Here is first 4 rows of my data;
X...Country.Name Country.Code Indicator.Name
1 Turkey TUR Inflation, GDP deflator (annual %)
2 Turkey TUR Unemployment, total (% of total labor force)
3 Afghanistan AFG Inflation, GDP deflator (annual %)
4 Afghanistan AFG Unemployment, total (% of total labor force)
Indicator.Code X2010
1 NY.GDP.DEFL.KD.ZG 5.675740
2 SL.UEM.TOTL.ZS 11.900000
3 NY.GDP.DEFL.KD.ZG 9.437322
4 SL.UEM.TOTL.ZS NA
I want my data reshaped into two colums, one of each Indicator code, and I want each row correspond to a country, something like this;
Country Name NY.GDP.DEFL.KD.ZG SL.UEM.TOTL.ZS
Turkey 5.6 11.9
Afghanistan 9.43 NA
I think I could do this with Excel, but I want to learn the R way, so that I don't need to rely on excel everytime I have a problem. Here is dput of data if you need it.
Edit: I actually want 3 colums, one for each indicator and one for the country's name.
Sticking with base R, use reshape. I took the liberty of cleaning up the column names. Here, I'm only showing you a few rows of the output. Remove head to see the full output. This assumes your data.frame is named "mydata".
names(mydata) <- c("CountryName", "CountryCode",
"IndicatorName", "IndicatorCode", "X2010")
head(reshape(mydata[-c(2:3)],
direction = "wide",
idvar = "CountryName",
timevar = "IndicatorCode"))
# CountryName X2010.NY.GDP.DEFL.KD.ZG X2010.SL.UEM.TOTL.ZS
# 1 Turkey 5.675740 11.9
# 3 Afghanistan 9.437322 NA
# 5 Albania 3.459343 NA
# 7 Algeria 16.245617 11.4
# 9 American Samoa NA NA
# 11 Andorra NA NA
Another option in base R is xtabs, but NA gets replaced with 0:
head(xtabs(X2010 ~ CountryName + IndicatorCode, mydata))
# IndicatorCode
# CountryName NY.GDP.DEFL.KD.ZG SL.UEM.TOTL.ZS
# Afghanistan 9.437322 0.0
# Albania 3.459343 0.0
# Algeria 16.245617 11.4
# American Samoa 0.000000 0.0
# Andorra 0.000000 0.0
# Angola 22.393924 0.0
The result of xtabs is a matrix, so if you want a data.frame, wrap the output with as.data.frame.matrix.
Related
I want to create a factor variables in my dataframes based on categorical variables.
My data:
# A tibble: 159 x 3
name.country gpd rate_suicide
<chr> <dbl> <dbl>
1 Afghanistan 2129. 6.4
2 Albania 12003. 5.6
3 Algeria 11624. 3.3
4 Angola 7103. 8.9
5 Antigua and Barbuda 19919. 0.5
6 Argentina 20308. 9.1
7 Armenia 10704. 5.7
8 Australia 47350. 11.7
9 Austria 52633. 11.4
10 Azerbaijan 14371. 2.6
# ... with 149 more rows
I want to create factor variable region, which contains a factors as:
region <- c('Asian', 'Europe', 'South America', 'North America', 'Africa')
region = factor(region, levels = c('Asian', 'Europe', 'South America', 'North America', 'Africa'))
I want to do this with dplyr packages, that can to choose a factor levels depends on name.countrybut it doesn't work. Example:
if (new_data$name.country[new_data$name.country == "N"]) {
mutate(new_data, region_ = region[1])
}
How i can solve the problem?
I think the way I would think about your problem is
Create a reproducible problem. (see How to make a great R reproducible example. ) Since you already have the data, use dput to make it easier for people like me to recreate your data in their environment.
dput(yourdf)
structure(list(name.country = c("Afghanistan", "Albania", "Algeria"
), gpd = c(2129L, 12003L, 11624L), rate_suicide = c(6.4, 5.6,
3.3)), class = "data.frame", row.names = c(NA, -3L))
raw_data<-structure(list(name.country = c("Afghanistan", "Albania", "Algeria"
), gpd = c(2129L, 12003L, 11624L), rate_suicide = c(6.4, 5.6,
3.3)), class = "data.frame", row.names = c(NA, -3L))
Define vectors that specify your regions
Use case_when to separate countries into regions
Use as.factor to convert your character variable to a factor
asia=c("Afghanistan","India","...","Rest of countries in Asia")
europe=c("Albania","France","...","Rest of countries in Europe")
africa=c("Algeria","Egypt","...","Rest of countries in Africa")
df<-raw_data %>%
mutate(region=case_when(
name.country %in% asia ~ "asia",
name.country %in% europe ~ "europe",
name.country %in% africa ~ "africa",
TRUE ~ "other"
)) %>%
mutate(region=region %>% as.factor())
You can check that your variable region is a factor using str
str(df)
'data.frame': 3 obs. of 4 variables:
$ name.country: chr "Afghanistan" "Albania" "Algeria"
$ gpd : int 2129 12003 11624
$ rate_suicide: num 6.4 5.6 3.3
$ region : Factor w/ 3 levels "africa","asia",..: 2 3 1
Here is a working example that combines data from the question with a file of countries and region information from Github. H/T to Luke Duncalfe for maintaining the region data, which is:
...a combination of the Wikipedia ISO-3166 article for alpha and numeric country codes and the UN Statistics site for countries' regional and sub-regional codes.
