| Title: | Tools for Descriptive Statistics |
|---|---|
| Description: | A toolbox for descriptive statistics, based on the computation of frequency and contingency tables. Several statistical functions and plot methods are provided to describe univariate or bivariate distributions of factors, integer series and numerical series either provided as individual values or as bins. |
| Authors: | Yves Croissant [aut, cre] |
| Maintainer: | Yves Croissant <[email protected]> |
| License: | GPL (>=2) |
| Version: | 0.1-3 |
| Built: | 2024-11-08 03:42:31 UTC |
| Source: | https://github.com/ycroissant/descstat |
Descriptive statistics consist on presenting the distribution of series for a sample in tables (frequency table for one series, contingency tables for two series), ploting this distribution and computing some statistics that summarise it. descstat provides a complete toolbox to perform this tasks. It has been writen using some packages of the tidyverse (especially dplyr, tidyr and purrr) and its usage follow the tidyverse conventions, especially the selection of series using their unquoted names and the use of the pipe operator and of tibbles.
In a frequency (or contingency table), continuous numerical series are presented as bins. Moreover, for some surveys, the individual values are not known, but only the fact that these values belongs to a bin. Therefore, it is crucial to be able to work easily with bins, ie:
creating bins from numerical values, which is performed by the
base::cut function which turns a numerical series to a bin,
coercing bins to numerical values, eg getting from the [10,20)
bin the lower bound (10), the upper bound (20), the center (15) or
whatever other value of the bin,
reducing the number of bins by merging some of them (for example
[0,10), [10, 20), [20,30), [30,Inf) to [0,20), [20,Inf)
these latter two tasks are performed using the new bin class
provided by this package and the accompanying as_numeric function
for the coercion to numeric and the cut method for bins
merging. Especially, coercing bins to their center values is the
basis of the computation of descripting statistics for bins.
The freq_table and cont_table are based on the dplyr::count
function but offer a much richer interface and performs easily
usual operations which are tedious to obtain with dplyr::count or
base::table functions. This includes:
adding a total,
for frequency tables, computing other kind of frequencies than the counts, for example relative frequencies, percentage, cummulative frequencies, etc.,
for contingency tables, computing easily the joint, marginal and conditional distributions,
printing easily the contingency table as a double entry table.
A pre_plot function is provided to put the tibble in form in
order to use classic plots for univariate or bivariate
distributions. This includes histogram, frequency plot, pie chart,
cummulative plot and Lorenz curve. The final plot can then be
obtained using some geoms of ggplot2.
A full set of statistical functions (of central tendency,
dispersion, shape, concentration and covariation) are provided and
can be applied directly on objects of class freq_table or
cont_table. Some of them are methods of generics defined by the
base or stats package, some other are defined as methods for
generics function provided by the descstat function when the
corresponding R function is not generic. For example,
mean is generic, so that we wrote a
mean.freq_table method to compute directly the mean of a series
from a frequency table.
var is not generic, so that we provide the variance generic
and a method for freq_table objects.
A new class called bin is provided, along with different
functions which enable to deal easily with bins, ie creating bin
objects (as_bin) coercing bins to numerical values
(as_numeric), merging bins (cut) and checking than an object is
a bin (is_bin).
as_bin(x) is_bin(x) as_numeric(x, pos = 0, xfirst = NULL, xlast = NULL, wlast = NULL) ## S3 method for class 'bin' cut(x, breaks = NULL, ...) ## S3 method for class 'character' cut(x, breaks = NULL, ...) ## S3 method for class 'factor' cut(x, breaks = NULL, ...) ## S3 method for class 'character' extract(data, ..., .name_repair = "check_unique") ## S3 method for class 'factor' extract(data, ..., .name_repair = "check_unique")as_bin(x) is_bin(x) as_numeric(x, pos = 0, xfirst = NULL, xlast = NULL, wlast = NULL) ## S3 method for class 'bin' cut(x, breaks = NULL, ...) ## S3 method for class 'character' cut(x, breaks = NULL, ...) ## S3 method for class 'factor' cut(x, breaks = NULL, ...) ## S3 method for class 'character' extract(data, ..., .name_repair = "check_unique") ## S3 method for class 'factor' extract(data, ..., .name_repair = "check_unique")
x |
a character or a factor: the first and last characters
should be any of |
pos |
a numeric between 0 and 1, 0 for the lower bond, 1 for
the upper bond, 0.5 for the center of the class (or any other
value between 0 and 1), which indicates to |
xfirst, xlast
|
the center of the first (last) class, if one wants to specify something different from the average of the lower and the upper bonds, |
wlast |
in the case where the upper bond is infinite and
|
breaks |
a numerical vector of breaks which should be a subset of the initial set of breaks. If only one break is provided, all the bins with greater values are merged, |
... |
see |
data |
a character or a factor containing bins, |
.name_repair |
see |
extract methods for characters and factors are provided which
split the character strings in a four tibble columns: the open
bracket, the lower bound, the upper bound and the closing bracket.
