.Rhistory

filenames
getwd
getwd(CovReg.Rmd)
getwd()
filedir <- getwd()
#main covid case data from jhu.edu
#download all csv in folder
setwd("/Users/nicholasgunady/Documents/covid-regression/Covid-Data")
dir <- "Covid-Data"
#main covid case data from jhu.edu
#download all csv in folder
setwd("/Users/nicholasgunady/Documents/covid-regression/Covid-Data")
dir <- "Covid-Data"
knitr::opts_chunk$set(echo = TRUE)
rm(list = ls())
filedir <- getwd()
library(magrittr)
library(plyr)
library(dplyr)
library(tinytex)
library(readr)
library(ALSM)
#main CA demographics dataset
demo <- read.csv(file="demographics.csv")
#main working df
df.demo <- demo[2:58,c(3,4,7,8,9,10,11,14,17,18,19,20,21,22,23)]
df.demo[,c(2,4,6,8,12)] <- df.demo[,c(2,4,6,8,12)] / 100 #change % columns to decimal
#main covid case data from jhu.edu
#download all csv in folder
setwd("/Users/nicholasgunady/Documents/covid-regression/Covid-Data")
dir <- "Covid-Data"
filenames <- list.files(path=dir, pattern="*.csv", full.names=TRUE)
df.cov <- ldply(files, read_csv)
knitr::opts_chunk$set(echo = TRUE)
rm(list = ls())
filedir <- getwd()
library(magrittr)
library(plyr)
library(dplyr)
library(tinytex)
library(readr)
library(ALSM)
#main CA demographics dataset
demo <- read.csv(file="demographics.csv")
#main working df
df.demo <- demo[2:58,c(3,4,7,8,9,10,11,14,17,18,19,20,21,22,23)]
df.demo[,c(2,4,6,8,12)] <- df.demo[,c(2,4,6,8,12)] / 100 #change % columns to decimal
#main covid case data from jhu.edu
#download all csv in folder
setwd("/Users/nicholasgunady/Documents/covid-regression/Covid-Data")
dir <- "Covid-Data"
filenames <- list.files(path=dir, pattern="*.csv", full.names=TRUE)
df.cov <- ldply(filenames, read_csv)
df.cov <- df.cov %>% filter(Country_Region == "CA")
#main covid case data from jhu.edu
#download all csv in folder
setwd("/Users/nicholasgunady/Documents/covid-regression/Covid-Data")
filenames <- list.files(path=dir, pattern="*.csv", full.names=TRUE)
setwd("~/Documents/covid-regression")
filenames <- list.files(path=dir, pattern="*.csv", full.names=TRUE) %>% str_remove("Covid-Data/")
library(stringr)
filenames <- list.files(path=dir, pattern="*.csv", full.names=TRUE) %>% str_remove("Covid-Data/")
df.cov <- ldply(filenames, read_csv)
getwd()
filedr
filedire
filedir
setwd("/Users/nicholasgunady/Documents/covid-regression/Covid-Data")
df.cov <- ldply(filenames, read_csv)
setwd("/Users/nicholasgunady/Documents/covid-regression/Covid-Data")
df.cov <- ldply(filenames, read_csv)
setwd("/Users/nicholasgunady/Documents/covid-regression/Covid-Data")
dir <- "Covid-Data"
filenames <- list.files(path=dir, pattern="*.csv", full.names=TRUE) %>% str_remove("Covid-Data/")
dir <- setwd("/Users/nicholasgunady/Documents/covid-regression/Covid-Data")
filenames <- list.files(path=dir, pattern="*.csv", full.names=TRUE) %>% str_remove("Covid-Data/")
setwd("/Users/nicholasgunady/Documents/covid-regression")
dir <- "Covid-Data"
filenames <- list.files(path=dir, pattern="*.csv", full.names=TRUE) %>% str_remove("Covid-Data/")
df.cov <- ldply(filenames, read_csv)
setwd("/Users/nicholasgunady/Documents/covid-regression/Covid-Data")
df.cov <- ldply(filenames, read_csv)
df.cov <- ldply(filenames, read.csv)
df.cov <- read.csv("owid-covid-data.csv")
