| \n | country | \nunemp_rate | \ngdp | \ntotal_pop | \n
|---|---|---|---|---|
| 0 | \nAustria | \n6.0 | \n356237.6 | \n8401940 | \n
| 1 | \nBelgium | \n7.8 | \n424660.3 | \n11000638 | \n
| 2 | \nBulgaria | \n7.6 | \n48128.6 | \n7364570 | \n
| 13 | \nCroatia | \n13.1 | \n46639.5 | \n4284889 | \n
| 4 | \nCyprus | \n13.0 | \n18490.2 | \n840407 | \n
| 5 | \nCzechia | \n4.0 | \n176370.1 | \n10436560 | \n
| 7 | \nDenmark | \n6.2 | \n282089.9 | \n5560628 | \n
| 8 | \nEstonia | \n6.8 | \n21682.6 | \n1294455 | \n
| 11 | \nFinland | \n8.8 | \n216073.0 | \n5375276 | \n
| 12 | \nFrance | \n10.1 | \n2228568.0 | \n64933400 | \n
| 6 | \nGermany | \n4.1 | \n3159750.0 | \n80219695 | \n
| 9 | \nGreece | \n23.6 | \n176487.9 | \n10816286 | \n
| 14 | \nHungary | \n5.1 | \n113903.8 | \n9937628 | \n
| 16 | \nIceland | \n3.0 | \n18646.1 | \n315556 | \n
| 15 | \nIreland | \n8.4 | \n273238.2 | \n4574888 | \n
| 17 | \nItaly | \n11.7 | \n1689824.0 | \n59433744 | \n
| 20 | \nLatvia | \n9.6 | \n25037.7 | \n2070371 | \n
| 18 | \nLithuania | \n7.9 | \n38849.4 | \n3043429 | \n
| 19 | \nLuxembourg | \n6.3 | \n53303.0 | \n512353 | \n
| 21 | \nMalta | \n4.7 | \n10344.1 | \n417432 | \n
| 22 | \nNetherlands | \n6.0 | \n708337.0 | \n16655799 | \n
| 23 | \nNorway | \n4.7 | \n335747.5 | \n4979954 | \n
| 24 | \nPoland | \n6.2 | \n426547.5 | \n38044565 | \n
| 25 | \nPortugal | \n11.2 | \n186480.5 | \n10562178 | \n
| 26 | \nRomania | \n5.9 | \n170393.6 | \n20121641 | \n
| 29 | \nSlovakia | \n9.7 | \n81226.1 | \n5397036 | \n
| 28 | \nSlovenia | \n8.0 | \n40357.2 | \n2050189 | \n
| 10 | \nSpain | \n19.6 | \n1118743.0 | \n46815910 | \n
| 27 | \nSweden | \n7.0 | \n463147.5 | \n9482855 | \n
| 3 | \nSwitzerland | \n5.0 | \n605753.7 | \n7954662 | \n
| 30 | \nTurkey | \n10.9 | \n780224.9 | \n7954662 | \n
| 31 | \nUnited Kingdom | \n4.8 | \n2403382.6 | \n63182180 | \n
| \\n | unemp_rate | \\n
|---|---|
| count | \\n32.000000 | \\n
| mean | \\n8.337500 | \\n
| std | \\n4.393378 | \\n
| min | \\n3.000000 | \\n
| 25% | \\n5.700000 | \\n
| 50% | \\n7.300000 | \\n
| 75% | \\n9.800000 | \\n
| max | \\n23.600000 | \\n
| \\n | country | \\nunemp_rate | \\n
|---|---|---|
| 16 | \\nIceland | \\n3.0 | \\n
| 5 | \\nCzechia | \\n4.0 | \\n
| 6 | \\nGermany | \\n4.1 | \\n
| 23 | \\nNorway | \\n4.7 | \\n
| 21 | \\nMalta | \\n4.7 | \\n
| \\n | country | \\nunemp_rate | \\n
|---|---|---|
| 17 | \\nItaly | \\n11.7 | \\n
| 4 | \\nCyprus | \\n13.0 | \\n
| 13 | \\nCroatia | \\n13.1 | \\n
| 10 | \\nSpain | \\n19.6 | \\n
| 9 | \\nGreece | \\n23.6 | \\n
| \\n | country | \\nunemp_rate | \\n
|---|---|---|
| 0 | \\nAustria | \\n6.0 | \\n
| 7 | \\nDenmark | \\n6.2 | \\n
| 21 | \\nMalta | \\n4.7 | \\n
| 22 | \\nNetherlands | \\n6.0 | \\n
| 23 | \\nNorway | \\n4.7 | \\n
| 24 | \\nPoland | \\n6.2 | \\n
| \\n | country | \\ngdp | \\n
|---|---|---|
| 0 | \\nCyprus | \\n18490.2 | \\n
| 1 | \\nLatvia | \\n25037.7 | \\n
| 2 | \\nLithuania | \\n38849.4 | \\n
| 3 | \\nLuxembourg | \\n53303.0 | \\n
| 4 | \\nHungary | \\n113903.8 | \\n
| \\n | country | \\nunemp_rate | \\ngdp | \\n
|---|---|---|---|
| 0 | \\nAustria | \\n6.0 | \\n356237.6 | \\n
| 1 | \\nBelgium | \\n7.8 | \\n424660.3 | \\n
| 2 | \\nBulgaria | \\n7.6 | \\n48128.6 | \\n
| 3 | \\nSwitzerland | \\n5.0 | \\n605753.7 | \\n
| 4 | \\nCyprus | \\n13.0 | \\n18490.2 | \\n
| \\n | country | \\navg_hrs_worked | \\n
|---|---|---|
| 0 | \\nAustria | \\n36.5 | \\n
| 1 | \\nBelgium | \\n37.0 | \\n
| 2 | \\nBulgaria | \\n40.8 | \\n
| 3 | \\nSwitzerland | \\n34.7 | \\n
| 4 | \\nCyprus | \\n39.2 | \\n
| \\n | country | \\nmedian_income | \\n
|---|---|---|
| 0 | \\nBelgium | \\n21335 | \\n
| 1 | \\nBulgaria | \\n6742 | \\n
| 2 | \\nCzechia | \\n12478 | \\n
| 3 | \\nDenmark | \\n21355 | \\n
| 4 | \\nGermany | \\n21152 | \\n
| \\n | Unnamed: 0 | \\nUnnamed: 1 | \\nUnnamed: 2 | \\n
|---|---|---|---|
| 0 | \\nThis is an overview of the population data for... | \\nNaN | \\nNaN | \\n
| 1 | \\nNaN | \\nNaN | \\nNaN | \\n
| 2 | \\ncountry | \\ntotal_pop | \\nprct_yng_adt_pop | \\n
| 3 | \\nBelgium | \\n11000638 | \\n18.485146 | \\n
| 4 | \\nBulgaria | \\n7364570 | \\n18.432577 | \\n
| \\n | country | \\ntotal_pop | \\nprct_yng_adt_pop | \\n
|---|---|---|---|
| 0 | \\nBelgium | \\n11000638 | \\n18.485146 | \\n
| 1 | \\nBulgaria | \\n7364570 | \\n18.432577 | \\n
| 2 | \\nEstonia | \\n1294455 | \\n19.688363 | \\n
| 3 | \\nIreland | \\n4574888 | \\n20.601335 | \\n
| 4 | \\nGreece | \\n10816286 | \\n17.604416 | \\n
| \\n | country | \\nunemp_rate | \\ngdp | \\ntotal_pop | \\n
|---|---|---|---|---|
| 0 | \\nAustria | \\n6.0 | \\n356237.6 | \\n8401940 | \\n
| 1 | \\nBelgium | \\n7.8 | \\n424660.3 | \\n11000638 | \\n
| 2 | \\nBulgaria | \\n7.6 | \\n48128.6 | \\n7364570 | \\n
| 13 | \\nCroatia | \\n13.1 | \\n46639.5 | \\n4284889 | \\n
| 4 | \\nCyprus | \\n13.0 | \\n18490.2 | \\n840407 | \\n
| \\n | unemp_rate | \\ngdp | \\ntotal_pop | \\n
|---|---|---|---|
| country | \\n\\n | \\n | \\n |
| Austria | \\n6.0 | \\n356237.6 | \\n8401940 | \\n
| Belgium | \\n7.8 | \\n424660.3 | \\n11000638 | \\n
| Bulgaria | \\n7.6 | \\n48128.6 | \\n7364570 | \\n
| Croatia | \\n13.1 | \\n46639.5 | \\n4284889 | \\n
| Cyprus | \\n13.0 | \\n18490.2 | \\n840407 | \\n
| \\n | unemp_rate | \\ngdp | \\nmedian_income | \\ntotal_pop | \\n
|---|---|---|---|---|
| count | \\n32.000000 | \\n3.200000e+01 | \\n32.000000 | \\n3.200000e+01 | \\n
| mean | \\n8.337500 | \\n5.218333e+05 | \\n15972.343750 | \\n1.637612e+07 | \\n
| std | \\n4.393378 | \\n7.781849e+05 | \\n6640.636617 | \\n2.198819e+07 | \\n
| min | \\n3.000000 | \\n1.034410e+04 | \\n4724.000000 | \\n3.155560e+05 | \\n
| 25% | \\n5.700000 | \\n4.775632e+04 | \\n10190.500000 | \\n3.974524e+06 | \\n
| 50% | \\n7.300000 | \\n2.012768e+05 | \\n16205.000000 | \\n7.954662e+06 | \\n
| 75% | \\n9.800000 | \\n4.987990e+05 | \\n21161.250000 | \\n1.241443e+07 | \\n
| max | \\n23.600000 | \\n3.159750e+06 | \\n28663.000000 | \\n8.021970e+07 | \\n
| \\n | country | \\nunemp_rate | \\ngdp | \\nmedian_income | \\ntotal_pop | \\n
|---|---|---|---|---|---|
| 0 | \\nAustria | \\n6.0 | \\n356237.6 | \\n23071 | \\n8401940 | \\n
| \\n | country | \\nunemp_rate | \\ngdp | \\nmedian_income | \\ntotal_pop | \\n
|---|---|---|---|---|---|
| 11 | \\nGreece | \\n23.6 | \\n176487.9 | \\n9048 | \\n10816286 | \\n
| \\n | country | \\nunemp_rate | \\ngdp | \\nmedian_income | \\ntotal_pop | \\n
|---|---|---|---|---|---|
| 1 | \\nBelgium | \\n7.8 | \\n424660.3 | \\n21335 | \\n11000638 | \\n
| 2 | \\nBulgaria | \\n7.6 | \\n48128.6 | \\n6742 | \\n7364570 | \\n
| 8 | \\nFinland | \\n8.8 | \\n216073.0 | \\n19997 | \\n5375276 | \\n
| 13 | \\nIceland | \\n3.0 | \\n18646.1 | \\n22193 | \\n315556 | \\n
