Assignment1_Mushroom

Read the file in table format and create a data frame from it

Mushroom_Family <- read.csv(url("https://archive.ics.uci.edu/ml/machine-learning-databases/mushroom/agaricus-lepiota.data"), header = FALSE)

Mushroom_Family is the dataframe created. Dimensions of the dataframe:

dim(Mushroom_Family)

## [1] 8124   23

The first six rows of the data frame are:

head.matrix(x = Mushroom_Family, 6L)

##   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

Mushroom_Sibling is the subset of the Mushroom_Family dataframe from the selected columns:

Mushroom_Sibling <- Mushroom_Family[,c(1,4,6,10,11,23)]

Renaming the column names of the subset dataframe:

names(Mushroom_Sibling) = c("Type", "Cap_Color", "Odor", "Gill_Color", "Stem_Shape","Habitat")

Column names of Mushroom_Sibling dataframe are renamed:

names(Mushroom_Sibling)

## [1] "Type"       "Cap_Color"  "Odor"       "Gill_Color" "Stem_Shape"
## [6] "Habitat"

Replace the abbreviation used in column Type:

Convert Columns to characters

Mushroom_Sibling$Type <- as.character(Mushroom_Sibling$Type)

Replace abbreviation with meaningful names

Mushroom_Sibling$Type[Mushroom_Sibling$Type == "e"] <- "edible"
Mushroom_Sibling$Type[Mushroom_Sibling$Type == "p"] <- "poisonous"

3.If needed to maintain columns as factors

Mushroom_Sibling$Type <- as.factor(Mushroom_Sibling$Type)

Display first six rows of the dataframe to verify the change

head.matrix(Mushroom_Sibling, 6L)

##        Type Cap_Color Odor Gill_Color Stem_Shape Habitat
## 1 poisonous         n    p          k          e       u
## 2    edible         y    a          k          e       g
## 3    edible         w    l          n          e       m
## 4 poisonous         w    p          n          e       u
## 5    edible         g    n          k          t       g
## 6    edible         y    a          n          e       g

Replace abbreviations of colum Cap_Color

Mushroom_Sibling$Cap_Color <- as.character(Mushroom_Sibling$Cap_Color)
Mushroom_Sibling$Cap_Color[Mushroom_Sibling$Cap_Color == "n"] <- "brown"
Mushroom_Sibling$Cap_Color[Mushroom_Sibling$Cap_Color == "b"] <- "buff"
Mushroom_Sibling$Cap_Color[Mushroom_Sibling$Cap_Color == "c"] <- "cinnamon"
Mushroom_Sibling$Cap_Color[Mushroom_Sibling$Cap_Color == "g"] <- "gray"
Mushroom_Sibling$Cap_Color[Mushroom_Sibling$Cap_Color == "r"] <- "green"
Mushroom_Sibling$Cap_Color[Mushroom_Sibling$Cap_Color == "p"] <- "pink"
Mushroom_Sibling$Cap_Color[Mushroom_Sibling$Cap_Color == "u"] <- "purple"
Mushroom_Sibling$Cap_Color[Mushroom_Sibling$Cap_Color == "e"] <- "red"
Mushroom_Sibling$Cap_Color[Mushroom_Sibling$Cap_Color == "w"] <- "white"
Mushroom_Sibling$Cap_Color[Mushroom_Sibling$Cap_Color == "y"] <- "yellow"
Mushroom_Sibling$Cap_Color <- as.factor(Mushroom_Sibling$Cap_Color)

Replace Abbreviations of column Odor

Mushroom_Sibling$Odor <- as.character(Mushroom_Sibling$Odor)
Mushroom_Sibling$Odor[Mushroom_Sibling$Odor == "a"] <- "almond"
Mushroom_Sibling$Odor[Mushroom_Sibling$Odor == "l"] <- "anise"
Mushroom_Sibling$Odor[Mushroom_Sibling$Odor == "c"] <- "creosote"
Mushroom_Sibling$Odor[Mushroom_Sibling$Odor == "y"] <- "fishy"
Mushroom_Sibling$Odor[Mushroom_Sibling$Odor == "f"] <- "foul"
Mushroom_Sibling$Odor[Mushroom_Sibling$Odor == "m"] <- "musty"
Mushroom_Sibling$Odor[Mushroom_Sibling$Odor == "n"] <- "none"
Mushroom_Sibling$Odor[Mushroom_Sibling$Odor == "p"] <- "pungent"
Mushroom_Sibling$Odor[Mushroom_Sibling$Odor == "s"] <- "spicy"
Mushroom_Sibling$Odor <- as.factor(Mushroom_Sibling$Odor)

Replace abbreviation of column Gill_Color

head.matrix(Mushroom_Sibling, 3L)

##        Type Cap_Color    Odor Gill_Color Stem_Shape Habitat
## 1 poisonous     brown pungent          k          e       u
## 2    edible    yellow  almond          k          e       g
## 3    edible     white   anise          n          e       m

