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Today is Valentine’s Day. And since both I and my sweetheart are R enthusiasts, here is how to say “I love you” using a statistical programming language:

library("dplyr")
library("gganimate")
library("ggplot2")

hrt_dat <- data.frame(t = seq(0, 2 * pi, by = 0.01)) %>%
  bind_rows(data.frame(t = rep(max(.$t), 300))) %>% 
  mutate(xhrt = 16 * sin(t) ^ 3,
         yhrt = 13 * cos(t) - 5 * cos(2 * t) - 2 * cos(3 * t) - cos(4 * t),
         frame = seq_along(t)) %>% 
  mutate(text = ifelse(frame > 300, "            J", "")) %>%
  mutate(text = ifelse(frame > 500, "A           J", text)) %>%
  mutate(text = ifelse(frame > 628, "A     +     J", text)) %>% 
  mutate(texty = 0, textx = 0)

ggplot(hrt_dat, aes(x = xhrt, y = yhrt)) +
  geom_line(colour = "#C8152B") +
  geom_polygon(fill = "#C8152B") +
  geom_text(aes(x = textx, y = texty, label = text), 
            size = 18, 
            colour = "white",
            vjust = "center") +
  theme_void() +
  transition_reveal(frame)

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Today is Valentine’s Day. And since both I and my sweetheart are R enthusiasts, here is how to say “I love you” using a statistical programming language:

library("dplyr")
library("gganimate")
library("ggplot2")

hrt_dat <- data.frame(t = seq(0, 2 * pi, by = 0.01)) %>%
  bind_rows(data.frame(t = rep(max(.$t), 300))) %>% 
  mutate(xhrt = 16 * sin(t) ^ 3,
         yhrt = 13 * cos(t) - 5 * cos(2 * t) - 2 * cos(3 * t) - cos(4 * t),
         frame = seq_along(t)) %>% 
  mutate(text = ifelse(frame > 300, "            J", "")) %>%
  mutate(text = ifelse(frame > 500, "A           J", text)) %>%
  mutate(text = ifelse(frame > 628, "A     +     J", text)) %>% 
  mutate(texty = 0, textx = 0)

ggplot(hrt_dat, aes(x = xhrt, y = yhrt)) +
  geom_line(colour = "#C8152B") +
  geom_polygon(fill = "#C8152B") +
  geom_text(aes(x = textx, y = texty, label = text), 
            size = 18, 
            colour = "white",
            vjust = "center") +
  theme_void() +
  transition_reveal(frame)

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CIS Primer Question 3.3.3 Posted on 14 February, 2019 by Brian Tags: CISP chapter 3, solutions, backdoor criteria Category: causal_inference_in_statistics_primer Here are my solutions to question 3.3.3 of Causal Inference in Statistics: a Primer (CISP). \(\DeclareMathOperator{\do}{do}\) The drug you have been assigned determines which ward you go to. Whether ... [Read more...]

CIS Primer Question 3.3.2 Posted on 14 February, 2019 by Brian Tags: CISP chapter 3, solutions, lord's paradox, simpson's paradox Category: causal_inference_in_statistics_primer Here are my solutions to question 3.3.2 of Causal Inference in Statistics: a Primer (CISP). Part a The following DAG is a possible casual graph representing the situation. We ... [Read more...]

CIS Primer Question 3.3.1 Posted on 14 February, 2019 by Brian Tags: CISP chapter 3, solutions, backdoor criteria Category: causal_inference_in_statistics_primer Here are my solutions to question 3.3.1 of Causal Inference in Statistics: a Primer (CISP). Part a and b For the causal effect of \(X\) on \(Y\), every backdoor path must ... [Read more...]

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The use case is to create the same plot in different languages. I used this technique for Wikipedia plots. We are going to build a list containing all translations, we will then loop over each language, generating and saving the plot. Our plots as a nice gallery : [Read more...]

This method can discretize a variable taking into consideration the target variable, similar to what decision tree do but with gain ratio. [Read more...]

Introduction: I have been told by a countless number of people that creating a personal website is a great way to showcase your skills and tell your story. I have been contemplating this for some time but kept procrastinating. The two biggest excuses I kept telling myself was that it ... [Read more...]

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One of the topics that is totally hyped at the moment is obviously Artificial Intelligence or AI for short. There are many self-proclaimed experts running around trying to sell you the stuff they have been doing all along under this new label. When you ask them what AI means you ... [Read more...]

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Self Service Data Preparation und Data Science Peter Jeitschko Peter presented Alteryx, a platform built for Business Analysts to master tasks like data management, data cleaning and modelling. The tool is windows only and will be ported to Linux... [Read more...]

This is the third part of the series of articles on R vocabulary. In this series, we explore most of the functions mentioned in Chapter 2 of the book Advanced R. The first part of the series can be read here and the second part of the series can be read ... [Read more...]

Self Service Data Preparation und Data Science Peter Jeitschko Peter presented Alteryx, a platform built for Business Analysts to master tasks like data management, data cleaning and modelling. The tool is windows only and will be ported to Linux soon. It can connect to multiple data sources and helps Business ... [Read more...]

Something that has been talked a bit about recently on twitter is the the use of unique playerIDs so that fan analysts, punters and bloggers can track players through time. There are some things that need to be thought about when creating unique playerIDS for analysis. Do you want your ... [Read more...]

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