Related
Is it possible to reproduce a meta-analysis type of flowchart as the one in the picture below using any R tool?
My attempt was using mermaid:
diagram = "
graph LR
subgraph Screening
b1-->b2
end
subgraph Eligibility
c1-->c2
end
subgraph Included
d1-->d2
end
subgraph Identification
a1-->a2
end
"
mermaid(diagram)
Which generated:
But I cannot find a way of connect the nodes accross the subgraphs.
Is there another tool better fitting to this kind of job? I am thinking on any package that I could use from within my Rmarkdown document.
I have found the DiagrammeR package easiest to do this. The general idea would be something like:
library(glue)
library(DiagrammeR)
excluded <- glue('Full text articles excluded
n = 1000
Reasons for exclusion
Reason 1
Reason 2')
grViz("
digraph cohort_flow_chart
{
node [fontname = Helvetica, fontsize = 12, shape = box, width = 4]
a[label = 'Records identified in original search']
b[label = 'Records identified with update']
c[label = 'Records after duplicates removed']
d[label = 'Records screened']
e[label = 'Records excluded']
f[label = 'Full text articles assessed']
g[label = 'Studies included']
h[label = '##1']
{ rank = same; a b}
{ rank = same; d, e}
{ rank = same; f, h}
a -> c;
b -> c;
c -> d;
d -> e [ minlen = 3 ];
d -> f;
f -> h [ minlen = 3 ];
f -> g;
}
[1]: excluded
")
Will look like:
Image with labels and empty nodes
grViz("
digraph cohort_flow_chart
{
node [fontname = Helvetica, fontsize = 12, shape = box, width = 4]
i[label = 'Identification', fillcolor = LightBlue,
style = filled, width = 2]
j[label = 'Screening',fillcolor = LightBlue, style = filled, width = 2]
k[label = 'Eligibility', fillcolor = LightBlue, style = filled,
width = 2]
l[label = 'Included', fillcolor = LightBlue, style = filled, width = 2]
a[label = 'Records identified in original search']
b[label = 'Records identified with update']
c[label = 'Records after duplicates removed']
d[label = 'Records screened']
e[label = 'Records excluded']
f[label = 'Full text articles assessed']
g[label = 'Studies included']
h[label = '##1']
blank_1[label = '', width = 0.01, height = 0.01]
blank_2[label = '', width = 0.01, height = 0.01]
blank_4[label = '', width = 4, color = White]
{ rank = same; a b i}
{ rank = same; blank_4 c j}
{ rank = same; f k}
{ rank = same; g l}
{ rank = same; blank_1 e}
{ rank = same; blank_2 h}
a -> c;
b -> c;
b -> blank_4 [ dir = none, color = White];
c -> d;
d -> blank_1 [ dir = none ];
blank_1 -> e [ minlen = 3 ];
blank_1 -> f;
f -> blank_2 [ dir = none ];
blank_2 -> h [ minlen = 3 ];
blank_2 -> g;
}
[1]: excluded
")
I'm trying to calculate determinants with any order using Lua. I can calculate determinants for order less than 4, but not for greater equals than 4 ones.
I have a 4x4 matrix and its determinant with the program is 0, but the real solution is 56.
I don't know if the problem is in getSubmatrix method or is in detMat method, because I don't have any error message from the console.
I've ported the methods from my own java code, where it works fine.
