{ "cells": [ { "cell_type": "markdown", "id": "c57b2ce5", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# R Data Import and Export\n", "\n", "\n", "Feng Li\n", "\n", "School of Statistics and Mathematics\n", "\n", "Central University of Finance and Economics\n", "\n", "[feng.li@cufe.edu.cn](mailto:feng.li@cufe.edu.cn)\n", "\n", "[https://feng.li/statcomp](https://feng.li/statcomp)\n", "\n", "_>>> Link to Python version_ [1](https://feng.li/files/python/P04-Python-for-Finance-Tasks/L04.1-Reading-and-Cleaning-Excel-Files.slides.html)" ] }, { "cell_type": "markdown", "id": "b2c313ec", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Plain Text Format\n", "\n", "- Read a file in table format (`.csv`, `.txt`, `.dat`) and creates a data frame from it.\n", "\n", " - `read.table()`\n", " - `read.csv()`\n", " - `read.csv2()`\n", "\n", "- Write a file in text format.\n", "\n", " - `write.table()`\n", " - `write.csv()`\n", " - `write.csv2()`" ] }, { "cell_type": "code", "execution_count": 1, "id": "36694fd9", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 50 × 5
STATEY1Y2X1X2
<chr><dbl><dbl><dbl><dbl>
AL2206.02186.0 92.7 91.4
AK 0.7 0.7151.0149.0
AZ 73.0 74.0 61.0 56.0
AR3620.03737.0 86.3 91.8
CA7472.07444.0 63.4 58.4
CO 788.0 873.0 77.8 73.0
CT1029.0 948.0106.0104.0
DE 168.0 164.0117.0113.0
FL2568.02537.0 62.0 57.2
GA4302.04301.0 80.6 80.8
HI 227.5 224.5 85.0 85.5
ID 187.0 203.0 79.1 72.9
IL 793.0 809.0 65.0 70.5
IN5445.05290.0 62.7 60.1
IA2151.02247.0 56.5 53.0
KS 404.0 389.0 54.5 47.8
KY 412.0 483.0 67.7 73.5
LA 273.0 254.0115.0115.0
ME1069.01070.0101.0 97.0
MD 885.0 898.0 76.6 75.4
MA 235.0 237.0105.0102.0
MI1406.01396.0 58.0 53.8
MN2499.02697.0 57.7 54.0
MS1434.01468.0 87.8 86.7
MO1580.01622.0 55.4 51.5
MT 172.0 164.0 68.0 66.0
NE1202.01400.0 50.3 48.9
NV 2.2 1.8 53.9 52.7
NH 43.0 49.0109.0104.0
NJ 442.0 491.0 85.0 83.0
NM 283.0 302.0 74.0 70.0
NY 975.0 987.0 68.1 64.0
NC3033.03045.0 82.8 78.7
ND 51.0 45.0 55.2 48.0
OH4667.04637.0 59.1 54.7
OK 869.0 830.0101.0100.0
OR 652.0 686.0 77.0 74.6
PA4976.05130.0 61.0 52.0
RI 53.0 50.0102.0 99.0
SC1422.01420.0 70.1 65.9
SD 435.0 602.0 48.0 45.8
TN 277.0 279.0 71.0 80.7
TX3317.03356.0 76.7 72.6
UT 456.0 486.0 64.0 59.0
VT 31.0 30.0106.0102.0
VA 934.0 988.0 86.3 81.2
WA1287.01313.0 74.1 71.5
WV 136.0 174.0104.0109.0
WI 910.0 873.0 60.1 54.0
WY 1.7 1.7 83.0 83.0
\n" ], "text/latex": [ "A data.frame: 50 × 5\n", "\\begin{tabular}{lllll}\n", " STATE & Y1 & Y2 & X1 & X2\\\\\n", " & & & & \\\\\n", "\\hline\n", "\t AL & 2206.0 & 2186.0 & 92.7 & 91.4\\\\\n", "\t AK & 0.7 & 0.7 & 151.0 & 149.0\\\\\n", "\t AZ & 73.0 & 74.0 & 61.0 & 56.0\\\\\n", "\t AR & 3620.0 & 3737.0 & 86.3 & 91.8\\\\\n", "\t CA & 7472.0 & 7444.0 & 63.4 & 58.4\\\\\n", "\t CO & 788.0 & 873.0 & 77.8 & 73.0\\\\\n", "\t CT & 1029.0 & 948.0 & 106.0 & 104.0\\\\\n", "\t DE & 168.0 & 164.0 & 117.0 & 113.0\\\\\n", "\t FL & 2568.0 & 2537.0 & 62.0 & 57.2\\\\\n", "\t GA & 4302.0 & 4301.0 & 80.6 & 80.8\\\\\n", "\t HI & 227.5 & 224.5 & 85.0 & 85.5\\\\\n", "\t ID & 187.0 & 203.0 & 79.1 & 72.9\\\\\n", "\t IL & 793.0 & 809.0 & 65.0 & 70.5\\\\\n", "\t IN & 5445.0 & 5290.0 & 62.7 & 60.1\\\\\n", "\t IA & 2151.0 & 2247.0 & 56.5 & 53.0\\\\\n", "\t KS & 404.0 & 389.0 & 54.5 & 47.8\\\\\n", "\t KY & 412.0 & 483.0 & 67.7 & 73.5\\\\\n", "\t LA & 273.0 & 254.0 & 115.0 & 115.0\\\\\n", "\t ME & 1069.0 & 1070.0 & 101.0 & 97.0\\\\\n", "\t MD & 885.0 & 898.0 & 76.6 & 75.4\\\\\n", "\t MA & 235.0 & 237.0 & 105.0 & 102.0\\\\\n", "\t MI & 1406.0 & 1396.0 & 58.0 & 53.8\\\\\n", "\t MN & 2499.0 & 2697.0 & 57.7 & 54.0\\\\\n", "\t MS & 1434.0 & 1468.0 & 87.8 & 86.7\\\\\n", "\t MO & 1580.0 & 1622.0 & 55.4 & 51.5\\\\\n", "\t MT & 172.0 & 164.0 & 68.0 & 66.0\\\\\n", "\t NE & 1202.0 & 1400.0 & 50.3 & 48.9\\\\\n", "\t NV & 2.2 & 1.8 & 53.9 & 52.7\\\\\n", "\t NH & 43.0 & 49.0 & 109.0 & 104.0\\\\\n", "\t NJ & 442.0 & 491.0 & 85.0 & 83.0\\\\\n", "\t NM & 283.0 & 302.0 & 74.0 & 70.0\\\\\n", "\t NY & 975.0 & 987.0 & 68.1 & 64.0\\\\\n", "\t NC & 3033.0 & 3045.0 & 82.8 & 78.7\\\\\n", "\t ND & 51.0 & 45.0 & 55.2 & 48.0\\\\\n", "\t OH & 4667.0 & 4637.0 & 59.1 & 54.7\\\\\n", "\t OK & 869.0 & 830.0 & 101.0 & 100.0\\\\\n", "\t OR & 652.0 & 686.0 & 77.0 & 74.6\\\\\n", "\t PA & 4976.0 & 5130.0 & 61.0 & 52.0\\\\\n", "\t RI & 53.0 & 50.0 & 102.0 & 99.0\\\\\n", "\t SC & 1422.0 & 1420.0 & 70.1 & 65.9\\\\\n", "\t SD & 435.0 & 602.0 & 48.0 & 45.8\\\\\n", "\t TN & 277.0 & 279.0 & 71.0 & 80.7\\\\\n", "\t TX & 3317.0 & 3356.0 & 76.7 & 72.6\\\\\n", "\t UT & 456.0 & 486.0 & 64.0 & 59.0\\\\\n", "\t VT & 31.0 & 30.0 & 106.0 & 102.0\\\\\n", "\t VA & 934.0 & 988.0 & 86.3 & 81.2\\\\\n", "\t WA & 1287.0 & 1313.0 & 74.1 & 71.5\\\\\n", "\t WV & 136.0 & 174.0 & 104.0 & 109.0\\\\\n", "\t WI & 910.0 & 873.0 & 60.1 & 54.0\\\\\n", "\t WY & 1.7 & 1.7 & 83.0 & 83.0\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 50 × 5\n", "\n", "| STATE <chr> | Y1 <dbl> | Y2 <dbl> | X1 <dbl> | X2 <dbl> |\n", "|---|---|---|---|---|\n", "| AL | 2206.0 | 2186.0 | 92.7 | 91.4 |\n", "| AK | 0.7 | 0.7 | 151.0 | 149.0 |\n", "| AZ | 73.0 | 74.0 | 61.0 | 56.0 |\n", "| AR | 3620.0 | 3737.0 | 86.3 | 91.8 |\n", "| CA | 7472.0 | 7444.0 | 63.4 | 58.4 |\n", "| CO | 788.0 | 873.0 | 77.8 | 73.0 |\n", "| CT | 1029.0 | 948.0 | 106.0 | 104.0 |\n", "| DE | 168.0 | 164.0 | 117.0 | 113.0 |\n", "| FL | 2568.0 | 2537.0 | 62.0 | 57.2 |\n", "| GA | 4302.0 | 4301.0 | 80.6 | 80.8 |\n", "| HI | 227.5 | 224.5 | 85.0 | 85.5 |\n", "| ID | 187.0 | 203.0 | 79.1 | 72.9 |\n", "| IL | 793.0 | 809.0 | 65.0 | 70.5 |\n", "| IN | 5445.0 | 5290.0 | 62.7 | 60.1 |\n", "| IA | 2151.0 | 2247.0 | 56.5 | 53.0 |\n", "| KS | 404.0 | 389.0 | 54.5 | 47.8 |\n", "| KY | 412.0 | 483.0 | 67.7 | 73.5 |\n", "| LA | 273.0 | 254.0 | 115.0 | 115.0 |\n", "| ME | 1069.0 | 1070.0 | 101.0 | 97.0 |\n", "| MD | 885.0 | 898.0 | 76.6 | 75.4 |\n", "| MA | 235.0 | 237.0 | 105.0 | 102.0 |\n", "| MI | 1406.0 | 1396.0 | 58.0 | 53.8 |\n", "| MN | 2499.0 | 2697.0 | 57.7 | 54.0 |\n", "| MS | 1434.0 | 1468.0 | 87.8 | 86.7 |\n", "| MO | 1580.0 | 1622.0 | 55.4 | 51.5 |\n", "| MT | 172.0 | 164.0 | 68.0 | 66.0 |\n", "| NE | 1202.0 | 1400.0 | 50.3 | 48.9 |\n", "| NV | 2.2 | 1.8 | 53.9 | 52.7 |\n", "| NH | 43.0 | 49.0 | 109.0 | 104.0 |\n", "| NJ | 442.0 | 491.0 | 85.0 | 83.0 |\n", "| NM | 283.0 | 302.0 | 74.0 | 70.0 |\n", "| NY | 975.0 | 987.0 | 68.1 | 64.0 |\n", "| NC | 3033.0 | 3045.0 | 82.8 | 78.7 |\n", "| ND | 51.0 | 45.0 | 55.2 | 48.0 |\n", "| OH | 4667.0 | 4637.0 | 59.1 | 54.7 |\n", "| OK | 869.0 | 830.0 | 101.0 | 100.0 |\n", "| OR | 652.0 | 686.0 | 77.0 | 74.6 |\n", "| PA | 4976.0 | 5130.0 | 61.0 | 52.0 |\n", "| RI | 53.0 | 50.0 | 102.0 | 99.0 |\n", "| SC | 1422.0 | 1420.0 | 70.1 | 65.9 |\n", "| SD | 435.0 | 602.0 | 48.0 | 45.8 |\n", "| TN | 277.0 | 279.0 | 71.0 | 80.7 |\n", "| TX | 3317.0 | 3356.0 | 76.7 | 72.6 |\n", "| UT | 456.0 | 486.0 | 64.0 | 59.0 |\n", "| VT | 31.0 | 30.0 | 106.0 | 102.0 |\n", "| VA | 934.0 | 988.0 | 86.3 | 81.2 |\n", "| WA | 1287.0 | 1313.0 | 74.1 | 71.5 |\n", "| WV | 136.0 | 174.0 | 104.0 | 109.0 |\n", "| WI | 910.0 | 873.0 | 60.1 | 54.0 |\n", "| WY | 1.7 | 1.7 | 83.0 | 83.0 |\n", "\n" ], "text/plain": [ " STATE Y1 Y2 X1 X2 \n", "1 AL 2206.0 2186.0 92.7 91.4\n", "2 AK 0.7 0.7 151.0 149.0\n", "3 AZ 73.0 74.0 61.0 56.0\n", "4 AR 3620.0 3737.0 86.3 91.8\n", "5 CA 7472.0 7444.0 63.4 58.4\n", "6 CO 788.0 873.0 77.8 73.0\n", "7 CT 1029.0 948.0 106.0 104.0\n", "8 DE 168.0 164.0 117.0 113.0\n", "9 FL 2568.0 2537.0 62.0 57.2\n", "10 GA 4302.0 4301.0 80.6 80.8\n", "11 HI 227.5 224.5 85.0 85.5\n", "12 ID 187.0 203.0 