This question already has answers here:
Aggregate one data frame by time intervals from another data frame
(3 answers)
Closed 1 year ago.
I've posted this as another question, but realised I've got my sample data wrong.
I've got two separate datasets. df1 looks like this:
loc_ID year observations
nin212 2002 90
nin212 2003 98
nin212 2004 102
cha670 2001 18
cha670 2002 19
cha670 2003 21
df2 looks like this:
loc_ID start_year end_year
nin212 2002 2003
nin212 2003 2004
cha670 2001 2002
cha670 2002 2003
I want to calculate the number of observations in the time intervals (start_year to end_year) per loc_ID. In the example above, I would like to achieve this final dataset:
loc_ID start_year end_year observations
nin212 2002 2003 188
nin212 2003 2004 200
cha670 2001 2002 37
cha670 2002 2003 40
How could I do this?
We can do a non-equi join
library(data.table)
setDT(df2)[, observations := setDT(df1)[df2, sum(observations),
on = .(loc_ID, year >= start_year, year <= end_year),
by = .EACHI]$V1]
-output
df2
# loc_ID start_year end_year observations
#1: nin212 2002 2003 188
#2: nin212 2003 2004 200
#3: cha670 2001 2002 37
#4: cha670 2002 2003 40
data
structure(list(loc_ID = c("nin212", "nin212", "nin212", "cha670",
"cha670", "cha670"), year = c(2002L, 2003L, 2004L, 2001L, 2002L,
2003L), observations = c(90L, 98L, 102L, 18L, 19L, 21L)),
class = "data.frame", row.names = c(NA,
-6L))
> dput(df2)
structure(list(loc_ID = c("nin212", "nin212", "cha670", "cha670"
), start_year = c(2002L, 2003L, 2001L, 2002L), end_year = c(2003L,
2004L, 2002L, 2003L)), class = "data.frame", row.names = c(NA,
-4L))
Related
I have a panel dataset that goes like this
year
id
treatment_year
time_to_treatment
outcome
2000
1
2011
-11
2
2002
1
2011
-10
3
2004
2
2015
-9
22
and so on and so forth. I am trying to deal with the outliers by 'Winsorize'. The end goal is to make a scatterplot with time_to_treatment on the X axis and outcome on the Y.
I would like to replace the outcomes for each time_to_treatment by its winsorized outcomes, i.e. replace all extreme values with the 5% and 95% quantile values.
So far what I have tried to do is this but it doesn't work.
for(i in range(dataset$time_to_treatment)){
dplyr::filter(dataset, time_to_treatment == i)$outcome <- DescTools::Winsorize(dplyr::filter(dataset,time_to_treatment==i)$outcome)
}
I get the error - Error in filter(dataset, time_to_treatment == i) <- *vtmp* :
could not find function "filter<-"
Would anyone able to give a better way?
Thanks.
my actual data
where: conflicts = outcome, commission = year of treatment, CD_mun = id.
The concerned time period indicator is time_to_t
Groups: year, CD_MUN, type [6]
type
CD_MUN
year
time_to_t
conflicts
commission
chr
dbl
dbl
dbl
int
dbl
manif
1100023
2000
-11
1
2011
manif
1100189
2000
-3
2
2003
manif
1100205
2000
-9
5
2009
manif
1500602
2000
-4
1
2004
manif
3111002
2000
-11
2
2011
manif
3147006
2000
-10
1
2010
Assuming, "time periods" refer to 'commission' column, you may use ave.
transform(dat, conflicts_w=ave(conflicts, commission, FUN=DescTools::Winsorize))
# type CD_MUN year time_to_t conflicts commission conflicts_w
# 1 manif 1100023 2000 -11 1 2011 1.05
# 2 manif 1100189 2000 -3 2 2003 2.00
# 3 manif 1100205 2000 -9 5 2009 5.00
# 4 manif 1500602 2000 -4 1 2004 1.00
# 5 manif 3111002 2000 -11 2 2011 1.95
# 6 manif 3147006 2000 -10 1 2010 1.00
Data:
dat <- structure(list(type = c("manif", "manif", "manif", "manif", "manif",
"manif"), CD_MUN = c(1100023L, 1100189L, 1100205L, 1500602L,
3111002L, 3147006L), year = c(2000L, 2000L, 2000L, 2000L, 2000L,
2000L), time_to_t = c(-11L, -3L, -9L, -4L, -11L, -10L), conflicts = c(1L,
2L, 5L, 1L, 2L, 1L), commission = c(2011L, 2003L, 2009L, 2004L,
2011L, 2010L)), class = "data.frame", row.names = c(NA, -6L))
For a start you may use this:
# The data
set.seed(123)
df <- data.frame(
time_to_treatment = seq(-15, 0, 1),
outcome = sample(1:30, 16, replace=T)
)
# A solution without Winsorize based solely on dplyr
library(dplyr)
df %>%
mutate(outcome05 = quantile(outcome, probs = 0.05), # 5% quantile
outcome95 = quantile(outcome, probs = 0.95), # 95% quantile
outcome = ifelse(outcome <= outcome05, outcome05, outcome), # replace
outcome = ifelse(outcome >= outcome95, outcome95, outcome)) %>%
select(-c(outcome05, outcome95))
You may adapt this to your exact problem.
