descr offers a concise description of each variable in a data frame. It is built as a wrapper around qsu, but also computes frequency tables for categorical variables, and quantiles and the number of distinct values for numeric variables.

descr(X, 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),
      cols = NULL, label.attr = "label", stepwise = FALSE, ...)

# S3 method for descr
[(x, ...)

# S3 method for descr
as.data.frame(x, ...)

# S3 method for descr
print(x, n = 14, perc = TRUE, digits = 2, t.table = TRUE,
      summary = TRUE, reverse = FALSE, stepwise = FALSE, ...)

Arguments

X

a 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.

Ndistinct

logical. TRUE (default) computes the number of distinct values on all variables using fndistinct.

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.

cols

select columns to describe using column names, indices, a logical vector or a function (e.g. is.numeric).

label.attr

character. The name of a label attribute to display for each variable (if variables are labeled).

...

for descr: other arguments passed to qsu.default. For [.descr: variable names or indices passed to [.list. The argument is unused in the print and as.data.frame methods.

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 grouped into an '... %s Others' category.

perc

logical. TRUE (default) adds percentages to the frequencies in the table for categorical variables.

digits

integer. The number of decimals to print in statistics and percentage tables.

t.table

logical. TRUE (default) prints a transposed table.

summary

logical. TRUE (default) computes and displays a summary of the frequencies, if the size of the table for a categorical variable exceeds n.

reverse

logical. TRUE prints contents in reverse order, starting with the last column, so that the dataset can be analyzed by scrolling up the console after calling descr.

stepwise

logical. TRUE prints one variable at a time. The user needs to press [enter] to see the printout for the next variable. If called from descr, the computation is also done one variable at a time, and the finished 'descr' object is returned invisibly. This is recommended for larger datasets, where precomputing the statistics for all variables can be time consuming.

Details

descr was heavily inspired by Hmisc::describe, but computes about 10x faster. The performance is comparable to summary. descr was built as a wrapper around qsu, to enrich the set of statistics computed by qsu for both numeric and categorical variables.

qsu itself is yet about 10x faster than descr, and is optimized for grouped, panel data and weighted statistics. It is possible to also compute grouped, panel data and/or weighted statistics with descr by passing group-ids to g, panel-ids to pid or a weight vector to w. These arguments are handed down to qsu.default and only affect the statistics natively computed by qsu, i.e. passing a weight vector produces a weighted mean, sd, skewness and kurtosis but not weighted quantiles.

The list-object returned from descr can be converted to a tidy data frame using as.data.frame. This representation will not include frequency tables computed for categorical variables, and the method cannot handle arrays of statistics (applicable when g or pid arguments are passed to descr, in that case as.data.frame.descr will throw an appropriate error).

Value

A 2-level nested list, the top-level containing 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. The object is given a class 'descr' and also has the number of observations in the dataset attached as an 'N' attribute, as well as an attribute 'arstat' indicating whether the object contains arrays of statistics, and an attribute 'table' indicating whether table = TRUE (i.e. the object could contain tables for categorical variables).

Examples

## Standard 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 ]

## 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
#> --------------------------------------------------------------------------------

