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How to sum a variable by group
(18 answers)
Closed 4 years ago.
This is the basically same problem I had in Excel a few days ago (Excel - find nth largest value based on criteria), but this time in R (the data set contains half a million entries and that is more than Excel seems to be able to handle).
I have a table that looks like this that I have imported from Excel:
Country Region Code Product name Year Value
Sweden Stockholm 123 Apple 1991 244
Sweden Kirruna 123 Apple 1987 100
Japan Kyoto 543 Pie 1987 544
Denmark Copenhagen 123 Apple 1998 787
Denmark Copenhagen 123 Apple 1987 100
Denmark Copenhagen 543 Pie 1991 320
Denmark Copenhagen 126 Candy 1999 200
Sweden Gothenburg 126 Candy 2013 300
Sweden Gothenburg 157 Tomato 1987 150
Sweden Stockholm 125 Juice 1987 250
Sweden Kirruna 187 Banana 1998 310
Japan Kyoto 198 Ham 1987 157
Japan Kyoto 125 Juice 1987 550
Japan Tokyo 125 Juice 1991 100
What I want to do is to make a code that can give me the sum of the nth largest value of products that have been sold in a specific country. For instance, the most sold product in Sweden is Apple so I want to code to find that apple is the most sold product (in total, which is what I am interested in) and then summaries all the values of the sold apples in the country Sweden, 344.
I also want to be able to find the nth largest value based on both country and year. That is, if I am looking for the most sold product in Sweden in the year 2013, it should return the product Candy and the value 300.
Solution for your first question (find most sold product per country, summarise value for this product) using dplyr:
library(tidyverse)
df %>%
group_by(Country, Product_name) %>%
summarise(sum_value = sum(Value, na.rm = TRUE)) %>%
ungroup() %>%
group_by(Country) %>%
filter(sum_value == max(sum_value))
# A tibble: 3 x 3
# Groups: Country [3]
Country Product_name sum_value
<fctr> <fctr> <int>
1 Denmark Apple 887
2 Japan Juice 650
3 Sweden Apple 344
Solution for second question (show nth most sold products per country and year, sum value):
df %>%
group_by(Country, Product_name, Year) %>%
summarise(sum_value = sum(Value, na.rm = TRUE)) %>%
ungroup() %>%
group_by(Country, Year) %>%
arrange(desc(sum_value), .by_group = TRUE) %>%
slice(., 1:2)
Had to change the data a bit to get a decent output, so here's the output with all years set to 1987 (change the 2 in the 1:2 within the last row for a different n):
# A tibble: 6 x 4
# Groups: Country, Year [3]
Country Product_name Year sum_value
<fctr> <fctr> <int> <int>
1 Denmark Apple 1987 887
2 Denmark Pie 1987 320
3 Japan Juice 1987 650
4 Japan Pie 1987 544
5 Sweden Apple 1987 344
6 Sweden Banana 1987 310
Related
I have created a simple data frame with simulated GDP data for Costa Rica and the US, using the following code
gdp_test <- read.table(text = "Country, Year, GDP
costa_rica 1979 200
costa_rica 1980 210
costa_rica 1981 250
usa 1979 350
usa 1980 375
usa 1981 421", header=T)
gdp_test <- as.data.frame(gdp_test)
The output is as follows
Country. Year. GDP
1 costa_rica 1979 200
2 costa_rica 1980 210
3 costa_rica 1981 250
4 usa 1979 350
5 usa 1980 375
6 usa 1981 421
What I would like to do is to create a new variable consisting of the ratio of each country's GDP, for each year, to the usa gdp for that same year (obviously the ratio wouldl be 1 for the usa every year).
