Criação do base de dados
# ----------------------------
#Criação da base de dados
# ----------------------------
Funcionarios <- data.frame(nome = c("Marx", "Weber", "Durkheim","Arendt", "Maquiavel", "Platão"),
sexo = c("M", "M", "M", "F","M","M"),
salario = c(1000, 1200, 1300, 2000, 500,1400),
stringsAsFactors = FALSE)
Funcionarios
## nome sexo salario
## 1 Marx M 1000
## 2 Weber M 1200
## 3 Durkheim M 1300
## 4 Arendt F 2000
## 5 Maquiavel M 500
## 6 Platão M 1400
mean (Funcionarios$salario)
## [1] 1233.333
Carregar base de dados
# ----------------------------
#Carregar uma base de dados do RData
# ----------------------------
load("C:/Users/Windows/Desktop/Base_de_dados-master/Titanic.RData")
Importar do Excel
# ----------------------------
#Importar do Excel
# ----------------------------
library(readxl)
Familias <- read_excel("C:/Users/Windows/Desktop/Base_de_dados-master/Familias.xls",
col_types = c("numeric", "text", "text",
"text", "numeric", "numeric"))
Importar arquivo de CSV
# ----------------------------
#Importar arquivo do CSV
# ----------------------------
library(readr)
Fifa <- read_csv("C:/Users/Windows/Desktop/Base_de_dados-master/FifaData.csv")
##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## Name = col_character(),
## Nationality = col_character(),
## National_Position = col_character(),
## Club = col_character(),
## Club_Position = col_character(),
## Club_Joining = col_character(),
## Height = col_character(),
## Weight = col_character(),
## Preffered_Foot = col_character(),
## Birth_Date = col_character(),
## Preffered_Position = col_character(),
## Work_Rate = col_character()
## )
## i Use `spec()` for the full column specifications.
Perguntas 01
# ---------------------------------
# Quantos brasileiros?
# Qual a proporção de Alemães?
# Qual a posição (país) mais comum?
# ---------------------------------
library(readr)
Fifa <- read_csv("C:/Users/Windows/Desktop/Base_de_dados-master/FifaData.csv")
##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## Name = col_character(),
## Nationality = col_character(),
## National_Position = col_character(),
## Club = col_character(),
## Club_Position = col_character(),
## Club_Joining = col_character(),
## Height = col_character(),
## Weight = col_character(),
## Preffered_Foot = col_character(),
## Birth_Date = col_character(),
## Preffered_Position = col_character(),
## Work_Rate = col_character()
## )
## i Use `spec()` for the full column specifications.
Respostas
tabela1 <- table(Fifa$Nationality)
tabela1
