QUESTÃO 1
MRT_1F <-c(517.1468515630205, 85.13094142168089, 30.333207896694553, 12.694776264558937, 3.3041601673945418, 1.1823111717498882, 1.1892293502386786)
MRT_3F <-c(156.68929936163462, 11.540837783562276, 0.4512835621696538, 0.4509797929766453, 0.4502068233039181, 0.4496185276300172, 0.4543157082191288)
MRT_5F <-c(83.90319666471157, 0.3068151086494968, 0.30522314133037304, 0.3072588968084928, 0.30655265997285697, 0.3055812715727718, 0.3053297166713006)
MRT_10F <-c(29.55430642951759, 0.19832832665772515, 0.1971923924717474, 0.19796648905716516, 0.19615594370806338, 0.2034569237883263, 0.19617420889447737)
MRT_15F <-c(11.317736530583566, 0.167364215666193, 0.16172168266811013, 0.16701085329580515, 0.1598052657153692, 0.1645934043532696, 0.16216563797118075)
MRT_sem_F <-c(11.93430909937736, 0.6095414637034009, 0.6060645101029295, 0.612167181646899, 0.6146761002685637, 0.6096747087200697, 0.6125810476877268)
clock <- c(0.1, 0.5, 1, 1.5, 2, 2.5, 3)
plot(clock,MRT_1F,type="o",pch=4,col="black",xlab="Time between Things requests
(seconds)",ylab="Response Time (sec.)",)
lines(clock,MRT_3F,type="o",pch=11,col="yellow")
lines(clock,MRT_5F,type="o",pch=1,col="red")
lines(clock,MRT_10F,type="o",pch=2,col="blue")
lines(clock,MRT_15F,type="o",pch=5,col="purple")
lines(clock,MRT_sem_F,type="o",pch=4,col="green")
legend("topright",pch=c(4,11,1,2,5,4),
col=c("black","yellow","red","blue","purple","green"),
legend=c("1 Fog","3 Fogs","5 Fogs","10 Fogs","15 Fogs","w/o Fog"))
par(mfrow = c(1, 2))
barplot(matrix(c(MRT_sem_F,MRT_1F),nrow=2,ncol=7,byrow=TRUE),log="y",
ylab="Response time (s)",xlab="Time between Things requests",
names.arg=clock,col=c("gray","azure4"),beside=TRUE)
legend("topright",pch=c(15,15),col=c("gray","azure4"),legend=c("w/o Fog","1 Fog"))
barplot(matrix(c(MRT_sem_F,MRT_3F),nrow=2,ncol=7,byrow=TRUE),log="y",
ylab="Response time (s)",xlab="Time between Things requests",
names.arg=clock,col=c("gray","azure4"),beside=TRUE)
legend("topright",pch=c(15,15),col=c("gray","azure4"),legend=c("w/o Fog","3 Fogs"))
barplot(matrix(c(MRT_sem_F,MRT_5F),nrow=2,ncol=7,byrow=TRUE),log="y",
ylab="Response time (s)",xlab="Time between Things requests",
names.arg=clock,col=c("gray","azure4"),beside=TRUE)
legend("topright",pch=c(15,15),col=c("gray","azure4"),legend=c("w/o Fog","5 Fogs"))
barplot(matrix(c(MRT_sem_F,MRT_10F),nrow=2,ncol=7,byrow=TRUE),log="y",
ylab="Response time (s)",xlab="Time between Things requests",
names.arg=clock,col=c("gray","azure4"),beside=TRUE)
legend("topright",pch=c(15,15),col=c("gray","azure4"),legend=c("w/o Fog","10 Fogs"))
barplot(matrix(c(MRT_sem_F,MRT_15F),nrow=2,ncol=7,byrow=TRUE),log="y",
ylab="Response time (s)",xlab="Time between Things requests",
names.arg=clock,col=c("gray","azure4"),beside=TRUE)
legend("topright",pch=c(15,15),col=c("gray","azure4"),legend=c("w/o Fog","15 Fogs"))
QUESTÃO 2
cores<-c("green","orange","brown")
metal_price<-c("$10-19","$20-29","$30-39","$40-49")
Porcentagens<-matrix(c(53.8,33.9,2.6,0.0,43.6,54.2,60.5,21.4,2.6,11.9,36.8,78.6),
nrow=3,ncol=4,byrow=TRUE)
quality_rating<-c("Good","Very Good","Excellent")
barplot(Porcentagens, main="Qualidade de Refeição",names.arg=metal_price,
ylab="Total (%)",xlab="Meal Price",col=cores)
legend("topright",pch=c(15,15,15),col=cores,legend=quality_rating)
QESTÃO 3
airquality_may<-subset(airquality,airquality$Month==5)
Temperature<-airquality_may$Temp
Temperature_C<-(Temperature-32)/1.8
hist(Temperature_C,main="Histograma da Temperatura",col="blue", xlab="Temperatura de Maio (°C)",ylab="Frequência",density=10)
