class: center, middle, inverse, title-slide # Visual Perception! ### Daniel Anderson ### Week 4 --- layout: true <script> feather.replace() </script> <div class="slides-footer"> <span> <a class = "footer-icon-link" href = "https://github.com/uo-datasci-specialization/c2-dataviz-2022/raw/main/static/slides/w4.pdf"> <i class = "footer-icon" data-feather="download"></i> </a> <a class = "footer-icon-link" href = "https://dataviz-2022.netlify.app/slides/w4.html"> <i class = "footer-icon" data-feather="link"></i> </a> <a class = "footer-icon-link" href = "https://github.com/uo-datasci-specialization/c2-dataviz-2022"> <i class = "footer-icon" data-feather="github"></i> </a> </span> </div> --- class: inverse-blue # Data viz in the wild * Ian * Zach * Ksenia ## Hava, Tingyu, and Dillon on deck --- class: inverse-red middle # Reminder Your final project proposals are due tonight at midnight. Please look at the syllabus for the requirements. --- # Agenda * Finish up on some text pattern matching/replacement stuff * Review Lab 1 * Aesthetic mappings and visual encodings of data * data/ink ratio * Some do's and don't's (which are all rules of 👍) * Lab 2 --- # Learning Objectives * Understand some of the basics of pattern matching for strings * Understand how decisions you make with your visualizations may help or hinder comprehension --- class: inverse-orange middle # String manipulations --- # Reminder: base vs tidyverse The tidyverse package is [{stringr}](https://stringr.tidyverse.org/) It is more consistent than base functions and occassionally faster However, I tend to prefer the base functions, and they are still more commonly seen "in the wild" than **stringr**. We'll therefore briefly cover both. --- # Special characters * `^`: Anchor - matches the start of a string * `$`: Anchor - matches the end of a string * `*`: Matches the preceding character **zero or more** times * `?`: Matches the preceding character **zero or one** times * `+`: Matches the preceding character **one or more** times * `{`: Used to specify number of matches, `a{n}`, `a{n,}`, and `a{n, m}` * `.`: Wildcard - matches any character * `|`: OR operator * `[`: Alternates, also used for character matching (e.g., `[:digit:]`) * `(`: Used for backreferencing, look aheads, or groups * `\`: Used to escape special characters --- # More detail Both of the below are good places to get more comprehensive information * [RStudio cheatsheet](https://raw.githubusercontent.com/rstudio/cheatsheets/main/strings.pdf) * [Regular expression](https://stringr.tidyverse.org/articles/regular-expressions.html) vignette --- # A few quick examples Please follow along ```r library(edld652) d <- get_data("EDFacts_acgr_lea_2011_2019") d ``` ``` ## # A tibble: 11,326 × 29 ## ALL_COHORT ALL_RATE CWD_COHORT CWD_RATE DATE_CUR ## <dbl> <chr> <dbl> <chr> <chr> ## 1 252 80 3 PS 03OCT15 ## 2 398 75 47 70-79 03OCT15 ## 3 1020 89 51 40-49 03OCT15 ## 4 750 91 35 60-69 03OCT15 ## 5 128 55-59 15 LT50 03OCT15 ## 6 166 90-94 9 GE50 03OCT15 ## # … with 11,320 more rows, and 24 more variables: ## # ECD_COHORT <dbl>, ECD_RATE <chr>, ## # FIPST <chr>, FILEURL <chr>, LEAID <chr>, ## # LEANM <chr>, LEP_COHORT <dbl>, ## # LEP_RATE <chr>, MAM_COHORT <dbl>, ## # MAM_RATE <chr>, MAS_COHORT <dbl>, ## # MAS_RATE <chr>, MBL_COHORT <dbl>, … ``` --- # Challenge ### Do three things: * Create a new variable that identifies if the LEA is associated with a city or a county * Drop "City" or "County" from the LEA name (e.g., "Albertville City" would be "Albertville") * Replace all `.` with `--DOT--` in `FILEURL` (so they are not actual links) You'll need `base::gsub()` or `stringr::str_replace_all()`
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--- # City or county Ideas? -- ```r d <- d %>% mutate(county = grepl("county$", tolower(LEANM))) ``` -- Another option ```r d <- d %>% mutate(county = grepl("[Cc]ounty$", LEANM)) ``` --- # Quick Check ```r d %>% select(LEANM, county) %>% print(n = 15) ``` ``` ## # A tibble: 11,326 × 2 ## LEANM county ## <chr> <lgl> ## 1 Albertville City FALSE ## 2 Marshall County TRUE ## 3 Hoover City FALSE ## 4 Madison City FALSE ## 5 Leeds City FALSE ## 6 Boaz City FALSE ## 7 Trussville City FALSE ## 8 Alexander City FALSE ## 9 Andalusia City FALSE ## 10 Anniston City FALSE ## 11 Arab City FALSE ## 12 Athens City FALSE ## 13 Attalla City FALSE ## 14 Auburn City FALSE ## 15 Autauga County TRUE ## # … with 11,311 more rows ``` --- # Remove city/county * Lots of ways to do this. Use `base::gsub()` or `stringr::str_replace_all()` * Replace everything after the space with nothing -- ```r d %>% select(LEANM) %>% mutate(new_name = gsub(" .+", "", LEANM)) ``` ``` ## # A tibble: 11,326 × 2 ## LEANM new_name ## <chr> <chr> ## 1 Albertville City Albertville ## 2 Marshall County Marshall ## 3 Hoover City Hoover ## 4 Madison City Madison ## 5 Leeds City Leeds ## 6 Boaz City Boaz ## # … with 11,320 more rows ``` -- You can also use `"\\s"` to mean "space" --- # Another way There are other ways too, of course ```r d %>% select(LEANM) %>% mutate(new_name = gsub(" City$| County$", "", LEANM)) ``` ``` ## # A tibble: 11,326 × 2 ## LEANM new_name ## <chr> <chr> ## 1 Albertville City Albertville ## 2 Marshall County Marshall ## 3 Hoover City Hoover ## 4 Madison City Madison ## 5 Leeds City Leeds ## 6 Boaz City Boaz ## # … with 11,320 more rows ``` --- # Final step Handling the URLs. This is a bit artificial, but it illustrates escaping, which is important. * Remember `.