Here's all my code:
function numMat(n, A)
local S = {}
for i = 1, #A, 1 do
local T = {}
S[i] = T
for j =1, #A[1], 1 do
T[j] = n * A[i][j]
end
end
return S
end
function sumMat(A, B)
local C = {}
for i = 1, #A do
local D = {}
C[i] = D
for j = 1, #A[1] do
D[j] = A[i][j] + B[i][j]
end
end
return C
end
function subMat(A, B)
return sumMat(A, numMat(-1, B))
end
function printMatrix(A)
for i, v in ipairs(A) do
for j, w in ipairs(v) do
print(w)
end
end
end
function escalarProduct(u, v)
local w = 0
for i = 1, #u do
w = w + u[i] * v[i]
end
return w
end
function prodMat(A, B)
local C = {}
for i = 1, #A do
C[i] = {}
for j = 1, #B[1] do
local num = A[i][1] * B[1][j]
for k = 2, #A[1] do
num = num + A[i][k] * B[k][j]
end
C[i][j] = num
end
end
return C
end
function powMat(A, power)
local B = {}
local C = {}
C = A
for i = 1, power - 1 do
B = prodMat(C, A)
C = B
end
return B
end
function trasposeMat(A)
local B = {}
for i = 1, #A do
local C = {}
B[i] = C
for j = 1, #A[1] do
C[j] = A[j][i]
end
end
return B
end
function productDiag(m)
local prod = 1
for i = 1, #m do
for j = 1, #m do
if i == j then prod = prod * m[i][i] end
end
end
return prod
end
function isDiagonal(A)
for i = 1, #A do
for j = 1, #A do
if i ~= j and A[i][j] ~= 0 then return false end
end
end
return true
end
function isTriangSup(m)
for i = 1, #m do
for j = 1, i do
if m[i][j] == 0 then return true end
end
end
return false
end
function isTriangInf(m)
return isTriangSup(trasposeMat(m))
end
function isTriang(m)
if(isTriangSup(m)) then return true
else
return false
end
end
function getSubmatrix(A, rows, cols, col)
local submatrix = {}
local k = 1
for j = 1, cols do
--local D = {}
--submatrix[j] = D
if j == col then
break
end
for i = 2, rows do
submatrix[i-1][k] = A[i][j]
--D[k] = A[i][j]
end
k = k + 1
end
return submatrix
end
function det2Mat(A)
assert(#A == 2 and #A == #A[1], 'Error: The matrix must be squared, order 2.')
return A[1][1] * A[2][2] - A[1][2] * A[2][1]
end
function det3Mat(A)
assert(#A == 3 and #A == #A[1], 'Error: The matrix must be squared, order 3.')
s1 = A[1][1] * A[2][2] * A[3][3] + A[2][1] * A[3][2] * A[1][3] + A[1][2] * A[2][3] * A[3][1]
s2 = A[1][3] * A[2][2] * A[3][1] + A[1][2] * A[2][1] * A[3][3] + A[2][3] * A[3][2] * A[1][1]
return s1 - s2
end
function detMat(A)
local submatrix = {}
local det
local sign = 1
local rows = #A
local cols = #A[1]
assert(rows == cols, 'Error: The matrix must be squared.')
if rows == 1 then
return A[1][1]
end
if rows == 2 then
return det2Mat(A)
end
if rows == 3 then
return det3Mat(A)
end
if isDiagonal(A) or isTriang(A) then return productDiag(A) end
if rows > 3 then
for column = 1, cols do
submatrix = getSubmatrix(A, rows, cols, column)
det = det + sign * A[1][column] * detMat(submatrix)
sign = -sign
end
end
return det
end
A = {{1, 3}, {5, 6}}
B = {{2, 4}, {3, 1}}
C = {{2, 3, 4}, {-5, 4, 7}, {7, 1, 0}}
D = {{2, 0, 0, 0}, {0, 4, 0, 0}, {0, 0, 7, 0}, {0, 0, 0, 6}}
E = {{2, 3, 4, -3}, {-5, 4, 7, -2}, {7, 1, 0, 5}, {3, 4, 5, 6}}
--printMatrix(numMat(-1, A))
--printMatrix(sumMat(A, B))
--printMatrix(subMat(A, B))
--print(escalarProduct({1, 3}, {5, 6}))
--printMatrix(prodMat(A, B))
--printMatrix(trasposeMat(A))
--printMatrix(powMat(A, 2))
--printMatrix(powMat(A, 3))
print(detMat(A))
print(detMat(B))
print(detMat(C))
print(detMat(D))
print(detMat(E)) --The solution must be 56
And the console solution is:
-9
-10
1
336
0
The error is when I want to find out the determinant of the matrix E.
I have black color png with transparent background.
I am trying to change color using hue-rotate(180deg) and invert(100%) CSS but failed.
In the case of other color png, all is good.
.huerotate{-webkit-filter: hue-rotate(180deg); filter: hue-rotate(180deg);}
<img src="blackXXX.png" class="huerotate"/>
Is it possible or impossible?