## Grouped Statistics
descr(iris, g = iris$Species)
#> Dataset: iris, 5 Variables, N = 150
#> --------------------------------------------------------------------------------
#> Sepal.Length (numeric): 
#> Statistics (66.67% NAs)
#>              N  Mean    SD  Min  Max  Skew  Kurt
#> setosa      50  5.01  0.35  4.3  5.8  0.12  2.65
#> versicolor  50  5.94  0.52  4.9    7   0.1   2.4
#> virginica   50  6.59  0.64  4.9  7.9  0.11  2.91
#> 
#> 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 (66.67% NAs)
#>              N  Mean    SD  Min  Max   Skew  Kurt
#> setosa      50  3.43  0.38  2.3  4.4   0.04  3.74
#> versicolor  50  2.77  0.31    2  3.4  -0.35  2.55
#> virginica   50  2.97  0.32  2.2  3.8   0.35  3.52
#> 
#> 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 (66.67% NAs)
#>              N  Mean    SD  Min  Max   Skew  Kurt
#> setosa      50  1.46  0.17    1  1.9    0.1   3.8
#> versicolor  50  4.26  0.47    3  5.1  -0.59  2.93
#> virginica   50  5.55  0.55  4.5  6.9   0.53  2.74
#> 
#> 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 (66.67% NAs)
#>              N  Mean    SD  Min  Max   Skew  Kurt
#> setosa      50  0.25  0.11  0.1  0.6   1.22  4.43
#> versicolor  50  1.33   0.2    1  1.8  -0.03  2.51
#> virginica   50  2.03  0.27  1.4  2.5  -0.13  2.34
#> 
#> 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(GGDC10S, g = GGDC10S$Region)
#> 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 (72.71% NAs)
#>                                  N     Mean     SD   Min   Max  Skew  Kurt
#> Asia                          1372  1980.69  18.01  1950  2012     0   1.8
#> Europe                        1004  1979.99  18.14  1948  2011    -0   1.8
#> Latin America                 1117  1980.53  17.92  1950  2012     0   1.8
#> Middle East and North Africa   257  1980.62   18.6  1948  2013    -0   1.8
#> North America                  129  1978.75  18.69  1947  2011     0   1.8
#> Sub-saharan Africa            1148  1985.59  15.07  1960  2013     0   1.8
#> 
#> Quantiles
#>     1%    5%   10%   25%   50%   75%   90%   95%   99%
#>   1950  1953  1957  1967  1982  1997  2006  2009  2011
#> --------------------------------------------------------------------------------
#> AGR (numeric): Agriculture 
#> Statistics (78.06% NAs)
#>                                  N         Mean           SD      Min
#> Asia                          1103  9'239810.75  73'388917.1     5.24
#> Europe                         791      8703.97     11906.16    69.65
#> Latin America                 1049    485922.33  3'170330.86        0
#> Middle East and North Africa   203     21797.51     38344.85   412.17
#> North America                  125     33982.97     42429.57  2061.82
#> Sub-saharan Africa            1093    283350.54   1'427564.7     0.04
#>                                          Max   Skew    Kurt
#> Asia                          1.19187778e+09  12.01  162.23
#> Europe                              57488.44   1.61    4.76
#> Latin America                      39'194000   8.82   86.32
#> Middle East and North Africa        243355.5   2.91   13.21
#> North America                         161017   1.25    3.36
#> Sub-saharan Africa               17'625142.9   7.33   64.98
#> 
#> 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 (78.06% NAs)
#>                                  N         Mean           SD     Min
#> Asia                          1103  6'714864.24  63'913433.6    0.16
#> Europe                         782      3388.68       7699.3     2.8
#> Latin America                 1049     501758.2  3'672600.12       0
#> Middle East and North Africa   203      10615.2     36879.15   10.34
#> North America                  125     35859.04     61778.02  550.95
#> Sub-saharan Africa            1093    176227.49  1'195624.72    0.01
#>                                          Max   Skew    Kurt
#> Asia                          1.10344053e+09   12.7   180.6
#> Europe                              64788.06   4.17   24.65
#> Latin America                      69'625000  11.68  171.51
#> Middle East and North Africa          290739   5.32   33.85
#> North America                         316776   2.37    8.87
#> Sub-saharan Africa               15'568361.4   8.87   88.61
#> 
#> 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 (78.06% NAs)
#>                                  N         Mean           SD     Min
#> Asia                          1103  20'767547.9   124'017608  136.89
#> Europe                         782     55223.95     91748.81  333.81
#> Latin America                 1049    960811.72  6'047028.87       0
#> Middle East and North Africa   203     22235.72     43022.59  240.69
#> North America                  125     343074.7    506122.03   12540
#> Sub-saharan Africa            1093     105260.5     555212.8    0.02
#>                                          Max   Skew    Kurt
#> Asia                          1.86843541e+09  10.41  126.86
#> Europe                                537796   2.36    9.34
#> Latin America                      78'124000   9.11   93.92
#> Middle East and North Africa          262505   2.92   13.02
#> North America                     1'766043.9   1.49    3.85
#> Sub-saharan Africa                  7'676500   8.67   88.74
#> 
#> 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 (78.08% NAs)
#>                                  N         Mean           SD     Min
#> Asia                          1102  1'081364.11  4'793872.69     7.6
#> Europe                         782      7111.43     13622.47   10.81
#> Latin America                 1049    223292.44  1'609737.47       0
#> Middle East and North Africa   203      2529.68      4910.63    7.74
#> North America                  125     44068.95     71925.42  463.21
#> Sub-saharan Africa            1093     22020.25    122089.09       0
#>                                       Max  Skew    Kurt
#> Asia                          65'324543.8  7.48   74.61
#> Europe                          102773.96     3   14.93
#> Latin America                   20'679000  9.54  100.25
#> Middle East and North Africa        21237  2.15     6.7
#> North America                   270330.51  1.64    4.45
#> Sub-saharan Africa               1'825400  9.03   99.43
#> 
#> 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 (78.06% NAs)
#>                                  N         Mean           SD      Min
#> Asia                          1103  6'600043.18  48'055878.8     43.1
#> Europe                         782     17622.97     28584.79   128.19
#> Latin America                 1049    436012.48   2'950538.7        0
#> Middle East and North Africa   203      6642.93     13037.94    26.83
