Funcionarios <- data.frame(nome = c("Marx", "Weber", "Durkheim","Arendt", "Maquiavel"),
sexo = c("M", "M", "M", "F","M"),
salario = c(1000, 1200, 1300, 2000, 500),
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
mean(Funcionarios$salario)
## [1] 1200
IIMPORTAR DO CSV
library(readr)
titanic3 <- read_csv("Base_de_dados-master/titanic3.csv")
##
## -- Column specification --------------------------------------------------------
## cols(
## pclass = col_double(),
## survived = col_double(),
## name = col_character(),
## sex = col_character(),
## age = col_double(),
## sibsp = col_double(),
## parch = col_double(),
## ticket = col_character(),
## fare = col_double(),
## cabin = col_character(),
## embarked = col_character(),
## boat = col_character(),
## body = col_double(),
## home.dest = col_character()
## )
View(titanic3)
IIMPORTAR DO EXCEL
library(readxl)
Familias <- read_excel("Base_de_dados-master/Familias.xls",
col_types = c("numeric", "text", "text",
"text", "numeric", "numeric"))
View(Familias)
IMPORTAR ARQUIVO DO CSV
library(readr)
FifaData <- read_csv("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.
View(FifaData)
library(readr)
Fifa <- read_csv("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.
View(Fifa)
pergunta 1 - QUANTOS BRASILEIROS NA FIFA
pergunta 2 - QUAL A PROPORCAO DE ALEMAES
pergunta 3 - QUAL A POSICAO NO PAIS
pergunta 4 - QUANTOS USAM O P A P NA BASE DE DADOS FAMILIA
pergunta 5 - PROPORCAO ENSINO MEDIO
tabela1 <- table(Fifa$Nationality)
View(tabela1)
options(scipen = 999*100)
prop.table(tabela1)
##
## Afghanistan Albania Algeria
## 0.00011371390 0.00210370707 0.00284284740
## Angola Antigua & Barbuda Argentina
## 0.00062542643 0.00022742779 0.06237207187
## Armenia Aruba Australia
## 0.00045485558 0.00005685695 0.01330452581
## Austria Azerbaijan Barbados
## 0.01512394815 0.00045485558 0.00005685695
## Belarus Belgium Belize
## 0.00090971117 0.01506709120 0.00005685695
## Benin Bermuda Bolivia
## 0.00090971117 0.00034114169 0.00170570844
## Bosnia Herzegovina Brazil Bulgaria
## 0.00295656129 0.05236524903 0.00198999318
## Burkina Faso Cameroon Canada
## 0.00079599727 0.00545826700 0.00335455993
## Cape Verde Central African Rep. Chad
## 0.00125085285 0.00034114169 0.00005685695
## Chile China PR Chinese Taipei
## 0.02262906527 0.00170570844 0.00005685695
## Colombia Comoros Congo
## 0.03365931317 0.00051171253 0.00102342506
## Costa Rica Croatia Cuba
## 0.00170570844 0.00659540596 0.00011371390
## Curacao Cyprus Czech Republic
## 0.00062542643 0.00034114169 0.00324084603
## Denmark Dominican Republic DR Congo
## 0.01944507619 0.00011371390 0.00329770298
## Ecuador Egypt El Salvador
## 0.00193313623 0.00170570844 0.00011371390
## England Equatorial Guinea Eritrea
## 0.09199454173 0.00045485558 0.00005685695
## Estonia Faroe Islands FIFA16_NationName_215
## 0.00045485558 0.00034114169 0.00011371390
## Fiji Finland France
## 0.00005685695 0.00341141688 0.05537866727
## FYR Macedonia Gabon Gambia
## 0.00102342506 0.00085285422 0.00079599727
## Georgia Germany Ghana
## 0.00159199454 0.03917443712 0.00676597680
## Gibraltar Greece Grenada
## 0.00005685695 0.00488969752 0.00005685695
## Guam Guatemala Guinea
## 0.00005685695 0.00011371390 0.00193313623
## Guinea Bissau Guyana Haiti
## 0.00096656811 0.00017057084 0.00068228338
## Honduras Hungary Iceland
## 0.00090971117 0.00233113486 0.00267227655
## India Iran Iraq
## 0.00170570844 0.00062542643 0.00045485558
## Israel Italy Ivory Coast
## 0.00079599727 0.04269956789 0.00511712531
## Jamaica Japan Kazakhstan
## 0.00204685013 0.02677962247 0.00011371390
## Kenya Korea DPR Korea Republic
## 0.00034114169 0.00011371390 0.01825108028
## Kosovo Kuwait Latvia
## 0.00176256539 0.00011371390 0.00045485558
## Lebanon Lesotho Liberia
## 0.00011371390 0.00005685695 0.00022742779
## Libya Liechtenstein Lithuania
## 0.00022742779 0.00039799864 0.00085285422
## Luxembourg Madagascar Mali
## 0.00051171253 0.00011371390 0.00261541960
## Malta Mauritania Mauritius
## 0.00022742779 0.00028428474 0.00005685695
## Mexico Moldova Montenegro
## 0.01938821924 0.00034114169 