I am new to R and to programming in general and am looking for feedback on how to approach what is probably a fairly simple problem in R.
I have the following dataset:
df <- data.frame(county = rep(c("QU","AN","GY"), 3),
park = (c("Downtown","Queens", "Oakville","Squirreltown",
"Pinhurst", "GarbagePile","LottaTrees","BigHill",
"Jaynestown")),
hectares = c(12,42,6,18,92,6,4,52,12))
df<-transform(df, parkrank = ave(hectares, county,
FUN = function(x) rank(x, ties.method = "first")))
Which returns a dataframe looking like this:
county park hectares parkrank
1 QU Downtown 12 2
2 AN Queens 42 1
3 GY Oakville 6 1
4 QU Squirreltown 18 3
5 AN Pinhurst 92 3
6 GY GarbagePile 6 2
7 QU LottaTrees 4 1
8 AN BigHill 52 2
9 GY Jaynestown 12 3
I want to use this to create a two-column data frame that lists each county and the park name corresponding to a specific rank (e.g. if when I call my function I add "2" as a variable, shows the second biggest park in each county).
I am very new to R and programming and have spent hours looking over the built in R help files and similar questions here on stack overflow but I am clearly missing something. Can anyone give a simple example of where to begin? It seems like I should be using split then lapply or maybe tapply, but everything I try leaves me very confused :(
Thanks.
Try,
df2 <- function(A,x) {
# A is the name of the data.frame() and x is the rank No
df <- A[A[,4]==x,]
return(df)
}
> df2(df,2)
county park hectares parkrank
1 QU Downtown 12 2
6 GY GarbagePile 6 2
8 AN BigHill 52 2
Related
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$n)))
c_tot<-rbind(c_tot, x)
next
}
print(c_tot)
Which works properly with the names but returns only the number of cases for the 8th district, but for all the districts. If you have any suggestion, even involving the use of other functions, it would be great. Thank you
Here's a base R solution:
number <- colSums(covid_bln[3:14])
district <- names(covid_bln[3:14])
c_tot <- cbind.data.frame(district, number)
rownames(c_tot) <- NULL
# If you don't want rownames:
rownames(c_tot) <- NULL
This gives us:
district number
1 mitte 16030
2 friedrichshain_kreuzberg 10679
3 pankow 10849
4 charlottenburg_wilmersdorf 10664
5 spandau 9450
6 steglitz_zehlendorf 9218
7 tempelhof_schoeneberg 12624
8 neukoelln 14922
9 treptow_koepenick 6760
10 marzahn_hellersdorf 6960
11 lichtenberg 7601
12 reinickendorf 9752
I want to provide a solution using tidyverse.
The final result is ordered alphabetically by districts
c_tot <- covid_bln %>%
select( mitte:reinickendorf) %>%
gather(district, number, mitte:reinickendorf) %>%
group_by(district) %>%
summarise(number = sum(number))
The rusult is
# A tibble: 12 x 2
district number
* <chr> <int>
1 charlottenburg_wilmersdorf 10736
2 friedrichshain_kreuzberg 10698
3 lichtenberg 7644
4 marzahn_hellersdorf 7000
5 mitte 16064
6 neukoelln 14982
7 pankow 10885
8 reinickendorf 9784
9 spandau 9486
10 steglitz_zehlendorf 9236
11 tempelhof_schoeneberg 12656
12 treptow_koepenick 6788
I have a huge dataset which is partly pooled cross section and partly panel data:
Year Country Respnr Power Nr
1 2000 France 1 1213 1
2 2001 France 2 1234 2
3 2000 UK 3 1726 3
4 2001 UK 3 6433 4
I would like to filter the panel data from the combined data and tried the following:
> anyDuplicated(df$Respnr)
[1] 45047 # Out of 340.000
dfpanel<- subset(df, duplicated(df$Respnr) == TRUE)
The new df is however reduced to zero observations. The following led to the expected amount of observations:
dfpanel<- subset(df, Nr < 3)
Any idea what could be the issue?
Although I have not figured out why the previous did not work, the following does provide a working solution. I have simply split the previous approach. The solution adds a column panel, which in my case is actually a welcome addition
df$panel <- duplicated(df$Respnr)
dfpanel <- subset(df, df$panel == TRUE)
This question already has answers here:
Split dataframe by levels of a factor and name dataframes by those levels
(3 answers)
Closed 5 years ago.
this is my first post.
I have this dataframe of the Nhl draft.
What I would like to do is to use some sort of recursive function to create 10 objects.
So, I want to create these 10 objects by subsetting the Nhl dataframe by Year.
Here are the first 6 rows of the data set (nhl_draft)
Year Overall Team
1 2000 1 New York Islanders
2 2000 2 Atlanta Thrashers
3 2000 3 Minnesota Wild
4 2000 4 Columbus Blue Jackets
5 2000 5 New York Islanders
6 2000 6 Nashville Predators
Player PS
1 Rick DiPietro 49.3
2 Dany Heatley 95.2
3 Marian Gaborik 103.6
4 Rostislav Klesla 34.5
5 Raffi Torres 28.4
6 Scott Hartnell 74.5
I want to create 10 objects by subsetting out the Years, 2000 ~ 2009.