##
## Afghanistan Albania Algeria
## 2 37 50
## Angola Antigua & Barbuda Argentina
## 11 4 1097
## Armenia Aruba Australia
## 8 1 234
## Austria Azerbaijan Barbados
## 266 8 1
## Belarus Belgium Belize
## 16 265 1
## Benin Bermuda Bolivia
## 16 6 30
## Bosnia Herzegovina Brazil Bulgaria
## 52 921 35
## Burkina Faso Cameroon Canada
## 14 96 59
## Cape Verde Central African Rep. Chad
## 22 6 1
## Chile China PR Chinese Taipei
## 398 30 1
## Colombia Comoros Congo
## 592 9 18
## Costa Rica Croatia Cuba
## 30 116 2
## Curacao Cyprus Czech Republic
## 11 6 57
## Denmark Dominican Republic DR Congo
## 342 2 58
## Ecuador Egypt El Salvador
## 34 30 2
## England Equatorial Guinea Eritrea
## 1618 8 1
## Estonia Faroe Islands FIFA16_NationName_215
## 8 6 2
## Fiji Finland France
## 1 60 974
## FYR Macedonia Gabon Gambia
## 18 15 14
## Georgia Germany Ghana
## 28 689 119
## Gibraltar Greece Grenada
## 1 86 1
## Guam Guatemala Guinea
## 1 2 34
## Guinea Bissau Guyana Haiti
## 17 3 12
## Honduras Hungary Iceland
## 16 41 47
## India Iran Iraq
## 30 11 8
## Israel Italy Ivory Coast
## 14 751 90
## Jamaica Japan Kazakhstan
## 36 471 2
## Kenya Korea DPR Korea Republic
## 6 2 321
## Kosovo Kuwait Latvia
## 31 2 8
## Lebanon Lesotho Liberia
## 2 1 4
## Libya Liechtenstein Lithuania
## 4 7 15
## Luxembourg Madagascar Mali
## 9 2 46
## Malta Mauritania Mauritius
## 4 5 1
## Mexico Moldova Montenegro
## 341 6 23
## Montserrat Morocco Mozambique
## 3 74 5
## Namibia Netherlands New Zealand
## 1 426 30
## Niger Nigeria Northern Ireland
## 2 122 83
## Norway Oman Pakistan
## 342 1 1
## Palestine Panama Papua New Guinea
## 4 11 1
## Paraguay Peru Philippines
## 75 34 2
## Poland Portugal Puerto Rico
## 328 360 2
## Qatar Republic of Ireland Romania
## 2 442 61
## Russia San Marino São Tomé & Príncipe
## 309 1 1
## Saudi Arabia Scotland Senegal
## 354 292 119
## Serbia Sierra Leone Slovakia
## 136 6 61
## Slovenia Somalia South Africa
## 58 1 78
## Spain St Kitts Nevis St Lucia
## 1008 3 1
## Suriname Sweden Switzerland
## 4 378 210
## Syria Tanzania Timor-Leste
## 5 2 1
## Togo Trinidad & Tobago Tunisia
## 10 7 35
## Turkey Uganda Ukraine
## 292 7 59
## United States Uruguay Uzbekistan
## 332 153 3
## Venezuela Wales Zambia
## 42 122 4
## Zimbabwe
## 10
# trocando a notação científica
options (scipen = 999)
# tabela de proporção
prop.table(tabela1)*100
##
## Afghanistan Albania Algeria
## 0.011371390 0.210370707 0.284284740
## Angola Antigua & Barbuda Argentina
## 0.062542643 0.022742779 6.237207187
## Armenia Aruba Australia
## 0.045485558 0.005685695 1.330452581
## Austria Azerbaijan Barbados
## 1.512394815 0.045485558 0.005685695
## Belarus Belgium Belize
## 0.090971117 1.506709120 0.005685695
## Benin Bermuda Bolivia
## 0.090971117 0.034114169 0.170570844
## Bosnia Herzegovina Brazil Bulgaria
## 0.295656129 5.236524903 0.198999318
## Burkina Faso Cameroon Canada
## 0.079599727 0.545826700 0.335455993
## Cape Verde Central African Rep. Chad
## 0.125085285 0.034114169 0.005685695
## Chile China PR Chinese Taipei
## 2.262906527 0.170570844 0.005685695
## Colombia Comoros Congo
## 3.365931317 0.051171253 0.102342506
## Costa Rica Croatia Cuba
## 0.170570844 0.659540596 0.011371390
## Curacao Cyprus Czech Republic
## 