data-manipulation

I'm using R. I have a matrix and I want to replace each element of it equal to zero with the corresponding element of the row above. For example, I created the following matrix: AA <- matrix(c(1,2,3,1,4,5,1,0,2), ncol=3, nrow=3) [,1] [,2] [,3] [1,] 1 1 1 [2,] 2 4 0 [3,] 3 5 2 I want to replace 0 with the element AA[1,3]. I would like a function able of doing this for each element of a matrix. We could find the row/column index of elements that are 0 in the matrix ('i1'), then extract the elements that correspond to 1 row above by subtracting one from the row index in 'i1' and replace the

I have a table: Table_Name: price_list --------------------------------------------------- | id | price_type_a | price_type_b | price_type_c | --------------------------------------------------- | 1 | 1234 | 5678 | 9012 | | 2 | 3456 | 7890 | 1234 | | 3 | 5678 | 9012 | 3456 | --------------------------------------------------- I need a select query in Postgres which gives result like this: --------------------------- | id | price_type | price | --------------------------- | 1 | type_a | 1234 | | 1 | type_b | 5678 | | 1 | type_c | 9012 | | 2 | type_a | 3456 | | 2 | type_b | 7890 | | 2 | type_c |

The code below should group the data by year and then create two new columns with the first and last value of each year. library(dplyr) set.seed(123) d <- data.frame( group = rep(1:3, each = 3), year = rep(seq(2000,2002,1),3), value = sample(1:9, r = T)) d %>% group_by(group) %>% mutate( first = dplyr::first(value), last = dplyr::last(value) ) However, it does not work as it should. The expected result would be group year value first last <int> <dbl> <int> <int> <int> 1 1 2000 3 3 4 2 1 2001 8 3 4 3 1 2002 4 3 4 4 2 2000 8 8 1 5 2 2001 9 8 1 6 2 2002 1 8 1 7 3 2000 5 5 5 8 3 2001 9 5 5 9 3

I have one table name Prv_Data which contain previous month of report, having Report_Id and Timeline column. Prv_Data --> Report_ID | Timeline ---------------|-------------- 01 | Weekly @Mon 01 | Weekly @Mon 01 | Weekly @Mon 01 | Weekly @Mon 02 | Weekly @Thru 02 | Weekly @Thru 02 | Weekly @Thru 02 | Weekly @Thru 02 | Weekly @Thru I have another table name as Cur_Month which contain current month details. Cur_Month--> Details | Count --------------|-------- First Date | 05/01/2017 Last Date | 05/31/2017 Friday | 4 Monday | 5 Saturday | 4 Sunday | 4 Thursday | 4 Tuesday | 5 Wednesday | 5 Now I

I would like to apply the same operation to multiple data frames in 'R' but cannot get how to deal with this matter. This is an example of pipe operation in dplyr : library(dplyr) iris %>% mutate(Sepal=rowSums(select(.,starts_with("Sepal"))), Length=rowSums(select(.,ends_with("Length"))), Width=rowSums(select(.,ends_with("Width")))) iris2 <- iris iris3 <- iris Could you suggest how to apply the same pipe function to iris , iris2 and isis3 ? I need to use dplyr piping operation. I suppose map function may help but as I have not fully understand its concept, I got errors to apply it. Sample

Tableau generally works best when input data is in "normalized" format , rather than cross-tab. This is also referred to as converting from "wide format" to "long format". That is, converting from: To: Tableau provides a "reshaping tool" for Excel users , but if you don't have Excel, you're stuck. So how can you get a spreadsheet into this format, without using Excel? Well, you can use this handy Google Sheets script I made. /* normalizeCrossTab: Converts crosstab format to normalized form. Given columns abcDE, the user puts the cursor somewhere in column D. The result is a new sheet,