I have a problem that is very similar to this:
R transform data frame with start and end year into time series however, none of the solutions have worked for me.
This the original df:
df <- data.frame(country = c("Albania", "Albania", "Albania"), leader = c("Sali Berisha", "Sali Berisha", "Sali Berisha"), term = c(2, 2, 2), yearbegin = c(2009,2009, 2009), yearend = c(2013, 2013, 2013))
And it currently looks like this:
#> country leader term yearbegin yearend
#> 1 Albania Sali Berisha 2 2009 2013
#> 2 Albania Sali Berisha 2 2009 2013
#> 3 Albania Sali Berisha 2 2009 2013
And I'm trying to get it to look like this:
#> 1 Albania Sali Berisha 2 2009
#> 2 Albania Sali Berisha 2 2010
#> 3 Albania Sali Berisha 2 2011
#> 4 Albania Sali Berisha 2 2012
#> 5 Albania Sali Berisha 2 2013
When using this code:
library(tidyverse)
gpd_df<- gpd_df %>%
mutate(year = map2(yearbegin, yearend, `:`)) %>%
select(-yearbegin, -yearend) %>%
unnest```
I get a column that looks like this:
```year
2009:2013
2009:2013
2009:2013
Many thanks in advance for your help!!
trying to transform date into time-series form year begin/year end. Have just found errors :')
Use distinct first:
library(dplyr)
library(tidyr)
gpd_df %>%
distinct() %>%
mutate(year = map2(yearbegin, yearend, `:`), .keep = "unused") %>%
unnest_longer(year)
country leader term year
1 Albania Sali Berisha 2 2009
2 Albania Sali Berisha 2 2010
3 Albania Sali Berisha 2 2011
4 Albania Sali Berisha 2 2012
5 Albania Sali Berisha 2 2013
Related
According to the answer of lhs,
https://stackoverflow.com/a/72467827/11124121
#From lhs
library(tidyverse)
data("population")
# create some data to interpolate
population_5 <- population %>%
filter(year %% 5 == 0) %>%
mutate(female_pop = population / 2,
male_pop = population / 2)
interpolate_func <- function(variable, data) {
data %>%
group_by(country) %>%
# can't interpolate if only one year
filter(n() >= 2) %>%
group_modify(~as_tibble(approx(.x$year, .x[[variable]],
xout = min(.x$year):max(.x$year)))) %>%
set_names(c("country", "year", paste0(variable, "_interpolated"))) %>%
ungroup()
}
The data that already exists, i.e. year 2000 and 2005 are also interpolated. I want to keep the orginal data and only interpolate the missing parts, that is,
2001-2004 ; 2006-2009
Therefore, I would like to construct a list:
population_5_list = list(population_5 %>% filter(year %in% c(2000,2005)),population_5 %>% filter(year %in% c(2005,2010)))
And impute the dataframes in the list one by one.
However, a error appeared:
Error in UseMethod("group_by") :
no applicable method for 'group_by' applied to an object of class "list"
I am wondering how can I change the interpolate_func into purrr format, in order to apply to list.
We need to loop over the list with map
library(purrr)
library(dplyr)
map(population_5_list,
~ map(vars_to_interpolate, interpolate_func, data = .x) %>%
reduce(full_join, by = c("country", "year")))
-output
[[1]]
# A tibble: 1,266 × 5
country year population_interpolated female_pop_interpolated male_pop_interpolated
<chr> <int> <dbl> <dbl> <dbl>
1 Afghanistan 2000 20595360 10297680 10297680
2 Afghanistan 2001 21448459 10724230. 10724230.
3 Afghanistan 2002 22301558 11150779 11150779
4 Afghanistan 2003 23154657 11577328. 11577328.
5 Afghanistan 2004 24007756 12003878 12003878
6 Afghanistan 2005 24860855 12430428. 12430428.
7 Albania 2000 3304948 1652474 1652474
8 Albania 2001 3283184. 1641592. 1641592.
9 Albania 2002 3261421. 1630710. 1630710.
10 Albania 2003 3239657. 1619829. 1619829.
# … with 1,256 more rows
# ℹ Use `print(n = ...)` to see more rows
[[2]]
# A tibble: 1,278 × 5
country year population_interpolated female_pop_interpolated male_pop_interpolated
