juanchiem

gruesa <- format(ISOdate(2000, c(6:12,1:3), 1), "%B")

tibble::tribble(
       ~sitio, ~pp_agosto, ~pp_septiembre, ~pp_octubre, ~pp_noviembre, ~pp_diciembre, ~pp_enero, ~pp_febrero, ~pp_marzo,
  "CHASCOMUS",          0,              0,         14L,           22L,           30L,       52L,         27L,       94L,
     "RIVERA",          0,             50,         48L,          109L,           15L,      158L,         25L,       85L
  ) |> 
  pivot_longer(contains("pp"), 
               names_prefix = "pp_") |> 

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library(tidyverse)
library(googlesheets4)
link <- "https://docs.google.com/spreadsheets/d/1OXHDK2S2rc5pp-aGKaTwvP8bSdo5faa6YN8QGqGDl7U/edit?usp=sharing"
gs4_deauth()
meteo_eea <- gs4_get(link)
bce22 <- read_sheet(meteo_eea, sheet="2022") %>% 
  mutate(date = ymd(date))
bce22
# remotes::install_github("ropensci/chirps", build_vignettes = TRUE)
pacman::p_load(chirps, terra, tidyverse)

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library(tidyverse)

# https://stats.stackexchange.com/questions/309047/zero-inflated-beta-regression-using-gamlss-for-vegetation-cover-data
# Since the data are percentages/proportions on the interval [0,1), 
# I figured a zero inflated beta regression would be appropriate. 
# I do this using the gamlss package in R:

df  <- data.frame(
    cover = c(0.0013,0,0.0208,0.0038,0,0,0,0.0043,
              0,0.002,0.0068,0.0213,0,0.0069,0.0075,0,0,0,0.013,

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pacman::p_load(tidyverse, sf, ggspatial, geodata)

# theme_set: background theme 
theme_set(theme_bw()+
            theme(
              panel.grid.major = element_line(color = gray(0.5), 
                                              linetype = "dashed", 
                                              linewidth = 0.1), 
              panel.background = element_rect(fill = "aliceblue"),
              axis.text.x =  element_text(size = 6),

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# Muy util para explorar diseños experimentales 

library(tidyverse)

# Agregamos algun NA
wb <- warpbreaks %>% 
  add_row(breaks= c(rep(30,5), NA), wool= rep(c("A","B"), 3), tension=rep(c("L","M", "H"), 2))

head(wb)
str(wb)

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# https://agronomy4future.org/?p=17137

pacman::p_load(tidyverse, nlraa, minpack.lm)
github <- "https://raw.githubusercontent.com/agronomy4future/raw_data_practice/main/sulphur%20application.csv"
dataA <- readr::read_csv(github)
  
# QUADRATIC PLATEAU
# to find reasonable initial values for parameters
fit.lm<-lm(yield ~ poly(sulphur,2, raw=TRUE),data=dataA)

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library(segmented)
library(tidyverse)

#view DataFrame
df <- data.frame(x=c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16),
                 y=c(2, 4, 5, 6, 8, 10, 12, 13, 15, 19, 24, 28, 31, 34, 39, 44))

df2 <- data.frame(x=c(2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)+2,
                 y=c(4, 5, 6, 8, 10, 12, 13, 15, 19, 24, 28, 31, 34, 39))

juanchiem

"https://tiagoolivoto.github.io/paper_pliman/code.html#batch-processing"

```{r}
# install.packages("BiocManager")
# BiocManager::install("EBImage")
# devtools::install_github("TiagoOlivoto/pliman")
pacman::p_load(tidyverse, pliman)
```

1. Seleccione el working directory 

juanchiem

library(tidyverse)

dat <- tibble::tribble(
  ~precio_cereal, ~gasto_comer, ~ingreso_neto, ~`kg/$`, ~producto, ~costo_dosis, ~mosquito, ~costo_apli, ~rinde_obj, ~`pisada_%`, ~pisada_kg, ~costo_trat_kg, ~costo_tot_kg,
  310L,          20L,           248,    4.03,      "MD",          22L,       8.5,        30.5,      2500L,         2.5,       62.5,           123L,          185L,
  310L,          20L,           248,    4.03,      "MD",          22L,       8.5,        30.5,      3000L,         2.5,         75,           123L,          198L,
  310L,          20L,           248,    4.03,      "MD",          22L,       8.5,        30.5,      3500L,         2.5,       87.5,           123L,          210L,
  310L,          20L,           248,    4.03,      "MD",          22L,       8.5,        30.5,      4000L,         2.5,        100,           123L,          223L,
  320L,          20L,           256,    3.91,      "MD",          22L,       8.5,        30.5,      2500L,         2.5,       62.5,           119L,        

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library(tidyverse)

dat <- tibble::tribble(
  ~hj, ~a_1, ~a_2, ~a_3, ~a_4, ~a_5, ~c_1, ~c_2, ~c_3, ~c_4, ~c_5,
  "hb",   0L,   0L,   0L,   0L,   0L,   0L,   0L,   0L,   0L,    1L,
  "hb-1",   0L,   0L,   0L,   0L,   0L,   0L,   0L,   2L,   1L,    3L,
  "hb-2",   0L,   2L,   3L,   1L,   0L,   4L,   5L,   5L,   8L,    10L,
  "hb-3",   3L,   7L,   3L,   5L,   7L,   2L,   8L,   14L,   14L,   10L
)