Select n lines after a specific number
I am working with data.frame like this:
Country Date balance_of_payment business_confidence_indicator consumer_confidence_indicator CPI Crisis_IMF
1 Australia 1980-01-01 -0.87 100.215 99.780 25.4 0
2 Australia 1980-04-01 -1.62 100.061 99.746 26.2 0
3 Australia 1980-07-01 -3.70 100.599 100.049 26.6 0
4 Australia 1980-10-01 -3.13 100.597 100.735 27.2 0
5 Australia 1981-01-01 -2.73 101.149 101.016 27.8 0
6 Australia 1981-04-01 -4.11 100.936 100.150 28.4 0
I want to generate summary statistics describe(dataset)
from a package Hmisc
.
I need to distinguish between the n-quaters of timespans before Crisis_IMF
is 1
, the time at which Crisis_IMF
is 1, and the n-quater of the states after Crisis_IMF
is 1
. To pick the time at which Crisis_IMF
1
I did describe(dataset[dataset$Crisis_IMF==1,"balance_of_payment"])
.
But I don't know how to execute the command within n-quarters time period (e.g. 8) after the event.
Edit:
dataset$Crisis_IMF
[1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[60] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[119] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[178] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[237] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[296] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[355] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[414] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[473] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[532] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[591] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[650] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[709] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0
[768] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[827] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[886] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[945] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[1004] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[1063] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[1122] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[1181] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[1240] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[1299] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[1358] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[1417] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[1476] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1
[1535] 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
[1594] 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[1653] 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[1712] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[1771] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0
[1830] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[1889] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[1948] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[2007] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[2066] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[2125] 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1
[2184] 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[2243] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[2302] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[2361] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[2420] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[2479] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[2538] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[2597] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[2656] 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[2715] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[2774] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[2833] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[2892] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[2951] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[3010] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[3069] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[3128] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[3187] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[3246] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[3305] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[3364] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[3423] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[3482] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[3541] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[3600] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[3659] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[3718] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[3777] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[3836] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[3895] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[3954] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1
[4013] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[4072] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[4131] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[4190] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[4249] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[4308] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Edit2; more information about the dataset:
Country Date balance_of_payment Crisis_IMF
1 Australia 1980-01-01 -0.87 0
2 Australia 1980-04-01 -1.62 0
3 Australia 1980-07-01 -3.70 0
4 Australia 1980-10-01 -3.13 0
5 Australia 1981-01-01 -2.73 0
6 Australia 1981-04-01 -4.11 0
7 Australia 1981-07-01 -3.98 0
8 Australia 1981-10-01 -5.27 0
9 Australia 1982-01-01 -5.31 0
10 Australia 1982-04-01 -4.67 0
11 Australia 1982-07-01 -3.30 0
12 Australia 1982-10-01 -3.24 0
13 Australia 1983-01-01 -3.45 0
14 Australia 1983-04-01 -2.86 0
15 Australia 1983-07-01 -3.58 0
...
137 Australia 2014-01-01 -2.18 0
138 Australia 2014-04-01 -3.44 0
139 Australia 2014-07-01 -3.04 0
140 Australia 2014-10-01 -2.39 0
141 Austria 1980-01-01 -3.97 0
142 Austria 1980-04-01 -3.89 0
143 Austria 1980-07-01 -1.84 0
144 Austria 1980-10-01 -1.60 0
145 Austria 1981-01-01 -2.74 0
146 Austria 1981-04-01 -2.88 0
147 Austria 1981-07-01 -2.83 0
148 Austria 1981-10-01 -2.06 0
149 Austria 1982-01-01 -0.63 0
150 Austria 1982-04-01 0.61 0
151 Austria 1982-07-01 2.42 0
152 Austria 1982-10-01 2.70 0
There can be more than one crisis period in one country. This, for example, in Australia - the crisis from 1990-01-01 to 1991-04-01 and 2002-01-01 to 2005-01-01
. I want to create 3 different commands describe
that show the behavior of a variable in the above states.
+3
source to share
1 answer
You did not provide your complete details, so I must guess that your column Crisis_IMF
has a continuous sequence of zeros (before the crisis) followed by a continuous sequence of ones (during which the IMF crisis was considered to be in effect), finally followed by a continuous sequence of zeros ( after the crisis).
