Package 'datana'

Description

The datana package provides the datasets and functions that accompany the book "Análisis de datos con el programa estadístico R: una introducción aplicada" by Salas-Eljatib (2021, ISBN: 9789566086109). You can visit the book's website at https://eljatib.com/rlibro.

Notice that most of the available dataframes have a counterpart with column names in Spanish. For instance, the dataframe 'crown' has column names in English, but 'crown2' has column names in Spanish. Both data frames have the same data.

Details

The package contains several datasets for exploratory data analysis in an array of disciplines. Furthermore, datana provides functions as tools for descriptive statistics and plotting.

To see the preferable citation of the package, type citation("datana").

Author(s)

Christian Salas-Eljatib [aut, cre] (<https://orcid.org/0000-0002-8468-0829>), Pino Nicolas [ctb] (up to 2020), Riquelme Joaquin [ctb] (up to 2020)

Maintainer: Christian Salas-Eljatib <[email protected]>

Christian Salas-Eljatib is also indebted to several people who have contributed to individual data frames and functions: see credits in help pages.

References

Salas-Eljatib C. 2021. Análisis de datos con el programa estadístico R: una introducción aplicada. Santiago, Chile: Ediciones Universidad Mayor. ISBN: 9789566086109. https://www.buscalibre.cl/libro-analisis-de-datos-con-el-programa-estadistico-r/9789566086109/p/53775485

Examples

##Scatter-plot and marginal histograms
data(treevolroble)
df <- treevolroble
xyhist(x=df$dbh,y=df$toth, xlab="Diameter (cm)",  ylab="Height (m)")

##Scatter-plot and box-plots 
data(fishgrowth)
df <- fishgrowth
xyboxplot(x=df$length,y=df$scale)

Description

A dataset that contains four pairs of columns with the same descriptive statistics; however, there is a difference when representing the points through a graph.

Usage

data(aboutrsq)

Format

The data frame contains four variables as follows:

X1

Integers values that represent X-axis for Y1, Y2 and Y3 column

Y1

Float values that represent Y-axis for X1 column

Y2

Float values that represent Y-axis for X1 column

Y3

Float values that represent Y-axis for X1 column

X2

Integers values that represent X-axis for Y4 column

Y4

Float values that represent Y-axis for X2 column

Source

Data were assembled by Dr Christian Salas-Eljatib (Santiago, Chile).

References

Anscombe FJ. 1973. Graphs in statistical analysis. The American Statistician 27:17-21. doi:10.2307/2682899

Examples

data(aboutrsq)    
head(aboutrsq)

Description

Dataset que contiene cuatro pares de columnas con la mismos estadísticos descriptivos, sin embargo, si existe diferencia al representar los puntos mediante un gráfico.

Usage

data(aboutrsq2)

Format

Variables se describen a continuación::

X1

Valores enteros que representan el eje X para las columnas Y1, Y2 e Y3

Y1

Valores flotantes que representan el eje Y para la columna X1

Y2

Valores flotantes que representan el eje Y para la columna X1

Y3

Valores flotantes que representan el eje Y para la columna X1

X2

Valores enteros que representan el eje X para las columnas Y4

Y4

Valores flotantes que representan el eje Y para la columna X2

Source

Datos fueron contribuidos por el Prof. Christian Salas-Eljatib (Universidad de Chile, Santiago, Chile).

References

Anscombe FJ. 1973. Graphs in statistical analysis. The American Statistician 27:17-21. doi:10.2307/2682899

Examples

data(aboutrsq2)    
head(aboutrsq2)

Description

Daily air quality measurements in New York, May to September 1973.

Usage

data(airquality)

Format

Contains 6 variables, as follows:

ozone

numeric Ozone (ppb).

solar

numeric Solar R (lang).

wind

numeric Wind (mph).

temp

numeric Temperature (degrees F).

month

numeric Month (1–12).

day

numeric Day of month (1–31).

Source

The data were obtained from the library $datasets$.

References

Chambers J, Cleveland W, Kleiner B, Tukey P. 1983. Graphical Methods for Data Analysis. Belmont. CA: Wadsworth.

Examples

data(airquality)    
head(airquality)

Description

Calidad del aire diario medido en New York, de Mayo a Septiembre de 1973.

Usage

data(airquality2)

Format

Contiene 6 variables:

ozone

Ozono (ppb).

solar

Solar R (largo).

wind

Viento (mph).

temp

Temperatura (grados F).

month

Mes del año (1–12).

day

Dia del mes (1–31).

Source

Los datos fueron obtenidos desde la librería 'datasets'.

References

Chambers J, Cleveland W, Kleiner B, Tukey P. 1983. Graphical Methods for Data Analysis. Belmont. CA: Wadsworth.

Examples

data(airquality2)    
head(airquality2)

Description

Data contains annual precipitations in six cities in Chile (Santiago, Talca, Chillán, Temuco, Valdivia, and Puerto Montt) at different years.

Usage

data(annualppCities)

Format

The dataframe contains three variables as follows:

city

Name of city.

year

Year of registry.

annual

Value of the annual precipitation of a given year (mm).

Source

The data were obtained from https://explorador.cr2.cl/.

Examples

data(annualppCities)    
head(annualppCities)

Description

Data contains annual precipitations in six cities in Chile (Santiago, Talca, Chillan, Temuco, Valdivia, and Puerto Montt) at different years.

Usage

data(annualppCities2)

Format

The dataframe contains three variables as follows:

ciudad

Name of city.

anho

Year of registry.

pp.anual

Value of the annual precipitation of a given year (mm).

Source

Los datos fueron obtenidos desde https://explorador.cr2.cl/.

Examples

data(annualppCities2)    
head(annualppCities2)

Description

These are stand variables data from Araucaria araucana forests in southern Chile, measured in 2009. The data was based on fixed-area plots of 1000 m$^{2}$. There are two forest stands.

Usage

data(araucaria)

Format

Contains plot-level variables as follows:

stand

Stand number.

plot.no

Plot sample identificator number.

x.utm

UTM coordinate in X-axis, in km.

y.utm

UTM coordinate in Y-axis, in km.

slope

Slope, in %.

aspect

Aspect, in degrees.

eleva

Elevation, in msnm.

nha

Tree density, in trees/ha.

gha

Basal area, in m$^{2}$/ha.

hdom

Dominant height, in m.

vha

Gross stand volume, in m$^{3}$/ha.

dg

Diameter of the average basal area tree of the plot, in cm.

Source

The data are provided courtesy of Dr Nelson Ojeda at Universidad de La Frontera (Temuco, Chile).

References

Salas C, Ene L, Ojeda N, Soto H. 2010. Metodos estadísticos parametricos y no parametricos para predecir variables de rodal basados en Landsat ETM+: una comparacion en un bosque de Araucaria araucana en Chile [Parametric and non-parametric statistical methods for predicting plotwise variables based on Landsat ETM+: a comparison in an Araucaria araucana forest in Chile]. Bosque 31(3): 179-194. doi:10.4067/S0717-92002010000300002

Examples

data(araucaria)    
head(araucaria)

Description

Estos son variables a nivel de parcela para bosques Araucaria araucana en el centro-sur de Chile, medidos en 2009. Estas variables se basan en mediciones realizadas en parcelas de muestreo de 1000 m$^{2}$. Hay dos rodales.

Usage

data(araucaria)

Format

Contiene las siguientes variables:

rodal

Rodal, con un número indentificador.

parce

Parcela de muestreo, con un número indentificador.

x.utm

Coordenada UTM en el eje X, en km.

y.utm

Coordenada UTM en el eje Y, en km.

pendiente

Pendiente, en %

exposicion

Exposición del terreno, en grados.

altitud

Altitud, en msnm.

nha

Densidad, en arb/ha.

gha

Área basal, en m$^{2}$/ha.

hdom

Altura dominante, en m.

vha

Volumen bruto, en m$^{3}$/ha.

dg

Diámetro del árbol de área basal media, en cm.

Source

Los datos a nivel de árbol fueron cedidos por el Dr. Nelson Ojeda de la Universidad de La Frontera (Temuco, Chile).

References

Salas C, Ene L, Ojeda N, Soto H. 2010. Metodos estadísticos parametricos y no parametricos para predecir variables de rodal basados en Landsat ETM+: una comparacion en un bosque de Araucaria araucana en Chile. Bosque 31(3): 179-194. doi:10.4067/S0717-92002010000300002

Examples

data(araucaria2)    
head(araucaria2)

Description

The dataset contains 157 observations of the last ten years in 6-8 adult trees of different species at three elevations of altitudinal gradients sampled in four locations in Chile and two in Spain.

Usage

data(baitreeline)

Format

Contains seven columns, as follows:

climate

Climate of each location, mediterranean and temperate.

site

Name of Location of study (termmas:Termas de Chillan , antillanca:Antillanca area within Puyehue National Park, castillo:Cerro Castillo Natural Reserve, farellones:Farellones in Central Chile, pyrenees: Sierra de Cutas area in Spanish Central Pyrenees, sierra:Sierra Nevada).

species

name species of study (lenga: Nothofagus pumilio, frangel: Kageneckia angustifolia, uncinata: Pinus uncinata, sylvestris: Pinus sylvestris).

elevation

Type of elevation. "Treeline", intermediate named as "inter", and closed or montane forest named as low.

tree

Id for tree.

bai

Value of annual basal area increment.

mean.bai

Mean of annual basal area increment.

Source

The data were obtained from the DRYAD repository at doi:10.5061/dryad.ks97h.

References

Piper F, Vinegla B, Linares J, Camarero J, Cavieres L, Fajardo A. 2016. Mediterranean and temperate treelines are controlled by different environmental drivers. Journal of of Ecology. 104: 691-702.

Examples

data(baitreeline)    
head(baitreeline)

Description

Este set de datps contiene 157 observaciones, de los ultimos 10 años en 6-8 árboles adultos de cuatro especies en un gradiente altitudinal. Las muestras se distribuyeron en cuatro localidades o sitios de Chile y dos en España.

Usage

data(baitreeline2)

Format

Contains seven columns, as follows:

clima

Climate of each location, mediterranean and temperate.

sitio

Name of Location of study (termmas:Termas de Chillan, antillanca:Antillanca area within Puyehue National Park, castillo:Cerro Castillo Natural Reserve, farellones:Farellones in Central Chile, pyrenees: Sierra de Cutas area in Spanish Central Pyrenees, sierra:Sierra Nevada).

especie

name species of study (lenga: Nothofagus pumilio, frangel: Kageneckia angustifolia, uncinata: Pinus uncinata, sylvestris: Pinus sylvestris).

tipo.altitud

Type of elevation. "Treeline", intermediate named as "inter", and closed or montane forest named as low.

arbol

Id for tree.

bai

Value of annual basal area increment.

bai.medio

Mean of annual basal area increment.

Source

The data were obtained from the DRYAD repository at doi:10.5061/dryad.ks97h.

References

Piper F, Vinegla B, Linares J, Camarero J, Cavieres L, Fajardo A. 2016. Mediterranean and temperate treelines are controlled by different environmental drivers. Journal of Ecology 104: 691-702.

Examples

data(baitreeline2)    
head(baitreeline2)

Description

Wild bears were anesthetized, and their bodies were measured and weighed. One goal of the study was to make a table (or perhaps a set of tables) for people interested in estimating the weight of a bear based on other measurements. Notice that there are missing values for some of the variables.

Usage

data(bears)

Format

Contains individual-level variables, as follows:

id

Bear id

age

Age in total number of months.

month

Month number within a given year.

sex

1 =male, 2 = female.

headL

Length of head, in cm.

headW

Width of head, in cm.

neckG

Girth of neck, in cm.

length

Body length, in cm.

chestG

Girth of chest, in cm.

weight

body weight, in kg.

obs

Temporal observation number for bear.

name

Name given to bear.

Source

According to Prof. Timothy Gregoire at Yale University (New Haven, CT, USA), the data set was supplied by Gary Alt.

References

Entertaining references are in Reader's Digest April, 1979, and Sports Afield September, 1981.

Examples

data(bears)    
head(bears) 
table(bears$sex)
boxplot(headL~sex, data=bears)

Description

Los osos salvajes fueron anestesiados y sus cuerpos medidos. Uno de los objetivos del estudio fue hacer una tabla (o quizas un conjunto de tablas) para las personas interesadas en estimar el peso de un oso basandose en otras medidas. Observe que faltan valores para algunas de las variables.

Usage

data(bears2)

Format

Contiene variables de nivel individual, como se describen a continuación:

id

Identificador del oso.

edad

edad en meses

mes

identificador del mes,dentro del año.

sexo

1 = macho, 2 = hembra

cabezaL

longitud de la cabeza, en cm

cabezaA

ancho de la cabeza, en cm

cuelloP

circunferencia del cuello, en cm

largo

longitud del cuerpo, en cm

pechoG

circunferencia del pecho, en cm

peso

peso corporal, en kg

obs

número de observación temporal para el oso

nombre

nombre dado al oso

Source

Segun el Prof. Timothy Gregoire de Yale University (New Haven, CT, USA), los datos fueron cedidos por Gary Alt. Minitab, Inc. La descripcion de los datos fue dada por él.

References

Algunas referencias generales estan en el Reader's Digest de Abril, 1979, y Sports Afield de Septiembre, 1981.

Examples

data(bears2)    
head(bears2) 
table(bears2$sexo)
boxplot(cabezaL~sexo, data=bears2)

Description

Wild bears were anesthetized, and their bodies were measured and weighed. One goal of the study was to make a table (or perhaps a set of tables) for people interested in estimating the weight of a bear based on other measurements.

Usage

data(bearsdepu)

Format

Individual-level variables, as follows:

id

Bear identificator.

age

Age in total number of months.

month

Month number within a given year.

sex

Sex code: 1 =male, 2 = female.

headL

Length of head, in cm.

headW

Width of head, in cm.

neckG

Girth of neck, in cm.

length

Body length, in cm.

chestG

Girth of chest, in cm.

weight

Body weight, in kg.

obs

Temporal observation number for bear.

name

name given to bear

Source

According to Prof. Timothy Gregoire at Yale University (New Haven, CT, USA), the data set was supplied by Gary Alt.

References

Entertaining references are in Reader's Digest April, 1979, and Sports Afield September, 1981.

Examples

data(bearsdepu)    
head(bearsdepu)
table(bearsdepu$sex)
boxplot(headL~sex, data=bearsdepu)

Description

Los osos salvajes fueron anestesiados y sus cuerpos medidos. Uno de los objetivos del estudio fue hacer una tabla (o quizas un conjunto de tablas) para las personas interesadas en estimar el peso de un oso basandose en otras medidas. Esta dataframe es igual que "bears" pero sin valores perdidos.

Usage

data(bearsdepu2)

Format

Contiene variables de nivel individual, como se describen a continuacion:

id

Identificador del oso.

edad

edad en meses.

mes

Diámetro a la altura del pecho, en cm.

sexo

1 = hombre, 2 = mujer.

cabezaL

longitud de la cabeza, en cm.

cabezaA

ancho de la cabeza, en cm.

cuelloP

circunferencia del cuello, en cm.

largo

longitud del cuerpo, en cm.

pechoG

circunferencia del pecho, en cm.

peso

peso corporal, en kg.

obs

número de observación temporal para el oso.

nombre

nombre dado al oso.

Source

Segun el Prof. Timothy Gregoire de Yale University (New Haven, CT, USA), los datos fueron cedidos por Gary Alt. Minitab, Inc. La descripcion de los datos fue dada por él.

References

Algunas referencias generales estan en el Reader's Digest de Abril, 1979, y Sports Afield de Septiembre, 1981.

