diff --git a/docs/src/data-diving-examples.md b/docs/src/data-diving-examples.md
index 39738f193d..100716ec26 100644
--- a/docs/src/data-diving-examples.md
+++ b/docs/src/data-diving-examples.md
@@ -160,11 +160,11 @@ CITRUS COUNTY 1332.9 79974.9 483785.1
stats2 -a corr,linreg-ols,r2 -f tiv_2011,tiv_2012
-tiv_2011_tiv_2012_corr 0.9730497632351692
-tiv_2011_tiv_2012_ols_m 0.9835583980337723
-tiv_2011_tiv_2012_ols_b 433854.6428968317
+tiv_2011_tiv_2012_corr 0.9730497632351701
+tiv_2011_tiv_2012_ols_m 0.9835583980337732
+tiv_2011_tiv_2012_ols_b 433854.6428968301
tiv_2011_tiv_2012_ols_n 36634
-tiv_2011_tiv_2012_r2 0.9468258417320189
+tiv_2011_tiv_2012_r2 0.9468258417320204
@@ -322,7 +322,7 @@ Look at bivariate stats by color and shape. In particular, `u,v` pairwise correl
u_v_corr w_x_corr
-0.1334180491027861 -0.011319841199866178
+0.1334180491027861 -0.011319841199852926
@@ -332,22 +332,22 @@ Look at bivariate stats by color and shape. In particular, `u,v` pairwise correl
color shape u_v_corr w_x_corr
- red circle 0.9807984401887236 -0.01856553658708754
-orange square 0.17685855992752927 -0.07104431573806054
- green circle 0.05764419437577255 0.01179572988801509
- red square 0.05574477124893523 -0.0006801456507510942
-yellow triangle 0.04457273771962798 0.024604310103081825
-yellow square 0.04379172927296089 -0.04462197201631237
-purple circle 0.03587354936895086 0.1341133954140899
- blue square 0.03241153095761164 -0.053507648119643196
- blue triangle 0.015356427073158766 -0.0006089997461435399
-orange circle 0.010518953877704048 -0.16279397329279383
- red triangle 0.00809782571528034 0.012486621357942596
-purple triangle 0.005155190909099334 -0.045057909256220656
-purple square -0.025680276963377404 0.05769429647930396
- green square -0.0257760734502851 -0.003265173252087127
-orange triangle -0.030456661186085785 -0.1318699981926352
-yellow circle -0.06477331572781474 0.07369449819706045
- blue circle -0.10234761901929677 -0.030528539069837757
- green triangle -0.10901825107358765 -0.04848782060162929
+ red circle 0.9807984401887242 -0.018565536587084836
+orange square 0.17685855992752933 -0.07104431573805543
+ green circle 0.05764419437577257 0.011795729888018455
+ red square 0.0557447712489348 -0.0006801456507506415
+yellow triangle 0.0445727377196281 0.024604310103079844
+yellow square 0.0437917292729612 -0.044621972016306265
+purple circle 0.03587354936895115 0.13411339541407613
+ blue square 0.03241153095761152 -0.05350764811965621
+ blue triangle 0.015356427073158612 -0.0006089997461408209
+orange circle 0.010518953877704181 -0.1627939732927932
+ red triangle 0.00809782571528054 0.01248662135795501
+purple triangle 0.005155190909099739 -0.04505790925621933
+purple square -0.02568027696337717 0.057694296479293694
+ green square -0.025776073450284875 -0.0032651732520739014
+orange triangle -0.030456661186085584 -0.13186999819263814
+yellow circle -0.06477331572781515 0.0736944981970553
+ blue circle -0.1023476190192966 -0.030528539069839333
+ green triangle -0.10901825107358747 -0.04848782060162855
diff --git a/docs/src/manpage.md b/docs/src/manpage.md
index 3321f17987..6f4ab1bbd6 100644
--- a/docs/src/manpage.md
+++ b/docs/src/manpage.md
@@ -19,9 +19,7 @@ Quick links:
This is simply a copy of what you should see on running `man mlr` at a command prompt, once Miller is installed on your system.
-MILLER(1) MILLER(1)
-
-
+4mMILLER24m(1) 4mMILLER24m(1)
1mNAME0m
Miller -- like awk, sed, cut, join, and sort for name-indexed data such
@@ -815,7 +813,7 @@ MILLER(1) MILLER(1)
markdown " " N/A "\n"
nidx " " N/A "\n"
pprint " " N/A "\n"
- tsv " " N/A "\n"
+ tsv " " N/A "\n"
xtab "\n" " " "\n\n"
--fs {string} Specify FS for input and output.