Below I have synthesized my own data for testing. I only synthesized columns Crisis_IMF
and balance_of_payment
because they are the only columns relevant to your problem. I used 30 rows, from the first 10 to the next 10 during and the last 10 after the crisis. I used a random parabolic arc for balance_of_payment
, but it was completely random.
library('Hmisc');
set.seed(1);
N <- 30;
df <- data.frame(balance_of_payment=-5+2*seq(-1.5,1.5,len=N)^2+rnorm(N,0,0.2), Crisis_IMF=c(rep(0,N/3),rep(1,N/3),rep(0,N/3)) );
df;
## balance_of_payment Crisis_IMF
## 1 -0.6252908 0
## 2 -1.0625579 0
## 3 -1.8228927 0
## 4 -1.8503850 0
## 5 -2.5744076 0
## 6 -3.2324647 0
## 7 -3.3561408 0
## 8 -3.6484112 0
## 9 -3.9805631 0
## 10 -4.4136342 0
## 11 -4.2642312 1
## 12 -4.6598435 1
## 13 -4.9904788 1
## 14 -5.3947830 1
## 15 -4.7696630 1
## 16 -5.0036359 1
## 17 -4.9550811 1
## 18 -4.6774634 1
## 19 -4.5735679 1
## 20 -4.4478071 1
## 21 -4.1687610 0
## 22 -3.9392921 0
## 23 -3.7811631 0
## 24 -3.8514970 0
## 25 -2.9444058 0
## 26 -2.6515349 0
## 27 -2.2006002 0
## 28 -1.9499174 0
## 29 -1.1949166 0
## 30 -0.4164117 0
crisisRange <- range(which(df$Crisis_IMF==1));
crisisRange;
## [1] 11 20
df$Off_Crisis <- c((1-crisisRange[1]):-1,rep(0,diff(crisisRange)+1),1:(nrow(df)-crisisRange[2]));
df;
## balance_of_payment Crisis_IMF Off_Crisis
## 1 -0.6252908 0 -10
## 2 -1.0625579 0 -9
## 3 -1.8228927 0 -8
## 4 -1.8503850 0 -7
## 5 -2.5744076 0 -6
## 6 -3.2324647 0 -5
## 7 -3.3561408 0 -4
## 8 -3.6484112 0 -3
## 9 -3.9805631 0 -2
## 10 -4.4136342 0 -1
## 11 -4.2642312 1 0
## 12 -4.6598435 1 0
## 13 -4.9904788 1 0
## 14 -5.3947830 1 0
## 15 -4.7696630 1 0
## 16 -5.0036359 1 0
## 17 -4.9550811 1 0
## 18 -4.6774634 1 0
## 19 -4.5735679 1 0
## 20 -4.4478071 1 0
## 21 -4.1687610 0 1
## 22 -3.9392921 0 2
## 23 -3.7811631 0 3
## 24 -3.8514970 0 4
## 25 -2.9444058 0 5
## 26 -2.6515349 0 6
## 27 -2.2006002 0 7
## 28 -1.9499174 0 8
## 29 -1.1949166 0 9
## 30 -0.4164117 0 10
n <- 8;
describe(df[df$Off_Crisis>=-n&df$Off_Crisis<=-1,'balance_of_payment']);
## df[df$Off_Crisis >= -n & df$Off_Crisis <= -1, "balance_of_payment"]
## n missing unique Info Mean
## 8 0 8 1 -3.11
##
## -4.41363415781177 (1, 12%), -3.98056311135777 (1, 12%), -3.64841115885525 (1, 12%), -3.35614082447269 (1, 12%), -3.23246466374394 (1, 12%), -2.57440760140387 (1, 12%), -1.85038498107066 (1, 12%), -1.82289266659616 (1, 12%)
describe(df[df$Off_Crisis==0,'balance_of_payment']);
## df[df$Off_Crisis == 0, "balance_of_payment"]
## n missing unique Info Mean .05 .10 .25 .50 .75 .90 .95
## 10 0 10 1 -4.774 -5.219 -5.043 -4.982 -4.724 -4.595 -4.429 -4.347
##
## -5.39478302143074 (1, 10%), -5.00363594891363 (1, 10%), -4.99047879387293 (1, 10%), -4.95508109661503 (1, 10%), -4.76966304348196 (1, 10%), -4.67746343562751 (1, 10%), -4.65984348113626 (1, 10%), -4.57356788939893 (1, 10%), -4.44780713171369 (1, 10%), -4.26423116226702 (1, 10%)
describe(df[df$Off_Crisis>=1&df$Off_Crisis<=n,'balance_of_payment']);
## df[df$Off_Crisis >= 1 & df$Off_Crisis <= n, "balance_of_payment"]
## n missing unique Info Mean
## 8 0 8 1 -3.186
##
## -4.16876100605885 (1, 12%), -3.93929212154225 (1, 12%), -3.85149697413106 (1, 12%), -3.78116310320806 (1, 12%), -2.94440583734139 (1, 12%), -2.65153490367274 (1, 12%), -2.20060024283928 (1, 12%), -1.949917420894 (1, 12%)
The solution works by first calculating the range of indices over which the crisis has acted like crisisRange
. It then adds a new column to the data.frame Off_Crisis
that captures how many quarters are offset from the crisis, a row, using negative numbers for previous and positive numbers for after, and assuming that each row represents exactly one quarter.