I am trying to melt/stack/gather multiple specific columns of a dataframe into 2 columns, retaining all the others.
I have tried many, many answers on stackoverflow without success (some below). I basically have a situation similar to this post here:
Reshaping multiple sets of measurement columns (wide format) into single columns (long format)
only many more columns to retain and combine. It is important to mention my year columns are factors and I have many, many more columns than the sample listed below so I want to call column names not positions.
>df
ID Code Country year.x value.x year.y value.y year.x.x value.x.x
1 A USA 2000 34.33422 2001 35.35241 2002 42.30042
1 A Spain 2000 34.71842 2001 39.82727 2002 43.22209
3 B USA 2000 35.98180 2001 37.70768 2002 44.40232
3 B Peru 2000 33.00000 2001 37.66468 2002 41.30232
4 C Argentina 2000 37.78005 2001 39.25627 2002 45.72927
4 C Peru 2000 40.52575 2001 40.55918 2002 46.62914
I tried using the pivot_longer in tidyr based on the post above which seemed very similar, which resulted in various errors depending on what I did:
pivot_longer(df,
cols = -c(ID, Code, Country),
names_to = c(".value", "group"),
names_sep = ".")
I also played with melt in reshape2 in various ways which either melted only the values columns or only the years columns. Such as:
new.df <- reshape2:::melt(df, id.var = c("ID", "Code", "Country"), measure.vars=c("value.x", "value.y", "value.x.x", "value.y.y", "value.x.x.x", "value.y.y.y"), value.name = "value", variable.vars=c('year.x','year.y', "year.x.x", "year.y.y", "year.x.x.x", "year.y.y.y", "value.x", variable.name = "year")
I also tried dplyr gather based on other posts but I find it extremely difficult to understand the help page and posts.
To be clear what I am looking to achieve:
ID Code Country year value
1 A USA 2000 34.33422
1 A Spain 2000 34.71842
3 B USA 2000 35.98180
3 B Peru 2000 33.00000
4 C Argentina2000 37.78005
4 C Peru 2000 40.52575
1 A USA 2001 35.35241
1 A Spain 2001 39.82727
3 B USA 2001 37.70768
3 B Peru 2001 37.66468
4 C Argentina2001 39.25627
4 C Peru 2001 40.55918
1 A USA 2002 42.30042
etc.
I really appreciate the help here.
We can specify the names_pattern
library(tidyr)
library(dplyr)
df %>%
pivot_longer(cols = -c(ID, Code, Country),
names_to = c(".value", "group"),names_pattern = "(.*)\\.(.*)")
Or use the names_sep with escaped . as according to ?pivot_longer
names_sep - names_sep takes the same specification as separate(), and can either be a numeric vector (specifying positions to break on), or a single string (specifying a regular expression to split on).
which implies that by default the regex is on and the . in regex matches any character and not the literal dot. To get the literal value, either escape or place it inside square bracket
pivot_longer(df,
cols = -c(ID, Code, Country),
names_to = c(".value", "group"),
names_sep = "\\.")
# A tibble: 18 x 6
# ID Code Country group year value
# <int> <chr> <chr> <chr> <int> <dbl>
# 1 1 A USA x 2000 34.3
# 2 1 A USA y 2001 35.4
# 3 1 A USA z 2002 42.3
# 4 1 A Spain x 2000 34.7
# 5 1 A Spain y 2001 39.8
# 6 1 A Spain z 2002 43.2
# 7 3 B USA x 2000 36.0
# 8 3 B USA y 2001 37.7
# 9 3 B USA z 2002 44.4
#10 3 B Peru x 2000 33
#11 3 B Peru y 2001 37.7
#12 3 B Peru z 2002 41.3
#13 4 C Argentina x 2000 37.8
#14 4 C Argentina y 2001 39.3
#15 4 C Argentina z 2002 45.7
#16 4 C Peru x 2000 40.5
#17 4 C Peru y 2001 40.6
#18 4 C Peru z 2002 46.6
Update
For the updated dataset
library(stringr)
df2 %>%
rename_at(vars(matches("year|value")), ~
str_replace(., "^([^.]+\\.[^.]+)\\.([^.]+)$", "\\1\\2")) %>%
pivot_longer(cols = -c(ID, Code, Country),
names_to = c(".value", "group"),names_pattern = "(.*)\\.(.*)")
Or without the rename, use regex lookaround
df2 %>%
pivot_longer(cols = -c(ID, Code, Country),
names_to = c(".value", "group"),
names_sep = "(?<=year|value)\\.")
