IBM|Educathon

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# Análise de dados de COVID 19: utilizando CP4D e DB2

## Educathon 2022

Thiago Pires | Abr 2022 
Repositório [github.com/th1460/ibm-educathon-2022](https://github.com/th1460/ibm-educathon-2022) 
Slides [th1460.github.io/ibm-educathon-2022](https://th1460.github.io/ibm-educathon-2022)

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# Thiago Pires

.pull-left[
<img class="rounded-corners" src="resources/images/avatar.jpeg" width="400px"/>
]
.pull-right[
<div id="htmlwidget-a99aba3969fcb4076863" style="width:504px;height:432px;" class="leaflet html-widget"></div>
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# Criando uma conta na IBM Cloud

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# Criando uma conta na IBM Cloud

.pull-left[
- Acesse https://cloud.ibm.com
- Clicar em Create an account

<img src="resources/images/ibm-cloud-login.png" width="90%" />
]

.pull-right[
- Preencha o formulário com o email e informações pessoais
- Não precisa incluir um cartão de crédito para utilizar os recursos da cloud necessários para este workshop!

]

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background-color: #ffcc31

# Cloud Pak for Data

---
# Cloud Pak for Data

>*O Cloud Pak for Data (CP4D) é uma plataforma de análise que ajuda a preparar dados para inteligência artificial (IA). Ele permite que engenheiros de dados, administradores de dados, cientistas de dados e analistas de negócios colaborem usando uma plataforma multicloud integrada.*

## Acessando o recurso na Cloud

.pull-left[
<img src="resources/images/cp4d.png" width="100%" style="display: block; margin: auto;" />
]
.pull-right[

Clicar em Launch para iniciar o recurso.

<img src="resources/images/cp4d-overview.png" width="60%" style="display: block; margin: auto;" />
]

---
# Bem-vindo ao Cloud Pak for Data

## Criar um projeto

.pull-left[
<img src="resources/images/cp4d-project.png" width="90%" style="display: block; margin: auto;" />
]

.pull-right[
Em seguida:
- Definir um none (mandatório)
- Colocar uma descrição (opcional)
- Associar um Cloud Object Storage (mandatório)
]

---
# Visão geral do projeto
<img src="resources/images/cp4d-assets.png" width="90%" style="display: block; margin: auto;" />

---
# Criar serviços

.pull-left[
- Clicar em Create a service.
<img src="resources/images/cp4d-service.png" width="90%" style="display: block; margin: auto;" />

- Adicionar Watson Studio
<img src="resources/images/cp4d-watson-studio.png" width="90%" style="display: block; margin: auto;" />

- Ir para https://dataplatform.cloud.ibm.com/projects/ e clicar no projeto criado anteriormente
]

.pull-right[
- Ir para Launch IDE>RStudio
<img src="resources/images/cp4d-ide.png" width="90%" style="display: block; margin: auto;" />

- Launch RStudio com a configuração sugerida
<img src="resources/images/cp4d-rstudio.png" width="90%" style="display: block; margin: auto;" />
]

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# RStudio
<img src="resources/images/cp4d-rstudio-start.png" width="100%" style="display: block; margin: auto;" />

---
# Adicionando o DB2 no projeto

>*O DB2 é um banco de dados SQL em cloud totalmente gerenciado e com escalabilidade sob demanda de computação e armazenamento.*

.pull-left[
- Ir na pesquisa por recursos
<img src="resources/images/cp4d-db2.png" width="90%" style="display: block; margin: auto;" />

- O DB2 estará listado como ativo
<img src="resources/images/cp4d-db2-active.png" width="90%" style="display: block; margin: auto;" />
]

.pull-right[
- Criar credencial de acesso
<img src="resources/images/cp4d-db2-credential.png" width="90%" style="display: block; margin: auto;" />

