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| 814 | werner | 2 | ## Example for DBH-Distribution barcharts |
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| 4 | library(RSQLite) |
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| 5 | library(ggplot2) |
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| 6 | library(plyr) |
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| 7 | library(reshape) |
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| 8 | library(scales) |
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| 9 | |||
| 10 | cols.species <- c("abal"="#088A29", |
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| 11 | "acca"="#F3F781", |
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| 12 | "acpl"="#86B404", |
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| 13 | "acps"="#58FAD0", |
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| 14 | "algl"="#61210B", |
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| 15 | "alin"="#A4A4A4", |
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| 16 | "alvi"="#0B3B17", |
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| 17 | "bepe"="#2E64FE", |
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| 18 | "cabe"= "#F7BE81", |
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| 19 | "casa"="#A9F5A9", |
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| 20 | "coav"="#58D3F7", |
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| 21 | "fasy"="#2EFE2E", |
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| 22 | "frex"="#FF0000", |
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| 23 | "lade"="#8A4B08", |
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| 24 | "piab"="#FFFF00", |
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| 25 | "pice"="#FF8000", |
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| 26 | "pini"="#610B21", |
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| 27 | "pisy"="#B18904", |
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| 28 | "poni"="#000000", |
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| 29 | "potr"="#610B5E", |
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| 30 | "psme"="#DF0174", |
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| 31 | "qupe"="#F6CED8", |
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| 32 | "qupu"="#FA5882", |
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| 33 | "quro"="#B40431", |
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| 34 | "rops"="#F781D8", |
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| 35 | "saca"="#F5ECCE", |
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| 36 | "soar"="#E6E0F8", |
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| 37 | "soau"="#B40404", |
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| 38 | "tico"="#9F81F7", |
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| 39 | "tipl"="#8000FF", |
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| 40 | "ulgl"="#DF7401") |
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| 41 | |||
| 42 | ### custom function for creating the dbh-distribution charts |
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| 43 | ### the data is generated by the "Dynamic stand" output of iLand. |
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| 44 | barchart_graphs <-function(data, label) { |
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| 45 | ## extract dbh classes |
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| 46 | count_ha.sp=data.frame(species=data$species, dbh10=data$if_dbh_10_1_0_sum/100, dbh20=data$if_dbh_10_and_dbh_20_1_0_sum/100, dbh30=data$if_dbh_20_and_dbh_30_1_0_sum/100, dbh40=data$if_dbh_30_and_dbh_40_1_0_sum/100, dbh50=data$if_dbh_40_and_dbh_50_1_0_sum/100, dbh60=data$if_dbh_50_and_dbh_60_1_0_sum/100, dbh70=data$if_dbh_60_and_dbh_70_1_0_sum/100, dbh80=data$if_dbh_70_and_dbh_80_1_0_sum/100, dbh90=data$if_dbh_80_and_dbh_90_1_0_sum/100, dbh100=data$if_dbh_90_and_dbh_100_1_0_sum/100, dbh100plus=data$if_dbh_100_1_0_sum/100) |
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| 47 | # reshape (melt) |
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| 48 | count_ha.sp.melted <- melt.data.frame(count_ha.sp, id.vars="species", na.rm=FALSE) |
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| 49 | # and calculate a total |
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| 50 | count_ha.total <- colSums(count_ha.sp[,2:12]) |
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| 51 | # now the plotting: |
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| 52 | barplot(count_ha.total+1, log="y", main=paste("Stemnumbers", label), ylab="N (+1)") |
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| 53 | ## the ggplot |
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| 54 | ggplot(count_ha.sp.melted, aes(x=variable,y=value,group=variable,fill=species)) + |
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| 55 | geom_bar(stat="identity") + scale_fill_manual(values=cols.species)+ |
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| 56 | labs(xlab="dbh classes", ylab="Stem number", title=paste("dbh distribution", label)) |
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| 57 | } |
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| 58 | |||
| 59 | #### load data from the database ###### |
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| 60 | conn<-dbConnect("SQLite", dbname = paste("C:/Users/werner/Desktop/bare_soil_afi413_pnv_buffer.sqlite",sep="")) |
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| 61 | dyn.out=dbReadTable(conn,"dynamicstand") |
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| 62 | dbDisconnect(conn) |
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| 63 | |||
| 64 | summary(dyn.out) |
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| 65 | |||
| 66 | |||
| 67 | |||
| 68 | ### create charts for several timesteps ######## |
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| 69 | #data <- dyn.out[dyn.out$year == 50,] |
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| 70 | |||
| 71 | barchart_graphs(dyn.out[dyn.out$year == 50,], "50") |
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| 72 | barchart_graphs(dyn.out[dyn.out$year == 100,], "100") |
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| 73 | barchart_graphs(dyn.out[dyn.out$year == 500,], "500") |
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| 74 | barchart_graphs(dyn.out[dyn.out$year == 1000,], "1000") |
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| 76 |