Saturday, 16 February 2019

Weather Forecast from MET Office

This is another function I wrote to access the MET office API and obtain a 5-day ahead weather forecast:

METDataDownload <- function(stationID, product, key){
  library("RJSONIO") #Load Library
  connectStr <- paste0("",stationID,"?res=",product,"&key=",key)
  con <- url(connectStr)
  data.json <- fromJSON(paste(readLines(con), collapse=""))
  LocID <- data.json$SiteRep$DV$Location$`i`
  LocName <- data.json$SiteRep$DV$Location$name
  Country <- data.json$SiteRep$DV$Location$country
  Lat <- data.json$SiteRep$DV$Location$lat
  Lon <- data.json$SiteRep$DV$Location$lon
  Elev <- data.json$SiteRep$DV$Location$elevation
  Details <- data.frame(LocationID = LocID,
                        LocationName = LocName,
                        Country = Country,
                        Lon = Lon,
                        Lat = Lat,
                        Elevation = Elev)
  param <-"rbind",data.json$SiteRep$Wx$Param)
  if(product == "daily"){
    dates <- unlist(lapply(data.json$SiteRep$DV$Location$Period, function(x){x$value}))
    DayForecast <-"rbind", lapply(data.json$SiteRep$DV$Location$Period, function(x){x$Rep[[1]]}))
    NightForecast <-"rbind", lapply(data.json$SiteRep$DV$Location$Period, function(x){x$Rep[[2]]}))
    colnames(DayForecast)[ncol(DayForecast)] <- "Type"
    colnames(NightForecast)[ncol(NightForecast)] <- "Type"
    ForecastDF <- plyr::rbind.fill.matrix(DayForecast, NightForecast) %>%
      as_tibble() %>%
      mutate(Date = as.Date(rep(dates, 2))) %>%
      mutate(Gn = as.numeric(Gn),
             Hn = as.numeric(Hn),
             PPd = as.numeric(PPd),
             S = as.numeric(S),
             Dm = as.numeric(Dm),
             FDm = as.numeric(FDm),
             W = as.numeric(W),
             U = as.numeric(U),
             Gm = as.numeric(Gm),
             Hm = as.numeric(Hm),
             PPn = as.numeric(PPn),
             Nm = as.numeric(Nm),
             FNm = as.numeric(FNm))
  } else {
    dates <- unlist(lapply(data.json$SiteRep$DV$Location$Period, function(x){x$value}))
    Forecast <-"rbind", lapply(lapply(data.json$SiteRep$DV$Location$Period, function(x){x$Rep}), function(x){"rbind",x)}))
    colnames(Forecast)[ncol(Forecast)] <- "Hour"
    DateTimes <- seq(ymd_hms(paste0(as.Date(dates[1])," 00:00:00")),ymd_hms(paste0(as.Date(dates[length(dates)])," 21:00:00")), "3 hours")
      extra_lines <- length(DateTimes)-nrow(Forecast)
      for(i in 1:extra_lines){
        Forecast <- rbind(rep("0", ncol(Forecast)), Forecast)
    ForecastDF <- Forecast %>%
      as_tibble() %>%
      mutate(Hour = DateTimes) %>%
      filter(D != "0") %>%
      mutate(F = as.numeric(F),
             G = as.numeric(G),
             H = as.numeric(H),
             Pp = as.numeric(Pp),
             S = as.numeric(S),
             T = as.numeric(T),
             U = as.numeric(U),
             W = as.numeric(W))
  list(Details, param, ForecastDF)

The API key can be obtained for free at this link:

Once we have an API key we can simply insert the station ID and the type of product we want to obtain the forecast. We can select between two products: daily and 3hourly

To obtain the station ID we need to use another query and download an XML with all stations names and ID:


url = paste0("",key)
XML_StationList <- read_xml(url)

write_xml(XML_StationList, "StationList.xml")

This will save an XML, which we can then open with a txt editor (e.g. Notepad++).

The function can be used as follows:

METDataDownload(stationID=3081, product="daily", key)

It will return a list with 3 elements:

  1. Station info: Name, ID, Lon, Lat, Elevation
  2. Parameter explanation
  3. Weather forecast: tibble format
I have not tested it much, so if you find any bug you are welcome to tweak it on GitHub:

Geocoding function

This is a very simple function to perform geocoding using the Google Maps API:

getGeoCode <- function(gcStr, key)  {
  library("RJSONIO") #Load Library
  gcStr <- gsub(' ','%20',gcStr) #Encode URL Parameters
  #Open Connection
  connectStr <- paste0('',gcStr, "&key=",key) 
  con <- url(connectStr)
  data.json <- fromJSON(paste(readLines(con), collapse=""))
  #Flatten the received JSON
  data.json <- unlist(data.json)
  if(data.json["status"]=="OK")   {
    lat <- data.json[""]
    lng <- data.json["results.geometry.location.lng"]
    gcodes <- c(lat, lng)
    names(gcodes) <- c("Lat", "Lng")
    return (gcodes)

Essentially, users need to get an API key from google and then use as an input (string) for the function. The function itself is very simple, and it is an adaptation of some code I found on-line (unfortunately I did not write down where I found the original version so I do not have a way to reference the source, sorry!!).

geoCodes <- getGeoCode(gcStr="11 via del piano, empoli", key)

To use the function we simply need to include an address, and it will return its coordinates in WGS84.
It can be used in a mutate call within dplyr and it is reasonably fast.

The repository is here:

Friday, 15 June 2018

Spreadsheet Data Manipulation in R

Today I decided to create a new repository on GitHub where I am sharing code to do spreadsheet data manipulation in R.

The first version of the repository and R script is available here: SpreadsheetManipulation_inR

As an example I am using a csv freely available from the IRS, the US Internal Revenue Service.

This spreadsheet has around 170'000 rows and 131 columns.

Please feel free to request new functions to be added or add functions and code yourself directly on GitHub.

