class: center, middle, inverse, title-slide # Join data <br> 🤝 --- layout: true <div class="my-footer"> <span> <a href="https://rstd.io/bootcamper" target="_blank">rstd.io/bootcamper</a> </span> </div> --- class: middle # Working with multiple data frames --- class: middle .hand[You...] .huge-blue[have] .hand[multiple data frames] .huge-pink[want] .hand[to slice it, and dice it, and juice it, and process it] --- ## 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. <img src="img/fisheries-data.png" width="60%" style="display: block; margin: auto;" /> .center[ .hand[...] ] .footnote[ - Source: https://en.wikipedia.org/wiki/Fishing_industry_by_country ] --- ## Load 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() ## ) ``` --- ## First peek at the data .xsmall[ ```r skim(fisheries) ``` ``` ## ── Data Summary ──────────────────────── ## Values ## Name fisheries ## Number of rows 216 ## Number of columns 4 ## _______________________ ## Column type frequency: ## character 1 ## numeric 3 ## ________________________ ## Group variables ## ## ── Variable type: character ──────────────────────────────────────────────────────────────────────────────────────────────────────────────── ## skim_variable n_missing complete_rate min max empty n_unique whitespace ## 1 country 0 1 4 32 0 215 0 ## ## ── Variable type: numeric ────────────────────────────────────────────────────────────────────────────────────────────────────────────────── ## skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist ## 1 capture 0 1 421916. 1478638. 0 3280. 33797 221884. 17800000 ▇▁▁▁▁ ## 2 aquaculture 0 1 508368. 4496073. 0 25.2 1574. 25998 63700000 ▇▁▁▁▁ ## 3 total 0 1 930284. 5846301. 0 7270. 44648. 271901. 81500000 ▇▁▁▁▁ ``` ] --- ## Some summary stats ```r fisheries %>% summarise( mean_cap = mean(capture), mean_aqc = mean(aquaculture), mean_tot = mean(total) ) ``` ``` ## # A tibble: 1 x 3 ## mean_cap mean_aqc mean_tot ## <dbl> <dbl> <dbl> ## 1 421916. 508368. 930284. ``` -- <br> .hand[ well, that was boring... ] --- ## A new approach! ```r fisheries %>% summarise(across(capture:total, mean)) ``` ``` ## # A tibble: 1 x 3 ## capture aquaculture total ## <dbl> <dbl> <dbl> ## 1 421916. 508368. 930284. ``` -- .hand[and once we learn more about data types...] ```r fisheries %>% summarise(across(is.numeric, mean)) ``` ``` ## # A tibble: 1 x 3 ## capture aquaculture total ## <dbl> <dbl> <dbl> ## 1 421916. 508368. 930284. ``` --- .discussion[ The (not-so-great) visualization below shows the distribution of fishery harvest of countries for 2016, by capture and aquaculture. What are some ways you would improve this visualization? Note that countries whose total harvest was less than 100,000 tons are not included in the 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. --- .huge-pink[goal:] .hand-blue[calculate summary statistics at the continent level and visualize them] --- ## Data prep ```r continents <- read_csv("data/continents.csv") ``` 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 ## # … with 76 more rows ``` --- class: 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 ## # … with 76 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 ## # … with 239 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 - `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[ <!-- --> ] --- ## `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[ <!-- --> ] --- ## `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[ <!-- --> ] --- ## `anti_join()` .pull-left[ ```r anti_join(x, y) ``` ``` ## Joining, by = "value" ``` ``` ## # A tibble: 1 x 1 ## value ## <dbl> ## 1 3 ``` ] .pull-right[ <!-- --> ] --- .discussion[ 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 ## # … with 76 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 ## # … with 239 more rows ``` ] --- ## Join fisheries and continents ```r fisheries <- left_join(fisheries, continents) ``` ``` ## Joining, by = "country" ``` -- .discussion[ 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 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> ``` --- .discussion[ What does the following code do? ] ```r fisheries %>% mutate(aquaculture_perc = aquaculture / total) ``` --- .your-turn[ - Go to RStudio Cloud and start the second assignment: `04 - Join Data` - Open the first R Markdown file: `fisheries.Rmd` - Knit the document and work on the Exercise 1. ] <div class="countdown" id="timer_5e94f203" style="right:0;bottom:0;" data-warnwhen="0"> <code class="countdown-time"><span class="countdown-digits minutes">10</span><span class="countdown-digits colon">:</span><span class="countdown-digits seconds">00</span></code> </div> .footnote[ RStudio Cloud workspace for this bootcamp is at [rstd.io/bootcamper-cloud](https://rstd.io/bootcamper-cloud). ] --- .your-turn[ - Go to RStudio Cloud and start the second assignment: `04 - Join Data` - Open the first R Markdown file: `fisheries.Rmd` - Knit the document and work on the Exercises 2 - 4. ] <div class="countdown" id="timer_5e94f0ac" style="right:0;bottom:0;" data-warnwhen="0"> <code class="countdown-time"><span class="countdown-digits minutes">10</span><span class="countdown-digits colon">:</span><span class="countdown-digits seconds">00</span></code> </div> .footnote[ RStudio Cloud workspace for this bootcamp is at [rstd.io/bootcamper-cloud](https://rstd.io/bootcamper-cloud). ] --- ## Visualize continent summary stats ```r ggplot(fisheries_summary, aes(x = continent, y = mean_ap)) + geom_col() ``` <!-- --> --- ## Improve visualization ```r ggplot(fisheries_summary, * aes(x = fct_reorder(continent, mean_ap), y = mean_ap)) + geom_col() ``` <!-- --> --- ## Improve visualization further ```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() ``` `\(\rightarrow\)` See next slide... --- <!-- --> --- .huge-pink[goal:] .hand-blue[map fishery data at the country level] --- ## 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 turns data from the **maps** package into 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> ``` --- ## Connect the dots <img src="img/connect-the-dots-australia.png" width="50%" style="display: block; margin: auto;" /> --- ## A few fixes for better matching .discussion[ 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() ``` <!-- --> ] --- ## 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 ## # … with 76 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 ## 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 ## 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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 Finland ## 931 Finland ## 932 Finland ## 933 Finland ## 934 Finland ## 935 Finland ## 936 Finland ## 937 Finland ## 939 Andorra ## 940 Andorra ## 941 Andorra ## 942 Andorra ## 943 Andorra ## 944 Andorra ## 945 Andorra ## 946 Andorra ## 947 Andorra ## 948 Andorra ## 949 Andorra ## 950 Andorra ## 951 Andorra ## 952 Andorra ## 953 Andorra ## 954 Andorra ## 955 Andorra ## 956 Andorra ## 957 Andorra ## 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 ## 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 ## 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 ## 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 ## 999 United Arab Emirates ## 1000 United Arab Emirates ## 1001 United Arab Emirates ## 1002 United Arab Emirates ## 1003 United Arab Emirates ## 1004 United Arab Emirates ## 1005 United Arab Emirates ## 1006 United Arab Emirates ## 1007 United Arab Emirates ## 1008 United Arab Emirates ## 1009 United Arab Emirates ## 1010 United Arab Emirates ## 1011 United Arab Emirates ## 1012 United Arab Emirates ## 1013 United Arab Emirates ## 1014 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", "Angola", "Angola", "A… ## $ capture <dbl> 486490, 486490, 486490, 486490, 486490, 486490, 486490, 486490… ## $ aquaculture <dbl> 655, 655, 655, 655, 655, 655, 655, 655, 655, 655, 655, 655, 65… ## $ total <dbl> 487145, 487145, 487145, 487145, 487145, 487145, 487145, 487145… ## $ continent <chr> "Africa", "Africa", "Africa", "Africa", "Africa", "Africa", "A… ## $ aquaculture_perc <dbl> 0.001344569, 0.001344569, 0.001344569, 0.001344569, 0.00134456… ## $ long <dbl> 23.96650, 23.98828, 24.01006, 24.02559, 24.04141, 24.04668, 24… ## $ lat <dbl> -10.87178, -11.00283, -11.18477, -11.31563, -11.37412, -11.405… ## $ group <dbl> 3, 3, 3, 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, 433, 434, 43… ## $ subregion <chr> NA, NA, NA, 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() ``` <!-- --> ] --- .discussion[ What is misleading about the following map? ] <img src="04-join-data_files/figure-html/unnamed-chunk-56-1.png" style="display: block; margin: auto;" /> --- ## Putting it altogether ```r ggplot() + # layer 1 geom_polygon(world_map, mapping = aes(x = long, y = lat, group = group), * fill = "lightgray") + # layer 2 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="04-join-data_files/figure-html/unnamed-chunk-58-1.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" ) ``` --- <img src="04-join-data_files/figure-html/unnamed-chunk-60-1.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="04-join-data_files/figure-html/unnamed-chunk-62-1.png" style="display: block; margin: auto;" /> --- <img src="04-join-data_files/figure-html/unnamed-chunk-63-1.png" style="display: block; margin: auto;" />