I am trying to use a select function within a mutate one in dplyr after grouping.
df <- data.frame(city = c(rep(1,4),rep(2,4)),
year = rep(1,8),
victory = rep(c(1,0,0,0),2),
affiliation = c("a","b","c","d","e","f","g","h"))
Non working Code:
data %>%
group_by(.dots=c("city","year")) %>%
mutate(group_affiliation = affiliation(victory==1))
Expectation:
group_affiliation = c(rep("a",4), rep("e",4)
For each city, year group I am trying to get the affiliation value for the entry defined by victory == 1 and then mutating to the whole group.
P.S. I would have done it in two parts, re-merging the groups but my computer is incapable of handling the vector size
library(dplyr)
df %>%
group_by(city,year) %>%
mutate(group_affiliation = affiliation[victory==1])
Related
I am a beginner in R and I am trying to solve a problem in R, which is I guess quite easy for experienced users.
The problem is the following: Customers (A, B, C) are coming in repeatedly using different programms (Prg). I would like to identify "typical sequences" of programs. Therefore, I identify the first programm, they consume, the second, and the third. In a next step, I would like to combine these information to sequences of programms by customer. For a customer first consuming Prg1, then Prg2, then Prg3, the final outcome should be "Prg1-Prg2-Prg3".
The code below produces a dataframe similar to the one I have. Prg is the Programm in the respective year, First is the first year the customer enters, Sec the second and Third the third.
The code produces columns that extract the program consumed in the first contract (Code_1_Prg), second contract (Code_2_Prg) and third contract (Code_3_Prg).
Unfortunately, I am not successful combining these 3 columns to the required goal. I tried to group by ID and save the frist element of the sequence in a new column called "chain1". Here I get the error message "Error in df %>% group_by(ID) %>% df$chain1 = df[df$Code_1_Prg != "NA", :
could not find function "%>%<-", even though I am using the magrittr and dplyr packages.
detach(package:plyr)
library(dplyr)
library(magrittr)
df %>%
group_by(ID) %>%
df$chain1 = df[df$Code_1_Prg!="NA", "Code_1_Prg"]
Below, I share some code, which produces the dataframe and the starting point for extracting the character variable in Code_1_Prg by group.
I would be really grateful, if you could help me with this. Thank you very much in advance!
df <- data.frame("ID"=c("A","A","A","A","B", "B", "B","B","B","C","C", "C", "C","C","C","C"),
"Year_Contract" =c("2010", "2015", "2017","2017","2010","2010", "2015","2015","2020","2015","2015","2017","2017","2017","2018","2018"),
"Prg"=c("AIB","AIB","LLA","LLA","BBU","BBU", "KLU","KLU","DDI","CKN","CKN","BBU","BBU","BBU","KLU","KLU"),
"First"=c("2010","2010","2010","2010","2010","2010", "2010","2010","2010","2015","2015","2015","2015","2015","2015","2015"),
"Sec"=c("2015","2015","2015","2015","2015","2015", "2015","2015","2015","2017","2017","2017","2017","2017","2017","2017"),
"Third"=c("2017","2017","2017","2017","2020","2020", "2020","2020","2020","2018","2018","2018","2018","2018","2018","2018")
)
df$Code_1_Prg <- ifelse(df$Year_Contract == df$First, df$Code_1_Prg <- df$Prg, NA)
df$Code_2_Prg <- ifelse(df$Year_Contract == df$Sec, df$Code_2_Prg <- df$Prg, NA)
df$Code_3_Prg <- ifelse(df$Year_Contract == df$Third, df$Code_3_Prg <- df$Prg, NA)
detach(package:plyr)
library(dplyr)
library(magrittr)
df %>%
group_by(ID) %>%
df$chain1 = df[df$Code_1_Prg!="NA", "Code_1_Prg"]
#This is the final column, I am trying to create
df2 <- data.frame("ID"=c("A","B", "C"),
"Goal" =c("AIB-LLA", "BBU-KLU-DDI", "CKN-BBU-KLU")
)
df <- merge(df, df2, by="ID")
Are you looking for something like this?
libra4ry(dplyr)
df %>%
group_by(ID) %>%
arrange(Year_Contract, .by_group = TRUE) %>%
distinct() %>%
summarise(sequence = toString(Prg))
ID sequence
<chr> <chr>
1 A AIB, AIB, LLA
2 B BBU, KLU, DDI
3 C CKN, BBU, KLU
My dataframe looks like this:
"count","stock"
5,"Google"
21,"Google"
...
27,"Google"
30,"Google"
22,"Google"
4,"Google"
18,"Google"
4,"Google"
12,"Apple"
27,"Apple"
5,"Apple"
25,"Apple"
...
I want to add another column named freq with the relative frequency of each count value:
df_stock %>% mutate(freq = count/sum(df_stock[df_stock$stock == (.)$stock, ]$count))
My problem is that the selection for the current stock value is not working, meaning that sum(df_stock[df_stock$stock == (.)$stock, ]$count) returns the sum of all count values not just the ones for the corresponding stock under evaluation (e.g., Google or Apple).
