Using R

Workflow and File Management

Here’s a quick workflow for starting a new project, which incorporates the key ideas of Reproducible Research:

  1. Open Rstudio and create a new Rstudio Project by clicking File > New Project. Name it peco-4980-thesis.
Figure 1: To create new project: (top) first click New Directory, then (middle) click New Project, then (bottom) fill in the directory (project) name, choose a good subdirectory for its home and click Create Project. source

  1. Check if now your RStudio Window shows the project name on top right corner. If not, go to folder and double-click the .RProj file.

  2. Download the 000-setup.R from here. Paste the 000-setup.R file in the main project folder. Open it in the same Rstudio window with the project and run the complete file. Your folder structure is created.

  3. Copy your raw data in Data/Raw folder. Similarly, your scripts if any, in Scripts/RScripts folder.

  4. Start your new .qmd file and save it in the main folder. Quarto is an in-built statistical programming and typesetting tool in R. The files are saved as .qmd files.

A Quarto document is saved as a .qmd file. You can edit this file in two ways: Programmatically by being in source button and visually by choosing the Visual button, both button on top left corner of the .qmd window. More details about workign with Quarto can be found on the quarto website here.

There are three building blocks in a .qmd file:

  1. Use here() package extensively in both, scripts and quarto file, when loading or saving the data. The example below in variable creation and merging section illustrates its usage.

  2. You can always zip the whole project folder for sharing. The receiver will just need to unzip and run the code after starting the associated .RProj file, without changing file paths on their computer.

Variable Creation and Merging Datasets

  1. Close the RStudio Window. Download the dataset for practice exercise from here. Unzip if needed and then paste the dataset inside ../Data/Raw/.

  2. Click on the .RProj file in the folder you just created.

  3. Open a new .qmd file. Save it (ctrl/cmd + s) as practice.qmd in the root/main folder of the project.

  4. Cope and paste the following code by creating a new R chunk in the qmd file. You can start a new R code chunk by pressing cmd + option + I or ctrl + alt + I.

```{r}

install.packages("here")
library(here)


 # See the output for each of the following lines | Use your own datasets
here()

# Make modification here after copying your dataset to this folder

here("Data","Raw","practice-datasets","V-Dem-CY-Full+Others-v12.rds")

# syntax is

# here("First subfolder from the root folder", "second subfolder",...., "file")


vdem_new <- readRDS(here("Data","Raw","practice-datasets","V-Dem-CY-Full+Others-v12.rds"))

```
  1. Load the other datasets (in simultated-datasets folder) by modifying the syntax above and storing it in objects economic_data_2000_2020 and economic_data_1995_2024. Note how you have to change file paths as well as use different functions to load different file types (here dta and rds)
```{r}
install.packages("readstata13") # Package for loading stata files in R
install.packages("tidyverse") # Package for data wrangling

library(tidyverse)
library(readstata13)

economic_data_2000_2020 <- read_csv(here("Data/Raw/practice-datasets/simulated-datasets/Economic_Data_2000_2020.csv"))

economic_data_1995_2024 <- read.dta13(here("Data/Raw/practice-datasets/simulated-datasets/Economic_Data_1995_2024.dta"))
```
  1. Inspect the datasets. There are multiple entries for each country corresponding to different years. Use the following code to merge and clean them.
```{r}
# Merge the datasets using a left join on 'country_code' and 'year'
merged_data <- left_join(economic_data_1995_2024,economic_data_2000_2020, by = c("country_code", "year"))

# Filter out rows with NA values in the GDP columns which indicate non-joined rows
filtered_data <- merged_data %>%
  filter(!is.na(gdp.x) & !is.na(gdp.y))

# View the filtered data
print(filtered_data)

```
  1. After merging create a new variable gdp_pc
```{r}
# Assuming 'gdp.x' is the GDP from the first dataset and 'population' is the population variable
filtered_data <- filtered_data %>%
  mutate(gdp_pc = ifelse(!is.na(gdp.x) & !is.na(population) & population > 0, gdp.x / population, NA))

# View the updated data with the new 'gdp_pc' column
print(filtered_data)
```
  1. Save the new dataset inside your project folder.
```{r}
write_csv(filtered_data, file = here("Data/Clean/country-year-gdppc.csv"))
```
  1. Save the file and close the project.