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require(plyr)
require(dplyr)

data.outliersRemoved<-ddply(data, .(withinIV1, withinIV2), function(d){
  limits.outliers = median(d$DV) + 2.5*c(-1, 1)*mad(d$DV))
  d$DV[which(((d$DV - limits.outliers[1])*(limits.outliers[2] - d$DV)) <= 0)]<-NA
  return(d)
})

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#######################################################################################
#example_metanalysis.R is licensed under a Creative Commons Attribution - Non commercial 3.0 Unported License. see full license at the end of this file.
#######################################################################################
#this script follows a combination of the guidelines proposed by Hadley Wickham http://goo.gl/c04kq as well as using the formatR package http://goo.gl/ri6ky
#if this is the first time you are conducting an analysis using this protocol, please watch http://goo.gl/DajIN while following step by step

#link to manuscript

#####################################################################################
#SETTING ENVIRONMENT

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library(tidyverse)
# devtools::install_github('rensa/ggflags')
library(ggflags) # https://github.com/rensa/ggflags
library(ggrepel)
library(ggthemes)
library(gganimate)

# Hecho por Rafa @GonzalezGouveia
# Con gusto para #DatosDeMiercoles, propuesto por @R4DS_es

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library(ggplot2)

ggplot(data = data.frame(x = c(-4, 6)), aes(x)) +
  stat_function(fun = dnorm, n = 101, args = list(mean = 0, sd = 1), geom = "area", fill = "#F5BB87", alpha = 0.9) +
  stat_function(fun = dnorm, n = 101, args = list(mean = 2.7, sd = 1), geom = "area", fill = "#BBD4EB", alpha = 0.9) +
  coord_fixed(xlim = c(-5, 7), ratio = 8, clip = "off") +
  annotate("segment", x = -3.9, y = 0, xend = 6, yend = 0, color = "grey60", size = 1.3) +
  annotate("segment", x = -3.5, y = -0.04, xend = -3.5, yend = 0.42, color = "grey60", size = 1.3) +
  annotate("text", x = 0, y = 0.18, label = "what people\nactually do\nin R", family = "Arial Bold", color = "white", size = 5.2) +
  annotate("text", x = 2.75, y = 0.18, label = "what people\nassume others\ndo in R", family = "Arial Bold", color = "white", size = 5.2) +

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library(tidyverse)
library(zoo)

df <- data.frame(
  date = seq(as.Date("2002-01-01"), by="1 day", length.out=50) , 
  var = 100 + c(0, cumsum(runif(49, -20, 20))))

df %>% 
  mutate(ma5=rollmean(var,5,align='right',fill=NA))%>% 
  mutate(sum5=rollsum(var,2,align='right',fill=NA))%>% 

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pacman::p_load(tidyverse, lme4, sjPlot, car)

dat <- read_csv("https://github.com/juanchiem/agro_data/raw/master/yield_wheat19.csv")

#Acondicionamiento del dataset
red_t=c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L)
id_trt <- tibble::tribble(
  ~trt,           ~trat, ~kg_trigo,
  1,         "1.Check",         0,
  2,        "2.MD_z32",       172,

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library(tidyverse)

dat <- expand.grid(sev = seq(0.01, 0.99, 0.01), 
                   inc = seq(0.01, 0.99, 0.01)) %>% 
  mutate(int = inc*sev)

dat %>% 
  ggplot(aes(inc, sev, fill= int)) + 
  geom_tile() +
  viridis::scale_fill_viridis(discrete=FALSE, direction = -1)+

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library(tidyverse)
library(ggforce)

tibble::tribble(
  ~patogeno, ~hum_min, ~hum_max, ~temp_min, ~temp_max,
  "RE",3L,6L,8L,14L,
  "RH",6L,10L,18L,25L,
  "RT",8L,12L,20L,30L,
  "MA",28L,32L,18L,28L,
  "SEP",72L,96L,10L,15L) %>% 

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library(tidyverse)
library(googlesheets4)
library(gapminder)

gs4_deauth()
red <- gs4_get("1HSoj5M--kcW-WioiwMbbngrqdkSC-bqpumeDIIlbx18")
gs4_browse(red)

# red %>% sheet_names() %>% 
#   map_df(~ read_sheet(red, sheet = .)) %>% 

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library(tidyverse) 
library(googlesheets4)

gapminder <- gs4_get("https://docs.google.com/spreadsheets/d/1U6Cf_qEOhiR9AZqTqS3mbMF3zt2db48ZP5v3rkrAEJY/edit#gid=780868077")

# gapminder es una planilla de google sheets que tiene 5 hojas (una por continente)

# Cuando cada hoja tiene toda la información que necesitamos (como este caso) 
gapminder_data <- gapminder %>% sheet_names() %>%
  map_df(~ read_sheet(gapminder, sheet = .)) %>%