@@ -1430,6 +1428,7 @@ MILLER(1) MILLER(1)
antimode Find least-frequently-occurring values for fields; first-found wins tie
sum Compute sums of specified fields
mean Compute averages (sample means) of specified fields
+ mad Compute mean absolute deviation
var Compute sample variance of specified fields
stddev Compute sample standard deviation of specified fields
meaneb Estimate error bars for averages (assuming no sample autocorrelation)
@@ -1928,6 +1927,7 @@ MILLER(1) MILLER(1)
antimode Find least-frequently-occurring values for fields; first-found wins tie
sum Compute sums of specified fields
mean Compute averages (sample means) of specified fields
+ mad Compute mean absolute deviation
var Compute sample variance of specified fields
stddev Compute sample standard deviation of specified fields
meaneb Estimate error bars for averages (assuming no sample autocorrelation)
@@ -3730,7 +3730,5 @@ MILLER(1) MILLER(1)
MIME Type for Comma-Separated Values (CSV) Files, the Miller docsite
https://miller.readthedocs.io
-
-
- 2024-05-09 MILLER(1)
+ 2024-05-11 4mMILLER24m(1)
diff --git a/docs/src/manpage.txt b/docs/src/manpage.txt
index 5ce10b9603..199946d457 100644
--- a/docs/src/manpage.txt
+++ b/docs/src/manpage.txt
@@ -1,6 +1,4 @@
-MILLER(1) MILLER(1)
-
-
+4mMILLER24m(1) 4mMILLER24m(1)
1mNAME0m
Miller -- like awk, sed, cut, join, and sort for name-indexed data such
@@ -794,7 +792,7 @@ MILLER(1) MILLER(1)
markdown " " N/A "\n"
nidx " " N/A "\n"
pprint " " N/A "\n"
- tsv " " N/A "\n"
+ tsv " " N/A "\n"
xtab "\n" " " "\n\n"
--fs {string} Specify FS for input and output.
@@ -1409,6 +1407,7 @@ MILLER(1) MILLER(1)
antimode Find least-frequently-occurring values for fields; first-found wins tie
sum Compute sums of specified fields
mean Compute averages (sample means) of specified fields
+ mad Compute mean absolute deviation
var Compute sample variance of specified fields
stddev Compute sample standard deviation of specified fields
meaneb Estimate error bars for averages (assuming no sample autocorrelation)
@@ -1907,6 +1906,7 @@ MILLER(1) MILLER(1)
antimode Find least-frequently-occurring values for fields; first-found wins tie
sum Compute sums of specified fields
mean Compute averages (sample means) of specified fields
+ mad Compute mean absolute deviation
var Compute sample variance of specified fields
stddev Compute sample standard deviation of specified fields
meaneb Estimate error bars for averages (assuming no sample autocorrelation)
@@ -3709,6 +3709,4 @@ MILLER(1) MILLER(1)
MIME Type for Comma-Separated Values (CSV) Files, the Miller docsite
https://miller.readthedocs.io
-
-
- 2024-05-09 MILLER(1)
+ 2024-05-11 4mMILLER24m(1)
diff --git a/docs/src/reference-verbs.md b/docs/src/reference-verbs.md
index d6589c459a..a90f0890ff 100644
--- a/docs/src/reference-verbs.md
+++ b/docs/src/reference-verbs.md
@@ -2093,6 +2093,7 @@ Options:
antimode Find least-frequently-occurring values for fields; first-found wins tie
sum Compute sums of specified fields
mean Compute averages (sample means) of specified fields
+ mad Compute mean absolute deviation
var Compute sample variance of specified fields
stddev Compute sample standard deviation of specified fields
meaneb Estimate error bars for averages (assuming no sample autocorrelation)
@@ -3266,6 +3267,7 @@ Options:
antimode Find least-frequently-occurring values for fields; first-found wins tie
sum Compute sums of specified fields
mean Compute averages (sample means) of specified fields
+ mad Compute mean absolute deviation
var Compute sample variance of specified fields
stddev Compute sample standard deviation of specified fields
meaneb Estimate error bars for averages (assuming no sample autocorrelation)
@@ -3433,14 +3435,14 @@ fields, optionally categorized by one or more fields.
