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library()
install.packages(c("GGally", "ggplot2"))
install.packages("tidyverse")
library(ggplot2)
install.packages(ggplot2)
install.packages("ggplot2")
mean(1,2,3)
mean(c(1,2,3))
??diamonds
ggplot(data = diamonds, aes(x = carat, y = price)) +
geom_point()
library(ggplot2)
ggplot(data = diamonds, aes(x = carat, y = price)) +
geom_point()
ggplot(data = diamonds, aes(x = carat, y = price, color = cut)) +
geom_point()
ggplot(data = diamonds, aes(x = carat, y = price, color = cut)) +
geom_point() +
facet_grid(. ~ clarity)
ggplot(data = diamonds, aes(x = carat, y = price, color = cut, size = color)) +
geom_jitter()
ggplot(data = diamonds, aes(x = carat, y = price, color = cut, size = color)) +
geom_jitter()
plot(data = diamonds, aes(x = carat, y = price, color = cut, size = color)) +
geom_jitter()
geom_point() +
facet_grid(. ~ clarity)
ggplot(data = diamonds, aes(x = carat, y = price, color = cut, size = color)) +
geom_jitter()
geom_point() +
facet_grid(. ~ clarity)
ggplot(data = diamonds, aes(x = carat, y = price, color = cut, size = color)) +
geom_jitter() +
geom_point() +
facet_grid(. ~ clarity
ggplot(data = diamonds, aes(x = carat, y = price, color = cut, size = color)) +
geom_jitter() +
geom_point() +
facet_grid(. ~ clarity)
ggplot(data = diamonds, aes(x = carat, y = log(price), color = cut, size = color)) +
geom_jitter() +
geom_point() +
facet_grid(. ~ clarity)
ggplot(data = diamonds, aes(x = carat, y = log(price), color = cut, size = color)) +
geom_jitter() +
geom_hex() +
facet_grid(. ~ clarity)
install.packages("hexbin")
ggplot(data = diamonds, aes(x = carat, y = log(price), color = cut, size = color)) +
geom_jitter() +
geom_hex() +
facet_grid(. ~ clarity)
install.packages(lmtest)
install.packages("lmtest")
library(lmtest)
model = lm(diamonds, price ~ carat + cut + color + clarity + x + y + z + depth + table)
model = lm(data = diamonds, price ~ carat + cut + color + clarity + x + y + z + depth + table)
summary(model)
library(dplyr)
library(memisc)
library(haven)
library(lmtest)
library(ggplot2)
library(ggfortify)
df = as.data.frame(read_spss("BD final - estudantes.sav"))
data = transmute(df, oportunidade = as.numeric(df$tri_oportunidade),
pressao = as.numeric(df$losango_motivacao),
racionalizacao = as.numeric(df$tri_dist_moral),
fraudenctx = as.numeric(df$norma_contexto),
fraudencop = as.numeric(df$norma_copiar),
fraudenplg = as.numeric(df$norma_plagio),
fraudeneud = as.numeric(df$norma_eu_desonesto),
fraudenavg = rowMeans(data[4:7], na.rm = TRUE),
fraudkfreq = rowMeans(df[34:50], na.rm = TRUE))
setwd("~/Documents/Comportamento Organizacional")
df = as.data.frame(read_spss("BD final - estudantes.sav"))
data = transmute(df, oportunidade = as.numeric(df$tri_oportunidade),
pressao = as.numeric(df$losango_motivacao),
racionalizacao = as.numeric(df$tri_dist_moral),
fraudenctx = as.numeric(df$norma_contexto),
fraudencop = as.numeric(df$norma_copiar),
fraudenplg = as.numeric(df$norma_plagio),
fraudeneud = as.numeric(df$norma_eu_desonesto),
fraudenavg = rowMeans(data[4:7], na.rm = TRUE),
fraudkfreq = rowMeans(df[34:50], na.rm = TRUE))
data = transmute(df, oportunidade = as.numeric(df$tri_oportunidade),
pressao = as.numeric(df$losango_motivacao),
racionalizacao = as.numeric(df$tri_dist_moral),
fraudenctx = as.numeric(df$norma_contexto),
fraudencop = as.numeric(df$norma_copiar),
fraudenplg = as.numeric(df$norma_plagio),
fraudeneud = as.numeric(df$norma_eu_desonesto),
fraudenavg = rowMeans(data[4:7], na.rm = TRUE),
fraudkfreq = rowMeans(df[34:50], na.rm = TRUE))
data = transmute(df, oportunidade = as.numeric(df$tri_oportunidade),
pressao = as.numeric(df$losango_motivacao),
racionalizacao = as.numeric(df$tri_dist_moral),
fraudenctx = as.numeric(df$norma_contexto),
fraudencop = as.numeric(df$norma_copiar),
fraudenplg = as.numeric(df$norma_plagio),
fraudeneud = as.numeric(df$norma_eu_desonesto),
fraudenavg = rowMeans(data[4:7], na.rm = TRUE),
fraudkfreq = rowMeans(df[34:50], na.rm = TRUE))
data = transmute(df, oportunidade = as.numeric(df$tri_oportunidade),
pressao = as.numeric(df$losango_motivacao),
racionalizacao = as.numeric(df$tri_dist_moral),
fraudenctx = as.numeric(df$norma_contexto),
fraudencop = as.numeric(df$norma_copiar),
fraudenplg = as.numeric(df$norma_plagio),
fraudeneud = as.numeric(df$norma_eu_desonesto),
fraudenavg = rowMeans(data[4:7], na.rm = TRUE),
fraudkfreq = rowMeans(df[34:50], na.rm = TRUE))
View(df)
data = transmute(df, oportunidade = as.numeric(df$tri_oportunidade),
pressao = as.numeric(df$losango_motivacao),
racionalizacao = as.numeric(df$tri_dist_moral),
fraudenctx = as.numeric(df$norma_contexto),
fraudencop = as.numeric(df$norma_copiar),
fraudenplg = as.numeric(df$norma_plagio),
fraudeneud = as.numeric(df$norma_eu_desonesto),
fraudenavg = rowMeans(df[68:71], na.rm = TRUE),
fraudkfreq = rowMeans(df[34:50], na.rm = TRUE))
GGally::ggpairs(data)
model11 = lm(data = data, fraudenctx ~ oportunidade + pressao + racionalizacao)
model12 = lm(data = data, fraudencop ~ oportunidade + pressao + racionalizacao)
model13 = lm(data = data, fraudenplg ~ oportunidade + pressao + racionalizacao)
model14 = lm(data = data, fraudeneud ~ oportunidade + pressao + racionalizacao)
model15 = lm(data = data, fraudenavg ~ oportunidade + pressao + racionalizacao)
### Comparing Models
AIC(model11, model12, model13, model14, model15)
model21 = lm(data = data, fraudkfreq ~ oportunidade + pressao + racionalizacao)
summary(model15)
summary(model14)
summary(model13)
summary(model12)
summary(model12)
summary(model11)
### Comparing Models
AIC(model11, model12, model13, model14, model15)
bptest(model15)
bptest(model14)