I have a dataframe for which I have date data and cumulative counts.
I am trying to do a reverse of cumsum to get the daily counts but also getting the counts per group.
I am trying to go from dataframe A to dataframe B.
I am using R and tidyr.
Here is the code :
df <- data.frame(cum_count = c(5, 14, 50, 5, 14, 50),
state = c("Alabama", "Alabama", "Alabama", "NY", "NY", "NY"),
Year = c(2012:2014, 2012:2014))
Dataframe A
cum_count state Year
1 5 Alabama 2012
2 14 Alabama 2013
3 50 Alabama 2014
4 5 NY 2012
5 14 NY 2013
6 50 NY 2014
Dataframe B
cum_count state Year
1 5 Alabama 2012
2 9 Alabama 2013
3 36 Alabama 2014
4 5 NY 2012
5 9 NY 2013
6 36 NY 2014
I have tried using the diff function :
df <- df %>%group_by(state)%>%
mutate(daily_count = diff(cum_count))
But I get
Error: Column daily_count must be length 3 (the number of rows) or one, not 2
Let me know what you think.
Thanks!
diff returns length one less than the original length and mutate requires the output column to have the same length as the original (or length 1 which can be recycled). We can append a value possibly NA or the first value of 'cum_count'
library(dplyr)
df %>%
group_by(state)%>%
mutate(daily_count = c(first(cum_count), diff(cum_count)))
# A tibble: 6 x 4
# Groups: state [2]
# cum_count state Year daily_count
# <dbl> <fct> <int> <dbl>
#1 5 Alabama 2012 5
#2 14 Alabama 2013 9
#3 50 Alabama 2014 36
#4 5 NY 2012 5
#5 14 NY 2013 9
#6 50 NY 2014 36
Or for this purpose, use lag and subtract from the column itself
df %>%
group_by(state)%>%
mutate(daily_count = replace_na(cum_count - lag(cum_count), first(cum_count)))
Related
I need to split my data so that when I use the facet_wrap I have the top 50 percentile for each year.
Here is a sample of my data:
# A tibble: 10,519 x 3
Species Abundance Year
<chr> <dbl> <chr>
1 Astropecten irregularis 2 2009
2 Asterias rubens 14 2009
3 Echinus esculentus 1 2009
4 Pagurus prideaux 1 2009
5 Raja clavata 1 2009
6 Astropecten irregularis 4 2009
7 Asterias rubens 47 2009
8 Henricia sp. 2 2009
9 Ophiura ophiura 8 2009
10 Solaster endeca 1 2009
# ... with 10,509 more rows
My current strategy is this:
Data <- All_years %>%
group_by(Species, Year) %>%
summarise(Abundance = sum(Abundance, na.rm = TRUE)) %>%
filter(quantile(Abundance, 0.50)<Abundance) %>%
filter(Abundance > 50)
The issue is that this gives me the top 50 percentile for the whole set while I would like it to give me the top 50 for each year so I can then display it with a facet_wrap in ggplot.
I have a big data frame of 80,000 rows. It was created by combining individual data frames from different years. The origin variable indicates the year of the entry's original data frame.
Here is an example of the first few of the big data frame rows that show how data frames from 2003 and 2011 were combined.
df_1:
ID City State origin
1 NY NY 2003
2 NY NY 2003
3 SF CA 2003
1 NY NY 2011
3 SF CA 2011
2 NY NY 2011
4 LA CA 2011
5 SD CA 2011
Now I want to create a new variable called first_appearance that takes the min of the origin variable for each ID:
final_df:
ID City State origin first_appearance
1 NY NY 2003 2003
2 NY NY 2003 2003
3 SF CA 2003 2003
1 NY NY 2011 2003
3 SF CA 2011 2003
2 NY NY 2011 2003
4 LA CA 2011 2011
5 SD CA 2011 2011
So far, I've tried using:
prestep_final <- df_1 %>% group_by(ID) %>% summarise(first_apperance = min(origin))
final_df <- merge(prestep_final, df_1, by = "ID")
Prestep_final works and produces a data frame with the ID and the first_appearance.
Unfortunately, the merge step doesn't work and yields a data frame with NA entries only.
How can I improve my code so that I can produce a table like final_df above. I'd appreciate any suggestions and don't have package preferences.
If you change summarise to mutate you get your desired result without merging:
library(tidyverse)
df <- tibble::tribble(
~ID, ~City, ~State, ~origin,
1, 'NY', 'NY', 2003,
2, 'NY', 'NY', 2003,
3, 'SF', 'CA', 2003,
1, 'NY', 'NY', 2011,
3, 'SF', 'CA', 2011,
2, 'NY', 'NY', 2011,
4, 'LA', 'CA', 2011,
5, 'SD', 'CA', 2011
)
df %>% group_by(ID) %>%
mutate(first_appearance = min(origin))
#> # A tibble: 8 x 5
#> # Groups: ID [5]
#> ID City State origin first_appearance
#> <dbl> <chr> <chr> <dbl> <dbl>
#> 1 1 NY NY 2003 2003
#> 2 2 NY NY 2003 2003
#> 3 3 SF CA 2003 2003
#> 4 1 NY NY 2011 2003
#> 5 3 SF CA 2011 2003
#> 6 2 NY NY 2011 2003
#> 7 4 LA CA 2011 2011
#> 8 5 SD CA 2011 2011
Created on 2020-06-10 by the reprex package (v0.3.0)
An option with data.table
library(data.table)
setDT(df)[, first_appearance := min(origin), ID]
Or in base R
df$first_appearance <- with(df, ave(origin, ID, FUN = min))
I have tried to find a solution via similar topics, but haven't found anything suitable. This may be due to the search terms I have used. If I have missed something, please accept my apologies.
