class: center, middle, inverse, title-slide # Joining and mapping
🗺 ### Dr. Çetinkaya-Rundel --- layout: true <div class="my-footer"> <span> Dr. Mine Çetinkaya-Rundel - <a href="https://introds.org" target="_blank">introds.org </a> </span> </div> --- ## So far this week... - Data tidying and reshaping - Improved data visualizations - ~~Merge conflicts~~ - HW 03 due today at 17:00 - OQ 03 due Friday at 17:00 .question[ .large[ Any questions? ] ] --- class: center, middle # Fisheries --- Fisheries and Aquaculture Department of the Food and Agriculture Organization of the United Nations collects data on fisheries production of countries. The (not-so-great) visualization belows shows the distribution of fishery harvest of countries for 2016, by capture and aquaculture. <img src="img/fisheries.png" width="70%" style="display: block; margin: auto;" /> .footnote[ - Countries whose total harvest was less than 100,000 tons are not included in the visualization. - Source: https://en.wikipedia.org/wiki/Fishing_industry_by_country ] --- .question[ What are some ways your teams discussed/implemented for improving this visualization? ] -- - Calculate summary statistics at the continent level and visualize them. - Map the data. --- ## Data prep Filter out countries whose total harvest was less than 100,000 tons since they are not included in the visualization: ```r fisheries <- fisheries %>% filter(total > 100000) fisheries ``` ``` ## # A tibble: 82 x 4 ## country capture aquaculture total ## <chr> <dbl> <dbl> <dbl> ## 1 Angola 486490 655 487145 ## 2 Argentina 755226 3673 758899 ## 3 Australia 174629 96847 271476 ## 4 Bangladesh 1674770 2203554 3878324 ## 5 Brazil 705000 581230 1286230 ## 6 Cambodia 629950 172500 802450 ## 7 Cameroon 233190 2315 235505 ## 8 Canada 874727 200765 1075492 ## 9 Chad 110000 94 110094 ## 10 Chile 1829238 1050117 2879355 ## # … with 72 more rows ``` --- class: center, middle # Data joins --- .pull-left[ ```r fisheries %>% select(country) ``` ``` ## # A tibble: 82 x 1 ## country ## <chr> ## 1 Angola ## 2 Argentina ## 3 Australia ## 4 Bangladesh ## 5 Brazil ## 6 Cambodia ## 7 Cameroon ## 8 Canada ## 9 Chad ## 10 Chile ## # … with 72 more rows ``` ] .pull-right[ ```r continents ``` ``` ## # A tibble: 245 x 2 ## country continent ## <chr> <chr> ## 1 Afghanistan Asia ## 2 Åland Islands Europe ## 3 Albania Europe ## 4 Algeria Africa ## 5 American Samoa Oceania ## 6 Andorra Europe ## 7 Angola Africa ## 8 Anguilla Americas ## 9 Antigua & Barbuda Americas ## 10 Argentina Americas ## # … with 235 more rows ``` ] --- ## Joining data frames ``` *_join(x, y) ``` - `inner_join()`: all rows from x where there are matching values in y, return all combination of multiple matches in the case of multiple matches - `left_join()`: all rows from x - `right_join()`: all rows from y - `full_join()`: all rows from both x and y - `semi_join()`: all rows from x where there are matching values in y, keeping just columns from x. - `anti_join()`: return all rows from x where there are not matching values in y, never duplicate rows of x - ... --- ## Setup For the next few slides... .pull-left[ ```r x ``` ``` ## # A tibble: 3 x 1 ## value ## <dbl> ## 1 1 ## 2 2 ## 3 3 ``` ] .pull-right[ ```r y ``` ``` ## # A tibble: 3 x 1 ## value ## <dbl> ## 1 1 ## 2 2 ## 3 4 ``` ] --- ## `inner_join()` .pull-left[ ```r inner_join(x, y) ``` ``` ## Joining, by = "value" ``` ``` ## # A tibble: 2 x 1 ## value ## <dbl> ## 1 1 ## 2 2 ``` ] .pull-right[ ![](img/inner-join.gif)<!