I'm trying to merge two datasets, by year and country. The first data set (df = GNIPC) represent Gross national income per capite for every country from 1980-2008.
Country Year GNIpc
(chr) (dbl) (dbl)
1 Afghanistan 1990 NA
2 Afghanistan 1991 NA
3 Afghanistan 1992 2010
4 Afghanistan 1993 NA
5 Afghanistan 1994 12550
6 Afghanistan 1995 NA
The second dataset (df = sanctions) represents the imposition of economic sanctions from 1946 to present day.
country imposition sanctiontype sanctions_period
(chr) (dbl) (chr) (chr)
1 Afghanistan 1 1 6 8 1997-2001
2 Afghanistan 1 7 1979-1979
3 Afghanistan 1 4 7 1995-2002
4 Albania 1 2 8 2005-2005
5 Albania 1 7 2005-2006
6 Albania 1 8 2004-2005
I would like to merge the two datasets so that for every GNI year i either have sanctions present in the country or not. For the GNI years that are not in the sanctions_period the value would be 0 and for those that are it would be 1. This is what i want it to look like:
Country Year GNIpc Imposition sanctiontype
(chr) (dbl) (dbl) (dbl) (chr)
1 Afghanistan 1990 NA 0 NA
2 Afghanistan 1991 NA 0 NA
3 Afghanistan 1992 2010 0 NA
4 Afghanistan 1993 NA 0 NA
5 Afghanistan 1994 12550 0 NA
6 Afghanistan 1995 NA 1 4 7
Some example data:
df1 <- data.frame(country = c('Afghanistan', 'Turkey'),
imposition = c(1, 0),
sanctiontype = c('1 6 8', '4'),
sanctions_period = c('1997-2001', '2003-ongoing')
)
country imposition sanctiontype sanctions_period
1 Afghanistan 1 1 6 8 1997-2001
2 Turkey 0 4 2012-ongoing
The "sanctions_period" column can be transformed with dplyr and tidyr:
library(tidyr)
library(dplyr)
df.new <- separate(df1, sanctions_period, c('start', 'end'), remove = F) %>%
mutate(end = ifelse(end == 'ongoing', '2016', end)) %>%
mutate(start = as.numeric(start), end = as.numeric(end)) %>%
group_by(country, sanctions_period) %>%
do(data.frame(country = .$country, imposition = .$imposition, sanctiontype = .$sanctiontype, year = .$start:.$end))
sanctions_period country imposition sanctiontype year
<fctr> <fctr> <dbl> <fctr> <int>
1 1997-2001 Afghanistan 1 1 6 8 1997
2 1997-2001 Afghanistan 1 1 6 8 1998
3 1997-2001 Afghanistan 1 1 6 8 1999
4 1997-2001 Afghanistan 1 1 6 8 2000
5 1997-2001 Afghanistan 1 1 6 8 2001
6 2012-ongoing Turkey 0 4 2012
7 2012-ongoing Turkey 0 4 2013
8 2012-ongoing Turkey 0 4 2014
9 2012-ongoing Turkey 0 4 2015
10 2012-ongoing Turkey 0 4 2016
From there, it should easy to merge with your first data frame. Note that your first data frame capitalizes Country and Year, while the second doesn't.
df.merged <- merge(df.first, df.new, by.x = c('Country', 'Year'), by.y = c('country', 'year'))
Using dplyr:
left_join(GNIPC, sanctions, by=c("Country"="country", "Year"="Year")) %>%
select(Country,Year, GNIpc, Imposition, sanctiontype)
Related
I am using the datase who (available in the library datasets
tidyr), which for 34 years counts the number of TB cases registered for 56 groups (combinations of gender, age and method of testing) for a number of countries. There is one row per country per year, and the first 4 entries are to do with year, country name and such.
I want to calculate the sum of new cases per country per year, but I just can't make it work.
I was ecpecting something like
group_by(who, country) %>% summarise(count = rowsum(.[5:60]))
would work, but it doesn't.
