juanchiem

library(titanic)
library(Amelia)
library(dplyr)
library(modeest)
library(ggplot2)
library(cowplot)
library(mice)
library(caTools)
library(caret)

juanchiem

library(tidyverse)
coop_dir = tempfile(fileext = ".xls")
dataURL <- "http://coopnecochea.com/web/download/lluvias/Lluvias-2020.xls"
download.file(dataURL, destfile=coop_dir)

total_2020 <- tibble(mes = coop_dir %>% readxl::excel_sheets()) %>% 
  mutate(data = map(mes, ~readxl::read_excel(coop_dir, sheet = .x) %>% 
                      mutate_all(as.character))) %>% 
  slice(n()) %>% 
  unnest(cols = c(data)) %>% 

juanchiem

library(tidyverse)
coop_dir = tempfile(fileext = ".xls")
dataURL <- "http://coopnecochea.com/web/download/lluvias/Lluvias-2021.xls"
download.file(dataURL, destfile=coop_dir)

ene_2021 <- tibble(mes = coop_dir %>% readxl::excel_sheets()) %>% 
  mutate(data = map(mes, ~readxl::read_excel(coop_dir, sheet = .x) %>% 
                      mutate_all(as.character))) %>% 
  filter(mes=="ENE") %>% 
  unnest(cols = c(data)) %>%  

juanchiem

library(tidyverse)
#temp
dates <- seq.Date(lubridate::dmy("1-6-2020"), lubridate::dmy("15-12-2020"), by = "1 day")
temp <- metR::GetSMNData(dates, type = "daily", bar = TRUE)
# temp %>% distinct(station) %>% pull()

temp %>% 
  filter(station == "TANDIL AERO") %>% 
  mutate(date = as.Date(date)) %>% 
  ggplot()+

juanchiem

# Definición matemática de los modelos logistico, gompertz e monomolecular
lo <- function(x, K, y0, r) {K/(1+((K-y0)/y0)*exp(-r*x))}
go <- function(x, K, y0, r) {K*(exp(-exp(y0-r*x)))}
mo <- function(x, K, y0, r) {K-(K-y0)*exp(-r*x)}

library(ggplot2)

# Graficar los 3 modelos con los mismos coeficientes

param = list(K=0.8, y0=0.01, r=0.3)

juanchiem

library(tidyverse)
# library(googlesheets4)
library(agricolae)

# gruesa <- gs4_get("1kga0p0jrgDihhoh-kEXVg5Fc5dsSBmj7zIuUOEWzVrY")  
# gs4_browse(gruesa)

design.rcbd(trt = 1:10, r=4, first=F, serie=2)$sketch %>%   
  as_tibble() %>% 
  rownames_to_column("fila") %>% 

juanchiem

dat <- tibble::tribble(
  ~date,  ~ndvi,
  "2020-05-18", 0.7655,
  "2020-06-14",  0.723,
  "2020-07-12", 0.6178,
  "2020-08-21",  0.437,
  "2020-09-07", 0.4763,
  "2020-09-10", 0.4928,
  "2020-09-12", 0.4831,
  "2020-09-22", 0.4774,

juanchiem

library("tidyverse")

cebada <- tibble::tribble(
  ~trt,        ~trat_id, ~bq,       ~rend_seco,
  1,          "Test",   1,  7527.0792385361,
  2,  "Allegro 1000",   1, 7834.14085058484,
  3, "Azoxy Pro 400",   1, 7105.33761425004,
  1,          "Test",   2,  7073.4451399919,
  2,  "Allegro 1000",   2, 8198.71524530747,
  3, "Azoxy Pro 400",   2, 7312.61874119017,

juanchiem

dat <- tibble::tribble(
  ~fila, ~col, ~plot, ~trt, ~yield,
      1,    1,     1,    1,   5879,
      1,    2,     2,    2,   6278,
      1,    3,     3,    3,   6114,
      2,    1,     6,    2,   5965,
      2,    2,     5,    3,   6208,
      2,    3,     4,    1,   6179,
      3,    1,     7,    3,   6833,
      3,    2,     8,    1,   5904,

juanchiem

library("tidyverse")
dat <- tibble::tribble(
                        ~localidad,  ~hibrido, ~inc_pc, ~sevm_pc,
                       "Napaleufú", "Syn3970",       0,        0,
                       "Napaleufú",  "Z74L60",      18,      3.6,
                       "Napaleufú", "Adv5205",       4,      0.4,
                          "Fulton", "Syn3970",       0,        0,
                          "Fulton",  "Z74L60",      30,        9,
                          "Fulton", "Adv5205",      15,        3,
                    "Pablo Acosta", "Adv5205",       5,     0.75,