Sunday, 25 March 2018

Data Visualization Website with Shiny

My second Shiny app is dedicated to data visualization.
Here users can simply upload any csv or txt file and create several plots:

  • Histograms (with option for faceting)
  • Barchart (with error bars, and option for color with dodging and faceting)
  • BoxPlots (with option for faceting)
  • Scatterplots (with options for color, size and faceting)
  • TimeSeries

  • Error bars in barcharts are computed with the mean_se function in ggplot2, which computes error bars as mean ± standard error. When the color option is set, barcharts are plotted one next to the other for each color (option dodging).

    For scatterplots, if the option for faceting is provided each plot will include a linear regression lines.

    Some examples are below:

    For the time being there is no option for saving plots, apart from saving the image from the screen. However, I would like to implement an option to have plots in tiff at 300dpi, but all the code I tried so far did not work. I will keep trying.

    The app can be accessed here:

    The R code is available here:

    Sunday, 11 March 2018

    Street Crime UK - Shiny App


    This is a shiny app to visualize heat maps of Street Crimes across Britain from 2010-12 to 2018-01 and test their spatial pattern.
    The code for both ui.R and server.R is available from my GitHub at:


    Please be aware that this apps downloads data from my personal Dropbox once it starts and every time the user changes some of the settings. This was the only work-around I could think of to use external data in for free. However, this also makes the app a bit slow, so please be patient.
    Users can select a date with two sliders (I personally do not like the dateInput tool), then a crime type and click Draw Map to update the map with new data. I also included a option to plot the Ripley K-function (function Kest in package spatstat) and the p-value of the quadrat.test (again from spatstat). Both tools work using the data shown within the screen area, so their results change as users interact with the map. The Ripley K function shows a red dashed line with the expected nearest neighbour distribution of points that are randomly distributed in space (i.e. follow a Poisson distribution). The black line is the one computed from the points shown on screen. If the black line is above the red means the observations shown on the map are clustered, while if it is below the red line means the crimes are scattered regularly in space. A more complete overview of the Ripley K function is available at this link from ESRI.
    The p-value from the quadrat test is testing a null hypothesis that the crimes are scattered randomly in space, against an alternative that they are clustered. If the p-value is below 0.05 (significance level of 5%) we can accept the alternative hypothesis that our data are clustered. Please be aware that this test does not account for regularly space crimes.


    Please not that the code here is not reproducible straight away. The app communicates with my Dropbox, though the package rdrop2, which requires a token to download data from Dropbox. More info
    I am sharing the code to potentially use a taken downloaded from elsewhere, but the url that points to my Dropbox will clearly not be shared.

    Preparing the dataset

    Csv files with crime data can be downloaded directly from the website. Please check the dates carefully, since each of these files contains more that one years of monthly data. The main issue with these data is that they are divided by local police forces, so for example we will have a csv for each month from the Bedfordshire Police, which only covers that part of the country. Moreover, these csv contain a lot of data, not only coordinates; they also contain the type of crimes, plus other details, which we do not need and which makes the full collection a couple of Gb in size.
    For these reasons I did some pre-processing, First of all I extracted all csv files into a folder named "CrimeUK" and then I ran the code below:
    lista = list.files("E:/CrimesUK",pattern="street",recursive=T,include.dirs=T,full.names=T, = T)
    for(i in lista){
      DF = read.csv(i)
       write.table(data.frame(LAT=DF$Latitude, LON=DF$Longitude, TYPE=DF$Crime.type),
                   sep=",",row.names=F,col.names=F, append=T)
    Here I first create a list of all csv files, with full link, searching inside all sub directory. Then I started a for loop to iterate through the files. The loop simply loads each file and than save part of its contents (namely coordinates and crime type) into new csv named after using year and month. This will help me identify which files to download from Dropbox, based on user inputs.
    Once I had these files I simply uploded them to my Dropbox.

    The link to test the app is:

    A snapshot of the screen is below:

    Tuesday, 25 July 2017

    Experiment designs for Agriculture

    This post is more for personal use than anything else. It is just a collection of code and functions to produce some of the most used experimental designs in agriculture and animal science. 

    I will not go into details about these designs. If you want to know more about what to use in which situation you can find material at the following links:

    Design of Experiments (Penn State):

    Statistical Methods for Bioscience (Wisconsin-Madison):

    R Packages to create several designs are presented here:

    A very good tutorial about the use of the package Agricolae can be found here:

    Complete Randomized Design

    This is probably the most common design, and it is generally used when conditions are uniform, so we do not need to account for variations due for example to soil conditions. 
    In R we can create a simple CRD with the function expand.grid and then with some randomization:

     TR.Structure = expand.grid(rep=1:3, Treatment1=c("A","B"), Treatment2=c("A","B","C"))  
     Data.CRD = TR.Structure[sample(1:nrow(TR.Structure),nrow(TR.Structure)),]  
     Data.CRD = cbind(PlotN=1:nrow(Data.CRD), Data.CRD[,-1])  
     write.csv(Data.CRD, "CompleteRandomDesign.csv", row.names=F)  

    The first line create a basic treatment structure, with rep that identifies the number of replicate, that looks like this:

     > TR.Structure  
       rep Treatment1 Treatment2  
     1  1     A     A  
     2  2     A     A  
     3  3     A     A  
     4  1     B     A  
     5  2     B     A  
     6  3     B     A  
     7  1     A     B  
     8  2     A     B  
     9  3     A     B  
     10  1     B     B  
     11  2     B     B  
     12  3     B     B  
     13  1     A     C  
     14  2     A     C  
     15  3     A     C  
     16  1     B     C  
     17  2     B     C  
     18  3     B     C  

    The second line randomizes the whole data.frame to obtain a CRD, then we add with cbind a column at the beginning with an ID for the plot, while also eliminating the columns with rep.