Can anyone help me understand why it doesn't work, and what to do instead?
You're missing a first step, which is to gather the data into a 'tidy' format. Try this:
who%>%
gather(key=type,value=cases,-country,-iso2,-iso3,-year)%>%
filter(!is.na(cases))%>%
group_by(country,year)%>%
summarise(sum(cases))
Which gives output:
# A tibble: 3,484 × 3
# Groups: country [219]
country year `sum(cases)`
<chr> <int> <int>
1 Afghanistan 1997 128
2 Afghanistan 1998 1778
3 Afghanistan 1999 745
4 Afghanistan 2000 2666
5 Afghanistan 2001 4639
library(tidyverse)
(long_who <- who |> pivot_longer(cols = -(1:4)))
long_who |> filter(startsWith(name,"new")) |> # dont want things like "Population"
group_by(country) |>
summarise(sum_of_new_ = sum(value,na.rm=TRUE))
A base r approach
data.frame(who[,c("country", "year")],
cnt = rowSums(who[5:60], na.rm = TRUE))
#> + country year cnt
#> 1 Afghanistan 1980 0
#> 2 Afghanistan 1981 0
#> 3 Afghanistan 1982 0
#> 4 Afghanistan 1983 0
#> 5 Afghanistan 1984 0
#> 6 Afghanistan 1985 0
You could also do without the long format by using rowSums and across:
library(dplyr)
who |>
group_by(country, year) |>
summarise(count = rowSums(across(5:58), na.rm = TRUE)) |>
ungroup()
Alternatives to across(5:58):
across(starts_with("new"))
across(-(1:4))
Output:
# A tibble: 20 × 3
# Groups: country [1]
country year count
<chr> <int> <dbl>
1 Afghanistan 1980 0
2 Afghanistan 1981 0
3 Afghanistan 1982 0
4 Afghanistan 1983 0
5 Afghanistan 1984 0
6 Afghanistan 1985 0
7 Afghanistan 1986 0
8 Afghanistan 1987 0
9 Afghanistan 1988 0
10 Afghanistan 1989 0
11 Afghanistan 1990 0
12 Afghanistan 1991 0
13 Afghanistan 1992 0
14 Afghanistan 1993 0
15 Afghanistan 1994 0
16 Afghanistan 1995 0
17 Afghanistan 1996 0
18 Afghanistan 1997 128
19 Afghanistan 1998 1778
20 Afghanistan 1999 745
I have a dataset (df1) on hundreds of national crises, where each observation is a crisis event at the country level with a start and an end date. I also have the date when the crisis was announced (yyyy-mm-dd format), and a bunch of other crisis characteristics.
df1 <- data.frame(cbind(eventID=c(1,2,3,4), country=c("ALB","ALB","ARG","ARG"), start=c(1994, 1998, 1998, 1991), end=c(1996,1999,1999,1993), announcement=c("1994-11-01","1998-03-01","1998-07-01","1992-01-01"), x1=c(6,2,8,7), x2=c("a","q","k","b")))
eventID country start end announcement x1 x2
1 ALB 1994 1996 1994-11-01 6 a
2 ALB 1998 1999 1998-03-01 2 q
3 ARG 1998 1999 1998-07-01 8 k
4 ARG 1991 1993 1992-01-01 7 b
I need to make df2, a panel of countries with annual observations from the earliest "start" year to the latest "end" year. I want to have a dummy variable, "crisis", that equals 1 for the years between "start" and "end" in df1, and 0 otherwise. I want "announcement" to contain the announcement date in df1 for the year with an announcement, and "NA" otherwise. I would like the extra crisis characteristics, x1 and x2, to show up for crisis years to which they correspond, and "NA" otherwise.