| 14 | \\nIreland | \\n8.4 | \\n273238.2 | \\n18286 | \\n4574888 | \\n
| 15 | \\nItaly | \\n11.7 | \\n1689824.0 | \\n16237 | \\n59433744 | \\n
| 19 | \\nMalta | \\n4.7 | \\n10344.1 | \\n17264 | \\n417432 | \\n
| 20 | \\nNetherlands | \\n6.0 | \\n708337.0 | \\n21189 | \\n16655799 | \\n
| 22 | \\nPoland | \\n6.2 | \\n426547.5 | \\n10865 | \\n38044565 | \\n
| 23 | \\nPortugal | \\n11.2 | \\n186480.5 | \\n10805 | \\n10562178 | \\n
| 25 | \\nSlovakia | \\n9.7 | \\n81226.1 | \\n10466 | \\n5397036 | \\n
| 26 | \\nSlovenia | \\n8.0 | \\n40357.2 | \\n15250 | \\n2050189 | \\n
| 29 | \\nSwitzerland | \\n5.0 | \\n605753.7 | \\n27692 | \\n7954662 | \\n
| \\n | country | \\nunemp_rate | \\ngdp | \\nmedian_income | \\ntotal_pop | \\n
|---|---|---|---|---|---|
| 1 | \\nBelgium | \\n7.8 | \\n424660.3 | \\n21335 | \\n11000638 | \\n
| 2 | \\nBulgaria | \\n7.6 | \\n48128.6 | \\n6742 | \\n7364570 | \\n
| 8 | \\nFinland | \\n8.8 | \\n216073.0 | \\n19997 | \\n5375276 | \\n
| 13 | \\nIceland | \\n3.0 | \\n18646.1 | \\n22193 | \\n315556 | \\n
| 14 | \\nIreland | \\n8.4 | \\n273238.2 | \\n18286 | \\n4574888 | \\n
| 15 | \\nItaly | \\n11.7 | \\n1689824.0 | \\n16237 | \\n59433744 | \\n
| 16 | \\nLatvia | \\n9.6 | \\n25037.7 | \\n9257 | \\n2070371 | \\n
| 17 | \\nLithuania | \\n7.9 | \\n38849.4 | \\n9364 | \\n3043429 | \\n
| 18 | \\nLuxembourg | \\n6.3 | \\n53303.0 | \\n28663 | \\n512353 | \\n
| 19 | \\nMalta | \\n4.7 | \\n10344.1 | \\n17264 | \\n417432 | \\n
| 20 | \\nNetherlands | \\n6.0 | \\n708337.0 | \\n21189 | \\n16655799 | \\n
| 22 | \\nPoland | \\n6.2 | \\n426547.5 | \\n10865 | \\n38044565 | \\n
| 23 | \\nPortugal | \\n11.2 | \\n186480.5 | \\n10805 | \\n10562178 | \\n
| 25 | \\nSlovakia | \\n9.7 | \\n81226.1 | \\n10466 | \\n5397036 | \\n
| 26 | \\nSlovenia | \\n8.0 | \\n40357.2 | \\n15250 | \\n2050189 | \\n
| 29 | \\nSwitzerland | \\n5.0 | \\n605753.7 | \\n27692 | \\n7954662 | \\n
| \\n | country | \\nunemp_rate | \\ngdp | \\nmedian_income | \\ntotal_pop | \\n
|---|---|---|---|---|---|
| 13 | \\nIceland | \\n3.0 | \\n18646.1 | \\n22193 | \\n315556 | \\n
| 18 | \\nLuxembourg | \\n6.3 | \\n53303.0 | \\n28663 | \\n512353 | \\n
| 19 | \\nMalta | \\n4.7 | \\n10344.1 | \\n17264 | \\n417432 | \\n
| 20 | \\nNetherlands | \\n6.0 | \\n708337.0 | \\n21189 | \\n16655799 | \\n
| 22 | \\nPoland | \\n6.2 | \\n426547.5 | \\n10865 | \\n38044565 | \\n
| 29 | \\nSwitzerland | \\n5.0 | \\n605753.7 | \\n27692 | \\n7954662 | \\n
| \\n | country | \\nunemp_rate | \\ngdp | \\nmedian_income | \\ntotal_pop | \\n
|---|---|---|---|---|---|
| 0 | \\nAustria | \\n6.0 | \\n356237.6 | \\n23071 | \\n8401940 | \\n
| 1 | \\nBelgium | \\n7.8 | \\n424660.3 | \\n21335 | \\n11000638 | \\n
| 2 | \\nBulgaria | \\n7.6 | \\n48128.6 | \\n6742 | \\n7364570 | \\n
| 5 | \\nCzechia | \\n4.0 | \\n176370.1 | \\n12478 | \\n10436560 | \\n
| 6 | \\nDenmark | \\n6.2 | \\n282089.9 | \\n21355 | \\n5560628 | \\n
| 7 | \\nEstonia | \\n6.8 | \\n21682.6 | \\n11867 | \\n1294455 | \\n
| 8 | \\nFinland | \\n8.8 | \\n216073.0 | \\n19997 | \\n5375276 | \\n
| 10 | \\nGermany | \\n4.1 | \\n3159750.0 | \\n21152 | \\n80219695 | \\n
| 12 | \\nHungary | \\n5.1 | \\n113903.8 | \\n8267 | \\n9937628 | \\n
| 13 | \\nIceland | \\n3.0 | \\n18646.1 | \\n22193 | \\n315556 | \\n
| 14 | \\nIreland | \\n8.4 | \\n273238.2 | \\n18286 | \\n4574888 | \\n
| 15 | \\nItaly | \\n11.7 | \\n1689824.0 | \\n16237 | \\n59433744 | \\n
| 18 | \\nLuxembourg | \\n6.3 | \\n53303.0 | \\n28663 | \\n512353 | \\n
| 19 | \\nMalta | \\n4.7 | \\n10344.1 | \\n17264 | \\n417432 | \\n
| 20 | \\nNetherlands | \\n6.0 | \\n708337.0 | \\n21189 | \\n16655799 | \\n
| 21 | \\nNorway | \\n4.7 | \\n335747.5 | \\n27670 | \\n4979954 | \\n
| 22 | \\nPoland | \\n6.2 | \\n426547.5 | \\n10865 | \\n38044565 | \\n
| 23 | \\nPortugal | \\n11.2 | \\n186480.5 | \\n10805 | \\n10562178 | \\n
| 24 | \\nRomania | \\n5.9 | \\n170393.6 | \\n4724 | \\n20121641 | \\n
| 25 | \\nSlovakia | \\n9.7 | \\n81226.1 | \\n10466 | \\n5397036 | \\n
| 26 | \\nSlovenia | \\n8.0 | \\n40357.2 | \\n15250 | \\n2050189 | \\n
| 29 | \\nSwitzerland | \\n5.0 | \\n605753.7 | \\n27692 | \\n7954662 | \\n
| 31 | \\nUnited Kingdom | \\n4.8 | \\n2403382.6 | \\n17296 | \\n63182180 | \\n
| \\n | country | \\nunemp_rate | \\ngdp | \\nmedian_income | \\ntotal_pop | \\n
|---|---|---|---|---|---|
| 1 | \\nBelgium | \\n7.8 | \\n424660.3 | \\n21335 | \\n11000638 | \\n
| 2 | \\nBulgaria | \\n7.6 | \\n48128.6 | \\n6742 | \\n7364570 | \\n
| 3 | \\nCroatia | \\n13.1 | \\n46639.5 | \\n8985 | \\n4284889 | \\n
| 4 | \\nCyprus | \\n13.0 | \\n18490.2 | \\n16173 | \\n840407 | \\n
| 8 | \\nFinland | \\n8.8 | \\n216073.0 | \\n19997 | \\n5375276 | \\n
| 9 | \\nFrance | \\n10.1 | \\n2228568.0 | \\n20621 | \\n64933400 | \\n
| 11 | \\nGreece | \\n23.6 | \\n176487.9 | \\n9048 | \\n10816286 | \\n
| 14 | \\nIreland | \\n8.4 | \\n273238.2 | \\n18286 | \\n4574888 | \\n
| 15 | \\nItaly | \\n11.7 | \\n1689824.0 | \\n16237 | \\n59433744 | \\n
| 16 | \\nLatvia | \\n9.6 | \\n25037.7 | \\n9257 | \\n2070371 | \\n
| 17 | \\nLithuania | \\n7.9 | \\n38849.4 | \\n9364 | \\n3043429 | \\n
| 23 | \\nPortugal | \\n11.2 | \\n186480.5 | \\n10805 | \\n10562178 | \\n
| 25 | \\nSlovakia | \\n9.7 | \\n81226.1 | \\n10466 | \\n5397036 | \\n
| 26 | \\nSlovenia | \\n8.0 | \\n40357.2 | \\n15250 | \\n2050189 | \\n
| 27 | \\nSpain | \\n19.6 | \\n1118743.0 | \\n15347 | \\n46815910 | \\n
| 28 | \\nSweden | \\n7.0 | \\n463147.5 | \\n20955 | \\n9482855 | \\n
| 30 | \\nTurkey | \\n10.9 | \\n780224.9 | \\n6501 | \\n7954662 | \\n
| \\n | country | \\nunemp_rate | \\ngdp | \\nmedian_income | \\ntotal_pop | \\n
|---|---|---|---|---|---|
| 9 | \\nFrance | \\n10.1 | \\n2228568.0 | \\n20621 | \\n64933400 | \\n
| 15 | \\nItaly | \\n11.7 | \\n1689824.0 | \\n16237 | \\n59433744 | \\n
| 27 | \\nSpain | \\n19.6 | \\n1118743.0 | \\n15347 | \\n46815910 | \\n
| 30 | \\nTurkey | \\n10.9 | \\n780224.9 | \\n6501 | \\n7954662 | \\n
| \\n | country | \\nunemp_rate | \\ngdp | \\nmedian_income | \\ntotal_pop | \\n
|---|---|---|---|---|---|
| 4 | \\nCyprus | \\n13.0 | \\n18490.2 | \\n16173 | \\n840407 | \\n
| 5 | \\nCzechia | \\n4.0 | \\n176370.1 | \\n12478 | \\n10436560 | \\n
| 7 | \\nEstonia | \\n6.8 | \\n21682.6 | \\n11867 | \\n1294455 | \\n
| 8 | \\nFinland | \\n8.8 | \\n216073.0 | \\n19997 | \\n5375276 | \\n
| 14 | \\nIreland | \\n8.4 | \\n273238.2 | \\n18286 | \\n4574888 | \\n
| 15 | \\nItaly | \\n11.7 | \\n1689824.0 | \\n16237 | \\n59433744 | \\n
| 19 | \\nMalta | \\n4.7 | \\n10344.1 | \\n17264 | \\n417432 | \\n
| 22 | \\nPoland | \\n6.2 | \\n426547.5 | \\n10865 | \\n38044565 | \\n
| 23 | \\nPortugal | \\n11.2 | \\n186480.5 | \\n10805 | \\n10562178 | \\n
