The following dataframe has one row per each patient (the rowids correspond to the patients), and one single column.

i have random df with likert scale answers.All columns are questions named as q1,q2,q3,…,q6.

I am working with the below data frame:

I need to group accounts based on multiple tier inputs:

I am trying to pattern-match all my targeted tissues ("heart", "muscle", "kidney", "liver") in a data frame (pasted below) and list the name of species that have all of the targeted tissues.

I am trying to filter targeted tissues ("heart", "muscle", "kidney", "liver") in my df which is pasted below and list name of species that have all of the targeted tissues.

I have two dataframes in R,

I have a data set with Likert scale answers. I want to create a new data frame that will calculate the percentages of each category of each level. Doing so i have made the following code:

I have a dataset with multiple columns that contains information from different schools. I am standardizing all the information, so all schools display the same information. To do so, I am trying to identify if the same information is displayed in all columns and identify which column and row does not display the same information.

I am trying to create a new column in my data frame (NEW) using the case_when functionality in dplyr. I am able to get the code below to run, but I am wondering if there is a way to create this new column based on the four columns that start with COL_ as opposed to how it is currently written, looking only at COL_1. Otherwise, I would have to repeat each case four times (once for each COL_1, COL_2, COL_3, and COL_4).