QUESTÃO 4
sales <- read.table("https://training-course-material.com/images/8/8f/Sales.txt",header=TRUE)
sales_pct<-round(sales$SALES/sum(sales$SALES)*100)
lbls<-paste(sales$COUNTRY,sales_pct)
lbls<-paste(lbls,"%",sep="")
pie(sales$SALES,lbls,main="Vendas por país",col=c("blue","green","red","yellow","magenta","cyan"))
legend("topright",pch=c(15,15),col=c("blue","green","red","yellow","magenta","cyan"),legend=sales$COUNTRY)
QUESTÃO 5
boxplot(count~spray,data=InsectSprays,ylab="Contagem de insetos",xlab="Inseticida",main="Dados de contagens de insetos tratados com inseticidas diferentes",outline=FALSE)
QUESTÃO 6
#Carregar pacotes necessários
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(ggplot2)
library(lubridate)
##
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, union
library(gridExtra)
##
## Attaching package: 'gridExtra'
## The following object is masked from 'package:dplyr':
##
## combine
#Carregar os dados
data_0.1 <- read.csv("monitoringCloudData_0.1.csv")
data_0.5 <- read.csv("monitoringCloudData_0.5.csv")
data_1 <- read.csv("monitoringCloudData_1.csv")
data_NONE <- read.csv("monitoringCloudData_NONE.csv")
#Ajustar coluna de tempo (currentTime) para um formato de data contínuo
data_0.1$currentTime <- ymd_hms(data_0.1$currentTime)
data_0.5$currentTime <- ymd_hms(data_0.5$currentTime)
## Warning: 194 failed to parse.
data_1$currentTime <- ymd_hms(data_1$currentTime)
data_NONE$currentTime <- ymd_hms(data_NONE$currentTime)
#Ajustar a coluna usedMemory para megabytes
#Definir função para converter valores de memória
convert_memory <- function(memory_str) {
if (grepl("TB", memory_str)) {
memory <- as.numeric(gsub("TB", "", memory_str)) * 1000000
} else if (grepl("GB", memory_str)) {
memory <- as.numeric(gsub("GB", "", memory_str)) * 1024
} else if (grepl("MB", memory_str)) {
memory <- as.numeric(gsub("MB", "", memory_str))
}
return(memory)
}
#Aplicar a função de conversão para todas as tabelas
data_0.1$usedMemory <- sapply(data_0.1$usedMemory, convert_memory)
data_0.5$usedMemory <- sapply(data_0.5$usedMemory, convert_memory)
data_1$usedMemory <- sapply(data_1$usedMemory, convert_memory)
data_NONE$usedMemory <- sapply(data_NONE$usedMemory, convert_memory)
#Criar gráficos para cada conjunto de dados
plot_NONE <-ggplot(data_NONE, aes(x = currentTime, y = usedMemory)) +
geom_line(color = "purple") +
labs(title = "Memory Analysis (None Workload)",
x = "Time (hour)",
y = "Used Memory (MB)")
plot_0.1 <-ggplot(data_0.1, aes(x = currentTime, y = usedMemory)) +
geom_line(color = "blue") +
labs(title = "Memory Analysis (Workload of 0.1)",
x = "Time (hour)",
y = "Used Memory (MB)")
plot_0.5 <-ggplot(data_0.5, aes(x = currentTime, y = usedMemory)) +
geom_line(color = "red") +
labs(title = "Memory Analysis (Workload of 0.5)",
x = "Time (hour)",
y = "Used Memory (MB)")
plot_1 <-ggplot(data_1, aes(x = currentTime, y = usedMemory)) +
geom_line(color = "green") +
labs(title = "Memory Analysis (Workload of 1.0)",
x = "Time (hour)",
y = "Used Memory (MB)")
grid.arrange(plot_NONE, plot_0.1, plot_0.5, plot_1, nrow = 2)
## Warning: Removed 194 rows containing missing values or values outside the scale range
## (`geom_line()`).
QUESTÃO 7
#Carregar as bibliotecas necessárias
library(dplyr)
library(plotly)
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
library(readr)
library(stringr)
#Carregar o dataset
netflix_df <- read_csv("netflix_titles.csv")
## Rows: 7787 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (11): show_id, type, title, director, cast, country, date_added, rating,...
