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Merge pull request #123 from worldbank/update-data-vis
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Updated data vis slides
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@mfiorina
mfiorina authored Mar 6, 2024
2 parents f41f937 + 71ae37c commit eccf006
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113 changes: 67 additions & 46 deletions Presentations/04-data-visualization.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@
title: "Session 4: Data Visualization"
subtitle: "R for Stata Users"
author: "Luiza Andrade, Rob Marty, Rony Rodriguez-Ramirez, Luis Eduardo San Martin, Leonardo Viotti, Marc-Andrea Fiorina"
date: "The World Bank -- DIME | [WB Github](https://github.com/worldbank) <br> March 2023"
date: "The World Bank -- DIME | [WB Github](https://github.com/worldbank) <br> March 2024"
output:
xaringan::moon_reader:
css: ["libs/remark-css/default.css",
Expand All @@ -20,13 +20,12 @@ output:
```{r setup, include = FALSE}
# Load packages
library(knitr)
library(tidyverse)
library(hrbrthemes)
library(fontawesome)
library(here)
library(xaringanExtra)
library(countdown)
if(!require(pacman)) install.packages("pacman")
pacman::p_load(
knitr, tidyverse, hrbrthemes, fontawesome, here, xaringanExtra, countdown, ggpubr
)
if(!require(flair)) devtools::install_github("r-for-educators/flair")
library(flair)
here::i_am("Presentations/04-data-visualization.Rmd")
Expand Down Expand Up @@ -76,17 +75,23 @@ name: intro
.panelset[

.panel[.panel-name[If You Attended Session 2]
1. Go to the `dime-r-training-mar2023` folder that you created yesterday, and open the `dime-r-training-mar2023` R project that you created there.
1. Go to the `dime-r-training-main` folder that you created yesterday, and open the `dime-r-training-main` R project that you created there.
]

.panel[.panel-name[If You Did Not Attend Session 2]
1. Create a folder named `dime-r-training-mar2023` in your preferred location in your computer.
1. Copy/paste the following code into a new RStudio script, **replacing "YOURFOLDERPATHHERE" with the folder within which you'll place this R project:
```{r, eval = FALSE}
library(usethis)
use_course(
url = "https://github.com/worldbank/dime-r-training/archive/main.zip",
destdir = "YOURFOLDERPATHHERE"
)
```

2. Go to the [OSF page of the course](https://osf.io/86g3b/) and download the file in: `R for Stata Users - 2023 March` > `Data` > `dime-r-training-mar2023.zip`.
2. In the console, type in the requisite number to delete the .zip file (we don't need it anymore).

3. Unzip `dime-r-training-mar2023.zip`.
3. A new RStudio environment will open. Use this for the session today.

4. Open the `dime-r-training-mar2023` R project.
]

]
Expand Down Expand Up @@ -167,8 +172,8 @@ First, we’re going to use base plot, i.e., using Base R default libraries. It

.exercise[**Exercise 1:** Exploratory Analysis.

**(1)** Create a vector called `vars` with the variables: `economy_gdp_per_capita`, `happiness_score`, `health_life_expectancy`, and `freedom`. <br>
**(2)** Select all the variables from the vector `vars` in the `whr_panel` dataset and assign to the object `whr_plot`. <br>
**(1)** Create a vector called `vars` with the strings: `"economy_gdp_per_capita"`, `"happiness_score"`, `"health_life_expectancy"`, and `"freedom"`. <br>
**(2)** Select all the variables from the vector `vars` in the `whr_panel` dataset and assign to the object `whr_plot`. Hint: use `select(all_of(vars))` for this. <br>
**(3)** Use the `plot()` function: `plot(whr_plot)`

]
Expand All @@ -180,7 +185,6 @@ First, we’re going to use base plot, i.e., using Base R default libraries. It
```{r}
# Vector of variables
vars <- c("economy_gdp_per_capita", "happiness_score", "health_life_expectancy", "freedom")
# Create a subset with only those variables, let's call this subset whr_plot
whr_plot <- whr_panel %>%
select(all_of(vars))
Expand Down Expand Up @@ -520,7 +524,8 @@ whr_panel %>%
y = economy_gdp_per_capita,
color = "blue" #<<
)
)
) +
geom_point()
```

]
Expand Down Expand Up @@ -836,10 +841,16 @@ Let's imagine now, that we would like to transform a variable before plotting.


```{r, out.width = "55%", eval = FALSE}
whr_panel %>%
whr_panel <- whr_panel %>%
mutate( #<<
latam = ifelse(region == "Latin America and Caribbean", TRUE, FALSE) #<<
) %>% #<<
latam = region == "Latin America and Caribbean" #<<
) #<<
whr_panel %>%
filter(
!is.na(latam) # Make sure that we don't include missing values in our graph
) %>%
ggplot(
aes(
x = happiness_score, y = economy_gdp_per_capita,
Expand All @@ -855,12 +866,21 @@ whr_panel %>%


```{r, out.width = "55%", echo = FALSE}
whr_panel %>%
whr_panel <- whr_panel %>%
mutate(
latam = ifelse(region == "Latin America and Caribbean", TRUE, FALSE)
latam = region == "Latin America and Caribbean" #<<
)
whr_panel %>%
filter(
!is.na(latam)
) %>%
ggplot(aes(x = happiness_score, y = economy_gdp_per_capita,
color = latam)) +
ggplot(
aes(
x = happiness_score, y = economy_gdp_per_capita,
color = latam)
) +
geom_point()
```
Expand Down Expand Up @@ -903,12 +923,13 @@ ggplot(data = whr_panel,