Any ideas of how to do it? It is an easy task in Excel, but I have found no way of doing it withing R
I have not been able to write any code that would do the task
That might do the trick, using tidyverse.
if(no_NA) {
Remove last pipe line
}
:)
gdp_test %>%filter(Country.=="usa") %>% group_by(Year.) %>% select(-Country.) %>%
left_join(gdp_test,by="Year.") %>%
rename(GDPus=GDP.x,GDP=GDP.y) %>%
mutate(ratio=GDP/GDPus) %>% ungroup() %>%
mutate(ratio=ifelse(ratio==1,NA,ratio))
Here is a very clumsy way of getting the job done. I am sure there are much better ways of doing it. Help would be enormously appreciated.
gdp_test <- read.table(text = "Country, Year, GDP
costa_rica 1979 200
costa_rica 1980 210
costa_rica 1981 250
usa 1979 350
usa 1980 375
usa 1981 421", header=T)
gdp_test <- as.data.frame(gdp_test) %>%
mutate(ID=row_number(),)
gdp_usa <- gdp_test$GDP[4:6]
usa <- as.data.frame(c(gdp_usa,gdp_usa)) %>%
mutate(ID=row_number(),)
gdp <-full_join(gdp_test,usa, by = "ID")
gdp <- gdp %>% mutate(ratio = GDP/gdp_usa)
I have this dataframe here which I filter down to only include counties in the state of Washington and only include columns that are relevant for the answer I am looking for. What I want to do is filter down the dataframe so that I have 10 rows only, which have the highest Black Prison Population out of all of the counties in Washington State regardless of year. The part that I am struggling with is that there can't be repeated counties, so each row should include the highest Black Prison Populations for the top 10 unique county names in the state of Washington. Some of the counties have Null data for the populations for the black prison populations as well. for You should be able to reproduce this to get the updated dataframe.
library(dplyr)
incarceration <- read.csv("https://raw.githubusercontent.com/vera-institute/incarceration-trends/master/incarceration_trends.csv")
blackPrisPop <- incarceration %>%
select(black_prison_pop, black_pop_15to64, year, fips, county_name, state) %>%
filter(state == "WA")
Sample of what the updated dataframe looks like (should include 1911 rows):
fips county_name state year black_pop_15to64 black_prison_pop
130 53005 Benton County WA 2001 1008 25
131 53005 Benton County WA 2002 1143 20
132 53005 Benton County WA 2003 1208 21
133 53005 Benton County WA 2004 1236 27
134 53005 Benton County WA 2005 1310 32
135 53005 Benton County WA 2006 1333 35
You can group_by the county county_name, and then use slice_max taking the row with maximum value for black_prison_pop. If you set n = 1 option you will get one row for each county. If you set with_ties to FALSE, you also will get one row even in case of ties.
You can arrange in descending order the black_prison_pop value to get the overall top 10 values across all counties.
library(dplyr)
incarceration %>%
select(black_prison_pop, black_pop_15to64, year, fips, county_name, state) %>%
filter(state == "WA") %>%
group_by(county_name) %>%
slice_max(black_prison_pop, n = 1, with_ties = FALSE) %>%
arrange(desc(black_prison_pop)) %>%
head(10)
Output
black_prison_pop black_pop_15to64 year fips county_name state
<dbl> <dbl> <int> <int> <chr> <chr>
1 1845 73480 2002 53033 King County WA
2 975 47309 2013 53053 Pierce County WA
3 224 5890 2005 53063 Spokane County WA
4 172 19630 2015 53061 Snohomish County WA
5 137 8129 2016 53011 Clark County WA
6 129 5146 2003 53035 Kitsap County WA
7 102 5663 2009 53067 Thurston County WA
8 58 706 1991 53021 Franklin County WA
9 50 1091 1991 53077 Yakima County WA
10 46 1748 2008 53073 Whatcom County WA
Assuming that the dataframe is stored as someData, and is in the following format:
ID Team Games Medal
1 Australia 1992 Summer NA
2 Australia 1994 Summer Gold
3 Australia 1992 Summer Silver
4 United States 1991 Winter Gold
5 United States 1992 Summer Bronze
6 Singapore 1991 Summer NA
How would I count the frequencies of the medal, based on the Team - while excluding NA as an variable. But at the same time, the total frequency of each country should be summed, rather than displayed separately for Gold, Silver and Bronze.
In other words, I am trying to display the total number of medals PER country, with the exception of NA.
