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xgao32/Calculating Means in R.ipynb

Created August 26, 2016 21:56
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Calculating Means in R.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# First we get our data.\n",
"mydata <- read.table(\"usparty.txt\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<ol class=list-inline>\n",
"\t<li>'YEAR'</li>\n",
"\t<li>'RCONG'</li>\n",
"\t<li>'DCONG'</li>\n",
"\t<li>'RPRES'</li>\n",
"\t<li>'DPRES'</li>\n",
"\t<li>'G'</li>\n",
"\t<li>'PRCONT'</li>\n",
"\t<li>'PDCONT'</li>\n",
"\t<li>'TOTPRES'</li>\n",
"\t<li>'TOTCONG'</li>\n",
"\t<li>'RREP'</li>\n",
"\t<li>'DREP'</li>\n",
"\t<li>'TR'</li>\n",
"\t<li>'INFLAT'</li>\n",
"\t<li>'PREVINFL'</li>\n",
"\t<li>'PREVGNP'</li>\n",
"\t<li>'GNP'</li>\n",
"\t<li>'TOTPOP18'</li>\n",
"\t<li>'ELIGIBLE'</li>\n",
"\t<li>'MRCONG'</li>\n",
"\t<li>'MDCONG'</li>\n",
"\t<li>'MRPRES'</li>\n",
"\t<li>'MDPRES'</li>\n",
"\t<li>'MTOTCONG'</li>\n",
"\t<li>'MTOTPRES'</li>\n",
"\t<li>'MTR'</li>\n",
"\t<li>'VRCONG'</li>\n",
"\t<li>'VDCONG'</li>\n",
"\t<li>'VRPRES'</li>\n",
"\t<li>'VDPRES'</li>\n",
"\t<li>'VTOTCONG'</li>\n",
"\t<li>'VTOTPRES'</li>\n",
"\t<li>'VTR'</li>\n",
"\t<li>'LMRCONG'</li>\n",
"\t<li>'LMDCONG'</li>\n",
"\t<li>'LRREP'</li>\n",
"\t<li>'LDREP'</li>\n",
"\t<li>'REPRATIO'</li>\n",
"\t<li>'DRATIO'</li>\n",
"\t<li>'RRATIO'</li>\n",
"\t<li>'REPSHIFT'</li>\n",
"\t<li>'MRATIO1'</li>\n",
"\t<li>'MRATIO2'</li>\n",
"\t<li>'ON'</li>\n",
"\t<li>'ONR1'</li>\n",
"\t<li>'ONR2'</li>\n",
"\t<li>'OND1'</li>\n",
"\t<li>'OND2'</li>\n",
"\t<li>'ON1'</li>\n",
"\t<li>'ON2'</li>\n",
"\t<li>'OFF'</li>\n",
"\t<li>'OFF1'</li>\n",
"\t<li>'OFF2'</li>\n",
"\t<li>'PRESDIF'</li>\n",
"\t<li>'RPRESDIF'</li>\n",
"\t<li>'LRPDIF'</li>\n",
"\t<li>'LPRESDIF'</li>\n",
"\t<li>'LMTCONG'</li>\n",
"\t<li>'DIFOYTC'</li>\n",
"\t<li>'SHIFT'</li>\n",
"\t<li>'FDR'</li>\n",
"\t<li>'CPOINT'</li>\n",
"\t<li>'CPCGNP'</li>\n",
"\t<li>'CGNP'</li>\n",
"\t<li>'LGNP'</li>\n",
"\t<li>'LINFLAT'</li>\n",
"</ol>\n"
],
"text/latex": [
"\\begin{enumerate*}\n",
"\\item 'YEAR'\n",
"\\item 'RCONG'\n",
"\\item 'DCONG'\n",
"\\item 'RPRES'\n",
"\\item 'DPRES'\n",
"\\item 'G'\n",
"\\item 'PRCONT'\n",
"\\item 'PDCONT'\n",
"\\item 'TOTPRES'\n",
"\\item 'TOTCONG'\n",
"\\item 'RREP'\n",
"\\item 'DREP'\n",
"\\item 'TR'\n",
"\\item 'INFLAT'\n",
"\\item 'PREVINFL'\n",
"\\item 'PREVGNP'\n",
"\\item 'GNP'\n",
"\\item 'TOTPOP18'\n",
"\\item 'ELIGIBLE'\n",
"\\item 'MRCONG'\n",
"\\item 'MDCONG'\n",
"\\item 'MRPRES'\n",
"\\item 'MDPRES'\n",
"\\item 'MTOTCONG'\n",
"\\item 'MTOTPRES'\n",
"\\item 'MTR'\n",
"\\item 'VRCONG'\n",
"\\item 'VDCONG'\n",
"\\item 'VRPRES'\n",
"\\item 'VDPRES'\n",
"\\item 'VTOTCONG'\n",
"\\item 'VTOTPRES'\n",
"\\item 'VTR'\n",
