Plastics
ofchurches
2 February 2021
Last updated: 2021-02-16
Checks: 6 1
Knit directory: TidyTuesday/
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Background
The read.me with instructions, notes, data and data-dictionary is here.
Get the data
plastics <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-01-26/plastics.csv')Clean up data
- Remove obvious typos/mistakes:
clean_plastics <- plastics %>%
dplyr::mutate(parent_company = str_to_title(parent_company),
country = str_to_title(country)) %>%
dplyr::filter(!parent_company %in% c("#Error!", "Unbranded", "null",
"NULL","Null", "Grand Total"),
!country %in% c("EMPTY"))- Remove duplicated rows
clean_plastics2<- clean_plastics %>%
split(x = ., f=.$country) %>%
lapply(function(x){
x %>% dplyr::arrange(parent_company, desc(grand_total)) %>%
dplyr::distinct(country, year, parent_company, num_events, volunteers, .keep_all=TRUE)
}) %>% bind_rows()- Replace NAs for plastic collected with 0:
# Get the columns associated with plastics collected
# and replace NA with 0
clean_plastics3 <- clean_plastics2[, c(4:12)]
clean_plastics3[is.na(clean_plastics3)] <- 0
clean_plastics2[, c(4:12)] <- clean_plastics3Initial data exploration
Countries’ participation in 2019 and 2020
countries_by_year <- clean_plastics2 %>%
dplyr::group_by(country, year) %>%
dplyr::summarise(n = n()) %>%
dplyr::arrange(country, year)`summarise()` regrouping output by 'country' (override with `.groups` argument)# 50 countries participated in 2019
countries_2019 <- countries_by_year %>%
dplyr::filter(year == 2019) %>%
as_tibble
countries_2019# A tibble: 51 x 3
country year n
<chr> <dbl> <int>
1 Argentina 2019 251
2 Australia 2019 1
3 Bangladesh 2019 24
4 Benin 2019 1
5 Bhutan 2019 4
6 Brazil 2019 132
7 Bulgaria 2019 35
8 Burkina Faso 2019 26
9 Cameroon 2019 150
10 Canada 2019 24
# … with 41 more rows# 55 countries participated in 2020
countries_2020 <- countries_by_year %>%
dplyr::filter(year == 2020) %>%
as_tibble
countries_2020# A tibble: 53 x 3
country year n
<chr> <dbl> <int>
1 Argentina 2020 224
2 Australia 2020 67
3 Bangladesh 2020 141
4 Benin 2020 1
5 Brazil 2020 20
6 Bulgaria 2020 13
7 Burkina Faso 2020 13
8 Canada 2020 41
9 Chile 2020 119
10 China 2020 216
# … with 43 more rows# 38 countries participated in both years
inner_join(countries_2019,
countries_2020,
by = c("country")) %>%
as_tibble()# A tibble: 39 x 5
country year.x n.x year.y n.y
<chr> <dbl> <int> <dbl> <int>
1 Argentina 2019 251 2020 224
2 Australia 2019 1 2020 67
3 Bangladesh 2019 24 2020 141
4 Benin 2019 1 2020 1
5 Brazil 2019 132 2020 20
6 Bulgaria 2019 35 2020 13
7 Burkina Faso 2019 26 2020 13
8 Canada 2019 24 2020 41
9 China 2019 605 2020 216
10 Colombia 2019 1 2020 23
# … with 29 more rowsHow many parent companies participated per country
companies_by_country_year <- clean_plastics2 %>%
dplyr::select(country, parent_company) %>%
dplyr::group_by(country) %>%
dplyr::summarise(n_companies = n()) %>%
dplyr::arrange(desc(n_companies))`summarise()` ungrouping output (override with `.groups` argument)companies_by_country_year # A tibble: 65 x 2
