class: center, middle, inverse, title-slide .title[ # Data Wrangling III ] .subtitle[ ## STAT 4380 ] .author[ ### Katie Fitzgerald, adpated from datasciencebox.org ] --- layout: true <div class="my-footer"> <span> <a href="https://nova-stat-4380.netlify.app" target="_blank">nova-stat-4380.netlify.app</a> </span> </div> --- # Goals Today we will practice: + pivoting data frames to wide or long format + planning a data wrangling pipeline to accomplished a desired task/visualization + computing (conditional) proportions using `dplyr` verbs --- class: middle # Case study: Grocery sales --- ## Grocery sales - Have: <!-- - Purchases: One row per customer per item, listing purchases they made --> - Customers: one row per customer, and a column for each item they purchased - Prices: one row per item in the store, listing their prices - Want: one row per customer per item - How much revenue was brought in per item (e.g. milk)? - How much was spent per person? - How much total revenue was brought into the store? -- .pull-left[ ``` r customers ``` ``` ## # A tibble: 2 × 4 ## customer_id item_1 item_2 item_3 ## <dbl> <chr> <chr> <chr> ## 1 1 bread milk banana ## 2 2 milk toilet paper <NA> ``` ] .pull-right[ ``` r prices ``` ``` ## # A tibble: 5 × 2 ## item price ## <chr> <dbl> ## 1 avocado 0.5 ## 2 banana 0.15 ## 3 bread 1 ## 4 milk 0.8 ## 5 toilet paper 3 ``` ] --- class: middle # .hand[We...] .huge[.green[have]] .hand[data organised in an unideal way for our analysis] .huge[.pink[want]] .hand[to reorganise the data to carry on with our analysis] --- ## Data: Sales <br> .pull-left[ ### .green[We have...] ``` ## # A tibble: 2 × 4 ## customer_id item_1 item_2 item_3 ## <dbl> <chr> <chr> <chr> ## 1 1 bread milk banana ## 2 2 milk toilet paper <NA> ``` ] -- .pull-right[ ### .pink[We want...] ``` ## # A tibble: 6 × 3 ## customer_id item_no item ## <dbl> <chr> <chr> ## 1 1 item_1 bread ## 2 1 item_2 milk ## 3 1 item_3 banana ## 4 2 item_1 milk ## 5 2 item_2 toilet paper ## 6 2 item_3 <NA> ``` ] --- ## A grammar of data tidying .pull-left[ <img src="img/tidyr-part-of-tidyverse.png" alt="" width="60%" style="display: block; margin: auto;" /> ] .pull-right[ The goal of tidyr is to help you tidy your data via - pivoting for going between wide and long data - splitting and combining character columns - nesting and unnesting columns - clarifying how `NA`s should be treated ] --- class: middle # Pivoting data --- ## Not this... <img src="img/pivot.gif" alt="" width="70%" style="display: block; margin: auto;" /> --- ## but this! .center[ <img src="img/tidyr-longer-wider.gif" alt="" width="45%" style="background-color: #FDF6E3" style="display: block; margin: auto;" /> ] --- ## Wider vs. longer .pull-left[ ### .green[wider] more columns ``` ## # A tibble: 2 × 4 ## customer_id item_1 item_2 item_3 ## <dbl> <chr> <chr> <chr> ## 1 1 bread milk banana ## 2 2 milk toilet paper <NA> ``` ] -- .pull-right[ ### .pink[longer] more rows ``` ## # A tibble: 6 × 3 ## customer_id item_no item ## <dbl> <chr> <chr> ## 1 1 item_1 bread ## 2 1 item_2 milk ## 3 1 item_3 banana ## 4 2 item_1 milk ## 5 2 item_2 toilet paper ## 6 2 item_3 <NA> ``` ] --- ## `pivot_longer()` .pull-left[ - `data` (as usual) ] .pull-right[ ``` r pivot_longer( * data, cols, names_to = "name", values_to = "value" ) ``` ] --- ## `pivot_longer()` .pull-left[ - `data` (as usual) - `cols`: columns to pivot into longer format ] .pull-right[ ``` r pivot_longer( data, * cols, names_to = "name", values_to = "value" ) ``` ] --- ## `pivot_longer()` .pull-left[ - `data` (as usual) - `cols`: columns to pivot into longer format - `names_to`: name of the column where column names of pivoted variables go (character string) ] .pull-right[ ``` r pivot_longer( data, cols, * names_to = "name", values_to = "value" ) ``` ] --- ## `pivot_longer()` .pull-left[ - `data` (as usual) - `cols`: columns to pivot into longer format - `names_to`: name of the column where column names of pivoted variables go (character string) - `values_to`: name of the column where data in pivoted variables go (character string) ] .pull-right[ ``` r pivot_longer( data, cols, names_to = "name", * values_to = "value" ) ``` ] --- ## Customers `\(\rightarrow\)` purchases ``` r purchases <- customers |> * pivot_longer( * cols = item_1:item_3, # variables item_1 to item_3 * names_to = "item_no", # column names -> new column called item_no * values_to = "item" # values in columns -> new column called item * ) purchases ``` ``` ## # A tibble: 6 × 3 ## customer_id item_no item ## <dbl> <chr> <chr> ## 1 1 item_1 bread ## 2 1 item_2 milk ## 3 1 item_3 banana ## 4 2 item_1 milk ## 5 2 item_2 toilet paper ## 6 2 item_3 <NA> ``` --- ## Why pivot? Most likely, because the next step of your analysis needs it -- .pull-left[ ``` r prices ``` ``` ## # A tibble: 5 × 2 ## item price ## <chr> <dbl> ## 1 avocado 0.5 ## 2 banana 0.15 ## 3 bread 1 ## 4 milk 0.8 ## 5 toilet paper 3 ``` ] .pull-right[ ``` r purchases |> * left_join(prices) ``` ``` ## # A tibble: 6 × 4 ## customer_id item_no item price ## <dbl> <chr> <chr> <dbl> ## 1 1 item_1 bread 1 ## 2 1 item_2 milk 0.8 ## 3 1 item_3 banana 0.15 ## 4 2 item_1 milk 0.8 ## 5 2 item_2 toilet paper 3 ## 6 2 item_3 <NA> NA ``` ] --- ## Grocery sales .panelset[ .panel[.panel-name[Total revenue] .pull-left[ ``` r purchases |> * left_join(prices) ``` ``` ## # A tibble: 6 × 4 ## customer_id item_no item price ## <dbl> <chr> <chr> <dbl> ## 1 1 item_1 bread 1 ## 2 1 item_2 milk 0.8 ## 3 1 item_3 banana 0.15 ## 4 2 item_1 milk 0.8 ## 5 2 item_2 toilet paper 3 ## 6 2 item_3 <NA> NA ``` ] .pull-right[ ``` r purchases |> left_join(prices) |> * summarise(total_revenue = sum(price)) ``` ``` ## # A tibble: 1 × 1 ## total_revenue ## <dbl> ## 1 NA ``` ] ] .panel[.panel-name[Revenue per customer] .pull-left[ ``` r purchases |> left_join(prices) ``` ``` ## # A tibble: 6 × 4 ## customer_id item_no item price ## <dbl> <chr> <chr> <dbl> ## 1 1 item_1 bread 1 ## 2 1 item_2 milk 0.8 ## 3 1 item_3 banana 0.15 ## 4 2 item_1 milk 0.8 ## 5 2 item_2 toilet paper 3 ## 6 2 item_3 <NA> NA ``` ] .pull-right[ ``` r purchases |> left_join(prices) |> * group_by(customer_id) |> summarise(total_revenue = sum(price)) ``` ``` ## # A tibble: 2 × 2 ## customer_id total_revenue ## <dbl> <dbl> ## 1 1 1.95 ## 2 2 NA ``` ] ] ] --- ## Purchases `\(\rightarrow\)` customers .pull-left-narrow[ - `data` (as usual) - `names_from`: which column in the long format contains the what should be column names in the wide format - `values_from`: which column in the long format contains the what should be values in the new columns in the wide format ] .pull-right-wide[ ``` r purchases |> * pivot_wider( * names_from = item_no, * values_from = item * ) ``` ``` ## # A tibble: 2 × 4 ## customer_id item_1 item_2 item_3 ## <dbl> <chr> <chr> <chr> ## 1 1 bread milk banana ## 2 2 milk