"\\item 'LMRCONG'\n",
"\\item 'LMDCONG'\n",
"\\item 'LRREP'\n",
"\\item 'LDREP'\n",
"\\item 'REPRATIO'\n",
"\\item 'DRATIO'\n",
"\\item 'RRATIO'\n",
"\\item 'REPSHIFT'\n",
"\\item 'MRATIO1'\n",
"\\item 'MRATIO2'\n",
"\\item 'ON'\n",
"\\item 'ONR1'\n",
"\\item 'ONR2'\n",
"\\item 'OND1'\n",
"\\item 'OND2'\n",
"\\item 'ON1'\n",
"\\item 'ON2'\n",
"\\item 'OFF'\n",
"\\item 'OFF1'\n",
"\\item 'OFF2'\n",
"\\item 'PRESDIF'\n",
"\\item 'RPRESDIF'\n",
"\\item 'LRPDIF'\n",
"\\item 'LPRESDIF'\n",
"\\item 'LMTCONG'\n",
"\\item 'DIFOYTC'\n",
"\\item 'SHIFT'\n",
"\\item 'FDR'\n",
"\\item 'CPOINT'\n",
"\\item 'CPCGNP'\n",
"\\item 'CGNP'\n",
"\\item 'LGNP'\n",
"\\item 'LINFLAT'\n",
"\\end{enumerate*}\n"
],
"text/markdown": [
"1. 'YEAR'\n",
"2. 'RCONG'\n",
"3. 'DCONG'\n",
"4. 'RPRES'\n",
"5. 'DPRES'\n",
"6. 'G'\n",
"7. 'PRCONT'\n",
"8. 'PDCONT'\n",
"9. 'TOTPRES'\n",
"10. 'TOTCONG'\n",
"11. 'RREP'\n",
"12. 'DREP'\n",
"13. 'TR'\n",
"14. 'INFLAT'\n",
"15. 'PREVINFL'\n",
"16. 'PREVGNP'\n",
"17. 'GNP'\n",
"18. 'TOTPOP18'\n",
"19. 'ELIGIBLE'\n",
"20. 'MRCONG'\n",
"21. 'MDCONG'\n",
"22. 'MRPRES'\n",
"23. 'MDPRES'\n",
"24. 'MTOTCONG'\n",
"25. 'MTOTPRES'\n",
"26. 'MTR'\n",
"27. 'VRCONG'\n",
"28. 'VDCONG'\n",
"29. 'VRPRES'\n",
"30. 'VDPRES'\n",
"31. 'VTOTCONG'\n",
"32. 'VTOTPRES'\n",
"33. 'VTR'\n",
"34. 'LMRCONG'\n",
"35. 'LMDCONG'\n",
"36. 'LRREP'\n",
"37. 'LDREP'\n",
"38. 'REPRATIO'\n",
"39. 'DRATIO'\n",
"40. 'RRATIO'\n",
"41. 'REPSHIFT'\n",
"42. 'MRATIO1'\n",
"43. 'MRATIO2'\n",
"44. 'ON'\n",
"45. 'ONR1'\n",
"46. 'ONR2'\n",
"47. 'OND1'\n",
"48. 'OND2'\n",
"49. 'ON1'\n",
"50. 'ON2'\n",
"51. 'OFF'\n",
"52. 'OFF1'\n",
"53. 'OFF2'\n",
"54. 'PRESDIF'\n",
"55. 'RPRESDIF'\n",
"56. 'LRPDIF'\n",
"57. 'LPRESDIF'\n",
"58. 'LMTCONG'\n",
"59. 'DIFOYTC'\n",
"60. 'SHIFT'\n",
"61. 'FDR'\n",
"62. 'CPOINT'\n",
"63. 'CPCGNP'\n",
"64. 'CGNP'\n",
"65. 'LGNP'\n",
"66. 'LINFLAT'\n",
"\n",
"\n"
],
"text/plain": [
" [1] \"YEAR\" \"RCONG\" \"DCONG\" \"RPRES\" \"DPRES\" \"G\" \n",
" [7] \"PRCONT\" \"PDCONT\" \"TOTPRES\" \"TOTCONG\" \"RREP\" \"DREP\" \n",
"[13] \"TR\" \"INFLAT\" \"PREVINFL\" \"PREVGNP\" \"GNP\" \"TOTPOP18\"\n",
"[19] \"ELIGIBLE\" \"MRCONG\" \"MDCONG\" \"MRPRES\" \"MDPRES\" \"MTOTCONG\"\n",
"[25] \"MTOTPRES\" \"MTR\" \"VRCONG\" \"VDCONG\" \"VRPRES\" \"VDPRES\" \n",
"[31] \"VTOTCONG\" \"VTOTPRES\" \"VTR\" \"LMRCONG\" \"LMDCONG\" \"LRREP\" \n",
"[37] \"LDREP\" \"REPRATIO\" \"DRATIO\" \"RRATIO\" \"REPSHIFT\" \"MRATIO1\" \n",
"[43] \"MRATIO2\" \"ON\" \"ONR1\" \"ONR2\" \"OND1\" \"OND2\" \n",
"[49] \"ON1\" \"ON2\" \"OFF\" \"OFF1\" \"OFF2\" \"PRESDIF\" \n",
"[55] \"RPRESDIF\" \"LRPDIF\" \"LPRESDIF\" \"LMTCONG\" \"DIFOYTC\" \"SHIFT\" \n",
"[61] \"FDR\" \"CPOINT\" \"CPCGNP\" \"CGNP\" \"LGNP\" \"LINFLAT\" "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"names(mydata) # Lets us see all the variable names.\n",
