Module 1: Intro to R programming

  1. R objects
  2. R packages
  3. Reading data in R
  4. Basic data wrangling

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Exercises

  1. Install R and RStudio on your computer.

  2. Download the entire folder 00-module-intro-r from the Google Drive link. For the meantime, keep the folder in your computer and wait for further instructions during the class.

  3. Before the class start, open the RStudio and paste the following code in the console to install the required packages. Just click the clipboard icon to copy the code.

## install required packages
install.packages(c("janitor", "readxl", "haven", "tidyverse", "skimr"))
Important

Please make sure to install the required packages before the class starts, as we may not have a secure internet connection. If you encounter any issues, please let me know.

Class demonstration in progress


# INTRO TO R AND BASIC DATA WRANGLING ----

## Install packages
# install.packages("readr")
# install.packages(c("janitor", "readxl", "haven", "tidyverse", "skimr"))


# 1. R Packages ----
library(readr) # reading csv files
library(readxl) # reading excel files
library(haven) # reading spss, sas, stata files
library(tidyverse) # load all packages under tidyverse environment
library(dplyr) # for data wrangling
library(skimr) # quick exploration of your data
library(janitor) # quick cleaning of dataset
library(gapminder)


# 2. Set the working directory ----
setwd("D:/Githu-repository/econ148-analytical-stat-packages/148-class-demo/00-module-intro-r")



# 3. Reading data into R ----

## 3.1 CSV files
### Load swimming_pools.csv files using the read_csv() function
swim_data <- read_csv("sample_dataset/swimming_pools.csv")
swim_data

### Load potatoes.csv using read_csv() and read.csv()
#### Observe the difference in the output

potato_data <- read_csv("sample_dataset/potatoes.csv")
potato_data_2 <- read.csv("sample_dataset/potatoes.csv")

View(potato_data)



## 3.2 Excel files
### Load urban_pop files and use the read_xls() and read_excel() functions
### Save the data into a variable named urban_pop

urban_pop <- read_xls("sample_dataset/urbanpop.xls")



### Transform the urban_pop data into long-format



## 3.2 SPSS files
### Load HBAT.sav

hbat_data <- read_sav("sample_dataset/HBAT.sav")

### Load data from Stata online @ https://www.stata-press.com/data/r9/u.html
### Use the link http://www.stata-press.com/data/r9/auto.dta and save the data into a variable named auto

auto_data <- read_dta("http://www.stata-press.com/data/r9/auto.dta")


## 3.3 Load SAS data
### Read the SAS data from the eventrepository.sas7bdat file
### Clean the variable names using the clean_names() function from janitor package

event_data <- read_sas("sample_dataset/eventrepository.sas7bdat")
event_data <- clean_names(event_data)

# 4. Basic data wrangling with tidyverse ----
### Use gapminder dataset from gapminder package and save it into a variable named gapminder_data

gapminder_data <- gapminder::gapminder

## 4.1 filter() ----
### Using the gapminder_data, filter dataset for the year 1957
gapminder_1957 <- filter(gapminder_data, year == 1957)


### Now, filter for Philippines in 2002
filter(gapminder_data, year < 2000, country == "Philippines")
filter(gapminder_data, country == "Philippines")

### Filter for the year 1957, then arrange in descending order of population
filter(gapminder_data, year == 1957)

arrange(filter(gapminder_data, year == 1957), desc(pop))

gapminder_data |> 
  filter(year == 1957) |> 
  arrange(pop)



## 4.2 mutate() ----
### Use mutate to compute for GDP