Examples

data(bearsdepu2)    
head(bearsdepu2)
table(bearsdepu2$sexo)
boxplot(cabezaL~sexo, data=bearsdepu2)

Description

Temporal measurements of density of beetles (Tribolium confusum) growing in different controlled environments.

Usage

beetles

Format

days

Number of days.

diet

The quantities of flour (in grams) of the environments where the beetles were growing. Six levels of the factor Diet.

type

The various stage of beetles, i.e., eggs, larvae, pupae, and adults.

density

The number of insects per environment.

Source

Data from Table No. 1, page 116, of Chapman (1928). Series of experiments under controlled conditions in which flour beetles (Tribolium confusum) are kept in environments of known size. The period from egg to adult is approximately forty days at 27C degrees. The data were entered by Miss Yamara Arancibia, a former student of Prof. Christian Salas-Eljatib.

References

- Chapman RN. 1928. The quantitative analysis of environmental factors. Ecology 9(2):111-122. doi:10.2307/1929348

Examples

data(beetles)
table(beetles$type)
name.diet<-unique(beetles$diet)
num.diet<-length(name.diet)
##Time series plot
#first, some computation
alys<-with(beetles,tapply(density,list(as.factor(days),as.factor(diet)),sum))
out<-as.data.frame(alys)
out$time<-row.names(out)
head(out)
#Figure 1 of the paper
matplot(out[,"time"], out[,1:num.diet], las=1, type=c("b"),pch=1,
        xlab="Time in days",ylab="Total individuals")
legend("topleft", legend = name.diet, title = "Diet (gr)",
       col = 1:6, lty = 1:6, pch = 1)

Description

Mediciones temporales de densidad de escarabajos (*Tribolium confusum*) creciendo en diferentes ambientes controlados.

Usage

beetles2

Format

dias

Número de dias.

dieta

La cantidad de harina (en gramos) de ambientes donde crecen los escarabajos. Seis niveles del factor Dieta.

tipo

Estados de desarrollo de los escarabajos, i.e., huevos, larvas, pupas, y adultos.

densidad

Número total de individuos por ambiente de crecimiento.

Source

Datos del Cuadro No. 1, page 116, de Chapman (1928). Serie de experimentos bajo condiciones controladas donde escarabajos (Tribolium confusum) se mantienen en ambientes de tamaño conocido. El periodo desde huevo a adulto es de aproximadamente de cuarenta dias a 27 grados Celsius. Los datos fueron digitados por la Srta. Yamara Arancibia, una estudiante del Prof. Christian Salas-Eljatib.

References

- Chapman RN. 1928. The quantitative analysis of environmental factors. Ecology 9(2):111-122. doi:10.2307/1929348

Examples

data(beetles2)    
table(beetles2$tipo)
nom.dieta<-unique(beetles2$dieta)
num.dieta<-length(nom.dieta)
##Grafico de serie de tiempo
#primero algunos calculos
alys<-with(beetles2,tapply(
          densidad,list(as.factor(dias),as.factor(dieta)),sum)
          )
out<-as.data.frame(alys)
out$tiempo<-row.names(out)
head(out)
##Figura 1 del paper
matplot(out[,"tiempo"], out[,1:num.dieta], las=1, type=c("b"),pch=1,
        xlab="Tiempo en dias",ylab="Densidad de individuos")
legend("topleft", legend = nom.dieta, title = "Dieta (gr)",
       col = 1:6, lty = 1:6, pch = 1)

Description

These are tree-level variables for several species in Canada.

Usage

biomass

Format

treenum

tree number.

spp

species common name.

dbh

diameter at breast height, in cm.

height

total height, in m.

totbiom

total biomass, in kg.

bolebiom

stem biomass, in kg.

branchbiom

branches biomass, in kg.

foliagebiom

foliage biomass, in kg.

Source

El archivo de datos fue preparado, y referido, por el Prof. Timothy Gregoire de Yale University (New Haven, CT, USA), mientras Christian Salas (el autor del presente paquete fue su Teaching Assistant).

Examples

data(biomass)    
head(biomass)
tapply(biomass$totbiom,biomass$spp,summary)

Description

These are tree-level variables for several species in Canada.

Usage

biomass2

Format

arbol

Número del árbol.

spp

Nombre común de la especie.

dap

Diámetro a la altura del pecho (1.3 m), en cm.

atot

Altura total, en m.

wtot

Biomasa total, en kg.

wfus

Biomasa del fuste, en kg.

wramas

Biomasa de las ramas, en kg.

whojas

Biomasa del follaje, en kg.

Source

The data are provided courtesy of Prof. Timothy Gregoire at the School of Forestry and Environmental Studies at Yale University (New Haven, CT, USA).

Examples

data(biomass2)    
head(biomass2) 
tapply(biomass2$wtot,biomass2$spp,summary)

Description

Dataset contains 14604 observations and sampling was carried out for two months during the dry season of 2013 and two months during the wet season of 2014. Each camera station is associated with a randomly placed camera and a trail-based camer, with the aim of comparing communities resulting from the two camera trap placement strategies.

Usage

data(cameratrap)

Format

Contains 6 variables, as follows:

reference

Number of observation od datasets.

placement

Type of "placement" placed in each station (random or trail).

season

Season where were made the samplings.

station

Station where were collected the data.

specie

Name of specie medium to large terrestrial mammals.

date.time

The date and time of each photographic event is also given.

Source

The data were provided by Dr Jeremy Cusack.

References

- Cusack J, Dickman A, Rowcliffe M, Carbone C, Macdonald D, Coulson T. 2016. Random versus game trail-based camera trap placement strategy for monitoring terrestrial mammal communities. PLoS ONE 10(5): e0126373.

Examples

data(cameratrap)    
head(cameratrap)

Description

Contains information of Camera trap data on medium to large terrestrial mammals collected at 54 camera stations in Ruaha National Park, southern Tanzania. Dataset contains 14604 observations and sampling was carried out for two months during the dry season of 2013 and two months during the wet season of 2014. Each camera station is associated with a randomly placed camera and a trail-based camer, with the aim of comparing communities resulting from the two camera trap placement strategies.

Usage

data(cameratrap2)

Format

Contiene 6 variables, como sigue:

referencia

Number of observation od datasets.

posicion

Type of "placement" placed in each station (random or trail).

temporada

Season where were made the samplings.

estacion

Station where were collected the data.

especie

Name of specie medium to large terrestrial mammals.

fecha.hora

The date and time of each photographic event is also given.

Source

Los datos fueron cedidos por el Dr Jeremy Cusack.

References

- Cusack J, Dickman A, Rowcliffe M, Carbone C, Macdonald D, Coulson T. 2016. Random versus game trail-based camera trap placement strategy for monitoring terrestrial mammal communities. PLoS ONE 10(5): e0126373.

Examples

data(cameratrap2)    
head(cameratrap2)

Description

Dataset contains 863 observations, about of total soluble carbohydrate, starch, and non structural carbohydrates concentrations per mass unit and per volume unit, in three tissues in early summer and early autumn 6-8 adult trees of different species at three elevations of altitudinal gradients sampled in four locations of Chile and Spain.

Usage

data(carbohtrees)

Format

Contains 16 variables, as follows:

climate

Climate of each location, mediterranean and temperate.

site

Name of Location of study (termas:Termas de Chillan, antillanca:Antillanca area within Puyehue National Park, castillo:Cerro Castillo Natural Reserve, farellones:Farellones in Central Chile, pyrenees: Sierra de Cutas area in Spanish Central Pyrenees, sierra:Sierra Nevada).

species

name species of study (lenga: Nothofagus pumilio, frangel: Kageneckia angustifolia, uncinata: Pinus uncinata, sylvestris: Pinus sylvestris).

tissue

Type of tissue, new developing twings, stem sapwood and branches.

time

Meauserement season (spring or autumn).

elevation

Type of elevation. "Treeline", intermediate named as "mid", and closed or montane forest named as "low".

tree

Id for tree.

tree.site

Id site for each location of study.

tss

Value of concentrations soluble carbohydrate per mass unit.

st

Value of concentrations starch per mass unit.

nsc

Value of concentrations non structural carbohydrates per mass unit.

tss.nsc

.

wd

It might be 'wood density', but not sure.

tss.mv

Value of concentrations soluble carbohydrate per volume unit.

st.mv

Value of concentrations starch per volume unit.

nsc.mv

Value of concentrations non structural carbohydrates per volume unit.

Source

The data were obtained from the DRYAD repository at doi:10.5061/dryad.ks97h.

References

Piper F, Vinegla B, Linares J, Camarero J, Cavieres L, Fajardo A. 2016. Mediterranean and temperate treelines are controlled by different environmental drivers. Journal of Ecology 104: 691-702. doi:10.1111/1365-2745.12555

Examples

data(carbohtrees)    
head(carbohtrees)

Description

Los datos contienen 863 observaciones, sobre carbohidratos totales solubles, almidon, y carbohidratos no-estructurales por unidad de masa y por unidad de volumen, en tres tejidos obtenidos al comienzo del verano y al comienzo del otoño. Lo anterior fue medido entre 6-8 árboles adultos de diferentes especies en un gradiente altitudinal, muestreados en cuatro sitios en Chile y España.

Usage

data(carbohtrees2)

Format

Hay 16 variables disponibles:

clima

Tipo de clima de cada sitio: mediterraneo o temperado.

sitio

Nombre del sitio de estudio, como sigue: "termas" (Termas de Chillán), "antillanca" (sector Antillanca dentro del Parque Nacional), "castillo" (Reserva Nacional Cerro Castillo), "farellones" (Farellones, a 20 Kms de Santiago, en Chile), "pyrenees" (Sierra de Cutas area in Spanish Central Pyrenees), "sierra" (Sierra Nevada).

especie

name species of study (lenga: Nothofagus pumilio, frangel: Kageneckia angustifolia, uncinata: Pinus uncinata, sylvestris: Pinus sylvestris).

tejido

Type of tissue, new developing twings, stem sapwood and branches.

temporada

Meauserement season (spring or autumn).

altitud

Type of elevation. "Treeline", intermediate named as "mid", and closed or montane forest named as "low".

arbol

Id for tree.

arb.sitio

Id site for each location of study.

cts

Concentración de carbohidratos solubles totales por unidad de masa.

almidon

Concentración de almidon por unidad de masa.

cne

Concentración de carbohidratos no-estructurales por unidad de masa.

cts.cne

División entre cts y cne.

dmade

It might be 'wood density', but not sure.

tss.mv

Value of concentrations soluble carbohydrate per volume unit.

st.mv

Value of concentrations starch per volume unit.

nsc.mv

Value of concentrations non structural carbohydrates per volume unit.

Source

Los datos fueron obtenidos desde el repositorio DRYAD en doi:10.5061/dryad.ks97h.

References

Piper F, Vinegla B, Linares J, Camarero J, Cavieres L, Fajardo A. 2016. Mediterranean and temperate treelines are controlled by different environmental drivers. Journal of Ecology 104:691-702. doi:10.1111/1365-2745.12555

Examples

data(carbohtrees2)    
head(carbohtrees2)

Description

Encuesta de Caracterización Socioeconómica Nacional (CASEN) de Chile, es realizada por el Ministerio de Desarrollo Social y Familia con el objetivo de disponer de información que permita conocer situación de los hogares y de la población. Estos datos corresponden a los de la encuesta CASEN 2022.

Usage

data(casen)

Format

Este set de datos contiene las siguientes columnas:

id.vivienda

Identificador de la vivienda.

id.persona

Identificador de la persona.

region

Región administrativa de Chile.

comuna

Comuna.

edad

Edad de la persona, en años.

sexo

Sexo de la persona.

esc

Años de escolaridad (edad >= 15).

educ

Clasificación de educación recibida.

personas.hogar

Número de personas que habitan en el hogar.

tipohogar

Nivel de tipo de hogar según encuesta.

activ

Nivel de actividad actual de la persona según encuesta.

ytot

Ingreso total.

ytoth

Ingreso total del hogar.

ypch

Ingreso total per cápita del hogar.

ytotcor

Ingreso total corregido.

ytotcorh

Ingreso total corregido del hogar.

ypc

Ingreso total corregido per cápita del hogar.

mayor.nivel.edu

¿Cuál es el nivel educacional al que asiste o el más alto al cual asistió?

area.edu.cinef

Clasificación Internacional Normalizada de Educación (CINE-F).

subarea.edu.cinef

Clasificación Internacional Normalizada de Sub-Area de Educación (CINE-F).

previ.salud

Sistema de previsión de salud.

Source

Los datos fueron obtenidos desde el web https://observatorio.ministeriodesarrollosocial.gob.cl/encuesta-casen. Note que solo algunas columnas son utilizadas aca, así como el nombre de algunas columnas fueron levemente cambiados.

Examples

data(casen)    
head(casen) 
table(casen$region)
table(casen$region,casen$sexo)
tapply(casen$ytotcor,casen$sexo,sum)

Description

Builds the cumulative distribution of a vector, using a step% of the data as fixed-intervals.

Usage

cdf(y = y, step = 0.05)

Arguments

Details

By default the cumulative distribution is build using 5% of the data as intervals, that is to say, from 0.05 (i.e., 5%) to 0.95 (i.e., 95%).

Value

returns a dataframe having two columns: the first contains the random variable values and the second the cumulative distribution for the variable.

Author(s)

Christian Salas-Eljatib

References

Salas-Eljatib, C. 2021. Análisis de datos con el programa estadístico R: una introducción aplicada. Ediciones Universidad Mayor, Santiago, Chile. 170 p. https://eljatib.com/rlibro

Examples

y.var <- rnorm(10)
cdf(y.var)
cdf(y.var, step=0.1)

Description

The body weights of the chicks were measured at birth and every second day thereafter until day 20. They were also measured on day 21. There were four groups on chicks on different protein diets.

Usage

data(chicksw)

Format

Contains four variables, as follows:

chick

An ordered factor with levels different giving a unique identifier for the chick. The ordering of the levels groups chicks on the same diet together and orders them according to their final weight (lightest to heaviest) within diet.

diet

A factor with levels 1,2,3 and 4 indicating which experimental diet the chick received.

time

A numeric vector giving the number of days since birth when the measurement was made.

weight

A numeric vector giving the body weight of the chick (gm).

Source

The data were obtained from the $alr4$ library.

References

Crowder M, Hand D. 1990. Analysis of Repeated Measures. Chapman and Hall

Examples

data(chicksw)    
head(chicksw)

Description

El peso de pollos fueron medidos al momento de nacer y cada dia por medio hasta el dia 20. Ellos también fueron medidos el día 21. Hubo cuatro grupos de pollos en diferentes dietas de proteinas.

Usage

data(chicksw2)

Format

Contine cuatro variables, como sigue:

pollo

Un identificador único para cada pollo. La numeracion esta ordenado segun el peso final dentro de cada dieta.

dieta

Un factor con cuatro nivels: 1,2,3 y 4 indicando que dieta recibió el pollo.

tiempo

Número de días desde el nacimiento.

peso

Peso del pollo (gm).

Source

Los datos fueron obtenidos desde la librería $alr4$.

References

Crowder M, Hand D. 1990. Analysis of Repeated Measures. Chapman and Hall

Examples

data(chicksw2)    
head(chicksw2)

Description

These data are the same as in the 'chicksw' dataframe, which is the one that should be preferred. Nonetheless, I kept the name of this dataframe (i.e., ChickWeight) to be able for using in the book of Salas-Eljatib (2021). Further details of the dataframe can be found by typing "?chicksw"

Usage

data(ChickWeight)

Format

Contains four variables, as follows:

weight

A numeric vector giving the body weight of the chick (gm).