    Add Control

    To add a Control we need to write two separate lines, one for the treatment structure and the other for the control:

     TR.Structure = expand.grid(rep=1:3, Treatment1=c("A","B"), Treatment2=c("A","B","C"))  
     CR.Structure = expand.grid(rep=1:3, Treatment1=c("Control"), Treatment2=c("Control"))  
     Data.CCRD = rbind(TR.Structure, CR.Structure)  

    This will generate the following table:

     > Data.CCRD  
       rep Treatment1 Treatment2  
     1  1     A     A  
     2  2     A     A  
     3  3     A     A  
     4  1     B     A  
     5  2     B     A  
     6  3     B     A  
     7  1     A     B  
     8  2     A     B  
     9  3     A     B  
     10  1     B     B  
     11  2     B     B  
     12  3     B     B  
     13  1     A     C  
     14  2     A     C  
     15  3     A     C  
     16  1     B     C  
     17  2     B     C  
     18  3     B     C  
     19  1  Control  Control  
     20  2  Control  Control  
     21  3  Control  Control  

    As you can see the control is totally excluded from the rest. Now we just need to randomize, again using the function sample:

     Data.CCRD = Data.CCRD[sample(1:nrow(Data.CCRD),nrow(Data.CCRD)),]  
     Data.CCRD = cbind(PlotN=1:nrow(Data.CCRD), Data.CCRD[,-1])  
     write.csv(Data.CCRD, "CompleteRandomDesign_Control.csv", row.names=F)  

    Block Design with Control

    The starting is the same as before. The difference starts when we need to randomize, because in CRD we randomize over the entire table, but with blocks, we need to do it by block.

     TR.Structure = expand.grid(Treatment1=c("A","B"), Treatment2=c("A","B","C"))  
     CR.Structure = expand.grid(Treatment1=c("Control"), Treatment2=c("Control"))  
     Data.CBD = rbind(TR.Structure, CR.Structure)  
     Block1 = Data.CBD[sample(1:nrow(Data.CBD),nrow(Data.CBD)),]  
     Block2 = Data.CBD[sample(1:nrow(Data.CBD),nrow(Data.CBD)),]  
     Block3 = Data.CBD[sample(1:nrow(Data.CBD),nrow(Data.CBD)),]  
     Data.CBD = rbind(Block1, Block2, Block3)  
     BlockID = rep(1:nrow(Block1),3)  
     Data.CBD = cbind(Block = BlockID, Data.CBD)  
     write.csv(Data.CBD, "BlockDesign_Control.csv", row.names=F)  

    As you can see from the code above, we've created three objects, one for each block, where we used the function sample to randomize.

    Other Designs with Agricolae

    The package agricolae includes many designs, which I am sure will cover all your needs in terms of setting up field and lab experiments.
    We will look at some of them, so first let's install the package:


    The main syntax for design in agricolae is the following:

     Trt1 = c("A","B","C")  
     design.crd(trt=Trt1, r=3)  

    The result is the output below:

     > design.crd(trt=Trt1, r=3)  
     [1] "crd"  
     [1] "A" "B" "C"  
     [1] 3 3 3  
     [1] 2  
     [1] 1572684797  
     [1] "Super-Duper"  
     [1] TRUE  
      plots r Trt1  
     1  101 1  A  
     2  102 1  B  
     3  103 2  B  
     4  104 2  A  
     5  105 1  C  
     6  106 3  A  
     7  107 2  C  
     8  108 3  C  
     9  109 3  B  

    As you can see the function takes only one argument for treatments and another for replicates. Therefore, if we need to include a more complex treatment structure we first need to work on them:

     Trt1 = c("A","B","C")  
     Trt2 = c("1","2")  
     Trt3 = c("+","-")  
     TRT.tmp = as.vector(sapply(Trt1, function(x){paste0(x,Trt2)}))  
     TRT = as.vector(sapply(TRT.tmp, function(x){paste0(x,Trt3)}))  
     TRT.Control = c(TRT, rep("Control", 3))  

    As you can see we have now three treatments, which are merged into unique strings within the function sapply:

     > TRT  
      [1] "A1+" "A1-" "A2+" "A2-" "B1+" "B1-" "B2+" "B2-" "C1+" "C1-" "C2+" "C2-"  

    Then we need to include the control, and then we can use the object TRT.Control with the function design.crd, from which we can directly obtain the data.frame with $book:

     > design.crd(trt=TRT.Control, r=3)$book  
       plots r TRT.Control  
     1  101 1     A2+  
     2  102 1     B1+  
     3  103 1   Control  
     4  104 1     B2+  
     5  105 1     A1+  
     6  106 1     C2+  
     7  107 2     A2+  
     8  108 1     C2-  
     9  109 2   Control  
     10  110 1     B2-  
     11  111 3   Control  
     12  112 1   Control  
     13  113 2     C2-  
     14  114 2   Control  
     15  115 1     C1+  
     16  116 2     C1+  
     17  117 2     B2-  
     18  118 1     C1-  
     19  119 2     C2+  
     20  120 3     C2-  
     21  121 1     A2-  
     22  122 2     C1-  
     23  123 2     A1+  
     24  124 3     C1+  
     25  125 1     B1-  
     26  126 3   Control  
     27  127 3     A1+  
     28  128 2     B1+  
     29  129 2     B2+  
     30  130 3     B2+  
     31  131 1     A1-  
     32  132 2     B1-  
     33  133 2     A2-  
     34  134 1   Control  
     35  135 3     C2+  
     36  136 2   Control  
     37  137 2     A1-  
     38  138 3     B1+  
     39  139 3   Control  
     40  140 3     A2-  
     41  141 3     A1-  
     42  142 3     A2+  
     43  143 3     B2-  
     44  144 3     C1-  
     45  145 3     B1-  

    A note about this design is that, since we repeated the string "Control" 3 times when creating the treatment structure, the design basically has additional repetition for the control. If this is what you want to do fine, otherwise you need to change from:

    TRT.Control = c(TRT, rep("Control", 3)) 


    TRT.Control = c(TRT, "Control") 

    This will create a design with 39 lines, and 3 controls.