-- --> ] --- ## `left_join()` .pull-left[ ```r left_join(x, y) ``` ``` ## Joining, by = "value" ``` ``` ## # A tibble: 3 x 1 ## value ## <dbl> ## 1 1 ## 2 2 ## 3 3 ``` ] .pull-right[ ![](img/left-join.gif)<!-- --> ] --- ## `right_join()` .pull-left[ ```r right_join(x, y) ``` ``` ## Joining, by = "value" ``` ``` ## # A tibble: 3 x 1 ## value ## <dbl> ## 1 1 ## 2 2 ## 3 4 ``` ] .pull-right[ ![](img/right-join.gif)<!-- --> ] --- ## `full_join()` .pull-left[ ```r full_join(x, y) ``` ``` ## Joining, by = "value" ``` ``` ## # A tibble: 4 x 1 ## value ## <dbl> ## 1 1 ## 2 2 ## 3 3 ## 4 4 ``` ] .pull-right[ ![](img/full-join.gif)<!-- --> ] --- ## `semi_join()` .pull-left[ ```r semi_join(x, y) ``` ``` ## Joining, by = "value" ``` ``` ## # A tibble: 2 x 1 ## value ## <dbl> ## 1 1 ## 2 2 ``` ] .pull-right[ ![](img/semi-join.gif)<!-- --> ] --- ## `anti_join()` .pull-left[ ```r anti_join(x, y) ``` ``` ## Joining, by = "value" ``` ``` ## # A tibble: 1 x 1 ## value ## <dbl> ## 1 3 ``` ] .pull-right[ ![](img/anti-join.gif)<!-- --> ] --- .question[ We want to keep all rows and columns from `fisheries` and add a column for corresponding continents. Which join function should we use? ] .pull-left[ ```r fisheries %>% select(country) ``` ``` ## # A tibble: 82 x 1 ## country ## <chr> ## 1 Angola ## 2 Argentina ## 3 Australia ## 4 Bangladesh ## 5 Brazil ## 6 Cambodia ## 7 Cameroon ## 8 Canada ## 9 Chad ## 10 Chile ## # … with 72 more rows ``` ] .pull-right[ ```r continents ``` ``` ## # A tibble: 245 x 2 ## country continent ## <chr> <chr> ## 1 Afghanistan Asia ## 2 Åland Islands Europe ## 3 Albania Europe ## 4 Algeria Africa ## 5 American Samoa Oceania ## 6 Andorra Europe ## 7 Angola Africa ## 8 Anguilla Americas ## 9 Antigua & Barbuda Americas ## 10 Argentina Americas ## # … with 235 more rows ``` ] --- ## Join fisheries and continents ```r fisheries <- left_join(fisheries, continents) ``` ``` ## Joining, by = "country" ``` -- .question[ How does `left_join()` know to join the two data frames by `country`? ] Hint: ```r names(fisheries) ``` ``` ## [1] "country" "capture" "aquaculture" "total" "continent" ``` ```r names(continents) ``` ``` ## [1] "country" "continent" ``` --- ## Check the data ```r fisheries %>% filter(is.na(continent)) ``` ``` ## # A tibble: 3 x 5 ## country capture aquaculture total continent ## <chr> <dbl> <dbl> <dbl> <chr> ## 1 Democratic Republic of the Congo 237372 3161 240533 <NA> ## 2 Hong Kong 142775 4258 147033 <NA> ## 3 Myanmar 2072390 1017644 3090034 <NA> ``` --- ## Implement fixes ```r fisheries <- fisheries %>% mutate(continent = case_when( country == "Democratic