"#attach(mydata) # This puts the variable names in memory. We will not be using this."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
" YEAR MTOTCONG \n",
" Min. :1896 Min. :0.2994 \n",
" 1st Qu.:1919 1st Qu.:0.4130 \n",
" Median :1942 Median :0.4638 \n",
" Mean :1942 Mean :0.4677 \n",
" 3rd Qu.:1965 3rd Qu.:0.5404 \n",
" Max. :1988 Max. :0.7579 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mysubsetdata<-subset(mydata, select=c(YEAR, MTOTCONG)) #This keeps only the two variables that we need.\n",
"summary(mysubsetdata) # Since no variables are listed, a summary for all variables in the data frame is printed."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th></th><th scope=col>YEAR</th><th scope=col>MTOTCONG</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><th scope=row>1</th><td>1896</td><td>0.7579143</td></tr>\n",
"\t<tr><th scope=row>2</th><td>1898</td><td>0.5706568</td></tr>\n",
"\t<tr><th scope=row>3</th><td>1900</td><td>0.6483015</td></tr>\n",
"\t<tr><th scope=row>4</th><td>1902</td><td>0.4801695</td></tr>\n",
"\t<tr><th scope=row>5</th><td>1904</td><td>0.5435825</td></tr>\n",
"\t<tr><th scope=row>6</th><td>1906</td><td>0.4302022</td></tr>\n",
"\t<tr><th scope=row>7</th><td>1908</td><td>0.5456067</td></tr>\n",
"\t<tr><th scope=row>8</th><td>1910</td><td>0.4343085</td></tr>\n",
"\t<tr><th scope=row>9</th><td>1912</td><td>0.4872044</td></tr>\n",
"\t<tr><th scope=row>10</th><td>1914</td><td>0.4638365</td></tr>\n",
"\t<tr><th scope=row>11</th><td>1916</td><td>0.5471928</td></tr>\n",
"\t<tr><th scope=row>12</th><td>1918</td><td>0.4141644</td></tr>\n",
"\t<tr><th scope=row>13</th><td>1920</td><td>0.411758</td></tr>\n",
"\t<tr><th scope=row>14</th><td>1922</td><td>0.3224323</td></tr>\n",
"\t<tr><th scope=row>15</th><td>1924</td><td>0.4078648</td></tr>\n",
"\t<tr><th scope=row>16</th><td>1926</td><td>0.2994402</td></tr>\n",
"\t<tr><th scope=row>17</th><td>1928</td><td>0.4795689</td></tr>\n",
"\t<tr><th scope=row>18</th><td>1930</td><td>0.3382249</td></tr>\n",
"\t<tr><th scope=row>19</th><td>1932</td><td>0.4993569</td></tr>\n",
"\t<tr><th scope=row>20</th><td>1934</td><td>0.4155684</td></tr>\n",
"\t<tr><th scope=row>21</th><td>1936</td><td>0.5372502</td></tr>\n",
"\t<tr><th scope=row>22</th><td>1938</td><td>0.442021</td></tr>\n",
"\t<tr><th scope=row>23</th><td>1940</td><td>0.5561004</td></tr>\n",
"\t<tr><th scope=row>24</th><td>1942</td><td>0.3221306</td></tr>\n",
"\t<tr><th scope=row>25</th><td>1944</td><td>0.5012781</td></tr>\n",
"\t<tr><th scope=row>26</th><td>1946</td><td>0.3714887</td></tr>\n",
"\t<tr><th scope=row>27</th><td>1948</td><td>0.4821603</td></tr>\n",
"\t<tr><th scope=row>28</th><td>1950</td><td>0.4116615</td></tr>\n",