| 25 | \\nSlovakia | \\n9.7 | \\n81226.1 | \\n10466 | \\n5397036 | \\n
| 26 | \\nSlovenia | \\n8.0 | \\n40357.2 | \\n15250 | \\n2050189 | \\n
| 27 | \\nSpain | \\n19.6 | \\n1118743.0 | \\n15347 | \\n46815910 | \\n
| 31 | \\nUnited Kingdom | \\n4.8 | \\n2403382.6 | \\n17296 | \\n63182180 | \\n
| \\n | country | \\nunemp_rate | \\ngdp | \\nmedian_income | \\ntotal_pop | \\n
|---|---|---|---|---|---|
| 4 | \\nCyprus | \\n13.0 | \\n18490.2 | \\n16173 | \\n840407 | \\n
| 5 | \\nCzechia | \\n4.0 | \\n176370.1 | \\n12478 | \\n10436560 | \\n
| 7 | \\nEstonia | \\n6.8 | \\n21682.6 | \\n11867 | \\n1294455 | \\n
| 8 | \\nFinland | \\n8.8 | \\n216073.0 | \\n19997 | \\n5375276 | \\n
| 14 | \\nIreland | \\n8.4 | \\n273238.2 | \\n18286 | \\n4574888 | \\n
| 15 | \\nItaly | \\n11.7 | \\n1689824.0 | \\n16237 | \\n59433744 | \\n
| 19 | \\nMalta | \\n4.7 | \\n10344.1 | \\n17264 | \\n417432 | \\n
| 22 | \\nPoland | \\n6.2 | \\n426547.5 | \\n10865 | \\n38044565 | \\n
| 23 | \\nPortugal | \\n11.2 | \\n186480.5 | \\n10805 | \\n10562178 | \\n
| 25 | \\nSlovakia | \\n9.7 | \\n81226.1 | \\n10466 | \\n5397036 | \\n
| 26 | \\nSlovenia | \\n8.0 | \\n40357.2 | \\n15250 | \\n2050189 | \\n
| 27 | \\nSpain | \\n19.6 | \\n1118743.0 | \\n15347 | \\n46815910 | \\n
| 31 | \\nUnited Kingdom | \\n4.8 | \\n2403382.6 | \\n17296 | \\n63182180 | \\n
| \\n | country | \\nunemp_rate | \\ngdp | \\nmedian_income | \\ntotal_pop | \\n
|---|---|---|---|---|---|
| 0 | \\nAustria | \\n6.0 | \\n356237.6 | \\n23071 | \\n8401940 | \\n
| 1 | \\nBelgium | \\n7.8 | \\n424660.3 | \\n21335 | \\n11000638 | \\n
| 2 | \\nBulgaria | \\n7.6 | \\n48128.6 | \\n6742 | \\n7364570 | \\n
| 3 | \\nCroatia | \\n13.1 | \\n46639.5 | \\n8985 | \\n4284889 | \\n
| 4 | \\nCyprus | \\n13.0 | \\n18490.2 | \\n16173 | \\n840407 | \\n
| \\n | country | \\ngdp | \\n
|---|---|---|
| 9 | \\nFrance | \\n2228568.0 | \\n
| 10 | \\nGermany | \\n3159750.0 | \\n
| \\n | country | \\nunemp_rate | \\ngdp | \\nmedian_income | \\n
|---|---|---|---|---|
| 9 | \\nFrance | \\n10.1 | \\n2228568.0 | \\n20621 | \\n
| 10 | \\nGermany | \\n4.1 | \\n3159750.0 | \\n21152 | \\n
| 11 | \\nGreece | \\n23.6 | \\n176487.9 | \\n9048 | \\n
| 12 | \\nHungary | \\n5.1 | \\n113903.8 | \\n8267 | \\n
| 13 | \\nIceland | \\n3.0 | \\n18646.1 | \\n22193 | \\n
| 14 | \\nIreland | \\n8.4 | \\n273238.2 | \\n18286 | \\n
| \\n | gdp | \\ntotal_pop | \\n
|---|---|---|
| country | \\n\\n | \\n |
| Cyprus | \\n18490.2 | \\n840407 | \\n
| Czechia | \\n176370.1 | \\n10436560 | \\n
| Denmark | \\n282089.9 | \\n5560628 | \\n
| Estonia | \\n21682.6 | \\n1294455 | \\n
| Finland | \\n216073.0 | \\n5375276 | \\n
| France | \\n2228568.0 | \\n64933400 | \\n
| \\n | country | \\nunemp_rate | \\ngdp | \\nmedian_income | \\ntotal_pop | \\n
|---|---|---|---|---|---|
| 3 | \\nCroatia | \\n13.1 | \\n46639.5 | \\n8985 | \\n4284889 | \\n
| 15 | \\nItaly | \\n11.7 | \\n1689824.0 | \\n16237 | \\n59433744 | \\n
| 16 | \\nLatvia | \\n9.6 | \\n25037.7 | \\n9257 | \\n2070371 | \\n
| 23 | \\nPortugal | \\n11.2 | \\n186480.5 | \\n10805 | \\n10562178 | \\n
| \\n | var1 | \\nvar2 | \\nvar3 | \\nvar4 | \\n
|---|---|---|---|---|
| one | \\n6 | \\n3 | \\n7 | \\n4 | \\n
| two | \\n6 | \\n9 | \\n2 | \\n6 | \\n
| \\n | country | \\nRegion | \\nsurface_area | \\ngdp | \\npopulation | \\npopulation_density | \\n
|---|---|---|---|---|---|---|
| 19 | \\nDominican Republic | \\nCaribbean | \\n48671 | \\n67103 | \\n10767 | \\n222.8 | \\n
| 27 | \\nHaiti | \\nCaribbean | \\n27750 | \\n8501 | \\n10981 | \\n398.4 | \\n
| 59 | \\nPuerto Rico | \\nCaribbean | \\n8868 | \\n102906 | \\n3663 | \\n413.0 | \\n
| 61 | \\nSaint Vincent and the Grenadines | \\nCaribbean | \\n389 | \\n738 | \\n110 | \\n281.8 | \\n
| 82 | \\nVirgin Islands | \\nCaribbean | \\n347 | \\n-99 | \\n105 | \\n299.7 | \\n
| \\n | Region | \\nsurface_area | \\ngdp | \\npopulation | \\npopulation_density | \\n
|---|---|---|---|---|---|
| 0 | \\nCaribbean | \\n1.720500e+04 | \\n3.582980e+04 | \\n5125.200000 | \\n323.140000 | \\n
| 1 | \\nCentralAmerica | \\n3.108195e+05 | \\n1.712796e+05 | \\n22164.750000 | \\n104.262500 | \\n
| 2 | \\nCentralAsia | \\n1.712745e+05 | \\n7.212500e+03 | \\n7483.000000 | \\n47.600000 | \\n
| 3 | \\nEasternAfrica | \\n4.816671e+05 | \\n1.883158e+04 | \\n25645.750000 | \\n139.458333 | \\n
| 4 | \\nEasternAsia | \\n5.582058e+06 | \\n5.585108e+06 | \\n706296.500000 | \\n76.050000 | \\n
| 5 | \\nEasternEurope | \\n3.186730e+05 | \\n4.854500e+04 | \\n24137.000000 | \\n99.800000 | \\n
| 6 | \\nMelanesia | \\n2.054250e+04 | \\n9.060000e+02 | \\n443.500000 | \\n22.250000 | \\n
| 7 | \\nMicronesia | \\n5.490000e+02 | \\n-9.900000e+01 | \\n164.000000 | \\n304.100000 | \\n
| 8 | \\nMiddleAfrica | \\n1.054169e+06 | \\n2.482600e+04 | \\n36885.000000 | \\n34.066667 | \\n
| 9 | \\nNorthernAfrica | \\n1.002000e+06 | \\n3.159170e+05 | \\n97553.000000 | \\n98.000000 | \\n
| 10 | \\nNorthernAmerica | \\n9.833517e+06 | \\n1.803665e+07 | \\n324460.000000 | \\n35.500000 | \\n
| 11 | \\nPolynesia | \\n2.842000e+03 | \\n7.740000e+02 | \\n196.000000 | \\n69.400000 | \\n
| 12 | \\nSouth-easternAsia | \\n5.205436e+05 | \\n2.298626e+05 | \\n76377.250000 | \\n153.762500 | \\n
| 13 | \\nSouthAmerica | \\n1.703150e+06 | \\n3.327079e+05 | \\n42953.250000 | \\n27.050000 | \\n
| 14 | \\nSouthernAfrica | \\n6.918360e+05 | \\n1.093567e+05 | \\n20494.666667 | \\n41.166667 | \\n
| 15 | \\nSouthernAsia | \\n9.843594e+05 | \\n4.851398e+05 | \\n320367.800000 | \\n197.200000 | \\n
| 16 | \\nSouthernEurope | \\n2.874800e+04 | \\n1.154100e+04 | \\n2930.000000 | \\n106.900000 | \\n
| 17 | \\nWesternAfrica | \\n4.164142e+05 | \\n5.688491e+04 | \\n30298.545455 | \\n89.181818 | \\n
| 18 | \\nWesternAsia | \\n2.060487e+05 | \\n1.389109e+05 | \\n19296.700000 | \\n243.040000 | \\n
| \\n | surface_area | \\ngdp | \\npopulation | \\npopulation_density | \\n
|---|---|---|---|---|
| \\n | mean | \\nmean | \\nmean | \\nmean | \\n
| Region | \\n\\n | \\n | \\n | \\n |
| Caribbean | \\n1.720500e+04 | \\n3.582980e+04 | \\n5125.200000 | \\n323.140000 | \\n
| CentralAmerica | \\n3.108195e+05 | \\n1.712796e+05 | \\n22164.750000 | \\n104.262500 | \\n
| CentralAsia | \\n1.712745e+05 | \\n7.212500e+03 | \\n7483.000000 | \\n47.600000 | \\n
| EasternAfrica | \\n4.816671e+05 | \\n1.883158e+04 | \\n25645.750000 | \\n139.458333 | \\n
| EasternAsia | \\n5.582058e+06 | \\n5.585108e+06 | \\n706296.500000 | \\n76.050000 | \\n
| EasternEurope | \\n3.186730e+05 | \\n4.854500e+04 | \\n24137.000000 | \\n99.800000 | \\n
| Melanesia | \\n2.054250e+04 | \\n9.060000e+02 | \\n443.500000 | \\n22.250000 | \\n
| Micronesia | \\n5.490000e+02 | \\n-9.900000e+01 | \\n164.000000 | \\n304.100000 | \\n
