >> 36L, 24L, 35L, 66L, 49L, 72L, 18L, 70L, 32L, 43L, 20L, 45L, 34L, >> 46L, 28L, 6L, 44L, 71L, 39L, 13L, 27L, 1L, 58L, 30L, 68L, 17L, >> 33L, 26L, 57L, 15L, 21L, 52L, 48L, 42L, 16L, 40L, 38L, 8L, 69L, >> 2L, 51L, 67L, 55L, 64L, 47L, 60L, 19L, 41L, 50L, 3L, 14L, 25L, >> 10L, 37L, 6L, 15L, 45L, 49L, 8L, 17L, 50L, 16L, 58L, 72L, 26L, >> 60L, 7L, 32L, 1L, 46L, 66L, 68L, 62L, 47L, 35L, 70L, 10L, 31L, >> 65L, 2L, 3L, 21L, 12L, 30L, 40L, 28L, 59L, 42L, 67L, 44L, 29L >> ), rep_num = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >> 1L, 1L, 1L, 1L, 1L, 1L, 1L), lp = c(NA, 41.64, 38.8, 44.45, 40.54, >> 38.54, 41.94, 39.6, 37.39, 40.95, 38.45, 43.47, 41.66, 40.91, >> 42.68, 43.12, 38.22, 40.95, 46.24, 42.95, 38.95, 39.88, 40.57, >> 40.13, 38.57, 45.45, 40.78, 43.52, 39.75, 39.93, 40.35, 37.6, >> 37.7, 43.05, 43.32, 41.31, 39.03, 42.5, 43.18, 41.32, 39.58, >> 40.62, 39.64, 39.85, 38.75, 40.18, 41.44, 40.5, 40.87, 40.75, >> 38.37, 40.26, 35.11, 40.89, 41.67, 38.87, 37.32, 35.85, 38.25, >> 42.21, 43.15, 38.69, 38.8, 38.77, 37.98, 39.8, 33.37, 40.09, >> 42.87, 44.07, 43.78, 42.47, 42.8, 41.1, 41.17, 44.07, 44.24, >> 43.16, 46.12, 42.64, 43.92, 42.16, 43.69, 42.89, 42.63, 43.58, >> 44.4, 43.17, 43.3, 42.5, 42.6, 44.05, 44.63, 43.09, 45.17, 45.17, >> 42.78, 42.38, 45.76, 42.16, 44.22, 43.67, 40.45, 41.95, 42.49, >> 41.98, 45.15, 46.01, 38.51, 43.08, 45.19, 44.53, 43.14, 39.93, >> 46.84, 43.59, 41.68, 43.7, 44.63, 44.02, 42.07, 43.88, 43.2, >> 46.2, 40.84, 39.14, 43.89, 42.58, 41.53, 45.32, 39.56, 43.77, >> 45.45, 45.16, 43.4, 40.08, 42.27, 43.74, 43.77, 41.31, 41.38, >> 45.01, 45.76, 40.4, 48.39, 46.27, 15.44, 44.76, 47.45, 44.87, >> 46.8, 41.83, 43.03, 43.96, 42.51, 45.06, 45.55, 44.9, 42.47, >> 44.9, 45, 44.77, 43.79, 44.26, 44.57, 44.4, 43.42, 42.31, 47.18, >> 43.83, 45.72, 44.83, 44.96, 40.28, 42.85, 41.23, 45.23, 47.09, >> 43.61, 42.69, 46.27, 45.16, 44.14, 43.34, 45.97, 43.81, 43.01, >> 39.82, 45.91, 45.97, 43.61, 45.12, 46.37, 42.67, 42.47, 45.86, >> 44.19, 44.46, 42.64, 43.95, 44.93, 40.33, 42.75, 39.92, 44.17, >> 44.49, 41.51, 42.43, 44.14, 40.5, 41.29, 44.89, 37.98, 39.02, >> 39.62, 42.13, 39.03, 44.16, 39.15, 41.49, 42.63, 40.11, 39.97, >> 42.85, 35.98, 39.45, 40.99, 42.44, 42.11, 37.36, 40.63, 40.69, >> 43.57, 39.04, 39.3, 42.19, 36.88, 40.39, 37.78, 38.6, 40.2, 40.98, >> 36.58, 43.59, 42.49, 39.96, 39.84, 40.43, 38.94, 42.72, 39.43, >> 42.13, 40.36, 40.58, 40.01, 42.17, 42.17, 41.35, 43.27, 40.15, >> 39.76, 40.94, 40.87, 