Time

A numeric vector giving the number of days since birth when the measurement was made.

Chick

An ordered factor with levels different giving a unique identifier for the chick. The ordering of the levels groups chicks on the same diet together and orders them according to their final weight (lightest to heaviest) within diet.

Diet

A factor with levels 1,2,3 and 4 indicating which experimental diet the chick received.

Source

See related-details on this, for the dataframe "chicksw".

References

- Salas-Eljatib, C. 2021. Análisis de datos con el programa estadístico R: una introducción aplicada. Ediciones Universidad Mayor, Santiago, Chile. 170 p. https://eljatib.com/rlibro

Examples

data(ChickWeight)    
head(ChickWeight)

Description

Data obtained from the $hockeystick$ package, which retrieves annual global carbon dioxide emissions since 1750 from the World Data repository https://github.com/owid/co2-data, as well as other climate-related variables.

Usage

data(co2temp)

Format

The data contains 75 variables, and the fully description can be reviewed in the references provided here.

country

Country.

year

Calendar year.

iso_code

TBA.

population

Population size, in number of people.

gdp

Gross domestic product, a measure of the value added created through the production of goods and services in a country.

cement_co2

TBA.

cement_co2_per_capita

TBA.

co2

TBA.

co2_growth_abs

TBA.

co2_growth_prct

TBA.

co2_including_luc

TBA.

co2_including_luc_growth_abs

TBA.

co2_including_luc_growth_prct

TBA.

co2_including_luc_per_capita

TBA.

co2_including_luc_per_gdp

TBA.

co2_including_luc_per_unit_energy

TBA.

co2_per_capita

TBA.

co2_per_gdp

TBA.

co2_per_unit_energy

TBA.

coal_co2

TBA.

coal_co2_per_capita

TBA.

consumption_co2

TBA.

consumption_co2_per_capita

TBA.

consumption_co2_per_gdp

TBA.

cumulative_cement_co2

TBA.

cumulative_co2

TBA.

cumulative_co2_including_luc

TBA.

cumulative_coal_co2

TBA.

cumulative_flaring_co2

TBA.

cumulative_gas_co2

TBA.

cumulative_luc_co2

TBA.

cumulative_oil_co2

TBA.

cumulative_other_co2

TBA.

energy_per_capita

TBA.

energy_per_gdp

TBA.

flaring_co2

TBA.

flaring_co2_per_capita

TBA.

gas_co2

TBA.

gas_co2_per_capita

TBA.

ghg_excluding_lucf_per_capita

TBA.

ghg_per_capita

TBA.

land_use_change_co2

TBA.

land_use_change_co2_per_capita

TBA.

methane

TBA.

methane_per_capita

TBA.

nitrous_oxide

TBA.

nitrous_oxide_per_capita

TBA.

oil_co2

TBA.

oil_co2_per_capita

TBA.

primary_energy_consumption

TBA.

share_global_cement_co2

TBA.

share_global_co2

TBA.

share_global_co2_including_luc

TBA.

share_global_coal_co2

TBA.

share_global_cumulative_cement_co2

TBA.

share_global_cumulative_co2

TBA.

share_global_cumulative_co2_including_luc

TBA.

share_global_cumulative_coal_co2

TBA.

share_global_cumulative_flaring_co2

TBA.

share_global_cumulative_gas_co2

TBA.

share_global_cumulative_luc_co2

TBA.

share_global_cumulative_oil_co2

TBA.

share_global_cumulative_other_co2

TBA.

share_global_flaring_co2

TBA.

share_global_gas_co2

TBA.

share_global_luc_co2

TBA.

share_global_oil_co2

TBA.

share_global_other_co2

TBA.

share_of_temperature_change_from_ghg

TBA.

temperature_change_from_ch4

TBA.

temperature_change_from_co2

TBA.

temperature_change_from_ghg

TBA.

temperature_change_from_n2o

TBA.

total_ghg

TBA.

total_ghg_excluding_lucf

TBA.

trade_co2

TBA.

trade_co2_share

TBA.

Source

The data were obtained from the $hockeystick$ library of R. Notice that in the dataframe only a portion of countries have been kept.

References

- https://www.globalcarbonproject.org/carbonbudget/

- Friedlingstein P. et al. 2020. Global Carbon Budget 2020, Earth System Science Data 12:3269-3340 doi:10.5194/essd-12-3269-2020

Examples

data(co2temp)    
names(co2temp)
table(co2temp$country)  
lattice::xyplot(co2~year|country,data=co2temp,type="l",as.table=TRUE)

Description

The function computes the statistics for inference in a given contrast, subject to a given significance level. Those statistics are as follows: estimated contrast, standard error of the contrast, and the confidence interval of the contrast.

Usage

contrast(
  model = model,
  coef.cont = coef.cont,
  grp.m = grp.m,
  grp.n = grp.n,
  alpha = 0.05,
  full = TRUE
)

Arguments

Details

The contrast is established based upon an already fitted statistical model that describe the relationship among variables. The significance level ('alpha') is defined by the user, although by default has been set to 0.05, that is to say, a 95% of statistical confidence.

Value

This function returns the above described statistics for a given contrast.

Author(s)

Christian Salas-Eljatib

References

- Salas-Eljatib C. 2025. datana: Datasets and Functions to Accompany Análisis de Datos con R. R package version 1.0.7, doi:10.32614/CRAN.package.datana, https://CRAN.R-project.org/package=datana

Examples

data(fertiliza)
table(fertiliza$treat)
means.trt <- tapply(fertiliza$volume,fertiliza$treat,mean);means.trt
sds.trt <- tapply(fertiliza$volume,fertiliza$treat,sd);sds.trt
ns.trt <- tapply(fertiliza$volume,fertiliza$treat,length);ns.trt
m1 <- lm(volume ~ treat, data=fertiliza)
anova(m1)
## Coefficients to be used in the contrast
#c1: (tmoA1-A2) - (tmoA3-A4)
C1.coeff <- c(0,1,1,-1,-1)
contrast(model=m1,C1.coeff,grp.m=means.trt,grp.n=ns.trt,alpha=0.1,full=TRUE)
contrast(model=m1,C1.coeff,grp.m=means.trt,grp.n=ns.trt,alpha=0.1,full=FALSE)
contrast(m1,C1.coeff,grp.m=means.trt,grp.n=ns.trt,alpha=0.05,full=TRUE)
contrast(m1,C1.coeff,grp.m=means.trt,grp.n=ns.trt)

Description

Measurements of cork weight in *Quercus suber* (Oak) trees in Portugal.

Usage

corkoak

Format

tree

A correlative number for each sample tree.

csc

is tree circumference at 1.3 m outside bark, in cm.

cbc

is tree circumference at 1.3 m under bark, in cm.

bt

bark thickness, in cm.

hdeb

is debarking height, in m.

hblc

height to base of live crown, in m.

nb

number of branches debarked

cr.diam

crown diameter, in m.

w

total green weight of the stripped cork, in kg

stratum

Stratum

Source

Data supplied electronically to Prof. Timothy Gregoire (Yale University) by authors accompanied by a note which said "After the article was published we discovered a problem with 2 of the observations so Teresa and I decided it was best just to delete them."

References

- Fonseca TJ, Parresol BR. 2001. A new model for cork weight estimation in northern Portugal with methodology for construction of confidence intervals. Forest Ecology and Management 152(1):131–139.

Examples

data(corkoak)    
head(corkoak)

Description

Mediciones de peso de corcho en árboles muestra de Quercus suber en Portugal.

Usage

corkoak2

Format

arbol

A correlative number for each sample tree.

perimetro.cc

is tree circumference at 1.3 m outside bark, in cm.

perimetro.sc

is tree circumference at 1.3 m under bark, in cm.

e.corteza

bark thickness, in cm.

h.desc

is debarking height, in m.

hcc

height to base of live crown, in m.

num.ram

number of branches debarked

diam.copa

crown diameter, in m.

biomasa

total green weight of the stripped cork, in kg

estrato

Estrato

Source

Datos cedidos por Prof. Timothy Gregoire (Yale University) y los autores originales mencionaron "After the article was published we discovered a problem with 2 of the observations so Teresa and I decided it was best just to delete them."

References

- Fonseca TJ, Parresol BR. 2001. A new model for cork weight estimation in northern Portugal with methodology for construction of confidence intervals. Forest Ecology and Management 152(1):131–139.

Examples

data(corkoak2)    
head(corkoak2)

Description

Crown radii measurements in cardinal directions for sample trees at the Rucamanque experimental forest, near Temuco, Chile. Data were collected within a sample plot of 250 m$^{2}$, located in a secondary forest stand dominated by Nothofagus obliqua.

Usage

data(crown)

Format

Contains of variables, as follows:

spp

Species code. 'Ro' is Nothofagus obliqua (roble), 'Co' is Nothofagus dombeyi (Coigue) and 'Ol' is Olivillo.

dbh

Diameter at breast height, in cm.

toth

Total height, in m.

crad.n

Crown radii towards the north, in m.

crad.e

Crown radii towards the east, in m.

crad.s

Crown radii towards the south, in m.

crad.w

Crown radii towards the west, in m.

x.coord

Cardinal position at the X-axis, in m.

y.coord

Cardinal position at the Y-axis, in m.

cr.diam

Crown diameter, in m.

Source

Data were provided by Dr Christian Salas-Eljatib, Universidad de Chile (Santiago, Chile).

References

- Salas C. 2001. Caracterización básica del relicto de Biodiversidad Rucamanque [Basic characterization of the biodiversity remnant Rucamanque]. Bosque Nativo, 29:3-9. https://eljatib.com/publication/2001-06-01_caracterizacion_basi/

- Salas C, and Garcia O. 2006. Modelling height development of mature Nothofagus obliqua. Forest Ecology and Management 229 (1-3): 1-6. doi:10.1016/j.foreco.2006.04.015

Examples

data(crown)    
table(crown$spp) 
descstat(crown[,c("dbh","cr.diam")])

Description

Mediciones de radios de copa en direcciones cardinales para árboles muestra en Rucamanque, cerca de Temuco, Chile. Los datos fueron colectados al interior de una parcela de muestreo de 250 m$^{2}$, establecidad en un bosque secundario dominado por Nothofagus obliqua.

Usage

data(crown2)

Format

Contiene las siguientes columnas:

espe

Código de especie, donde: 'Ro' es Nothofagus obliqua (Roble), 'Co' es Nothofagus dombeyi (Coigue) y 'Ol' es Olivillo.

dap

Diámetro a la altura del pecho, en cm.

atot

Altura total, en m.

rc.n

Radio de copa hacia el Norte, en m.

rc.e

Radio de copa hacia el Este, en m.

rc.s

Radio de copa hacia el Sur, en m.

rc.w

Radio de copa hacia el Oeste, en m.

coord.x

Posición cartesiana en el eje-X, en m.

coord.y

Posición cartesiana en el eje-Y, en m.

dcopa

Diámetro de copa, en m.

Source

Datos cedidos por el Prof. Christian Salas-Eljatib, Universidad de Chile (Santiago, Chile).

References

- Salas C. 2001. Caracterización básica del relicto de Biodiversidad Rucamanque [Basic characterization of the biodiversity remnant Rucamanque]. Bosque Nativo, 29:3-9. https://eljatib.com/publication/2001-06-01_caracterizacion_basi/

- Salas C, and Garcia O. 2006. Modelling height development of mature Nothofagus obliqua. Forest Ecology and Management 229 (1-3):1-6. doi:10.1016/j.foreco.2006.04.015

Examples

data(crown2)    
table(crown2$espe) 
descstat(crown2[,c("dap","dcopa")])

Description

Function to delete the last n-characters of a string from the right-hand side.

Usage

deleteRight(fac, n)

Arguments

Details

It is specially set to arrange data vector having alphanumeric format.

Value

This function returns an object having n-less characters from the right-hand side.

Author(s)

Christian Salas-Eljatib

References

Salas-Eljatib, C. 2021. Análisis de datos con el programa estadístico R: una introducción aplicada. Ediciones Universidad Mayor, Santiago, Chile. 170 p. https://eljatib.com/rlibro

Examples

last.names.id <- c("Stage-1924","Gregoire-1958","Robinson-1967")
deleteRight(last.names.id,5)
deleteRight(last.names.id,4)

Description

Dataset contains 61 observations about life histories values for each species and site, as obtained from the parameterization carried out in studies that used the model SORTIE

Usage

data(demograph)

Format

Contains 15 variables, as follows:

sp

Name specie.

site

Name of site of study.

country

Name of country.

site.n

Code of site.

code

Code of specie.

genus

Genus of specie.

sps

Abbreviated name specie.

family

Family of specie.

phyl

Type of phylogeny.

l.hab

Type of leaf habit.

l.type

.

leaf

Type of leaf.

growth.l

Growth at full light (time in years).

growth.d

Growth in shade.

surv.d

Survival in shade.

Source

The data were obtained from the DRYAD repository.

References

- Ameztegui A, Paquette A, Shipley B, Heym M, Messier C, Gravel D. 2016. Shade tolerance and the functional trait: demography relationship in temperate and boreal forests. Functional Ecology 31: 821-830.

Examples

data(demograph)    
head(demograph)

Description

Function to create a descriptive statistics table for continuous variables from a dataframe.

Usage

descstat(data = data, decnum = 4, full = FALSE)

Arguments

Details

The resulting table offers the main central and dispersion statistics.

Value

This function wraps descriptive statistics into a summarize table having the following descriptive statistics: sample size, minimum, maximum, mean, median, SD, and coefficient of variation. If the full option is set to TRUE, the following statistics are added to the table: 25th and 75th percentiles, the interquartile range, skewness, and kurtosis.

Author(s)

Christian Salas-Eljatib and Tomas Cayul.

References

Salas-Eljatib, C. 2021. Análisis de datos con el programa estadístico R: una introducción aplicada. Ediciones Universidad Mayor, Santiago, Chile. 170 p. https://eljatib.com/rlibro

Examples

#creating a fictitious dataframe
set.seed(1234)
df <- as.data.frame(cbind(variable1=rnorm(5, 0), variable2=rnorm(5, 2)))
## adding one missing value
df[3,1] <- NA
df
#' #using the function
descstat(data=df)
descstat(data=df,decnum=1)
descstat(df,2)

Description

County-by-county vote for president in Florida in 2000 for Bush, Gore and Buchanan.

Usage

data(election)

Format

Contains three variables, as follows:

gore

Vote for Gore.

bush

Vote for Bush.

buchanan

Vote for Pat Buchanan.

Source

The data were obtained from the $alr4$ library.

References

Weisberg S. 2014. Applied Linear Regression. 4th edition. Hoboken NJ: Wiley

Examples

data(election)    
head(election)

Description

Conteo de votos a nivel de condado en el estado de Florida, año 2000.

Usage

data(election2)

Format

Contiene las siguientes tres columnas:

gore

Votos para Gore. Número de votos para Al Gore.

bush

Votos para Bush. Número de votos para George W. Bush.

buchanan

Votos para Buchaman. Número de votos para Pat Buchanan.

Source

Los datos se obtuvieron desde el paquete $alr4$ de R.

References

Weisberg S. 2014. Applied Linear Regression. 4th edition. Hoboken NJ: Wiley

Examples

data(election2)    
head(election2)

Description

The length, width, and area of Eucalyptus nitens leaves were measured.

Usage

data(eucaleaf)

Format

Contains leaf-level variables, as follows:

time

Time factor, in two levels: early or Late.

tree

Sample tree code identificator.

shoot

Shoot description factor, in three levels.

l

Length of the leaf, in mm.

w

Width of the leaf, in mm.

la

leaf area, in cm$^{2}$.

