Clase 2 El Hadleyverso y el Tidyverso
Derek Corcoran
"12/10, 2017"
transition: rotate
Hadleyverso: Conjunto de paquetes creado por Hadley Wickham, generados para hacer el trabajo con Tidy Data mucho más fácil
Tidyverso: Desde que David Robinson y algunos otros empezaron a aportar
install.packages("tidyverse")
library(tidyverse)
Paquetes del Tidyverso
En el corazón del Tidyverso
- dplyr (Clase anterior)
- tidyr (Hoy)
- ggplot2 (Próxima clase)
- readr (ya la estamos usando)
purrr (En clase sobre loops)
Adyacente al Tidyverso
- stringr para carácteres (Palabras)
- lubridate para fechas y fechas/horas
- forcats Para variables categóricas
- hms para horas
Tidyr
Dos funciones
- gather hace que tablas anchas se vuelvan largas
- spread hace que tablas largas se vuelvan anchan
Gather
data(iris)
mini_iris <- iris[c(1, 51, 101), ]
kable(mini_iris)
| Sepal.Length | Sepal.Width | Petal.Length | Petal.Width | Species | |
|---|---|---|---|---|---|
| 1 | 5.1 | 3.5 | 1.4 | 0.2 | setosa |
| 51 | 7.0 | 3.2 | 4.7 | 1.4 | versicolor |
| 101 | 6.3 | 3.3 | 6.0 | 2.5 | virginica |
Gather (cont)
MI <- mini_iris %>% gather(key = flower_att, value = measurement, -Species)
kable(MI)
| Species | flower_att | measurement |
|---|---|---|
| setosa | Sepal.Length | 5.1 |
| versicolor | Sepal.Length | 7.0 |
| virginica | Sepal.Length | 6.3 |
| setosa | Sepal.Width | 3.5 |
| versicolor | Sepal.Width | 3.2 |
| virginica | Sepal.Width | 3.3 |
| setosa | Petal.Length | 1.4 |
| versicolor | Petal.Length | 4.7 |
| virginica | Petal.Length | 6.0 |
| setosa | Petal.Width | 0.2 |
| versicolor | Petal.Width | 1.4 |
| virginica | Petal.Width | 2.5 |
spread
M <- spread(MI, flower_att, measurement)
kable(M)
| Species | Petal.Length | Petal.Width | Sepal.Length | Sepal.Width |
|---|---|---|---|---|
| setosa | 1.4 | 0.2 | 5.1 | 3.5 |
| versicolor | 4.7 | 1.4 | 7.0 | 3.2 |
| virginica | 6.0 | 2.5 | 6.3 | 3.3 |
Duda clase anterior
library(dplyr)
data("iris")
DF <- iris %>% group_by(Species) %>% select(contains("Petal")) %>% summarise_all(funs(mean, sd))
kable(DF)
| Species | Petal.Length_mean | Petal.Width_mean | Petal.Length_sd | Petal.Width_sd |
|---|---|---|---|---|
| setosa | 1.462 | 0.246 | 0.1736640 | 0.1053856 |
| versicolor | 4.260 | 1.326 | 0.4699110 | 0.1977527 |
| virginica | 5.552 | 2.026 | 0.5518947 | 0.2746501 |
Ejercicios
- ¿Como paso de esto?
counts_df <- data.frame(
day = c("Monday", "Tuesday", "Wednesday"),
wolf = c(2, 1, 3),
hare = c(20, 25, 30),
fox = c(4, 4, 4)
)
kable(counts_df)
| day | wolf | hare | fox |
|---|---|---|---|
| Monday | 2 | 20 | 4 |
| Tuesday | 1 | 25 | 4 |
| Wednesday | 3 | 30 | 4 |
- a tidy data?
Solución
DF <- counts_df %>% gather(key = Species, value = Count, -day)
kable(DF)
| day | Species | Count |
|---|---|---|
| Monday | wolf | 2 |
| Tuesday | wolf | 1 |
| Wednesday | wolf | 3 |
| Monday | hare | 20 |
| Tuesday | hare | 25 |
| Wednesday | hare | 30 |
| Monday | fox | 4 |
| Tuesday | fox | 4 |
| Wednesday | fox | 4 |
Ejercicio 2
a. Quedarse con solo las observaciones que tienen coordenadas geograficas
b. Cuantas observaciones son de observacion humana y cuantas de especimen de museo?