toilet paper <NA> ``` ] --- class: middle # Case study: Approval rating of Donald Trump --- <img src="img/trump-approval.png" alt="" width="70%" style="display: block; margin: auto;" /> .footnote[ Source: [FiveThirtyEight](https://projects.fivethirtyeight.com/trump-approval-ratings/adults/) ] --- ## Goal Write psuedocode required to create this visualization <img src="08-data-wrangling-3_files/figure-html/unnamed-chunk-25-1.png" alt="" width="80%" style="display: block; margin: auto;" /> --- ## Data .pull-left-wide[ ``` r trump ``` ``` ## # A tibble: 2,702 × 4 ## subgroup date approval disapproval ## <chr> <date> <dbl> <dbl> ## 1 Voters 2020-10-04 44.7 52.2 ## 2 Adults 2020-10-04 43.2 52.6 ## 3 Adults 2020-10-03 43.2 52.6 ## 4 Voters 2020-10-03 45.0 51.7 ## 5 Adults 2020-10-02 43.3 52.4 ## 6 Voters 2020-10-02 44.5 52.1 ## 7 Voters 2020-10-01 44.1 52.8 ## 8 Adults 2020-10-01 42.7 53.3 ## 9 Adults 2020-09-30 42.2 53.7 ## 10 Voters 2020-09-30 44.2 52.7 ## # ℹ 2,692 more rows ``` ] -- .pull-right-narrow[ **Aesthetic mappings:** ✅ x = `date` ❌ y = `rating_value` ❌ color = `rating_type` **Facet:** ✅ `subgroup` (Adults and Voters) ] --- ## Goal <img src="08-data-wrangling-3_files/figure-html/unnamed-chunk-27-1.png" alt="" width="100%" style="display: block; margin: auto;" /> --- ## Pivot ``` r trump_longer <- trump |> pivot_longer( cols = c(approval, disapproval), names_to = "rating_type", values_to = "rating_value" ) trump_longer ``` ``` ## # A tibble: 5,404 × 4 ## subgroup date rating_type rating_value ## <chr> <date> <chr> <dbl> ## 1 Voters 2020-10-04 approval 44.7 ## 2 Voters 2020-10-04 disapproval 52.2 ## 3 Adults 2020-10-04 approval 43.2 ## 4 Adults 2020-10-04 disapproval 52.6 ## 5 Adults 2020-10-03 approval 43.2 ## 6 Adults 2020-10-03 disapproval 52.6 ## 7 Voters 2020-10-03 approval 45.0 ## 8 Voters 2020-10-03 disapproval 51.7 ... ``` --- ## Plot ``` r ggplot(trump_longer, aes(x = date, y = rating_value, color = rating_type, group = rating_type)) + geom_line() + facet_wrap(~ subgroup) ``` <img src="08-data-wrangling-3_files/figure-html/unnamed-chunk-29-1.png" alt="" width="60%" style="display: block; margin: auto;" /> --- .panelset[ .panel[.panel-name[Code] ``` r ggplot(trump_longer, aes(x = date, y = rating_value, color = rating_type, group = rating_type)) + geom_line() + facet_wrap(~ subgroup) + * scale_color_manual(values = c("darkgreen", "orange")) + * labs( * x = "Date", y = "Rating", * color = NULL, * title = "How (un)popular is Donald Trump?", * subtitle = "Estimates based on polls of all adults and polls of likely/registered voters", * caption = "Source: FiveThirtyEight modeling estimates" * ) ``` ] .panel[.panel-name[Plot] <img src="08-data-wrangling-3_files/figure-html/unnamed-chunk-30-1.png" alt="" width="75%" style="display: block; margin: auto;" /> ] ] --- .panelset[ .panel[.panel-name[Code] ``` r ggplot(trump_longer, aes(x = date, y = rating_value, color = rating_type, group = rating_type)) + geom_line() + facet_wrap(~ subgroup) + scale_color_manual(values = c("darkgreen", "orange")) + labs( x = "Date", y = "Rating", color = NULL, title = "How (un)popular is Donald Trump?", subtitle = "Estimates based on polls of all adults and polls of likely/registered voters", caption = "Source: FiveThirtyEight modeling estimates" ) + * theme_minimal() + * theme(legend.position = "bottom") ``` ] .panel[.panel-name[Plot] <img src="08-data-wrangling-3_files/figure-html/unnamed-chunk-31-1.png" alt="" width="75%" style="display: block; margin: auto;" /> ] ] --- class: middle # Deeper dive into counts and proportions with `group_by()`, `count()`, and `summarize()` --- ## Case study: The Titanic The `titanic` package contains data on 891 passengers aboard the Titanic. ``` ## Rows: 891 ## Columns: 12 ## $ PassengerId <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, … ## $ Survived <int> 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0… ## $ Pclass <int> 3, 1, 3, 1, 3, 3, 1, 3, 3, 2, 3, 1, 3, 3, 3… ## $ Name <chr> "Braund, Mr. Owen Harris", "Cumings, Mrs. J… ## $ Sex <chr> "male", "female", "female", "female", "male… ## $ Age <dbl> 22, 38, 26, 35, 35, NA, 54, 2, 27, 14, 4, 5… ## $ SibSp <int> 1, 1, 0, 1, 0, 0, 0, 3, 0, 1, 1, 0, 0, 1, 0… ## $ Parch <int> 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 1, 0, 0, 5, 0… ## $ Ticket <chr> "A/5 21171", "PC 17599", "STON/O2. 3101282"… ## $ Fare <dbl> 7.2500, 71.2833, 7.9250, 53.1000, 8.0500, 8… ## $ Cabin <chr> "", "C85", "", "C123", "", "", "E46", "", "… ## $ Embarked <chr> "S", "C", "S", "S", "S", "Q", "S", "S", "S"… ``` --- ## Case study: The Titanic We will consider three categorical variables in particular: `survived`, `sex`, and `class`. ``` r titanic_df <- titanic::titanic_train |> select( survived = Survived, sex = Sex, class = Pclass ) |> mutate( survived = if_else(survived == 1, "Survived", "Died"), class = factor(class, labels = c("1st", "2nd", "3rd")) ) glimpse(titanic_df) ``` ``` ## Rows: 891 ## Columns: 3 ## $ survived <chr> "Died", "Survived", "Survived", "Survived", "D… ## $ sex <chr> "male", "female", "female", "female", "male", … ## $ class <fct> 3rd, 1st, 3rd, 1st, 3rd, 3rd, 1st, 3rd, 3rd, 2… ``` --- ## What does group_by() do? `group_by()` takes an existing data frame and converts it into a grouped data frame where subsequent operations are performed "once per group" .pull-left[ ``` r titanic_df ``` ``` ## survived sex class ## 1 Died male 3rd ## 2 Survived female 1st ## 3 Survived female 3rd ## 4 Survived female 1st ## 5 Died male 3rd ## 6 Died male 3rd ## 7 Died male 1st ## 8 Died male 3rd ## 9 Survived female 3rd ## 10 Survived female 2nd ## 11 Survived female 3rd ## 12 Survived female 1st ## 13 Died male 3rd ## 14 Died male 3rd ## 15 Died female 3rd ## 16 Survived female 2nd ## 17 Died male 3rd ## 18 Survived male 2nd ## 19 Died female 3rd ## 20 Survived female 3rd ## 21 Died male 2nd ## 22 Survived male 2nd ## 23 Survived female 3rd ## 24 Survived male 1st ## 25 Died female 3rd ## 26 Survived female 3rd ## 27 Died male 3rd ## 28 Died male 1st ## 29 Survived female 3rd ## 30 Died male 3rd ## 31 Died male 1st ## 32 Survived female 1st ## 33 Survived female 3rd ## 34 Died male 2nd ## 35 Died male 1st ## 36 Died male 1st ## 37 Survived male 3rd ## 38 Died male 3rd ## 39 Died female 3rd ## 40 Survived female 3rd ## 41 Died female 3rd ## 42 Died female 2nd ## 43 Died male 3rd ## 44 Survived female 2nd ## 45 Survived female 3rd ## 46 Died male 3rd ## 47 Died male 3rd ## 48 Survived female 3rd ## 49 Died male 3rd ## 50 Died female 3rd ## 51 Died male 3rd ## 52 Died male 3rd ## 53 Survived female 1st ## 54 Survived female 2nd ## 55 Died male 1st ## 56 Survived male 1st ## 57 Survived female 2nd ## 58 Died male 3rd ## 59 Survived female 2nd ## 60 Died male 3rd ## 61 Died male 3rd ## 62 Survived female 1st ## 63 Died male 1st ## 64 Died male 3rd ## 65 Died