#This is the page for topic 7k
# generate the data
source("../gnrnd4.R")
gnrnd4(296288901,800066)
# be sure that we have the right data
L1
# we are looking at the frequency of each value
# in the data. Find those frequencies.
table(L1)
# then, just because we know how to do it,
# we will make a bar plot of the frequencies
barplot( table(L1),
main="Data from gnrnd4(296288901,800066)",
xlab="Values in the data set",
ylab="Frequency",
ylim=c(0,20))
abline(h=0)
abline(h=seq(5,20,5), lty="dotted")
par(new=TRUE)
barplot( table(L1),
main="Data from gnrnd4(296288901,800066)",
xlab="Values in the data set",
ylab="Frequency",
ylim=c(0,20))
# now, rather than re-enter the frequencies we
# will have R compute them again and store them
# in a variable we will call freqs
freqs <- table(L1)
# to compute the relative frequency we divide
# the frequencies by the total number of items
total <- length(L1)
rel_freq <- freqs/total
rel_freq
# to compute the cumulative frequencies we
# use the cumsum() function
cum_count <- cumsum( freqs)
cum_count
# to compute the cumulative relative
# frequencies we just divide the cumulative
# frequencies by the total number of items
cum_rel_freq <- cum_count/total
cum_rel_freq
# to compute the degrees to allocate in a pie
# chart we just multiply the relative frequency
# times 360
deg_pie <- 360*rel_freq
deg_pie
##############################
## We will end up doing each of these steps for any
## similar problem. It makes more sense to capture
## all of the steps in a function and then to just
## be able to run the function when we want a
## frequency table.
###############################
source("../make_freq_table.R")
make_freq_table( L1 )
#### there is one more special feature that we
#### should see here. We can get a nicer looking
#### table by using the View() function
View( make_freq_table(L1) )