Source

Although the original source of the measurements is the Dissertation of Dr Candy (1999), the data file used here was courtesy of Prof. Timothy Gregoire at Yale University (New Haven, CT, USA). Furthermore, these data were used by Gregoire and Salas (2009).

References

- Candy SG. 1999. Predictive models for integrated pest management of the leaf beetle *Chrysophtharta bimaculata* in *Eucalyptus nitens* in Tasmania. Doctoral dissertation, University of Tasmania, Hobart, Australia.

- Gregoire TG, and Salas C. 2009. Ratio estimation with measurement error in the auxiliary variate. Biometrics 65(2):590-598 doi:10.1111/j.1541-0420.2008.01110.x

Examples

data(eucaleaf)    
head(eucaleaf)

Description

Mediciones de largo, ancho y area de hojas de Eucalyptus nitens.

Usage

data(eucaleaf2)

Format

Contiene variables a nivel de hoja, como sigue:

tiempo

Factor a dos niveles: Temprano o Tardío.

arbol

Identificador del árbol muestra.

meristema

Factor de la descripción del meristema, en tres niveles.

largo

Largo de la hoja, en mm.

ancho

Ancho de la hoja, en mm.

area

Área foliar, en cm$^{2}$.

Source

Aunque la fuente original de estas mediciones proviene de la tesis del Dr. Candy (1999), el archivo de datos fue cortesía del Prof. Timothy Gregoire de Yale University (New Haven, CT, USA). Además, estos datos fueron ocupados en el estudio de Gregoire y Salas (2009).

References

- Candy SG. 1999. Predictive models for integrated pest management of the leaf beetle Chrysophtharta bimaculata in Eucalyptus nitens in Tasmania. Doctoral dissertation, University of Tasmania, Hobart, Australia.

- Gregoire TG, and Salas C. 2009. Ratio estimation with measurement error in the auxiliary variate. Biometrics 65(2):590-598 doi:10.1111/j.1541-0420.2008.01110.x

Examples

data(eucaleaf2)    
head(eucaleaf2)

Description

The length, width, and area of Eucalyptus nitens leaves were measured for all the samples of Candy (1999).

Usage

data(eucaleafAll)

Format

Contains leaf-level variables, as follows:

time

Time factor, in two levels: early or Late.

tree

Sample tree code identificator.

shoot

Shoot description factor, in three levels.

l

Length of the leaf, in mm.

w

Width of the leaf, in mm.

la

leaf area, in cm$^{2}$.

Source

Although the original source of the measurements is the Dissertation of Dr Candy (1999), the data file used here was courtesy of Prof. Timothy Gregoire at Yale University (New Haven, CT, USA). Furthermore, these data were used by Gregoire and Salas (2009).

References

- Candy SG. 1999. Predictive models for integrated pest management of the leaf beetle Chrysophtharta bimaculata in Eucalyptus nitens in Tasmania. Doctoral dissertation, University of Tasmania, Hobart, Australia.

Examples

data(eucaleafAll)    
head(eucaleafAll)

Description

Mediciones de largo, ancho y área de hojas de Eucalyptus nitens para toda la muestra de Candy (1999).

Usage

data(eucaleafAll2)

Format

Contiene variables a nivel de hoja, como sigue:

tiempo

Factor a dos niveles: Temprano o Tardío

arbol

Identificador del árbol muestra

meristema

Factor de la descripción del meristema, en tres niveles.

largo

Largo de la hoja, en mm

ancho

Ancho de la hoja, en mm

area

Área foliar, en cm$^{2}$

Source

Aunque la fuente original de estas mediciones proviene de la tesis del Dr. Candy (1999), el archivo de datos fue cortesía del Prof. Timothy Gregoire de Yale University (New Haven, CT, USA).

References

- Candy SG. 1999. Predictive models for integrated pest management of the leaf beetle Chrysophtharta bimaculata in Eucalyptus nitens in Tasmania. Doctoral dissertation, University of Tasmania, Hobart, Australia.

Examples

data(eucaleafAll2)    
head(eucaleafAll2)

Description

Tree-level variables collected for all trees (even the variable height) within a sample plot in a forestry plantation of *Eucalyptus globulus* near Gorbea, southern Chile. The plot size is 500 m$^{2}$. The plantation is 15 yr-old and had been subject to three thinnings.

Usage

data(eucaplot)

Format

The dataframe contains four variables as follows:

dbh

Diameter at breast height, in cm.

health

health status (1: good, 2: medium, 3: bad).

shape

stem shape for timber purposes (1: good, 2: medium, 3: bad).

crown.class

Crown class (1: superior, 2: intermedium, 3: lower).

toth

Total height, in m.

Source

The data were provided courtesy of Dr Christian Salas-Eljatib (Universidad de Chile, Santiago, Chile).

Examples

data(eucaplot)    
head(eucaplot$health) 
descstat(eucaplot[,c("dbh","toth")])

Description

Variables a nivel individual medidas en todos los árboles (incluso la variable altura) encontrados en una parcela de muestreo en una plantación forestal de *Eucalyptus globulus* cerca de Gorbea, en el sur de Chile. La superficie de la parcela es de 500 m$^{2}$. La plantación tiene 15 años de edad y ha estado sujeta a tres raleos.

Usage

data(eucaplot2)

Format

Los datos contienen las siguientes cuatro columnas:

dap

Diámetro a la altura del pecho, en cm.

sanidad

Evaluación cualitativa de la sanidad del árbol (1: buena, 2: media, 3: mala).

forma

Evaluación cualtitativa de la forma del fuste (1: buena, 2: media, 3: mala).

clase.copa

Clase de copa (1: superior, 2: intermedio, 3: inferior).

atot

Altura total, en m.

Source

Los datos fueron cedidos por el Prof. Christian Salas (Universidad de Chile, Santiago, Chile), y colectados por él mientras fue Profesor del Departamento de Ciencias Forestales en la Universidad de La Frontera (Temuco, Chile). La plantación se encontraba dentro de un predio del colega (QEPD) Hugo Castro.

Examples

data(eucaplot2)    
table(eucaplot2$sanidad) 
descstat(eucaplot2[,c("dap","atot")])

Description

Tree-level variables collected in a sample plot (area=500 m$^{2}$) in a forestry plantation of *Eucalyptus globulus* near Gorbea, in southern Chile. The variable height, was only measured in a sub-sample of trees within the plot. The plantation is 15 yr-old and had been subject to three thinnings.

Usage

data(eucaplotr)

Format

The dataframe contains four variables as follows:

dbh

Diameter at breast height, in cm.

health

health status (1: good, 2: medium, 3: bad).

shape

stem shape for timber purposes (1: good, 2: medium, 3: bad).

crown.class

Crown class (1: superior, 2: intermedium, 3: lower).

toth

Total height, in m.

Source

The data were provided courtesy of Dr Christian Salas-Eljatib (Universidad de Chile, Santiago, Chile).

Examples

data(eucaplotr)    
head(eucaplotr$health)
descstat(eucaplotr[,c("dbh","toth")])

Description

Variables a nivel individual medidas en los árboles encontrados en una parcela de muestreo (de 500 m$^{2}$) en una plantación forestal de *Eucalyptus globulus*, cerca de Gorbea (Sur de Chile). La variable altura fue medida solo en una sub-muestra de árboles. La plantación tiene 15 años de edad y ha estado sujeta a tres raleos.

Usage

data(eucaplotr2)

Format

Los datos contienen las siguientes cuatro columnas:

dap

Diámetro a la altura del pecho, en cm.

sanidad

Evaluación cualitativa de la sanidad del árbol (1: buena, 2: media, 3: mala).

forma

Evaluación cualtitativa de la forma del fuste (1: buena, 2: media, 3: mala).

clase.copa

Clase de copa (1: superior, 2: intermedio, 3: inferior).

atot

Altura total, en m. Esta variable fue medida solo en una submuestra de árboles.

Source

Los datos fueron cedidos por el Prof. Christian Salas-Eljatib (Universidad de Chile, Santiago, Chile), y colectados por él mientras fue Profesor del Departamento de Ciencias Forestales en la Universidad de La Frontera (Temuco, Chile). La plantación se encontraba dentro de un predio del colega (QEPD) Hugo Castro.

Examples

data(eucaplotr2)    
table(eucaplotr2$sanidad) 
descstat(eucaplotr2[,c("dap","atot")])

Description

Data contains volume data at plot-level for a fertilization experiment.

Usage

data(fertiliza)

Format

Contains two variables, as follows:

treat

Treatment level.

volume

Plot-level volume, in m$^{3}$.

Source

The data were provided by Dr Christian Salas-Eljatib (Universidad de Chile, Santiago, Chile).

References

not yet

Examples

data(fertiliza)
head(fertiliza)
class(fertiliza$treat)
unique(fertiliza$treat)
means.g <- tapply(fertiliza$volume,fertiliza$treat,mean);means.g
sds.g <- tapply(fertiliza$volume,fertiliza$treat,sd);sds.g
ns.g <- tapply(fertiliza$volume,fertiliza$treat,length);ns.g

Description

Datos a nivel de parcela de un experimento de fertilización con tratamientos y replicas.

Usage

data(fertiliza2)

Format

Contiene tres columnas como sigue:

tmo

Tratamiento.Factor medido en diferentes niveles.

vol

Volumen de madera en la parcela experimental, en m$^{3}$.

Source

Datos cedidos por el Prof. Christian Salas.

References

not yet

Examples

data(fertiliza2)
head(fertiliza2)
class(fertiliza2$tmo)
unique(fertiliza2$tmo)
media.g <- tapply(fertiliza2$vol,fertiliza2$tmo,mean);media.g
desvst.g <- tapply(fertiliza2$vol,fertiliza2$tmo,sd);desvst.g
n.g <- tapply(fertiliza2$vol,fertiliza2$tmo,length);n.g

Description

The 'ficdiamgr' is a fictitious dataframe built to show the structure of longitudinal data. The dataframe has records of tree diameter growth of five sample trees, spanning three species.

Usage

data(ficdiamgr)

Format

A time series data containing the following columns:

tree.id

an ordered factor indicating the tree on which the measurement is made. The ordering is according to increasing maximum diameter.

time

a numeric vector giving the numbers of days since establishment.

dbh

a numeric vector of diameter at breast height, in cm.

site

a factor variable, representing site conditions with two levels.

spp

a factor variable, representing tree species with three levels.

Source

This dataframe was built from the 'Orange' data of the $datasets$ package, by Christian Salas-Eljatib.

Examples

data(ficdiamgr)

coplot(dbh ~ time | tree, data = ficdiamgr, show.given = FALSE)

Description

Los datos 'ficdiamgr2' son ficticios, y fue construida para mostrar la estructura de datos longitudinales. Los datos tienen registro de crecimiento en cinco árboles muestra, representando a tres especies.

Usage

data(ficdiamgr2)

Format

Una serie de tiempo conteniendo las siguientes columnas:

arbol

indica el identificador del árbol.

tiempo

número de dias desde el inicio de las mediciones.

dap

diámetro a la altura del pecho, en cm.

sitio

un factor, representando condiciones de sitio, en dos niveles.

espe

un factor, representando especie del árbol, en tres niveles.

Source

Estos datos fueron modificados desde la dataframe 'Orange' de la librería 'datasets', por Christian Salas-Eljatib.

Examples

data(ficdiamgr2)

coplot(dap ~ tiempo | arbol, data = ficdiamgr2, show.given = FALSE)

Description

Sometimes in data manipulation we face the task of locating the position of a specific variable within a dataframe. The function finds the position in which a column name is within an object.

Usage

findColumn.byname(data = data, col.name = col.name)

Arguments

Details

Although the function finds the position of a specific variable, can also be used for more than one variable.

Value

This function returns the number of a specific column-name.

Note

It can be used for a vector of specified column-names as well.

Author(s)

Christian Salas-Eljatib

References

Salas-Eljatib, C. 2021. Análisis de datos con el programa estadístico R: una introducción aplicada. Ediciones Universidad Mayor, Santiago, Chile. 170 p. https://eljatib.com/rlibro

Examples

df <- data.frame(varX=1:5, varY=letters[1:5], varZ=rep("a",5), 
varK=rep("b",5))
df
#using the function
findColumn.byname(df, c("varY","varZ"))
findColumn.byname(df, "varK")
#Creating an example vector
vector <- letters
vector
findColumn.byname(vector, c("h","z"))

Description

Data on samples of small mouth bass collected in West Bearskin Lake, Minnesota, in 1991. The file wblake includes only fish of ages 8 or younger.

Usage

data(fishgrowth)

Format

Contains 3 variables, as follows:

years

Year at capture.

length

Length at capture (mm).

scale

radius of a key scale (mm).

Source

The data were obtained from the $alr4$ library of R.

References

Weisberg S. 2014. Applied Linear Regression. 4th edition. Hoboken NJ: Wiley

Examples

data(fishgrowth)    
head(fishgrowth)

Description

Data on samples of small mouth bass collected in West Bearskin Lake, Minnesota, in 1991. The file wblake includes only fish of ages 8 or younger.

Usage

data(fishgrowth2)

Format

Contiene tres variables, como sigue:

edad

Year at capture.

largo

Length at capture, en mm.

escala

radius of a key scale, en mm.

Source

Datos obtenidos desde el paquete $alr4$ de R.

References

Weisberg S. 2014. Applied Linear Regression. 4th edition. Hoboken NJ: Wiley

Examples

data(fishgrowth2)    
head(fishgrowth2)

Description

Dataset contains taxonomic level information segregatted by latitude.

Usage

data(floraChile)

Format

Contains seven columns, as follows:

family

.

genus

.

scientific.name

.

author

.

origin

.

life.form

.

lat...

.

Source

The data are provided courtesy of Dr Jan Bannister at the Instituto Forestal (Chiloe, Chile).

References

- Bannister JR, Vidal OJ, Teneb E, Sandoval V. 2012. Latitudinal patterns and regionalization of plant diversity along a 4270‐km gradient in continental Chile. Austral Ecology 37(4):500-509. doi:10.1111/j.1442-9993.2011.02312.x

Examples

data(floraChile)    
head(floraChile)

Description

Contiene informacion taxonomica segregada por latitude.

Usage

data(floraChile)

Format

Contains seven columns, as follows:

family

.

genus

.

scientific.name

.

author

.

origin

.

life.form

.

lat...

.

Source

Datos cedidos por el Dr Jan Bannister del Instituto Forestal (Chiloe, Chile).

References

- Bannister JR, Vidal OJ, Teneb E, Sandoval V. 2012. Latitudinal patterns and regionalization of plant diversity along a 4270‐km gradient in continental Chile. Austral Ecology 37(4):500-509. doi:10.1111/j.1442-9993.2011.02312.x

Examples

data(floraChile2)    
head(floraChile2)

Description

Data about anaerobic variables of football players.

Usage

data(football)

Format

The data frame contains 13 variables as follows:

WPM

WPMk

WPm

WPmk

WTT

WTTk

WIF

W5

W10

W15

W20

W25

W30

Source

Data were provided by Dr Aquiles Yanez-Silva at Universidad Mayor (Santiago, Chile).

References

Not yet.

Examples

data(football)    
head(football)

Description

Datos sobre potencia anaerobica de jugadores de football.

Usage

data(football2)

Format

Contiene variables de nivel individual, como se describen a continuación::

WPM

WPMk

WPm

WPmk

WTT

WTTk

WIF

W5

W10

W15

W20

W25

W30

Source

Los datos fueron cedidos por el Dr Aquiles Yañez-Silva de la Universidad Mayor (Santiago, Chile).

References

Not yet.