` is a special character, so needs to be escaped * `\` itself is a special character so it needs to be escaped. Functionally, then, you escape special characters with `\\`, not `\`. --- ```r d %>% select(FILEURL) ``` ``` ## # A tibble: 11,326 × 1 ## # … with 11,320 more rows, and 1 more variable: ## # FILEURL <chr> ``` --- ```r d %>% select(FILEURL) %>% mutate(FILEURL = gsub("\\.", "--DOT--", FILEURL)) %>% as.data.frame() ``` ``` ## FILEURL ## 1 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 2 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 3 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 4 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 5 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 6 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 7 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 8 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 9 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 10 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 11 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 12 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 13 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 14 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 15 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 16 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 17 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 18 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 19 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 20 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 21 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 22 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 23 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 24 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 25 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 26 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 27 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 28 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 29 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 30 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 31 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 32 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 33 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 34 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 35 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 36 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 37 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 38 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 39 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 40 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 41 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 42 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 43 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 44 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 45 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 46 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 47 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 48 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 49 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 50 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 51 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 52 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 53 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 54 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 55 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 56 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 57 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 58 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 59 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 60 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 61 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 62 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 63 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 64 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 65 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 66 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 67 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 68 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 69 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 70 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 71 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 72 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 73 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 74 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 75 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 76 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 77 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 78 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 79 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 80 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 81 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 82 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 83 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 84 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 85 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 86 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 87 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 88 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 89 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 90 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 91 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 