I am new to R and am trying to work on a data frame from a csv file (as seen from the code below). It has hospital data with 46 columns and 4706 rows (one of those columns being 'State'). I made a table showing counts of rows for each value in the State column. So in essence the table shows each state and the number of hospitals in that state. Now what I want to do is subset the data frame and create a new one without the entries for which the state has less than 20 hospitals.
How do I count the occurrences of values in the State column and then remove those that count up to less than 20? Maybe I am supposed to use the table() function, remove the undesired data and put that into a new data frame using something like lappy(), but I'm not sure due to my lack of experience in programming with R.
Any help will be much appreciated. I have seen other examples of removing rows that have certain column values in this site, but not one that does that based on the count of a particular column value.
> outcome <- read.csv("outcome-of-care-measures.csv", colClasses = "character")
> hospital_nos <- table(outcome$State)
> hospital_nos
AK AL AR AZ CA CO CT DC DE FL GA GU HI IA ID IL IN KS KY LA MA MD ME MI
17 98 77 77 341 72 32 8 6 180 132 1 19 109 30 179 124 118 96 114 68 45 37 134
MN MO MS MT NC ND NE NH NJ NM NV NY OH OK OR PA PR RI SC SD TN TX UT VA
133 108 83 54 112 36 90 26 65 40 28 185 170 126 59 175 51 12 63 48 116 370 42 87
VI VT WA WI WV WY
2 15 88 125 54 29
Here is one way to do it. Starting with the following data frame :
df <- data.frame(x=c(1:10), y=c("a","a","a","b","b","b","c","d","d","e"))
If you want to keep only the rows with more than 2 occurrences in df$y, you can do :
tab <- table(df$y)
df[df$y %in% names(tab)[tab>2],]
Which gives :
x y
1 1 a
2 2 a
3 3 a
4 4 b
5 5 b
6 6 b
And here is a one line solution with the plyr package :
ddply(df, "y", function(d) {if(nrow(d)>2) d else NULL})
Related
i have such a difficult question (at least to me) that i spend 2 hours just writing it. Complete impossible to program it by my self. I try to be very clear and i´m sorry if i didn´t. I´m doing this in a very rustic way in excel, but i really need to program this.
i have a data.frame like this
id_pix id_lote clase f1 f2
45 4 Sg 2460 2401
46 4 Sg 2620 2422
47 4 Sg 2904 2627
48 5 M 2134 2044
49 5 M 2180 2104
50 5 M 2127 2069
83 11 S 2124 2062
84 11 S 2189 2336
85 11 S 2235 2162
86 11 S 2162 2153
87 11 S 2108 2124
with 17451 "id_pixel"(rows), 2080 "id_lote" and 9 "clase"
this is the "id_lote" count per "clase" (v1 is the id_lote count)
clase v1
1: S 1099
2: P 213
3: Sg 114
4: M 302
5: Alg 27
6: Az 77
7: Po 228
8: Cit 13
9: Ma 7
i need to split the "id_lote" randomly within the "clase". I mean i have 1099 "id_lote" for the "S" "clase" that are 9339 "id_pixel" (rows) and i want to randomly select 50 % of "id_lote" that are x "id_pixel"(rows). And do this for every "clase" considering that the size (number of "id_lote") of every "clase" are different. I also would like to be able to change the size of the selection (50 %, 30 %, etc). And i also want to keep the not selected set of "id_lote". I hope some one can help me with this!
here is the reproducible example
this is the data with 2 clase (S and Az), with 6 id_lote and 13 id_pixel
id_pix id_lote clase f1 f2
1 1 S 2909 2381
2 1 S 2515 2663
3 1 S 2628 3249
30 2 S 3021 2985
31 2 S 3020 2596
71 9 S 4725 4404
72 9 S 4759 4943
75 11 S 2728 2225
218 21 Az 4830 3007
219 21 Az 4574 2761
220 21 Az 5441 3092
1155 126 Az 7209 2449
1156 126 Az 7035 2932
and one result could be:
id_pix id_lote clase f1 f2
1 1 S 2909 2381
2 1 S 2515 2663
3 1 S 2628 3249
75 11 S 2728 2225
1155 126 Az 7209 2449
1156 126 Az 7035 2932
were 50% of id_lote were randomly selected in clase "S" (2 of 4 id_lote) but all the id_pixel in selected id_lote were keeped. The same for clase "Az", one id_lote was randomly selected (1 of 2 in this case) and all the id_pixel in selected id_lote were keeped.
what colemand77 proposed helped a lot. I think dplyr package is usefull for this but i think that if i do
df %>%
group_by(clase, id_lote) %>%
sample_frac(.3, replace = FALSE)
i get the 30 % of the data of each clase but not grouped by id_lote like i need! I mean 30 % of the rows (id_pixel) were selected instead of id_lote.
i hope this example help to understand what i want to do and make it usefull for everybody. I´m sorry if i wasn´t clear enough the first time.
