I have a large dataframe (AT_df) with many years for many countries, but no annual totals. The initial dataset has already been slimmed down to Pollutant_name (x1="CO2"), I dropped all subcategories, and to one country.
I am preparing this data to afterwards run ggplot2, but for this I need to add a row for each year with the total of the categories (=1-6).
The data looks like this (excerpt):
x y x1 x2 x4 x6
1553 1993 0.00000 CO2 Austria 6 6 - Other Sector
1554 2006 0.00000 CO2 Austria 6 6 - Other Sector
1555 2015 0.00000 CO2 Austria 6 6 - Other Sector
2243 1998 12.07760 CO2 Austria 5 5 - Waste management
2400 1992 11.12720 CO2 Austria 5 5 - Waste management
2401 1995 11.11040 CO2 Austria 5 5 - Waste management
2402 2006 10.26000 CO2 Austria 5 5 - Waste management
2489 1998 0.00000 CO2 Austria 6 6 - Other Sector
I would like to insert a row which is labelled (x6= aggregate) and sums up the values for y (emissions) under the condition of x= year xyz & x2=country_xyz.
Basically something like this
sum(AT_df, x4 %in% c("1", "2", "3", "4", "5", "6") & x ="yearxyz" &
x2="Austria").
This then should be inserted into the dataframe FOR EACH YEAR (16 years in total)
While I have tried some things I've read on stackoverflow, such as:
rbind(AT_df, data.frame(x1='Aggregate', y = sum(AT_df$y)))
... I was not able to write any correctly working code
Thanks in any case and for any sort of help.
You could first prepare a data frame with summary data in the same shape as your AT_df and afterwards combine the two. There are many ways to do this in R. Here I am using the dplyr package. Since the sample data is not enough to fully show this, I am also creating some artificial data first. After that, one has to do the follwing steps:
Name all the columns that should be retained when summarising (function group_by).
Summarise some column and assigning the output to a column (function summarise).
Add a column for the now missing variable(s) (function mutate).
Combine the resulting data frame with the original one (function union_all)
The final filter is only used to show some representative data.
set.seed(42)
df <- expand.grid(year = 1993:2015,
pollutant = "CO2",
country = LETTERS,
sector = 1L:6L)
df$amount <- runif(nrow(df), 0, 15)
library("dplyr")
df %>%
group_by(year, pollutant, country) %>%
summarise(amount = sum(amount)) %>%
mutate(sector = -1L) %>%
union_all(df) %>%
filter(country == "A" & year == 1996)
#> # A tibble: 7 x 5
#> # Groups: year, pollutant [1]
#> year pollutant country amount sector
#> <int> <fct> <fct> <dbl> <int>
#> 1 1996 CO2 A 41.5 -1
#> 2 1996 CO2 A 12.5 1
#> 3 1996 CO2 A 4.24 2
#> 4 1996 CO2 A 6.70 3
#> 5 1996 CO2 A 1.88 4
#> 6 1996 CO2 A 9.40 5
#> 7 1996 CO2 A 6.82 6
Related
I've looked around but I can't find an answer to this!
I've imported a large number of datasets to R.
Each dataset contains information for a single year (ex. df_2012, df_2013, df_2014 etc).
All the datasets have the same variables/columns (ex. varA_2012 in df_2012 corresponds to varA_2013 in df_2013).
I want to create a df with my id variable and varA_2012, varB_2012, varA_2013, varB_2013, varA_2014, varB_2014 etc
I'm trying to create a loop that helps me extract the few columns that I'm interested in (varA_XXXX, varB_XXXX) in each data frame and then do a full join based on my id var.
I haven't used R in a very long time...