## dbl (1): release_year
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#Filtrar os conteúdos com apenas um país de origem
single_country_df <- netflix_df %>%
filter(!is.na(country) & !str_detect(country, ","))
#Contar a quantidade de conteúdos por país e selecionar os 10 principais
country_counts <- single_country_df %>%
count(country, sort = TRUE) %>%
slice_max(n, n = 10) %>%
arrange(desc(n))
#Em caso de empate, remover o último país
if (nrow(country_counts) > 10) {
country_counts <- country_counts[1:10,]
}
#Exibir os dados dos 10 países com mais conteúdos
country_counts
## # A tibble: 10 × 2
## country n
## <chr> <int>
## 1 United States 2555
## 2 India 923
## 3 United Kingdom 397
## 4 Japan 226
## 5 South Korea 183
## 6 Canada 177
## 7 Spain 134
## 8 France 115
## 9 Egypt 101
## 10 Mexico 100
#Criar o gráfico de pizza
fig <- plot_ly(country_counts, labels = ~country, values = ~n, type = 'pie') %>%
layout(title = 'Top 10 Países com Mais Conteúdos na Netflix',
xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
#Exibir o gráfico
fig
QUESTÃO 8
#Criar a tabela com Plotly
fig <- plot_ly(
type = 'table',
header = list(
values = c("País", "Total de conteúdos"),
align = c('center', 'center'),
line = list(width = 1, color = 'black'),
fill = list(color = 'grey'),
font = list(family = "Arial", size = 12, color = "white")
),
cells = list(
values = rbind(country_counts$country, country_counts$n),
align = c('center', 'center'),
line = list(color = "black", width = 1),
fill = list(color = c('white', 'white')),
font = list(family = "Arial", size = 11, color = c("black"))
)
)
#Exibir a tabela
fig
QUESTÃO 9
#Extrair a década do ano de lançamento
netflix_df <- netflix_df %>%
mutate(decade = (release_year %/% 10) * 10)
#Contar a quantidade de conteúdos por década e tipo
content_by_decade <- netflix_df %>%
group_by(decade, type) %>%
summarise(count = n()) %>%
ungroup()
## `summarise()` has grouped output by 'decade'. You can override using the
## `.groups` argument.
#Exibir os dados
content_by_decade
## # A tibble: 18 × 3
## decade type count
## <dbl> <chr> <int>
## 1 1920 TV Show 1
## 2 1940 Movie 13
## 3 1940 TV Show 1
## 4 1950 Movie 11
## 5 1960 Movie 22
## 6 1960 TV Show 3
## 7 1970 Movie 63
## 8 1970 TV Show 4
## 9 1980 Movie 99
## 10 1980 TV Show 7
## 11 1990 Movie 194
## 12 1990 TV Show 31
## 13 2000 Movie 601
## 14 2000 TV Show 127
## 15 2010 Movie 3951
## 16 2010 TV Show 1760
## 17 2020 Movie 423
## 18 2020 TV Show 476
#Criar o gráfico de linha com pontos
fig <- plot_ly(content_by_decade, x = ~decade, y = ~count, type = 'scatter', mode = 'lines+markers', color = ~type,
colors = c( 'Movie' = 'orange','TV Show' = 'blue'),
line = list(shape = 'linear')) %>%
layout(title = 'Quantidade de Conteúdos por Década na Netflix',
xaxis = list(title = 'Década'),
yaxis = list(title = 'Qnd. Conteúdo'))
#Exibir o gráfico
fig
QESTÃO 10
#Filtrar os filmes lançados entre 2000 e 2010
filtered_movies <- netflix_df %>%
filter(type == "Movie", release_year >= 2000, release_year <= 2010) %>%
mutate(main_genre = str_split(listed_in, ",") %>% sapply(`[`, 1))
#Filtrar apenas os gêneros de interesse
genres_of_interest <- c("Dramas", "Action & Adventure", "Comedies")
filtered_movies <- filtered_movies %>%
filter(main_genre %in% genres_of_interest)
#Contar a quantidade de filmes por ano e por gênero
movies_by_year_genre <- filtered_movies %>%
group_by(release_year, main_genre) %>%
summarise(count = n(), .groups = 'drop')
#Exibir os dados
movies_by_year_genre
## # A tibble: 33 × 3
## release_year main_genre count
## <dbl> <chr> <int>
## 1 2000 Action & Adventure 7
## 2 2000 Comedies 8
## 3 2000 Dramas 5
## 4 2001 Action & Adventure 8
## 5 2001 Comedies 11
## 6 2001 Dramas 7
## 7 2002 Action & Adventure 12
## 8 2002 Comedies 3
## 9 2002 Dramas 14
## 10 2003 Action & Adventure 12
## # ℹ 23 more rows
#Criar o gráfico de barras lado-a-lado
