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adamkaplan0adamkaplan0
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Added presentation slides
Added workshop presentation slides and updated the README accordingly.
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‎.Rhistory

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setwd("C:/Users/adamk/Dropbox (MIT)/Current/PML_Good_coding_practices_example")
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renv::init()
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renv::snapshot()
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setwd("C:/Users/adamk/Dropbox (MIT)/Current/PML_Good_coding_practices/PML_Good_coding_practices_example")
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renv::activate()
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install.packages("styler")
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styler:::set_style_transformers()
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styler:::style_active_file()
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# Libraries and setup #######
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#
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library(here)
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library(readr)
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library(stargazer)
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library(ggplot2)
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# Let `here` know what file this is using RELATIVE paths
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here::i_am("analysis/analysis.R")
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# Analysis ##################
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#
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# Can use here to access data using RELATIVE paths
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data <- readr::read_csv(here("data/simulated-data.csv"))
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# Run regression
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regression <- lm(y ~ x + 1, data = data)
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# Outputs ###################
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#
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## Save table ###############
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summary(regression)
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stargazer::stargazer(regression, out = here("report/tables/regression.tex"))
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## Plot results #############
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### Base R version ##########
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png(here("report/figures/regression.png"))
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plot(data$x, data$y)
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lines(data$x, predict(regression), col = "red")
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dev.off()
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# Libraries and setup #######
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#
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library(here)
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library(readr)
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library(stargazer)
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library(ggplot2)
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# Let `here` know what file this is using RELATIVE paths
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here::i_am("analysis/analysis.R")
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# Analysis ##################
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#
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# Can use here to access data using RELATIVE paths
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data <- readr::read_csv(here("data/simulated-data.csv"))
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# Run regression
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regression <- lm(y ~ x + 1, data = data)
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# Outputs ###################
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#
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## Save table ###############
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summary(regression)
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stargazer::stargazer(regression, out = here("report/tables/regression.tex"))
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## Plot results #############
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### Base R version ##########
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png(here("report/figures/regression.png"))
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plot(data$x, data$y)
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lines(data$x, predict(regression), col = "red")
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dev.off()
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### GGplot version ##########
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plotting_data <- data
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plotting_data$predictions <- predict(regression)
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ggplot2::ggplot(plotting_data, aes(x = x, y = y)) +
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geom_point() +
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geom_line(aes(y = predictions), color = "red")
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ggplot2::ggsave(here("report/figures/regression_ggplot.png"))
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debugSource("C:/Users/adamk/Dropbox (MIT)/Current/PML_Good_coding_practices/PML_Good_coding_practices_example/analysis/analysis.R")
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plot(data$x, data$y)
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lines(datas$x, predict(regression), col = "red")
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print(datas$x)

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‎.Rproj.user/D8C8E36F/sources/per/t/38D9610B-contents

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# Libraries and setup #######
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#
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library(here)
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library(readr)
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# Let `here` know what file this is using RELATIVE paths
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here::i_am("simulation/create-data.R")
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# Generate simple data ######
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#
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number_of_samples <- 1000
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x <- rnorm(number_of_samples, 0, 1)
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# Beta = [1, 2]^T
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y <- 1 + 2 * x + rnorm(number_of_samples, 0, 1)
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data <- data.frame(x = x, y = y)
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# Save the data using RELATIVE paths
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readr::write_csv(data, here("data/simulated-data.csv"))

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‎.Rproj.user/D8C8E36F/sources/per/t/5A302946-contents

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# Libraries and setup #######
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#
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library(here)
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library(readr)
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library(stargazer)
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library(ggplot2)
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# Let `here` know what file this is using RELATIVE paths
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here::i_am("analysis/analysis.R")
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# Analysis ##################
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#
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# Can use here to access data using RELATIVE paths
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data <- readr::read_csv(here("data/simulated-data.csv"))
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# Run regression
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regression <- lm(y ~ x + 1, data = data)
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# Outputs ###################
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#
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## Save table ###############
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summary(regression)
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stargazer::stargazer(regression, out = here("report/tables/regression.tex"))
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## Plot results #############
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### Base R version ##########
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png(here("report/figures/regression.png"))
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plot(data$x, data$y)
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print(datas$x)
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lines(datas$x, predict(regression), col = "red")
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dev.off()
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### GGplot version ##########
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plotting_data <- data
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plotting_data$predictions <- predict(regression)
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ggplot2::ggplot(plotting_data, aes(x = x, y = y)) +
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geom_point() +
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geom_line(aes(y = predictions), color = "red")
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ggplot2::ggsave(here("report/figures/regression_ggplot.png"))

‎.Rproj.user/D8C8E36F/sources/per/t/8300BEF5

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library(readr)
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library(ggplot2)
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library(stargazer)
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simdata <- read_csv("C:/Users/adamk/Dropbox (MIT)/Current/PML_Good_coding_practices/PML_Good_coding_practices_example/data/simulated-data.csv")
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reg <- lm(y ~ x + 1, data = simdata)
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summary(reg)
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# stargazer(reg, out = "C:/Users/adamk/Dropbox (MIT)/Current/PML_Good_coding_practices/PML_Good_coding_practices_example/report/tables/regression.tex")
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# png("C:/Users/adamk/Dropbox (MIT)/Current/PML_Good_coding_practices/PML_Good_coding_practices_example/report/figures/regression.png")
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plot(simdata$x, simdata$y)
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lines(simdata$x, predict(reg), col = "red")
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# dev.off()
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simdata$preds <- predict(reg)
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ggplot(simdata, aes(x = x, y = y)) +
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geom_point() +
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geom_line(aes(y = preds), color = "red")
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# ggsave("C:/Users/adamk/Dropbox (MIT)/Current/PML_Good_coding_practices/PML_Good_coding_practices_example/report/figures/regression_ggplot.png")

‎.Rproj.user/D8C8E36F/sources/per/t/98DB3E73

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‎.Rproj.user/D8C8E36F/sources/per/t/98DB3E73-contents

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---
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title: "A Fancy Regression"
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author: "Adam Kaplan"
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date: "11/19/2021"
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output: pdf_document
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---
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```{r setup, include=FALSE}
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library(knitr)
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library(here)
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library(readr)
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here::i_am("report/report.Rmd")
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knitr::opts_chunk$set(echo = TRUE)
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```
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## Data creation
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I created the data using the following code:
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```{r create-data, eval = FALSE, code = readr::read_lines(here("simulation/create-data.R"), skip = 10, n_max = 5)}
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```
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## Results
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Here is my fancy table:
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\input{`r here("report/tables/regression.tex")`}
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And here is the super fancy plot:
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\begin{figure}[!h]
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\centering
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\includegraphics[width=\textwidth]{`r here("report/figures/regression.png")`}
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\end{figure}
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And here is the even fancier ggplot:
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\begin{figure}[!h]
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\centering
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\includegraphics[width=\textwidth]{`r here("report/figures/regression_ggplot.png")`}
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\end{figure}

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