>*SQL significa Standard Query Language, literalmente a linguagem padrão para realizar queries. A linguagem SQL é utilizada de maneira relativamente parecida entre os principais bancos de dados relacionais do mercado*

>*Bancos de dados relacionais são baseados no modelo relacional, uma maneira intuitiva e direta de representar dados em tabelas.*
]

---
# Preparando o ambiente

Baixar repositório no RStudio

```bash
git clone https://github.com/th1460/ibm-educathon-2022.git
```

Instalar bibliotecas que serão utilizadas no projeto

```r
install.packages("renv")
renv::restore()
```

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# Análises

---
# Ler dados bruto

```r
require(magrittr, include.only = "%>%")
require(dbplyr)

url <- "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv"

covid19 <- 
 read.csv(url, stringsAsFactors = FALSE)

covid19 %>% 
  dplyr::select(1:9) %>%
  dplyr::glimpse()
```

```
Rows: 284
Columns: 9
$ Province.State <chr> "", "", "", "", "", "", "", "", "", "Australian Capital…
$ Country.Region <chr> "Afghanistan", "Albania", "Algeria", "Andorra", "Angola…
$ Lat <dbl> 33.93911, 41.15330, 28.03390, 42.50630, -11.20270, -71.…
$ Long <dbl> 67.709953, 20.168300, 1.659600, 1.521800, 17.873900, 23…
$ X1.22.20 <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ X1.23.20 <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ X1.24.20 <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ X1.25.20 <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ X1.26.20 <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 1, 0, 0, 0…
```

---
# Preparar dados

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```r
(covid19 <- 
 covid19 %>% 
 dplyr::filter(Country.Region %in% 
 c("Brazil", "Peru", "Bolivia", 
 "Chile", "Argentina", "Colombia",
 "Venezuela", "Ecuador", "Uruguay",
 "Paraguay")) %>% 
 dplyr::select(- Province.State) %>% 
 tidyr::pivot_longer(!Country.Region:Long, 
 names_to = "date", 
 values_to = "cumulate") %>% 
 dplyr::group_by(Country.Region) %>% 
 dplyr::mutate(date = gsub("X", "", date) %>% 
 gsub("\\.", "-", .) %>% 
 lubridate::mdy(),
 value = cumulate - dplyr::lag(cumulate)) %>% 
 dplyr::ungroup() %>%
 dplyr::rename(country = Country.Region))
```
]

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```
# A tibble: 8,260 × 6
 country Lat Long date cumulate value
 <chr> <dbl> <dbl> <date> <int> <int>
 1 Argentina -38.4 -63.6 2020-01-22 0 NA
 2 Argentina -38.4 -63.6 2020-01-23 0 0
 3 Argentina -38.4 -63.6 2020-01-24 0 0
 4 Argentina -38.4 -63.6 2020-01-25 0 0
 5 Argentina -38.4 -63.6 2020-01-26 0 0
 6 Argentina -38.4 -63.6 2020-01-27 0 0
 7 Argentina -38.4 -63.6 2020-01-28 0 0
 8 Argentina -38.4 -63.6 2020-01-29 0 0
 9 Argentina -38.4 -63.6 2020-01-30 0 0
10 Argentina -38.4 -63.6 2020-01-31 0 0
# … with 8,250 more rows
```
]

---
# Salvar dados no DB2

```r
# Ler variáveis de ambiente
readRenviron("../.Renviron")

# Criar conexão com o banco
drv <-
 RJDBC::JDBC("com.ibm.db2.jcc.DB2Driver", "../jars/db2jcc4.jar")

host <- Sys.getenv("DB2_HOST")
user <- Sys.getenv("DB2_USER")
password <- Sys.getenv("DB2_PASSWORD")

uri <- 
 sprintf("jdbc:db2://%s/bludb:user=%s;password=%s;sslConnection=true;", 
 host, user, password)

db2 <-
 DBI::dbConnect(drv, uri)
```

```r
# Escrever tabela no banco de dados
DBI::dbWriteTable(db2, "COVID19", value = covid19, overwrite = TRUE)
```

