Detailed Statistical Description of Data Frame
descr.Rddescr offers a fast and detailed description of each variable in a data frame. Since v1.9.0 it fully supports grouped and weighted computations.
Usage
descr(X, ...)
# Default S3 method
descr(X, by = NULL, w = NULL, cols = NULL,
Ndistinct = TRUE, higher = TRUE, table = TRUE, sort.table = "freq",
Qprobs = c(0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99), Qtype = 7L,
label.attr = "label", stepwise = FALSE, ...)
# S3 method for class 'grouped_df'
descr(X, w = NULL,
Ndistinct = TRUE, higher = TRUE, table = TRUE, sort.table = "freq",
Qprobs = c(0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99), Qtype = 7L,
label.attr = "label", stepwise = FALSE, ...)
# S3 method for class 'descr'
as.data.frame(x, ..., gid = "Group")
# S3 method for class 'descr'
print(x, n = 14, perc = TRUE, digits = .op[["digits"]], t.table = TRUE, total = TRUE,
compact = FALSE, summary = !compact, reverse = FALSE, stepwise = FALSE, ...)Arguments
- X
a (grouped) data frame or list of atomic vectors. Atomic vectors, matrices or arrays can be passed but will first be coerced to data frame using
qDF.- by
a factor,
GRPobject, or atomic vector / list of vectors (internally grouped withGRP), or a one- or two-sided formula e.g.~ group1orvar1 + var2 ~ group1 + group2to groupX. See Examples.- w
a numeric vector of (non-negative) weights. the default method also supports a one-sided formulas i.e.
~ weightcolor~ log(weightcol). Thegrouped_dfmethod supports lazy-expressions (same without~). See Examples.- cols
select columns to describe using column names, indices a logical vector or selector function (e.g.
is.numeric). Note:colsis ignored if a two-sided formula is passed toby.- Ndistinct
logical.
TRUE(default) computes the number of distinct values on all variables usingfndistinct.- higher
logical. Argument is passed down to
qsu:TRUE(default) computes the skewness and the kurtosis.- table
logical.
TRUE(default) computes a (sorted) frequency table for all categorical variables (excluding Date variables).- sort.table
an integer or character string specifying how the frequency table should be presented:
Int. String Description 1 "value" sort table by values. 2 "freq" sort table by frequencies. 3 "none" return table in first-appearance order of values, or levels for factors (most efficient). - Qprobs
double. Probabilities for quantiles to compute on numeric variables, passed down to
.quantile. If something non-numeric is passed (i.e.NULL,FALSE,NA,""etc.), no quantiles are computed.- Qtype
integer. Quantile types 5-9 following Hyndman and Fan (1996) who recommended type 8, default 7 as in
quantile.- label.attr
character. The name of a label attribute to display for each variable (if variables are labeled).
- ...
for
descr: other arguments passed toqsu.default. For[.descr: variable names or indices passed to[.list. The argument is unused in theprintandas.data.framemethods.- x
an object of class 'descr'.
- n
integer. The maximum number of table elements to print for categorical variables. If the number of distinct elements is
<= n, the whole table is printed. Otherwise the remaining items are summed into an '... %s Others' category.- perc
logical.
TRUE(default) adds percentages to the frequencies in the table for categorical variables, and, if!is.null(by), the percentage of observations in each group.- digits
integer. The number of decimals to print in statistics, quantiles and percentage tables.
- t.table
logical.
TRUE(default) prints a transposed table.- total
logical.
TRUE(default) adds a 'Total' column for grouped tables (when usingbyargument).- compact
logical.
TRUEcombines statistics and quantiles to generate a more compact printout. Especially useful with groups (by).- summary
logical.
TRUE(default) computes and displays a summary of the frequencies, if the size of the table for a categorical variable exceedsn.- reverse
logical.
TRUEprints contents in reverse order, starting with the last column, so that the dataset can be analyzed by scrolling up the console after callingdescr.- stepwise
logical.
TRUEprints one variable at a time. The user needs to press [enter] to see the printout for the next variable. If called fromdescr, the computation is also done one variable at a time, and the finished 'descr' object is returned invisibly.- gid
character. Name assigned to the group-id column, when describing data by groups.
Details
descr was heavily inspired by Hmisc::describe, but is much faster and has more advanced statistical capabilities. It is principally a wrapper around qsu, fquantile (.quantile), and fndistinct for numeric variables, and computes frequency tables for categorical variables using qtab. Date variables are summarized with fnobs, fndistinct and frange.
Since v1.9.0 grouped and weighted computations are fully supported. The use of sampling weights will produce a weighted mean, sd, skewness and kurtosis, and weighted quantiles for numeric data. For categorical data, tables will display the sum of weights instead of the frequencies, and percentage tables as well as the percentage of missing values indicated next to 'Statistics' in print, be relative to the total sum of weights. All this can be done by groups. Grouped (weighted) quantiles are computed using BY.
For larger datasets, calling the stepwise option directly from descr() is recommended, as precomputing the statistics for all variables before digesting the results can be time consuming.
The list-object returned from descr can efficiently be converted to a tidy data frame using the as.data.frame method. This representation will not include frequency tables computed for categorical variables.
Value
A 2-level nested list-based object of class 'descr'. The list has the same size as the dataset, and contains the statistics computed for each variable, which are themselves stored in a list containing the class, the label, the basic statistics and quantiles / tables computed for the variable (in matrix form).
The object has attributes attached providing the 'name' of the dataset, the number of rows in the dataset ('N'), an attribute 'arstat' indicating whether arrays of statistics where generated by passing arguments (e.g. pid) down to qsu.default, an attribute 'table' indicating whether table = TRUE (i.e. the object could contain tables for categorical variables), and attributes 'groups' and/or 'weights' providing a GRP object and/or weight vector for grouped and/or weighted data descriptions.
Examples
## Simple Use
descr(iris)
#> Dataset: iris, 5 Variables, N = 150
#> --------------------------------------------------------------------------------
#> Sepal.Length (numeric):
#> Statistics
#> N Ndist Mean SD Min Max Skew Kurt
#> 150 35 5.84 0.83 4.3 7.9 0.31 2.43
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95% 99%
#> 4.4 4.6 4.8 5.1 5.8 6.4 6.9 7.25 7.7
#> --------------------------------------------------------------------------------
#> Sepal.Width (numeric):
#> Statistics
#> N Ndist Mean SD Min Max Skew Kurt
#> 150 23 3.06 0.44 2 4.4 0.32 3.18
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95% 99%
#> 2.2 2.34 2.5 2.8 3 3.3 3.61 3.8 4.15
#> --------------------------------------------------------------------------------
#> Petal.Length (numeric):
#> Statistics
#> N Ndist Mean SD Min Max Skew Kurt
#> 150 43 3.76 1.77 1 6.9 -0.27 1.6
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95% 99%
#> 1.15 1.3 1.4 1.6 4.35 5.1 5.8 6.1 6.7
#> --------------------------------------------------------------------------------
#> Petal.Width (numeric):
#> Statistics
#> N Ndist Mean SD Min Max Skew Kurt
#> 150 22 1.2 0.76 0.1 2.5 -0.1 1.66
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95% 99%
#> 0.1 0.2 0.2 0.3 1.3 1.8 2.2 2.3 2.5
#> --------------------------------------------------------------------------------
#> Species (factor):
#> Statistics
#> N Ndist
#> 150 3
#> Table
#> Freq Perc
#> setosa 50 33.33
#> versicolor 50 33.33
#> virginica 50 33.33
#> --------------------------------------------------------------------------------
descr(wlddev)
#> Dataset: wlddev, 13 Variables, N = 13176
#> --------------------------------------------------------------------------------
#> country (character): Country Name
#> Statistics
#> N Ndist
#> 13176 216
#> Table
#> Freq Perc
#> Afghanistan 61 0.46
#> Albania 61 0.46
#> Algeria 61 0.46
#> American Samoa 61 0.46
#> Andorra 61 0.46
#> Angola 61 0.46
#> Antigua and Barbuda 61 0.46
#> Argentina 61 0.46
#> Armenia 61 0.46
#> Aruba 61 0.46
#> Australia 61 0.46
#> Austria 61 0.46
#> Azerbaijan 61 0.46
#> Bahamas, The 61 0.46
#> ... 202 Others 12322 93.52
#>
#> Summary of Table Frequencies
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 61 61 61 61 61 61
#> --------------------------------------------------------------------------------
#> iso3c (factor): Country Code
#> Statistics
#> N Ndist
#> 13176 216
#> Table
#> Freq Perc
#> ABW 61 0.46
#> AFG 61 0.46
#> AGO 61 0.46
#> ALB 61 0.46
#> AND 61 0.46
#> ARE 61 0.46
#> ARG 61 0.46
#> ARM 61 0.46
#> ASM 61 0.46
#> ATG 61 0.46
#> AUS 61 0.46
#> AUT 61 0.46
#> AZE 61 0.46
#> BDI 61 0.46
#> ... 202 Others 12322 93.52
#>
#> Summary of Table Frequencies
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 61 61 61 61 61 61
#> --------------------------------------------------------------------------------
#> date (Date): Date Recorded (Fictitious)
#> Statistics
#> N Ndist Min Max
#> 13176 61 1961-01-01 2021-01-01
#> --------------------------------------------------------------------------------
#> year (integer): Year
#> Statistics
#> N Ndist Mean SD Min Max Skew Kurt
#> 13176 61 1990 17.61 1960 2020 -0 1.8
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95% 99%
#> 1960 1963 1966 1975 1990 2005 2014 2017 2020
#> --------------------------------------------------------------------------------
#> decade (integer): Decade
#> Statistics
#> N Ndist Mean SD Min Max Skew Kurt
#> 13176 7 1985.57 17.51 1960 2020 0.03 1.79
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95% 99%
#> 1960 1960 1960 1970 1990 2000 2010 2010 2020
#> --------------------------------------------------------------------------------
#> region (factor): Region
#> Statistics
#> N Ndist
#> 13176 7
#> Table
#> Freq Perc
#> Europe & Central Asia 3538 26.85
#> Sub-Saharan Africa 2928 22.22
#> Latin America & Caribbean 2562 19.44
#> East Asia & Pacific 2196 16.67
#> Middle East & North Africa 1281 9.72
#> South Asia 488 3.70
#> North America 183 1.39
#> --------------------------------------------------------------------------------
#> income (factor): Income Level
#> Statistics
#> N Ndist
#> 13176 4
#> Table
#> Freq Perc
#> High income 4819 36.57
#> Upper middle income 3660 27.78
#> Lower middle income 2867 21.76
#> Low income 1830 13.89
#> --------------------------------------------------------------------------------
#> OECD (logical): Is OECD Member Country?