Yes, you can do it... the black is tricky.
Here's how:
background: url(black.png);
filter: brightness(0.9) invert(.7) sepia(.5) hue-rotate(100deg) saturate(200%);
This makes black -> blue.
This website can help you generate the FILTER from a HEX color:
https://isotropic.co/tool/hex-color-to-css-filter/
If you need a custom color, just try
this fiddle
The javascript:
'use strict';
class Color {
constructor(r, g, b) {
this.set(r, g, b);
}
toString() {
return `rgb(${Math.round(this.r)}, ${Math.round(this.g)}, ${Math.round(this.b)})`;
}
set(r, g, b) {
this.r = this.clamp(r);
this.g = this.clamp(g);
this.b = this.clamp(b);
}
hueRotate(angle = 0) {
angle = angle / 180 * Math.PI;
const sin = Math.sin(angle);
const cos = Math.cos(angle);
this.multiply([
0.213 + cos * 0.787 - sin * 0.213,
0.715 - cos * 0.715 - sin * 0.715,
0.072 - cos * 0.072 + sin * 0.928,
0.213 - cos * 0.213 + sin * 0.143,
0.715 + cos * 0.285 + sin * 0.140,
0.072 - cos * 0.072 - sin * 0.283,
0.213 - cos * 0.213 - sin * 0.787,
0.715 - cos * 0.715 + sin * 0.715,
0.072 + cos * 0.928 + sin * 0.072,
]);
}
grayscale(value = 1) {
this.multiply([
0.2126 + 0.7874 * (1 - value),
0.7152 - 0.7152 * (1 - value),
0.0722 - 0.0722 * (1 - value),
0.2126 - 0.2126 * (1 - value),
0.7152 + 0.2848 * (1 - value),
0.0722 - 0.0722 * (1 - value),
0.2126 - 0.2126 * (1 - value),
0.7152 - 0.7152 * (1 - value),
0.0722 + 0.9278 * (1 - value),
]);
}
sepia(value = 1) {
this.multiply([
0.393 + 0.607 * (1 - value),
0.769 - 0.769 * (1 - value),
0.189 - 0.189 * (1 - value),
0.349 - 0.349 * (1 - value),
0.686 + 0.314 * (1 - value),
0.168 - 0.168 * (1 - value),
0.272 - 0.272 * (1 - value),
0.534 - 0.534 * (1 - value),
0.131 + 0.869 * (1 - value),
]);
}
saturate(value = 1) {
this.multiply([
0.213 + 0.787 * value,
0.715 - 0.715 * value,
0.072 - 0.072 * value,
0.213 - 0.213 * value,
0.715 + 0.285 * value,
0.072 - 0.072 * value,
0.213 - 0.213 * value,
0.715 - 0.715 * value,
0.072 + 0.928 * value,
]);
}
multiply(matrix) {
const newR = this.clamp(this.r * matrix[0] + this.g * matrix[1] + this.b * matrix[2]);
const newG = this.clamp(this.r * matrix[3] + this.g * matrix[4] + this.b * matrix[5]);
const newB = this.clamp(this.r * matrix[6] + this.g * matrix[7] + this.b * matrix[8]);
this.r = newR;
this.g = newG;
this.b = newB;
}
brightness(value = 1) {
this.linear(value);
}
contrast(value = 1) {
this.linear(value, -(0.5 * value) + 0.5);
}
linear(slope = 1, intercept = 0) {
this.r = this.clamp(this.r * slope + intercept * 255);
this.g = this.clamp(this.g * slope + intercept * 255);
this.b = this.clamp(this.b * slope + intercept * 255);
}
invert(value = 1) {
this.r = this.clamp((value + this.r / 255 * (1 - 2 * value)) * 255);
this.g = this.clamp((value + this.g / 255 * (1 - 2 * value)) * 255);
this.b = this.clamp((value + this.b / 255 * (1 - 2 * value)) * 255);
}
hsl() {
// Code taken from https://stackoverflow.com/a/9493060/2688027, licensed under CC BY-SA.