# setwd("/Users/nicholasgunady/Documents/covid-regression")
# dir <- "Covid-Data"
# #get all csv filenames
# filenames <- list.files(path=dir, pattern="*.csv", full.names=TRUE) %>% str_remove("Covid-Data/")
setwd("/Users/nicholasgunady/Documents/covid-regression/Covid-Data")
setwd("/Users/nicholasgunady/Documents/covid-regression")
df.cov <- read.csv("owid-covid-data.csv")
df.cov <- df.cov %>% filter(location == "United States")
View(df.cov)
source("~/Documents/covid-regression/Covid-Data/compile.R", echo=TRUE)
source("~/Documents/covid-regression/Covid-Data/compile.R", echo=TRUE)
source("~/Documents/covid-regression/Covid-Data/compile.R", echo=TRUE)
source("~/Documents/covid-regression/Covid-Data/compile.R", echo=TRUE)
View(df.cov)
df <- df.cov %>% filter(Country_Region == "United States")
View(df)
df <- df.cov %>% filter(Country_Region == "CA")
View(df.cov)
df <- df.cov %>% filter(Province_State == "CA")
View(df.cov)
df <- df.cov %>% filter(Province_State == "California")
View(df)
df <- df.cov %>% filter(Province_State == "California") %>% rename(County = Admin2)
df <- df.cov %>% filter(Province_State == "California") %>% rename("County" = "Admin2")
library(dplyr)
df <- df.cov %>% filter(Province_State == "California") %>% rename("County" = "Admin2")
df <- df.cov %>% filter(Province_State == "California") %>% rename(County = Admin2)
df <- df %>% rename(County = Admin2)
df <- df %>% dplyr::rename(County = Admin2)
df <- df.cov %>% filter(Province_State == "California") %>% dplyr::rename(County = Admin2)
View(df)
df <- df %>% group_by(County) %>% count(name="Case.Count")
dir <- "Covid-Data2"
#get all csv filenames
filenames <- list.files(path=dir, pattern="*.csv", full.names=TRUE) %>% str_remove("Covid-Data/")
#get all csv filenames
filenames <- list.files(path=dir, pattern="*.csv", full.names=TRUE)
setwd("/Users/nicholasgunady/Documents/covid-regression")
dir <- "Covid-Data"
#get all csv filenames
filenames <- list.files(path=dir, pattern="*.csv", full.names=TRUE) %>% str_remove("Covid-Data/")
setwd("/Users/nicholasgunady/Documents/covid-regression/Covid-Data")
#mount all data to df.cov VERY COMPUTE HEAVY
df.cov <- ldply(filenames, read_csv) #careful, this is a very long loop
df <- df.cov %>% filter(Province_State == "California") %>% dplyr::rename(County = Admin2)
df <- df %>% group_by(County) %>% count(name="Case.Count")
df <- df %>% group_by(County) %>% sum(name="Case.Count")
df <- df %>% group_by(County) %>% sum()
df <- df %>% group_by(County) %>% summarise(Case.Count=sum)
View(df)
#mount all data to df.cov VERY COMPUTE HEAVY
# df.cov <- ldply(filenames, read_csv) #careful, this is a very long loop
df <- df.cov %>% filter(Province_State == "California") %>% dplyr::rename(County = Admin2)
View(df)
df <- df %>% group_by(County) %>% summarise(Case.Count=sum(Confirmed))
View(df)
View(df)
#mount all data to df.cov VERY COMPUTE HEAVY
# df.cov <- ldply(filenames, read_csv) #careful, this is a very long loop
df <- df.cov %>% filter(Province_State == "California") %>% dplyr::rename(County = Admin2)
df <- df %>% group_by(County)
View(df)
df <- df %>% group_by(County) %>% summarise(Case.Count=sum(Confirmed))
df <- df %>% group_by(County) %>% dplyr::summarise(Case.Count=sum(Confirmed))
#mount all data to df.cov VERY COMPUTE HEAVY
# df.cov <- ldply(filenames, read_csv) #careful, this is a very long loop
df <- df.cov %>% filter(Province_State == "California") %>% dplyr::rename(County = Admin2)