#> Statistics
#> N Ndist
#> 13176 2
#> Table
#> Freq Perc
#> FALSE 10980 83.33
#> TRUE 2196 16.67
#> --------------------------------------------------------------------------------
#> PCGDP (numeric): GDP per capita (constant 2010 US$)
#> Statistics (28.13% NAs)
#> N Ndist Mean SD Min Max Skew Kurt
#> 9470 9470 12048.78 19077.64 132.08 196061.42 3.13 17.12
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95%
#> 227.71 399.62 555.55 1303.19 3767.16 14787.03 35646.02 48507.84
#> 99%
#> 92340.28
#> --------------------------------------------------------------------------------
#> LIFEEX (numeric): Life expectancy at birth, total (years)
#> Statistics (11.43% NAs)
#> N Ndist Mean SD Min Max Skew Kurt
#> 11670 10548 64.3 11.48 18.91 85.42 -0.67 2.67
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95% 99%
#> 35.83 42.77 46.83 56.36 67.44 72.95 77.08 79.34 82.36
#> --------------------------------------------------------------------------------
#> GINI (numeric): Gini index (World Bank estimate)
#> Statistics (86.76% NAs)
#> N Ndist Mean SD Min Max Skew Kurt
#> 1744 368 38.53 9.2 20.7 65.8 0.6 2.53
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95% 99%
#> 24.6 26.3 27.6 31.5 36.4 45 52.6 55.98 60.5
#> --------------------------------------------------------------------------------
#> ODA (numeric): Net official development assistance and official aid received (constant 2018 US$)
#> Statistics (34.67% NAs)
#> N Ndist Mean SD Min Max Skew
#> 8608 7832 454'720131 868'712654 -997'679993 2.56715605e+10 6.98
#> Kurt
#> 114.89
#> Quantiles
#> 1% 5% 10% 25% 50% 75%
#> -12'593999.7 1'363500.01 8'347000.31 44'887499.8 165'970001 495'042503
#> 90% 95% 99%
#> 1.18400697e+09 1.93281696e+09 3.73380782e+09
#> --------------------------------------------------------------------------------
#> POP (numeric): Population, total
#> Statistics (1.95% NAs)
#> N Ndist Mean SD Min Max Skew Kurt
#> 12919 12877 24'245971.6 102'120674 2833 1.39771500e+09 9.75 108.91
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90%
#> 8698.84 31083.3 62268.4 443791 4'072517 12'816178 46'637331.4
#> 95% 99%
#> 81'177252.5 308'862641
#> --------------------------------------------------------------------------------
descr(GGDC10S)
#> Dataset: GGDC10S, 16 Variables, N = 5027
#> --------------------------------------------------------------------------------
#> Country (character): Country
#> Statistics
#> N Ndist
#> 5027 43
#> Table
#> Freq Perc
#> USA 129 2.57
#> EGY 129 2.57
#> MOR 128 2.55
#> IDN 126 2.51
#> PHL 126 2.51
#> TWN 126 2.51
#> DNK 126 2.51
#> ESP 126 2.51
#> FRA 126 2.51
#> GBR 126 2.51
#> ITA 126 2.51
#> NLD 126 2.51
#> SWE 126 2.51
#> CHN 125 2.49
#> ... 29 Others 3256 64.77
#>
#> Summary of Table Frequencies
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 4 105 124 117 126 129
#> --------------------------------------------------------------------------------
#> Regioncode (character): Region code
#> Statistics
#> N Ndist
#> 5027 6
#> Table
#> Freq Perc
#> ASI 1372 27.29
#> SSA 1148 22.84
#> LAM 1117 22.22
#> EUR 1004 19.97
#> MENA 257 5.11
#> NAM 129 2.57
#> --------------------------------------------------------------------------------
#> Region (character): Region
#> Statistics
#> N Ndist
#> 5027 6
#> Table
#> Freq Perc
#> Asia 1372 27.29
#> Sub-saharan Africa 1148 22.84
#> Latin America 1117 22.22
#> Europe 1004 19.97
#> Middle East and North Africa 257 5.11
#> North America 129 2.57
#> --------------------------------------------------------------------------------
#> Variable (character): Variable
#> Statistics
#> N Ndist
#> 5027 2
#> Table
#> Freq Perc
#> EMP 2516 50.05
#> VA 2511 49.95
#> --------------------------------------------------------------------------------
#> Year (numeric): Year
#> Statistics
#> N Ndist Mean SD Min Max Skew Kurt
#> 5027 67 1981.58 17.57 1947 2013 -0.05 1.86
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95% 99%
#> 1950 1953 1957 1967 1982 1997 2006 2009 2011
#> --------------------------------------------------------------------------------
#> AGR (numeric): Agriculture
#> Statistics (13.19% NAs)
#> N Ndist Mean SD Min Max Skew Kurt
#> 4364 4353 2'526696.5 37'129098.1 0 1.19187778e+09 23.95 642.16
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95%
#> 0.09 23.18 144.56 930.7 4394.52 29781.04 315403 2'393977.49
#> 99%
#> 24'932575.1
#> --------------------------------------------------------------------------------
#> MIN (numeric): Mining
#> Statistics (13.37% NAs)
#> N Ndist Mean SD Min Max Skew Kurt
#> 4355 4224 1'867908.95 32'334251.7 0 1.10344053e+09 25.27 712.33
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95% 99%
#> 0.02 1.2 3.78 38.95 173.22 4841.26 64810.08 713420.36 14'309891.9
#> --------------------------------------------------------------------------------
#> MAN (numeric): Manufacturing
#> Statistics (13.37% NAs)
#> N Ndist Mean SD Min Max Skew Kurt
#> 4355 4353 5'538491.36 63'090998.4 0 1.86843541e+09 20.71 498.7
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95%
#> 0.05 27.31 103.84 620.44 3718.1 52805.35 516077.08 2'978846.99
#> 99%
#> 108'499037
#> --------------------------------------------------------------------------------
#> PU (numeric): Utilities
#> Statistics (13.39% NAs)
#> N Ndist Mean SD Min Max Skew Kurt
#> 4354 4237 335679.47 2'576027.41 0 65'324543.8 13.5 244.29
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95% 99%
#> 0 2.16 6.3 25.74 167.95 4892.25 63004.56 291356.48 11'866259.3
#> --------------------------------------------------------------------------------
#> CON (numeric): Construction
#> Statistics (13.37% NAs)
#> N Ndist Mean SD Min Max Skew Kurt
#> 4355 4339 1'801597.63 24'382598 0 860'638677 26.15 774.73
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95%
#> 0.02 15.03 43.37 215.57 1473.45 13514.84 132609.51 829361.57
#> 99%
#> 37'430603.6
#> --------------------------------------------------------------------------------
#> WRT (numeric): Trade, restaurants and hotels
#> Statistics (13.37% NAs)
#> N Ndist Mean SD Min Max Skew Kurt
#> 4355 4344 3'392909.52 36'950812.9 0 1.15497404e+09 21.19 530.06
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95%
#> 0.03 25.07 96.85 650.38 3773.64 41648.17 475116.68 2'646521.57
#> 99%
#> 79'618054.2
#> --------------------------------------------------------------------------------
#> TRA (numeric): Transport, storage and communication
#> Statistics (13.37% NAs)
#> N Ndist Mean SD Min Max Skew Kurt
#> 4355 4334 1'473269.72 16'815143.2 0 547'047040 22.77 604.58
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95%
#> 0.05 12.28 37.35 205.79 1174.8 18927.21 195055.31 1'059843.16
#> 99%
#> 31'750009.1
#> --------------------------------------------------------------------------------
#> FIRE (numeric): Finance, insurance, real estate and business services
#> Statistics (13.37% NAs)
#> N Ndist Mean SD Min Max Skew Kurt
#> 4355 4349 1'657114.84 13'709981.9 -2848.81 387'997506 16.48 356.43
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95% 99%
#> 0 3.87 14.3 128.18 960.13 13460.4 252299.08 1'599086.92 55'536957
#> --------------------------------------------------------------------------------
#> GOV (numeric): Government services
#> Statistics (30.73% NAs)
#> N Ndist Mean SD Min Max Skew Kurt
#> 3482 3470 1'712300.28 16'967383.7 0 485'535400 18.67 430.18
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95%
#> 0.02 48.14 121.87 723.98 3928.51 37689.12 331990.24 1'400263.37
#> 99%
#> 56'340246.3
#> --------------------------------------------------------------------------------
#> OTH (numeric): Community, social and personal services
#> Statistics (15.5% NAs)
#> N Ndist Mean SD Min Max Skew Kurt
#> 4248 4238 1'684527.32 15'613923.6 0 402'671182 14.93 273.79
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95%
#> 0.02 15.92 49.56 310.09 1433.17 13321.29 107230.29 605013.39
#> 99%
#> 42'264477.4
#> --------------------------------------------------------------------------------
#> SUM (numeric): Summation of sector GDP
#> Statistics (13.19% NAs)
#> N Ndist Mean SD Min Max Skew Kurt
#> 4364 4364 21'566436.8 251'812500 0 8.06794210e+09 22.53 589.58
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95%
#> 0.38 269.63 1242.98 4803.94 23186.19 284646.08 2'644610.11 15'030223.5
#> 99%
#> 435'513356
#> --------------------------------------------------------------------------------
# Some useful print options (also try stepwise argument)
print(descr(GGDC10S), reverse = TRUE, t.table = FALSE)
#> SUM (numeric): Summation of sector GDP
#> Statistics (13.19% NAs)
#> N Ndist Mean SD Min Max Skew Kurt
#> 4364 4364 21'566436.8 251'812500 0 8.06794210e+09 22.53 589.58
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95%
#> 0.38 269.63 1242.98 4803.94 23186.19 284646.08 2'644610.11 15'030223.5
#> 99%
#> 435'513356
#> --------------------------------------------------------------------------------
#> OTH (numeric): Community, social and personal services
#> Statistics (15.5% NAs)
#> N Ndist Mean SD Min Max Skew Kurt
#> 4248 4238 1'684527.32 15'613923.6 0 402'671182 14.93 273.79
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95%
#> 0.02 15.92 49.56 310.09 1433.17 13321.29 107230.29 605013.39
#> 99%
#> 42'264477.4
#> --------------------------------------------------------------------------------
#> GOV (numeric): Government services
#> Statistics (30.73% NAs)
#> N Ndist Mean SD Min Max Skew Kurt
#> 3482 3470 1'712300.28 16'967383.7 0 485'535400 18.67 430.18
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95%
#> 0.02 48.14 121.87 723.98 3928.51 37689.12 331990.24 1'400263.37
#> 99%
#> 56'340246.3
#> --------------------------------------------------------------------------------
#> FIRE (numeric): Finance, insurance, real estate and business services
#> Statistics (13.37% NAs)
#> N Ndist Mean SD Min Max Skew Kurt
#> 4355 4349 1'657114.84 13'709981.9 -2848.81 387'997506 16.48 356.43
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95% 99%
#> 0 3.87 14.3 128.18 960.13 13460.4 252299.08 1'599086.92 55'536957
#> --------------------------------------------------------------------------------
#> TRA (numeric): Transport, storage and communication
#> Statistics (13.37% NAs)
#> N Ndist Mean SD Min Max Skew Kurt
#> 4355 4334 1'473269.72 16'815143.2 0 547'047040 22.77 604.58
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95%
#> 0.05 12.28 37.35 205.79 1174.8 18927.21 195055.31 1'059843.16
#> 99%
#> 31'750009.1
#> --------------------------------------------------------------------------------
#> WRT (numeric): Trade, restaurants and hotels
#> Statistics (13.37% NAs)
#> N Ndist Mean SD Min Max Skew Kurt
#> 4355 4344 3'392909.52 36'950812.9 0 1.15497404e+09 21.19 530.06
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95%
#> 0.03 25.07 96.85 650.38 3773.64 41648.17 475116.68 2'646521.57
#> 99%
#> 79'618054.2
#> --------------------------------------------------------------------------------
#> CON (numeric): Construction
#> Statistics (13.37% NAs)
#> N Ndist Mean SD Min Max Skew Kurt
#> 4355 4339 1'801597.63 24'382598 0 860'638677 26.15 774.73
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95%
#> 0.02 15.03 43.37 215.57 1473.45 13514.84 132609.51 829361.57
#> 99%
#> 37'430603.6
#> --------------------------------------------------------------------------------
#> PU (numeric): Utilities
#> Statistics (13.39% NAs)
#> N Ndist Mean SD Min Max Skew Kurt
#> 4354 4237 335679.47 2'576027.41 0 65'324543.8 13.5 244.29
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95% 99%
#> 0 2.16 6.3 25.74 167.95 4892.25 63004.56 291356.48 11'866259.3
#> --------------------------------------------------------------------------------
#> MAN (numeric): Manufacturing
#> Statistics (13.37% NAs)
#> N Ndist Mean SD Min Max Skew Kurt
#> 4355 4353 5'538491.36 63'090998.4 0 1.86843541e+09 20.71 498.7
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95%
#> 0.05 27.31 103.84 620.44 3718.1 52805.35 516077.08 2'978846.99
#> 99%
#> 108'499037
#> --------------------------------------------------------------------------------
#> MIN (numeric): Mining
#> Statistics (13.37% NAs)
#> N Ndist Mean SD Min Max Skew Kurt
