In Epiconcept-Paris/GADMTools: Easy Use of 'GADM' Maps
Epiconcept is made up of a team of doctors, epidemiologists, data scientists and digital specialists.
For more than 20 years, Epiconcept has been contributing to the improvement of public health programs by providing software, epidemiological studies, counseling, evaluation and training to better prevent, detect and treat people.
Epiconcept delivers software and services in the following areas :
- Software for managing public health programs,
- Secure cloud solutions for health data collection, reporting and processing,
- The implementation of research projects on measuring the effectiveness and impact of vaccines,
- Services in the field of epidemiology (protocols, analyzes, training, etc.),
- Expertise in data analysis,
- Counseling, coaching and assistance to project owners for public health programs,
- Training (short introductory modules, training through long-term practice).
To achieve such goals Epiconcept :
- Recognized research organization,
- Certified datacenter for hosting personal health data,
- Training organisation.
Epiconcept relies on :
- Its expertise in epidemiology
- Its IT expertise,
- Ethical values rooted in practice (responsibility and quality of services, data security and confidentiality, scientific independence, etc.),
- Capabilities to answer and anticipate tomorrow’s challenges (Research - evaluation, e-health, Big Data, IoT, etc.),
- A desire to build long-term relationships with its clients and partners.
Its current customers and partners include some of the greatest names in the world such as : Santé Publique France (and many public health organizations around the world), WHO, eCDC, AFD, MSF, World Bank, etc.
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Graphics
functions
Function | Description
:------------------- | :------------------------------------------
choropleth | Draws a choropleth on selected regions
classDots | Plots dots on a map with values between different fixed classestries
dots | Plots dots on a map
dotDensity | Draws a dot-density map
isopleth | Draws an isopleth on selected regions
gadm_showNorth | Displays a north arrow on a plot
gadm_showScale | Displays a scale on a plot
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Display a north arrow on a plot
library(GADMTools)
data("Corsica")
plotmap(Corsica) %>% gadm_showNorth("tl")
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Display a scale on a plot
library(GADMTools)
data("Corsica")
plotmap(Corsica) %>% gadm_showNorth("tl") %>% gadm_showScale("bl")
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Plotting dots on a map
# Preparing data.frame
# --------------------
data("Corsica")
Corse <- gadm_union(Corsica, 0)
Cantons <- listNames(Corsica, 4)
L <- length(Cantons)
Pop <- floor(runif(L, min=15200, max=23500))
longitude <- runif(6, min=8.74, max = 9.25)
latitude <- runif(6, min=41.7, max = 42.6)
Cases <- floor(runif(6, 25, 80))
Type <- rep(c("TYPE A", "TYPE B", "TYPE C"), 6, length.out = 6)
DAT <- data.frame(longitude, latitude, Cases)
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# Simple dots
#--------------------------------------------------------------------
dots(Corsica, DAT, color="red", size = 3)
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# Classified dots
#--------------------------------------------------------------------
dots(Corse, points = DAT,
palette = "Reds",
value="Cases")
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# Typed points
#--------------------------------------------------------------------
DAT2 <- data.frame(longitude, latitude, Type)
dots(Corse, points = DAT2,
color = "#ee00ee",
strate="Type")
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Plotting proportionals dots
# Test of propDots with default parameters
# ------------------------------------------------------------------------------
propDots(Corse,
data = DAT,
value="Cases",
color = "blue")
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# Test of propDots with defined breaks
# ------------------------------------------------------------------------------
propDots(Corse,
data = DAT,
value="Cases", breaks=c(30, 40, 50, 70, 100),
color = "blue")
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# Test of propDots with forced range of breaks
# ------------------------------------------------------------------------------
propDots(Corse, data = DAT, value="Cases",
breaks=c(0, 25, 50, 75, 100),
range = c(25, 100))
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Plotting dots with classified size
library(GADMTools)
classDots(Corse, DAT, color="blue", value = "Cases", steps = 4)
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Dot-Density
library(GADMTools)
data("Corsica")
# Creates test data.frame -------------------------------------------------
# --------------------------------------------------------------------------
VAR_1 <- as.integer(runif(n = 43, min = 800, max = 15800))
VAR_2 <- as.integer(runif(n = 43, min = 1000, max = 15800))
VAR_3 <- as.integer(runif(n = 43, min = 1500, max = 15800))
Cantons <- listNames(Corsica, 4)
DF <- data.frame(Cantons, VAR_1, VAR_2, VAR_3, stringsAsFactors = FALSE)
dotDensity(Corsica,
DF, adm.join="Cantons", dot.size = 0.5, cases.by.dots = 1000,
values = c("VAR_1", "VAR_2", "VAR_3"),
labels = c("H1N1", "H1N2", "H2N2"),
palette = c("#ffff00", "#ffaa00", "#FF3200"))
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Plotting density
library(GADMTools)
isopleth(Corse, data = DAT, palette = "Blues")
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Plotting a choropleth
DAT <- data.frame(Cantons, Pop, stringsAsFactors = FALSE)
choropleth(Corsica, data = DAT, value = "Pop", adm.join = "Cantons",
breaks = "sd", palette = "Greens")
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fast.choropleth()
fast.choropleth(
x, data, value=NULL,
breaks = NULL, steps = 5,
adm.join=NULL, legend = NULL,
labels = NULL,
palette=NULL, title=""
)
Parameter | Description
--------- | ---------------------------------------------------------------------------------
x | Object GADMWrapper
data | data.frame - data to plot
value | String - the name of the column in the data.frame we want to plot (eg: an incidence in epidemiology studies)
breaks |
steps | Integer - number of breaks. Default = 5. If breaks is NOT NULL this value is used internally with cut().
