12/13/2022 0 Comments Dplyr rename![]() ![]() Whether you're an R user looking to switch to pandas (or the other way around), I hope this guide will help ease the transition. If you know other and better ways of solving the task above, please let me know in the comments below.The comparison is just on syntax (verbage), not performance. I am constantly learning and I am sure there are easier approaches out there that are way more efficient and more readable. When we have a big data frame with many columns, this makes a big difference. )) -> second_approachĪfter comparing algorithms we can see that the second approach is faster. dir = "backward") %>%ĭplyr::mutate_at(vars(everything()), ~ ifelse(. A neat trick, to get the final years in the end, is to add a base year (in our case 2009) to all the indexes we have found. The rest is just using basic dplyr and tidyr functions. lapply(u, lapply, purrr::detect_index, is_one. What we will be doing now is looping over the list of lists with lapply(list, lapply, …) and identify the index which contains the first one starting from the end with purrr’s detect_index() function. Then we create a helper function that is able to recognize the value one. Second Approach With pmap() and Other purrr Functionsįirst, we take the column names of the data frame and remove all the underscores and numbers. There are so many ways to do one task that is very exciting to solve a problem in various ways. The beautiful thing about coding and data munging is that we can get creative with the ways we solve problems. #Dplyr rename code#However, the code took around 10-15 minutes to run. Now, for every variable ( SCORE, TAX, FISCAL, FAM, TEA, and COFFEE) we know the most recent year where one occurred. ![]() We rbind all the data frames in the list and cbind the output to our original data frame. ![]() Afterward we filter out the 0 in the Value column.Īnd we are done. Now, we spread the data frame so the entries in the type column become variables with the particular year where one occurred. So, by default, we will be picking the most recent year that has a one in the value column. This is exactly what we wanted because we ordered by year and value in descending order. keep_all = TRUE))īy default, dplyr::distinct() picks the first unique value of the type column. Purrr::map(~dplyr::arrange(., desc(value), desc(year)))Īfterward, we arrange the data frame by value and year in descending order. Purrr::map(~dplyr::mutate(., year = as.integer(year))) %>% Now, we separate the column names into type and year. There are two columns with the value (0 or 1) and the column name (SCORE_2010, SCORE_2011, SCORE_2012, SCORE_2013 etc.). Second, we map over the list entries and transform the named vectors into a data frame. Tibble::rownames_to_column(var = "type") %>% # TEA_2012 TEA_2013 COFFEE_2010 COFFEE_2011 COFFEE_2012 COFFEE_2013įirst, we use pmap() to get a list of 4 row named vectors. In this post, I will be going over a small example data set which outlines the problem we wanted to solve. Last time, we talked about row-wise operations with purrr and pmap() after a colleague of mine got me thinking about row-wise operations in R. By Pascal Schmidt programming R Tidyverse Tutorial ![]()
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