as_bin takes as argument a character or a factor that
represents a bin, check the consistency of the string and return a
bin object with levels in the correct order and NAs when the
strings are malformed,
the default cut method takes a numerical series as argument and
returns a factor containing bins according to a break vector; for
the bin's method, the break should be a subset of the original
set of breaks and a bin with fewer levels results,
as_numeric converts a bin to a value of the underlying variable
defined by its relative position (from 0 lower bound to 1 upper
bound in the bin),
is_bin check if the argument is a bin.
as_bin returns a bin object, is_bin a logical, the
extract method a tibble, as_numeric a numeric and the cut
method a bin object with fewer levels.
Yves Croissant
# create a factor containing bins using cut on a numeric z <- c(1, 5, 10, 12, 4, 9, 8) bin1 <- cut(z, breaks = c(1, 8, 12, Inf), right = FALSE) # extract the elements of the levels in a tibble extract(bin1) # coerce to a bin object bin2 <- as_bin(bin1) # coerce to a numeric using the center of the bins as_numeric(bin2, pos = 0.5) # special values for the center of the first and of the last bin as_numeric(bin2, pos = 0.5, xfirst = 5, xlast = 16) # same, but indicating that the width of the last class should be # twice the one of the before last as_numeric(bin2, pos = 0.5, xfirst = 5, wlast = 2) # merge in order to get only two bins cut(bin2, breaks = c(1, 12)) # if length of breaks is 1, this is the value for which all the bins # containing greater values are merged cut(bin2, breaks = 8) # check that bin1 and bin2 are objects of class bin is_bin(bin1) is_bin(bin2)# create a factor containing bins using cut on a numeric z <- c(1, 5, 10, 12, 4, 9, 8) bin1 <- cut(z, breaks = c(1, 8, 12, Inf), right = FALSE) # extract the elements of the levels in a tibble extract(bin1) # coerce to a bin object bin2 <- as_bin(bin1) # coerce to a numeric using the center of the bins as_numeric(bin2, pos = 0.5) # special values for the center of the first and of the last bin as_numeric(bin2, pos = 0.5, xfirst = 5, xlast = 16) # same, but indicating that the width of the last class should be # twice the one of the before last as_numeric(bin2, pos = 0.5, xfirst = 5, wlast = 2) # merge in order to get only two bins cut(bin2, breaks = c(1, 12)) # if length of breaks is 1, this is the value for which all the bins # containing greater values are merged cut(bin2, breaks = 8) # check that bin1 and bin2 are objects of class bin is_bin(bin1) is_bin(bin2)
These functions are intended to compute from a cont_table objects
covariation statistics, ie the covariance, the correlation
coefficient, variance decomposition and regression line.