    Other possible designs are:

     #Random Block Design  
     design.rcbd(trt=TRT.Control, r=3)$book  
     #Incomplete Block Design  
     design.bib(trt=TRT.Control, r=7, k=3)  
     #Split-Plot Design  
     design.split(Trt1, Trt2, r=3, design=c("crd"))  
     #Latin Square  

    Others not included above are: Alpha designs, Cyclic designs, Augmented block designs, Graeco - latin square designs, Lattice designs, Strip Plot Designs, Incomplete Latin Square Design

    Update 26/07/2017 - Plotting your Design

    Today I received an email from Kevin Wright, creator of the package desplot.
    This is a very cool package that allows you to plot your design with colors and text, so that it becomes quite informative for the reader. On the link above you will find several examples on how to plot designs for existing datasets. In this paragraph I would like to focus on how to create cool plots when we are designing our experiments.
    Let's look at some code:

    #Complete Randomized Design  
     CRD = design.crd(trt=TRT.Control, r=3)$book  
     CRD = CRD[order(CRD$r),]  
     CRD$col = CRD$r  
     CRD$row = rep(1:13,3)  
    desplot(form=TRT.Control ~ col+row, data=CRD, text=TRT.Control, out1=col, out2=row,   
          cex=1, main="Complete Randomized Design")  

    After installing the package desplot I created an example for plotting the comple randomized design we created above.

    To use the function desplot we first need to include in the design columns and rows, so that the function knows what to plot and where. For this I first ordered the data.frame based on the column r, which stands for replicates. Then I added a column named col, with values equal to r (I could use the column r, but I wanted to make clear the procedure), and another named row. Here I basically repeated a vector from 1 to 13 (which is the total number of treatments per replicate), 3 times (i.e. the number of replicates).

    The function desplot returns the following plot, which I think is very informative:

    We could do the same with the random block design:

     #Random Block Design  
     RBD = design.rcbd(trt=TRT.Control, r=6)$book  
     RBD = RBD[order(RBD$block),]  
     RBD$col = RBD$block  
     RBD$row = rep(1:13,6)  
     desplot(form=block~row+col, data=RBD, text=TRT.Control, col=TRT.Control, out1=block, out2=row, cex=1, main="Randomized Block Design")  

    thus obtaining the following image:

    Final Note

    For repeated measures and crossover designs I think we can create designs simply using again the function expand.grid and including time and subjects, as I did in my previous post about Power Analysis. However, there is also the package Crossover that deals specifically with crossover design and on this page you can find more packages that deal with clinical designs:

    Friday, 21 July 2017

    Power analysis and sample size calculation for Agriculture

    Power analysis is extremely important in statistics since it allows us to calculate how many chances we have of obtaining realistic results. Sometimes researchers tend to underestimate this aspect and they are just interested in obtaining significant p-values. The problem with this is that a significance level of 0.05 does not necessarily mean that what you are observing is real.
    In the book "Statistics Done Wrong" by Alex Reinhart (which you can read for free here: this problem is discussed with an example where we can clearly see that a significance of 0.05 does not mean that we have 5% chances of getting it wrong, but actually we have closer to 30% chances of obtaining unrealistic results. This is because there are two types of errors in which we can incur (for example when performing an ANOVA), the type I (i.e. rejecting a null hypothesis when it is actually true) and type II (i.e. accepting a null hypothesis when it is actually false).

    Image taken from:

    The probability of incurring in a type I error is indicated by α (or significance level) and usually takes a value of 5%; this means that we are happy to consider a scenario where we have 5% chances of rejecting the null hypothesis when it is actually true. If we are not happy with this, we can further decrease this probability by decreasing the value of α (for example to 1%). On the contrary the probability of incurring in a type II error is expressed by β, which usually takes a value of 20% (meaning a power of 80%). This means we are happy to work assuming that we have a 20% chance of accepting the null hypothesis when it is actually false.
    If our experiment is not designed properly we cannot be sure whether we actually incurred in one of these two errors. In other words, if we run a bad experiment and we obtain a insignificant p-value it may be that we incurred in a type II error, meaning that in reality our treatment works but its effect cannot be detected by our experiment. However, it may also be that we obtained a significant p-value but we incurred in a type I error, and if we repeat the experiment we will find different results.
    The only way we can be sure to run a good experiment is by running a power analysis. By definition power is the probability of obtaining statistical significance (not necessarily a small p-value, but at least a realistic outcome). Power analysis can be used before an experiment to test whether our design has good chances of succeeding (a priori) or after to test whether the results we obtained were realistic.

    Update 17/11/2017
    How many subjects to compute a robust mean?

    This is a question that I get sometimes when talking with students that are planning descriptive experiments. How many subjects do I need for the mean value I compute to be robust?

    The answer is provided by Berkowitz here:

    The simplified formula to compute the minimum number of samples is:

    where SD is the standard deviation and SE is the standard error. These values can be obtained from previous experiments, or from the literature. 

    Effect Size

    A simple and effective definition of effect size is provided in the book "Power Analysis for Experimental Research" by Bausell & Li. They say: 
    "effect size is nothing more than a standardized measure of the size of the mean difference(s) among the study’s groups or of the strength of the relationship(s) among its variables".

    Despite its simple definition the calculation of the effect size is not always straightforward and many indexes have been proposed over the years. Bausell & Li propose the following definition, in line with what proposed by Cohen in his "Statistical Power Analysis for the Behavioral Sciences":

    where ES is the effect size (in Cohen this is referred as d). In this equation, Ya is the mean of the measures for treatment A, and Yb is the mean for treatment B. The denominator is the pooled standard deviation, which is computed as follows:

    where SD are the standard deviation for treatments B and A, and n are the number of samples for treatment B and A.

    This is the main definition but then every software or functions tend to use indexes correlated to this but not identical. We will see each way of calculating the effect size case by case.

    One-Way ANOVA 

    Sample size

    For simple models the power calculating can be performed with the package pwr:


    In the previous post (Linear Models) we worked on a dataset where we tested the impact on yield of 6 levels of nitrogen. Let's assume that we need to run a similar experiment and we would like to know how many samples we should collect (or how many plants we should use in the glass house) for each level of nitrogen. To calculate this we need to do a power analysis.