Republic of the Congo" ~ "Africa", country == "Hong Kong" ~ "Asia", country == "Myanmar" ~ "Asia", TRUE ~ continent ) ) ``` ...and check again ```r fisheries %>% filter(is.na(continent)) ``` ``` ## # A tibble: 0 x 5 ## # … with 5 variables: country <chr>, capture <dbl>, aquaculture <dbl>, ## # total <dbl>, continent <chr> ``` --- .question[ What does the following code do? ] ```r fisheries_summary <- fisheries %>% mutate(aquaculture_perc = aquaculture / total) %>% group_by(continent) %>% summarise( min_ap = min(aquaculture_perc), mean_ap = mean(aquaculture_perc), max_ap = max(aquaculture_perc) ) ``` --- ## Summarise by continent ```r fisheries_summary %>% arrange(desc(mean_ap)) ``` ``` ## # A tibble: 5 x 4 ## continent min_ap mean_ap max_ap ## <chr> <dbl> <dbl> <dbl> ## 1 Asia 0 0.367 0.782 ## 2 Americas 0 0.192 0.529 ## 3 Europe 0.00682 0.165 0.618 ## 4 Oceania 0.0197 0.150 0.357 ## 5 Africa 0 0.0943 0.803 ``` --- ## Visualize continent summary stats ```r ggplot(fisheries_summary, aes(x = continent, y = mean_ap)) + geom_col() ``` ![](w4_d2-joining-mapping_files/figure-html/unnamed-chunk-32-1.png)<!-- --> --- ## Improve visualization ```r ggplot(fisheries_summary, * aes(x = reorder(continent, mean_ap), y = mean_ap)) + geom_col() + coord_flip() + * scale_y_continuous(labels = percent) + labs( x = "", y = "", title = "Average share of aquaculture by continent", subtitle = "out of total fisheries harvest, 2016", caption = "Source: bit.ly/2VrawTt" ) + * theme_minimal() ``` --- ![](w4_d2-joining-mapping_files/figure-html/unnamed-chunk-34-1.png)<!-- --> --- ## Improve visualization further ```r ggplot(fisheries_summary, aes(x = reorder(continent, mean_ap), y = mean_ap)) + geom_col() + coord_flip() + * scale_y_continuous(labels = percent_format(accuracy = 1)) + labs( x = "", y = "", title = "Average share of aquaculture by continent", subtitle = "out of total fisheries harvest, 2016", caption = "Source: bit.ly/2VrawTt" ) + theme_minimal() ``` --- ![](w4_d2-joining-mapping_files/figure-html/unnamed-chunk-36-1.png)<!-- --> --- class: center, middle # Mapping --- ## Mapping the fisheries data - Obtain country boundaries and store as a data frame - Join the fisheries and country boundaries data frames - Plot the country boundaries, and fill by fisheries harvest data --- ## `map_data()` The `map_data()` function easily turns data from the maps package in to a data frame suitable for plotting with ggplot2: ```r map_data("world") ``` ``` ## long lat group order region subregion ## 1 -69.89912 12.45200 1 1 Aruba <NA> ## 2 -69.89571 12.42300 1 2 Aruba <NA> ## 3 -69.94219 12.43853 1 3 Aruba <NA> ## 4 -70.00415 12.50049 1 4 Aruba <NA> ## 5 -70.06612 12.54697 1 5 Aruba <NA> ## 6 -70.05088 12.59707 1 6 Aruba <NA> ## 7 -70.03511 12.61411 1 7 Aruba <NA> ## 8 -69.97314 12.56763 1 8 Aruba <NA> ## 9 -69.91181 12.48047 1 9 Aruba <NA> ## 10 -69.89912 12.45200 1 10 Aruba <NA> ## 12 74.89131 37.23164 2 12 Afghanistan <NA> ## 13 74.84023 37.22505 2 13 Afghanistan <NA> ## 14 74.76738 37.24917 2 14 Afghanistan <NA> ## 15 74.73896 