regionFile <- "https://raw.githubusercontent.com/lukes/ISO-3166-Countries-with-Regional-Codes/master/all/all.csv"
regionData <- read.csv(regionFile,header=TRUE)
textFile <- "rowID|country|gdp|suicideRate
1|Afghanistan|2129.|6.4
2|Albania|12003.|5.6
3|Algeria|11624.|3.3
4|Angola|7103.|8.9
5|Antigua and Barbuda|19919.|0.5
6|Argentina|20308.|9.1
7|Armenia|10704.|5.7
8|Australia|47350.|11.7
9|Austria|52633.|11.4
10|Azerbaijan|14371.|2.6"
data <- read.csv(text=textFile,sep="|")
library(dplyr)
data %>%
left_join(.,regionData,by = c("country" = "name"))
...and the output:
rowID country gdp suicideRate alpha.2 alpha.3 country.code
1 1 Afghanistan 2129 6.4 AF AFG 4
2 2 Albania 12003 5.6 AL ALB 8
3 3 Algeria 11624 3.3 DZ DZA 12
4 4 Angola 7103 8.9 AO AGO 24
5 5 Antigua and Barbuda 19919 0.5 AG ATG 28
6 6 Argentina 20308 9.1 AR ARG 32
7 7 Armenia 10704 5.7 AM ARM 51
8 8 Australia 47350 11.7 AU AUS 36
9 9 Austria 52633 11.4 AT AUT 40
10 10 Azerbaijan 14371 2.6 AZ AZE 31
iso_3166.2 region sub.region intermediate.region
1 ISO 3166-2:AF Asia Southern Asia
2 ISO 3166-2:AL Europe Southern Europe
3 ISO 3166-2:DZ Africa Northern Africa
4 ISO 3166-2:AO Africa Sub-Saharan Africa Middle Africa
5 ISO 3166-2:AG Americas Latin America and the Caribbean Caribbean
6 ISO 3166-2:AR Americas Latin America and the Caribbean South America
7 ISO 3166-2:AM Asia Western Asia
8 ISO 3166-2:AU Oceania Australia and New Zealand
9 ISO 3166-2:AT Europe Western Europe
10 ISO 3166-2:AZ Asia Western Asia
region.code sub.region.code intermediate.region.code
1 142 34 NA
2 150 39 NA
3 2 15 NA
4 2 202 17
5 19 419 29
6 19 419 5
7 142 145 NA
8 9 53 NA
9 150 155 NA
10 142 145 NA
At this point one can decide whether to use the region, sub region, or intermediate region and convert it to a factor.
We can set region to a factor by adding a mutate() function to the dplyr pipeline:
data %>%
left_join(.,regionData,by = c("country" = "name")) %>%
mutate(region = factor(region)) -> mergedData
At this point mergedData$region is a factor.
str(mergedData$region)
table(mergedData$region)
> str(mergedData$region)
Factor w/ 5 levels "Africa","Americas",..: 3 4 1 1 2 2 3 5 4 3
> table(mergedData$region)
Africa Americas Asia Europe Oceania
2 2 3 2 1
Now the data is ready for further analysis. We will generate a table of average suicide rates by region.
library(knitr) # for kable
mergedData %>% group_by(region) %>%
summarise(suicideRate = mean(suicideRate)) %>%
kable(.)
