Introduction


The code and data employed here can be found at the original repository. The data employed on this report is a sample of the commits made on some of the repositories on Github each day from 2016 to 2017.


Data Overview


readr::read_csv(here::here("evidences/github-users-committing-filetypes.csv"),
                      progress = FALSE,
                      col_types = cols(
                        file_extension = col_character(),
                        month_day = col_integer(),
                        the_month = col_integer(),
                        the_year = col_integer(),
                        users = col_integer()
                        )) -> data
data %>% 
  glimpse()
## Observations: 13,802
## Variables: 5
## $ file_extension <chr> "md", "md", "md", "md", "md", "md", "md", "md", "…
## $ month_day      <int> 18, 17, 27, 16, 26, 21, 4, 22, 23, 1, 12, 3, 2, 2…
## $ the_month      <int> 2, 2, 1, 2, 1, 3, 2, 2, 2, 2, 4, 2, 2, 2, 4, 3, 4…
## $ the_year       <int> 2016, 2016, 2016, 2016, 2016, 2017, 2016, 2016, 2…
## $ users          <int> 10279, 10208, 10118, 10045, 10020, 10015, 9991, 9…


Popularity


We will refer to popularity as the median number of users that commited files of a certain language. In other words, the more people commit files in a programming language the more popular it is.


Is it Weekend yet?


  • We will recreate a date object based on the data-frame columns (month_day, the_month, the_year) and from this date object deduce whether the day of that observation was a weekend day or not (isWeekend).
data %>%
  mutate(cronology = lubridate::ymd(paste(the_year,
                         the_month,
                         month_day)),
         isWeekend = timeDate::isWeekend(cronology)) -> data
data %>%
  sample_n(10)
## # A tibble: 10 x 7
##    file_extension month_day the_month the_year users cronology  isWeekend
##    <chr>              <int>     <int>    <int> <int> <date>     <lgl>    
##  1 yaml                   1        12     2016   557 2016-12-01 FALSE    
##  2 xml                    9        12     2016  2810 2016-12-09 FALSE    
##  3 css                    4         1     2017  2717 2017-01-04 FALSE    
##  4 go                    23        12     2016   746 2016-12-23 FALSE    
##  5 js                    11         2     2017  4527 2017-02-11 TRUE     
##  6 css                    7         1     2017  1888 2017-01-07 TRUE     
##  7 yml                   16         9     2016  2309 2016-09-16 FALSE    
##  8 rb                     5         3     2017   601 2017-03-05 TRUE     
##  9 cs                    12         3     2016   867 2016-03-12 TRUE     
## 10 xml                   12         2     2017  1941 2017-02-12 TRUE


All file extensions

data %>%
  group_by(file_extension, isWeekend) %>%
  summarise(popularity = sum(users)) %>%
  ggplot(aes(x=reorder(file_extension,popularity),
             y = popularity,
             fill=isWeekend)) + 
  geom_bar(stat="identity") + 
  theme(axis.text.x = element_text(angle=60, hjust=1)) +
  labs(x="File extension",y="Number of editing users")

  • A lot of md and web related files (json, js, html) were committed on weekends. Looks like documenting and working on the front-end goes on during weekends, poor front-end developers…


data %>%
  group_by(cronology,isWeekend) %>%
  summarise(popularity = sum(users)) %>%
  ggplot(aes(popularity,cronology,color=isWeekend)) +
  geom_segment(aes(x = 0, y = cronology, 
                 xend = popularity, 
                 yend = cronology),
               color = "grey50",
               size=0.15) +
  geom_point() +
  coord_flip() +
  labs(y="Point in Time",
       x="Number of editing users")

  • We can see a sizable decrease in user’s commits around January, this matches typical holidays.


Python and Java

data %>%
  filter(file_extension %in% c("py","java")) %>%
  group_by(file_extension, isWeekend) %>%
  summarise(popularity = median(users)) %>%
  ggplot(aes(x=reorder(file_extension,popularity),
             y = popularity,
             fill=isWeekend)) + 
  geom_bar(stat="identity") + 
  theme(axis.text.x = element_text(angle=60, hjust=1)) +
  labs(x="File extension",y="Popularity")

  • In terms of sample Python seems to be more popular on weekends than Java.
  • In terms of sample both Java and Python seem to more popular on weekdays than on weekends.


data %>%
  filter(file_extension %in% c("py","java")) %>%
  ggplot(aes(users,cronology,color=isWeekend)) +
  geom_segment(aes(x = 0, y = cronology, 
                 xend = users, 
                 yend = cronology),
               color = "grey50",
               size=0.15) +
  geom_point() +
  facet_wrap(~ file_extension,
             nrow = 2) + 
  coord_flip() +
  labs(y="Point in Time",
       x="Number of editing users")

  • Both Python and Java reflect the same drop on file editions around January.



Statistical Inference


As talking about the sample isn’t enough to draw conclusions about the population (coders in Github), further into this report we will make use of statistical inference.