Examples

data(football2)    
head(football2)

Description

Data of forest fire occurrence from Altamirano et al. (2013) as our population, containing 7210 total observations (N), with only 890 cases of fire occurrence (N 1 ) and 6320 cases of non occurrence (N0). The binary variable (Y) is the occurrence of forest fire, where Y equal to 1 denotes occurrence and Y equal to 0 otherwise.

Usage

data(forestfire)

Format

The data frame contains four variables as follows:

fire

Presence of forest fire (1 yes, 0 no)

xcoord

Geographic coordinate x.utm

ycoord

Geographic coordinate y.utm

aspect

Exposure (degrees from north)

eleva

Elevation (m)

slope

Slope (degrees)

distr

Distance to dirt roads

distcity

Distance to cities

distriver

Distance to paved roads

covera

Land use classifications according to a polygon

coverb

Land use classifications according to a polygon

tempe

Minimum temperature of the coldest month

ppan

Annual precipitation

ndii

Normalized difference infrared index

nvdi

Normalized difference vegetation index

tempe2

Minimum temperature of the warmest month

ppan2

Precipitation of the driest month

frec.fire

Frequency of fires

perc.fire

Percentage of fire frequency

fireClass

Class for frecuency fire

asp.class

Class of variable exposure

eleva.class

Class of numerical variable elevation

slope.class

Class of numerical variable slope

ndii.class

Normalized difference infrared index class

nvdi.class

Normalized difference vegetation index class

Source

Data were provided by Dr Adison Altamirano at the Universidad de La Frontera (Temuco, Chile).

References

- Altamirano A, Salas C, Yaitul V, Smith-Ramirez C, Avila A. 2013. Infuencia de la heterogeneidad del paisaje en la ocurrencia de incendios forestales en Chile Central. Revista de Geografia del Norte Grande, 55:157-170, 2013. -Salas-Eljatib C, Fuentes-Ramírez A, Gregoire TG, Altamirano A, Yaitul V. 2018. A study on the effects of unbalanced data when fitting logistic regression models in ecology. Ecological Indicators 85:502-508. doi:10.1016/j.ecolind.2017.10.030

Examples

data(forestfire)    
head(forestfire)

Description

Datos de ocurrencia de incendios forestales de Altamirano et al. (2013) como nuestra poblacion, que contiene 7210 observaciones totales (N), con solo 890 casos de ocurrencia de incendios (N1) y 6320 casos de no ocurrencia (N0). La variable binaria (Y) es la ocurrencia de un incendio forestal, donde Y igual a 1 denota ocurrencia e igual a 0 en caso contrario.

Usage

data(forestfire2)

Format

Variables se describen a continuacion:

fire

Presencia de incendio forestal (1 si, 0 no)

xcoord

Coordenada geografica x.utm

ycoord

Coordenada geografica y.utm

aspect

Exposicion (grados desde el norte)

eleva

Elevacion (m)

slope

Pendiente (grados)

distr

Distancia a caminos de tierra

distcity

Distancia a ciudades

distriver

Distancia a caminos pavimentados

covera

Clasificaciones de uso del suelo segun un poligono

coverb

Clasificaciones de uso del suelo segun un poligono

tempe

Temperatura m?nima del mes m?s frio

ppan

Precipitacion anual

ndii

Indice infrarrojo de diferencia normalizado

nvdi

Indice de vegetacion de diferencia normalizado

tempe2

Temperatura m?nima del mes mas calido

ppan2

Precipitacion del mes mas seco

frec.fire

Frecuencia de incendios

perc.fire

Porcentajede la frecuencia de incendios

fireClass

Clase para variable frecuencia de incendio

asp.class

Clase de variable exposicion

eleva.class

Clase de variable numerica elevacion

slope.class

Clase de variable numerica pendiente

ndii.class

Clase de indice infrarrojo de diferencia normalizado

nvdi.class

Clase de indice de vegetacion de diferencia normalizado

Source

Datos fueron cedidos por el Dr Christian Salas-Eljatib (Santiago, Chile).

References

- Altamirano A, Salas C, Yaitul V, Smith-Ramirez C, Avila A. 2013. Infuencia de la heterogeneidad del paisaje en la ocurrencia de incendios forestales en Chile Central. Revista de Geografia del Norte Grande, 55:157-170, 2013. -Salas-Eljatib C, Fuentes-Ramírez A, Gregoire TG, Altamirano A, Yaitul V. 2018. A study on the effects of unbalanced data when fitting logistic regression models in ecology. Ecological Indicators 85:502-508. doi:10.1016/j.ecolind.2017.10.030

Examples

data(forestfire2)    
head(forestfire2)

Description

Computes the geometric mean of a numeric vector. It is the n-th root of the product of n numbers, as follows.

$y_g = \left(\prod_{i=1}^{n} y_i\right)^{1/n}$

for $y_i > 0$. It can also be understood as the average of the logarithmic values of a data set, converted back to a base 10 number. The geometric mean is a central position statistics of a random variable.

Usage

gmean(v)

Arguments

Details

Notice that can only be computed for positive values. For negative values, there are alternatives, but not covered here.

Value

This function returns the geometric mean, a numeric scalar.

Author(s)

Christian Salas-Eljatib.

References

Salas-Eljatib, C. 2021. Análisis de datos con el programa estadístico R: una introducción aplicada. Ediciones Universidad Mayor, Santiago, Chile. 170 p. https://eljatib.com/rlibro

Examples

y.var <- runif(10, min=10, max=45)
gmean(y.var)

Description

Tree size, competition, and diameter growth increment of *Metrosideros polymorpha* trees collected in the Kilauea Volcano, Hawaii. Data containing 64 observations at the current annual growth rate (defined as dbh increment within one calendar year) of each tree was measured from 1986 to 1988.

Usage

data(hawaii)

Format

The dataframe has the following columns:

tree.code

Tree number identification. The first letter of the ID representsa cohort. Six cohorts representing a chronosequence were sampled.

dbh

Initial stem diameter, in cm.

toth

Total height, in m.

crown.area

Crown outline area, in square meters.

comp.ind

Competition index (Basal area of nearest neighbor divided by square of distance to nearest neighbor plus basal area of second nearest neighbor divided by square of distance to second nearest neighbor).

cai.1986

Current annual stem diameter increment during 1986, in mm.

cai.1987

Current annual stem diameter increment during 1987, in mm.

cai.1988

Current annual stem diameter increment during 1988, in mm.

Source

The data were obtained from Gerrish and Mueller-Dombois (1999).

References

Gerrish G, Mueller-Dombois D. 1999. Measuring stem growth rates for determining age and cohort analysis of a tropical evergreen tree. Pacific Science. 53(4): 418-429.

Examples

data(hawaii)    
head(hawaii)

Description

Tamaño del árbol, competencia, e incremento corriente anual de árboles de *Metrosideros polymorpha* colectado en el volcan Kilauea, en Hawaii. Los datos contienen 64 observaciones de incremento corriente anual (definido como el incremento en diámetro en un año calendario) de cada árbol. Estos incrementos fueron medidos desde el año 1986 a 1988.

Usage

data(hawaii)

Format

Estos datos contienen las siguientes columnas:

arb.id

Codigo identificador del árbol. La primera letra del ID representa un cohorte. Hay seis cohortes que representan una cronosecuencia.

dap

Diámetro a la altura del pecho, en cm.

atot

Altura total, en m.

area.copa

Area de copa, en metros cuadrados.

ind.comp

Competition index (Basal area of nearest neighbor divided by square of distance to nearest neighbor plus basal area of second nearest neighbor divided by square of distance to second nearest neighbor).

ica.1986

Incremento corriente anual durante el año 1986, en mm.

ica.1987

Incremento corriente anual durante el año 1987, en mm.

ica.1988

Incremento corriente anual durante el año 1988, en mm.

Source

Los datos fueron obtenidos desde Gerrish and Mueller-Dombois (1999).

References

Gerrish G, Mueller-Dombois D. 1999. Measuring stem growth rates for determining age and cohort analysis of a tropical evergreen tree. Pacific Science. 53(4): 418-429.

Examples

data(hawaii2)    
head(hawaii2)

Description

Data contains 148 observations on the height growth of dominant trees of Pseudotsguga mensiezzi in the Northwest of the United States.

Usage

data(hgrdfir)

Format

The data frame contains seven variables as follows:

natfor.id

Code identifier.

plot.code

Plot number identification

tree.code

Tree number identification.

dbh

Diameter at breast height at sampling, in in.

toth

Total height at sa,pling, in ft.

age

Age of tree, yr.

height

Height at a given age, in ft.

Source

The data were provided by Dr Christian Salas.

References

- Monserud RA. 1984. Height growth and site index curves for Inland Douglas-fir based on stem analysis data and forest habitat type. Forest Science 30(4):943-965.

- Salas C, Stage AR, and Robinson AP. 2008. Modeling effects of overstory density and competing vegetation on tree height growth. Forest Science 54(1):107-122. doi:10.1093/forestscience/54.1.107

Examples

data(hgrdfir)    
head(hgrdfir)
unique(hgrdfir$tree.code)
table(hgrdfir$plot.code,hgrdfir$tree.code)
tapply(hgrdfir$dbh, hgrdfir$tree.code, mean)
tapply(hgrdfir$dbh, hgrdfir$tree.code, mean) #dbh of each sample tree
tapply(hgrdfir$toth, hgrdfir$tree.code, mean) #toth of each sample tree

Description

Data contiene 148 obserrvaciones sobre el crecimiento en altura de árboles dominantes de Pseudotsguga mensiezzi en el Nor-Oeste de los Estados Unidos

Usage

data(hgrdfir2)

Format

La data frame contiene siete variables:

bosque.id

Codigo identificador del bosque.

parcela

Codigo identificador de la parcela.

arbol

Número de identificacion árbol.

dap

Diámetro a la altura del pecho, en pulgadas.

atot

Altura total, en pies

edad

Edad, en os

altura

Altura para cada edad del árbol, en pies

Source

La data fue cedida por el Dr Christian Salas-Eljatib.

References

Monserud RA. 1984. Height growth and site index curves for Inland Douglas-fir based on stem analysis data and forest habitat type. Forest Science 30(4):943-965.

Salas C, Stage AR, and Robinson AP. 2008. Modeling effects of overstory density and competing vegetation on tree height growth. Forest Science 54(1):107-122. doi:10.1093/forestscience/54.1.107

Examples

data(hgrdfir2)    
head(hgrdfir2)
unique(hgrdfir2$arbol.id)
table(hgrdfir2$parcela,hgrdfir2$arbol.id)
tapply(hgrdfir2$dap, hgrdfir2$arbol.id, mean) #dap de cada arbol muestra
tapply(hgrdfir2$atot, hgrdfir2$arbol.id, mean) #atot de cada arbol muestra

Description

These data are forest inventory measures from the Upper Flat Creek stand of the University of Idaho Experimental Forest, dated 1991.

Usage

data(idahohd)

Format

Contains five variables, as follows:

plot

Plot number.

tree

Tree within plot.

species

A factor with levels DF = Douglas-fir, GF = Grand fir, SF = Subalpine fir, WL = Western larch, WC = Western red cedar, WP = White pine.

dbh

Diameter 137 cm perpendicular to the bole, cm.

height

Height of the tree, in m.

Source

The data were assembled from the 'ufc' dataframe from the $alr4$ library.

References

Weisberg S. 2014. Applied Linear Regression. 4th edition. New York: Wiley.

Examples

data(idahohd)    
head(idahohd) 
plot(height~dbh, data=idahohd)

Description

Estos datos provienen de un muestreo en el bosque experimental de la University of Idaho, en Upper Flat Creek, Idaho, USA. Medido en 1991.

Usage

data(idahohd2)

Format

Contiene cinco variables detalladas a continuación:

parce

Número de la parcela de muestreo.

arbol

Número del árbol dentro de la parcela.

spp

Especie del árbol, una variable factor con niveles DF = Douglas-fir, GF = Grand fir, SF = Subalpine fir, WL = Western larch, WC = Western red cedar, WP = White pine.

dap

Diámetro del fuste a los 1.3 m sobre el suelo, en cm.

atot

Altura del árbol, en m.

Source

Los datos fueron obtenidos desde la dataframe 'ufc' de la librería $alr4$.

References

Weisberg S. 2014. Applied Linear Regression. 4th edition. New York: Wiley.

Examples

data(idahohd2)    
head(idahohd2)
plot(atot~dap, data=idahohd2)

Description

These are plot-level measurement (2x2 m) data from the forests in the Robinson Crusoe Island, located in the Pacific Ocean, 667 km from mainland Chile. Measurements were collected in transects of 100 to 240 meters in which, 398 squared plots (2x2 m) were set to include canopy gaps, gap borders and closed forest conditions.

Usage

data(invasivesRCI)

Format

Data has the following columns

plot.id

Plot identification code

Gap.type

Canopy gap classified as invaded=Inv, non invaded= Nat or treated =Treat(considering the estimated cover of invasive plant species)

Forest.zone

Location of the plot (gap, border or forest)

Ferns

Estimated cover of fern species (in 2x2 plots)

Moss.liverw

Estimated cover of mosses and liverworts (in 2x2 plots)

Cwd

Estimated cover of coarse woody debris > 3 cm diameter (in 2x2 plots)

Litter

Estimated cover of litter (in 2x2 plots)

Ms

Estimated cover of mineral soil ( in 2x2 plots)

Rock

Estimated cover of rocks (in 2x2 plots)

Est.age

Age category for the canopy gap associated to each plot

Source

The data are provided courtesy of Prof. Rodrigo Vargas-Gaete at Universidad de La Frontera (Temuco, Chile).

References

Vargas-Gaete R, Salas-Eljatib C, Gärtner SM, Vidal OJ, Bannister JR, Pauchard A. 2018. Invasive plant species thresholds in the forests of Robinson Crusoe Island, Chile. Plant Ecology & Diversity, 11(2), 205-215.

Description

The kurtosis is about the tailedness, or the degree of heaviness of the tails, in the frequency distribution. The function computes an estimator of the kurtosis.

Usage

kurto(x, na.rm = TRUE)

Arguments

Details

The kurtosis of a random variable is the fourth moment of the standardized variable. There are several ways of parameterizing a kurtosis estimator, such as depending on the fourth moment and the standard deviation of the random variable.

Value

An estimator of the kurtosis.

Author(s)

Christian Salas-Eljatib

Examples

y.var<-rnorm(100);x.var<-rbeta(100,.2,2)
kurto(y.var)
kurto(x.var)

Description

dataset contains 476 observations, 34 categorical and 442 numerical. Land-cover data was generated through remote sensing classification techniques using Sentinel-2 satellite images from year 2016. Temperatures were obtained from TIRS band 10 of Landsat 8 satellites images. Particulate matter concentrations were estimated using spatial modelling techniques from 10 pollution stations distributed in the city. Altitude was generated from a Digital Elevation Model. Population and poverty were gathered from Casen 2017 survey.

Usage

data(landcover)

Format

The data frame contains four variables as follows:

county

Name of Municipality

built.p

Percentage of surface covered by built-up area

vegeta.p

Percentage of surface covered by vegetation

naked.p

Percentage of surface covered by bare soil

grass.p

Percentage of surface covered by deciduous vegetation

p.Deciduo

Percentage of surface covered by evergreen vegetation

p.Siempreverde

Percentage of surface covered by evergreen vegetation

temp.winter

Land surface temperature in celsius degrees at 2pm on a winter 0% cloud day

temp.summer

Land surface temperature in celsius degrees at 2pm on a summer 0% cloud day

pm10.winter

Average particulate matter 10 micron during winter months

pm10.summer

Average particulate matter 10 micron during summer months

poor.p

Percentage of people under poverty line year 2017.

eleva

Average altitude of municipal area.

pop

Total population of municipality

Source

Data were provided by Dr Ignacio Fernandez at Universidad Adolfo Ibañez (Santiago, Chile).