const r = this.r / 255;
const g = this.g / 255;
const b = this.b / 255;
const max = Math.max(r, g, b);
const min = Math.min(r, g, b);
let h, s, l = (max + min) / 2;
if (max === min) {
h = s = 0;
} else {
const d = max - min;
s = l > 0.5 ? d / (2 - max - min) : d / (max + min);
switch (max) {
case r:
h = (g - b) / d + (g < b ? 6 : 0);
break;
case g:
h = (b - r) / d + 2;
break;
case b:
h = (r - g) / d + 4;
break;
}
h /= 6;
}
return {
h: h * 100,
s: s * 100,
l: l * 100,
};
}
clamp(value) {
if (value > 255) {
value = 255;
} else if (value < 0) {
value = 0;
}
return value;
}
}
class Solver {
constructor(target, baseColor) {
this.target = target;
this.targetHSL = target.hsl();
this.reusedColor = new Color(0, 0, 0);
}
solve() {
const result = this.solveNarrow(this.solveWide());
return {
values: result.values,
loss: result.loss,
filter: this.css(result.values),
};
}
solveWide() {
const A = 5;
const c = 15;
const a = [60, 180, 18000, 600, 1.2, 1.2];
let best = { loss: Infinity };
for (let i = 0; best.loss > 25 && i < 3; i++) {
const initial = [50, 20, 3750, 50, 100, 100];
const result = this.spsa(A, a, c, initial, 1000);
if (result.loss < best.loss) {
best = result;
}
}
return best;
}
solveNarrow(wide) {
const A = wide.loss;
const c = 2;
const A1 = A + 1;
const a = [0.25 * A1, 0.25 * A1, A1, 0.25 * A1, 0.2 * A1, 0.2 * A1];
return this.spsa(A, a, c, wide.values, 500);
}
spsa(A, a, c, values, iters) {
const alpha = 1;
const gamma = 0.16666666666666666;
let best = null;
let bestLoss = Infinity;
const deltas = new Array(6);
const highArgs = new Array(6);
const lowArgs = new Array(6);
for (let k = 0; k < iters; k++) {
const ck = c / Math.pow(k + 1, gamma);
for (let i = 0; i < 6; i++) {
deltas[i] = Math.random() > 0.5 ? 1 : -1;
highArgs[i] = values[i] + ck * deltas[i];
lowArgs[i] = values[i] - ck * deltas[i];
}
const lossDiff = this.loss(highArgs) - this.loss(lowArgs);
for (let i = 0; i < 6; i++) {
const g = lossDiff / (2 * ck) * deltas[i];
const ak = a[i] / Math.pow(A + k + 1, alpha);
values[i] = fix(values[i] - ak * g, i);
}
const loss = this.loss(values);
if (loss < bestLoss) {
best = values.slice(0);
bestLoss = loss;
}
}
return { values: best, loss: bestLoss };
function fix(value, idx) {
let max = 100;
if (idx === 2 /* saturate */) {
max = 7500;
} else if (idx === 4 /* brightness */ || idx === 5 /* contrast */) {
max = 200;
}
if (idx === 3 /* hue-rotate */) {
if (value > max) {
value %= max;
} else if (value < 0) {
value = max + value % max;
}
} else if (value < 0) {
value = 0;
} else if (value > max) {
value = max;
}
return value;
}
}
loss(filters) {
// Argument is array of percentages.
