How to use STATA to draw a bin-scatter plot ?

[Yaqi Liu]

Suppose I want to put x variable in a decile, and get the mean of y variable for x variable in each decile. Plot the mean y variable against the mean x variable in the decile.

Thanks in advance.

[Joseph Coveney]

Probably something along these lines.

version 16.0

clear *

set seed `=strreverse("1516573")'
quietly set obs 100

generate double x = runiform(0, 100)
generate double y = runiform(0, 100)

*
* Begin here
*
egen byte d = cut(x), group(10)
set type double
collapse (mean) x = x y = y, by(d)

graph twoway scatter y x, mcolor(black) msize(small) ylabel( , angle(horizontal) nogrid)

exit

A line graph might be more the look that you want.

help graph twoway line

[Yaqi Liu]

Thanks a lot.
It works!!!
Btw I found there is another way: just install binscatter demand.

[Jiale Zhang]

Probably something along these lines.

version 16.0

clear *

set seed `=strreverse("1516573")'
quietly set obs 100

generate double x = runiform(0, 100)
generate double y = runiform(0, 100)

*
* Begin here
*
egen byte d = cut(x), group(10)
set type double
collapse (mean) x = x y = y, by(d)

graph twoway scatter y x, mcolor(black) msize(small) ylabel( , angle(horizontal) nogrid)

exit

A line graph might be more the look that you want.

help graph twoway line

Any idea on how to cut the variables unevenly? for example 10th percentile and 50th percentile?

[Nick Cox]

Here is some further technique addressing #4 but also pertinent to earlier posts.

. sysuse auto, clear

. _pctile weight, p(10, 50, 90)

. return list

scalars:
                 r(r1) =  2020
                 r(r2) =  3190
                 r(r3) =  4060

. gen binned_weight = cond(weight <= r(r1), 1, cond(weight <= r(r2), 2, cond(weight <= r(r3), 3, 4))) if weight < .

. tab weight binned_weight

    Weight |                binned_weight
    (lbs.) |         1          2          3          4 |     Total
-----------+--------------------------------------------+----------
     1,760 |         1          0          0          0 |         1
     1,800 |         2          0          0          0 |         2
     1,830 |         1          0          0          0 |         1
     1,930 |         1          0          0          0 |         1
     1,980 |         1          0          0          0 |         1
     1,990 |         1          0          0          0 |         1
     2,020 |         1          0          0          0 |         1
     2,040 |         0          1          0          0 |         1
     2,050 |         0          1          0          0 |         1
     2,070 |         0          1          0          0 |         1
     2,110 |         0          1          0          0 |         1
     2,120 |         0          1          0          0 |         1
     2,130 |         0          1          0          0 |         1
     2,160 |         0          1          0          0 |         1
     2,200 |         0          2          0          0 |         2
     2,230 |         0          1          0          0 |         1
     2,240 |         0          1          0          0 |         1
     2,280 |         0          1          0          0 |         1
     2,370 |         0          1          0          0 |         1
     2,410 |         0          1          0          0 |         1
     2,520 |         0          1          0          0 |         1
     2,580 |         0          1          0          0 |         1
     2,640 |         0          1          0          0 |         1
     2,650 |         0          2          0          0 |         2
     2,670 |         0          1          0          0 |         1
     2,690 |         0          1          0          0 |         1
     2,730 |         0          1          0          0 |         1
     2,750 |         0          2          0          0 |         2
     2,830 |         0          2          0          0 |         2
     2,930 |         0          1          0          0 |         1
     3,170 |         0          1          0          0 |         1
     3,180 |         0          1          0          0 |         1
     3,200 |         0          0          1          0 |         1
     3,210 |         0          0          1          0 |         1
     3,220 |         0          0          1          0 |         1
     3,250 |         0          0          1          0 |         1
     3,260 |         0          0          1          0 |         1
     3,280 |         0          0          1          0 |         1
     3,300 |         0          0          1          0 |         1
     3,310 |         0          0          1          0 |         1
     3,330 |         0          0          1          0 |         1
     3,350 |         0          0          1          0 |         1
     3,370 |         0          0          2          0 |         2
     3,400 |         0          0          1          0 |         1
     3,420 |         0          0          2          0 |         2
     3,430 |         0          0          1          0 |         1
     3,470 |         0          0          1          0 |         1
     3,600 |         0          0          2          0 |         2
     3,670 |         0          0          1          0 |         1
     3,690 |         0          0          2          0 |         2
     3,700 |         0          0          1          0 |         1
     3,720 |         0          0          1          0 |         1
     3,740 |         0          0          1          0 |         1
     3,830 |         0          0          1          0 |         1
     3,880 |         0          0          1          0 |         1
     3,900 |         0          0          1          0 |         1
     4,030 |         0          0          1          0 |         1
     4,060 |         0          0          2          0 |         2
     4,080 |         0          0          0          1 |         1
     4,130 |         0          0          0          1 |         1
     4,290 |         0          0          0          1 |         1
     4,330 |         0          0          0          1 |         1
     4,720 |         0          0          0          1 |         1
     4,840 |         0          0          0          1 |         1
-----------+--------------------------------------------+----------
     Total |         8         29         31          6 |        74

. quantile weight, ms(none) mlabel(binned_weight) mlabpos(0)

. egen mean = mean(mpg), by(binned_weight)

. scatter mean binned_weight

. scatter mean weight, ms(Oh) jitter(1)