The following is the setup to get the data from the UCI machine learning databases.

#install.packages("RCurl")

library(RCurl)
## Loading required package: bitops
url <- "https://archive.ics.uci.edu/ml/machine-learning-databases/mushroom/agaricus-lepiota.data"

mushroom_raw <-getURL(url)

mushroom_df <- read.csv(text=mushroom_raw,header=F,sep=",", stringsAsFactors = FALSE)

I have selected the columns Edible, Shape, Cap Color, Odor, and Habitat to represent at a high level the characteristics one needs to look for to determine if the mushoom is edible or poisonous.

mushroom_df <- mushroom_df[,c(1,2,4,6,23)]

#V1.
# Edible - e or Poisinous -p are the only two options

mushroom_df$V1[mushroom_df$V1=="e"] <- "Edible"
mushroom_df$V1[mushroom_df$V1=="p"] <- "Poisonous"

#V2.
# Cap Shape bell=b,conical=c,convex=x,flat=f,knobbed=k,sunken=s

mushroom_df$V2[mushroom_df$V2=="b"] <- "bell"
mushroom_df$V2[mushroom_df$V2=="c"] <- "conical"
mushroom_df$V2[mushroom_df$V2=="x"] <- "convex"
mushroom_df$V2[mushroom_df$V2=="f"] <- "flat"
mushroom_df$V2[mushroom_df$V2=="k"] <- "knobbed"
mushroom_df$V2[mushroom_df$V2=="s"] <- "sunken"

#V4
#brown=n,buff=b,cinnamon=c,gray=g,green=r,pink=p,purple=u,red=e,white=w,yellow=y
mushroom_df$V4[mushroom_df$V4=="n"] <- "brown"
mushroom_df$V4[mushroom_df$V4=="b"] <- "buff"
mushroom_df$V4[mushroom_df$V4=="c"] <- "cinnamon"
mushroom_df$V4[mushroom_df$V4=="g"] <- "gray"
mushroom_df$V4[mushroom_df$V4=="r"] <- "green"
mushroom_df$V4[mushroom_df$V4=="p"] <- "pink"
mushroom_df$V4[mushroom_df$V4=="u"] <- "purple"
mushroom_df$V4[mushroom_df$V4=="e"] <- "red"
mushroom_df$V4[mushroom_df$V4=="w"] <- "white"
mushroom_df$V4[mushroom_df$V4=="y"] <- "yellow"

#V6
#Odor of the mushroom  almond=a,anise=l,creosote=c,fishy=y,foul=f, musty=m,none=n,pungent=p,spicy=s
mushroom_df$V6[mushroom_df$V6=="a"] <- "almond"
mushroom_df$V6[mushroom_df$V6=="l"] <- "anise"
mushroom_df$V6[mushroom_df$V6=="c"] <- "creosote"
mushroom_df$V6[mushroom_df$V6=="y"] <- "fishy"
mushroom_df$V6[mushroom_df$V6=="f"] <- "foul"
mushroom_df$V6[mushroom_df$V6=="m"] <- "musty"
mushroom_df$V6[mushroom_df$V6=="n"] <- "none"
mushroom_df$V6[mushroom_df$V6=="p"] <- "pungent"
mushroom_df$V6[mushroom_df$V6=="s"] <- "spicy"

#V23
#Habitat - grasses=g,leaves=l,meadows=m,paths=p,urban=u,waste=w,woods=d
mushroom_df$V23[mushroom_df$V23=="g"] <- "grasses"
mushroom_df$V23[mushroom_df$V23=="l"] <- "leaves"
mushroom_df$V23[mushroom_df$V23=="m"] <- "meadows"
mushroom_df$V23[mushroom_df$V23=="p"] <- "paths"
mushroom_df$V23[mushroom_df$V23=="u"] <- "urban"
mushroom_df$V23[mushroom_df$V23=="w"] <- "waste"
mushroom_df$V23[mushroom_df$V23=="d"] <- "woods"

#updating column names
colnames(mushroom_df) <- c("Can I Eat It?","Shape","Cap Color","Odor","Common Habitat")

Overall the data frame had 8124 mushrooms and 5 columsn that I renamed to Can I eat It?, Shape, Cap Color, Odor and Common Habitat for easier identification.

dim(mushroom_df)
## [1] 8124    5
head(mushroom_df)
##   Can I Eat It?  Shape Cap Color    Odor Common Habitat
## 1     Poisonous convex     brown pungent          urban
## 2        Edible convex    yellow  almond        grasses
## 3        Edible   bell     white   anise        meadows
## 4     Poisonous convex     white pungent          urban
## 5        Edible convex      gray    none        grasses
## 6        Edible convex    yellow  almond        grasses