| MiddleAfrica | \\n1.054169e+06 | \\n2.482600e+04 | \\n36885.000000 | \\n34.066667 | \\n
| NorthernAfrica | \\n1.002000e+06 | \\n3.159170e+05 | \\n97553.000000 | \\n98.000000 | \\n
| NorthernAmerica | \\n9.833517e+06 | \\n1.803665e+07 | \\n324460.000000 | \\n35.500000 | \\n
| Polynesia | \\n2.842000e+03 | \\n7.740000e+02 | \\n196.000000 | \\n69.400000 | \\n
| South-easternAsia | \\n5.205436e+05 | \\n2.298626e+05 | \\n76377.250000 | \\n153.762500 | \\n
| SouthAmerica | \\n1.703150e+06 | \\n3.327079e+05 | \\n42953.250000 | \\n27.050000 | \\n
| SouthernAfrica | \\n6.918360e+05 | \\n1.093567e+05 | \\n20494.666667 | \\n41.166667 | \\n
| SouthernAsia | \\n9.843594e+05 | \\n4.851398e+05 | \\n320367.800000 | \\n197.200000 | \\n
| SouthernEurope | \\n2.874800e+04 | \\n1.154100e+04 | \\n2930.000000 | \\n106.900000 | \\n
| WesternAfrica | \\n4.164142e+05 | \\n5.688491e+04 | \\n30298.545455 | \\n89.181818 | \\n
| WesternAsia | \\n2.060487e+05 | \\n1.389109e+05 | \\n19296.700000 | \\n243.040000 | \\n
| \\n | surface_area | \\ngdp | \\npopulation | \\npopulation_density | \\n||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| \\n | mean | \\nmedian | \\nsum | \\nmean | \\nmedian | \\nsum | \\nmean | \\nmedian | \\nsum | \\nmean | \\nmedian | \\nsum | \\n
| Region | \\n\\n | \\n | \\n | \\n | \\n | \\n | \\n | \\n | \\n | \\n | \\n | \\n |
| Caribbean | \\n1.720500e+04 | \\n8868.0 | \\n86025 | \\n3.582980e+04 | \\n8501.0 | \\n179149 | \\n5125.200000 | \\n3663.0 | \\n25626 | \\n323.140000 | \\n299.70 | \\n1615.7 | \\n
| CentralAmerica | \\n3.108195e+05 | \\n92104.5 | \\n2486556 | \\n1.712796e+05 | \\n38991.0 | \\n1370237 | \\n22164.750000 | \\n6298.0 | \\n177318 | \\n104.262500 | \\n74.60 | \\n834.1 | \\n
| CentralAsia | \\n1.712745e+05 | \\n171274.5 | \\n342549 | \\n7.212500e+03 | \\n7212.5 | \\n14425 | \\n7483.000000 | \\n7483.0 | \\n14966 | \\n47.600000 | \\n47.60 | \\n95.2 | \\n
| EasternAfrica | \\n4.816671e+05 | \\n589626.5 | \\n5780005 | \\n1.883158e+04 | \\n13530.0 | \\n225979 | \\n25645.750000 | \\n17858.0 | \\n307749 | \\n139.458333 | \\n54.35 | \\n1673.5 | \\n
| EasternAsia | \\n5.582058e+06 | \\n5582058.0 | \\n11164116 | \\n5.585108e+06 | \\n5585107.5 | \\n11170215 | \\n706296.500000 | \\n706296.5 | \\n1412593 | \\n76.050000 | \\n76.05 | \\n152.1 | \\n
| EasternEurope | \\n3.186730e+05 | \\n318673.0 | \\n637346 | \\n4.854500e+04 | \\n48545.0 | \\n97090 | \\n24137.000000 | \\n24137.0 | \\n48274 | \\n99.800000 | \\n99.80 | \\n199.6 | \\n
| Melanesia | \\n2.054250e+04 | \\n20542.5 | \\n41085 | \\n9.060000e+02 | \\n906.0 | \\n1812 | \\n443.500000 | \\n443.5 | \\n887 | \\n22.250000 | \\n22.25 | \\n44.5 | \\n
| Micronesia | \\n5.490000e+02 | \\n549.0 | \\n549 | \\n-9.900000e+01 | \\n-99.0 | \\n-99 | \\n164.000000 | \\n164.0 | \\n164 | \\n304.100000 | \\n304.10 | \\n304.1 | \\n
| MiddleAfrica | \\n1.054169e+06 | \\n475650.0 | \\n3162508 | \\n2.482600e+04 | \\n28416.0 | \\n74478 | \\n36885.000000 | \\n24054.0 | \\n110655 | \\n34.066667 | \\n35.90 | \\n102.2 | \\n
| NorthernAfrica | \\n1.002000e+06 | \\n1002000.0 | \\n1002000 | \\n3.159170e+05 | \\n315917.0 | \\n315917 | \\n97553.000000 | \\n97553.0 | \\n97553 | \\n98.000000 | \\n98.00 | \\n98.0 | \\n
| NorthernAmerica | \\n9.833517e+06 | \\n9833517.0 | \\n9833517 | \\n1.803665e+07 | \\n18036648.0 | \\n18036648 | \\n324460.000000 | \\n324460.0 | \\n324460 | \\n35.500000 | \\n35.50 | \\n35.5 | \\n
| Polynesia | \\n2.842000e+03 | \\n2842.0 | \\n2842 | \\n7.740000e+02 | \\n774.0 | \\n774 | \\n196.000000 | \\n196.0 | \\n196 | \\n69.400000 | \\n69.40 | \\n69.4 | \\n
| South-easternAsia | \\n5.205436e+05 | \\n315483.5 | \\n4164349 | \\n2.298626e+05 | \\n127921.0 | \\n1838901 | \\n76377.250000 | \\n61204.5 | \\n611018 | \\n153.762500 | \\n112.90 | \\n1230.1 | \\n
| SouthAmerica | \\n1.703150e+06 | \\n927341.5 | \\n13625203 | \\n3.327079e+05 | \\n145302.5 | \\n2661663 | \\n42953.250000 | \\n17340.0 | \\n343626 | \\n27.050000 | \\n24.65 | \\n216.4 | \\n
| SouthernAfrica | \\n6.918360e+05 | \\n824116.0 | \\n2075508 | \\n1.093567e+05 | \\n11491.0 | \\n328070 | \\n20494.666667 | \\n2534.0 | \\n61484 | \\n41.166667 | \\n46.80 | \\n123.5 | \\n
| SouthernAsia | \\n9.843594e+05 | \\n652864.0 | \\n4921797 | \\n4.851398e+05 | \\n20658.0 | \\n2425699 | \\n320367.800000 | \\n35530.0 | \\n1601839 | \\n197.200000 | \\n204.40 | \\n986.0 | \\n
| SouthernEurope | \\n2.874800e+04 | \\n28748.0 | \\n28748 | \\n1.154100e+04 | \\n11541.0 | \\n11541 | \\n2930.000000 | \\n2930.0 | \\n2930 | \\n106.900000 | \\n106.90 | \\n106.9 | \\n
| WesternAfrica | \\n4.164142e+05 | \\n238537.0 | \\n4580556 | \\n5.688491e+04 | \\n11065.0 | \\n625734 | \\n30298.545455 | \\n15851.0 | \\n333284 | \\n89.181818 | \\n82.30 | \\n981.0 | \\n
| WesternAsia | \\n2.060487e+05 | \\n78150.0 | \\n2060487 | \\n1.389109e+05 | \\n43833.0 | \\n1389109 | \\n19296.700000 | \\n9012.0 | \\n192967 | \\n243.040000 | \\n107.10 | \\n2430.4 | \\n
| \\n | surface_area | \\ngdp | \\npopulation | \\npopulation_density | \\n||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| \\n | tsem | \\ntstd | \\nskew | \\ntsem | \\ntstd | \\nskew | \\ntsem | \\ntstd | \\nskew | \\ntsem | \\ntstd | \\nskew | \\n
| Region | \\n\\n | \\n | \\n | \\n | \\n | \\n | \\n | \\n | \\n | \\n | \\n | \\n |
| Caribbean | \\n9.321432e+03 | \\n2.084335e+04 | \\n0.685299 | \\n2.091220e+04 | \\n4.676111e+04 | \\n0.607884 | \\n2435.292270 | \\n5445.479061 | \\n0.215651 | \\n36.100853 | \\n80.723962 | \\n0.030881 | \\n
| CentralAmerica | \\n2.366682e+05 | \\n6.693987e+05 | \\n2.250712 | \\n1.387055e+05 | \\n3.923185e+05 | \\n2.253862 | \\n15379.223415 | \\n43499.012664 | \\n2.214198 | \\n32.501153 | \\n91.927144 | \\n1.489869 | \\n
| CentralAsia | \\n2.867450e+04 | \\n4.055187e+04 | \\n0.000000 | \\n6.405000e+02 | \\n9.058038e+02 | \\n0.000000 | \\n1438.000000 | \\n2033.639103 | \\n0.000000 | \\n16.100000 | \\n22.768838 | \\n0.000000 | \\n
| EasternAfrica | \\n9.050907e+04 | \\n3.135326e+05 | \\n-0.263652 | \\n5.318552e+03 | \\n1.842400e+04 | \\n1.434065 | \\n4595.949529 | \\n15920.836188 | \\n0.939269 | \\n47.199301 | \\n163.503175 | \\n1.306365 | \\n
| EasternAsia | \\n4.017942e+06 | \\n5.682228e+06 | \\n0.000000 | \\n5.573349e+06 | \\n7.881906e+06 | \\n0.000000 | \\n703220.500000 | \\n994503.968439 | \\n0.000000 | \\n74.050000 | \\n104.722514 | \\n0.000000 | \\n
| EasternEurope | \\n2.848270e+05 | \\n4.028062e+05 | \\n0.000000 | \\n4.207000e+04 | \\n5.949596e+04 | \\n0.000000 | \\n20086.000000 | \\n28405.893614 | \\n0.000000 | \\n23.500000 | \\n33.234019 | \\n0.000000 | \\n