I also need observations for each country for years in which no country has a crisis (in df2: 1997).
df2 <- data.frame(cbind(year=c(1991,1992,1993,1994,1995,1996,1997,1998,1999,1991,1992,1993,1994,1995,1996,1997,1998,1999), country=c("ALB","ALB","ALB","ALB","ALB","ALB","ALB","ALB","ALB","ARG","ARG","ARG","ARG","ARG","ARG","ARG","ARG","ARG"),crisis=c(0,0,0,1,1,1,0,1,1,1,1,1,0,0,0,0,1,1), announcement=c(NA, NA,NA,"1994-11-01",NA,NA,NA,"1998-03-01",NA,NA,"1992-01-01",NA,NA,NA,NA,NA,"1998-07-01"), x1=c(NA,NA,NA,6,6,6,NA,2,2,8,8,8,NA,NA,NA,NA,7,7), x2=c(NA,NA,NA,"a","a","a",NA,"q","q","k","k","k",NA,NA,NA,NA,"b","b")))
year country crisis announcement x1 x2
1991 ALB 0 NA NA NA
1992 ALB 0 NA NA NA
1993 ALB 0 NA NA NA
1994 ALB 1 1994-11-01 6 a
1995 ALB 1 NA 6 a
1996 ALB 1 NA 6 a
1997 ALB 0 NA NA NA
1998 ALB 1 1998-03-01 2 q
1999 ALB 1 NA 2 q
1991 ARG 1 NA 8 k
1992 ARG 1 1992-01-01 8 k
1993 ARG 1 NA 8 k
1994 ARG 0 NA NA NA
1995 ARG 0 NA NA NA
1996 ARG 0 NA NA NA
1997 ARG 0 NA NA NA
1998 ARG 1 1998-07-01 7 b
1999 ARG 1 NA 7 b
I would love any suggestions! I'm stumped as to how to replicate the observations for each year, but only include x1 and x2 values when my new "crisis" dummy = 1
Thanks!
Making use of dplyr and tidyr this could be achieved like so:
library(dplyr)
library(tidyr)
df1 <- data.frame(cbind(eventID=c(1,2,3,4), country=c("ALB","ALB","ARG","ARG"), start=c(1994, 1998, 1998, 1991), end=c(1996,1999,1999,1993), announcement=c("1994-11-01","1998-03-01","1998-07-01","1992-01-01"), x1=c(6,2,8,7), x2=c("a","q","k","b")))
df1 %>%
mutate(year = factor(start, levels = min(start):max(end))) %>%
complete(year, country) %>%
mutate(year = as.numeric(as.character(year))) %>%
arrange(country, year) %>%
group_by(country) %>%
fill(eventID, end, x1, x2) %>%
ungroup() %>%
mutate(across(c(eventID, end, x1, x2), ~ ifelse(end < year, NA, .)),
crisis = as.numeric(!is.na(eventID)))
#> # A tibble: 18 x 9
#> year country eventID start end announcement x1 x2 crisis
#> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 1991 ALB <NA> <NA> <NA> <NA> <NA> <NA> 0
#> 2 1992 ALB <NA> <NA> <NA> <NA> <NA> <NA> 0
#> 3 1993 ALB <NA> <NA> <NA> <NA> <NA> <NA> 0
#> 4 1994 ALB 1 1994 1996 1994-11-01 6 a 1
#> 5 1995 ALB 1 <NA> 1996 <NA> 6 a 1
#> 6 1996 ALB 1 <NA> 1996 <NA> 6 a 1
#> 7 1997 ALB <NA> <NA> <NA> <NA> <NA> <NA> 0
#> 8 1998 ALB 2 1998 1999 1998-03-01 2 q 1
#> 9 1999 ALB 2 <NA> 1999 <NA> 2 q 1
#> 10 1991 ARG 4 1991 1993 1992-01-01 7 b 1
#> 11 1992 ARG 4 <NA> 1993 <NA> 7 b 1
#> 12 1993 ARG 4 <NA> 1993 <NA> 7 b 1
#> 13 1994 ARG <NA> <NA> <NA> <NA> <NA> <NA> 0
#> 14 1995 ARG <NA> <NA> <NA> <NA> <NA> <NA> 0
#> 15 1996 ARG <NA> <NA> <NA> <NA> <NA> <NA> 0
#> 16 1997 ARG <NA> <NA> <NA> <NA> <NA> <NA> 0
#> 17 1998 ARG 3 1998 1999 1998-07-01 8 k 1
#> 18 1999 ARG 3 <NA> 1999 <NA> 8 k 1
I have a data frame with three columns: birth_year, death_year, gender.
