library(tidyverse)
I feel like there is a simple solution for this but I'm stuck. The code below creates a simple list of two dataframes (they are the same for simplicity of the example, but the real data has different values)
Loc<-c("Montreal","Toronto","Vancouver","Quebec","Ottawa","Hamilton","Total")
Count<-c("2344","2322","122","45","4544","44","9421")
Data<-data_frame(Loc,Count)
Data2<-data_frame(Loc,Count)
Data3<-list(Data,Data2)
Each dataframe has "Total" within the "Loc" column with the corresponding overall total of the "Count" column. I would like to calculate percentages for each dataframe by dividing each value in the "Count" column by the total, which is the last number in the "Count" column.
I would like the percentages to be added as new columns for each dataframe.
For this example, the total is the last number in the column, but in reality, it may be mixed anywhere in the column and can be found by the corresponding "Total" value in the "Loc" column.
I would like to use purrr and Tidyverse:
Below is an example of the code, but I'm stuck on the percentage...
Data3%>%map(~mutate(.x,paste0(round(100* (MISSING PERCENTAGE),2),"%"))
This solution uses only base-R:
for (i in seq_along(Data3)) {
Data3[[i]]$Count <- as.numeric(Data3[[i]]$Count)
n <- nrow(Data3[[i]])
Data3[[i]]$perc <- Data3[[i]]$Count / Data3[[i]]$Count[n]
}
> Data3
[[1]]
# A tibble: 7 x 3
Loc Count perc
<chr> <dbl> <dbl>
1 Montreal 2344 0.248805859
2 Toronto 2322 0.246470651
3 Vancouver 122 0.012949793
4 Quebec 45 0.004776563
5 Ottawa 4544 0.482326717
6 Hamilton 44 0.004670417
7 Total 9421 1.000000000
[[2]]
# A tibble: 7 x 3
Loc Count perc
<chr> <dbl> <dbl>
1 Montreal 2344 0.248805859
2 Toronto 2322 0.246470651
3 Vancouver 122 0.012949793
4 Quebec 45 0.004776563
5 Ottawa 4544 0.482326717
6 Hamilton 44 0.004670417
7 Total 9421 1.000000000
Related
I have ~4000 observations in my data frame, test_11, and have pasted part of the data frame below:
data frame snippit
The k_hidp column represents matching households, the k_fihhmnnet1_dv column is their reported household income and the percentage_income_rounded reports each participant's income contribution to the total household income
I want to filter my data to remove all k_hidp observations where their collective income in the percentage_income_rounded does not equal 100.
So for example, the first household 68632420 reported a contribution of 83% (65+13) instead of the 100% as the other households report.
Is there any way to remove these household observations so I am only left with households with a collective income of 100%?
Thank you!
Try this:
## Creating the dataframe
df=data.frame(k_hidp = c(68632420,68632420,68632420,68632420,68632420,68632420,68632422,68632422,68632422,68632422,68632428,68632428),
percentage_income_rounded = c(65,18,86,14,49,51,25,25,25,25,50,50))
## Loading the libraries
library(dplyr)
## Aggregating and determining which household collective income is 100%
df1 = df %>%
group_by(k_hidp) %>%
mutate(TotalPercentage = sum(percentage_income_rounded)) %>%
filter(TotalPercentage == 100)
Output
> df1
# A tibble: 6 x 3
# Groups: k_hidp [2]
k_hidp percentage_income_rounded TotalPercentage
<dbl> <dbl> <dbl>
1 68632422 25 100
2 68632422 25 100
3 68632422 25 100
4 68632422 25 100
5 68632428 50 100
6 68632428 50 100
I know this is a classic question and there are also similar ones in the archive, but I feel like the answers did not really apply to this case. Basically I want to take one dataframe (covid cases in Berlin per district), calculate the sum of the columns and create a new dataframe with a column representing the name of the district and another one representing the total number. So I wrote
covid_bln <- read.csv('https://www.berlin.de/lageso/gesundheit/infektionsepidemiologie-infektionsschutz/corona/tabelle-bezirke-gesamtuebersicht/index.php/index/all.csv?q=', sep=';')
c_tot<-data.frame('district'=c(), 'number'=c())
for (n in colnames(covid_bln[3:14])){
x<-data.frame('district'=c(n), 'number'=c(sum(covid_bln$n)))
c_tot<-rbind(c_tot, x)
next
}
print(c_tot)
Which works properly with the names but returns only the number of cases for the 8th district, but for all the districts. If you have any suggestion, even involving the use of other functions, it would be great. Thank you
Here's a base R solution:
number <- colSums(covid_bln[3:14])
district <- names(covid_bln[3:14])
c_tot <- cbind.data.frame(district, number)
rownames(c_tot) <- NULL
# If you don't want rownames:
rownames(c_tot) <- NULL
This gives us:
district number
1 mitte 16030
2 friedrichshain_kreuzberg 10679
3 pankow 10849
4 charlottenburg_wilmersdorf 10664
5 spandau 9450
6 steglitz_zehlendorf 9218
7 tempelhof_schoeneberg 12624
8 neukoelln 14922
9 treptow_koepenick 6760
10 marzahn_hellersdorf 6960
11 lichtenberg 7601
12 reinickendorf 9752
I want to provide a solution using tidyverse.