male 1st ## 66 Survived male 3rd ## 67 Survived female 2nd ## 68 Died male 3rd ## 69 Survived female 3rd ## 70 Died male 3rd ## 71 Died male 2nd ## 72 Died female 3rd ## 73 Died male 2nd ## 74 Died male 3rd ## 75 Survived male 3rd ## 76 Died male 3rd ## 77 Died male 3rd ## 78 Died male 3rd ## 79 Survived male 2nd ## 80 Survived female 3rd ## 81 Died male 3rd ## 82 Survived male 3rd ## 83 Survived female 3rd ## 84 Died male 1st ## 85 Survived female 2nd ## 86 Survived female 3rd ## 87 Died male 3rd ## 88 Died male 3rd ## 89 Survived female 1st ## 90 Died male 3rd ## 91 Died male 3rd ## 92 Died male 3rd ## 93 Died male 1st ## 94 Died male 3rd ## 95 Died male 3rd ## 96 Died male 3rd ## 97 Died male 1st ## 98 Survived male 1st ## 99 Survived female 2nd ## 100 Died male 2nd ## 101 Died female 3rd ## 102 Died male 3rd ## 103 Died male 1st ## 104 Died male 3rd ## 105 Died male 3rd ## 106 Died male 3rd ## 107 Survived female 3rd ## 108 Survived male 3rd ## 109 Died male 3rd ## 110 Survived female 3rd ## 111 Died male 1st ## 112 Died female 3rd ## 113 Died male 3rd ## 114 Died female 3rd ## 115 Died female 3rd ## 116 Died male 3rd ## 117 Died male 3rd ## 118 Died male 2nd ## 119 Died male 1st ## 120 Died female 3rd ## 121 Died male 2nd ## 122 Died male 3rd ## 123 Died male 2nd ## 124 Survived female 2nd ## 125 Died male 1st ## 126 Survived male 3rd ## 127 Died male 3rd ## 128 Survived male 3rd ## 129 Survived female 3rd ## 130 Died male 3rd ## 131 Died male 3rd ## 132 Died male 3rd ## 133 Died female 3rd ## 134 Survived female 2nd ## 135 Died male 2nd ## 136 Died male 2nd ## 137 Survived female 1st ## 138 Died male 1st ## 139 Died male 3rd ## 140 Died male 1st ## 141 Died female 3rd ## 142 Survived female 3rd ## 143 Survived female 3rd ## 144 Died male 3rd ## 145 Died male 2nd ## 146 Died male 2nd ## 147 Survived male 3rd ## 148 Died female 3rd ## 149 Died male 2nd ## 150 Died male 2nd ## 151 Died male 2nd ## 152 Survived female 1st ## 153 Died male 3rd ## 154 Died male 3rd ## 155 Died male 3rd ## 156 Died male 1st ## 157 Survived female 3rd ## 158 Died male 3rd ## 159 Died male 3rd ## 160 Died male 3rd ## 161 Died male 3rd ## 162 Survived female 2nd ## 163 Died male 3rd ## 164 Died male 3rd ## 165 Died male 3rd ## 166 Survived male 3rd ## 167 Survived female 1st ## 168 Died female 3rd ## 169 Died male 1st ## 170 Died male 3rd ## 171 Died male 1st ## 172 Died male 3rd ## 173 Survived female 3rd ## 174 Died male 3rd ## 175 Died male 1st ## 176 Died male 3rd ## 177 Died male 3rd ## 178 Died female 1st ## 179 Died male 2nd ## 180 Died male 3rd ## 181 Died female 3rd ## 182 Died male 2nd ## 183 Died male 3rd ## 184 Survived male 2nd ## 185 Survived female 3rd ## 186 Died male 1st ## 187 Survived female 3rd ## 188 Survived male 1st ## 189 Died male 3rd ## 190 Died male 3rd ## 191 Survived female 2nd ## 192 Died male 2nd ## 193 Survived female 3rd ## 194 Survived male 2nd ## 195 Survived female 1st ## 196 Survived female 1st ## 197 Died male 3rd ## 198 Died male 3rd ## 199 Survived female 3rd ## 200 Died female 2nd ## 201 Died male 3rd ## 202 Died male 3rd ## 203 Died male 3rd ## 204 Died male 3rd ## 205 Survived male 3rd ## 206 Died female 3rd ## 207 Died male 3rd ## 208 Survived male 3rd ## 209 Survived female 3rd ## 210 Survived male 1st ## 211 Died male 3rd ## 212 Survived female 2nd ## 213 Died male 3rd ## 214 Died male 2nd ## 215 Died male 3rd ## 216 Survived female 1st ## 217 Survived female 3rd ## 218 Died male 2nd ## 219 Survived female 1st ## 220 Died male 2nd ## 221 Survived male 3rd ## 222 Died male 2nd ## 223 Died male 3rd ## 224 Died male 3rd ## 225 Survived male 1st ## 226 Died male 3rd ## 227 Survived male 2nd ## 228 Died male 3rd ## 229 Died male 2nd ## 230 Died female 3rd ## 231 Survived female 1st ## 232 Died male 3rd ## 233 Died male 2nd ## 234 Survived female 3rd ## 235 Died male 2nd ## 236 Died female 3rd ## 237 Died male 2nd ## 238 Survived female 2nd ## 239 Died male 2nd ## 240 Died male 2nd ## 241 Died female 3rd ## 242 Survived female 3rd ## 243 Died male 2nd ## 244 Died male 3rd ## 245 Died male 3rd ## 246 Died male 1st ## 247 Died female 3rd ## 248 Survived female 2nd ## 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463 Died male 1st ## 464 Died male 2nd ## 465 Died male 3rd ## 466 Died male 3rd ## 467 Died male 2nd ## 468 Died male 1st ## 469 Died male 3rd ## 470 Survived female 3rd ## 471 Died male 3rd ## 472 Died male 3rd ## 473 Survived female 2nd ## 474 Survived female 2nd ## 475 Died female 3rd ## 476 Died male 1st ## 477 Died male 2nd ## 478 Died male 3rd ## 479 Died male 3rd ## 480 Survived female 3rd ## 481 Died male 3rd ## 482 Died male 2nd ## 483 Died male 3rd ## 484 Survived female 3rd ## 485 Survived male 1st ## 486 Died female 3rd ## 487 Survived female 1st ## 488 Died male 1st ## 489 Died male 3rd ## 490 Survived male 3rd ## 491 Died male 3rd ## 492 Died male 3rd ## 493 Died male 1st ## 494 Died male 1st ## 495 Died male 3rd ## 496 Died male 3rd ## 497 Survived female 1st ## 498 Died male 3rd ## 499 Died female 1st ## 500 Died male 3rd ## 501 Died male 3rd ## 502 Died female 3rd ## 503 Died female 3rd ## 504 Died female 3rd ## 505 Survived female 1st ## 506 Died male 1st ## 507 Survived female 2nd ## 508 Survived male 1st ## 509 Died male 3rd ## 510 Survived male 3rd ## 511 Survived male 3rd ## 512 Died male 3rd ## 513 Survived male 1st ## 514 Survived female 1st ## 515 Died male 3rd ## 516 Died male 1st ## 517 Survived female 2nd ## 518 Died male 3rd ## 519 Survived female 2nd ## 520 Died male 3rd ## 521 Survived female 1st ## 522 Died male 3rd ## 523 Died male 3rd ## 524 Survived female 1st ## 525 Died male 3rd ## 526 Died male 3rd ## 527 Survived female 2nd ## 528 Died male 1st ## 529 Died male 3rd ## 530 Died male 2nd ## 531 Survived female 2nd ## 532 Died male 3rd ## 533 Died male 3rd ## 534 Survived female 3rd ## 535 Died female 3rd ## 536 Survived female 2nd ## 537 Died male 1st ## 538 Survived female 1st ## 539 Died male 3rd ## 540 Survived female 1st ## 541 Survived female 1st ## 542 Died female 3rd ## 543 Died female 3rd ## 544 Survived male 2nd ## 545 Died male 1st ## 546 Died male 1st ## 547 Survived female 2nd ## 548 Survived male 2nd ## 549 Died male 3rd ## 550 Survived male 2nd ## 551 Survived male 1st ## 552 Died male 2nd ## 553 Died male 3rd ## 554 Survived male 3rd ## 555 Survived female 3rd ## 556 Died male 1st ## 557 Survived female 1st ## 558 Died male 1st ## 559 Survived female 1st ## 560 Survived female 3rd ## 561 Died male 3rd ## 562 Died male 3rd ## 563 Died male 2nd ## 564 Died male 3rd ## 565 Died female 3rd ## 566 Died male 3rd ## 567 Died male 3rd ## 568 Died female 3rd ## 569 Died male 3rd ## 570 Survived male 3rd ## 571 Survived male 2nd ## 572 Survived female 1st ## 573 Survived male 1st ## 574 Survived female 3rd ## 575 Died male 3rd ## 576 Died male 3rd ## 577 Survived female 2nd ## 578 Survived female 1st ## 579 Died female 3rd ## 580 Survived male 3rd ## 581 Survived female 2nd ## 582 Survived female 1st ## 583 Died male 2nd ## 584 Died male 1st ## 585 Died male 3rd ## 586 Survived female 1st ## 587 Died male 2nd ## 588 Survived male 1st ## 589 Died male 3rd ## 590 Died male 3rd ## 591 Died male 3rd ## 592 Survived female 1st ## 593 Died male 3rd ## 594 Died female 3rd ## 595 Died male 2nd ## 596 Died male 3rd ## 597 Survived female 2nd ## 598 Died male 3rd ## 599 Died male 3rd ## 600 Survived male 1st ## 601 Survived female 2nd ## 602 Died male 3rd ## 603 Died male 1st ## 604 Died male 3rd ## 605 Survived male 1st ## 606 Died male 3rd ## 607 Died male 3rd ## 608 Survived male 1st ## 609 Survived female 2nd ## 610 Survived female 1st ## 611 Died female 3rd ## 612 Died male 3rd ## 613 Survived female 3rd ## 614 Died male 3rd ## 615 Died male 3rd ## 616 Survived female 2nd ## 617 Died male 3rd ## 618 Died female 3rd ## 619 Survived female 2nd ## 620 Died male 2nd ## 621 Died male 3rd ## 622 Survived male 1st ## 623 Survived male 3rd ## 624 Died male 3rd ## 625 Died male 3rd ## 626 Died male 1st ## 627 Died male 2nd ## 628 Survived female 1st ## 629 Died male 3rd ## 630 Died male 3rd ## 631 Survived male 1st ## 632 Died male 3rd ## 633 Survived male 1st ## 634 Died male 1st ## 635 Died female 3rd ## 636 Survived female 2nd ## 637 Died male 3rd ## 638 Died male 2nd ## 639 Died female 3rd ## 640 Died male 3rd ## 641 Died male 3rd ## 642 Survived female 1st ## 643 Died female 3rd ## 644 Survived male 3rd ## 645 Survived female 3rd ## 646 Survived male 1st ## 647 Died male 3rd ## 648 Survived male 1st ## 649 Died male 3rd ## 650 Survived female 3rd ## 651 Died male 3rd ## 652 Survived female 2nd ## 653 Died male 3rd ## 654 Survived female 3rd ## 655 Died female 3rd ## 656 Died male 2nd ## 657 Died male 3rd ## 658 Died female 3rd ## 659 Died male 2nd ## 660 Died male 1st ## 661 Survived male 1st ## 662 Died male 3rd ## 663 Died male 1st ## 664 Died male 3rd ## 665 Survived male 3rd ## 666 Died male 2nd ## 667 Died male 2nd ## 668 Died male 3rd ## 669 Died male 3rd ## 670 Survived female 1st ## 671 Survived female 2nd ## 672 Died male 1st ## 673 Died male 2nd ## 674 Survived male 2nd ## 675 Died male 2nd ## 676 Died male 3rd ## 677 Died male 3rd ## 678 Survived female 3rd ## 679 Died female 3rd ## 680 Survived male 1st ## 681 Died female 3rd ## 682 Survived male 1st ## 683 Died male 3rd ## 684 Died male 3rd ## 685 Died male 2nd ## 686 Died male 2nd ## 687 Died male 3rd ## 688 Died male 3rd ## 689 Died male 3rd ## 690 Survived female 1st ## 691 Survived male 1st ## 692 Survived female 3rd ## 693 Survived male 3rd ## 694 Died male 3rd ## 695 Died male 1st ## 696 Died male 2nd ## 697 Died male 3rd ## 698 Survived female 3rd ## 699 Died male 1st ## 700 Died male 3rd ## 701 Survived female 1st ## 702 Survived male 1st ## 703 Died female 3rd ## 704 Died male 3rd ## 705 Died male 3rd ## 706 Died male 2nd ## 707 Survived female 2nd ## 708 Survived male 1st ## 709 Survived female 1st ## 710 Survived male 3rd ## 711 Survived female 1st ## 712 Died male 1st ## 713 Survived male 1st ## 714 Died male 3rd ## 715 Died male 2nd ## 716 Died male 3rd ## 717 Survived female 1st ## 718 Survived female 2nd ## 719 Died male 3rd ## 720 Died male 3rd ## 721 Survived female 2nd ## 722 Died male 3rd ## 723 Died male 2nd ## 724 Died male 2nd ## 725 Survived male 1st ## 726 Died male 3rd ## 727 Survived female 2nd ## 728 Survived female 3rd ## 729 Died male 2nd ## 730 Died female 3rd ## 731 Survived female 1st ## 732 Died male 3rd ## 733 Died male 2nd ## 734 Died male 2nd ## 735 Died male 2nd ## 736 Died male 3rd ## 737 Died female 3rd ## 738 Survived male 1st ## 739 Died male 3rd ## 740 Died male 3rd ## 741 Survived male 1st ## 742 Died male 1st ## 743 Survived female 1st ## 744 Died male 3rd ## 745 Survived male 3rd ## 746 Died male 1st ## 747 Died male 3rd ## 748 Survived female 2nd ## 749 Died male 1st ## 750 Died male 3rd ## 751 Survived female 2nd ## 752 Survived male 3rd ## 753 Died male 3rd ## 754 Died male 3rd ## 755 Survived female 2nd ## 756 Survived male 2nd ## 757 Died male 3rd ## 758 Died male 2nd ## 759 Died male 3rd ## 760 Survived female 1st ## 761 Died male 3rd ## 762 Died male 3rd ## 763 Survived male 3rd ## 764 Survived female 1st ## 765 Died male 3rd ## 766 Survived female 1st ## 767 Died male 1st ## 768 Died female 3rd ## 769 Died male 3rd ## 770 Died male 3rd ## 771 Died male 3rd ## 772 Died male 3rd ## 773 Died female 2nd ## 774 Died male 3rd ## 775 Survived female 2nd ## 776 Died male 3rd ## 777 Died male 3rd ## 778 Survived female 3rd ## 779 Died male 3rd ## 780 Survived female 1st ## 781 Survived female 3rd ## 782 Survived female 1st ## 783 Died male 1st ## 784 Died male 3rd ## 785 Died male 3rd ## 786 Died male 3rd ## 787 Survived female 3rd ## 788 Died male 3rd ## 789 Survived male 3rd ## 790 Died male 1st ## 791 Died male 3rd ## 792 Died male 2nd ## 793 Died female 3rd ## 794 Died male 1st ## 795 Died male 3rd ## 796 Died male 2nd ## 797 Survived female 1st ## 798 Survived female 3rd ## 799 Died male 3rd ## 800 Died female 3rd ## 801 Died male 2nd ## 802 Survived female 2nd ## 803 Survived male 1st ## 804 Survived male 3rd ## 805 Survived male 3rd ## 806 Died male 3rd ## 807 Died male 1st ## 808 Died female 3rd ## 809 Died male 2nd ## 810 Survived female 1st ## 811 Died male 3rd ## 812 Died male 3rd ## 813 Died male 2nd ## 814 Died female 3rd ## 815 Died male 3rd ## 816 Died male 1st ## 817 Died female 3rd ## 818 Died male 2nd ## 819 Died male 3rd ## 820 Died male 3rd ## 821 Survived female 1st ## 822 Survived male 3rd ## 823 Died male 1st ## 824 Survived female 3rd ## 825 Died male 3rd ## 826 Died male 3rd ## 827 Died male 3rd ## 828 Survived male 2nd ## 829 Survived male 3rd ## 830 Survived female 1st ## 831 Survived female 3rd ## 832 Survived male 2nd ## 833 Died male 3rd ## 834 Died male 3rd ## 835 Died male 3rd ## 836 Survived female 1st ## 837 Died male 3rd ## 838 Died male 3rd ## 839 Survived male 3rd ## 840 Survived male 1st ## 841 Died male 3rd ## 842 Died male 2nd ## 843 Survived female 1st ## 844 Died male 3rd ## 845 Died male 3rd ## 846 Died male 3rd ## 847 Died male 3rd ## 848 Died male 3rd ## 849 Died male 2nd ## 850 Survived female 1st ## 851 Died male 3rd ## 852 Died male 3rd ## 853 Died female 3rd ## 854 Survived female 1st ## 855 Died female 2nd ## 856 Survived female 3rd ## 857 Survived female 1st ## 858 Survived male 1st ## 859 Survived female 3rd ## 860 Died male 3rd ## 861 Died male 3rd ## 862 Died male 2nd ## 863 Survived female 1st ## 864 Died female 3rd ## 865 Died male 2nd ## 866 Survived female 2nd ## 867 Survived female 2nd ## 868 Died male 1st ## 869 Died male 3rd ## 870 Survived male 3rd ## 871 Died male 3rd ## 872 Survived female 1st ## 873 Died