79.1 72.9\n", "13 IL 793.0 809.0 65.0 70.5\n", "14 IN 5445.0 5290.0 62.7 60.1\n", "15 IA 2151.0 2247.0 56.5 53.0\n", "16 KS 404.0 389.0 54.5 47.8\n", "17 KY 412.0 483.0 67.7 73.5\n", "18 LA 273.0 254.0 115.0 115.0\n", "19 ME 1069.0 1070.0 101.0 97.0\n", "20 MD 885.0 898.0 76.6 75.4\n", "21 MA 235.0 237.0 105.0 102.0\n", "22 MI 1406.0 1396.0 58.0 53.8\n", "23 MN 2499.0 2697.0 57.7 54.0\n", "24 MS 1434.0 1468.0 87.8 86.7\n", "25 MO 1580.0 1622.0 55.4 51.5\n", "26 MT 172.0 164.0 68.0 66.0\n", "27 NE 1202.0 1400.0 50.3 48.9\n", "28 NV 2.2 1.8 53.9 52.7\n", "29 NH 43.0 49.0 109.0 104.0\n", "30 NJ 442.0 491.0 85.0 83.0\n", "31 NM 283.0 302.0 74.0 70.0\n", "32 NY 975.0 987.0 68.1 64.0\n", "33 NC 3033.0 3045.0 82.8 78.7\n", "34 ND 51.0 45.0 55.2 48.0\n", "35 OH 4667.0 4637.0 59.1 54.7\n", "36 OK 869.0 830.0 101.0 100.0\n", "37 OR 652.0 686.0 77.0 74.6\n", "38 PA 4976.0 5130.0 61.0 52.0\n", "39 RI 53.0 50.0 102.0 99.0\n", "40 SC 1422.0 1420.0 70.1 65.9\n", "41 SD 435.0 602.0 48.0 45.8\n", "42 TN 277.0 279.0 71.0 80.7\n", "43 TX 3317.0 3356.0 76.7 72.6\n", "44 UT 456.0 486.0 64.0 59.0\n", "45 VT 31.0 30.0 106.0 102.0\n", "46 VA 934.0 988.0 86.3 81.2\n", "47 WA 1287.0 1313.0 74.1 71.5\n", "48 WV 136.0 174.0 104.0 109.0\n", "49 WI 910.0 873.0 60.1 54.0\n", "50 WY 1.7 1.7 83.0 83.0" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mytable <- read.table(file = \"data/table1.txt\", sep = \"\\t\", header = TRUE)\n", "mytable" ] }, { "cell_type": "code", "execution_count": 3, "id": "8a0dc0cc", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", 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A matrix: 50 × 4 of type dbl
Y1Y2X1X2
2206.02186.0 92.7 91.4
0.7 0.7151.0149.0
73.0 74.0 61.0 56.0
3620.03737.0 86.3 91.8
7472.07444.0 63.4 58.4
788.0 873.0 77.8 73.0
1029.0 948.0106.0104.0
168.0 164.0117.0113.0
2568.02537.0 62.0 57.2
4302.04301.0 80.6 80.8
227.5 224.5 85.0 85.5
187.0 203.0 79.1 72.9
793.0 809.0 65.0 70.5
5445.05290.0 62.7 60.1
2151.02247.0 56.5 53.0
404.0 389.0 54.5 47.8
412.0 483.0 67.7 73.5
273.0 254.0115.0115.0
1069.01070.0101.0 97.0
885.0 898.0 76.6 75.4
235.0 237.0105.0102.0
1406.01396.0 58.0 53.8
2499.02697.0 57.7 54.0
1434.01468.0 87.8 86.7
1580.01622.0 55.4 51.5
172.0 164.0 68.0 66.0
1202.01400.0 50.3 48.9
2.2 1.8 53.9 52.7
43.0 49.0109.0104.0
442.0 491.0 85.0 83.0
283.0 302.0 74.0 70.0
975.0 987.0 68.1 64.0
3033.03045.0 82.8 78.7
51.0 45.0 55.2 48.0
4667.04637.0 59.1 54.7
869.0 830.0101.0100.0
652.0 686.0 77.0 74.6
4976.05130.0 61.0 52.0
53.0 50.0102.0 99.0
1422.01420.0 70.1 65.9
435.0 602.0 48.0 45.8
277.0 279.0 71.0 80.7
3317.03356.0 76.7 72.6
456.0 486.0 64.0 59.0
31.0 30.0106.0102.0
934.0 988.0 86.3 81.2
1287.01313.0 74.1 71.5
136.0 174.0104.0109.0
910.0 873.0 60.1 54.0
1.7 1.7 83.0 83.0
\n" ], "text/latex": [ "A matrix: 50 × 4 of type dbl\n", "\\begin{tabular}{llll}\n", " Y1 & Y2 & X1 & X2\\\\\n", "\\hline\n", "\t 2206.0 & 2186.0 & 92.7 & 91.4\\\\\n", "\t 0.7 & 0.7 & 151.0 & 149.0\\\\\n", "\t 73.0 & 74.0 & 61.0 & 56.0\\\\\n", "\t 3620.0 & 3737.0 & 86.3 & 91.8\\\\\n", "\t 7472.0 & 7444.0 & 63.4 & 58.4\\\\\n", "\t 788.0 & 873.0 & 77.8 & 73.0\\\\\n", "\t 1029.0 & 948.0 & 106.0 & 104.0\\\\\n", "\t 168.0 & 164.0 & 117.0 & 113.0\\\\\n", "\t 2568.0 & 2537.0 & 62.0 & 57.2\\\\\n", "\t 4302.0 & 4301.0 & 80.6 & 80.8\\\\\n", "\t 227.5 & 224.5 & 85.0 & 85.5\\\\\n", "\t 187.0 & 203.0 & 79.1 & 72.9\\\\\n", "\t 793.0 & 809.0 & 65.0 & 70.5\\\\\n", "\t 5445.0 & 5290.0 & 62.7 & 60.1\\\\\n", "\t 2151.0 & 2247.0 & 56.5 & 53.0\\\\\n", "\t 404.0 & 389.0 & 54.5 & 47.8\\\\\n", "\t 412.0 & 483.0 & 67.7 & 73.5\\\\\n", "\t 273.0 & 254.0 & 115.0 & 115.0\\\\\n", "\t 1069.0 & 1070.0 & 101.0 & 97.0\\\\\n", "\t 885.0 & 898.0 & 76.6 & 75.4\\\\\n", "\t 235.0 & 237.0 & 105.0 & 102.0\\\\\n", "\t 1406.0 & 1396.0 & 58.0 & 53.8\\\\\n", "\t 2499.0 & 2697.0 & 57.7 & 54.0\\\\\n", "\t 1434.0 & 1468.0 & 87.8 & 86.7\\\\\n", "\t 1580.0 & 1622.0 & 55.4 & 51.5\\\\\n", "\t 172.0 & 164.0 & 68.0 & 66.0\\\\\n", "\t 1202.0 & 1400.0 & 50.3 & 48.9\\\\\n", "\t 2.2 & 1.8 & 53.9 & 52.7\\\\\n", "\t 43.0 & 49.0 & 109.0 & 104.0\\\\\n", "\t 442.0 & 491.0 & 85.0 & 83.0\\\\\n", "\t 283.0 & 302.0 & 74.0 & 70.0\\\\\n", "\t 975.0 & 987.0 & 68.1 & 64.0\\\\\n", "\t 3033.0 & 3045.0 & 82.8 & 78.7\\\\\n", "\t 51.0 & 45.0 & 55.2 & 48.0\\\\\n", "\t 4667.0 & 4637.0 & 59.1 & 54.7\\\\\n", "\t 869.0 & 830.0 & 101.0 & 100.0\\\\\n", "\t 652.0 & 686.0 & 77.0 & 74.6\\\\\n", "\t 4976.0 & 5130.0 & 61.0 & 52.0\\\\\n", "\t 53.0 & 50.0 & 102.0 & 99.0\\\\\n", "\t 1422.0 & 1420.0 & 70.1 & 65.9\\\\\n", "\t 435.0 & 602.0 & 48.0 & 45.8\\\\\n", "\t 277.0 & 279.0 & 71.0 & 80.7\\\\\n", "\t 3317.0 & 3356.0 & 76.7 & 72.6\\\\\n", "\t 456.0 & 486.0 & 64.0 & 59.0\\\\\n", "\t 31.0 & 30.0 & 106.0 & 102.0\\\\\n", "\t 934.0 & 988.0 & 86.3 & 81.2\\\\\n", "\t 1287.0 & 1313.0 & 74.1 & 71.5\\\\\n", "\t 136.0 & 174.0 & 104.0 & 109.0\\\\\n", "\t 910.0 & 873.0 & 60.1 & 54.0\\\\\n", "\t 1.7 & 1.7 & 83.0 & 83.0\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A matrix: 50 × 4 of type dbl\n", "\n", "| Y1 | Y2 | X1 | X2 |\n", "|---|---|---|---|\n", "| 2206.0 | 2186.0 | 92.7 | 91.4 |\n", "| 0.7 | 0.7 | 151.0 | 149.0 |\n", "| 73.0 | 74.0 | 61.0 | 56.0 |\n", "| 3620.0 | 3737.0 | 86.3 | 91.8 |\n", "| 7472.0 | 7444.0 | 63.4 | 58.4 |\n", "| 788.0 | 873.0 | 77.8 | 73.0 |\n", "| 1029.0 | 948.0 | 106.0 | 104.0 |\n", "| 168.0 | 164.0 | 117.0 | 113.0 |\n", "| 2568.0 | 2537.0 | 62.0 | 57.2 |\n", "| 4302.0 | 4301.0 | 80.6 | 80.8 |\n", "| 227.5 | 224.5 | 85.0 | 85.5 |\n", "| 187.0 | 203.0 | 79.1 | 72.9 |\n", "| 793.0 | 809.0 | 65.0 | 70.5 |\n", "| 5445.0 | 5290.0 | 62.7 | 60.1 |\n", "| 2151.0 | 2247.0 | 56.5 | 53.0 |\n", "| 404.0 | 389.0 | 54.5 | 47.8 |\n", "| 412.0 | 483.0 | 67.7 | 73.5 |\n", "| 273.0 | 254.0 | 115.0 | 115.0 |\n", "| 1069.0 | 1070.0 | 101.0 | 97.0 |\n", "| 885.0 | 898.0 | 76.6 | 75.4 |\n", "| 235.0 | 237.0 | 105.0 | 102.0 |\n", "| 1406.0 | 1396.0 | 58.0 | 53.8 |\n", "| 2499.0 | 2697.0 | 57.7 | 54.0 |\n", "| 1434.0 | 1468.0 | 87.8 | 86.7 |\n", "| 1580.0 | 1622.0 | 55.4 | 51.5 |\n", "| 172.0 | 164.0 | 68.0 | 66.0 |\n", "| 1202.0 | 1400.0 | 50.3 | 48.9 |\n", "| 2.2 | 1.8 | 53.9 | 52.7 |\n", "| 43.0 | 49.0 | 109.0 | 104.0 |\n", "| 442.0 | 491.0 | 85.0 | 83.0 |\n", "| 283.0 | 302.0 | 74.0 | 70.0 |\n", "| 975.0 | 987.0 | 68.1 | 64.0 |\n", "| 3033.0 | 3045.0 | 82.8 | 78.7 |\n", "| 51.0 | 45.0 | 55.2 | 48.0 |\n", "| 4667.0 | 4637.0 | 59.1 | 54.7 |\n", "| 869.0 | 830.0 | 101.0 | 100.0 |\n", "| 652.0 | 686.0 | 77.0 | 74.6 |\n", "| 4976.0 | 5130.0 | 61.0 | 52.0 |\n", "| 53.0 | 50.0 | 102.0 | 99.0 |\n", "| 1422.0 | 1420.0 | 70.1 | 65.9 |\n", "| 435.0 | 602.0 | 48.0 | 45.8 |\n", "| 277.0 | 279.0 | 71.0 | 80.7 |\n", "| 3317.0 | 3356.0 | 76.7 | 72.6 |\n", "| 456.0 | 486.0 | 64.0 | 59.0 |\n", "| 31.0 | 30.0 | 106.0 | 102.0 |\n", "| 934.0 | 988.0 | 86.3 | 81.2 |\n", "| 1287.0 | 1313.0 | 74.1 | 71.5 |\n", "| 136.0 | 174.0 | 104.0 | 109.0 |\n", "| 910.0 | 873.0 | 60.1 | 54.0 |\n", "| 1.7 | 1.7 | 83.0 | 83.0 |\n", "\n" ], "text/plain": [ " Y1 Y2 X1 X2 \n", " [1,] 2206.0 2186.0 92.7 91.4\n", " [2,] 0.7 0.7 151.0 149.0\n", " [3,] 73.0 74.0 61.0 56.0\n", " [4,] 3620.0 3737.0 86.3 91.8\n", " [5,] 7472.0 7444.0 63.4 58.4\n", " [6,] 788.0 873.0 77.8 73.0\n", " [7,] 1029.0 948.0 106.0 104.0\n", " [8,] 168.0 164.0 117.0 113.0\n", " [9,] 2568.0 2537.0 62.0 57.2\n", "[10,] 4302.0 4301.0 80.6 80.8\n", "[11,] 227.5 224.5 85.0 85.5\n", "[12,] 187.0 203.0 79.1 72.9\n", "[13,] 793.0 809.0 65.0 70.5\n", "[14,] 5445.0 5290.0 62.7 60.1\n", "[15,] 2151.0 2247.0 56.5 53.0\n", "[16,] 404.0 389.0 54.5 