| Melanesia | \\n8.353500e+03 | \\n1.181363e+04 | \\n0.000000 | \\n1.690000e+02 | \\n2.390021e+02 | \\n0.000000 | \\n167.500000 | \\n236.880772 | \\n0.000000 | \\n0.450000 | \\n0.636396 | \\n0.000000 | \\n
| Micronesia | \\nNaN | \\nNaN | \\n0.000000 | \\nNaN | \\nNaN | \\n0.000000 | \\nNaN | \\nNaN | \\n0.000000 | \\nNaN | \\nNaN | \\n0.000000 | \\n
| MiddleAfrica | \\n6.464966e+05 | \\n1.119765e+06 | \\n0.695791 | \\n8.583308e+03 | \\n1.486673e+04 | \\n-0.417756 | \\n22879.972909 | \\n39629.275555 | \\n0.532459 | \\n10.288883 | \\n17.820868 | \\n-0.186994 | \\n
| NorthernAfrica | \\nNaN | \\nNaN | \\n0.000000 | \\nNaN | \\nNaN | \\n0.000000 | \\nNaN | \\nNaN | \\n0.000000 | \\nNaN | \\nNaN | \\n0.000000 | \\n
| NorthernAmerica | \\nNaN | \\nNaN | \\n0.000000 | \\nNaN | \\nNaN | \\n0.000000 | \\nNaN | \\nNaN | \\n0.000000 | \\nNaN | \\nNaN | \\n0.000000 | \\n
| Polynesia | \\nNaN | \\nNaN | \\n0.000000 | \\nNaN | \\nNaN | \\n0.000000 | \\nNaN | \\nNaN | \\n0.000000 | \\nNaN | \\nNaN | \\n0.000000 | \\n
| South-easternAsia | \\n2.110175e+05 | \\n5.968477e+05 | \\n1.783458 | \\n1.038262e+05 | \\n2.936648e+05 | \\n1.333305 | \\n30201.355172 | \\n85422.332172 | \\n1.382405 | \\n40.638603 | \\n114.943327 | \\n0.866714 | \\n
| SouthAmerica | \\n9.847489e+05 | \\n2.785290e+06 | \\n2.162869 | \\n2.090794e+05 | \\n5.913659e+05 | \\n2.125620 | \\n24368.804859 | \\n68925.388662 | \\n2.057515 | \\n7.109526 | \\n20.108776 | \\n0.932068 | \\n
| SouthernAfrica | \\n3.500259e+05 | \\n6.062627e+05 | \\n-0.381757 | \\n1.026437e+05 | \\n1.777841e+05 | \\n0.704844 | \\n18111.375103 | \\n31369.821873 | \\n0.707034 | \\n20.545586 | \\n35.585999 | \\n-0.283531 | \\n
| SouthernAsia | \\n5.934819e+05 | \\n1.327066e+06 | \\n1.284080 | \\n4.107010e+05 | \\n9.183553e+05 | \\n1.449172 | \\n257017.757855 | \\n574709.177989 | \\n1.436007 | \\n77.116950 | \\n172.438743 | \\n0.439830 | \\n
| SouthernEurope | \\nNaN | \\nNaN | \\n0.000000 | \\nNaN | \\nNaN | \\n0.000000 | \\nNaN | \\nNaN | \\n0.000000 | \\nNaN | \\nNaN | \\n0.000000 | \\n
| WesternAfrica | \\n1.298416e+05 | \\n4.306358e+05 | \\n0.995896 | \\n4.390537e+04 | \\n1.456176e+05 | \\n2.814527 | \\n16239.477886 | \\n53860.254939 | \\n2.732611 | \\n17.592038 | \\n58.346188 | \\n0.460914 | \\n
| WesternAsia | \\n8.687679e+04 | \\n2.747285e+05 | \\n1.157696 | \\n7.048297e+04 | \\n2.228867e+05 | \\n1.993615 | \\n7749.004611 | \\n24504.504171 | \\n1.776751 | \\n84.348314 | \\n266.732788 | \\n1.290031 | \\n
| \\n | country | \\nsurface_area | \\ngdp | \\npopulation | \\npopulation_density | \\n
|---|---|---|---|---|---|
| \\n | get_max_alt | \\nget_max_alt | \\nget_max_alt | \\nget_max_alt | \\nget_max_alt | \\n
| Region | \\n\\n | \\n | \\n | \\n | \\n |
| Caribbean | \\nVirgin Islands | \\n48671 | \\n102906 | \\n10981 | \\n413.0 | \\n
| CentralAmerica | \\nPanama | \\n1964375 | \\n1140724 | \\n129163 | \\n307.8 | \\n
| CentralAsia | \\nTajikistan | \\n199949 | \\n7853 | \\n8921 | \\n63.7 | \\n
| EasternAfrica | \\nZimbabwe | \\n947303 | \\n63399 | \\n57310 | \\n494.9 | \\n
| EasternAsia | \\nMongolia | \\n9600000 | \\n11158457 | \\n1409517 | \\n150.1 | \\n
| EasternEurope | \\nUkraine | \\n603500 | \\n90615 | \\n44223 | \\n123.3 | \\n
| Melanesia | \\nVanuatu | \\n28896 | \\n1075 | \\n611 | \\n22.7 | \\n
| Micronesia | \\nGuam | \\n549 | \\n-99 | \\n164 | \\n304.1 | \\n
| MiddleAfrica | \\nThe Democratic Republic of the Congo | \\n2344858 | \\n37569 | \\n81340 | \\n50.9 | \\n
| NorthernAfrica | \\nEgypt | \\n1002000 | \\n315917 | \\n97553 | \\n98.0 | \\n
| NorthernAmerica | \\nUnited States | \\n9833517 | \\n18036648 | \\n324460 | \\n35.5 | \\n
| Polynesia | \\nSamoa | \\n2842 | \\n774 | \\n196 | \\n69.4 | \\n
| South-easternAsia | \\nVietnam | \\n1910931 | \\n861934 | \\n263991 | \\n351.9 | \\n
| SouthAmerica | \\nSuriname | \\n8515767 | \\n1772591 | \\n209288 | \\n66.9 | \\n
| SouthernAfrica | \\nSouth Africa | \\n1221037 | \\n314571 | \\n56717 | \\n73.6 | \\n
| SouthernAsia | \\nPakistan | \\n3287263 | \\n2116239 | \\n1339180 | \\n450.4 | \\n
| SouthernEurope | \\nAlbania | \\n28748 | \\n11541 | \\n2930 | \\n106.9 | \\n
| WesternAfrica | \\nTogo | \\n1240192 | \\n494583 | \\n190886 | \\n209.6 | \\n
| WesternAsia | \\nYemen | \\n783562 | \\n717888 | \\n80745 | \\n817.4 | \\n
| \\n | get_max_alt | \\n
|---|---|
| Region | \\n\\n |
| Caribbean | \\n48671 | \\n
| CentralAmerica | \\n1964375 | \\n
| CentralAsia | \\n199949 | \\n
| EasternAfrica | \\n947303 | \\n
| EasternAsia | \\n9600000 | \\n
| EasternEurope | \\n603500 | \\n
| Melanesia | \\n28896 | \\n
| Micronesia | \\n549 | \\n
| MiddleAfrica | \\n2344858 | \\n
| NorthernAfrica | \\n1002000 | \\n
| NorthernAmerica | \\n9833517 | \\n
| Polynesia | \\n2842 | \\n
| South-easternAsia | \\n1910931 | \\n
| SouthAmerica | \\n8515767 | \\n
| SouthernAfrica | \\n1221037 | \\n
| SouthernAsia | \\n3287263 | \\n
| SouthernEurope | \\n28748 | \\n
| WesternAfrica | \\n1240192 | \\n
| WesternAsia | \\n783562 | \\n
| \\n | Region | \\nsurface_area | \\npopulation | \\n|
|---|---|---|---|---|
| \\n | \\n | sum | \\nmean | \\nmean | \\n
| 0 | \\nCaribbean | \\n86025 | \\n1.720500e+04 | \\n5125.200000 | \\n
| 1 | \\nCentralAmerica | \\n2486556 | \\n3.108195e+05 | \\n22164.750000 | \\n
| 2 | \\nCentralAsia | \\n342549 | \\n1.712745e+05 | \\n7483.000000 | \\n
| 3 | \\nEasternAfrica | \\n5780005 | \\n4.816671e+05 | \\n25645.750000 | \\n
| 4 | \\nEasternAsia | \\n11164116 | \\n5.582058e+06 | \\n706296.500000 | \\n
| 5 | \\nEasternEurope | \\n637346 | \\n3.186730e+05 | \\n24137.000000 | \\n
| 6 | \\nMelanesia | \\n41085 | \\n2.054250e+04 | \\n443.500000 | \\n
| 7 | \\nMicronesia | \\n549 | \\n5.490000e+02 | \\n164.000000 | \\n
| 8 | \\nMiddleAfrica | \\n3162508 | \\n1.054169e+06 | \\n36885.000000 | \\n
| 9 | \\nNorthernAfrica | \\n1002000 | \\n1.002000e+06 | \\n97553.000000 | \\n
| 10 | \\nNorthernAmerica | \\n9833517 | \\n9.833517e+06 | \\n324460.000000 | \\n
| 11 | \\nPolynesia | \\n2842 | \\n2.842000e+03 | \\n196.000000 | \\n
| 12 | \\nSouth-easternAsia | \\n4164349 | \\n5.205436e+05 | \\n76377.250000 | \\n
| 13 | \\nSouthAmerica | \\n13625203 | \\n1.703150e+06 | \\n42953.250000 | \\n
| 14 | \\nSouthernAfrica | \\n2075508 | \\n6.918360e+05 | \\n20494.666667 | \\n
| 15 | \\nSouthernAsia | \\n4921797 | \\n9.843594e+05 | \\n320367.800000 | \\n
| 16 | \\nSouthernEurope | \\n28748 | \\n2.874800e+04 | \\n2930.000000 | \\n
| 17 | \\nWesternAfrica | \\n4580556 | \\n4.164142e+05 | \\n30298.545455 | \\n
| 18 | \\nWesternAsia | \\n2060487 | \\n2.060487e+05 | \\n19296.700000 | \\n
| \\n | Region | \\nsum_surface_area | \\nmean_population | \\n
|---|---|---|---|
| 0 | \\nCaribbean | \\n86025 | \\n5125.200000 | \\n