Thanks a lot!
First glimpse I'd say the dplyr package is your friend here.
df %>%
group_by(clase, id_lote) %>%
sample_frac(.3, replace = FALSE)
so you first use group_by() and include the grouping levels you want to sample from, then you use sample_frac to sample the fraction of the results you want for each group.
As near as I can tell this is what you are asking for. If not, please consider re-stating your question to include either a reproducible example or clarify. Cheers.
to "keep" the not-selected members, I would add a column of unique ids, and use an anti-join anti_join()(also from the dplyr package) to find the id's that are not in common between the two data.frames (the results of the sampling and the original).
## Update ##
I'm understanding better now, I believe. Think about this as a two step process...
1) you want to select x% (50 in example) of the id_lote from each clase and return those id_lote #s (i'm assuming that a given id_lote does not exist for multiple clase?)
2) you want to see all of the id_pixels that correspond to each id_lote, all in one data.frame
I've broken this down into multiple steps for illustration, not because it is the fastest / prettiest.
raw data: (couldn't read your data into R.)
df<-data.frame(id_pix = c(1:200),
id_lote = sample(1:20,200, replace = TRUE),
clase = sample(letters[seq_along(1:10)], 200, replace = TRUE),
f1 = sample(1000:2000,200, replace = TRUE),
f2 = sample(2000:3000,200, replace = TRUE))
1) figure out which id_lote correspond to which clase - for this we use the dplyr summarise function and store it in a variable
summary<-df %>%
ungroup() %>%
group_by(clase, id_lote) %>%
summarise()
returns:
Source: local data frame [125 x 2]
Groups: clase
clase id_lote
1 a 1
2 a 2
3 a 4
4 a 5
5 a 6
6 a 7
7 a 8
8 a 9
9 a 11
10 a 12
.. ... ...
then we sample to get the 30% of the id_lote for each clase..
sampled_summary <- summary %>%
group_by(clase) %>%
sample_frac(.3,replace = FALSE)
so the result of this is a data table with two columns, (clase and id_lote) with 30% of the id_lotes shown for each clase.
2) ok so now we have the id_lotes randomly selected from each class but not the id_pix that are associated with that class. To accomplish this we do a join to get the corresponding full data set including the id_pix, etc.
result <- sampled_summary %>%
left_join(df)
The above makes a copy of the data set a bunch, so if you have a substantial data set you could just do it all at one go:
result <- df %>%
ungroup() %>%
group_by(clase, id_lote) %>%
summarise() %>%
group_by(clase) %>%
sample_frac(.5,replace = FALSE) %>%
left_join(df)
if this doesn't get you what you want, let me know and we'll take another crack at it.
I am building a streambed hydrology calculator in R using multiple tables from an Access database. I am having trouble automating and calculating the same set of indices for multiple sites. The following sample dataset describes my data structure:
> Thalweg
StationID AB0 AB1 AB2 AB3 AB4 AB5 BC1 BC2 BC3 BC4 Xdep_Vdep
1 1AAUA017.60 47 45 44 55 54 6 15 39 15 11 18.29
2 1AXKR000.77 30 27 24 19 20 18 9 12 21 13 6.46
3 2-BGU005.95 52 67 62 42 28 25 23 26 11 19 20.18
4 2-BLG011.41 66 85 77 83 63 35 10 70 95 90 67.64
5 2-CSR003.94 29 35 46 14 19 14 13 13 21 48 6.74
where each column represents certain field-measured parameters (i.e. depth of a reach section) and each row represents a different site.
I have successfully used the apply functions to simultaneously calculate simple functions on multiple rows:
> Xdepth <- apply(Thalweg[, 2:11], 1, mean) # Mean Depth
> Xdepth
1 2 3 4 5
33.1 19.3 35.5 67.4 25.2
and appending the results back to the proper station in a dataframe.
However, I am struggling when I want to calculate and save variables that are subsequently used for further calculations. I cannot seem to loop or apply the same function to multiple columns on a single row and complete the same calculations over the next row without mixing variables and data.