"\t<tr><th scope=row>29</th><td>1952</td><td>0.574667</td></tr>\n",
"\t<tr><th scope=row>30</th><td>1954</td><td>0.4172303</td></tr>\n",
"\t<tr><th scope=row>31</th><td>1956</td><td>0.5608612</td></tr>\n",
"\t<tr><th scope=row>32</th><td>1958</td><td>0.4228058</td></tr>\n",
"\t<tr><th scope=row>33</th><td>1960</td><td>0.5797567</td></tr>\n",
"\t<tr><th scope=row>34</th><td>1962</td><td>0.4559034</td></tr>\n",
"\t<tr><th scope=row>35</th><td>1964</td><td>0.5898774</td></tr>\n",
"\t<tr><th scope=row>36</th><td>1966</td><td>0.456073</td></tr>\n",
"\t<tr><th scope=row>37</th><td>1968</td><td>0.5519243</td></tr>\n",
"\t<tr><th scope=row>38</th><td>1970</td><td>0.436432</td></tr>\n",
"\t<tr><th scope=row>39</th><td>1972</td><td>0.5057479</td></tr>\n",
"\t<tr><th scope=row>40</th><td>1974</td><td>0.3574957</td></tr>\n",
"\t<tr><th scope=row>41</th><td>1976</td><td>0.4879879</td></tr>\n",
"\t<tr><th scope=row>42</th><td>1978</td><td>0.3506242</td></tr>\n",
"\t<tr><th scope=row>43</th><td>1980</td><td>0.4754198</td></tr>\n",
"\t<tr><th scope=row>44</th><td>1982</td><td>0.3806203</td></tr>\n",
"\t<tr><th scope=row>45</th><td>1984</td><td>0.4757716</td></tr>\n",
"\t<tr><th scope=row>46</th><td>1986</td><td>0.3340337</td></tr>\n",
"\t<tr><th scope=row>47</th><td>1988</td><td>0.4478284</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|ll}\n",
" & YEAR & MTOTCONG\\\\\n",
"\\hline\n",
"\t1 & 1896 & 0.7579143\\\\\n",
"\t2 & 1898 & 0.5706568\\\\\n",
"\t3 & 1900 & 0.6483015\\\\\n",
"\t4 & 1902 & 0.4801695\\\\\n",
"\t5 & 1904 & 0.5435825\\\\\n",
"\t6 & 1906 & 0.4302022\\\\\n",
"\t7 & 1908 & 0.5456067\\\\\n",
"\t8 & 1910 & 0.4343085\\\\\n",
"\t9 & 1912 & 0.4872044\\\\\n",
"\t10 & 1914 & 0.4638365\\\\\n",
"\t11 & 1916 & 0.5471928\\\\\n",
"\t12 & 1918 & 0.4141644\\\\\n",
"\t13 & 1920 & 0.411758\\\\\n",
"\t14 & 1922 & 0.3224323\\\\\n",
"\t15 & 1924 & 0.4078648\\\\\n",
"\t16 & 1926 & 0.2994402\\\\\n",
"\t17 & 1928 & 0.4795689\\\\\n",
"\t18 & 1930 & 0.3382249\\\\\n",
"\t19 & 1932 & 0.4993569\\\\\n",
"\t20 & 1934 & 0.4155684\\\\\n",
"\t21 & 1936 & 0.5372502\\\\\n",
"\t22 & 1938 & 0.442021\\\\\n",
"\t23 & 1940 & 0.5561004\\\\\n",
"\t24 & 1942 & 0.3221306\\\\\n",
"\t25 & 1944 & 0.5012781\\\\\n",
"\t26 & 1946 & 0.3714887\\\\\n",
"\t27 & 1948 & 0.4821603\\\\\n",
"\t28 & 1950 & 0.4116615\\\\\n",
"\t29 & 1952 & 0.574667\\\\\n",
"\t30 & 1954 & 0.4172303\\\\\n",
"\t31 & 1956 & 0.5608612\\\\\n",
"\t32 & 1958 & 0.4228058\\\\\n",
"\t33 & 1960 & 0.5797567\\\\\n",
"\t34 & 1962 & 0.4559034\\\\\n",
"\t35 & 1964 & 0.5898774\\\\\n",
"\t36 & 1966 & 0.456073\\\\\n",
"\t37 & 1968 & 0.5519243\\\\\n",
"\t38 & 1970 & 0.436432\\\\\n",
"\t39 & 1972 & 0.5057479\\\\\n",
"\t40 & 1974 & 0.3574957\\\\\n",
"\t41 & 1976 & 0.4879879\\\\\n",