covariance(data, ...) correlation(data, ...) ## S3 method for class 'cont_table' covariance(data, ...) ## S3 method for class 'cont_table' correlation(data, ...) ## S3 method for class 'cont_table' anova(object, x, ...) ## S3 method for class 'anova.cont_table' summary(object, ...) regline(formula, data)covariance(data, ...) correlation(data, ...) ## S3 method for class 'cont_table' covariance(data, ...) ## S3 method for class 'cont_table' correlation(data, ...) ## S3 method for class 'cont_table' anova(object, x, ...) ## S3 method for class 'anova.cont_table' summary(object, ...) regline(formula, data)
data, object
|
a |
... |
further arguments. |
x |
the series for which the analyse of variance should be computed, |
formula |
symbolic description of the model, |
a numeric or a tibble
Yves Croissant
# the covariance and the linear correlation coefficient are # computed using only the `cont_table` # First reduce the number of bins wages2 <- wages %>% dplyr::mutate(size = cut(as_bin(size), c(20, 50, 100)), wage = cut(as_bin(wage), c(10, 30, 50))) wages2 %>% cont_table(wage, size) %>% covariance wages2 %>% cont_table(wage, size) %>% correlation # For the analyse of variance, one of the two series should be # indicated wages2 %>% cont_table(wage, size) %>% anova(wage) wages2 %>% cont_table(wage, size) %>% anova(wage) %>% summary # For the regression line, a formula should be provided wages2 %>% cont_table(wage, size) %>% regline(formula = wage ~ size)# the covariance and the linear correlation coefficient are # computed using only the `cont_table` # First reduce the number of bins wages2 <- wages %>% dplyr::mutate(size = cut(as_bin(size), c(20, 50, 100)), wage = cut(as_bin(wage), c(10, 30, 50))) wages2 %>% cont_table(wage, size) %>% covariance wages2 %>% cont_table(wage, size) %>% correlation # For the analyse of variance, one of the two series should be # indicated wages2 %>% cont_table(wage, size) %>% anova(wage) wages2 %>% cont_table(wage, size) %>% anova(wage) %>% summary # For the regression line, a formula should be provided wages2 %>% cont_table(wage, size) %>% regline(formula = wage ~ size)
A contingency table returns the counts of all the combinations of
the modalities of two series in a table for which every modality of
the first series is a row and every modality of the second series
is a column. The joint, marginal and conditional functions
compute these three distributions from the contingency table (by
indicating one series for the last two).
cont_table( data, x1, x2, weights = NULL, freq = NULL, total = FALSE, xfirst1 = NULL, xlast1 = NULL, wlast1 = NULL, xfirst2 = NULL, xlast2 = NULL, wlast2 = NULL ) joint(data) conditional(data, x = NULL) marginal(data, x = NULL, f = "f", vals = NULL)cont_table( data, x1, x2, weights = NULL, freq = NULL, total = FALSE, xfirst1 = NULL, xlast1 = NULL, wlast1 = NULL, xfirst2 = NULL, xlast2 = NULL, wlast2 = NULL ) joint(data) conditional(data, x = NULL) marginal(data, x = NULL, f = "f", vals = NULL)
data |
a tibble, |
x1, x2
|
the two series used the construct the contingency table, the distinct values of the first and the second will respectively be the rows and the columns of the contingency table, |
weights |
a series containing the weights that should be used to mimic the population, |
freq |
the frequencies (in the case where data is already contingency table), |
total |
if |
xfirst1, xfirst2, xlast1, xlast2, wlast1, wlast2
|
see |
x |
the series on which the operation should be computed, |
f |
see |
vals |
see |
cont_table actually returns a tibble in "long format", as the
dplyr::count table does. As the returned object is of class
cont_table, this is the format and print methods that turns
the tibble in a wide format before printing.
The conditional and joint functions return a cont_table
object, as the marginal function returns a freq_table object.
a tibble
Yves Croissant
library("dplyr") # get a contingency table containing education and sex cont_table(employment, education, sex) # instead of counts, sum the weights cont_table(employment, education, sex, weights = weights) # get the joint distribution and the conditional and marginal # distribution of sex cont_table(employment, education, sex) %>% joint cont_table(employment, education, sex) %>% marginal(sex) cont_table(employment, education, sex) %>% conditional(sex)library("dplyr") # get a contingency table containing education and sex cont_table(employment, education, sex) # instead of counts, sum the weights cont_table(employment, education, sex, weights = weights) # get the joint distribution and the conditional and marginal # distribution of sex cont_table(employment, education, sex) %>% joint cont_table(employment, education, sex) %>% marginal(sex) cont_table(employment, education, sex) %>% conditional(sex)
The employment survey gives information about characteristics of a sample of individuals (employed/unemployed, part/full time job, education, etc.).
a tibble containing
activity : a factor with levels occupied, unemployed and
inactive,
time : job time a factor with levels part, full and
unknown,
education : level of education,
age : age in years,
sex : one of male or female,
household : kind of household, single, monop (mono-parental
family), couple (couple without children), family (couple with
families) and other,
weights : weights to mimic the population.
Employment survey 2018, INSEE's website.