So far, I've tried this:
id <- c("France", "Belgium", "Spain")
varA_2012 <- c(1,2,3)
varB_2012 <- c(7,2,9)
varC_2012 <- c(1,56,0)
varD_2012 <- c(13,55,8)
varA_2013 <- c(34,3,56)
varB_2013 <- c(2,53,5)
varC_2013 <- c(24,3,45)
varD_2013 <- c(27,13,8)
varA_2014 <- c(9,10,5)
varB_2014 <- c(95,30,75)
varC_2014 <- c(99,0,51)
varD_2014 <- c(9,40,1)
df_2012 <-data.frame(id, varA_2012, varB_2012, varC_2012, varD_2012)
df_2013 <-data.frame(id, varA_2013, varB_2013, varC_2013, varD_2013)
df_2014 <-data.frame(id, varA_2014, varB_2014, varC_2014, varD_2014)
year = c(2012:2014)
for(i in 1:length(year)) {
df_[i] <- df_[I][df_[i]$id, df_[i]$varA_[i], df_[i]$varB_[i], ]
list2env(df_[i], .GlobalEnv)
}
panel_df <- Reduce(function(x, y) merge(x, y, by="if"), list(df_2012, df_2013, df_2014))
I know that there are probably loads of errors in here.
Here are a couple of options; however, it's unclear what you want the expected output to look like.
If you want a wide format, then we can use tidyverse to do:
library(tidyverse)
results <-
map(list(df_2012, df_2013, df_2014), function(x)
x %>% dplyr::select(id, starts_with("varA"), starts_with("varB"))) %>%
reduce(., function(x, y)
left_join(x, y, all = TRUE, by = "id"))
Output
id varA_2012 varB_2012 varA_2013 varB_2013 varA_2014 varB_2014
1 Belgium 2 2 3 53 10 30
2 France 1 7 34 2 9 95
3 Spain 3 9 56 5 5 75
However, if you need it in a long format, then we could pivot the data:
results %>%
pivot_longer(-id, names_to = c("variable", "year"), names_sep = "_")
Output
id variable year value
<chr> <chr> <chr> <dbl>
1 France varA 2012 1
2 France varB 2012 7
3 France varA 2013 34
4 France varB 2013 2
5 France varA 2014 9
6 France varB 2014 95
7 Belgium varA 2012 2
8 Belgium varB 2012 2
9 Belgium varA 2013 3
10 Belgium varB 2013 53
11 Belgium varA 2014 10
12 Belgium varB 2014 30
13 Spain varA 2012 3
14 Spain varB 2012 9
15 Spain varA 2013 56
16 Spain varB 2013 5
17 Spain varA 2014 5
18 Spain varB 2014 75
Or if using base R for the wide format, then we can do:
results <-
lapply(list(df_2012, df_2013, df_2014), function(x)
subset(x, select = c("id", names(x)[startsWith(names(x), "varA")], names(x)[startsWith(names(x), "varB")])))
results <-
Reduce(function(x, y)
merge(x, y, all = TRUE, by = "id"), results)
From your initial for loop attempt, it seems the code below may help
> (df <- Reduce(merge, list(df_2012, df_2013, df_2014)))[grepl("^(id|var(A|B))",names(df))]
id varA_2012 varB_2012 varA_2013 varB_2013 varA_2014 varB_2014
1 Belgium 2 2 3 53 10 30
2 France 1 7 34 2 9 95
3 Spain 3 9 56 5 5 75
I have data for each Country's happiness (https://www.kaggle.com/unsdsn/world-happiness), and I made data for each year of the reports. Now, I don't know how to get the values for each year subtracted from each other e.g. how did happiness rank change from 2015 to 2017/2016 to 2017? I'd like to make a new df of differences for each.
I was able to bind the tables for columns in common and started to work on removing Countries that don't have data for all 3 years. I'm not sure if I'm going down a complicated path.
