summarize

I am grouping data and then summarizing it, but would also like to retain another column. I do not need to do any evaluations of that column's content as it will always be the same as the group_by column. I can add it to the group_by statement but that does not seem "right". I want to retain State.Full.Name after grouping by State . Thanks TDAAtest <- data.frame(State=sample(state.abb,1000,replace=TRUE)) TDAAtest$State.Full.Name <- state.name[match(TDAAtest$State,state.abb)] TDAA.states <- TDAAtest %>% filter(!is.na(State)) %>% group_by(State) %>% summarize(n=n()) %>% ungroup() %>% arrange

I'm fooling around with babynames pkg. A group_by command works, but after the summarize , one of the groups is dropped from the group list. library(babynames) babynames[1:10000, ] %>% group_by(year, name) %>% head(1) # A tibble: 1 x 5 # Groups: year, name [1] year sex name n prop <dbl> <chr> <chr> <int> <dbl> 1 1880 F Mary 7065 0.07238433 This is fine---two groups, year, name . But after a summarize (which respects the groups correctly), the name group is dropped. Am I missing an easy mistake? babynames[1:10000, ] %>% group_by(year, name) %>% summarise(n = sum(n)) %>% head(1) # A tibble: 1 x

I would like to count the occurrences of a string in a column ....per group. In this case the string is often a substring in a character column. I have some data e.g. ID String village 1 fd_sec, ht_rm, A 2 NA, ht_rm A 3 fd_sec, B 4 san, ht_rm, C The code that I began with is obviously incorrect, but I am failing on my search to find out I could use the grep function in a column and group by village impacts <- se %>% group_by(village) %>% summarise(c_NA = round(sum(sub$en41_1 == "NA")), c_ht_rm = round(sum(sub$en41_1 == "ht_rm")), c_san = round(sum(sub$en41_1 == "san")), c_fd_sec = round(sum

I am wondering if there is a way to use functions with summarise ( dplyr 0.1.2 ) that return multiple values (for instance the describe function from psych package). If not, is it just because it hasn't been implemented yet, or is there a reason that it wouldn't be a good idea? Example: require(psych) require(ggplot2) require(dplyr) dgrp <- group_by(diamonds, cut) describe(dgrp$price) summarise(dgrp, describe(price)) produces: Error: expecting a single value With dplyr >= 0.2 we can use do function for this: library(ggplot2) library(psych) library(dplyr) diamonds %>% group_by(cut) %>% do