0.062542643 0.034114169 0.324084603
## Denmark Dominican Republic DR Congo
## 1.944507619 0.011371390 0.329770298
## Ecuador Egypt El Salvador
## 0.193313623 0.170570844 0.011371390
## England Equatorial Guinea Eritrea
## 9.199454173 0.045485558 0.005685695
## Estonia Faroe Islands FIFA16_NationName_215
## 0.045485558 0.034114169 0.011371390
## Fiji Finland France
## 0.005685695 0.341141688 5.537866727
## FYR Macedonia Gabon Gambia
## 0.102342506 0.085285422 0.079599727
## Georgia Germany Ghana
## 0.159199454 3.917443712 0.676597680
## Gibraltar Greece Grenada
## 0.005685695 0.488969752 0.005685695
## Guam Guatemala Guinea
## 0.005685695 0.011371390 0.193313623
## Guinea Bissau Guyana Haiti
## 0.096656811 0.017057084 0.068228338
## Honduras Hungary Iceland
## 0.090971117 0.233113486 0.267227655
## India Iran Iraq
## 0.170570844 0.062542643 0.045485558
## Israel Italy Ivory Coast
## 0.079599727 4.269956789 0.511712531
## Jamaica Japan Kazakhstan
## 0.204685013 2.677962247 0.011371390
## Kenya Korea DPR Korea Republic
## 0.034114169 0.011371390 1.825108028
## Kosovo Kuwait Latvia
## 0.176256539 0.011371390 0.045485558
## Lebanon Lesotho Liberia
## 0.011371390 0.005685695 0.022742779
## Libya Liechtenstein Lithuania
## 0.022742779 0.039799864 0.085285422
## Luxembourg Madagascar Mali
## 0.051171253 0.011371390 0.261541960
## Malta Mauritania Mauritius
## 0.022742779 0.028428474 0.005685695
## Mexico Moldova Montenegro
## 1.938821924 0.034114169 0.130770980
## Montserrat Morocco Mozambique
## 0.017057084 0.420741415 0.028428474
## Namibia Netherlands New Zealand
## 0.005685695 2.422105981 0.170570844
## Niger Nigeria Northern Ireland
## 0.011371390 0.693654765 0.471912668
## Norway Oman Pakistan
## 1.944507619 0.005685695 0.005685695
## Palestine Panama Papua New Guinea
## 0.022742779 0.062542643 0.005685695
## Paraguay Peru Philippines
## 0.426427109 0.193313623 0.011371390
## Poland Portugal Puerto Rico
## 1.864907892 2.046850125 0.011371390
## Qatar Republic of Ireland Romania
## 0.011371390 2.513077098 0.346827382
## Russia San Marino São Tomé & Príncipe
## 1.756879691 0.005685695 0.005685695
## Saudi Arabia Scotland Senegal
## 2.012735956 1.660222879 0.676597680
## Serbia Sierra Leone Slovakia
## 0.773254492 0.034114169 0.346827382
## Slovenia Somalia South Africa
## 0.329770298 0.005685695 0.443484194
## Spain St Kitts Nevis St Lucia
## 5.731180350 0.017057084 0.005685695
## Suriname Sweden Switzerland
## 0.022742779 2.149192631 1.193995906
## Syria Tanzania Timor-Leste
## 0.028428474 0.011371390 0.005685695
## Togo Trinidad & Tobago Tunisia
## 0.056856948 0.039799864 0.198999318
## Turkey Uganda Ukraine
## 1.660222879 0.039799864 0.335455993
## United States Uruguay Uzbekistan
## 1.887650671 0.869911303 0.017057084
## Venezuela Wales Zambia
## 0.238799181 0.693654765 0.022742779
## Zimbabwe
## 0.056856948
# arredondar
round(prop.table(tabela1)*100,2)