I have to calculate total alive male and female population for every year in a given range (1950:1980).
The data frame looks like this:
birth_year death_year gender
1934 1988 male
1922 1993 female
1890 1966 male
1901 1956 male
1946 2009 female
1909 1976 female
1899 1945 male
1887 1949 male
1902 1984 female
The person is alive in year x if death_year > x & birth year <= x
The output I am looking for is something like this:
year male female
1950 3 4
1951 2 3
1952 4 3
1953 4 5
.
.
1980 6 3
Thanks!
Does this work:
library(tidyr)
library(purrr)
library(dplyr)
df %>% mutate(year = map2(1950,1980, seq)) %>% unnest(year) %>%
mutate(isalive = case_when(year >= birth_year & year < death_year ~ 1, TRUE ~ 0)) %>%
group_by(year, gender) %>% summarise(alive = sum(isalive)) %>%
pivot_wider(names_from = gender, values_from = alive) %>% print( n = 50)
`summarise()` regrouping output by 'year' (override with `.groups` argument)
# A tibble: 31 x 3
# Groups: year [31]
year female male
<int> <dbl> <dbl>
1 1950 4 3
2 1951 4 3
3 1952 4 3
4 1953 4 3
5 1954 4 3
6 1955 4 3
7 1956 4 2
8 1957 4 2
9 1958 4 2
10 1959 4 2
11 1960 4 2
12 1961 4 2
13 1962 4 2
14 1963 4 2
15 1964 4 2
16 1965 4 2
17 1966 4 1
18 1967 4 1
19 1968 4 1
20 1969 4 1
21 1970 4 1
22 1971 4 1
23 1972 4 1
24 1973 4 1
25 1974 4 1
26 1975 4 1
27 1976 3 1
28 1977 3 1
29 1978 3 1
30 1979 3 1
31 1980 3 1
Data used:
df
# A tibble: 9 x 3
birth_year death_year gender
<dbl> <dbl> <chr>
1 1934 1988 male
2 1922 1993 female
3 1890 1966 male
4 1901 1956 male
5 1946 2009 female
6 1909 1976 female
7 1899 1945 male
8 1887 1949 male
9 1902 1984 female
Here's a simple base R solution. Summing a logical vector will get you your count of alive or dead because TRUE is 1 and FALSE is 0.
number_alive <- function(range, df){
sapply(range, function(x) sum((df$death_year > x) & (df$birth_year <= x)))
}
output <- data.frame('year' = 1950:1980,
'female' = number_alive(1950:1980, df[df$gender == 'female']),
'male' = number_alive(1950:1980, df[df$gender == 'male']))
# year female male
# 1 1950 4 3
# 2 1951 4 3
# 3 1952 4 3
# 4 1953 4 3
# 5 1954 4 3
# 6 1955 4 3
# 7 1956 4 2
# 8 1957 4 2
# 9 1958 4 2
# 10 1959 4 2
# 11 1960 4 2
# 12 1961 4 2
# 13 1962 4 2
# 14 1963 4 2
# 15 1964 4 2
# 16 1965 4 2
# 17 1966 4 1
# 18 1967 4 1
# 19 1968 4 1
# 20 1969 4 1
# 21 1970 4 1
# 22 1971 4 1
# 23 1972 4 1
# 24 1973 4 1
# 25 1974 4 1
# 26 1975 4 1
# 27 1976 3 1
# 28 1977 3 1
# 29 1978 3 1
# 30 1979 3 1
# 31 1980 3 1
This approach uses an ifelse to determine if alive (1) or dead (0).