keepcols <- c("Country","Happiness.Rank","Economy..GDP.per.Capita.","Family","Health..Life.Expectancy.","Freedom","Trust..Government.Corruption.","Generosity","Dystopia.Residual","Year")
mydata2015 = read.csv("C:\\Users\\mmcgown\\Downloads\\2015.csv")
mydata2015$Year <- "2015"
data2015 <- subset(mydata2015, select = keepcols )
mydata2016 = read.csv("C:\\Users\\mmcgown\\Downloads\\2016.csv")
mydata2016$Year <- "2016"
data2016 <- subset(mydata2016, select = keepcols )
mydata2017 = read.csv("C:\\Users\\mmcgown\\Downloads\\2017.csv")
mydata2017$Year <- "2017"
data2017 <- subset(mydata2017, select = keepcols )
df <- rbind(data2015,data2016,data2017)
head(df, n=10)
tail(df, n=10)
df15 <- df[df['Year']=='2015',]
df16 <- df[df['Year']=='2016',]
df17 <- df[df['Year']=='2017',]
nocon <- rbind(setdiff(unique(df16['Country']),unique(df17['Country'])),setdiff(unique(df15['Country']),unique(df16['Country'])))
Don't have a clear path to accomplish what I want but it would look like
df16_to_17
Country Happiness.Rank ...(other columns)
Yemen (Yemen[Happiness Rank in 2017] - Yemen[Happiness Rank in 2016])
USA (USA[Happiness Rank in 2017] - USA[Happiness Rank in 2016])
(other countries)
df15_to_16
Country Happiness.Rank ...(other columns)
Yemen (Yemen[Happiness Rank in 2016] - Yemen[Happiness Rank in 2015])
USA (USA[Happiness Rank in 2016] - USA[Happiness Rank in 2015])
(other countries)
It's very straightforward with dplyr, and involves grouping by country and then finding the differences between consecutive values with base R's diff. Just make sure to use df and not df15, etc.:
library(dplyr)
rank_diff_df <- df %>%
group_by(Country) %>%
mutate(Rank.Diff = c(NA, diff(Happiness.Rank)))
The above assumes that the data are arranged by year, which they are in your case because of the way you combined the dataframes. If not, you'll need to call arrange(Year) before the call to mutate. Filtering out countries with missing year data isn't necessary, but can be done after group_by() with filter(n() == 3).
If you would like to view the differences it would make sense to drop some variables and rearrange the data:
rank_diff_df %>%
select(Year, Country, Happiness.Rank, Rank.Diff) %>%
arrange(Country)
Which returns:
# A tibble: 470 x 4
# Groups: Country [166]
Year Country Happiness.Rank Rank.Diff
<chr> <fct> <int> <int>
1 2015 Afghanistan 153 NA
2 2016 Afghanistan 154 1
3 2017 Afghanistan 141 -13
4 2015 Albania 95 NA
5 2016 Albania 109 14
6 2017 Albania 109 0
7 2015 Algeria 68 NA
8 2016 Algeria 38 -30
9 2017 Algeria 53 15
10 2015 Angola 137 NA
# … with 460 more rows
The above data frame will work well with ggplot2 if you are planning on plotting the results.
If you don't feel comfortable with dplyr you can use base R's merge to combine the dataframes, and then create a new dataframe with the differences as columns:
df_wide <- merge(merge(df15, df16, by = "Country"), df17, by = "Country")
rank_diff_df <- data.frame(Country = df_wide$Country,
Y2015.2016 = df_wide$Happiness.Rank.y -
df_wide$Happiness.Rank.x,
Y2016.2017 = df_wide$Happiness.Rank -
df_wide$Happiness.Rank.y
)
Which returns:
head(rank_diff_df, 10)
Country Y2015.2016 Y2016.2017
1 Afghanistan 1 -13
2 Albania 14 0
3 Algeria -30 15
4 Angola 4 -1
5 Argentina -4 -2
6 Armenia -6 0
7 Australia -1 1
8 Austria -1 1
9 Azerbaijan 1 4
10 Bahrain -7 -1
Assuming the three datasets are present in your environment with the name data2015, data2016 and data2017, we can add a year column with the respective year and keep the columns which are present in keepcols vector. arrange the data by Country and Year, group_by Country, keep only those countries which are present in all 3 years and then subtract the values from previous rows using lag or diff.
library(dplyr)
data2015$Year <- 2015
data2016$Year <- 2016
data2017$Year <- 2017
df <- bind_rows(data2015, data2016, data2017)
data <- df[keepcols]
data %>%
arrange(Country, Year) %>%
group_by(Country) %>%
filter(n() == 3) %>%
mutate_at(-1, ~. - lag(.)) #OR
#mutate_at(-1, ~c(NA, diff(.)))
