Package {amazonasdatahub}

agriculture_amazonas - Plant production data from the Institute of Sustainable Agricultural and Forestry Development of Amazonas - IDAM (2023).

Description

This database, which is the consolidation of data from IDAM's activity reports, provides crop production data for crops that this institute considers a priority.

Usage

agriculture_amazonas

Format

‘agriculture_amazonas’ A data frame with 1072 rows and 13 columns:

municipality

Municipality

local_unit

Local Unit of the Institute

n_beneficiary

Number of assisted beneficiaries

planted

Planted area in hectares (ha)

harvested

Harvested area in hectares (ha)

production

Production (in the unit of measurement - measure)

estimated_n_beneficiary

Estimate of the number of beneficiaries

estimated_planted

Estimate of the planted area (ha)

estimated_harvested

Estimate of the harvested area (ha)

estimated_production

Estimate of production (in the unit of measurement - measure)

measure

Unit of Measurement of production

cultivation

Cultivation

year

Year of cultivation

Source

INSTITUTO DE DESENVOLVIMENTO AGROPECUÁRIO E FLORESTAL SUSTENTÁVEL DO ESTADO DO AMAZONAS (IDAM). Relatório de Atividades (RAT 23-2). Manaus: IDAM, 2023. Disponível em: https://www.idam.am.gov.br/biblioteca/relatorio-de-atividades-rat-23-2/.

Examples

# Cassava production plot (Gráfico de Produção de Mandioca)
mandioca_prod <- agriculture_amazonas[agriculture_amazonas$cultivation == "Mandioca", ]
plot(
  mandioca_prod$planted,
  mandioca_prod$harvested,
  xlab = "Planted area (hectare)",
  ylab = "Harvested area (hectare)",
  main = "Cassava production in Amazonas",
  sub = "Planted area x Harvested Area"
)

aids_amazonas - A dataset of AIDS occurrences from 2011 to 2023 in Amazonas

Description

This dataset contains data of the AIDS occurrences in each municipality from Amazonas.

Usage

aids_amazonas

Format

‘aids_amazonas’ A data frame with 1612 rows and 5 columns:

code_muni

Municipality code

name_muni

Municipality name

cases

Case count

gender

Gender of the infected individual - M for Male and F for female

year

Year of observation

Source

BRASIL. Ministério da Saúde. Departamento de HIV, Aids, Tuberculose, Hepatites Virais e Infecções Sexualmente Transmissíveis. Indicadores HIV, Aids. Available in: https://indicadores.aids.gov.br/.

Examples

# Aids - case count in Manaus
# Loading dplyr and ggplot to structure the data
require(dplyr)
require(ggplot2)

# Filtering by municipality and plotting case count by gender
aids_amazonas %>%
  filter(name_muni == "Manaus") %>%
  group_by(gender) %>%
  ggplot(aes(x = year, y = cases, group = gender, color = gender)) +
  geom_line() +
  scale_color_manual(values = c("blue", "red")) +
  theme_minimal() +
  labs(
    title = "AIDS ocurrences in Manaus (2011-2023)",
    x = "Year",
    y = "Case count",
    color = "Gender"
  )

gdp_amazonas - Gross Domestic Product - GDP of Amazonas

Description

This database contains information about the Gross Domestic Product (GDP) of the state of Amazonas over the years (from 2010 to 2021). It includes values in R$ 1,000,000.