47.8\n", "[17,] 412.0 483.0 67.7 73.5\n", "[18,] 273.0 254.0 115.0 115.0\n", "[19,] 1069.0 1070.0 101.0 97.0\n", "[20,] 885.0 898.0 76.6 75.4\n", "[21,] 235.0 237.0 105.0 102.0\n", "[22,] 1406.0 1396.0 58.0 53.8\n", "[23,] 2499.0 2697.0 57.7 54.0\n", "[24,] 1434.0 1468.0 87.8 86.7\n", "[25,] 1580.0 1622.0 55.4 51.5\n", "[26,] 172.0 164.0 68.0 66.0\n", "[27,] 1202.0 1400.0 50.3 48.9\n", "[28,] 2.2 1.8 53.9 52.7\n", "[29,] 43.0 49.0 109.0 104.0\n", "[30,] 442.0 491.0 85.0 83.0\n", "[31,] 283.0 302.0 74.0 70.0\n", "[32,] 975.0 987.0 68.1 64.0\n", "[33,] 3033.0 3045.0 82.8 78.7\n", "[34,] 51.0 45.0 55.2 48.0\n", "[35,] 4667.0 4637.0 59.1 54.7\n", "[36,] 869.0 830.0 101.0 100.0\n", "[37,] 652.0 686.0 77.0 74.6\n", "[38,] 4976.0 5130.0 61.0 52.0\n", "[39,] 53.0 50.0 102.0 99.0\n", "[40,] 1422.0 1420.0 70.1 65.9\n", "[41,] 435.0 602.0 48.0 45.8\n", "[42,] 277.0 279.0 71.0 80.7\n", "[43,] 3317.0 3356.0 76.7 72.6\n", "[44,] 456.0 486.0 64.0 59.0\n", "[45,] 31.0 30.0 106.0 102.0\n", "[46,] 934.0 988.0 86.3 81.2\n", "[47,] 1287.0 1313.0 74.1 71.5\n", "[48,] 136.0 174.0 104.0 109.0\n", "[49,] 910.0 873.0 60.1 54.0\n", "[50,] 1.7 1.7 83.0 83.0" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "as.matrix(mytable[, 2:5]) # Convert to a Matrix format" ] }, { "cell_type": "code", "execution_count": 2, "id": "6431bc2e", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "write.table(mytable, file = \"mysavedtable.txt\", sep = \",\") # save with comma separator" ] }, { "cell_type": "markdown", "id": "587e119f", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Microsoft Excel\n", "\n", "- Install and load the `openxlsx` package.\n", "- Read Excel files with `read.xlsx()` function\n", "\n", " read.xlsx(xlsxFile, sheet, startRow = 1, colNames = TRUE, rowNames = FALSE, ...)\n", "\n", "- Write Excel files\n", " \n", " write.xlsx(x, file, asTable = FALSE, overwrite = TRUE, ...)\n" ] }, { "cell_type": "code", "execution_count": 10, "id": "743b3fe9", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Installing package into ‘/home/fli/.R/library’\n", "(as ‘lib’ is unspecified)\n", "\n" ] } ], "source": [ "# install.packages(\"openxlsx\")" ] }, { "cell_type": "code", "execution_count": 9, "id": "0b2cb571", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 20 × 11
TeamSlides/ReportCodesVideoUse.public.dataUse.big.dataThemeInnovationMethodsPresentationVisualization
<chr><chr><chr><chr><chr><chr><dbl><dbl><dbl><dbl><dbl>
1Scipopulis YesYesYesYesYes43344
2Analysts Against Poverty YesYesYesYesNo 33232
3Upside Down YesYesNo YesYes22212
4safe-encounter.ai YesYesYesYesYes32131
5Big Data and Disasters - ADB YesYesYesYesYes54544
6Datains - Nippon Koei YesYesYesYesYes53344
7Frame YesYesYesYesYes53232
8GC-TICS YesYesYesYesYes33342
9Meaning Maker YesYesYesYesYes43344
10Satria Garuda YesYesYesYesYes32221
11Vegetation Growth YesYesYesYesYes33332
12YOLO (You Only Live Once) YesYesYesYesYes44333
13ZU_ProMinds YesYesYesYesYes33333
14GEMy YesYesYesYesYes43332
15Not wee Data YesYesYesYesYes33332
16MKIT_AIMS2018 No YesNo YesYes22211
17Explorers YesYesYesYesYes44555
18Multiverse of Data YesYesYesYesYes43433
19QUT Centre for Data Science No YesNo YesYes32211
20UAE Diversified Big Data Expert TeamYesYesYesYesYes44323
\n" ], "text/latex": [ "A data.frame: 20 × 11\n", "\\begin{tabular}{r|lllllllllll}\n", " & Team & Slides/Report & Codes & Video & Use.public.data & Use.big.data & Theme & Innovation & Methods & Presentation & Visualization\\\\\n", " & & & & & & & & & & & \\\\\n", "\\hline\n", "\t1 & Scipopulis & Yes & Yes & Yes & Yes & Yes & 4 & 3 & 3 & 4 & 4\\\\\n", "\t2 & Analysts Against Poverty & Yes & Yes & Yes & Yes & No & 3 & 3 & 2 & 3 & 2\\\\\n", "\t3 & Upside Down & Yes & Yes & No & Yes & Yes & 2 & 2 & 2 & 1 & 2\\\\\n", "\t4 & safe-encounter.ai & Yes & Yes & Yes & Yes & Yes & 3 & 2 & 1 & 3 & 1\\\\\n", "\t5 & Big Data and Disasters - ADB & Yes & Yes & Yes & Yes & Yes & 5 & 4 & 5 & 4 & 4\\\\\n", "\t6 & Datains - Nippon Koei & Yes & Yes & Yes & Yes & Yes & 5 & 3 & 3 & 4 & 4\\\\\n", "\t7 & Frame & Yes & Yes & Yes & Yes & Yes & 5 & 3 & 2 & 3 & 2\\\\\n", "\t8 & GC-TICS & Yes & Yes & Yes & Yes & Yes & 3 & 3 & 3 & 4 & 2\\\\\n", "\t9 & Meaning Maker & Yes & Yes & Yes & Yes & Yes & 4 & 3 & 3 & 4 & 4\\\\\n", "\t10 & Satria Garuda & Yes & Yes & Yes & Yes & Yes & 3 & 2 & 2 & 2 & 1\\\\\n", "\t11 & Vegetation Growth & Yes & Yes & Yes & Yes & Yes & 3 & 3 & 3 & 3 & 2\\\\\n", "\t12 & YOLO (You Only Live Once) & Yes & Yes & Yes & Yes & Yes & 4 & 4 & 3 & 3 & 3\\\\\n", "\t13 & ZU\\_ProMinds & Yes & Yes & Yes & Yes & Yes & 3 & 3 & 3 & 3 & 3\\\\\n", "\t14 & GEMy & Yes & Yes & Yes & Yes & Yes & 4 & 3 & 3 & 3 & 2\\\\\n", "\t15 & Not wee Data & Yes & Yes & Yes & Yes & Yes & 3 & 3 & 3 & 3 & 2\\\\\n", "\t16 & MKIT\\_AIMS2018 & No & Yes & No & Yes & Yes & 2 & 2 & 2 & 1 & 1\\\\\n", "\t17 & Explorers & Yes & Yes & Yes & Yes & Yes & 4 & 4 & 5 & 5 & 5\\\\\n", "\t18 & Multiverse of Data & Yes & Yes & Yes & Yes & Yes & 4 & 3 & 4 & 3 & 3\\\\\n", "\t19 & QUT Centre for Data Science & No & Yes & No & Yes & Yes & 3 & 2 & 2 & 1 & 1\\\\\n", "\t20 & UAE Diversified Big Data Expert Team & Yes & Yes & Yes & Yes & Yes & 4 & 4 & 3 & 2 & 3\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 20 × 11\n", "\n", "| | Team <chr> | Slides/Report <chr> | Codes <chr> | Video <chr> | Use.public.data <chr> | Use.big.data <chr> | Theme <dbl> | Innovation <dbl> | Methods <dbl> | Presentation <dbl> | Visualization <dbl> |\n", "|---|---|---|---|---|---|---|---|---|---|---|---|\n", "| 1 | Scipopulis | Yes | Yes | Yes | Yes | Yes | 4 | 3 | 3 | 4 | 4 |\n", "| 2 | Analysts Against Poverty | Yes | Yes | Yes | Yes | No | 3 | 3 | 2 | 3 | 2 |\n", "| 3 | Upside Down | Yes | Yes | No | Yes | Yes | 2 | 2 | 2 | 1 | 2 |\n", "| 4 | safe-encounter.ai | Yes | Yes | Yes | Yes | Yes | 3 | 2 | 1 | 3 | 1 |\n", "| 5 | Big Data and Disasters - ADB | Yes | Yes | Yes | Yes | Yes | 5 | 4 | 5 | 4 | 4 |\n", "| 6 | Datains - Nippon Koei | Yes | Yes | Yes | Yes | Yes | 5 | 3 | 3 | 4 | 4 |\n", "| 7 | Frame | Yes | Yes | Yes | Yes | Yes | 5 | 3 | 2 | 3 | 2 |\n", "| 8 | GC-TICS | Yes | Yes | Yes | Yes | Yes | 3 | 3 | 3 | 4 | 2 |\n", "| 9 | Meaning Maker | Yes | Yes | Yes | Yes | Yes | 4 | 3 | 3 | 4 | 4 |\n", "| 10 | Satria Garuda | Yes | Yes | Yes | Yes | Yes | 3 | 2 | 2 | 2 | 1 |\n", "| 11 | Vegetation Growth | Yes | Yes | Yes | Yes | Yes | 3 | 3 | 3 | 3 | 2 |\n", "| 12 | YOLO (You Only Live Once) | Yes | Yes | Yes | Yes | Yes | 4 | 4 | 3 | 3 | 3 |\n", "| 13 | ZU_ProMinds | Yes | Yes | Yes | Yes | Yes | 3 | 3 | 3 | 3 | 3 |\n", "| 14 | GEMy | Yes | Yes | Yes | Yes | Yes | 4 | 3 | 3 | 3 | 2 |\n", "| 15 | Not wee Data | Yes | Yes | Yes | Yes | Yes | 3 | 3 | 3 | 3 | 2 |\n", "| 16 | MKIT_AIMS2018 | No | Yes | No | Yes | Yes | 2 | 2 | 2 | 1 | 1 |\n", "| 17 | Explorers | Yes | Yes | Yes | Yes | Yes | 4 | 4 | 5 | 5 | 5 |\n", "| 18 | Multiverse of Data | Yes | Yes | Yes | Yes | Yes | 4 | 3 | 4 | 3 | 3 |\n", "| 19 | QUT Centre for Data Science | No | Yes | No | Yes | Yes | 3 | 2 | 2 | 1 | 1 |\n", "| 20 | UAE Diversified Big Data Expert Team | Yes | Yes | Yes | Yes | Yes | 4 | 4 | 3 | 2 | 3 |\n", "\n" ], "text/plain": [ " Team Slides/Report Codes Video\n", "1 Scipopulis Yes Yes Yes \n", "2 Analysts Against Poverty Yes Yes Yes \n", "3 Upside Down Yes Yes No \n", "4 safe-encounter.ai Yes Yes Yes \n", "5 Big Data and Disasters - ADB Yes Yes Yes \n", "6 Datains - Nippon Koei Yes Yes Yes \n", "7 Frame Yes Yes Yes \n", "8 GC-TICS Yes Yes Yes \n", "9 Meaning Maker Yes Yes Yes \n", "10 Satria Garuda Yes Yes Yes \n", "11 Vegetation Growth Yes Yes Yes \n", "12 YOLO (You Only Live Once) Yes Yes Yes \n", "13 ZU_ProMinds Yes Yes Yes \n", "14 GEMy Yes Yes Yes \n", "15 Not wee Data Yes Yes Yes \n", "16 MKIT_AIMS2018 No Yes No \n", "17 Explorers Yes Yes Yes \n", "18 Multiverse of Data Yes Yes Yes \n", "19 QUT Centre for Data Science No Yes No \n", "20 UAE Diversified Big Data Expert Team Yes Yes Yes \n", " Use.public.data Use.big.data Theme Innovation Methods Presentation\n", "1 Yes Yes 4 3 3 4 \n", "2 Yes No 3 3 2 3 \n", "3 Yes Yes 2 2 2 1 \n", "4 Yes Yes 3 2 1 3 \n", "5 Yes Yes 5 4 5 4 \n", "6 Yes Yes 5 3 3 4 \n", "7 Yes Yes 5 3 2 3 \n", "8 Yes Yes 3 3 3 4 \n", "9 Yes Yes 4 3 3 4 \n", "10 Yes Yes 3 2 2 2 \n", "11 Yes Yes 3 3 3 3 \n", "12 Yes Yes 4 4 3 3 \n", "13 Yes Yes 3 3 3 3 \n", "14 Yes Yes 4 3 3 3 \n", "15 Yes Yes 3 3 3 3 \n", "16 Yes Yes 2 2 2 1 \n", "17 Yes Yes 4 4 5 5 \n", "18 Yes Yes 4 3 4 3 \n", "19 Yes Yes 3 2 2 1 \n", "20 Yes Yes 4 4 3 2 \n", " Visualization\n", "1 4 \n", "2 2 \n", "3 2 \n", "4 1 \n", "5 4 \n", "6 4 \n", "7 2 \n", "8 2 \n", "9 4 \n", "10 1 \n", "11 2 \n", "12 3 \n", "13 3 \n", "14 2 \n", "15 2 \n", "16 1 \n", "17 5 \n", "18 3 \n", "19 1 \n", "20 3 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "require(\"openxlsx\")\n", "mydata <- read.xlsx(\"data/UNHBDScore.xlsx\",\n", " sheet = 1,\n", " startRow = 1,\n", " colNames = TRUE)\n", "mydata" ] }, { "cell_type": "markdown", "id": "6742ab84", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Read and Write MAT Files\n", "\n", "- Install and load the `R.matlab` package.\n", "- No Matlab installation required.\n", "- Methods `readMat()` and `writeMat()` for reading and writing MAT files. " ] }, { "cell_type": "code", "execution_count": 11, "id": "5eb246c4", "metadata": { "scrolled": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "R.matlab v3.7.0 (2022-08-25 21:52:34 UTC) successfully loaded. See ?R.matlab for help.\n", "\n", "\n", "Attaching package: ‘R.matlab’\n", "\n", "\n", "The following objects are masked from ‘package:base’:\n", "\n", " getOption, isOpen\n", "\n", "\n" ] }, { "data": { "text/html": [ "
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A matrix: 4405 × 5 of type dbl
10.15283128 1.04527506.046561 0.08774734
10.59341052 1.64792983.939774 0.18836954
10.24067336 2.85774545.757311 0.24105133
10.37908270 0.68596074.942442 0.10643459
10.22463373 0.81535653.644353 0.10099751
10.28268554 2.00069005.687355 0.10290431
10.16413260 0.85672307.444632 0.07588428
10.64726939 1.05477386.007547 0.10247049
10.10341836 0.87605566.427695-0.04920358
10.12944697 2.07064806.069407 0.14460620
10.10986147 1.48786376.850210 0.18828204
10.22716095 0.88281604.145006 0.10592493
10.10519763 3.66748372.216809 0.41223156
10.88186163 1.01948524.973619 0.31369309
10.44649656 4.17629868.968512 0.31245069
10.68209559 0.90980374.418684 0.08361230
10.09845585 1.25328364.988076 0.12320417
10.30317060 0.89657705.179500 0.10322459
10.27682052 1.26509144.286548 0.11287619
10.19914411 1.00422233.970990 0.05652562
10.21840969 0.90084264.563952 0.08914537
10.36891574 0.77645821.599792 0.04900973
10.0831839416.75166802.215719 0.03011832
10.07090499 3.29403234.797549 0.19508910
10.30679425 0.90371794.584620 0.05960136
10.13470736 1.21516196.310098 0.05064859
10.04878252 1.03638316.610284 0.10701220
10.43966075 2.03060094.536859 0.28045713
10.10106310 0.79128123.201648 0.12439597
10.16227839 0.83579083.221033 0.06801826
⋮⋮⋮⋮⋮
10.231630571.4541071 1.5096176-0.45443400
10.014125141.9036772 4.4757333 0.10464538
10.103291013.1218296 3.6441697 0.20205401
10.069157152.6923948 3.2967624 0.44818360
10.247965274.5749457 0.4187103-0.25908844
10.063443154.3150962 4.2255336 0.17914748
10.061351373.0522308 3.3308107 0.24496727
10.040141194.1432166 2.8499548 0.14002478
10.010685104.4860544 1.1749559 0.10708674
10.072082576.1268956 3.6317269 0.11618639
10.023403384.1160044 2.0272268 0.13732224
10.367952173.1915709 3.2789544 0.10186379
10.330900410.8523913 6.4572217 0.05928239
10.306857091.4329358 4.5266571 0.23338493
10.319437521.6375911 7.9338800 0.16894862
10.664187331.2743732 9.3798299 0.12295684
10.240864220.9936688 9.4355879 0.04494273
10.692665640.9936683 6.1993483 0.17269039
10.898999560.1482478 5.6734504 0.04860495
10.933145900.3128039 6.0399737 0.07985335
10.707253700.9642831 7.9493340 0.18984009
10.606401931.4674369 6.7021385 0.09358254
10.714170701.3699697 5.3074902 0.20615239
10.124618271.174256310.2837032 0.07930871
10.357185101.8335471 6.7345917 0.17267889
10.543076371.1319547 5.1073566 0.08069173
10.499455220.7121362 6.1899834 0.04285418
10.955068710.7826259 4.3393540 0.05073775
10.271265701.4070131 4.5467993 0.02018504
10.552913841.2432457 7.7006338 0.09380608
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A matrix: 4405 × 1 of type dbl
0.48061406
0.49789298
0.08513211
0.72337394
0.87229293
0.25224938
0.65029221
0.82784745
0.96586876
0.29379814
0.25159485
0.69433447
0.08579703
0.52781013
0.12396548
0.82881142
0.25703244
0.52647353
0.47460682
0.56260611
0.69551821
0.27355197
0.05781200
0.13346465
0.57969422
0.69786571
0.23100059
0.13815073
0.32689204
0.79901292
⋮
0.14383677
0.30137774
0.32939544
0.68419846
0.07815812
0.06176844
0.07326558
0.04638021
0.03154198
0.04460804
0.01265112
0.18559067
0.79289621
0.36263210
0.33696546
0.60398084
0.65082213
0.56969612
0.43006658
0.18838975
0.57448000
0.44272956
0.53810913
0.67301374
0.30994720
0.48864363
0.65195421
0.93939365
0.19348394
0.45197044
\n", "
\n", "\t
$yName
\n", "\t\t
\n", "\n", "\n", "\t\n", "\n", "
A matrix: 1 × 1 of type chr
debtratio
\n", "
\n", "\t
$Description
\n", "\t\t
\n", "\n", "\n", "\t\n", "\n", "
A matrix: 1 × 1 of type chr
Rajan-Zingales JF 1995 data, but with definitions from Cook, Kieschnick and McCullough, Journal of Empirical Finance 2008
\n", "
\n", "\t
$XName
\n", "\t\t
\n", "\n", "\n", "\t\n", "\n", "
A matrix: 1 × 5
consttangmbtrlogsaleprofit
\n", "
\n", "
\n" ], "text/latex": [ "\\begin{description}\n", "\\item[\\$X] A matrix: 4405 × 5 of type dbl\n", "\\begin{tabular}{lllll}\n", "\t 1 & 0.15283128 & 1.0452750 & 6.046561 & 0.08774734\\\\\n", "\t 1 & 0.59341052 & 1.6479298 & 3.939774 & 0.18836954\\\\\n", "\t 1 & 0.24067336 & 2.8577454 & 5.757311 & 0.24105133\\\\\n", "\t 1 & 0.37908270 & 0.6859607 & 4.942442 & 0.10643459\\\\\n", "\t 1 & 0.22463373 & 0.8153565 & 3.644353 & 0.10099751\\\\\n", "\t 1 & 0.28268554 & 2.0006900 & 5.687355 & 0.10290431\\\\\n", "\t 1 & 0.16413260 & 0.8567230 & 7.444632 & 0.07588428\\\\\n", "\t 1 & 0.64726939 & 1.0547738 & 6.007547 & 0.10247049\\\\\n", "\t 1 & 0.10341836 & 0.8760556 & 6.427695 & -0.04920358\\\\\n", "\t 1 & 0.12944697 & 2.0706480 & 6.069407 & 0.14460620\\\\\n", "\t 1 & 0.10986147 & 1.4878637 & 6.850210 & 0.18828204\\\\\n", "\t 1 & 0.22716095 & 0.8828160 & 4.145006 & 0.10592493\\\\\n", "\t 1 & 0.10519763 & 3.6674837 & 2.216809 & 0.41223156\\\\\n", "\t 1 & 0.88186163 & 1.0194852 & 4.973619 & 0.31369309\\\\\n", "\t 1 & 0.44649656 & 4.1762986 & 8.968512 & 0.31245069\\\\\n", "\t 1 & 0.68209559 & 0.9098037 & 4.418684 & 0.08361230\\\\\n", "\t 1 & 0.09845585 & 1.2532836 & 4.988076 & 0.12320417\\\\\n", "\t 1 & 0.30317060 & 0.8965770 & 5.179500 & 0.10322459\\\\\n", "\t 1 & 0.27682052 & 1.2650914 & 4.286548 & 0.11287619\\\\\n", "\t 1 & 0.19914411 & 1.0042223 & 3.970990 & 0.05652562\\\\\n", "\t 1 & 0.21840969 & 0.9008426 & 4.563952 & 0.08914537\\\\\n", "\t 1 & 0.36891574 & 0.7764582 & 1.599792 & 0.04900973\\\\\n", "\t 1 & 0.08318394 & 16.7516680 & 2.215719 & 0.03011832\\\\\n", "\t 1 & 0.07090499 & 3.2940323 & 4.797549 & 0.19508910\\\\\n", "\t 1 & 0.30679425 & 0.9037179 & 4.584620 & 0.05960136\\\\\n", "\t 1 & 0.13470736 & 1.2151619 & 6.310098 & 0.05064859\\\\\n", "\t 1 & 0.04878252 & 1.0363831 & 6.610284 & 0.10701220\\\\\n", "\t 1 & 0.43966075 & 2.0306009 & 4.536859 & 0.28045713\\\\\n", "\t 1 & 0.10106310 & 0.7912812 & 3.201648 & 0.12439597\\\\\n", "\t 1 & 0.16227839 & 0.8357908 & 3.221033 & 0.06801826\\\\\n", "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n", "\t 1 & 0.23163057 & 1.4541071 & 1.5096176 & -0.45443400\\\\\n", "\t 1 & 0.01412514 & 1.9036772 & 4.4757333 & 0.10464538\\\\\n", "\t 1 & 0.10329101 & 3.1218296 & 3.6441697 & 0.20205401\\\\\n", "\t 1 & 0.06915715 & 2.6923948 & 3.2967624 & 0.44818360\\\\\n", "\t 1 & 0.24796527 & 4.5749457 & 0.4187103 & -0.25908844\\\\\n", "\t 1 & 0.06344315 & 4.3150962 & 4.2255336 & 0.17914748\\\\\n", "\t 1 & 0.06135137 & 3.0522308 & 3.3308107 & 0.24496727\\\\\n", "\t 1 & 0.04014119 & 4.1432166 & 2.8499548 & 0.14002478\\\\\n", "\t 1 & 0.01068510 & 4.4860544 & 1.1749559 & 0.10708674\\\\\n", "\t 1 & 0.07208257 & 6.1268956 & 3.6317269 & 0.11618639\\\\\n", "\t 1 & 0.02340338 & 4.1160044 & 2.0272268 & 0.13732224\\\\\n", "\t 1 & 0.36795217 & 3.1915709 & 3.2789544 & 0.10186379\\\\\n", "\t 1 & 0.33090041 & 0.8523913 & 6.4572217 & 0.05928239\\\\\n", "\t 1 & 0.30685709 & 1.4329358 & 4.5266571 & 0.23338493\\\\\n", "\t 1 & 0.31943752 & 1.6375911 & 7.9338800 & 0.16894862\\\\\n", "\t 1 & 0.66418733 & 1.2743732 & 9.3798299 & 0.12295684\\\\\n", "\t 1 & 0.24086422 & 0.9936688 & 9.4355879 & 0.04494273\\\\\n", "\t 1 & 0.69266564 & 0.9936683 & 6.1993483 & 0.17269039\\\\\n", "\t 1 & 0.89899956 & 0.1482478 & 5.6734504 & 0.04860495\\\\\n", "\t 1 & 0.93314590 & 0.3128039 & 6.0399737 & 0.07985335\\\\\n", "\t 1 & 0.70725370 & 0.9642831 & 7.9493340 & 0.18984009\\\\\n", "\t 1 & 0.60640193 & 1.4674369 & 6.7021385 & 0.09358254\\\\\n", "\t 1 & 0.71417070 & 1.3699697 & 5.3074902 & 0.20615239\\\\\n", "\t 1 & 0.12461827 & 1.1742563 & 10.2837032 & 0.07930871\\\\\n", "\t 1 & 0.35718510 & 1.8335471 & 6.7345917 & 0.17267889\\\\\n", "\t 1 & 0.54307637 & 1.1319547 & 5.1073566 & 0.08069173\\\\\n", "\t 1 & 0.49945522 & 0.7121362 & 6.1899834 & 0.04285418\\\\\n", "\t 1 & 0.95506871 & 0.7826259 & 4.3393540 & 0.05073775\\\\\n", "\t 1 & 0.27126570 & 1.4070131 & 4.5467993 & 0.02018504\\\\\n", "\t 1 & 0.55291384 & 1.2432457 & 7.7006338 & 0.09380608\\\\\n", "\\end{tabular}\n", "\n", "\\item[\\$y] A matrix: 4405 × 1 of type dbl\n", "\\begin{tabular}{l}\n", "\t 0.48061406\\\\\n", "\t 0.49789298\\\\\n", "\t 0.08513211\\\\\n", "\t 0.72337394\\\\\n", "\t 0.87229293\\\\\n", "\t 0.25224938\\\\\n", "\t 0.65029221\\\\\n", "\t 0.82784745\\\\\n", "\t 0.96586876\\\\\n", "\t 0.29379814\\\\\n", "\t 0.25159485\\\\\n", "\t 0.69433447\\\\\n", "\t 0.08579703\\\\\n", "\t 0.52781013\\\\\n", "\t 0.12396548\\\\\n", "\t 0.82881142\\\\\n", "\t 0.25703244\\\\\n", "\t 0.52647353\\\\\n", "\t 0.47460682\\\\\n", "\t 0.56260611\\\\\n", "\t 0.69551821\\\\\n", "\t 0.27355197\\\\\n", "\t 0.05781200\\\\\n", "\t 0.13346465\\\\\n", "\t 0.57969422\\\\\n", "\t 0.69786571\\\\\n", "\t 0.23100059\\\\\n", "\t 0.13815073\\\\\n", "\t 0.32689204\\\\\n", "\t 0.79901292\\\\\n", "\t ⋮\\\\\n", "\t 0.14383677\\\\\n", "\t 0.30137774\\\\\n", "\t 0.32939544\\\\\n", "\t 0.68419846\\\\\n", "\t 0.07815812\\\\\n", "\t 0.06176844\\\\\n", "\t 0.07326558\\\\\n", "\t 0.04638021\\\\\n", "\t 0.03154198\\\\\n", "\t 0.04460804\\\\\n", "\t 0.01265112\\\\\n", "\t 0.18559067\\\\\n", "\t 0.79289621\\\\\n", "\t 0.36263210\\\\\n", "\t 0.33696546\\\\\n", "\t 0.60398084\\\\\n", "\t 0.65082213\\\\\n", "\t 0.56969612\\\\\n", "\t 0.43006658\\\\\n", "\t 0.18838975\\\\\n", "\t 0.57448000\\\\\n", "\t 0.44272956\\\\\n", "\t 0.53810913\\\\\n", "\t 0.67301374\\\\\n", "\t 0.30994720\\\\\n", "\t 0.48864363\\\\\n", "\t 0.65195421\\\\\n", "\t 0.93939365\\\\\n", "\t 0.19348394\\\\\n", "\t 0.45197044\\\\\n", "\\end{tabular}\n", "\n", "\\item[\\$yName] A matrix: 1 × 1 of type chr\n", "\\begin{tabular}{l}\n", "\t debtratio\\\\\n", "\\end{tabular}\n", "\n", "\\item[\\$Description] A matrix: 1 × 1 of type chr\n", "\\begin{tabular}{l}\n", "\t Rajan-Zingales JF 1995 data, but with definitions from Cook, Kieschnick and McCullough, Journal of Empirical Finance 2008\\\\\n", "\\end{tabular}\n", "\n", "\\item[\\$XName] A matrix: 1 × 5\n", "\\begin{tabular}{lllll}\n", "\t const & tang & mbtr & logsale & profit\\\\\n", "\\end{tabular}\n", "\n", "\\end{description}\n" ], "text/markdown": [ "$X\n", ": \n", "A matrix: 4405 × 5 of type dbl\n", "\n", "| 1 | 0.15283128 | 1.0452750 | 6.046561 | 0.08774734 |\n", "| 1 | 0.59341052 | 1.6479298 | 3.939774 | 0.18836954 |\n", "| 1 | 0.24067336 | 2.8577454 | 5.757311 | 0.24105133 |\n", "| 1 | 0.37908270 | 0.6859607 | 4.942442 | 0.10643459 |\n", "| 1 | 0.22463373 | 0.8153565 | 3.644353 | 0.10099751 |\n", "| 1 | 0.28268554 | 2.0006900 | 5.687355 | 0.10290431 |\n", "| 1 | 0.16413260 | 0.8567230 | 7.444632 | 0.07588428 |\n", "| 1 | 0.64726939 | 1.0547738 | 6.007547 | 0.10247049 |\n", "| 1 | 0.10341836 | 0.8760556 | 6.427695 | -0.04920358 |\n", "| 1 | 0.12944697 | 2.0706480 | 6.069407 | 0.14460620 |\n", "| 1 | 0.10986147 | 1.4878637 | 6.850210 | 0.18828204 |\n", "| 1 | 0.22716095 | 0.8828160 | 4.145006 | 0.10592493 |\n", "| 1 | 0.10519763 | 3.6674837 | 2.216809 | 0.41223156 |\n", "| 1 | 0.88186163 | 1.0194852 | 4.973619 | 0.31369309 |\n", "| 1 | 0.44649656 | 4.1762986 | 8.968512 | 0.31245069 |\n", "| 1 | 0.68209559 | 0.9098037 | 4.418684 | 0.08361230 |\n", "| 1 | 0.09845585 | 1.2532836 | 4.988076 | 0.12320417 |\n", "| 1 | 0.30317060 | 0.8965770 | 5.179500 | 0.10322459 |\n", "| 1 | 0.27682052 | 1.2650914 | 4.286548 | 0.11287619 |\n", "| 1 | 0.19914411 | 1.0042223 | 3.970990 | 0.05652562 |\n", "| 1 | 0.21840969 | 0.9008426 | 4.563952 | 0.08914537 |\n", "| 1 | 0.36891574 | 0.7764582 | 1.599792 | 0.04900973 |\n", "| 1 | 0.08318394 | 16.7516680 | 2.215719 | 0.03011832 |\n", "| 1 | 0.07090499 | 3.2940323 | 4.797549 | 0.19508910 |\n", "| 1 | 0.30679425 | 0.9037179 | 4.584620 | 0.05960136 |\n", "| 1 | 0.13470736 | 1.2151619 | 6.310098 | 0.05064859 |\n", "| 1 | 0.04878252 | 1.0363831 | 6.610284 | 0.10701220 |\n", "| 1 | 0.43966075 | 2.0306009 | 4.536859 | 0.28045713 |\n", "| 1 | 0.10106310 | 0.7912812 | 3.201648 | 0.12439597 |\n", "| 1 | 0.16227839 | 0.8357908 | 3.221033 | 0.06801826 |\n", "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n", "| 1 | 0.23163057 | 1.4541071 | 1.5096176 | -0.45443400 |\n", "| 1 | 0.01412514 | 1.9036772 | 4.4757333 | 0.10464538 |\n", "| 1 | 0.10329101 | 3.1218296 | 3.6441697 | 0.20205401 |\n", "| 1 | 0.06915715 | 2.6923948 | 3.2967624 | 0.44818360 |\n", "| 1 | 0.24796527 | 4.5749457 | 0.4187103 | -0.25908844 |\n", "| 1 | 0.06344315 | 4.3150962 | 4.2255336 | 0.17914748 |\n", "| 1 | 0.06135137 | 3.0522308 | 3.3308107 | 0.24496727 |\n", "| 1 | 0.04014119 | 4.1432166 | 2.8499548 | 0.14002478 |\n", "| 1 | 0.01068510 | 4.4860544 | 1.1749559 | 0.10708674 |\n", "| 1 | 0.07208257 | 6.1268956 | 3.6317269 | 0.11618639 |\n", "| 1 | 0.02340338 | 4.1160044 | 2.0272268 | 0.13732224 |\n", "| 1 | 0.36795217 | 3.1915709 | 3.2789544 | 0.10186379 |\n", "| 1 | 0.33090041 | 0.8523913 | 6.4572217 | 0.05928239 |\n", "| 1 | 0.30685709 | 1.4329358 | 4.5266571 | 0.23338493 |\n", "| 1 | 0.31943752 | 1.6375911 | 7.9338800 | 0.16894862 |\n", "| 1 | 0.66418733 | 1.2743732 | 9.3798299 | 0.12295684 |\n", "| 1 | 0.24086422 | 0.9936688 | 9.4355879 | 0.04494273 |\n", "| 1 | 0.69266564 | 0.9936683 | 6.1993483 | 0.17269039 |\n", "| 1 | 0.89899956 | 0.1482478 | 5.6734504 | 0.04860495 |\n", "| 1 | 0.93314590 | 0.3128039 | 6.0399737 | 0.07985335 |\n", "| 1 | 0.70725370 | 0.9642831 | 7.9493340 | 0.18984009 |\n", "| 1 | 0.60640193 | 1.4674369 | 6.7021385 | 0.09358254 |\n", "| 1 | 0.71417070 | 1.3699697 | 5.3074902 | 0.20615239 |\n", "| 1 | 0.12461827 | 1.1742563 | 10.2837032 | 0.07930871 |\n", "| 1 | 0.35718510 | 1.8335471 | 6.7345917 | 0.17267889 |\n", "| 1 | 0.54307637 | 1.1319547 | 5.1073566 | 0.08069173 |\n", "| 1 | 0.49945522 | 0.7121362 | 6.1899834 | 0.04285418 |\n", "| 1 | 0.95506871 | 0.7826259 | 4.3393540 | 0.05073775 |\n", "| 1 | 0.27126570 | 1.4070131 | 4.5467993 | 0.02018504 |\n", "| 1 | 0.55291384 | 1.2432457 | 7.7006338 | 0.09380608 |\n", "\n", "\n", "$y\n", ": \n", "A matrix: 4405 × 1 of type dbl\n", "\n", "| 0.48061406 |\n", "| 0.49789298 |\n", "| 0.08513211 |\n", "| 0.72337394 |\n", "| 0.87229293 |\n", "| 0.25224938 |\n", "| 0.65029221 |\n", "| 0.82784745 |\n", "| 0.96586876 |\n", "| 0.29379814 |\n", "| 0.25159485 |\n", "| 0.69433447 |\n", "| 0.08579703 |\n", "| 0.52781013 |\n", "| 0.12396548 |\n", "| 0.82881142 |\n", "| 0.25703244 |\n", "| 0.52647353 |\n", "| 0.47460682 |\n", "| 0.56260611 |\n", "| 0.69551821 |\n", "| 0.27355197 |\n", "| 0.05781200 |\n", "| 0.13346465 |\n", "| 0.57969422 |\n", "| 0.69786571 |\n", "| 0.23100059 |\n", "| 0.13815073 |\n", "| 0.32689204 |\n", "| 0.79901292 |\n", "| ⋮ |\n", "| 0.14383677 |\n", "| 0.30137774 |\n", "| 0.32939544 |\n", "| 0.68419846 |\n", "| 0.07815812 |\n", "| 0.06176844 |\n", "| 0.07326558 |\n", "| 0.04638021 |\n", "| 0.03154198 |\n", "| 0.04460804 |\n", "| 0.01265112 |\n", "| 0.18559067 |\n", "| 0.79289621 |\n", "| 0.36263210 |\n", "| 0.33696546 |\n", "| 0.60398084 |\n", "| 0.65082213 |\n", "| 0.56969612 |\n", "| 0.43006658 |\n", "| 0.18838975 |\n", "| 0.57448000 |\n", "| 0.44272956 |\n", "| 0.53810913 |\n", "| 0.67301374 |\n", "| 0.30994720 |\n", "| 0.48864363 |\n", "| 0.65195421 |\n", "| 0.93939365 |\n", "| 0.19348394 |\n", "| 0.45197044 |\n", "\n", "\n", "$yName\n", ": \n", "A matrix: 1 × 1 of type chr\n", "\n", "| debtratio |\n", "\n", "\n", "$Description\n", ": \n", "A matrix: 1 × 1 of type chr\n", "\n", "| Rajan-Zingales JF 1995 data, but with definitions from Cook, Kieschnick and McCullough, Journal of Empirical Finance 2008 |\n", "\n", "\n", "$XName\n", ": \n", "A matrix: 1 × 5\n", "\n", "| const | tang | mbtr | logsale | profit |\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "$X\n", " [,1] [,2] [,3] [,4] [,5]\n", " [1,] 1 0.1528312816 1.0452750 6.046560975 8.774734e-02\n", " [2,] 1 0.5934105203 1.6479298 3.939774350 1.883695e-01\n", " [3,] 1 0.2406733621 2.8577454 5.757310603 2.410513e-01\n", " [4,] 1 0.3790827025 0.6859607 4.942442103 1.064346e-01\n", " [5,] 1 0.2246337282 0.8153565 3.644352689 1.009975e-01\n", " [6,] 1 0.2826855409 2.0006900 5.687354945 1.029043e-01\n", " [7,] 1 0.1641325993 0.8567230 7.444631617 7.588428e-02\n", " [8,] 1 0.6472693865 1.0547738 6.007547028 1.024705e-01\n", " [9,] 1 0.1034183561 0.8760556 6.427694849 -4.920358e-02\n", " [10,] 1 0.1294469738 2.0706480 6.069406588 1.446062e-01\n", " [11,] 1 0.1098614740 1.4878637 6.850209849 1.882820e-01\n", " [12,] 1 0.2271609521 0.8828160 4.145005990 1.059249e-01\n", " [13,] 1 0.1051976346 3.6674837 2.216809316 4.122316e-01\n", " [14,] 1 0.8818616342 1.0194852 4.973618550 3.136931e-01\n", " [15,] 1 0.4464965606 4.1762986 8.968512348 3.124507e-01\n", " [16,] 1 0.6820955863 0.9098037 4.418683969 8.361230e-02\n", " [17,] 1 0.0984558515 1.2532836 4.988076071 1.232042e-01\n", " [18,] 1 0.3031706040 0.8965770 5.179500046 1.032246e-01\n", " [19,] 1 0.2768205208 1.2650914 4.286547691 1.128762e-01\n", " [20,] 1 0.1991441060 1.0042223 3.970989783 5.652562e-02\n", " [21,] 1 0.2184096863 0.9008426 4.563952280 8.914537e-02\n", " [22,] 1 0.3689157435 0.7764582 1.599791535 4.900973e-02\n", " [23,] 1 0.0831839369 16.7516680 2.215719160 3.011832e-02\n", " [24,] 1 0.0709049938 3.2940323 4.797549329 1.950891e-01\n", " [25,] 1 0.3067942477 0.9037179 4.584620480 5.960136e-02\n", " [26,] 1 0.1347073599 1.2151619 6.310098262 5.064859e-02\n", " [27,] 1 0.0487825220 1.0363831 6.610284116 1.070122e-01\n", " [28,] 1 0.4396607489 2.0306009 4.536859225 2.804571e-01\n", " [29,] 1 0.1010630954 0.7912812 3.201648279 1.243960e-01\n", " [30,] 1 0.1622783921 0.8357908 3.221033495 6.801826e-02\n", " [31,] 1 0.1657467360 0.7174072 3.120424718 -1.836323e-02\n", " [32,] 1 0.4787551922 1.3814365 7.322833367 2.915955e-01\n", " [33,] 1 0.4089845336 1.0886473 5.423905419 -1.925939e-02\n", " [34,] 1 0.3727144000 0.9929970 3.385372831 2.334544e-01\n", " [35,] 1 0.4843575256 3.3312814 6.025757272 2.285542e-01\n", " [36,] 1 0.5149931730 1.2677765 4.160771923 8.103140e-02\n", " [37,] 1 0.6112966426 1.7129220 8.076297777 1.807596e-01\n", " [38,] 1 0.7848746179 0.9403037 2.429217744 1.225986e-01\n", " [39,] 1 0.9502898729 1.1588416 -3.688879454 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4.814474414 2.446924e-01\n", "[1887,] 1 0.3464325268 0.8507030 5.127451937 1.155182e-01\n", "[1888,] 1 0.1772935245 0.9122054 5.502808029 1.541528e-01\n", "[1889,] 1 0.1885087719 2.6027193 3.062034863 2.486842e-01\n", "[1890,] 1 0.5009180753 1.9233880 3.551540823 1.319058e-01\n", "[1891,] 1 0.0683297180 0.6827549 1.137512438 1.822126e-01\n", "[1892,] 1 0.1582965525 1.2615412 1.764902015 8.588811e-03\n", "[1893,] 1 0.0440121311 1.1993676 3.280648023 8.876396e-02\n", "[1894,] 1 0.1150015707 1.2275725 7.704691141 1.205393e-01\n", "[1895,] 1 0.4602671496 1.0386103 2.851746423 7.312656e-02\n", "[1896,] 1 0.4473355659 1.0373464 7.588673996 1.526870e-01\n", "[1897,] 1 0.2062274368 0.7554095 5.219398610 -3.025722e-02\n", "[1898,] 1 0.7111433676 0.9991905 4.207405355 6.138343e-02\n", "[1899,] 1 0.1630282239 4.8776898 2.322191623 -2.990020e-01\n", "[1900,] 1 0.6805299471 0.9957325 5.533456680 -3.965186e-05\n", "[1901,] 1 0.1188888382 1.1666976 7.071694749 1.150053e-01\n", "[1902,] 1 0.0252656434 4.6660725 0.754242280 1.617473e-01\n", "[1903,] 1 0.2146222594 0.9190144 4.888422461 6.706460e-02\n", "[1904,] 1 0.2707945483 1.0383505 6.283299858 8.574934e-02\n", "[1905,] 1 0.0091934709 0.9150625 5.719193118 1.865231e-02\n", "[1906,] 1 0.3746805987 1.5275312 6.487738812 1.940581e-01\n", "[1907,] 1 0.3505387978 1.3699372 6.558502721 1.850588e-01\n", "[1908,] 1 0.1182705407 1.1070781 6.342651208 6.026940e-02\n", "[1909,] 1 0.2996879008 1.5351933 6.167969219 1.533140e-01\n", "[1910,] 1 0.2040748410 1.0004622 4.552908001 1.758061e-01\n", "[1911,] 1 0.1204928564 2.0350622 6.612137804 2.189847e-01\n", "[1912,] 1 0.3524508584 1.7681373 7.704225902 1.682010e-01\n", "[1913,] 1 0.2207975201 2.9081302 2.048466737 -3.254896e-02\n", "[1914,] 1 0.1439809086 2.1162056 1.761472068 -3.950857e-02\n", "[1915,] 1 0.2215794204 1.9926421 3.494596646 1.668164e-01\n", "[1916,] 1 0.3115271563 1.2150793 5.194483463 1.281957e-01\n", "[1917,] 1 0.0582135178 0.8863301 4.717203390 4.394536e-02\n", "[1918,] 1 0.1644189668 1.5032763 4.797054210 2.319659e-01\n", "[1919,] 1 0.4502411142 1.3967631 5.039954585 1.356942e-01\n", "[1920,] 1 0.5652686145 1.2175037 6.077100812 1.782180e-01\n", "[1921,] 1 0.0541591358 0.9909472 4.775225148 1.011517e-01\n", "[1922,] 1 0.0760733961 1.4284832 5.557835276 1.428752e-01\n", "[1923,] 1 0.1146261023 2.6445869 6.531406642 1.346383e-01\n", "[1924,] 1 0.3877231732 0.8144086 3.289110592 -2.136021e-01\n", "[1925,] 1 0.3128280301 0.9590900 5.582657611 4.788385e-02\n", "[1926,] 1 0.5368720649 1.0071343 8.616259943 6.671410e-02\n", "[1927,] 1 0.3961834555 1.1842110 