##
## Afghanistan Albania Algeria
## 0.01 0.21 0.28
## Angola Antigua & Barbuda Argentina
## 0.06 0.02 6.24
## Armenia Aruba Australia
## 0.05 0.01 1.33
## Austria Azerbaijan Barbados
## 1.51 0.05 0.01
## Belarus Belgium Belize
## 0.09 1.51 0.01
## Benin Bermuda Bolivia
## 0.09 0.03 0.17
## Bosnia Herzegovina Brazil Bulgaria
## 0.30 5.24 0.20
## Burkina Faso Cameroon Canada
## 0.08 0.55 0.34
## Cape Verde Central African Rep. Chad
## 0.13 0.03 0.01
## Chile China PR Chinese Taipei
## 2.26 0.17 0.01
## Colombia Comoros Congo
## 3.37 0.05 0.10
## Costa Rica Croatia Cuba
## 0.17 0.66 0.01
## Curacao Cyprus Czech Republic
## 0.06 0.03 0.32
## Denmark Dominican Republic DR Congo
## 1.94 0.01 0.33
## Ecuador Egypt El Salvador
## 0.19 0.17 0.01
## England Equatorial Guinea Eritrea
## 9.20 0.05 0.01
## Estonia Faroe Islands FIFA16_NationName_215
## 0.05 0.03 0.01
## Fiji Finland France
## 0.01 0.34 5.54
## FYR Macedonia Gabon Gambia
## 0.10 0.09 0.08
## Georgia Germany Ghana
## 0.16 3.92 0.68
## Gibraltar Greece Grenada
## 0.01 0.49 0.01
## Guam Guatemala Guinea
## 0.01 0.01 0.19
## Guinea Bissau Guyana Haiti
## 0.10 0.02 0.07
## Honduras Hungary Iceland
## 0.09 0.23 0.27
## India Iran Iraq
## 0.17 0.06 0.05
## Israel Italy Ivory Coast
## 0.08 4.27 0.51
## Jamaica Japan Kazakhstan
## 0.20 2.68 0.01
## Kenya Korea DPR Korea Republic
## 0.03 0.01 1.83
## Kosovo Kuwait Latvia
## 0.18 0.01 0.05
## Lebanon Lesotho Liberia
## 0.01 0.01 0.02
## Libya Liechtenstein Lithuania
## 0.02 0.04 0.09
## Luxembourg Madagascar Mali
## 0.05 0.01 0.26
## Malta Mauritania Mauritius
## 0.02 0.03 0.01
## Mexico Moldova Montenegro
## 1.94 0.03 0.13
## Montserrat Morocco Mozambique
## 0.02 0.42 0.03
## Namibia Netherlands New Zealand
## 0.01 2.42 0.17
## Niger Nigeria Northern Ireland
## 0.01 0.69 0.47
## Norway Oman Pakistan
## 1.94 0.01 0.01
## Palestine Panama Papua New Guinea
## 0.02 0.06 0.01
## Paraguay Peru Philippines
## 0.43 0.19 0.01
## Poland Portugal Puerto Rico
## 1.86 2.05 0.01
## Qatar Republic of Ireland Romania
## 0.01 2.51 0.35
## Russia San Marino São Tomé & Príncipe
## 1.76 0.01 0.01
## Saudi Arabia Scotland Senegal
## 2.01 1.66 0.68
## Serbia Sierra Leone Slovakia
## 0.77 0.03 0.35
## Slovenia Somalia South Africa
## 0.33 0.01 0.44
## Spain St Kitts Nevis St Lucia
## 5.73 0.02 0.01
## Suriname Sweden Switzerland
## 0.02 2.15 1.19
## Syria Tanzania Timor-Leste
## 0.03 0.01 0.01
## Togo Trinidad & Tobago Tunisia
## 0.06 0.04 0.20
## Turkey Uganda Ukraine
## 1.66 0.04 0.34
## United States Uruguay Uzbekistan
## 1.89 0.87 0.02
## Venezuela Wales Zambia
## 0.24 0.69 0.02
## Zimbabwe
## 0.06
tabela2 <- table(Fifa$National_Position)
tabela2
##
## CAM CB CDM CM GK LAM LB LCB LCM LDM LF LM LS LW LWB RAM RB RCB RCM RDM
## 19 9 9 9 47 4 39 48 25 19 3 32 18 7 4 4 38 46 25 18
## RF RM RS RW RWB ST Sub
## 3 34 18 7 4 30 556
round(prop.table(tabela2)*100,2)