<chr> <int> <dbl> <dbl> <dbl>
1 Afghanistan 2005 24860855 12430428. 12430428.
2 Afghanistan 2006 25568246. 12784123. 12784123.
3 Afghanistan 2007 26275638. 13137819. 13137819.
4 Afghanistan 2008 26983029. 13491515. 13491515.
5 Afghanistan 2009 27690421. 13845210. 13845210.
6 Afghanistan 2010 28397812 14198906 14198906
7 Albania 2005 3196130 1598065 1598065
8 Albania 2006 3186933. 1593466. 1593466.
9 Albania 2007 3177735. 1588868. 1588868.
10 Albania 2008 3168538. 1584269. 1584269.
# … with 1,268 more rows
I'm trying to create a new variable in R containing the initial values of another variable (crime) based on groups (countries) considering the initial period of time observable per group (on panel data framework), my current data looks like this:
country
year
Crime
Albania
2016
2.7369478
Albania
2017
2.0109779
Argentina
2002
9.474084
Argentina
2003
7.7898825
Argentina
2004
6.0739941
And I want it to look like this:
country
year
Crime
Initial_Crime
Albania
2016
2.7369478
2.7369478
Albania
2017
2.0109779
2.7369478
Argentina
2002
9.474084
9.474084
Argentina
2003
7.7898825
9.474084
Argentina
2004
6.0739941
9.474084
I saw that ddply could make it work this way, but the problem is that it is not longer supported by the latest R updates.
Thank you in advance.
Maybe arrange by year, then after grouping by country set Initial_Crime to be the first Crime in the group.
library(tidyverse)
df %>%
arrange(year) %>%
group_by(country) %>%
mutate(Initial_Crime = first(Crime))
Output
country year Crime Initial_Crime
<chr> <int> <dbl> <dbl>
1 Argentina 2002 9.47 9.47
2 Argentina 2003 7.79 9.47
3 Argentina 2004 6.07 9.47
4 Albania 2016 2.74 2.74
5 Albania 2017 2.01 2.74
library(data.table)
setDT(data)[, Initial_Crime:=.SD[1,Crime], by=country]
country year Crime Initial_Crime
1: Albania 2016 2.736948 2.736948
2: Albania 2017 2.010978 2.736948
3: Argentina 2002 9.474084 9.474084
4: Argentina 2003 7.789883 9.474084
5: Argentina 2004 6.073994 9.474084
A data.table solution
setDT(df)
df[, x := 1:.N, country
][x==1, initial_crime := crime
][, initial_crime := nafill(initial_crime, type = "locf")
][, x := NULL
]
I have a data set where I am looking at longitudinal data for countries.
master.set <- data.frame(
Country = c(rep("Afghanistan", 3), rep("Albania", 3)),
Country.ID = c(rep("Afghanistan", 3), rep("Albania", 3)),
Year = c(2015, 2016, 2017, 2015, 2016, 2017),
Happiness.Score = c(3.575, 3.360, 3.794, 4.959, 4.655, 4.644),
GDP.PPP = c(1766.593, 1757.023, 1758.466, 10971.044, 11356.717, 11803.282),
GINI = NA,
Status = 2,
stringsAsFactors = F
)
> head(master.set)
Country Country.ID Year Happiness.Score GDP.PPP GINI Status
1 Afghanistan Afghanistan 2015 3.575 1766.593 NA 2
2 Afghanistan Afghanistan 2016 3.360 1757.023 NA 2
3 Afghanistan Afghanistan 2017 3.794 1758.466 NA 2
4 Albania Albania 2015 4.959 10971.044 NA 2
5 Albania Albania 2016 4.655 11356.717 NA 2
6 Albania Albania 2017 4.644 11803.282 NA 2
I created that Country.ID variable with the intent of turning them into numerical values 1:159.
I am hoping to avoid doing something like this to replace the value at each individual observation:
master.set$Country.ID <- master.set$Country.ID[master.set$Country.ID == "Afghanistan"] <- 1
As I implied, there are 159 countries listed in the data set. Because it' longitudinal, there are 460 observations.
Is there any way to use a for loop to save me a lot of time? Here is what I attempted. I made a couple of lists and attempted to use an ifelse command to tell R to label each country the next number.
