I am performing WGCNA analysis on my RNAseq dataset for the first time and getting this error message:

        > net = blockwiseModules(expression, power = 6,
    +                        TOMType = "unsigned", minModuleSize = 30,
    +                        reassignThreshold = 0, mergeCutHeight = 0.25,
    +                        numericLabels = TRUE, pamRespectsDendro = FALSE,
    +                        saveTOMs = TRUE,
    +                        saveTOMFileBase = "SW_TOM", 
    +                        verbose = 3)
     Calculating module eigengenes block-wise from all genes
       Flagging genes and samples with too many missing values...
        ..step 1
      ..Excluding 722 genes from the calculation due to too many missing samples or zero variance.
        ..step 2
     ....pre-clustering genes to determine blocks..
       Projective K-means:
       ..k-means clustering..
       ..merging smaller clusters...
    Block sizes:
    gBlocks
       1    2    3    4    5 
    4999 4998 4945 4424 4306 
     ..Working on block 1 .
    Error in blockwiseModules(expression, power = 6, TOMType = "unsigned",  : 
      REAL() can only be applied to a 'numeric', not a 'integer'

`

The code I used is given below. How do I solve this error?

    library(WGCNA)
#Setting string not as factor
options(stringsAsFactors = FALSE)
#Enable multithread
enableWGCNAThreads()
#Reading the raw data (rows are the sample and columns the genes)
SWexpressiondata = read.csv("edgeR_normalized.csv")

#Create a new format expression data - remove gene name column
expression = as.data.frame(expressiondata[, -c(1)]) 
expression = t(expression)

#Column 1 -  gene names
colnames(expression) = expressiondata$genes
rownames(expression) = names(expressiondata)[-c(1)]

#Group data in a dendogram to check outliers
sampleTree = hclust(dist(expression), method = "average")
dev.off()
sizeGrWindow(12,9)
par(cex = 0.6)
par(mar = c(0,4,2,0))
plot(sampleTree, main = "Sample clustering to detect outliers", sub="", xlab="", cex.lab = 1.5, 
     cex.axis = 1.5, cex.main = 2)


# Choose a set of soft-thresholding powers
powers = c(c(1:10), seq(from = 12, to = 20, by = 2))
# Call the network topology analysis function
sft = pickSoftThreshold(expression,             # <= Input data
  #blockSize = 30,
  powerVector = powers,
  verbose = 5)
# Plot the results:
sizeGrWindow(9, 5)
par(mfrow = c(1,2));
cex1 = 0.9;

# Scale-free topology fit index as a function of the soft-thresholding power
plot(sft$fitIndices[, 1],
     -sign(sft$fitIndices[, 3]) * sft$fitIndices[, 2],
     xlab = "Soft Threshold (power)",
     ylab = "Scale Free Topology Model Fit, signed R^2",type="n",
     main = paste("Scale independence"))

text(sft$fitIndices[, 1],
     -sign(sft$fitIndices[, 3]) * sft$fitIndices[, 2],
     labels = powers, cex = cex1, col = "red")

# this line corresponds to using an R^2 cut-off of h
abline(h = 0.90, col = "red")
# Mean connectivity as a function of the soft-thresholding power
plot(sft$fitIndices[, 1],
     sft$fitIndices[, 5],
     xlab = "Soft Threshold (power)",
     ylab = "Mean Connectivity",
     type = "n",
     main = paste("Mean connectivity"))
text(sft$fitIndices[, 1],
     sft$fitIndices[, 5],
     labels = powers,
     cex = cex1, col = "red")

> sessionInfo()
R version 3.6.3 (2020-02-29)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19043)