I want to do:
Residual_AB0 <- min(Xdep_Vdep, Thalweg$AB0)
Residual_AB1 <- min((Residual_AB0 + other_variables), Thalweg$AB1)
Residual_AB2 <- min((Residual_AB1 + other_variables), Thalweg$AB2)
Residual_AB3 <- min((Residual_AB2 + other_variables), Thalweg$AB3)
# etc.
Depth_AB0 <- (Thalweg$AB0 - Residual_AB0)
Depth_AB1 <- (Thalweg$AB1 - Residual_AB1)
Depth_AB2 <- (Thalweg$AB2 - Residual_AB2)
# etc.
I have tried and subsequently failed at for loops such as:
for (i in nrow(Thalweg)){
Residual_AB0 <- min(Xdep_Vdep, Thalweg$AB0)
Residual_AB1 <- min((Residual_AB0 + Stacks_Equation), Thalweg$AB1)
Residual_AB2 <- min((Residual_AB1 + Stacks_Equation), Thalweg$AB2)
Residual_AB3 <- min((Residual_AB2 + Stacks_Equation), Thalweg$AB3)
Residuals <- data.frame(Thalweg$StationID, Residual_AB0, Residual_AB1, Residual_AB2, Residual_AB3)
}
Is there a better way to approach looping through multiple lines of data when I need unique variables saved for each specific row that I am currently calculating? Thank you for any suggestions.
your exact problem is still a mistery to me...
but it looks like you want a double for loop
for(i in 1:nrow(thalweg)){
residual=thalweg[i,"Xdep_Vdep"]
for(j in 2:11){
residual=min(residual,thalweg[i,j])
}
}
edited to improve the quality of the question as a result of the (wholly appropriate) spanking received by Spacedman!
I have a k-nearest neighbors object (an igraph) which I created as such, by using the file I have uploaded here:
I performed the following operations on the data, in order to create an adjacency matrix of distances between observations:
W <- read.csv("/path/sim_matrix.csv")
W <- W[, -c(1,3)]
W <- scale(W)
sim_matrix <- dist(W, method = "euclidean", upper=TRUE)
sim_matrix <- as.matrix(sim_matrix)
mygraph <- nng(sim_matrix, k=10)
This give me a nice list of vertices and their ten closest neighbors, a small sample follows:
1 -> 25 26 28 30 32 144 146 151 177 183 2 -> 4 8 32 33 145 146 154 156 186 199
3 -> 1 25 28 51 54 106 144 151 177 234 4 -> 7 8 89 95 97 158 160 170 186 204
5 -> 9 11 17 19 21 112 119 138 145 158 6 -> 10 12 14 18 20 22 147 148 157 194
7 -> 4 13 123 132 135 142 160 170 173 174 8 -> 4 7 89 90 95 97 158 160 186 204
So far so good.
What I'm struggling with, however, is how to to get access to the values for the weights between the vertices that I can do meaningful calculations on. Shouldn't be so hard, this is a common thing to want from graphs, no?
Looking at the documentation, I tried:
degree(mygraph)
which gives me the sum of the weights for each node. But I don't want the sum, I want the raw data, so I can do my own calculations.
I tried
get.data.frame(mygraph,"E")[1:10,]
but this has none of the distances between nodes:
from to
1 1 25
2 1 26
3 1 28
4 1 30
5 1 32
6 1 144
7 1 146
8 1 151
9 1 177
10 1 183
I have attempted to get values for the weights between vertices out of the graph object, that I can work with, but no luck.
If anyone has any ideas on how to go about approaching this, I'd be grateful. Thanks.
It's not clear from your question whether you are starting with a dataset, or with a distance matrix, e.g. nng(x=mydata,...) or nng(dx=mydistancematrix,...), so here are solutions with both.
library(cccd)
df <- mtcars[,c("mpg","hp")] # extract from mtcars dataset
# knn using dataset only
g <- nng(x=as.matrix(df),k=5) # for each car, 5 other most similar mpg and hp
V(g)$name <- rownames(df) # meaningful names for the vertices
dm <- as.matrix(dist(df)) # full distance matrix
E(g)$weight <- apply(get.edges(g,1:ecount(g)),1,function(x)dm[x[1],x[2]])
# knn using distance matrix (assumes you have dm already)
h <- nng(dx=dm,k=5)
V(h)$name <- rownames(df)
E(h)$weight <- apply(get.edges(h,1:ecount(h)),1,function(x)dm[x[1],x[2]])
# same result either way
identical(get.data.frame(g),get.data.frame(h))
# [1] TRUE
So these approaches identify the distances from each vertex to it's five nearest neighbors, and set the edge weight attribute to those values. Interestingly, plot(g) works fine, but plot(h) fails. I think this might be a bug in the plot method for cccd.
