I need to find the average prices for all the different weeks. I need to make a ggplot to show how the price is during the year.
When you find the mean how does the empty cells affect the mean?
I have tried several thing including using the melt() function so I only have 3 variables. The variable are factors which I want to find the mean of.
Company variable value
ns Price week 24 1749
ns Price week 24
ns Price week 24 1599
ns Price week 24
ns Price week 24
ns Price week 24 359
ns Price week 24 460
I got more than 300K obs, and would love to have a small data.frame where I only have the Company, Price of different weeks as a mean. Now I have all observations for each week and I need to use the mean for using GGplot.
When I use following code
dat %in% mutate(means=mean(value), na.rm=TRUE)
I got a warning message saying the argument is not numeric or logical: returning NA.
I am looking forward to getting your help!
Clean code from PavoDive's comment
dt[!is.na(value), mean(value), by = .(price, week)]
and even better
dt[ , mean(value, na.rm = TRUE), by = .(price, week)]
Original:
This works using data.table. The first part filters out rows that don't have a number in value. Next is to say we want the average from the value column. Final the by defines how to group the rows.
Code:
dt[value >0 | value<1, .(MeanValues = mean(`value`)), by = c("Price", "Week")][]
Input:
dt <- data.table(`Price` = c("A","B","B","A","A","B","B","A"),
`Week`= c(1,2,1,1,2,2,1,2),
`value` = c(3,7,2,NA,1,46,1,NA))
Price Week value
1: A 1 3
2: B 2 7
3: B 1 2
4: A 1 NA
5: A 2 1
6: B 2 46
7: B 1 1
8: A 2 NA
Output:
1: A 1 3.0
2: B 2 26.5
3: B 1 1.5
4: A 2 1.0
Related
I am trying to average values in different months over vectors of dates. Basically, I have a dataframe with monthly values of a variable, and I'm trying to get a representative average of the experienced values for samples that sometimes span month boundaries.
I've ended up with a dataframe of monthly values, and vectors of the representative number of "month-year" combinations of every sampling duration (e.g. if a sample was out from Jan 28, 2000 to Feb 1, 2000, the vector would show 4 values of Jan 2000, 1 value of Feb 2000). Later I'm going to average the values with these weights, so it's important that the returned variable values appear in representative numbers.
I am having trouble figuring out how to index the dataframe pulling the representative value repeatedly. See below.
# data frame of monthly values
reprex_df <-
tribble(
~my, ~value,
"2000-01", 10,
"2000-02", 11,
"2000-03", 15,
"2000-04", 9,
"2000-05", 13
) %>%
as.data.frame()
# vector of month-year dates from Jan 28 to Feb 1:
reprex_vec <- c("2000-01","2000-01","2000-01","2000-01","2000-02")
# I want to index the df using the vector to get a return vector of
# January value*4, Feb value*1, or 10, 10, 10, 10, 11
# I tried this:
reprex_df[reprex_df$my %in% reprex_vec,"value"]
# but %in% only returns each value once ("10 11", not "10 10 10 10 11").
# is there a different way I should be indexing to account for repeated values?
# eventually I will take an average, e.g.:
mean(reprex_df[reprex_df$my %in% reprex_vec,"value"])
# but I want this average to equal 10.2 for mean(c(10,10,10,10,11)), not 10.5 for mean(c(10,11))
Simple tidy solution with inner_join:
dplyr::inner_join(reprex_df, data.frame(my = reprex_vec), by = "my")$value
in base R:
merge(reprex_df, list(my = reprex_vec))
my value
1 2000-01 10
2 2000-01 10
3 2000-01 10
4 2000-01 10
5 2000-02 11
Perhaps use match from base R to get the index
reprex_df[match(reprex_vec, reprex_df$my),]
my value
1 2000-01 10
1.1 2000-01 10
1.2 2000-01 10
1.3 2000-01 10
2 2000-02 11
Another base R option using setNames
with(
reprex_df,
data.frame(
my = reprex_vec,
value = setNames(value, my)[reprex_vec]
)
)
gives
my value
1 2000-01 10
2 2000-01 10
3 2000-01 10
4 2000-01 10
5 2000-02 11
In R, I need to calculate several time interval variables between resightings of marked individuals. I have a dataset similar to this:
ID Time Day Month
a 11.15 13 6
a 12.35 13 6
a 10.02 14 6
a 19.30 15 6
a 20.46 15 6
.
.
.
b 11.12 8 7
etc
In which each ID represents a different animal marked for individual recognition, and each row contains the date and time in which it was relocated.
For each individual, I'd need to calculate the number of days each animal was observed, the mean and standard deviation of the number of relocations per day, and the mean and standard deviation of the days elapsed between relocations (including 0 days between observations on the same day.
