tidyverse

I am looking to calculate occupancy in emergency department (ED) with tidyverse. Occupancy is understood here in this particular problem as Admitted but did not leave the hospital within the same hour they were admitted. A clearer example would be: if I came at ED at 12:00:00 and did not leave within the hour I was admitted, then I am occupying the bospital. So for this I need to create a new column Occupancy. (A little insight to give - I want to plot occupancy by hour of the day. Yet I know how to plot this, but do not know how to calculate occupancy. Thus no need for you to be bogged down

For the dataset mtcars2 mtcars2 = mtcars mtcars2 = mtcars2 %>% mutate(cyl9=cyl, disp9=disp, gear2=gear) I want to get a new column which is the sum of multiple columns, by using regular expressions to capture the pattern. This is a solution, however this is done by hard-coding select(mtcars2, cyl9) + select(mtcars2, disp9) + select(mtcars2, gear2) I tried something like this but it gives me a number instead of a vector mtcars2 %>% select(matches("[0-9]")) %>% sum Please dplyr solutions only, since i need to apply these functions to a sql table later on. Thanks! Update.. I need the solution to

I have a data frame df1 that summarises the depth of different fishes over time at different places. On the other hand, I have df2 that summarises the intensity of the currents over time (EVERY THREE HOURS) from the surface to 39 meters depth at intervals of 8 meters ( m0-7 , m8-15 , m16-23 , m24-31 and m32-39 ) in a specific place. As an example: df1<-data.frame(Datetime=c("2016-08-01 15:34:07","2016-08-01 16:25:16","2016-08-01 17:29:16","2016-08-01 18:33:16","2016-08-01 20:54:16","2016-08-01 22:48:16"),Site=c("BD","HG","BD","BD","BD","BD"),Ind=c(16,17,19,16,17,16), Depth=c(5.3,24,36.4,42,NA

I want to mutate multiple columns containing the string "account". Specifically, I want these columns to take "NA" when a certain condition is met, and another value when the condition is not met. Below I present my attempt inspired on here and here . So far, unsuccessful. Still trying, nevertheless any help would be much appreciated. My data df<-as.data.frame(structure(list(low_account = c(1, 1, 0.5, 0.5, 0.5, 0.5), high_account = c(16, 16, 56, 56, 56, 56), mid_account_0 = c(8.5, 8.5, 28.25, 28.25, 28.25, 28.25), mean_account_0 = c(31.174, 30.1922101449275, 30.1922101449275, 33.3055555555556,

I have data frame df1 that summarises fish depths over time. df1$Site tells you the site where the fish was, df1$Ind tells you the individual and df1$Depth tells you the depth where the fish was at a specific df1$Datetime . On the other hand, I have df2 that summarises the intensity of the currents over time (EVERY THREE HOURS) from the surface to 39 meters depth at intervals of 8 meters ( m0-7 , m8-15 , m16-23 , m24-31 and m32-39 ). As an example: df1<-data.frame(Datetime=c("2016-08-01 15:34:07","2016-08-01 16:25:16","2016-08-01 17:29:16","2016-08-01 18:33:16","2016-08-01 20:54:16","2016-08

I wrote a simple function to create tables of percentages in dplyr : library(dplyr) df = tibble( Gender = sample(c("Male", "Female"), 100, replace = TRUE), FavColour = sample(c("Red", "Blue"), 100, replace = TRUE) ) quick_pct_tab = function(df, col) { col_quo = enquo(col) df %>% count(!! col_quo) %>% mutate(Percent = (100 * n / sum(n))) } df %>% quick_pct_tab(FavColour) # Output: # A tibble: 2 x 3 FavColour n Percent <chr> <int> <dbl> 1 Blue 58 58 2 Red 42 42 This works great. However, when I tried to build on top of this, writing a new function that calculated the same percentages with