apply

问题 I got a function which was solved here. This function takes a column filled with annotations and another grouping column and propagates the annotation to rows with missing values. f1 <- function(data, group_col, expand_col){ data %>% dplyr::group_by({{group_col}}) %>% dplyr::mutate( {{expand_col}} := dplyr::case_when( !is.na({{expand_col}}) ~ {{expand_col}} , any( !is.na({{expand_col}}) ) & is.na({{expand_col}}) ~ paste(unique(unlist(str_split(na.omit({{expand_col}}), " ")) ), collapse = " ")

问题 I am working with several datasets that measure the same variables over many years. I am trying to add a year variable to each dataset, but more generally I want to loop through elements in a vector and add each as a new column in a list of dataframes. This question was similar to mine but I want to iteratively add each element in a vector to the corresponding dataframe as a new column: R - New variables over several data frames in a loop Here's sample data: year <- c(1:3) data1 <- data.frame

问题 I am working with several datasets that measure the same variables over many years. I am trying to add a year variable to each dataset, but more generally I want to loop through elements in a vector and add each as a new column in a list of dataframes. This question was similar to mine but I want to iteratively add each element in a vector to the corresponding dataframe as a new column: R - New variables over several data frames in a loop Here's sample data: year <- c(1:3) data1 <- data.frame

问题 Calculating Euclidean Distances in R is easy. A good example can be found HERE. The vectorised form is: sqrt((known_data[, 1] - unknown_data[, 1])^2 + (known_data[, 2] - unknown_data[, 2])^2) What would be the fastest, most efficient way to get Euclidean Distances for each row of one data frame with all rows of another data frame? A particular function from apply() family? Thanks! 回答1: Maybe you can try outer + dist like below outer( 1:nrow(known_data), 1:nrow(unknown_data), FUN = Vectorize

问题 I'm trying to prepare some demographic data retrieved from Eurostat for further processing, amongst others replacing any missing data with corresponding approximated ones. First I was using data.frames only, but then I got convinced that data.tables might offer some advantages over regular data.frames, so I migrated to data.tables. One thing I've observed while doing so was getting different results when using "na.spline" in combination with "apply" versus "na.spline" as part of the data

问题 I am new to R program and currently working on a set of financial data. Now I got around 10 csv files under my working directory and I want to analyze one of them and apply the same command to the rest of csv files. Here are all the names of these files: ("US%10y.csv", "UK%10y.csv", "GER%10y.csv","JAP%10y.csv", "CHI%10y.csv", "SWI%10y.csv","SOA%10y.csv", "BRA%10y.csv", "CAN%10y.csv", "AUS%10y.csv") For example, because the Date column in CSV files are Factor so I need to change them to Date

问题 I've got the following function that allows me to do some comparison between the rows of two dataframes ( data and ref )and return the index of both rows if there's a match. def get_gene(row): m = np.equal(row[0], ref.iloc[:,0].values) & np.greater_equal(row[2], ref.iloc[:,2].values) & np.less_equal(row[3], ref.iloc[:,3].values) return ref.index[m] if m.any() else None Being a process that takes time (25min for 1.6M rows in data versus 20K rows in ref ), I tried to speed things up by

问题 I've got the following function that allows me to do some comparison between the rows of two dataframes ( data and ref )and return the index of both rows if there's a match. def get_gene(row): m = np.equal(row[0], ref.iloc[:,0].values) & np.greater_equal(row[2], ref.iloc[:,2].values) & np.less_equal(row[3], ref.iloc[:,3].values) return ref.index[m] if m.any() else None Being a process that takes time (25min for 1.6M rows in data versus 20K rows in ref ), I tried to speed things up by

问题 Let's say I have created 10 selectInput dropdowns for a multi plot export and these selectInputs are called "xaxis_1", "xaxis_2", ..... , "xaxis_10" for a single 1 I can write: if(!is.null(input$xaxis_1)) { .... do stuff } to stop it running export when the user hasn't entered any name, and presses submit, to avoid crashes. A bit more general you can check this: if(!is.null(input[[paste('xaxis', i, sep = '_')]])) { ...} how can you write it elegantly so that 1 line of code checks whether ANY

问题 Let's say I have created 10 selectInput dropdowns for a multi plot export and these selectInputs are called "xaxis_1", "xaxis_2", ..... , "xaxis_10" for a single 1 I can write: if(!is.null(input$xaxis_1)) { .... do stuff } to stop it running export when the user hasn't entered any name, and presses submit, to avoid crashes. A bit more general you can check this: if(!is.null(input[[paste('xaxis', i, sep = '_')]])) { ...} how can you write it elegantly so that 1 line of code checks whether ANY