"\t42 & 1978 & 0.3506242\\\\\n",
"\t43 & 1980 & 0.4754198\\\\\n",
"\t44 & 1982 & 0.3806203\\\\\n",
"\t45 & 1984 & 0.4757716\\\\\n",
"\t46 & 1986 & 0.3340337\\\\\n",
"\t47 & 1988 & 0.4478284\\\\\n",
"\\end{tabular}\n"
],
"text/plain": [
" YEAR MTOTCONG\n",
"1 1896 0.7579143\n",
"2 1898 0.5706568\n",
"3 1900 0.6483015\n",
"4 1902 0.4801695\n",
"5 1904 0.5435825\n",
"6 1906 0.4302022\n",
"7 1908 0.5456067\n",
"8 1910 0.4343085\n",
"9 1912 0.4872044\n",
"10 1914 0.4638365\n",
"11 1916 0.5471928\n",
"12 1918 0.4141644\n",
"13 1920 0.4117580\n",
"14 1922 0.3224323\n",
"15 1924 0.4078648\n",
"16 1926 0.2994402\n",
"17 1928 0.4795689\n",
"18 1930 0.3382249\n",
"19 1932 0.4993569\n",
"20 1934 0.4155684\n",
"21 1936 0.5372502\n",
"22 1938 0.4420210\n",
"23 1940 0.5561004\n",
"24 1942 0.3221306\n",
"25 1944 0.5012781\n",
"26 1946 0.3714887\n",
"27 1948 0.4821603\n",
"28 1950 0.4116615\n",
"29 1952 0.5746670\n",
"30 1954 0.4172303\n",
"31 1956 0.5608612\n",
"32 1958 0.4228058\n",
"33 1960 0.5797567\n",
"34 1962 0.4559034\n",
"35 1964 0.5898774\n",
"36 1966 0.4560730\n",
"37 1968 0.5519243\n",
"38 1970 0.4364320\n",
"39 1972 0.5057479\n",
"40 1974 0.3574957\n",
"41 1976 0.4879879\n",
"42 1978 0.3506242\n",
"43 1980 0.4754198\n",
"44 1982 0.3806203\n",
"45 1984 0.4757716\n",
"46 1986 0.3340337\n",
"47 1988 0.4478284"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mysubsetdata #This prints out all the variable values."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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" <use xlink:href=\"#glyph1-12\" x=\"12.396484\" y=\"256.117188\"/>\n",
" <use xlink:href=\"#glyph1-18\" x=\"12.396484\" y=\"249.445312\"/>\n",
" <use xlink:href=\"#glyph1-19\" x=\"12.396484\" y=\"242.773438\"/>\n",
" <use xlink:href=\"#glyph1-20\" x=\"12.396484\" y=\"240.109375\"/>\n",
" <use xlink:href=\"#glyph1-21\" x=\"12.396484\" y=\"236.773438\"/>\n",
" <use xlink:href=\"#glyph1-11\" x=\"12.396484\" y=\"226.777344\"/>\n",
" <use xlink:href=\"#glyph1-22\" x=\"12.396484\" y=\"220.105469\"/>\n",
" <use xlink:href=\"#glyph1-17\" x=\"12.396484\" y=\"213.433594\"/>\n",
" <use xlink:href=\"#glyph1-19\" x=\"12.396484\" y=\"210.769531\"/>\n",
" <use xlink:href=\"#glyph1-17\" x=\"12.396484\" y=\"208.105469\"/>\n",
" <use xlink:href=\"#glyph1-23\" x=\"12.396484\" y=\"205.441406\"/>\n",
" <use xlink:href=\"#glyph1-18\" x=\"12.396484\" y=\"199.441406\"/>\n",
" <use xlink:href=\"#glyph1-24\" x=\"12.396484\" y=\"192.769531\"/>\n",
" <use xlink:href=\"#glyph1-17\" x=\"12.396484\" y=\"189.433594\"/>\n",
" <use xlink:href=\"#glyph1-11\" x=\"12.396484\" y=\"186.769531\"/>\n",
" <use xlink:href=\"#glyph1-12\" x=\"12.396484\" y=\"180.097656\"/>\n",
"</g>\n",
"</g>\n",
"</svg>\n"
],
"text/plain": [