male 1st ## 874 Died male 3rd ## 875 Survived female 2nd ## 876 Survived female 3rd ## 877 Died male 3rd ## 878 Died male 3rd ## 879 Died male 3rd ## 880 Survived female 1st ## 881 Survived female 2nd ## 882 Died male 3rd ## 883 Died female 3rd ## 884 Died male 2nd ## 885 Died male 3rd ## 886 Died female 3rd ## 887 Died male 2nd ## 888 Survived female 1st ## 889 Died female 3rd ## 890 Survived male 1st ## 891 Died male 3rd ``` ] .pull-right[ ``` r titanic_df |> group_by(sex) ``` ``` ## # A tibble: 891 × 3 ## # Groups: sex [2] ## survived sex class ## <chr> <chr> <fct> ## 1 Died male 3rd ## 2 Survived female 1st ## 3 Survived female 3rd ## 4 Survived female 1st ## 5 Died male 3rd ## 6 Died male 3rd ## 7 Died male 1st ## 8 Died male 3rd ## 9 Survived female 3rd ## 10 Survived female 2nd ## # ℹ 881 more rows ``` ] --- ## What does group_by() not do? `group_by()` does not sort the data, `arrange()` does .pull-left[ ``` r titanic_df |> group_by(sex) ``` ``` ## # A tibble: 891 × 3 ## # Groups: sex [2] ## survived sex class ## <chr> <chr> <fct> ## 1 Died male 3rd ## 2 Survived female 1st ## 3 Survived female 3rd ## 4 Survived female 1st ## 5 Died male 3rd ## 6 Died male 3rd ## 7 Died male 1st ## 8 Died male 3rd ## 9 Survived female 3rd ## 10 Survived female 2nd ## # ℹ 881 more rows ``` ] .pull-right[ ``` r titanic_df |> arrange(sex) ``` ``` ## survived sex class ## 1 Survived female 1st ## 2 Survived female 3rd ## 3 Survived female 1st ## 4 Survived female 3rd ## 5 Survived female 2nd ## 6 Survived female 3rd ## 7 Survived female 1st ## 8 Died female 3rd ## 9 Survived female 2nd ## 10 Died female 3rd ## 11 Survived female 3rd ## 12 Survived female 3rd ## 13 Died female 3rd ## 14 Survived female 3rd ## 15 Survived female 3rd ## 16 Survived female 1st ## 17 Survived female 3rd ## 18 Died female 3rd ## 19 Survived female 3rd ## 20 Died female 3rd ## 21 Died female 2nd ## 22 Survived female 2nd ## 23 Survived female 3rd ## 24 Survived female 3rd ## 25 Died female 3rd ## 26 Survived female 1st ## 27 Survived female 2nd ## 28 Survived female 2nd ## 29 Survived female 2nd ## 30 Survived female 1st ## 31 Survived female 2nd ## 32 Survived female 3rd ## 33 Died female 3rd ## 34 Survived female 3rd ## 35 Survived female 3rd ## 36 Survived female 2nd ## 37 Survived female 3rd ## 38 Survived female 1st ## 39 Survived female 2nd ## 40 Died female 3rd ## 41 Survived female 3rd ## 42 Survived female 3rd ## 43 Died female 3rd ## 44 Died female 3rd ## 45 Died female 3rd ## 46 Died female 3rd ## 47 Survived female 2nd ## 48 Survived female 3rd ## 49 Died female 3rd ## 50 Survived female 2nd ## 51 Survived female 1st ## 52 Died female 3rd ## 53 Survived female 3rd ## 54 Survived female 3rd ## 55 Died female 3rd ## 56 Survived female 1st ## 57 Survived female 3rd ## 58 Survived female 2nd ## 59 Survived female 1st ## 60 Died female 3rd ## 61 Survived female 3rd ## 62 Died female 1st ## 63 Died female 3rd ## 64 Survived female 3rd ## 65 Survived female 3rd ## 66 Survived female 2nd ## 67 Survived female 3rd ## 68 Survived female 1st ## 69 Survived female 1st ## 70 Survived female 3rd ## 71 Died female 2nd ## 72 Died female 3rd ## 73 Survived female 3rd ## 74 Survived female 2nd ## 75 Survived female 1st ## 76 Survived female 3rd ## 77 Survived female 1st ## 78 Died female 3rd ## 79 Survived female 1st ## 80 Survived female 3rd ## 81 Died female 3rd ## 82 Survived female 2nd ## 83 Died female 3rd ## 84 Survived female 3rd ## 85 Died female 3rd ## 86 Survived female 2nd ## 87 Died female 3rd ## 88 Died female 3rd ## 89 Survived female 3rd ## 90 Survived female 1st ## 91 Survived female 1st ## 92 Survived female 1st ## 93 Survived female 2nd ## 94 Died female 3rd ## 95 Survived female 1st ## 96 Survived female 1st ## 97 Survived female 2nd ## 98 Survived female 3rd ## 99 Survived female 1st ## 100 Died female 3rd ## 101 Survived female 3rd ## 102 Survived female 3rd ## 103 Survived female 1st ## 104 Survived female 1st ## 105 Died female 3rd ## 106 Died female 1st ## 107 Survived female 1st ## 108 Survived female 3rd ## 109 Survived female 2nd ## 110 Survived female 1st ## 111 Survived female 1st ## 112 Survived female 1st ## 113 Survived female 1st ## 114 Survived female 1st ## 115 Died female 2nd ## 116 Survived female 3rd ## 117 Survived female 2nd ## 118 Survived female 1st ## 119 Survived female 1st ## 120 Survived female 2nd ## 121 Survived female 2nd ## 122 Survived female 1st ## 123 Survived female 2nd ## 124 Survived female 3rd ## 125 Survived female 1st ## 126 Survived female 3rd ## 127 Survived female 1st ## 128 Survived female 1st ## 129 Survived female 1st ## 130 Survived female 2nd ## 131 Survived female 2nd ## 132 Survived female 3rd ## 133 Survived female 1st ## 134 Died female 2nd ## 135 Survived female 3rd ## 136 Survived female 3rd ## 137 Died female 3rd ## 138 Survived female 1st ## 139 Survived female 3rd ## 140 Survived female 3rd ## 141 Survived female 1st ## 142 Died female 3rd ## 143 Survived female 1st ## 144 Survived female 3rd ## 145 Survived female 1st ## 146 Survived female 3rd ## 147 Survived female 1st ## 148 Survived female 2nd ## 149 Survived female 2nd ## 150 Survived female 1st ## 151 Survived female 3rd ## 152 Died female 3rd ## 153 Survived female 2nd ## 154 Died female 3rd ## 155 Died female 3rd ## 156 Died female 3rd ## 157 Survived female 1st ## 158 Died female 3rd ## 159 Survived female 2nd ## 160 Survived female 2nd ## 161 Died female 3rd ## 162 Died female 3rd ## 163 Survived female 2nd ## 164 Survived female 2nd ## 165 Survived female 3rd ## 166 Survived female 2nd ## 167 Survived female 1st ## 168 Died female 3rd ## 169 Survived female 2nd ## 170 Survived female 2nd ## 171 Survived female 2nd ## 172 Survived female 2nd ## 173 Survived female 3rd ## 174 Survived female 1st ## 175 Survived female 2nd ## 176 Survived female 3rd ## 177 Survived female 2nd ## 178 Survived female 2nd ## 179 Died female 3rd ## 180 Survived female 3rd ## 181 Survived female 3rd ## 182 Died female 3rd ## 183 Survived female 1st ## 184 Survived female 1st ## 185 Died female 1st ## 186 Died female 3rd ## 187 Died female 3rd ## 188 Died female 3rd ## 189 Survived female 1st ## 190 Survived female 2nd ## 191 Survived female 1st ## 192 Survived female 2nd ## 193 Survived female 2nd ## 194 Survived female 1st ## 195 Survived female 1st ## 196 Survived female 2nd ## 197 Survived female 2nd ## 198 Survived female 3rd ## 199 Died female 3rd ## 200 Survived female 2nd ## 201 Survived female 1st ## 202 Survived female 1st ## 203 Survived female 1st ## 204 Died female 3rd ## 205 Died female 3rd ## 206 Survived female 2nd ## 207 Survived female 3rd ## 208 Survived female 1st ## 209 Survived female 1st ## 210 Survived female 3rd ## 211 Died female 3rd ## 212 Died female 3rd ## 213 Survived female 1st ## 214 Survived female 3rd ## 215 Survived