I have never really used dplyr and wondering how I can use it in the following context. So, I have following two data frames:
trainData <- read.csv("train.csv", header = TRUE, stringsAsFactors = FALSE)
subscriptionData <- read.csv("subscriptions.csv", header = TRUE, stringsAsFactors = FALSE)
> head(trainData)
account.id total
1 001i000000NuOGY 0
2 001i000000NuS8r 0
3 001i000000NuPGw 0
4 001i000000NuO7a 0
5 001i000000NuQ2f 0
6 001i000000NuOQz 0
> head(subscriptionData)
account.id season package no.seats location section price.level total multiple.subs
1 001i000000LhyR3 2009-2010 Quartet 2 San Francisco Premium Orchestra 1 1.0 no
2 001i000000NuOeY 2000-2001 Full 2 San Francisco Orchestra 2 2.0 no
3 001i000000NuNvb 2001-2002 Full 2 Berkeley Saturday Balcony Front 3 2.0 no
4 001i000000NuOIz 1993-1994 Quartet 1 Contra Costa Orchestra 2 0.5 no
5 001i000000NuNVE 1998-1999 Full 2 Berkeley Sunday Balcony Rear 4 2.0 no
Now I want to take subset of subscriptionData based on the account.id of trainData. I basically want to take subset of subscriptionData with account.id that are also present in trainData.
I know it's a very basic question but I am totally new dplyr and have no clue.
You want a semi join:
subscriptionData %>% semi_join(trainData, by = "account.id")
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 want to create a spatial map showing drug mortality rates by US county, but I'm having trouble merging the drug mortality dataset, crude_rate, with the shapefile, usa_county_df. Can anyone help out?
I've created a key variable, "County", in both sets to merge on but I don't know how to format them to make the data mergeable. How can I make the County variables correspond? Thank you!
head(crude_rate, 5)
Notes County County.Code Deaths Population Crude.Rate
1 Autauga County, AL 1001 74 975679 7.6
2 Baldwin County, AL 1003 440 3316841 13.3
3 Barbour County, AL 1005 16 524875 Unreliable
4 Bibb County, AL 1007 50 420148 11.9
5 Blount County, AL 1009 148 1055789 14.0
head(usa_county_df, 5)
long lat order hole piece id group County
1 -97.01952 42.00410 1 FALSE 1 0 0.1 1
2 -97.01952 42.00493 2 FALSE 1 0 0.1 2
3 -97.01953 42.00750 3 FALSE 1 0 0.1 3
4 -97.01953 42.00975 4 FALSE 1 0 0.1 4
5 -97.01953 42.00978 5 FALSE 1 0 0.1 5
crude_rate$County <- as.factor(crude_rate$County)
usa_county_df$County <- as.factor(usa_county_df$County)
merge(usa_county_df, crude_rate, "County")
[1] County long lat order hole
[6] piece id group Notes County.Code
[11] Deaths Population Crude.Rate
<0 rows> (or 0-length row.names)`
My take on this. First, you cannot expect a full answer with code because you did not provide a link to you data. Next time, please provide a full description of the problem with the data.
I just used the data you provided here to illustrate.
require(tidyverse)
# Load the data
crude_rate = read.csv("county_crude.csv", header = TRUE)
usa_county = read.csv("usa_county.csv", header = TRUE)
# Create the variable "county_join" within the county_crude to "left_join" on with the usa_county data. Note that you have to have the same type of data variable between the two tables and the same values as well
crude_rate = crude_rate %>%
mutate(county_join = c(1:5))
# Join the dataframes using a left join on the county_join and County variables
df_all = usa_county %>%
left_join(crude_rate, by = c("County"="county_join")) %>%
distinct(order,hole,piece,id,group, .keep_all = TRUE)
Data link: county_crude
Data link: usa_county
Blockquote
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
I have data containing a unique identifier, a category, and a description.
Below is a toy dataset.
prjnumber <- c(1,2,3,4,5,6,7,8,9,10)
category <- c("based","trill","lit","cold",NA,"epic", NA,NA,NA,NA)
description <- c("skip class",
"dunk on brayden",
"record deal",
"fame and fortune",
NA,
"female attention",
NA,NA,NA,NA)
toy.df <- data.frame(prjnumber, category, description)
> toy.df
prjnumber category description
1 1 based skip class
2 2 trill dunk on brayden
3 3 lit record deal
4 4 cold fame and fortune
5 5 <NA> <NA>
6 6 epic female attention
7 7 <NA> <NA>
8 8 <NA> <NA>
9 9 <NA> <NA>
10 10 <NA> <NA>
I want to randomly sample the 'category' and 'description' columns from rows that have been filled in to use as infill for rows with missing data.
The final data frame would be complete and would only rely on the initial 5 rows which contain data. The solution would preserve between-column correlation.