data/medium
-x_y_cov 0.000042574820827444476
-x_y_corr 0.0005042001844467462
-y_y_cov 0.08461122467974003
+x_y_cov 0.00004257482082749404
+x_y_corr 0.0005042001844473328
+y_y_cov 0.08461122467974005
y_y_corr 1
-x2_xy_cov 0.04188382281779374
-x2_xy_corr 0.630174342037994
-x2_y2_cov -0.00030953725962542085
-x2_y2_corr -0.0034249088761121966
+x2_xy_cov 0.041883822817793716
+x2_xy_corr 0.6301743420379936
+x2_y2_cov -0.0003095372596253918
+x2_y2_corr -0.003424908876111875
@@ -3449,12 +3451,12 @@ x2_y2_corr -0.0034249088761121966
data/medium
-a x_y_ols_m x_y_ols_b x_y_ols_n x_y_r2 y_y_ols_m y_y_ols_b y_y_ols_n y_y_r2 xy_y2_ols_m xy_y2_ols_b xy_y2_ols_n xy_y2_r2
-pan 0.01702551273681908 0.5004028922897639 2081 0.00028691820445814767 1 0 2081 1 0.8781320866715662 0.11908230147563566 2081 0.41749827377311266
-eks 0.0407804923685586 0.48140207967651016 1965 0.0016461239223448587 1 0 1965 1 0.8978728611690183 0.10734054433612333 1965 0.45563223864254526
-wye -0.03915349075204814 0.5255096523974456 1966 0.0015051268704373607 1 0 1966 1 0.8538317334220835 0.1267454301662969 1966 0.38991721818599295
-zee 0.0027812364960399147 0.5043070448033061 2047 0.000007751652858786137 1 0 2047 1 0.8524439912011013 0.12401684308018937 2047 0.39356598090006495
-hat -0.018620577041095078 0.5179005397264935 1941 0.0003520036646055585 1 0 1941 1 0.8412305086345014 0.13557328318623216 1941 0.3687944261732265
+a x_y_ols_m x_y_ols_b x_y_ols_n x_y_r2 y_y_ols_m y_y_ols_b y_y_ols_n y_y_r2 xy_y2_ols_m xy_y2_ols_b xy_y2_ols_n xy_y2_r2
+pan 0.017025512736819345 0.500402892289764 2081 0.00028691820445815624 1 -0.00000000000000002890430283104539 2081 1 0.8781320866715664 0.11908230147563569 2081 0.4174982737731127
+eks 0.04078049236855813 0.4814020796765104 1965 0.0016461239223448218 1 0.00000000000000017862676354313703 1965 1 0.897872861169018 0.1073405443361234 1965 0.4556322386425451
+wye -0.03915349075204785 0.5255096523974457 1966 0.0015051268704373377 1 0.00000000000000004464425401127647 1966 1 0.8538317334220837 0.1267454301662969 1966 0.3899172181859931
+zee 0.0027812364960401333 0.5043070448033061 2047 0.000007751652858787357 1 0.00000000000000004819404567023685 2047 1 0.8524439912011011 0.12401684308018947 2047 0.39356598090006495
+hat -0.018620577041095272 0.5179005397264937 1941 0.00035200366460556604 1 -0.00000000000000003400445761787692 1941 1 0.8412305086345017 0.13557328318623207 1941 0.3687944261732266
Here's an example simple line-fit. The `x` and `y`
@@ -3540,11 +3542,11 @@ upsec_count_pca_quality 0.9999590846136102
donesec 92.33051350964094
color purple
-upsec_count_pca_m -39.03009744795354
-upsec_count_pca_b 979.9883413064914
+upsec_count_pca_m -39.030097447953594
+upsec_count_pca_b 979.9883413064917
upsec_count_pca_n 21
upsec_count_pca_quality 0.9999908956206317
-donesec 25.10852919630297
+donesec 25.108529196302943
## step
@@ -3821,9 +3823,9 @@ distinct_count 5 5 10000 10000 10000
mode pan wye 1 0.3467901443380824 0.7268028627434533
sum 0 0 50005000 4986.019681679581 5062.057444929905
mean - - 5000.5 0.49860196816795804 0.5062057444929905
-stddev - - 2886.8956799071675 0.2902925151144007 0.290880086426933
-var - - 8334166.666666667 0.08426974433144456 0.08461122467974003
-skewness - - 0 -0.0006899591185521965 -0.017849760120133784
+stddev - - 2886.8956799071675 0.29029251511440074 0.2908800864269331
+var - - 8334166.666666667 0.08426974433144457 0.08461122467974005
+skewness - - 0 -0.0006899591185517494 -0.01784976012013298
minlen 3 3 1 15 13
maxlen 3 3 5 22 22
min eks eks 1 0.00004509679127584487 0.00008818962627266114
diff --git a/docs/src/two-pass-algorithms.md b/docs/src/two-pass-algorithms.md
index 146f3a81e1..e475aebf3b 100644
--- a/docs/src/two-pass-algorithms.md
+++ b/docs/src/two-pass-algorithms.md
@@ -598,8 +598,8 @@ hat pan 0.4643355557376876
x_count 10000
x_sum 4986.019681679581
x_mean 0.49860196816795804
-x_var 0.08426974433144456
-x_stddev 0.2902925151144007
+x_var 0.08426974433144457
+x_stddev 0.29029251511440074
diff --git a/man/manpage.txt b/man/manpage.txt
index 5ce10b9603..199946d457 100644
--- a/man/manpage.txt
+++ b/man/manpage.txt
@@ -1,6 +1,4 @@
-MILLER(1) MILLER(1)
-
-
+4mMILLER24m(1) 4mMILLER24m(1)
1mNAME0m
Miller -- like awk, sed, cut, join, and sort for name-indexed data such
@@ -794,7 +792,7 @@ MILLER(1) MILLER(1)
markdown " " N/A "\n"
nidx " " N/A "\n"
pprint " " N/A "\n"
- tsv " " N/A "\n"
+ tsv " " N/A "\n"
xtab "\n" " " "\n\n"
--fs {string} Specify FS for input and output.