I have tried something like this:
library(plyr)
counts <- ddply(olympics, .(olympics$Team, olympics$Medal), nrow)
names(counts) <- c("Country", "Medal", "Freq")
counts
But this just gives me a massive table of every medal for every country separately, including NA.
What I would like to do is the following:
Australia 2
United States 2
Any help would be greatly appreciated.
Thank you!
We can use count
library(dplyr)
df1 %>%
filter(!is.na(Medal)) %>%
count(Team)
# A tibble: 2 x 2
# Team n
# <fct> <int>
#1 Australia 2
#2 United States 2
You can do that in base R with table and colSums
colSums(table(someData$Medal, someData$Team))
Australia Singapore United States
2 0 2
Data
someData = read.table(text="ID Team Games Medal
1 Australia '1992 Summer' NA
2 Australia '1994 Summer' Gold
3 Australia '1992 Summer' Silver
4 'United States' '1991 Winter' Gold
5 'United States' '1992 Summer' Bronze
6 Singapore '1991 Summer' NA",
header=TRUE)
I want to spread this data below (first 12 rows shown here only) by the column 'Year', returning the sum of 'Orders' grouped by 'CountryName'. Then calculate the % change in 'Orders' for each 'CountryName' from 2014 to 2015.
CountryName Days pCountry Revenue Orders Year
United Kingdom 0-1 days India 2604.799 13 2014
Norway 8-14 days Australia 5631.123 9 2015
US 31-45 days UAE 970.8324 2 2014
United Kingdom 4-7 days Austria 94.3814 1 2015
Norway 8-14 days Slovenia 939.8392 3 2014
South Korea 46-60 days Germany 1959.4199 15 2014
UK 8-14 days Poland 1394.9096 6. 2015
UK 61-90 days Lithuania -170.8035 -1 2015
US 8-14 days Belize 1687.68 5 2014
Australia 46-60 days Chile 888.72 2. 0 2014
US 15-30 days Turkey 2320.7355 8 2014
Australia 0-1 days Hong Kong 672.1099 2 2015
I can make this work with a smaller test dataframe, but can only seem to return endless errors like 'sum not meaningful for factors' or 'duplicate identifiers for rows' with the full data. After hours of reading the dplyr docs and trying things I've given up. Can anyone help with this code...
data %>%
spread(Year, Orders) %>%
group_by(CountryName) %>%
summarise_all(.funs=c(Sum='sum'), na.rm=TRUE) %>%
mutate(percent_inc=100*((`2014_Sum`-`2015_Sum`)/`2014_Sum`))
The expected output would be a table similar to below. (Note: these numbers are for illustrative purposes, they are not hand calculated.)
CountryName percent_inc
UK 34.2
US 28.2
Norway 36.1
... ...
Edit
I had to make a few edits to the variable names, please note.
Sum first, while your data are still in long format, then spread. Here's an example with fake data:
set.seed(2)
dat = data.frame(Country=sample(LETTERS[1:5], 500, replace=TRUE),
Year = sample(2014:2015, 500, replace=TRUE),
Orders = sample(-1:20, 500, replace=TRUE))
dat %>% group_by(Country, Year) %>%
summarise(sum_orders = sum(Orders, na.rm=TRUE)) %>%
spread(Year, sum_orders) %>%
mutate(Pct = (`2014` - `2015`)/`2014` * 100)
Country `2014` `2015` Pct
1 A 575 599 -4.173913
2 B 457 486 -6.345733
3 C 481 319 33.679834
4 D 423 481 -13.711584
5 E 528 551 -4.356061
If you have multiple years, it's probably easier to just keep it in long format until you're ready to make a nice output table:
set.seed(2)
dat = data.frame(Country=sample(LETTERS[1:5], 500, replace=TRUE),
Year = sample(2010:2015, 500, replace=TRUE),
Orders = sample(-1:20, 500, replace=TRUE))
dat %>% group_by(Country, Year) %>%
summarise(sum_orders = sum(Orders, na.rm=TRUE)) %>%
group_by(Country) %>%
arrange(Country, Year) %>%
mutate(Pct = c(NA, -diff(sum_orders))/lag(sum_orders) * 100)
Country Year sum_orders Pct
<fctr> <int> <int> <dbl>
1 A 2010 205 NA
2 A 2011 144 29.756098
3 A 2012 226 -56.944444
4 A 2013 119 47.345133
5 A 2014 177 -48.739496
6 A 2015 303 -71.186441
7 B 2010 146 NA
8 B 2011 159 -8.904110
9 B 2012 152 4.402516
10 B 2013 180 -18.421053
# ... with 20 more rows
This is not an answer because you haven't really asked a reproducible question, but just to help out.