Data:
df <- "birth_year death_year gender
1934 1988 male
1922 1993 female
1890 1966 male
1901 1956 male
1946 2009 female
1909 1976 female
1899 1945 male
1887 1949 male
1902 1984 female"
df <- read.table(text = df, header = TRUE)
Code:
library(dplyr)
library(tidyr)
library(tibble)
library(purrr)
df %>%
mutate(year = map2(1950,1980, seq)) %>%
unnest(year) %>%
select(year, birth_year, death_year, gender) %>%
mutate(
alive = ifelse(year >= birth_year & year <= death_year, 1, 0)
) %>%
group_by(year, gender) %>%
summarise(
is_alive = sum(alive)
) %>%
pivot_wider(
names_from = gender,
values_from = is_alive
) %>%
select(year, male, female)
Output:
#> # A tibble: 31 x 3
#> # Groups: year [31]
#> year male female
#> <int> <dbl> <dbl>
#> 1 1950 3 4
#> 2 1951 3 4
#> 3 1952 3 4
#> 4 1953 3 4
#> 5 1954 3 4
#> 6 1955 3 4
#> 7 1956 3 4
#> 8 1957 2 4
#> 9 1958 2 4
#> 10 1959 2 4
#> # … with 21 more rows
Created on 2020-11-11 by the reprex package (v0.3.0)
This question already has answers here:
How to reshape data from long to wide format
(14 answers)
Closed 3 years ago.
I currently have a data frame that looks like this.
country2<-c("Afghanistan","Afghanistan","Afghanistan")
continent2<-c("Asia","Asia","Asia")
series<-c('lifeexp','pop','gdp')
y1901<-c('1','3','100')
y1902<-c('2','4','101')
y1903<-c('2','4','101')
y1904<-c('2','4','101')
y1905<-c('2','4','101')
y1906<-c('2','4','101')
y1907<-c('2','4','101')
df<-data.frame(country2,continent2,series,y1901,y1902,y1903,y1904,y1905,y1906,y1907)
country2 continent2 series y1901 y1902 y1903 y1904 y1905 y1906 y1907
1 Afghanistan Asia lifeexp 1 2 2 2 2 2 2
2 Afghanistan Asia pop 3 4 4 4 4 4 4
3 Afghanistan Asia gdp 100 101 101 101 101 101 101
How can I reshape this data so that it will look like this?
country<-c("Afghanistan","Afghanistan","Afghanistan","Afghanistan","Afghanistan","Afghanistan","Afghanistan")
continent<-c("Asia","Asia","Asia","Asia","Asia","Asia","Asia")
year<-c("1901","1902","1903","1904","1905","1906","1907")
lifeexp<-c("1","2","2","2","2","2","2")
pop<-c('3','4','4','4','4','4','4')
gdp<-c('100','101','101','101','101','101','101')
df<-data.frame(country,continent,year,lifeexp,pop,gdp)
country continent year lifeexp pop gdp
1 Afghanistan Asia 1901 1 3 100
2 Afghanistan Asia 1902 2 4 101
3 Afghanistan Asia 1903 2 4 101
4 Afghanistan Asia 1904 2 4 101
5 Afghanistan Asia 1905 2 4 101
6 Afghanistan Asia 1906 2 4 101
7 Afghanistan Asia 1907 2 4 101
I have tried using dcast2 from the reshape2 to reshape the data but I can only enter 1 column for value.var.
dcast(df,country+region~series,value.var ='y1901',fun.aggregate = sum)
I also tried using ftable and xtabs but I'm still not sure how to enter more than 1 column for the value. The code below gives an error.
