class: center, middle, inverse, title-slide # Tidying data ## <br><br> Introduction to Data Science with R and Tidyverse ### based on datasciencebox.org --- layout: true <div class="my-footer"> <span> Introduction to Data Science with R and Tidyverse | Lukas Jürgensmeier, Matteo Fina, Jan Bischoff | based on <a href="https://datasciencebox.org" target="_blank">datasciencebox.org</a> </span> </div> --- class: middle # We... .huge[.green[have]] data organised in an unideal way for our analysis .huge[.pink[want]] to reorganise the data to carry on with our analysis --- ## Data: Sales <br> .pull-left[ ### .green[We have...] ``` ## # A tibble: 2 x 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 x 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" 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" width="70%" style="display: block; margin: auto;" /> --- ## but this! .center[ <img src="img/tidyr-longer-wider.gif" width="45%" style="background-color: #FDF6E3" style="display: block; margin: auto;" /> ] --- ## Wider vs. longer .pull-left[ ### .green[wider] more columns ``` ## # A tibble: 2 x 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 x 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 x 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 x 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 x 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 ``` ] --- ## 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 x 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" width="70%" style="display: block; margin: auto;" /> .footnote[ Source: [FiveThirtyEight](https://projects.fivethirtyeight.com/trump-approval-ratings/adults/) ] --- ## Data ```r trump ``` ``` ## # A tibble: 2,702 x 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 ## # ... with 2,692 more rows ``` --- ## Goal .pull-left-wide[ <img src="u2-d09-tidying_files/figure-html/unnamed-chunk-20-1.png" width="100%" style="display: block; margin: auto;" /> ] -- .pull-right-narrow[ **Aesthetic mappings:** ✅ x = `date` ❌ y = `rating_value` ❌ color = `rating_type` **Facet:** ✅ `subgroup` (Adults and Voters) ] --- ## 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 x 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="u2-d09-tidying_files/figure-html/unnamed-chunk-22-1.png" 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="u2-d09-tidying_files/figure-html/unnamed-chunk-23-1.png" 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="u2-d09-tidying_files/figure-html/unnamed-chunk-24-1.png" width="75%" style="display: block; margin: auto;" /> ] ]