An expected output would be:
> toy.df
prjnumber category description
1 1 based skip class
2 2 trill dunk on brayden
3 3 lit record deal
4 4 cold fame and fortune
5 5 lit record deal
6 6 epic female attention
7 7 based skip class
8 8 based skip class
9 9 lit record deal
10 10 trill dunk on brayden
complete = na.omit(toy.df)
toy.df[is.na(toy.df$category), c("category", "description")] =
complete[sample(1:nrow(complete), size = sum(is.na(toy.df$category)), replace = TRUE),
c("category", "description")]
toy.df
# prjnumber category description
# 1 1 based skip class
# 2 2 trill dunk on brayden
# 3 3 lit record deal
# 4 4 cold fame and fortune
# 5 5 lit record deal
# 6 6 epic female attention
# 7 7 cold fame and fortune
# 8 8 based skip class
# 9 9 epic female attention
# 10 10 epic female attention
Though it would seem a little more straightforward if you didn't start with the unique identifiers filled out for the NA rows...
You could try
library(dplyr)
toy.df %>%
mutate_each(funs(replace(., is.na(.), sample(.[!is.na(.)]))), 2:3)
Based on new information, we may need a numeric index to use in the funs.
toy.df %>%
mutate(indx= replace(row_number(), is.na(category),
sample(row_number()[!is.na(category)], replace=TRUE))) %>%
mutate_each(funs(.[indx]), 2:3) %>%
select(-indx)
Using Base R to fill in a single field a at a time, use something like (not preserving the correlation between the fields):
fields <- c('category','description')
for(field in fields){
missings <- is.na(toy.df[[field]])
toy.df[[field]][missings] <- sample(toy.df[[field]][!missings],sum(missings),T)
}
and to fill them in simultaneously (preserving the correlation between the fields) use something like:
missings <- apply(toy.df[,fields],
1,
function(x)any(is.na(x)))
toy.df[missings,fields] <- toy.df[!missings,fields][sample(sum(!missings),
sum(missings),
T),]
and of course, to avoid the implicit for loop in the apply(x,1,fun), you could use:
rowAny <- function(x) rowSums(x) > 0
missings <- rowAny(toy.df[,fields])
I am working with data in the following form:
Country Player Goals
"USA" "Tim" 0
"USA" "Tim" 0
"USA" "Dempsey" 3
"USA" "Dempsey" 5
"Brasil" "Neymar" 6
"Brasil" "Neymar" 2
"Brasil" "Hulk" 5
"Brasil" "Luiz" 2
"England" "Rooney" 4
"England" "Stewart" 2
Each row represents the number of goals that a player scored per game, and also contains that player's country. I would like to have the data in the form such that I can run pairwise correlations to see whether being from the same country has some association with the number of goals that a player scores. The data would look like this:
Player_1 Player_2
0 8 # Tim Dempsey
8 5 # Neymar Hulk
8 2 # Neymar Luiz
5 2 # Hulk Luiz
4 2 # Rooney Stewart
(You can ignore the comments, they are there simply to clarify what each row contains).
How would I do this?
table(df$player)
gets me the number of goals per player, but then how to I generate these pairwise combinations?
This is a pretty classic self-join problem. I'm gonna start by summarizing your data to get the total goals for each player. I like dplyr for this, but aggregate or data.table work just fine too.
library(dplyr)
df <- df %>% group_by(Player, Country) %>% dplyr::summarize(Goals = sum(Goals))
> df
Source: local data frame [7 x 3]
Groups: Player
Player Country Goals
1 Dempsey USA 8
2 Hulk Brasil 5
3 Luiz Brasil 2
4 Neymar Brasil 8
5 Rooney England 4
6 Stewart England 2
7 Tim USA 0
Then, using good old merge, we join it to itself based on country, and then so we don't get each row twice (Dempsey, Tim and Tim, Dempsey---not to mention Dempsey, Dempsey), we'll subset it so that Player.x is alphabetically before Player.y. Since I already loaded dplyr I'll use filter, but subset would do the same thing.
df2 <- merge(df, df, by.x = "Country", by.y = "Country")
df2 <- filter(df2, as.character(Player.x) < as.character(Player.y))
> df2
Country Player.x Goals.x Player.y Goals.y
2 Brasil Hulk 5 Luiz 2
3 Brasil Hulk 5 Neymar 8
6 Brasil Luiz 2 Neymar 8
11 England Rooney 4 Stewart 2
15 USA Dempsey 8 Tim 0
The self-join could be done in dplyr if we made a little copy of the data and renamed the Player and Goals columns so they wouldn't be joined on. Since merge is pretty smart about the renaming, it's easier in this case.
There is probably a smarter way to get from the aggregated data to the pairs, but assuming your data is not too big (national soccer data), you can always do something like:
A<-aggregate(df$Goals~df$Player+df$Country,data=df,sum)
players_in_c<-table(A[,2])
dat<-NULL
for(i in levels(df$Country)) {
count<-players_in_c[i]
pair<-combn(count,m=2)
B<-A[A[,2]==i,]
dat<-rbind(dat, cbind(B[pair[1,],],B[pair[2,],]) )
}
dat
> dat
df$Player df$Country df$Goals df$Player df$Country df$Goals
1 Hulk Brasil 5 Luiz Brasil 2
1.1 Hulk Brasil 5 Neymar Brasil 8
2 Luiz Brasil 2 Neymar Brasil 8
4 Rooney England 4 Stewart England 2
6 Dempsey USA 8 Tim USA 0