df <- setNames(data.frame(matrix(, nrow = 100, ncol = 2)), c(“V1”, “V2”)) df$V1 <- “a, b, c, d, e” df$V2 <- “b, c, a, b, e” df$V3 <- paste(df$V1, df$V2, sep = “, “) Hope this helps. solved Join columns of the same data.frame
as.numeric(res$mean) Will do the job. As per your comment, so Forecast is the actual forecast and it is not always just the mean. Consider the following example using a data set taken from here library(forecast) births <- scan(“http://robjhyndman.com/tsdldata/data/nybirths.dat”) birthstimeseries <- ts(births, frequency=12, start=c(1946,1)) fit <- auto.arima(birthstimeseries) res <- forecast(fit, 12) plot(res) As you can see … Read more
Given the explanation of your problem, the following may work: # get all overlapping names bindNames <- intersect(names(ww), names(dd03)) # get a list of rbinded data.frames, keeping unique observations newList <- lapply(bindNames, function(i) unique(rbind(ww[[i]], dd03[[i]]))) If at this point, you want to append all of your data.frames into a single data.frame, you can once again … Read more
list.files() lists all the files in a directory, use this with lapply() and read_xlsx(). In this example I set the working directory to the folder containing the files. library(readxl) files <- list.files() dat <- lapply(files, read_xlsx) dat # [[1]] # # A tibble: 1 x 3 # a a__1 a__2 # <dbl> <dbl> <dbl> # … Read more
This is very difficult to answer given the vagueness of the question, but I’ll make a reproducible example of what I think you’re asking and will give a solution. Say I have a function that returns a data frame: MyFun <- function(x)randu[1:x,] And I have a data frame df that will act an input to … Read more
Here are a couple ways to only keep the text that comes before your pattern, if it exists a <- “GCUGUGGAGAUAACUGCGC” b <- “CUGGCUGAGGUAGUAGUUUGUGCUGUUGGUCGGGUUGUGACAUUGCCCGCUGUGGAGAUAACUGCGCAAGC” strsplit(b, a)[[1]][1] sub(paste0(a, “.*$”), “”, b) Now, you just need to read the files into R and loop over each pattern. I’m not exactly sure what you are looking for, but … Read more
Do this instead (add your labels to the data object, and then use geom_tile and then geom_text) gg <- ggplot(data = cbind(melted_cormat,ids, ages), aes(x=Var1, y=Var2, fill=value)) + scale_x_discrete(labels = ids ) + scale_y_discrete(labels = ids) gg + geom_raster( aes(fill=value)) + geom_text( aes(x=Var1, y=Var2, label = ages), color=”red”, size=3) This brings the needed data into the … Read more
Spearman correlations can be produced with the corr.test() function from the psych package. To illustrate use of Spearman correlation with ranked data, we’ll create two ranked variables for the Motor Trend Cars data set, mtcars, install the psych package, and then run corr.test(). install.packages(“psych”) library(psych) # create a couple of rank variables with mtcars data … Read more
library(stringr) # Get the context of the page thepage = readLines(‘https://data.giss.nasa.gov/impacts/agmipcf/agmerra/’) # Find the lines that contain the names for netcdf files nc4.lines <- grep(‘*.nc4’, thepage) # Subset the original dataset leaving only those lines thepage <- thepage[nc4.lines] #extract the file names str.loc <- str_locate(thepage,’A.*nc4?”‘) #substring file.list <- substring(thepage,str.loc[,1], str.loc[,2]-1) # download all files for … Read more
Converting to French country names is a feature (one of many) that was recently added to the countrycode package, and is in the currently available version (0.19) on CRAN with version 0.19 or higher installed and loaded, you can convert to French country names with a command like… countrycode(c(‘Germany’, ‘USA’), ‘country.name’, ‘country.name.fr’) which will return… … Read more
Because in general l[n] returns a sublist (possibly of length one), whereas l[[n]] returns an element: > lfile[2] [[1]] [1] “file2.xls” # returns SUBLIST of length one, not n’th ELEMENT > lfile[[2]] [1] “file2.xls” # returns ELEMENT From R intro manual: 6.1 Lists: It is very important to distinguish Lst[[1]] from Lst[1]. ‘[[…]]’ is the … Read more
There’s no argument check.names for read_csv. Assuming you’re actually using readr, see the reference: https://cran.r-project.org/web/packages/readr/readr.pdf. But more likely, you’re trying to use read.csv. solved ‘unused argument’ error in read_csv, check.names = FALSE [closed]
If you mean to replace 1000 elements in the x vector with values between 500 and 700, you first need to generate these 1000 elements: r <- runif(1000, min=500, max=700) I am assuming here that random values are uniformly between 500 and 700. Then you need to select places to put these values in: idx … Read more
The table command does what you want: table(df$V1, df$V2, useNA = “ifany”) Table will work on all distinct values. If you want blanks “” to be equivalent to missing values NA, you need to make that change in your data: df[df == “”] = NA Similarly, if the 1 x or 2 x doesn’t matter, … Read more
Based on your comments you might be looking for: library(tidyverse) plot1 <- df %>% gather(key = measure, value = value, -year) %>% ggplot(aes(x = year, y = value, color = measure))+ geom_point()+ geom_line()+ facet_wrap(~measure) plot1 The biggest points here are gather and facet_wrap. I recommend the following two links: https://ggplot2.tidyverse.org/reference/facet_grid.html https://ggplot2.tidyverse.org/reference/facet_wrap.html solved Multiple plots in … Read more