fig <- plot_ly(movies_by_year_genre, x = ~release_year, y = ~count, color = ~main_genre,
type = 'bar', barmode = 'group', colors = c('Dramas' = 'blue',
'Action & Adventure' = 'red',
'Comedies' = 'green')) %>%
layout(title = 'Quantidade de Filmes por Gênero entre 2000 e 2010',
xaxis = list(title = 'Ano de Lançamento'),
yaxis = list(title = 'Quantidade de Filmes'),
barmode = 'group',
legend = list(title = list(text = 'Gênero')))
#Exibir o gráfico
fig
## Warning: 'bar' objects don't have these attributes: 'barmode'
## Valid attributes include:
## '_deprecated', 'alignmentgroup', 'base', 'basesrc', 'cliponaxis', 'constraintext', 'customdata', 'customdatasrc', 'dx', 'dy', 'error_x', 'error_y', 'hoverinfo', 'hoverinfosrc', 'hoverlabel', 'hovertemplate', 'hovertemplatesrc', 'hovertext', 'hovertextsrc', 'ids', 'idssrc', 'insidetextanchor', 'insidetextfont', 'legendgroup', 'legendgrouptitle', 'legendrank', 'marker', 'meta', 'metasrc', 'name', 'offset', 'offsetgroup', 'offsetsrc', 'opacity', 'orientation', 'outsidetextfont', 'selected', 'selectedpoints', 'showlegend', 'stream', 'text', 'textangle', 'textfont', 'textposition', 'textpositionsrc', 'textsrc', 'texttemplate', 'texttemplatesrc', 'transforms', 'type', 'uid', 'uirevision', 'unselected', 'visible', 'width', 'widthsrc', 'x', 'x0', 'xaxis', 'xcalendar', 'xhoverformat', 'xperiod', 'xperiod0', 'xperiodalignment', 'xsrc', 'y', 'y0', 'yaxis', 'ycalendar', 'yhoverformat', 'yperiod', 'yperiod0', 'yperiodalignment', 'ysrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'
## Warning: 'bar' objects don't have these attributes: 'barmode'
## Valid attributes include:
## '_deprecated', 'alignmentgroup', 'base', 'basesrc', 'cliponaxis', 'constraintext', 'customdata', 'customdatasrc', 'dx', 'dy', 'error_x', 'error_y', 'hoverinfo', 'hoverinfosrc', 'hoverlabel', 'hovertemplate', 'hovertemplatesrc', 'hovertext', 'hovertextsrc', 'ids', 'idssrc', 'insidetextanchor', 'insidetextfont', 'legendgroup', 'legendgrouptitle', 'legendrank', 'marker', 'meta', 'metasrc', 'name', 'offset', 'offsetgroup', 'offsetsrc', 'opacity', 'orientation', 'outsidetextfont', 'selected', 'selectedpoints', 'showlegend', 'stream', 'text', 'textangle', 'textfont', 'textposition', 'textpositionsrc', 'textsrc', 'texttemplate', 'texttemplatesrc', 'transforms', 'type', 'uid', 'uirevision', 'unselected', 'visible', 'width', 'widthsrc', 'x', 'x0', 'xaxis', 'xcalendar', 'xhoverformat', 'xperiod', 'xperiod0', 'xperiodalignment', 'xsrc', 'y', 'y0', 'yaxis', 'ycalendar', 'yhoverformat', 'yperiod', 'yperiod0', 'yperiodalignment', 'ysrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'
## Warning: 'bar' objects don't have these attributes: 'barmode'
## Valid attributes include:
## '_deprecated', 'alignmentgroup', 'base', 'basesrc', 'cliponaxis', 'constraintext', 'customdata', 'customdatasrc', 'dx', 'dy', 'error_x', 'error_y', 'hoverinfo', 'hoverinfosrc', 'hoverlabel', 'hovertemplate', 'hovertemplatesrc', 'hovertext', 'hovertextsrc', 'ids', 'idssrc', 'insidetextanchor', 'insidetextfont', 'legendgroup', 'legendgrouptitle', 'legendrank', 'marker', 'meta', 'metasrc', 'name', 'offset', 'offsetgroup', 'offsetsrc', 'opacity', 'orientation', 'outsidetextfont', 'selected', 'selectedpoints', 'showlegend', 'stream', 'text', 'textangle', 'textfont', 'textposition', 'textpositionsrc', 'textsrc', 'texttemplate', 'texttemplatesrc', 'transforms', 'type', 'uid', 'uirevision', 'unselected', 'visible', 'width', 'widthsrc', 'x', 'x0', 'xaxis', 'xcalendar', 'xhoverformat', 'xperiod', 'xperiod0', 'xperiodalignment', 'xsrc', 'y', 'y0', 'yaxis', 'ycalendar', 'yhoverformat', 'yperiod', 'yperiod0', 'yperiodalignment', 'ysrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'