References

Not yet

Examples

data(landcover)    
head(landcover)

Description

El conjunto de datos contiene 476 observaciones, 34 categoricas y 442 numericas. Los datos de cobertura terrestre se generaron mediante tecnicas de clasificacion de teledeteccion utilizando imagenes de satelite Sentinel-2 del año 2016. Las temperaturas se obtuvieron de la banda TIRS 10 de las imagenes de los satelites Landsat 8. Las concentraciones de material particulado se estimaron mediante tecnicas de modelado espacial de 10 estaciones de contaminacion distribuidas en la ciudad. La altitud se genero a partir de un modelo de elevacion digital. La poblacion y la pobreza se obtuvieron de la encuesta Casen 2017.

Usage

data(landcover2)

Format

Variables se describen a continuacion:

comuna

Name of Municipality

const.p

Porcentaje de superficie cubierta por area construida

vegeta.p

Porcentaje de superficie cubierta por vegetacion

desnu.p

Porcentaje de superficie cubierta por suelo desnudo

pasto.p

Porcentaje de superficie cubierta por cesped

deci.p

Porcentaje de superficie cubierta por vegetacion de hoja caduca

sverde.p

Porcentaje de superficie cubierta por vegetacion siempre verde

temp.inv

Temperatura de la superficie terrestre en grados celsius a las 2 p.m.en un dia de invierno con 0% de nubes

temp.ver

Temperatura de la superficie de la tierra en grados celsius a las 2 p.m.en un dia de verano con 0% de nubes

pm10.inv

Material particulado promedio de 10 micrones durante los meses de invierno

pm10.ver

Material particulado promedio de 10 micrones durante los meses de verano

pobreza.p

Porcentaje de personas por debajo de la linea de pobreza año 2017

altitud

Altitud media del termino municipal

pob

Poblacion total del municipio

Source

Los datos fueron cedidos por el Dr Ignacio Fernandez de la Universidad Adolfo Ibañez (Santiago, Chile).

References

Not yet

Examples

data(landcover2)    
head(landcover2)

Description

The study area is situated in the municipality of Tolga, located in Hedmark County, Eastern Norway. Field plots 32 m × 32 m in size were established in forests. A total of 1109 plots were sampled. In each plot, Scots pines (Pinus sylvestris L.). trees with a stem diameter larger than 35 cm were measured and counted.

Usage

data(largetrees)

Format

Contains two variables, as follows:

plot

Plot code.

y

Number of large-diameter trees in a given sample plot.

Source

Although Christian Salas was part of the study, he just reproduced the needed data to mimic the distribution of the random variable of interest, as shown in the study of Korkhonen et al (2016).

References

- Korhonen L, Salas C, Ostgard T, Lien V, Gobakken T, Naesset E. 2016. Predicting the occurrence of large-diameter trees using airborne laser scanning. Canadian Journal of Forest Research 46:461–469. doi:10.1139/cjfr-2015-0384

Examples

data(largetrees)    
head(largetrees) 
hist(largetrees$y)

Description

El área de estudio esta ubicada en la municiplaidad de Tolga, en la comuna de Hedmark, al Este de Noruega. 1109 parcelas de muestreo de 32 m × 32 m se establecieron en los bosques. En cada parcela, los árboles de pino escoses (Pinus sylvestris L.). que tuvieran un diámetro mayor a 35 cm fueron medidos y contados.

Usage

data(largetrees2)

Format

Los datos poseen las siguientes dos columnas:

parc

Identificador de la parcela de muestreo.

y

Número de árboles de gran diámetro encontrados en una parcela de muestreo.

Source

Aunque el Prof. Christian Salas fue parte del estudio, acá se han reproducido los datos necesarios que imitan la distribución de la variable aleatoria de interés, tal como se muestra en el estudio de Korkhonen et al (2016).

References

- Korhonen L, Salas C, Ostgard T, Lien V, Gobakken T, Naesset E. 2016. Predicting the occurrence of large-diameter trees using airborne laser scanning. Canadian Journal of Forest Research 46:461–469. doi:10.1139/cjfr-2015-0384

Examples

data(largetrees2)    
head(largetrees2) 
hist(largetrees2$y)

Description

The Cartesian position, species, and diameter of trees within a plot were measured. The sample plot is rectangular of 130 m by 70 m. Further details can be #' reviewed in the reference.

Usage

data(llancahue)

Format

Contains tree-level variables, as follows:

tree.code

Tree identificator

spp

species abreviation as follows: AP=Aextocicon puncatatum, EC=Eucryphia cordifolia, GA=Gevuina avellana, LP=Laureliopsis philippiana, LS=Laurelia sempervirens, ND=Nothofagus dombeyi, Ot=Other, PS=Podocarpus saligna

dbh

diameter at breast height, in cm.

x.coord

Cartesian position in the X-axis, in m.

y.coord

Cartesian position in the Y-axis, in m.

Source

The data are provided courtesy of Prof. Daniel Soto at Universidad de Aysen (Coyhaique, Chile).

References

- Soto DP, Salas C, Donoso PJ, Uteau D. 2010. Heterogeneidad estructural y espacial de un bosque mixto dominado por Nothofagus dombeyi después de un disturbio parcial. Revista Chilena de Historia Natural 83(3): 335-347.

Examples

data(llancahue)    
head(llancahue) 
descstat(llancahue$dbh)
boxplot(dbh~spp, data=llancahue)

Description

Corresponde a la posición cartesiana, especie, y diámetro de árboles en una parcela de muestreo en el bosque de Llancahue, cerca de Valdivia, Chile. La parcela es rectangular con dimensiones de 130 m por 70 m. Mayores antecedentes aparecen en las referencias.

Usage

data(llancahue2)

Format

Contains tree-level variables, as follows:

arb.id

Identificador del árbol.

spp

Codificación de la especie como sigue: AP= Aextocicon puncatatum, EC=Eucryphia cordifolia, GA=Gevuina avellana, LP=Laureliopsis philippiana, LS=Laurelia sempervirens, ND=Nothofagus dombeyi, Ot=Other, PS=Podocarpus saligna.

dap

Diámetro a la altura del pecho, en cm.

coord.x

Posición cartesiana en el eje-X, en m.

coord.y

Posición cartesiana en el eje-Y, en m.

Source

Los datos fueron cedidos por el Prof. Daniel Soto de Universidad de Aysen (Coyhaique, Chile).

References

- Soto DP, Salas C, Donoso PJ, Uteau D. 2010. Heterogeneidad estructural y espacial de un bosque mixto dominado por Nothofagus dombeyi después de un disturbio parcial. Revista Chilena de Historia Natural 83(3): 335-347.

Examples

data(llancahue2)    
head(llancahue2) 
descstat(llancahue2$dap)
boxplot(dap~spp, data=llancahue2)

Description

Contains species composition data for forests with presence of Lleuque (Prumnopitys andina)

Usage

lleuque

Format

The dataframe has the following columns

stand

Stand number

plot.num

Sample plot number

cipres

Tree density of ciprés de la Cordillera (Austrocedrus chilensis), in trees/ha.

ldura

Tree density of leña dura (Maytenus disticha), in trees/ha.

roble

Tree density of roble (Nothofagus obliqua), in trees/ha.

lleuque

Tree density of lleuque (Prumnopitys andina), in trees/ha.

Source

The data are provided courtesy of Prof. Rodrigo Vargas-Gaete at Universidad de La Frontera (Temuco, Chile).

References

Vargas-Gaete R, Salas-Eljatib C, Penneckamp D, Neira Z, Diez MC, Vargas-Picón, R. 2020. Estructura y regeneración de bosques de Prumnopitys andina en los Andes del sur de Chile. Gayana Botánica 77(1):48-58. doi:10.4067/S0717-66432020000100048

Description

Para cada unidad de muestreo (o parcela) hay valores de densidad por especie arbórea. Las parcelas fueron establecidas en bosques de *Prumnopitys andina* (Lleuque) forests

Usage

lleuque2

Format

Los datos contienen las siguientes columnas:

rodal

Rodal, en código.

parce

Parcela de muestreo, en código.

cipres

Densidad de Ciprés de la Cordillera (Austrocedrus chilensis), en arb/ha.

ldura

Densidad de leña dura (Maytenus disticha), en arb/ha.

roble

Densidad de roble (Nothofagus obliqua), en arb/ha.

lleuque

Densidad de lleuque (Prumnopitys andina), en arb/ha.

Source

Los datos fueron cedidos por el Prof. Rodrigo Vargas-Gaete de la Universidad de La Frontera (Temuco, Chile).

References

Vargas-Gaete R, Salas-Eljatib C, Penneckamp D, Neira Z, Diez MC, Vargas-Picón, R. 2020. Estructura y regeneración de bosques de Prumnopitys andina en los Andes del sur de Chile. Gayana Botánica 77(1):48-58. doi:10.4067/S0717-66432020000100048

Description

Computes a likelihood ratio test between a reduced model (modr) and a full model (modf). Both models must be previously fitted by maximum likelihood.

Usage

lrt(modr, modf)

Arguments

Details

Double-check the order of the reduced and full model, before of using the model

Value

This function returns an object having the following elements: "loglik.Modr" maximized log-likelihood of modr; "loglik.Modf" maximized log-likelihood of modf; "dif.loglik" difference in log-likelihood between both models, and "dif.df" difference in degrees of freedong of both models, and "p-value" is the p-value for the LRT.

Author(s)

Christian Salas-Eljatib.

References

Pinheiro JC, and Bates DM. 2000. Mixed-effects models in S and Splus. Springer-Verlag, New York, NY. 528 p.

Examples

#not yet implemented

Description

Computes the mode of a random variable.

Usage

moda(y = y)

Arguments

Details

The mode is an statistics representing the most "used" value of the random variable as a way of central position.

Value

The function returns the mode, a numeric scalar.

Author(s)

Christian Salas-Eljatib.

Examples

set.seed(1234)
variable <- rnorm(10, mean=45,sd=6)
#using the function
moda(y=variable)
moda(variable)

Description

These are tree-level measurement data, with cartesian location of each tree, from Pinus contorta invasion in Patagonian steppe in Coyhaique in southhern Chile, measured in 2011. There are 3 plots, each of 10.000 m$^{2}$.

Usage

data(pcontorta)

Format

Contains eight variables, as follows:

plot.id

Plot sample ID.

tree.id

Tree identificator number in each plot. Same indv/id for multi-stem trees.

y.coord

coordinate of S latitude.

x.coord

coordinate of W longitude.

substrate

Ground cover in which each pine grow. Bare soil, Festuca pallescens, Baccharis magellanica, Oreopulus glacialis, Acaena integerrima and others species.

drc

Diameter at the root collar on trees, in mm.

h

Height of trees, in cm.

canopy.area

Proyection of canopy area of each tree, in square meters.

Source

The data are provided courtesy of Drs Anibal Pauchard and Rafael Garcia at the Laboratorio de Invasiones Biologicas, Universidad de Concepción (Concepción, Chile).

References

Pauchard A, Escudero A, García RA, de la Cruz M, Langdon B, Cavieres LA, Esquivel J. 2016. Pine invasions in treeless environments: dispersal overruns microsite heterogeneity. Ecology and Evolution. 6(2): 447-459.

Examples

data(pcontorta)    
head(pcontorta)
unique(pcontorta$plot.id)

Description

Mediciones a nivel de árbol, con la ubicación cartesian de cada árbol de Pinus contorta, en parcelas de muestreo para estudio de invasion en la estepa Patagonica en Coyhaique en el sur de Chile. Hay tres parcelas, cada una de 10.000 m$^{2}$.

Usage

data(pcontorta2)

Format

Contiene ocho variables, como siguen:

parcela

Parcela.

arbol

Número de árbol en cada parcela. Mismo árbol/id para árboles multifustales.

coord.y

coordinada de latitud W.

coord.x

coordinada de longitud W.

substrato

Cobertura del suelo donde cada pino crece. Bare soil, Festuca pallescens, Baccharis magellanica, Oreopulus glacialis, Acaena integerrima and others species.

h

Height of trees, in cm.

diam.cuello

Diámetro del cuello, en mm.

area.copa

Area de copa, en m$^{2}$.

Source

Los datos fueron cedidos por los Drs. Anibal Pauchard y Rafael Garcia del Laboratorio de Invasiones Biologicas, Universidad de Concepcion (Chile).

References

Pauchard A, Escudero A, Garcia RA, de la Cruz M, Langdon B, Cavieres LA, Esquivel J. 2016. Pine invasions in treeless environments: dispersal overruns microsite heterogeneity. Ecology and Evolution. 6(2): 447-459. doi:10.1002/ece3.1877

Examples

data(pcontorta2)    
head(pcontorta2)
unique(pcontorta2$plot.id)

Description

These are volume measurements data of sample trees in the Baixo-Mino region in Galicia, Spain.

Usage

data(pinaster)

Format

Contains tree-level variables, as follows:

stand

stand number from the sample tree was selected.

si

Site index of the stand.

tree.no

tree number.

dbh

Diameter at breast height, in cm.

toth

Total height, in m.

d4

Upper-stem diameter at 4 m, in cm.

volwb

Tree gross volume, in m$^{3}$ with bark.

volwob

Tree gross volume, in m$^{3}$ without bark.

Source

The data are provided courtesy of Dr Christian Salas-Eljatib at the Universidad de Chile (Santiago, Chile).

References

- Salas C, Nieto L, Irisarri A. 2005. Modelos de volumen para Pinus pinaster Ait. en la comarca del Baixo Mino, Galicia, España. Quebracho 12: 11-22. https://eljatib.com/publication/2005-12-01_modelos_de_volumen_p/

Examples

data(pinaster)    
head(pinaster)

Description

Variables de volumen y otras a nivel de árbol para una muestra de árboles de Pinus pinaster en la comarca del Baixo-Mino en Galicia, España.

Usage

data(pinaster2)

Format

Contiene las siguientes variables a nivel de árbol:

rodal

Rodal desde donde el árbol fue muestreado

ind.sitio

Indice de sitio del rodal, en m.

arbol

Número del árbol.

dap

Diámetro a la altura del pecho, en cm.

atot

Altura total, en m.

d4

Diámetro fustal a los 4 m, en cm.

vtcc

Volumen bruto total con corteza, en m$^{3}$.

vtsc

Volumen bruto total sin corteza, en m$^{3}$.

Source

Los datos fueron cedidos por el Dr Christian Salas (Chile).

References

- Salas C, Nieto L, Irisarri A. 2005. Modelos de volumen para Pinus pinaster Ait. en la comarca del Baixo Miño, Galicia, España. Quebracho 12: 11-22. https://eljatib.com/publication/2005-12-01_modelos_de_volumen_p/

Examples

data(pinaster2)    
head(pinaster2)

Description

These are tree-lavel measurement data from Pinus spp invasion in *Araucaria-Nothofagus* forests in the Malalcahuello National Reserve in La Araucania region in southhern Chile, measured in 2012. There are 26 plots and plot size is 100 m$^{2}$.

Usage

data(pinusSpp)

Format

Contains eight variables, as follows:

plot.id

Plot sample ID.

plot.size

Plot size, en m$^{2}$.

lat.s

Decimal coordinate of S latitude.

long.w

Decimal coordinate of W longitude.

indv.id

Tree identificator number in each plot. Same indv/id for multi-stem trees.

stem.id

Stem identificator number in each plot.

spp

Specie.

dbh

Diameter at breast-height, in cm.

toth

Total height, in m.

hcb

Height to crown base, in m.

crown.lenght

Crown lenght, in m.