const color = this.reusedColor;
color.set(0, 0, 0);
color.invert(filters[0] / 100);
color.sepia(filters[1] / 100);
color.saturate(filters[2] / 100);
color.hueRotate(filters[3] * 3.6);
color.brightness(filters[4] / 100);
color.contrast(filters[5] / 100);
const colorHSL = color.hsl();
return (
Math.abs(color.r - this.target.r) +
Math.abs(color.g - this.target.g) +
Math.abs(color.b - this.target.b) +
Math.abs(colorHSL.h - this.targetHSL.h) +
Math.abs(colorHSL.s - this.targetHSL.s) +
Math.abs(colorHSL.l - this.targetHSL.l)
);
}
css(filters) {
function fmt(idx, multiplier = 1) {
return Math.round(filters[idx] * multiplier);
}
return `filter: invert(${fmt(0)}%) sepia(${fmt(1)}%) saturate(${fmt(2)}%) hue-rotate(${fmt(3, 3.6)}deg) brightness(${fmt(4)}%) contrast(${fmt(5)}%);`;
}
}
function hexToRgb(hex) {
// Expand shorthand form (e.g. "03F") to full form (e.g. "0033FF")
const shorthandRegex = /^#?([a-f\d])([a-f\d])([a-f\d])$/i;
hex = hex.replace(shorthandRegex, (m, r, g, b) => {
return r + r + g + g + b + b;
});
const result = /^#?([a-f\d]{2})([a-f\d]{2})([a-f\d]{2})$/i.exec(hex);
return result
? [
parseInt(result[1], 16),
parseInt(result[2], 16),
parseInt(result[3], 16),
]
: null;
}
$(document).ready(() => {
$('button.execute').click(() => {
const rgb = hexToRgb($('input.target').val());
if (rgb.length !== 3) {
alert('Invalid format!');
return;
}
const color = new Color(rgb[0], rgb[1], rgb[2]);
const solver = new Solver(color);
const result = solver.solve();
let lossMsg;
if (result.loss < 1) {
lossMsg = 'This is a perfect result.';
} else if (result.loss < 5) {
lossMsg = 'The is close enough.';
} else if (result.loss < 15) {
lossMsg = 'The color is somewhat off. Consider running it again.';
} else {
lossMsg = 'The color is extremely off. Run it again!';
}
$('.realPixel').css('background-color', color.toString());
$('.filterPixel').attr('style', result.filter);
$('.filterDetail').text(result.filter);
$('.lossDetail').html(`Loss: ${result.loss.toFixed(1)}. <b>${lossMsg}</b>`);
});
});
I have a table which looks like this:
table =
{
{
id = 1,
name = 'john',
png = 'john.png',
descr = "..."
},
{
id = 2,
name = 'sam',
png = "sam.png",
descr = "..."
}
...
}
What function could I use to display each picture like this and make them buttons
so that when I click on their image I can open their info.
This is where I am stuck:
local buttons = display.newGroup()
local xpos = -20
local ypos = 0
local e = -1
function addpicture ()
for i=1, #table do
xpos = (xpos + 100) % 300
e = e + 1
ypos = math.modf((e)*1/3) * 100 + 100
local c = display.newImage( table[i].name, system.TemporaryDirectory, xpos, ypos)
c:scale( 0.4, 0.4 )
c.name = table[i].tvname
buttons:insert(c)
end
end
function buttons:touch( event )
if event.phase == "began" then
print(self, event.id)
end
end
buttons:addEventListener('touch', buttons)
addpicture()
How can I recognize which image is touched in order to go back to the persons info?
I solved my problem by adding the listener inside of the loop like this:
function addpicture ()
for i=1, #table do
xpos = (xpos + 100) % 300
e = e + 1
ypos = math.modf((e)*1/3) * 100 + 100
local c = display.newImage( table[i].name, system.TemporaryDirectory, xpos, ypos)
c:scale( 0.4, 0.4 )
c.name = table[i].tvname
buttons:insert(c)
function c:touch( event )
if event.phase == "began" then
print(self, event.id)
end
end
c:addEventListener('touch', c)
end
end
addpicture()
I have few tables in database that are having huge amount of data. My need is
1 : To query data exist for more than one year.
2 : Export and archive them to some file.
3 : At any point of time I can insert those data back to database.
The data may or may not contain COMMA, so not sure if I should export them to csv format.
Which is the best file format I should go for ??
What should be the file size limitation here ??