Error 1 You're getting this error duplicate identifiers for rows likely because of spread. spread wants to make N columns of your N unique values but it needs to know which unique row to place those values. If you have duplicate value-combinations, for instance:
CountryName Days pCountry Revenue
United Kingdom 0-1 days India 2604.799
United Kingdom 0-1 days India 2604.799
shows up twice, then spread gets confused which row it should place the data in. The quick fix is to data %>% mutate(row=row_number()) %>% spread... before spread.
Error 2 You're getting this error sum not meaningful for factors likely because of summarise_all. summarise_all will operate on all columns but some columns contain strings (or factors). What does United Kingdom + United Kingdom equal? Try instead summarise(2014_Sum = sum(2014), 2015_Sum = sum(2015)).
I would like to create a 'Category' column in the below dataset based on the sales and year.
set.seed(30)
df <- data.frame(
Year = rep(2010:2015, each = 6),
Country = rep(c('India', 'China', 'Japan', 'USA', 'Germany', 'Russia'), 6),
Sales = round(runif(18, 100, 900))
)
head(df)
Year Country Sales
1 2010 India 661
2 2010 China 888
3 2010 Japan 285
4 2010 USA 272
5 2010 Germany 332
6 2010 Russia 660
Categories are:
Top 2 countries with highest sales in each year: Category - 1
Bottom 2 countries with lowest sales in each year: Category - 3
Remaining countries by year: Category - 2
Expected dataset might look like:
Year Country Sales Category
1 2010 India 661 1
2 2010 China 888 1
3 2010 Japan 285 3
4 2010 USA 272 3
5 2010 Germany 332 2
6 2010 Russia 660 2
You don't need much here; just group_by year, arrange from greatest to least sales, and then add a new column with mutate that fills with 2:
df %>% group_by(Year) %>%
arrange(desc(Sales)) %>%
mutate(Category = c(1, 1, rep(2, n()-4), 3, 3))
# Source: local data frame [36 x 4]
# Groups: Year [6]
#
# Year Country Sales Category
# (int) (fctr) (dbl) (dbl)
# 1 2010 China 491 1
# 2 2010 USA 436 1
# 3 2010 Japan 391 2
# 4 2010 Germany 341 2
# 5 2010 Russia 218 3
# 6 2010 India 179 3
# 7 2011 Japan 873 1
# 8 2011 India 819 1
# 9 2011 Russia 418 2
# 10 2011 China 279 2
# .. ... ... ... ...
It will fail with fewer than four countries, but that doesn't sound like an issue from the question.
We can use cut to create a 'Category' column after grouping by "Year".
library(dplyr)
df %>%
group_by(Year) %>%
mutate(Category = as.numeric(cut(-Sales, breaks=c(-Inf,
quantile(-Sales, prob = c(0, .5, 1))))))
Or using data.table
library(data.table)
setDT(df)[order(-Sales), Category := if(.N > 4) rep(1:3,
c(2, .N - 4, 2)) else rep(seq(.N), each = ceiling(.N/3)) ,by = Year]
This should also work when there are fewer elements than 4 in each "Year". i.e. if we remove the first five observations in 2010.
df1 <- df[-(1:5),]
setDT(df1)[order(-Sales), Category := if(.N > 4) rep(1:3,
c(2, .N - 4, 2)) else rep(seq(.N), each = ceiling(.N/3)) ,by = Year]
head(df1)
# Year Country Sales Category
#1: 2010 Russia 218 1
#2: 2011 India 819 1
#3: 2011 China 279 2
#4: 2011 Japan 873 1
#5: 2011 USA 213 3
#6: 2011 Germany 152 3