# A tibble: 438 x 10
# Groups: Country [146]
# Country Happiness.Rank Economy..GDP.pe… Family Health..Life.Ex… Freedom
# <chr> <int> <dbl> <dbl> <dbl> <dbl>
# 1 Afghan… NA NA NA NA NA
# 2 Afghan… 1 0.0624 -0.192 -0.130 -0.0698
# 3 Afghan… -13 0.0192 0.471 0.00731 -0.0581
# 4 Albania NA NA NA NA NA
# 5 Albania 14 0.0766 -0.303 -0.0832 -0.0387
# 6 Albania 0 0.0409 0.302 0.00109 0.0628
# 7 Algeria NA NA NA NA NA
# 8 Algeria -30 0.113 -0.245 0.00038 -0.0757
# 9 Algeria 15 0.0392 0.313 -0.000455 0.0233
#10 Angola NA NA NA NA NA
# … with 428 more rows, and 4 more variables: Trust..Government.Corruption. <dbl>,
# Generosity <dbl>, Dystopia.Residual <dbl>, Year <dbl>
The value of first row for each Year would always be NA, rest of the values would be subtracted by it's previous values.
Can anyone help me figure out how to calculate the difference in values based on my monthly data? For example I would like to calculate the difference in groundwater values between Jan-Jul, Feb-Aug, Mar-Sept etc, for each well by year. Note in some years there will be some months missing. Any tidyverse solutions would be appreciated.
Well year month value
<dbl> <dbl> <fct> <dbl>
1 222 1995 February 8.53
2 222 1995 March 8.69
3 222 1995 April 8.92
4 222 1995 May 9.59
5 222 1995 June 9.59
6 222 1995 July 9.70
7 222 1995 August 9.66
8 222 1995 September 9.46
9 222 1995 October 9.49
10 222 1995 November 9.31
# ... with 18,400 more rows
df1 <- subset(df, month %in% c("February", "August"))
test <- df1 %>%
dcast(site + year + Well ~ month, value.var = "value") %>%
mutate(Diff = February - August)
Thanks,
Simon
So I attempted to manufacture a data set and use dplyr to create a solution. It is best practice to include a method of generating a sample data set, so please do so in future questions.
# load required library
library(dplyr)
# generate data set of all site, well, and month combinations
## define valid values
sites = letters[1:3]
wells = 1:5
months = month.name
## perform a series of merges
full_sites_wells_months_set <-
merge(sites, wells) %>%
dplyr::rename(sites = x, wells = y) %>% # this line and the prior could be replaced on your system with initial_tibble %>% dplyr::select(sites, wells) %>% unique()
merge(months) %>%
dplyr::rename(months = y) %>%
dplyr::arrange(sites, wells)
# create sample initial_tibble
## define fraction of records to simulate missing months
data_availability <- 0.8
initial_tibble <-
full_sites_wells_months_set %>%
dplyr::sample_frac(data_availability) %>%
dplyr::mutate(values = runif(nrow(full_sites_wells_months_set)*data_availability)) # generate random groundwater values
# generate final result by joining full expected set of sites, wells, and months to actual data, then group by sites and wells and perform lag subtraction
final_tibble <-
full_sites_wells_months_set %>%
dplyr::left_join(initial_tibble) %>%
dplyr::group_by(sites, wells) %>%
dplyr::mutate(trailing_difference_6_months = values - dplyr::lag(values, 6L))
I have tried to find a solution via similar topics, but haven't found anything suitable. This may be due to the search terms I have used. If I have missed something, please accept my apologies.
Here is a excerpt of my data UN_ (the provided sample should be sufficient):
country year sector UN
AT 1990 1 1.407555
AT 1990 2 1.037137
AT 1990 3 4.769618
AT 1990 4 2.455139
AT 1990 5 2.238618
AT 1990 Total 7.869005
AT 1991 1 1.484667
AT 1991 2 1.001578
AT 1991 3 4.625927
AT 1991 4 2.515453
AT 1991 5 2.702081
AT 1991 Total 8.249567
....
