You should post the output from dput(Ft_max), that would make it easier for people to load your example. Use functions from the tidyverse package to gather your data in two columns: “key” for the grouping and “values” for the actual values require(tidyverse) Ft_max %>% gather(“key”, “values”) %>% ggplot(aes(x = values, fill = as.factor(key))) + geom_histogram() … Read more
data$sal.cat <- cut(data$salary, breaks=seq(1855, 1915, by=10), right=FALSE) data$ex.cat <- cut(data$ex, breaks=seq(1855, 1915, by=10), right=FALSE) cbind( xtabs(~ sal.cat+ex.cat, data=data), total=rowSums(xtabs(~ sal.cat+ex.cat, data=data))) [1855,1865) [1865,1875) [1875,1885) [1885,1895) [1855,1865) 0 0 0 0 [1865,1875) 0 0 0 0 [1875,1885) 1 0 0 0 [1885,1895) 2 0 0 0 [1895,1905) 4 5 0 1 [1905,1915) 0 0 0 0 … Read more
Please add a reproducible sample next time. Below is the sample input based on the data from the given link. Input: url <- c(“/history/apollo/”, “/shuttle/countdown/”, “/shuttle/missions/sts-73/mission-sts-73.html”, “/shuttle/countdown/liftoff.html”, “/shuttle/missions/sts-73/sts-73-patch-small.gif”, “/images/NASA-logosmall.gif”, “/shuttle/countdown/video/livevideo.gif”, “/shuttle/countdown/countdown.html”, “/shuttle/countdown/”, “https://stackoverflow.com/”, “/shuttle/countdown/count.gif”, “/images/NASA-logosmall.gif”, “/images/KSC-logosmall.gif”, “/shuttle/countdown/count.gif”, “/images/NASA-logosmall.gif”, “/images/KSC-logosmall.gif”, “/images/ksclogo-medium.gif”, “/images/launch-logo.gif”, “/facts/about_ksc.html”, “/shuttle/missions/sts-71/images/KSC-95EC-0916.jpg”) return_code <- c(200, 200, 200, 304, 200, 304, 200, 200, 200, 200, … Read more
Currently, color = ifelse(‘VARIACION'< -0,’#ed1c16′,’#0ca649’) is evaluating whether the string VARIACION is less than 0. While that is not a particularly meaningful question, R does indeed evaluates that to FALSE (in all your cases) and thus prints things in green as a result. More generally, you do not want to use ifelse() in this case. … Read more
Your question isn’t 100% clear, but I believe this is what you’re asking for. I’ve used Matlab. len = 1000; a_ = linspace(0, 5, len); b_ = linspace(0, 5, len); x = zeros(len); for a = 1:len for b = 1:len val(1) = (a_(a)^2 * (1-b_(b))) / (a_(a) + b_(b) – a_(a)*b_(b)) + (1-a_(a))*a_(a); val(2) … Read more
You need to combine DATA2 and DATA3 to one vector and create a vector to define your data. library(ggplot2) x1 <- c(0.2, 0.27, 0.4, 0.37, 0.42, 0.45, 0.70) x2 <- c(0.25, 0.32, 0.28, 0.24, 0.20, 0.19, 0.20) data <- data.frame(x = rep(c(1:7), 2), label = rep(c(“x1”, “x2”), each = 7), y = c(x1, x2)) ggplot(data … Read more
We can use pivot_longer from tidyr library(dplyr) library(tidyr) dt %>% pivot_longer(cols = Apples:Oranges, names_to = ‘Type’, values_to = ‘Values’) %>% arrange(Year, Type) -output # A tibble: 6 x 5 # Year Month Location Type Values # <dbl> <chr> <chr> <chr> <dbl> #1 2020 Jan Store_1 Apples 100 #2 2020 Jan Store_1 Apples 150 #3 2020 … Read more
Using base R you can do something like this do.call(“rbind”, lapply(split(dd, 1:nrow(dd)), function(x) { data.frame(dd[1:3], seat=dd[1,4]:dd[1,5], dd[6:7]) })) tested with dd <- read.csv(text=”event , section , row, seat from , seat to, price, n.tickets 1, A, 10, 6, 8, 120, 3″) Here we split up the data.frame by rows, then create a new data.frame by … Read more
To remove NA values. dataNoNa <- data[!is.na(data$custnr), ] To remove negative values. dataClean <- dataNoNa[dataNoNa$custnr > 0, ] solved Find errors in data with R [closed]
The reason for the observed behavior is the fact, that the factor levels are handled as string. Because of that, the sort is done in an alphabetical order. This results in “100” being before “99” in an ascending order. The workaround was a bit tricky, I have used the stringr package for easier manipulation of … Read more
Data: lorem <- “\nLorem ipsum dolor sit amet, consectetur adipisicing elit,\nsed do eiusmod tempor incididunt ut labore et dolore magna aliqua.\nUt enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi\nut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit\nin voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur\nsint occaecat cupidatat non … Read more
You need joins, and something like tidyr::fill: library(dplyr) library(tidyr) x %>% mutate(slopezz = slopezz[1:n()]) %>% right_join(veh, by = c(‘psi’ = ‘Tim’)) %>% fill(slopezz, .direction = ‘up’) # psi slopezz # 1 169.7 -2.1920 # 2 169.8 -2.1920 # 3 169.9 -2.1920 # 4 170.0 -2.1920 # 5 170.1 -2.1920 # 6 170.2 -2.1920 # . … Read more
We can use row/column indexing to extract the elements from the first dataset and create the ‘result’ column in ‘d2’ d2$result <- d1[-1][cbind(1:nrow(d1), match(d2$mycol, names(d1)[-1]))] d2 # mycol result #1 a 40 #2 bxb 88 #3 bxb 76 #4 ct 33 It could be also that there is a single dataset available i.e. ‘d1’ and … Read more
Your issue is that R won’t like the final table because it has duplicate column names. Maybe you need the data in that format but it’s a bad way to store data because it would be difficult to put the columns back into rows again without a load of manual work. That said, if you … Read more
Using dplyr from the tidyverse you should be able to do this: library(tidyverse) AB <- B %>% rename(ID = `Cust No`, Name = `Customer Name`) %>% full_join(A, ., by = “ID”) rename will let you change the names in B manually so that they match A in the form new_name = old_name. full_join will keep … Read more