##
## CAM CB CDM CM GK LAM LB LCB LCM LDM LF LM LS
## 1.77 0.84 0.84 0.84 4.37 0.37 3.63 4.47 2.33 1.77 0.28 2.98 1.67
## LW LWB RAM RB RCB RCM RDM RF RM RS RW RWB ST
## 0.65 0.37 0.37 3.53 4.28 2.33 1.67 0.28 3.16 1.67 0.65 0.37 2.79
## Sub
## 51.72
Perguntas 01
#---------------------------------
# Quantos usam o P.A.P?
# Proporção de Ensino médio?
#---------------------------------
tabela3 <- table(Familias$p.a.p)
tabela3
##
## Não usa Usa
## 42 78
round(prop.table(tabela3)*100,2)
##
## Não usa Usa
## 35 65
tabela4 <- table(Familias$instr)
tabela4
##
## Ensino fundamental Ensino médio Sem Instrução
## 38 44 38
round(prop.table(tabela4)*100,2)
##
## Ensino fundamental Ensino médio Sem Instrução
## 31.67 36.67 31.67
Todas as pizzas
#---------------------------------
# Gráfico de pizza
#---------------------------------
#simples
pie(tabela3)
#simples + título
pie(tabela3,main="Gráfico 1 - Uso do programa de alimentação popular")
#simples + título + cor
pie(tabela3,col = c("coral2", "turquoise4"), main="Gráfico 1 - Uso do programa de alimentação popular")
#definir a quantidade de cores
colors()
## [1] "white" "aliceblue" "antiquewhite"
## [4] "antiquewhite1" "antiquewhite2" "antiquewhite3"
## [7] "antiquewhite4" "aquamarine" "aquamarine1"
## [10] "aquamarine2" "aquamarine3" "aquamarine4"
## [13] "azure" "azure1" "azure2"
## [16] "azure3" "azure4" "beige"
## [19] "bisque" "bisque1" "bisque2"
## [22] "bisque3" "bisque4" "black"
## [25] "blanchedalmond" "blue" "blue1"
## [28] "blue2" "blue3" "blue4"
## [31] "blueviolet" "brown" "brown1"
## [34] "brown2" "brown3" "brown4"
## [37] "burlywood" "burlywood1" "burlywood2"
## [40] "burlywood3" "burlywood4" "cadetblue"
## [43] "cadetblue1" "cadetblue2" "cadetblue3"
## [46] "cadetblue4" "chartreuse" "chartreuse1"
## [49] "chartreuse2" "chartreuse3" "chartreuse4"
## [52] "chocolate" "chocolate1" "chocolate2"
## [55] "chocolate3" "chocolate4" "coral"
## [58] "coral1" "coral2" "coral3"
## [61] "coral4" "cornflowerblue" "cornsilk"
## [64] "cornsilk1" "cornsilk2" "cornsilk3"
## [67] "cornsilk4" "cyan" "cyan1"
## [70] "cyan2" "cyan3" "cyan4"
## [73] "darkblue" "darkcyan" "darkgoldenrod"
## [76] "darkgoldenrod1" "darkgoldenrod2" "darkgoldenrod3"
## [79] "darkgoldenrod4" "darkgray" "darkgreen"
## [82] "darkgrey" "darkkhaki" "darkmagenta"
## [85] "darkolivegreen" "darkolivegreen1" "darkolivegreen2"
## [88] "darkolivegreen3" "darkolivegreen4" "darkorange"
## [91] "darkorange1" "darkorange2" "darkorange3"
## [94] "darkorange4" "darkorchid" "darkorchid1"
## [97] "darkorchid2" "darkorchid3" "darkorchid4"
## [100] "darkred" "darksalmon" "darkseagreen"
## [103] "darkseagreen1" "darkseagreen2" "darkseagreen3"
## [106] "darkseagreen4" "darkslateblue" "darkslategray"
## [109] "darkslategray1" "darkslategray2" "darkslategray3"
