In this article, we will discuss how to perform Univariate Analysis in R Programming Language. Univariate Analysis means doing an Analysis of one variable.
The analysis of univariate data is thus the simplest form of analysis since the information deals with only one quantity that changes.
Example: R program to create a vector with 10 elements and display the Summary statistics.
# create a vector with 10 elements data = c (1: 10)
# display print (data)
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Output:
[1] 1 2 3 4 5 6 7 8 9 10
Summary Statistics
Summary statistics include:
Minimum:
Get the Minimum element from the data.
Syntax:
min(data)
# minimum print ( min (data))
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Output
[1] 1
Maximum:
Get the Maximum element from the data.
Syntax:
max(data)
# maximum print ( max (data))
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Output:
[1] 10
Mean:
Get the mean of the given elements from the data.
Syntax:
mean(data)
# mean print ( mean (data))
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Output
[1] 5.5
Median:
Get the median of the given elements from the data.
Syntax:
median(data)
# median print ( median (data))
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Output
[1] 5.5
Inter Quartile Range:
Get the IQR of the given elements from the data.
Syntax:
IQR(data)
# IQR print ( IQR (data))
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Output
[1] 4.5
Standard Deviation:
Get the standard deviation of the given elements from the data.
Syntax:
sd(data)
# standard deviation print ( sd (data))
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Output
[1] 3.02765
Range:
Get a range of the elements from the data.
Syntax:
max(data)-min(data)
# range print ( max (data)- min (data))
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Output
[1] 9
Frequency Table
We can display the frequency table using the table() method, This will return the count of element occurrence.
Syntax:
table(data)
Example:
# create a vector with 10 elements data = c (1: 10)
# display print (data)
# display frequency table print ( table (data))
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Output:
[1] 1 2 3 4 5 6 7 8 9 10
display frequency table
data
1 2 3 4 5 6 7 8 9 10
1 1 1 1 1 1 1 1 1 1
Visualization
Here we can visualize the data using some plots
Boxplot
boxplot() function will result in a five-point summary(min, max, median, 1st quartile, 3rd quartile)
Syntax:
boxplot(data)
Example:
# create a vector with 10 elements data = c (1: 10)
# display print (data)
# display boxplot print ( boxplot (data))
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Output:
$stats
[,1]
[1,] 1.0
[2,] 3.0
[3,] 5.5
[4,] 8.0
[5,] 10.0
$n
[1] 10
$conf
[,1]
[1,] 3.001801
[2,] 7.998199
$out
numeric(0)
$group
numeric(0)
$names
[1] "1"
Histogram
This will return the histogram of the data and the function used is hist()
Syntax:
hist(data)
Example:
# create a vector with 10 elements data = c (1: 10)
# display print (data)
# display histogram print ( hist (data))
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Output:
[1] 1 2 3 4 5 6 7 8 9 10
$breaks
[1] 0 2 4 6 8 10
$counts
[1] 2 2 2 2 2
$density
[1] 0.1 0.1 0.1 0.1 0.1
$mids
[1] 1 3 5 7 9
$xname
[1] "data"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"
Density plot
This will display the density plot. We have to use the density() function along with the plot() function.
Syntax:
plot(density(data))
Example:
# create a vector with 10 elements data = c (1: 10)
# display print (data)
# display density plot print ( plot ( density (data)))
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Output:
[1] 1 2 3 4 5 6 7 8 9 10
NULL