Factor-mapping protocol

# version information: v0.3
# date of update: 4/Apr/2018

library(wordcloud)  # "wordcloud" is the name of an R package.

data_pre <- read.csv("filename.csv")  # "filename" is the name of the csv file

# An example of auto-scaled data prepared for Factor-mapping approach
# Sample No. |Variable 1 |Variable 2 |Variable 3 | ...
# 1          |0.7        |-0.9       |0.3        | ...
# 2          |0.9        |-0.8       |-0.5       | ...
# 3          |0.2        |-0.6       |0.2        | ...
# ...        |...        |...        |...        | ...
# In this case, you need to delete the first column (Sample No.) using the next command
# data_pre <- data_pre[,-1]

# Gain a Spearman's correlation-coefficient matrix
data_pair <- cor(data_pre, method = "s")

# Convert the correlation matrix to a distant matrix
dis_pair_mat <- matrix(1, nrow = nrow(data_pair), ncol = ncol(data_pair)) - data_pair
dis_pair     <- dist(dis_pair_mat, method ="euclidean")

# Map the distant matrix using multidimensional scaling (MDS) 
data_map <- cmdscale(dis_pair)
plot(data_map 
     , type = "n"
     , xlab = "Dimension 1"
     , ylab = "Dimension 2"
)
textplot(x = data_map[,1]
         , y = data_map[,2]
         , new = F
         , words = rownames(data_map)
         , col = c("color name for variable 1", ...)
)