#> 4355 4224 1'867908.95 32'334251.7 0 1.10344053e+09 25.27 712.33
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95% 99%
#> 0.02 1.2 3.78 38.95 173.22 4841.26 64810.08 713420.36 14'309891.9
#> --------------------------------------------------------------------------------
#> AGR (numeric): Agriculture
#> Statistics (13.19% NAs)
#> N Ndist Mean SD Min Max Skew Kurt
#> 4364 4353 2'526696.5 37'129098.1 0 1.19187778e+09 23.95 642.16
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95%
#> 0.09 23.18 144.56 930.7 4394.52 29781.04 315403 2'393977.49
#> 99%
#> 24'932575.1
#> --------------------------------------------------------------------------------
#> Year (numeric): Year
#> Statistics
#> N Ndist Mean SD Min Max Skew Kurt
#> 5027 67 1981.58 17.57 1947 2013 -0.05 1.86
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95% 99%
#> 1950 1953 1957 1967 1982 1997 2006 2009 2011
#> --------------------------------------------------------------------------------
#> Variable (character): Variable
#> Statistics
#> N Ndist
#> 5027 2
#> Table
#> EMP VA
#> Freq 2516 2511
#> Perc 50.05 49.95
#> --------------------------------------------------------------------------------
#> Region (character): Region
#> Statistics
#> N Ndist
#> 5027 6
#> Table
#> Asia Sub-saharan Africa Latin America Europe
#> Freq 1372 1148 1117 1004
#> Perc 27.29 22.84 22.22 19.97
#> Middle East and North Africa North America
#> Freq 257 129
#> Perc 5.11 2.57
#> --------------------------------------------------------------------------------
#> Regioncode (character): Region code
#> Statistics
#> N Ndist
#> 5027 6
#> Table
#> ASI SSA LAM EUR MENA NAM
#> Freq 1372 1148 1117 1004 257 129
#> Perc 27.29 22.84 22.22 19.97 5.11 2.57
#> --------------------------------------------------------------------------------
#> Country (character): Country
#> Statistics
#> N Ndist
#> 5027 43
#> Table
#> USA EGY MOR IDN PHL TWN DNK ESP FRA GBR ITA NLD
#> Freq 129 129 128 126 126 126 126 126 126 126 126 126
#> Perc 2.57 2.57 2.55 2.51 2.51 2.51 2.51 2.51 2.51 2.51 2.51 2.51
#> SWE CHN ... 29 Others
#> Freq 126 125 3256
#> Perc 2.51 2.49 64.77
#>
#> Summary of Table Frequencies
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 4 105 124 117 126 129
#> --------------------------------------------------------------------------------
#> Dataset: GGDC10S, 16 Variables, N = 5027
# For bigger data consider: descr(big_data, stepwise = TRUE)
# Generating a data frame
as.data.frame(descr(wlddev, table = FALSE))
#> Variable Class Label N Ndist Min Max Mean SD
#> 1 country character Country Name 13176 216 NA NA NA NA
#> 2 iso3c factor Country Code 13176 216 NA NA NA NA
#> 3 date Date Date Recorded (Fictitious) 13176 61 -3287 18628 NA NA
#> Skew Kurt 1% 5% 10% 25% 50% 75% 90% 95% 99%
#> 1 NA NA NA NA NA NA NA NA NA NA NA
#> 2 NA NA NA NA NA NA NA NA NA NA NA
#> 3 NA NA NA NA NA NA NA NA NA NA NA
#> [ reached 'max' / getOption("max.print") -- omitted 10 rows ]
## Weighted Desciptions
descr(wlddev, w = ~ replace_na(POP)) # replacing NA's with 0's for fquantile()
#> Dataset: wlddev, 12 Variables, N = 13176, WeightSum = 313233706778
#> --------------------------------------------------------------------------------
#> country (character): Country Name
#> Statistics
#> WeightSum Ndist
#> 3.13233707e+11 216
#> Table
#> WeightSum Perc
#> China 65272180000 20.84
#> India 52835203044 16.87
#> United States 15226426293 4.86
#> Indonesia 10681870259 3.41
#> Brazil 8711884458 2.78
#> Russian Federation 8388319293 2.68
#> Japan 7088669911 2.26
#> Pakistan 6865420747 2.19
#> Bangladesh 6217567789 1.98
#> Nigeria 6191168112 1.98
#> Mexico 4948012523 1.58
#> Germany 4773054666 1.52
#> Vietnam 3955178878 1.26
#> Philippines 3805345278 1.21
#> ... 202 Others 108273405527 34.57
#>
#> Summary of Table WeightSums
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 5.0e+05 3.1e+07 2.6e+08 1.5e+09 8.3e+08 6.5e+10
#> --------------------------------------------------------------------------------
#> iso3c (factor): Country Code
#> Statistics
#> WeightSum Ndist
#> 3.13233707e+11 216
#> Table
#> WeightSum Perc
#> CHN 65272180000 20.84
#> IND 52835203044 16.87
#> USA 15226426293 4.86
#> IDN 10681870259 3.41
#> BRA 8711884458 2.78
#> RUS 8388319293 2.68
#> JPN 7088669911 2.26
#> PAK 6865420747 2.19
#> BGD 6217567789 1.98
#> NGA 6191168112 1.98
#> MEX 4948012523 1.58
#> DEU 4773054666 1.52
#> VNM 3955178878 1.26
#> PHL 3805345278 1.21
#> ... 202 Others 108273405527 34.57
#>
#> Summary of Table WeightSums
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 5.0e+05 3.1e+07 2.6e+08 1.5e+09 8.3e+08 6.5e+10
#> --------------------------------------------------------------------------------
#> date (Date): Date Recorded (Fictitious)
#> Statistics
#> N Ndist Min Max
#> 13176 61 1961-01-01 2021-01-01
#> --------------------------------------------------------------------------------
#> year (integer): Year
#> Statistics (1.95% NAs)
#> N Ndist WeightSum Mean SD Min Max Skew Kurt
#> 12919 61 3.13233707e+11 1994.1 16.75 1960 2019 -0.32 1.97
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95% 99%
#> 1961 1965 1969 1981 1996 2009 2015 2017 2019
#> --------------------------------------------------------------------------------
#> decade (integer): Decade
#> Statistics (1.95% NAs)
#> N Ndist WeightSum Mean SD Min Max Skew Kurt
#> 12919 7 3.13233707e+11 1989.47 16.52 1960 2010 -0.33 1.91
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95% 99%
#> 1960 1960 1960 1980 1990 2000 2010 2010 2010
#> --------------------------------------------------------------------------------
#> region (factor): Region
#> Statistics
#> WeightSum Ndist
#> 3.13233707e+11 7
#> Table
#> WeightSum Perc
#> East Asia & Pacific 103222500256 32.95
#> South Asia 69206603131 22.09
#> Europe & Central Asia 49081067014 15.67
#> Sub-Saharan Africa 33308413094 10.63
#> Latin America & Caribbean 26135854126 8.34
#> North America 16881058226 5.39
#> Middle East & North Africa 15398210931 4.92
#> --------------------------------------------------------------------------------
#> income (factor): Income Level
#> Statistics
#> WeightSum Ndist
#> 3.13233707e+11 4
#> Table
#> WeightSum Perc
#> Upper middle income 119606023798 38.18
#> Lower middle income 113837684528 36.34
#> High income 58840837058 18.78
#> Low income 20949161394 6.69
#> --------------------------------------------------------------------------------
#> OECD (logical): Is OECD Member Country?
#> Statistics
#> WeightSum Ndist
#> 3.13233707e+11 2
#> Table
#> WeightSum Perc
#> FALSE 249344473835 79.6
#> TRUE 63889232943 20.4
#> --------------------------------------------------------------------------------
#> PCGDP (numeric): GDP per capita (constant 2010 US$)
#> Statistics (28.13% NAs)
#> N Ndist WeightSum Mean SD Min Max Skew Kurt
#> 9470 9470 2.95445830e+11 7956.24 12984.91 132.08 196061.42 2.19 7.22
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95%
#> 164.32 263.48 370.57 711.93 1875.79 7786.69 29257.66 41278.55
#> 99%
#> 51732.26
#> --------------------------------------------------------------------------------
#> LIFEEX (numeric): Life expectancy at birth, total (years)
#> Statistics (11.51% NAs)
#> N Ndist WeightSum Mean SD Min Max Skew Kurt
#> 11659 10548 3.12878084e+11 65.88 9.75 18.91 85.42 -0.73 2.96
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95% 99%
#> 41.73 46.11 50.79 60.14 68.29 72.98 76.58 78.69 82.5
#> --------------------------------------------------------------------------------
#> GINI (numeric): Gini index (World Bank estimate)
#> Statistics (86.76% NAs)
#> N Ndist WeightSum Mean SD Min Max Skew Kurt
#> 1744 368 8.26010770e+10 39.52 7.61 20.7 65.8 0.88 3.63
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95% 99%
#> 26 29.2 31.3 34.3 39.16 42.2 52.03 55.6 59.98
#> --------------------------------------------------------------------------------
#> ODA (numeric): Net official development assistance and official aid received (constant 2018 US$)
#> Statistics (34.75% NAs)
#> N Ndist WeightSum Mean SD Min
#> 8597 7832 2.31451603e+11 1.61325042e+09 1.63323654e+09 -997'679993
#> Max Skew Kurt
#> 2.56715605e+10 1.75 11.89
#> Quantiles
#> 1% 5% 10% 25% 50%
#> -907'107452 -327'497633 56'138226.8 332'261483 1.34542532e+09
#> 75% 90% 95% 99%
#> 2.51256879e+09 3.52051981e+09 4.48714786e+09 8.06805848e+09
#> --------------------------------------------------------------------------------
## Grouped Desciptions
descr(GGDC10S, ~ Variable)
#> Dataset: GGDC10S, 15 Variables, N = 5027
#> Grouped by: Variable [2]
#> N Perc
#> EMP 2516 50.05
#> VA 2511 49.95
#> --------------------------------------------------------------------------------
#> Country (character): Country
#> Statistics (N = 5027)
#> N Perc Ndist
#> EMP 2516 50.05 42
#> VA 2511 49.95 43
#>
#> Table (Freq Perc)
#> EMP VA Total
#> USA 64 2.5 65 2.6 129 2.6
#> EGY 65 2.6 64 2.5 129 2.6
#> MOR 65 2.6 63 2.5 128 2.5
#> IDN 63 2.5 63 2.5 126 2.5
#> PHL 63 2.5 63 2.5 126 2.5
#> TWN 63 2.5 63 2.5 126 2.5
#> DNK 64 2.5 62 2.5 126 2.5
#> ESP 64 2.5 62 2.5 126 2.5
#> FRA 64 2.5 62 2.5 126 2.5
#> GBR 64 2.5 62 2.5 126 2.5
#> ITA 64 2.5 62 2.5 126 2.5
#> NLD 64 2.5 62 2.5 126 2.5
#> SWE 64 2.5 62 2.5 126 2.5
#> CHN 62 2.5 63 2.5 125 2.5
#> ... 29 Others 1623 64.5 1633 65.0 3256 64.8
#>
#> Summary of Table Frequencies
#> EMP VA Total
#> Min. : 0.00 Min. : 4.0 Min. : 4.0
#> 1st Qu.:52.00 1st Qu.:53.0 1st Qu.:105.0
#> Median :62.00 Median :62.0 Median :124.0
#> Mean :58.51 Mean :58.4 Mean :116.9
#> 3rd Qu.:63.00 3rd Qu.:62.0 3rd Qu.:126.0
#> Max. :65.00 Max. :65.0 Max. :129.0
#> --------------------------------------------------------------------------------
#> Regioncode (character): Region code
#> Statistics (N = 5027)
#> N Perc Ndist
#> EMP 2516 50.05 6
#> VA 2511 49.95 6
#>
#> Table (Freq Perc)
#> EMP VA Total
#> ASI 684 27.2 688 27.4 1372 27.3
#> SSA 571 22.7 577 23.0 1148 22.8
#> LAM 558 22.2 559 22.3 1117 22.2
#> EUR 509 20.2 495 19.7 1004 20.0
#> MENA 130 5.2 127 5.1 257 5.1
#> NAM 64 2.5 65 2.6 129 2.6
#> --------------------------------------------------------------------------------
#> Region (character): Region
#> Statistics (N = 5027)
#> N Perc Ndist
#> EMP 2516 50.05 6
#> VA 2511 49.95 6
#>
#> Table (Freq Perc)
#> EMP VA Total
#> Asia 684 27.2 688 27.4 1372 27.3
#> Sub-saharan Africa 571 22.7 577 23.0 1148 22.8
#> Latin America 558 22.2 559 22.3 1117 22.2
#> Europe 509 20.2 495 19.7 1004 20.0
#> Middle East and North Africa 130 5.2 127 5.1 257 5.1
#> North America 64 2.5 65 2.6 129 2.6
#> --------------------------------------------------------------------------------
#> Year (numeric): Year
#> Statistics (N = 5027)
#> N Perc Ndist Mean SD Min Max Skew Kurt
#> EMP 2516 50.05 66 1981.38 17.61 1947 2012 -0.05 1.86
#> VA 2511 49.95 67 1981.78 17.53 1947 2013 -0.05 1.85
#>
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95% 99%
#> EMP 1950 1953 1957 1967 1982 1997 2006 2009 2011
#> VA 1950 1953 1958 1967 1982 1997 2006 2009 2011
#> --------------------------------------------------------------------------------
#> AGR (numeric): Agriculture
#> Statistics (N = 4364, 13.19% NAs)
#> N Perc Ndist Mean SD Min Max Skew
#> EMP 2225 50.99 2219 16746.43 55644.84 5.24 390980 4.58
#> VA 2139 49.01 2135 5'137560.88 52'913681.8 0 1.19187778e+09 16.74
#> Kurt
#> EMP 23.76
#> VA 314.45
#>
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95%
#> EMP 7.67 67.33 187.94 752.73 2168.96 5762.23 18285.84 48898.1
#> VA 0 3.11 72.85 1976.5 21040.45 156589.16 2'563619.99 8'693829.74
#> 99%
#> EMP 310671.2
#> VA 47'607622.9
#> --------------------------------------------------------------------------------
#> MIN (numeric): Mining
#> Statistics (N = 4355, 13.37% NAs)
#> N Perc Ndist Mean SD Min Max Skew
#> EMP 2216 50.88 2153 359.61 1295.29 0.11 12908.36 6.64
#> VA 2139 49.12 2072 3'802686.57 46'062895.7 0 1.10344053e+09 17.68
#> Kurt
#> EMP 50.77
#> VA 349.32
#>
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95%
#> EMP 0.21 1.19 2.37 18.05 56.44 144.22 675.54 1145.26
#> VA 0 1.49 12.29 327.55 4642 35481.94 746910.9 1'843491.16
#> 99%
#> EMP 8839.77
#> VA 35'804017.4
#> --------------------------------------------------------------------------------