Mushroom_Sibling$Gill_Color <- as.character(Mushroom_Sibling$Gill_Color)
Mushroom_Sibling$Gill_Color[Mushroom_Sibling$Gill_Color == "k"] <- "black"
Mushroom_Sibling$Gill_Color[Mushroom_Sibling$Gill_Color == "n"] <- "brown"
Mushroom_Sibling$Gill_Color[Mushroom_Sibling$Gill_Color == "b"] <- "buff"
Mushroom_Sibling$Gill_Color[Mushroom_Sibling$Gill_Color == "h"] <- "chocolate"
Mushroom_Sibling$Gill_Color[Mushroom_Sibling$Gill_Color == "g"] <- "gray"
Mushroom_Sibling$Gill_Color[Mushroom_Sibling$Gill_Color == "r"] <- "green"
Mushroom_Sibling$Gill_Color[Mushroom_Sibling$Gill_Color == "o"] <- "orange"
Mushroom_Sibling$Gill_Color[Mushroom_Sibling$Gill_Color == "p"] <- "pink"
Mushroom_Sibling$Gill_Color[Mushroom_Sibling$Gill_Color == "u"] <- "purple"
Mushroom_Sibling$Gill_Color[Mushroom_Sibling$Gill_Color == "e"] <- "red"
Mushroom_Sibling$Gill_Color[Mushroom_Sibling$Gill_Color == "w"] <- "white"
Mushroom_Sibling$Gill_Color[Mushroom_Sibling$Gill_Color == "y"] <- "yellow"
Mushroom_Sibling$Gill_Color <- as.factor(Mushroom_Sibling$Gill_Color)

Replace abbreviation of column Stem_Shape

Mushroom_Sibling$Stem_Shape <- as.character(Mushroom_Sibling$Stem_Shape)
Mushroom_Sibling$Stem_Shape[Mushroom_Sibling$Stem_Shape == "e"] <- "enlarging"
Mushroom_Sibling$Stem_Shape[Mushroom_Sibling$Stem_Shape == "t"] <- "tapering"
Mushroom_Sibling$Stem_Shape <- as.factor(Mushroom_Sibling$Stem_Shape)

Replace abbreviations of column Habitat

Mushroom_Sibling$Habitat <- as.character(Mushroom_Sibling$Habitat)
Mushroom_Sibling$Habitat[Mushroom_Sibling$Habitat == "g"] <- "grasses"
Mushroom_Sibling$Habitat[Mushroom_Sibling$Habitat == "l"] <- "leaves"
Mushroom_Sibling$Habitat[Mushroom_Sibling$Habitat == "m"] <- "meadows"
Mushroom_Sibling$Habitat[Mushroom_Sibling$Habitat == "p"] <- "paths"
Mushroom_Sibling$Habitat[Mushroom_Sibling$Habitat == "u"] <- "urban"
Mushroom_Sibling$Habitat[Mushroom_Sibling$Habitat == "w"] <- "waste"
Mushroom_Sibling$Habitat[Mushroom_Sibling$Habitat == "d"] <- "woods"
Mushroom_Sibling$Habitat <- as.factor(Mushroom_Sibling$Habitat)

Display the table to verify:

head.matrix(x = Mushroom_Sibling, 6L)

##        Type Cap_Color    Odor Gill_Color Stem_Shape Habitat
## 1 poisonous     brown pungent      black  enlarging   urban
## 2    edible    yellow  almond      black  enlarging grasses
## 3    edible     white   anise      brown  enlarging meadows
## 4 poisonous     white pungent      brown  enlarging   urban
## 5    edible      gray    none      black   tapering grasses
## 6    edible    yellow  almond      brown  enlarging grasses

By exploring and analyzing the dataset and the data description of Mushrooms ,the following conclusions can be drawn:

Edible and poisonous mushrooms can be analyzed based on two strong factors: Odor and Habitat With Odor being the strongest factor.
Therefore Let’s calculate the frequencies of both the factors affecting the edibility of the mushrooms:

table = table(Mushroom_Sibling$Type, Mushroom_Sibling$Odor,Mushroom_Sibling$Habitat)
result_data = as.data.frame(table)
names(result_data)[1] = 'Type'
names(result_data)[2] = 'Odor'
names(result_data)[3] = 'Habitat'
result_data