.panel[.panel-name[Log]

```{r, out.width = "50%"}
```{r, out.width = "45%"}
ggplot(data = whr_panel,
aes(x = happiness_score,
y = economy_gdp_per_capita)) +
geom_point() +
scale_x_log10() #<<
scale_x_continuous(limits = c(0, 10), #<<
breaks = c(0, 2, 4, 6, 8, 10)) #<<
```

]
Expand Down Expand Up @@ -945,8 +966,8 @@ We are going to do the following to this plot:

```{r, out.width = "40%", eval = FALSE}
whr_panel %>%
mutate(
latam = ifelse(region == "Latin America and Caribbean", TRUE, FALSE)
filter(
!is.na(latam) # Make sure that we don't include missing values in our graph
) %>%
filter(year == 2015) %>% #<<
ggplot(aes(x = happiness_score, y = economy_gdp_per_capita,
Expand All @@ -965,8 +986,8 @@ whr_panel %>%
.panel[.panel-name[Plot]
```{r, out.width = "60%", echo = FALSE}
whr_panel %>%
mutate(
latam = ifelse(region == "Latin America and Caribbean", TRUE, FALSE)
filter(
!is.na(latam) # Make sure that we don't include missing values in our graph
) %>%
filter(year == 2015) %>%
ggplot(aes(x = happiness_score, y = economy_gdp_per_capita,
Expand All @@ -992,8 +1013,8 @@ whr_panel %>%

```{r, out.width = "40%", eval = FALSE}
whr_panel %>%
mutate(
latam = ifelse(region == "Latin America and Caribbean", TRUE, FALSE)
filter(
!is.na(latam) # Make sure that we don't include missing values in our graph
) %>%
filter(year == 2015) %>%
ggplot(aes(x = happiness_score, y = economy_gdp_per_capita,
Expand All @@ -1014,8 +1035,8 @@ whr_panel %>%
.panel[.panel-name[Plot]
```{r, out.width = "70%", echo = FALSE}
whr_panel %>%
mutate(
latam = ifelse(region == "Latin America and Caribbean", TRUE, FALSE)
filter(
!is.na(latam) # Make sure that we don't include missing values in our graph
) %>%
filter(year == 2015) %>%
ggplot(aes(x = happiness_score, y = economy_gdp_per_capita,
Expand All @@ -1042,8 +1063,8 @@ whr_panel %>%

```{r, out.width = "60%", eval = FALSE}
whr_panel %>%
mutate(
latam = ifelse(region == "Latin America and Caribbean", TRUE, FALSE)
filter(
!is.na(latam) # Make sure that we don't include missing values in our graph
) %>%
filter(year == 2015) %>%
ggplot(aes(x = happiness_score, y = economy_gdp_per_capita,
Expand All @@ -1065,8 +1086,8 @@ whr_panel %>%
.panel[.panel-name[Plot]
```{r, out.width = "70%", echo = FALSE}
whr_panel %>%
mutate(
latam = ifelse(region == "Latin America and Caribbean", TRUE, FALSE)
filter(
!is.na(latam) # Make sure that we don't include missing values in our graph
) %>%
filter(year == 2015) %>%
ggplot(aes(x = happiness_score, y = economy_gdp_per_capita,
Expand Down Expand Up @@ -1140,8 +1161,8 @@ library(RColorBrewer)

```{r, out.width = "60%", eval = FALSE}
whr_panel %>%
mutate(
latam = ifelse(region == "Latin America and Caribbean", TRUE, FALSE)
filter(
!is.na(latam) # Make sure that we don't include missing values in our graph
) %>%
filter(year == 2015) %>%
ggplot(aes(x = happiness_score, y = economy_gdp_per_capita,
Expand All @@ -1163,8 +1184,8 @@ whr_panel %>%
.panel[.panel-name[Plot]
```{r, out.width = "70%", echo = FALSE}
whr_panel %>%
mutate(
latam = ifelse(region == "Latin America and Caribbean", TRUE, FALSE)
filter(
!is.na(latam) # Make sure that we don't include missing values in our graph
) %>%
filter(year == 2015) %>%
ggplot(aes(x = happiness_score, y = economy_gdp_per_capita,
Expand Down Expand Up @@ -1217,8 +1238,8 @@ Remember that in R we can always assign our functions to an object. In this case

```{r, eval = FALSE}
fig <- whr_panel %>%
mutate(
latam = ifelse(region == "Latin America and Caribbean", TRUE, FALSE)
filter(
!is.na(latam) # Make sure that we don't include missing values in our graph
) %>%
filter(year == 2015) %>%
ggplot(aes(x = happiness_score, y = economy_gdp_per_capita,
Expand Down Expand Up @@ -1256,8 +1277,8 @@ The syntax is `ggsave(OBJECT, filename = FILEPATH, heigth = ..., width = ..., dp

```{r, echo = FALSE}
fig <- whr_panel %>%
mutate(
latam = ifelse(region == "Latin America and Caribbean", TRUE, FALSE)
filter(
!is.na(latam) # Make sure that we don't include missing values in our graph
) %>%
filter(year == 2015) %>%
ggplot(aes(x = happiness_score, y = economy_gdp_per_capita,
Expand Down
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