juanchiem

# remotes::install_github("ropensci/gistr")
pacman::p_load(tidyverse, gistr)
options(github.username = 'juanchiem')
gists('mineall')
gist("3690306a2f7d7b36ac251fd55cf05e09") %>% gist_save() %>% gist_open()

# Get country centroids---
pacman::p_load(rgeos, rworldmap)
# get world map
wmap <- getMap(resolution="low")

juanchiem

AntroSO42<-read.csv("antroSO42-.csv", header = TRUE)
Bp <- AntroSO42[ ,(2:4), ]

library(tidyverse)
Bp %>% 
  gather(value, variable, -Class) %>% 
  ggplot(aes(Class, 
             variable)) +
  geom_boxplot() +
  facet_wrap( ~ value) +

juanchiem

library(tidyverse)
library(lubridate)

u <- "https://raw.githubusercontent.com/juanchiem/agro_data/master/weather_africa.csv"
sol <- rio::import(u)

sol %>% glimpse

sol %>% 
  mutate(date1= update(mdy(date), year = 1800)) %>% 

juanchiem

How to use R with SQLlite
================
  
  This is a walkthrough of how you can use SQLite with R from my twitter
[@neilgcurrie](https://twitter.com/neilgcurrie).

Often in R we read in files of mixed formats. This gets messy fast and
when the data is too big R will lag or fall over. Enter the database.
SQLite is software used to interact with databases and is one of the
most popular engines around. We can work with SQLite directly from R.

juanchiem

# devtools::install_github('byzheng/weaana')
library(weaana)

file <- system.file("extdata/WeatherRecordsDemo1.met", package = "weaana")

# Read weather file - met file
met <- readWeatherRecords(file)

x_temp <- c(0, 26, 34)
y_temp <- c(0, 26, 0)

juanchiem

dat <- tibble(
  x_1 = runif(10),
  x_2 = runif(10),
  x_3 = runif(10)
)
dat |> 
  mutate(
    across(
      .cols = everything(),
      .fns = rank,

juanchiem

# the first argument gives expansion equal to its multiplication by limit range;
ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  scale_x_continuous(limits = c(1, 7), expand = c(.1, 0))
# right position will be 7 + (7-1) * .1 = 7.6

# the second gives the absolute expansion added to both end of the axis:
ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  scale_x_continuous(limits = c(1, 7), expand = c(0, 1))

juanchiem

```{r}
pacman::p_load(
  tidyverse,
  skimr, janitor,# exploracion de los datos
  lme4, gamlss,  # modelado
  ggResidpanel,  # diagnostico 
  DHARMa, 
  performance,   # evaluar performance de los modelos
  emmeans,       # medias estimadas por el modelo  

juanchiem

library(purrr)
library(tidyverse)

newdata=data.frame(Sepal.Length=seq(4, 8, by=.5))
# newdata$Sepal.Length
fitted_models <- iris %>%
  nest(data = -Species) %>% 
  mutate(fit = map(data, ~ lm(Petal.Length ~ Sepal.Length + I(Sepal.Length^2), data = .x)),
         # fitted = map(fit, "fitted.values"),
         predicted_Petal.Length= map(fit, ~ predict(.x, newdata = newdata))

juanchiem

library("tidyverse")
library("geometry")

data.frame(trap_degree = c(0,0,90,90), 
           distance_m = c(100, 200, 100, 200))%>% 
  mutate(data.frame(pol2cart(theta = trap_degree, r = distance_m))) %>% 
  ggplot()+
  aes(x=y, y=x)+
  geom_point() +
  geom_text(aes(label=paste0(trap_degree,",", distance_m), vjust=1))+