---
# Ler dados do DB2

```r
dplyr::tbl(db2, "COVID19") %>%
  dplyr::filter(COUNTRY == "Brazil")
```

```
# Source: lazy query [?? x 6]
# Database: JDBCConnection
 COUNTRY LAT LONG DATE CUMULATE VALUE
 <chr> <dbl> <dbl> <chr> <dbl> <dbl>
 1 Brazil -14.2 -51.9 2020-01-22 0 NA
 2 Brazil -14.2 -51.9 2020-01-23 0 0
 3 Brazil -14.2 -51.9 2020-01-24 0 0
 4 Brazil -14.2 -51.9 2020-01-25 0 0
 5 Brazil -14.2 -51.9 2020-01-26 0 0
 6 Brazil -14.2 -51.9 2020-01-27 0 0
 7 Brazil -14.2 -51.9 2020-01-28 0 0
 8 Brazil -14.2 -51.9 2020-01-29 0 0
 9 Brazil -14.2 -51.9 2020-01-30 0 0
10 Brazil -14.2 -51.9 2020-01-31 0 0
# … with more rows
```

---
# Análises

```r
brazil <- dplyr::tbl(db2, "COVID19") %>%
 dplyr::filter(COUNTRY == "Brazil") %>%
 dplyr::as_tibble() %>%
 dplyr::mutate(DATE = DATE %>% lubridate::ymd())
```

## Distribuição do número de casos diários

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```r
brazil %>%
  ggplot2::ggplot() +
  ggplot2::aes(DATE, VALUE) +
  ggplot2::geom_line() +
  ggplot2::theme_minimal() +
  ggplot2::labs(x = "Data", 
                y = "# de casos de COVID 19")
```
]

.pull-right[
<img src="index_files/figure-html/plot1-1.png" width="504" style="display: block; margin: auto;" />
]

---
# Análises
## Distribuição da média móvel de 7 dias de casos

```r
brazil <- 
 brazil %>%
* dplyr::mutate(MOVING_AVERAGE = zoo::rollmean(VALUE, 7, align = "right", fill = NA))
```

.pull-left[

```r
brazil %>%
  ggplot2::ggplot() +
  ggplot2::aes(DATE, MOVING_AVERAGE) +
  ggplot2::geom_line() +
  ggplot2::theme_minimal() +
  ggplot2::labs(x = "Data", 
                y = "Média móvel de casos de COVID 19")
```

]
.pull-right[
<img src="index_files/figure-html/plot2-1.png" width="504" style="display: block; margin: auto;" />
]

---
# Análises
## Valor máximo da distribuição de média móvel de 7 dias de casos

```r
brazil %>%
  dplyr::filter(MOVING_AVERAGE == max(MOVING_AVERAGE, na.rm = TRUE)) %>%
  dplyr::select(COUNTRY, DATE, MOVING_AVERAGE) %>%
  reactable::reactable()
```

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---
# Análises
## Distribuição da média móvel de 7 dias de casos (plotly)

.pull-left[

```r
gg <- brazil %>%
 ggplot2::ggplot() +
 ggplot2::aes(DATE, MOVING_AVERAGE) +
 ggplot2::geom_line() +
 ggplot2::theme_minimal() +
 ggplot2::labs(x = "Data", 
 y = "Média móvel de casos de COVID 19")
gg %>% 
* plotly::ggplotly()
```
]
.pull-right[
<div id="htmlwidget-efa98006cd1a83343183" style="width:504px;height:360px;" class="plotly html-widget"></div>
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]

---
# Análises
## Variação entre a média móvel mais atual e a média móvel anterior

