missing-data

I feel this should be something easy, I have looked x the internet, but I keep getting error messages. I have done plenty of analytics in the past but am new to R and programming. I have a pretty basic function to calculate means x columns of data: columnmean <-function(y){ nc <- ncol(y) means <- numeric(nc) for(i in 1:nc) { means[i] <- mean(y[,i]) } means } I'm in RStudio and testing it using the included 'airquality' dataset. When I load the AQ dataset and run my function: data("airquality") columnmean(airquality) I get back: NA NA 9.957516 77.882353 6.993464 15.803922 Because the first two

This is what my data.table/dataframe looks lke library(data.table) dt <- fread(' STATE ZIP PA 19333 PA 19327 PA 19333 PA NA PA 19355 PA 19333 PA NA PA 19355 PA NA ') I have three missing values in the ZIP column. I want to fill the missing values with nonmissing sample values of ZIPs according to their probability of occuring in the dataset. So for example ZIP 19333 occurs three times in the dataset and ZIP 19355 occurs twice in the dataset and 19327 occurs once. So ZIP 19333 has 50% probability of occurring in the dataset for PA , and 19355 has a 33.33% chance and 19327 has a 16.17% chance of

Whis this data frame, df <- expand.grid(id="01", parameter=c("blood", "saliva"), visit=c("V1", "V2", "V3")) df$value <- c(1:6) df$sex <- rep("f", 6) df > df id parameter visit value sex 1 01 blood V1 1 f 2 01 saliva V1 2 f 3 01 blood V2 3 f 4 01 saliva V2 4 f 5 01 blood V3 5 f 6 01 saliva V3 6 f When I reshape it in the "wide" format, I get identical results with both the base reshape function and the dcast function from reshape2 . reshape(df, timevar="visit", idvar=c("id", "parameter", "sex"), direction="wide") id parameter sex value.V1 value.V2 value.V3 1 01 blood f 1 3 5 2 01 saliva f 2 4 6

I have the following table which records a value per day. The problem is that sometimes days are missing. I want to write a SQL query that will: Return the missing days Calculate the missing value using linear interpolation So from the following source table: Date Value -------------------- 2010/01/10 10 2010/01/11 15 2010/01/13 25 2010/01/16 40 I want to return: Date Value -------------------- 2010/01/10 10 2010/01/11 15 2010/01/12 20 2010/01/13 25 2010/01/14 30 2010/01/15 35 2010/01/16 40 Any help would be greatly appreciated. declare @MaxDate date declare @MinDate date select @MaxDate = MAX

I have a dataframe and in a particular column I want to find the index of all NA values. How can I do it? which(is.na(my.df$col.I.care.about)) 来源： https://stackoverflow.com/questions/23070665/how-can-i-find-the-index-of-all-na-in-a-dataframe-column

There appears to be a quirk with the pandas merge function. It considers NaN values to be equal, and will merge NaN s with other NaN s: >>> foo = DataFrame([ ['a',1,2], ['b',4,5], ['c',7,8], [np.NaN,10,11] ], columns=['id','x','y']) >>> bar = DataFrame([ ['a',3], ['c',9], [np.NaN,12] ], columns=['id','z']) >>> pd.merge(foo, bar, how='left', on='id') Out[428]: id x y z 0 a 1 2 3 1 b 4 5 NaN 2 c 7 8 9 3 NaN 10 11 12 [4 rows x 4 columns] This is unlike any RDB I've seen, normally missing values are treated with agnosticism and won't be merged together as if they are equal. This is especially

I have two datasets a = raw dataset with thousands of observations of different weather events STATE EVTYPE 1 AL WINTER STORM 2 AL TORNADO 3 AL TSTM WIND 4 AL TSTM WIND 5 AL TSTM WIND 6 AL HAIL 7 AL HIGH WIND 8 AL TSTM WIND 9 AL TSTM WIND 10 AL TSTM WIND b = a dictionary table, which has a standard spelling for some weather events. EVTYPE evmatch 1 HIGH SURF ADVISORY <NA> 2 COASTAL FLOOD COASTAL FLOOD 3 FLASH FLOOD FLASH FLOOD 4 LIGHTNING LIGHTNING 5 TSTM WIND <NA> 6 TSTM WIND (G45) <NA> both are merged into df_new by evtype library(dplyr) df_new <- left_join(a, b, by = c("EVTYPE")) STATE