group-by

问题 EDIT: input very new to this. I have a similar problem to this: group by and then count missing variables? Taking the input data from that question: df1 <- data.frame( Z = sample(LETTERS[1:5], size = 10000, replace = T), X1 = sample(c(1:10,NA), 10000, replace = T), X2 = sample(c(1:25,NA), 10000, replace = T), X3 = sample(c(1:5,NA), 10000, replace = T)) as one user proposed, it's possible to use summarise_each : df1 %>% group_by(Z) %>% summarise_each(funs(sum(is.na(.)))) #Source: local data

问题 EDIT: input very new to this. I have a similar problem to this: group by and then count missing variables? Taking the input data from that question: df1 <- data.frame( Z = sample(LETTERS[1:5], size = 10000, replace = T), X1 = sample(c(1:10,NA), 10000, replace = T), X2 = sample(c(1:25,NA), 10000, replace = T), X3 = sample(c(1:5,NA), 10000, replace = T)) as one user proposed, it's possible to use summarise_each : df1 %>% group_by(Z) %>% summarise_each(funs(sum(is.na(.)))) #Source: local data

问题 I am wondering if there is an easy way to create a lag (or lead) of a time series variable in Julia according to a by group or condition? For example: I have a dataset of the following form julia> df1 = DataFrame(var1=["a","a","a","a","b","b","b","b"], var2=[0,1,2,3,0,1,2,3]) 8×2 DataFrame │ Row │ var1 │ var2 │ │ │ String │ Int64 │ ├─────┼────────┼───────┤ │ 1 │ a │ 0 │ │ 2 │ a │ 1 │ │ 3 │ a │ 2 │ │ 4 │ a │ 3 │ │ 5 │ b │ 0 │ │ 6 │ b │ 1 │ │ 7 │ b │ 2 │ │ 8 │ b │ 3 │ And I want to create a

问题 I am wondering if there is an easy way to create a lag (or lead) of a time series variable in Julia according to a by group or condition? For example: I have a dataset of the following form julia> df1 = DataFrame(var1=["a","a","a","a","b","b","b","b"], var2=[0,1,2,3,0,1,2,3]) 8×2 DataFrame │ Row │ var1 │ var2 │ │ │ String │ Int64 │ ├─────┼────────┼───────┤ │ 1 │ a │ 0 │ │ 2 │ a │ 1 │ │ 3 │ a │ 2 │ │ 4 │ a │ 3 │ │ 5 │ b │ 0 │ │ 6 │ b │ 1 │ │ 7 │ b │ 2 │ │ 8 │ b │ 3 │ And I want to create a

问题 I am wondering if there is an easy way to create a lag (or lead) of a time series variable in Julia according to a by group or condition? For example: I have a dataset of the following form julia> df1 = DataFrame(var1=["a","a","a","a","b","b","b","b"], var2=[0,1,2,3,0,1,2,3]) 8×2 DataFrame │ Row │ var1 │ var2 │ │ │ String │ Int64 │ ├─────┼────────┼───────┤ │ 1 │ a │ 0 │ │ 2 │ a │ 1 │ │ 3 │ a │ 2 │ │ 4 │ a │ 3 │ │ 5 │ b │ 0 │ │ 6 │ b │ 1 │ │ 7 │ b │ 2 │ │ 8 │ b │ 3 │ And I want to create a

问题 I am wondering if there is an easy way to create a lag (or lead) of a time series variable in Julia according to a by group or condition? For example: I have a dataset of the following form julia> df1 = DataFrame(var1=["a","a","a","a","b","b","b","b"], var2=[0,1,2,3,0,1,2,3]) 8×2 DataFrame │ Row │ var1 │ var2 │ │ │ String │ Int64 │ ├─────┼────────┼───────┤ │ 1 │ a │ 0 │ │ 2 │ a │ 1 │ │ 3 │ a │ 2 │ │ 4 │ a │ 3 │ │ 5 │ b │ 0 │ │ 6 │ b │ 1 │ │ 7 │ b │ 2 │ │ 8 │ b │ 3 │ And I want to create a

问题 I am wondering if there is an easy way to create a lag (or lead) of a time series variable in Julia according to a by group or condition? For example: I have a dataset of the following form julia> df1 = DataFrame(var1=["a","a","a","a","b","b","b","b"], var2=[0,1,2,3,0,1,2,3]) 8×2 DataFrame │ Row │ var1 │ var2 │ │ │ String │ Int64 │ ├─────┼────────┼───────┤ │ 1 │ a │ 0 │ │ 2 │ a │ 1 │ │ 3 │ a │ 2 │ │ 4 │ a │ 3 │ │ 5 │ b │ 0 │ │ 6 │ b │ 1 │ │ 7 │ b │ 2 │ │ 8 │ b │ 3 │ And I want to create a

问题 I am looking at a stackoverflow post over here: R: Count Number of Observations within a group Here, daily data is created and summed/grouped at monthly intervals (as well as weekly intervals): library(xts) library(dplyr) #create data date_decision_made = seq(as.Date("2014/1/1"), as.Date("2016/1/1"),by="day") date_decision_made <- format(as.Date(date_decision_made), "%Y/%m/%d") property_damages_in_dollars <- rnorm(731,100,10) final_data <- data.frame(date_decision_made, property_damages_in

问题 I am looking at a stackoverflow post over here: R: Count Number of Observations within a group Here, daily data is created and summed/grouped at monthly intervals (as well as weekly intervals): library(xts) library(dplyr) #create data date_decision_made = seq(as.Date("2014/1/1"), as.Date("2016/1/1"),by="day") date_decision_made <- format(as.Date(date_decision_made), "%Y/%m/%d") property_damages_in_dollars <- rnorm(731,100,10) final_data <- data.frame(date_decision_made, property_damages_in

问题 I am looking at a stackoverflow post over here: R: Count Number of Observations within a group Here, daily data is created and summed/grouped at monthly intervals (as well as weekly intervals): library(xts) library(dplyr) #create data date_decision_made = seq(as.Date("2014/1/1"), as.Date("2016/1/1"),by="day") date_decision_made <- format(as.Date(date_decision_made), "%Y/%m/%d") property_damages_in_dollars <- rnorm(731,100,10) final_data <- data.frame(date_decision_made, property_damages_in