Fail when reading population structure

[cyadrogarcia]

I am trying to run prepareEnv as follow:
`prepareEnv("WC_climate_data.csv", "BIOC_pop.csv",

•        separator = ";",
  

•        maxCorr = 1.0,
  

•        idName = 'ID',
  

•        genoFile = gdsFile,
  

•        numPc = 0.2,
  

•        mafThresh = 0.05,
  

•        missingnessThresh=0.1,
  

•        ldThresh=0.2,
  

•        numPop=2,
  

•        x ='lon',
  

•        y ='lat',
  

•        popStrCol = c("ligustica", "carnica"),
  

•        interactiveChecks=FALSE,
  

•        locationProj=4326)
  

`

But i am alway getting the following error:
"Checking correlation between kept env variables and population var. If correlation env-pop > 70%, the environmental variable will be printed here" Error in cor(env_kept[, j], as.numeric(popvect2[, i]), use = "complete.obs") : no complete element pairs In addition: Warning message: In is.data.frame(y) : NAs introduced by coercion

as I understood it seem to be that the enviromental dataset and the population structure dont have the same number of elements. However I loaded my enviromental dataset with read.csv and used str and length to check, and all the vectors appears to have the same length:

`env <- read.csv("/home/carlosgarcia-dora/ITALY/SAMBADAr/WC_climate_data.csv", sep = ";", dec = ",")

str(env)
'data.frame': 225 obs. of 19 variables:
$ ID : chr "I_3281_" "I_3282_" "I_3283_" "I_3284_" ...
$ pop_ID : chr "NorthWest" "NorthEast" "South" "NorthEast" ...
$ ecotype : chr "NorthWest" "NorthEast" "South" "NorthEast" ...
$ lat : num 46.2 44.4 40.8 45.5 42.9 ...
$ lon : num 9.85 11.33 14.83 10.94 12.63 ...
$ Mean_Temperature_of_Warmest_Quarter: num 14.9 22.5 21 21.6 22.2 ...
$ Mean_Temperature_of_Coldest_Quarter: num -1.45 3.92 6.78 2.77 5.6 ...
$ Precipitation_Seasonality : num 38.5 22 42.9 24.4 23.7 ...
$ Precipitation_of_Wettest_Quarter : int 300 266 344 250 229 141 227 426 219 249 ...
$ Precipitation_of_Driest_Quarter : int 115 171 97 138 136 76 74 107 145 149 ...
$ Precipitation_of_Warmest_Quarter : int 300 178 97 242 136 76 108 107 163 204 ...
$ Precipitation_of_Coldest_Quarter : int 115 171 301 138 157 97 151 333 152 158 ...
$ Mean_Diurnal_Range : num 10.24 9.29 8.34 9.27 10.84 ...
$ Isothermality : num 36.2 31.3 34.8 31.1 36.6 ...
$ Temperature_Annual_Range : num 28.3 29.7 24 29.8 29.6 ...
$ Mean_Temperature_of_Wettest_Quarter: num 14.9 13.7 11 17.5 14.5 ...
$ Mean_Temperature_of_Driest_Quarter : num -0.25 3.92 21.02 2.77 22.22 ...
$ ligustica : num 0.53 0.63 0.93 0.6 1 0.92 0.83 0.45 0.61 1 ...
$ carnica : num 0.47 0.37 0.07 0.4 0 0.08 0.17 0.55 0.39 0 ...
lengths <- sapply(env, length)
print(lengths)
ID pop_ID
225 225
ecotype lat
225 225
lon Mean_Temperature_of_Warmest_Quarter
225 225
Mean_Temperature_of_Coldest_Quarter Precipitation_Seasonality
225 225
Precipitation_of_Wettest_Quarter Precipitation_of_Driest_Quarter
225 225
Precipitation_of_Warmest_Quarter Precipitation_of_Coldest_Quarter
225 225
Mean_Diurnal_Range Isothermality
225 225
Temperature_Annual_Range Mean_Temperature_of_Wettest_Quarter
225 225
Mean_Temperature_of_Driest_Quarter ligustica
225 225
carnica
225
`
Really sorry to bother, Would you please bear with my ignorance on this issue?
Carlos