class: center, middle, inverse, title-slide # Tidy up your data science workflow with the tidyverse ## rstd.io/tidy-up-london --- ## tidyverse .pull-left[ - The **tidyverse** is an opinionated collection of R packages designed for data science. - All packages share an underlying design philosophy, grammar, and data structures. <br> <img src="img/tidyverse.png" width="50%" style="display: block; margin: auto;" /> ] .pull-right[ <img src="img/tidyverse-packages.png" width="100%" /> ] --- class: middle, center # Fisheries of the world --- 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. <br> .pull-left[ <img src="img/fisheries-data.png" width="100%" style="display: block; margin: auto;" /> ] .pull-right[ - Countries whose total harvest was less than 100,000 tons are not included in the visualization. - Source: [Fishing industry by country](https://en.wikipedia.org/wiki/Fishing_industry_by_country) ] --- .question[ What are some ways you would improve this visualization? ] <img src="img/fisheries.png" width="60%" style="display: block; margin: auto;" /> -- - Calculate summary statistics at the continent level and visualize them. - Map the data. --- ## Get the data ```r fisheries <- read_csv("data/fisheries.csv") ``` ``` ## Parsed with column specification: ## cols( ## country = col_character(), ## capture = col_double(), ## aquaculture = col_double(), ## total = col_double() ## ) ``` ```r names(fisheries) ``` ``` ## [1] "country" "capture" "aquaculture" "total" ``` --- ## Inspect the data .midi[ ```r fisheries ``` ``` ## # A tibble: 216 x 4 ## country capture aquaculture total ## <chr> <dbl> <dbl> <dbl> ## 1 Afghanistan 1000 1200 2200 ## 2 Albania 7886 950 8836 ## 3 Algeria 95000 1361 96361 ## 4 American Samoa 3047 20 3067 ## 5 Andorra 0 0 0 ## 6 Angola 486490 655 487145 ## 7 Antigua and Barbuda 3000 10 3010 ## 8 Argentina 755226 3673 758899 ## 9 Armenia 3758 16381 20139 ## 10 Aruba 142 0 142 ## # … with 206 more rows ``` ] --- ## Data prep Filter out countries whose total harvest was less than 100,000 tons since they are not included in the visualization: .pull-left[ ```r fisheries <- fisheries %>% filter(total > 100000) ``` ] .pull-right[ ```r 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 ``` ] --- ## Load continent data ```r continents <- read_csv("data/continents.csv") ``` ``` ## Parsed with column specification: ## cols( ## country = col_character(), ## continent = col_character() ## ) ``` --- 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 ``` something_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 - `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[ <!-- --> ] --- ## `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[ <!-- --> ] --- ## `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[ <!-- --> ] --- ## `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[ <!-- --> ] --- ## `anti_join()` .pull-left[ ```r anti_join(x, y) ``` ``` ## Joining, by = "value" ``` ``` ## # A tibble: 1 x 1 ## value ## <dbl> ## 1 3 ``` ] .pull-right[ <!-- --> ] --- .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: - Variables in the original fisheries dataset: ``` ## [1] "country" "capture" "aquaculture" "total" ``` - Variables in the continents dataset: ``` ## [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 Con… 237372 3161 2.41e5 <NA> ## 2 Hong Kong 142775 4258 1.47e5 <NA> ## 3 Myanmar 2072390 1017644 3.09e6 <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 <- fisheries %>% mutate(aquaculture_perc = aquaculture / total) ``` --- ## Continent-level statistics ```r fisheries_summary <- fisheries %>% group_by(continent) %>% summarise( min_ap = min(aquaculture_perc), mean_ap = mean(aquaculture_perc), max_ap = max(aquaculture_perc) ) fisheries_summary ``` ``` ## # A tibble: 5 x 4 ## continent min_ap mean_ap max_ap ## <chr> <dbl> <dbl> <dbl> ## 1 Africa 0 0.0943 0.803 ## 2 Americas 0 0.192 0.529 ## 3 Asia 0 0.367 0.782 ## 4 Europe 0.00682 0.165 0.618 ## 5 Oceania 0.0197 0.150 0.357 ``` --- ## Visualize continent summary stats ```r ggplot(fisheries_summary, aes(x = continent, y = mean_ap)) + geom_col() ``` <img src="tidy-up_files/figure-html/unnamed-chunk-37-1.png" width="2100" /> --- ## Improve visualization ```r ggplot(fisheries_summary, aes(y = fct_reorder(continent, mean_ap), x = mean_ap)) + geom_col() + * scale_x_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() ``` --- <img src="tidy-up_files/figure-html/unnamed-chunk-39-1.png" width="1500" /> --- 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() ``` <img src="tidy-up_files/figure-html/unnamed-chunk-42-1.png" width="3600" /> ] --- ## 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 ## 65 Afghanistan ## 66 Afghanistan ## 67 Afghanistan ## 68 Afghanistan ## 69 Afghanistan ## 70 Afghanistan ## 71 Afghanistan ## 72 Afghanistan ## 73 Afghanistan ## 74 Afghanistan ## 75 Afghanistan ## 76 Afghanistan ## 77 Afghanistan ## 78 Afghanistan ## 79 Afghanistan ## 80 Afghanistan ## 81 Afghanistan ## 82 Afghanistan ## 83 Afghanistan ## 84 Afghanistan ## 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758 Angola ## 759 Anguilla ## 760 Anguilla ## 761 Anguilla ## 762 Anguilla ## 763 Anguilla ## 764 Anguilla ## 765 Albania ## 766 Albania ## 767 Albania ## 768 Albania ## 769 Albania ## 770 Albania ## 771 Albania ## 772 Albania ## 773 Albania ## 774 Albania ## 775 Albania ## 776 Albania ## 777 Albania ## 778 Albania ## 779 Albania ## 780 Albania ## 781 Albania ## 782 Albania ## 783 Albania ## 784 Albania ## 785 Albania ## 786 Albania ## 787 Albania ## 788 Albania ## 789 Albania ## 790 Albania ## 791 Albania ## 792 Albania ## 793 Albania ## 794 Albania ## 795 Albania ## 796 Albania ## 797 Albania ## 798 Albania ## 799 Albania ## 800 Albania ## 801 Albania ## 802 Albania ## 803 Albania ## 804 Albania ## 805 Albania ## 806 Albania ## 807 Albania ## 808 Albania ## 809 Albania ## 810 Albania ## 811 Albania ## 812 Albania ## 813 Albania ## 814 Albania ## 815 Albania ## 816 Albania ## 817 Albania ## 818 Albania ## 819 Albania ## 820 Albania ## 821 Albania ## 822 Albania ## 823 Albania ## 824 Albania ## 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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) ``` ``` ## Rows: 72,685 ## Columns: 11 ## $ country <chr> "Angola", "Angola", "Angola", "Angola", "A… ## $ capture <dbl> 486490, 486490, 486490, 486490, 486490, 48… ## $ aquaculture <dbl> 655, 655, 655, 655, 655, 655, 655, 655, 65… ## $ total <dbl> 487145, 487145, 487145, 487145, 487145, 48… ## $ continent <chr> "Africa", "Africa", "Africa", "Africa", "A… ## $ aquaculture_perc <dbl> 0.001344569, 0.001344569, 0.001344569, 0.0… ## $ long <dbl> 23.96650, 23.98828, 24.01006, 24.02559, 24… ## $ lat <dbl> -10.87178, -11.00283, -11.18477, -11.31563… ## $ group <dbl> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, … ## $ order <int> 423, 424, 425, 426, 427, 428, 429, 430, 43… ## $ subregion <chr> 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() ``` <img src="tidy-up_files/figure-html/unnamed-chunk-47-1.png" width="3600" /> ] --- .question[ What is misleading about the following map? ] .midi[ <img src="tidy-up_files/figure-html/unnamed-chunk-48-1.png" width="3600" 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="tidy-up_files/figure-html/unnamed-chunk-50-1.png" width="3000" 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="tidy-up_files/figure-html/unnamed-chunk-52-1.png" width="3000" 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="tidy-up_files/figure-html/unnamed-chunk-54-1.png" width="3000" style="display: block; margin: auto;" /> --- <img src="tidy-up_files/figure-html/unnamed-chunk-55-1.png" width="3000" style="display: block; margin: auto;" /> --- class: center, middle # Instructional staff employment trends --- The American Association of University Professors (AAUP) is a nonprofit membership association of faculty and other academic professionals. [This report](https://www.aaup.org/sites/default/files/files/AAUP_Report_InstrStaff-75-11_apr2013.pdf) by the AAUP shows trends in instructional staff employees between 1975 and 2011, and contains an image very similar to the one given below. <img src="img/staff-employment.png" width="80%" style="display: block; margin: auto;" /> --- ## Data Each row in this dataset represents a faculty type, and the columns are the years for which we have data. The values are percentage of hires of that type of faculty for each