Compute the frequency table of a categorical or a numerical series.
freq_table( data, x, f = "n", vals = NULL, weights = NULL, total = FALSE, max = NULL, breaks = NULL, right = NULL, xfirst = NULL, xlast = NULL, wlast = NULL, freq = NULL, mass = NULL, center = NULL )freq_table( data, x, f = "n", vals = NULL, weights = NULL, total = FALSE, max = NULL, breaks = NULL, right = NULL, xfirst = NULL, xlast = NULL, wlast = NULL, freq = NULL, mass = NULL, center = NULL )
data |
a tibble, |
x |
a categorical or numerical series, |
f |
a string containing |
vals |
a character containing letters indicating the values of
the variable that should be returned; |
weights |
a series that contain the weights that enable the sample to mimic the population, |
total |
a logical indicating whether the total should be returned, |
max |
if the series is a discrete numerical value, this
argument indicates that all the values greater than |
breaks |
a numerical vector of class limits, |
right |
a logical indicating whether classes should be closed
( |
xfirst, xlast, wlast
|
see |
freq |
a series that contains the frequencies (only relevant
if |
mass |
a series that contains the masses of the variable (only
relevant if |
center |
a series that contains the center of the class of the
variable (only relevant if |
a tibble containing the specified values of vals and
f.
Yves Croissant
# in table padova, price is a numeric variable, a vector of breaks should be provided library("dplyr") padova %>% freq_table(price, breaks = c(50, 100, 150, 200, 250, 300, 350, 400), right = TRUE) # return relative frequencies and densities, and the center value # of the series and the width of the bin padova %>% freq_table(price, breaks = c(50, 100, 150, 200, 250, 300, 350, 400), right = TRUE, f = "fd", vals = "xa") # in table wages, wage is a factor that represents the classes wages %>% freq_table(wage, "d") # a breaks argument is provided to reduce the number of classes wages %>% freq_table(wage, breaks = c(10, 20, 30, 40, 50)) # a total argument add a total to the frequency table wages %>% freq_table(wage, breaks = c(10, 20, 30, 40, 50), total = TRUE) # ìncome is already a frequency table, the freq argument # is mandatory income %>% freq_table(inc_class, freq = number) # the mass argument can be indicated if on column contains the # mass of the series in each bin. In this case, the center of the # class are exactly the mean of the series in each bin income %>% freq_table(inc_class, freq = number, mass = tot_inc) # rgp contains a children series which indicates the number of # children of the households rgp %>% freq_table(children) # a max argument can be indicated to merge the unusual high # values of number of childre rgp %>% freq_table(children, max = 4) # employment is a non random survey, there is a weights series # that can be used to compute the frequency table according to the # sum of weights and not to counts employment %>% freq_table(education) employment %>% freq_table(education, weights = weights)# in table padova, price is a numeric variable, a vector of breaks should be provided library("dplyr") padova %>% freq_table(price, breaks = c(50, 100, 150, 200, 250, 300, 350, 400), right = TRUE) # return relative frequencies and densities, and the center value # of the series and the width of the bin padova %>% freq_table(price, breaks = c(50, 100, 150, 200, 250, 300, 350, 400), right = TRUE, f = "fd", vals = "xa") # in table wages, wage is a factor that represents the classes wages %>% freq_table(wage, "d") # a breaks argument is provided to reduce the number of classes wages %>% freq_table(wage, breaks = c(10, 20, 30, 40, 50)) # a total argument add a total to the frequency table wages %>% freq_table(wage, breaks = c(10, 20, 30, 40, 50), total = TRUE) # ìncome is already a frequency table, the freq argument # is mandatory income %>% freq_table(inc_class, freq = number) # the mass argument can be indicated if on column contains the # mass of the series in each bin. In this case, the center of the # class are exactly the mean of the series in each bin income %>% freq_table(inc_class, freq = number, mass = tot_inc) # rgp contains a children series which indicates the number of # children of the households rgp %>% freq_table(children) # a max argument can be indicated to merge the unusual high # values of number of childre rgp %>% freq_table(children, max = 4) # employment is a non random survey, there is a weights series # that can be used to compute the frequency table according to the # sum of weights and not to counts employment %>% freq_table(education) employment %>% freq_table(education, weights = weights)
Bins of income classes, number of households and mass of income.
a tibble containing :
bin: bin of income,
number: number of households in the bin,
income: mass of income in the bin.