df <- df %>% group_by(County) %>% dplyr::summarise(Case.Count=sum(Confirmed))
View(df)
source("~/Documents/covid-regression/Covid-Data/compile.R", echo=TRUE)
source("~/Documents/covid-regression/Covid-Data/compile.R", echo=TRUE)
source("~/Documents/covid-regression/Covid-Data/compile.R", echo=TRUE)
#main covid case data from jhu.edu
covid <- read.csv(file="CA-2020-cov-data.csv")
knitr::opts_chunk$set(echo = TRUE)
rm(list = ls())
filedir <- getwd()
library(magrittr)
library(plyr)
library(dplyr)
library(tinytex)
library(readr)
library(ALSM)
library(stringr)
#main CA demographics dataset
demo <- read.csv(file="demographics.csv")
#main working df
df.demo <- demo[2:58,c(3,4,7,8,9,10,11,14,17,18,19,20,21,22,23)]
df.demo[,c(2,4,6,8,12)] <- df.demo[,c(2,4,6,8,12)] / 100 #change % columns to decimal
#main covid case data from jhu.edu
covid <- read.csv(file="CA-2020-cov-data.csv")
plot(df.demo)
df.cov <- covid %>% select(-"Unassigned")
View(covid)
View(demo)
df.main <- left_join(df.demo, covid, by=County)
df.main <- left_join(df.demo, covid, by="County")
View(df.main)
View(df.main)
df.main <- left_join(df.demo, covid, by="County") %>% dplyr::select(County,Case.Count.2020,-X,everything())
View(df.main)
#main covid case data from jhu.edu
covid <- read.csv(file="CA-2020-cov-data.csv") %>% subset(select=-"X")
#main covid case data from jhu.edu
covid <- read.csv(file="CA-2020-cov-data.csv") %>% subset(select=-c("X"))
df.main <- left_join(df.demo, covid, by="County") %>% dplyr::select(County,Case.Count.2020,everything() )%>% subset(select=-c("X"))
df.main <- df.main[,1:16]
View(df.main)
plot(df.main)
df.main <- left_join(df.demo, covid, by="County") %>% dplyr::select(Case.Count.2020,County,-X,everything())
df.main <- df.main[,1:16]
plot(df.main)
#main covid case data from jhu.edu
covid <- read.csv(file="CA-2020-cov-data.csv") %>% rename(Cases = Case.Count.2020)
#main covid case data from jhu.edu
covid <- read.csv(file="CA-2020-cov-data.csv") %>% dplyr::rename(Cases = Case.Count.2020)
df.main <- left_join(df.demo, covid, by="County") %>% dplyr::select(Case.Count.2020,County,-X,everything())
df.main <- df.main[,1:16]
#main covid case data from jhu.edu
covid <- read.csv(file="CA-2020-cov-data.csv") %>% dplyr::rename(Cases = Case.Count.2020)
df.main <- left_join(df.demo, covid, by="County") %>% dplyr::select(Case.Count.2020,County,-X,everything())
df.main <- left_join(df.demo, covid, by="County") %>% dplyr::select(Cases,County,-X,everything())
df.main <- df.main[,1:16]
df.main <- left_join(df.demo, covid, by="County") %>% dplyr::select(Cases,County,-X,everything())
df.main <- df.main[,1:16]
```{r initial plotting}
plot(df.main)
mod.unemp <- lm(Cases~PERC.UNEMPLOYED,df.main)
mod.unemp <- lm(Cases~PERC.UNEMPLOYED,df.main)
summary(mod.unemp)
anova(mod.unemp)
mod.unemp <- lm(Cases~NUM.UNEMPLOYED,df.main)
summary(mod.unemp)
anova(mod.unemp)
mod.unemp <- lm(Cases~NUM.UNEMPLOYED,df.main)
summary(mod.unemp)
anova(mod.unemp)
mod.unemp <- lm(Cases~NUM.UNEMPLOYED,df.main)
summary(mod.unemp)
anova(mod.unemp)
trace1 <- predict(mod.unemp)
fig.age65 <- plot_ly(data = df.main, x = ~NUM.UNEMPLOYED, y = ~Cases,name='Data Points',type='scatter',mode='markers') %>%
add_trace(y = ~trace1, name = 'OLS',mode = 'lines') %>%
layout(title = 'Model Fit Covid-19 Cases with Unemployment')