female 2nd ## 216 Survived female 1st ## 217 Died female 3rd ## 218 Survived female 2nd ## 219 Survived female 1st ## 220 Survived female 1st ## 221 Survived female 1st ## 222 Died female 3rd ## 223 Survived female 2nd ## 224 Survived female 2nd ## 225 Survived female 2nd ## 226 Survived female 1st ## 227 Died female 3rd ## 228 Survived female 3rd ## 229 Survived female 2nd ## 230 Died female 3rd ## 231 Survived female 2nd ## 232 Survived female 1st ## 233 Died female 3rd ## 234 Survived female 2nd ## 235 Died female 3rd ## 236 Survived female 1st ## 237 Died female 3rd ## 238 Survived female 3rd ## 239 Survived female 3rd ## 240 Survived female 2nd ## 241 Survived female 3rd ## 242 Died female 3rd ## 243 Died female 3rd ## 244 Survived female 1st ## 245 Survived female 2nd ## 246 Survived female 3rd ## 247 Died female 3rd ## 248 Died female 3rd ## 249 Survived female 1st ## 250 Survived female 3rd ## 251 Survived female 3rd ## 252 Survived female 1st ## 253 Died female 3rd ## 254 Survived female 2nd ## 255 Survived female 1st ## 256 Survived female 1st ## 257 Survived female 1st ## 258 Survived female 2nd ## 259 Survived female 2nd ## 260 Survived female 2nd ## 261 Survived female 3rd ## 262 Died female 3rd ## 263 Survived female 1st ## 264 Died female 3rd ## 265 Survived female 1st ## 266 Survived female 2nd ## 267 Survived female 2nd ## 268 Survived female 2nd ## 269 Survived female 1st ## 270 Survived female 1st ## 271 Survived female 1st ## 272 Died female 3rd ## 273 Died female 2nd ## 274 Survived female 2nd ## 275 Survived female 3rd ## 276 Survived female 1st ## 277 Survived female 3rd ## 278 Survived female 1st ## 279 Survived female 3rd ## 280 Died female 3rd ## 281 Survived female 1st ## 282 Survived female 3rd ## 283 Died female 3rd ## 284 Survived female 2nd ## 285 Died female 3rd ## 286 Survived female 1st ## 287 Died female 3rd ## 288 Died female 3rd ## 289 Survived female 1st ## 290 Survived female 3rd ## 291 Survived female 1st ## 292 Survived female 3rd ## 293 Survived female 1st ## 294 Survived female 1st ## 295 Survived female 1st ## 296 Died female 3rd ## 297 Survived female 1st ## 298 Died female 2nd ## 299 Survived female 3rd ## 300 Survived female 1st ## 301 Survived female 3rd ## 302 Survived female 1st ## 303 Died female 3rd ## 304 Survived female 2nd ## 305 Survived female 2nd ## 306 Survived female 1st ## 307 Survived female 2nd ## 308 Survived female 3rd ## 309 Survived female 1st ## 310 Survived female 2nd ## 311 Died female 3rd ## 312 Died female 3rd ## 313 Survived female 1st ## 314 Died female 3rd ## 315 Died male 3rd ## 316 Died male 3rd ## 317 Died male 3rd ## 318 Died male 1st ## 319 Died male 3rd ## 320 Died male 3rd ## 321 Died male 3rd ## 322 Died male 3rd ## 323 Survived male 2nd ## 324 Died male 2nd ## 325 Survived male 2nd ## 326 Survived male 1st ## 327 Died male 3rd ## 328 Died male 1st ## 329 Died male 3rd ## 330 Died male 1st ## 331 Died male 2nd ## 332 Died male 1st ## 333 Died male 1st ## 334 Survived male 3rd ## 335 Died male 3rd ## 336 Died male 3rd ## 337 Died male 3rd ## 338 Died male 3rd ## 339 Died male 3rd ## 340 Died male 3rd ## 341 Died male 3rd ## 342 Died male 1st ## 343 Survived male 1st ## 344 Died male 3rd ## 345 Died male 3rd ## 346 Died male 3rd ## 347 Died male 1st ## 348 Died male 3rd ## 349 Died male 1st ## 350 Survived male 3rd ## 351 Died male 3rd ## 352 Died male 3rd ## 353 Died male 2nd ## 354 Died male 2nd ## 355 Died male 3rd ## 356 Survived male 3rd ## 357 Died male 3rd ## 358 Died male 3rd ## 359 Died male 3rd ## 360 Survived male 2nd ## 361 Died male 3rd ## 362 Survived male 3rd ## 363 Died male 1st ## 364 Died male 3rd ## 365 Died male 3rd ## 366 Died male 3rd ## 367 Died male 3rd ## 368 Died male 3rd ## 369 Died male 1st ## 370 Died male 3rd ## 371 Died male 3rd ## 372 Died male 3rd ## 373 Died male 1st ## 374 Survived male 1st ## 375 Died male 2nd ## 376 Died male 3rd ## 377 Died male 1st ## 378 Died male 3rd ## 379 Died male 3rd ## 380 Died male 3rd ## 381 Survived male 3rd ## 382 Died male 3rd ## 383 Died male 1st ## 384 Died male 3rd ## 385 Died male 3rd ## 386 Died male 3rd ## 387 Died male 2nd ## 388 Died male 1st ## 389 Died male 2nd ## 390 Died male 3rd ## 391 Died male 2nd ## 392 Died male 1st ## 393 Survived male 3rd ## 394 Died male 3rd ## 395 Survived male 3rd ## 396 Died male 3rd ## 397 Died male 3rd ## 398 Died male 3rd ## 399 Died male 2nd ## 400 Died male 2nd ## 401 Died male 1st ## 402 Died male 3rd ## 403 Died male 1st ## 404 Died male 3rd ## 405 Died male 2nd ## 406 Died male 2nd ## 407 Survived male 3rd ## 408 Died male 2nd ## 409 Died male 2nd ## 410 Died male 2nd ## 411 Died male 3rd ## 412 Died male 3rd ## 413 Died male 3rd ## 414 Died male 1st ## 415 Died male 3rd ## 416 Died male 3rd ## 417 Died male 3rd ## 418 Died male 3rd ## 419 Died male 3rd ## 420 Died male 3rd ## 421 Died male 3rd ## 422 Survived male 3rd ## 423 Died male 1st ## 424 Died male 3rd ## 425 Died male 1st ## 426 Died male 3rd ## 427 Died male 3rd ## 428 Died male 1st ## 429 Died male 3rd ## 430 Died male 3rd ## 431 Died male 2nd ## 432 Died male 3rd ## 433 Died male 2nd ## 434 Died male 3rd ## 435 Survived male 2nd ## 436 Died male 1st ## 437 Survived male 1st ## 438 Died male 3rd ## 439 Died male 3rd ## 440 Died male 2nd ## 441 Survived male 2nd ## 442 Died male 3rd ## 443 Died male 3rd ## 444 Died male 3rd ## 445 Died male 3rd ## 446 Died male 3rd ## 447 Died male 3rd ## 448 Survived male 3rd ## 449 Died male 3rd ## 450 Survived male 3rd ## 451 Survived male 1st ## 452 Died male 3rd ## 453 Died male 3rd ## 454 Died male 2nd ## 455 Died male 3rd ## 456 Died male 2nd ## 457 Died male 2nd ## 458 Survived male 3rd ## 459 Died male 2nd ## 460 Died male 3rd ## 461 Died male 3rd ## 462 Survived male 1st ## 463 Died male 3rd ## 464 Survived male 2nd ## 465 Died male 3rd ## 466 Died male 2nd ## 467 Died male 3rd ## 468 Died male 2nd ## 469 Died male 2nd ## 470 Died male 2nd ## 471 Died male 2nd ## 472 Died male 2nd ## 473 Died male 2nd ## 474 Died male 3rd ## 475 Died male 3rd ## 476 Died male 1st ## 477 Survived male 1st ## 478 Died male 2nd ## 479 Died male 3rd ## 480 Died male 1st ## 481 Died male 3rd ## 482 Died male 3rd ## 483 Survived male 3rd ## 484 Died male 1st ## 485 Died male 1st ## 486 Died male 2nd ## 487 Died male 3rd ## 488 Survived male 3rd ## 489 Died male 1st ## 490 Survived male 3rd ## 491 Died male 1st ## 492 Died male 2nd ## 493 Died male 3rd ## 494 Died male 3rd ## 495 Died male 3rd ## 496 Died male 3rd ## 497 Survived male 3rd ## 498 Died male 1st ## 499 Died male 3rd ## 500 Survived male 3rd ## 501 Died male 3rd ## 502 Survived male 2nd ## 503 Died male 2nd ## 504 Died male 3rd ## 505 Died male 1st ## 506 Died male 3rd ## 507 Survived male 1st ## 508 Survived male 3rd ## 509 Died male 3rd ## 510 Died male 3rd ## 511 Survived male 1st ## 