Descriptive Statistics in R

1. Descriptive statistics in R
Iris dataset is imported by default in R, you only
need to load it by running iris:
data <- iris # load the iris dataset and renamed it dat
head(data) # first 6 observations
str(data) # structure of dataset
The dataset contains 150 observations and 5
variables, representing the length and width of the
sepal and petal and the species of 150 flowers.
Length and width of the sepal and petal are
numeric variables and the species is a factor with 3
levels

2. • Regarding plots, we present the default graphs
and the graphs from the well-
known {ggplot2} package.
• Graphs from the {ggplot2}
• Minimum and maximum
• Minimum and maximum can be found thanks
to the min() and max() functions:
• min(data$Sepal.Length)
• max(data$Sepal.Length)
• Alternatively the range() function:
• rng <- range(data$Sepal.Length)

3. Range
• The range can then be easily computed, as you have guessed, by
subtracting the minimum from the maximum:
• max(dat$Sepal.Length) - min(dat$Sepal.Length)
• there is no default function to compute the range.
• Mean
• The mean can be computed with the mean() function:
• mean(data$Sepal.Length)
• if there is at least one missing value in your dataset, use
• mean(data$Sepal.Length, na.rm = TRUE) to compute the mean
with the NA excluded.
• This argument can be used for most functions presented in this
article, not only the mean
• numbers<-c(5,7,8,6,4,2,5,6)
• mean(numbers)
• barplot(numbers)

4. Frequency and Mode
• Frequency is a summary of data occurrence in a
collection of non-overlapping types
• In R freq() used to find frequency distribution of
vector inputs
• Head(subset(mtcars, select=‘gear’))
• Factor(mtcars$gear)
• w=table(mtcars$gear)
• t=as.data.frame(w)
• cbind() function can be used to display result in
column format
• cbind(w)

5. Mode
• Mode is similar to frequency , except that the
value of mode returns highest number of
occurrences in a dataset
• Mode can take both numeric and character as
inputdata
• V<-c(5,6,4,2,5,6,7,8,9,6,3,6)
• Calculate mode with userdefined function
• resultMode<-get Mode(v)

6. Median
• Median is the middle value of given inputs
• In R median can be found using median() function
• Median(x,na.rm=FALSE)
• X<-c(15,54,6,5,9.2,36,5.3,8,-7,-5)
• Median.result<-median(x)
• Numbers<-c(1,3,5,6,4,7,8,10)
• Median(numbers)
• Barplot(numbers)

7. Standard Deviation
• To determine the Standard Deviation, to plot
the numbers in a barplot and have straight line
run through the plot at mean and another
straight line run through the plot at
mean+Standard Deviation
• V<-c(5,6,4,2,5,6,7,8,9,6,3,6)
• Mean(v)
• deviation<- sd(v)
• Barplot(numbers)

8. First and third quartile
• As the median, the first and third quartiles can be
computed to the quantile() function and by setting the
second argument to 0.25 or 0.75:
• quantile(dat$Sepal.Length, 0.25) # first quartile
• quantile(dat$Sepal.Length, 0.75) # third quartile
• Interquartile range
• The interquartile range (i.e., the difference between the
first and third quartile) can be computed with
the IQR() function:
• IQR(dat$Sepal.Length)
• uantile(dat$Sepal.Length, 0.75) -
quantile(dat$Sepal.Length, 0.25)