8.519107558 1.700338e-01\n", "[1928,] 1 0.2366425428 0.9637535 7.327443425 8.745530e-02\n", "[1929,] 1 0.1989395996 1.8245772 6.186755799 2.144753e-01\n", "[1930,] 1 0.4950671454 0.9171977 6.875010988 1.158726e-01\n", "[1931,] 1 0.0466672229 3.3117771 6.373193502 3.171327e-01\n", "[1932,] 1 0.2537146688 2.2815560 2.210579447 2.155066e-01\n", "[1933,] 1 0.1752979969 5.8713790 6.167629692 2.500441e-01\n", "[1934,] 1 0.1833304910 0.8941891 4.708529701 -1.061999e-02\n", "[1935,] 1 0.3030738294 1.0910899 0.484276289 -2.642919e-02\n", "[1936,] 1 0.2029230583 2.3965435 5.050336661 3.224464e-01\n", "[1937,] 1 0.1424799248 0.6307603 4.060684361 1.169066e-01\n", "[1938,] 1 0.1958212379 1.4220169 4.524859943 -8.672903e-03\n", "[1939,] 1 0.0583060365 2.7153393 3.608076041 8.606458e-02\n", "[1940,] 1 0.5290382614 1.4395427 6.114387683 1.212270e-01\n", "[1941,] 1 0.4080532483 0.9975796 3.776730062 5.863909e-02\n", "[1942,] 1 0.3296311363 0.8189112 7.532806150 1.227289e-01\n", "[1943,] 1 0.1727090836 1.1933974 4.225796711 1.061224e-01\n", "[1944,] 1 0.4763713453 1.5882284 4.126230751 1.975385e-01\n", "[1945,] 1 0.6158787679 1.1784820 9.062536177 3.640257e-02\n", "[1946,] 1 0.0747333691 1.2913757 2.080690761 1.143252e-01\n", "[1947,] 1 0.2965960750 1.5127378 7.422194366 1.586096e-01\n", "[1948,] 1 0.6747895174 0.7625030 1.370926884 -3.794616e-03\n", "[1949,] 1 0.6355441082 0.8978896 5.419326304 1.559482e-02\n", "[1950,] 1 0.7096765632 1.0132540 2.559240787 -7.579001e-02\n", "[1951,] 1 0.2242613878 0.9510668 7.360943951 1.114150e-01\n", "[1952,] 1 0.2269312720 1.3959485 5.496840303 1.521183e-01\n", "[1953,] 1 0.1619857726 1.0460986 5.444160779 -1.320795e-02\n", "[1954,] 1 0.1798492418 1.1983135 7.019749145 1.543774e-01\n", "[1955,] 1 0.3538968724 1.3732693 9.271651911 1.384261e-01\n", "[1956,] 1 0.4896593537 2.6025438 5.784790870 2.502227e-01\n", "[1957,] 1 0.2065396786 2.4382546 4.857219899 2.310604e-01\n", "[1958,] 1 0.0842445146 2.4025097 3.258673295 1.677919e-01\n", "[1959,] 1 0.3251546252 0.8520515 3.084109384 -1.518385e-01\n", "[1960,] 1 0.2432902105 1.0862643 2.809944717 -8.824097e-02\n", "[1961,] 1 0.3897062975 4.5860447 5.417317439 3.270818e-01\n", "[1962,] 1 0.3631718307 2.2051842 3.711887722 1.449807e-01\n", "[1963,] 1 0.1273868368 0.8993375 4.854153009 1.528806e-01\n", "[1964,] 1 0.6576818331 1.0517355 2.922085733 9.739333e-02\n", "[1965,] 1 0.8031914894 1.6589096 1.401429249 3.065160e-01\n", "[1966,] 1 0.0509501188 1.4730602 3.740831282 -3.052257e-02\n", "[1967,] 1 0.1532801994 1.1610592 5.843370489 1.043849e-01\n", "[1968,] 1 0.1125864454 1.1783541 3.275671951 -1.166667e-01\n", "[1969,] 1 0.5890939918 0.9709109 5.767142176 1.634872e-01\n", "[1970,] 1 0.2509335623 2.8702798 4.623108331 2.240949e-01\n", "[1971,] 1 0.1541642056 3.3524316 1.978100714 -4.400473e-02\n", "[1972,] 1 0.2088126190 3.3345240 5.348145088 1.838476e-01\n", "[1973,] 1 0.3678046349 2.3909793 7.642524134 2.178990e-01\n", "[1974,] 1 0.1931928687 8.5481323 0.803345973 2.197731e-01\n", "[1975,] 1 0.3190911228 2.6696998 9.092995553 1.768020e-01\n", "[1976,] 1 0.1742657304 1.7400620 4.494138062 1.078538e-01\n", "[1977,] 1 0.2679702650 1.8819570 4.090102243 -3.267615e-01\n", "[1978,] 1 0.0916775510 3.0032568 6.344772830 2.113039e-01\n", "[1979,] 1 0.4379306424 1.3227724 4.776565603 1.300891e-01\n", "[1980,] 1 0.3835489787 1.3036526 8.299422431 1.484599e-01\n", "[1981,] 1 0.0287703087 1.3294280 4.616644108 5.205405e-02\n", "[1982,] 1 0.3872972319 1.1364700 3.392560188 9.543217e-02\n", "[1983,] 1 0.9440235690 0.6800821 -1.087672349 -1.683502e-03\n", "[1984,] 1 0.4241480396 1.4032155 9.025335218 1.751557e-01\n", "[1985,] 1 0.1537035607 1.5772766 3.696177757 1.326948e-01\n", "[1986,] 1 0.0150313647 0.4517103 2.698336509 5.681146e-02\n", "[1987,] 1 0.2750585608 1.2113431 4.854869978 1.107888e-01\n", "[1988,] 1 0.3667750426 4.8058846 2.158137189 -1.259225e-02\n", "[1989,] 1 0.0981976383 0.7998757 0.886655927 -2.175264e-02\n", "[1990,] 1 0.1223309609 1.8669929 0.852711750 1.089858e-01\n", "[1991,] 1 0.1538054130 0.9669830 5.766954437 1.162285e-01\n", "[1992,] 1 0.4241154375 7.2821467 6.528123109 5.643210e-01\n", "[1993,] 1 0.3031948388 1.2052728 7.619192669 1.066019e-01\n", "[1994,] 1 0.3873803329 4.6512162 5.170614668 2.720958e-01\n", "[1995,] 1 0.1677954128 0.9839290 8.464311900 1.352556e-01\n", "[1996,] 1 0.2995262301 1.3032631 4.874181540 1.323100e-01\n", "[1997,] 1 0.3473243113 1.3683528 5.694166388 1.534123e-01\n", "[1998,] 1 0.1733854381 0.8869021 1.802369648 1.162073e-01\n", "[1999,] 1 0.1730484227 1.2278775 4.294192192 1.434534e-01\n", "[2000,] 1 0.1960179506 0.8427850 5.191004947 1.059156e-01\n", "[2001,] 1 0.0064920528 2.6134990 2.951989351 2.010298e-01\n", "[2002,] 1 0.0424724784 1.1234938 2.675940147 1.277893e-01\n", "[2003,] 1 0.3669849932 1.2393148 4.590604621 1.744094e-01\n", "[2004,] 1 0.0336091003 0.4783914 0.842428883 1.949328e-01\n", "[2005,] 1 0.1145658733 0.5679894 4.134254141 1.319980e-03\n", "[2006,] 1 0.2209141871 1.0465502 4.596583425 8.587753e-02\n", "[2007,] 1 0.0777176586 1.0308387 1.837210732 2.242991e-01\n", "[2008,] 1 0.2273570898 1.2668662 2.103402179 1.185969e-01\n", "[2009,] 1 0.2993695531 0.9951048 7.137675815 1.132023e-01\n", "[2010,] 1 0.1405230963 0.9970441 4.571210012 1.426933e-01\n", "[2011,] 1 0.3081240896 1.1025179 7.167996522 1.442029e-01\n", "[2012,] 1 0.3769616027 0.8169032 1.191799003 1.068447e-01\n", "[2013,] 1 0.1732084745 0.7196570 4.158508013 7.896659e-02\n", "[2014,] 1 0.5495412137 1.2730049 5.632751814 1.152518e-01\n", "[2015,] 1 0.0896015870 1.5590042 1.932245787 -1.075660e-01\n", "[2016,] 1 0.2067191874 1.4479067 7.968180986 1.267661e-01\n", "[2017,] 1 0.3731172805 1.5284588 6.340592081 1.512045e-01\n", "[2018,] 1 0.1776083318 1.1645550 2.862772146 8.062116e-02\n", "[2019,] 1 0.1243593202 1.1960615 2.266439646 5.853790e-02\n", "[2020,] 1 0.2688471539 1.8321372 5.555127775 1.171921e-01\n", "[2021,] 1 0.1190488145 2.1577886 5.370731047 1.600305e-01\n", "[2022,] 1 0.2069587075 1.1126550 7.905877763 4.117299e-02\n", "[2023,] 1 0.5547705964 0.6155422 3.010522359 -7.957560e-02\n", "[2024,] 1 0.5393473307 1.0566406 3.024028143 5.509440e-02\n", "[2025,] 1 0.8657404330 0.7009811 2.255807901 4.227880e-02\n", "[2026,] 1 0.2654121612 1.0460873 3.273932031 1.172588e-01\n", "[2027,] 1 0.0327628912 0.8886914 5.126983226 7.856982e-02\n", "[2028,] 1 0.3522033057 1.9792335 5.370000616 2.070658e-01\n", "[2029,] 1 0.3444283647 1.2634256 9.483340186 8.128316e-02\n", "[2030,] 1 0.0469018933 1.3568725 2.818995095 -4.790591e-02\n", "[2031,] 1 0.4013707692 1.3544789 6.272086128 1.273579e-01\n", "[2032,] 1 0.3018307768 0.9640184 2.124295397 1.673676e-01\n", "[2033,] 1 0.1419514458 1.6489099 3.034039108 6.394000e-02\n", "[2034,] 1 0.1531948882 2.1166134 2.169510739 8.817891e-02\n", "[2035,] 1 0.4443076455 0.8205886 6.852713917 3.159683e-02\n", "[2036,] 1 0.5857956294 1.2111108 10.513579158 9.929978e-02\n", "[2037,] 1 0.2505095109 1.5593899 2.729550733 2.939029e-01\n", "[2038,] 1 0.5060136108 0.9200026 6.407109333 8.471840e-02\n", "[2039,] 1 0.4113789778 1.3672475 8.914626128 1.986500e-01\n", "[2040,] 1 0.1459746534 2.3706115 3.479977754 3.901592e-01\n", "[2041,] 1 0.5613289082 1.1036853 5.795741591 2.053792e-01\n", "[2042,] 1 0.2329042062 0.9247964 3.760339280 6.302148e-02\n", "[2043,] 1 0.0644532526 1.0778930 9.029321966 7.975804e-02\n", "[2044,] 1 0.2298539114 1.1549982 3.352007137 1.833176e-01\n", "[2045,] 1 0.2679114431 1.3960554 6.853269526 5.251710e-02\n", "[2046,] 1 0.6222516556 1.0695188 