I tried,
for (i in 2000:2009) {
nhl_draft.i <- subset(nhl_draft, Year == "i")
}
BUT this doesn't do anything. What's the problem with this for-loop? Can you suggest any other ways?
Please tell me if this is confusing after all, this is my first post......
The following code may fix your error.
# Create an empty list
nhl_list <- list()
for (i in 2000:2009) {
# Subset the data frame based on Year
nhl_draft_temp <- subset(nhl_draft, Year == i)
# Assign the subset to the list
nhl_list[[as.character(i)]] <- nhl_draft_temp
}
But you can consider split, which is more concise.
nhl_list <- split(nhl_draft, f = nhl_draft$Year)
I would like to apply a function on sets of data based on their category. Given the following data frame
pet <- c(rep("cat",5),rep("dog",5))
year <- c(rep(1991:1995,2))
karma <- c(5,4,1,1,1,6,4,3,2,6)
df <- data.frame(pet,year,karma)
that looks like this
pet year karma
1 cat 1991 5
2 cat 1992 4
3 cat 1993 1
4 cat 1994 1
5 cat 1995 1
6 dog 1991 6
7 dog 1992 4
8 dog 1993 3
9 dog 1994 2
10 dog 1995 6
I would like to perform operations on the karma column for each year. If I wanted to apply a function like sum, this can be done with ddply:
ddply(df, .(year),summarize, sum(karma))
How would I apply it to a function I have written myself, for example
calc <- function(d,c){(d*5+c*7)/12}
where d is a value corresponding to the dog's karma for each given year and c corresponding to that of the cat.
Ideally, I would like to have five more entries appended to this data frame with the pet both, a year and the karma value calculated by the function above. What would be the best way of doing that?
(Terribly sorry if this is trivial, but I really couldn't find a similar question this time.)
You can use spread to make your data frame wide and then mutate to implement your function
library('tidyr')
library('dplyr')
df %>%
spread(pet, karma, drop = FALSE) %>%
mutate(karma = calc(dog, cat), pet = "both") %>%
select(year, pet, karma) %>%
rbind(df)
For an assignment for my graduate program, I have been asked to extract data from datasets of English Premier League results (located here). I am very close to being done but need help on the last two outputs.
We must create a function that can receive two arguments, a date and a season. The function must return a data frame with the table of the respective season on that date. It must include wins, losses, home record, away record, etc. The only ones I have not managed to figure out are W/L streak and the results of the last 10 matches.
Here is an example of what the initial dataset looks like:
e.Date e.HomeTeam e.AwayTeam e.FTHG e.FTAG e.FTR
1 2015-08-08 Bournemouth Aston Villa 0 1 A
2 2015-08-08 Chelsea Swansea 2 2 D
3 2015-08-08 Everton Watford 2 2 D
4 2015-08-08 Leicester Sunderland 4 2 H
5 2015-08-08 Man United Tottenham 1 0 H
My plan was to get Home and Away data sorted out for each club then merge them together before doing the analysis to find streak and last 10 results.
I manipulated the data to look like this:
HomeTeam FTR Date freq
1 Arsenal L 2015-08-09 1
2 Arsenal D 2015-08-24 1
3 Arsenal W 2015-09-12 1
4 Aston Villa L 2015-08-14 1
5 Aston Villa L 2015-09-19 1
6 Aston Villa D 2015-08-29 1
And now I'm kinda lost. My idea was to run some kind of loop (for? ddply? data.table?) to create a data frame for each club with their results in it and then loop again to do whatever calculations to get the desired variables (streak and last 10) and somehow push those back into the main data frame where I am housing all of the other outputs.
I don't want to be told the answer outright since it's important I learn this on my own. However, if someone could point me in the right direction that would be great. Thanks so much.
I created some dummy data just to demonstrate a few commands and maybe give you some ideas.
set.seed(321)
dat <- data.frame(team = sample(letters[1:3], 20, replace=TRUE),
season = rep("season1", 20),
time = rnorm(20),
win_loss = sample(c("win", "loss"), 20, replace=TRUE))
Problem 1. Find win/loss streak
Take a look at the rle function example below
# 1. find wl streak of team 'a'
tmp <- dat[dat$team == "a", ]
tmp <- tmp[order(tmp$time), ]
> tmp
team season time win_loss
19 a season1 -1.12032742 loss
14 a season1 -1.07223880 loss
16 a season1 0.09500072 loss
3 a season1 0.18832552 loss
8 a season1 0.42033257 loss
4 a season1 2.44325982 win
# shows runs of 5 consecutive losses, then 1 consecutive win
rle(tmp$win_loss == "win")
Run Length Encoding
lengths: int [1:2] 5 1
values : logi [1:2] FALSE TRUE
Here's a very helpful post on rle How can I count runs in a sequence?
Problem 2. Last 3 results
I reversed the order of time and then picked the top 3 results.
# 2. find last 3 matches for team 'b'
tmp <- dat[dat$team == "b", ]
tmp <- tmp[rev(order(tmp$time)), ]
> tmp[1:3, ]
team season time win_loss
11 b season1 0.9172555 loss
9 b season1 0.5775845 win
7 b season1 0.4560691 loss