data
df <- structure(list(ID = c(1L, 1L, 3L, 3L, 4L, 4L), Code = c("A",
"A", "B", "B", "C", "C"), Country = c("USA", "Spain", "USA",
"Peru", "Argentina", "Peru"), year.x = c(2000L, 2000L, 2000L,
2000L, 2000L, 2000L), value.x = c(34.33422, 34.71842, 35.9818,
33, 37.78005, 40.52575), year.y = c(2001L, 2001L, 2001L, 2001L,
2001L, 2001L), value.y = c(35.35241, 39.82727, 37.70768, 37.66468,
39.25627, 40.55918), year.z = c(2002L, 2002L, 2002L, 2002L, 2002L,
2002L), value.z = c(42.30042, 43.22209, 44.40232, 41.30232, 45.72927,
46.62914)), class = "data.frame", row.names = c(NA, -6L))
df2 <- structure(list(ID = c(1L, 1L, 3L, 3L, 4L, 4L), Code = c("A",
"A", "B", "B", "C", "C"), Country = c("USA", "Spain", "USA",
"Peru", "Argentina", "Peru"), year.x = c(2000L, 2000L, 2000L,
2000L, 2000L, 2000L), value.x = c(34.33422, 34.71842, 35.9818,
33, 37.78005, 40.52575), year.y = c(2001L, 2001L, 2001L, 2001L,
2001L, 2001L), value.y = c(35.35241, 39.82727, 37.70768, 37.66468,
39.25627, 40.55918), year.x.x = c(2002L, 2002L, 2002L, 2002L,
2002L, 2002L), value.x.x = c(42.30042, 43.22209, 44.40232, 41.30232,
45.72927, 46.62914)), class = "data.frame", row.names = c(NA,
-6L))
I have two data sets (one for each country) that look like this:
dfGermany
Country Sales Year Code
Germany 2000 2000 221
Germany 1500 2001 150
Germany 2150 2002 270
dfJapan
Country Sales Year Code
Japan 500 2000 221
Japan 750 2001 221
Japan 800 2001 270
Japan 1000 2002 270
Code here is the "name" of the product. What I want to do is to take half the Japanese sell and add it to the df for Germany if the code and the year matches.
So for instance, half of the sales value for product 221 and 270 in dfJapan (250 € and 500 €) should be added to dfGermany for year 2000 and 2002. But nothing should happen to the values for 2001 since the code does not match with the year.
I tried with merge, but that function did not work since the data is of different size and I also want to match both year and value.
We can do a join on 'Year', 'Code' and then update the 'dfGermany' 'Sales' column
library(data.table)
setDT(dfGermany)[dfJapan, Sales := Sales + i.Sales/2, on = .(Year, Code)]
dfGermany
# Country Sales Year Code
#1: Germany 2250 2000 221
#2: Germany 1500 2001 150
#3: Germany 2650 2002 270
data
dfGermany <- structure(list(Country = c("Germany", "Germany", "Germany"),
Sales = c(2000, 1500, 2150), Year = 2000:2002, Code = c(221L,
150L, 270L)), row.names = c(NA, -3L), class = "data.frame")
dfJapan <- structure(list(Country = c("Japan", "Japan", "Japan", "Japan"
), Sales = c(500L, 750L, 800L, 1000L), Year = c(2000L, 2001L,
2001L, 2002L), Code = c(221L, 221L, 270L, 270L)),
class = "data.frame", row.names = c(NA, -4L))
Using dplyr and #akrun's provided data:
library(dplyr)
dfGermany %>%
left_join(dfJapan %>%
select(Year, Code, sales_japan = Sales),
by = c('Year', 'Code')) %>%
mutate(Sales = Sales + coalesce(sales_japan / 2, 0)) %>%
select(-sales_japan)
> dfGermany
Country Sales Year Code
1 Germany 2250 2000 221
2 Germany 1500 2001 150
3 Germany 2650 2002 270
Quarterly data from a data provider has the issue that for some variables the quarterly data values are actually Year-to-date figures. That means the values are the sum of all previous quarters (Q2 = Q1 + Q2 , Q3 = Q1 + Q2 + Q3, ...).