    To compute the sample size required to reach good power we can run the following line of code:

     pwr.anova.test(k=6, f=0.25, sig.level=0.05, power=0.8)  

    Let's start describing the options from the end. We have the option power, to specify the power you require for your experiment. In general, this can be set to 0.8, as mentioned above. The significance level is alpha and usually we are happy to accept a significance of 5%. Another option is k, which is the number of groups in our experiment, in this case we have 6 groups.
    Finally we have the option f, which is the effect size. As I mentioned above, there are many indexes to express the effect size and f is one of them.
    According to Cohen, f can be expressed as:

    where the numerator is the is the standard deviation of the effects that we want to test and the denominator is the common standard deviation. For two means, as in the equation we have seen above, f is simply equal to:

    Clearly, before running the experiment we do not really know what the effect size would be. In some case we may have an idea, for example from previous experiments or a pilot study. However, most of the times we do not have a clue. In such cases we can use the classification suggested by Cohen, who considered the following values for f:

    The general rule is that if we do not know anything about our experiment we should use a medium effect size, so in this case 0.25. This was suggested in the book Bausell & Li and it is based on a review of 302 studies in the social and behavioral sciences. for this reason it may well be that the effect size of your experiment would be different. However, if you do not have any additional information this is the only thing the literature suggest.

    The function above returns the following output:

     > pwr.anova.test(k=6, f=0.25, sig.level=0.05, power=0.8)  
        Balanced one-way analysis of variance power calculation   
            k = 6  
            n = 35.14095  
            f = 0.25  
        sig.level = 0.05  
          power = 0.8  
     NOTE: n is number in each group  

    In this example we would need 36 samples for each nitrogen level to achieve a power of 80% with a significance of 5%.

    Power Calculation

    As I mentioned above, sometimes we have a dataset we collected assuming we could reach good power but we are not actually sure if that is the case. In those instances what we can do is the a posteriori power analysis, where we basically compute the power for a model we already fitted.

    As you remember is the previous post about linear models, we fitted the following:

     mod1 = aov(yield ~ nf, data=dat)  

    To compute the power we achieved here we first need to calculate the effect size. As discussed above we have several options: d, f and another index called partial eta squared.
    Let's start from d, which can be simply calculated using means and standard deviation of two groups, for example N0 (control) and N5:

     Control = dat[dat$nf=="N0","yield"]
     Treatment1 = dat[dat$nf=="N5","yield"]
     numerator = (mean(Treatment1)-mean(Control))
     denominator = sqrt((((length(Treatment1)-1)*sd(Treatment1)^2)+((length(Control)-1)*sd(Control)^2))/(length(Treatment1)+length(Control)-2))
     d = numerator/denominator

    This code simply computes the numerator (difference in means) and the denominator (pooled standard deviation) and then computes the Cohen's d, you just need to change the vectors for objects Control and Treatment1. The effect size results in 0.38.

    Again Cohen provides some values for the d, so that we can determine how large is our effects, which are presented below:

    From this table we can see that our effect size is actually low, and not medium as we assumed for the a priori analysis. This is important because if we run the experiment with 36 samples per group we may end up with unrealistic results simply due to low power. For this reason it is my opinion that we should always be a bit more conservative and maybe include some additional replicates or blocks, just to account for potential unforeseen differences between our assumptions and reality.

    The function to compute power is again pwr.anova.test, in which the effect size is expressed as f. We have two ways of doing that, the first is by using the d values we just calculated and halve it, so in this case f = 0.38/2 = 0.19. However, this will tell you the specific effects size for the relation between N0 and N5, and not for the full set of treatments.

    At this link there is an Excel file that you can use to convert between indexes of effect size:

    Another way to get a fuller picture is by using the partial Eta Squared, which can be calculated using the sum of squares:

    This will tell us the average effect size for all the treatments we applied, so not only for N5 compared to N0, but for all of them.
    To compute the partial eta squared we first need to access the anova table, with the function anova:

     > anova(mod1)  
     Analysis of Variance Table  
     Response: yield  
           Df Sum Sq Mean Sq F value  Pr(>F)    
     nf      5  23987 4797.4 12.396 6.075e-12 ***  
     Residuals 3437 1330110  387.0             
     Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1  

    From this table we can extract the sum of squares for the treatment (i.e. nf) and the sum of squares of the residuals and then solve the equation above:

     > EtaSQ = 23987/(23987+1330110)  
     > print(EtaSQ)  
     [1] 0.01771439  

    As for the other indexes, eta squares also has its table of interpretation:

    The relation between f and eta squared is the following:

    so to compute the f related to the full treatment we can simply do the following:

     > f = sqrt(EtaSQ / (1-EtaSQ))  
     > print(f)  
     [1] 0.1342902  

    So now we have everything we need to calculate the power of our model:

     > pwr.anova.test(k=6, n=571, f=f, sig.level=0.05)  
        Balanced one-way analysis of variance power calculation   
            k = 6  
            n = 571  
            f = 0.1342902  
        sig.level = 0.05  
          power = 1  
     NOTE: n is number in each group  

    To compute the power we need to run again the function pwr.anova.test, but this time without specifying the option power, but replacing it with the option n, which is the number of samples per group.
    As you remember from the previous post this was an unbalanced design, so the number of samples per group is not constant. We could either use a vector as input for n, with all the samples per each group. In that case the function will return a power for each group. However, what I did here is putting the lowest number, so that we are sure to reach good power for the lowest sample size.

    As you can see even with the small effect size we are still able to reach a power of 1, meaning 100%. This is because the sample size is more than adequate to catch even such a small effect. You could try to run again the sample size calculation to actually see what would be the minimum sample requirement for the observed effect size.

    Linear Model

    The method we have seen above is only valid for one-way ANOVAs. For more complex model, which may simply be ANOVA with two treatments we should use the function specific for linear models.

