DATA 607—Homework No. 1

Load libraries:

library(RCurl)

First, let’s load the data directly from the source (though a copy is saved in the ./data/ directory):

data_url <- 'https://archive.ics.uci.edu/ml/machine-learning-databases/mushroom/agaricus-lepiota.data'
original <- getURL(data_url) 
df <- read.csv(text=original, header=FALSE, stringsAsFactors=FALSE)
head(df)

##   V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V20
## 1  p  x  s  n  t  p  f  c  n   k   e   e   s   s   w   w   p   w   o   p
## 2  e  x  s  y  t  a  f  c  b   k   e   c   s   s   w   w   p   w   o   p
## 3  e  b  s  w  t  l  f  c  b   n   e   c   s   s   w   w   p   w   o   p
## 4  p  x  y  w  t  p  f  c  n   n   e   e   s   s   w   w   p   w   o   p
## 5  e  x  s  g  f  n  f  w  b   k   t   e   s   s   w   w   p   w   o   e
## 6  e  x  y  y  t  a  f  c  b   n   e   c   s   s   w   w   p   w   o   p
##   V21 V22 V23
## 1   k   s   u
## 2   n   n   g
## 3   n   n   m
## 4   k   s   u
## 5   n   a   g
## 6   k   n   g

Fill in the column names and subset just a handful:

colnames(df) <- c('poisonous',
                  'cap_shape', 
                  'cap_surface', 
                  'cap_color', 
                  'bruises', 
                  'odor', 
                  'gill_attachment', 
                  'gill_spacing', 
                  'gill_size', 
                  'gill_color', 
                  'stalk_shape', 
                  'stalk_root', 
                  'stalk_surface_above_ring', 
                  'stalk_surface_below_ring', 
                  'stalk_color_above_ring', 
                  'stalk_color_below_ring', 
                  'veil_type', 
                  'veil_color', 
                  'ring_number', 
                  'ring_type', 
                  'spore_print_color', 
                  'population', 
                  'habitat')

cols <- c('poisonous', 'bruises', 'gill_size', 'ring_number')
df <- df[cols]
head(df)

##   poisonous bruises gill_size ring_number
## 1         p       t         n           o
## 2         e       t         b           o
## 3         e       t         b           o
## 4         p       t         n           o
## 5         e       f         b           o
## 6         e       t         b           o

The remaining task is the de-abbreviate the data, converting each entry to a meaningful designation.

One way to do this would be to use many gsub() commands. However, a custom function that makes multiple substitutions at one go might make the job a little cleaner and easier to read.

The function gsub_map() accepts a string and a mapping (named list) of pattern-replacements, performing multiple gsub() operations together:

gsub_map <- function(s, mapping) {
    # Accepts a mapping of pattern-replacements on a string s, allowing more 
    # compact operations involving mutliple substitutions on the same string
    # sequentially
    for (i in 1:length(mapping)) {
        pattern <- names(mapping[i])
        replacement <- mapping[i]
        s <- gsub(pattern, replacement, s)
    }
    return(s)
}

# Example
gsub_map('foo bar', list(foo='foo1', bar='bar1'))

## [1] "foo1 bar1"

Using sapply() to apply to function to each row of the columns:

df$poisonous <- sapply(df$poisonous, gsub_map, list(e='edible', p='poisonous'))
df$bruises <- sapply(df$bruises, gsub_map, list(t='TRUE', f='FALSE'))
df$gill_size <- sapply(df$gill_size, gsub_map, list(b='broad', n='narrow'))
df$ring_number <- sapply(df$ring_number, gsub_map, list(n='0', 'o'='1', 't'='2'))

head(df)

##   poisonous bruises gill_size ring_number
## 1 poisonous    TRUE    narrow           1
## 2    edible    TRUE     broad           1
## 3    edible    TRUE     broad           1
## 4 poisonous    TRUE    narrow           1
## 5    edible   FALSE     broad           1
## 6    edible    TRUE     broad           1

Finally, convert to proper R data types:

df$bruises <- as.logical(df$bruises)
df$ring_number <- as.integer(df$ring_number)

head(df)

##   poisonous bruises gill_size ring_number
## 1 poisonous    TRUE    narrow           1
## 2    edible    TRUE     broad           1
## 3    edible    TRUE     broad           1
## 4 poisonous    TRUE    narrow           1
## 5    edible   FALSE     broad           1
## 6    edible    TRUE     broad           1

sapply(df, class)

##   poisonous     bruises   gill_size ring_number 
## "character"   "logical" "character"   "integer"