To prepare for loading the data, column names are specified based on the ‘Attribute Information’ section at https://archive.ics.uci.edu/ml/datasets/Mushroom:
mushroom_fields <- c('class', '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')The data is then read in from https://archive.ics.uci.edu/ml/machine-learning-databases/mushroom/agaricus-lepiota.data:
mushroom <- read.table('https://archive.ics.uci.edu/ml/machine-learning-databases/mushroom/agaricus-lepiota.data',
header=FALSE, sep=",", col.names = mushroom_fields)A new data.frame is then constructed with 5 colums from mushroom:
my_mushrooms <- mushroom[ , c('class', 'bruises', 'gill_size',
'stalk_shape', 'veil_type')]Using the atrribute information at https://archive.ics.uci.edu/ml/machine-learning-databases/mushroom/agaricus-lepiota.names, the abbreviations in the data are replaced:
my_mushrooms$class <- ifelse(my_mushrooms$class == 'e', 'edible', 'poisonous')
my_mushrooms$bruises <- ifelse(my_mushrooms$bruises == 't', 'bruises', 'no')
my_mushrooms$gill_size <- ifelse(my_mushrooms$gill_size == 'b', 'broad', 'narrow')
my_mushrooms$stalk_shape <- ifelse(my_mushrooms$stalk_shape == 'e', 'enlarging', 'tapering')
my_mushrooms$veil_type <- ifelse(my_mushrooms$veil_type == 'p', 'partial', 'universal')The resulting data.frame:
head(my_mushrooms)## class bruises gill_size stalk_shape veil_type
## 1 poisonous bruises narrow enlarging partial
## 2 edible bruises broad enlarging partial
## 3 edible bruises broad enlarging partial
## 4 poisonous bruises narrow enlarging partial
## 5 edible no broad tapering partial
## 6 edible bruises broad enlarging partial