From e2f321b645234413e3dcb858e027d6c7dbae9681 Mon Sep 17 00:00:00 2001 From: David Keyes Date: Mon, 4 Nov 2024 11:10:41 -0800 Subject: [PATCH] Update gganiamte --- gganimate/gganimate.md | 62 ++++- gganimate/gganimate.qmd | 46 +++- gganimate/gganimate.rmarkdown | 242 ++++++++++++++++++ .../figure-commonmark/unnamed-chunk-11-1.gif | Bin 0 -> 651855 bytes .../figure-commonmark/unnamed-chunk-8-1.gif | Bin 651855 -> 141671 bytes gganimate/refugees_data.rds | Bin 221169 -> 138927 bytes 6 files changed, 325 insertions(+), 25 deletions(-) create mode 100644 gganimate/gganimate.rmarkdown create mode 100644 gganimate/gganimate_files/figure-commonmark/unnamed-chunk-11-1.gif diff --git a/gganimate/gganimate.md b/gganimate/gganimate.md index 4b9d21f..ee4f089 100644 --- a/gganimate/gganimate.md +++ b/gganimate/gganimate.md @@ -6,9 +6,15 @@ David Keyes ## Intro -The {gganimate} package is the easiest way to make animated plots in R. If you know how to make plots in ggplot, you can animate them with {gganimate}. +The {gganimate} package is the easiest way to make animated plots in R. If you know how to make plots in ggplot, you can animate them with {gganimate}. Animated plots are a great way to capture attention, especially online where the next shiny object is just a scroll away. If you want to get attention on your data, using animation with a package like {gganimate} is a great option. -To show how {gganimate} works, let’s use data from the United Nations High Commissioner for Refugees (UNHCR). [Their package](https://www.unhcr.org/refugee-statistics/insights/explainers/refugees-r-package.html), called {refugees}, has data on a number of different datasets related to refugee populations. I’ll use one of these datasets, called `population`, which has “data on forcibly displaced and stateless persons by year, including refugees, asylum-seekers, internally displaced people (IDPs) and stateless people.” I begin by loading the {refugees} package: +In this blog post, I’ll show how to use the {gganimate} package by animating data on refugees. As part of our [one percent for people, one percent for the planet giving program](https://rfortherestofus.com/1percent), we regularly support the work of the United Nations High Commissioner for Refugees (UNHCR). If you want to learn more about how the UNHCR uses R, please see our interviews with UNHCR statistician Ahmadou Dicko and information management officer Cédric Vidonne. TODO: Add links + +## Import Data + +To show how {gganimate} works, let’s use data from UNHCR. [Their {refugees} package](https://www.unhcr.org/refugee-statistics/insights/explainers/refugees-r-package.html) has data on a number of different datasets related to refugee populations. I’ll use one of these datasets, called `population`, which has “data on forcibly displaced and stateless persons by year, including refugees, asylum-seekers, internally displaced people (IDPs) and stateless people.” + +I begin by loading the {refugees} package: ``` r library(refugees) @@ -36,29 +42,52 @@ population #> # stateless , ooc , oip , hst ``` -``` r +This dataset has more variables than we need. I’ve done some work to calculate the refugee population from each country as a percentage of its total population in all years since 2000 (you can see the code for that [here](https://github.com/rfortherestofus/blog/blob/main/gganimate/gganimate.qmd)). To see the data that I end up with, I’ll load the {tidyverse}: +``` r library(tidyverse) -library(sf) -library(scales) -library(hrbrthemes) -library(gganimate) ``` -## Data +Next, I’ll import the data I’ve created and saved as an RDS file: ``` r refugees_data <- read_rds("https://github.com/rfortherestofus/blog/raw/refs/heads/main/gganimate/refugees_data.rds") ``` +We can take a look at the `refugees_data` tibble we now have to work with: + ``` r -refugees_data_geospatial <- - read_sf("https://github.com/rfortherestofus/blog/raw/refs/heads/main/gganimate/refugees_data_geospatial.geojson") +refugees_data +#> # A tibble: 5,136 × 5 +#> year country_abbreviation total_population number_of_refugees +#> +#> 1 2018 SYR 19333463 6654374 +#> 2 2017 SYR 18983373 6310498 +#> 3 2019 