The calls describe()
can then be made subset in the column Off_Crisis
, getting only the quarters offset from the crisis you want for each call.
Change: . This was hard. Pretty sure I got it though:
library('Hmisc');
set.seed(1);
N <- 60;
df <- data.frame(balance_of_payment=rep(-5+2*seq(-1.5,1.5,len=N/2)^2,2)+rnorm(N,0,0.2), Crisis_IMF=c(rep(0,N/6),rep(1,N/6),rep(0,N/3),rep(1,N/6),rep(0,N/6)) );
df;
## balance_of_payment Crisis_IMF
## 1 -0.6252908 0
## 2 -1.0625579 0
## 3 -1.8228927 0
## 4 -1.8503850 0
## 5 -2.5744076 0
## 6 -3.2324647 0
## 7 -3.3561408 0
## 8 -3.6484112 0
## 9 -3.9805631 0
## 10 -4.4136342 0
## 11 -4.2642312 1
## 12 -4.6598435 1
## 13 -4.9904788 1
## 14 -5.3947830 1
## 15 -4.7696630 1
## 16 -5.0036359 1
## 17 -4.9550811 1
## 18 -4.6774634 1
## 19 -4.5735679 1
## 20 -4.4478071 1
## 21 -4.1687610 0
## 22 -3.9392921 0
## 23 -3.7811631 0
## 24 -3.8514970 0
## 25 -2.9444058 0
## 26 -2.6515349 0
## 27 -2.2006002 0
## 28 -1.9499174 0
## 29 -1.1949166 0
## 30 -0.4164117 0
## 31 -0.2282641 0
## 32 -1.1198441 0
## 33 -1.5782326 0
## 34 -2.1802021 0
## 35 -2.9157211 0
## 36 -3.1513699 0
## 37 -3.5324846 0
## 38 -3.8079388 0
## 39 -3.8757143 0
## 40 -4.1999213 0
## 41 -4.5994921 1
## 42 -4.7884845 1
## 43 -4.7268380 1
## 44 -4.8405104 1
## 45 -5.1324004 1
## 46 -5.1361483 1
## 47 -4.8789267 1
## 48 -4.7125241 1
## 49 -4.7602814 1
## 50 -4.3903659 1
## 51 -4.2729353 0
## 52 -4.2181247 0
## 53 -3.7278522 0
## 54 -3.6794993 0
## 55 -2.7817662 0
## 56 -2.2442292 0
## 57 -2.2428854 0
## 58 -1.8645939 0
## 59 -0.9853426 0
## 60 -0.5270109 0
df$Off_Crisis <- ifelse(df$Crisis_IMF==1,0,with(rle(df$Crisis_IMF),{ mids <- lengths[-c(1,length(lengths))]; c(-lengths[1]:-1,sequence(mids)-rep(rbind(0,mids+1),rbind(ceiling(mids/2),floor(mids/2))),1:lengths[length(lengths)]); }));
df;
## balance_of_payment Crisis_IMF Off_Crisis
## 1 -0.6252908 0 -10
## 2 -1.0625579 0 -9
## 3 -1.8228927 0 -8
## 4 -1.8503850 0 -7
## 5 -2.5744076 0 -6
## 6 -3.2324647 0 -5
## 7 -3.3561408 0 -4
## 8 -3.6484112 0 -3
## 9 -3.9805631 0 -2
## 10 -4.4136342 0 -1
## 11 -4.2642312 1 0
## 12 -4.6598435 1 0
## 13 -4.9904788 1 0
## 14 -5.3947830 1 0
## 15 -4.7696630 1 0
## 16 -5.0036359 1 0
## 17 -4.9550811 1 0
## 18 -4.6774634 1 0
## 19 -4.5735679 1 0
## 20 -4.4478071 1 0
## 21 -4.1687610 0 1
## 22 -3.9392921 0 2
## 23 -3.7811631 0 3
## 24 -3.8514970 0 4
## 25 -2.9444058 0 5
## 26 -2.6515349 0 6
## 27 -2.2006002 0 7
## 28 -1.9499174 0 8
## 29 -1.1949166 0 9
## 30 -0.4164117 0 10
## 31 -0.2282641 0 -10
## 32 -1.1198441 0 -9
## 33 -1.5782326 0 -8
## 34 -2.1802021 0 -7
## 35 -2.9157211 0 -6
## 36 -3.1513699 0 -5
## 37 -3.5324846 0 -4
## 38 -3.8079388 0 -3
## 39 -3.8757143 0 -2
## 