#> MAN (numeric): Manufacturing
#> Statistics (N = 4355, 13.37% NAs)
#> N Perc Ndist Mean SD Min Max Skew
#> EMP 2216 50.88 2214 5204.33 13924.82 1.04 145898.4 6.18
#> VA 2139 49.12 2139 11'270966.4 89'674720.3 0 1.86843541e+09 14.5
#> Kurt
#> EMP 48.12
#> VA 245.16
#>
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95%
#> EMP 10.85 63.22 114.55 439.01 1188.59 4235.75 11914.54 18920.24
#> VA 0 3 51.07 3220.02 48182.47 267410.31 3'034481.4 24'608297
#> 99%
#> EMP 84869.43
#> VA 220'767719
#> --------------------------------------------------------------------------------
#> PU (numeric): Utilities
#> Statistics (N = 4354, 13.39% NAs)
#> N Perc Ndist Mean SD Min Max Skew
#> EMP 2215 50.87 2141 153.42 365.12 0.12 3903.81 6.47
#> VA 2139 49.13 2097 683126.98 3'643270.26 0 65'324543.8 9.43
#> Kurt
#> EMP 54.8
#> VA 120.69
#>
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95%
#> EMP 1.28 3.97 6.19 14.85 40.69 144.25 356.06 589.6
#> VA 0 0.37 7.7 329.13 5185.74 34756.69 305247.84 1'843310.67
#> 99%
#> EMP 1661.89
#> VA 17'121707.3
#> --------------------------------------------------------------------------------
#> CON (numeric): Construction
#> Statistics (N = 4355, 13.37% NAs)
#> N Perc Ndist Mean SD Min Max Skew
#> EMP 2216 50.88 2209 1793.61 5114.13 1.71 69887.56 7.17
#> VA 2139 49.12 2130 3'666191.22 34'696912 0 860'638677 18.32
#> Kurt
#> EMP 63.74
#> VA 380.89
#>
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95%
#> EMP 11.65 32.58 45.26 140.32 450.36 1664.01 3991.46 5910.12
#> VA 0 1.34 30.97 964.91 12628.28 80096.67 871467.04 7'884387.9
#> 99%
#> EMP 29914.57
#> VA 58'732994.5
#> --------------------------------------------------------------------------------
#> WRT (numeric): Trade, restaurants and hotels
#> Statistics (N = 4355, 13.37% NAs)
#> N Perc Ndist Mean SD Min Max Skew
#> EMP 2216 50.88 2212 4368.38 8616.85 1.64 84165.11 4.29
#> VA 2139 49.12 2132 6'903431.8 52'500538.5 0 1.15497404e+09 14.85
#> Kurt
#> EMP 25.93
#> VA 260.94
#>
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90%
#> EMP 15.15 58.31 111.22 459.61 1447.36 4228.6 11405.12
#> VA 0 3.6 65.03 2955.05 39897.72 256568.47 2'712856.32
#> 95% 99%
#> EMP 18215.36 49580.74
#> VA 18'710060.6 94'112882.1
#> --------------------------------------------------------------------------------
#> TRA (numeric): Transport, storage and communication
#> Statistics (N = 4355, 13.37% NAs)
#> N Perc Ndist Mean SD Min Max Skew
#> EMP 2216 50.88 2203 1442.44 3289.42 1.73 31222.74 5.31
#> VA 2139 49.12 2131 2'998080.02 23'900671.1 0 547'047040 15.96
#> Kurt
#> EMP 36.86
#> VA 297.62
#>
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95%
#> EMP 5.53 22.39 38.99 135.87 406.94 1178.6 3545.03 5644.88
#> VA 0 1.72 28.86 1374.15 18552.4 113658.26 1'098376.42 7'637158.2
#> 99%
#> EMP 20475
#> VA 53'478514.1
#> --------------------------------------------------------------------------------
#> FIRE (numeric): Finance, insurance, real estate and business services
#> Statistics (N = 4355, 13.37% NAs)
#> N Perc Ndist Mean SD Min Max Skew
#> EMP 2216 50.88 2216 1330.68 3113.74 0.78 28092.69 5.07
#> VA 2139 49.12 2133 3'372504.14 19'416463 -2848.81 387'997506 11.55
#> Kurt
#> EMP 35.06
#> VA 176.34
#>
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95%
#> EMP 2.56 7.03 14.42 68.77 298.8 1034.59 3356.25 6749.82
#> VA -48.78 0.14 13.28 894.75 12776.55 143622.56 1'627030.46 11'657805.1
#> 99%
#> EMP 18330.87
#> VA 67'958580.7
#> --------------------------------------------------------------------------------
#> GOV (numeric): Government services
#> Statistics (N = 3482, 30.73% NAs)
#> N Perc Ndist Mean SD Min Max Skew
#> EMP 1780 51.12 1772 4196.81 7278.04 0 44817.34 3.14
#> VA 1702 48.88 1698 3'498683.46 24'143501.2 0 485'535400 13.04
#> Kurt
#> EMP 13.76
#> VA 210.91
#>
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95%
#> EMP 20.8 57.72 120.85 409.9 1413.06 4413.38 10919.28 20592.43
#> VA 0 13.88 129.17 3029.88 37694.55 232193 1'448746.76 4'846006.68
#> 99%
#> EMP 38132.65
#> VA 81'906146
#> --------------------------------------------------------------------------------
#> OTH (numeric): Community, social and personal services
#> Statistics (N = 4248, 15.5% NAs)
#> N Perc Ndist Mean SD Min Max Skew
#> EMP 2109 49.65 2106 2268.11 8022.24 4.07 104517.87 9.48
#> VA 2139 50.35 2132 3'343192.42 21'880087.7 0 402'671182 10.55
#> Kurt
#> EMP 102.76
#> VA 137.84
#>
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95%
#> EMP 20.32 34.82 84.19 233.75 699.45 1672.3 4121.24 7461.79
#> VA 0 2.1 20.09 787.54 10963.92 65040.92 598083.51 8'741638.08
#> 99%
#> EMP 23123.63
#> VA 91'548932.3
#> --------------------------------------------------------------------------------
#> SUM (numeric): Summation of sector GDP
#> Statistics (N = 4364, 13.19% NAs)
#> N Perc Ndist Mean SD Min Max Skew
#> EMP 2225 50.99 2225 36846.87 96318.65 173.88 764200 5.02
#> VA 2139 49.01 2139 43'961639.1 358'350627 0 8.06794210e+09 15.77
#> Kurt
#> EMP 30.98
#> VA 289.46
#>
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90%
#> EMP 256.12 599.38 1599.27 3555.62 9593.98 24801.5 66975.01
#> VA 0 25.01 444.54 21302 243186.47 1'396139.11 15'926968.3
#> 95% 99%
#> EMP 152402.28 550909.6
#> VA 104'405351 692'993893
#> --------------------------------------------------------------------------------
descr(wlddev, ~ income)
#> Dataset: wlddev, 12 Variables, N = 13176
#> Grouped by: income [4]
#> N Perc
#> High income 4819 36.57
#> Low income 1830 13.89
#> Lower middle income 2867 21.76
#> Upper middle income 3660 27.78
#> --------------------------------------------------------------------------------
#> country (character): Country Name
#> Statistics (N = 13176)
#> N Perc Ndist
#> High income 4819 36.57 79
#> Low income 1830 13.89 30
#> Lower middle income 2867 21.76 47
#> Upper middle income 3660 27.78 60
#>
#> Table (Freq Perc)
#> High income Low income Lower middle income
#> Afghanistan 0 0.00 61 3.33 0 0.00
#> Albania 0 0.00 0 0.00 0 0.00
#> Algeria 0 0.00 0 0.00 0 0.00
#> American Samoa 0 0.00 0 0.00 0 0.00
#> Andorra 61 1.27 0 0.00 0 0.00
#> Angola 0 0.00 0 0.00 61 2.13
#> Antigua and Barbuda 61 1.27 0 0.00 0 0.00
#> Argentina 0 0.00 0 0.00 0 0.00
#> Armenia 0 0.00 0 0.00 0 0.00
#> Aruba 61 1.27 0 0.00 0 0.00
#> Australia 61 1.27 0 0.00 0 0.00
#> Austria 61 1.27 0 0.00 0 0.00
#> Azerbaijan 0 0.00 0 0.00 0 0.00
#> Bahamas, The 61 1.27 0 0.00 0 0.00
#> Upper middle income Total
#> Afghanistan 0 0.00 61 0.46
#> Albania 61 1.67 61 0.46
#> Algeria 61 1.67 61 0.46
#> American Samoa 61 1.67 61 0.46
#> Andorra 0 0.00 61 0.46
#> Angola 0 0.00 61 0.46
#> Antigua and Barbuda 0 0.00 61 0.46
#> Argentina 61 1.67 61 0.46
#> Armenia 61 1.67 61 0.46
#> Aruba 0 0.00 61 0.46
#> Australia 0 0.00 61 0.46
#> Austria 0 0.00 61 0.46
#> Azerbaijan 61 1.67 61 0.46
#> Bahamas, The 0 0.00 61 0.46
#> [ reached 'max' / getOption("max.print") -- omitted 1 row ]
#>
#> Summary of Table Frequencies
#> High income Low income Lower middle income Upper middle income
#> Min. : 0.00 Min. : 0.000 Min. : 0.00 Min. : 0.00
#> 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 0.00 1st Qu.: 0.00
#> Median : 0.00 Median : 0.000 Median : 0.00 Median : 0.00
#> Mean :22.31 Mean : 8.472 Mean :13.27 Mean :16.94
#> 3rd Qu.:61.00 3rd Qu.: 0.000 3rd Qu.: 0.00 3rd Qu.:61.00
#> Max. :61.00 Max. :61.000 Max. :61.00 Max. :61.00
#> Total
#> Min. :61
#> 1st Qu.:61
#> Median :61
#> Mean :61
#> 3rd Qu.:61
#> Max. :61
#> --------------------------------------------------------------------------------
#> iso3c (factor): Country Code
#> Statistics (N = 13176)
#> N Perc Ndist
#> High income 4819 36.57 79
#> Low income 1830 13.89 30
#> Lower middle income 2867 21.76 47
#> Upper middle income 3660 27.78 60
#>
#> Table (Freq Perc)
#> High income Low income Lower middle income
#> ABW 61 1.27 0 0.00 0 0.00
#> AFG 0 0.00 61 3.33 0 0.00
#> AGO 0 0.00 0 0.00 61 2.13
#> ALB 0 0.00 0 0.00 0 0.00
#> AND 61 1.27 0 0.00 0 0.00
#> ARE 61 1.27 0 0.00 0 0.00
#> ARG 0 0.00 0 0.00 0 0.00
#> ARM 0 0.00 0 0.00 0 0.00
#> ASM 0 0.00 0 0.00 0 0.00
#> ATG 61 1.27 0 0.00 0 0.00
#> AUS 61 1.27 0 0.00 0 0.00
#> AUT 61 1.27 0 0.00 0 0.00
#> AZE 0 0.00 0 0.00 0 0.00
#> BDI 0 0.00 61 3.33 0 0.00
#> Upper middle income Total
#> ABW 0 0.00 61 0.46
#> AFG 0 0.00 61 0.46
#> AGO 0 0.00 61 0.46
#> ALB 61 1.67 61 0.46
#> AND 0 0.00 61 0.46
#> ARE 0 0.00 61 0.46
#> ARG 61 1.67 61 0.46
#> ARM 61 1.67 61 0.46
#> ASM 61 1.67 61 0.46
#> ATG 0 0.00 61 0.46
#> AUS 0 0.00 61 0.46
#> AUT 0 0.00 61 0.46
#> AZE 61 1.67 61 0.46
#> BDI 0 0.00 61 0.46
#> [ reached 'max' / getOption("max.print") -- omitted 1 row ]
#>
#> Summary of Table Frequencies
#> High income Low income Lower middle income Upper middle income
#> Min. : 0.00 Min. : 0.000 Min. : 0.00 Min. : 0.00
#> 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 0.00 1st Qu.: 0.00
#> Median : 0.00 Median : 0.000 Median : 0.00 Median : 0.00
#> Mean :22.31 Mean : 8.472 Mean :13.27 Mean :16.94
#> 3rd Qu.:61.00 3rd Qu.: 0.000 3rd Qu.: 0.00 3rd Qu.:61.00
#> Max. :61.00 Max. :61.000 Max. :61.00 Max. :61.00
#> Total
#> Min. :61
#> 1st Qu.:61
#> Median :61
#> Mean :61
#> 3rd Qu.:61
#> Max. :61
#> --------------------------------------------------------------------------------
#> date (Date): Date Recorded (Fictitious)
#> Statistics (N = 13176)
#> N Perc Ndist Min Max
#> High income 4819 36.57 61 1961-01-01 2021-01-01
#> Low income 1830 13.89 61 1961-01-01 2021-01-01
#> Lower middle income 2867 21.76 61 1961-01-01 2021-01-01
#> Upper middle income 3660 27.78 61 1961-01-01 2021-01-01
#> --------------------------------------------------------------------------------
#> year (integer): Year
#> Statistics (N = 13176)
#> N Perc Ndist Mean SD Min Max Skew Kurt
#> High income 4819 36.57 61 1990 17.61 1960 2020 0 1.8
#> Low income 1830 13.89 61 1990 17.61 1960 2020 -0 1.8
#> Lower middle income 2867 21.76 61 1990 17.61 1960 2020 -0 1.8
#> Upper middle income 3660 27.78 61 1990 17.61 1960 2020 0 1.8
#>
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95% 99%
#> High income 1960 1963 1966 1975 1990 2005 2014 2017 2020
#> Low income 1960 1963 1966 1975 1990 2005 2014 2017 2020
#> Lower middle income 1960 1963 1966 1975 1990 2005 2014 2017 2020
#> Upper middle income 1960 1963 1966 1975 1990 2005 2014 2017 2020
#> --------------------------------------------------------------------------------
#> decade (integer): Decade
#> Statistics (N = 13176)
#> N Perc Ndist Mean SD Min Max Skew Kurt
#> High income 4819 36.57 7 1985.57 17.51 1960 2020 0.03 1.79
#> Low income 1830 13.89 7 1985.57 17.52 1960 2020 0.03 1.79
#> Lower middle income 2867 21.76 7 1985.57 17.51 1960 2020 0.03 1.79
#> Upper middle income 3660 27.78 7 1985.57 17.51 1960 2020 0.03 1.79
#>
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95% 99%
#> High income 1960 1960 1960 1970 1990 2000 2010 2010 2020
#> Low income 1960 1960 1960 1970 1990 2000 2010 2010 2020
#> Lower middle income 1960 1960 1960 1970 1990 2000 2010 2010 2020
#> Upper middle income 1960 1960 1960 1970 1990 2000 2010 2010 2020
#> --------------------------------------------------------------------------------
#> region (factor): Region
#> Statistics (N = 13176)
#> N Perc Ndist
#> High income 4819 36.57 6
#> Low income 1830 13.89 5
#> Lower middle income 2867 21.76 6
#> Upper middle income 3660 27.78 6
#>
#> Table (Freq Perc)
#> High income Low income Lower middle income
#> Europe & Central Asia 2257 46.8 61 3.3 244 8.5
#> Sub-Saharan Africa 61 1.3 1464 80.0 1037 36.2
#> Latin America & Caribbean 1037 21.5 61 3.3 244 8.5
#> East Asia & Pacific 793 16.5 0 0.0 793 27.7
#> Middle East & North Africa 488 10.1 122 6.7 305 10.6
#> South Asia 0 0.0 122 6.7 244 8.5
#> North America 183 3.8 0 0.0 0 0.0
#> Upper middle income Total
#> Europe & Central Asia 976 26.7 3538 26.9
#> Sub-Saharan Africa 366 10.0 2928 22.2
#> Latin America & Caribbean 1220 33.3 2562 19.4
#> East Asia & Pacific 610 16.7 2196 16.7
#> Middle East & North Africa 366 10.0 1281 9.7
#> South Asia 122 3.3 488 3.7
#> North America 0 0.0 183 1.4
#> --------------------------------------------------------------------------------
#> OECD (logical): Is OECD Member Country?
