In this page for sake of clarity we will save the table at intermediate steps as my_table, adding flextable functions bit-by-bit.

# 7 Total 3440 1469 42.7% 22 1971 57.3% 22Īfter doing this, we can progressively pipe the my_table object through more flextable formatting functions. # hospital N_Known N_Recover Pct_Recover ct_value_Recover N_Death Pct_Death ct_value_Death Table % # Get summary values per hospital-outcome group # group_by ( hospital, outcome ) %>% # Group data summarise ( # Create new summary columns of indicators of interest N = n ( ), # Number of rows per hospital-outcome group ct_value = median ( ct_blood, na.rm = T ) ) %>% # median CT value per group # add totals # bind_rows ( # Bind the previous table with this mini-table of totals linelist %>% filter ( ! is.na ( outcome ) & hospital != 'Missing' ) %>% group_by ( outcome ) %>% # Grouped only by outcome, not by hospital summarise ( N = n ( ), # Number of rows for whole dataset ct_value = median ( ct_blood, na.rm = T ) ) ) %>% # Median CT for whole dataset # Pivot wider and format # mutate (hospital = replace_na ( hospital, 'Total' ) ) %>% pivot_wider ( # Pivot from long to wide values_from = c ( ct_value, N ), # new values are from ct and count columns names_from = outcome ) %>% # new column names are from outcomes mutate ( # Add new columns N_Known = N_Death + N_Recover, # number with known outcome Pct_Death = scales :: percent ( N_Death / N_Known, 0.1 ), # percent cases who died (to 1 decimal) Pct_Recover = scales :: percent ( N_Recover / N_Known, 0.1 ) ) %>% # percent who recovered (to 1 decimal) select ( # Re-order columns hospital, N_Known, # Intro columns N_Recover, Pct_Recover, ct_value_Recover, # Recovered columns N_Death, Pct_Death, ct_value_Death ) %>% # Death columns arrange ( N_Known ) # Arrange rows from lowest to highest (Total row at bottom) table # print # A tibble: 7 × 8 46 Version control and collaboration with Git and Github.33 Demographic pyramids and Likert-scales.

19 Univariate and multivariable regression.

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