Matrix products: default

locale:
[1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United States.1252   
[3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C                          
[5] LC_TIME=English_United States.1252    

attached base packages:
[1] parallel  stats4    stats     graphics  grDevices utils     datasets  methods  
[9] base     

other attached packages:
 [1] magrittr_2.0.1         edgeR_3.28.1           limma_3.42.2          
 [4] WGCNA_1.70-3           fastcluster_1.2.3      dynamicTreeCut_1.63-1 
 [7] ggplot2_3.3.5          org.Mm.eg.db_3.10.0    AnnotationDbi_1.48.0  
[10] IRanges_2.20.2         S4Vectors_0.24.4       Biobase_2.46.0        
[13] BiocGenerics_0.32.0    DOSE_3.12.0            clusterProfiler_3.14.3
[16] dplyr_1.0.7           

loaded via a namespace (and not attached):
  [1] fgsea_1.12.0          colorspace_2.0-2      ellipsis_0.3.2       
  [4] ggridges_0.5.3        qvalue_2.18.0         htmlTable_2.2.1      
  [7] base64enc_0.1-3       rstudioapi_0.13       farver_2.1.0         
 [10] urltools_1.7.3        graphlayouts_0.7.1    ggrepel_0.9.1        
 [13] bit64_4.0.5           fansi_0.5.0           xml2_1.3.2           
 [16] codetools_0.2-18      splines_3.6.3         doParallel_1.0.16    
 [19] impute_1.60.0         cachem_1.0.5          GOSemSim_2.12.1      
 [22] knitr_1.33            polyclip_1.10-0       Formula_1.2-4        
 [25] jsonlite_1.7.2        cluster_2.1.2         GO.db_3.10.0         
 [28] png_0.1-7             ggforce_0.3.3         BiocManager_1.30.16  
 [31] compiler_3.6.3        httr_1.4.2            backports_1.2.1      
 [34] rvcheck_0.1.8         assertthat_0.2.1      Matrix_1.3-4         
 [37] fastmap_1.1.0         tweenr_1.0.2          htmltools_0.5.1.1    
 [40] prettyunits_1.1.1     tools_3.6.3           igraph_1.2.6         
 [43] gtable_0.3.0          glue_1.4.2            reshape2_1.4.4       
 [46] DO.db_2.9             fastmatch_1.1-0       Rcpp_1.0.7           
 [49] enrichplot_1.6.1      vctrs_0.3.8           preprocessCore_1.48.0
 [52] iterators_1.0.13      ggraph_2.0.5          xfun_0.24            
 [55] stringr_1.4.0         lifecycle_1.0.0       europepmc_0.4        
 [58] MASS_7.3-54           scales_1.1.1          tidygraph_1.2.0      
 [61] hms_1.1.0             RColorBrewer_1.1-2    memoise_2.0.0        
 [64] gridExtra_2.3         triebeard_0.3.0       rpart_4.1-15         
 [67] latticeExtra_0.6-29   stringi_1.7.2         RSQLite_2.2.7        
 [70] foreach_1.5.1         checkmate_2.0.0       BiocParallel_1.20.1  
 [73] rlang_0.4.11          pkgconfig_2.0.3       matrixStats_0.59.0   
 [76] lattice_0.20-41       purrr_0.3.4           htmlwidgets_1.5.3    
 [79] labeling_0.4.2        cowplot_1.1.1         bit_4.0.4            
 [82] tidyselect_1.1.1      plyr_1.8.6            R6_2.5.0             
 [85] generics_0.1.0        Hmisc_4.5-0           DBI_1.1.1            
 [88] pillar_1.6.1          foreign_0.8-75        withr_2.4.2          
 [91] survival_3.2-11       nnet_7.3-16           tibble_3.1.2         
 [94] crayon_1.4.1          utf8_1.2.1            viridis_0.6.1        
 [97] jpeg_0.1-8.1          progress_1.2.2        locfit_1.5-9.4       
[100] grid_3.6.3            data.table_1.14.2     blob_1.2.1           
[103] digest_0.6.27         tidyr_1.1.3           gridGraphics_0.5-1   
[106] munsell_0.5.0         viridisLite_0.4.0     ggplotify_0.0.7