## [112] "darkslategray4" "darkslategrey" "darkturquoise"
## [115] "darkviolet" "deeppink" "deeppink1"
## [118] "deeppink2" "deeppink3" "deeppink4"
## [121] "deepskyblue" "deepskyblue1" "deepskyblue2"
## [124] "deepskyblue3" "deepskyblue4" "dimgray"
## [127] "dimgrey" "dodgerblue" "dodgerblue1"
## [130] "dodgerblue2" "dodgerblue3" "dodgerblue4"
## [133] "firebrick" "firebrick1" "firebrick2"
## [136] "firebrick3" "firebrick4" "floralwhite"
## [139] "forestgreen" "gainsboro" "ghostwhite"
## [142] "gold" "gold1" "gold2"
## [145] "gold3" "gold4" "goldenrod"
## [148] "goldenrod1" "goldenrod2" "goldenrod3"
## [151] "goldenrod4" "gray" "gray0"
## [154] "gray1" "gray2" "gray3"
## [157] "gray4" "gray5" "gray6"
## [160] "gray7" "gray8" "gray9"
## [163] "gray10" "gray11" "gray12"
## [166] "gray13" "gray14" "gray15"
## [169] "gray16" "gray17" "gray18"
## [172] "gray19" "gray20" "gray21"
## [175] "gray22" "gray23" "gray24"
## [178] "gray25" "gray26" "gray27"
## [181] "gray28" "gray29" "gray30"
## [184] "gray31" "gray32" "gray33"
## [187] "gray34" "gray35" "gray36"
## [190] "gray37" "gray38" "gray39"
## [193] "gray40" "gray41" "gray42"
## [196] "gray43" "gray44" "gray45"
## [199] "gray46" "gray47" "gray48"
## [202] "gray49" "gray50" "gray51"
## [205] "gray52" "gray53" "gray54"
## [208] "gray55" "gray56" "gray57"
## [211] "gray58" "gray59" "gray60"
## [214] "gray61" "gray62" "gray63"
## [217] "gray64" "gray65" "gray66"
## [220] "gray67" "gray68" "gray69"
## [223] "gray70" "gray71" "gray72"
## [226] "gray73" "gray74" "gray75"
## [229] "gray76" "gray77" "gray78"
## [232] "gray79" "gray80" "gray81"
## [235] "gray82" "gray83" "gray84"
## [238] "gray85" "gray86" "gray87"
## [241] "gray88" "gray89" "gray90"
## [244] "gray91" "gray92" "gray93"
## [247] "gray94" "gray95" "gray96"
## [250] "gray97" "gray98" "gray99"
## [253] "gray100" "green" "green1"
## [256] "green2" "green3" "green4"
## [259] "greenyellow" "grey" "grey0"
## [262] "grey1" "grey2" "grey3"
## [265] "grey4" "grey5" "grey6"
## [268] "grey7" "grey8" "grey9"
## [271] "grey10" "grey11" "grey12"
## [274] "grey13" "grey14" "grey15"
## [277] "grey16" "grey17" "grey18"
## [280] "grey19" "grey20" "grey21"
## [283] "grey22" "grey23" "grey24"
## [286] "grey25" "grey26" "grey27"
## [289] "grey28" "grey29" "grey30"
## [292] "grey31" "grey32" "grey33"
## [295] "grey34" "grey35" "grey36"
## [298] "grey37" "grey38" "grey39"
## [301] "grey40" "grey41" "grey42"
## [304] "grey43" "grey44" "grey45"
## [307] "grey46" "grey47" "grey48"
## [310] "grey49" "grey50" "grey51"
## [313] "grey52" "grey53" "grey54"
## [316] "grey55" "grey56" "grey57"
## [319] "grey58" "grey59" "grey60"
## [322] "grey61" "grey62" "grey63"
## [325] "grey64" "grey65" "grey66"
## [328] "grey67" "grey68" "grey69"
## [331] "grey70" "grey71" "grey72"
