library(tm)
library(wordcloud)
library(SnowballC)
library(readxl)

# set your workspace to where the .csv file for the ENM2020 question responses is
setwd("")
sheet <- read.csv("ENM 2020 Questions (Responses) - Form Responses.csv")

dt <- Corpus(VectorSource(sheet$Please.provide.your.question.here.))
toSomething <- content_transformer(function (x , pattern, y) gsub(pattern, y, x))
dt <- tm_map(dt, toSomething, "specie", "species")
dt <- tm_map(dt, toSomething, "speciess", "species")

# Convert the text to lower case
dt <- tm_map(dt, content_transformer(tolower))
# Remove numbers
dt <- tm_map(dt, removeNumbers)
# Remove English common stopwords
dt <- tm_map(dt, removeWords, stopwords("english"))
# Remove your own stop word
# specify your stopwords as a character vector
dt <- tm_map(dt, removeWords, c("like","can","used","way","dear","one","best","use",
                                "question","need","week","different","will","questions",
                                "affect","understand","using","know","thank","run","well",
                                "also","really","type","due","consider","better","however",
                                "find","lot","possible","account","certain","much","new",
                                "many","useful","mentioned","think","case","particular","sorry",
                                "yes","put","file","specifically","depending","per","explained",
                                "remove","choosing","weeks","close","start","con","wondering",
                                "emilio","add","single","often","tell","big","sincerely",
                                "got","presented","long","far","probably","end","atte","taken",
                                "included","selecting","making","least","seen","looking",
                                "sometimes","others","provide","usually","actually","going",
                                "smaller","una","three","might","read","already","show",
                                "made","words","previous","los","las","rather","especially",
                                "interested","para","must","trying","try","quite","among",
                                "works","ones","thinking","second","instead","higher","something",
                                "last","given","sure","done","low","large","seems","taking",
                                "enough","found","able","less","bit","deal","little",
                                "now","maybe","within","ask","said","set","according","always",
                                "without","give","right","several","still","que","high","say",
                                "etc","great","take","may","considering","first","please",
                                "just","want","get","make","two","hello","good","thanks"))
# Remove punctuations
dt <- tm_map(dt, removePunctuation)
# Eliminate extra white spaces
dt <- tm_map(dt, stripWhitespace)

dtm <- TermDocumentMatrix(dt)
m <- as.matrix(dtm)
v <- sort(rowSums(m),decreasing=TRUE)
d <- data.frame(word = names(v),freq=v)
set.seed(1234)
wordcloud(words = d$word, freq = d$freq, min.freq = 5,
          max.words=200, random.order=FALSE, rot.per=0.3, 
          colors=brewer.pal(8, "Dark2"))