"Plot with title “Figure 1: Plot of U.S. Congressional Mobilization”"
]
},
"metadata": {
"image/svg+xml": {
"isolated": true
}
},
"output_type": "display_data"
}
],
"source": [
"# Now let us look at what the on-year/off-year pattern looks like.\n",
"# Note that to reference a variable, you need to put the data set name followed by a $ before the variable name.\n",
"# We can reference the variables directly without using the data set name and the $ if we used the attach command above.\n",
"# But we do not want to use the attach command now because we will be creating other data sets that contain the same variable names.\n",
"plot(mysubsetdata$YEAR, mysubsetdata$MTOTCONG, xlab=\"\", ylab=\"\", ylim=c(0.2,0.8), pch=19, type=\"o\")\n",
"title(xlab=\"Year\", ylab=\"Congressional Mobilization\", main=\"Figure 1: Plot of U.S. Congressional Mobilization\", cex=1.5, col=\"black\", font=2)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"0.467712890033299"
],
"text/latex": [
"0.467712890033299"
],
"text/markdown": [
"0.467712890033299"
],
"text/plain": [
"[1] 0.4677129"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Now let us get the overall mean for congressional mobilization\n",
"mean(mysubsetdata$MTOTCONG) # Here we are getting the mean only of the MTOTCONG variable."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# not needed in RStudio or jupyter notebook\n",
"# windows() # This prevents the next plot from erasing the previous plot by giving us a new graphics window."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th></th><th scope=col>YEAR</th><th scope=col>MTOTCONG</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><th scope=row>28</th><td>1950</td><td>0.4116615</td></tr>\n",
"\t<tr><th scope=row>29</th><td>1952</td><td>0.574667</td></tr>\n",
"\t<tr><th scope=row>30</th><td>1954</td><td>0.4172303</td></tr>\n",
"\t<tr><th scope=row>31</th><td>1956</td><td>0.5608612</td></tr>\n",
"\t<tr><th scope=row>32</th><td>1958</td><td>0.4228058</td></tr>\n",
"\t<tr><th scope=row>33</th><td>1960</td><td>0.5797567</td></tr>\n",
"\t<tr><th scope=row>34</th><td>1962</td><td>0.4559034</td></tr>\n",
"\t<tr><th scope=row>35</th><td>1964</td><td>0.5898774</td></tr>\n",
"\t<tr><th scope=row>36</th><td>1966</td><td>0.456073</td></tr>\n",
"\t<tr><th scope=row>37</th><td>1968</td><td>0.5519243</td></tr>\n",
"\t<tr><th scope=row>38</th><td>1970</td><td>0.436432</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|ll}\n",
" & YEAR & MTOTCONG\\\\\n",
"\\hline\n",
"\t28 & 1950 & 0.4116615\\\\\n",
"\t29 & 1952 & 0.574667\\\\\n",
"\t30 & 1954 & 0.4172303\\\\\n",
"\t31 & 1956 & 0.5608612\\\\\n",
"\t32 & 1958 & 0.4228058\\\\\n",
"\t33 & 1960 & 0.5797567\\\\\n",
"\t34 & 1962 & 0.4559034\\\\\n",
"\t35 & 1964 & 0.5898774\\\\\n",