Usage

gdp_amazonas

Format

‘gdp_amazonas’ A data frame with 48 rows and 21 columns:

year

Indicates the year in which the GDP info was collected

quarter

Specifies the quarter of the year

agriculture

Refers to the economic output from agricultural activities, including cultivation, harvesting, and logistical support after harvest

livestock

Represents the economic contribution from livestock farming, covering meat, dairy production, and related support services

forest_fishing_prod

Outputs from forestry (timber and non-timber products), fishing activities and aquaculture (fish farming)

extractive_ind

Extractive industries of natural resources as minearls and oil

manufacturing_ind

Refers to the transformation of raw materials into finished goods industries

basic_infrastructure

Encompasses services related to electricity and gas generation, water supply, sewage treatment, and waste management.

construction

Represents economic activities related to building construction.

vehicle_retail_repair

Includes all commercial activities related to selling goods and services as well as vehicle repair services.

transport_storage_post

Covers services related to transporting people and goods, storage facilities, and postal services.

accomodation_food

Refers to economic activities in tourism-related sectors such as hotels and restaurants.

information_communication

Encompasses sectors dealing with information technology and telecommunications.

financial_activities

Includes banks, financial institutions, insurance companies, and various financial services.

real_state_activities

Refers to transactions involving buying, selling, or renting properties.

professional_activities

Covers a wide range of specialized professional services including consulting and administrative support.

public_admin_defense_health_edu

Represents public sector spending on government administration, national defense, public education, health care services, and social security programs.

private_edu_health

Refers to educational institutions and health care providers operating in the private sector.

art_culture_sport

Encompasses activities related to entertainment, cultural events, sports facilities, and other service-oriented sectors.

domestic_services

Includes personal services provided within households such as cleaning or caregiving.

taxes

Represents taxes levied on goods and services included in GDP calculations.

Source

Scientific Journal of Applied Social and Clinical Science - TIME SERIES ANALYSIS FOR THE QUARTERLY GROSS DOMESTIC PRODUCT OF AMAZONAS

Examples

# Time series - Gross Domestic Product of Amazonas
# Loading dplyr and ggplot2
require(dplyr)
require(ggplot2)

# Selecting only year and taxes and ploting
gdp_amazonas %>%
  select(year, taxes) %>%
  ggplot(., aes(x = year, y = taxes)) +
  geom_line(linewidth = 1L, colour = "#cb181d") +
  geom_hline(
    yintercept = mean(gdp_amazonas$taxes),
    linetype = "dashed",
    size = 1
  ) +
  theme_light() +
  theme(
    plot.title = element_text(face = "bold", size = 16)
  ) +
  labs(
    x = "Year",
    y = "Taxes",
    title = "Quarterly GDP of Amazonas",
    subtitle = "2010 - 2021"
  )

humidity_manaus - Time series with Extreme Values of Limited Range

Description

A time series dataset consisting of the minimum relative humidity observed in the city of Manaus from January 2009 to December 2020. This dataset is a sample of size n = 144 monthly blocks.

Usage

humidity_manaus

Format

‘humidity_manaus’ A tibble with 144 rows and 5 columns

year

The year in which this data was observed

rh

Relative Humidity

dbt

Dry Bulb Temperature

wbt

Wet Bulb Temperature

month

The month in which this data was observed

Source

NASCIMENTO, Leonardo Brandão Freitas; LIMA, Max Sousa; DUCZMAL, Luiz H. P-min-stable regression models for time series with extreme values of limited range. Environmetrics, Issue 2, v. 36, 2025.

Examples

# Relative humidity time series of Amazonas
# Loading dplyr and ggplot to structure the data
require(dplyr)
require(ggplot2)

# Creating date column and plotting the time series
humidity_manaus %>%
  mutate(date = as.Date(paste0(year, "-", month, "-","01"))) %>%
  ggplot(aes(x = date, y = rh)) +
  geom_line() +
  theme_minimal() +
  labs(
    title = "Relative Humidity of Amazonas (2010 - 2020)",
    x = "Date",
    y = "Relative Humidity"
  )

malaria_amazonas - An integrated dataset of malaria notifications in the state of Amazonas

Description

This dataset contains medical records of patients who underwent malaria screening tests in the Legal Amazon. The data in this dataset were filtered to include only the cases reported in the state of Amazonas and for patients who reside specifically in the state of Amazonas between 2014 and 2019.

