Joachim Schork – Author & Founder of Statistics Globe

Hi, my name is Joachim Schork and I’m the guy behind Statistics Globe.

On this page, I’ll give you a brief overview about my background in statistics and why I started this website.

I have spent a lot of time to increase my skills in statistics and programming within the last 15 years. My academic career started with a bachelor in Educational Science at the University of Tübingen, Germany.

During my Bachelor studies, I fell in love with statistics the first time. I focused as much as possible on statistical research methods for the monitoring of students’ skills and the assessment of educational interventions.

Due to my increasing interest in statistics and the corresponding software tools, I decided to focus even more on this field in my Master studies. For that reason, I moved to Trier University where I have finished a Master of Survey Statistics and an EMOS Certificate in 2017.

Afterwards, I started a job as Microdata Expert located at STATEC, the national statistical institute of Luxembourg.

This job position was definitely another boost for my statistical skills, since I was able to work on many different data sets such as the Statistics on Income and Living Conditions (SILC), the Labour Force Survey (LFS), or the Business and Leisure Tourism Survey.

In discussions with colleagues or at research conferences, I noticed how important it is to exchange with other statisticians and researchers of different fields and this was definitely one of the main reasons why I started Statistics Globe.

While working at STATEC, I also started a side-business for online marketing and webdesign. This business was initially a part-time job, but at the end of 2019 I decided to quit my job at STATEC to dedicate all my time to my own business.

Since then, Statistics Globe is not only a way to exchange with other programmers and researchers; It also allows me to combine both of my interests (statistics and webdesign).

Social Media & Contact

As already mentioned: A major goal of this website is to exchange with other statisticians, data scientists, programmers, and researchers of any field. So please let me know in the comments or on social media, in case you have any questions or topics you want to discuss!

In case you want to follow me on social media or if you want to contact me via email, you can find all details below.

14 Comments. Leave new

SHRINIVAS
November 4, 2020 1:40 am

Hello Jo

We like your portal “statistics Globe” and I am a frequent visitor.
Can you do some tutorials in Debug functions in R. Also if you do so, please include a layman’s explanation about how to interpret explainations that debug functions give because these are equally technical .

Thanks

Reply
- Joachim
  November 4, 2020 1:35 pm
  
  Hey Shrinivas,
  
  Nice to hear from you!
  
  I don’t have a tutorial on debug functions yet, but you may have a look at this article: https://statisticsglobe.com/errors-warnings-r
  
  It contains a list of some of the most common error messages in R and provides introductions on how to fix these errors.
  
  I hope that helps!
  
  Joachim
  
  Reply

Daniele

December 20, 2020 8:59 pm

Hallo Joachim, coole Seite. Gibt es die Möglichkeit Beobachtungen in denen Ergebnisse von 1%-100% existieren und Skalen von 1-5 umzuwandeln? Also das man sagt, alles was in dieser Spalte ist, in der die Prozente sind und im Prozentbereich von 1-20% liegt wird in eine 1 umgewandelt und alles was zwischen 21-40% liegt ist eine 2 usw usw. Dafür würde ich direkt Trinkgeld auf Paypal schicken.

Joachim

December 21, 2020 7:39 am

Hi Daniele,

vielen Dank für die netten Worte!

Du kannst deine Daten wie folgt umwandeln:

# Create example data
set.seed(287346)
x <- paste0(round(runif(20, 1, 100)), "%")
x
#  [1] "32%" "35%" "64%" "70%" "13%" "23%" "70%" "94%" "95%" "68%" "42%" "32%" "39%" "15%" "54%" "57%" "22%" "34%" "61%" "71%"
 
# Convert data into groups
x_new <- rep(NA, length(x))
x_new[x %in% paste0(1:20, "%")] <- 1
x_new[x %in% paste0(21:40, "%")] <- 2
x_new[x %in% paste0(41:60, "%")] <- 3
x_new[x %in% paste0(61:80, "%")] <- 4
x_new[x %in% paste0(81:100, "%")] <- 5
x_new
# [1] 2 2 4 4 1 2 4 5 5 4 3 2 2 1 3 3 2 2 4 4

Ich hoffe, das hilft dir weiter!

Falls du mich unterstützen möchtest, kannst du dir gerne mal meinen Patreon Account anschauen (ist natürlich völlig freiwillig! 🙂 ): https://www.patreon.com/statisticsglobe

Viele Grüße und schöne Feiertage!

Joachim

Carsten Grube
January 30, 2021 2:49 pm

Congratulations, Joachim, for your great work on statisticsglobe.com – and a huge ‘Thank you’ as it is really helpful in my studying of statistics and R programming. Very well and detailed explanations and yet helt super simple. Just seen your vid on ‘Join Data with dplyr in R’ – what a great way of explaining. This 9 min. video has saved me hours of reading and has made it much more clear to me using your graphical explanations – THANK YOU SO MUCH!

Reply
- Joachim
  January 30, 2021 3:00 pm
  
  Carsten,
  
  Thank you so much for this amazing feedback! I’m very happy to hear that you enjoy my content and that it helps to improve your statistics and R programming skills.
  
  Don’t hesitate to let me know in the comments in case you have any questions in your future learning progress.
  
  Regards,
  
  Joachim
  
  Reply
Dr. Kamal Nain Kapoor
January 30, 2021 6:33 pm

Dear Joachim,
You are doing a great Job. Its really very helpful for all.