37.28564 2 15 Afghanistan <NA> ## 16 74.72666 37.29072 2 16 Afghanistan <NA> ## 17 74.66895 37.26670 2 17 Afghanistan <NA> ## 18 74.55899 37.23662 2 18 Afghanistan <NA> ## 19 74.37217 37.15771 2 19 Afghanistan <NA> ## 20 74.37617 37.13735 2 20 Afghanistan <NA> ## 21 74.49796 37.05722 2 21 Afghanistan <NA> ## 22 74.52646 37.03066 2 22 Afghanistan <NA> ## 23 74.54140 37.02217 2 23 Afghanistan <NA> ## 24 74.43106 36.98369 2 24 Afghanistan <NA> ## 25 74.19473 36.89688 2 25 Afghanistan <NA> ## 26 74.03887 36.82573 2 26 Afghanistan <NA> ## 27 74.00185 36.82310 2 27 Afghanistan <NA> ## 28 73.90781 36.85293 2 28 Afghanistan <NA> ## 29 73.76914 36.88848 2 29 Afghanistan <NA> ## 30 73.73183 36.88779 2 30 Afghanistan <NA> ## 31 73.41113 36.88169 2 31 Afghanistan <NA> ## 32 73.11680 36.86856 2 32 Afghanistan <NA> ## 33 72.99374 36.85161 2 33 Afghanistan <NA> ## 34 72.76621 36.83501 2 34 Afghanistan <NA> ## 35 72.62286 36.82959 2 35 Afghanistan <NA> ## 36 72.53135 36.80200 2 36 Afghanistan <NA> ## 37 72.43115 36.76582 2 37 Afghanistan <NA> ## 38 72.32696 36.74239 2 38 Afghanistan <NA> ## 39 72.24980 36.73472 2 39 Afghanistan <NA> ## 40 72.15674 36.70088 2 40 Afghanistan <NA> ## 41 72.09560 36.63374 2 41 Afghanistan <NA> ## 42 71.92070 36.53418 2 42 Afghanistan <NA> ## 43 71.82227 36.48608 2 43 Afghanistan <NA> ## 44 71.77266 36.43184 2 44 Afghanistan <NA> ## 45 71.71641 36.42656 2 45 Afghanistan <NA> ## 46 71.62051 36.43647 2 46 Afghanistan <NA> ## 47 71.54590 36.37769 2 47 Afghanistan <NA> ## 48 71.46328 36.29326 2 48 Afghanistan <NA> ## 49 71.31260 36.17119 2 49 Afghanistan <NA> ## 50 71.23291 36.12178 2 50 Afghanistan <NA> ## 51 71.18506 36.04209 2 51 Afghanistan <NA> ## 52 71.22021 36.00068 2 52 Afghanistan <NA> ## 53 71.34287 35.93853 2 53 Afghanistan <NA> ## 54 71.39756 35.88018 2 54 Afghanistan <NA> ## 55 71.42754 35.83374 2 55 Afghanistan <NA> ## 56 71.48359 35.71460 2 56 Afghanistan <NA> ## 57 71.51904 35.59751 2 57 Afghanistan <NA> ## 58 71.57198 35.54683 2 58 Afghanistan <NA> ## 59 71.58740 35.46084 2 59 Afghanistan <NA> ## 60 71.60059 35.40791 2 60 Afghanistan <NA> ## 61 71.57198 35.37041 2 61 Afghanistan <NA> ## 62 71.54551 35.32851 2 62 Afghanistan <NA> ## 63 71.54551 35.28886 2 63 Afghanistan <NA> ## 64 71.57725 35.24800 2 64 Afghanistan <NA> ## 65 71.60527 35.21177 2 65 Afghanistan <NA> ## 66 71.62051 35.18301 2 66 Afghanistan <NA> ## 67 71.60166 35.15068 2 67 Afghanistan <NA> ## 68 71.54551 35.10141 2 68 Afghanistan <NA> ## 69 71.51709 35.05112 2 69 Afghanistan <NA> ## 70 71.45508 34.96694 2 70 Afghanistan <NA> ## 71 71.35811 34.90962 2 71 Afghanistan <NA> ## 72 71.29414 34.86773 2 72 Afghanistan <NA> ## 73 71.22578 34.77954 2 73 Afghanistan <NA> ## 74 71.11328 34.68159 2 74 Afghanistan <NA> ## 75 71.06563 34.59961 2 75 Afghanistan <NA> ## 76 71.01631 34.55464 2 76 Afghanistan <NA> ## 77 70.96562 34.53037 2 77 Afghanistan <NA> ## 78 70.97891 34.48628 2 78 Afghanistan <NA> ## 79 71.02295 34.43115 2 79 Afghanistan <NA> ## 80 71.09570 34.36943 2 80 Afghanistan <NA> ## 81 71.09238 34.27324 2 81 Afghanistan <NA> ## 82 71.08906 34.20406 2 82 Afghanistan <NA> ## 83 71.09131 34.12027 2 83 Afghanistan <NA> ## 84 71.05156 34.04971 2 84 Afghanistan <NA> ## 85 70.84844 33.98188 2 85 Afghanistan <NA> ## 86 70.65401 33.95229 2 86 Afghanistan <NA> ## 87 70.41573 33.95044 2 87 Afghanistan <NA> ## 88 70.32568 33.96113 2 88 Afghanistan <NA> ## 89 70.25361 33.97598 2 89 Afghanistan <NA> ## 90 69.99473 34.05181 2 90 Afghanistan <NA> ## 91 69.88965 34.00727 2 91 Afghanistan <NA> ## 92 69.86806 33.89766 2 92 Afghanistan <NA> ## 93 70.05664 33.71987 2 93 Afghanistan <NA> ## 94 70.13418 33.62075 2 94 Afghanistan <NA> ## 95 70.21973 33.45469 2 95 Afghanistan <NA> ## 96 70.28418 33.36904 2 96 Afghanistan <NA> ## 97 70.26113 33.28901 2 97 Afghanistan <NA> ## 98 70.09023 33.19810 2 98 Afghanistan <NA> ## 99 69.92012 33.11250 2 99 Afghanistan <NA> ## 100 69.70371 33.09473 2 100 Afghanistan <NA> ## 101 69.56777 33.06416 2 101 Afghanistan <NA> ## 102 69.50156 33.02007 2 102 Afghanistan <NA> ## 103 69.45312 32.83281 2 103 Afghanistan <NA> ## 104 69.40459 32.76426 2 104 Afghanistan <NA> ## 105 69.40537 32.68272 2 105 Afghanistan <NA> ## 106 69.35947 32.59033 2 106 Afghanistan <NA> ## 107 69.28994 32.53057 2 107 Afghanistan <NA> ## 108 69.24140 32.43354 2 108 Afghanistan <NA> ## 109 69.25654 32.24946 2 109 Afghanistan <NA> ## 110 69.27930 31.93682 2 110 Afghanistan <NA> ## 111 69.18691 31.83809 2 111 Afghanistan <NA> ## 112 69.08311 31.73848 2 112 Afghanistan <NA> ## 113 68.97343 31.66738 2 113 Afghanistan <NA> ## 114 68.86895 31.63423 2 114 Afghanistan <NA> ## 115 68.78233 31.64643 2 115 Afghanistan <NA> ## 116 68.71367 31.70806 2 116 Afghanistan <NA> ## 117 68.67324 31.75972 2 117 Afghanistan <NA> ## 118 68.59766 31.80298 2 118 Afghanistan <NA> ## 119 68.52071 31.79414 2 119 Afghanistan <NA> ## 120 68.44326 31.75449 2 120 Afghanistan <NA> ## 121 68.31982 31.76767 2 121 Afghanistan <NA> ## 122 68.21397 31.80737 2 122 Afghanistan <NA> ## 123 68.16103 31.80298 2 123 Afghanistan <NA> ## 124 68.13017 31.76328 2 124 Afghanistan <NA> ## 125 68.01719 31.67798 2 125 Afghanistan <NA> ## 126 67.73985 31.54819 2 126 Afghanistan <NA> ## 127 67.62675 31.53877 2 127 Afghanistan <NA> ## 128 67.57822 31.50649 2 128 Afghanistan <NA> ## 129 67.59756 31.45332 2 129 Afghanistan <NA> ## 130 67.64706 31.40996 2 130 Afghanistan <NA> ## 131 67.73350 31.37925 2 131 Afghanistan <NA> ## 132 67.73789 31.34395 2 132 Afghanistan <NA> ## 133 67.66152 31.31299 2 133 Afghanistan <NA> ## 134 67.59639 31.27769 2 134 Afghanistan <NA> ## 135 67.45283 31.23462 2 135 Afghanistan <NA> ## 136 67.28730 31.21782 2 136 Afghanistan <NA> ## 137 67.11592 31.24292 2 137 Afghanistan <NA> ## 138 67.02773 31.30024 2 138 Afghanistan <NA> ## 139 66.92432 31.30561 2 139 Afghanistan <NA> ## 140 66.82929 31.26367 2 140 Afghanistan <NA> ## 141 66.73135 31.19453 2 141 Afghanistan <NA> ## 142 66.62422 31.04604 2 142 Afghanistan <NA> ## 143 66.59580 31.01997 2 143 Afghanistan <NA> ## 144 66.56680 30.99658 2 144 Afghanistan <NA> ## 145 66.49736 30.96455 2 145 Afghanistan <NA> ## 146 66.39717 