SYR 20098251 6615249 +#> 4 2020 SYR 20772595 6702910 +#> 5 2021 SYR 21324367 6848865 +#> 6 2022 SYR 22125249 6559736 +#> 7 2016 SYR 18964252 5524511 +#> 8 2023 SYR 23227014 6355788 +#> 9 2015 SYR 19205178 4873236 +#> 10 2017 SSD 10658226 2439888 +#> # ℹ 5,126 more rows +#> # ℹ 1 more variable: refugees_as_pct ``` +I’ve sorted the data to show the single year and country with the highest percentage of refugees as a proportion of the total population. As you can see, Syria (with the country abbreviation SYR) has the top 9 observations, a result of its [decade-plus civil war](https://www.cfr.org/article/syrias-civil-war). + ## Plot +We’re now ready to + +``` r +library(scales) +library(hrbrthemes) +library(gganimate) +``` + ``` r refugees_data |> filter(country_abbreviation == "SYR") |> @@ -81,10 +110,19 @@ refugees_data |> transition_reveal(year) ``` - + ## Map +``` r +library(sf) +``` + +``` r +refugees_data_geospatial <- + read_sf("https://github.com/rfortherestofus/blog/raw/refs/heads/main/gganimate/refugees_data_geospatial.geojson") +``` + ``` r refugees_data_geospatial |> left_join( @@ -129,4 +167,4 @@ refugees_data_geospatial |> transition_manual(year) ``` - + diff --git a/gganimate/gganimate.qmd b/gganimate/gganimate.qmd index 21ae12a..23687d9 100644 --- a/gganimate/gganimate.qmd +++ b/gganimate/gganimate.qmd @@ -58,7 +58,7 @@ refugees_data <- join_by(year, country_abbreviation) ) |> mutate(refugees_as_pct = number_of_refugees / total_population) |> - select(year, country_abbreviation, total_population, number_of_refugees, refugees_as_pct) |> + select(year, country_abbreviation, refugees_as_pct) |> complete(year, country_abbreviation) |> arrange(desc(refugees_as_pct)) @@ -80,9 +80,13 @@ refugees_data_geospatial |> ## Intro -The {gganimate} package is the easiest way to make animated plots in R. If you know how to make plots in ggplot, you can animate them with {gganimate}. In this blog post, I'll show how to use the {gganimate} package by animating data on refugees. As part of our [one percent for people, one percent for the planet giving program](https://rfortherestofus.com/1percent), we regularly support the work of the United Nations High Commissioner for Refugees (UNHCR). If you want to learn more about how the UNHCR uses R, please see our interviews with UNHCR statistician Ahmadou Dicko and information management officer Cédric Vidonne. TODO: Add links +The {gganimate} package is the easiest way to make animated plots in R. If you know how to make plots in ggplot, you can animate them with {gganimate}. Animated plots are a great way to capture attention, especially online where the next shiny object is just a scroll away. If you want to get attention on your data, using animation with a package like {gganimate} is a great option. -To show how {gganimate} works, let's use data from UNHCR. [Their package](https://www.unhcr.org/refugee-statistics/insights/explainers/refugees-r-package.html), called {refugees}, has data on a number of different datasets related to refugee populations. I'll use one of these datasets, called `population`, which has "data on forcibly displaced and stateless persons by year, including refugees, asylum-seekers, internally displaced people (IDPs) and stateless people." +In this blog post, I'll show how to use the {gganimate} package by animating data on refugees. As part of our [one percent for people, one percent for the planet giving program](https://rfortherestofus.com/1percent), we regularly support the work of the United Nations High Commissioner for Refugees (UNHCR). If you want to learn more about how the UNHCR uses R, please see our interviews with UNHCR statistician Ahmadou Dicko and information management officer Cédric Vidonne. TODO: Add links + +## Import Data + +To show how {gganimate} works, let's use data from UNHCR. [Their {refugees} package](https://www.unhcr.org/refugee-statistics/insights/explainers/refugees-r-package.html) has data on a number of different datasets related to refugee populations. I'll use one of these datasets, called `population`, which has "data on forcibly displaced and stateless persons by year, including