adm.join | String - the name in GADM spdf dataset which will be joined with a column of the data.
legend | String - legend title. Default NULL.
labels | String vector labels for the legend. Default NULL
palette | String - An RColorBrewer palette name or a String vector vector of colors. Default NULL.
title | String - Title of the plot. Default is an empty string.
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Example
MAP <- gadm_sp_loadCountries("BEL", level = 3, simplify=0.01)
DAT = read.csv2("BE_clamydia_incidence.csv")
# Rewriting District names
# ------------------------
DAT$district <- as.character(DAT$district)
DAT[7,1] = "Brussel"
DAT[20,1] <- "Liège"
DAT[22,1] = "Marche-en-Famenne"
DAT[27,1] = "Neufchâteau"
DAT <- rename(DAT, NAME_3 = district)
fast.choropleth(MAP, DAT,
adm.join = "NAME_3",
value = "rate03",
steps = 4,
breaks = "jenks",
palette="Greens",
legend = "Incidence",
title="Chlamydia incidence by Belgian district (2003)")
Epiconcept-Paris/GADMTools documentation built on March 6, 2020, 10:24 a.m.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Epiconcept is made up of a team of doctors, epidemiologists, data scientists and digital specialists. For more than 20 years, Epiconcept has been contributing to the improvement of public health programs by providing software, epidemiological studies, counseling, evaluation and training to better prevent, detect and treat people.
Epiconcept delivers software and services in the following areas :
- Software for managing public health programs,
- Secure cloud solutions for health data collection, reporting and processing,
- The implementation of research projects on measuring the effectiveness and impact of vaccines,
- Services in the field of epidemiology (protocols, analyzes, training, etc.),
- Expertise in data analysis,
- Counseling, coaching and assistance to project owners for public health programs,
- Training (short introductory modules, training through long-term practice).
To achieve such goals Epiconcept :
- Recognized research organization,
- Certified datacenter for hosting personal health data,
- Training organisation.
Epiconcept relies on :
- Its expertise in epidemiology
- Its IT expertise,
- Ethical values rooted in practice (responsibility and quality of services, data security and confidentiality, scientific independence, etc.),
- Capabilities to answer and anticipate tomorrow’s challenges (Research - evaluation, e-health, Big Data, IoT, etc.),
- A desire to build long-term relationships with its clients and partners.
Its current customers and partners include some of the greatest names in the world such as : Santé Publique France (and many public health organizations around the world), WHO, eCDC, AFD, MSF, World Bank, etc.