Here is a excerpt of my data UN_ (the provided sample should be sufficient):
country year sector UN
AT 1990 1 1.407555
AT 1990 2 1.037137
AT 1990 3 4.769618
AT 1990 4 2.455139
AT 1990 5 2.238618
AT 1990 Total 7.869005
AT 1991 1 1.484667
AT 1991 2 1.001578
AT 1991 3 4.625927
AT 1991 4 2.515453
AT 1991 5 2.702081
AT 1991 Total 8.249567
....
BE 1994 1 3.008115
BE 1994 2 1.550344
BE 1994 3 1.080667
BE 1994 4 1.768645
BE 1994 5 7.208295
BE 1994 Total 1.526016
BE 1995 1 2.958820
BE 1995 2 1.571759
BE 1995 3 1.116049
BE 1995 4 1.888952
BE 1995 5 7.654881
BE 1995 Total 1.547446
....
What I want to do is, to add another row with UN_$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)
Below is the summary and structure of the two data sets I tried to merge claimants and unemp, they can me found here claims.csv and unemp.csv
> tbl_df(claimants)
# A tibble: 6,960 × 5
X County Month Year Claimants
<int> <fctr> <fctr> <int> <int>
1 1 ALAMEDA Jan 2007 13034
2 2 ALPINE Jan 2007 12
3 3 AMADOR Jan 2007 487
4 4 BUTTE Jan 2007 3496
5 5 CALAVERAS Jan 2007 644
6 6 COLUSA Jan 2007 1244
7 7 CONTRA COSTA Jan 2007 8475
8 8 DEL NORTE Jan 2007 328
9 9 EL DORADO Jan 2007 2120
10 10 FRESNO Jan 2007 19974
# ... with 6,950 more rows
> tbl_df(unemp)
# A tibble: 6,960 × 7
County Year Month laborforce emplab unemp unemprate
* <chr> <int> <chr> <int> <int> <int> <dbl>
1 Alameda 2007 Jan 743100 708300 34800 4.7
2 Alameda 2007 Feb 744800 711000 33800 4.5
3 Alameda 2007 Mar 746600 713200 33300 4.5
4 Alameda 2007 Apr 738200 705800 32400 4.4
5 Alameda 2007 May 739100 707300 31800 4.3
6 Alameda 2007 Jun 744900 709100 35800 4.8
7 Alameda 2007 Jul 749600 710900 38700 5.2
8 Alameda 2007 Aug 746700 709600 37000 5.0
9 Alameda 2007 Sep 748200 712100 36000 4.8
10 Alameda 2007 Oct 749000 713000 36100 4.8
# ... with 6,950 more rows
I thought first I should change all the factor columns to character columns.
unemp[sapply(unemp, is.factor)] <- lapply(unemp[sapply(unemp, is.factor)], as.character)
claimants[sapply(claimants, is.factor)] <- lapply(claimants[sapply(claimants, is.factor)], as.character)
m <-merge(unemp, claimants, by = c("County", "Month", "Year"))
dim(m)
[1] 0 10
In the output of dim(m), 0 rows are in the resulting dataframe. All the 6960 rows should match each other uniquely.
To verify that the two data frames have unique combination of the the 3 columns 'County', 'Month', and 'Year' I reorder and rearrange these columns within the dataframes as below:
a <- claimants[ order(claimants[,"County"], claimants[,"Month"], claimants[,"Year"]), ]
b <- unemp[ order(unemp[,"County"], unemp[,"Month"], unemp[,"Year"]), ]
b[2:4] <- b[c(2,4,3)]
a[2:4] %in% b[2:4]
[1] TRUE TRUE TRUE
This last output confirms that all 'County', 'Month', and 'Year' columns match each other in these two dataframes.
I have tried looking into the documentation for merge and could not gather where do I go wrong, I have also tried the inner_join function from dplyr:
> m <- inner_join(unemp[2:8], claimants[2:5])
Joining, by = c("County", "Year", "Month")
> dim(m)
[1] 0 8
I am missing something and don't know what, would appreciate the help with understanding this, I know I should not have to rearrange the rows by the three columns to run merge R should identify the matching rows and merge the non-matching columns.
The claimants df has the counties in all uppercase, the unemp df has them in lower case.
