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)
Related
Background
I've got two dataframes about baseball cards and their market value. This information comes from Baseball Card "Almanacs", guides to cards' value published every year.
The first, d, is a table with the card_id of each card, as well as an indicator almanac_flag, which tells you if the card_id in that row came from the either the 1999 or 2009 editions of the Baseball Card Almanac:
d <- data.frame(card_id = c("48","2100","F7","2729","F4310","27700"),
almanac_flag = c(0,0,1,0,1,0), # 0 = 1999 Almanac, 1 = 2009 almanac
stringsAsFactors=T)
It looks like this:
The second dataframe is d2, which contains (not all) equivalent id's for 1999 and 2009, along with a description of which baseball player is depicted in that card. Note that d2 doesn't have all the ID's that appear in d -- it only has 3 "matches" and that's totally fine.
d2 <- data.frame(card_id_1999 = c("48","2100","31"),
card_id_2009 = c("J18","K02","F7"),
description = c("Wade Boggs","Frank Thomas","Mickey Mantle"),
stringsAsFactors=T)
d2 looks like this:
The Problem
I want to join these two tables so I get a table that looks like this:
What I've Tried
So of course, I could use left_join with the key being either card_id = card_id_1999 or card_id = card_id_2009, but that only gets me half of what I need, like so:
d_tried <- left_join(d, d2, by = c("card_id" = "card_id_1999"))
Which gives me this:
In a sense I'm asking to do 2 joins in one go, but I'm not sure how to do that.
Any thoughts?
If we do the reshape to 'long' format from 'd2', it should work
library(dplyr)
library(tidyr)
d2 %>%
pivot_longer(cols = starts_with('card'),
values_to = 'card_id', names_to = NULL) %>%
right_join(d) %>%
select(names(d), everything())
-output
# A tibble: 6 x 3
card_id almanac_flag description
<fct> <dbl> <fct>
1 48 0 Wade Boggs
2 2100 0 Frank Thomas
3 F7 1 Mickey Mantle
4 2729 0 <NA>
5 F4310 1 <NA>
6 27700 0 <NA>
or another option is to match separately for each column (or join separately) and then do a coalesce such as the first non-NA will be selected
d %>%
mutate(description = coalesce(d2$description[match(card_id,
d2$card_id_1999)], d2$description[match(card_id, d2$card_id_2009)]))
card_id almanac_flag description
1 48 0 Wade Boggs
2 2100 0 Frank Thomas
3 F7 1 Mickey Mantle
4 2729 0 <NA>
5 F4310 1 <NA>
6 27700 0 <NA>
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 need to create a panel data set (long format) from multiple yearly (cross-sectional) data sets. The variables of interest have different names in the single data sets and i need to harmonize them.
I loaded the dataframes to a list and now want to manipulate the names using lapply or a chunk of code that allows binding the dataframes. I can see several ways of doing this, but would like to use one which works with little code on a large list of data.frames, so that I can do this for several variables and easily change specifics later on.
So what I am looking for is either a way to rename the columns, so that I able to simple use bind_rows() from dplyr or an equivalent method, or a way to rename and bind the datasets in one step. Since I need to do this for several variables it might be safer to keep the two steps apart.
To illustrate, here an example:
a <- data.frame(id=c("Marc", "Julia", "Rico"), year=2000:2002, laborincome=1:3)
b <- data.frame(id=c("Marc", "Julia", "Rico"), earningsfromlabor=2:4, year=2003:2005)
dflist <- list(a, b)
equivalent_vars <- c("laborincome", "earningsfromlabor")
newnanme <- "income"
Desired result:
data.frame(id=c("Marc", "Julia", "Rico"), income=c(1,2,3,2,3,4), year=2000:2005)
id income year
1 Marc 1 2000
2 Julia 2 2001
3 Rico 3 2002
4 Marc 2 2003
5 Julia 3 2004
6 Rico 4 2005
We could use setnames from data.table
library(data.table)
do.call(rbind, Map(setnames, dflist, old = equivalent_vars, new = newnanme))
# id year income
#1 Marc 2000 1
#2 Julia 2001 2
#3 Rico 2002 3
#4 Marc 2003 2
#5 Julia 2004 3
#6 Rico 2005 4
Or we can use the :=
library(dplyr)
library(purrr)
map2_df(dflist, equivalent_vars, ~ .x %>%
rename(!! (newnanme) := !! .y)) %>%
select(id, income, year)
# id income year
#1 Marc 1 2000
#2 Julia 2 2001
#3 Rico 3 2002
#4 Marc 2 2003
#5 Julia 3 2004
#6 Rico 4 2005
My data frame consists of three columns: state name, year, and the tax receipt for each year and each state. Below is an example for just one state.
year RealTaxRevs
1 1971 8335046
2 1972 9624026
3 1973 10498935
4 1974 10052305
5 1975 8708381
6 1976 8911262
7 1977 10759032
I'd like to compute the change in tax receipt from one year to the next, for each state. I used the following code:
data %>% group_by(state) %>% summarise(diff(RealTaxRevs, lag = 1, differences = 1))
but it gives me "Error: expecting a single value".
Could anyone explain this error message, and help me do this correctly using dplyr? Thank you.
If you want to use diff like function, then consider using the zoo library as well. Then you can have code which looks like the following:
library(zoo)
diff(as.zoo(1:4), na.pad=T)
In a data frame setting it would be like:
dat <- data.frame(a=c(8335046, 9624026, 10498935, 10052305, 8708381, 8911262, 10759032))
dat %>% mutate(b=diff(as.zoo(a), na.pad=T))
# a b
# 1 8335046 NA
# 2 9624026 1288980
# 3 10498935 874909
# 4 10052305 -446630
# 5 8708381 -1343924
# 6 8911262 202881
# 7 10759032 1847770
This way you can easily increase the number of lags, without continually adding NA
dat %>% mutate(b2=diff(as.zoo(a), lag=2, na.pad=T))
# a b2
# 1 8335046 NA
# 2 9624026 NA
# 3 10498935 2163889
# 4 NA NA
# 5 8708381 -1790554
# 6 8911262 NA
# 7 10759032 2050651
We can use data.table
library(data.table)
setDT(data)[, Diffs := RealTaxRevs - shift(RealTaxRevs)[[1]], state]
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