This script exports rows from specified tables to INSERT statement for any tables structure. So, you'll just need to copy the result and run it in sql document of SSMS -
DECLARE
#TableName SYSNAME
, #ObjectID INT
, #IsImportIdentity BIT = 1
DECLARE [tables] CURSOR READ_ONLY FAST_FORWARD LOCAL FOR
SELECT
'[' + s.name + '].[' + t.name + ']'
, t.[object_id]
FROM (
SELECT DISTINCT
t.[schema_id]
, t.[object_id]
, t.name
FROM sys.objects t WITH (NOWAIT)
JOIN sys.partitions p WITH (NOWAIT) ON p.[object_id] = t.[object_id]
WHERE p.[rows] > 0
AND t.[type] = 'U'
) t
JOIN sys.schemas s WITH (NOWAIT) ON t.[schema_id] = s.[schema_id]
WHERE t.name IN ('<your table name>')
OPEN [tables]
FETCH NEXT FROM [tables] INTO
#TableName
, #ObjectID
DECLARE
#SQLInsert NVARCHAR(MAX)
, #SQLColumns NVARCHAR(MAX)
, #SQLTinyColumns NVARCHAR(MAX)
WHILE ##FETCH_STATUS = 0 BEGIN
SELECT
#SQLInsert = ''
, #SQLColumns = ''
, #SQLTinyColumns = ''
;WITH cols AS
(
SELECT
c.name
, datetype = t.name
, c.column_id
FROM sys.columns c WITH (NOWAIT)
JOIN sys.types t WITH (NOWAIT) ON c.system_type_id = t.system_type_id AND c.user_type_id = t.user_type_id
WHERE c.[object_id] = #ObjectID
AND (c.is_identity = 0 OR #IsImportIdentity = 1)
AND c.is_computed = 0
AND t.name NOT IN ('xml', 'geography', 'geometry', 'hierarchyid')
)
SELECT
#SQLInsert = 'INSERT INTO ' + #TableName + ' (' + STUFF((
SELECT ', [' + c.name + ']'
FROM cols c
ORDER BY c.column_id
FOR XML PATH, TYPE, ROOT).value('.', 'NVARCHAR(MAX)'), 1, 2, '') + ')'
, #SQLTinyColumns = STUFF((
SELECT ', ' + c.name
FROM cols c
ORDER BY c.column_id
FOR XML PATH, TYPE, ROOT).value('.', 'NVARCHAR(MAX)'), 1, 2, '')
, #SQLColumns = STUFF((SELECT CHAR(13) +
CASE
WHEN c.datetype = 'uniqueidentifier'
THEN ' + '', '' + ISNULL('''''''' + CAST([' + c.name + '] AS VARCHAR(MAX)) + '''''''', ''NULL'')'
WHEN c.datetype IN ('nvarchar', 'varchar', 'nchar', 'char', 'varbinary', 'binary')
THEN ' + '', '' + ISNULL('''''''' + CAST(REPLACE([' + c.name + '], '''''''', '''''''''''' ) AS NVARCHAR(MAX)) + '''''''', ''NULL'')'
WHEN c.datetype = 'datetime'
THEN ' + '', '' + ISNULL('''''''' + CONVERT(VARCHAR, [' + c.name + '], 120) + '''''''', ''NULL'')'
ELSE
' + '', '' + ISNULL(CAST([' + c.name + '] AS NVARCHAR(MAX)), ''NULL'')'
END
FROM cols c
ORDER BY c.column_id
FOR XML PATH, TYPE, ROOT).value('.', 'NVARCHAR(MAX)'), 1, 10, 'CHAR(13) + '', ('' +')
DECLARE #SQL NVARCHAR(MAX) = '
SET NOCOUNT ON;
DECLARE
#SQL NVARCHAR(MAX) = ''''
, #x INT = 1
, #count INT = (SELECT COUNT(1) FROM ' + #TableName + ')
IF EXISTS(
SELECT 1
FROM tempdb.dbo.sysobjects
WHERE ID = OBJECT_ID(''tempdb..#import'')
)
DROP TABLE #import;
SELECT ' + #SQLTinyColumns + ', ''RowNumber'' = ROW_NUMBER() OVER (ORDER BY ' + #SQLTinyColumns + ')
INTO #import
FROM ' + #TableName + '
WHILE #x < #count BEGIN
SELECT #SQL = ''VALUES '' + STUFF((
SELECT ' + #SQLColumns + ' + '')''' + '
