I have a data set that records the averages of air pollution coming from different kinds of monitors by county and year. If the monitor is known to only be Monitor 1 it is coded as such, otherwise the average is coded as "all". If there isn't anything other than Monitor 1 though, so far there isn't an All. I want to take the values of Monitor 1 and create a new row with the exact same information labeled as All, but only if All doesn't already exist. Example:
Year County Type Average
2001 Adams Monitor 1 8.9
2001 Benton Monitor 1 6.5
2001 Benton All 7.1
In this case, I would want it to become:
Year County Type Average
2001 Adams Monitor 1 8.9
2001 Adams All 8.9 ***identical to the above
2001 Benton Monitor 1 6.5
2001 Benton All 7.1
I can think of a few kludgy, convoluted starts to doing this, or I could try to mess with conditionals. But I am trying to improve my R ability and keep my coding consistent with how R works best (there's a phrase for this I'm forgetting...!) Does anyone have any suggestions?
As a first step, I would use the ave function to determine if each row is of Type "Monitor 1" and is the only row for a particular county:
(to.duplicate <- ave(as.character(dat$Type), dat$County, FUN=function(x) if(identical(x, "Monitor 1")) { TRUE } else {rep(FALSE, length(x))}) == "TRUE")
# [1] TRUE FALSE FALSE
Then I would generate all the new rows in one shot and use rbind to add it to the data frame:
new.dat <- dat[to.duplicate,]
new.dat$Type <- "All"
rbind(dat, new.dat)
# Year County Type Average
# 1 2001 Adams Monitor 1 8.9
# 2 2001 Benton Monitor 1 6.5
# 3 2001 Benton All 7.1
# 4 2001 Adams All 8.9
Related
I have a huge dataset which is partly pooled cross section and partly panel data:
Year Country Respnr Power Nr
1 2000 France 1 1213 1
2 2001 France 2 1234 2
3 2000 UK 3 1726 3
4 2001 UK 3 6433 4
I would like to filter the panel data from the combined data and tried the following:
> anyDuplicated(df$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)
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
*****This is to deal with the question asked in Coursera and hence I may not be able to reveal the complete code*****
hi,
below is my data frame (outcome_H)
Hospital_Name H_A H_F PN
ABC 4.5 5 6
CDE 4.5 1 3
EFG 5 2 1
1) I need to rank the column provided in the function call (it could be one of H_A ,H_F,PN)
2) there will also a rank be provided in the call. Need to match that rank with the rank calculated above and return the respective Hospital_Name
I had used ties.method="first" to solve the tie problem. But however when I look at the final output the hospital name is not sorted.
Example: if i give rank =2, I expect CDE to be printed, but due to some problems(which I am note aware) ABC gets printed for rank=2 and CDE is printed for rank=1.
Below are some parts of code for better understanding:
H_A <- as.numeric(outcome_H$H_A)
HA <- H_A[order(H_A)] // newly added piece to order the value
df <- data.frame(HA,round(rank(HA,ties.method="first")),outcome_H$Hospital_Name)
rowss <- df[order(df$round.rank.HA..),]
Before ordering Output:
HA round.rank.HA.. outcome_H.Hospital.Name
42 8.1 1 FORT DUNCAN MEDICAL CENTER
192 8.5 2 TOMBALL REGIONAL MEDICAL CENTER
61 8.7 4 DETAR HOSPITAL NAVARRO
210 8.7 4 CYPRESS FAIRBANKS MEDICAL CENTER
69 8.8 6 MISSION REGIONAL MEDICAL CENTER
117 8.8 6 METHODIST HOSPITAL,THE
After Ordering output:
HA round.rank.HA..ties.method....first... outcome_H.Hospital.Name
1 8.1 1 PROVIDENCE MEMORIAL HOSPITAL
2 8.5 2 MEMORIAL HERMANN BAPTIST ORANGE HOSPITAL
3 8.7 3 PETERSON REGIONAL MEDICAL CENTER
4 8.7 4 CHILDREN'S HOSPITAL -SCOTT & WHITE HEALTHCARE
5 8.8 5 UNITED REGIONAL HEALTH CARE SYSTEM
6 8.8 6 ST JOSEPH REGIONAL HEALTH CENTER
As you can see, the data with hospital names are completely incorrect.
Any help is very much appreciated.
Thanks,
Pravellika J
You could try H_A <- as.numeric(as.character(outcome_H$H_A))
Output
HA round.rank.HA..ties.method....first... outcome_H.Hospital_Name
1 4.5 1 ABC
2 4.5 2 CDE
3 5.0 3 EFG
I figured it myself. I had initialy assigned HA only with one of the three cols(H_A,H_F,PN). Now i clubbed it with hospital_Name and ordered it based on both the attributes.
Thanks,
Pravellika J
I currently have a data frame that looks like this...
Year School AveragePoints
2012-2013 Alabama 2.5
2012-2013 Alabama 5.4
2012-2013 Alabama 10.4
2012-2013 Alabama 1.2
2012-2013 Alabama 9.2
2012-2013 Alabama 7.3
Each row represents a player on that team for that year. So the first row means that one player on Alabama for 2012-2013 averaged 2.5 points that year. The data frame is much longer than this, with more teams and the years continuing through 1997-1998. I want to know how to find out how many players averaged between 0-4, 4.1-9, 9.1-14, and >14.1 for each year per school. In other words for 2012-2013, how many players averaged in those 4 categories for Alabama. But I would need those numbers for each year for Alabama and the other schools involved. I think some form of an apply function should be used but I'm not sure.