fig.age65 <- plot_ly(data = df.main, x = ~NUM.UNEMPLOYED, y = ~Cases,name='Data Points',type='scatter',mode='markers') %>%
add_trace(y = ~trace1, name = 'OLS',mode = 'lines') %>%
layout(title = "Model Fit Covid-19 Cases with Unemployment")
mod.unemp <- lm(Cases~NUM.UNEMPLOYED,df.main)
summary(mod.unemp)
anova(mod.unemp)
trace1 <- predict(mod.unemp)
fig.age65 <- plot_ly(data = df.main, x = ~NUM.UNEMPLOYED, y = ~Cases,name='Data Points',type='scatter',mode='markers') %>%
add_trace(y = ~trace1, name = 'OLS',mode = 'lines')
library(magrittr)
library(plyr)
library(dplyr)
library(tinytex)
library(readr)
library(ALSM)
library(stringr)
library(plotly)
mod.unemp <- lm(Cases~NUM.UNEMPLOYED,df.main)
summary(mod.unemp)
anova(mod.unemp)
trace1 <- predict(mod.unemp)
fig.age65 <- plot_ly(data = df.main, x = ~NUM.UNEMPLOYED, y = ~Cases,name='Data Points',type='scatter',mode='markers') %>%
add_trace(y = ~trace1, name = 'OLS',mode = 'lines') %>%
layout(title = "Model Fit Covid-19 Cases with Unemployment")
fig.age65
library(stats)
confint(mod.unemp)
fig.age65 <- plot_ly(data = df.main, x = ~NUM.UNEMPLOYED, y = ~Cases,name='Data Points',type='scatter',mode='markers') %>%
add_trace(y = ~trace1, name = 'Linear Model',mode = 'lines') %>%
layout(title = "Model Fit Covid-19 Cases with Unemployment")
mod.unemp <- lm(Cases~NUM.UNEMPLOYED,df.main)
summary(mod.unemp)
anova(mod.unemp)
confint(mod.unemp)
trace1 <- predict(mod.unemp)
fig.age65 <- plot_ly(data = df.main, x = ~NUM.UNEMPLOYED, y = ~Cases,name='Data Points',type='scatter',mode='markers') %>%
add_trace(y = ~trace1, name = 'Linear Model',mode = 'lines') %>%
layout(title = "Model Fit Covid-19 Cases with Unemployment")
fig.age65
View(covid)
mod.fi <- lm(Cases~NUM.FOOD.INSECURE, df.main)
summary(mod.fi)
anova(mod.fi)
knitr::opts_chunk$set(echo = TRUE)
rm(list = ls())
filedir <- getwd()
library(magrittr)
library(plyr)
library(dplyr)
library(tinytex)
library(readr)
library(ALSM)
library(stringr)
library(plotly)
library(stats)
#main CA demographics dataset
demo <- read.csv(file="demographics.csv")
#main working df
df.demo <- demo[2:58,c(3,4,7,8,9,10,11,14,17,18,19,20,21,22,23)]
df.demo[,c(2,4,6,8,12)] <- df.demo[,c(2,4,6,8,12)] / 100 #change % columns to decimal
#main covid case data from jhu.edu
covid <- read.csv(file="CA-2020-cov-data.csv") %>% dplyr::rename(Cases = Case.Count.2020)
df.main <- left_join(df.demo, covid, by="County") %>% dplyr::select(Cases,County,-X,everything())
df.main <- df.main[,1:16]
plot(df.main)
mod.fi <- lm(Cases~NUM.FOOD.INSECURE, df.main)
summary(mod.fi)
anova(mod.fi)
#plot linear model and CI
newx = seq(min(x),max(x),by = 0.05)
#plot linear model and CI
newx = seq(min(df.main$NUM.FOOD.INSECURE),max(df.main$NUM.FOOD.INSECURE),by = 0.05)
mod.fi <- lm(Cases~NUM.FOOD.INSECURE, df.main)
summary(mod.fi)
anova(mod.fi)
#plot linear model and CI
newx = seq(min(df.main$NUM.FOOD.INSECURE),max(df.main$NUM.FOOD.INSECURE),by = 0.05)
ci.trace <- predict(lm.out, newdata=data.frame(x=newx), interval="confidence",
level = 0.95)
mod.fi <- lm(Cases~NUM.FOOD.INSECURE, df.main)
summary(mod.fi)
anova(mod.fi)
#plot linear model and CI
newx = seq(min(df.main$NUM.FOOD.INSECURE),max(df.main$NUM.FOOD.INSECURE),by = 0.05)
ci.trace <- predict(mod.fi, newdata=data.frame(x=newx), interval="confidence",
level = 0.95)
View(df.main)
mod.fi <- lm(Cases~NUM.FOOD.INSECURE, df.main)
summary(mod.fi)
anova(mod.fi)
