magrittr

I would like to understand why, in the the dplyr or magrittr package, and more specifically the chaining function %>% has some trouble with the basic operators + , - , * , and / Chaining takes the output of previous statement and feeds it as first argument of the next: 1:10 %>% sum # [55] Thus how come this doesn't work 1:10 %>% *2 %>% sum 1:10 %>% .*2 %>% sum I also found that the following syntax works for adding/substracting, but not multiply or divide. why so? 1:10 %>% +(2) # works OK 1:10 %>% *(2) # nope... So should I write an anonymous function even to do a *2 operation on my data.frame

I've been experimenting quite a bit with the increasingly popular %>% operator from the magrittr package. I've used it enough that I've set a keyboard shortcut to save me typing: shift + command + . instead of space , shift + 5 , shift + . , shift + 5 , space . This is great in SublimeTetxt2 but Rstudio does not allow services it does not work if I'm working within Rstudio projects. So my question is: Can you define text-inserting key-bindings or shortcuts within Rstudio ? This would be exactly synonymous with the alt + - binding for the assignment <- operator that is oxygen to the otter . If

Given the following data: set.seed(1) data <- data.frame(o=c('a','a','a','a','b','b','b','b','c','c','c','c'), t=c(1,2,3,4,1,2,3,4,1,2,3,4), u=runif(12), v=runif(12)) data o t u v 1 a 1 0.26550866 0.6870228 2 a 2 0.37212390 0.3841037 3 a 3 0.57285336 0.7698414 4 a 4 0.90820779 0.4976992 5 b 1 0.20168193 0.7176185 6 b 2 0.89838968 0.9919061 7 b 3 0.94467527 0.3800352 8 b 4 0.66079779 0.7774452 9 c 1 0.62911404 0.9347052 10 c 2 0.06178627 0.2121425 11 c 3 0.20597457 0.6516738 12 c 4 0.17655675 0.1255551 I want to calculate the rolling mean (package zoo) of u per group defined by the coloumn o.

I'm running an example in R, going through the steps and everything is working so far except for this code produces an error: words <- dtm %>% as.matrix %>% colnames %>% (function(x) x[nchar(x) < 20]) Error: could not find function "%>%" I don't understand what the benefit of using this special operator %>% is, and any feedback would be great. You need to load a package (like magrittr or dplyr ) that defines the function first, then it should work. install.packages("magrittr") # package installations are only needed the first time you use it install.packages("dplyr") # alternative installation

My question is where does the piping operator of magrittr package %>% come in the order of operations? I have a problem simmilar to the following: set.seed(10) df <- data.frame(a=rnorm(3),b=rnorm(3),c=rnorm(3)) df/rowSums(df) %>% round(.,3) This results in the following non rounded figures: a b c 1 -0.0121966 0.119878 0.8922125 To get the rounded figures I need to put df/rowSums(df) between brackets. I experimented with the + , - , * , / and ^ and from the results I found the order of operation is as follow: Exponents Piping Multiplication and division Addition and subtraction Is that right or

My dataframe a1 <- c("a","a","b","b","c","d","e","e") b2 <- c("01.01.2015", "02.02.2015", "14.02.2012", "16.08.2008", "17.06.2003", "31.01.2015", "07.01.2022", "09.05.2001") c3 <- c("1a", "2b", "3c", "4d", "5e", "6f", "7g", "8h") d3 <- c(1:8) df2 <- data.frame(a1,b2,c3,d3, stringsAsFactors = F) My code. library(dplyr) library(magrittr) test <- df2 %>% group_by(a1) %>% as.Date(b2, format = "%d.%m.%Y") Error in as.Date.default(., b2, format = "%d.%m.%Y") : do not know how to convert '.' to class “Date” Well, I tried without the pipe: df$b2 <- as.Date(df$b2, format = "%d.%m.%Y") Error in df$b2 :

I want to add a suffix or prefix to most variable names in a data.frame, typically after they've all been transformed in some way and before performing a join. I don't have a way to do this without breaking up my piping. For example, with this data: library(dplyr) set.seed(1) dat14 <- data.frame(ID = 1:10, speed = runif(10), power = rpois(10, 1), force = rexp(10), class = rep(c("a", "b"),5)) I want to get to this result (note variable names): class speed_mean_2014 power_mean_2014 force_mean_2014 1 a 0.5572500 0.8 0.5519802 2 b 0.2850798 0.6 1.0888116 My current approach is: means14 <- dat14 %>