DescriptiveStats.OBeu
estimates the descriptive statistical measures, needed at OpenBudgets.eu.
You can measure central tendency and dispersion of numeric variables along with their distributions and correlations and the frequencies of categorical variables for a given dataset on OpenBudgets.eu data mining tool platform.
The vignette provides an effective way to use functions of DescriptiveStats.OBeu
with datasets including datasets of OpenBudgets.eu.
tojson
parameter is used in ds.analysis
, ds.statistics
, ds.hist
, ds.boxplot
, ds.correlation
, ds.frequency
, ds.kurtosis
, ds.skewness
functions in order to specify if the resulted object should be in json format.
First you have to load the library
# load DescriptiveStats.OBeu library(DescriptiveStats.OBeu)
The data in the package include the budget of Wuppertal for 2009 to 2020, as a data frame Wuppertal_df
and as a json link Wuppertal_openspending
as well as a sample json link sample_json_link_openspending
, which you can access them using fromJSON
of jsonlite
package or copy paste the link to a browser.
Wuppertal internal structure
str(Wuppertal_df)
ds.analysis
is used to estimate minimum, maximum, range, mean, median, first and third quantiles, variance, standart deviation, skewness and kurtosis, boxplot, histogram parameters needed for visualization of numeric variables and frequencies of factor variables of a given vector, matrix or data frame of data.
+---------------+------------------------+-----------------------------------------------------------+
| Component | Output | Description |
+===============+========================+===========================================================+
| statistics
| - Min | - The minimum observed value of the input data |
| | - Max | - The maximum observed value of the input data |
| | - Range | - The difference between maximum and minimum |
| | - Mean | - The average value of the input data |
| | - Median | - The median value of the input data |
| | - Quantiles | - The 25%, 75% percentiles |
| | - Variance | - The variance of the input data |
| | - StandardDeviation | - The standard deviation of the input data |
| | - Skewness | - The Skewness of the input data |
| | - Kurtosis | - The Kurtosis of the input data |
+---------------+------------------------+-----------------------------------------------------------+
| boxplot
| - lo.whisker | - Lower horizontal line out of the box |
| | - lo.hinge | - Lower horizontal line of the box |
| | - median | - Horizontal line in the box |
| | - up.hinge | - Upper horizontal line of the box |
| | - up.whisker | - Upper horizontal line out of the box |
| | - box.width | - The box width of each variable |
| | - lo.out | - Lower outliers |
| | - up.out | - Upper outliers |
| | - n | - The number of non-NA observations |
+---------------+------------------------+-----------------------------------------------------------+
| histogram
| - cuts | - The boundaries of the histogram classes |
| | - counts | - The frequency of each histogram class |
| | - mean | - The average value of the input vector |
| | - median | - The median value of the input data |
+---------------+------------------------+-----------------------------------------------------------+
| frequencies
| - Variable name | - The name of the calculated variable |
| | - frequencies | - The frequency value |
| | - "_row" | - Name of the categories of the variable |
| | - relative.frequencies | - Relative frequency values |
+---------------+------------------------+-----------------------------------------------------------+
| correlation
| - Variable name | - The name of the calculated variable |
| | - Correlation value | - The correlation value |
| | - "_row" | - The corresponding correlation variable |
+---------------+------------------------+-----------------------------------------------------------+
Table: ds.analysis
components
ds.analysis
returns by default a list object, we set tojson
parameter TRUE
, outliers
parameter FALSE
, fr.select = "Produktbereich"
. Correlation component is empty because there is one numeric variable.
wuppertalanalysis = ds.analysis(Wuppertal_df,outliers=FALSE, fr.select = "Produktbereich", tojson=TRUE) # json string format jsonlite::prettify(wuppertalanalysis) # use prettify of jsonlite library to add indentation to the returned JSON string
ds.analysis
uses internally the functions ds.statistics
,ds.hist
,ds.boxplot
,ds.correlation
and ds.frequency
. However, these functions can be used independently and depends on the user requirements.
ds.statistics
is used to estimate minimum, maximum, range, mean, median, first and third quantiles, variance, standart deviation, skewness and kurtosis values of a given vector, matrix or data frame of data.
ds.statistics
returns by default a list object:
ds.statistics(Wuppertal_df) # list format
The results can be extracted in json format for further use you should set the parameter tojson
to TRUE
:
wuppertalstats = ds.statistics(Wuppertal_df, tojson = TRUE) # json format jsonlite::prettify(wuppertalstats) # use prettify of jsonlite library to add indentation to the returned JSON string
ds.hist
computes the parameters needed to visualize a histogram of a numeric input vector, specifying the breaks
as in base hist
function.
ds.hist(Wuppertal_df$Amount, breaks= "Sturges") # list format
Return the results as json string:
wuppertalhist = ds.hist(Wuppertal_df$Amount, breaks= "Sturges", tojson=TRUE) # json format jsonlite::prettify(wuppertalhist) # use prettify of jsonlite library to add indentation to the returned JSON string
The ds.boxplot
returns the parameters needed for a boxplot visualization of an input vector, matrix or data frame.
If outl
is TRUE
the outliers will be computed at the selected out.level
level (default is 1.5
times the Interquartile Range) and the width level is determined 0.15 times the square root of the size of the input data.
ds.boxplot
uses the numeric variables of the input data, you do not have to exclude factor or character variables.
wuppertalbox = ds.boxplot(Wuppertal_df, width = 0.15 , outl = FALSE, tojson=TRUE) # json format jsonlite::prettify(wuppertalbox) # use prettify of jsonlite library to add indentation to the returned JSON string
ds.correlation
estimate the correlation coefficient (default is "pearson"
) of the input vectors, matrix or data frame.
In this example iris
dataset is used. Factor or character variables in the input matrix or data frame will be filtered out by default.
iriscorr = ds.correlation(iris, cor.method="pearson", tojson=TRUE) # json format jsonlite::prettify(iriscorr) # use prettify of jsonlite library to add indentation to the returned JSON string
Frequencies and relative frequencies of factors/characters of the input dataset using ds.frequency
for Produktbereich
from Wuppertal_df
dataset and return as json string.
wuppertalfreq = ds.frequency(Wuppertal_df$Produktbereich, tojson = TRUE) jsonlite::prettify(wuppertalfreq) # use prettify of jsonlite library to add indentation to the returned JSON string
If the input is a dataframe and the select
parameter is not specified, all the factor variables will be returned.
All the numeric variables of the input data are filtered out of the estimations internally.
This function calculates kurtosis of the input vector, matrix or data frame. Factor or character variables that may be included in the input matrix or data frame, will be omitted in the estimations.
ds.kurtosis(Wuppertal_df$Amount, tojson=TRUE)
This function calculates skewness of the input vector, matrix or data frame. Factor or character variables that may be included in the input matrix or data frame, will be omitted in the estimations.
ds.skewness(Wuppertal_df$Amount, tojson=TRUE)
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