512 Died male 2nd ## 513 Died male 3rd ## 514 Died male 2nd ## 515 Died male 2nd ## 516 Died male 3rd ## 517 Died male 3rd ## 518 Died male 3rd ## 519 Died male 3rd ## 520 Died male 1st ## 521 Died male 1st ## 522 Died male 3rd ## 523 Died male 3rd ## 524 Died male 1st ## 525 Survived male 3rd ## 526 Died male 1st ## 527 Survived male 2nd ## 528 Died male 2nd ## 529 Died male 2nd ## 530 Died male 2nd ## 531 Survived male 3rd ## 532 Died male 3rd ## 533 Died male 3rd ## 534 Died male 1st ## 535 Died male 3rd ## 536 Died male 3rd ## 537 Died male 3rd ## 538 Died male 3rd ## 539 Died male 3rd ## 540 Died male 2nd ## 541 Died male 3rd ## 542 Died male 3rd ## 543 Died male 3rd ## 544 Survived male 1st ## 545 Died male 3rd ## 546 Died male 3rd ## 547 Died male 1st ## 548 Died male 1st ## 549 Died male 3rd ## 550 Died male 3rd ## 551 Died male 3rd ## 552 Died male 3rd ## 553 Died male 2nd ## 554 Died male 3rd ## 555 Died male 3rd ## 556 Survived male 1st ## 557 Survived male 3rd ## 558 Died male 3rd ## 559 Died male 3rd ## 560 Died male 2nd ## 561 Died male 2nd ## 562 Survived male 3rd ## 563 Died male 3rd ## 564 Died male 3rd ## 565 Died male 2nd ## 566 Died male 3rd ## 567 Survived male 2nd ## 568 Died male 3rd ## 569 Died male 3rd ## 570 Died male 3rd ## 571 Died male 2nd ## 572 Survived male 3rd ## 573 Died male 2nd ## 574 Died male 3rd ## 575 Died male 3rd ## 576 Died male 3rd ## 577 Died male 3rd ## 578 Died male 3rd ## 579 Died male 3rd ## 580 Survived male 3rd ## 581 Survived male 1st ## 582 Died male 3rd ## 583 Died male 1st ## 584 Died male 1st ## 585 Died male 2nd ## 586 Died male 3rd ## 587 Died male 3rd ## 588 Survived male 3rd ## 589 Survived male 1st ## 590 Survived male 1st ## 591 Survived male 1st ## 592 Died male 2nd ## 593 Died male 3rd ## 594 Died male 1st ## 595 Survived male 1st ## 596 Died male 3rd ## 597 Survived male 3rd ## 598 Died male 1st ## 599 Died male 3rd ## 600 Survived male 1st ## 601 Died male 3rd ## 602 Died male 1st ## 603 Died male 2nd ## 604 Died male 3rd ## 605 Died male 3rd ## 606 Died male 2nd ## 607 Died male 1st ## 608 Died male 3rd ## 609 Died male 3rd ## 610 Died male 3rd ## 611 Died male 1st ## 612 Died male 2nd ## 613 Died male 3rd ## 614 Died male 3rd ## 615 Died male 3rd ## 616 Died male 2nd ## 617 Died male 3rd ## 618 Survived male 1st ## 619 Died male 1st ## 620 Died male 3rd ## 621 Survived male 3rd ## 622 Died male 3rd ## 623 Died male 3rd ## 624 Died male 1st ## 625 Died male 1st ## 626 Died male 3rd ## 627 Died male 3rd ## 628 Died male 3rd ## 629 Died male 3rd ## 630 Died male 3rd ## 631 Died male 1st ## 632 Survived male 1st ## 633 Died male 3rd ## 634 Survived male 3rd ## 635 Survived male 3rd ## 636 Died male 3rd ## 637 Survived male 1st ## 638 Died male 3rd ## 639 Died male 1st ## 640 Died male 3rd ## 641 Died male 3rd ## 642 Died male 3rd ## 643 Died male 3rd ## 644 Died male 3rd ## 645 Died male 3rd ## 646 Died male 1st ## 647 Died male 3rd ## 648 Died male 2nd ## 649 Died male 3rd ## 650 Died male 3rd ## 651 Died male 1st ## 652 Died male 3rd ## 653 Survived male 2nd ## 654 Died male 1st ## 655 Died male 1st ## 656 Survived male 2nd ## 657 Died male 3rd ## 658 Survived male 2nd ## 659 Survived male 1st ## 660 Died male 2nd ## 661 Died male 3rd ## 662 Survived male 3rd ## 663 Died male 1st ## 664 Died male 1st ## 665 Died male 3rd ## 666 Died male 3rd ## 667 Died male 2nd ## 668 Died male 3rd ## 669 Died male 3rd ## 670 Died male 3rd ## 671 Died male 3rd ## 672 Survived male 3rd ## 673 Survived male 2nd ## 674 Survived male 1st ## 675 Died male 3rd ## 676 Died male 3rd ## 677 Survived male 3rd ## 678 Died male 2nd ## 679 Died male 1st ## 680 Died male 3rd ## 681 Died male 2nd ## 682 Survived male 1st ## 683 Died male 3rd ## 684 Died male 3rd ## 685 Died male 3rd ## 686 Died male 3rd ## 687 Died male 2nd ## 688 Died male 3rd ## 689 Died male 3rd ## 690 Died male 3rd ## 691 Survived male 1st ## 692 Died male 3rd ## 693 Died male 1st ## 694 Died male 3rd ## 695 Survived male 1st ## 696 Died male 3rd ## 697 Died male 3rd ## 698 Survived male 1st ## 699 Died male 3rd ## 700 Died male 3rd ## 701 Died male 3rd ## 702 Died male 3rd ## 703 Died male 2nd ## 704 Died male 3rd ## 705 Survived male 1st ## 706 Survived male 3rd ## 707 Died male 3rd ## 708 Died male 3rd ## 709 Died male 1st ## 710 Died male 2nd ## 711 Died male 3rd ## 712 Died male 3rd ## 713 Survived male 1st ## 714 Died male 3rd ## 715 Survived male 1st ## 716 Died male 1st ## 717 Died male 3rd ## 718 Died male 2nd ## 719 Died male 3rd ## 720 Died male 3rd ## 721 Survived male 3rd ## 722 Survived male 1st ## 723 Died male 3rd ## 724 Survived male 1st ## 725 Died male 3rd ## 726 Died male 3rd ## 727 Died male 3rd ## 728 Died male 2nd ## 729 Died male 3rd ## 730 Died male 2nd ## 731 Died male 1st ## 732 Survived male 1st ## 733 Died male 3rd ## 734 Died male 1st ## 735 Died male 3rd ## 736 Survived male 3rd ## 737 Died male 2nd ## 738 Died male 2nd ## 739 Died male 3rd ## 740 Died male 3rd ## 741 Died male 1st ## 742 Died male 2nd ## 743 Survived male 2nd ## 744 Died male 2nd ## 745 Died male 3rd ## 746 Died male 3rd ## 747 Survived male 1st ## 748 Survived male 1st ## 749 Died male 3rd ## 750 Died male 3rd ## 751 Died male 2nd ## 752 Died male 2nd ## 753 Died male 3rd ## 754 Died male 3rd ## 755 Died male 3rd ## 756 Survived male 1st ## 757 Survived male 3rd ## 758 Died male 3rd ## 759 Died male 1st ## 760 Died male 2nd ## 761 Died male 3rd ## 762 Died male 1st ## 763 Died male 3rd ## 764 Survived male 1st ## 765 Died male 3rd ## 766 Died male 3rd ## 767 Died male 2nd ## 768 Survived male 1st ## 769 Survived male 3rd ## 770 Died male 1st ## 771 Survived male 1st ## 772 Died male 3rd ## 773 Died male 2nd ## 774 Died male 3rd ## 775 Died male 3rd ## 776 Died male 3rd ## 777 Died male 3rd ## 778 Died male 2nd ## 779 Died male 2nd ## 780 Survived male 1st ## 781 Died male 3rd ## 782 Died male 2nd ## 783 Died male 3rd ## 784 Died male 2nd ## 785 Died male 2nd ## 786 Died male 2nd ## 787 Died male 3rd ## 788 Survived male 1st ## 789 Died male 3rd ## 790 Died male 3rd ## 791 Survived male 1st ## 792 Died male 1st ## 793 Died male 3rd ## 794 Survived male 3rd ## 795 Died male 1st ## 796 Died male 3rd ## 797 Died male 1st ## 798 Died male 3rd ## 799 Survived male 3rd ## 800 Died male 3rd ## 801 Died male 3rd ## 802 Survived male 2nd ## 803 Died male 3rd ## 804 Died male 2nd ## 805 Died male 3rd ## 806 Died male 3rd ## 807 Died male 3rd ## 808 Survived male 3rd ## 809 Died male 3rd ## 810 Died male 1st ## 811 Died male 3rd ## 812 Died male 3rd ## 813 Died male 3rd ## 814 Died male 3rd ## 815 Died male 3rd ## 816 Died male 3rd ## 817 Died male 3rd ## 818 Died male 3rd ## 819 Died male 1st ## 820 Died male 3rd ## 821 Died male 3rd ## 822 Died male 3rd ## 823 Died male 3rd ## 824 Survived male 3rd ## 825 Died male 1st ## 826 