    Sample Size

    To calculate the sample size for this analysis we can refer once again to the package pwr, but now use the function pwr.f2.test.
    Using this function is slightly more complex because here we start reasoning in terms of degrees of freedom for the F ratio, which can be obtained using the following equation:

    where MS between is the mean square variance between groups and MS within is the mean square variance within each group.
    These two terms have the following equations (again from: :

    The degrees of freedom we need to consider are the denominators of the last two equations. For an a priori power analysis we need to input the option u, with the degrees of freedom of the numerator of the F ratio, thus MS between. As you can see this can be computed as k-1, for a one-way ANOVA.
    For more complex model we need to calculate the degrees of freedom ourselves. This is not difficult because we can generate dummy datasets in R with the specific treatment structure we require, so that R will compute the degrees of freedom for us.
    We can generate dummy dataset very easily with the function expand.grid:

     > data = expand.grid(rep=1:3, FC1=c("A","B","C"), FC2=c("TR1","TR2"))  
     > data  
       rep FC1 FC2  
     1  1  A TR1  
     2  2  A TR1  
     3  3  A TR1  
     4  1  B TR1  
     5  2  B TR1  
     6  3  B TR1  
     7  1  C TR1  
     8  2  C TR1  
     9  3  C TR1  
     10  1  A TR2  
     11  2  A TR2  
     12  3  A TR2  
     13  1  B TR2  
     14  2  B TR2  
     15  3  B TR2  
     16  1  C TR2  
     17  2  C TR2  
     18  3  C TR2  

    Working with expand.grid is very simple. We just need to specify the level for each treatment and the number of replicates (or blocks) and the function will generate a dataset with every combination.
    Now we just need to add the dependent variable, which we can generate randomly from a normal distribution:

     data$Y = rnorm(nrow(data))  

    Now our dataset is ready so we can fit a linear model to it and generate the ANOVA table:

     > mod.pilot = lm(Y ~ FC1*FC2, data=data)  
     > anova(mod.pilot)  
     Analysis of Variance Table  
     Response: Y  
          Df Sum Sq Mean Sq F value Pr(>F)  
     FC1    2 0.8627 0.4314 0.3586 0.7059  
     FC2    1 3.3515 3.3515 2.7859 0.1210  
     FC1:FC2  2 1.8915 0.9458 0.7862 0.4777  
     Residuals 12 14.4359 1.2030  

    Since this is a dummy dataset all the sum of squares and the other values are meaningless. We are only interested in looking at the degrees of freedom.
    To calculate the sample size for this analysis we can refer once again to the package pwr, but now use the function pwr.f2.test, as follows:

     pwr.f2.test(u = 2, f2 = 0.25, sig.level = 0.05, power=0.8)  

    The first option in the function is u, which represents the degrees of freedom of the numerator of the F ratio. This is related to the degrees of freedom of the component we want to focus on. As you probably noticed from the model, we are trying to see if there is an interaction between two treatments. From the ANOVA table above we can see that the degrees of freedom of the interaction are equal to 2, so that it what we include as u.
    Other options are again power and significance level, which we already discussed. Moreover, in this function the effect size is f2, which is again different from the f we've seen before. F2 again has its own table:

    Since we assume we have no idea about the real effect size we use a medium value for the a priori testing.

    The function returns the following table:

     > pwr.f2.test(u = 2, f2 = 0.25, sig.level = 0.05, power=0.8)  
        Multiple regression power calculation   
            u = 2  
            v = 38.68562  
            f2 = 0.25  
        sig.level = 0.05  
          power = 0.8  

    As you can see what the function is actually providing us is the value of the degrees of freedom for the denominator of the F test (with v), which results in 38.68, so 39 since we always round it by excess.
    If we look to the equation to compute MS withing we can see that the degrees of freedom is given by n-k, meaning that to transform the degrees of freedom into a sample size we need to add what we calculated before for the option u. The sample size is then equal to n = v + u + 1, so in this case the sample size is equal 39 + 2 + 1 = 42

    This is not the number of samples per group but it is the total number of samples.

    Another way of looking at the problem would be to compute the total power of our model, and not just how much power we have to discriminate between levels of one of the treatments (as we saw above). To do so we can still use the function pwr.f2.test, but with some differences. The first is that we need to compute u using all elements in the model, so basically sum the decrees of freedom of the ANOVA table, or sum all the coefficients in the model minus the intercept:

     u = length(coef(mod3))-1  

    Another difference is in how we compute the effects size f2. Before we used its relation with partial eta square, now we can use its relation with the R2 of the model:

    With these additional element we can compute the power of the model.

    Power Calculation

    Now we look at estimating the power for a model we've already fitted, which can be done with the same function.
    We will work with one of the models we used in the post about Linear Models:

     mod3 = lm(yield ~ nf + bv, data=dat)   

    Once again we first need to calculate the observed effect size as the eta squared, using again the sum of squares:

     > Anova(mod3, type="III")  
     Anova Table (Type III tests)  
     Response: yield  
           Sum Sq  Df F value  Pr(>F)    
     (Intercept) 747872  1 2877.809 < 2.2e-16 ***  
     nf      24111  5  18.555 < 2.2e-16 ***  
     bv     437177  1 1682.256 < 2.2e-16 ***  
     Residuals  892933 3436              
     Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1  

    In this example, I used the function Anova (with option type="III") in the package car just to remind you that if you have an unbalanced design, like in this case, you should use the type III sum of squares.
    From this table we can obtain the sum of squares we need to compute the eta squared, for example for nf we will use the following code:

     > EtaSQ = 24111/(24111+892933)  
     > EtaSQ  
     [1] 0.02629209  

    Then we need to transform this into f2 (of f squared), which is what the pwr.f2.test function uses:

     > f2 = EtaSQ / (1-EtaSQ)  
     > f2  
     [1] 0.02700203  

    The only thing we need to do now is calculating the value of v, i.e. the denominator degrees of freedom. This is equal to the n (number of samples) - u - 1, but a quick way of obtaining this number is looking at the anova table above and take the degrees of freedom of the residuals, i.e. 3436.

    Now we have everything we need to obtain the observed power:

     > pwr.f2.test(u = 5, v = 3436, f2 = f2, sig.level = 0.05)  
        Multiple regression power calculation   
            u = 5  
            v = 3436  
            f2 = 0.02700203  
        sig.level = 0.05  
          power = 1  

    which again returns a very high power, since we have a lot of samples.

    Generalized Linear Models

    For GLM we need to install the package lmsupport:


    Sample Size

    For calculating the sample size for GLM we can use the same procedure we used for linear models.