#> Statistics (N = 13176)
#> N Perc Ndist
#> High income 4819 36.57 2
#> Low income 1830 13.89 1
#> Lower middle income 2867 21.76 1
#> Upper middle income 3660 27.78 2
#>
#> Table (Freq Perc)
#> High income Low income Lower middle income Upper middle income
#> FALSE 2745 57.0 1830 100.0 2867 100.0 3538 96.7
#> TRUE 2074 43.0 0 0.0 0 0.0 122 3.3
#> Total
#> FALSE 10980 83.3
#> TRUE 2196 16.7
#> --------------------------------------------------------------------------------
#> PCGDP (numeric): GDP per capita (constant 2010 US$)
#> Statistics (N = 9470, 28.13% NAs)
#> N Perc Ndist Mean SD Min Max
#> High income 3179 33.57 3179 30280.73 23847.05 932.04 196061.42
#> Low income 1311 13.84 1311 597.41 288.44 164.34 1864.79
#> Lower middle income 2246 23.72 2246 1574.25 858.72 144.99 4818.19
#> Upper middle income 2734 28.87 2734 4945.33 2979.56 132.08 20532.95
#> Skew Kurt
#> High income 2.17 10.34
#> Low income 1.24 4.71
#> Lower middle income 0.91 3.72
#> Upper middle income 1.23 4.94
#>
#> Quantiles
#> 1% 5% 10% 25% 50% 75%
#> High income 3053.83 5395.18 7768.74 14369.61 24745.65 38936.22
#> Low income 191.73 234.77 289.48 396.52 535.96 745.29
#> Lower middle income 194.43 398.88 585.24 961.12 1437.78 1987.89
#> Upper middle income 466.58 1248.19 1835.98 2864.47 4219.97 6452.07
#> 90% 95% 99%
#> High income 57259 75529.1 116493.28
#> Low income 985.45 1180.37 1513.83
#> Lower middle income 2829.09 3192.75 4191.8
#> Upper middle income 8966.02 10867.95 14416.71
#> --------------------------------------------------------------------------------
#> LIFEEX (numeric): Life expectancy at birth, total (years)
#> Statistics (N = 11670, 11.43% NAs)
#> N Perc Ndist Mean SD Min Max Skew Kurt
#> High income 3831 32.83 3566 73.62 5.67 42.67 85.42 -1.01 5.56
#> Low income 1800 15.42 1751 49.73 9.09 26.17 74.43 0.27 2.67
#> Lower middle income 2790 23.91 2694 58.15 9.31 18.91 76.7 -0.34 2.68
#> Upper middle income 3249 27.84 3083 66.65 7.54 36.53 80.28 -1.1 4.23
#>
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95%
#> High income 55.62 63.95 67.12 70.5 73.93 77.61 80.67 81.79
#> Low income 31.5 35.78 38.2 43.52 49.04 56.06 61.98 65.87
#> Lower middle income 36.62 42.66 45.73 51.5 58.53 65.79 70 71.72
#> Upper middle income 42.66 51.22 55.93 62.8 68.36 71.95 74.66 75.94
#> 99%
#> High income 83.22
#> Low income 71.71
#> Lower middle income 74.91
#> Upper middle income 78.42
#> --------------------------------------------------------------------------------
#> GINI (numeric): Gini index (World Bank estimate)
#> Statistics (N = 1744, 86.76% NAs)
#> N Perc Ndist Mean SD Min Max Skew Kurt
#> High income 680 38.99 213 33.3 6.79 20.7 58.9 1.49 5.68
#> Low income 107 6.14 88 41.13 6.58 29.5 65.8 0.75 4.24
#> Lower middle income 369 21.16 219 40.05 9.3 24 63.2 0.44 2.22
#> Upper middle income 588 33.72 280 43.16 8.95 25.2 64.8 0.08 2.35
#>
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95%
#> High income 23.42 25.2 26.39 28.48 32.35 35.5 41.01 48.72
#> Low income 29.81 32.35 33.26 35.65 41.1 44.9 48.26 51.61
#> Lower middle income 24.77 26.84 28.88 32.7 38.7 46.6 54.52 56.94
#> Upper middle income 26.21 27.87 30.6 36.77 42.45 49.5 54.83 58.56
#> 99%
#> High income 56.62
#> Low income 60.99
#> Lower middle income 59.82
#> Upper middle income 63
#> --------------------------------------------------------------------------------
#> ODA (numeric): Net official development assistance and official aid received (constant 2018 US$)
#> Statistics (N = 8608, 34.67% NAs)
#> N Perc Ndist Mean SD
#> High income 1575 18.3 1407 153'663194 425'918409
#> Low income 1692 19.66 1678 631'660165 941'498380
#> Lower middle income 2544 29.55 2503 692'072692 1.02452490e+09
#> Upper middle income 2797 32.49 2700 301'326218 765'116131
#> Min Max Skew Kurt
#> High income -464'709991 4.34612988e+09 5.25 36.27
#> Low income -500000 1.04032100e+10 4.46 32.13
#> Lower middle income -605'969971 1.18790801e+10 3.79 25.24
#> Upper middle income -997'679993 2.56715605e+10 16.31 464.86
#>
#> Quantiles
#> 1% 5% 10% 25%
#> High income -54'802401.1 -755999.99 264000 4'400000.1
#> Low income 1'100000.02 33'997999.8 71'296000.7 151'814999
#> Lower middle income 209999.99 14'721500.3 41'358000.2 100'485003
#> Upper middle income -73'793201.9 4'558000.18 12'666000 38'000000
#> 50% 75% 90% 95%
#> High income 21'209999.1 104'934998 375'347992 661'426996
#> Low income 332'904999 692'777496 1.47914895e+09 2.14049348e+09
#> Lower middle income 336'494995 810'707520 1.84614302e+09 2.59226945e+09
#> Upper middle income 105'139999 311'519989 714'823975 1.18504797e+09
#> 99%
#> High income 2.31632209e+09
#> Low income 4.82899863e+09
#> Lower middle income 4.69573516e+09
#> Upper middle income 2.98750435e+09
#> --------------------------------------------------------------------------------
#> POP (numeric): Population, total
#> Statistics (N = 12919, 1.95% NAs)
#> N Perc Ndist Mean SD Min
#> High income 4737 36.67 4712 12'421540.4 34'160829.5 2833
#> Low income 1792 13.87 1791 11'690380.2 13'942313.8 365047
#> Lower middle income 2790 21.6 2790 40'802037.5 137'302296 41202
#> Upper middle income 3600 27.87 3596 33'223895.5 143'647992 4375
#> Max Skew Kurt
#> High income 328'239523 5.5 39.75
#> Low income 112'078730 3.22 16.57
#> Lower middle income 1.36641775e+09 6.7 52.4
#> Upper middle income 1.39771500e+09 7.53 61.78
#>
#> Quantiles
#> 1% 5% 10% 25%
#> High income 7467.08 18432.4 30517 84449
#> Low income 594755.89 1'206251.55 1'919035.6 3'842838.75
#> Lower middle income 58452.97 105620.75 224651.9 1'188469
#> Upper middle income 7357.28 47202.15 93885.2 609166.25
#> 50% 75% 90% 95%
#> High income 1'632114 8'336605 37'508393.4 58'933084.2
#> Low income 7'181772 13'579920.8 25'964845.3 36'596246.7
#> Lower middle income 5'914923.5 25'966431.8 81'157844 146'949299
#> Upper middle income 3'763490 16'347212.5 47'556659.8 104'771148
#> 99%
#> High income 209'091400
#> Low income 76'539171.5
#> Lower middle income 893'256928
#> Upper middle income 1.02344515e+09
#> --------------------------------------------------------------------------------
print(descr(wlddev, ~ income), compact = TRUE)
#> Dataset: wlddev, 12 Variables, N = 13176
#> Grouped by: income [4]
#> N Perc
#> High income 4819 36.57
#> Low income 1830 13.89
#> Lower middle income 2867 21.76
#> Upper middle income 3660 27.78
#> --------------------------------------------------------------------------------
#> country (character): Country Name
#> Statistics (N = 13176)
#> N Perc Ndist
#> High income 4819 36.57 79
#> Low income 1830 13.89 30
#> Lower middle income 2867 21.76 47
#> Upper middle income 3660 27.78 60
#>
#> Table (Freq Perc)
#> High income Low income Lower middle income
#> Afghanistan 0 0.00 61 3.33 0 0.00
#> Albania 0 0.00 0 0.00 0 0.00
#> Algeria 0 0.00 0 0.00 0 0.00
#> American Samoa 0 0.00 0 0.00 0 0.00
#> Andorra 61 1.27 0 0.00 0 0.00
#> Angola 0 0.00 0 0.00 61 2.13
#> Antigua and Barbuda 61 1.27 0 0.00 0 0.00
#> Argentina 0 0.00 0 0.00 0 0.00
#> Armenia 0 0.00 0 0.00 0 0.00
#> Aruba 61 1.27 0 0.00 0 0.00
#> Australia 61 1.27 0 0.00 0 0.00
#> Austria 61 1.27 0 0.00 0 0.00
#> Azerbaijan 0 0.00 0 0.00 0 0.00
#> Bahamas, The 61 1.27 0 0.00 0 0.00
#> Upper middle income Total
#> Afghanistan 0 0.00 61 0.46
#> Albania 61 1.67 61 0.46
#> Algeria 61 1.67 61 0.46
#> American Samoa 61 1.67 61 0.46
#> Andorra 0 0.00 61 0.46
#> Angola 0 0.00 61 0.46
#> Antigua and Barbuda 0 0.00 61 0.46
#> Argentina 61 1.67 61 0.46
#> Armenia 61 1.67 61 0.46
#> Aruba 0 0.00 61 0.46
#> Australia 0 0.00 61 0.46
#> Austria 0 0.00 61 0.46
#> Azerbaijan 61 1.67 61 0.46
#> Bahamas, The 0 0.00 61 0.46
#> [ reached 'max' / getOption("max.print") -- omitted 1 row ]
#> --------------------------------------------------------------------------------
#> iso3c (factor): Country Code
#> Statistics (N = 13176)
#> N Perc Ndist
#> High income 4819 36.57 79
#> Low income 1830 13.89 30
#> Lower middle income 2867 21.76 47
#> Upper middle income 3660 27.78 60
#>
#> Table (Freq Perc)
#> High income Low income Lower middle income
#> ABW 61 1.27 0 0.00 0 0.00
#> AFG 0 0.00 61 3.33 0 0.00
#> AGO 0 0.00 0 0.00 61 2.13
#> ALB 0 0.00 0 0.00 0 0.00
#> AND 61 1.27 0 0.00 0 0.00
#> ARE 61 1.27 0 0.00 0 0.00
#> ARG 0 0.00 0 0.00 0 0.00
#> ARM 0 0.00 0 0.00 0 0.00
#> ASM 0 0.00 0 0.00 0 0.00
#> ATG 61 1.27 0 0.00 0 0.00
#> AUS 61 1.27 0 0.00 0 0.00
#> AUT 61 1.27 0 0.00 0 0.00
#> AZE 0 0.00 0 0.00 0 0.00