Impot sur le revenu par commune (IRCOM) DGI's website.
This data set documents characteristics (including the prices) of a sample of housings in Padova.
a tibble containing
zone : one of the 12 zones of Padova,
condition : new for new housings, ordinary or good for old ones,
house : dummy for houses,
floor : floor,
rooms : number of rooms,
bathrooms : number of bathrooms,
parking : dummy for parkings,
energy : energy cathegory for the house (A for the best, G for the worst),
area : area of the house in square meters,
price : price of the house in thousands of euros.
Data in Brief's website.
Bonifaci P, Copiello S (2015). "Real estate market and building energy performance: Data for a mass appraisal approach." Data in Brief, 5, 1060-1065. ISSN 2352-3409.
Convert a tibble built using freq_table or cont_table in a
shape that makes it easy to plot.
pre_plot(data, f = NULL, plot = NULL, ...) ## S3 method for class 'freq_table' pre_plot( data, f = NULL, plot = c("histogram", "freqpoly", "lorenz", "stacked", "cumulative"), ... ) ## S3 method for class 'cont_table' pre_plot(data, ...)pre_plot(data, f = NULL, plot = NULL, ...) ## S3 method for class 'freq_table' pre_plot( data, f = NULL, plot = c("histogram", "freqpoly", "lorenz", "stacked", "cumulative"), ... ) ## S3 method for class 'cont_table' pre_plot(data, ...)
data |
a tibble returned by the |
f |
mandatory argument if the tibble contains more than one frequency or density, |
plot |
for object of class |
... |
further arguments. |
The pre_plot function returns a tibble containing:
if plot = histogram, x, y that should be
passed to geom_polygon,
if plot = freqpoly x and y that should be passed to geom_line,
if plot = stacked x and ypos that should be passed
respectively to geom_col and to geom_text to draw labels on
the right position,
if plot = cumulative x, y, xend and yend that should be passed to
geom_segment,
if plot = lorenz for the Lorenz curve, F and M for the
coordinates of the polygons under the Lorenz curve, pts is
logical which the defines the subset of points that belongs to the
Lorenz curve.
a tibble
Yves Croissant
library("dplyr") library("ggplot2") pad <- padova %>% freq_table(price, breaks = c(100, 200, 300, 400, 500, 1000), right = TRUE, f = "Npd") pad %>% pre_plot(f = "d") %>% ggplot() + geom_polygon(aes(x, y)) pad %>% pre_plot(f = "d", plot = "freqpoly") %>% ggplot() + geom_line(aes(x, y)) ## A pie chart wages %>% freq_table(sector, "p", total = FALSE) %>% pre_plot("p", plot = "stacked") %>% ggplot(aes(x = 2, y = p, fill = sector)) + geom_col() + geom_text(aes(y = ypos, label = sector)) + coord_polar(theta = "y") + theme_void() + guides(fill = FALSE)library("dplyr") library("ggplot2") pad <- padova %>% freq_table(price, breaks = c(100, 200, 300, 400, 500, 1000), right = TRUE, f = "Npd") pad %>% pre_plot(f = "d") %>% ggplot() + geom_polygon(aes(x, y)) pad %>% pre_plot(f = "d", plot = "freqpoly") %>% ggplot() + geom_line(aes(x, y)) ## A pie chart wages %>% freq_table(sector, "p", total = FALSE) %>% pre_plot("p", plot = "stacked") %>% ggplot(aes(x = 2, y = p, fill = sector)) + geom_col() + geom_text(aes(y = ypos, label = sector)) + coord_polar(theta = "y") + theme_void() + guides(fill = FALSE)
freq_table and cont_table are tibbles with specific format and
print methods for pretty printing. A pre_print generic is
provided with specific methods to put in form freq_table and
cont_table objects.
