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How to sum a variable by group
(18 answers)
Closed 4 years ago.
I have a df that resembles this:
Year Country Sales($M)
2013 Australia 120
2013 Australia 450
2013 Armenia 80
2013 Armenia 175
2013 Armenia 0
2014 Australia 500
2014 Australia 170
2014 Armenia 0
2014 Armenia 100
I'd like to combine the rows that match Year and Country, adding the Sales column. The result should be:
Year Country Sales($M)
2013 Australia 570
2013 Armenia 255
2014 Australia 670
2014 Armenia 100
I'm sure I could write a long loop to check whether Year and Country are the same and then add the Sales from those rows, but this is R so there must be a simple function that I'm totally missing.
Many thanks in advance.
library(tidyverse)
df %>%
group_by(Year,Country) %>%
summarise(Sales = sum(Sales))
Related
The question says: Find the number of storms per year since 2010.
So far, I have this as my code in R.
The data set is "storms" which is a dataset that is loaded into R, and is a subset of the NOAA Atlantic hurricane database.
storms %>%
select(status, year) %>%
filter(year == 2010) %>%
tally()
What I don't know is if the "since" keyword means before 2010 or should I just count the number of storms found in 2010?
Storms since 2010 per year means including 2010 and afterwards the number of storms each year. Maybe this is what the question is asking:
storms2 = storms %>% filter(year>= 2010)
storms2 %>% count(year)
# A tibble: 11 × 2
year n
<dbl> <int>
1 2010 402
2 2011 323
3 2012 454
4 2013 202
5 2014 139
6 2015 220
7 2016 396
8 2017 306
9 2018 266
10 2019 330
11 2020 570
I have the following dataframe:
Count Year
32 2018
346 2017
524 2016
533 2015
223 2014
1 2010
3 2008
1 1992
Is it possible to exclude the years 1992 and 2008. I tried different ways, but don't find a flexible solution.
I would like to have the same dataframe without the years 1993 and 2008.
Many thanks in advance,
jeemer
library(dplyr); filter(df, year != 1992 | year != 2008)
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'm trying to perform analysis on a time series data of inflation rates from the year 1960 to 2015. The dataset is a yearly time series over 56 years with 1 real value per each year, which is the following:
Year Inflation percentage
1960 1.783264746
1961 1.752021563
1962 3.57615894
1963 2.941176471
1964 13.35403727
1965 9.479452055
1966 10.81081081
1967 13.0532972
1968 2.996404315
1969 0.574712644
1970 5.095238095
1971 3.081105573
1972 6.461538462
1973 16.92815855
1974 28.60169492
1975 5.738605162
1976 -7.63438068
1977 8.321619342
1978 2.517518817
1979 6.253164557
1980 11.3652609
1981 13.11510484
1982 7.887270664
1983 11.86886396
1984 8.32157969
1985 5.555555556
1986 8.730811404
1987 8.798689021
1988 9.384775808
1989 3.26256011
1990 8.971233545
1991 13.87024609
1992 11.78781925
1993 6.362038664
1994 10.21150033
1995 10.22488756
1996 8.977149075
1997 7.16425362
1998 13.2308409
1999 4.669821024
2000 4.009433962
2001 3.684807256
2002 4.392199745
2003 3.805865922
2004 3.76723848
2005 4.246353323
2006 6.145522388
2007 6.369996746
2008 8.351816444
2009 10.87739112
2010 11.99229692
2011 8.857845297
2012 9.312445605
2013 10.90764331
2014 6.353194544
2015 5.872426595
'stock1' contains my data where the first column stands for Year, and the second for 'Inflation.percentage', as follows:
stock1<-read.csv("India-Inflation time series.csv", header=TRUE, stringsAsFactors=FALSE, as.is=TRUE)
The following is my code for creating the time series object:
stock <- ts(stock1$Inflation.percentage,start=(1960), end=(2015),frequency=1)
Following this, I am trying to decompose the time series object 'stock' using the following line of code:
decom_add <- (decompose(stock, type ="additive"))
Here I get an error:
Error in decompose(stock, type = "additive") : time series has no
or less than 2 periods
Why is this so? I initially thought it has something to do with frequency, but since the data is annual, the frequency has to be 1 right? If it is 1, then aren't there definitely more than 2 periods in the data?
Why isn't decompose() working? What am I doing wrong?
Thanks a lot in advance!
Please try for frequency=2, because frequency needs to be greater than 1. Because this action will change your model, for me the better way is to load data which contain and month column, so the frequency will be 12.
I have a data.frame of the following structure (panel data), with 16 levels of time(quarters) 14 levels of geo (countries) and 20 levels of citizen, each of them repeating accordingly in the dataframe.
time geo citizen X
2008Q1 Belgium Afghanistan 22
2008Q1 Belgium Armenia 10
2008Q1 Belgium Bangladesh 25
2008Q1 Belgium Democratic Republic of the Congo 55
2008Q1 Belgium China (including Hong Kong) 5
2008Q1 Belgium Eritrea 8
I would like to create a new column lets say MOVSUM where it will sum variable X for each level of citizen and geo and time for the previous 4 quarters, so that I would have for each quarter, t, how many X's of each citizen in each geo were available during t-4 to t-1 quarters.
Thanks in advance