The structure of the original data looks the following:
library(data.table)
library(plyr)
dt.quarter.test <- structure(list(Year = c(2000L, 2000L, 2000L, 2000L, 2001L, 2001L, 2001L, 2001L)
, Quarter = c(1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L)
, Data.Year.to.Date = c(162, 405, 610, 938, 331, 1467, 1981, 2501))
, .Names = c("Year", "Quarter", "Data.Year.to.Date"), class = c("data.table", "data.frame"), row.names = c(NA, -8L))
In order to calculate the quarterly values I therefore need to subtract the previous Quarter from Q2, Q3 and Q4.
I've managed to get the desired results by using the ddply function from the plyr package.
dt.quarter.result <- ddply(dt.quarter.test, "Year"
, transform
, Data.Quarterly = Data.Year.to.Date - shift(Data.Year.to.Date, n = 1L, type = "lag", fill = 0))
dt.quarter.result
Year Quarter Data.Year.to.Date Data.Quarterly
1 2000 1 162 162
2 2000 2 405 243
3 2000 3 610 205
4 2000 4 938 328
5 2001 1 331 331
6 2001 2 1467 1136
7 2001 3 1981 514
8 2001 4 2501 520
But I am not really happy with the command, since it seems quite clumsy and I would like to get some input on how to improve it and especially do it directly within the data.table.
Here is the data.table syntax, and you might find data.table cheat sheet helpful:
library(data.table)
dt.quarter.test[, Data.Quarterly := Data.Year.to.Date - shift(Data.Year.to.Date, fill = 0), Year][]
# Year Quarter Data.Year.to.Date Data.Quarterly
# 1: 2000 1 162 162
# 2: 2000 2 405 243
# 3: 2000 3 610 205
# 4: 2000 4 938 328
# 5: 2001 1 331 331
# 6: 2001 2 1467 1136
# 7: 2001 3 1981 514
# 8: 2001 4 2501 520
This question already has answers here:
Adding a column of means by group to original data [duplicate]
(4 answers)
Closed 6 years ago.
Imagine I have the following data:
Year Month State ppo
2011 Jan CA 220
2011 Feb CA 250
2012 Jan CA 230
2011 Jan WA 200
2011 Feb WA 210
I need to calculate the mean for each state for the year, so the output would look something like this:
Year Month State ppo annualAvg
2011 Jan CA 220 230
2011 Feb CA 240 230
2012 Jan CA 260 260
2011 Jan WA 200 205
2011 Feb WA 210 205
where the annual average is the mean of any entries for that state in the same year. If the year and state were constant I would know how to do this, but somehow the fact that they are variable is throwing me off.
Looking around, it seems like maybe ddply is what I want to be using for this (https://stats.stackexchange.com/questions/8225/how-to-summarize-data-by-group-in-r), but when I tried to use it I was doing something wrong and kept getting errors (I have tried so many variations of it that I won't bother to post them all here). Any idea how I am actually supposed to be doing this?
Thanks for the help!
Try this:
library(data.table)
setDT(df)
df[ , annualAvg := mean(ppo) , by =.(Year, State) ]
Base R: df$ppoAvg <- ave(df$ppo, df$State, df$Year, FUN = mean)
Using dplyr with group_by %>% mutate to add a column:
library(dplyr)
df %>% group_by(Year, State) %>% mutate(annualAvg = mean(ppo))
#Source: local data frame [5 x 5]
#Groups: Year, State [3]
# Year Month State ppo annualAvg
# (int) (fctr) (fctr) (int) (dbl)
#1 2011 Jan CA 220 235
#2 2011 Feb CA 250 235
#3 2012 Jan CA 230 230
#4 2011 Jan WA 200 205
#5 2011 Feb WA 210 205
Using data.table:
library(data.table)
setDT(df)[, annualAvg := mean(ppo), .(Year, State)]
df
# Year Month State ppo annualAvg
#1: 2011 Jan CA 220 235
#2: 2011 Feb CA 250 235
#3: 2012 Jan CA 230 230
#4: 2011 Jan WA 200 205
#5: 2011 Feb WA 210 205
Data:
structure(list(Year = c(2011L, 2011L, 2012L, 2011L, 2011L), Month = structure(c(2L,
1L, 2L, 2L, 1L), .Label = c("Feb", "Jan"), class = "factor"),
State = structure(c(1L, 1L, 1L, 2L, 2L), .Label = c("CA",
"WA"), class = "factor"), ppo = c(220L, 250L, 230L, 200L,
210L), annualAvg = c(235, 235, 230, 205, 205)), .Names = c("Year",
"Month", "State", "ppo", "annualAvg"), class = c("data.table",
"data.frame"), row.names = c(NA, -5L), .internal.selfref = <pointer: 0x105000778>)