FROM #import
WHERE RowNumber BETWEEN #x AND #x + 9
FOR XML PATH, TYPE, ROOT).value(''.'', ''NVARCHAR(MAX)''), 1, 2, CHAR(13) + '' '') + '';''
PRINT(''' + #SQLInsert + ''')
PRINT(#SQL)
SELECT #x = #x + 10
END'
EXEC sys.sp_executesql #SQL
FETCH NEXT FROM [tables] INTO
#TableName
, #ObjectID
END
CLOSE [tables]
DEALLOCATE [tables]
In output you get something like this (AdventureWorks.Person.Address):
INSERT INTO [Person].[Address] ([AddressID], [AddressLine1], [AddressLine2], [City], [StateProvinceID], [PostalCode], [rowguid], [ModifiedDate])
VALUES
(1, '1970 Napa Ct.', NULL, 'Bothell', 79, '98011', '9AADCB0D-36CF-483F-84D8-585C2D4EC6E9', '2002-01-04 00:00:00')
, (2, '9833 Mt. Dias Blv.', NULL, 'Bothell', 79, '98011', '32A54B9E-E034-4BFB-B573-A71CDE60D8C0', '2003-01-01 00:00:00')
, (3, '7484 Roundtree Drive', NULL, 'Bothell', 79, '98011', '4C506923-6D1B-452C-A07C-BAA6F5B142A4', '2007-04-08 00:00:00')
, (4, '9539 Glenside Dr', NULL, 'Bothell', 79, '98011', 'E5946C78-4BCC-477F-9FA1-CC09DE16A880', '2003-03-07 00:00:00')
, (5, '1226 Shoe St.', NULL, 'Bothell', 79, '98011', 'FBAFF937-4A97-4AF0-81FD-B849900E9BB0', '2003-01-20 00:00:00')
, (6, '1399 Firestone Drive', NULL, 'Bothell', 79, '98011', 'FEBF8191-9804-44C8-877A-33FDE94F0075', '2003-03-17 00:00:00')
, (7, '5672 Hale Dr.', NULL, 'Bothell', 79, '98011', '0175A174-6C34-4D41-B3C1-4419CD6A0446', '2004-01-12 00:00:00')
, (8, '6387 Scenic Avenue', NULL, 'Bothell', 79, '98011', '3715E813-4DCA-49E0-8F1C-31857D21F269', '2003-01-18 00:00:00')
, (9, '8713 Yosemite Ct.', NULL, 'Bothell', 79, '98011', '268AF621-76D7-4C78-9441-144FD139821A', '2006-07-01 00:00:00')
, (10, '250 Race Court', NULL, 'Bothell', 79, '98011', '0B6B739D-8EB6-4378-8D55-FE196AF34C04', '2003-01-03 00:00:00');
UPDATE:
And this script exports rows from specified tables to CSV format in output window for any tables structure.
DECLARE
#TableName SYSNAME
, #ObjectID INT
DECLARE [tables] CURSOR READ_ONLY FAST_FORWARD LOCAL FOR
SELECT
'[' + s.name + '].[' + t.name + ']'
, t.[object_id]
FROM (
SELECT DISTINCT
t.[schema_id]
, t.[object_id]
, t.name
FROM sys.objects t WITH (NOWAIT)
JOIN sys.partitions p WITH (NOWAIT) ON p.[object_id] = t.[object_id]
WHERE p.[rows] > 0
AND t.[type] = 'U'
) t
JOIN sys.schemas s WITH (NOWAIT) ON t.[schema_id] = s.[schema_id]
WHERE t.name IN ('<your table name>')
OPEN [tables]
FETCH NEXT FROM [tables] INTO
#TableName
, #ObjectID
DECLARE
#SQLInsert NVARCHAR(MAX)
, #SQLColumns NVARCHAR(MAX)
, #SQLTinyColumns NVARCHAR(MAX)
WHILE ##FETCH_STATUS = 0 BEGIN
SELECT
#SQLInsert = ''
, #SQLColumns = ''
, #SQLTinyColumns = ''
;WITH cols AS
(
SELECT
c.name
, datetype = t.name
, c.column_id
FROM sys.columns c WITH (NOWAIT)
JOIN sys.types t WITH (NOWAIT) ON c.system_type_id = t.system_type_id AND c.user_type_id = t.user_type_id
WHERE c.[object_id] = #ObjectID
AND c.is_computed = 0