1.878395776 1.006623e-01\n", "[2047,] 1 0.3375909698 1.7673028 2.135940016 -1.090153e-01\n", "[2048,] 1 0.0498217001 2.0042453 3.823803626 1.863456e-02\n", "[2049,] 1 0.2651041168 1.6856502 6.957569680 1.544506e-01\n", "[2050,] 1 0.2710075971 0.8702862 6.042048340 1.026242e-01\n", "[2051,] 1 0.4481913810 1.3595445 5.550794452 2.374743e-01\n", "[2052,] 1 0.5755997475 0.9504852 1.476363558 9.075126e-02\n", "[2053,] 1 0.0814432658 2.2227869 5.611093191 1.440529e-01\n", "[2054,] 1 0.0766475645 10.3197230 1.306439136 -9.789876e-03\n", "[2055,] 1 0.2745776772 1.3741893 6.606289310 3.111840e-01\n", "[2056,] 1 0.4138475064 0.7506356 4.072286393 8.356918e-02\n", "[2057,] 1 0.1814964783 1.3794362 6.012217703 1.151791e-01\n", "[2058,] 1 0.2263787593 0.6172816 3.762733823 3.880193e-02\n", "[2059,] 1 0.1975150587 1.6903163 5.271860448 2.121225e-01\n", "[2060,] 1 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0.792896207\n", "[4389,] 0.362632102\n", "[4390,] 0.336965464\n", "[4391,] 0.603980842\n", "[4392,] 0.650822130\n", "[4393,] 0.569696125\n", "[4394,] 0.430066579\n", "[4395,] 0.188389753\n", "[4396,] 0.574480004\n", "[4397,] 0.442729561\n", "[4398,] 0.538109128\n", "[4399,] 0.673013735\n", "[4400,] 0.309947196\n", "[4401,] 0.488643634\n", "[4402,] 0.651954208\n", "[4403,] 0.939393651\n", "[4404,] 0.193483942\n", "[4405,] 0.451970443\n", "\n", "$yName\n", " [,1] \n", "[1,] \"debtratio\"\n", "\n", "$Description\n", " [,1] \n", "[1,] \"Rajan-Zingales JF 1995 data, but with definitions from Cook, Kieschnick and McCullough, Journal of Empirical Finance 2008\"\n", "\n", "$XName\n", " [,1] [,2] [,3] [,4] [,5] \n", "[1,] list,1 list,1 list,1 list,1 list,1\n", "\n", "attr(,\"header\")\n", "attr(,\"header\")$description\n", "[1] \"MATLAB 5.0 MAT-file, Platform: GLNX86, Created on: Mon Oct 11 15:08:10 2010 \"\n", "\n", "attr(,\"header\")$version\n", "[1] \"5\"\n", "\n", "attr(,\"header\")$endian\n", "[1] \"little\"\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# install.packages(R.matlab)\n", "library(\"R.matlab\")\n", "readMat(\"data/RajanData.mat\")" ] }, { "cell_type": "markdown", "id": "50a03a62", "metadata": {}, "source": [ "## Minitab, S-PLUS, SAS, SPSS, Stata, Systat\n", "\n", "The recommended R package `foreign` provides import facilities for files\n", "produced by these statistical systems.\n", "\n", "- Function `read.xport()` reads a file in SAS Transport (XPORT) format\n", " and return a list of data frames.\n", "\n", "- Function `read.mtp()` imports a 'Minitab Portable Worksheet'. This\n", " returns the components of the worksheet as an R list\n", "\n", "- Function `read.spss()` can read files created by the 'save' and\n", " 'export' commands in SPSS\n", "\n", "- Files from Stata can be read and written by functions `read.dta()`\n", " and `write.dta()`." ] }, { "cell_type": "markdown", "id": "e5921b5d", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Images\n", "\n", "- JPEG Format\n", "\n", " install.packages(\"jpeg\")\n", " require(\"jpeg\")\n", " readJPEG()\n", " rasterImage()\n", "\n", "- Packages **bmp**, **jpeg** and **png** read the formats after which\n", " they are named. See also packages **biOps** and **Momocs**, and\n", " Bioconductor package **EBImage**." ] }, { "cell_type": "code", "execution_count": 13, "id": "59237f0f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
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  3. 3
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b371j9SdZReYpVoNG9Z0xpN1vpLVA6LsJpoAqGlGW8wxJVB1JnWZbJloKCfqGKx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\ngw+qqqpQnQHgW9/61pYtW8aPH3/33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rgffvhhufo3iaf70keUtynIVHSWwHUtx5k+ohxGlLe9/xAgcVacZoyNnZk7hH0l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\nuZx+bXL7ns34LLAPRgyyBraFd2+B0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KRuHE/IqShgnFfkEsNj7HSzf/IEnR0F1zVWDwx7ioMv9mZXZLHtjFwfQcB8HfH9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\nK+7AtJneYKtIM3qwi2P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HMMpatOcGLIgc/KjC+8RzTM4/uHFglni6760Se447Eb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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 180, "width": 240 } }, "output_type": "display_data" } ], "source": [ "# install.packages(\"jpeg\")\n", "library(\"jpeg\")\n", " dim(myprofile)\n", "image(1:1539, 1:1154, myprofile[, , 1])\n", "image(1:1539, 1:1154, myprofile[, , 2])\n", "image(1:1539, 1:1154, myprofile[, , 3])" ] }, { "cell_type": "code", "execution_count": 14, "id": "7c70daed", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "image/png": 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68EBkDo0CwlYY+SAIDQi5llYOOyv/bfKGWtG6eANXukoIZbmbwB6swJfJqevLZb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\nIMOgpzfreHnLmBEjjckLBZmmlX//8W+UkhzAusr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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 180, "width": 240 } }, "output_type": "display_data" } ], "source": [ "plot(c(100, 250), c(300, 450), type = \"n\", xlab = \"\", ylab = \"\")\n", "rasterImage(myprofile, 100, 300, 150, 350, interpolate = FALSE)\n", "rasterImage(myprofile, 100, 400, 150, 450)\n", "rasterImage(myprofile, 200, 300, 200 + xinch(.5),\n", " 300 + yinch(.3), interpolate = FALSE)\n", "rasterImage(myprofile, 200, 400, 250, 450,\n", " angle = 15, interpolate = FALSE)" ] }, { "cell_type": "markdown", "id": "93234bdf", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## R and Database\n", "\n", "- SQL has limited numerical and statistical features. For example, it\n", " has no least squares fitting procedures, and to find quantiles\n", " requires a sophisticated query.\n", "\n", "- Not only are basic statistical functions missing from SQL, but in\n", " many cases the numerical algorithms used in the basic aggregate\n", " functions are not implemented to safeguard numerical accuracy.\n", "\n", "- For these reasons, it may be desirable or even necessary to perform\n", " a statistical analysis in a statistical package rather than in the\n", " database. One way to do this, is to extract the data from the\n", " database and import it into statistical software." ] }, { "cell_type": "markdown", "id": "4e876b30", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "- The `RODBC` package provides access to databases (including\n", " Microsoft Access and Microsoft SQL Server) through an ODBC\n", " interface.\n", "\n", " odbcConnect(dsn, uid=\"\", pwd=\"\")\n", " sqlFetch(channel, sqtable)\n", " sqlQuery(channel, query)\n", " sqlSave(channel, mydf, tablename = sqtable, append = FALSE)\n", " sqlDrop(channel, sqtable)\n", "\n", "- Use the `RODBC` package to read Oracle Database.\n", "\n", "- The `DBI` package in R provides a uniform, client- side interface\n", " to different database management systems, such as MySQL, PostgreSQL,\n", " and Oracle. \n", "\n", "- The `RMySQL` package provides an interface to MySQL.\n", "\n", "- The `ROracle` package provides an interface for Oracle.\n", "\n", "- The `RJDBC` package provides access to databases through a JDBC\n", " interface." ] }, { "cell_type": "markdown", "id": "2fde12d9", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Further Suggested Read\n", "\n", "- R Data Import/Export\n", "\n", " [http://cran.r-project.org/doc/manuals/r-release/R-data.pdf](http://cran.r-project.org/doc/manuals/r-release/R-data.pdf)" ] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "4.3.2" } }, "nbformat": 4, "nbformat_minor": 5 }