#plot linear model and CI
newx = seq(min(df.main$NUM.FOOD.INSECURE),max(df.main$NUM.FOOD.INSECURE),by = 100)
ci.trace <- predict(mod.fi, newdata=data.frame(x=newx), interval="confidence",
level = 0.95)
mod.fi <- lm(Cases~NUM.FOOD.INSECURE, df.main)
summary(mod.fi)
anova(mod.fi)
#plot linear model and CI
newx = seq(min(df.main$NUM.FOOD.INSECURE),max(df.main$NUM.FOOD.INSECURE),by = 100)
ci.trace <- predict(mod.fi, df.main$NUM.FOOD.INSECURE, interval="confidence",
level = 0.95)
ci.trace <- predict(mod.fi,newx, interval="confidence",
level = 0.95)
#plot linear model and CI
newx = seq(min(df.main$NUM.FOOD.INSECURE),max(df.main$NUM.FOOD.INSECURE),by = 100) %>%
as.data.frame
ci.trace <- predict(mod.fi,newx, interval="confidence",
level = 0.95)
#plot linear model and CI
newx = seq(min(df.main$NUM.FOOD.INSECURE),max(df.main$NUM.FOOD.INSECURE),by = 100) %>%
list
ci.trace <- predict(mod.fi,newx, interval="confidence",
level = 0.95)
df.main$NUM.FOOD.INSECURE
newx
#plot linear model and CI
newx = seq(min(df.main$NUM.FOOD.INSECURE),max(df.main$NUM.FOOD.INSECURE),by = 1200) %>%
list
ci.trace <- predict(mod.fi,newx, interval="confidence",
level = 0.95)
library(car)
ci.trace <- car::predict(mod.fi,newx, interval="confidence",
level = 0.95)
ci.trace <- predict(mod.fi,newx, interval="confidence",
level = 0.95)
ci.trace <- stats::predict(mod.fi,newx, interval="confidence",
level = 0.95)
ci.trace <- car::predict(mod.fi,newx, interval="confidence",
level = 0.95)
ci.trace <- car::Predict(mod.fi,newx, interval="confidence",
level = 0.95)
fig.age65 <- plot_ly(data = df.main, x = ~NUM.UNEMPLOYED, y = ~Cases,name='Data    Points',type='scatter',mode='markers') %>%
add_trace(y = ~lm.trace, name = 'Linear Model',mode = 'lines') %>%
add_trace(y = ~ci.trace, name = '95% Confidence Interval',mode = 'lines') %>%
layout(title = "Model Fit Covid-19 Cases with Food Insecurity")
fig.fi <- plot_ly(data = df.main, x = ~NUM.UNEMPLOYED, y = ~Cases,name='Data    Points',type='scatter',mode='markers') %>%
add_trace(y = ~lm.trace, name = 'Linear Model',mode = 'lines') %>%
add_trace(y = ~ci.trace, name = '95% Confidence Interval',mode = 'lines') %>%
layout(title = "Model Fit Covid-19 Cases with Food Insecurity")
fig.fi
mod.fi <- lm(Cases~NUM.FOOD.INSECURE, df.main)
summary(mod.fi)
anova(mod.fi)
#plot linear model and CI
# newx = seq(min(df.main$NUM.FOOD.INSECURE),max(df.main$NUM.FOOD.INSECURE),by = 1200) %>%
#   list
# ci.trace <- car::Predict(mod.fi,newx, interval="confidence",
#                          level = 0.95)
lm.trace <- predict(mod.unemp)
mod.fi <- lm(Cases~NUM.FOOD.INSECURE, df.main)
summary(mod.fi)
anova(mod.fi)
#plot linear model and CI
# newx = seq(min(df.main$NUM.FOOD.INSECURE),max(df.main$NUM.FOOD.INSECURE),by = 1200) %>%
#   list
# ci.trace <- car::Predict(mod.fi,newx, interval="confidence",
#                          level = 0.95)
lm.trace <- predict(mod.fi)
fig.fi <- plot_ly(data = df.main, x = ~NUM.UNEMPLOYED, y = ~Cases,name='Data    Points',type='scatter',mode='markers') %>%
add_trace(y = ~lm.trace, name = 'Linear Model',mode = 'lines') %>%
# add_trace(y = ~ci.trace, name = '95% Confidence Interval',mode = 'lines') %>%
layout(title = "Model Fit Covid-19 Cases with Food Insecurity")
fig.fi
plot.lm(mod.fi)
plot(mod.fi)
#plot linear model and CI
plot(mod.fi,abline(mod.fi))
#plot data with regression line