    Power Calculation

    For this example we are going to use one of the model we discussed in the post about GLM, using the dataset beall.webworms (n = 1300):

     dat = beall.webworms  
     pois.mod2 = glm(y ~ block + spray*lead, data=dat, family=c("poisson"))   

    Once again we would need to compute effect size and degrees of freedom. As before, we can use the function anova to generate the data we need:

     > anova(pois.mod2)  
     Analysis of Deviance Table  
     Model: poisson, link: log  
     Response: y  
     Terms added sequentially (first to last)  
           Df Deviance Resid. Df Resid. Dev  
     NULL            1299   1955.9  
     block   12 122.040   1287   1833.8  
     spray    1 188.707   1286   1645.2  
     lead    1  42.294   1285   1602.8  
     spray:lead 1  4.452   1284   1598.4  

    Let's say we are interested in looking at the interaction between spray and lead, its degrees of freedom are 1, so this is our u. On its side we also have the residuals degrees of freedom, so v is 1284.
    The other thing we need is the effect size, which we can compute with the function modelEffectSizes from the package lmSupport:

     > modelEffectSizes(pois.mod2)  
     glm(formula = y ~ block + spray * lead, family = c("poisson"),   
       data = dat)  
           SSR df pEta-sqr dR-sqr  
     block 122.0402 12  0.0709   NA  
     spray 142.3487 1  0.0818 0.0849  
     lead  43.7211 1  0.0266 0.0261  
     Sum of squared errors (SSE): 1598.4  
     Sum of squared total (SST): 1675.9  

    This function calculates the partial eta squares, and it works also for lm models. As you can see it does not provide the eta squared for the interaction, but just to be on the safe side we can use the lowest value (0.03) from the values provided for spray and lead.
    Now that we have the observed eta squared we can use the function modelPower:

    > modelPower(u=1, v=1284, alpha=0.05, peta2=0.03)
    Results from Power Analysis
    pEta2 = 0.030
    u =     1 
    v =     1284.0 
    alpha = 0.050 
    power = 1.000 

    This function can take the option f2, as we've seen before for the package pwr. However, since computing the partial eta square is generally easier, we can use the option peta2 and use directly this index.
    Once again our power is very high.

    Note 12/12/2017

    Please note that the line above only works with the older version of the package lmSupport (version 2.9.8). The new version features a different syntax.
    You can download the old version from here:

    Linear Mixed Effects Models

    For power analysis with mixed effects models we would need to install the following packages:


    In this example we will be working with models fitted with the package lme4, but what is discussed here should work also with models fitted with nlme.

    Sample Size

    A priori power analysis for mixed effect model is not easy. There are packages that should provide functions to do that (e.g. simstudy and longpower), but they are probably more related to the medical sciences and I found them difficult to use. For this reason I decided that probably the easiest way to test the power of an experiment for which we need to use a mixed-effect model (e.g. involving clustering or repeated measures) would be to use a dummy dataset again and simulation. However, please be advised that I am not 100% sure of the validity of this procedure.

    To create the dummy dataset we can use the same procedure we employed above, with expand.grid:

     data = expand.grid(subject=1:5, treatment=c("Tr1", "Tr2", "Tr3"))  
     data$Y = numeric(nrow(data))  

    In this case we are simulating a simple experiment with 5 subjects, 3 treatments and a within subject design, like a crossover I suppose.
    As you can see the Y has not been drawn from a normal distribution, this is because for the time being it is just a list of zeroes. We need to create data for each treatment as follows:

     data[data$treatment=="Tr1","Y"] = rnorm(nrow(data[data$treatment=="Tr1",]), mean=20, sd=1)  
     data[data$treatment=="Tr2","Y"] = rnorm(nrow(data[data$treatment=="Tr2",]), mean=20.5, sd=1)  
     data[data$treatment=="Tr3","Y"] = rnorm(nrow(data[data$treatment=="Tr3",]), mean=21, sd=1)  

    In these lines I created three samples, from normal distributions, which means differ by half their standard deviation. This (when SD = 1)  provides an effect size (d) of 0.5, so medium.

    Now we can create the model:

     mod1 = lmer(Y ~ treatment + (1|subject), data=data)  

    and then test its power with the function powerSim from the package simr. This function runs 1000 simulation and provide a measure for the power of the experiment:

     > powerSim(mod1, alpha=0.05)  
     Power for predictor 'treatment', (95% confidence interval):  
        25.90% (23.21, 28.73)  
     Test: Likelihood ratio  
     Based on 1000 simulations, (84 warnings, 0 errors)  
     alpha = 0.05, nrow = 15  
     Time elapsed: 0 h 3 m 2 s  
     nb: result might be an observed power calculation  
     Warning message:  
     In observedPowerWarning(sim) :  
      This appears to be an "observed power" calculation  

    From this output we can see that our power is very low, so we probably need to increase the number of subjects and then try again the simulation.

    Let's now look at repeated measures. In this case we do not only have the effect size to account for in the data, but also the correlation between in time between measures.

     sigma <- matrix(c(1, 0.5, 0.5, 0.5,   
              0.5, 1, 0.5, 0.5,  
              0.5, 0.5, 1, 0.5,  
                    0.5, 0.5, 0.5 ,1 ), ncol=4, byrow=T)  
     data = expand.grid(subject=1:4, treatment=c("Tr1", "Tr2", "Tr3"), time=c("t1","t2","t3","t4"))  
     data$Y = numeric(nrow(data))  
     T1 = rmvnorm(4, mean=rep(20, 4), sigma=sigma)  
     T2 = rmvnorm(4, mean=rep(20.5, 4), sigma=sigma)  
     T3 = rmvnorm(4, mean=rep(21, 4), sigma=sigma)  
     data[data$subject==1&data$treatment=="Tr1","Y"] = T1[,1]  
     data[data$subject==2&data$treatment=="Tr1","Y"] = T1[,2]  
     data[data$subject==3&data$treatment=="Tr1","Y"] = T1[,3]  
     data[data$subject==4&data$treatment=="Tr1","Y"] = T1[,4]  
     data[data$subject==1&data$treatment=="Tr2","Y"] = T2[,1]  
     data[data$subject==2&data$treatment=="Tr2","Y"] = T2[,2]  
     data[data$subject==3&data$treatment=="Tr2","Y"] = T2[,3]  
     data[data$subject==4&data$treatment=="Tr2","Y"] = T2[,4]  
     data[data$subject==1&data$treatment=="Tr3","Y"] = T3[,1]  
     data[data$subject==2&data$treatment=="Tr3","Y"] = T3[,2]  
     data[data$subject==3&data$treatment=="Tr3","Y"] = T3[,3]  
     data[data$subject==4&data$treatment=="Tr3","Y"] = T3[,4]  
     modRM = lmer(Y ~ treatment + (time|subject), data=data)  
     powerSim(modRM, alpha=0.05)  

