Last updated: 2021-09-21

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TidyTuesday

Join the R4DS Online Learning Community in the weekly #TidyTuesday event! Every week we post a raw dataset, a chart or article related to that dataset, and ask you to explore the data. While the dataset will be “tamed”, it will not always be tidy! As such you might need to apply various R for Data Science techniques to wrangle the data into a true tidy format. The goal of TidyTuesday is to apply your R skills, get feedback, explore other’s work, and connect with the greater #RStats community! As such we encourage everyone of all skills to participate!

Load the weekly Data

Dowload the weekly data and make available in the tt object.

tt <- tidytuesdayR::tt_load("2020-09-01")

    Downloading file 1 of 5: `arable_land_pin.csv`
    Downloading file 2 of 5: `cereal_crop_yield_vs_fertilizer_application.csv`
    Downloading file 3 of 5: `cereal_yields_vs_tractor_inputs_in_agriculture.csv`
    Downloading file 4 of 5: `key_crop_yields.csv`
    Downloading file 5 of 5: `land_use_vs_yield_change_in_cereal_production.csv`

Readme

Take a look at the readme for the weekly data to get insight on the dataset. This includes a data dictionary, source, and a link to an article on the data.

tt

Glimpse Data

Take an initial look at the format of the data available.

tt %>%
  map(glimpse)

Wrangle

Explore the data and process it into a nice format for plotting! Access each dataset by name by using a dollarsign after the tt object and then the name of the data set.

nutrient <- tt$cereal_crop_yield_vs_fertilizer_application

nutrient_rates <- nutrient %>%
  mutate(
    Cereal = `Cereal yield (tonnes per hectare)`,
    Nitrogen = `Nitrogen fertilizer use (kilograms per hectare)`, Nitrogen = if_else(is.na(Nitrogen), 0, Nitrogen),
    Efficiency = Cereal / Nitrogen, .keep = "unused"
  ) %>%
  filter(Efficiency < 10, !is.na(Code)) %>%
  tidyr::nest(data = c(Efficiency, Nitrogen, Cereal, Year)) %>%
  mutate(fit = map(.x = data, .f = ~ lm(Efficiency ~ Year, data = .))) %>%
  mutate(slope = purrr::map_dbl(.x = fit, .f = ~ broom::tidy(.)$estimate[2])) %>%
  tidyr::unnest(data) %>%
  group_by(Entity) %>%
  mutate(avg_yield = mean(Cereal)) %>%
  ungroup()

btm <- quantile(nutrient_rates$slope, probs = c(0.2, 0.8))[1]
top <- quantile(nutrient_rates$slope, probs = c(0.2, 0.8))[2]

nutrient_rates <- nutrient_rates %>%
  mutate(
    bin = case_when(
      slope < btm ~ "Decrease",
      slope > top ~ "Increase",
      TRUE ~ "Flat"
    ),
    Year = as.integer(Year)
  )

nutrient_rates %>%
  filter(bin %in% c("Decrease", "Increase"), avg_yield > 5) %>%
  ggplot(aes(Year, Efficiency)) +
  geom_point() +
  geom_smooth(aes(color = bin),
    method = "loess",
    formula = "y ~ x",
    alpha = 0.1, show.legend = FALSE
  ) +
  scale_x_continuous(n.breaks = 4) +
  facet_wrap(~Entity) +
  labs(
    title = "Nutrient Efficiency",
    subtitle = "Cereal Yield Tonnes per Hectare divided by Nitrogen Equivalent Fertilizer kg per Hectare",
    caption = "FAO and Our World in Data | @jim_gruman"
  )

key_crop_yields <- tt$key_crop_yields

land_use <- tt$land_use_vs_yield_change_in_cereal_production
top_countries <- land_use %>%
  janitor::clean_names() %>%
  filter(
    !is.na(code),
    entity != "World"
  ) %>%
  group_by(entity) %>%
  filter(year == max(year)) %>%
  ungroup() %>%
  slice_max(total_population_gapminder, n = 30) %>%
  pull(entity)
tidy_yields <- key_crop_yields %>%
  janitor::clean_names() %>%
  pivot_longer(wheat_tonnes_per_hectare:bananas_tonnes_per_hectare,
    names_to = "crop",
    values_to = "yield"
  ) %>%
  mutate(crop = str_remove(crop, "_tonnes_per_hectare")) %>%
  filter(
    crop %in% c("wheat", "rice", "maize", "barley"),
    entity %in% top_countries,
    !is.na(yield)
  )
tidy_yields %>%
  ggplot(aes(year, yield, color = crop)) +
  geom_point(alpha = 0.7, size = 1.5) +
  geom_line(alpha = 0.7, size = 1.5) +
  facet_wrap(~entity) +
  scale_x_continuous(n.breaks = 2) +
  labs(
    x = NULL,
    subtitle = "yields (tons per hectare)",
    title = "Global increases in yields of cereal grains"
  )

Many Models

Let’s use statistical modeling to measure the country to country differences and changes over time. In this case, a simple linear estimate is useful to compare each country’s growth rates.

This chart depicts the estimate of growth on the x-axis, and the p-value on the y-axis. Consider the p-value the likelihood that the growth estimate is no different than random chance. A smaller p-value is a greater confidence in the effect.

tidy_lm <- tidy_yields %>%
  nest(yields = c(year, yield)) %>%
  mutate(model = map(yields, ~ lm(yield ~ year, data = .x)))

slopes <- tidy_lm %>%
  mutate(coefs = map(model, broom::tidy)) %>%
  unnest(coefs) %>%
  filter(term == "year") %>%
  mutate(p.value = p.adjust(p.value))
slopes %>%
  ggplot(aes(estimate, p.value, label = entity)) +
  geom_vline(xintercept = 0, lty = 2, size = 1.5, alpha = 0.7, color = "gray50") +
  geom_point(aes(color = crop), alpha = 0.8, size = 2.5, show.legend = FALSE) +
  ggrepel::geom_text_repel(size = 3) +
  scale_y_log10() +
  facet_wrap(~crop) +
  labs(
    y = "p.value effect size: Likelihood that this slope was no different than chance, \nsmaller is more confident",
    x = "estimate of the slope, values greater than 0 are yearly increases"
  )


sessionInfo()
R version 4.1.1 (2021-08-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19043)

Matrix products: default

locale:
[1] LC_COLLATE=English_United States.1252 
[2] LC_CTYPE=English_United States.1252   
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] forcats_0.5.1   stringr_1.4.0   dplyr_1.0.7     purrr_0.3.4    
 [5] readr_2.0.1     tidyr_1.1.3     tibble_3.1.4    ggplot2_3.3.5  
 [9] tidyverse_1.3.1 workflowr_1.6.2

loaded via a namespace (and not attached):
  [1] readxl_1.3.1       backports_1.2.1    systemfonts_1.0.2 
  [4] workflows_0.2.3    plyr_1.8.6         selectr_0.4-2     
  [7] tidytuesdayR_1.0.1 splines_4.1.1      listenv_0.8.0     
 [10] usethis_2.0.1      digest_0.6.27      foreach_1.5.1     
 [13] htmltools_0.5.2    yardstick_0.0.8    viridis_0.6.1     
 [16] parsnip_0.1.7.900  fansi_0.5.0        magrittr_2.0.1    
 [19] tune_0.1.6         tzdb_0.1.2         recipes_0.1.16    
 [22] globals_0.14.0     modelr_0.1.8       gower_0.2.2       
 [25] extrafont_0.17     R.utils_2.10.1     vroom_1.5.5       
 [28] extrafontdb_1.0    hardhat_0.1.6      rsample_0.1.0     
 [31] dials_0.0.10       colorspace_2.0-2   ggrepel_0.9.1     
 [34] rvest_1.0.1        textshaping_0.3.5  haven_2.4.3       
 [37] xfun_0.26          crayon_1.4.1       jsonlite_1.7.2    
 [40] survival_3.2-11    iterators_1.0.13   glue_1.4.2        
 [43] gtable_0.3.0       ipred_0.9-12       R.cache_0.15.0    
 [46] Rttf2pt1_1.3.9     future.apply_1.8.1 scales_1.1.1      
 [49] infer_1.0.0        DBI_1.1.1          Rcpp_1.0.7        
 [52] viridisLite_0.4.0  bit_4.0.4          GPfit_1.0-8       
 [55] lava_1.6.10        prodlim_2019.11.13 httr_1.4.2        
 [58] ellipsis_0.3.2     farver_2.1.0       pkgconfig_2.0.3   
 [61] R.methodsS3_1.8.1  nnet_7.3-16        sass_0.4.0        
 [64] dbplyr_2.1.1       janitor_2.1.0      utf8_1.2.2        
 [67] here_1.0.1         labeling_0.4.2     tidyselect_1.1.1  
 [70] rlang_0.4.11       DiceDesign_1.9     later_1.3.0       
 [73] munsell_0.5.0      cellranger_1.1.0   tools_4.1.1       
 [76] cachem_1.0.6       cli_3.0.1          generics_0.1.0    
 [79] broom_0.7.9        evaluate_0.14      fastmap_1.1.0     
 [82] yaml_2.2.1         ragg_1.1.3         bit64_4.0.5       
 [85] knitr_1.34         fs_1.5.0           nlme_3.1-152      
 [88] future_1.22.1      whisker_0.4        R.oo_1.24.0       
 [91] xml2_1.3.2         compiler_4.1.1     rstudioapi_0.13   
 [94] curl_4.3.2         reprex_2.0.1       lhs_1.1.3         
 [97] bslib_0.3.0        stringi_1.7.4      highr_0.9         
[100] gdtools_0.2.3      hrbrthemes_0.8.0   lattice_0.20-44   
[103] Matrix_1.3-4       styler_1.6.1       conflicted_1.0.4  
[106] vctrs_0.3.8        tidymodels_0.1.3   pillar_1.6.2      
[109] lifecycle_1.0.0    furrr_0.2.3        jquerylib_0.1.4   
[112] httpuv_1.6.3       R6_2.5.1           promises_1.2.0.1  
[115] gridExtra_2.3      parallelly_1.28.1  codetools_0.2-18  
[118] MASS_7.3-54        assertthat_0.2.1   rprojroot_2.0.2   
[121] withr_2.4.2        mgcv_1.8-36        parallel_4.1.1    
[124] hms_1.1.0          grid_4.1.1         rpart_4.1-15      
[127] timeDate_3043.102  class_7.3-19       snakecase_0.11.0  
[130] rmarkdown_2.11     git2r_0.28.0       pROC_1.18.0       
[133] lubridate_1.7.10