I am trying to gather two sets of columns. Each set needs to end up in a new column, but the rows data is linked, so sequential uses of tidyr::gather() won’t guarantee retention of cross-column organization. It looks like tidyr::pivot_longer() can accomplish this, but I am stumped. A similar post is here, but the answer… Read More R: gather multiple sets of columns with tidyr pivot_longer()

This is what I want to achieve: For each child, I would like to have the mother right below him/her. library(tidyverse) mydata <- tribble( ~id, ~child, ~mother, 1, "John", "Jane", 1, "George", "Jane", 2, "Matthew", "Angela", 3, "Gwel", "Macrine", 3, "Tim", "Macrine", 3, "Jim", "Macrine" ) # Desired newdata <- tribble( ~id, ~child, ~mother, 1,… Read More Is it possible to lengthen a given column based another column

There is a dataset like this df <- data.frame( Pseudonym = c("aa", "bb"), KE_date_1 = c("2022-04-01", "2022-04-03"), KE_content_2 = c("high pot", "high pot"), KE_date_3 = c("2022-08-01", "2022-08-04"), KE_content_4 = c("high pot return", "high pot return") ) Pseudonym | KE_date_1 | KE_content_2| KE_date_3 | KE_content_4 ——————————————————————– aa | 2022-04-01| high pot | 2022-08-01 | high pot… Read More Pivot_longer from multiple columns pairwise

I have the following dataset. As you could see, the columns names are composed by three elements. I would like to separate each of the element separted by the underscore: structure(list(post_name_mo = c("3433243juhy234_2323526", "3433243juhy234_2323526", "3433243juhy234_2323526"), minna_yout_qu = c("3433243juhy234_2323526", "3433243juhy234_2323526", "3433243juhy234_2323526"), tour_sima_ta = c("3433243juhy234_2323526", "3433243juhy234_2323526", "3433243juhy234_2323526")), class = "data.frame", row.names = c(NA, -3L)) in a way… Read More How to split elements separated by underscoe in columns names and create

From my dataset of 5000 odd samples, I made a frequency table of dates by survey type and I want to weight the frequencies based on year So in this table I’ve got three columns of data that I want to perform the same case_when series on, so I thought I’d make a ‘for’ loop… Read More for loop with case_when giving error "4 arguments passed to 'for' which requires 3"

I am trying to unnest a dataframe where some columns have lists for values. Ideally, the values would be unnested in such a way that each value creates a new row and values get filled with NA if there are fewer values than those in the list. Example Data: dat <- data.frame(matrix(ncol = 4, nrow… Read More Unnest dataframe that has lists for some values

I have a dataset that I am trying to copy an index date variable for controls based on their matched case’s index date. In this data, case = 1, control = 0. Each pair has a unique ID in the "matchid" column and time = the timepoint. I have the below sample dataset: Study_ID time… Read More Copy values to rows based on conditions

df_wide <- structure(list(name = c("Crushers GC", "4Aces GC"), first_name = c("Charles", "Peter"), last_name = c("Howell III", "Uihlein"), first_name_1 = c("Paul", "Pat"), last_name_1 = c("Casey", "Perez"), first_name_2 = c("Bryson", "Dustin"), last_name_2 = c("DeChambeau", "Johnson"), first_name_3 = c("Anirban", "Patrick"), last_name_3 = c("Lahiri", "Reed")), row.names = c(NA, -2L), class = c("tbl_df", "tbl", "data.frame")) name first_name last_name first_name_1 last_name_1… Read More pivot_longer across multiple columns, how to use names_pattern parameter

I have dataframe with two pieces of information per row: the name of a substrate and its mass. I want to pivot longer the data frame and in the output the name and the mass of each substrate should go into seperate columns. Here is an example of the data frame: library(tidyr) df <- tibble(… Read More R pivot longer – separating multiple observations per row

I have a registration database, that looks like that reg <- tibble( name = c("John","Bill","Chuck"), email= c("john@john.com","bill@acme.com","chuck@someplace.org"), option1=c("meal","meal","drink"), value1=c("vegetarian","vegetarian","beer"), option2=c(NA,"drink","room"), value2=c(NA,"beer","single"), option3=c(NA,"room",NA), value3=c(NA,"single",NA) ) in other words, each of the three options (more in the real data, of course) can end up in different columns, and each registrant may have none, one or many options.… Read More pivot_wider on multiple key:value pairs