Source

The data are provided courtesy of Drs Anibal Pauchard and Rafael García at the Laboratorio de Invasiones Biológicas, Universidad de Concepcion (Concepción, Chile).

References

Cobar-Carranza A, Garcia R, Pauchard A, Pena E. 2014. Effect of Pinus contorta invasion on forest fuel properties and its potential implications on the fire regime of Araucaria araucana and Nothofagus antarctica forests. Biological Invasions. 16(11): 2273-2291. doi:10.1007/s10530-014-0663-8

Examples

data(pinusSpp)    
head(pinusSpp) 
length(unique(pinusSpp$plot.id)) 
boxplot(dbh~plot.id, data=pinusSpp)

Description

Mediciones a nivel de árbol para estudiar la invasion de Pinus spp en bosques de Araucaria-Nothofagus en la Reserva Nacional Malalcahuello en la region de la Araucania en el sur de Chile. Hay 26 parcelas, y la superficie de cada una es de 100 m$^{2}$.

Usage

data(pinusSpp2)

Format

Los datos contienen ocho columnas que se detallan a continuacion:

parcela

Número de la parcela.

sup.parcela

Superficie de la parcela, en m$^{2}$.

lat.s

Decimal coordinate of S latitude.

long.w

Decimal coordinate of W longitude.

indv.id

Identificador del árbol en la parcelaeach plot. Same indv/id for multi-stem trees.

fuste.id

Identificador del fuste.

espe

Especie.

dap

Diámetro a la altura del pecho, en cm.

atot

Altura total, en m.

hcc

Altura comienzo de copa, en m.

largo.copa

Largo de copa, en m.

Source

Los datos fueron cedidos por los Drs. Anibal Pauchard y Rafael García del Laboratorio de Invasiones Biológicas, Universidad de Concepción (Concepción, Chile).

References

Cobar-Carranza A, Garcia R, Pauchard A & Pena E. 2014. Effect of Pinus contorta invasion on forest fuel properties and its potential implications on the fire regime of *Araucaria araucana* and Nothofagus antarctica forests. Biological Invasions. 16(11):2273-2291. doi:10.1007/s10530-014-0663-8

Examples

data(pinusSpp2)    
head(pinusSpp2) 
length(unique(pinusSpp2$parce)) 
boxplot(dap~parce, data=pinusSpp2)

Description

Data containing 52717 observations about presence of sea ice from logbook records of annual cruises to the B-C-B in an unbroken record between years 1850 to 1910.

Usage

data(presenceIce)

Format

The dataframe contains the following columns:

ship.id

The code number for ships.

move.type

Type of movement of ships. 0 indicates a sail-powered vessel and 1 indicates an auxiliary-powered vessel.

year

Year of registry.

month

Month of registry.

day

Day of registry.

lat.dec

Decimal latitude.

long.dec

Decimal longitude.

e.w

East or west of the Prime Meridian.

ice.cov

Sea Ice Observed. 0 no see (Not registered) and 1 presence sea ice (Registered).

Source

The data were provided from Sea Ice Group at the Geophysical Institute.

References

Mahoney A, Bockstoce J, Botkin D, Eicken H, Nisbet R. 2011. Sea-Ice Distribution in the Bering and Chukchi Seas: Information from Historical Whaleships' Logbooks and Journals ARCTIC. 64(4): 465-477.

Examples

data(presenceIce)    
head(presenceIce)

Description

Datos de mesa de la eleccion presidencial del 2012 en Chile. La eleccion se llevo a cabo el 19 de Diciembre del 2021.

Usage

data(president)

Format

Los datos contienen las siguientes columnas:

region.no

Número de la region adminsitrativa de Chile.

region

Nombre de la region administrativa de Chile

provincia

Provincia.

circu.senatorial

Circunscripcion senatorial.

distrito

Distrit.

comuna

County.

circu.elec

Circunscripcion electoral.

local

Local de votacion. Generalmente es un colegio.

no.mesa

Número de mesa.

tipo.mesa

Tipo de mesa de votacion.

mesas.fusionadas

Mesa de votacion fucionada.

electores

Electores.

nro.en.voto

.

candidato

Candidato, ya sea Gabriel Boric o Jose A. Kast

votos.tricel

Número total de votos segun el TRICEL (Tribunal calificador de elecciones).

Source

Los datos fueron obtenidos desde el sitio web del Servicio Electoral del Gobierno de Chilean (SERVEL) en https://www.servel.cl. El archivo de datos descargado el 24 de Octubre del 2022 tenia el nombre Resultados mesa presidencial TRICEL 2v 2021-1.xlsx.

Examples

data(president)    
head(president)

Description

Datos a nivel de mesa de la votación para elecciones primarias para Presidente de Chile en 2021.

Usage

data(primary)

Format

Este set de datos contiene las siguientes columnas:

region.no

Región administrativa de Chile.

region

Nombre de la región.

provincia

Provincia.

distrito

Distrito.

comuna

Comuna.

circu.elec

Circunscripción electoral.

local

Local de votación.

tipo.mesa

tipo de mesa.

mesa

Código identificador de la mesa.

mesas.fusionadas

Mesas fusionadas.

nro.voto

.

lista

Lista política del candidato.

pacto

Pacto político del candidato.

partido

Partido político del candidato.

candidato

Nombre del candidato.

votos

Número total de votos.

Source

Los datos fueron obtenidos desde el servicio electoral de Chile (SERVEL) en el web https://www.servel.cl. El nombre del archivo era Resultados Primarias Presidenciales 2021 CHILE.xlsx, y fue descargado el 4 de octubre del 2022. Los datos fueron ordenados, y solo aquellas filas que contenian información en la columna 'votos' son parte de la dataframe.

Examples

data(primary)    
head(primary) 
table(primary$region)
table(primary$region,primary$candidato)
tapply(primary$votos,primary$candidato,sum)

Description

Tree-level variables and spatial coordinates in a permanent sample plot of 1 ha (100 x 100m) in the Rucamanque experimental forest, near Temuco, Chile.

Usage

data(pspruca)

Format

The data frame contains four variables for the standing-alive trees as follows:

tree.no

tree number

species

Species name, "N. obliqua" is Nothofagus obliqua, "Ap" is Aexitocicum puncatatum, etc.

crown.class

Crown class (1: superior, 2: intermediate, 3; inferior)

dbh

diameter at breast-height, in cm

x.coord

Cartesian position at the X-axis, in m

y.coord

Cartesian position at the Y-axis, in m

Source

Data were provided by Dr Christian Salas-Eljatib (Universidad de Chile, Santiago, Chile).

References

Salas C, LeMay V, Nunez P, Pacheco P, and Espinosa A. 2006. Spatial patterns in an old-growth Nothofagus obliqua forest in south-central Chile. Forest Ecology and Management 231(1-3): 38-46. doi:10.1016/j.foreco.2006.04.037

Examples

data(pspruca)    
head(pspruca) 
table(pspruca$species)

Description

Medidas a nivel de árbol y coordenadas espaciales en un parcela de muestreo permanente de 1 ha (100 x 100m) en el bosque de Rucamanque, cerca de Temuco, Chile. Mayores antecedentes en las referencias.

Usage

data(pspruca2)

Format

Las columnas describen características de los árboles vivos en pie, como sigue:

arbol

Número del árbol

especie

Nombre de la especie, "N. obliqua" es Nothofagus obliqua, "Ap" es Aexitocicum puncatatum, etc.

clase.copa

Clase de copa (1: superior, 2: intermedio, 3; inferior)

dap

Diámetro a la altura del pecho, en cm

coord.x

Posicion cartesiana en el eje X, en m

coord.y

Posicion cartesiana en el eje Y, en m

Source

Los datos fueron cedidos por el Dr Christian Salas-Eljatib (Santiago, Chile).

References

Salas C, LeMay V, Nunez P, Pacheco P, and Espinosa A. 2006. Spatial patterns in an old-growth Nothofagus obliqua forest in south-central Chile. Forest Ecology and Management 231(1-3): 38-46. doi:10.1016/j.foreco.2006.04.037

Examples

data(pspruca2)    
table(pspruca2$especie)

Description

The Loblolly data frame has 84 rows and tree columns of records of the tree height growth of Loblolly pine trees. This dataframe is a slight modification to the original dataframe "Loblolly" from the $datasets$ R package.

Usage

data(ptaeda, package="datana")

Format

A dataframe containing the following columns:

seed.id

an ordered factor indicating the seed source for the tree. The ordering is according to increasing maximum height.

age

a numeric vector of tree ages, in yr.

toth

a numeric vector of tree heights, in m.

Source

Pinheiro, J. C. and Bates, D. M. (2000) Mixed-effects Models in S and S-PLUS. Springer.

Examples

data(ptaeda, package="datana")
head(ptaeda)
plot(toth ~ age, data = subset(ptaeda, seed.id == 329),
     xlab = "Age (yr)", las = 1,
     ylab = "Height (m)")

Description

Esta dataframe contiene 84 folas y tres columnas de crecimiento en altura de árboles de Pinus taeda (Loblolly pine). Es una modificación de la dataframe "Loblolly" del paquete 'datasets' de R.

Usage

data(ptaeda2)

Format

Los datos contienen las siguientes columnas:

semilla.id

Un factor indicando el origen de la semilla del árbol.

edad

Edad del árbol, en años.

atot

Altura total, en m.

Source

Pinheiro, J. C. and Bates, D. M. (2000) Mixed-effects Models in S and S-PLUS. Springer.

Examples

data(ptaeda2, package="datana")
head(ptaeda2)
plot(atot ~ edad, data = subset(ptaeda2, semilla.id == 329),
     xlab = "Edad (años)", las = 1,
     ylab = "Altura (m)")

Description

Function to compute the P-value for a Standard t-distributed random variable.

Usage

pvalt(t.value, df, decnum = 5)

Arguments

Details

It is suited to compute the P-value for any random variable following a Standard t probability density function. For instance, to obtain the p-value in a t-test.

Value

The function returns the P-value or probability of getting a value as large as t.value.

Author(s)

Christian Salas-Eljatib

Examples

## Load dataset
df <- datana::araucaria
#
## Computes the t-test statistics (from the 'stats' package)
t.value <- stats::t.test(df$hdom)
t.v <- as.numeric(t.value$statistic)
deg.f <- as.numeric(t.value$parameter)

## Obtaining the p value
pvalt(t.v,deg.f)

Description

Function to computes the P-value for a Standard Gaussian random variable.

Usage

pvalz(zval, decnum = 5)

Arguments

Details

It is suited to compute the P-value for any random variable following a Standard Gaussian probability density function.

Value

This function returns the P-value or probability of getting a value as large as 'zval'.

Author(s)

Christian Salas-Eljatib

Examples

pvalz(1.96)

Description

Tree-level information collected within sample plots in a forestry plantation of Pinus radiata near Capitan Pastene, in southern Chile. Sample plots size is 150 m$^{2}$.

Usage

data(radiatapl)

Format

The data frame contains four variables as follows:

plot

Plot number identification.

tree

Tree number identification.

dbh

Diameter at breast height, in cm.

heigth

Total height, in m.

Source

The data are provided courtesy of Mr. Mauricio Lobos-Beneventi (Temuco, Chile).

Examples

data(radiatapl)    
head(radiatapl)

Description

Es un listado de árboles con características medidas dentro de unidades de muestreo en una plantación de Pinus radiata cercana a Capitan Pastene, Region de la Araucania, Chile. Las parcelas de muestreo tienen 150 m$^{2}$.

Usage

data(radiatapl2)

Format

Los datos contienen las siguientes columnas

parce

Número de identificación de la parcela de muestreo.

arbol

Número de identificación del árbol dentro de la parcela.

dap

Diámetro a los 1.3 m en el fuste, en cm.

atot

Altura total, en m. Solo registrada para algunos árboles muestra.

Source

Los datos son cortesía del Ing. Forestal Mauricio Lobos-Beneventi (Temuco, Chile).

Examples

data(radiatapl2)    
head(radiatapl2)

Description

Time series data of height for rauli (Nothofagus alpina) trees in south-central Chile. These sampled trees are part of the ones used in Salas-Eljatib (2021, Ecological Applications). The full citation is provided below.

Usage

data(raulihg)

Format

The data frame contains four variables as follows:

tree.code

tree id code

spp

species common name

bha.t

breast-height age, in yrs.

h.t

total height, in m.

Source

Data were provided by Dr Christian Salas-Eljatib (Santiago, Chile).

References

- Salas-Eljatib C. 2021. An approach to quantify climate-productivity relationships: an example from a widespread Nothofagus forest. Ecological Applications 31(4): e02285. doi:10.1002/eap.2285

- Salas-Eljatib, C. 2021. Time series height-data for Nothofagus alpina trees. doi:10.6084/m9.figshare.13521602.v5

Examples

data(raulihg)    
head(raulihg)

Description

Datos de series de tiempo de altura para árboles muestreados de Nothofagus alpina (raulí) en el centro-sur de Chile. Estos árboles son parte de los usados en Salas-Eljatib (2021, Ecological Applications). La cita completa se da en referencias.

Usage

data(raulihg2)

Format

Contiene variables de nivel individual, como se describen a continuacion::

tree.code

Codigo del árbol

spp

Nombre comun especie

bha.t

Edad a la altura del pecho, en años.

h.t

Altura total, en m.

Source

Datos cedidos por el Prof. Christian Salas-Eljatib.

References

- Salas-Eljatib C. 2021. An approach to quantify climate-productivity relationships: an example from a widespread Nothofagus forest. Ecological Applications 31(4): e02285. doi:10.1002/eap.2285

- Salas-Eljatib C. 2021. Time series height-data for Nothofagus alpina trees. doi:10.6084/m9.figshare.13521602.v5

Examples

data(raulihg2)    
head(raulihg2)

Description

Dataset contains 442 observations.

Usage

data(regNothofagus)

Format

Contains 15 variables, as follows:

site

Id site of study.

plot

Number of plot.

scar

Scarification in percentage of total area.

x.trans.total

Transmitted radiation in percentage.

kPa

Soil resistance to penetration.

SWC

Soil water content.

SM

Exposed mineral soil.

litter

Litter cover in percentage.

CWD

Ocular estimation in the regeneration plot in percentage.

MT

Microtopography. 1 plane, 2 convex, 3 concave, 4 mixed (convex and concave) in the regeneration plot.

S

Ground-layer vascular species richness in the regeneration plot..

LLES

Long-lived early-seral tree species (N. dombeyi , N. alpina , Nothofagus pumilio ).

SLES

Short-lived early-seral plants (Ribes spp. and Fuchsia sp).

LLLS

Long-lived late-seral tree species (L. philippiana and Dasyphyllum diacantaoides ).

log.bam

Logarithm of the cover of bamboo (%) in the regeneration plot.

Source

The data were obtained from the DRYAD repository at doi:10.5061/dryad.3q977

References

Soto D, Puettmann K.2018. Topsoil removal through scarification improves natural regeneration in high-graded Nothofagus old-growth forests. Journal Applied Ecology 55: 967- 976.

Examples

data(regNothofagus)    
head(regNothofagus)

Description

The yield tables of simulated plantations of Pinus radiata, Eucalyptus globulus, and Eucalyptus nitens are obtained from the Radiata simulator and EucaSim simulator built in Chile. Several stand-level variables are part of the output.