@@ -1409,6 +1407,7 @@ MILLER(1) MILLER(1)
antimode Find least-frequently-occurring values for fields; first-found wins tie
sum Compute sums of specified fields
mean Compute averages (sample means) of specified fields
+ mad Compute mean absolute deviation
var Compute sample variance of specified fields
stddev Compute sample standard deviation of specified fields
meaneb Estimate error bars for averages (assuming no sample autocorrelation)
@@ -1907,6 +1906,7 @@ MILLER(1) MILLER(1)
antimode Find least-frequently-occurring values for fields; first-found wins tie
sum Compute sums of specified fields
mean Compute averages (sample means) of specified fields
+ mad Compute mean absolute deviation
var Compute sample variance of specified fields
stddev Compute sample standard deviation of specified fields
meaneb Estimate error bars for averages (assuming no sample autocorrelation)
@@ -3709,6 +3709,4 @@ MILLER(1) MILLER(1)
MIME Type for Comma-Separated Values (CSV) Files, the Miller docsite
https://miller.readthedocs.io
-
-
- 2024-05-09 MILLER(1)
+ 2024-05-11 4mMILLER24m(1)
diff --git a/man/mlr.1 b/man/mlr.1
index d660b99a89..f5b66e015f 100644
--- a/man/mlr.1
+++ b/man/mlr.1
@@ -2,12 +2,12 @@
.\" Title: mlr
.\" Author: [see the "AUTHOR" section]
.\" Generator: ./mkman.rb
-.\" Date: 2024-05-09
+.\" Date: 2024-05-11
.\" Manual: \ \&
.\" Source: \ \&
.\" Language: English
.\"
-.TH "MILLER" "1" "2024-05-09" "\ \&" "\ \&"
+.TH "MILLER" "1" "2024-05-11" "\ \&" "\ \&"
.\" -----------------------------------------------------------------
.\" * Portability definitions
.\" ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -1778,6 +1778,7 @@ Options:
antimode Find least-frequently-occurring values for fields; first-found wins tie
sum Compute sums of specified fields
mean Compute averages (sample means) of specified fields
+ mad Compute mean absolute deviation
var Compute sample variance of specified fields
stddev Compute sample standard deviation of specified fields
meaneb Estimate error bars for averages (assuming no sample autocorrelation)
@@ -2408,6 +2409,7 @@ Options:
antimode Find least-frequently-occurring values for fields; first-found wins tie
sum Compute sums of specified fields
mean Compute averages (sample means) of specified fields
+ mad Compute mean absolute deviation
var Compute sample variance of specified fields
stddev Compute sample standard deviation of specified fields
meaneb Estimate error bars for averages (assuming no sample autocorrelation)
diff --git a/pkg/transformers/utils/stats1_accumulators.go b/pkg/transformers/utils/stats1_accumulators.go
index 02756a9a34..3e8aadd942 100644
--- a/pkg/transformers/utils/stats1_accumulators.go
+++ b/pkg/transformers/utils/stats1_accumulators.go
@@ -72,6 +72,11 @@ var stats1AccumulatorInfos []stats1AccumulatorInfo = []stats1AccumulatorInfo{
"Compute averages (sample means) of specified fields",
NewStats1MeanAccumulator,
},
+ {
+ "mad",
+ "Compute mean absolute deviation",
+ NewStats1MeanAbsDevAccumulator,
+ },
{
"var",
@@ -504,6 +509,47 @@ func (acc *Stats1MeanAccumulator) Reset() {
acc.count = 0
}
+// ----------------------------------------------------------------