The final result is ordered alphabetically by districts
c_tot <- covid_bln %>%
select( mitte:reinickendorf) %>%
gather(district, number, mitte:reinickendorf) %>%
group_by(district) %>%
summarise(number = sum(number))
The rusult is
# A tibble: 12 x 2
district number
* <chr> <int>
1 charlottenburg_wilmersdorf 10736
2 friedrichshain_kreuzberg 10698
3 lichtenberg 7644
4 marzahn_hellersdorf 7000
5 mitte 16064
6 neukoelln 14982
7 pankow 10885
8 reinickendorf 9784
9 spandau 9486
10 steglitz_zehlendorf 9236
11 tempelhof_schoeneberg 12656
12 treptow_koepenick 6788
I want to select a random sample of rows from a large R data frame df (around 10 million rows) in such a way that all distinct values of two columns are included in the resulting sample. df looks like:
StoreID WEEK Units Value ProdID
2001 1 1 3.5 20702
2001 2 2 3 20705
2002 32 3 6 23568
2002 35 5 15 24025
2003 1 2 10 21253
I have the following unique values in the respective columns: StoreID: 1433 and WEEK: 52. When I generate a random sample of rows from df, I must have at least one row each for each StoreID and each WEEK value.
I used the function sample_frac in dplyr in various trials but that does not ensure that all distinct values of StoreID and WEEK are included at least once in the resulting sample. How can I achieve what I want?
It sounds like you need to group the desired columns before sampling rows. The last line will return one random row for each unique storeID-week pairing.
df <- data.frame(storeid=sample(c(2000:2010),1000,T),
week=sample(c(1:52),1000,T),
value=runif(1000))
# count number of duplicated storeid-week pairs
df %>% count(storeid,week) %>% filter(n>1)
df %>% group_by(storeid,week) %>% sample_n(1)
# A tibble: 468 x 3
# Groups: storeid, week [468]
storeid week value
<int> <int> <dbl>
1 2000 1 0.824
2 2000 2 0.0987
3 2000 6 0.916
4 2000 8 0.289
5 2000 9 0.610
6 2000 11 0.0807
7 2000 12 0.592
8 2000 13 0.849
9 2000 14 0.0181
10 2000 16 0.182
# ... with 458 more rows
Not sure if I have read the problem correctly. I would have tried the following using sample function.
Assuming your dataframe is called MyDataFrame and is two dimensional, I would have done it like this.
RandomizedDF <- MyDataFrame[sample(dim(MyDataFrame)[1],dim(MyDataFrame)[1],replace=FALSE),]
Let me know if this is what you wanted or something else?
I am attempting to combine 2 rows into 1 row and select the value to keep depending on a different column.
ID score date std error
123 87 1/15/2018 5
123 92 1/15/2018 10
155 78 3/10/2018 8
155 82 1/15/2018 7
In the data set I only want 1 row per ID. When there are two different test scores I want to keep the score value with the corresponding test date that is closest to present day. If the date is the same then I want to take the test score with the smallest standard error.
End result would look like this:
ID score test date std error
123 87 1/15/2018 5
155 78 3/10/2018 8
Being going at it few a few hours and cannot seem to figure this out.
Thanks
arrange by date (descending order) and std error (ascending order) then take the first row from each group:
df %>%
arrange(desc(as.Date(date, '%m/%d/%Y')), std.error) %>%
group_by(ID) %>% slice(1)
# A tibble: 2 x 4
# Groups: ID [2]
# ID score date std.error
# <int> <int> <fct> <int>
#1 123 87 1/15/2018 5
#2 155 78 3/10/2018 8
I have an issue that I just cannot seem to sort out. I have a dataset that was derived from a raster in arcgis. The dataset represents every fire occurrence during a 10-year period. Some raster cells had multiple fires within that time period (and, thus, will have multiple rows in my dataset) and some raster cells will not have had any fire (and, thus, will not be represented in my dataset). So, each row in the dataset has a column number (sequential integer) and a row number assigned to it that corresponds with the row and column ID from the raster. It also has the date of the fire.
I would like to assign a unique ID (fire_ID) to all of the fires that are within 4 days of each other and in adjacent pixels from one another (within the 8-cell neighborhood) and put this into a new column.
To clarify, if there were an observation from row 3, col 3, Jan 1, 2000 and another from row 2, col 4, Jan 4, 2000, those observations would be assigned the same fire_ID.