If I understand your question correctly, you should look into cut first, and then into aggregate (or tapply) or possibly even table.
Here's how I would proceed:
Add a column with the cut results.
mydf$AP <- with(mydf, cut(AveragePoints, c(0, 4.1, 9.1, 14.1)))
Here's a table approach:
table(mydf[c("AP", "Year", "School")])
# , , School = Alabama
#
# Year
# AP 2012-2013
# (0,4.1] 2
# (4.1,9.1] 2
# (9.1,14.1] 2
However, the output for aggregate would probably be a much more useful format.
aggregate(. ~ Year + School + AP, mydf, length)
# Year School AP AveragePoints
# 1 2012-2013 Alabama (0,4.1] 2
# 2 2012-2013 Alabama (4.1,9.1] 2
# 3 2012-2013 Alabama (9.1,14.1] 2
So what I have is data of cod weights at different ages. This data is taken at several locations over time.
What I would like to create is "weight at age", basically a mean value of weights at a certain age. I want do this for each location at each year.
However, the ages are not sampled the same way (all old fish caught are measured, while younger fish are sub sampled), so I can't just create a normal average, I would like to bootstrap samples.
The bootstrap should take out 5 random values of weight at an age, create a mean value and repeat this a 1000 times, and then create an average of the means. The values should be able to be used again (replace). This should be done for each age at every AreaCode for every year. Dependent factors: Year-location-Age.
So here's an example of what my data could look like.
df <- data.frame( Year= rep(c(2000:2008),2), AreaCode = c("39G4", "38G5","40G5"), Age = c(0:8), IndWgt = c(rnorm(18, mean=5, sd=3)))
> df
Year AreaCode Age IndWgt
1 2000 39G4 0 7.317489899
2 2001 38G5 1 7.846606144
3 2002 40G5 2 0.009212455
4 2003 39G4 3 6.498688035
5 2004 38G5 4 3.121134937
6 2005 40G5 5 11.283096043
7 2006 39G4 6 0.258404136
8 2007 38G5 7 6.689780137
9 2008 40G5 8 10.180511929
10 2000 39G4 0 5.972879108
11 2001 38G5 1 1.872273650
12 2002 40G5 2 5.552962065
13 2003 39G4 3 4.897882549
14 2004 38G5 4 5.649438631
15 2005 40G5 5 4.525012587
16 2006 39G4 6 2.985615831
17 2007 38G5 7 8.042884181
18 2008 40G5 8 5.847629941
AreaCode contains the different locations, in reality I have 85 different levels. The time series stretches 1991-2013, the ages 0-15. IndWgt contain the weight. My whole data frame has a row length of 185726.
Also, every age does not exist for every location and every year. Don't know if this would be a problem, just so the scripts isn't based on references to certain row number. There are some NA values in the weight column, but I could just remove them before hand.
I was thinking that I maybe should use replicate, and apply or another plyr function. I've tried to understand the boot function but I don't really know if I would write my arguments under statistics, and in that case how. So yeah, basically I have no idea.
I would be thankful for any help I can get!
How about this with plyr. I think from the question you wanted to bootstrap only the "young" fish weights and use actual means for the older ones. If not, just replace the ifelse() statement with its last argument.
require(plyr)
#cod<-read.csv("cod.csv",header=T) #I loaded your data from csv
bootstrap<-function(Age,IndWgt){
ifelse(Age>2, # treat differently for old/young fish
res<-mean(IndWgt), # old fish mean
res<-mean(replicate(1000,sample(IndWgt,5,replace = TRUE))) # young fish bootstrap
)
return(res)
}
ddply(cod,.(Year,AreaCode,Age),summarize,boot_mean=bootstrap(Age,IndWgt))
Year AreaCode Age boot_mean
1 2000 39G4 0 6.650294
2 2001 38G5 1 4.863024
3 2002 40G5 2 2.724541
4 2003 39G4 3 5.698285
5 2004 38G5 4 4.385287
6 2005 40G5 5 7.904054
7 2006 39G4 6 1.622010
8 2007 38G5 7 7.366332
9 2008 40G5 8 8.014071
PS: If you want to sample all ages in the same way, no need for the function, just:
ddply(cod,.(Year,AreaCode,Age),
summarize,
boot_mean=mean(replicate(1000,mean(sample(IndWgt,5,replace = TRUE)))))
Since you don't provide enough code, it's too hard (lazy) for me to test it properly. You should get your first step using the following code. If you wrap this into replicate, you should get your end result that you can average.
part.result <- aggregate(IndWgt ~ Year + AreaCode + Age, data = data, FUN = function(x) {
rws <- length(x)
get.em <- sample(x, size = 5, replace = TRUE)
out <- mean(get.em)
out
})
To handle any missing combination of year/age/location, you could probably add an if statement checking for NULL/NA and producing a warning and/or skipping the iteration.