Your task is to study the dataset and the associated description of the data (i.e. “data dictionary”). You may need to look around a bit, but it’s there! You should take the data, and create a data frame with a subset of the columns (and if you like rows) in the dataset. You should include the column that indicates edible or poisonous and three or four other columns. You should also add meaningful column names and replace the abbreviations used in the data-for example, in the appropriate column, “e” might become “edible.” Your deliverable is the R code to perform these transformation tasks.
mushr_full_df<-read.csv("https://archive.ics.uci.edu/ml/machine-learning-databases/mushroom/agaricus-lepiota.data", header= FALSE, sep=",")
head (mushr_full_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 gmushr_dict <- read.table("C:\\m_dictionary.txt",row.names = 1, sep=":")
head(mushr_dict)## V2
## class poison=p,edible=e
## cap-shape bell=b,conical=c,convex=x,flat=f,knobbed=k,sunken=s
## cap-surface fibrous=f,grooves=g,scaly=y,smooth=s
## cap-color brown=n,buff=b,cinnamon=c,gray=g,green=r,pink=p,purple=u,red=e,white=w,yellow=y
## bruises? bruises=t,no=f
## odor almond=a,anise=l,creosote=c,fishy=y,foul=f,musty=m,none=n,pungent=p,spicy=snames(mushr_full_df) <- row.names(mushr_dict)
head(mushr_full_df)## class cap-shape cap-surface cap-color bruises? odor gill-attachment
## 1 p x s n t p f
## 2 e x s y t a f
## 3 e b s w t l f
## 4 p x y w t p f
## 5 e x s g f n f
## 6 e x y y t a f
## gill-spacing gill-size gill-color stalk-shape stalk-root
## 1 c n k e e
## 2 c b k e c
## 3 c b n e c
## 4 c n n e e
## 5 w b k t e
## 6 c b n e c
## stalk-surface-above-ring stalk-surface-below-ring stalk-color-above-ring
## 1 s s w
## 2 s s w
## 3 s s w
## 4 s s w
## 5 s s w
## 6 s s w
## stalk-color-below-ring veil-type veil-color ring-number ring-type
## 1 w p w o p
## 2 w p w o p
## 3 w p w o p
## 4 w p w o p
## 5 w p w o e
## 6 w p w o p
## spore-print-color population habitat
## 1 k s u
## 2 n n g
## 3 n n m
## 4 k s u
## 5 n a g
## 6 k n gmushr_sub_df <- subset(mushr_full_df, select=c("class","cap-color","habitat", "ring-number","odor"))
head(mushr_sub_df)## class cap-color habitat ring-number odor
## 1 p n u o p
## 2 e y g o a
## 3 e w m o l
## 4 p w u o p
## 5 e g g o n
## 6 e y g o agetTranslation <- function(a) {
return(sapply(1:nrow(mushr_sub_df), function(x)
gsub(paste('(^|.*,)(.*)=',mushr_sub_df[x,a],'.*',sep=""),"\\2",mushr_dict[a,1] )
))
}mushr_translated_df <- sapply(colnames(mushr_sub_df), function(x) getTranslation(x))
head(mushr_translated_df)## class cap-color habitat ring-number odor
## [1,] "poison" "brown" "urban" "one" "pungent"
## [2,] "edible" "yellow" "grasses" "one" "almond"
## [3,] "edible" "white" "meadows" "one" "anise"
## [4,] "poison" "white" "urban" "one" "pungent"
## [5,] "edible" "gray" "grasses" "one" "none"
## [6,] "edible" "yellow" "grasses" "one" "almond"