Rmarkdown - code run ok at script, but when knit gives error - html

Question

I have one Rmarkdown document, that was given to me and worked fine with the person who given to me.
But when I do with my datas, I dont know what's going on, when I run the exactly code with script document works well, but when I try to knit to html, give me an error.
bhv_df <- plyr::ddply(bhv_df, ~segmentid, function(d){
d= bhv_df[bhv_df$segmentid == bhv_df$segmentid[1],
# predictions are made based on the mid time between start and end of the message
predObj <- crawl::crwPredict(object.crwFit = crawl_models_list[[d$segmentid[1]]], predTime = d$MidTime, speedEst=TRUE, flat=TRUE)
predObj_dives <- predObj[predObj$locType == "p",]
# reproject into lat/long, because the crawl models have been built in a mercator pacific centered CRS
coord_points <- predObj_dives
coordinates(coord_points) =~ mu.x + mu.y
proj4string(coord_points) <- CRS("+proj=merc +lon_0=0 +k=1 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs")
predObj_dives[c("lon", "lat")] <- coordinates(spTransform(coord_points, CRS("+proj=longlat +datum=WGS84")))
# calculate the lon360
return(cbind(d, predObj_dives[c("mu.x", "mu.y", "lon", "lat")]))
})
#load("./Outputs/crawl_argos_df.RData")
######### MERGE DIVE DATASET with CRAWL-derived MOUVEMENT DATA
bhv_df <- plyr::ddply(bhv_df, ~segmentid, function(d){ # for each tag dive data
cr <- crawl_argos_df[crawl_argos_df$segmentid == d$segmentid[1], ] # select the corresponding crawl track
fl <- filtered_argos_df_crawl[filtered_argos_df_crawl$segmentid == d$segmentid[1], ] # select the corresponding filtered track
d[c("lq_closest_filt","dt_closest_filt", "dist_closest_filt", "rel.angle","speed")] <- NA # add empty columns
for (i in 1:nrow(d)){ # for each dive...
fl$dist_diff <- as.vector(spDists(x = as.matrix(d[i,c("lon", "lat")]), y = as.matrix(fl[c("lon", "lat")]), longlat = T)) # distance calculated in km
fl$dt_diff <- as.numeric(difftime(d$MidTime[i], fl$time, units = "hours"))
d[i, "dt_closest_filt"] <- min(abs(fl$dt_diff)) # select the filtered position closest in time to the dive, time in hours
d[i, "dist_closest_filt"] <- fl[which(abs(fl$dt_diff) == min(abs(fl$dt_diff))), "dist_diff"][1] # retrieve the distance from this filtered position to the dive location (predicted by crawl)
d[i, "lq_closest_filt"] <- fl[which(abs(fl$dt_diff) == min(abs(fl$dt_diff))), "lq"][1] # argos quality of closest filtered argos position
cr$diff <- as.numeric(difftime(d$MidTime[i], cr$time, units = "hours")) # look at the time diff between this dive and all positions recorded in crawl for that same tag
d[i, c("rel.angle","speed")] <- cr[abs(cr$diff) == min(abs(cr$diff)), c("rel.angle", "speed")] # select closest position recorded when dive occurred
}
return(d)
})
bhv_df$depth_bin <- cut(bhv_df$DepthMean, seq(0, 700, 50))
ggplot(bhv_df[bhv_df$What == "Dive" & bhv_df$depth_range == "deep" & bhv_df$DepthMean < 1000,], aes(x = lon, y = lat)) +
stat_contour(data = bathyNOAA_df, aes(x, y, z=z), binwidth = 500, color = "grey60", size = 0.2) +
geom_tile(data = bathyNOAA_df_shallow[bathyNOAA_df_shallow$z >= 0, ], aes(x, y), fill = "grey10") +
geom_jitter(aes(fill = -DepthMean, size = DepthMean), col="black", alpha=0.8, pch = 21, width=0.1) +
scale_fill_viridis(option = "magma", name = "Dive Depth (m)", direction = 1, begin = 0.2) +
xlab("Longitude") +
ylab("Latitude") +
coord_fixed(xlim = c(-50, -26), ylim = c(-55, -15), expand = F)
The error (is it just at beginning of the code, at 4 line):
Erro: unexpected symbol in:
" predObj <- crawl::crwPredict(object.crwFit = crawl_models_list[[d$segmentid1]], predTime = d$MidTime, speedEst=TRUE, flat=TRUE)
predObj_dives"
And this:
I tryed to see if have conflicts, but apparently dont have
> conflicts()
[1] "lines" "cividis" "inferno" "magma" "plasma" "viridis" "viridis.map"
[8] "summary" "days" "hours" "minutes" "origin" "seconds" "show"
[15] "years" "hour" "isoweek" "mday" "minute" "month" "quarter"
[22] "second" "wday" "week" "yday" "year" "coerce" "coerce"
[29] "plot" "show" "summary" "%>%" "%>%" "%>%" "between"
[36] "count" "first" "intersect" "last" "setdiff" "union" "%>%"
[43] "flatten" "map" "transpose" "%>%" "add_row" "as_data_frame" "as_tibble"
[50] "data_frame" "data_frame_" "frame_data" "glimpse" "lst" "lst_" "tbl_sum"
[57] "tibble" "tribble" "trunc_mat" "type_sum" "enexpr" "enexprs" "enquo"
[64] "enquos" "ensym" "ensyms" "expr" "quo" "quo_name" "quos"
[71] "sym" "syms" "vars" "filter" "lag" "lines" "plot"
[78] "as.raster" "data" "Arith" "coerce" "Compare" "initialize" "show"
[85] "as.difftime" "body<-" "date" "intersect" "kronecker" "merge" "Position"
[92] "setdiff" "setequal" "split" "subset" "summary" "union"
>
Someone know what's going on?
Thanks!

