library(tidyverse) df <- data.frame(continent = c(“europe”, “africa”), region = c(“north”, “west”, “south”), number = 1:18) I am trying to convert from a long to a wide format but the following syntaxis didn’t work. df_long <- df |> pivot_wider(names_from = region, values_from = number) r tidyverse

I have R dataframe which has one column delivery_by_region which is a JSON string which when parsed returns a multiple row dataframe. I want to create a new dataframe which has all the rows from parsed dataframe of each row and also keep some of the orignal columns like spend and impressions. Here’s my code:

I have searched for an answer over the past two days and can’t figure this out. I have tidyverse loaded but can use any solution.

I have a data.frame that looks like this.

I have a folder of csv files which I need to load into the R environment, gather statistics by multiple groups and then update a list, with the end result being accumulated values per group. Here is some dummy data:-

I have a dataset in R with messy data where each person’s test information is stored in separate corrosponding columns: test_name, test_result, and test_status. All three columns contain comma-separated values for each test if more than one test was performed. I want to extract the individual tests, their corresponding results, and statuses for each person in the most efficient way possible. My end goal is to be able to pull the different tests and their results and status for each person. This is a sample of the data.

I am trying this very simple code:

I am currently working on identifying the largest overlaps of features in samples. For this purpose, I have a data.frame with n samples. Each sample has the same features i. In addition, I have the column state which indicates whether the feature is available for the sample:

# dataframe df <- tibble( # groups group = c(“A”, “A”, “A”, “B”, “A”, “A”, “A”, “C”, “B”, “B”, “C”, “A”), # running count of all groups count = c(1,1,1,2,3,3,3,4,5,5,6,7) In my data there are a series of repeating groups. In this example, rows 1-3 are the first occurrence of group “A”, row 4 is […]

I am trying to execute a staggered Difference-in-Difference with the agg_gt() function, but I keep getting the error “Arguments must have same length”. Of course my dataframe is very small but I tried this for simplification and it gives the same error as my actual dataframe.