#> North America                  125     106162.3    178402.17  3676.41
#> Sub-saharan Africa            1093      73497.7    550630.53     0.02
#>                                      Max   Skew    Kurt
#> Asia                          860'638677   13.2  197.66
#> Europe                         181205.76   2.09    7.57
#> Latin America                  42'701000  10.26  122.29
#> Middle East and North Africa       76747    2.9   11.81
#> North America                     692087   1.91    5.54
#> Sub-saharan Africa             10'588300  12.92  201.92
#> 
#> 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 (78.06% NAs)
#>                                  N         Mean           SD       Min
#> Asia                          1103  12'283373.8    72'524320     152.9
#> Europe                         782     40872.35     67182.39    344.71
#> Latin America                 1049    847999.29  5'294147.93         0
#> Middle East and North Africa   203     18578.82     37541.51     150.8
#> North America                  125    400759.76    642511.02  12579.48
#> Sub-saharan Africa            1093    230722.57  1'341125.15      0.01
#>                                          Max  Skew    Kurt
#> Asia                          1.15497404e+09  10.7  135.87
#> Europe                             402210.76  2.07    7.41
#> Latin America                      69'154000  8.99   92.21
#> Middle East and North Africa          236592  3.16   14.71
#> North America                    2'308153.02  1.73    4.73
#> Sub-saharan Africa                 19'486200  9.12   98.41
#> 
#> 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 (78.06% NAs)
#>                                  N        Mean           SD      Min
#> Asia                          1103  5'245717.6  33'003641.5       79
#> Europe                         782    21148.54     36686.15   126.24
#> Latin America                 1049   466527.62  2'956458.95        0
#> Middle East and North Africa   203    10652.28     23253.57    71.99
#> North America                  125    134171.2    216436.13  4523.41
#> Sub-saharan Africa            1093     96252.2    634046.34     0.02
#>                                       Max   Skew    Kurt
#> Asia                           547'047040  11.55   155.7
#> Europe                          215350.33   2.36     9.1
#> Latin America                   38'249000   9.14   95.02
#> Middle East and North Africa       144115   3.43   16.24
#> North America                   788678.73   1.72    4.74
#> Sub-saharan Africa            9'773441.71  10.72  131.86
#> 
#> 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 (78.06% NAs)
#>                                  N         Mean           SD       Min
#> Asia                          1103  5'592444.02  26'438065.9      23.1
#> Europe                         782     17666.27      42118.1     63.39
#> Latin America                 1049    797458.31  4'792126.14  -2848.81
#> Middle East and North Africa   203     14224.06        29896     12.45
#> North America                  125    683714.58   1'307383.3   4237.91
#> Sub-saharan Africa            1093    100245.92       723873      0.01
#>                                       Max   Skew   Kurt
#> Asia                           387'997506   8.59  96.57
#> Europe                             310690   3.62  17.44
#> Latin America                 61'965134.9   8.63  87.76
#> Middle East and North Africa       146593   2.54   9.01
#> North America                 6'235285.98   2.24    7.3
#> Sub-saharan Africa            11'650290.1  11.09  139.1
#> 
#> 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 (82.73% NAs)
#>                                  N         Mean         SD       Min
#> Asia                           868  6'551795.16  33'523686         0
#> Europe                         782     57514.37  103507.97    154.58
#> Latin America                  457    104151.17  376988.85         0
#> Middle East and North Africa   203     12613.11    25698.6    115.56
#> North America                  125    439395.77  747123.15  11825.64
#> Sub-saharan Africa            1047    119592.28  722111.59      0.02
#>                                       Max   Skew    Kurt
#> Asia                           485'535400   9.29  107.95
#> Europe                          649612.02   2.56   10.43
#> Latin America                 3'729607.87   5.78   43.44
#> Middle East and North Africa     174713.3   3.53   17.75
#> North America                 3'065984.37   1.96    5.88
#> Sub-saharan Africa            12'973353.5  11.06  154.29
#> 
#> 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 (78.08% NAs)
#>                                  N         Mean           SD      Min
#> Asia                          1102  5'482995.35    29'561814    78.01
#> Europe                         782     11692.75     20661.59    91.22
#> Latin America                 1049  1'023708.74  6'978936.81        0
#> Middle East and North Africa    97     20033.42     24498.46   111.81
#> North America                  125      83558.9    148752.62  2534.34
#> Sub-saharan Africa            1093     16659.52     86821.36        0
#>                                       Max   Skew    Kurt
#> Asia                           402'671182   7.99   77.77
#> Europe                          133230.69   2.43    9.68
#> Latin America                   92'333000   9.21   96.28
#> Middle East and North Africa        90814   1.38    3.85
#> North America                   559681.65      2    5.77
#> Sub-saharan Africa            1'411950.45  10.75  139.55
#> 
#> 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 (78.06% NAs)
#>                                  N         Mean           SD      Min
#> Asia                          1103    78'158110   495'362238    650.9
#> Europe                         791    238302.84    400180.41   513.59
#> Latin America                 1049   5'788864.9  37'131918.6        0
#> Middle East and North Africa   203    129461.93     261686.6  1791.12
#> North America                  125  2'304748.18  3'896447.24  62539.2
#> Sub-saharan Africa            1093   1'218795.8  6'786969.12     0.13
#>                                          Max   Skew    Kurt
#> Asia                          8.06794210e+09  11.37   150.7
#> Europe                           2'539942.74   2.29    9.09
#> Latin America                     512'024135   9.32  100.02
#> Middle East and North Africa      1'650962.8   3.11   14.22
#> North America                    15'918306.8   1.91    5.57
#> Sub-saharan Africa               91'111865.3   8.63    87.7
#> 
#> 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
#> --------------------------------------------------------------------------------