subset

df <- data.frame(a=LETTERS[1:4], b=rnorm(4) ) vals <- c("B","D") I can filter/subset df with values in val with: dplyr::filter(df, a %in% vals) subset(df, a %in% vals) Both gives: a b 2 B 0.4481627 4 D 0.2916513 What if I have a variable name in a vector, e.g.: > names(df)[1] [1] "a" Then it doesnt work - I guess because its quoted dplyr::filter(df, names(df)[1] %in% vals) [1] a b <0 rows> (or 0-length row.names) How do you do this ? UPDATE ( what if its dplyr::tbl_df(df) ) Answers below work fine for data.frames, but not for dplyr::tbl_df wrapped data: df<-dplyr::tbl_df(df) dplyr::filter(df,

I have a couple of large data frames (1 million+ rows x 6-10 columns) I need to subset repeatedly. The subsetting section is the slowest part of my code and I curious if there is way to do this faster. load("https://dl.dropbox.com/u/4131944/Temp/DF_IOSTAT_ALL.rda") start_in <- strptime("2012-08-20 13:00", "%Y-%m-%d %H:%M") end_in<- strptime("2012-08-20 17:00", "%Y-%m-%d %H:%M") system.time(DF_IOSTAT_INT <- DF_IOSTAT_ALL[DF_IOSTAT_ALL$date_stamp >= start_in & DF_IOSTAT_ALL$date_stamp <= end_in,]) > system.time(DF_IOSTAT_INT <- DF_IOSTAT_ALL[DF_IOSTAT_ALL$date_stamp >= start_in & DF_IOSTAT_ALL

This is a follow-up question of the question I asked here . There I learned a) how to do this for columns (see below) and b) that the selection of rows and columns seems to be quite differently handled in R which means that I cannot use the same approach for rows. So suppose I have a pandas dataframe like this: import pandas as pd import numpy as np df = pd.DataFrame(np.random.randint(10, size=(6, 6)), columns=['c' + str(i) for i in range(6)], index=["r" + str(i) for i in range(6)]) c0 c1 c2 c3 c4 c5 r0 4 2 3 9 9 0 r1 9 0 8 1 7 5 r2 2 6 7 5 4 7 r3 6 9 9 1 3 4 r4 1 1 1 3 0 3 r5 0 8 5 8 2 9 then

I am using R to analyze a number of time series (1951-2013) containing daily values of Max and Min temperatures. The data has the following structure: YEAR MONTH DAY MAX MIN 1985 1 1 22.8 9.4 1985 1 2 28.6 11.7 1985 1 3 24.7 12.2 1985 1 4 17.2 8.0 1985 1 5 17.9 7.6 1985 1 6 17.7 8.1 I need to find the frequency of heat waves based on this definition: A period of three or more consecutive days ‎with a daily maximum and minimum temperature exceeding the 90th percentile of the maximum ‎and minimum temperatures for all days in the studied period. Basically, I want to subset those consecutive days

Note: This is basically the same question as another one I've posted on Stackoverflow yesterday. However, I figured that I used a poor example in that question that didn't quite boil it down to the essence of what I had in mind. As all replies to that original post refer to that first question I thought it might be a better idea to put the new example in a separate question — no duplication intended. Model Game Characters That Can Move Let's define an enum of directions for use in a simple game: enum Direction { case up case down case left case right } Now in the game I need two kinds of

Given an input json string of keys from an array, return an object with only the entries that had keys in the original object and in the input array. I have a solution but I think that it isn't elegant ( {($k):$input[$k]} feels especially clunky...) and that this is a chance for me to learn. jq -n '{"1":"a","2":"b","3":"c"}' \ | jq --arg keys '["1","3","4"]' \ '. as $input | ( $keys | fromjson ) | map( . as $k | $input | select(has($k)) | {($k):$input[$k]} ) | add' Any ideas how to clean this up? I feel like Extracting selected properties from a nested JSON object with jq is a good starting

I have two dataframes. In the first one, I have a KEY/ID column and two variables: KEY V1 V2 1 10 2 2 20 4 3 30 6 4 40 8 5 50 10 In the second dataframe, I have a KEY/ID column and a third variable KEY V3 1 5 2 10 3 20 I would like to extract the rows of the first dataframe that are also in the second dataframe by matching them according to the KEY column. I would also like to add the V3 column to final dataset. KEY V1 V2 V3 1 10 2 5 2 20 4 10 3 30 6 20 This are my attempts by using the subset and the merge function subset(data1, data1$KEY == data2$KEY) merge(data1, data2, by.x = "KEY", by.y =