Died male 3rd ## 827 Died male 2nd ## 828 Died male 1st ## 829 Died male 3rd ## 830 Died male 2nd ## 831 Died male 3rd ## 832 Died male 2nd ## 833 Survived male 1st ## 834 Survived male 3rd ## 835 Survived male 3rd ## 836 Died male 3rd ## 837 Died male 1st ## 838 Died male 2nd ## 839 Died male 3rd ## 840 Died male 3rd ## 841 Died male 2nd ## 842 Died male 3rd ## 843 Died male 1st ## 844 Died male 2nd ## 845 Died male 3rd ## 846 Died male 3rd ## 847 Survived male 3rd ## 848 Died male 1st ## 849 Died male 3rd ## 850 Died male 3rd ## 851 Died male 3rd ## 852 Survived male 2nd ## 853 Survived male 3rd ## 854 Survived male 2nd ## 855 Died male 3rd ## 856 Died male 3rd ## 857 Died male 3rd ## 858 Died male 3rd ## 859 Died male 3rd ## 860 Survived male 3rd ## 861 Survived male 1st ## 862 Died male 3rd ## 863 Died male 2nd ## 864 Died male 3rd ## 865 Died male 3rd ## 866 Died male 3rd ## 867 Died male 3rd ## 868 Died male 3rd ## 869 Died male 2nd ## 870 Died male 3rd ## 871 Died male 3rd ## 872 Survived male 1st ## 873 Died male 3rd ## 874 Died male 3rd ## 875 Died male 2nd ## 876 Died male 2nd ## 877 Died male 1st ## 878 Died male 3rd ## 879 Survived male 3rd ## 880 Died male 3rd ## 881 Died male 1st ## 882 Died male 3rd ## 883 Died male 3rd ## 884 Died male 3rd ## 885 Died male 3rd ## 886 Died male 3rd ## 887 Died male 2nd ## 888 Died male 3rd ## 889 Died male 2nd ## 890 Survived male 1st ## 891 Died male 3rd ``` ] --- ## What does group_by() not do? `group_by()` does not create frequency tables, `count()` does .pull-left[ ``` r titanic_df |> group_by(sex) ``` ``` ## # A tibble: 891 × 3 ## # Groups: sex [2] ## survived sex class ## <chr> <chr> <fct> ## 1 Died male 3rd ## 2 Survived female 1st ## 3 Survived female 3rd ## 4 Survived female 1st ## 5 Died male 3rd ## 6 Died male 3rd ## 7 Died male 1st ## 8 Died male 3rd ## 9 Survived female 3rd ## 10 Survived female 2nd ## # ℹ 881 more rows ``` ] .pull-right[ ``` r titanic_df |> count(sex) ``` ``` ## sex n ## 1 female 314 ## 2 male 577 ``` ] --- ## count() is a short-hand `count()` is a short-hand for `group_by()` and then `summarise()` to count the number of observations in each group .pull-left[ ``` r titanic_df |> group_by(sex) |> summarise(n = n()) ``` ``` ## # A tibble: 2 × 2 ## sex n ## <chr> <int> ## 1 female 314 ## 2 male 577 ``` ] .pull-right[ ``` r titanic_df |> count(sex) ``` ``` ## sex n ## 1 female 314 ## 2 male 577 ``` ] --- # Adding on `mutate()` for proportions .pull-left[ ``` r titanic_df |> group_by(sex) |> summarise(n = n()) |> mutate(prop = n/sum(n)) ``` ``` ## # A tibble: 2 × 3 ## sex n prop ## <chr> <int> <dbl> ## 1 female 314 0.352 ## 2 male 577 0.648 ``` ] .pull-right[ ``` r titanic_df |> count(sex) |> mutate(prop = n/sum(n)) ``` ``` ## sex n prop ## 1 female 314 0.352413 ## 2 male 577 0.647587 ``` ] --- ## count can take multiple arguments .pull-left[ ``` r titanic_df |> group_by(sex, survived) |> summarise(n = n()) ``` ``` ## # A tibble: 4 × 3 ## # Groups: sex [2] ## sex survived n ## <chr> <chr> <int> ## 1 female Died 81 ## 2 female Survived 233 ## 3 male Died 468 ## 4 male Survived 109 ``` ] .pull-right[ ``` r titanic_df |> count(sex, survived) ``` ``` ## sex survived n ## 1 female Died 81 ## 2 female Survived 233 ## 3 male Died 468 ## 4 male Survived 109 ``` ] --- # Notice the difference - `count()` ungroups after itself - `summarise()` peels off one layer of grouping by default .pull-left[ ``` r titanic_df |> group_by(sex, survived) |> summarise(n = n()) |> mutate(prop = n/sum(n)) ``` ``` ## # A tibble: 4 × 4 ## # Groups: sex [2] ## sex survived n prop ## <chr> <chr> <int> <dbl> ## 1 female Died 81 0.258 ## 2 female Survived 233 0.742 ## 3 male Died 468 0.811 ## 4 male Survived 109 0.189 ``` ] .pull-right[ ``` r titanic_df |> count(sex, survived) |> mutate(prop = n/sum(n)) ``` ``` ## sex survived n prop ## 1 female Died 81 0.09090909 ## 2 female Survived 233 0.26150393 ## 3 male Died 468 0.52525253 ## 4 male Survived 109 0.12233446 ``` ``` r titanic_df |> count(sex, survived) |> group_by(sex) |> mutate(prop = n/sum(n)) ``` ] --- ## Undo grouping with ungroup() .pull-left[ ``` r titanic_df |> count(sex, survived) |> group_by(sex) |> mutate(prop = n/sum(n)) ``` ``` ## # A tibble: 4 × 4 ## # Groups: sex [2] ## sex survived n prop ## <chr> <chr> <int> <dbl> ## 1 female Died 81 0.258 ## 2 female Survived 233 0.742 ## 3 male Died 468 0.811 ## 4 male Survived 109 0.189 ``` ] .pull-right[ ``` r titanic_df |> count(sex, survived) |> group_by(sex) |> mutate(prop = n/sum(n)) |> ungroup() ``` ``` ## # A tibble: 4 × 4 ## sex survived n prop ## <chr> <chr> <int> <dbl> ## 1 female Died 81 0.258 ## 2 female Survived 233 0.742 ## 3 male Died 468 0.811 ## 4 male Survived 109 0.189 ``` ] --- ## Grouping by more than one variable ``` r titanic_df |> count(class, sex, survived) |> group_by(sex, class) |> mutate(prop = n/sum(n)) ``` ``` ## # A tibble: 12 × 5 ## # Groups: sex, class [6] ## class sex survived n prop ## <fct> <chr> <chr> <int> <dbl> ## 1 1st female Died 3 0.0319 ## 2 1st female Survived 91 0.968 ## 3 1st male Died 77 0.631 ## 4 1st male Survived 45 0.369 ## 5 2nd female Died 6 0.0789 ## 6 2nd female Survived 70 0.921 ## 7 2nd male Died 91 0.843 ## 8 2nd male Survived 17 0.157 ## 9 3rd female Died 72 0.5 ## 10 3rd female Survived 72 0.5 ## # ℹ 2 more rows ``` --- ## If you only need one category .pull-left[ ``` r titanic_df |> group_by(class, sex) |> summarize(prop_survived = sum(survived == "Survived")/n()) ``` ``` ## # A tibble: 6 × 3 ## # Groups: class [3] ## class sex prop_survived ## <fct> <chr> <dbl> ## 1 1st female 0.968 ## 2 1st male 0.369 ## 3 2nd female 0.921 ## 4 2nd male 0.157 ## 5 3rd female 0.5 ## 6 3rd male 0.135 ``` ] .pull-right[ ``` r titanic_df |> count(class, sex, survived) |> group_by(sex, class) |> mutate(prop = n/sum(n)) |> filter(survived == "Survived") ``` ``` ## # A tibble: 6 × 5 ## # Groups: sex, class [6] ## class sex survived n prop ## <fct> <chr> <chr> <int> <dbl> ## 1 1st female Survived 91 0.968 ## 2 1st male Survived 45 0.369 ## 3 2nd female Survived 70 0.921 ## 4 2nd male Survived 17 0.157 ## 5 3rd female Survived 72 0.5 ## 6 3rd male Survived 47 0.135 ``` ] --- ## Displaying in wide format ``` r titanic_df |> group_by(class, sex) |> summarize(prop_survived = sum(survived == "Survived")/n()) |> pivot_wider(names_from = class, values_from = prop_survived) ``` ``` ## # A tibble: 2 × 4 ## sex `1st` `2nd` `3rd` ## <chr> <dbl> <dbl> <dbl> ## 1 female 0.968 0.921 0.5 ## 2 male 0.369 0.157 0.135 ``` --- ## Cleaning up with `kableExtra` ``` r titanic_df |> group_by(class, sex) |> summarize(prop_survived = sum(survived == "Survived")/n()) |> pivot_wider(names_from = class, values_from = prop_survived) |> kbl(digits = 2) |> kable_minimal() ``` <img src="img/titanic_wide_table.png" alt="" width="60%" style="display: block; margin: auto;" />