40 -4.1999213 0 -1
## 41 -4.5994921 1 0
## 42 -4.7884845 1 0
## 43 -4.7268380 1 0
## 44 -4.8405104 1 0
## 45 -5.1324004 1 0
## 46 -5.1361483 1 0
## 47 -4.8789267 1 0
## 48 -4.7125241 1 0
## 49 -4.7602814 1 0
## 50 -4.3903659 1 0
## 51 -4.2729353 0 1
## 52 -4.2181247 0 2
## 53 -3.7278522 0 3
## 54 -3.6794993 0 4
## 55 -2.7817662 0 5
## 56 -2.2442292 0 6
## 57 -2.2428854 0 7
## 58 -1.8645939 0 8
## 59 -0.9853426 0 9
## 60 -0.5270109 0 10
n <- 8;
describe(df[df$Off_Crisis>=-n&df$Off_Crisis<=-1,'balance_of_payment']);
## df[df$Off_Crisis >= -n & df$Off_Crisis <= -1, "balance_of_payment"]
## n missing unique Info Mean .05 .10 .25 .50 .75 .90 .95
## 16 0 16 1 -3.133 -4.253 -4.090 -3.825 -3.294 -2.476 -1.837 -1.762
##
## -4.41363415781177 (1, 6%), -4.19992133068899 (1, 6%), -3.98056311135777 (1, 6%), -3.87571430729169 (1, 6%), -3.80793877922333 (1, 6%), -3.64841115885525 (1, 6%)
## -3.53248462570045 (1, 6%), -3.35614082447269 (1, 6%), -3.23246466374394 (1, 6%), -3.15136989958027 (1, 6%), -2.91572106713267 (1, 6%), -2.57440760140387 (1, 6%)
## -2.1802021496148 (1, 6%), -1.85038498107066 (1, 6%), -1.82289266659616 (1, 6%), -1.57823262180228 (1, 6%)
describe(df[df$Off_Crisis==0,'balance_of_payment']);
## df[df$Off_Crisis == 0, "balance_of_payment"]
## n missing unique Info Mean .05 .10 .25 .50 .75 .90 .95
## 20 0 20 1 -4.785 -5.149 -5.133 -4.964 -4.765 -4.645 -4.442 -4.384
##
## lowest : -5.395 -5.136 -5.132 -5.004 -4.990, highest: -4.599 -4.574 -4.448 -4.390 -4.264
describe(df[df$Off_Crisis>=1&df$Off_Crisis<=n,'balance_of_payment']);
## df[df$Off_Crisis >= 1 & df$Off_Crisis <= n, "balance_of_payment"]
## n missing unique Info Mean .05 .10 .25 .50 .75 .90 .95
## 16 0 16 1 -3.157 -4.232 -4.193 -3.873 -3.312 -2.244 -2.075 -1.929
##
## -4.27293530430708 (1, 6%), -4.21812466033862 (1, 6%), -4.16876100605885 (1, 6%), -3.93929212154225 (1, 6%), -3.85149697413106 (1, 6%), -3.78116310320806 (1, 6%)
## -3.72785216159621 (1, 6%), -3.67949925417454 (1, 6%), -2.94440583734139 (1, 6%), -2.78176624658013 (1, 6%), -2.65153490367274 (1, 6%), -2.24422917606577 (1, 6%)
## -2.24288543679152 (1, 6%), -2.20060024283928 (1, 6%), -1.949917420894 (1, 6%), -1.86459386937746 (1, 6%)
For this demonstration, I synthesized five periods: 10 series of non-crisis, 10 series of crises (first), 20 series of non-crisis, 10 series of crises (second) and 10 series of non-crisis, One and the same algorithm - calculating the column Off_Crisis
(which was much more difficult this time !) And then use it on a subset of data.frame for each call describe()
. Only now will the data points from different crises be combined into subsets.
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