rev - explanation on palette

docs/learn/02.Tidyverse.Rmd

@@ -409,6 +409,51 @@ Now prepare top 10 countries of asylum for asylum seekers in 2021!
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 Each can be used to add new variables, pick variables based on their names, pick cases based on their values, reduce multiple values down to a single summary, and change the ordering of the rows, respectively
 
+---
+
+## Data Manipulation: base R versus the tidyverse way...
+
+Let’s say we have a data frame called `df` with two columns: `x` and `y`. We want to filter the rows where x is greater than 5 and then calculate the `mean` of y for those rows.
+
+.pull-left[ 
+
+__base R way__ 
+
+```{r, eval=FALSE}
+
+# Filter the rows where x > 5
+df2 <- df[df$x > 5, ]
+
+# Calculate the mean of y for those rows
+mean(df2$y)
+
+
+```
+
+
+ ]
+.pull-right[ 
+__tidyverse way__ 
+
+```{r, eval=FALSE}
+
+library(tidyverse)
+
+df |>
+  filter(x > 5) |>
+  summarize(mean(y))
+
+
+```
+
+As you can see, the tidyverse code is more concise and easier to read.
+
+It uses the __|>__ pipe operator to chain together the `filter()` and `summarize()` functions, which makes the code more readable and easier to understand. 
+
+The `summarize()` function is used to calculate the mean of y for the filtered rows.
+
+]
+
 
 ---
 
@@ -1441,6 +1486,36 @@ ggplot(data = fd_last_ten_years,
 
 ]
 
+---
+
+## Selecting the pallette
+
+.pull-left[
+
+We select the palette: "pal_unhcr_poc"
+
+```{r, fig.height=3}
+display_unhcr_pal(9, "pal_unhcr_poc")
+ 
+```
+]
+
+.pull-right[
+
+Now check the factor level for our population type to map them correctly
+
+```{r }
+levels(as.factor(fd_last_ten_years$pop_type))
+ 
+```
+So we have 5 levels to map to map to the same color order meaning
+
+> c(4, 1:3, 9, 8)
+
+]
+
+
+
 ---
 
 ## Mapping the pallette
@@ -1685,6 +1760,7 @@ class: inverse, center, middle
 
 ## Resources  
 
+Workday Training
  - [R Essential Training: Wrangling and Visualizing Data](https://wd3.myworkday.com/unhcr/learning/course/046437bef6c810195cefc58c829f0006?type=9882927d138b100019b928e75843018d)
 
  - [learn R tidyverse](https://wd3.myworkday.com/unhcr/learning/course/046437bef6c810195cfa6ae8c4f30003?type=9882927d138b100019b928e75843018d)
@@ -1695,6 +1771,8 @@ class: inverse, center, middle
 
 - [Creating Maps with R](https://wd3.myworkday.com/unhcr/learning/course/046437bef6c810195cefd839751a0006?type=9882927d138b100019b928e75843018d)
 
+Documentation
+
 - [Ggplot main doc](https://ggplot2.tidyverse.org/index.html)
 
 - [The ggplot flipbook](https://evamaerey.github.io/ggplot_flipbook/ggplot_flipbook_xaringan.html#1) by Gina Reynolds
@@ -1703,6 +1781,8 @@ class: inverse, center, middle
 
 - Ggplot workshop [Part1](https://www.youtube.com/watch?v=h29g21z0a68)/[Part2](https://www.youtube.com/watch?v=0m4yywqNPVY) by Thomas Lin Pedersen (one of the main maintainer of ggplot)
 
+- [Using R for data journalism](https://learn.r-journalism.com/en/)
+
 
 ---