+type Stats1MeanAbsDevAccumulator struct {
+ samples []*mlrval.Mlrval
+}
+
+func NewStats1MeanAbsDevAccumulator() IStats1Accumulator {
+ return &Stats1MeanAbsDevAccumulator{
+ samples: make([]*mlrval.Mlrval, 0, 1000),
+ }
+}
+func (acc *Stats1MeanAbsDevAccumulator) Ingest(value *mlrval.Mlrval) {
+ if value.IsNumeric() {
+ acc.samples = append(acc.samples, value)
+ }
+}
+func (acc *Stats1MeanAbsDevAccumulator) Emit() *mlrval.Mlrval {
+ n := len(acc.samples)
+ if n == 0 {
+ return mlrval.VOID
+ }
+ mn := mlrval.FromInt(int64(n))
+
+ mean := mlrval.FromInt(0)
+ for i := 0; i < n; i++ {
+ mean = bifs.BIF_plus_binary(mean, acc.samples[i])
+ }
+ mean = bifs.BIF_divide(mean, mn)
+
+ meanAbsDev := mlrval.FromInt(0)
+ for i := 0; i < n; i++ {
+ diff := bifs.BIF_minus_binary(mean, acc.samples[i])
+ meanAbsDev = bifs.BIF_plus_binary(meanAbsDev, bifs.BIF_abs(diff))
+ }
+ meanAbsDev = bifs.BIF_divide(meanAbsDev, mn)
+
+ return meanAbsDev
+}
+func (acc *Stats1MeanAbsDevAccumulator) Reset() {
+ acc.samples = make([]*mlrval.Mlrval, 0, 1000)
+}
+
// ----------------------------------------------------------------
type Stats1MinAccumulator struct {
min *mlrval.Mlrval
diff --git a/test/cases/cli-help/0001/expout b/test/cases/cli-help/0001/expout
index 33eed96d5d..6d6cdea85c 100644
--- a/test/cases/cli-help/0001/expout
+++ b/test/cases/cli-help/0001/expout
@@ -555,6 +555,7 @@ Options:
antimode Find least-frequently-occurring values for fields; first-found wins tie
sum Compute sums of specified fields
mean Compute averages (sample means) of specified fields
+ mad Compute mean absolute deviation
var Compute sample variance of specified fields
stddev Compute sample standard deviation of specified fields
meaneb Estimate error bars for averages (assuming no sample autocorrelation)
@@ -1075,6 +1076,7 @@ Options:
antimode Find least-frequently-occurring values for fields; first-found wins tie
sum Compute sums of specified fields
mean Compute averages (sample means) of specified fields
+ mad Compute mean absolute deviation
var Compute sample variance of specified fields
stddev Compute sample standard deviation of specified fields
meaneb Estimate error bars for averages (assuming no sample autocorrelation)
diff --git a/test/cases/verb-stats1/0001/cmd b/test/cases/verb-stats1/0001/cmd
index 1e5931d0fc..93753529ce 100644
--- a/test/cases/verb-stats1/0001/cmd
+++ b/test/cases/verb-stats1/0001/cmd
@@ -1 +1 @@
-mlr --oxtab stats1 -a mean,sum,count,min,max,antimode,mode -f i,x,y test/input/abixy
+mlr --oxtab stats1 -a mean,sum,count,min,max,antimode,mode,mad -f i,x,y test/input/abixy
diff --git a/test/cases/verb-stats1/0001/expout b/test/cases/verb-stats1/0001/expout
index e99cdf2b07..8c52b51663 100644
--- a/test/cases/verb-stats1/0001/expout
+++ b/test/cases/verb-stats1/0001/expout
@@ -5,6 +5,7 @@ i_min 1
i_max 10
i_antimode 1
i_mode 1
+i_mad 2.50000000
x_mean 0.45362938
x_sum 4.53629384
x_count 10
@@ -12,6 +13,7 @@ x_min 0.03144188
x_max 0.75867996
x_antimode 0.34679014
x_mode 0.34679014
+x_mad 0.17005656
y_mean 0.59445424
y_sum 5.94454242
y_count 10
@@ -19,3 +21,4 @@ y_min 0.13418874
y_max 0.97618139
y_antimode 0.72680286
y_mode 0.72680286
+y_mad 0.25930133