Reply
- Joachim
  February 1, 2021 7:10 am
  
  Hi Kamal,
  
  Thanks a lot for your kind words! It’s very motivating to get such positive feedback! 🙂
  
  Regards
  
  Joachim
  
  Reply
ahmed
March 24, 2021 9:34 pm

Fuzzy clustering:
I want to do (time-series data fuzzy clustering) using R program

Use my data rather than (CharTraj ) in this code but use same style (CharTraj )

data <-read.csv(file.choose(),sep = ',')

library("dtwclust")
data("uciCT")

# Calculate autocorrelation up to 50th lag
acf_fun <- function(series, …) {
lapply(series, function(x) {
as.numeric(acf(x, lag.max = 50, plot = FALSE)$acf)
})
}

# Fuzzy c-means
fc <- tsclust(CharTraj[1:25], type = "f", k = 4L,
preproc = acf_fun, distance = "L2",
seed = 42)

# Fuzzy membership matrix
fc@fcluster

example results

## cluster_1 cluster_2 cluster_3 cluster_4
## A.V1 0.944079794 0.010596054 0.020895926 0.0244282262
## A.V2 0.973024707 0.004558053 0.009814713 0.0126025278
## A.V3 0.910457782 0.013363454 0.026818391 0.0493603740
## A.V4 0.487954179 0.212700292 0.219111649 0.0802338802
## A.V5 0.557762811 0.172923239 0.188579412 0.0807345380
## B.V1 0.128665544 0.034803979 0.082738850 0.7537916278
## B.V2 0.010999524 0.002277317 0.004997756 0.9817254027
## B.V3 0.197222739 0.033052784 0.061935472 0.7077890056
## B.V4 0.166409909 0.031366546 0.050323544 0.7519000007
## B.V5 0.427121633 0.235092628 0.187510917 0.1502748225
## C.V1 0.311652169 0.047492672 0.197978128 0.4428770302
## C.V2 0.007458354 0.002748052 0.986187858 0.0036057365
## C.V3 0.075206881 0.051338895 0.840850637 0.0326035878
## C.V4 0.340863672 0.055549042 0.357239701 0.2463475850
## C.V5 0.015607418 0.006151640 0.970146090 0.0080948526
## D.V1 0.017714824 0.958605028 0.016256793 0.0074233544
## D.V2 0.047929862 0.903236104 0.030495920 0.0183381136
## D.V3 0.002225743 0.994942451 0.001865065 0.0009667418
## D.V4 0.004954758 0.988846881 0.004040801 0.0021575597
## D.V5 0.018867912 0.954708141 0.017683168 0.0087407796

Reply
- Joachim
  March 25, 2021 6:50 am
  
  Hi Ahmed,
  
  Thank you for your comment. Do you have a specific question? 🙂
  
  Regards
  
  Joachim
  
  Reply
ahmed
March 25, 2021 6:06 pm

how can I do (time-series data fuzzy clustering, for data 25 columns)?

add to, which code to make my data from column to lists?
or
which code can I use to (R Create Data Frame where a Column is a List | Different Variable Types )

Reply
- Joachim
  March 26, 2021 8:35 am
  
  I’m not an expert for Fuzzy Clustering, but this tutorial seems to explain it quite well: https://cran.r-project.org/web/packages/ppclust/vignettes/fcm.html
  
  Here you can learn how to convert columns to lists: https://statisticsglobe.com/convert-data-frame-columns-to-list-elements-in-r
  
  Regards
  
  Joachim
  
  Reply
Elsie
April 20, 2021 8:42 pm

Hi Joachim, your contents are very helpful:). I have a large data (with 11 variables) in .txt and when i use the “read.table (“data.txt”, ….) I do not like how R has read the data. Please see head (data) below. How can i ensure that data is read properly as a data frame or table with 11 dimensions/variables? Your ideas are greatly appreciated, thanks

combined head(data)
SNP.Name.Sample.ID.Allele1…Forward.Allele2…Forward.Allele1…Top.Allele2…Top.Allele1…AB.Allele2…AB.GC.Score.X.Y
1 1-65462706-C-G-rs43237859 JERGBRF000000051557 C C C C A A 0 2250 523
2 1-69673871-C-T-rs209885271 JERGBRF000000051557 C C G G B B 0 639 2749
3 1-69756947-C-G-rs469945562 JERGBRF000000051557 C C C C A A 0 1405 455.4
4 1-69832336-G-A-rs209887380 JERGBRF000000051557 G G G G B B 0 1159 6483
5 11-19037605-G-A-rs381309800 JERGBRF000000051557 A G A G A B 0 1878 2468
6 11-19079043-C-T-rs208164936 JERGBRF000000051557 T C A G A B 0 1710 1730

Reply
- Joachim
  April 23, 2021 9:53 am
  Hi again Elsie,
  
  Thank you for the kind words! 🙂
  
  Regarding your question: It seems like you have to change the separator, i.e. the sep argument within the read.table function.
  
  For example, you could try the following codes:
  read.table ("data.txt", sep = ",")
  read.table ("data.txt", sep = ",")
  or
  read.table ("data.txt", sep = ";")
  read.table ("data.txt", sep = ";")
  Does this help?
  
  Joachim
  Reply

Joachim Schork – Author & Founder of Statistics Globe

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