R Summary Statistics Table
R Summary Statistics Table, The describe() and describeBy() methods from the psych package are the simplest way to produce summary tables in R.
library(psych)
The syntax for the summary table
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describe(df)
Now we can create a summary table, grouped by a specific variable
describeBy(df, group=df$varname)
R Summary Statistics Table
The following examples show how to use these functions in practice.
Example 1:- Create a Basic Summary Table
Let’s say we have the following R data frame.
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Let’s take the iris dataset for illustration purposes.
df <- iris
Now we can view the data frame
head(df)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species 1 5.1 3.5 1.4 0.2 setosa 2 4.9 3.0 1.4 0.2 setosa 3 4.7 3.2 1.3 0.2 setosa 4 4.6 3.1 1.5 0.2 setosa 5 5.0 3.6 1.4 0.2 setosa 6 5.4 3.9 1.7 0.4 setosa
To construct a summary table for each variable in the data frame, we may use the describe() function.
library(psych) describe(df)
vars n mean sd median trimmed mad min max range skew kurtosis se Sepal.Length 1 150 5.84 0.83 5.80 5.81 1.04 4.3 7.9 3.6 0.31 -0.61 0.07 Sepal.Width 2 150 3.06 0.44 3.00 3.04 0.44 2.0 4.4 2.4 0.31 0.14 0.04 Petal.Length 3 150 3.76 1.77 4.35 3.76 1.85 1.0 6.9 5.9 -0.27 -1.42 0.14 Petal.Width 4 150 1.20 0.76 1.30 1.18 1.04 0.1 2.5 2.4 -0.10 -1.36 0.06 Species* 5 150 2.00 0.82 2.00 2.00 1.48 1.0 3.0 2.0 0.00 -1.52 0.07
The following are some examples of how to interpret each value in the output.
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vars: number of columns
n: The total number of legitimate cases
mean: The average price
median: The median value has been cut to The average after trimming (default trims 10 percent of observations from each end)
mad: The median value
trimmed: The trimmed mean (default trims 10% of observations from each end)
range: The range of values (max – min)
skew: The skewness
kurtosis: The kurtosis
se: The standard error
Any variable marked with an asterisk (*) is a categorical or logical variable that has been converted to a numerical variable with values that represent the numerical ordering of the values.
Because the variable ‘Species1’ has been changed to a numerical variable in our example, the summary statistics for it should not be taken literally.
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Also, the parameter fast=TRUE can be used to only calculate the most common summary statistics.
reduce the size of the summary table
describe(df, fast=TRUE)
vars n mean sd min max range se Sepal.Length 1 150 5.84 0.83 4.3 7.9 3.6 0.07 Sepal.Width 2 150 3.06 0.44 2.0 4.4 2.4 0.04 Petal.Length 3 150 3.76 1.77 1.0 6.9 5.9 0.14 Petal.Width 4 150 1.20 0.76 0.1 2.5 2.4 0.06 Species 5 150 NaN NA Inf -Inf -Inf NA
We may also select to construct summary statistics for only a subset of the data frame’s variables:
only the ‘Petal.Length’ and ‘Sepal.Length’ columns should be included in the summary table
describe(df[ , c('Petal.Length', 'Sepal.Length')], fast=TRUE)vars n mean sd min max range se Petal.Length 1 150 3.76 1.77 1.0 6.9 5.9 0.14 Sepal.Length 2 150 5.84 0.83 4.3 7.9 3.6 0.07
Example 2: Make a summary table with specific variables grouped together.
The following code demonstrates how to group the data frame by the ‘ Species’ variable and use the describeBy() function to build a summary table.
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Make a summary table based on the ‘Species’ variable.
describeBy(df[,-5], group=df$Species, fast=TRUE)
Descriptive statistics by group
group: setosa vars n mean sd min max range se Sepal.Length 1 50 5.01 0.35 4.3 5.8 1.5 0.05 Sepal.Width 2 50 3.43 0.38 2.3 4.4 2.1 0.05 Petal.Length 3 50 1.46 0.17 1.0 1.9 0.9 0.02 Petal.Width 4 50 0.25 0.11 0.1 0.6 0.5 0.01 --------------------------------------------------------------------------- group: versicolor vars n mean sd min max range se Sepal.Length 1 50 5.94 0.52 4.9 7.0 2.1 0.07 Sepal.Width 2 50 2.77 0.31 2.0 3.4 1.4 0.04 Petal.Length 3 50 4.26 0.47 3.0 5.1 2.1 0.07 Petal.Width 4 50 1.33 0.20 1.0 1.8 0.8 0.03 --------------------------------------------------------------------------- group: virginica vars n mean sd min max range se Sepal.Length 1 50 6.59 0.64 4.9 7.9 3.0 0.09 Sepal.Width 2 50 2.97 0.32 2.2 3.8 1.6 0.05 Petal.Length 3 50 5.55 0.55 4.5 6.9 2.4 0.08 Petal.Width 4 50 2.03 0.27 1.4 2.5 1.1 0.04
The summary statistics for each of the three Species in the data frame are displayed in the output.
