Categorical Frequency Distribution

# Data
grade <- c("C", "A", "B", "C", "D", "F", "B", "B", "A", "C", "C", "F", "C", "B", "D", 
           "A", "C", "C", "C", "F", "C", "C")
# Frequency
freq <- table(grade)
freq
## grade
##  A  B  C  D  F 
##  3  4 10  2  3

\[\mbox{Relative Frequency}=\frac{\mbox{Frequency}}{\mbox{Total Frequency}}\]

# Relative Frequency
relfreq <- freq/length(grade)
round(relfreq, 3)
## grade
##     A     B     C     D     F 
## 0.136 0.182 0.455 0.091 0.136
# Cumulative Relative Frequency
cumsum_relfreq <- cumsum(relfreq)
round(cumsum_relfreq,3)
##     A     B     C     D     F 
## 0.136 0.318 0.773 0.864 1.000
library(readr)
Credit <- read_csv("Credit.csv")
## Parsed with column specification:
## cols(
##   id = col_double(),
##   Income = col_double(),
##   Limit = col_double(),
##   Rating = col_double(),
##   Cards = col_double(),
##   Age = col_double(),
##   Education = col_double(),
##   Gender = col_character(),
##   Student = col_character(),
##   Married = col_character(),
##   Ethnicity = col_character(),
##   Balance = col_double()
## )
ethnty <- Credit$Ethnicity

# Frequency
freq_ethnty <- table(ethnty)

# Relative Frequency
relfreq_ethnty <- freq_ethnty/length(ethnty)
round(relfreq_ethnty, 3)
## ethnty
## African American            Asian        Caucasian 
##            0.248            0.255            0.498
# Cumulative Relative Frequency
cumm_relfreq_ethnty <- cumsum(relfreq_ethnty)
round(cumm_relfreq_ethnty, 3)
## African American            Asian        Caucasian 
##            0.248            0.502            1.000