melt

问题 I am new to data analysis with R. I recently got a pre-formatted environmental observation-model dataset, an example subset of which is shown below: date site obs mod site obs mod 2000-09-01 00:00:00 campus NA 61.63 city centre 66 56.69 2000-09-01 01:00:00 campus 52 62.55 city centre NA 54.75 2000-09-01 02:00:00 campus 52 63.52 city centre 56 54.65 Basically, the data include the time series of hourly observed and modelled concentrations of a pollutant at various sites in "reoccurring columns

问题 I have a dataframe which I want to melt the data into multiple target columns. The below code I used grp2 = pd.lreshape(grp1, cols.groupby(cols.str.split('_').str[1])).sort_values('ACCT_NAME') The above line I lose the column names grp2 = pd.melt(grp1 , id_vars = ['Client' , 'Industry'] , var_name = "H Year" , value_name = 'Count') The above line I dont get multiple target columns From DF Client INDUSTRY 1H2016_6MO 2H2016_6MO 1H2017_6MO 2H2017_6MO 1H2016_12MO 2H2016_12MO 1H2017_12MO 2H2017

问题 This question already has answers here : Split a column of concatenated comma-delimited data and recode output as factors (2 answers) Closed 2 years ago . As shown in the above pic, I've a column, genres, with a list of genres the corresponding movie belongs to. There are in total 19 unique genres. I'd like to know if I can manipulate this data into appending 19 columns to the data set each corresponding to each of the genres identifiers and label the corresponding cells as 0 or 1 indicating

问题 This question is raising different points as the one I posted yesterday, with a better description, so I hope for your understanding. I have the following Data: Data <- data.frame(LMX = c(1.92, 2.33, 3.52, 5.34, 6.07, 4.23, 3.45, 5.64), Thriving = c(4.33, 6.54, 6.13, 4.85, 4.26, 6.32, 5.63, 4.55), Wellbeing = c(1.92, 2.33, 3.52, 2.34, 4.07, 3.23, 3.45, 4.64)) rownames(Data) <- 1:8 Now, my aim is to generate a flipped over bar chart that is showing one bar for each variable with all bars

问题 Let's say I have this molten data.frame molten <- data.frame( gene = c("a1", "b1", "a1", "b1", "a1", "b1"), count = c(3, 4, 5, 2, 6, 7), condition = c("A", "A", "B", "B", "C", "C") ) # gene count condition # 1 a1 3 A # 2 b1 4 A # 3 a1 5 B # 4 b1 2 B # 5 a1 6 C # 6 b1 7 C Which looks like this unmolten molten %>% dcast(gene ~ condition, value.var = "count") # gene A B C # 1 a1 3 5 6 # 2 b1 4 2 7 How can I subtract column A from all the other numeric columns (B and C in this example). I want

问题 I have a pandas DataFrame like this: df = pd.DataFrame({'custid':[1,2,3,4], ...: 'prod1':['jeans','tshirt','jacket','tshirt'], ...: 'prod1_hnode1':[1,2,3,2], ...: 'prod1_hnode2':[6,7,8,7], ...: 'prod2':['tshirt','jeans','jacket','shirt'], ...: 'prod2_hnode1':[2,1,3,4], ...: 'prod2_hnode2':[7,6,8,7]}) In [54]: df Out[54]: custid prod1 prod1_hnode1 prod1_hnode2 prod2 prod2_hnode1 \ 0 1 jeans 1 6 tshirt 2 1 2 tshirt 2 7 jeans 1 2 3 jacket 3 8 jacket 3 3 4 tshirt 2 7 shirt 4 prod2_hnode2 0 7 1 6

问题 I'm working with a large data frame that I want to pivot, so that variables in a column become rows across the top. I've found the reshape package very useful in such cases, except that the cast function defaults to fun.aggregate=length. Presumably this is because I'm performing these operations by "case" and the number of variables measured varies among cases. I would like to pivot so that missing variables are denoted as "NA"s in the pivoted data frame. So, in other words, I want to go from

问题 I have searched and found many solutions that came close, but never quite worked in the end. This is probably something very simple, for those with experience... Here is a snippet of my data. This was created automatically from a JSON import by the package jsonlite. The data is very nicely structured, but I am nevertheless helpless. Update2: I have added the relevant data below structure(list(rightsize = c(42L, 50L, 52L, 49L, 41L, 41L, 41L, 41L, 41L, 45L, 47L, 42L, 45L, 46L, 42L, 44L, 44L,

问题 I have a data frame (read from RDS file) with 140 variables. I have subsetted 3 of them. But the subset has only one row with three column variables. I have to present it as a table and make a bar chart too. The subset data frame looks like this. HomeCondn_Good HomeCondn_Livabl HomeCondn_Dilapdtd (dbl) (dbl) (dbl) 1 65.9 29.7 4.3 The reproducible example is as follows: structure(list(HomeCondn_Good = 65.9, HomeCondn_Livabl = 29.7, HomeCondn_Dilapdtd = 4.3), .Names = c("HomeCondn_Good",

问题 I have data which has data like id name model_# ms bp1 cd1 sf1 sa1 rq1 bp2 cd2 sf2 sa2 rq2 ... 1 John 23984 1 23 234 124 25 252 252 62 194 234 234 ... 2 John 23984 2 234 234 242 62 262 622 262 622 26 262 ... for hundreds of models with up to 10 ms and variables counting up to 21. I have usually used pd.melt for doing my analysis where i look at bp1:bp21 or whatever. I currently have a need to create a melt where I look at bp1 values along with rq 1 values. I am looking to effectively create