| 1 | \\nCentralAmerica | \\n2486556 | \\n22164.750000 | \\n
| 2 | \\nCentralAsia | \\n342549 | \\n7483.000000 | \\n
| 3 | \\nEasternAfrica | \\n5780005 | \\n25645.750000 | \\n
| 4 | \\nEasternAsia | \\n11164116 | \\n706296.500000 | \\n
| 5 | \\nEasternEurope | \\n637346 | \\n24137.000000 | \\n
| 6 | \\nMelanesia | \\n41085 | \\n443.500000 | \\n
| 7 | \\nMicronesia | \\n549 | \\n164.000000 | \\n
| 8 | \\nMiddleAfrica | \\n3162508 | \\n36885.000000 | \\n
| 9 | \\nNorthernAfrica | \\n1002000 | \\n97553.000000 | \\n
| 10 | \\nNorthernAmerica | \\n9833517 | \\n324460.000000 | \\n
| 11 | \\nPolynesia | \\n2842 | \\n196.000000 | \\n
| 12 | \\nSouth-easternAsia | \\n4164349 | \\n76377.250000 | \\n
| 13 | \\nSouthAmerica | \\n13625203 | \\n42953.250000 | \\n
| 14 | \\nSouthernAfrica | \\n2075508 | \\n20494.666667 | \\n
| 15 | \\nSouthernAsia | \\n4921797 | \\n320367.800000 | \\n
| 16 | \\nSouthernEurope | \\n28748 | \\n2930.000000 | \\n
| 17 | \\nWesternAfrica | \\n4580556 | \\n30298.545455 | \\n
| 18 | \\nWesternAsia | \\n2060487 | \\n19296.700000 | \\n
| \\n | country | \\nRegion | \\nsurface_area | \\ngdp | \\npopulation | \\npopulation_density | \\n
|---|---|---|---|---|---|---|
| 1 | \\nAlbania | \\nSouthernEurope | \\n28748 | \\n11541 | \\n2930 | \\n106.9 | \\n
| 19 | \\nDominican Republic | \\nCaribbean | \\n48671 | \\n67103 | \\n10767 | \\n222.8 | \\n
| 25 | \\nGuam | \\nMicronesia | \\n549 | \\n-99 | \\n164 | \\n304.1 | \\n
| 27 | \\nHaiti | \\nCaribbean | \\n27750 | \\n8501 | \\n10981 | \\n398.4 | \\n
| 59 | \\nPuerto Rico | \\nCaribbean | \\n8868 | \\n102906 | \\n3663 | \\n413.0 | \\n
| 61 | \\nSaint Vincent and the Grenadines | \\nCaribbean | \\n389 | \\n738 | \\n110 | \\n281.8 | \\n
| 82 | \\nVirgin Islands | \\nCaribbean | \\n347 | \\n-99 | \\n105 | \\n299.7 | \\n
| \\n | country | \\nRegion | \\nsurface_area | \\ngdp | \\npopulation | \\npopulation_density | \\n
|---|---|---|---|---|---|---|
| 10 | \\nBurundi | \\nEasternAfrica | \\n27830 | \\n2735 | \\n10864 | \\n423.1 | \\n
| 21 | \\nEgypt | \\nNorthernAfrica | \\n1002000 | \\n315917 | \\n97553 | \\n98.0 | \\n
| 34 | \\nKenya | \\nEasternAfrica | \\n591958 | \\n63399 | \\n49700 | \\n87.3 | \\n
| 40 | \\nMadagascar | \\nEasternAfrica | \\n587295 | \\n9739 | \\n25571 | \\n44.0 | \\n
| 41 | \\nMalawi | \\nEasternAfrica | \\n118484 | \\n6420 | \\n18622 | \\n197.5 | \\n
| 47 | \\nMozambique | \\nEasternAfrica | \\n799380 | \\n14806 | \\n29669 | \\n37.7 | \\n
| 60 | \\nRwanda | \\nEasternAfrica | \\n26338 | \\n8096 | \\n12208 | \\n494.9 | \\n
| 66 | \\nSomalia | \\nEasternAfrica | \\n637657 | \\n1559 | \\n14742 | \\n23.5 | \\n
| 68 | \\nSouth Sudan | \\nEasternAfrica | \\n658841 | \\n13167 | \\n12576 | \\n20.6 | \\n
| 71 | \\nTanzania | \\nEasternAfrica | \\n947303 | \\n45628 | \\n57310 | \\n64.7 | \\n
| 77 | \\nUganda | \\nEasternAfrica | \\n241550 | \\n25282 | \\n42863 | \\n214.5 | \\n
| 79 | \\nUnited States | \\nNorthernAmerica | \\n9833517 | \\n18036648 | \\n324460 | \\n35.5 | \\n
| 84 | \\nZambia | \\nEasternAfrica | \\n752612 | \\n21255 | \\n17094 | \\n23.0 | \\n
| 85 | \\nZimbabwe | \\nEasternAfrica | \\n390757 | \\n13893 | \\n16530 | \\n42.7 | \\n
![](\"https://raw.githubusercontent.com/hpcng/singularity-userdocs/v3.6.0/logo.png\"){kind=logo}
\n#### Your Host: Nathan Weeks\n\n---\n\n# Roadmap\n\n### Part 1 (this presentation)\n\n* What are (Singularity) containers?\n* Finding container images\n* Using Singularity on Cannon\n\n### Part 2 (next week)\n\n* Building Singularity container images\n* Common pitfalls\n* Useful tips & tricks\n\n---\n\n### Prerequisites (to follow along with the exercises)...\n\n1. Log into Cannon (either via [SSH](https://docs.rc.fas.harvard.edu/kb/terminal-access/), or a web browser using the [FASRC VDI portal](https://docs.rc.fas.harvard.edu/kb/virtual-desktop/) ([FASRC VPN](https://docs.rc.fas.harvard.edu/kb/vpn-setup/) connection required)\n\n2. Launch an [interactive job](https://docs.rc.fas.harvard.edu/kb/running-jobs/#Interactive_jobs_and_salloc) on a compute node:\n```\n salloc -p test,shared -t 2:00:00 --mem=4g\n```\n *NOTE:* the `singularity` *command is not available on Cannon login nodes*\n\n\n---\n\n# What is a *container*?\n\nA set of one or more processes (*running programs*) that share (at least) a different root file system (\"/\"; usually provided by a *container image*) than processes running outside of the container on the same host operating system kernel (*typically Linux*).\n\n---\n\n# What is Singularity?\n* An open-source container platform for Linux\n - https://github.com/hpcng/singularity\n - [Old beta for macOS](https://sylabs.io/singularity-desktop-macos/) (don't bother)\n* Started in 2015 by Greg Kurtzer from Lawrence Berkeley National Laboratory; now commercially developed/supported by [Sylabs](https://sylabs.io/)\n* Provides software tooling for both building *container images* and creating *containers* in which commands are run\n* Used:\n - Primarily for HPC clusters\n - Cannon: [FASRC VDI portal](https://docs.rc.fas.harvard.edu/kb/virtual-desktop/) (Open OnDemand) to run JupyterLab, RStudio Server, and other interactive apps\n - Workflow management systems, such as [Snakemake](https://snakemake.readthedocs.io/en/stable/snakefiles/deployment.html#running-jobs-in-containers), [NextFlow](https://www.nextflow.io/docs/latest/singularity.html), and [Galaxy](https://docs.galaxyproject.org/en/master/admin/special_topics/mulled_containers.html)\n - [Open Science Grid](https://support.opensciencegrid.org/support/solutions/articles/12000024676-docker-and-singularity-containers)\n\n---\n\n# Singularity Image\n\n* A Singularity *image* is a file containing a (compressed, read-only [SquashFS](https://en.wikipedia.org/wiki/SquashFS)) file system, usually containing:\n * A base (Linux) operating system (e.g., Ubuntu, CentOS, Alpine)\n * Target software (e.g., NCBI BLAST+) and all software dependencies\n* Typical file extension: *.sif* ([Singularity Image Format]()); older (pre Singularity 3.x) may use \".simg\"\n* Since it's a *file*, can be copied to other hosts (e.g., `scp`), or archived/shared (e.g., alongside code and research data)\n\n\n---\n\n# Singularity Container\n\n* A Singularity *container* runs user processes with a software environment reflecting the (read-only) file system within the Singularity image file, plus select (generally-writable) directories from the host that are *bind-mounted* onto this file system.\n * On Cannon, this includes **/n/** (network) file systems, like **/n/holyscratch01** and **/n/home* **\n\n![](\"images/singularity_container.jpeg\")
\n\n---\n\n# Why use containers?\n\n![](\"images/installing_software.gif\")