#> BDI 0 0.00 61 3.33 0 0.00
#> Upper middle income Total
#> ABW 0 0.00 61 0.46
#> AFG 0 0.00 61 0.46
#> AGO 0 0.00 61 0.46
#> ALB 61 1.67 61 0.46
#> AND 0 0.00 61 0.46
#> ARE 0 0.00 61 0.46
#> ARG 61 1.67 61 0.46
#> ARM 61 1.67 61 0.46
#> ASM 61 1.67 61 0.46
#> ATG 0 0.00 61 0.46
#> AUS 0 0.00 61 0.46
#> AUT 0 0.00 61 0.46
#> AZE 61 1.67 61 0.46
#> BDI 0 0.00 61 0.46
#> [ reached 'max' / getOption("max.print") -- omitted 1 row ]
#> --------------------------------------------------------------------------------
#> date (Date): Date Recorded (Fictitious)
#> Statistics (N = 13176)
#> N Perc Ndist Min Max
#> High income 4819 36.57 61 1961-01-01 2021-01-01
#> Low income 1830 13.89 61 1961-01-01 2021-01-01
#> Lower middle income 2867 21.76 61 1961-01-01 2021-01-01
#> Upper middle income 3660 27.78 61 1961-01-01 2021-01-01
#> --------------------------------------------------------------------------------
#> year (integer): Year
#> Statistics (N = 13176)
#> N Perc Ndist Mean SD Min Max Skew Kurt
#> High income 4819 36.57 61 1990 17.61 1960 2020 0 1.8
#> Low income 1830 13.89 61 1990 17.61 1960 2020 -0 1.8
#> Lower middle income 2867 21.76 61 1990 17.61 1960 2020 -0 1.8
#> 1% 5% 10% 25% 50% 75% 90% 95% 99%
#> High income 1960 1963 1966 1975 1990 2005 2014 2017 2020
#> Low income 1960 1963 1966 1975 1990 2005 2014 2017 2020
#> Lower middle income 1960 1963 1966 1975 1990 2005 2014 2017 2020
#> [ reached 'max' / getOption("max.print") -- omitted 1 row ]
#> --------------------------------------------------------------------------------
#> decade (integer): Decade
#> Statistics (N = 13176)
#> N Perc Ndist Mean SD Min Max Skew Kurt
#> High income 4819 36.57 7 1985.57 17.51 1960 2020 0.03 1.79
#> Low income 1830 13.89 7 1985.57 17.52 1960 2020 0.03 1.79
#> Lower middle income 2867 21.76 7 1985.57 17.51 1960 2020 0.03 1.79
#> 1% 5% 10% 25% 50% 75% 90% 95% 99%
#> High income 1960 1960 1960 1970 1990 2000 2010 2010 2020
#> Low income 1960 1960 1960 1970 1990 2000 2010 2010 2020
#> Lower middle income 1960 1960 1960 1970 1990 2000 2010 2010 2020
#> [ reached 'max' / getOption("max.print") -- omitted 1 row ]
#> --------------------------------------------------------------------------------
#> region (factor): Region
#> Statistics (N = 13176)
#> N Perc Ndist
#> High income 4819 36.57 6
#> Low income 1830 13.89 5
#> Lower middle income 2867 21.76 6
#> Upper middle income 3660 27.78 6
#>
#> Table (Freq Perc)
#> High income Low income Lower middle income
#> Europe & Central Asia 2257 46.8 61 3.3 244 8.5
#> Sub-Saharan Africa 61 1.3 1464 80.0 1037 36.2
#> Latin America & Caribbean 1037 21.5 61 3.3 244 8.5
#> East Asia & Pacific 793 16.5 0 0.0 793 27.7
#> Middle East & North Africa 488 10.1 122 6.7 305 10.6
#> South Asia 0 0.0 122 6.7 244 8.5
#> North America 183 3.8 0 0.0 0 0.0
#> Upper middle income Total
#> Europe & Central Asia 976 26.7 3538 26.9
#> Sub-Saharan Africa 366 10.0 2928 22.2
#> Latin America & Caribbean 1220 33.3 2562 19.4
#> East Asia & Pacific 610 16.7 2196 16.7
#> Middle East & North Africa 366 10.0 1281 9.7
#> South Asia 122 3.3 488 3.7
#> North America 0 0.0 183 1.4
#> --------------------------------------------------------------------------------
#> OECD (logical): Is OECD Member Country?
#> Statistics (N = 13176)
#> N Perc Ndist
#> High income 4819 36.57 2
#> Low income 1830 13.89 1
#> Lower middle income 2867 21.76 1
#> Upper middle income 3660 27.78 2
#>
#> Table (Freq Perc)
#> High income Low income Lower middle income Upper middle income
#> FALSE 2745 57.0 1830 100.0 2867 100.0 3538 96.7
#> TRUE 2074 43.0 0 0.0 0 0.0 122 3.3
#> Total
#> FALSE 10980 83.3
#> TRUE 2196 16.7
#> --------------------------------------------------------------------------------
#> PCGDP (numeric): GDP per capita (constant 2010 US$)
#> Statistics (N = 9470, 28.13% NAs)
#> N Perc Ndist Mean SD Min Max
#> High income 3179 33.57 3179 30280.73 23847.05 932.04 196061.42
#> Low income 1311 13.84 1311 597.41 288.44 164.34 1864.79
#> Lower middle income 2246 23.72 2246 1574.25 858.72 144.99 4818.19
#> Skew Kurt 1% 5% 10% 25% 50%
#> High income 2.17 10.34 3053.83 5395.18 7768.74 14369.61 24745.65
#> Low income 1.24 4.71 191.73 234.77 289.48 396.52 535.96
#> Lower middle income 0.91 3.72 194.43 398.88 585.24 961.12 1437.78
#> 75% 90% 95% 99%
#> High income 38936.22 57259 75529.1 116493.28
#> Low income 745.29 985.45 1180.37 1513.83
#> Lower middle income 1987.89 2829.09 3192.75 4191.8
#> [ reached 'max' / getOption("max.print") -- omitted 1 row ]
#> --------------------------------------------------------------------------------
#> LIFEEX (numeric): Life expectancy at birth, total (years)
#> Statistics (N = 11670, 11.43% NAs)
#> N Perc Ndist Mean SD Min Max Skew Kurt
#> High income 3831 32.83 3566 73.62 5.67 42.67 85.42 -1.01 5.56
#> Low income 1800 15.42 1751 49.73 9.09 26.17 74.43 0.27 2.67
#> Lower middle income 2790 23.91 2694 58.15 9.31 18.91 76.7 -0.34 2.68
#> 1% 5% 10% 25% 50% 75% 90% 95%
#> High income 55.62 63.95 67.12 70.5 73.93 77.61 80.67 81.79
#> Low income 31.5 35.78 38.2 43.52 49.04 56.06 61.98 65.87
#> Lower middle income 36.62 42.66 45.73 51.5 58.53 65.79 70 71.72
#> 99%
#> High income 83.22
#> Low income 71.71
#> Lower middle income 74.91
#> [ reached 'max' / getOption("max.print") -- omitted 1 row ]
#> --------------------------------------------------------------------------------
#> GINI (numeric): Gini index (World Bank estimate)
#> Statistics (N = 1744, 86.76% NAs)
#> N Perc Ndist Mean SD Min Max Skew Kurt
#> High income 680 38.99 213 33.3 6.79 20.7 58.9 1.49 5.68
#> Low income 107 6.14 88 41.13 6.58 29.5 65.8 0.75 4.24
#> Lower middle income 369 21.16 219 40.05 9.3 24 63.2 0.44 2.22
#> 1% 5% 10% 25% 50% 75% 90% 95%
#> High income 23.42 25.2 26.39 28.48 32.35 35.5 41.01 48.72
#> Low income 29.81 32.35 33.26 35.65 41.1 44.9 48.26 51.61
#> Lower middle income 24.77 26.84 28.88 32.7 38.7 46.6 54.52 56.94
#> 99%
#> High income 56.62
#> Low income 60.99
#> Lower middle income 59.82
#> [ reached 'max' / getOption("max.print") -- omitted 1 row ]
#> --------------------------------------------------------------------------------
#> ODA (numeric): Net official development assistance and official aid received (constant 2018 US$)
#> Statistics (N = 8608, 34.67% NAs)
#> N Perc Ndist Mean SD
#> High income 1575 18.3 1407 153'663194 425'918409
#> Low income 1692 19.66 1678 631'660165 941'498380
#> Lower middle income 2544 29.55 2503 692'072692 1.02452490e+09
#> Min Max Skew Kurt 1%
#> High income -464'709991 4.34612988e+09 5.25 36.27 -54'802401.1
#> Low income -500000 1.04032100e+10 4.46 32.13 1'100000.02
#> Lower middle income -605'969971 1.18790801e+10 3.79 25.24 209999.99
#> 5% 10% 25% 50%
#> High income -755999.99 264000 4'400000.1 21'209999.1
#> Low income 33'997999.8 71'296000.7 151'814999 332'904999
#> Lower middle income 14'721500.3 41'358000.2 100'485003 336'494995
#> 75% 90% 95% 99%
#> High income 104'934998 375'347992 661'426996 2.31632209e+09
#> Low income 692'777496 1.47914895e+09 2.14049348e+09 4.82899863e+09
#> Lower middle income 810'707520 1.84614302e+09 2.59226945e+09 4.69573516e+09
#> [ reached 'max' / getOption("max.print") -- omitted 1 row ]
#> --------------------------------------------------------------------------------
#> POP (numeric): Population, total
#> Statistics (N = 12919, 1.95% NAs)
#> N Perc Ndist Mean SD Min
#> High income 4737 36.67 4712 12'421540.4 34'160829.5 2833
#> Low income 1792 13.87 1791 11'690380.2 13'942313.8 365047
#> Lower middle income 2790 21.6 2790 40'802037.5 137'302296 41202
#> Max Skew Kurt 1% 5%
#> High income 328'239523 5.5 39.75 7467.08 18432.4
#> Low income 112'078730 3.22 16.57 594755.89 1'206251.55
#> Lower middle income 1.36641775e+09 6.7 52.4 58452.97 105620.75
#> 10% 25% 50% 75%
#> High income 30517 84449 1'632114 8'336605
#> Low income 1'919035.6 3'842838.75 7'181772 13'579920.8
#> Lower middle income 224651.9 1'188469 5'914923.5 25'966431.8
#> 90% 95% 99%
#> High income 37'508393.4 58'933084.2 209'091400
#> Low income 25'964845.3 36'596246.7 76'539171.5
#> Lower middle income 81'157844 146'949299 893'256928
#> [ reached 'max' / getOption("max.print") -- omitted 1 row ]
#> --------------------------------------------------------------------------------
## Grouped & Weighted Desciptions
descr(wlddev, ~ income, w = ~ replace_na(POP))
#> Dataset: wlddev, 11 Variables, N = 13176, WeightSum = 313233706778
#> Grouped by: income [4]