92 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 93 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 94 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 95 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 96 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 97 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 98 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 99 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 100 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 101 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 102 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 103 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 104 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 105 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 106 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 107 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 108 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 109 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 110 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 111 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 112 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 113 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 114 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 115 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 116 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 117 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 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868 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 869 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 870 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 871 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 872 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 873 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 874 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 875 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 876 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 877 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 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888 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 889 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 890 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 891 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 892 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 893 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 894 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 895 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 896 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 897 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 898 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 899 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 900 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 901 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 902 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 903 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 904 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 905 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 906 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 907 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 908 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 909 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 910 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 911 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 912 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 913 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 914 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 915 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 916 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 917 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 918 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 919 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 920 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 921 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 922 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 923 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 924 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 925 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 926 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 927 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 928 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 929 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 930 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 931 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 932 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 933 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 934 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 935 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 936 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 937 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 938 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 939 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 940 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 941 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 942 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 943 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 944 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 945 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 946 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 947 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 948 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 949 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 950 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 951 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 952 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 953 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 954 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 955 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 956 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 957 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 958 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 959 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 960 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 961 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 962 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 963 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 964 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 965 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 966 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 967 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 968 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 969 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 970 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 971 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 972 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 973 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 974 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 975 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 976 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 977 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 978 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 979 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 980 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 981 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 982 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 983 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 984 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 985 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 986 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 987 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 988 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 989 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 990 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 991 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 992 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 993 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 994 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 995 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 996 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 997 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 998 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 999 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## 1000 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv ## [ reached 'max' / getOption("max.print") -- omitted 10326 rows ] ``` --- # A few more examples ```r some_strings <- c("abc", "a23", "def", "Ad6") ``` -- Remove the digits ```r gsub("\\d", "", some_strings) ``` ``` ## [1] "abc" "a" "def" "Ad" ``` -- Extract the digits ```r gsub("\\D", "", some_strings) ``` ``` ## [1] "" "23" "" "6" ``` --- Starts with A ```r grepl("^[aA]", some_strings) ``` ``` ## [1] TRUE TRUE FALSE TRUE ``` --- # There's lots more * Please checkout the vignette, in particular * I end up using back-referencing a lot -- ```r cities <- c("Eugene, Oregon", "Denver, Colorado") gsub( "^(.+), (.+)$", # pattern with two groupings "The state of \\2 includes the city of \\1", # reference the groups cities) ``` ``` ## [1] "The state of Oregon includes the city of Eugene" ## [2] "The state of Colorado includes the city of Denver" ``` --- class: inverse-blue middle # Review Lab 1 --- class: inverse-red middle # Visual perception --- class: inverse-red center middle # Disclaimer ### I'm not a psychologist I don't really know why we perceive things certain ways. I mainly care that we do, and that your visualizations should account for them. --- # Visual Cues .footnote[Taken from *Modern Data Science with R*, p. 15] * **Position:** *Numeric*. Where in relation to other things? * **Length:** *Numeric*. How big (in one dimension)? * **Angle:** *Numeric*. How wide? Parallel to something else? * **Direction:** *Numeric*. At what slope? In a time series, going up or down? --- # Visual Cues .footnote[Taken from *Modern Data Science with R*, p. 15] * **Shape:** *Categorical*. Belonging to which group? * **Area:** *Numeric*. How big (in two dimensions)? * **Volume:** *Numeric*. How big (in three dimensions)? * **Shade:** *Numeric or Categorical*. To what extent? How Severely? * **Color:** *Numeric or Categorical*. To what extent? How Severely? --- class: middle center # Encoding data ![](w4_files/figure-html/data-encoding-1.png)<!