##          Type     Odor Habitat Freq
## 1      edible   almond grasses  176
## 2   poisonous   almond grasses    0
## 3      edible    anise grasses  176
## 4   poisonous    anise grasses    0
## 5      edible creosote grasses    0
## 6   poisonous creosote grasses    0
## 7      edible    fishy grasses    0
## 8   poisonous    fishy grasses    0
## 9      edible     foul grasses    0
## 10  poisonous     foul grasses  576
## 11     edible    musty grasses    0
## 12  poisonous    musty grasses    0
## 13     edible     none grasses 1056
## 14  poisonous     none grasses   36
## 15     edible  pungent grasses    0
## 16  poisonous  pungent grasses  128
## 17     edible    spicy grasses    0
## 18  poisonous    spicy grasses    0
## 19     edible   almond  leaves    0
## 20  poisonous   almond  leaves    0
## 21     edible    anise  leaves    0
## 22  poisonous    anise  leaves    0
## 23     edible creosote  leaves    0
## 24  poisonous creosote  leaves    0
## 25     edible    fishy  leaves    0
## 26  poisonous    fishy  leaves  192
## 27     edible     foul  leaves    0
## 28  poisonous     foul  leaves  192
## 29     edible    musty  leaves    0
## 30  poisonous    musty  leaves    0
## 31     edible     none  leaves  240
## 32  poisonous     none  leaves   16
## 33     edible  pungent  leaves    0
## 34  poisonous  pungent  leaves    0
## 35     edible    spicy  leaves    0
## 36  poisonous    spicy  leaves  192
## 37     edible   almond meadows  128
## 38  poisonous   almond meadows    0
## 39     edible    anise meadows  128
## 40  poisonous    anise meadows    0
## 41     edible creosote meadows    0
## 42  poisonous creosote meadows    0
## 43     edible    fishy meadows    0
## 44  poisonous    fishy meadows    0
## 45     edible     foul meadows    0
## 46  poisonous     foul meadows    0
## 47     edible    musty meadows    0
## 48  poisonous    musty meadows    0
## 49     edible     none meadows    0
## 50  poisonous     none meadows   36
## 51     edible  pungent meadows    0
## 52  poisonous  pungent meadows    0
## 53     edible    spicy meadows    0
## 54  poisonous    spicy meadows    0
## 55     edible   almond   paths   48
## 56  poisonous   almond   paths    0
## 57     edible    anise   paths   48
## 58  poisonous    anise   paths    0
## 59     edible creosote   paths    0
## 60  poisonous creosote   paths    0
## 61     edible    fishy   paths    0
## 62  poisonous    fishy   paths  192
## 63     edible     foul   paths    0
## 64  poisonous     foul   paths  624
## 65     edible    musty   paths    0
## 66  poisonous    musty   paths    0
## 67     edible     none   paths   40
## 68  poisonous     none   paths    0
## 69     edible  pungent   paths    0
## 70  poisonous  pungent   paths    0
## 71     edible    spicy   paths    0
## 72  poisonous    spicy   paths  192
## 73     edible   almond   urban    0
## 74  poisonous   almond   urban    0
## 75     edible    anise   urban    0
## 76  poisonous    anise   urban    0
## 77     edible creosote   urban    0
## 78  poisonous creosote   urban    0
## 79     edible    fishy   urban    0
## 80  poisonous    fishy   urban    0
## 81     edible     foul   urban    0
## 82  poisonous     foul   urban  144
## 83     edible    musty   urban    0
## 84  poisonous    musty   urban    0
## 85     edible     none   urban   96
## 86  poisonous     none   urban    0
## 87     edible  pungent   urban    0
## 88  poisonous  pungent   urban  128
## 89     edible    spicy   urban    0
## 90  poisonous    spicy   urban    0
## 91     edible   almond   waste    0
## 92  poisonous   almond   waste    0
## 93     edible    anise   waste    0
## 94  poisonous    anise   waste    0
## 95     edible creosote   waste    0
## 96  poisonous creosote   waste    0
## 97     edible    fishy   waste    0
## 98  poisonous    fishy   waste    0
## 99     edible     foul   waste    0
## 100 poisonous     foul   waste    0
## 101    edible    musty   waste    0
## 102 poisonous    musty   waste    0
## 103    edible     none   waste  192
## 104 poisonous     none   waste    0
## 105    edible  pungent   waste    0
## 106 poisonous  pungent   waste    0
## 107    edible    spicy   waste    0
## 108 poisonous    spicy   waste    0
## 109    edible   almond   woods   48
## 110 poisonous   almond   woods    0
## 111    edible    anise   woods   48
## 112 poisonous    anise   woods    0
## 113    edible creosote   woods    0
## 114 poisonous creosote   woods  192
## 115    edible    fishy   woods    0
## 116 poisonous    fishy   woods  192
## 117    edible     foul   woods    0
## 118 poisonous     foul   woods  624
## 119    edible    musty   woods    0
## 120 poisonous    musty   woods   36
## 121    edible     none   woods 1784
## 122 poisonous     none   woods   32
## 123    edible  pungent   woods    0
## 124 poisonous  pungent   woods    0
## 125    edible    spicy   woods    0
## 126 poisonous    spicy   woods  192

Assignment1_Mushroom

Priya Shaji

January 31, 2019

Read the file in table format and create a data frame from it

Mushroom_Family is the dataframe created. Dimensions of the dataframe:

The first six rows of the data frame are:

Mushroom_Sibling is the subset of the Mushroom_Family dataframe from the selected columns:

Renaming the column names of the subset dataframe:

Column names of Mushroom_Sibling dataframe are renamed:

Replace the abbreviation used in column Type:

Display first six rows of the dataframe to verify the change

Replace abbreviations of colum Cap_Color

Replace Abbreviations of column Odor

Replace abbreviation of column Gill_Color

Replace abbreviation of column Stem_Shape

Replace abbreviations of column Habitat

Display the table to verify:

By exploring and analyzing the dataset and the data description of Mushrooms ,the following conclusions can be drawn: