【R】コーヒー生産量
2020年12月23日
コーヒーは、ちょっとリラックスしたいときの最高の飲み物だと思っています。子供たちに、世界的にどこで生産されているのか。と質問を受けましたが、十分に答えられなかったので、調べてみることにしました。
世界のコーヒー生産量をWikipediaの”List of countries by coffee production”で調べました。せっかくなので、Rでスクレイピングして、地図に表示してみます。
まず、rvestでhtmlからtableを取得します。それをデータフレームにします。
library(tidyverse)
library(rvest)
coffee_html <- read_html("https://en.wikipedia.org/wiki/List_of_countries_by_coffee_production")
coffee_html
results <- coffee_html %>%
html_table(fill=TRUE)
coffee_df <- results[1] %>%
as.data.frame()
> coffee_html
{html_document}
<html class="client-nojs" lang="en" dir="ltr">
[1] <head>\n<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">\n<meta ...
[2] <body class="mediawiki ltr sitedir-ltr mw-hide-empty-elt ns-0 ns-subject mw-editab ...
>扱いやすいように、データフレームをちょっと操作します。
coffee_dat <- coffee_df %>% rename(sovereignt=Country) %>% mutate(Metric.Tons = str_remove_all(Metric.Tons, ",")) %>% select(Rank, sovereignt, Metric.Tons)
> head(coffee_dat)
Rank sovereignt Metric.Tons
1 1 Brazil 2652000
2 2 Vietnam 1650000
3 3 Colombia 810000
4 4 Indonesia 660000
5 5 Ethiopia 384000
6 6 Honduras 348000rnaturalearthで世界地図を取得して、コーヒーのデータとマージします。
world <- ne_countries(scale = "medium", returnclass = "sf") world_joined <- left_join(world, coffee_dat, key = sovereignt)
> head(world_joined)
Simple feature collection with 6 features and 65 fields
geometry type: MULTIPOLYGON
dimension: XY
bbox: xmin: -73.41544 ymin: -55.25 xmax: 75.15803 ymax: 42.68825
CRS: +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
scalerank featurecla labelrank sovereignt sov_a3 adm0_dif level
1 1 Admin-0 country 3 Afghanistan AFG 0 2
2 1 Admin-0 country 3 Angola AGO 0 2
3 1 Admin-0 country 6 Albania ALB 0 2
4 1 Admin-0 country 4 United Arab Emirates ARE 0 2
5 1 Admin-0 country 2 Argentina ARG 0 2
6 1 Admin-0 country 6 Armenia ARM 0 2
type admin adm0_a3 geou_dif geounit gu_a3
1 Sovereign country Afghanistan AFG 0 Afghanistan AFG
2 Sovereign country Angola AGO 0 Angola AGO
3 Sovereign country Albania ALB 0 Albania ALB
4 Sovereign country United Arab Emirates ARE 0 United Arab Emirates ARE
5 Sovereign country Argentina ARG 0 Argentina ARG
6 Sovereign country Armenia ARM 0 Armenia ARM
su_dif subunit su_a3 brk_diff name name_long
1 0 Afghanistan AFG 0 Afghanistan Afghanistan
2 0 Angola AGO 0 Angola Angola
3 0 Albania ALB 0 Albania Albania
4 0 United Arab Emirates ARE 0 United Arab Emirates United Arab Emirates
5 0 Argentina ARG 0 Argentina Argentina
6 0 Armenia ARM 0 Armenia Armenia
brk_a3 brk_name brk_group abbrev postal formal_en
1 AFG Afghanistan <NA> Afg. AF Islamic State of Afghanistan
2 AGO Angola <NA> Ang. AO People's Republic of Angola
3 ALB Albania <NA> Alb. AL Republic of Albania
4 ARE United Arab Emirates <NA> U.A.E. AE United Arab Emirates
5 ARG Argentina <NA> Arg. AR Argentine Republic
6 ARM Armenia <NA> Arm. ARM Republic of Armenia
formal_fr note_adm0 note_brk name_sort name_alt mapcolor7 mapcolor8
1 <NA> <NA> <NA> Afghanistan <NA> 5 6
2 <NA> <NA> <NA> Angola <NA> 3 2
3 <NA> <NA> <NA> Albania <NA> 1 4
4 <NA> <NA> <NA> United Arab Emirates <NA> 2 1
5 <NA> <NA> <NA> Argentina <NA> 3 1
6 <NA> <NA> <NA> Armenia <NA> 3 1
mapcolor9 mapcolor13 pop_est gdp_md_est pop_year lastcensus gdp_year
1 8 7 28400000 22270 NA 1979 NA
2 6 1 12799293 110300 NA 1970 NA
3 1 6 3639453 21810 NA 2001 NA
4 3 3 4798491 184300 NA 2010 NA
5 3 13 40913584 573900 NA 2010 NA
6 2 10 2967004 18770 NA 2001 NA
economy income_grp wikipedia fips_10 iso_a2 iso_a3
1 7. Least developed region 5. Low income NA <NA> AF AFG
2 7. Least developed region 3. Upper middle income NA <NA> AO AGO
3 6. Developing region 4. Lower middle income NA <NA> AL ALB
4 6. Developing region 2. High income: nonOECD NA <NA> AE ARE
5 5. Emerging region: G20 3. Upper middle income NA <NA> AR ARG
6 6. Developing region 4. Lower middle income NA <NA> AM ARM
iso_n3 un_a3 wb_a2 wb_a3 woe_id adm0_a3_is adm0_a3_us adm0_a3_un adm0_a3_wb
1 004 004 AF AFG NA AFG AFG NA NA
2 024 024 AO AGO NA AGO AGO NA NA
3 008 008 AL ALB NA ALB ALB NA NA
4 784 784 AE ARE NA ARE ARE NA NA
5 032 032 AR ARG NA ARG ARG NA NA
6 051 051 AM ARM NA ARM ARM NA NA
continent region_un subregion region_wb name_len long_len
1 Asia Asia Southern Asia South Asia 11 11
2 Africa Africa Middle Africa Sub-Saharan Africa 6 6
3 Europe Europe Southern Europe Europe & Central Asia 7 7
4 Asia Asia Western Asia Middle East & North Africa 20 20
5 South America Americas South America Latin America & Caribbean 9 9
6 Asia Asia Western Asia Europe & Central Asia 7 7
abbrev_len tiny homepart Rank Metric.Tons geometry
1 4 NA 1 NA <NA> MULTIPOLYGON (((61.21082 35...
2 4 NA 1 44 2100 MULTIPOLYGON (((16.32653 -5...
3 4 NA 1 NA <NA> MULTIPOLYGON (((20.59025 41...
4 6 NA 1 NA <NA> MULTIPOLYGON (((51.57952 24...
5 4 NA 1 NA <NA> MULTIPOLYGON (((-65.5 -55.2...
6 4 NA 1 NA <NA> MULTIPOLYGON (((43.58275 41...最後に地図を描きます。
ggplot(data = world_joined)+
geom_sf(aes(fill = as.numeric(world_joined$Metric.Tons)),
color = "black")+
labs(fill='Coffee production') +
scale_fill_viridis_c(option = "viridis")
データは表であらわします。
library(kableExtra) library(clipr) coffee_df %>% kable(align = "c", row.names=FALSE) %>% kable_styling(full_width = F) %>% column_spec(1, bold = T) %>% collapse_rows(columns = 1, valign = "middle") %>% write_clip
| Rank | Country | X60.kilogram.bags | Metric.Tons | Pounds |
|---|---|---|---|---|
| 1 | Brazil | 44,200,000 | 2,652,000 | 5,714,381,000 |
| 2 | Vietnam | 27,500,000 | 1,650,000 | 3,637,627,000 |
| 3 | Colombia | 13,500,000 | 810,000 | 1,785,744,000 |
| 4 | Indonesia | 11,000,000 | 660,000 | 1,455,050,000 |
| 5 | Ethiopia | 6,400,000 | 384,000 | 846,575,000 |
| 6 | Honduras | 5,800,000 | 348,000 | 767,208,000 |
| 7 | India | 5,800,000 | 348,000 | 767,208,000 |
| 8 | Uganda | 4,800,000 | 288,000 | 634,931,000 |
| 9 | Mexico | 3,900,000 | 234,000 | 515,881,000 |
| 10 | Guatemala | 3,400,000 | 204,000 | 449,743,000 |
| 11 | Peru | 3,200,000 | 192,000 | 423,287,000 |
| 12 | Nicaragua | 2,200,000 | 132,000 | 291,010,000 |
| 13 | China(2013/14 est.)[7] | 1,947,000 | 116,820 | 257,544,000 |
| 14 | Ivory Coast | 1,800,000 | 108,000 | 238,099,000 |
| 15 | Costa Rica | 1,492,000 | 89,520 | 197,357,000 |
| 16 | Kenya | 833,000 | 49,980 | 110,187,000 |
| 17 | Papua New Guinea | 800,000 | 48,000 | 105,821,000 |
| 18 | Tanzania | 800,000 | 48,000 | 105,821,000 |
| 19 | El Salvador | 762,000 | 45,720 | 100,795,000 |
| 20 | Ecuador | 700,000 | 42,000 | 92,594,000 |
| 21 | Cameroon | 570,000 | 34,200 | 75,398,000 |
| 22 | Laos | 520,000 | 31,200 | 68,784,000 |
| 23 | Madagascar | 520,000 | 31,200 | 68,784,000 |
| 24 | Gabon | 500,000 | 30,000 | 66,138,000 |
| 25 | Thailand | 500,000 | 30,000 | 66,138,000 |
| 26 | Venezuela | 500,000 | 30,000 | 66,138,000 |
| 27 | Dominican Republic | 400,000 | 24,000 | 52,910,000 |
| 28 | Haiti | 350,000 | 21,000 | 46,297,000 |
| 29 | Democratic Republic of the Congo | 335,000 | 20,100 | 44,312,000 |
| 30 | Rwanda | 250,000 | 15,000 | 33,069,000 |
| 31 | Burundi | 200,000 | 12,000 | 26,455,000 |
| 32 | Philippines | 200,000 | 12,000 | 26,455,000 |
| 33 | Togo | 200,000 | 12,000 | 26,455,000 |
| 34 | Guinea | 160,000 | 9,600 | 21,164,000 |
| 35 | Yemen | 120,000 | 7,200 | 15,873,000 |
| 36 | Cuba | 100,000 | 6,000 | 13,227,000 |
| 37 | Panama | 100,000 | 6,000 | 13,227,000 |
| 38 | Bolivia | 90,000 | 5,400 | 11,904,000 |
| 39 | Timor Leste | 80,000 | 4,800 | 10,582,000 |
| 40 | Central African Republic | 65,000 | 3,900 | 8,598,000 |
| 41 | Nigeria | 40,000 | 2,400 | 5,291,000 |
| 42 | Ghana | 37,000 | 2,220 | 4,894,000 |
| 43 | Sierra Leone | 36,000 | 2,160 | 4,761,000 |
| 44 | Angola | 35,000 | 2,100 | 4,629,000 |
| 45 | Jamaica | 21,000 | 1,260 | 2,777,000 |
| 46 | Paraguay | 20,000 | 1,200 | 2,645,000 |
| 47 | Malawi | 16,000 | 960 | 2,116,000 |
| 48 | Trinidad and Tobago | 12,000 | 720 | 1,587,000 |
| 49 | Zimbabwe | 10,000 | 600 | 1,322,000 |
| 50 | Liberia | 6,000 | 360 | 793,000 |
| 51 | Zambia | 2,000 | 120 | 264,000 |
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