I am trying to generate groupwise (hearing - my independent variable, so HL and NH are the two groups) summary statistics (mean, sd, min, max, standard error etc. ) for each of the 10 dependent variables. I was able to do this for one variable (R_PTA) using these 2 codes:
1.
RightPTA <- mydata %>% group_by(NHL) %>% summarise(n=length(R_PTA), mean_R_PTA=mean(R_PTA), sd_R_PTA=sd(R_PTA), se_R_PTA=sd(R_PTA)/sqrt(length(R_PTA)), min_R_PTA=min(R_PTA), max_R_PTA=max(R_PTA))
2.
mydata mean<-tapply(mydata$R_PTA, mydata$NHL, mean) mean sd<-tapply(mydata$R_PTA, mydata$NHL, sd) sd median<-tapply(mydata$R_PTA, mydata$NHL, median) median max<-tapply(mydata$R_PTA, mydata$NHL, max) max min<-tapply(mydata$R_PTA, mydata$NHL, min) min cbind(mean, sd, median, max, min) round(cbind(mean, sd, median, max, min), digits = 1) t1<-round(cbind(mean, sd, median, max, min), digits = 1) t1
Here is the output:
RightearPTA mean sd median max min HL 26.9 7.3 27.5 37.5 8.8 NH 11.6 4.1 12.5 16.2 2.5
I want the same exact thing for all the remaining 9 variables (L_PTA, B_PTA etc.) but in one shot if possible. Is there no way to do this? Do I have to code for each single dependent variable? I am sure its out there, but I cant find it! Any hep would be appreciated!!
Consider a base R solution with by (the object-oriented wrapper to tapply to subset dataframe into factor groups) and nested sapply (to build matrix of stats). Below demonstrates with random, seeded data for 10 stats columns:
set.seed(88) df <- data.frame( GROUP = sapply(seq(50), function(i) sample(c("NH", "HL"), 1, replace=TRUE)), STAT1 = rnorm(50)*100, STAT2 = rnorm(50), STAT3 = runif(50)*100, STAT4 = runif(50), STAT5 = rgamma(50, shape = 2)*100, STAT6 = rgamma(50, shape = 2), STAT7 = rpois(50, lambda = 100)*100, STAT8 = rpois(50, lambda = 100), STAT9 = rexp(50, rate = 1)*100, STAT10 = rexp(50, rate = 1) ) dfList <- by(df, df$GROUP, FUN = function(d) sapply(d[2:ncol(d)], function(i) c(mean = mean(i, na.rm=TRUE), sd = sd(i, na.rm=TRUE), median = median(i, na.rm=TRUE), min = min(i, na.rm=TRUE), max = max(i, na.rm=TRUE) ) ) )
Output
dfList$HL # STAT1 STAT2 STAT3 STAT4 STAT5 STAT6 STAT7 STAT8 STAT9 STAT10 # mean -6.594221 -0.04059519 52.990723 0.58753311 157.55220 1.9196911 10103.4483 101.17241 113.089148 0.771495372 # sd 102.512709 0.99159105 31.055376 0.27339871 152.37034 1.4880694 709.3673 10.02165 121.360898 0.720117072 # median 8.034055 0.01163562 56.416484 0.56894472 136.58274 1.5150241 10200.0000 103.00000 77.302150 0.599291434 # min -199.786535 -1.84703449 1.345751 0.00207128 22.56936 0.1553518 8400.0000 82.00000 2.396641 0.006532798 # max 251.976970 2.55701655 98.612123 0.99413520 806.38484 7.1030277 11900.0000 120.00000 487.719745 3.133768953 dfList$NH # STAT1 STAT2 STAT3 STAT4 STAT5 STAT6 STAT7 STAT8 STAT9 STAT10 # mean 26.51853 -0.13748799 44.1973692 0.46621478 155.7555 1.880407 9961.9048 104.38095 150.596480 1.1243476 # sd 90.57645 0.77843518 29.9227560 0.30340507 121.5361 1.105004 868.6059 8.44083 131.123059 1.1627959 # median 24.52202 -0.02949522 46.1950960 0.33646282 114.7845 1.736198 9700.0000 105.00000 122.841835 0.7819896 # min -105.54741 -1.58980314 0.2636007 0.02044767 17.3282 0.291350 8900.0000 89.00000 7.799051 0.1108107 # max 194.78958 1.35889041 96.0175463 0.99160167 434.5724 4.368176 12000.0000 120.00000 554.307036 5.1537741