BE 1994 1 3.008115
BE 1994 2 1.550344
BE 1994 3 1.080667
BE 1994 4 1.768645
BE 1994 5 7.208295
BE 1994 Total 1.526016
BE 1995 1 2.958820
BE 1995 2 1.571759
BE 1995 3 1.116049
BE 1995 4 1.888952
BE 1995 5 7.654881
BE 1995 Total 1.547446
....
What I want to do is, to add another row with UN_$sector = Residual. The value of residual will be (UN_$sector = Total) - (the sum of column UN for the sectors c("1", "2", "3", "4", "5")) for a given year AND country.
This is how it should look like:
country year sector UN
AT 1990 1 1.407555
AT 1990 2 1.037137
AT 1990 3 4.769618
AT 1990 4 2.455139
AT 1990 5 2.238618
----> AT 1990 Residual TO BE CALCULATED
AT 1990 Total 7.869005
As I don't want to write many, many lines of code I'm looking for a way to automate this. I was told about loops, but can't really follow the concept at the moment.
Thank you very much for any type of help!!
Best,
Constantin
PS: (for Parfait)
country year sector UN ETS
UK 2012 1 190336512 NA
UK 2012 2 18107910 NA
UK 2012 3 8333564 NA
UK 2012 4 11269017 NA
UK 2012 5 2504751 NA
UK 2012 Total 580957306 NA
UK 2013 1 177882200 NA
UK 2013 2 20353347 NA
UK 2013 3 8838575 NA
UK 2013 4 11051398 NA
UK 2013 5 2684909 NA
UK 2013 Total 566322778 NA
Consider calculating residual first and then stack it with other pieces of data:
# CALCULATE RESIDUALS BY MERGED COLUMNS
agg <- within(merge(aggregate(UN ~ country + year, data = subset(df, sector!='Total'), sum),
aggregate(UN ~ country + year, data = subset(df, sector=='Total'), sum),
by=c("country", "year")),
{UN <- UN.y - UN.x
sector = 'Residual'})
# ROW BIND DIFFERENT PIECES
final_df <- rbind(subset(df, sector!='Total'),
agg[c("country", "year", "sector", "UN")],
subset(df, sector=='Total'))
# ORDER ROWS AND RESET ROWNAMES
final_df <- with(final_df, final_df[order(country, year, as.character(sector)),])
row.names(final_df) <- NULL
Rextester demo
final_df
# country year sector UN
# 1 AT 1990 1 1.407555
# 2 AT 1990 2 1.037137
# 3 AT 1990 3 4.769618
# 4 AT 1990 4 2.455139
# 5 AT 1990 5 2.238618
# 6 AT 1990 Residual -4.039062
# 7 AT 1990 Total 7.869005
# 8 AT 1991 1 1.484667
# 9 AT 1991 2 1.001578
# 10 AT 1991 3 4.625927
# 11 AT 1991 4 2.515453
# 12 AT 1991 5 2.702081
# 13 AT 1991 Residual -4.080139
# 14 AT 1991 Total 8.249567
# 15 BE 1994 1 3.008115
# 16 BE 1994 2 1.550344
# 17 BE 1994 3 1.080667
# 18 BE 1994 4 1.768645
# 19 BE 1994 5 7.208295
# 20 BE 1994 Residual -13.090050
# 21 BE 1994 Total 1.526016
# 22 BE 1995 1 2.958820
# 23 BE 1995 2 1.571759
# 24 BE 1995 3 1.116049
# 25 BE 1995 4 1.888952
# 26 BE 1995 5 7.654881
# 27 BE 1995 Residual -13.643015
# 28 BE 1995 Total 1.547446
I think there are multiple ways you can do this. What I may recommend is to take advantage of the tidyverse suite of packages which includes dplyr.
Without getting too far into what dplyr and tidyverse can achieve, we can talk about the power of dplyr's inline commands group_by(...), summarise(...), arrange(...) and bind_rows(...) functions. Also, there are tons of great tutorials, cheat sheets, and documentation on all tidyverse packages.
