guidocor

## ordinal_response_by_group.R
library(ggplot2)
library(dplyr)
set.seed(123)
# Problem: I want to plot the responses to certain items
# by each condition (a response on other )

# lets simulate some data.
# here we have two conditions, 10 items and 5 response options
n = 6000
df <- data.frame(

## plot_four_scales.R


if (!require("pacman")) install.packages("pacman")
pacman::p_load("tidyverse",  "ggpubr", "see")

set.seed(123)
n = 100
df = data.frame(
  part = paste0("id", 1:n),
  scale_A = rnorm(n, m = 10, sd = 5),

## ordinal_response_plot.R


# Imagine that you want to plot the responses of a ordinal questionnaire
# besides the common practice, is not always correct to use ordinal data as
# continous. There are good resources like https://bookdown.org/Rmadillo/likert/always-visualize.html
# which tell you how to proper represent ordinal data. However, I found that if your scale has missing
# response options in one question... the package likert does not deal with that
# so, I self-made the plot(ex_2_likert, type = "heat") graph

# Creating a fake "ordinal" data with seven questions with 0-4 responses

## trichotomize.R
# Sometimes, you will find useful to discretize a variable.
# An option to do that is creating three equal sized groups
# this is, trichotomization.
# Reference http://www.stat.columbia.edu/~gelman/research/published/thirds5.pdf

library(Hmisc)

# We use cut2 to create three equal groups
# then, by taking the number with as.numeric
# we will have a column filled with the group membership

## datosejemplo.csv

          
            cond_1
            cond_2
            measure_1
            measure_2
            measure_3

            
              316
              1
              0
              26
              20
              34

            
              476
              0
              0
              14
              10
              12

            
              339
              1
              1
              21
              6
              16

            
              271
              0
              0
              15
              8
              25

            
              541
              0
              1
              20
              14
              24

            
              376
              1
              1
              26
              15
              30

            
              147
              0
              1
              26
              24
              22

            
              471
              1
              0
              14
              7
              23

            
              328
              0
              0
              19
              10
              20

## fake_messy_data.csv
id;edad;LETRA FAVORITA;humor;calida de su letra ñ
A1;21;a;bueno;1
A2;22;B;bueno;2
A3;333;C;bueno;3
A4;89;D;bueno;4
A5;90;c;malo;5
A6;45;d;malo;6
A7;65;c;malo;7
A8;34;a;malo;8
A9;23;b;malo;9

## clusters_valoracion_politicos.R

######################################################################
# Cluster Analysis de la valoración
######################################################################
rm(list=ls()); gc()

options(scipen=20)
if (!require("pacman")) install.packages("pacman")
pacman::p_load("tidyverse", "psych", "haven", "reshape2", "qgraph", "Rmisc", "Hmisc", "mclust", "corrplot")
# Sacamos del CIS la encuesta

## simpatia_analisis_Factorial.R
Creados para tuitear sobre la encuesta preelectoral y hacer este tuit https://twitter.com/GuidoBCor/status/1120248040848334849

## plot_individual_estimates.R
if (!require("pacman")) install.packages("pacman")
pacman::p_load("ggplot2", "Rmisc")
#############################################################
# Preparing data (you can skip this and go to the plot : )
#############################################################
# Simulate some date by trial
parts = 30 # Our participants
trials = 60 # Our Trials
df=data.frame()

## ploting_stimates_in_R.R
#############################################################
## Estimating and plotting Generalized Linear Mixed Models ##
#############################################################

# This script will
# 1) Simulate some experimental data
# 2) Run a glmer with afex
# 3) Estimate predicted marginal means with emmeans
# 4) Plot estimates
	library(ggplot2)
	library(dplyr)
	set.seed(123)
	# Problem: I want to plot the responses to certain items
	# by each condition (a response on other )

	# lets simulate some data.
	# here we have two conditions, 10 items and 5 response options
	n = 6000
	df <- data.frame(


	if (!require("pacman")) install.packages("pacman")
	pacman::p_load("tidyverse", "ggpubr", "see")

	set.seed(123)
	n = 100
	df = data.frame(
	part = paste0("id", 1:n),
	scale_A = rnorm(n, m = 10, sd = 5),


	# Imagine that you want to plot the responses of a ordinal questionnaire
	# besides the common practice, is not always correct to use ordinal data as
	# continous. There are good resources like https://bookdown.org/Rmadillo/likert/always-visualize.html
	# which tell you how to proper represent ordinal data. However, I found that if your scale has missing
	# response options in one question... the package likert does not deal with that
	# so, I self-made the plot(ex_2_likert, type = "heat") graph

	# Creating a fake "ordinal" data with seven questions with 0-4 responses
	# Sometimes, you will find useful to discretize a variable.
	# An option to do that is creating three equal sized groups
	# this is, trichotomization.
	# Reference http://www.stat.columbia.edu/~gelman/research/published/thirds5.pdf

	library(Hmisc)

	# We use cut2 to create three equal groups
	# then, by taking the number with as.numeric
	# we will have a column filled with the group membership
	cond_1	cond_2	measure_1	measure_2	measure_3
316	1	0	26	20	34
476	0	0	14	10	12
339	1	1	21	6	16
271	0	0	15	8	25
541	0	1	20	14	24
376	1	1	26	15	30
147	0	1	26	24	22
471	1	0	14	7	23
328	0	0	19	10	20
	id;edad;LETRA FAVORITA;humor;calida de su letra ñ
	A1;21;a;bueno;1
	A2;22;B;bueno;2
	A3;333;C;bueno;3
	A4;89;D;bueno;4
	A5;90;c;malo;5
	A6;45;d;malo;6
	A7;65;c;malo;7
	A8;34;a;malo;8
	A9;23;b;malo;9

	######################################################################
	# Cluster Analysis de la valoración
	######################################################################
	rm(list=ls()); gc()

	options(scipen=20)
	if (!require("pacman")) install.packages("pacman")
	pacman::p_load("tidyverse", "psych", "haven", "reshape2", "qgraph", "Rmisc", "Hmisc", "mclust", "corrplot")
	# Sacamos del CIS la encuesta
	if (!require("pacman")) install.packages("pacman")
	pacman::p_load("ggplot2", "Rmisc")
	#############################################################
	# Preparing data (you can skip this and go to the plot : )
	#############################################################
	# Simulate some date by trial
	parts = 30 # Our participants
	trials = 60 # Our Trials
	df=data.frame()
	#############################################################
	## Estimating and plotting Generalized Linear Mixed Models ##
	#############################################################

	# This script will
	# 1) Simulate some experimental data
	# 2) Run a glmer with afex
	# 3) Estimate predicted marginal means with emmeans
	# 4) Plot estimates