#> N Perc WeightSum Perc
#> High income 4819 36.57 5.88408371e+10 18.78
#> Low income 1830 13.89 2.09491614e+10 6.69
#> Lower middle income 2867 21.76 1.13837685e+11 36.34
#> Upper middle income 3660 27.78 1.19606024e+11 38.18
#> --------------------------------------------------------------------------------
#> country (character): Country Name
#> Statistics (WeightSum = 313233706778)
#> WeightSum Perc Ndist
#> High income 5.88408371e+10 18.78 79
#> Low income 2.09491614e+10 6.69 30
#> Lower middle income 1.13837685e+11 36.34 47
#> Upper middle income 1.19606024e+11 38.18 60
#>
#> Table (WeightSum Perc)
#> High income Low income Lower middle income
#> China 0 0.0 0 0.0 0 0.0
#> India 0 0.0 0 0.0 52835203044 46.4
#> United States 15226426293 25.9 0 0.0 0 0.0
#> Indonesia 0 0.0 0 0.0 10681870259 9.4
#> Brazil 0 0.0 0 0.0 0 0.0
#> Russian Federation 0 0.0 0 0.0 0 0.0
#> Japan 7088669911 12.0 0 0.0 0 0.0
#> Pakistan 0 0.0 0 0.0 6865420747 6.0
#> Bangladesh 0 0.0 0 0.0 6217567789 5.5
#> Nigeria 0 0.0 0 0.0 6191168112 5.4
#> Mexico 0 0.0 0 0.0 0 0.0
#> Germany 4773054666 8.1 0 0.0 0 0.0
#> Vietnam 0 0.0 0 0.0 3955178878 3.5
#> Philippines 0 0.0 0 0.0 3805345278 3.3
#> Upper middle income Total
#> China 65272180000 54.6 65272180000 20.8
#> India 0 0.0 52835203044 16.9
#> United States 0 0.0 15226426293 4.9
#> Indonesia 0 0.0 10681870259 3.4
#> Brazil 8711884458 7.3 8711884458 2.8
#> Russian Federation 8388319293 7.0 8388319293 2.7
#> Japan 0 0.0 7088669911 2.3
#> Pakistan 0 0.0 6865420747 2.2
#> Bangladesh 0 0.0 6217567789 2.0
#> Nigeria 0 0.0 6191168112 2.0
#> Mexico 4948012523 4.1 4948012523 1.6
#> Germany 0 0.0 4773054666 1.5
#> Vietnam 0 0.0 3955178878 1.3
#> Philippines 0 0.0 3805345278 1.2
#> [ reached 'max' / getOption("max.print") -- omitted 1 row ]
#>
#> Summary of Table WeightSums
#> High income Low income Lower middle income
#> Min. :0.000e+00 Min. :0.000e+00 Min. :0.000e+00
#> 1st Qu.:0.000e+00 1st Qu.:0.000e+00 1st Qu.:0.000e+00
#> Median :0.000e+00 Median :0.000e+00 Median :0.000e+00
#> Mean :2.724e+08 Mean :9.699e+07 Mean :5.270e+08
#> 3rd Qu.:1.083e+07 3rd Qu.:0.000e+00 3rd Qu.:0.000e+00
#> Max. :1.523e+10 Max. :3.280e+09 Max. :5.284e+10
#> Upper middle income Total
#> Min. :0.000e+00 Min. :5.006e+05
#> 1st Qu.:0.000e+00 1st Qu.:3.067e+07
#> Median :0.000e+00 Median :2.553e+08
#> Mean :5.537e+08 Mean :1.450e+09
#> 3rd Qu.:5.643e+06 3rd Qu.:8.257e+08
#> Max. :6.527e+10 Max. :6.527e+10
#> --------------------------------------------------------------------------------
#> iso3c (factor): Country Code
#> Statistics (WeightSum = 313233706778)
#> WeightSum Perc Ndist
#> High income 5.88408371e+10 18.78 79
#> Low income 2.09491614e+10 6.69 30
#> Lower middle income 1.13837685e+11 36.34 47
#> Upper middle income 1.19606024e+11 38.18 60
#>
#> Table (WeightSum Perc)
#> High income Low income Lower middle income
#> CHN 0 0.0 0 0.0 0 0.0
#> IND 0 0.0 0 0.0 52835203044 46.4
#> USA 15226426293 25.9 0 0.0 0 0.0
#> IDN 0 0.0 0 0.0 10681870259 9.4
#> BRA 0 0.0 0 0.0 0 0.0
#> RUS 0 0.0 0 0.0 0 0.0
#> JPN 7088669911 12.0 0 0.0 0 0.0
#> PAK 0 0.0 0 0.0 6865420747 6.0
#> BGD 0 0.0 0 0.0 6217567789 5.5
#> NGA 0 0.0 0 0.0 6191168112 5.4
#> MEX 0 0.0 0 0.0 0 0.0
#> DEU 4773054666 8.1 0 0.0 0 0.0
#> VNM 0 0.0 0 0.0 3955178878 3.5
#> PHL 0 0.0 0 0.0 3805345278 3.3
#> Upper middle income Total
#> CHN 65272180000 54.6 65272180000 20.8
#> IND 0 0.0 52835203044 16.9
#> USA 0 0.0 15226426293 4.9
#> IDN 0 0.0 10681870259 3.4
#> BRA 8711884458 7.3 8711884458 2.8
#> RUS 8388319293 7.0 8388319293 2.7
#> JPN 0 0.0 7088669911 2.3
#> PAK 0 0.0 6865420747 2.2
#> BGD 0 0.0 6217567789 2.0
#> NGA 0 0.0 6191168112 2.0
#> MEX 4948012523 4.1 4948012523 1.6
#> DEU 0 0.0 4773054666 1.5
#> VNM 0 0.0 3955178878 1.3
#> PHL 0 0.0 3805345278 1.2
#> [ reached 'max' / getOption("max.print") -- omitted 1 row ]
#>
#> Summary of Table WeightSums
#> High income Low income Lower middle income
#> Min. :0.000e+00 Min. :0.000e+00 Min. :0.000e+00
#> 1st Qu.:0.000e+00 1st Qu.:0.000e+00 1st Qu.:0.000e+00
#> Median :0.000e+00 Median :0.000e+00 Median :0.000e+00
#> Mean :2.724e+08 Mean :9.699e+07 Mean :5.270e+08
#> 3rd Qu.:1.083e+07 3rd Qu.:0.000e+00 3rd Qu.:0.000e+00
#> Max. :1.523e+10 Max. :3.280e+09 Max. :5.284e+10
#> Upper middle income Total
#> Min. :0.000e+00 Min. :5.006e+05
#> 1st Qu.:0.000e+00 1st Qu.:3.067e+07
#> Median :0.000e+00 Median :2.553e+08
#> Mean :5.537e+08 Mean :1.450e+09
#> 3rd Qu.:5.643e+06 3rd Qu.:8.257e+08
#> Max. :6.527e+10 Max. :6.527e+10
#> --------------------------------------------------------------------------------
#> date (Date): Date Recorded (Fictitious)
#> Statistics (N = 13176)
#> N Perc Ndist Min Max
#> High income 4819 36.57 61 1961-01-01 2021-01-01
#> Low income 1830 13.89 61 1961-01-01 2021-01-01
#> Lower middle income 2867 21.76 61 1961-01-01 2021-01-01
#> Upper middle income 3660 27.78 61 1961-01-01 2021-01-01
#> --------------------------------------------------------------------------------
#> year (integer): Year
#> Statistics (N = 12919, 1.95% NAs)
#> N Ndist WeightSum Perc Mean SD Min
#> High income 4737 61 5.88408371e+10 18.78 1991.75 17.15 1960
#> Low income 1792 61 2.09491614e+10 6.69 1997.27 16.31 1960
#> Lower middle income 2790 61 1.13837685e+11 36.34 1995.41 16.47 1960
#> Upper middle income 3600 61 1.19606024e+11 38.18 1993.44 16.69 1960
#> Max Skew Kurt
#> High income 2019 -0.15 1.84
#> Low income 2019 -0.56 2.23
#> Lower middle income 2019 -0.42 2.08
#> Upper middle income 2019 -0.27 1.95
#>
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95% 99%
#> High income 1960 1963 1967 1977 1993 2007 2015 2017 2019
#> Low income 1961 1966 1972 1985 2001 2011 2016 2018 2019
#> Lower middle income 1961 1965 1970 1983 1998 2010 2016 2018 2019
#> Upper middle income 1961 1964 1969 1980 1995 2008 2015 2017 2019
#> --------------------------------------------------------------------------------
#> decade (integer): Decade
#> Statistics (N = 12919, 1.95% NAs)
#> N Ndist WeightSum Perc Mean SD Min
#> High income 4737 7 5.88408371e+10 18.78 1987.19 16.92 1960
#> Low income 1792 7 2.09491614e+10 6.69 1992.55 16.05 1960
#> Lower middle income 2790 7 1.13837685e+11 36.34 1990.76 16.25 1960
#> Upper middle income 3600 7 1.19606024e+11 38.18 1988.84 16.48 1960
#> Max Skew Kurt
#> High income 2010 -0.16 1.77
#> Low income 2010 -0.59 2.18
#> Lower middle income 2010 -0.44 2.02
#> Upper middle income 2010 -0.28 1.88
#>
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95% 99%
#> High income 1960 1960 1960 1970 1990 2000 2010 2010 2010
#> Low income 1960 1960 1970 1980 2000 2010 2010 2010 2010
#> Lower middle income 1960 1960 1970 1980 1990 2010 2010 2010 2010
#> Upper middle income 1960 1960 1960 1980 1990 2000 2010 2010 2010
#> --------------------------------------------------------------------------------
#> region (factor): Region
#> Statistics (WeightSum = 313233706778)
#> WeightSum Perc Ndist
#> High income 5.88408371e+10 18.78 6
#> Low income 2.09491614e+10 6.69 5
#> Lower middle income 1.13837685e+11 36.34 6
#> Upper middle income 1.19606024e+11 38.18 6
#>
#> Table (WeightSum Perc)
#> High income Low income
#> East Asia & Pacific 11407808149 19.3876 0 0.0000
#> South Asia 0 0.0000 2252259680 10.7511
#> Europe & Central Asia 27285316560 46.3714 311485944 1.4869
#> Sub-Saharan Africa 4222055 0.0072 16384603068 78.2113
#> Latin America & Caribbean 1466292826 2.4920 429756890 2.0514
#> North America 16881058226 28.6894 0 0.0000
#> Middle East & North Africa 1796139242 3.0525 1571055812 7.4994
#> Lower middle income Upper middle income
#> East Asia & Pacific 22174820629 19.4793 69639871478 58.2244
#> South Asia 65947845945 57.9315 1006497506 0.8415
#> Europe & Central Asia 4511786205 3.9634 16972478305 14.1903
#> Sub-Saharan Africa 14399976836 12.6496 2519611135 2.1066
#> Latin America & Caribbean 1290800630 1.1339 22949003780 19.1872
#> North America 0 0.0000 0 0.0000
#> Middle East & North Africa 5512454283 4.8424 6518561594 5.4500
#> Total
#> East Asia & Pacific 103222500256 32.9538
#> South Asia 69206603131 22.0942
#> Europe & Central Asia 49081067014 15.6692
#> Sub-Saharan Africa 33308413094 10.6337
#> Latin America & Caribbean 26135854126 8.3439
#> North America 16881058226 5.3893
#> Middle East & North Africa 15398210931 4.9159
#> --------------------------------------------------------------------------------
#> OECD (logical): Is OECD Member Country?