Although it is less and less relevant these days, we generally want to avoid for loops in R. Therefore, we will create a new data frame which contains all of the Residual values then bring it back into your original data frame.
Step 1: Calculating all residual values
We want to calculate the sum of UN values, grouped by country and year. We can achieve this by this value
res_UN = UN_ %>% group_by(country, year) %>% summarise(UN = sum(UN, na.rm = T))
Step 2: Add sector column to res_UN with value 'residual'
This should yield a data frame which contains country, year, and UN, we now need to add a column sector which the value 'Residual' to satisfy your specifications.
res_UN$sector = 'Residual'
Step 3 : Add res_UN back to UN_ and order accordingly
res_UN and UN_ now have the same columns and they can now be added back together.
UN_ = bind_rows(UN_, res_UN) %>% arrange(country, year, sector)
Piecing this all together, should answer your question and can be achieved in a couple lines!
TLDR:
res_UN = UN_ %>% group_by(country, year) %>% summarise(UN = sum(UN, na.rm = T))`
res_UN$sector = 'Residual'
UN_ = bind_rows(UN_, res_UN) %>% arrange(country, year, sector)
I am not very experienced with loops so I am not sure where I went wrong here...
I have a dataframe that looks like:
month year day mean.temp mean.temp.year.month
1 1961 1 4.85 4.090323
1 1961 2 4.90 4.090323
1 1961 3 2.95 4.090323
1 1961 4 3.40 4.090323
1 1961 5 2.90 4.090323
dataset showing 3 months for 2 years can be found here:
https://drive.google.com/file/d/1w7NVeoEh8b7cAkU3cu1sXx6yCh75Inqg/view?usp=sharing
and I want to subset this dataframe by year and month so that I can run one nls model per year and month. Since my dataset contains 56 years (and each year has 12 months), that will give 672 models. Then I want to store the parameter estimates in a separate table.
I've created this code, but I can't work out why it is only giving me the parameter estimates for month 12 (all 56 years, but just month 12):
table <- matrix(99999, nrow=672, ncol=4)
YEARMONTHsel <- unique(df_weather[c("year", "month")])
YEARsel <- unique(df_weather$year)
MONTHsel <- unique(df_weather$month)
for (i in 1:length(YEARsel)) {
for (j in 1:length(MONTHsel)) {
temp2 <- df_weather[df_weather$year==YEARsel[i] & df_weather$month==MONTHsel[j],]
mn <- nls(mean.temp~mean.temp.year.month+alpha*sin(day*pi*2/30+phi),
data = temp2, control=nlc,
start=list(alpha=-6.07043, phi = -10))
cr <- as.vector(coef(mn))
nv <-length(coef(mn))
table[i,1:nv] <- cr
table[i,nv+1]<- YEARsel[i]
table[i,nv+2]<- MONTHsel[j]
}
}
I've tried several options (i.e. without using nested loop) but I'm not getting anywhere.
Any help would be greatly appreciated!Thanks.
Based on your loop, it looks like you want to run the regression grouped by year and month and then extract the coefficients in a new dataframe (correct me if thats wrong)
library(readxl)
library(tidyverse)
df <- read_excel("~/Downloads/df_weather.xlsx")
df %>% nest(-month, -year) %>%
mutate(model = map(data, ~nls(mean.temp~mean.temp.year.month+alpha*sin(day*pi*2/30+phi),
data = .x, control= "nlc",
start=list(alpha=-6.07043, phi = -10))),
coeff = map(model, ~coefficients(.x))) %>%
unnest(coeff %>% map(broom::tidy)) %>%
spread(names, x) %>%
arrange(year)
#> # A tibble: 6 x 4
#> month year alpha phi
#> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1961 0.561 -10.8
#> 2 2 1961 -1.50 -10.5
#> 3 3 1961 -2.06 -9.77
#> 4 1 1962 -3.35 -5.48
#> 5 2 1962 -2.27 -9.97
#> 6 3 1962 0.959 -10.8
First we nest the data based on your groups (in this case year and month), then we map the model for each group, then we map the coefficients for each group, lastly we unnest the coefficients and spread the data from long to wide.