Mean(unname(unlist(df))) # works with numeric vector Sum(unname(unlist(df))) # works with numeric vector In fault(df) : argument is not numeric or logical: returning NA However, mean and many other common functions expect a (numeric) vector as its first argument: class(df) As shown above with sum you can use them nearly interchangeably. The function you want to apply will necessitate, which verb you use. The row-wise output of c_across is a vector (hence the c_), while the row-wise output of pick is a 1-row tibble object: df %>% Mutate(sumrange = sum(pick(x1:x5), na.rm = T))
DPLYR SUMMARIZE SUM IF CODE
In the particular case of the sum function, pick and c_across give the same output for much of the code above: sum_pick % Pivot_longer(cols = starts_with("x")) %>% Though there are probably faster non-tidyverse options, here is a tidyverse option (using tidyr::pivot_longer): library(tidyr) If there isn't a row-wise variant for your function and you have a large data frame, consider a long-format, which is more efficient than rowwise. Large data frame without a row-wise variant function Mutate(sumrange = sum(c_across(x1:x5), na.rm = T)), However, it is inefficient.įor this example, the the row-wise variant rowSums is much faster: library(microbenchmark) Rowwise makes a pipe chain very readable and works fine for smaller data frames. Mutate(sumrow = rowSums(pick(x1:x5), na.rm = T)) However, in your specific case a row-wise variant exists ( rowSums) so you can do the following (note the use of pick instead), which will be faster: df %>% Rowise() will work for any summary function. Mutate(sum_startswithx = sum(c_across(starts_with("x")), na.rm = T)) You can use any number of tidy selection helpers like starts_with, ends_with, contains, etc. Mutate(sumnumeric = sum(c_across(where(is.numeric)), na.rm = T)) # %>% ungroup() # you'll likely want to ungroup after using rowwise() Mutate(sumrange = sum(c_across(x1:x5), na.rm = T)) Since rowwise() is just a special form of grouping and changes the way verbs work you'll likely want to pipe it to ungroup() after doing your row-wise operation. In newer versions of dplyr you can use rowwise() along with c_across to perform row-wise aggregation for functions that do not have specific row-wise variants, but if the row-wise variant exists it should be faster than using rowwise (eg rowSums, rowMeans).
Operation so I would like to try avoid having to give any column names.Īny assistance would be greatly appreciated. In addition, the column names change at different iterations of the loop in which I want to implement this I could use something like: df % mutate(sumrow= x1 + x2 + x3 + x4 + x5)īut this would involve writing out the names of each of the columns. Below is a minimal example of the data frame: library(dplyr) I am thinking of a row-wise analog of the summarise_each or mutate_each function of dplyr. The data entries in the columns are binary(0,1). In case you have any further questions on this topic, please let me know in the comments.My question involves summing up values across multiple columns of a data frame and creating a new column corresponding to this summation using dplyr.
summarizing values by a group such as dates, names, or countries.
DPLYR SUMMARIZE SUM IF HOW TO
This tutorial explained how to add values in order to compute the sum of a column, a variable, or a simple vector, i.e. For that reason, you might want to have a look at some of the other R tutorials that I have published on my website: However, there is much more to learn on the addition of numeric values and also there is much more to learn regarding the R programming language. This tutorial showed how to calculate group sums based on the R programming language. # 3 virginica 329.Īs you can see, the values are the same as in Example 1 (besides the fact that they are rounded). List (name = sum ) ) # Specify function # A tibble: 3 x 2 # Species name # 1 setosa 250. Group_by (Species ) %>% # Specify group indicator
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