plot(X,Y,pch = 16, cex = 1.3, col = "blue", main = "Confirmed COVID-19 Cases vs. Food Insecurity", xlab = "Food Insecurity", ylab = "Confirmed COVID-19 Cases")
Y <- df.main$Cases
X <- df.main$NUM.FOOD.INSECURE
mod.fi <- lm(Y~X, df.main)
summary(mod.fi)
anova(mod.fi)
#plot linear model data
plot(mod.fi)
#plot data with regression line
plot(X,Y,pch = 16, cex = 1.3, col = "blue", main = "Confirmed COVID-19 Cases vs. Food Insecurity", xlab = "Food Insecurity", ylab = "Confirmed COVID-19 Cases")
abline(mod.fi)
Y <- df.main$Cases
X <- df.main$NUM.FOOD.INSECURE
mod.fi <- lm(Y~X, df.main)
summary(mod.fi)
anova(mod.fi)
#plot linear model data
plot(mod.fi)
#plot data with regression line
plot(X,Y,pch = 16, cex = 1.3, col = "blue", main = "Confirmed COVID-19 Cases vs. Food Insecurity", xlab = "Food Insecurity", ylab = "Confirmed COVID-19 Cases")
abline(mod.fi)
install.packages('reshape2')
install.packages('tidymodels')
Y <- df.main$Cases
X <- df.main$NUM.FOOD.INSECURE
mod.fi <- lm(Y~X, df.main)
summary(mod.fi)
anova(mod.fi)
#plot linear model data
plot(mod.fi)
#plot data with regression line
plot(X,Y,pch = 16, cex = 1.3, col = "blue", main = "Confirmed COVID-19 Cases vs. Food Insecurity", xlab = "Food Insecurity", ylab = "Confirmed COVID-19 Cases")
abline(mod.fi)
#Plotly Regression
lm_model <- linear_reg() %>%
set_engine('lm') %>%
set_mode('regression') %>%
fit(Y ~ X, data = df.main)
Y <- df.main$Cases
X <- df.main$NUM.FOOD.INSECURE
mod.fi <- lm(Y~X, df.main)
summary(mod.fi)
anova(mod.fi)
#plot linear model data
plot(mod.fi)
#plot data with regression line
plot(X,Y,pch = 16, cex = 1.3, col = "blue", main = "Confirmed COVID-19 Cases vs. Food Insecurity", xlab = "Food Insecurity", ylab = "Confirmed COVID-19 Cases")
abline(mod.fi)
#Plotly Regression
lm_model <- linear_reg() %>%
set_engine('lm') %>%
set_mode('regression') %>%
tidymodels::fit(Y ~ X, data = df.main)
library(magrittr)
library(plyr)
library(dplyr)
library(tinytex)
library(readr)
library(ALSM)
library(stringr)
library(plotly)
library(stats)
#plotly Regression packages
library(reshape2) # to load tips data
library(tidyverse)
library(tidymodels) # for the fit() function
install.packages('tidymodels')
install.packages("tidymodels")
install.packages("tidymodels")
install.packages("tidymodels")
knitr::opts_chunk$set(echo = TRUE)
rm(list = ls())
filedir <- getwd()
library(tidymodels) # for the fit() function
library(magrittr)
library(plyr)
library(dplyr)
library(tinytex)
library(readr)
library(ALSM)
library(stringr)
library(plotly)
library(stats)
#plotly Regression packages
library(reshape2) # to load tips data
library(tidyverse)
library(magrittr)
library(tinytex)
library(ALSM)
library(stringr)
library(stats)
#plotly Regression packages
library(reshape2) # to load tips data
library(tidyverse)
library(magrittr)
library(tinytex)
library(ALSM)
library(stringr)
library(stats)
#plotly Regression packages
library(reshape2) # to load tips data
library(tidymodels) # for the fit() function
install.packages('tidyr')
install.packages("tidyr")
library(magrittr)
library(tinytex)
library(ALSM)
library(stringr)
library(stats)
#plotly Regression packages
library(reshape2) # to load tips data
library(tidymodels) # for the fit() function
library(tidyr)
detach("package:tidyr", unload = TRUE)
source("~/.active-rstudio-document", echo=TRUE)
source("~/.active-rstudio-document")