See ?complete for details and the Working with dates page for an example. You can set them to 0 using the fill = argument of complete(), which expects a named list (if your counts column is named n, provide fill = list(n = 0). By default, the case count values in any new “expanded” rows will be NA. Within complete() you re-define your date column as a sequence of dates seq.Date() from the minimum to the maximum - thus the dates are expanded. Without this step, a week with no cases reported might not appear in your data!
#Dplyr summarize issues with list series
Use complete() from tidyr so that the aggregated date series is complete including all possible date units within the range. One additional step common for date situations, is to “fill-in” any dates in the sequence that are not present in the data. Once you have this column, you can use count() from dplyr to group the rows by those unique date values and achieve aggregate counts. You can make this column using floor_date() from lubridate, as explained in the Epidemiological weeks section of the Working with dates page. When grouping data by date, you must have (or create) a column for the date unit of interest - for example “day”, “epiweek”, “month”, etc. To add more complex totals rows that involve summary statistics other than sums, see this section of the Descriptive Tables page. Linelist %>% # case linelist tabyl ( age_cat, gender ) %>% # cross-tabulate counts of two columns adorn_totals (where = "row" ) %>% # add a total row adorn_percentages (denominator = "col" ) %>% # convert to proportions with column denominator adorn_pct_formatting ( ) %>% # convert proportions to percents adorn_ns (position = "front" ) %>% # display as: "count (percent)" adorn_title ( # adjust titles row_name = "Age Category", col_name = "Gender" ) # Gender # ℹ 19 more variables: age_years, age_cat, age_cat5, lon, lat, infector, source , # 10 Missing 1469 1389ca 4 NA Death f 27 years # 9 Missing 1469 f393b4 4 NA Recover m 61 years # 7 Missing 1469 07e3e8 4 Recover f 16 years # 6 Port Hos… 1762 be99c8 3 Recover f 16 years # 5 Military… 896 893f25 3 Recover m 3 years # 2 Missing 1469 8689b7 4 NA Recover f 3 years # hospital n case_id generation date_infection date_onset date_hospitalisation date_outcome outcome gender age age_unit Linelist %>% as_tibble ( ) %>% # convert to tibble for nicer printing add_count ( hospital ) %>% # add column n with counts by hospital select ( hospital, n, everything ( ) ) # re-arrange for demo purposes # A tibble: 5,888 × 31 The statistics returned are produced from the entire dataset. You can use sum() to count the number of rows that meet a logical criteria (with double equals =).īelow is an example of summarise() applied without grouped data.
Within the statistical function, list the column to be operated on and any relevant argument (e.g. For example, min(), max(), median(), or sd(). The syntax of summarise() is such that you provide the name(s) of the new summary column(s), an equals sign, and then a statistical function to apply to the data, as shown below. Applying summarise() to grouped data produces those summary statistics for each group. On an ungrouped data frame, the summary statistics will be calculated from all rows. The dplyr function summarise() (or summarize()) takes a data frame and converts it into a new summary data frame, with columns containing summary statistics that you define.
Here we briefly address how its behavior changes when applied to grouped data.
#Dplyr summarize issues with list how to
See the dplyr section of the Descriptive tables page for a detailed description of how to produce summary tables with summarise().
# time_admission, bmi, days_onset_hosp # wt_kg, ht_cm, ct_blood, fever, chills, cough, aches, vomit, temp , # ℹ 19 more variables: age_cat, age_cat5, hospital, lon, lat, infector, source , # case_id generation date_infection date_onset date_hospitalisation date_outcome outcome gender age age_unit age_years # print to see which groups are active ll_by_outcome # A tibble: 5,888 × 30 46 Version control and collaboration with Git and Github.33 Demographic pyramids and Likert-scales.19 Univariate and multivariable regression.
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