solved comments from reviews

Presentations/01-intro-to-R-Part-I.Rmd

@@ -101,15 +101,14 @@ whr <- read.csv(file.path(finalData,"whr_panel.csv"),
 1. [Installation](#installation)
 2. [Introduction](#introduction)
 3. [Getting started](#getting-started)
-4. [RStudio interface](#rstudio-interface)
-5. [Data in R](#data-in-r)
-6. [Functions](#functions)
-7. [R objects](#r-objects)
-8. [Basic types of data](#basic-types-of-data)
-9. [Advanced types of data](#advanced-types-of-data)
-10. [Help, Google, and Stack Overflow](#help-google-and-stack-overflow)
-11. [Useful resources](#useful-resources)
-12. [Appendix](#appendix)
+4. [Data in R](#data-in-r)
+5. [Functions](#functions)
+6. [R objects](#r-objects)
+7. [Basic types of data](#basic-types-of-data)
+8. [Advanced types of data](#advanced-types-of-data)
+9. [Help, Google, and Stack Overflow](#help-google-and-stack-overflow)
+10. [Useful resources](#useful-resources)
+11. [Appendix](#appendix)
 
 ---
 
@@ -180,7 +179,7 @@ name: introduction
 
 # Introduction
 
-These training sessions will offer a quick introduction to R, its amazing features and why it is so much better than Stata. 
+These training sessions will offer an introduction to R, its amazing features and how Stata users can adapt to utilize the features that are better in R
 
 ---
 
@@ -190,7 +189,7 @@ This first session will present the basic concepts you will need to use R.
 
 The next sessions will include:
 
-* __Introduction to R part II__
+* __Introduction to R - part II__
 
 * __Data processing__
 
@@ -219,7 +218,7 @@ Some advantages of R over Stata:
   + More resources online, which makes using Google a lot easier. You'll never want to see Statalist again in your life!
   + Development of new features and bug fixes happen faster.
 
-* It is waaaay cooler in general.
+* It is cooler in general.
 
 ---
 
@@ -230,7 +229,9 @@ Some possible disadvantages of R:
 * Higher cost of entry than Stata for learning how to use it.
 
 * Stata is more specialized:
-  + Certain common tasks are simpler in Stata.
+  + Certain common tasks are simpler in Stata. For example:
+    * Running a regression with clustered standard errors
+    * Analyzing survey data with weights
 
 * Stata has wider adoption among micro-econometricians (though R adoption is steadily increasing).
   + Network externalities in your work environment.
@@ -260,12 +261,12 @@ Here are some other advantages:
 
 # Introduction
 
-But Python is even more flexible and has more users than R, so why should I bother to learn R?
-  
+But Python is even more flexible and has more users than R, so why should I learn R?
+
 * Despite being super popular for data science, Python has fewer libraries developed for econometrics.
-  
+
 * Python is a bit harder to set up and get started.
-  
+
 * It can be a harder to find help only for statistics and econometrics in Python than in R, especially for beginners.
 
 ---
@@ -279,89 +280,80 @@ name: getting-started
 
 ---
 
-# Getting started
-
-Let's start by loading the data set we'll be using:
+# Getting started - RStudio interface
 
-## Exercise 1: Import data
-
-1. In RStudio, go to `File > Import Dataset > From Text (base)` and open the `whr_panel.csv` file. 
-  + Depending on your Rstudio version, it might be `File > Import Dataset > From CSV`
-
-2. The file should be in `GitHub/dime-r-training/DataWork/DataSets/Final/whr_panel.csv`.
-
-3. Assign the name `whr` to the dataset on the import window.
+```{r echo = FALSE, out.width = "70%"}
+knitr::include_graphics("img/Interface.png")
+```
 
 ---
 
-# Getting started
+# Getting started - RStudio interface
 
-```{r echo = FALSE, out.width = "50%"}
-knitr::include_graphics("img/import_data1.png")
+```{r echo = FALSE, out.width = "70%"}
+knitr::include_graphics("img/environment.png")
 ```
 
 ---
 
-# Getting started
+# Getting started - RStudio interface
 
-```{r echo = FALSE, out.width = "50%"}
-knitr::include_graphics("img/import_data2.png")
+```{r echo = FALSE, out.width = "70%"}
+knitr::include_graphics("img/scritpt1.png")
 ```
 