Here is what I have:
#List of country names
N.Countries <- length(unique(master.set$Country))
Country <- unique(master.set$Country)
Country.ID <- unique(master.set$Country.ID)
CountryList <- unique(master.set$Country)
#For Loop to make Country ID numerically match Country
for (i in 1:460){
for (j in N.Countries){
master.set[[Country.ID[i]]] <- ifelse(master.set[[Country[i]]] == CountryList[j], j, master.set$Country)
}
}
I received this error:
Error in `[[<-.data.frame`(`*tmp*`, Country.ID[i], value = logical(0)) :
replacement has 0 rows, data has 460
Does anyone know how I can accomplish this task? Or will I be stuck using the ifelse command 159 times?
Thanks!
Maybe something like
master.set$Country.ID <- as.numeric(as.factor(master.set$Country.ID))
Or alternatively, using dplyr
library(tidyverse)
master.set <- master.set %>% mutate(Country.ID = as.numeric(as.factor(Country.ID)))
Or this, which creates a new variable Country.ID2based on a key-value pair between Country.ID and a 1:length(unique(Country)).
library(tidyverse)
master.set <- left_join(master.set,
data.frame( Country = unique(master.set$Country),
Country.ID2 = 1:length(unique(master.set$Country))))
master.set
#> Country Country.ID Year Happiness.Score GDP.PPP GINI Status
#> 1 Afghanistan Afghanistan 2015 3.575 1766.593 NA 2
#> 2 Afghanistan Afghanistan 2016 3.360 1757.023 NA 2
#> 3 Afghanistan Afghanistan 2017 3.794 1758.466 NA 2
#> 4 Albania Albania 2015 4.959 10971.044 NA 2
#> 5 Albania Albania 2016 4.655 11356.717 NA 2
#> 6 Albania Albania 2017 4.644 11803.282 NA 2
#> Country.ID2
#> 1 1
#> 2 1
#> 3 1
#> 4 2
#> 5 2
#> 6 2
library(dplyr)
df<-data.frame("Country"=c("Afghanistan","Afghanistan","Afghanistan","Albania","Albania","Albania"),
"Year"=c(2015,2016,2017,2015,2016,2017),
"Happiness.Score"=c(3.575,3.360,3.794,4.959,4.655,4.644),
"GDP.PPP"=c(1766.593,1757.023,1758.466,10971.044,11356.717,11803.282),
"GINI"=NA,
"Status"=rep(2,6))
df1<-df %>% arrange(Country) %>% mutate(Country_id = group_indices_(., .dots="Country"))
View(df1)
This question already has answers here:
Reshaping data.frame from wide to long format
(8 answers)
Closed 4 years ago.
I'm struggling with a sizeable panel data in long format with multiple variables. It looks like this
set.seed(42)
dat_0=
data.frame(
c(rep('AFG',2),rep('UK',2)),
c(rep(c('GDP','pop'),2)),
runif(4),
runif(4))
colnames(dat_0)<-c('country','variable','2010','2011')
Producing a data frame like this:
country variable 2010 2011
1 AFG GDP 0.535761290 0.7515226
2 AFG pop 0.002272966 0.4527316
3 UK GDP 0.608937453 0.5357900
4 UK pop 0.836801559 0.5373767
And I am trying/struggling to coerce it to this structure
country year GDP pop
1 AFG 2010 0.5357612 0.0022729
2 AFG 2011 0.7515226 0.4527316
3 UK 2010 0.6089374 0.8368015
4 UK 2011 0.5357900 0.5373767
Apologies if repeated, I seem to be struggling with reshape/tidyr/dplyr
You need to gather and then spread:
library(tidyverse)
set.seed(42)
dat_0 <- data.frame(c(rep("AFG", 2), rep("UK", 2)), c(rep(c("GDP", "pop"), 2)), runif(4), runif(4))
colnames(dat_0) <- c("country", "variable", "2010", "2011")
dat_0 %>%
gather(year, value, `2010`, `2011`) %>%
spread(variable, value)
#> country year GDP pop
#> 1 AFG 2010 0.9148060 0.9370754
#> 2 AFG 2011 0.6417455 0.5190959
#> 3 UK 2010 0.2861395 0.8304476
#> 4 UK 2011 0.7365883 0.1346666
Created on 2019-02-20 by the reprex package (v0.2.1)
Looks like you could solve your problem with a mix from spread and gather functions from the tidyverse package.
Edit: actually the package is tidyr, which is part of the tidyverse package
You can solve this problem in two steps.
