Plotting global rice production using FAO data: 1961–2021

Photo by Sergio Camalich on Unsplash

Rice is one of the most important staple crops worldwide, and analyzing its production trends can provide valuable insights for policymakers, researchers, and agricultural stakeholders.

In this post, we will explore how to plot global rice production using data from the Food and Agriculture Organization (FAO): https://www.fao.org/faostat/en/#data/QCL

The data underwent some wrangling to extract Continent data based on the corresponding countries as shown on an earlier post. The cleaned data is available here.

Let’s now have a glimpse of the data:

>  headtail(df)
         country year  value Continent incGroup
1    Afghanistan 2011 210000      Asia      LIC
2    Afghanistan 2016 119000      Asia      LIC
3    Afghanistan 2014 220000      Asia      LIC
4    Afghanistan 1971  2e+05      Asia      LIC
...         <NA>  ...    ...      <NA>     <NA>
7316    Zimbabwe 2018   3371    Africa     LMIC
7317    Zimbabwe 2019   2529    Africa     LMIC
7318    Zimbabwe 2020   1260    Africa     LMIC
7319    Zimbabwe 2021   3466    Africa     LMIC

Now it’s time to bring ggplotto the picture. We will plot the volume of production across the years by continent:

df %>%
    drop_na() %>%
    group_by(Continent, year) %>% 
    summarize(value = mean(value)) %>%
    ggplot(aes(x=year, y=value, color=Continent)) +
    geom_line() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1)  ,
        axis.title = element_text(size = 16),
        axis.text = element_text(size = 12),
        plot.title = element_text(size = 20, face = "bold") 
    ) +
    scale_x_continuous(breaks = seq(min(df$year), max(df$year), by = 5))

And then by income groups (WB classification):

df %>%
    drop_na() %>%
    group_by(incGroup, year) %>% 
    summarize(value = mean(value)) %>%
    ggplot(aes(x=year, y=value, color=incGroup)) +
    geom_line() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1)  ,
        axis.title = element_text(size = 16),
        axis.text = element_text(size = 12),
        plot.title = element_text(size = 20, face = "bold") 
    ) +
    scale_x_continuous(breaks = seq(min(df$year), max(df$year), by = 5))

Of course, we can stratify the plot both by continent and income groups:

df %>%
    drop_na() %>%
    group_by(Continent, incGroup, year) %>%
    summarize(value = mean(value)) %>
    ggplot(aes(x=year, y=value, color=Continent)) +
    geom_line() +
    scale_x_continuous(breaks = seq(min(df$year), max(df$year), by = 5)) +
    facet_wrap(~incGroup) +
    theme(
        axis.title = element_text(size = 16),
        axis.text = element_text(size = 12),
        axis.text.x = element_text(angle = 90, hjust = 1),  
        plot.title = element_text(size = 20, face = "bold")
    ) 

Happy ggplotting!