0.00130770980
## Montserrat Morocco Mozambique
## 0.00017057084 0.00420741415 0.00028428474
## Namibia Netherlands New Zealand
## 0.00005685695 0.02422105981 0.00170570844
## Niger Nigeria Northern Ireland
## 0.00011371390 0.00693654765 0.00471912668
## Norway Oman Pakistan
## 0.01944507619 0.00005685695 0.00005685695
## Palestine Panama Papua New Guinea
## 0.00022742779 0.00062542643 0.00005685695
## Paraguay Peru Philippines
## 0.00426427109 0.00193313623 0.00011371390
## Poland Portugal Puerto Rico
## 0.01864907892 0.02046850125 0.00011371390
## Qatar Republic of Ireland Romania
## 0.00011371390 0.02513077098 0.00346827382
## Russia San Marino São Tomé & PrÃncipe
## 0.01756879691 0.00005685695 0.00005685695
## Saudi Arabia Scotland Senegal
## 0.02012735956 0.01660222879 0.00676597680
## Serbia Sierra Leone Slovakia
## 0.00773254492 0.00034114169 0.00346827382
## Slovenia Somalia South Africa
## 0.00329770298 0.00005685695 0.00443484194
## Spain St Kitts Nevis St Lucia
## 0.05731180350 0.00017057084 0.00005685695
## Suriname Sweden Switzerland
## 0.00022742779 0.02149192631 0.01193995906
## Syria Tanzania Timor-Leste
## 0.00028428474 0.00011371390 0.00005685695
## Togo Trinidad & Tobago Tunisia
## 0.00056856948 0.00039799864 0.00198999318
## Turkey Uganda Ukraine
## 0.01660222879 0.00039799864 0.00335455993
## United States Uruguay Uzbekistan
## 0.01887650671 0.00869911303 0.00017057084
## Venezuela Wales Zambia
## 0.00238799181 0.00693654765 0.00022742779
## Zimbabwe
## 0.00056856948
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 & PrÃncipe
## 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$National_Position)
View(tabela2)
round(prop.table(tabela2)*100, 2)
##
## CAM CB CDM CM GK LAM LB LCB LCM LDM LF LM LS
## 1.77 0.84 0.84 0.84 4.37 0.37 3.63 4.47 2.33 1.77 0.28 2.98 1.67
## LW LWB RAM RB RCB RCM RDM RF RM RS RW RWB ST
## 0.65 0.37 0.37 3.53 4.28 2.33 1.67 0.28 3.16 1.67 0.65 0.37 2.79
## Sub
## 51.72
trocando anotacao cientifica pra decimal,
tabela de proporcao,
arredondar,
proporcao tabela 2,
respondendo pergunta 4 e 5.
tabela3 <- table(Familias$p.a.p)
View(tabela3)
tabela4 <- table(Familias$instr)
View(tabela4)
round(prop.table(tabela4)*100, 2)
##
## Ensino fundamental Ensino médio Sem Instrução
## 31.67 36.67 31.67
grafico de pizza simples.
pie(tabela3)
simples + titulo.
pie(tabela3, main = "Grafico 1 - uso programa alimentação popular")
simples + tilulo + cor
pie(tabela3,col = c("lightgreen","pink"), main = "Grafico 1 - uso programa alimentação popular")
para saber as cores dentro do R colors()
pegar cores no google - no endereco colocar #ffffff, escolher a cor, copiar o codigo e colar
pie(tabela3,col = c("#e35454","pink"), main = "Grafico 1 - uso programa alimentação popular")
pizza de cor posicao do jogador
pie(tabela2)
pizza tabela 4
pie(tabela4)
pie(tabela4, main = " Graficoo 2- Posição do jogador")
pie(tabela4, col = c("#9394c9","#424382","#181bc7"), main = "Grafico 2 - Posição do jogador")
pizza facilmente fica poluido e dificil de verificar valores
como fazer grafico de barras tabela 4
barplot(tabela4)
como colocar categorias em hierarquias, em ordem
tabela4
##
## Ensino fundamental Ensino médio Sem Instrução
## 38 44 38
Familias$instr <- factor(Familias$instr, levels = c("Sem Instrução", "Ensino fundamental","Ensino médio"))
barplot(tabela4)
tabela4 <- table(Familias$instr)
tabela4
##
## Sem Instrução Ensino fundamental Ensino médio
## 38 38 44
agora o grafico ficara na ordem correta (sem instruao, fundamental e medio)
barplot(tabela4)
colocar titulo e cor
barplot(tabela4, main = "Tabela 2 - Escolaridade", col = c("#8f1414", "#e04848", "#eda1a1"))
acertar limite da reta y
barplot(tabela4, main = "Tabela 2 - Escolaridade", col = c("#8f1414", "#e04848", "#eda1a1"), ylim = c(0,50))
para colocar legenda dentro do grafico, cor da legenda, posicao da legenda, tamanho da fonte
tabela4 <-table(Familias$instr)
barras <- barplot(tabela4, main = "Tabela 2 - Escolaridade", col = c("#8f1414", "#e04848", "#eda1a1"), ylim = c(0,50))
text(barras, 5, tabela4, cex =2, pos =3, col = c("black", "black", "black"))