#> Statistics (WeightSum = 313233706778)
#> WeightSum Perc Ndist
#> High income 5.88408371e+10 18.78 2
#> Low income 2.09491614e+10 6.69 1
#> Lower middle income 1.13837685e+11 36.34 1
#> Upper middle income 1.19606024e+11 38.18 2
#>
#> Table (WeightSum Perc)
#> High income Low income Lower middle income
#> FALSE 3113623360 5.3 20949161394 100.0 113837684528 100.0
#> TRUE 55727213698 94.7 0 0.0 0 0.0
#> Upper middle income Total
#> FALSE 111444004553 93.2 249344473835 79.6
#> TRUE 8162019245 6.8 63889232943 20.4
#> --------------------------------------------------------------------------------
#> PCGDP (numeric): GDP per capita (constant 2010 US$)
#> Statistics (N = 9470, 28.13% NAs)
#> N Ndist WeightSum Perc Mean SD
#> High income 3179 3179 5.55288564e+10 18.79 31284.74 13807.6
#> Low income 1311 1311 1.69031453e+10 5.72 557.14 279.41
#> Lower middle income 2246 2246 1.10267107e+11 37.32 1238.83 823.89
#> Upper middle income 2734 2734 1.12746722e+11 38.16 4145.68 3515.97
#> Min Max Skew Kurt
#> High income 932.04 196061.42 0.2 3.21
#> Low income 164.34 1864.79 1.07 4.08
#> Lower middle income 144.99 4818.19 1.25 4.57
#> Upper middle income 132.08 20532.95 0.66 2.52
#>
#> Quantiles
#> 1% 5% 10% 25% 50% 75%
#> High income 3268.18 8837.9 13369.8 20951.55 31079.21 41791.15
#> Low income 179.91 205 234.62 356.02 495.11 710.27
#> Lower middle income 236.75 365.72 396.21 580.69 1039.84 1665.26
#> Upper middle income 141.8 196.78 260.78 781.64 3543.37 6803.24
#> 90% 95% 99%
#> High income 48866.19 52164.71 60264.2
#> Low income 945.3 1128.55 1396.37
#> Lower middle income 2337.98 2885.88 3973.91
#> Upper middle income 9014.51 10737.23 12916.96
#> --------------------------------------------------------------------------------
#> LIFEEX (numeric): Life expectancy at birth, total (years)
#> Statistics (N = 11659, 11.51% NAs)
#> N Ndist WeightSum Perc Mean SD Min
#> High income 3828 3566 5.87959699e+10 18.79 75.69 4.53 42.67
#> Low income 1792 1751 2.09491614e+10 6.7 53.51 8.87 26.17
#> Lower middle income 2790 2694 1.13837685e+11 36.38 60.59 8.36 18.91
#> Upper middle income 3249 3083 1.19295269e+11 38.13 68.27 7.19 36.53
#> Max Skew Kurt
#> High income 85.42 -0.73 4.94
#> Low income 74.43 -0.01 2.47
#> Lower middle income 76.7 -0.56 2.52
#> Upper middle income 80.28 -1.42 4.95
#>
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95%
#> High income 62.63 69.38 70.21 72.5 76.03 78.74 81.44 82.57
#> Low income 33.82 39.38 42.65 46.95 53 60.36 65.27 67.1
#> Lower middle income 41.43 45.37 47.75 54.69 62.3 67.54 69.77 71.27
#> Upper middle income 44.11 51.87 58.2 65.86 69.5 73.51 75.62 76.45
#> 99%
#> High income 83.79
#> Low income 72.68
#> Lower middle income 74.91
#> Upper middle income 76.91
#> --------------------------------------------------------------------------------
#> GINI (numeric): Gini index (World Bank estimate)
#> Statistics (N = 1744, 86.76% NAs)
#> N Ndist WeightSum Perc Mean SD Min Max
#> High income 680 213 2.07396836e+10 25.11 36.03 4.93 20.7 58.9
#> Low income 107 88 1.90256783e+09 2.3 39.76 5.99 29.5 65.8
#> Lower middle income 369 219 2.16883977e+10 26.26 35.16 5.52 24 63.2
#> Upper middle income 588 280 3.82704279e+10 46.33 43.88 7.52 25.2 64.8
#> Skew Kurt
#> High income 0.18 3.61
#> Low income 0.58 4.23
#> Lower middle income 1.44 6.24
#> Upper middle income 0.68 2.86
#>
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95%
#> High income 25.38 28.18 29.87 32.3 35.35 40.47 41.1 41.4
#> Low income 29.83 30.14 32.84 35 40.28 43.55 46.13 48.5
#> Lower middle income 24.95 28.63 29.8 31.77 34.4 37 41.6 46.5
#> Upper middle income 28.35 34.4 36.6 38.7 42 48.7 55.6 58.93
#> 99%
#> High income 47.26
#> Low income 53.98
#> Lower middle income 55.5
#> Upper middle income 61.32
#> --------------------------------------------------------------------------------
#> ODA (numeric): Net official development assistance and official aid received (constant 2018 US$)
#> Statistics (N = 8597, 34.75% NAs)
#> N Ndist WeightSum Perc Mean
#> High income 1572 1407 5.60343429e+09 2.42 469'519277
#> Low income 1684 1678 2.05403995e+10 8.87 1.27976069e+09
#> Lower middle income 2544 2503 1.11067918e+11 47.99 2.23495473e+09
#> Upper middle income 2797 2700 9.42398516e+10 40.72 1.02122309e+09
#> SD Min Max Skew Kurt
#> High income 823'186883 -464'709991 4.34612988e+09 2.18 8.3
#> Low income 1.36723776e+09 -500000 1.04032100e+10 2.17 9.67
#> Lower middle income 1.71188944e+09 -605'969971 1.18790801e+10 1.48 6.42
#> Upper middle income 1.31969536e+09 -997'679993 2.56715605e+10 2.58 38.51
#>
#> Quantiles
#> 1% 5% 10% 25%
#> High income -211'221010 -111'039383 -51'563267 12'609999.7
#> Low income 18'581027.3 102'699417 170'565775 340'775627
#> Lower middle income -1'186156.61 169'613235 351'778004 955'472807
#> Upper middle income -951'313387 -674'066685 -464'822389 127'570913
#> 50% 75% 90%
#> High income 73'609429.8 636'421870 1.67398341e+09
#> Low income 792'634263 1.73644025e+09 3.15899287e+09
#> Lower middle income 2.02836117e+09 2.90852841e+09 4.33204816e+09
#> Upper middle income 653'751768 1.89074430e+09 2.85101616e+09
#> 95% 99%
#> High income 1.97758747e+09 4.03283599e+09
#> Low income 4.28987656e+09 6.46379868e+09
#> Lower middle income 5.37706240e+09 8.56557908e+09
#> Upper middle income 3.48410000e+09 3.84146040e+09
#> --------------------------------------------------------------------------------
## Passing Arguments down to qsu.default: for Panel Data Statistics
descr(iris, pid = iris$Species)
#> Dataset: iris, 5 Variables, N = 150
#> --------------------------------------------------------------------------------
#> Sepal.Length (numeric):
#> Statistics
#> N/T Mean SD Min Max Skew Kurt
#> Overall 150 5.84 0.83 4.3 7.9 0.31 2.43
#> Between 3 5.84 0.8 5.01 6.59 -0.21 1.5
#> Within 50 5.84 0.51 4.16 7.16 0.12 3.26
#>
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95% 99%
#> 4.4 4.6 4.8 5.1 5.8 6.4 6.9 7.25 7.7
#> --------------------------------------------------------------------------------
#> Sepal.Width (numeric):
#> Statistics
#> N/T Mean SD Min Max Skew Kurt
#> Overall 150 3.06 0.44 2 4.4 0.32 3.18
#> Between 3 3.06 0.34 2.77 3.43 0.43 1.5
#> Within 50 3.06 0.34 1.93 4.03 0.03 3.51
#>
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95% 99%
#> 2.2 2.34 2.5 2.8 3 3.3 3.61 3.8 4.15
#> --------------------------------------------------------------------------------
#> Petal.Length (numeric):
#> Statistics
#> N/T Mean SD Min Max Skew Kurt
#> Overall 150 3.76 1.77 1 6.9 -0.27 1.6
#> Between 3 3.76 2.09 1.46 5.55 -0.42 1.5
#> Within 50 3.76 0.43 2.5 5.11 0.12 3.89
#>
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95% 99%
#> 1.15 1.3 1.4 1.6 4.35 5.1 5.8 6.1 6.7
#> --------------------------------------------------------------------------------
#> Petal.Width (numeric):
#> Statistics
#> N/T Mean SD Min Max Skew Kurt
#> Overall 150 1.2 0.76 0.1 2.5 -0.1 1.66
#> Between 3 1.2 0.9 0.25 2.03 -0.25 1.5
#> Within 50 1.2 0.2 0.57 1.67 -0.05 3.36
#>
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95% 99%
#> 0.1 0.2 0.2 0.3 1.3 1.8 2.2 2.3 2.5
#> --------------------------------------------------------------------------------
#> Species (factor):
#> Statistics
#> N Ndist
#> 150 3
#>
#> Table
#> Freq Perc
#> setosa 50 33.33
#> versicolor 50 33.33
#> virginica 50 33.33
#> --------------------------------------------------------------------------------
descr(wlddev, pid = wlddev$iso3c)
#> Dataset: wlddev, 13 Variables, N = 13176
#> --------------------------------------------------------------------------------
#> country (character): Country Name
#> Statistics
#> N Ndist
#> 13176 216
#>
#> Table
#> Freq Perc
#> Afghanistan 61 0.46
#> Albania 61 0.46
#> Algeria 61 0.46
#> American Samoa 61 0.46
#> Andorra 61 0.46
#> Angola 61 0.46
#> Antigua and Barbuda 61 0.46
#> Argentina 61 0.46
#> Armenia 61 0.46
#> Aruba 61 0.46
#> Australia 61 0.46
#> Austria 61 0.46
#> Azerbaijan 61 0.46
#> Bahamas, The 61 0.46
#> ... 202 Others 12322 93.52
#>
#> Summary of Table Frequencies
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 61 61 61 61 61 61
#> --------------------------------------------------------------------------------
#> iso3c (factor): Country Code
#> Statistics
#> N Ndist
#> 13176 216
#>
#> Table
#> Freq Perc
#> ABW 61 0.46
#> AFG 61 0.46
#> AGO 61 0.46
#> ALB 61 0.46
#> AND 61 0.46
#> ARE 61 0.46
#> ARG 61 0.46
#> ARM 61 0.46
#> ASM 61 0.46
#> ATG 61 0.46
#> AUS 61 0.46
#> AUT 61 0.46
#> AZE 61 0.46
#> BDI 61 0.46
#> ... 202 Others 12322 93.52
#>
#> Summary of Table Frequencies
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 61 61 61 61 61 61
#> --------------------------------------------------------------------------------
#> date (Date): Date Recorded (Fictitious)
#> Statistics
#> N Ndist Min Max
#> 13176 61 1961-01-01 2021-01-01
#> --------------------------------------------------------------------------------
#> year (integer): Year
#> Statistics
#> N/T Mean SD Min Max Skew Kurt
#> Overall 13176 1990 17.61 1960 2020 -0 1.8
#> Between 216 1990 0 1990 1990 - -
#> Within 61 1990 17.61 1960 2020 -0 1.8
#>
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95% 99%
#> 1960 1963 1966 1975 1990 2005 2014 2017 2020
#> --------------------------------------------------------------------------------
#> decade (integer): Decade
#> Statistics
#> N/T Mean SD Min Max Skew Kurt
#> Overall 13176 1985.57 17.51 1960 2020 0.03 1.79
#> Between 216 1985.57 0 1985.57 1985.57 - -
#> Within 61 1985.57 17.51 1960 2020 0.03 1.79
#>
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95% 99%
#> 1960 1960 1960 1970 1990 2000 2010 2010 2020
#> --------------------------------------------------------------------------------
#> region (factor): Region
#> Statistics
#> N Ndist
#> 13176 7
#>
#> Table
#> Freq Perc
#> Europe & Central Asia 3538 26.85
#> Sub-Saharan Africa 2928 22.22
#> Latin America & Caribbean 2562 19.44
#> East Asia & Pacific 2196 16.67
#> Middle East & North Africa 1281 9.72
#> South Asia 488 3.70
#> North America 183 1.39
#> --------------------------------------------------------------------------------
#> income (factor): Income Level
#> Statistics
#> N Ndist
#> 13176 4
#>
#> Table
#> Freq Perc
#> High income 4819 36.57
#> Upper middle income 3660 27.78
#> Lower middle income 2867 21.76
#> Low income 1830 13.89
#> --------------------------------------------------------------------------------
#> OECD (logical): Is OECD Member Country?
#> Statistics
#> N Ndist
#> 13176 2
#>
#> Table
#> Freq Perc
#> FALSE 10980 83.33
#> TRUE 2196 16.67
#> --------------------------------------------------------------------------------
#> PCGDP (numeric): GDP per capita (constant 2010 US$)
#> Statistics (28.13% NAs)
#> N/T Mean SD Min Max Skew Kurt
#> Overall 9470 12048.78 19077.64 132.08 196061.42 3.13 17.12
#> Between 206 12962.61 20189.9 253.19 141200.38 3.13 16.23
#> Within 45.97 12048.78 6723.68 -33504.87 76767.53 0.66 17.2
#>
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95%
#> 227.71 399.62 555.55 1303.19 3767.16 14787.03 35646.02 48507.84
#> 99%
#> 92340.28
#> --------------------------------------------------------------------------------
#> LIFEEX (numeric): Life expectancy at birth, total (years)
#> Statistics (11.43% NAs)
#> N/T Mean SD Min Max Skew Kurt
#> Overall 11670 64.3 11.48 18.91 85.42 -0.67 2.67
#> Between 207 64.95 9.89 40.97 85.42 -0.5 2.17
#> Within 56.38 64.3 6.08 32.91 84.42 -0.26 3.7
#>
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95% 99%
#> 35.83 42.77 46.83 56.36 67.44 72.95 77.08 79.34 82.36
#> --------------------------------------------------------------------------------
#> GINI (numeric): Gini index (World Bank estimate)
#> Statistics (86.76% NAs)
#> N/T Mean SD Min Max Skew Kurt
#> Overall 1744 38.53 9.2 20.7 65.8 0.6 2.53
#> Between 167 39.42 8.14 24.87 61.71 0.58 2.83
#> Within 10.44 38.53 2.93 25.39 55.36 0.33 5.34
#>
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90% 95% 99%
#> 24.6 26.3 27.6 31.5 36.4 45 52.6 55.98 60.5
#> --------------------------------------------------------------------------------
#> ODA (numeric): Net official development assistance and official aid received (constant 2018 US$)
#> Statistics (34.67% NAs)
#> N/T Mean SD Min Max Skew
#> Overall 8608 454'720131 868'712654 -997'679993 2.56715605e+10 6.98
#> Between 178 439'168412 569'049959 468717.92 3.62337432e+09 2.36
#> Within 48.36 454'720131 650'709624 -2.44379420e+09 2.45610972e+10 9.6
#> Kurt
#> Overall 114.89
#> Between 9.95
#> Within 263.37
#>
#> Quantiles
#> 1% 5% 10% 25% 50% 75%
#> -12'593999.7 1'363500.01 8'347000.31 44'887499.8 165'970001 495'042503
#> 90% 95% 99%
#> 1.18400697e+09 1.93281696e+09 3.73380782e+09
#> --------------------------------------------------------------------------------
#> POP (numeric): Population, total
#> Statistics (1.95% NAs)
#> N/T Mean SD Min Max Skew
#> Overall 12919 24'245971.6 102'120674 2833 1.39771500e+09 9.75
#> Between 216 24'178573 98'616506.7 8343.33 1.08786967e+09 9
#> Within 59.81 24'245971.6 26'803077.4 -405'793067 510'077008 -0.41
#> Kurt
#> Overall 108.91
#> Between 90.02
#> Within 149.24
#>
#> Quantiles
#> 1% 5% 10% 25% 50% 75% 90%
#> 8698.84 31083.3 62268.4 443791 4'072517 12'816178 46'637331.4
#> 95% 99%
#> 81'177252.5 308'862641
#> --------------------------------------------------------------------------------