I used the options(stringsAsFactors = FALSE) when reading in your data. A few suggestions drop the X column in both, it doesn't seem useful.
options(stringsAsFactors = FALSE)
claims <- read.csv("claims.csv",header=TRUE)
claims$X <- NULL
unemp <- read.csv("unemp.csv",header=TRUE)
unemp$X <- NULL
unemp$County <- toupper(unemp$County)
m <- inner_join(unemp, claims)
dim(m)
# [1] 6960 8
I have a dataset in R which I am trying to aggregate by column level and year which looks like this:
City State Year Status Year_repealed PolicyNo
Pitt PA 2001 InForce 6
Phil. PA 2001 Repealed 2004 9
Pitt PA 2002 InForce 7
Pitt PA 2005 InForce 2
What I would like to create is where for each Year, I aggregate the PolicyNo across states taking into account the date the policy was repealed. The results I would then get is:
Year State PolicyNo
2001 PA 15
2002 PA 22
2003 PA 22
2004 PA 12
2005 PA 14
I am not sure how to go about splitting and aggregating the data conditional on the repeal data and was wondering if there is a way to achieve this is R easily.
It may help you to break this up into two distinct problems.
Get a table that shows the change in PolicyNo in every city-state-year.
Summarize that table to show the PolicyNo in each state-year.
To accomplish (1) we add the missing years with NA PolicyNo, and add repeals as negative PolicyNo observations.
library(dplyr)
df = structure(list(City = c("Pitt", "Phil.", "Pitt", "Pitt"), State = c("PA", "PA", "PA", "PA"), Year = c(2001L, 2001L, 2002L, 2005L), Status = c("InForce", "Repealed", "InForce", "InForce"), Year_repealed = c(NA, 2004L, NA, NA), PolicyNo = c(6L, 9L, 7L, 2L)), .Names = c("City", "State", "Year", "Status", "Year_repealed", "PolicyNo"), class = "data.frame", row.names = c(NA, -4L))
repeals = df %>%
filter(!is.na(Year_repealed)) %>%
mutate(Year = Year_repealed, PolicyNo = -1 * PolicyNo)
repeals
# City State Year Status Year_repealed PolicyNo
# 1 Phil. PA 2004 Repealed 2004 -9
all_years = expand.grid(City = unique(df$City), State = unique(df$State),
Year = 2001:2005)
df = bind_rows(df, repeals, all_years)
# City State Year Status Year_repealed PolicyNo
# 1 Pitt PA 2001 InForce NA 6
# 2 Phil. PA 2001 Repealed 2004 9
# 3 Pitt PA 2002 InForce NA 7
# 4 Pitt PA 2005 InForce NA 2
# 5 Phil. PA 2004 Repealed 2004 -9
# 6 Pitt PA 2001 <NA> NA NA
# 7 Phil. PA 2001 <NA> NA NA
# 8 Pitt PA 2002 <NA> NA NA
# 9 Phil. PA 2002 <NA> NA NA
# 10 Pitt PA 2003 <NA> NA NA
# 11 Phil. PA 2003 <NA> NA NA
# 12 Pitt PA 2004 <NA> NA NA
# 13 Phil. PA 2004 <NA> NA NA
# 14 Pitt PA 2005 <NA> NA NA
# 15 Phil. PA 2005 <NA> NA NA
Now the table shows every city-state-year and incorporates repeals. This is a table we can summarize.
df = df %>%
group_by(Year, State) %>%
summarize(annual_change = sum(PolicyNo, na.rm = TRUE))
df
# Source: local data frame [5 x 3]
# Groups: Year [?]
#
# Year State annual_change
# <int> <chr> <dbl>
# 1 2001 PA 15
# 2 2002 PA 7
# 3 2003 PA 0
# 4 2004 PA -9
# 5 2005 PA 2
That gets us PolicyNo change in each state-year. A cumulative sum over the changes gets us levels.
df = df %>%
ungroup() %>%
mutate(PolicyNo = cumsum(annual_change))
df
# # A tibble: 5 × 4
# Year State annual_change PolicyNo
# <int> <chr> <dbl> <dbl>
# 1 2001 PA 15 15
# 2 2002 PA 7 22
# 3 2003 PA 0 22
# 4 2004 PA -9 13
# 5 2005 PA 2 15
With the data.table package you could do it as follows:
melt(setDT(dat),
measure.vars = c(3,5),
value.name = 'Year',
value.factor = FALSE)[!is.na(Year)
][variable == 'Year_repealed', PolicyNo := -1*PolicyNo
][CJ(Year = min(Year):max(Year), State = State, unique = TRUE), on = .(Year, State)
][is.na(PolicyNo), PolicyNo := 0
][, .(PolicyNo = sum(PolicyNo)), by = .(Year, State)
][, .(Year, State, PolicyNo = cumsum(PolicyNo))]
The result of the above code:
Year State PolicyNo
1: 2001 PA 15
2: 2002 PA 22
3: 2003 PA 22
4: 2004 PA 13
5: 2005 PA 15
As you can see, there are several steps needed to come to the desired endresult:
First you convert to a data.table (setDT(dat)) and reshape this into long format and remove the rows with no Year
Then you make the value for the rows that have 'Year_repealed' to negative.
With a cross-join (CJ) you make sure that alle the years for each state are present and convert the NA-values in the PolicyNo column to zero.
Finally, you summarise by year and do a cumulative sum on the result.