Created
May 30, 2018 09:48
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Assorted R utilities
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| # Load required packages | |
| load_requirements <- function(pkg){ | |
| new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])] | |
| if (length(new.pkg)) | |
| install.packages(new.pkg, dependencies = TRUE) | |
| sapply(pkg, require, character.only = TRUE) | |
| } | |
| #Returns all items in a list that are not contained in toMatch | |
| #toMatch can be a single item or a list of items | |
| exclude <- function (theList, toMatch){ | |
| return(setdiff(theList,include(theList,toMatch))) | |
| } | |
| #Returns all items in a list that ARE contained in toMatch | |
| #toMatch can be a single item or a list of items | |
| include <- function (theList, toMatch){ | |
| matches <- unique (grep(paste(toMatch,collapse="|"), | |
| theList, value=TRUE)) | |
| return(matches) | |
| } | |
| # Clean up bad XLS formatting | |
| remove_comma_from_numeric <- function(s) { | |
| gsub(",", "", s, fixed = TRUE) | |
| } | |
| # Read in x rows, format as header and sub that in as the header | |
| read_datafile_2header <- function(filename, skip_rows=0, header_rows=1){ | |
| filepath <- paste(data_src_path, filename, sep='') | |
| # Read and format the headers which span multiple lines | |
| headers <- read.csv(filepath, nrows=header_rows, header=FALSE) | |
| headers_names <- sapply(headers,paste,collapse="_") | |
| headers_names <- str_replace_all(headers_names, '-', '_') | |
| headers_names <- str_replace_all(headers_names, ' ', '_') | |
| headers_names <- str_replace_all(headers_names, '__', '_') | |
| headers_names <- str_replace_all(headers_names, '__', '_') | |
| df <- read.csv(file=filepath, skip = skip_rows, header=FALSE, stringsAsFactors=FALSE ) | |
| names(df) <- headers_names | |
| names(df) <- gsub(' ', '_', names(df), fixed = TRUE) | |
| names(df) <- gsub(' ', '_', names(df), fixed = TRUE) | |
| names(df) <- gsub('__', '_', names(df), fixed = TRUE) | |
| names(df) <- gsub('-', '_', names(df), fixed = TRUE) | |
| names(df) <- gsub(',', '', names(df), fixed = TRUE) | |
| names(df) <- gsub('.', '', names(df), fixed = TRUE) | |
| return(df) | |
| } | |
| # https://stackoverflow.com/questions/12945687/read-all-worksheets-in-an-excel-workbook-into-an-r-list-with-data-frames | |
| read_excel_allsheets <- function(filename, tibble = FALSE) { | |
| # I prefer straight data.frames | |
| # but if you like tidyverse tibbles (the default with read_excel) | |
| # then just pass tibble = TRUE | |
| sheets <- readxl::excel_sheets(filename) | |
| x <- lapply(sheets, function(X) readxl::read_excel(filename, sheet = X)) | |
| if(!tibble) x <- lapply(x, as.data.frame) | |
| names(x) <- sheets | |
| x | |
| } | |
| cleaner_worksheet <- function(df){ | |
| # # drop rows with missing values | |
| # df <- df[rowSums(is.na(df)) == 0,] | |
| # # remove serial comma from all variables | |
| # df[,-1] <- as.numeric(gsub(",", "", as.matrix(df[,-1]))) | |
| # # create numeric version of year variable for graphing | |
| # df$Year <- as.numeric(substr(df$year, 1, 4)) | |
| # return cleaned df | |
| return(df) | |
| } |
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