Quite often when using the tidyverse to manipulate data, I come up with a situation like the one below. Can someone explain why option 3 works
I am trying to transform a dataset: [1]: https://i.stack.imgur.com/09Ioo.png To something like this: [2]: https://i.stack.imgur.com/vKKu2.png How can I do this
I'm scraping data from a website and depending on the structure of the page. I have an inner join in my final table that either joins clean on WON and LOST vari
In R 4.1 a native pipe operator was introduced that is "more streamlined" than previous implementations. I already noticed one difference between the native |&g
I'd like to convert the below list to a data frame but I'm failing at doing it. The list is taken from Microsoft Azure's API listing all resource types with tec
I have a dataframe like below (the real data has many more people and club): Year Player Club 2005 Phelan Chicago Fire 2007 Phelan Boston Pant 2
Here are two datasets: (this is fake data) library(tidyverse) myfruit <- tibble(fruit_name = c("apple", "pear", "banana", "cherry"), number
I just need to write some code that will look at the difference between the "est_age" and "known_age" columns in my data set. Then I need to know what percenta
I have a data frame that some rows that need to be further grouped by some of the overlapped values among rows col1, col2 a1, 2;3 a2, 2 a3, 3;4 a4, 4 a
I have seen similar posts on this topic (see, for example, here and here) but not one that is specific to the sf-tidyverse ecosystem. I have a series of lakes,
I know this is a duplicate Q but I can't seem to find the post again Using the following data df <- data.frame(A=c(1,1,2,2),B=c(NA,2,NA,4),C=c(3,NA,NA,5),D