## [334] "grey73" "grey74" "grey75"
## [337] "grey76" "grey77" "grey78"
## [340] "grey79" "grey80" "grey81"
## [343] "grey82" "grey83" "grey84"
## [346] "grey85" "grey86" "grey87"
## [349] "grey88" "grey89" "grey90"
## [352] "grey91" "grey92" "grey93"
## [355] "grey94" "grey95" "grey96"
## [358] "grey97" "grey98" "grey99"
## [361] "grey100" "honeydew" "honeydew1"
## [364] "honeydew2" "honeydew3" "honeydew4"
## [367] "hotpink" "hotpink1" "hotpink2"
## [370] "hotpink3" "hotpink4" "indianred"
## [373] "indianred1" "indianred2" "indianred3"
## [376] "indianred4" "ivory" "ivory1"
## [379] "ivory2" "ivory3" "ivory4"
## [382] "khaki" "khaki1" "khaki2"
## [385] "khaki3" "khaki4" "lavender"
## [388] "lavenderblush" "lavenderblush1" "lavenderblush2"
## [391] "lavenderblush3" "lavenderblush4" "lawngreen"
## [394] "lemonchiffon" "lemonchiffon1" "lemonchiffon2"
## [397] "lemonchiffon3" "lemonchiffon4" "lightblue"
## [400] "lightblue1" "lightblue2" "lightblue3"
## [403] "lightblue4" "lightcoral" "lightcyan"
## [406] "lightcyan1" "lightcyan2" "lightcyan3"
## [409] "lightcyan4" "lightgoldenrod" "lightgoldenrod1"
## [412] "lightgoldenrod2" "lightgoldenrod3" "lightgoldenrod4"
## [415] "lightgoldenrodyellow" "lightgray" "lightgreen"
## [418] "lightgrey" "lightpink" "lightpink1"
## [421] "lightpink2" "lightpink3" "lightpink4"
## [424] "lightsalmon" "lightsalmon1" "lightsalmon2"
## [427] "lightsalmon3" "lightsalmon4" "lightseagreen"
## [430] "lightskyblue" "lightskyblue1" "lightskyblue2"
## [433] "lightskyblue3" "lightskyblue4" "lightslateblue"
## [436] "lightslategray" "lightslategrey" "lightsteelblue"
## [439] "lightsteelblue1" "lightsteelblue2" "lightsteelblue3"
## [442] "lightsteelblue4" "lightyellow" "lightyellow1"
## [445] "lightyellow2" "lightyellow3" "lightyellow4"
## [448] "limegreen" "linen" "magenta"
## [451] "magenta1" "magenta2" "magenta3"
## [454] "magenta4" "maroon" "maroon1"
## [457] "maroon2" "maroon3" "maroon4"
## [460] "mediumaquamarine" "mediumblue" "mediumorchid"
## [463] "mediumorchid1" "mediumorchid2" "mediumorchid3"
## [466] "mediumorchid4" "mediumpurple" "mediumpurple1"
## [469] "mediumpurple2" "mediumpurple3" "mediumpurple4"
## [472] "mediumseagreen" "mediumslateblue" "mediumspringgreen"
## [475] "mediumturquoise" "mediumvioletred" "midnightblue"
## [478] "mintcream" "mistyrose" "mistyrose1"
## [481] "mistyrose2" "mistyrose3" "mistyrose4"
## [484] "moccasin" "navajowhite" "navajowhite1"
## [487] "navajowhite2" "navajowhite3" "navajowhite4"
## [490] "navy" "navyblue" "oldlace"
## [493] "olivedrab" "olivedrab1" "olivedrab2"
## [496] "olivedrab3" "olivedrab4" "orange"
## [499] "orange1" "orange2" "orange3"
## [502] "orange4" "orangered" "orangered1"
## [505] "orangered2" "orangered3" "orangered4"
## [508] "orchid" "orchid1" "orchid2"