"\t36 & 1966 & 0.456073\\\\\n",
"\t37 & 1968 & 0.5519243\\\\\n",
"\t38 & 1970 & 0.436432\\\\\n",
"\\end{tabular}\n"
],
"text/plain": [
" YEAR MTOTCONG\n",
"28 1950 0.4116615\n",
"29 1952 0.5746670\n",
"30 1954 0.4172303\n",
"31 1956 0.5608612\n",
"32 1958 0.4228058\n",
"33 1960 0.5797567\n",
"34 1962 0.4559034\n",
"35 1964 0.5898774\n",
"36 1966 0.4560730\n",
"37 1968 0.5519243\n",
"38 1970 0.4364320"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Now let us work with just the years 1950 through 1970.\n",
"my5070data <- subset(mysubsetdata, YEAR >= 1950 & YEAR <= 1970)\n",
"my5070data\n",
"\n",
"# attach(my5070data) # Let us avoid variable confusion with these data sets by not doing this."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"</g>\n",
"</g>\n",
"</svg>\n"
],
"text/plain": [
"Plot with title “Figure 2: Plot of U.S. Congressional Mobilization, 1950-70”"
]
},
"metadata": {
"image/svg+xml": {
"isolated": true
}
},
"output_type": "display_data"
}
],
"source": [
"plot(my5070data$YEAR, my5070data$MTOTCONG, xlab=\"\", ylab=\"\", ylim=c(0.2,0.8), pch=19, type=\"o\", axes=FALSE)\n",
"axis(1, at=c(1952, 1956, 1960, 1964, 1968)) # This defines the X axis tick marks.\n",
"axis(2, yaxs=\"r\") # This defines the Y axis.\n",
"box()\n",
"title(xlab=\"Year\", ylab=\"Congressional Mobilization\", main=\"Figure 2: Plot of U.S. Congressional Mobilization, 1950-70\", cex=1.5, col=\"black\", font=2)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"0.496108424917445"
],
"text/latex": [
"0.496108424917445"
],
"text/markdown": [
"0.496108424917445"
],
"text/plain": [
"[1] 0.4961084"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mean(my5070data$MTOTCONG)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th></th><th scope=col>YEAR</th><th scope=col>MTOTCONG</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><th scope=row>39</th><td>1972</td><td>0.5057479</td></tr>\n",
"\t<tr><th scope=row>40</th><td>1974</td><td>0.3574957</td></tr>\n",
"\t<tr><th scope=row>41</th><td>1976</td><td>0.4879879</td></tr>\n",
"\t<tr><th scope=row>42</th><td>1978</td><td>0.3506242</td></tr>\n",
"\t<tr><th scope=row>43</th><td>1980</td><td>0.4754198</td></tr>\n",
"\t<tr><th scope=row>44</th><td>1982</td><td>0.3806203</td></tr>\n",
"\t<tr><th scope=row>45</th><td>1984</td><td>0.4757716</td></tr>\n",
"\t<tr><th scope=row>46</th><td>1986</td><td>0.3340337</td></tr>\n",
"\t<tr><th scope=row>47</th><td>1988</td><td>0.4478284</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|ll}\n",
" & YEAR & MTOTCONG\\\\\n",
"\\hline\n",
"\t39 & 1972 & 0.5057479\\\\\n",
"\t40 & 1974 & 0.3574957\\\\\n",
"\t41 & 1976 & 0.4879879\\\\\n",
"\t42 & 1978 & 0.3506242\\\\\n",
"\t43 & 1980 & 0.4754198\\\\\n",
"\t44 & 1982 & 0.3806203\\\\\n",