If all you want to know is the distances from each vertex to the nearest neighbors, the code below does not require package cccd.
knn <- t(apply(dm,1,function(x)sort(x)[2:6]))
rownames(knn) <- rownames(df)
Here, the matrix knn has a row for each vertex and columns specifying the distance from that vertex to it's 5 nearest neighbors. It does not tell you which neighbors those are, though.
Okay, I've found a nng function in cccd package. Is that it? If so.. then mygraph is just an igraph object and you can just do E(mygraph)$whatever to get the names of the edge attributes.
Following one of the cccd examples to create G1 here, you can get a data frame of all the edges and attributes thus:
get.data.frame(G1,"E")[1:10,]
You can get/set individual edge attributes with E(g)$whatever:
> E(G1)$weight=1:250
> E(G1)$whatever=runif(250)
> get.data.frame(G1,"E")[1:10,]
from to weight whatever
1 1 3 1 0.11861240
2 1 7 2 0.06935047
3 1 22 3 0.32040316
4 1 29 4 0.86991432
5 1 31 5 0.47728632
Is that what you are after? Any igraph package tutorial will tell you more!
I have a data frame having 20 columns. I need to filter / remove noise from one column. After filtering using convolve function I get a new vector of values. Many values in the original column become NA due to filtering process. The problem is that I need the whole table (for later analysis) with only those rows where the filtered column has values but I can't bind the filtered column to original table as the number of rows for both are different. Let me illustrate using the 'age' column in 'Orange' data set in R:
> head(Orange)
Tree age circumference
1 1 118 30
2 1 484 58
3 1 664 87
4 1 1004 115
5 1 1231 120
6 1 1372 142
Convolve filter used
smooth <- function (x, D, delta){
z <- exp(-abs(-D:D/delta))
r <- convolve (x, z, type='filter')/convolve(rep(1, length(x)),z,type='filter')
r <- head(tail(r, -D), -D)
r
}
Filtering the 'age' column
age2 <- smooth(Orange$age, 5,10)
data.frame(age2)
The number of rows for age column and age2 column are 35 and 15 respectively. The original dataset has 2 more columns and I like to work with them also. Now, I only need 15 rows of each column corresponding to the 15 rows of age2 column. The filter here removed first and last ten values from age column. How can I apply the filter in a way that I get truncated dataset with all columns and filtered rows?
You would need to figure out how the variables line up. If you can add NA's to age2 and then do Orange$age2 <- age2 followed by na.omit(Orange) you should have what you want. Or, equivalently, perhaps this is what you are looking for?
df <- tail(head(Orange, -10), -10) # chop off the first and last 10 observations
df$age2 <- age2
df
Tree age circumference age2
11 2 1004 156 915.1678
12 2 1231 172 876.1048
13 2 1372 203 841.3156
14 2 1582 203 911.0914
15 3 118 30 948.2045
16 3 484 51 1008.0198
17 3 664 75 955.0961
18 3 1004 108 915.1678
19 3 1231 115 876.1048
20 3 1372 139 841.3156
21 3 1582 140 911.0914
22 4 118 32 948.2045
23 4 484 62 1008.0198
24 4 664 112 955.0961
25 4 1004 167 915.1678
Edit: If you know the first and last x observations will be removed then the following works:
x <- 2
df <- tail(head(Orange, -x), -x) # chop off the first and last x observations
df$age2 <- age2
I'm working on some exercises based on this dataset.
There's a State column listing the rate of deaths per month by heart attack for each hospital of the state (column 11):
> table(data$State)
AK AL AR AZ CA CO CT DC DE FL GA GU HI IA ID IL IN KS KY
17 98 77 77 341 72 32 8 6 180 132 1 19 109 30 179 124 118 96
Now I try to filter out these states where at least 20 values are available:
> table(data$State)>20
AK AL AR AZ CA CO CT DC DE FL GA GU
FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE FALSE
So using subset I try to get a subset of data based on the above conditions, but that gives me a result I can't follow:
> data_subset <- subset(data, table(data$State)>20)
> table(data_subset$State)
AK AL AR AZ CA CO CT DC DE FL GA GU HI IA ID IL IN KS KY
14 84 66 65 288 64 25 8 5 155 109 1 19 93 24 153 107 100 83
Why am I getting AK 14, when I would expect that state to be filtered out by the condition?
You can use the following approach to filter out the data with less than 20 rows:
tab <- table(data$State)
data[data$State %in% names(tab)[tab > 19], ]
Your code
subset(data, table(data$State)>20)
does not work because table(data$State)>20 returns a boolean vector of length length(table$State). In your data, the boolean vector is shorter than the number of rows in your data frame. Due to vector recycling, the vector is combined with itself until the longer length is reached. E.g., have a look at (1:3)[c(TRUE, FALSE)].