Below is a sample dataset with "rows", which are the row IDs of the raster, "cols", which are the column IDs of the raster, and "dates" which are the dates the fire was detected.
rows<-sample(seq(1,50,1),600, replace=TRUE)
cols<-sample(seq(1,50,1),600, replace=TRUE)
dates<-sample(seq(from=as.Date("2000/01/01"), to=as.Date("2000/02/01"), by="day"),600, replace=TRUE)
fire_df<-data.frame(rows, cols, dates)
I've tried sorting the data by "row", then "column", then "date" and looping through, to create a new fire_ID if the row and column ID were within one value and the date was within 4 days, but this obviously doesn't work, as fires which should be assigned the same fire_ID are assigned different fire_IDs if there are observations in between them in the list that belong to a different fire_ID.
fire_df2<-fire_df[order(fire_df$rows, fire_df$cols, fire_df$date),]
fire_ID=numeric(length=nrow(fire_df2))
fire_ID[1]=1
for (i in 2:nrow(fire_df2)){
fire_ID[i]=ifelse(
fire_df2$rows[i]-fire_df2$rows[i-1]<=abs(1) & fire_df2$cols[i]-fire_df2$cols[i-1]<=abs(1) & fire_df2$date[i]-fire_df2$date[i-1]<=abs(4),
fire_ID[i-1],
i)
}
length(unique(fire_ID))
fire_df2$fire_ID<-fire_ID
Please let me know if you have any suggestions.
I think this task requires something along the lines of hierarchical clustering.
Note, however, that there will be necessarily some degree of arbitrariness in the ids. This is because it is entirely possible that the cluster of fires itself is longer than 4 days yet every fire is less than 4 days away from some other fire in that cluster (and thus should have the same id).
library(dplyr)
# Create the distances
fire_dist <- fire_df %>%
# Normalize dates
mutate( norm_dates = as.numeric(dates)/4) %>%
# Only keep the three variables of interest
select( rows, cols, norm_dates ) %>%
# Compute distance using L-infinite-norm (maximum)
dist( method="maximum" )
# Do hierarchical clustering with "single" aggl method
fire_clust <- hclust(fire_dist, method="single")
# Cut the tree at height 1 and obtain groups
group_id <- cutree(fire_clust, h=1)
# First attach the group ids back to the data frame
fire_df2 <- cbind( fire_df, group_id ) %>%
# Then sort the data
arrange( group_id, dates, rows, cols )
# Print the first 20 records
fire_df2[1:10,]
(Make sure you have dplyr library installed. You can run install.packages("dplyr",dep=TRUE) if not installed. It is a really good and very popular library for data manipulations)
A couple of simple tests:
Test #1. The same forest fire moving.
rows<-1:6
cols<-1:6
dates<-seq(from=as.Date("2000/01/01"), to=as.Date("2000/01/06"), by="day")
fire_df<-data.frame(rows, cols, dates)
gives me this:
rows cols dates group_id
1 1 1 2000-01-01 1
2 2 2 2000-01-02 1
3 3 3 2000-01-03 1
4 4 4 2000-01-04 1
5 5 5 2000-01-05 1
6 6 6 2000-01-06 1
Test #2. 6 different random forest fires.
set.seed(1234)
rows<-sample(seq(1,50,1),6, replace=TRUE)
cols<-sample(seq(1,50,1),6, replace=TRUE)
dates<-sample(seq(from=as.Date("2000/01/01"), to=as.Date("2000/02/01"), by="day"),6, replace=TRUE)
fire_df<-data.frame(rows, cols, dates)
output:
rows cols dates group_id
1 6 1 2000-01-10 1
2 32 12 2000-01-30 2
3 31 34 2000-01-10 3
4 32 26 2000-01-27 4
5 44 35 2000-01-10 5
6 33 28 2000-01-09 6
Test #3: one expanding forest fire
dates <- seq(from=as.Date("2000/01/01"), to=as.Date("2000/01/06"), by="day")
rows_start <- 50
cols_start <- 50
fire_df <- data.frame(dates = dates) %>%
rowwise() %>%
do({
diff = as.numeric(.$dates - as.Date("2000/01/01"))
expand.grid(rows=seq(rows_start-diff,rows_start+diff),
cols=seq(cols_start-diff,cols_start+diff),
dates=.$dates)
})
gives me:
rows cols dates group_id
1 50 50 2000-01-01 1
2 49 49 2000-01-02 1
3 49 50 2000-01-02 1
4 49 51 2000-01-02 1
5 50 49 2000-01-02 1
6 50 50 2000-01-02 1
7 50 51 2000-01-02 1
8 51 49 2000-01-02 1
9 51 50 2000-01-02 1
10 51 51 2000-01-02 1
and so on. (All records identified correctly to belong to the same forest fire.)