\newpage
Graphics
functions
Function | Description :------------------- | :------------------------------------------ choropleth | Draws a choropleth on selected regions classDots | Plots dots on a map with values between different fixed classestries dots | Plots dots on a map dotDensity | Draws a dot-density map isopleth | Draws an isopleth on selected regions gadm_showNorth | Displays a north arrow on a plot gadm_showScale | Displays a scale on a plot
\newpage
Display a north arrow on a plot
library(GADMTools) data("Corsica") plotmap(Corsica) %>% gadm_showNorth("tl")
\newpage
Display a scale on a plot
library(GADMTools) data("Corsica") plotmap(Corsica) %>% gadm_showNorth("tl") %>% gadm_showScale("bl")
\newpage
Plotting dots on a map
# Preparing data.frame # -------------------- data("Corsica") Corse <- gadm_union(Corsica, 0) Cantons <- listNames(Corsica, 4) L <- length(Cantons) Pop <- floor(runif(L, min=15200, max=23500)) longitude <- runif(6, min=8.74, max = 9.25) latitude <- runif(6, min=41.7, max = 42.6) Cases <- floor(runif(6, 25, 80)) Type <- rep(c("TYPE A", "TYPE B", "TYPE C"), 6, length.out = 6) DAT <- data.frame(longitude, latitude, Cases)
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# Simple dots #-------------------------------------------------------------------- dots(Corsica, DAT, color="red", size = 3)
\newpage
# Classified dots #-------------------------------------------------------------------- dots(Corse, points = DAT, palette = "Reds", value="Cases")
\newpage
# Typed points #-------------------------------------------------------------------- DAT2 <- data.frame(longitude, latitude, Type) dots(Corse, points = DAT2, color = "#ee00ee", strate="Type")
\newpage
Plotting proportionals dots
# Test of propDots with default parameters # ------------------------------------------------------------------------------ propDots(Corse, data = DAT, value="Cases", color = "blue")
\newpage
# Test of propDots with defined breaks # ------------------------------------------------------------------------------ propDots(Corse, data = DAT, value="Cases", breaks=c(30, 40, 50, 70, 100), color = "blue")
\newpage
# Test of propDots with forced range of breaks # ------------------------------------------------------------------------------ propDots(Corse, data = DAT, value="Cases", breaks=c(0, 25, 50, 75, 100), range = c(25, 100))
\newpage
Plotting dots with classified size
library(GADMTools) classDots(Corse, DAT, color="blue", value = "Cases", steps = 4)
\newpage
Dot-Density
library(GADMTools) data("Corsica") # Creates test data.frame ------------------------------------------------- # -------------------------------------------------------------------------- VAR_1 <- as.integer(runif(n = 43, min = 800, max = 15800)) VAR_2 <- as.integer(runif(n = 43, min = 1000, max = 15800)) VAR_3 <- as.integer(runif(n = 43, min = 1500, max = 15800)) Cantons <- listNames(Corsica, 4) DF <- data.frame(Cantons, VAR_1, VAR_2, VAR_3, stringsAsFactors = FALSE) dotDensity(Corsica, DF, adm.join="Cantons", dot.size = 0.5, cases.by.dots = 1000, values = c("VAR_1", "VAR_2", "VAR_3"), labels = c("H1N1", "H1N2", "H2N2"), palette = c("#ffff00", "#ffaa00", "#FF3200"))
\newpage
Plotting density
library(GADMTools) isopleth(Corse, data = DAT, palette = "Blues")
\newpage
Plotting a choropleth
DAT <- data.frame(Cantons, Pop, stringsAsFactors = FALSE) choropleth(Corsica, data = DAT, value = "Pop", adm.join = "Cantons", breaks = "sd", palette = "Greens")
\newpage
fast.choropleth()
fast.choropleth(
x, data, value=NULL,
breaks = NULL, steps = 5,
adm.join=NULL, legend = NULL,
labels = NULL,
palette=NULL, title=""
)
Parameter | Description
--------- | ---------------------------------------------------------------------------------
x | Object GADMWrapper
data | data.frame - data to plot
value | String - the name of the column in the data.frame we want to plot (eg: an incidence in epidemiology studies)
breaks |
steps | Integer - number of breaks. Default = 5. If breaks is NOT NULL this value is used internally with cut().
adm.join | String - the name in GADM spdf dataset which will be joined with a column of the data.
legend | String - legend title. Default NULL.
labels | String vector labels for the legend. Default NULL
palette | String - An RColorBrewer palette name or a String vector vector of colors. Default NULL.
title | String - Title of the plot. Default is an empty string.
\newpage
Example
MAP <- gadm_sp_loadCountries("BEL", level = 3, simplify=0.01) DAT = read.csv2("BE_clamydia_incidence.csv") # Rewriting District names # ------------------------ DAT$district <- as.character(DAT$district) DAT[7,1] = "Brussel" DAT[20,1] <- "Liège" DAT[22,1] = "Marche-en-Famenne" DAT[27,1] = "Neufchâteau" DAT <- rename(DAT, NAME_3 = district) fast.choropleth(MAP, DAT, adm.join = "NAME_3", value = "rate03", steps = 4, breaks = "jenks", palette="Greens", legend = "Incidence", title="Chlamydia incidence by Belgian district (2003)")
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