42.32, 41.81, 41.97, 43.72, 41.32, 40.83, >> 37.64, 41.03, 38.98, 40.61, 41.17, 41.96, 40.07, 38.48, 42.85, >> 38.68, 39.09, 42.16, 38.14, 37.99, 41.06, 37.4, 41.88, 39.35, >> 39.73, 38.38, 41.34, 40.67, 40.89, 39.28, 37.59, 39.3, 39.72, >> 40.79, 39.42, 34.5, 37.61, 37.76, 40.24, 41.17, 41.24, 42.14, >> 42.53, 39.71, 39.44, 40.19, 42.51, 40.15, 36.26, 37.48, 40.43, >> 37.5, 42.11, 41.44, 40.29, 39.75, 37.58, 41.25, 40.39, 41.63, >> 42.18, 43.71, 38.69, 43.47, 41.98, 35.51, 42.22, 38.51, 40.17, >> 39.4, 39.54, 41.7, 40.93, 40.01, 35.97, 41.44, 41.89, 40.9, 39.95, >> 40.69, 43.18, 37.03, 39.91, 36.75, 42.75, 41.61, 42.6, 38.55, >> 42.84, 38.41, 42.55, 41.44, 41.03, 40, 41.35, 42.18, 42.66, 40.06, >> 42.48, 41.33, 41.92, 40.88, 40.99, 42.78, 38.26, 40.81, 39.95, >> 37.89, 40.81, 38.33, 39.33, 39.67, 40.12, 41.75, 39.81, 41.92, >> 44.59, 37.8, 40.54, 40.49, 41.82, 42.13, 39.93, 39.94, 42.54, >> 41.74, 43.06, 41.72, 41.27, 39.42, 42.07, 40.06, 42.1, 38.91, >> 39.28, 42.94, 39.94, 41.86, 38.56, 38.15, 41.47, 42.34, 38.14, >> 40.19, 40.2, 43.01, 42.35, 40.89, 41.44, 42.89, 44.49, 39.1, >> 38.42, 37.44, 43.28, 37.96, 40.56, 41.53, 41.59, 41.45, 42.55, >> 40.6, 44.04, 39.31, 41.08, 37.14, 40.03, 41.49, 40.82, 38.47, >> 43.43, 38.82, 40.4, 41.09, 42.26, 41.85, 41.13, 37.27, 41.97, >> 45.24, 43.35, 40.53, 43.14, 39.71, 42.77, 42.2, 42.13, 41.91, >> 42.59, 41.99, 42.11, 40.43, 40.02, 43.53, 43.01, 39.55, 43.2, >> 39.95, 42.51, 42, 42.26, 42.39, 42.64, 39.9, 41.89, 43.05, 41.02, >> 41.95, 39.92, 43.42, 44.01, 42.6, 40.84, 42.86, 44.31, 42.24, >> 41.25, 42.38, 44.99, 41.24, 43.97, 41.12, 37.88, 41.53, 41.56, >> 39.18, 40.83, 42.96, 41.92, 43.86, 42.48, 42.65, 43.3, 41.99, >> 42.51, 40.65, 42.77, 34.71, 40.97, 38.15, 39.76, 36.74, 37.95, >> 39.17, 38.22, 39.31, 43.57, 37.21, 39.35, 42.37, 42.01, 42.39, >> 43.21, 36.91, 36.69, 41.07, 41.91, 34.63, 40.61, 36.23, 38.12, >> 40.76, 39.14, 41.81, 39.14, 41.04, 37.32, 40.83, 40.81, 38.29, >> 39.61, 40.96, 40.71, 40.47, 38.64, 39.76, 38.19, 39.05, 38.04, >> 41.14, 38.35, 42.3, 34.44, 40.93, 39.3, 41.44, 38.3, 42.74, 36.66, >> 40.02, 36.62, 40.48, 41.72, 41.23, 41.81, 42.07, 40.22, 37.83, >> 36.54, 37.77, 40.49, 38.65, 43.2, 43.32, 40.67, 41.95, 36.11, >> 39.28, 42.38, 40.35, 40.3, 43.48, 40.55, 40.54, 44.03, 41.94, >> 37.97, 41.98, 41.53, 38.19, 38.66, 41.18, 41.95, 42.53, 38.7, >> 44.55, 42.39, 41.55, 38.46, 42.27, 42.19, 41.95, 41.81, 39.81, >> 38.12, 42.94, 42.99, 38.85, 41.26, 43.13, 44.21, 38.54, 