-- --> --- # Other elements to consider * Text + How is the text displayed (e.g., font, face, location)? + What is the purpose of the text? -- * Transparency + Are there overlapping pieces? + Can transparency help? -- * Type of data + Continuous/categorical + Which can be mapped to each aesthetic? - e.g., shape and line type can only be mapped to categorical data, whereas color and size can be mapped to either. --- # Talk with a neighbor How would you encode each column of data? | Month | Day | Location | Station ID | Temperature | |:-----:|:---:|:-------------|-------------| :----------:| | Jan | 1 | Chicago | USW00014819 | 25.6 | | Jan | 1 | San Diego | USW00093107 | 55.2 | | Jan | 1 | Houston | USW00012918 | 53.9 | | Jan | 1 | Death Valley | USC00042319 | 51.0 | | Jan | 2 | Chicago | USW00014819 | 25.5 | | Jan | 2 | San Diego | USW00093107 | 55.3 | | Jan | 2 | Houston | USW00012918 | 53.8 | | Jan | 2 | Death Valley | USC00042319 | 51.2 | | Jan | 3 | Chicago | USW00014819 | 25.3 | .footnote[You can assume that month and day can be collapsed to a single *date* column] --- # Scales > A scale defines a unique mapping between data and aesthetics. Importantly, a scale must be one-to-one, such that for each specific data value there is exactly one aesthetics value and vice versa. If a scale isn't one-to-one, then the data visualization becomes ambiguous. -- * Which data values correspond to specific aesthetic values? --- class: middle center # Basic Scales ![](w4_files/figure-html/scales-wilke-1.png)<!-- --> --- # Putting it to practice ![](w4_files/figure-html/temp-change-1.png)<!-- --> --- # Basic code for previous plot ```r ggplot(temps_long, aes(date, temperature)) + geom_line(aes(color = location)) ``` ![](w4_files/figure-html/unnamed-chunk-18-1.png)<!-- --> --- # Change colors If you want to change the colors on the previous plot, you have to change the colors of the scale for the color mapping. -- In other words, color is being mapped to data, and you have to change the color scale. -- ```r ggplot(temps_long, aes(date, temperature)) + geom_line(aes(color = location)) + scale_color_brewer(palette = "Dark2") ``` --- ![](w4_files/figure-html/unnamed-chunk-20-1.png)<!-- --> --- # One more note on colors There are lots of different scales and some work better than others. We'll talk about them more next week. -- You **do not** use `scale_color_*()` if you are not mapping data to color -- Make sure to keep straight `scale_color_*()` and `scale_fill_*()` --- class: inverse-orange middle # Alternative represenation Can you think of other ways to show this relation? --- # Alternative representation ### Same plot as before, but with different scales ![](w4_files/figure-html/temp-change2-1.png)<!-- --> --- # Basic code for previous plot ```r temps_long %>% group_by(location, month) %>% summarize(temp = mean(temperature)) %>% ggplot(aes(month, location)) + geom_tile(aes(fill = temp), color = "white") + coord_fixed() ``` ![](w4_files/figure-html/unnamed-chunk-21-1.png)<!-- --> --- # Change the fill ```r temps_long %>% group_by(location, month) %>% summarize(temp = mean(temperature)) %>% ggplot(aes(month, location)) + geom_tile(aes(fill = temp), color = "white") + coord_fixed() + scico::scale_fill_scico(palette = "tokyo") ``` ![](w4_files/figure-html/unnamed-chunk-22-1.png)<!-- --> --- # Comparison * Both represent three scales + Two position scales (x/y axis) + One color scale (categorical for the first, continuous for the second) * More scales are possible --- class: center middle ![](w4_files/figure-html/five-scales-1.png)<!-- --> --- background-image:url(http://socviz.co/dataviz-pdfl_files/figure-html4/ch-01-multichannel-1.png) background-size:contain ## Additional scales can become lost without high structure in the data --- class: inverse-blue center middle # Data ink ratio --- # What is it? -- > ## Above all else, show the data <br> \-Edward Tufte -- * Data-Ink Ratio = Ink devoted to the data / total ink used to produce the figure -- * Common goal: Maximize the data-ink ratio --- # Example ![](img/six-boxplots.png) -- * First thought might be - Cool! --- class: inverse-red background-image:url(https://theamericanreligion.files.wordpress.com/2012/10/lee-corso-sucks.jpeg?w=660) background-size:cover --- # Minimize cognitive load * Empirically, Tufte's plot was **the most difficult** for viewers to interpret. -- * Visual cues (labels, gridlines) *reduce* the data-ink ratio, but can also reduce cognitive load. --- # Another example ### Which do you prefer? .pull-left[ ![](w4_files/figure-html/h3_bad-1.png)<!-- --> ] .pull-right[ ![](w4_files/figure-html/h3_good-1.png)<!-- --> ] --- # Advice from Wilke > Whenever possible, visualize your data with solid, colored shapes rather than with lines that outline those shapes. Solid shapes are more easily perceived, are less likely to create visual artifacts or optical illusions, and do more immediately convey amounts than do outlines. --- # Another example .pull-left[ ![](w4_files/figure-html/iris_lines-1.png)<!-- --> ] .pull-right[ ![](w4_files/figure-html/iris_colored_lines-1.png)<!-- --> ] --- class: center middle ![](w4_files/figure-html/iris_filled-1.png)<!-- --> --- class: inverse-red middle background-image:url(img/monstrous-costs.png) background-size: contain ## This? --- # The takeaway? * It can often be helpful to remove "chart junk" + Remove background + Unnecessary frills + Certainly don't use 3D when it's not clearly warranted -- ### But... * Infographics can often be more memorable --- # Compromise? In some cases, it may be easy and more memorable to use glyphs instead of points or squares -- * Install packages ```r install.packages("extrafont") remotes::install_github("wch/extrafontdb") remotes::install_github("wch/Rttf2pt1") remotes::install_github("hrbrmstr/waffle") ``` -- * Create data ```r parts <- c(`Un-breached\nUS Population` = (318 - 11 - 79), `Premera` = 11, `Anthem` = 79) ``` --- # Basic plot ```r library(waffle) waffle(parts, rows = 8, colors = c("#969696", "#1879bf", "#009bda")) ``` ![](w4_files/figure-html/waffle1-1.png)<!