## [511] "orchid3" "orchid4" "palegoldenrod"
## [514] "palegreen" "palegreen1" "palegreen2"
## [517] "palegreen3" "palegreen4" "paleturquoise"
## [520] "paleturquoise1" "paleturquoise2" "paleturquoise3"
## [523] "paleturquoise4" "palevioletred" "palevioletred1"
## [526] "palevioletred2" "palevioletred3" "palevioletred4"
## [529] "papayawhip" "peachpuff" "peachpuff1"
## [532] "peachpuff2" "peachpuff3" "peachpuff4"
## [535] "peru" "pink" "pink1"
## [538] "pink2" "pink3" "pink4"
## [541] "plum" "plum1" "plum2"
## [544] "plum3" "plum4" "powderblue"
## [547] "purple" "purple1" "purple2"
## [550] "purple3" "purple4" "red"
## [553] "red1" "red2" "red3"
## [556] "red4" "rosybrown" "rosybrown1"
## [559] "rosybrown2" "rosybrown3" "rosybrown4"
## [562] "royalblue" "royalblue1" "royalblue2"
## [565] "royalblue3" "royalblue4" "saddlebrown"
## [568] "salmon" "salmon1" "salmon2"
## [571] "salmon3" "salmon4" "sandybrown"
## [574] "seagreen" "seagreen1" "seagreen2"
## [577] "seagreen3" "seagreen4" "seashell"
## [580] "seashell1" "seashell2" "seashell3"
## [583] "seashell4" "sienna" "sienna1"
## [586] "sienna2" "sienna3" "sienna4"
## [589] "skyblue" "skyblue1" "skyblue2"
## [592] "skyblue3" "skyblue4" "slateblue"
## [595] "slateblue1" "slateblue2" "slateblue3"
## [598] "slateblue4" "slategray" "slategray1"
## [601] "slategray2" "slategray3" "slategray4"
## [604] "slategrey" "snow" "snow1"
## [607] "snow2" "snow3" "snow4"
## [610] "springgreen" "springgreen1" "springgreen2"
## [613] "springgreen3" "springgreen4" "steelblue"
## [616] "steelblue1" "steelblue2" "steelblue3"
## [619] "steelblue4" "tan" "tan1"
## [622] "tan2" "tan3" "tan4"
## [625] "thistle" "thistle1" "thistle2"
## [628] "thistle3" "thistle4" "tomato"
## [631] "tomato1" "tomato2" "tomato3"
## [634] "tomato4" "turquoise" "turquoise1"
## [637] "turquoise2" "turquoise3" "turquoise4"
## [640] "violet" "violetred" "violetred1"
## [643] "violetred2" "violetred3" "violetred4"
## [646] "wheat" "wheat1" "wheat2"
## [649] "wheat3" "wheat4" "whitesmoke"
## [652] "yellow" "yellow1" "yellow2"
## [655] "yellow3" "yellow4" "yellowgreen"
# Hexadecimal
pie(tabela3,col = c("#FFFFFF", "#CCCCCC"), main="Gráfico 1 - Uso do programa de alimentação popular")
pie(tabela2)
pie (tabela4)
Todas as barras
#---------------------------------
# Gráfico de barras
#---------------------------------
barplot(tabela4)
tabela4
##
## Ensino fundamental Ensino médio Sem Instrução
## 38 44 38
# corrigir a ordem dos fatores
Familias$instr <- factor(Familias$instr,
levels = c("Sem Instrução",
"Ensino fundamental",
"Ensino médio"))
tabela4 <- table(Familias$instr)
barplot(tabela4)
barras <- barplot(tabela4,col = c("skyblue", "royalblue", "darkblue"),
main="Gráfico 2 - Escolaridade",
ylim = c(0,50))
# Rótulo
text(barras, 0, tabela4, cex=3, pos=3, col = "white")