Usage

data(simula)

Format

Contains stand-level variables, as follows:

species

"P. radiata" is Pinus radiata, "E. globulus" is Eucalyptus globulus, and "E. nitens" is Eucalyptus nitens.

age

plantation age, in years.

tph

Tree density, in trees/ha.

gha

Basal area, in m$^{2}$/ha.

toph

Dominant height, in m.

qmd

quadratic mean diameter, in cm.

totvol

gross stand volume, in m$^{3}$/ha

viu.10

stand volume below an utilizacion index of 10 cm, in m$^{3}$/ha.

viu.15

stand volume below an utilizacion index of 15 cm, in m$^{3}$/ha.

viu.20

stand volume below an utilizacion index of 20 cm, in m$^{3}$/ha.

viu.25

stand volume below an utilizacion index of 25 cm, in m$^{3}$/ha.

Source

The data were obtained as outputs for plantations without management in Chile. The academic version of the simulator was used. You can visit mnssimulacion.cl

Examples

data(simula)

Description

The skewness is about the departure from symmetry of a frequency distribution. Therefore, It is about asymmetry. One way to assess asymmetry of a random variable is to compute an statistics representing its skewness. The current function an dimensionless statistics of the skewness of given vector.

Usage

skew(x, na.rm = TRUE)

Arguments

Details

The skewness of a random variable is the third moment of the standardized variable. There are several ways of parameterizing an skewness estimator, such as depending on the third moment and the standard deviation of the random variable.

Value

The value of the the skewness of given vector

Author(s)

Christian Salas-Eljatib.

Examples

y.var<-rnorm(100);x.var<-rbeta(100,.2,2)
skew(y.var)
skew(x.var)

Description

Dataset that contains nine pairs of columns with information about biomass of 40 samples.

Usage

data(slashpine)

Format

The data frame contains nine variables as follows:

tree_id

tree code

dbh

diameter

h

heigth

lcl

live crown lenght

age

age tree

wood

wood biomass

bark

bark biomass

crown

crown biomass

tree

tree biomass

Source

Data were provided by Dr Christian Salas-Eljatib (Universidad de Chile, Santiago, Chile).

References

Parresol BR. 2001. Additivity of nonlinear biomass equations. Canadian Journal of Forest Research, 31:865-878.

Examples

data(slashpine)    
head(slashpine)

Description

Dataset que contiene nueve pares de columnas con informacion sobre la biomasa de 40 árboles.

Usage

data(slashpine2)

Format

Variables se describen a continuación:

tree_id

Identifcador del árbol

dbh

diámetro

h

altura total

lcl

largo de copa

age

edad árbol

wood

biomasa madera

bark

biomasa corteza

crown

biomasa copa

tree

biomasa total

Source

Datos fueron contribuidos por el Dr Christian Salas-Eljatib (Universidad de Chile, Santiago, Chile).

References

Parresol BR. 2001. Additivity of nonlinear biomass equations. Canadian Journal of Forest Research, 31:865-878.

Examples

data(slashpine2)    
head(slashpine2)

Description

Dataset contains 36 observations

Usage

data(sludge)

Format

Contains four variables, as follows:

city

Name of city.

rate

Concentration rate of sludge.

zinc

Value of concentration ( in ppm).

trt.comb

Combination between city and rate factors.

Source

The data were provided from.. still remember.

References

not yet

Examples

data(sludge2)    
table(sludge$city,sludge$rate) 
levels(sludge$city)
tapply(sludge$zinc, list(sludge$city,sludge$rate), mean)

Description

Datos de contenido de Zinc en el tratamiento de lodos

Usage

data(sludge2)

Format

Contiene las siguinetes cuatro variables:

ciudad

Nombre de la ciudad.

tasa

Tasa de concentracion de lodo.

zinc

Concentracion de Zinc, en ppm.

trt.comb

Identificador de la combinacion de niveles entre los factores ciudad y tasa.

Source

The data were provided from.. still remember.

References

not yet

Examples

data(sludge2)    
table(sludge2$ciudad,sludge2$tasa) 
levels(sludge2$ciudad)
tapply(sludge2$zinc, list(sludge2$ciudad,sludge2$tasa), mean)

Description

Units of the National System of State Protected Wild Areas (SNASPE).

Usage

data(snaspe)

Format

Contains the following variables:

unit.id

Number for the unit.

unit

Name of the protected area.

category

Category of the unit. It can be either a National Park, a National Reserve or a Natural Monument.

county

Name of the county where the unit is located.

province

Province where the unit is located.

region

Region where the unit is located.

perim.km

Perimeter, in km.

area.ha

Area, in hectares.

area.m2

Area, in m$^{2}$.

Source

These data are freely available at https://ide.minagri.gob.cl

References

The Chilean SNASPE is under the direction of the Chilean Forest Service (CONAF). Further information and documentation can be found at https://www.conaf.cl

Examples

data(snaspe)    
head(snaspe) 
table(snaspe$category)
tapply(snaspe$area.ha,snaspe$category,mean)

Description

Contiene variables general de las unidades del sistema de areas protegidas por el estado de Chile (SNASPE).

Usage

data(snaspe2)

Format

Contiene las siguientes variables para cada unidad del SNASPE:

uni.id

Número indentificador de la unidad.

unidad

Nombre de la unidad.

categoria

Categoría de la unidad. Puede ser Parque Nacional, Reserva Nacional, o Monumento Natural.

comuna

Nombre de la communa donde esta la unidad.

province

Nombre de la provincia donde esta la unidad.

region

Nombre de la región.

perim.km

Perimetro, en km.

area.ha

Área, en hectareas.

area.m2

Área, en m$^{2}$.

Source

Estos datos fueron obtenidos desde https://ide.minagri.gob.cl

References

EL SNASPE esta bajo la administración de la Corporación Nacional Forestal (CONAF) de Chile. Mayor información se puede encontrar en https://www.conaf.cl

Examples

data(snaspe2)    
head(snaspe2) 
table(snaspe2$categoria)
tapply(snaspe2$area.ha,snaspe2$categoria,mean)

Description

A test was made of the effect of three soil treatments on the height growth of 2-year-old seedlings. Treatments were assigned at random to the three plots within each of 11 blocks. Each plot was made up of 50 seedlings. Average 5-year height growth was the criterion for evaluating treatments.

Usage

data(soiltreat)

Format

Contains the four following columns, at the plot-level,

block

Block unit.

treat

Treatment level.

ini.h

Initial height, in m.

inc.h

Increment in height during 5-year, in m.

Source

Table in page 71 of Freese (1967). The data were entered by Miss Nayeli Ramirez, a former student of Prof. Christian Salas-Eljatib.

References

- Freese, F 1967. Elementary statistical methods for foresters. Agriculture Handbook 3171, USDA Forest Service.

Examples

data(soiltreat)    
head(soiltreat) 
tapply(soiltreat$inc.h,soiltreat$treat,summary)
tapply(soiltreat$inc.h,soiltreat$treat,sd)

Description

Un experimento sobre el efecto de tres tratamientos del suelo en el crecimiento en altura de plantulas de 2-años de edad. Los tratamientos fueron asignados aleatoriamente a tres parcelas dentro de cada uno de 11 bloques. Cada parcela esta constituida por hasta 50 plantulas. El promedio del incremento en altura de los últimos 5 años fue la variable de interes para evaluar los tratamientos.

Usage

data(soiltreat2)

Format

Los datos, a nivel de parcela, tienen las siguientes columnas,

bloque

Bloque del experimento.

tmo

Factor tratamiento, medido en tres nivels.

alt.ini

Altura initial, rn m.

alt.inc

Incremento en altura durante los últimos cinco años, en m.

Source

Cuadro de la página 71 de Freese (1967). Los datos fueron digitados por la Srta. Nayeli Ramirez, una estudiante del Prof. Christian Salas-Eljatib.

References

- Freese, F 1967. Elementary statistical methods for foresters. Agriculture Handbook 3171, USDA Forest Service.

Examples

data(soiltreat2)    
head(soiltreat2) 
tapply(soiltreat2$alt.inc,soiltreat2$tmo,summary)
tapply(soiltreat2$alt.inc,soiltreat2$tmo,sd)

Description

The Cartesian position, species, year, ID tree , and diameter of trees within a plot were measured.

Usage

data(spataustria)

Format

Contains cartesian position of trees, and covariates, in sample plots, as follows:

plot.code

Plot identificator

tree.code

Tree identificator

spp.name

species abreviation as follows: PCAB=Picea abies, FASY= Fagus sylvatica, QCPE=Quercus petraea , PNSY= Pinus Sylvestris, LADC=Larix decidua

x.coord

Cartesian position in the X-axis, in m

y.coord

Cartesian position in the Y-axis, in m

year

Measurement year

dbh

diameter at breast-height, in cm

References

- Kindermann G. Kristofel F, Neumann M, Rossler G, Ledermann T & Schueler. 2018. 109 years of forest growth measurements from individual Norway spruce trees. Sci. Data 5:180077 doi:10.1038/sdata.2018.77

Examples

data(spataustria)    
head(spataustria)
pos<-spataustria
oldpar<-par(mar=c(4,4,0,0))
bord<-data.frame(
 x=c(min(pos$x.coord),max(pos$x.coord),min(pos$x.coord),max(pos$x.coord)),
 y=c(min(pos$y.coord),min(pos$y.coord),max(pos$y.coord),min(pos$y.coord))
 )
plot(bord,type="n", xlab="x (m)", ylab="y (m)", asp=1, bty='n')
points(pos$x.coord,pos$y.coord,col=pos$plot.code,cex=0.5) 
par(oldpar)

Description

This data set provides names (taxonomy), of plant species. Includes codes and name abbreviations used by the Biometrics group at the Forest Biometrics and Modelling Lab, Universidad de Chile, Santiago, Chile.

Usage

data(species)

Format

A data frame with 63 observations on 31 variables

nesp

Unique correlative specie number

spp.ci.name

Species scientific name

spp.ci.abb

Species scientific name abbreviation

common.name

Species common name. No blank spaces, no special characters

common.nameBlank

Species common name. With blank spaces, no special characters

esp

Species code: code given by CEM Biometrics to identify species for different processing routines

common.nameLatex

Species common name formatted for Latex

nTaxon

Unique number of the taxon (i.e., species)

kingdom

Taxonomic rank Kingdom. In this datase, all species belong to the Kingdom Plantae

division

Taxonomic rank division or phylum within the Kingdom

class

Taxonomic rank Class within the Kingdom

order

Taxonomic rank Order within the Class

family

Taxonomic rank Family within the Order

spp.ci.full

Full scientific name including author

genus

Taxonomic rank Genus within the Family

epithet

Specific epithet

spp.aut

Species author

subspp

Subespecies: one of two or more populations of a species varying from one another by morphological characteristics

subspp.aut

Subespecies author

varspp

Species variety or varietas

varspp.aut

Variety author

formspp

Form or forma

formspp.aut

Form author

commonNamesList

List of common names per species, separated by commas

synonyms

Synonyms of the scientific name by which the species has been or is known

borCountries

Border countries given the species distribution range

habit

Habit. The general appearance, growth form, or architecture e.g., tree, shrub, grass

lifeCycle

Life cycle

statusOri

Status according to the species origin: Native or Endemic

regDist

Distribution range of the species, within Chile administrative regions

elev.range

Distribution range of the species, in terms of elevation. Meters above sea level

notes

Notes

Source

Data available at https://investigacion.conaf.cl, but modified by the Forest Biometrics and Modelling Laboratory at the Universidad de Chile.

References

Proyecto 004/2016 Lista sistematica actualizada de la flora vascular nativa de Chile, origen y distribucion geografica. VII Concurso del Fondo de Investigacion del Bosque Nativo.

Examples

data(species)    
table(species$family)

Description

Los datos proveen diferentes características de la clasificación taxonómica de especies de plantas. Incluye codigos y otros que son especialmente usados en biometría de bosques.

Usage

data(species2)

Format

Los datos contienen31 variables (columnas)

nesp

Unique correlative specie number

spp.ci.name

Species scientific name

spp.ci.abb

Species scientific name abbreviation

nom.com

Species common name. No blank spaces, no special characters

nom.com.vacio

Species common name. With blank spaces, no special characters

esp

Species code: code given by CEM Biometrics to identify species for different processing routines

nom.com.latex

Species common name formatted for Latex

ntaxon

Unique number of the taxon (i.e., species)

reino

Taxonomic rank Kingdom. In this datase, all species belong to the Kingdom Plantae

division

Taxonomic rank division or phylum within the Kingdom

clase

Taxonomic rank Class within the Kingdom

orden

Taxonomic rank Order within the Class

familia

Taxonomic rank Family within the Order

spp.ci.comple

Full scientific name including author

genero

Taxonomic rank Genus within the Family

epiteto

Specific epithet

spp.aut

Species author

subspp

Subespecies: one of two or more populations of a species varying from one another by morphological characteristics

subspp.aut

Subespecies author

varspp

Species variety or varietas

varspp.aut

Variety author

formaspp

Forma de vida de la especie

formaspp.aut

Autor que asigno la forma de vida

nomcom.list

List of common names per species, separated by commas

sinonimia

Sinonimia of the scientific name by which the species has been or is known

pais.limi

Border countries given the species distribution range

habito

Habit. The general appearance, growth form, or architecture e.g., tree, shrub, grass

ciclo.vida

Life cycle

estatus.ori

Status according to the species origin: Native or Endemic

dist.regional

Distribution range of the species, within Chile administrative regions

rango.alti

Rango altitudinal, en metros sobre el nivel del mar, de la especie.

notas

Notas

Source

Datos disponibles en https://investigacion.conaf.cl, con ciertas modificaciones (no botanicas) por el Laboratorio de Biometría y Modelación Forestal de la Universidad de Chile.

References

Proyecto 004/2016 Lista sistematica actualizada de la flora vascular nativa de Chile, origen y distribucion geografica. VII Concurso del Fondo de Investigacion del Bosque Nativo.

Examples

data(species2)    
table(species2$familia)

Description

Abundance of plant species [50 total] (at parcel scale [100 m$^{2}$]) in burned Araucaria-Nothofagus forests with different levels of fire severity (ie, unburned = unburned, low_sev = low severity, mid_sev = medium severity , high_sev = high severity) in the China Muerta National Reserve, Andes of central-southern Chile.

Usage

data(sppAbundance)

Format

Contains 6 variables, as follows:

sp.name

name of specie.

sp.code.name

code of specie

unburned

Abundance of plants unburned.

low.sev

Abundance of plants for low severity of burned.

mid.sev

Abundance of plants for middle severity of burned.

high.sev

Abundance of plants for high severity of burned.

Source

The data are provided courtesy of Dr Andres Fuentes-Ramirez at the Universidad de La Frontera (Temuco, Chile)

References

- Fuentes-Ramirez A, Salas-Eljatib C, Gonzalez M, Urrutia J, Arroyo P, Santibanez P. 2020. Initial response of understorey vegetation and tree regeneration to a mixed-severity fire in old-growth Araucaria-Nothofagus forests. Applied Vegetation Science. 23:210-222.

Examples

data(sppAbundance)    
head(sppAbundance)

Description

Dataset contains 48 observations about about functional trait values for each of the 48 study species, including 23 evergreen and 25 deciduous.

Usage

data(sppTraits)

Format

Contains 17 variables, as follows:

sp

Abbreviated name of specie.

sp.name

Name of specie.

family

Family of specie.

genus

Genus of specie.

phyl

Type of phylogeny.

l.hab

Type of leaf habit.

leaf

Type of leaf.

lt

.

lma

Leaf mass area.

amass

Photosynthetic capacity per unit leaf mass.

n.mass

Leaf N content per unit mass.

pmass

Leaf P content per unit mass.

l.lifespan

Leaf life span.

l.length

Leaf length.

sem

Seed mass.

wd

Wood density.

max.h

Maximum height.