Usage

malaria_amazonas

Format

‘malaria_amazonas’ A data frame with 551633 rows and 35 columns:

notification_hr

Health region of notification

notification_county

County of notification

infection_state

Probable state where the patient was infected

infection_hr

Probable health region where the patient was infected

infection_county

Probable county where the patient was infected

home_hr

Health region of residence of the patient

home_county

County of residence of the patient

exam_interval

Time interval between notification and examination

treatment_interval

Time interval between examination and beginning of treatment

notification_interval

Time interval between symptom and notification

notification_month

Month in which the notification was recorded

notification_year

Year in which the notification was recorded

symptom_month

Month in which the patient felt the first symptoms of malaria

symptom_year

Year in which the patient felt the first symptoms of malaria

exam_month

Month in which the examination was performed

exam_year

Year in which the examination was performed

treatment_month

Month in which the treatment started

treatment_year

Year in which the treatment started

migration

Health region of residence different than that of notification

exam_result

Result of examination

detection_type

Type of detection

exam_type

Type of examination

symptom

Indicates if the patient felt a symptom

hemiparasite

The result of the examination for other hemiparasites

previous_treatment

Previous treatment for P. vivax or P.falciparum

occupation

Main activity in the last 15 days

education_level

Level of education of the patient

age

Interval of the age of the patient

race

Race/color of the patient

gender

Gender of the patient

pregnancy

Indicates pregnancy and the gestational age

crosses

Indicates the amount of parasitemia in crosses

cvl_case

Indicates the existence of cases of canine visceral leishmaniasis

scheme

Indicates the treatment scheme employed

qty_parasites

Indicates the number of parasites per mm^3

Source

Lais Baroni, M. P. (2020). An Integrated Dataset of Malaria Notifications in the Legal Amazon (Dataset). Synapse. https://doi.org/10.7303/SYN21552203

Examples

# Bar plot of malaria tests results (2019)
library(dplyr)
library(ggplot2)

malaria_amazonas %>%
  filter(exam_year == 2019) %>%
  count(exam_result) %>%
  ggplot(aes(x = reorder(exam_result, -n), y = n)) +
  geom_bar(stat = "identity") +
  geom_text(
    aes(label = n),
    vjust = -0.5
  ) +
  theme_minimal() +
  labs(
    title = "Results of malaria tests conducted in 2019",
    subtitle = "Documented cases in Amazonas and of patients residing in the state",
    x = "Result",
    y = "Count"
  )

prf_amazonas - Traffic accident data from Amazonas recorded by Federal Highway Police (2012-2025)

Description

Traffic accident data from Amazonas (2012-2025)

Usage

prf_amazonas

Format

‘prf_amazonas’ A data frame with 116 rows and 30 columns:

id

Record ID

date

Date of the record - yyyy-mm-dd

days_of_week

Day of the week

time

Hour of the record

federative_unit

Federative unit (UF)

br

Federal Highway

km

Kilometer of BR

municipality

Municipality

accident_cause

Accident cause

accident_type

Accident type

accident_classification

Accident classification: victims, no victims, unharmed victims, fatal victims

road_direction

Road direction

weather

Weather conditions at the time of the accident

track_type

Track type: Simples, Múltipla ou Dupla

track_layout

Track layout

land_use

Land use

people

People involved in the accident

deceased

Death toll

minor_injuries

Number of victims with minor injuries

serious_injuries

Number of victims with serious injuries

unharmed

Number of unharmed victims

ignored

Ignored

harmed

Number of harmed victims

latitude

Latitude

longitude

Longitude

regional

Superintendency of the Federal Highway Police of Brazil

police_station

Police station where the report was filed

operating_unit

Operating unit and police station

Source

Brasil. Polícia Rodoviária Federal (PRF). Dados Abertos da PRF: Acidentes de Trânsito. https://www.gov.br/prf/pt-br/acesso-a-informacao/dados-abertos/dados-abertos-da-prf.

