• Leer el archivo IB15Tem.csv de la carpeta T&H
• Este archivo es la temperatura medida cada una hora del sitio de estudio IB15
• Asegurarse que las fechas estén en formato fecha y no texto
• Eliminar columnas innecesarias
• Agregar una columna con el ID del sitio, en este caso IB15

library(readr)
library(lubridate)
library(dplyr)
library(ggplot2)

IB15Tem <- read_csv("~/Documents/CursoR/Clase6/T&H/IB15Tem.csv")

IB15Tem <- read_csv("~/Documents/CursoR/Clase6/T&H/IB15Tem.csv") %>% rename(Temperatura = Value, Date.Time = `Date/Time`)

IB15Tem <- read_csv("~/Documents/CursoR/Clase6/T&H/IB15Tem.csv") %>% rename(Temperatura = Value, Date.Time = `Date/Time`) %>% select(Date.Time, Temperatura)

IB15Tem <- read_csv("~/Documents/CursoR/Clase6/T&H/IB15Tem.csv") %>% rename(Temperatura = Value, Date.Time = `Date/Time`) %>% select(Date.Time, Temperatura) %>% mutate(Date.Time = dmy_hms(Date.Time)) %>% mutate(ID = "IB15")

knitr::kable(IB15Tem)

IB15Tem <- read_csv("~/Documents/CursoR/Clase6/T&H/IB15Tem.csv") %>% rename(Temperatura = Value, Date.Time = `Date/Time`) %>% select(Date.Time, Value) %>% mutate(Date.Time = dmy_hms(Date.Time)) %>% mutate(ID = "IB15")

• Vamos a la primera parte

IB15Tem <- read_csv("~/Documents/CursoR/Clase6/T&H/IB15Tem.csv")

• ¿Que queremos?

Temperaturas <- map(.x = ArchivosTemp, .f = read_csv)

• Necesitamos un vector o lista ArchivosTemp que contenga todos los archivos que vamos a leer

Temperaturas <- map(.x = ArchivosTemp, .f = read_csv)

• Si le agregamos argumentos a una función agregamos un ~ antes de la función y un .x en vez de los datos

Temperaturas <- map(.x = ArchivosTemp, .f = read_csv) %>% map(~rename(.x, Temperatura = Value, Date.Time = `Date/Time`))

• A partir de esto podemos avanzar mucho

Temperaturas <- ArchivosTemp %>% map(read_csv) %>% map(~rename(.x, Temperatura = Value, Date.Time = `Date/Time`)) %>% map(~mutate(.x, Date.Time = dmy_hms(Date.Time))) %>% map(~select(.x, Date.Time, Temperatura))

IB15Tem <- read_csv("~/Documents/CursoR/Clase6/T&H/IB15Tem.csv") %>% rename(Temperatura = Value, Date.Time = `Date/Time`) %>% select(Date.Time, Value) %>% mutate(Date.Time = dmy_hms(Date.Time)) %>% mutate(ID = "IB15")

... %>% mutate(ID = "IB15")

• Necesitamos un vector con todos lo ID de los sitios
• stringr, paquete adyacente al tidyverso que nos permite trabajar con caracteres
• Donde tenemos los nombres de los sitios?

library(stringr)
setwd("/home/derek/Documents/CursoR/Clase6/T&H")
ArchivosTemp <- list.files(pattern = "Tem.csv")
IDs <- str_replace(ArchivosTemp, "Tem.csv", "")
IDs