\n\n---\n\n# Why use containers?\n* Save time\n - (Re)creating a complex software environment on a different host can be difficult\n* Portability\n - A container image can run on another (Linux) host, or...\n - Easily shared with other users on the same system (e.g., on Cannon with other members of the same lab)\n* Reproducibility\n - Guarantees software environment (including exact versions) recorded & reproduced faithfully\n\n#### SEE ALSO\n* [Introduction to Singularity - Use Cases](https://sylabs.io/guides/3.7/user-guide/introduction.html#use-cases)\n\n---\n\n## Example: [Trinity](https://trinityrnaseq.github.io/) de novo transcriptome assembler\n\n* Difficult to install; many software dependencies\n - Bespoke environment modules for a couple older versions exist on Cannon (see `module-query trinityrnaseq`)\n* \"Official\" *container image* encapsulates Trinity & dependencies (see the [Dockerfile](https://github.com/trinityrnaseq/trinityrnaseq/blob/v2.12.0/Docker/Dockerfile) container image \"recipe\")\n - Can be used on Cannon\n\n![](\"images/trinity.svg\")
This is the second session covering tidyverse commands. We will show you how to use some new functions, but we will also use some of the functions we previously used last week which will be good for extra practice. If you missed the previous session, that should not be a problem.
\nFrom last time, we looked at this cheat sheet to occasionally guide us.
\nIt’s typical to load all the libraries you need at the top of your code.
\n\n\n\nlibrary(tidyverse)For this week, rather than use an example dataset, we are going to use several publically available datasets from the NYT github repository on the COVID-19 pandemic. This is because “wild-caught” datasets are often much messier than example datasets, so it will help us become more familiar with how you would actually go about processing data. We are going to keep our analysis purely descriptive, as it would be irresponsible to make any conclusive claims after fiddling around with R for only an hour!
\nLet’s load in the first data set on mask usage in the US, which is in the form of a comma separated list. This data is from a NYT survey where they asked the question: “How often do you wear a mask in public when you expect to be within six feet of another person?” (As a note, this survey was done several months ago, so these numbers are not the most up-to-date.)
\n\n\n\nmask_use_wide <- read_delim(file="~/Documents/GitHub/bioinformatics-coffee-hour/tidyverse/part2_winter2021/mask-use-by-county.csv", delim=",")Parsed with column specification:\ncols(\n COUNTYFP = \u001b[31mcol_character()\u001b[39m,\n NEVER = \u001b[32mcol_double()\u001b[39m,\n RARELY = \u001b[32mcol_double()\u001b[39m,\n SOMETIMES = \u001b[32mcol_double()\u001b[39m,\n FREQUENTLY = \u001b[32mcol_double()\u001b[39m,\n ALWAYS = \u001b[32mcol_double()\u001b[39m\n)mask_useIt looks like for all the counties are represented with a 5-digit FIPS code, which is not exactly ideal because we don’t know what these numbers mean. We’ll get the names in a moment by combining these data with another file that also has these FIPS codes AND county names. We can thus match these data tables based on shared FIPS codes to combine them.
\nIn addition to the mask data, let’s also load in data on COVID-19 case counts across the US, also supplied by the NYT, and combine it with our mask use data. Unlike the previous data that we loaded in from our local file system, these data we will directly download because they are so large. This will also show how you can specify web addresses instead of file names for read_delim(). This file is also comma separated, so we will let read_delim() know this.
\n\n\nThis is a lot of data. For ~3000 US counties, there’s an estimate of cumulative COVID-19 cases and deaths for each day since late January (it’s now March again!!!). For now, let’s just look at the start of this month.
\n\n\nAs a refresher, we are using the filter() function to select only the rows that match the string “2021-03-01” in the date column, then using the arrange() function to sort the counties by FIPS code.
\nLet’s load in some additional data on population size by county, which are obtained from www.census.gov.
\n\n\nWhen I’m working with larger files and I want to make sure they looks like I expect them to, instead of opening up the file and scrolling through all the lines, checking each by eye, I typically use various commands to just get an idea of what it looks like before proceeding. Oftentimes, this is sufficient; we don’t need to check every line.
\nThese are just a few commands I might use to look at the first and last few lines, how many rows there are overall, and also get an idea of how much missing data there might be:
\n\n\n\nhead(pop_sizes) nrow(pop_sizes) [1] 3149sum(!is.na(pop_sizes$'2019')) # how many counties have size estimates for 2019? it's even closer to the 3142 we expect![1] 3143tail(pop_sizes)Ok so these data are a little messy and need to be cleaned up.
\nFrom these commands, we can see that the entire US was included in this table, which is not a county. We should remove that. Also, county names are all preceded with a “.”, something we’ll deal with in a moment. The number of rows in the table is very similar to what we expect (3,142 according to wikipedia), but maybe larger by 7 rows or so. The row containing the entire US contributes to this excess of rows beyond our expectation.
\nLooking at the bottom of the table, we see some footnotes were left in. These are probably contributing to the extra number of rows and should absolutely be removed.
\nLet’s remove these rows at the beginning and end with the slice() function, which pulls out rows from a data frame/tibble based on their ordinal position. It keeps all the columns, in contrast to the select() function that we discussed last week.
\n\n\nWith this command, we are slicing out the middle of the data frame to remove the row with data from the whole US, as well as the footnotes at the bottom.
\nThe ‘Geographic Area’ column has two pieces of info in it: county and state. We should split this column into two columns so that we can separately access the info. For instance, later on we will do an analysis not by county but by state, summing up across a state’s counties, and this requires having this information in it’s own separate column. To separate this info into 2 seperate columns, we will use the separate() function:
\n\n\nNote that unlike last time we are specifying what we want to separate on with the “sep=” argument, which is a comma followed by a white space.