...and the output:
|region | suicideRate|
|:--------|-----------:|
|Africa | 6.1|
|Americas | 4.8|
|Asia | 4.9|
|Europe | 8.5|
|Oceania | 11.7|
When rendered in an HTML / markdown viewer, the result looks like this:
I want to make a worldmap visualization using an data frame, which look like this:
Country Year Sex Age Suicides Population Suicides_per_100k Country_Year HDI/Year Year_GDP
1 Albania 1987 Male 15-24 years 21 312900 6.71 Albania1987 NA 2156624900
2 Albania 1987 Male 35-54 years 16 308000 5.19 Albania1987 NA 2156624900
3 Albania 1987 Female 15-24 years 14 289700 4.83 Albania1987 NA 2156624900
4 Albania 1987 Male 75+ years 1 21800 4.59 Albania1987 NA 2156624900
5 Albania 1987 Male 25-34 years 9 274300 3.28 Albania1987 NA 2156624900
6 Albania 1987 Female 75+ years 1 35600 2.81 Albania1987 NA 2156624900
GDP_Per_Capita Generation Continent
1 796 Generation X Europe
2 796 Silent Europe
3 796 Generation X Europe
4 796 G.I. Generation Europe
5 796 Boomers Europe
6 796 G.I. Generation Europe
I tried to use the following code:
world <- ggplot2::map_data('world')
worldstart <- left_join(df,world,by = c("Country"="region")
This code created a new dataframe with 14 million observations.
But, I'd like to keep the same number of the dataset "df".
What is the best approach?
Indeed, the map_data functions returns the values for each point of each multipolygons in the world (~10k rows). As mentioned earlier, you cannot chose what point to keep.
You can use the sf library to go around this difficulty, keeping the geometry (here multipolygons) on one side and your data on the other.
My proposal would be the following :
library(dplyr)
library(sf)
library(ggplot2)
df <- tibble(Country = "Albania",
GDP_per_Capita = 796)
world <- maps::map('world', plot = F, fill = T) %>% st_as_sf(stringsAsFactors = F)
world_df <- df %>%
left_join(world, by = c("Country" = "ID"))
In my example, you would have only one row of data, but the geometry columns contains all necessary information for plotting.
sf and ggplot2 are well linked so you are good to go.
Best regards
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)
I have a panel dataset with data on conflicts for which I want to identify the post-conflict years.
So I constructed a variable myself, which codes a transition from conflict to peace with "3". Whenever the values for a new country begin, I coded that same variable with NA. S
What I want to do now is to create a new binary variable which identifies post-conflict years with a 1 and conflict years and never conflict with 0. For that I would have to assign every year, following a 3 in the transition variable with a 1 until there is an NA in the same column. As follows:
Country Year transition post-conflict
Afghanistan 1994 0 0
Afghanistan 1995 0 0
Afghanistan 1996 3 1
Afghanistan 1997 2 1
Afghanistan 1998 2 1
Albania 1994 NA 0
Albania 1994 2 0
How could I go about this?
You probably shouldn't use NA like that. It prevents functions like which, sum, and cumsum from working as you may want them to. You likely don't need to mark the first row of a new country anyway, since most R functions you would use for your analysis can group by Country without needing a special marker showing where each group starts.
Below I change NA to something different, and make transition a factor. Then you can use cumsum to create your new column.
library(data.table)
setDT(df) # assuming your data is called df
# fix transition column
df[is.na(transition), transition := 90]
df[, transition := as.factor(transition)]
# create post_conflict column
df[, post_conflict := cumsum(transition == 3), by = Country]
# Country Year transition post_conflict
# 1: Afghanistan 1994 0 0
# 2: Afghanistan 1995 0 0
# 3: Afghanistan 1996 3 1
# 4: Afghanistan 1997 2 1
# 5: Afghanistan 1998 2 1
# 6: Albania 1994 90 0
# 7: Albania 1994 2 0
I hope I don't ask a question that has been asked already, but I couldn't quite find what I was looking for. I am fairly new to R and have no experience with programming.
I want to make a table with the top 10 values of three sections for each year If my data looks somthing like this:
Year Country Test1 Test2 Test3
2000 ALB 500 497 501
2001 ALB NA NA NA
...
2000 ARG 502 487 354
2001 ARG NA NA NA
...
(My years go from 2000 to 2015, I only have observations for every three years, and even in those years still a lot of NA's for some countries or tests)
I would like to get a table in which I can see the 10 top values for each test for each year. So for the year 2000,2003,2006,...,2015 the top ten values and the countries that reached those values for test 1,2&3.
AND then (I am not sure if this should be a separate question) I would like to get the table into Latex.
Easier to see top values this way.
You could use dcast and melt from data.table package:
# convert to data table
setDT(df)
# convert it to long format and select the columns to used
df1 <- melt(df, id.vars=1:2)
df1 <- df1[,c(1,2,4)]
# get top values year and country
df1 <- df1[,top_value := .(list(sort(value, decreasing = T))), .(Year, Country)][,.(Year, Country, top_value)]
print(df1)
Year Country top_value
1: 2000 ALB 501,500,497
2: 2001 ALB
3: 2000 ARG 502,487,354
4: 2001 ARG
5: 2000 ALB 501,500,497
6: 2001 ALB
7: 2000 ARG 502,487,354
8: 2001 ARG
9: 2000 ALB 501,500,497
10: 2001 ALB
11: 2000 ARG 502,487,354
12: 2001 ARG