FRG scalars
## StandID count m2 total weight
## 1 63437 1362536 1226282400 412127346 0.003306104
## 2 51690 999382 899443800 412127346 0.002424935
## 3 62761 880643 792578700 412127346 0.002136823
## 4 63431 851508 766357200 412127346 0.002066128
## 5 8380 779152 701236800 412127346 0.001890561
## 6 4689 743339 669005100 412127346 0.001803663## StandID count m2 total weight
## 1 64438 649516 584564400 36914631 0.017595083
## 2 40797 643370 579033000 36914631 0.017428591
## 3 37913 583398 525058200 36914631 0.015803978
## 4 37952 492879 443591100 36914631 0.013351860
## 5 40228 397812 358030800 36914631 0.010776540
## 6 40806 289181 260262900 36914631 0.007833777fig <- plot_ly(alpha = .6)
fig = fig %>% add_histogram(x = ~frg1.freq$StandID, name = 'frg1')
fig = fig %>% add_histogram(x = ~frg2.freq$StandID, name = 'frg2')
fig = fig %>% add_histogram(x = ~frg3.freq$StandID, name = 'frg3')
fig = fig %>% add_histogram(x = ~frg4.freq$StandID, name = 'frg4')
fig = fig %>% add_histogram(x = ~frg5.freq$StandID, name = 'frg5')
figfrg1.carb = AGB%>%
filter(StandID %in% frg1.freq$StandID)%>%
left_join(frg1.freq, AGB, by = 'StandID')%>%
filter(StandID != 0)%>%
#filter(count > 25)%>%
mutate(total = sum(count))%>%
mutate(weight = count / total)%>%
group_by(StandID, MgmtID)%>%
mutate(w.Carb = d.prop * weight)%>%
select(MgmtID, Year, d.prop, w.Carb, weight)%>%
mutate(FRG = 1)## Adding missing grouping variables: `StandID`frg2.carb = AGB%>%
filter(StandID %in% frg2.freq$StandID)%>%
left_join(frg2.freq, AGB, by = 'StandID')%>%
filter(StandID != 0)%>%
filter(count > 25)%>%
mutate(total = sum(count))%>%
mutate(weight = count / total)%>%
group_by(StandID, MgmtID)%>%
mutate(w.Carb = d.prop * weight)%>%
select(MgmtID, Year, d.prop, w.Carb, weight)%>%
mutate(FRG = 2)## Adding missing grouping variables: `StandID`frg3.carb = AGB%>%
filter(StandID %in% frg3.freq$StandID)%>%
left_join(frg3.freq, AGB, by = 'StandID')%>%
filter(StandID != 0)%>%
#filter(count > 25)%>%
mutate(total = sum(count))%>%
mutate(weight = count / total)%>%
group_by(StandID, MgmtID)%>%
mutate(w.Carb = d.prop * weight)%>%
select(MgmtID, Year, d.prop, w.Carb, weight)%>%
mutate(FRG = 3)## Adding missing grouping variables: `StandID`frg4.carb = AGB%>%
filter(StandID %in% frg4.freq$StandID)%>%
left_join(frg4.freq, AGB, by = 'StandID')%>%
filter(StandID != 0)%>%
#filter(count > 25)%>%
mutate(total = sum(count))%>%
mutate(weight = count / total)%>%
group_by(StandID, MgmtID)%>%
mutate(w.Carb = d.prop * weight)%>%
select(MgmtID, Year, d.prop, w.Carb, weight)%>%
mutate(FRG = 4)## Adding missing grouping variables: `StandID`frg5.carb = AGB%>%
filter(StandID %in% frg5.freq$StandID)%>%
left_join(frg5.freq, AGB, by = 'StandID')%>%
filter(StandID != 0)%>%
#filter(count > 25)%>%
mutate(total = sum(count))%>%
mutate(weight = count / total)%>%
group_by(StandID, MgmtID)%>%
mutate(w.Carb = d.prop * weight)%>%
select(MgmtID, Year, d.prop, w.Carb, weight)%>%
mutate(FRG = 5)## Adding missing grouping variables: `StandID`fig <- plot_ly(alpha = .8)
fig = fig %>% add_histogram(x = ~frg2.carb$w.Carb, name = 'FIC2 - Weighted')
fig = fig %>% add_histogram(x = ~frg2.carb$d.prop, name = 'FIC2 - Unweighted')
figfig <- plot_ly(alpha = .8)
fig = fig %>% add_histogram(x = ~frg3.carb$d.prop, name = 'FIC3')
fig = fig %>% add_histogram(x = ~frg2.carb$d.prop, name = 'FIC2')
fig = fig %>% add_histogram(x = ~frg1.carb$d.prop, name = 'FIC1')
fig = fig %>% add_histogram(x = ~frg4.carb$d.prop, name = 'FIC4')
fig = fig %>% add_histogram(x = ~frg5.carb$d.prop, name = 'FIC5')
fig