```r
delta <- dplyr::tbl(db2, "COVID19") %>%
 dplyr::filter(DATE %in% c("2022-04-22", "2022-04-23")) %>%
 dplyr::as_tibble() %>%
 dplyr::mutate(DATE = DATE %>% lubridate::ymd(),
 MOVING_AVERAGE = zoo::rollmean(VALUE, 7, align = "right", fill = NA)) %>%
 dplyr::group_by(COUNTRY) %>%
* dplyr::mutate(`DELTA%` = (MOVING_AVERAGE - dplyr::lag(MOVING_AVERAGE))/dplyr::lag(MOVING_AVERAGE) * 100,
 `DELTA%` = ifelse(is.na(`DELTA%`), 0, round(`DELTA%`, 1))) %>%
 dplyr::filter(DATE == "2022-04-23")

gg <- delta %>%
 dplyr::select(COUNTRY, `DELTA%`) %>%
 ggplot2::ggplot() +
 ggplot2::aes(COUNTRY, `DELTA%`) +
 ggplot2::geom_col(fill = ifelse(delta$`DELTA%` > 0, "#cb6e5d", "#005c5b")) +
 ggplot2::theme_minimal() +
 ggplot2::labs(x = "Países", y = "Delta %")
plotly::ggplotly(gg)
```

---
# Análises
## Variação entre a média móvel mais atual e a média móvel anterior

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---
# Análises
## Mapa com as variações percentuais

.pull-left[

```r
conpal <- 
 leaflet::colorNumeric(palette = "RdBu", 
 domain = delta$`DELTA%`, 
 na.color = "black", 
 reverse = TRUE)

delta %>%
 dplyr::select(COUNTRY, LAT, LONG, `DELTA%`) %>%
 leaflet::leaflet() %>%
 leaflet::addProviderTiles("CartoDB.DarkMatter") %>%
 leaflet::addCircleMarkers(~LONG, ~LAT, 
 label = paste(delta$COUNTRY, " ", 
 round(delta$`DELTA%`, 1)),
 color = ~conpal(delta$`DELTA%`)) %>%
 leaflet::addLegend(position = "bottomleft", 
 title = "Delta %",
 pal = conpal, 
 values = delta$`DELTA%`,
 opacity = 0.8)
```
]
.pull-right[
<div id="htmlwidget-8872949b8a5abc936f56" style="width:504px;height:432px;" class="leaflet html-widget"></div>
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]

---
# Funções SQL

```r
dplyr::tbl(db2, "COVID19") %>%
* dplyr::mutate(REF_DATE = dplyr::sql("VARCHAR_FORMAT(LAST_DAY(TO_DATE(DATE, 'YYYYMMDD')), 'YYYY-MM-DD')")) %>%
  dplyr::group_by(COUNTRY, REF_DATE) %>%
  dplyr::summarise(SUM = sum(VALUE, na.rm = TRUE), .groups = "drop") %>%
  dplyr::filter(COUNTRY == "Brazil")
```

```
# Source: lazy query [?? x 3]
# Database: JDBCConnection
 COUNTRY REF_DATE SUM
 <chr> <chr> <dbl>
 1 Brazil 2020-01-31 0
 2 Brazil 2020-02-29 2
 3 Brazil 2020-03-31 5715
 4 Brazil 2020-04-30 81470
 5 Brazil 2020-05-31 428950
 6 Brazil 2020-06-30 896532
 7 Brazil 2020-07-31 1257782
 8 Brazil 2020-08-31 1244378
 9 Brazil 2020-09-30 902536
10 Brazil 2020-10-31 720398
# … with more rows
```

---
class: inverse center middle
background-color: #000

.pull-left[
# Obrigado!
]

.pull-right[
[th1460.github.io](https://th1460.github.io/) 
[github.com/th1460](https://github.com/) 
[medium.com/@thopr](https://medium.com/@thopr) 
[linkedin.com/in/thop](https://www.linkedin.com/in/thop) 
slides [th1460.github.io/ibm-educathon-2022](https://th1460.github.io/ibm-educathon-2022)
]