"\t45 & 1984 & 0.4757716\\\\\n",
"\t46 & 1986 & 0.3340337\\\\\n",
"\t47 & 1988 & 0.4478284\\\\\n",
"\\end{tabular}\n"
],
"text/plain": [
" YEAR MTOTCONG\n",
"39 1972 0.5057479\n",
"40 1974 0.3574957\n",
"41 1976 0.4879879\n",
"42 1978 0.3506242\n",
"43 1980 0.4754198\n",
"44 1982 0.3806203\n",
"45 1984 0.4757716\n",
"46 1986 0.3340337\n",
"47 1988 0.4478284"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Now let us work with just the years 1972 through 1988.\n",
"my7288data <- subset(mysubsetdata, YEAR >= 1972 & YEAR <= 1988)\n",
"my7288data"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"0.423947722281327"
],
"text/latex": [
"0.423947722281327"
],
"text/markdown": [
"0.423947722281327"
],
"text/plain": [
"[1] 0.4239477"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
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],
"text/plain": [
"Plot with title “Figure 2: Plot of U.S. Congressional Mobilization, 1972-88”"
]
},
"metadata": {
"image/svg+xml": {
"isolated": true
}
},
"output_type": "display_data"
}
],
"source": [
"# attach(my7288data) # Again, let us avoid variable confusion with these data sets by not doing this.\n",
"plot(my7288data$YEAR, my7288data$MTOTCONG, xlab=\"\", ylab=\"\", ylim=c(0.2,0.8), pch=19, type=\"o\", axes=FALSE)\n",
"axis(1, at=c(1972, 1976, 1980, 1984, 1988)) # This defines the X axis tick marks.\n",
"axis(2, yaxs=\"r\") # This defines the Y axis.\n",
"box()\n",
"title(xlab=\"Year\", ylab=\"Congressional Mobilization\", main=\"Figure 2: Plot of U.S. Congressional Mobilization, 1972-88\", cex=1.5, col=\"black\", font=2)\n",
"mean(my7288data$MTOTCONG)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"0.571417322005834"
],
"text/latex": [
"0.571417322005834"
],
"text/markdown": [
"0.571417322005834"
],
"text/plain": [
"[1] 0.5714173"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Now let us work with on-year and off-year separately to get the means\n",
"myON5070data <- subset(mysubsetdata, YEAR == 1952 | YEAR == 1956 | YEAR == 1960 | YEAR == 1964 | YEAR == 1968)\n",
"mean(myON5070data$MTOTCONG) "
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"0.433351010677121"
],
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"0.433351010677121"
],
"text/markdown": [
"0.433351010677121"
],
"text/plain": [
"[1] 0.433351"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"myOFF5070data <- subset(mysubsetdata, YEAR == 1950 | YEAR == 1954 | YEAR == 1958 | YEAR == 1962 | YEAR == 1966 | YEAR == 1970)\n",
"mean(myOFF5070data$MTOTCONG)"
]
}
],
"metadata": {
"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": "3.2.2"
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"state": {},
"version": "1.1.2"
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