 ---
 
-class: inverse, center, middle
-name: rstudio-interface
-
-# RStudio interface
-
-<html><div style='float:left'></div><hr color='#D38C28' size=1px width=1100px></html>
+# Getting started - RStudio interface
 
+```{r echo = FALSE, out.width = "70%"}
+knitr::include_graphics("img/scritpt2.png")
+```
 ---
 
-# RStudio interface
+# Getting started - RStudio interface
 
 ```{r echo = FALSE, out.width = "70%"}
-knitr::include_graphics("img/Interface.png")
+knitr::include_graphics("img/scritpt3.png")
 ```
 
 ---
 
-# RStudio interface
+# Getting started - RStudio interface
 
 ```{r echo = FALSE, out.width = "70%"}
-knitr::include_graphics("img/environment.png")
+knitr::include_graphics("img/scritpt4.png")
 ```
 
 ---
 
-# RStudio interface
+# Getting started - Importing data
 
-```{r echo = FALSE, out.width = "70%"}
-knitr::include_graphics("img/scritpt1.png")
-```
+Let's start by loading the data set we'll be using:
 
----
+## Exercise 1: Import data
 
-# RStudio interface
+1. In RStudio, go to `File > Import Dataset > From Text (base)` and open the `whr_panel.csv` file. 
+  + Depending on your Rstudio version, it might be `File > Import Dataset > From CSV`
+
+2. The file should be in `GitHub/dime-r-training/DataWork/DataSets/Final/whr_panel.csv`.
+
+3. Assign the name `whr` to the dataset on the import window.
 
-```{r echo = FALSE, out.width = "70%"}
-knitr::include_graphics("img/scritpt2.png")
-```
 ---
 
-# RStudio interface
+# Getting started - Importing data
 
-```{r echo = FALSE, out.width = "70%"}
-knitr::include_graphics("img/scritpt3.png")
+```{r echo = FALSE, out.width = "50%"}
+knitr::include_graphics("img/import_data1.png")
 ```
 
 ---
 
-# RStudio interface
+# Getting started - Importing data
 
-```{r echo = FALSE, out.width = "70%"}
-knitr::include_graphics("img/scritpt4.png")
+```{r echo = FALSE, out.width = "50%"}
+knitr::include_graphics("img/import_data2.png")
 ```
 
 ---
@@ -466,6 +458,8 @@ subset(whr, year == 2015)
 head(whr)
 ```
 
+#### Important: Always write your code in the script window and run it from there
+
 ---
 
 # Data in R
@@ -525,7 +519,7 @@ head(whr2015)
 
 ```{r, echo= F}
 # Use the head() function again
-head(whr2015[,1:5])
+head(whr2015)
 ```
 
 ---
@@ -538,7 +532,7 @@ head(whr)
 
 ```{r, echo= F}
 # Use the head() function again
-head(whr[,1:5])
+head(whr)
 ```
 
 ---
@@ -553,7 +547,7 @@ You can also see that your environment pane now has two objects:
 
 # Data in R
 
-### Two important concepts to take note:
+### Important concepts to take note:
  1. In R, if you want to change your data, you need to __store it in an object__.
 
  2.  It is possible to simply replace the original data. This happens if you assign the new object to the same name as the original.
@@ -911,7 +905,7 @@ print(boolean_false)
 Create a boolean vector with the condition of annual income below average:
 ```{r}
 # Create vector
-bool_vec <- whr$happy_rank < mean(whr$happy_rank)
+bool_vec <- whr$happiness_score < mean(whr$happiness_score)
 
 # See the 6 first elements of the vector
 head(bool_vec)
@@ -951,7 +945,7 @@ Instead of indexing the lines with the boolean vector `bool_vec`, we could also
 # Replace with 1 those obs that meet the condition
 whr$rank_low [bool_vec] <- 1 
 # is the same as
-whr$rank_low [whr$happy_rank < mean(whr$happy_rank)] <- 1 
+whr$rank_low [whr$happiness_score < mean(whr$happiness_score)] <- 1 
 # This in stata would be
 # gen     rank_low = 0
 # replace rank_low = 1 if (...)