First: gather by year and values, creating a new column called "measurement"
> dat_1 <- dat_0 %>% gather(key="year",value="measurement","2010":"2011")
> dat_1
country variable year measurement
1 AFG GDP 2010 0.9148060
2 AFG pop 2010 0.9370754
3 UK GDP 2010 0.2861395
4 UK pop 2010 0.8304476
5 AFG GDP 2011 0.6417455
6 AFG pop 2011 0.5190959
7 UK GDP 2011 0.7365883
8 UK pop 2011 0.1346666
Second: spread by your new "variable" and "measurement"
> dat_2 <- dat_1 %>% spread(key="variable",value="measurement")
> dat_2
country year GDP pop
1 AFG 2010 0.9148060 0.9370754
2 AFG 2011 0.6417455 0.5190959
3 UK 2010 0.2861395 0.8304476
4 UK 2011 0.7365883 0.1346666
I sincerly hope this solves your problem.
So, I currently have a dataframe that looks like:
country continent year lifeExp pop gdpPercap
<fctr> <fctr> <int> <dbl> <int> <dbl>
1 Afghanistan Asia 1952 28.801 8425333 779.4453
2 Afghanistan Asia 1957 30.332 9240934 820.8530
3 Afghanistan Asia 1962 31.997 10267083 853.1007
4 Afghanistan Asia 1967 34.020 11537966 836.1971
5 Afghanistan Asia 1972 36.088 13079460 739.9811
6 Afghanistan Asia 1977 38.438 14880372 786.1134
There are 140+ countries. The years are in 5 year intervals. From 1952- 2007 I want to reshape my dataframe such that I get.
Country gdpPercap(1952) gdpPercap(1957) ... gdpPercap(2007)
<fctr> <dbl>
1 Afghanistan 974.5803 .... ...
2 Albania 5937.0295 ... ...
3 Algeria 6223.3675 ... ...
4 Angola 4797.2313
5 Argentina 12779.3796
6 Australia 34435.3674
7 Austria 36126.4927
8 Bahrain 29796.0483
9 Bangladesh 1391.2538
10 Belgium 33692.6051
My attempt is this:
gapminder %>% #my dataframe
filter(year >= 1952) %>%
group_by(country) %>%
summarise(gdpPercap = mean(gdpPercap))
OUTPUT:
country gdpPercap <- but this takes the mean of gdpPercap from 1952-2007
<fctr> <dbl>
1 Afghanistan 802.6746
2 Albania 3255.3666
3 Algeria 4426.0260
4 Angola 3607.1005
5 Argentina 8955.5538
6 Australia 19980.5956
7 Austria 20411.9163
8 Bahrain 18077.6639
9 Bangladesh 817.5588
10 Belgium 19900.7581
# ... with 132 more rows
Any ideas? PS: I'm new to R. I'm also looking at melt(). Any help will be appreciated!
tidyr::spread() would solve your problem
library(dplyr); library(tidyr)
gapminder %>%
select(country, year, gdpPercap) %>%
spread(year, gdpPercap)
You should use year also in group_by, and after summary, just reshape the data the way you want using dcast or rehape
Here is a sample solution :
library(dplyr)
library(reshape2)
gapminder <- data.frame(cbind(gdpPercap=runif(10000), year =as.integer(seq(from=1952, to=2007, by=5)), country = c("India", "US", "UK")))
gapminder$gdpPercap <- as.numeric(as.character(gapminder$gdpPercap))
gapminder$year <- as.integer(as.character(gapminder$year))
gapminder %>% #my dataframe
filter(year >= 1952) %>%
group_by(country, year) %>%
summarise(gdpPercap = mean(gdpPercap)) %>%
dcast(country ~ year, value.var="gdpPercap")
I have to generate a new data, because your example is not reproducible. Go through the link How to make a great R reproducible example?. It helps in answering and understanding the problem, as well as, quicker answers.
Built-in reshape can do this.
foo.data.frame <- data.frame(
Country=rep(c("Here", "There"), each=3),
year=rep(c(1952, 1957, 1962),2),
gdpPercap=779:784
# ... other variables
)
reshape(foo.data.frame[, c("Country", "year", "gdpPercap")],
timevar="year", idvar="Country", direction="wide", sep=" ")
# Country gdpPercap 1952 gdpPercap 1957 gdpPercap 1962
# 1 Here 779 780 781
# 4 There 782 783 784