ftable(xtabs(c(y2000,y2001)~country+region+series,df))
Thanks
A data.table approach using melt and dcast could be
library(data.table)
setDT(df)
dcast(melt(df,measure = patterns("^y\\d+")),country2 + continent2 + variable~series)
# country2 continent2 variable gdp lifeexp pop
#1: Afghanistan Asia y1901 100 1 3
#2: Afghanistan Asia y1902 101 2 4
#3: Afghanistan Asia y1903 101 2 4
#4: Afghanistan Asia y1904 101 2 4
#5: Afghanistan Asia y1905 101 2 4
#6: Afghanistan Asia y1906 101 2 4
#7: Afghanistan Asia y1907 101 2 4
I know that you are looking for a solution with ftable or dcast but just for your knowledge, you can achieve it using tidyr:
library(tidyverse)
df %>%
pivot_longer(., cols = starts_with("y190"), names_to = "year", values_to = "Value") %>%
pivot_wider(., names_from = "series", values_from = "Value") %>%
mutate(year = gsub("y","", year)) %>%
rename(country = country2, continent = continent2)
# A tibble: 7 x 6
country continent year lifeexp pop gdp
<fct> <fct> <chr> <fct> <fct> <fct>
1 Afghanistan Asia 1901 1 3 100
2 Afghanistan Asia 1902 2 4 101
3 Afghanistan Asia 1903 2 4 101
4 Afghanistan Asia 1904 2 4 101
5 Afghanistan Asia 1905 2 4 101
6 Afghanistan Asia 1906 2 4 101
7 Afghanistan Asia 1907 2 4 101
My data frame currently looks like
country_txt Year nkill_yr Countrycode Population deathsPer100k
<chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Afghanistan 1973 0 4 12028 0.000000e+00
2 Afghanistan 1979 53 4 13307 3.982866e-05
3 Afghanistan 1987 0 4 11503 0.000000e+00
4 Afghanistan 1988 128 4 11541 1.109089e-04
5 Afghanistan 1989 10 4 11778 8.490406e-06
6 Afghanistan 1990 12 4 12249 9.796718e-06
It contains a list of al countries, and the terrorist Deaths per 100,000 population.
Ideally I would Like a data frame in wide format that has the structure of:
country_txt 1970 1971 1972 1973 1974 1975
Afghanistan 3.98 1.1 0 4.3 0.8 0.09
Albania 0 0.4 0.5 0 0 0
Algeria 0 0 0 0.1 0.2 0
Angola 0 0.3 0 0 0 0
Except my function currently repeats like this:
YearCountryRatio<- spread(data = YearCountryRatio, Year, deathsPer100k )
country_txt 1970 1971 1972 1973
Afghanistan 3.98 NA NA NA
Afghanistan NA 1.1 NA NA
Afghanistan NA NA 0 NA
Afghanistan NA NA NA 4.3
And similarly for other countries,
Is there any way to either:
Collapse all of the NA values to show only one country or
Put it directly into wide format?
I've assumed you want each country_txt value reduced to a single row and are happy to drop the unused variables. (Note: I added a dummy country_txt value of "XYZ" to the sample data to show how multiple countries spread)
library(dplyr)
library(tidyr)
df <- read.table(text = "country_txt Year nkill_yr Countrycode Population deathsPer100k
1 Afghanistan 1973 0 4 12028 0.000000e+00
2 Afghanistan 1979 53 4 13307 3.982866e-05
3 Afghanistan 1987 0 4 11503 0.000000e+00
4 XYZ 1988 128 4 11541 1.109089e-04
5 XYZ 1989 10 4 11778 8.490406e-06
6 XYZ 1990 12 4 12249 9.796718e-06", header = TRUE)
df <- mutate(df, deathsPer100k = round(deathsPer100k*100000, 2))
select(df, country_txt, Year, deathsPer100k) %>% spread(Year, deathsPer100k, fill = 0)
#> country_txt 1973 1979 1987 1988 1989 1990
#> 1 Afghanistan 0 3.98 0 0.00 0.00 0.00
#> 2 XYZ 0 0.00 0 11.09 0.85 0.98