Examples

# Accident count by weather condition
# Loading dplyr and ggplot
require(dplyr)
require(ggplot2)
require(forcats)

# Counting accidents by weather condition and ploting
prf_amazonas %>%
  count(weather) %>%
  mutate(
    weather = fct_reorder(weather, -n)
  ) %>%
  ggplot(aes(x = weather, y = n)) +
  geom_bar(stat = "identity", fill = "steelblue") +
  geom_text(aes(label = n), vjust = -0.5) +
  theme_minimal() +
  labs(
    title = "Accident count by weather condition",
    x = "Weather",
    y = "Count"
  )

rionegro_amazonas - A dataset of the Rio Negro River (Amazonas) level from 2003 to 2023

Description

This dataset contains data of the Rio Negro River level in the state of Amazonas (Brazil), compiling information on years, semesters and days, along with the river's level in meters and whether it filled or dropped (in centimeters).

Usage

rionegro_amazonas

Format

‘rionegro_amazonas’ A data frame with 8003 rows and 7 columns:

level_m

River level in meters

increase_decrease_cm

Wheter the river level filled or dropped (in centimeters)

day

Measurement day

month

Measurement month

year

Measurement year

semester

Measurement semester

Source

Porto de Manaus. Nível do Rio Negro. https://portodemanaus.com.br/nivel-do-rio-negro/.

Examples

# Rio Negro - Boxplot (water level by year)

# Loading ggplot
require(ggplot2)
require(dplyr)

rionegro_amazonas %>%
  ggplot(aes(x = year, y = level_m, group = year)) +
  stat_boxplot(geom = "errorbar") +
  geom_boxplot() +
  theme_minimal() +
  labs(
    x = "Year",
    y = "Water level (m)"
  )

srl_muni - School Read Levels - Physical literacy and reading performance of amazonian schoolchildren

Description

This is a dataset from an association, cross-sectional, quantitative study carried out with Amazonian schoolchildren (n=100; aged between 10 and 13 years) from 3 public schools in the municipality of Borba (Amazonas).

Usage

srl_muni

Format

‘srl_muni’ A data frame with 100 rows and 17 columns:

school

School of observation

sex

Sex - 1 for Masculine and 2 for Feminine

age

Age in years

weight_kg

Weight in kilograms (kg)

height_m

Height in meters (m)

bmi

Body Mass Index (BMI)

bmi_status

Body Mass Index Status - A categorical description of the BMI

phv

Peak Height Velocity (PHV) is a continuous variable, measured by age (in years) relative to the peak height in the period where maximum rate of growth occurs and this is an indication of maturity

phv_state

Categorical description for Peak Height Velocity (PHV)

eses

Exercise Self-Efficacy Scale measures how confident the students are with regard to carrying out regular physical activities and exercise, measured by a psychometric scale

pmc

Perceived Motor Competence (PMC) assesses the child's self-perception regarding their ability to perform motor movements. Measured by a psychometric scale

kupa

Knowledge and Understanding of Physical Activity (KUPA) - Evaluates knowledge regarding the benefits, safety, and engagement in physical activity. Measured by scores obtained in objective questions

sb

Sedentary Behavior - Evalutes the exposition time to sedentary behaviors. Measured by psychometric scale

pa

Pyshical Activity (PA) - Level of physical activity ian children, based on the frequency and duration of routine daily activities. Measured by score on the physical activity frequency questions

mc

Motor Competence - Supine To Stand (in seconds) - Measures the time (in seconds) it takes for the child to stand up from the floor, from a supine position to a standing position

tde

Total Score on the TDE test - Measures reading level based on time and accuracy in word reading

tde_categorical

Categorical description of the TDE Score

Source

ALMEIDA, Thiago da Cruz de. Physical Literacy e desempenho em leitura de escolares amazônicos: um estudo de associação. 2024. 104 f. Dissertação (Mestrado em Educação) - Universidade Federal do Amazonas, Manaus, 2024. Disponível em: https://tede.ufam.edu.br/handle/tede/10627.