year. ```r staff <- read_csv("data/instructional-staff.csv") staff ``` ``` ## # A tibble: 5 x 12 ## faculty_type `1975` `1989` `1993` `1995` `1999` `2001` `2003` `2005` ## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 Full-Time T… 29 27.6 25 24.8 21.8 20.3 19.3 17.8 ## 2 Full-Time T… 16.1 11.4 10.2 9.6 8.9 9.2 8.8 8.2 ## 3 Full-Time N… 10.3 14.1 13.6 13.6 15.2 15.5 15 14.8 ## 4 Part-Time F… 24 30.4 33.1 33.2 35.5 36 37 39.3 ## 5 Graduate St… 20.5 16.5 18.1 18.8 18.7 19 20 19.9 ## # … with 3 more variables: `2007` <dbl>, `2009` <dbl>, `2011` <dbl> ``` --- ## Recreate the visualization In order to recreate this visualization we need to first reshape the data to have one variable for faculty type and one variable for year. In other words, we will convert the data from the long format to wide format. But before we do so... .question[ If the long data will have a row for each year/faculty type combination, and there are 5 faculty types and 11 years of data, how many rows will the data have? ] --- class: center, middle <img src="img/pivot.gif" width="80%" style="display: block; margin: auto;" /> --- ## `pivot_*()` functions <!-- --> --- ## `pivot_longer()` ```r pivot_longer(data, cols, names_to = "name", values_to = "value") ``` - The first argument is `data` as usual. - The second argument, `cols`, is where you specify which columns to pivot into longer format -- in this case all columns except for the `faculty_type` - The third argument, `names_to`, is a string specifying the name of the column to create from the data stored in the column names of data -- in this case `year` - The fourth argument, `values_to`, is a string specifying the name of the column to create from the data stored in cell values, in this case `percentage` --- ## Pivot staff data .midi[ ```r staff_long <- staff %>% pivot_longer(cols = -faculty_type, names_to = "year", values_to = "percentage") %>% mutate(percentage = as.numeric(percentage)) staff_long ``` ``` ## # A tibble: 55 x 3 ## faculty_type year percentage ## <chr> <chr> <dbl> ## 1 Full-Time Tenured Faculty 1975 29 ## 2 Full-Time Tenured Faculty 1989 27.6 ## 3 Full-Time Tenured Faculty 1993 25 ## 4 Full-Time Tenured Faculty 1995 24.8 ## 5 Full-Time Tenured Faculty 1999 21.8 ## 6 Full-Time Tenured Faculty 2001 20.3 ## 7 Full-Time Tenured Faculty 2003 19.3 ## 8 Full-Time Tenured Faculty 2005 17.8 ## 9 Full-Time Tenured Faculty 2007 17.2 ## 10 Full-Time Tenured Faculty 2009 16.8 ## # … with 45 more rows ``` ] --- .question[ This doesn't look quite right, how would you fix it? ] .small[ ```r staff_long %>% ggplot(aes(x = percentage, y = year, color = faculty_type)) + geom_col(position = "dodge") ``` <img src="tidy-up_files/figure-html/unnamed-chunk-61-1.png" width="1500" /> ] --- .midi[ ```r staff_long %>% ggplot(aes(x = percentage, y = year, fill = faculty_type)) + geom_col(position = "dodge") ``` <img src="tidy-up_files/figure-html/unnamed-chunk-62-1.png" width="1500" /> ] --- ## Some improvement... .midi[ ```r staff_long %>% ggplot(aes(x = percentage, y = year, fill = faculty_type)) + geom_col() ``` <img src="tidy-up_files/figure-html/unnamed-chunk-63-1.png" width="1500" /> ] --- ## More improvement .midi[ ```r staff_long %>% ggplot(aes(x = year, y = percentage, group = faculty_type, color = faculty_type)) + geom_line() + theme_minimal() ``` <img src="tidy-up_files/figure-html/unnamed-chunk-64-1.png" width="2100" /> ] --- .midi[ <img src="tidy-up_files/figure-html/unnamed-chunk-65-1.png" width="2100" /> ] --- .midi[ ```r staff_long %>% mutate(part_time = if_else(faculty_type == "Part-Time Faculty", "Part-Time Faculty", "Other Faculty")) %>% ggplot(aes(x = year, y = percentage/100, group = faculty_type, color = part_time)) + geom_line() + scale_color_manual(values = c("gray", "red")) + scale_y_continuous(labels = percent_format(accuracy = 1)) + theme_minimal() + labs( title = "Instructional staff employment trends", x = "Year", y = "Percentage", color = "" ) + theme(legend.position = "bottom") ``` ] --- class: middle, center background-image: url("img/greg-rakozy-oMpAz-DN-9I-unsplash.jpg") <font color="white" style="bold" size="100px">Learn more at tidyverse.org!</font> .footnote[ Photo by Greg Rakozy on Unsplash. ]