Variáveis categóricas
Diovanna
24/05/2021
#Criaçao da base de dados
#---------------------------------------
#Criaçao da base de dados
Funcionarios <- data.frame(nome = c("Marx", "Weber", "Durkheim","Arendt", "Maquiavel","platao"),
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 platao M 1400mean(Funcionarios$salario)## [1] 1233.333#------------------------------------------------#Carregar uma base de dados
#------------------------------------------------
#carregar uma base de dados
#---------------------------------------------
load("C:/Users/diova/Desktop/Base_de_dados-master/Titanic.RData")#Importar do excel
#---------------------------------------------
#importar do excel
#-----------------------------------------------
library(readxl)
Familias <- read_excel("C:/Users/diova/Desktop/Base_de_dados-master/Familias.xls",
col_types = c("numeric", "text", "text",
"text", "numeric", "numeric"))
head(Familias)## # A tibble: 6 x 6
## familia local p.a.p instr tam renda
## <dbl> <chr> <chr> <chr> <dbl> <dbl>
## 1 1 Monte Verde Não usa Ensino médio 4 10.3
## 2 2 Monte Verde Não usa Ensino médio 4 15.4
## 3 3 Monte Verde Usa Ensino fundamental 4 9.6
## 4 4 Monte Verde Não usa Ensino fundamental 5 5.5
## 5 5 Monte Verde Usa Ensino médio 4 9
## 6 6 Monte Verde Usa Sem Instrução 1 2.4#importar arquivo do CSV
#-------------------------------------------------
#Importar arquivo do CSV
#-------------------------------------------------
library(readr)
FifaData <- read_csv("C:/Users/diova/Desktop/Base_de_dados-master/FifaData.csv")##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## Name = col_character(),
## Nationality = col_character(),
## National_Position = col_character(),
## Club = col_character(),
## Club_Position = col_character(),
## Club_Joining = col_character(),
## Height = col_character(),
## Weight = col_character(),
## Preffered_Foot = col_character(),
## Birth_Date = col_character(),
## Preffered_Position = col_character(),
## Work_Rate = col_character()
## )
## i Use `spec()` for the full column specifications.Quantos Brasileiros?
Qual a proporção de Alemães?
Qual a posição (país) mais comum?
tabela1 <-table(FifaData$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
## 0.06tabela2 <- table(FifaData$National_Position)
tabela2##
## CAM CB CDM CM GK LAM LB LCB LCM LDM LF LM LS LW LWB RAM RB RCB RCM RDM
## 19 9 9 9 47 4 39 48 25 19 3 32 18 7 4 4 38 46 25 18
## RF RM RS RW RWB ST Sub
## 3 34 18 7 4 30 556round(prop.table(tabela2)*100,2)##
## CAM CB CDM CM GK LAM LB LCB LCM LDM LF LM LS
## 1.77 0.84 0.84 0.84 4.37 0.37 3.63 4.47 2.33 1.77 0.28 2.98 1.67
## LW LWB RAM RB RCB RCM RDM RF RM RS RW RWB ST
## 0.65 0.37 0.37 3.53 4.28 2.33 1.67 0.28 3.16 1.67 0.65 0.37 2.79
## Sub
## 51.72um texto com as minhas respostas
Quantos usam P. A. P?
Proporção do ensino médio?
tabela3 <- table(Familias$p.a.p)
tabela3##
## Não usa Usa
## 42 78tabela4 <- table(Familias$instr)
tabela4##
## Ensino fundamental Ensino médio Sem Instrução
## 38 44 38round(prop.table(tabela4)*100,2)##
## Ensino fundamental Ensino médio Sem Instrução
## 31.67 36.67 31.67#Gráfico de pizza
#----------------------------------------------
#Gráfico de pizza
#---------------------------------------------------
#smples
pie(tabela3)
#simples + título
pie(tabela3,main = "Gráfico 1 - Uso de programa de alimentação popular")
#simples + título + cor
pie(tabela3,col= c("lightgreen", "pink"), main = "Gráfico 1 - Uso de programa alimentar popular")
#simples + título + cor
pie(tabela3,col= c("tomato3", "tomato4"), main = "Gráfico 2 - Uso de programa alimentar popular")
##simples + título + cor
pie(tabela2,col= c("#967dc9", "#c97dc1"), main = "Gráfico 3 - posição dos jogadores")
#Gráfico de barras
#----------------------------------
#Gráfico de barras
#---------------------------------------------
barplot(tabela4)
tabela4##
## Ensino fundamental Ensino médio Sem Instrução
## 38 44 38#corrigir a ordem dos fatores
Familias$instr2 <- factor(Familias$instr,
levels = c("Sem Instrução",
"Ensino fundamental",
"Ensino médio"))
table(Familias$instr)##
## Ensino fundamental Ensino médio Sem Instrução
## 38 44 38table(Familias$instr2)##
## Sem Instrução Ensino fundamental Ensino médio
## 38 38 44tabela4 <- table(Familias$instr2)
barras <- barplot(tabela4, main = "Gráfico 2 - Escolaridade",
col = c("skyblue","royalblue","darkblue"),
ylim = c(0,50))
#RÓTULO
text(barras, 0, tabela4,cex=1,pos=3,
col = c("black", "white","white"))