Ideally, I need to obtain a data frame such this:
ID N.Obs N.days mean.Obs.per.Day m.O.D.sd mean.days.elapsed mde.sd
a 27 7 4.2 1.1 1.5 0.5
b 32 5 3.4 0.4 3.2 0.7
c 17 6 4.4 0.2 4.5 1.2
d etc
I've been doing it in using the tapply function and transferring the results to an Excel, but I am sure there must be a relatively simple code which could help me to ignite the process in R.
The OP has requested to aggregate 6 statistics per ID. Four of them can by directly aggregated by grouping by ID. Two (mean.Obs.per.Day and m.O.D.sd) need to be grouped by date and ID first.
Unfortunately, the time stamps are split up in three different fields, Time, Day, and Month with the year missing. As four of the statistics are based on dates, we need to construct a Date column which combines Day, Month, and a dummy year.
The code below utilises the data.table and lubridate packages for efficiency.
library(data.table)
# coerce to data.table and add Date column
setDT(DF)[, Date := lubridate::make_date(, Month, Day)]
# aggregate by ID,
# use temporary variable to hold the day differences between resightings
agg_per_id <- DF[, {
tmp <- as.numeric(diff(Date))
.(N.Obs = .N, N.days = uniqueN(Date),
mean.days.elapsed = mean(tmp),
mde.sd = sd(tmp))
} , by = ID]
# aggregate by Date and ID
agg_per_day_and_id <- DF[, .N, by = .(ID, Date)][
, .(mean.Obs.per.Day = mean(N), m.O.D.sd = sd(N)), by = ID]
# join partial results
result <- agg_per_day_and_id[agg_per_id, on = "ID"]
# reorder columns (for comparison with expected result)
setcolorder(result, c("ID", "N.Obs", "N.days", "mean.Obs.per.Day",
"m.O.D.sd", "mean.days.elapsed", "mde.sd"))
result
ID N.Obs N.days mean.Obs.per.Day m.O.D.sd mean.days.elapsed mde.sd
1: a 5 3 1.666667 0.5773503 0.5 0.5773503
2: b 1 1 1.000000 NA NaN NA
Note that the figures differ from the expected result of the OP due to different input data.
Data
As far as provided by the OP
DF <- readr::read_table(
"ID Time Day Month
a 11.15 13 6
a 12.35 13 6
a 10.02 14 6
a 19.30 15 6
a 20.46 15 6
b 11.12 8 7"
)
>head(df)
person week target actual drop_out organization agency
1: QJ1 1 30 19 TRUE BB LLC
2: GJ2 1 30 18 FALSE BB LLC
3: LJ3 1 30 22 TRUE CC BBR
4: MJ4 1 30 24 FALSE CC BBR
5: PJ5 1 35 55 FALSE AA FUN
6: EJ6 1 35 50 FALSE AA FUN
There are around ~30 weeks in the dataset with a repeating Person ID each week.
I want to look at each person's values FOUR weeks at a time (so week 1-4, 5-9, 10-13, and so on). For each of these chunks, I want to add up all the "actual" columns and divide it by the sum of the "target" columns. Then we could put that value in a column called "monthly percent."
As per Shape's recommendation I've created a month column like so
fullReshapedDT$month <- with(fullReshapedDT, ceiling(week/4))
Trying to figure out how to iterate over the month column and calculate averages now. Trying something like this, but it obviously doesn't work:
fullReshapedDT[,.(monthly_attendance = actual/target,by=.(person_id, month)]
Have you tried creating a group variable? It will allow you to group operations by the four-week period:
setDT(df1)[,grps:=ceiling(week/4) #Create 4-week groups
][,sum(actual)/sum(target), .(person, grps) #grouped operations
][,grps:=NULL][] #Remove unnecessary columns
# person V1
# 1: QJ1 1.1076923
# 2: GJ2 1.1128205
# 3: LJ3 0.9948718
# 4: MJ4 0.6333333
# 5: PJ5 1.2410256
# 6: EJ6 1.0263158
# 7: QJ1 1.2108108
# 8: GJ2 0.6378378
# 9: LJ3 0.9891892
# 10: MJ4 0.8564103
# 11: PJ5 1.1729730
# 12: EJ6 0.8666667
Have a look at the simplified table below. I want for each product a vector containing the quantities sold within each delivery time. A delivery time is defined as 4 days. So if we look at product A, we see that it starts at 03/12/15 and within the first delivery term (until 07/12/15) it has sold a quantity of 4. The second delivery term starts at 08/12/15 and ends at 12/12/15. So for this period there is 1 quantity sold. The following delivery term starts at 13/12/15 and ends at 17/12/15. During these period there are no quantities sold and thus for this period the vector must have a value of 0. In the last period, finally, 2 products are sold. So basically the problem here is that information regarding the periods were no products are sold is missing.