30.91221 2 146 Afghanistan <NA> ## 147 66.34687 30.80278 2 147 Afghanistan <NA> ## 148 66.28691 30.60791 2 148 Afghanistan <NA> ## 149 66.30097 30.50298 2 149 Afghanistan <NA> ## 150 66.30547 30.32114 2 150 Afghanistan <NA> ## 151 66.28184 30.19346 2 151 Afghanistan <NA> ## 152 66.23848 30.10962 2 152 Afghanistan <NA> ## 153 66.24717 30.04351 2 153 Afghanistan <NA> ## 154 66.31338 29.96856 2 154 Afghanistan <NA> ## 155 66.28691 29.92002 2 155 Afghanistan <NA> ## 156 66.23125 29.86572 2 156 Afghanistan <NA> ## 157 66.17706 29.83559 2 157 Afghanistan <NA> ## 158 65.96162 29.77891 2 158 Afghanistan <NA> ## 159 65.66621 29.70132 2 159 Afghanistan <NA> ## 160 65.47099 29.65156 2 160 Afghanistan <NA> ## 161 65.18047 29.57763 2 161 Afghanistan <NA> ## 162 65.09550 29.55947 2 162 Afghanistan <NA> ## 163 64.91895 29.55278 2 163 Afghanistan <NA> ## 164 64.82734 29.56416 2 164 Afghanistan <NA> ## 165 64.70351 29.56714 2 165 Afghanistan <NA> ## 166 64.52110 29.56450 2 166 Afghanistan <NA> ## 167 64.39375 29.54433 2 167 Afghanistan <NA> ## [ reached 'max' / getOption("max.print") -- omitted 99172 rows ] ``` --- # A few fixes for better matching .question[ What does the following code do? ] ```r world_map <- map_data("world") %>% mutate(region = case_when( region == "UK" ~ "United Kingdom", region == "USA" ~ "United States", subregion == "Hong Kong" ~ "Hong Kong", TRUE ~ region ) ) ``` --- ## Map the world .midi[ ```r ggplot(world_map, aes(x = long, y = lat, group = group)) + geom_polygon(fill = "gray") + theme_minimal() ``` ![](w4_d2-joining-mapping_files/figure-html/unnamed-chunk-39-1.png)<!-- --> ] --- ## Join fisheries and world map .pull-left[ ```r fisheries %>% select(country) ``` ``` ## # A tibble: 82 x 1 ## country ## <chr> ## 1 Angola ## 2 Argentina ## 3 Australia ## 4 Bangladesh ## 5 Brazil ## 6 Cambodia ## 7 Cameroon ## 8 Canada ## 9 Chad ## 10 Chile ## # … with 72 more rows ``` ] .pull-right[ ```r world_map %>% select(region) ``` ``` ## region ## 1 Aruba ## 2 Aruba ## 3 Aruba ## 4 Aruba ## 5 Aruba ## 6 Aruba ## 7 Aruba ## 8 Aruba ## 9 Aruba ## 10 Aruba ## 11 Afghanistan ## 12 Afghanistan ## 13 Afghanistan ## 14 Afghanistan ## 15 Afghanistan ## 16 Afghanistan ## 17 Afghanistan ## 18 Afghanistan ## 19 Afghanistan ## 20 Afghanistan ## 21 Afghanistan ## 22 Afghanistan ## 23 Afghanistan ## 24 Afghanistan ## 25 Afghanistan ## 26 Afghanistan ## 27 Afghanistan ## 28 Afghanistan ## 29 Afghanistan ## 30 Afghanistan ## 31 Afghanistan ## 32 Afghanistan ## 33 Afghanistan ## 34 Afghanistan ## 35 Afghanistan ## 36 Afghanistan ## 37 Afghanistan ## 38 Afghanistan ## 39 Afghanistan ## 40 Afghanistan ## 41 Afghanistan ## 42 Afghanistan ## 43 Afghanistan ## 44 Afghanistan ## 45 Afghanistan ## 46 Afghanistan ## 47 Afghanistan ## 48 Afghanistan ## 49 Afghanistan ## 50 Afghanistan ## 51 Afghanistan ## 52 Afghanistan ## 53 Afghanistan ## 54 Afghanistan ## 55 Afghanistan ## 56 Afghanistan ## 57 Afghanistan ## 58 Afghanistan ## 59 Afghanistan ## 60 Afghanistan ## 61 Afghanistan ## 62 Afghanistan ## 63 Afghanistan ## 64 Afghanistan ## 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868 Albania ## 869 Albania ## 870 Albania ## 871 Albania ## 872 Albania ## 873 Albania ## 874 Albania ## 875 Albania ## 876 Albania ## 877 Albania ## 878 Finland ## 879 Finland ## 880 Finland ## 881 Finland ## 882 Finland ## 883 Finland ## 884 Finland ## 885 Finland ## 886 Finland ## 887 Finland ## 888 Finland ## 889 Finland ## 890 Finland ## 891 Finland ## 892 Finland ## 893 Finland ## 894 Finland ## 895 Finland ## 896 Finland ## 897 Finland ## 898 Finland ## 899 Finland ## 900 Finland ## 901 Finland ## 902 Finland ## 903 Finland ## 904 Finland ## 905 Finland ## 906 Finland ## 907 Finland ## 908 Finland ## 909 Finland ## 910 Finland ## 911 Finland ## 912 Finland ## 913 Finland ## 914 Finland ## 915 Finland ## 916 Finland ## 917 Finland ## 918 Finland ## 919 Finland ## 920 Finland ## 921 Finland ## 922 Finland ## 923 Finland ## 924 Finland ## 925 Finland ## 926 Finland ## 927 Finland ## 928 Finland ## 929 Finland ## 930 Andorra ## 931 Andorra ## 932 Andorra ## 933 Andorra ## 934 Andorra ## 935 Andorra ## 936 Andorra ## 937 Andorra ## 938 Andorra ## 939 Andorra ## 940 Andorra ## 941 Andorra ## 942 Andorra ## 943 Andorra ## 944 Andorra ## 945 Andorra ## 946 Andorra ## 947 Andorra ## 948 Andorra ## 949 United Arab Emirates ## 950 United Arab Emirates ## 951 United Arab Emirates ## 952 United Arab Emirates ## 953 United Arab Emirates ## 954 United Arab Emirates ## 955 United Arab Emirates ## 956 United Arab Emirates ## 957 United Arab Emirates ## 958 United Arab Emirates ## 959 United Arab Emirates ## 960 United Arab Emirates ## 961 United Arab Emirates ## 962 United Arab Emirates ## 963 United Arab Emirates ## 964 United Arab Emirates ## 965 United Arab Emirates ## 966 United Arab Emirates ## 967 United Arab Emirates ## 968 United Arab Emirates ## 969 United Arab Emirates ## 970 United Arab Emirates ## 971 United Arab Emirates ## 972 United Arab Emirates ## 973 United Arab Emirates ## 974 United Arab Emirates ## 975 United Arab Emirates ## 976 United Arab Emirates ## 977 United Arab Emirates ## 978 United Arab Emirates ## 979 United Arab Emirates ## 980 United Arab Emirates ## 981 United Arab Emirates ## 982 United Arab Emirates ## 983 United Arab Emirates ## 984 United Arab Emirates ## 985 United Arab Emirates ## 986 United Arab Emirates ## 987 United Arab Emirates ## 988 United Arab Emirates ## 989 United Arab Emirates ## 990 United Arab Emirates ## 991 United Arab Emirates ## 992 United Arab Emirates ## 993 United Arab Emirates ## 994 United Arab Emirates ## 995 United Arab Emirates ## 996 United Arab Emirates ## 997 United Arab Emirates ## 998 United Arab Emirates ## 999 United Arab Emirates ## 1000 United Arab Emirates ## [ reached 'max' / getOption("max.print") -- omitted 98338 rows ] ``` ] --- ## Join fisheries and world map ```r fisheries_map <- left_join(fisheries, world_map, by = c("country" = "region")) ``` ```r glimpse(fisheries_map) ``` ``` ## Observations: 72,685 ## Variables: 10 ## $ country <chr> "Angola", "Angola", "Angola", "Angola", "Angola", "A… ## $ capture <dbl> 486490, 486490, 486490, 486490, 486490, 486490, 4864… ## $ aquaculture <dbl> 655, 655, 655, 655, 655, 655, 655, 655, 655, 655, 65… ## $ total <dbl> 487145, 487145, 487145, 487145, 487145, 487145, 4871… ## $ continent <chr> "Africa", "Africa", "Africa", "Africa", "Africa", "A… ## $ long <dbl> 23.96650, 23.98828, 24.01006, 24.02559, 24.04141, 24… ## $ lat <dbl> -10.87178, -11.00283, -11.18477, -11.31563, -11.3741… ## $ group <dbl> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3… ## $ order <int> 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 43… ## $ subregion <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, … ``` --- ## Mapping fisheries .midi[ ```r ggplot(fisheries_map, mapping = aes(x = long, y = lat, group = group)) + geom_polygon(aes(fill = capture)) + scale_fill_viridis_c() + theme_minimal() ``` ![](w4_d2-joining-mapping_files/figure-html/unnamed-chunk-44-1.png)<!-- --> ] --- .question[ What is misleading about the following map? ] .midi[ <img src="w4_d2-joining-mapping_files/figure-html/unnamed-chunk-45-1.png" style="display: block; margin: auto;" /> ] --- ## Putting it altogether ```r ggplot() + geom_polygon(world_map, mapping = aes(x = long, y = lat, group = group), * fill = "lightgray") + geom_polygon(fisheries_map, mapping = aes(x = long, y = lat, group = group, * fill = capture)) + scale_fill_viridis_c() + theme_minimal() + theme(legend.position = "bottom") + labs( x = "", y = "", title = "Fisheries harvest by capture, 2016", subtitle = "Capture measured in tonnes", caption = "Source: bit.ly/2VrawTt" ) ``` --- <img src="w4_d2-joining-mapping_files/figure-html/unnamed-chunk-47-1.png" style="display: block; margin: auto;" /> --- ## Log scale ```r ggplot() + geom_polygon(world_map, mapping = aes(x = long, y = lat, group = group), fill = "lightgray") + geom_polygon(fisheries_map, mapping = aes(x = long, y = lat, group = group, * fill = log(capture))) + scale_fill_viridis_c() + theme_minimal() + theme(legend.position = "bottom") + labs( x = "", y = "", title = "Fisheries harvest by capture, 2016", subtitle = "Capture measured in logged tonnes", caption = "Source: bit.ly/2VrawTt" ) ``` --- ## Log scale <img src="w4_d2-joining-mapping_files/figure-html/unnamed-chunk-49-1.png" style="display: block; margin: auto;" /> --- ## Aquaculture ```r ggplot() + geom_polygon(world_map, mapping = aes(x = long, y = lat, group = group), fill = "lightgray") + geom_polygon(fisheries_map, mapping = aes(x = long, y = lat, group = group, * fill = log(aquaculture+1))) + scale_fill_viridis_c() + theme_minimal() + theme(legend.position = "bottom") + labs( x = "", y = "", title = "Fisheries harvest by aquaculture, 2016", subtitle = "Aquaculture measured in logged tonnes", fill = "log(aquaculture)", caption = "Source: bit.ly/2VrawTt" ) ``` --- <img src="w4_d2-joining-mapping_files/figure-html/unnamed-chunk-51-1.png" style="display: block; margin: auto;" /> --- <img src="w4_d2-joining-mapping_files/figure-html/unnamed-chunk-52-1.png" style="display: block; margin: auto;" />