library(dismo)
library(dplyr)
Huemul <- gbif('Hippocamelus', 'bisulcus', down=TRUE)
colnames(Huemul)
[1] "accessRights"
[2] "adm1"
[3] "adm2"
[4] "basisOfRecord"
[5] "catalogNumber"
[6] "class"
[7] "classKey"
[8] "cloc"
[9] "collectionCode"
[10] "collectionID"
[11] "continent"
[12] "coordinatePrecision"
[13] "coordinateUncertaintyInMeters"
[14] "country"
[15] "crawlId"
[16] "datasetID"
[17] "datasetKey"
[18] "datasetName"
[19] "dateIdentified"
[20] "day"
[21] "depth"
[22] "depthAccuracy"
[23] "disposition"
[24] "dynamicProperties"
[25] "elevation"
[26] "elevationAccuracy"
[27] "endDayOfYear"
[28] "establishmentMeans"
[29] "eventDate"
[30] "eventRemarks"
[31] "eventTime"
[32] "family"
[33] "familyKey"
[34] "fieldNumber"
[35] "fullCountry"
[36] "gbifID"
[37] "genericName"
[38] "genus"
[39] "genusKey"
[40] "geodeticDatum"
[41] "georeferencedBy"
[42] "georeferencedDate"
[43] "georeferenceProtocol"
[44] "georeferenceSources"
[45] "georeferenceVerificationStatus"
[46] "higherClassification"
[47] "higherGeography"
[48] "http://unknown.org/occurrenceDetails"
[49] "identificationID"
[50] "identificationQualifier"
[51] "identificationRemarks"
[52] "identificationVerificationStatus"
[53] "identifiedBy"
[54] "identifier"
[55] "individualCount"
[56] "informationWithheld"
[57] "institutionCode"
[58] "institutionID"
[59] "ISO2"
[60] "key"
[61] "kingdom"
[62] "kingdomKey"
[63] "language"
[64] "lastCrawled"
[65] "lastInterpreted"
[66] "lastParsed"
[67] "lat"
[68] "license"
[69] "locality"
[70] "locationAccordingTo"
[71] "lon"
[72] "modified"
[73] "month"
[74] "nomenclaturalCode"
[75] "occurrenceID"
[76] "occurrenceRemarks"
[77] "occurrenceStatus"
[78] "order"
[79] "orderKey"
[80] "organismID"
[81] "organismRemarks"
[82] "otherCatalogNumbers"
[83] "ownerInstitutionCode"
[84] "phylum"
[85] "phylumKey"
[86] "preparations"
[87] "previousIdentifications"
[88] "protocol"
[89] "publishingCountry"
[90] "publishingOrgKey"
[91] "recordedBy"
[92] "recordNumber"
[93] "references"
[94] "rights"
[95] "rightsHolder"
[96] "scientificName"
[97] "sex"
[98] "species"
[99] "speciesKey"
[100] "specificEpithet"
[101] "startDayOfYear"
[102] "taxonID"
[103] "taxonKey"
[104] "taxonRank"
[105] "type"
[106] "verbatimCoordinateSystem"
[107] "verbatimEventDate"
[108] "verbatimLocality"
[109] "vernacularName"
[110] "year"
[111] "downloadDate"
Solucion a
Sola <- Huemul %>% dplyr::select(lon, lat, basisOfRecord) %>% filter(!is.na(lat) & !is.na(lon))
kable(Sola)
| lon | lat | basisOfRecord |
|---|---|---|
| -72.97712 | -51.01511 | HUMAN_OBSERVATION |
| -72.43751 | -47.20485 | HUMAN_OBSERVATION |
| -73.01456 | -51.03635 | HUMAN_OBSERVATION |
| -73.03190 | -51.17531 | HUMAN_OBSERVATION |
| -72.72944 | -46.25602 | HUMAN_OBSERVATION |
| -71.31538 | -41.30110 | PRESERVED_SPECIMEN |
| -71.31538 | -41.30110 | PRESERVED_SPECIMEN |
| -71.30989 | -40.81978 | PRESERVED_SPECIMEN |
| -71.31538 | -41.30110 | PRESERVED_SPECIMEN |
| -73.02467 | -50.46476 | PRESERVED_SPECIMEN |
| -71.33186 | -41.26523 | PRESERVED_SPECIMEN |
| -73.01764 | -50.46747 | PRESERVED_SPECIMEN |
| -71.70000 | -45.26667 | PRESERVED_SPECIMEN |
| -71.70000 | -45.26667 | PRESERVED_SPECIMEN |
| -71.70000 | -45.26667 | PRESERVED_SPECIMEN |
| -72.08000 | -47.25000 | PRESERVED_SPECIMEN |
| -71.64718 | -40.22605 | PRESERVED_SPECIMEN |
| -67.88534 | -43.99376 | PRESERVED_SPECIMEN |
| -71.71094 | -42.75692 | HUMAN_OBSERVATION |
| -71.36714 | -41.13574 | PRESERVED_SPECIMEN |
Solucion b
Solb <- Huemul %>% group_by(basisOfRecord) %>% summarize(N = n())
kable(Solb)
| basisOfRecord | N |
|---|---|
| HUMAN_OBSERVATION | 100 |
| PRESERVED_SPECIMEN | 60 |
El regalo
install.packages("mapr", dependencies = TRUE)
- probar map_leaflet(dat)
library("mapr")
library("spocc")
dat <- occ(query = 'Hippocamelus bisulcus', from = 'gbif', has_coords = TRUE, limit = 50)
map_ggmap(dat)
Ejercicio 3
- Entrar a INE ambiental y bajar la base de datos de Dimensión Aire.
- Generar una base de datos tidy con las siguientes 5 columnas
- El nombre de la localidad donde se encuntra la estación
- El año en que se tomo la medida
- El mes en que se tomo la medida
- La temperatura media de ese mes
- La media del mp25 de ese mes
- Humedad relativa media mensual
- De la base de datos anterior obterner un segundo data frame en la cual calculen para cada variable y estación la media y desviación estandar para cada mes