    In this case we need to use the function rmvnorm to draw, from a normal distribution, samples with a certain correlation. For this example I followed the approach suggested by William Huber here:

    Basically, if we assume a correlation equal to 0.5 between time samples (which is what the software G*Power does for repeated measures), we first need to create a symmetrical matrix in sigma. This will allow rmvnorm to produce values from distributions with standard deviation equal to 1 and 0.5 correlation.
    A more elegant approach is the one suggested by Ben Amsel on his blog:

     sigma = 1 # population standard deviation  
     rho = 0.5 #Correlation between repeated measurs  
     # create k x k matrix populated with sigma  
     sigma.mat <- rep(sigma, 4)  
     S <- matrix(sigma.mat, ncol=length(sigma.mat), nrow=length(sigma.mat))  
     # compute covariance between measures  
     Sigma <- t(S) * S * rho   
     # put the variances on the diagonal   
     diag(Sigma) <- sigma^2   

    The result is the same but at least here you can specify different values for SD and correlation.

    The other elementS the function needs are mean values, for which I used the same as before. This should guarantee a difference of around half a standard deviation between treatments.
    The remaining of the procedure is the same we used before with no changes.

    As I said before, I am not sure if this is the correct way of computing power for linear mixed effects models. It may be completely or partially wrong, and if you know how to do this or you have comments please do not hesitate to write them below.

    Power Analysis

    As we have seen with the a priori analysis, computing the power of mixed effects models is actually very easy with the function powerSim.


    PWR package Vignette:

    William E. Berndtson (1991). "A simple, rapid and reliable method for selecting or assessing the number of replicates for animal experiments"

    This paper is what some of my colleagues, who deal particularly with animal experiments, use to calculate how many subjects to use for their experiments. The method presented here is base on the coefficient of variation (CV%), which is something that also in agriculture is often used to estimate the number of replicates needed.

    Berkowitz J. "Sample Size Estimation" -

    This document gives you some rule of thumb to determine the sample size for a number of experiments.

    Update 26/07/2017

    For computing effect size automatically you also have the option to use the package sjstats. This has function to compute eta-squared, partial eta-squared and others, but it also has an option to print a comprehensive ANOVA table with everything you get from a normal call to anova plus the effects sizes.
    You can find some example on this blog post from the author of the package Daniel Lüdecke here:

    Final Note about the use of CV% 

    AS I mentioned above, CV% and the percentage of difference between means is one of the indexes used to estimate the number of replicates needed to run experiments. For this reason I decided to create some code to test whether power analysis and the method based on CV% provide similar results.
    Below is the function I created for this:

     d.ES = function(n, M, SD, DV){  
     PC.DIFF = (abs(M1-M2)/((M1+M2)/2))*100  
     numerator = (mean(M2)-mean(M1))  
     denominator = sqrt((((n-1)*SD^2)+((n-1)*SD^2))/(n+n-2))  
     samp = sapply(ES, function(x){pwr.anova.test(k=2, f=x/2, sig.level=0.05, power=0.8)$n})  
     return(list(EffectSize=ES, PercentageDifference=PC.DIFF, CV.Control=CV1*100, n.samples=samp))  

    This function takes 4 arguments: number of samples (n), mean of control (M), standard deviation (here we assume the standard deviation to be identical between groups), and DV, which indicates the number of times to divide the standard deviation to compute the mean of the treatment. If DV is equal to 2 then the mean of the treatment will be half the mean of control.

    The equation for the percentage of difference was taken from:

    Now we can use this function to estimate Effect Size, percentage of differences in means, CV% and number of samples from power analysis (assuming an ANOVA with 2 groups).

    The first example looks at changing the standard deviation, and keeping everything else constant:

     > d.ES(n=10, M=20, SD=seq(1, 15, by=1), DV=8)  
      [1] 1.00000000 0.50000000 0.33333333 0.25000000 0.20000000 0.16666667  
      [7] 0.14285714 0.12500000 0.11111111 0.10000000 0.09090909 0.08333333  
     [13] 0.07692308 0.07142857 0.06666667  
      [1] 0.623053 1.242236 1.857585 2.469136 3.076923 3.680982 4.281346 4.878049  
      [9] 5.471125 6.060606 6.646526 7.228916 7.807808 8.383234 8.955224  
      [1] 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75  
      [1]  10.54166  39.15340  86.88807 153.72338 239.65639 344.68632  
      [7] 468.81295 612.03614 774.35589 955.77211 1156.28483 1375.89403  
     [13] 1614.59972 1872.40183 2149.30043  

    If you look at the tables presented in the paper by Berndtson you will see that the results are similar in terms of number of samples.

    Larger differences are seen when we look at changes in mean, while everything else stays constant:

     > d.ES(n=10, M=seq(1,25,1), SD=5, DV=8)  
     [1] 0.125  
      [1] 47.619048 27.027027 18.867925 14.492754 11.764706 9.900990 8.547009  
      [8] 7.518797 6.711409 6.060606 5.524862 5.076142 4.694836 4.366812  
     [15] 4.081633 3.831418 3.610108 3.412969 3.236246 3.076923 2.932551  
     [22] 2.801120 2.680965 2.570694 2.469136  
      [1] 500.00000 250.00000 166.66667 125.00000 100.00000 83.33333 71.42857  
      [8] 62.50000 55.55556 50.00000 45.45455 41.66667 38.46154 35.71429  
     [15] 33.33333 31.25000 29.41176 27.77778 26.31579 25.00000 23.80952  
     [22] 22.72727 21.73913 20.83333 20.00000  
     [1] 1005.615  

    In this case the mean of the treatment is again 1/8 of the mean of the control, and the standard deviation is fixed at 5. Since the difference in means is the same, and the standard deviation is constant, the effect size also stays constant at 0.125, so very small.
    However, both percentage of difference and CV% change quite a bit and therefore the estimates from Berndtson could differ.