refugees, asylum-seekers, internally displaced people (IDPs) and stateless people." I begin by loading the {refugees} package: @@ -96,32 +100,38 @@ And then showing the `population` data: population ``` - - +This dataset has more variables than we need. I've done some work to calculate the refugee population from each country as a percentage of its total population in all years since 2000 (you can see the code for that [here](https://github.com/rfortherestofus/blog/blob/main/gganimate/gganimate.qmd)). To see the data that I end up with, I'll load the {tidyverse}: ```{r} - library(tidyverse) -library(sf) -library(scales) -library(hrbrthemes) -library(gganimate) ``` -## Data +Next, I'll import the data I've created and saved as an RDS file: ```{r} refugees_data <- read_rds("https://github.com/rfortherestofus/blog/raw/refs/heads/main/gganimate/refugees_data.rds") ``` +We can take a look at the `refugees_data` tibble we now have to work with: + ```{r} -refugees_data_geospatial <- - read_sf("https://github.com/rfortherestofus/blog/raw/refs/heads/main/gganimate/refugees_data_geospatial.geojson") +refugees_data ``` +I've sorted the data to show the single year and country with the highest percentage of refugees as a proportion of the total population. As you can see, Syria (with the country abbreviation SYR) has the top 9 observations, a result of its [decade-plus civil war](https://www.cfr.org/article/syrias-civil-war). + ## Plot +We're now ready to + +```{r} +library(scales) +library(hrbrthemes) +library(gganimate) +``` + + ```{r} refugees_data |> filter(country_abbreviation == "SYR") |> @@ -146,6 +156,16 @@ refugees_data |> ## Map +```{r} +library(sf) +``` + + +```{r} +refugees_data_geospatial <- + read_sf("https://github.com/rfortherestofus/blog/raw/refs/heads/main/gganimate/refugees_data_geospatial.geojson") +``` + ```{r} refugees_data_geospatial |> left_join( diff --git a/gganimate/gganimate.rmarkdown b/gganimate/gganimate.rmarkdown new file mode 100644 index 0000000..3bc9d00 --- /dev/null +++ b/gganimate/gganimate.rmarkdown @@ -0,0 +1,242 @@ +--- +title: "Refugees animation" +author: David Keyes +date: 1/17/2024 +toc: false +format: gfm +wrap: none +execute: + warning: false + message: false +knitr: + opts_chunk: + out.width: 100% + fig.height: 8 + dpi: 300 + collapse: true + comment: "#>" + warning: false + message: false +editor_options: + chunk_output_type: console +--- + + + +## Data {.unnumbered .hidden} + + + +```{r} +#| echo: false +#| eval: false + +# This code chunk is where I import the data, but I'm not showing it in the blog post + +library(tidyverse) +library(refugees) +library(wbstats) +library(rnaturalearth) +library(here) +library(sf) + +refugees <- + population |> + rename(country_abbreviation = coo_iso) |> + group_by(year, country_abbreviation) |> + summarise(number_of_refugees = sum(refugees, na.rm = TRUE)) |> + ungroup() |> + filter(year >= 2000) |> + arrange(country_abbreviation) + +country_population <- + wb_data("SP.POP.TOTL", start_date = 2000, end_date = 2023) |> + select(date, iso3c, SP.POP.TOTL) |> + set_names("year", "country_abbreviation", "total_population") |> + labelled::remove_var_label() + +refugees_data <- + refugees |> + left_join( + country_population, + join_by(year, country_abbreviation) + ) |> + mutate(refugees_as_pct = number_of_refugees / total_population) |> + select(year, country_abbreviation, refugees_as_pct) |> + complete(year, country_abbreviation) |> + arrange(desc(refugees_as_pct)) + +refugees_data |> + write_rds(here("gganimate/refugees_data.rds")) + +refugees_data_geospatial <- + ne_countries() |> + select(iso_a3, continent, subregion) |> + rename(country_abbreviation = iso_a3) + +refugees_data_geospatial |> + st_write( + here("gganimate/refugees_data_geospatial.geojson"), + driver = "GeoJSON", + delete_dsn = TRUE # This overwrites existing file if present + ) +``` + + + +## Intro + +The {gganimate} package is the easiest way to make animated plots in R. If you know how to make plots in ggplot, you can animate them with {gganimate}. Animated plots are a great way to capture attention, especially online where the next shiny object is just a scroll away. If you