Answer 1

After many tries, I removed one part of the code, and everything worked well
# I removed: d= bhv_df[bhv_df$segmentid == bhv_df$segmentid[1],
bhv_df <- plyr::ddply(bhv_df, ~segmentid, function(d){
# predictions are made based on the mid time between start and end of the message
predObj <- crawl::crwPredict(object.crwFit = crawl_models_list[[d$segmentid[1]]], predTime = d$MidTime, speedEst=TRUE, flat=TRUE)
predObj_dives <- predObj[predObj$locType == "p",]
# reproject into lat/long, because the crawl models have been built in a mercator pacific centered CRS
coord_points <- predObj_dives
coordinates(coord_points) =~ mu.x + mu.y
proj4string(coord_points) <- CRS("+proj=merc +lon_0=0 +k=1 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs")
predObj_dives[c("lon", "lat")] <- coordinates(spTransform(coord_points, CRS("+proj=longlat +datum=WGS84")))
# calculate the lon360
return(cbind(d, predObj_dives[c("mu.x", "mu.y", "lon", "lat")]))
})
######### MERGE DIVE DATASET with CRAWL-derived MOUVEMENT DATA
bhv_df <- plyr::ddply(bhv_df, ~segmentid, function(d){ # for each tag dive data
cr <- crawl_argos_df[crawl_argos_df$segmentid == d$segmentid[1], ] # select the corresponding crawl track
fl <- filtered_argos_df_crawl[filtered_argos_df_crawl$segmentid == d$segmentid[1], ] # select the corresponding filtered track
d[c("lq_closest_filt","dt_closest_filt", "dist_closest_filt", "rel.angle","speed")] <- NA # add empty columns
for (i in 1:nrow(d)){ # for each dive...
fl$dist_diff <- as.vector(spDists(x = as.matrix(d[i,c("lon", "lat")]), y = as.matrix(fl[c("lon", "lat")]), longlat = T)) # distance calculated in km
fl$dt_diff <- as.numeric(difftime(d$MidTime[i], fl$time, units = "hours"))
d[i, "dt_closest_filt"] <- min(abs(fl$dt_diff)) # select the filtered position closest in time to the dive, time in hours
d[i, "dist_closest_filt"] <- fl[which(abs(fl$dt_diff) == min(abs(fl$dt_diff))), "dist_diff"][1] # retrieve the distance from this filtered position to the dive location (predicted by crawl)
d[i, "lq_closest_filt"] <- fl[which(abs(fl$dt_diff) == min(abs(fl$dt_diff))), "lq"][1] # argos quality of closest filtered argos position
cr$diff <- as.numeric(difftime(d$MidTime[i], cr$time, units = "hours")) # look at the time diff between this dive and all positions recorded in crawl for that same tag
d[i, c("rel.angle","speed")] <- cr[abs(cr$diff) == min(abs(cr$diff)), c("rel.angle", "speed")] # select closest position recorded when dive occurred
}
return(d)
}) #warnings probably due to RT being equal to NA at beginning and end of the track
Thank you