44.02, >> 43.46, 41.64, 42.06, 42.11, 42.34, 41.86, 37.91, 40.89, 40.5, >> 39.54, 37.87, 40.86, 41.36, 41.77, 42.03, 39.15, 40.04, 44.11, >> 41.34, 42.97, 38.42, 37.28, 41.04, 41.48, 38.82, 41.94, 37.95, >> 40.9, 40.39, 40.31, 43.19, 41.22, 41.49, 41.25, 40.07, 36.7, >> 39.97, 39.99, 41.7, 37.09, 42.58, 43.01, 37.7, 41.81, 39.99, >> 42.95, 43.19, 42.69, 41.5, 40.64, 43.24, 41.14, 41.21, 41.29, >> 41.43, 44.21, 38.52, 42.54, 40.54, 42.49, 43.2, 38.12, 40.08, >> 39.02, 41.45, 42.33, 41.11, 38.93, 41.63, 44.22, 41.41, 39.08, >> 40.9, 41.1, 43.88, 40.96, 46.75, 47.54, 40.35, 41.97, 44.94, >> 44.91, 44.66, 44.5, 44.4, 46.4, 47.97, 46.05, 45.57, 42.83, 41.48, >> 47.48, 45.43, 41.98, 43.14, 45.6, 44.78, 45.45, 45.69, 44.82, >> 44.24, 41.14, 43.14, 46.61, 43.92, 43.56, 43.68, 45.37, 45.15, >> 40.76, 43.78, 44.67, 41.36, 41.4, 40.97, 41.87, 39.83, 43.8, >> 48.36, 44.28, 43.29, 44.69, 43.26, 43.35, 44.34, 45.08, 42.26, >> 39.7, 42.4, 44.03, 43.22, 42.71, 45.89, 44.89, 44.81, 42.59, >> 40.85, 43.82, 44.85, 47.47, 43.64, 42.65, 45.67, 43.24, 42.33, >> 40.61, 38.3, 39.84, 41.08, 42.33, 44.44, 40.85, 39.58, 42.55, >> 41.75, 39.44, 41.79, 39.31, 41.34, 42.76, 40.79, 37.31, 42.85, >> 42.88, 42.01, 44.63, 38.85, 41.13, 40.43, 41.34, 43.14, 40.58, >> 42.21, 38.94, 44.88, 42.33, 42.61, 41.88, 41.13, 41.83, 42.8)), .Names >> c("field", >> "set", "ent_num", "rep_num", "lp"), class = "data.frame", row.names >> c(NA, >> -787L)) >> >> # session info >> >> R version 3.4.1 (2017-06-30) >> >> Platform: i386-w64-mingw32/i386 (32-bit) >> >> Running under: Windows 7 x64 (build 7601) Service Pack 1 >> >> >> >> Matrix products: default >> >> >> >> locale: >> >> [1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United >> States.1252 >> >> [3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C >> >> [5] LC_TIME=English_United States.1252 >> >> >> >> attached base packages: >> >> [1] stats graphics grDevices utils datasets methods base >> >> >> >> other attached packages: >> >> [1] bindrcpp_0.2 tidyr_0.6.3 dplyr_0.7.4 >> >> >> >> loaded via a namespace (and not attached): >> >> [1] compiler_3.4.1 magrittr_1.5 assertthat_0.2.0 R6_2.2.2 >> tools_3.4.1 >> >> [6] glue_1.1.1 tibble_1.3.3 Rcpp_0.12.11 stringi_1.1.5 >> pkgconfig_2.0.1 >> >> [11] rlang_0.1.2 bindr_0.1 >> >> This email and any attachments were sent from a Monsanto email account >> and may contain confidential and/or privileged information. 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