AND t.name NOT IN ('xml', 'geography', 'geometry', 'hierarchyid')
)
SELECT
#SQLTinyColumns = STUFF((
SELECT ', [' + c.name + ']'
FROM cols c
ORDER BY c.column_id
FOR XML PATH, TYPE, ROOT).value('.', 'NVARCHAR(MAX)'), 1, 2, '')
, #SQLColumns = STUFF((SELECT CHAR(13) +
CASE
WHEN c.datetype = 'uniqueidentifier'
THEN ' + '';'' + ISNULL('''' + CAST([' + c.name + '] AS VARCHAR(MAX)) + '''', ''NULL'')'
WHEN c.datetype IN ('nvarchar', 'varchar', 'nchar', 'char', 'varbinary', 'binary')
THEN ' + '';'' + ISNULL('''' + CAST(REPLACE([' + c.name + '], '''', '''''''') AS NVARCHAR(MAX)) + '''', ''NULL'')'
WHEN c.datetype = 'datetime'
THEN ' + '';'' + ISNULL('''' + CONVERT(VARCHAR, [' + c.name + '], 120) + '''', ''NULL'')'
ELSE
' + '';'' + ISNULL(CAST([' + c.name + '] AS NVARCHAR(MAX)), ''NULL'')'
END
FROM cols c
ORDER BY c.column_id
FOR XML PATH, TYPE, ROOT).value('.', 'NVARCHAR(MAX)'), 1, 10, 'CHAR(13) + '''' +')
DECLARE #SQL NVARCHAR(MAX) = '
SET NOCOUNT ON;
DECLARE
#SQL NVARCHAR(MAX) = ''''
, #x INT = 1
, #count INT = (SELECT COUNT(1) FROM ' + #TableName + ')
IF EXISTS(
SELECT 1
FROM tempdb.dbo.sysobjects
WHERE ID = OBJECT_ID(''tempdb..#import'')
)
DROP TABLE #import;
SELECT ' + #SQLTinyColumns + ', ''RowNumber'' = ROW_NUMBER() OVER (ORDER BY ' + #SQLTinyColumns + ')
INTO #import
FROM ' + #TableName + '
WHILE #x < #count BEGIN
SELECT #SQL = STUFF((
SELECT ' + #SQLColumns + ' + ''''' + '
FROM #import
WHERE RowNumber BETWEEN #x AND #x + 9
FOR XML PATH, TYPE, ROOT).value(''.'', ''NVARCHAR(MAX)''), 1, 1, '''')
PRINT(#SQL)
SELECT #x = #x + 10
END'
EXEC sys.sp_executesql #SQL
FETCH NEXT FROM [tables] INTO
#TableName
, #ObjectID
END
CLOSE [tables]
DEALLOCATE [tables]
In output you get something like this (AdventureWorks.Person.Person):
1;EM;0;NULL;Ken;J;Sánchez;NULL;0;92C4279F-1207-48A3-8448-4636514EB7E2;2003-02-08 00:00:00
2;EM;0;NULL;Terri;Lee;Duffy;NULL;1;D8763459-8AA8-47CC-AFF7-C9079AF79033;2002-02-24 00:00:00
3;EM;0;NULL;Roberto;NULL;Tamburello;NULL;0;E1A2555E-0828-434B-A33B-6F38136A37DE;2001-12-05 00:00:00
4;EM;0;NULL;Rob;NULL;Walters;NULL;0;F2D7CE06-38B3-4357-805B-F4B6B71C01FF;2001-12-29 00:00:00
5;EM;0;Ms.;Gail;A;Erickson;NULL;0;F3A3F6B4-AE3B-430C-A754-9F2231BA6FEF;2002-01-30 00:00:00
6;EM;0;Mr.;Jossef;H;Goldberg;NULL;0;0DEA28FD-EFFE-482A-AFD3-B7E8F199D56F;2002-02-17 00:00:00
Try using the bcp command line utility, which is very efficient at handling import/export for large data sets:
bcp "select * from [YourTable]" queryout data.dat -n -S YourServer -d "YourDatabase" -T
-T means Trusted Authentication. -n means native format, so you don't need to worry about data types, commas, etc. However, this does mean you can't view the data in an editor; it's only available for loading back into SQL Server. You can use -c instead if you want CSV format.
To import back in:
bcp "[YourTable]" in data.dat -n -S YourServer -d "YourDatabase" -T