\nLet’s get rid of extra characters in the new ‘County’ column. We don’t need the “.” that precedes each name, and it’s pointless that each individual county name is followed by “County”… we know they’re counties based on the name of the column. We can use the mutate() function to add new rows with new names, but if the name of the new row we want is the same as an existing one, it just replaces it!
\n\n\nWe are combining the mutate() function with str_remove() which, as the name suggests, will remove a given pattern from a character column, the County column in this case.
\nThis table includes population size data for quite a few years. Let’s just get the most recent population estimate, selecting the 2019 column with the select() function, which as mentioned above pulls out specific columns:
\n\n\nAs we did above for the data sets we used last week, we can also combine all of these individual commands into one compact command that might look something like this:
\n\n\nWe can take a quick peek at these population size data, now that they’re cleaned, using the summary() function, which shows there’s a ton of variability in county sizes!
\n\n\n\nsummary(pop_sizes) County State 2019 \n Length:3142 Length:3142 Min. : 86 \n Class :character Class :character 1st Qu.: 10902 \n Mode :character Mode :character Median : 25726 \n Mean : 104468 \n 3rd Qu.: 68073 \n Max. :10039107 Let’s combine these data on population sizes with our previous data on case counts and mask usage, as it may reveal interesting dynamics that vary by county size.
\nWe will first use the join command on the case count data and the county population size data, joining by BOTH county and state. There are many different types of joining functions and it can get fairly confusing, so today we will focus just on inner_join.
\n\n\nThe “x=” and “y=” arguments specify which datasets we want to merge. Inner_join() returns all rows in dataset x that has matching values in dataset y, and all the columns in both x and y.
\nThe “by=” arugment tells what variables the function should merge by, as a character vector. In other words we tell the function what columns we want to match: it compares the values in the columns in x and y, and if they match it merges them. We are matching the “county” coumn in cases_latest with the “County” column in pop_sizes, and the “state” and “State” columns in the same.
\nNote that because the column names (i.e. variable names) are slightly different between datasets, we have to specify which we are matching using an “=”; if they had the same names, we could just list them.
\nNow let’s merge this table that has case counts and population size with the mask data! For now, let’s not add in all the mask data. Let’s only incorporate the the proportion of people who are “ALWAYS” masked for each county.
\n\n\nNote that we are nesting the select() function within our inner_join() call, allowing us to subset the mask useage dataset without making an entirely new data frame.
\nLooking at this data table, for each county we now have cases, deaths, population size, and the proportion of people who are always masked.
\nYou may also notice the population size data had a column entitled “2019” for size estimates during that year. Let’s make this more informative and change it to “pop_size”, and rename the ALWAYS column so that we know it’s referring to always masked.
\n\n\nLet’s quickly probe these data just to get an idea of what they look like. The main goal here is to teach you how to use R/tidyverse commands to quickly and easily explore your data. Again, due to the context of these data, let’s keep any results as fairly descriptive observations. We can’t say anything conclusive without more complicated analyses that we’ll leave to the public health officials.
\nLet’s assume these case count data are accurately measuring the number of people who are getting coronavirus infections (which is obviously an underestimate, as many cases can be asymptomatic!). Assuming this, what fraction of a county’s population is testing positive, and how does this fraction vary with population size? Let’s use the mutate() function to add a new column with number of cases normalized by population size:
\n\n\n\ncases_popsize_masked <- cases_popsize_masked %>%\n mutate(FracPos = cases/pop_size)Do counties with large populations have a higher proportion of case counts? Let’s use a simple plotting function to plot population size on the x axis and proportion of cases on y axis. Since we will look at the proportion of cases by dividing the data in the case counts column by the data in the population size column, let’s just make a new column called ‘FracPos’ that contains this information
\nWe can make a quick plot of these data using a base R (i.e. not tidyverse) plotting function:
\n\n\n\ncases_popsize_masked <- cases_popsize_masked %>%\n mutate(FracPos = cases/pop_size)\nplot(x=cases_popsize_masked$pop_size,\n y=cases_popsize_masked$FracPos,\n log="x",\n ylab="Fraction of cases",\n xlab="pop size")# DON'T RUN CORRELATION ANALYSES WITH HETEROSCEDASTICITYLooks interesting. Counties with larger population sizes may look like they have higher proportion of cases, but it’s a little complicated because there’s a lot of variability for counties with smaller population sizes.
\nOne thing we might be interested in are those outliers with very high rates of positive cases. What counties are theses, and in what states? We can very easily check this with the following:
\n\n\n\ncases_popsize_masked %>%\n filter(FracPos > 0.08) %>%\n arrange(desc(FracPos))Let’s add another column for death rate, and visualize the relationship between the fraction of positive cases and the death rate:
\n\n\n\ncases_popsize_masked <- cases_popsize_masked %>%\n mutate(FracDeaths = deaths/cases)\nplot(x=cases_popsize_masked$FracPos,\n y=cases_popsize_masked$FracDeaths,\n log="",\n xlab="FracCases",\n ylab="FracDeaths")What are these counties that have been severely impacted by deaths? Is this just noise from counties with small sizes and/or small case counts?
\n\n\n\ncases_popsize_masked %>%\n filter(FracDeaths > 0.06) %>%\n arrange(desc(FracDeaths))The use of the group_by() and summarize() functions allows us to quickly get extremely informative information from our data.
\nWe’ll show how they’re useful in many ways, but first we need to do a little more reshaping of our data.
\nLet’s go back to our original mask use data table, not the combined one. What if we wanted to quickly get the overall mean values of ALWAYS, … , NEVER across all the counties? We could use the original data in wide format, and calculate the mean for each of these columns. We could also use the simple summary() function which outputs a table:
\n\n\n\nmean(mask_use_wide$NEVER)[1] 0.07993953mean(mask_use_wide$RARELY)[1] 0.08291852mean(mask_use_wide$SOMETIMES)[1] 0.121318mean(mask_use_wide$FREQUENTLY)[1] 0.2077247mean(mask_use_wide$ALWAYS)[1] 0.5080936summary(mask_use_wide) COUNTYFP NEVER RARELY SOMETIMES FREQUENTLY \n Length:3142 Min. :0.00000 Min. :0.00000 Min. :0.0010 Min. :0.0290 \n Class :character 1st Qu.:0.03400 1st Qu.:0.04000 1st Qu.:0.0790 1st Qu.:0.1640 \n Mode :character Median :0.06800 Median :0.07300 Median :0.1150 Median :0.2040 \n Mean :0.07994 Mean :0.08292 Mean :0.1213 Mean :0.2077 \n 3rd Qu.:0.11300 3rd Qu.:0.11500 3rd Qu.:0.1560 3rd Qu.:0.2470 \n Max. :0.43200 Max. :0.38400 Max. :0.4220 Max. :0.5490 \n ALWAYS \n Min. :0.1150 \n 1st Qu.:0.3932 \n Median :0.4970 \n Mean :0.5081 \n 3rd Qu.:0.6138 \n Max. :0.8890 You can do it with way, but it is kind of clunky, and it wouldn’t be practical if your dataset has too many variables. It is easier to first convert the data in long format:
\n\n\nAs a reminder, the “cols=” argument specifies which columns we are reshaping into rows (all except the “COUNTYFP” column), the “names_to=” argument specifies the name of the column that will hold the names of the reshaped columns, and the “values_to=” argument gives the name of the column that will hold the values of the reshaped columns.
\nWe can now use the group_by() function, which takes our existing dataset and converts it to a grouped dataset based on a given variable, and combine it with the summarize() function (different from summary used above!) to get mean for each group:
\n\n\n\nmask_use_long %>% \n group_by(MaskUseResponse) %>% \n summarize(mean(MaskUseProportion))Let’s explore another example of group_by() and summarize() using our tibble that has all our combined information.
\nInstead of focusing on counties as our unit of analysis, we can use group_by() to easily switch to a state level analysis by telling the summarize() function to perform analyses for each state, combining all the rows that have the same value under the “state” column. When we combine the group_by() function with summarize(), we can easily do some pretty powerful analyses that would take a lot of effort using other programs.
\n\n\n\ncases_popsize_masked %>%\n group_by(state) %>%\n summarize(MeanMasked = mean(AlwaysMasked)) %>%\n arrange(desc(MeanMasked))This is potentially a bad analysis because we are calculating a mean for a state based on it’s counties, and some of these counties may be represented by fewer people. So if we truly wanted the average behavior of a state, we should weight each county by its population size. Why should a county with a size of 400 contribute equally to the mean as a county of size 40,000?
\nThe following code calculates a custom mean value, where each county is weighted by its population size:
\n\n\n\ncases_popsize_masked %>%\n group_by(state) %>%\n summarize(WeightedMeanMasked = sum(AlwaysMasked*pop_size)/sum(pop_size)) %>%\n arrange(desc(WeightedMeanMasked))Did this weighting by county size actually make a difference? Let’s store these analyses as ‘x’ and ‘y’ and compare them:
\n\n\n\nx <- cases_popsize_masked %>%\n group_by(state) %>%\n summarize(MeanMasked = mean(AlwaysMasked)) %>%\n arrange(state)\ny <- cases_popsize_masked %>%\n group_by(state) %>%\n summarize(WeightedMeanMasked = sum(AlwaysMasked*pop_size)/sum(pop_size)) %>%\n arrange(state)\nhist(x$MeanMasked - y$WeightedMeanMasked)It looks like on average, AlwaysMasked estimates that don’t take pop_size into account are systematically lower than those that do. When we don’t take county size into account, we effectively give more weight to smaller counties.
\n
\\n\",\n \"\\n\",\n \"*source: Brian Arnold, https://github.com/harvardinformatics/shortRead_mapping_variantCalling/ *\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"### Definition\\n\",\n \"