library(tidyverse) # for everything #set directory setwd("F:/ARTIKKELIT/ARTIKKELI4/r/results/hgam_m2_m2_genus") directory = getwd() #set factors response = c ("heig", "SLA", "seed", "LDMC", "leaA", "leaN", "leaP") predictors = c ("mois", "temp", "pH", "radi") #create dataframe varimp = data.frame() #specify files files = dir(pattern = "deviance", full.names = TRUE, ignore.case = TRUE) for (i in files){ #specify file file = read.csv(paste0 (directory,i)) df_temp <- data.frame(trait = substr(strsplit(i,"_")[[1]][1], 3, 1000), var = gsub(".csv", "", strsplit(i,"_")[[1]][3]), file %>% filter(model_name == "excluded_var1") %>% select(deviance)) varimp <- bind_rows(varimp, df_temp) } varimp %>% pivot_wider(id_cols = trait, names_from = var, values_from = deviance) %>% select(1,predictors) %>% mutate(trait = factor(trait, levels = response)) %>% arrange(trait) -> varimp write.csv(varimp, file = "F:/ARTIKKELIT/ARTIKKELI4/r/results/hgam_m2_m2_genus/varimp.csv", dec = ".", row.names = F)