This should work
library(tidyverse)
df_stock %>%
group_by(stock) %>%
mutate(freq = count/sum(count)
Base R solution:
cbind(df, freq = with(df, ave(count, stock, FUN = prop.table)))
I have a data frame like DF below which will be imported directly from the database (as tibble).
library(tidyverse)
library(lubridate)
date_until <- dmy("31.05.2019")
date_val <- dmy("30.06.2018")
DF <- data.frame( date_bal = as.Date(c("2018-04-30", "2018-05-31", "2018-06-30", "2018-05-31", "2018-06-30")),
department = c("A","A","A","B","B"),
amount = c(10,20,30,40,50)
)
DF <- DF %>%
as_tibble()
DF
It represents the amount of money spent by each department in a specific month. My task is to project how much money will be spent by each department in the following months until a specified date in the future (in this case date_until=31.05.2019)
I would like to use tidyverse in order to generate additional rows for each department where the first column date_bal would be a sequence of dates from the last one from "original" DF up until date_until which is predefined. Then I would like to add additional column called "DIFF" which would represent the difference between DATE_BAL and DATE_VAL, where DATE_VAL is also predefined. My final result would look like this:
Final result
I have managed to do this in the following way:
first filter data from DF for department A
Create another DF2 by populating it with date sequence from min(dat_bal) to date_until from 1.
Merge data frames from 1. and 2. and then add calculated columns using mutate
Since I will have to repeat this procedure for many departments I wonder if it's possible to add rows (create date sequence) in existing DF (without creating a second DF and then merging).
Thanks in advance for your help and time.
I add one day to the dates, create a sequence and then rollback to the last day of the previous month.
seq(min(date_val + days(1)), date_until + days(1), by = 'months')[-1] %>%
rollback() %>%
tibble(date_bal = .) %>%
crossing(DF %>% distinct(department)) %>%
bind_rows(DF %>% select(date_bal, department)) %>%
left_join(DF) %>%
arrange(department, date_bal) %>%
mutate(
amount = if_else(is.na(amount), 0, amount),
DIFF = interval(
rollback(date_val, roll_to_first = TRUE),
rollback(date_bal, roll_to_first = TRUE)) %/% months(1)
)
I have 11 variables in my dataframe. The first is unique identifier of observation (a plane). The second one is a number from 1 to 21 representing flight of a given plane. The rest of the variables are time, velocity, distance, etc.
What I want to do is make new variables for every group (number) of flight e.g. time_1, time_2,..., velocity_1, velocity_2, etc. and consequently, reduce the number of observations (the repeating ones).
I don't really have idea how to start. I was thinking about a mutate function like:
mutate(df, time_1 = ifelse(n_flight == 1, time, NA))
But that would be a lot of typing and a new problem may appear, perhaps.
Basically, you want to convert long to wide data for each variable. You can lapply over these with tidyr::spread in that case. Suppose the data looks like the following:
library(dplyr)
library(tidyr)
df <- data.frame(
ID = c(rep("A", 3), rep("B", 3)),
n_flight = rep(seq(3), 2),
time = seq(19, 24),
velocity = rev(seq(65, 60))
)
Then the following will generate your outcome of interest, as long as you get rid of the extra ID variables.
lapply(
setdiff(names(df), c("ID", "n_flight")), function(x) {
df %>%
select(ID, n_flight, !!x) %>%
tidyr::spread(., key = "n_flight", value = x) %>%
setNames(paste(x, names(.), sep = "_"))
}
) %>%
bind_cols()
Let me know if this wasn't what you were going for.
I am working with data that stops in a specific year and is NA afterwards. And I need to calculate allot of variables based on lagged values of other variables. I would like to find a way that a whole series is calculated instead of each time one year when one of the variables is NA. I was looking at dplyr given that I am working with panel data and thus need to group it by ID.
I provide the example below:
set.seed(1)
df <- data.frame( year = c(seq(2000, 2018), seq(2000, 2018)) , id = c(rep(1, 19),rep(2, 19)), varA = floor(rnorm(38)*100), varB= floor(rnorm(38)*100), varC= floor(rnorm(38)*100))
df <- df %>% mutate(varA = if_else(year>2010, as.double(NA) , varA) ,
varB = if_else(year>2010, as.double(NA) , varB),
varC = if_else(year>2010, as.double(NA) , varC)) %>% group_by(id) %>% arrange(year)
What I would like is to find a way to calculate a variable that is equal to variable C when it is available, but afterwards is equal to a formula based on lagged values of variable C, B and A. When executing the code below, varResult and D are ony calculated for one year given that the lags are only available for one year:
df <- df %>% mutate( varD = lag(varA)*lag(varB),
varRESULT = if_else(is.na(varC), lag(varC, 1)/lag(varD, 2)*lag(varD, 1), varC))
But I would like to find a way to calculate immidiatly the whole serries (taking into account the panel dimension of the data) instead of heaving to repeat the code 7 times. Preferably a solution where you can calculate varD seperatly from varResults, given that in the final application I have multiple variables that are linked to each other.
Proposed solution:
Starting with the first NA, the "recursive" lags of vars varA, varB, and varC are equal to the last value of these variables.
Thus, starting from these initial variables, we can create new variables: varA1, varB1, and varC1 where we fill the NAs with the last value, by id:
library(dplyr)
library(tidyr) # for the function `fill`
df <- df %>%
mutate(varA1 = varA, varB1 = varB, varC1 = varC) %>%
group_by(id) %>%
arrange(year) %>%
fill(varA1, varB1, varC1) # fills with last value
Then, we apply the formula:
df <- df %>%
mutate( varD = lag(varA1)*lag(varB1),
varRESULT = if_else(is.na(varC), lag(varC1, 1)/lag(varD, 2)*lag(varD, 1), varC)) %>%
select(-varA1, -varB1, -varC1)