want to get attention on your data, using animation with a package like {gganimate} is a great option. + +In this blog post, I'll show how to use the {gganimate} package by animating data on refugees. As part of our [one percent for people, one percent for the planet giving program](https://rfortherestofus.com/1percent), we regularly support the work of the United Nations High Commissioner for Refugees (UNHCR). If you want to learn more about how the UNHCR uses R, please see our interviews with UNHCR statistician Ahmadou Dicko and information management officer Cédric Vidonne. TODO: Add links + +## Import Data + +To show how {gganimate} works, let's use data from UNHCR. [Their {refugees} package](https://www.unhcr.org/refugee-statistics/insights/explainers/refugees-r-package.html) has data on a number of different datasets related to refugee populations. I'll use one of these datasets, called `population`, which has "data on forcibly displaced and stateless persons by year, including refugees, asylum-seekers, internally displaced people (IDPs) and stateless people." + +I begin by loading the {refugees} package: + + + +```{r} +library(refugees) +``` + + + +And then showing the `population` data: + + + +```{r} +population +``` + + + +This dataset has more variables than we need. I've done some work to calculate the refugee population from each country as a percentage of its total population in all years since 2000 (you can see the code for that [here](https://github.com/rfortherestofus/blog/blob/main/gganimate/gganimate.qmd)). To see the data that I end up with, I'll load the {tidyverse}: + + + +```{r} +library(tidyverse) +``` + + + +Next, I'll import the data I've created and saved as an RDS file: + + + +```{r} +refugees_data <- + read_rds("https://github.com/rfortherestofus/blog/raw/refs/heads/main/gganimate/refugees_data.rds") +``` + + + +We can take a look at the `refugees_data` tibble we now have to work with: + + + +```{r} +refugees_data +``` + + + +I've sorted the data to show the single year and country with the highest percentage of refugees as a proportion of the total population. As you can see, Syria (with the country abbreviation SYR) has the top 9 observations, a result of its [decade-plus civil war](https://www.cfr.org/article/syrias-civil-war). + +## Plot + +We're now ready to + + + +```{r} +library(scales) +library(hrbrthemes) +library(gganimate) +``` + +```{r} +refugees_data |> + filter(country_abbreviation == "SYR") |> + ggplot( + aes( + x = year, + y = refugees_as_pct + ) + ) + + scale_y_continuous( + labels = percent_format() + ) + + geom_line() + + labs( + x = NULL, + y = NULL + ) + + theme_ipsum_inter() + + theme(panel.grid.minor = element_blank()) + + transition_reveal(year) +``` + + + +## Map + + + +```{r} +library(sf) +``` + +```{r} +refugees_data_geospatial <- + read_sf("https://github.com/rfortherestofus/blog/raw/refs/heads/main/gganimate/refugees_data_geospatial.geojson") +``` + +```{r} +refugees_data_geospatial |> + left_join( + refugees_data, + join_by(country_abbreviation) + ) |> + filter(continent == "Asia") |> + filter(subregion == "Western Asia") |> + ggplot() + + geom_sf( + aes(fill = refugees_as_pct), + linewidth = 0.1, + color = "white" + ) + + theme_ipsum_inter( + grid = FALSE + ) + + labs( + title = "Refugees as a percentage of total population in {current_frame}", + # caption = "{current_frame}", + fill = NULL + ) + + scale_fill_viridis_c( + labels = scales::percent_format(), + limits = c(0, .4), + option = "C", + guide = guide_colorsteps(show.limits = TRUE) + ) + + theme( + legend.position = "top", + plot.title = element_text( + face = "bold", + hjust = 0.5 + ), + axis.text.x = element_blank(), + axis.text.y = element_blank(), + legend.key.width = unit(2, "cm"), + legend.text = element_text( + size = 12 + ) + ) + + transition_manual(year) +``` + diff --git a/gganimate/gganimate_files/figure-commonmark/unnamed-chunk-11-1.gif b/gganimate/gganimate_files/figure-commonmark/unnamed-chunk-11-1.gif new file mode 100644 index 0000000000000000000000000000000000000000..f6828c419432c618ca00d601b622dd72140a7acb GIT 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