pre_print(x, ...) ## S3 method for class 'freq_table' pre_print(x, ...) ## S3 method for class 'cont_table' pre_print(x, ..., row_name = TRUE, total_name = "Total") ## S3 method for class 'freq_table' format(x, ..., n = NULL, width = NULL, n_extra = NULL) ## S3 method for class 'cont_table' format( x, ..., n = NULL, width = NULL, n_extra = NULL, row_name = TRUE, total_name = "Total" ) ## S3 method for class 'cont_table' print( x, ..., n = NULL, width = NULL, n_extra = NULL, row_name = TRUE, total_name = "Total" ) ## S3 method for class 'bin' print(x, ...)pre_print(x, ...) ## S3 method for class 'freq_table' pre_print(x, ...) ## S3 method for class 'cont_table' pre_print(x, ..., row_name = TRUE, total_name = "Total") ## S3 method for class 'freq_table' format(x, ..., n = NULL, width = NULL, n_extra = NULL) ## S3 method for class 'cont_table' format( x, ..., n = NULL, width = NULL, n_extra = NULL, row_name = TRUE, total_name = "Total" ) ## S3 method for class 'cont_table' print( x, ..., n = NULL, width = NULL, n_extra = NULL, row_name = TRUE, total_name = "Total" ) ## S3 method for class 'bin' print(x, ...)
x |
a |
... |
further arguments, |
row_name |
a logical that indicates whether the first column in the two-ways contingency table, that contains the levels of the first series, should be named, |
total_name |
the name of the line (and of the column for
|
n, width, n_extra
|
see tibble::formatting and tibble::formatting. |
a tibble, for the cont_table it is a tibble in wide
format as the cont_table object is in long format.
This extract of the French census gives information about a sample of French households.
a tibble containing :
cars : number of cars,
rooms : number of rooms of the housing,
children : number of children,
type : type of household ; couple or monop (for mono-parental families),
INSEE's website.
descstat provide functions to compute statistics on an univariate distribution. This includes central tendency, dispersion, shape and concentration.
variance(x, ...) gmean(x, r = 1, ...) gini(x, ...) stdev(x, ...) madev(x, ...) modval(x, ...) medial(x, ...) kurtosis(x, ...) skewness(x, ...) ## Default S3 method: variance(x, w = NULL, ...) ## Default S3 method: gmean(x, r = 1, ...) ## Default S3 method: stdev(x, w = NULL, ...) ## Default S3 method: madev(x, w = NULL, center = c("median", "mean"), ...) ## Default S3 method: skewness(x, ...) ## Default S3 method: kurtosis(x, ...) ## S3 method for class 'freq_table' mean(x, ...) ## S3 method for class 'freq_table' gmean(x, r = 1, ...) ## S3 method for class 'freq_table' variance(x, ...) ## S3 method for class 'freq_table' stdev(x, ...) ## S3 method for class 'freq_table' skewness(x, ...) ## S3 method for class 'freq_table' kurtosis(x, ...) ## S3 method for class 'freq_table' madev(x, center = c("median", "mean"), ...) ## S3 method for class 'freq_table' modval(x, ...) ## S3 method for class 'freq_table' quantile(x, y = c("value", "mass"), probs = c(0.25, 0.5, 0.75), ...) ## S3 method for class 'freq_table' median(x, ..., y = c("value", "mass")) ## S3 method for class 'freq_table' medial(x, ...) ## S3 method for class 'freq_table' gini(x, ...) ## S3 method for class 'cont_table' modval(x, ...) ## S3 method for class 'cont_table' gini(x, ...) ## S3 method for class 'cont_table' skewness(x, ...) ## S3 method for class 'cont_table' kurtosis(x, ...) ## S3 method for class 'cont_table' madev(x, center = c("median", "mean"), ...) ## S3 method for class 'cont_table' mean(x, ...) ## S3 method for class 'cont_table' variance(x, ...) ## S3 method for class 'cont_table' stdev(x, ...)variance(x, ...) gmean(x, r = 1, ...) gini(x, ...) stdev(x, ...) madev(x, ...) modval(x, ...) medial(x, ...) kurtosis(x, ...) skewness(x, ...) ## Default S3 method: variance(x, w = NULL, ...) ## Default S3 method: gmean(x, r = 1, ...) ## Default S3 method: stdev(x, w = NULL, ...) ## Default S3 method: madev(x, w = NULL, center = c("median", "mean"), ...) ## Default S3 method: skewness(x, ...) ## Default S3 method: kurtosis(x, ...) ## S3 method for class 'freq_table' mean(x, ...) ## S3 method for class 'freq_table' gmean(x, r = 1, ...) ## S3 method for class 'freq_table' variance(x, ...) ## S3 method for class 'freq_table' stdev(x, ...) ## S3 method for class 'freq_table' skewness(x, ...) ## S3 method for class 'freq_table' kurtosis(x, ...) ## S3 method for class 'freq_table' madev(x, center = c("median", "mean"), ...) ## S3 method for class 'freq_table' modval(x, ...) ## S3 method for class 'freq_table' quantile(x, y = c("value", "mass"), probs = c(0.25, 0.5, 0.75), ...) ## S3 method for class 'freq_table' median(x, ..., y = c("value", "mass")) ## S3 method for class 'freq_table' medial(x, ...) ## S3 method for class 'freq_table' gini(x, ...) ## S3 method for class 'cont_table' modval(x, ...) ## S3 method for class 'cont_table' gini(x, ...) ## S3 method for class 'cont_table' skewness(x, ...) ## S3 method for class 'cont_table' kurtosis(x, ...) ## S3 method for class 'cont_table' madev(x, center = c("median", "mean"), ...) ## S3 method for class 'cont_table' mean(x, ...) ## S3 method for class 'cont_table' variance(x, ...) ## S3 method for class 'cont_table' stdev(x, ...)