Any ideas on how the vector I want can be created using R? I've been thinking of for or while loops, but these do not seem to give the requested results. Note that the code must be applicable on a real dataset containing over 1000 product categories, so it has to be 'automatized' in one way.
I would be very gratefull if somebody could point me in the right direction.
Product Quantity Date
A 1 03/12/15
A 2 04/12/15
A 1 05/12/15
A 1 08/12/15
A 1 17/12/16
A 1 18/12/16
B 1 19/12/15
B 2 10/05/15
B 2 11/05/15
C 1 01/06/15
C 1 02/06/15
C 1 12/06/15
Assume that dt is the dataset you provided. You'll get a better understanding of the process if you run it step by step (and maybe with an even simpler dataset).
library(lubridate)
library(dplyr)
# create date time columns
dt$Date = dmy(dt$Date)
dt %>%
group_by(Product) %>%
do(data.frame(days = seq(min(.$Date), max(.$Date), by="1 day"))) %>% # create all combinations between product and days
mutate(dist = as.numeric(difftime(days,min(days), units="days"))) %>% # create distance of each day with min date
ungroup() %>%
left_join(dt, by=c("Product"="Product","days"="Date")) %>% # join info to get quantities for each day
mutate(Quantity = ifelse(is.na(Quantity), 0, Quantity), # replace NAs with 0s
id = floor(dist/5 + 1)) %>% # create the 4 period id
group_by(Product, id) %>%
summarise(Sum = sum(Quantity),
min_date = min(days),
max_date = max(days)) %>%
ungroup
# Product id Sum min_date max_date
# 1 A 1 4 2015-12-03 2015-12-07
# 2 A 2 1 2015-12-08 2015-12-12
# 3 A 3 0 2015-12-13 2015-12-17
# 4 A 4 0 2015-12-18 2015-12-22
# 5 A 5 0 2015-12-23 2015-12-27
# 6 A 6 0 2015-12-28 2016-01-01
# 7 A 7 0 2016-01-02 2016-01-06
# 8 A 8 0 2016-01-07 2016-01-11
# 9 A 9 0 2016-01-12 2016-01-16
# 10 A 10 0 2016-01-17 2016-01-21
# .. ... .. ... ... ...
First row of the output tells you that for product A in the first 4 days period (id = 1) you had 4 quantities in total and the period is from 3/12 to 7/12.
I would suggest {dplyr}'s summarise(),mutate() and group_by() functions. group_by() groups your data by desired variables (in your case - product and delivery term),mutate() allows operations on grouped columns, and summarise() applies a summarising function over these groups (in your case sum(Quantity)).
So this is how it will look:
convert date into proper format:
library(dplyr)
df=tbl_df(df)
df$Date=as.Date(df$Date,format="%d/%m/%y")
calculating delivery terms
df=group_by(df,Product) %>% arrange(Date)
df=mutate(df,term=1+unclass((Date-min(Date)))%/%4)
group by product and terms and calculate sum of quantity:
df=group_by(df,Product,term)
summarise(df,sum=sum(Quantity))
Here's a base R way:
df$groups <- ave(as.numeric(df$Date), df$Product, FUN=function(x) {
intrvl <- findInterval(x, seq(min(x), max(x),4))
as.numeric(factor(intrvl))
})
df
# Product Quantity Date groups
# 1 A 1 2015-12-03 1
# 2 A 2 2015-12-04 1
# 3 A 1 2015-12-05 1
# 4 A 1 2015-12-08 2
# 5 A 1 2016-12-17 3
# 6 A 1 2016-12-18 3
# 7 B 1 2015-12-19 2
# 8 B 2 2015-05-10 1
# 9 B 2 2015-05-11 1
# 10 C 1 2015-06-01 1
# 11 C 1 2015-06-02 1
# 12 C 1 2015-06-12 2
The dates should be converted to one of the date classes. I chose as.Date. When it converts to numeric, the output will be the number of days from a specified date. From there, we are able to group by 4 day increments.
Data
df$Date <- as.Date(df$Date, format="%d/%m/%y")
I have an issue that I just cannot seem to sort out. I have a dataset that was derived from a raster in arcgis. The dataset represents every fire occurrence during a 10-year period. Some raster cells had multiple fires within that time period (and, thus, will have multiple rows in my dataset) and some raster cells will not have had any fire (and, thus, will not be represented in my dataset). So, each row in the dataset has a column number (sequential integer) and a row number assigned to it that corresponds with the row and column ID from the raster. It also has the date of the fire.
I would like to assign a unique ID (fire_ID) to all of the fires that are within 4 days of each other and in adjacent pixels from one another (within the 8-cell neighborhood) and put this into a new column.
To clarify, if there were an observation from row 3, col 3, Jan 1, 2000 and another from row 2, col 4, Jan 4, 2000, those observations would be assigned the same fire_ID.