  [1] "H1F102"  "H1F105"  "H1F106"  "H1F107"  "H1F108"  "H1FS3"   "H1FS4"  
  [8] "H1FS5"   "H1FS8"   "H1NF109" "H1NF111" "H1NF112" "H1NF113" "H1NF114"
 [15] "H1NF115" "H1NF116" "H1NF118" "H1NFS11" "H1NFS12" "H1NFS5"  "H1NFS9" 
 [22] "H2F119"  "H2F120"  "H2F121"  "H2F123"  "H2F124"  "H2F126"  "H2FS12" 
 [29] "H2FS2"   "H2FS5"   "H2FS6"   "H2N128"  "H2NF127" "H2NF130" "H2NF132"
 [36] "H2NF133" "H2NF134" "H2NF135" "H2NF137" "H2NF138" "H2NF140" "H2NF141"
 [43] "H2NF142" "H2NF143" "H2NFS4"  "H2NFS5"  "H2NFS6"  "H2NFS8"  "H2NFS9" 
 [50] "H3F144"  "H3F146"  "H3F147"  "H3F148"  "H3F150"  "H3F151"  "H3FS12" 
 [57] "H3FS7"   "H3FS8"   "H3NF152" "H3NF153" "H3NF154" "H3NF155" "H3NF156"
 [64] "H3NF157" "H3NF159" "H3NFS10" "H3NFS1"  "H3NFS3"  "H3NFS4"  "H3NFS9" 
 [71] "H4F160"  "H4F161"  "H4F164"  "H4F165"  "H4F166"  "H4FS11"  "H4FS7"  
 [78] "H4FS8"   "H4FS9"   "H4NF170" "H4NF171" "H4NF172" "H4NF173" "H4NF174"
 [85] "H4NF175" "H4NFS10" "H4NFS1"  "H4NFS4"  "H4NFS5"  "H4NFS9"  "H5F176" 
 [92] "H5F177"  "H5F179"  "H5F182"  "H5FS10"  "H5FS12"  "H5FS3"   "H5FS4"  
 [99] "H5FS7"   "H5NF183" "H5NF184" "H5NF185" "H5NF186" "H5NF187" "H5NF190"
[106] "H5NF192" "H5NFS10" "H5NFS2"  "H5NFS6"  "IB10"    "IB15"    "IB26"   
[113] "IB27"    "OB4"    

• Tenemos una lista con 114 data frames
• Necesitamos guardarlo en uno
• funcion reduce de purrr

library(purrr)
x <- c(1,2,3,4,5,6,7,8,9,10)
map(.x =x, .f = sqrt)

library(purrr)
x <- c(1,2,3,4,5,6,7,8,9,10)
map(.x =x, .f = sqrt) %>% reduce(c)

 [1] 1.000000 1.414214 1.732051 2.000000 2.236068 2.449490 2.645751
 [8] 2.828427 3.000000 3.162278

• Para unir varios data frame rbind

library(readr)
library(lubridate)
library(dplyr)
library(ggplot2)
library(stringr)
library(stringr)
setwd("/home/derek/Documents/CursoR/Clase6/T&H")
ArchivosTemp <- list.files(pattern = "Tem.csv")
IDs <- str_replace(ArchivosTemp, "Tem.csv", "")
Temperaturas <- ArchivosTemp %>% map(read_csv) %>% map(~rename(.x, Temperatura = Value, Date.Time = `Date/Time`)) %>% map(~mutate(.x, Date.Time = dmy_hms(Date.Time, truncated = 1))) %>% map(~select(.x, Date.Time, Temperatura)) %>% map2(.y = IDs, ~ mutate(.x, ID = .y)) %>% reduce(rbind) 

Temperaturas

# A tibble: 20,223 x 3
             Date.Time Temperatura     ID
                <dttm>       <int>  <chr>
 1 2016-07-01 14:55:00          28 H1F102
 2 2016-07-01 15:55:00          24 H1F102
 3 2016-07-01 16:55:00          25 H1F102
 4 2016-07-01 17:55:00          25 H1F102
 5 2016-07-01 18:55:00          25 H1F102
 6 2016-07-01 19:55:00          25 H1F102
 7 2016-07-01 20:55:00          24 H1F102
 8 2016-07-01 21:55:00          24 H1F102
 9 2016-07-01 22:55:00          25 H1F102
10 2016-07-01 23:55:00          25 H1F102
# ... with 20,213 more rows

library(animation)
saveGIF(
for(i in 1:8){
  plot(bio.stack[[i]], col = colors, breaks = brks, main = paste("Mean temperature", years[i]))
}
,movie.name = "Mean_temp.gif", img.name = "Rplot", convert = "convert", clean = TRUE)