country n_companies
<chr> <int>
1 Empty 1267
2 United States Of America 945
3 Indonesia 874
4 Ukraine 825
5 China 821
6 Philippines 818
7 Vietnam 802
8 Nigeria 683
9 Hong Kong 556
10 India 552
# … with 55 more rowsVisualising the types of plastics collected per country in 2020
grandtotals <- clean_plastics2 %>%
dplyr::filter(year == 2020) %>%
dplyr::select(country, grand_total, volunteers) %>%
dplyr::group_by(country) %>%
dplyr::summarise(sum = sum(grand_total),
sum_volunteers = sum(volunteers)) %>%
dplyr::mutate(norm_total = sum/sum_volunteers)`summarise()` ungrouping output (override with `.groups` argument)clean_plastics2 %>%
dplyr::filter(year == 2020) %>%
dplyr::select(country, parent_company, hdpe, ldpe, o, pet, pp, ps, pvc) %>%
melt(measure.vars= c("hdpe", "ldpe", "o", "pet", "pp", "ps", "pvc")) %>%
dplyr::rename(plasticType = variable) %>%
dplyr::left_join(grandtotals, by = "country") %>%
ggplot(aes(x = reorder(country, sum), y = value, fill = plasticType)) +
geom_bar(stat="identity") +
coord_flip() +
scale_fill_brewer(palette="Accent") +
labs(x = "Number of units", y = "Country")+
ggtitle("Plastics collected in 2020")
- And 2019:
grandtotals <- clean_plastics2 %>%
dplyr::filter(year == 2019) %>%
dplyr::select(country, grand_total) %>%
dplyr::group_by(country) %>%
dplyr::summarise(sum = sum(grand_total))`summarise()` ungrouping output (override with `.groups` argument)clean_plastics2 %>%
dplyr::filter(year == 2019) %>%
dplyr::select(country, parent_company, hdpe, ldpe, o, pet, pp, ps, pvc) %>%
melt(measure.vars= c("hdpe", "ldpe", "o", "pet", "pp", "ps", "pvc")) %>%
dplyr::rename(plasticType = variable) %>%
dplyr::left_join(grandtotals, by = "country") %>%
ggplot(aes(x = reorder(country, sum), y = value, fill = plasticType)) +
geom_bar(stat="identity") +
coord_flip() +
scale_fill_brewer(palette="Accent") +
labs(x = "Number of units", y = "Country") +
ggtitle("Plastics collected in 2019")
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Mojave 10.14.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] reshape2_1.4.4 dplyr_1.0.2 stringr_1.4.0 magrittr_2.0.1 ggalt_0.4.0
[6] ggplot2_3.3.2 readr_1.4.0
loaded via a namespace (and not attached):
[1] tidyselect_1.1.0 xfun_0.19 purrr_0.3.4 colorspace_2.0-0
[5] vctrs_0.3.5 generics_0.1.0 htmltools_0.5.1.1 yaml_2.2.1
[9] utf8_1.1.4 rlang_0.4.9 later_1.1.0.1 pillar_1.4.7
[13] glue_1.4.2 withr_2.3.0 RColorBrewer_1.1-2 lifecycle_0.2.0
[17] plyr_1.8.6 munsell_0.5.0 gtable_0.3.0 workflowr_1.6.2
[21] evaluate_0.14 labeling_0.4.2 knitr_1.30 extrafont_0.17
[25] httpuv_1.5.4 curl_4.3 fansi_0.4.1 Rttf2pt1_1.3.8
[29] Rcpp_1.0.5 KernSmooth_2.23-17 promises_1.1.1 scales_1.1.1
[33] farver_2.0.3 fs_1.5.0 proj4_1.0-10 hms_0.5.3
[37] digest_0.6.27 stringi_1.5.3 ash_1.0-15 grid_4.0.3
[41] rprojroot_2.0.2 cli_2.2.0 tools_4.0.3 maps_3.3.0
[45] tibble_3.0.4 crayon_1.3.4 whisker_0.4 extrafontdb_1.0
[49] pkgconfig_2.0.3 ellipsis_0.3.1 MASS_7.3-53 assertthat_0.2.1
[53] rmarkdown_2.5 rstudioapi_0.13 R6_2.5.0 git2r_0.27.1
[57] compiler_4.0.3