-- --> --- # Glyph plot Doesn't seem to work anymore...🤷♂️ * Download and install `fontawesome-webfont.ttf` on your machine locally (see [here](https://fontawesome.com/v4.7.0/)) * Import new fonts (including glyphs, via font awesome) ```r library(extrafont) font_import() loadfonts() ``` ```r waffle(parts/10, rows = 3, colors = c("#969696", "#1879bf", "#009bda"), * use_glyph = "medkit") ``` --- # Should look like this ![](https://github.com/hrbrmstr/waffle/raw/master/README_files/figure-gfm/medkit-1.png) -- Despite glyphs not (easily) working anymore, I still recommend you [check it out](https://github.com/hrbrmstr/waffle). It's a neat package and does have some integration with ggplot2 now. --- class: inverse center middle background-image: url(https://pbs.twimg.com/media/DxiychAVYAAJ4CY.jpg) background-size: 100% 100% --- # You can create them! * Create plots * Use illustrator or similar to put them together * Add some annotations * Consider using glyphs for greater memory * You can do a lot in R without going to illustrator etc. by just using [**{patchwork}**](https://patchwork.data-imaginist.com) or [**{cowplot}**](https://wilkelab.org/cowplot/index.html) --- class: center middle # More visual properties ![](http://socviz.co/assets/ch-01-perception-adelson-checkershow.jpg) --- # Or in real life <iframe width="560" height="315" src="https://www.youtube.com/embed/z9Sen1HTu5o" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> --- background-image:url(http://socviz.co/dataviz-pdfl_files/figure-html4/ch-01-dual-search-1.png) background-size:contain ## Where's the blue circle in each plot? --- background-image:url(http://socviz.co/assets/ch-01-cleveland-task-types.png) background-size:contain # What are we good at perceiving? --- background-image:url(http://socviz.co/assets/ch-01-heer-bostock-results.png) background-size:contain --- background-image:url(http://socviz.co/assets/ch-01-channels-for-cont-data-vertical.png) background-size:contain ## Ordered ## data ## mappings: ## Ranked --- # Unordered data mappings ![](http://socviz.co/assets/ch-01-channels-for-cat-data-vertical.png) --- class: inverse-blue center middle # Some things to avoid --- # Line drawings ### As discussed earlier .pull-left[ ![](w4_files/figure-html/iris_lines2-1.png)<!-- --> ] .pull-right[ ![](w4_files/figure-html/iris_filled2-1.png)<!-- --> ] --- # Much worse ### Unnecessary 3D .pull-left[ ![](img/3d-pie-10-v2.png) ] .pull-right[ ![](img/3d-pie-20-v2.png) ] --- # Much worse ### Unnecessary 3D .pull-left[ ![](img/3d-pie-40-v2.png) ] .pull-right[ ![](img/3d-pie-80-v2.png) ] --- # Horrid example ### Used relatively regularly ![](img/3d_bar.png) --- # Pie charts w/lots of categories ![](https://serialmentor.com/dataviz/visualizing_proportions_files/figure-html/marketshare-pies-1.png) --- # Alternative representation ![](https://serialmentor.com/dataviz/visualizing_proportions_files/figure-html/marketshare-side-by-side-1.png) --- # A case for pie charts * `\(n\)` categories low, * differences are relatively large * familiar for some audiences ![](w4_files/figure-html/pie-1.png)<!-- --> --- # The anatomy of a pie chart Pie charts are just stacked bar charts with a radial coordinate system ![](w4_files/figure-html/stacked_bars_nopie-1.png)<!-- --> --- # Alternative represenation ![](w4_files/figure-html/horiz_stacked-1.png)<!-- --> --- # Or one of these ![](w4_files/figure-html/dodged_bars-1.png)<!-- -->![](w4_files/figure-html/dodged_bars-2.png)<!-- --> --- # Dual axes * One exception - if second axis is a direct transformation of the first + e.g., Miles/Kilometers, Fahrenheit/Celsius ![](img/dual_axes.png) --- # Another example ![](img/bedsheet-tangled.png) .footnote[See more examples [here](http://www.tylervigen.com/spurious-correlations)] --- # Truncated axes ![](img/truncated_axes.png) --- class: middle ![](img/truncated_axes2.png) --- # Not always a bad thing > It is tempting to lay down inflexible rules about what to do in terms of producing your graphs, and to dismiss people who don’t follow them as producing junk charts or lying with statistics. But **being honest with your data is a bigger problem than can be solved by rules of thumb** about making graphs. In this case there is a moderate level of agreement that bar charts should generally include a zero baseline (or equivalent) given that bars encode their variables as lengths. But it would be a mistake to think that a dot plot was by the same token deliberately misleading, just because it kept itself to the range of the data instead. --- # Bars ![](http://socviz.co/dataviz-pdfl_files/figure-html4/ch-01-bar-simple-1.png) --- # Points ![](http://socviz.co/dataviz-pdfl_files/figure-html4/ch-01-bar-simple-2.png) --- # Law school enrollments ![](http://socviz.co/dataviz-pdfl_files/figure-html4/ch-01-law-enrollments-1.png) --- # Start at zero ![](http://socviz.co/dataviz-pdfl_files/figure-html4/ch-01-law-enrollments-2.png) --- # Scaling issues ![](img/area_size.png) --- class: middle center # Poor binning choices ![](img/poor_binning.png) --- class: inverse-blue middle # Conclusions --- # Essentially never * Use dual axes (unles they are direct transformations, just produce separate plots instead) * Use 3D unnecessarily -- # Be wary of + Truncated axes -- # Do * Minimize cognitive load * Be as clear as possible --- class: inverse-green middle # Lab 2