Below is a sample dataset with "rows", which are the row IDs of the raster, "cols", which are the column IDs of the raster, and "dates" which are the dates the fire was detected.
rows<-sample(seq(1,50,1),600, replace=TRUE)
cols<-sample(seq(1,50,1),600, replace=TRUE)
dates<-sample(seq(from=as.Date("2000/01/01"), to=as.Date("2000/02/01"), by="day"),600, replace=TRUE)
fire_df<-data.frame(rows, cols, dates)
I've tried sorting the data by "row", then "column", then "date" and looping through, to create a new fire_ID if the row and column ID were within one value and the date was within 4 days, but this obviously doesn't work, as fires which should be assigned the same fire_ID are assigned different fire_IDs if there are observations in between them in the list that belong to a different fire_ID.
fire_df2<-fire_df[order(fire_df$rows, fire_df$cols, fire_df$date),]
fire_ID=numeric(length=nrow(fire_df2))
fire_ID[1]=1
for (i in 2:nrow(fire_df2)){
fire_ID[i]=ifelse(
fire_df2$rows[i]-fire_df2$rows[i-1]<=abs(1) & fire_df2$cols[i]-fire_df2$cols[i-1]<=abs(1) & fire_df2$date[i]-fire_df2$date[i-1]<=abs(4),
fire_ID[i-1],
i)
}
length(unique(fire_ID))
fire_df2$fire_ID<-fire_ID
Please let me know if you have any suggestions.
I think this task requires something along the lines of hierarchical clustering.
Note, however, that there will be necessarily some degree of arbitrariness in the ids. This is because it is entirely possible that the cluster of fires itself is longer than 4 days yet every fire is less than 4 days away from some other fire in that cluster (and thus should have the same id).
library(dplyr)
# Create the distances
fire_dist <- fire_df %>%
# Normalize dates
mutate( norm_dates = as.numeric(dates)/4) %>%
# Only keep the three variables of interest
select( rows, cols, norm_dates ) %>%
# Compute distance using L-infinite-norm (maximum)
dist( method="maximum" )
# Do hierarchical clustering with "single" aggl method
fire_clust <- hclust(fire_dist, method="single")
# Cut the tree at height 1 and obtain groups
group_id <- cutree(fire_clust, h=1)
# First attach the group ids back to the data frame
fire_df2 <- cbind( fire_df, group_id ) %>%
# Then sort the data
arrange( group_id, dates, rows, cols )
# Print the first 20 records
fire_df2[1:10,]
(Make sure you have dplyr library installed. You can run install.packages("dplyr",dep=TRUE) if not installed. It is a really good and very popular library for data manipulations)
A couple of simple tests:
Test #1. The same forest fire moving.
rows<-1:6
cols<-1:6
dates<-seq(from=as.Date("2000/01/01"), to=as.Date("2000/01/06"), by="day")
fire_df<-data.frame(rows, cols, dates)
gives me this:
rows cols dates group_id
1 1 1 2000-01-01 1
2 2 2 2000-01-02 1
3 3 3 2000-01-03 1
4 4 4 2000-01-04 1
5 5 5 2000-01-05 1
6 6 6 2000-01-06 1
Test #2. 6 different random forest fires.
set.seed(1234)
rows<-sample(seq(1,50,1),6, replace=TRUE)
cols<-sample(seq(1,50,1),6, replace=TRUE)
dates<-sample(seq(from=as.Date("2000/01/01"), to=as.Date("2000/02/01"), by="day"),6, replace=TRUE)
fire_df<-data.frame(rows, cols, dates)
output:
rows cols dates group_id
1 6 1 2000-01-10 1
2 32 12 2000-01-30 2
3 31 34 2000-01-10 3
4 32 26 2000-01-27 4
5 44 35 2000-01-10 5
6 33 28 2000-01-09 6
Test #3: one expanding forest fire
dates <- seq(from=as.Date("2000/01/01"), to=as.Date("2000/01/06"), by="day")
rows_start <- 50
cols_start <- 50
fire_df <- data.frame(dates = dates) %>%
rowwise() %>%
do({
diff = as.numeric(.$dates - as.Date("2000/01/01"))
expand.grid(rows=seq(rows_start-diff,rows_start+diff),
cols=seq(cols_start-diff,cols_start+diff),
dates=.$dates)
})
gives me:
rows cols dates group_id
1 50 50 2000-01-01 1
2 49 49 2000-01-02 1
3 49 50 2000-01-02 1
4 49 51 2000-01-02 1
5 50 49 2000-01-02 1
6 50 50 2000-01-02 1
7 50 51 2000-01-02 1
8 51 49 2000-01-02 1
9 51 50 2000-01-02 1
10 51 51 2000-01-02 1
and so on. (All records identified correctly to belong to the same forest fire.)