x |
a series or a |
... |
further arguments, |
r |
the order of the mean for the |
w |
a vector of weights, |
center |
the center value used to compute the mean absolute
deviations, one of |
y |
for the quantile method, one of |
probs |
the probabilities for which the quantiles have to be computed. |
The following functions are provided:
central tendency: mean, median, medial, modval (for the mode),
dispersion: variance, stdev, maddev (for mean absolute
deviation) and quantile,
shape: skewness and kurtosis,
concentration: gini.
When a generic function exists in base R (or in the stats
package), methods are provided for freq_table or cont_table,
this is a case for mean, median and quantile. When a function
exists, but is not generic, we provide a generic and relevant
methods using different names (stdev, variance and madev
instead respectively of sd, var and mad). Finally some
function don't exist in base R and recommended packages, we
therefore provide a modval function to compute the mode, gini
for the Gini concentration index, skewness and kurtosis for
Fisher's shape statistics and gmean for generalized means (which
include the geometric, the quadratic and the harmonic means).
madev has a center argument which indicates whether the
deviations should be computed respective to the mean or to the
median.
gmean has a r argument: values of -1, 0, 1 and 2 lead
respectively to the harmonic, geometric, arithmetic and quadratic
means.
a numeric or a tibble.
Yves Croissant
library("dplyr") z <- wages %>% freq_table(wage) z %>% median # the medial is the 0.5 quantile of the mass of the distribution z %>% medial # the modval function returns the mode, it is a one line tibble z %>% modval z %>% quantile(probs = c(0.25, 0.5, 0.75)) # quantiles can compute for the frequency (the default) or the mass # of the series z %>% quantile(y = "mass", probs = c(0.25, 0.5, 0.75)) # univariate statistics can be computed on the joint, marginal or # conditional distributions for cont_table objects wages %>% cont_table(wage, size) %>% joint wages %>% cont_table(wage, size) %>% marginal(size) %>% mean wages %>% cont_table(wage, size) %>% conditional(size) %>% meanlibrary("dplyr") z <- wages %>% freq_table(wage) z %>% median # the medial is the 0.5 quantile of the mass of the distribution z %>% medial # the modval function returns the mode, it is a one line tibble z %>% modval z %>% quantile(probs = c(0.25, 0.5, 0.75)) # quantiles can compute for the frequency (the default) or the mass # of the series z %>% quantile(y = "mass", probs = c(0.25, 0.5, 0.75)) # univariate statistics can be computed on the joint, marginal or # conditional distributions for cont_table objects wages %>% cont_table(wage, size) %>% joint wages %>% cont_table(wage, size) %>% marginal(size) %>% mean wages %>% cont_table(wage, size) %>% conditional(size) %>% mean
The DADS survey (Declaration Annuelle des Données Sociales) provides characteristics of wage earners (wages in class, number of working hours, etc.).
a tibble containing
sector : activity sector, industry, building, business,
services and administration,
age : the age in years,
hours : annual number of hours worked,
sex : sex of the wage earner, male or female,
wage : class of yearly wages, in thousands of euros,
size : class of working force size of the firm.
DADS survey 2015, INSEE's website.