Java


Overview

data %>%
  filter(file_extension == "java") %>%
  ggplot(aes(x="", 
             y=users,
             group=isWeekend, 
             fill=isWeekend)) + 
  geom_boxplot() +
  labs(y="Number of editing users") +
  ggtitle("Java popularity (weekdays vs weekend)") +
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank())

  • In terms of sample we see more clearly that Java coders work way more during the week.


Inference on two samples


  • We will make use of confidence intervals at a 95% degree of confidence
data %>%
  filter(file_extension == "java") %>%
  filter(!isWeekend) %>%
  bootstrap(median(users), R = 10000) %>% 
    CI.percentile(probs = c(.025, .975)) -> java.week
data %>%
  filter(file_extension == "java") %>%
  filter(isWeekend) %>%
  bootstrap(median(users), R = 10000) %>% 
    CI.percentile(probs = c(.025, .975)) -> java.weekend

cat(paste("Java on week days:\n"))
java.week
cat(paste("\n\nJava on weekend days:\n"))
java.weekend
## Java on week days:
##               2.5% 97.5%
## median(users) 3533  3667
## 
## 
## Java on weekend days:
##               2.5% 97.5%
## median(users) 2058  2167



df = data.frame(rbind(java.week, 
                      java.weekend[rownames(java.weekend),]))

df$medida = c("JAVA (Week)", "JAVA (Weekend)")

df %>% 
  ggplot(aes(x = medida, ymin = X2.5., ymax = X97.5.)) + 
  geom_errorbar(width = .2) +
  labs(y= "Popularity", x="Group")


Looking at the confidence intervals (C.I.) of Java popularity during the week and during the weekend we can say at a 95% degree of confidence that there’s a statistically significant difference between Java popularity during the week and the weekend.


Inference on the unpaired difference of two samples


  • Let’s bootstrap the unpaired difference between java popularity during the week and java popularity during the weekend.


data %>%
  filter(file_extension == "java") -> java

b.diff.means <- bootstrap2(java$users, 
                          treatment = java$isWeekend, 
                          median, R = 10000)

means.diff = CI.percentile(b.diff.means, probs = c(.025, .975))
means.diff

data.frame(means.diff) %>%
  ggplot(aes(x = "Difference",ymin = X2.5., ymax = X97.5.)) +
  geom_errorbar(width = .2) +
  geom_hline(yintercept = 0, colour = "darkorange") +
  labs(x="")

##                      2.5%    97.5%
## median: FALSE-TRUE 1389.5 1566.329
  • The C.I shows us that Java is more popular during the week (Interval is exclusively above 0). This was expected given the business feel around the Java community.


Looking at the confidence intervals (C.I.) of the unpaired difference between java popularity on the week and java popularity during the weekend we can say at a 95% degree of confidence that Java is more popular during the week than during the weekend.


Python


Overview

data %>%
  filter(file_extension == "py") %>%
  ggplot(aes(x="", 
             y=users,
             group=isWeekend, 
             fill=isWeekend)) + 
  geom_boxplot() + 
  labs(x="", y="Number of editing users") +
  ggtitle("Python popularity (weekday vs weekend)") +
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank())

  • In terms of sample we see more clearly that Python coders work way more during the week.


Inference on two samples

data %>%
  filter(file_extension == "py") %>%
  filter(!isWeekend) %>%
  bootstrap(median(users), R = 10000) %>% 
    CI.percentile(probs = c(.025, .975)) -> python.week
data %>%
  filter(file_extension == "py") %>%
  filter(isWeekend) %>%
  bootstrap(median(users), R = 10000) %>% 
    CI.percentile(probs = c(.025, .975)) -> python.weekend

cat(paste("Python on week days:\n"))
python.week
cat(paste("\n\nPython on weekend days:\n"))
python.weekend
## Python on week days:
##                 2.5%  97.5%
## median(users) 4668.5 4789.5
## 
## 
## Python on weekend days:
##               2.5% 97.5%
## median(users) 2523  2644



df = data.frame(rbind(python.week, 
                      python.weekend[rownames(python.week),]))
df$medida = c("PYTHON (Week)", "PYTHON (Weekend)")

df %>% 
  ggplot(aes(x = medida, ymin = X2.5., ymax = X97.5.)) + 
  geom_errorbar(width = .2) +
  labs(y= "Popularity", x="Group")


Looking at the confidence intervals (C.I.) of Python popularity during the week and the weekend we can say at a 95% degree of confidence that there’s a statistically significant difference between Python popularity during the week and Python popularity during the weekend.


Inference on the unpaired difference of two samples


  • Let’s bootstrap the unpaired difference between Python popularity during the week and Python popularity during the weekend.


data %>%
  filter(file_extension == "py") -> python

b.diff.means <- bootstrap2(python$users, 
                          treatment = python$isWeekend, 
                          median, R = 10000)

means.diff = CI.percentile(b.diff.means, probs = c(.025, .975))
means.diff

data.frame(means.diff) %>%
  ggplot(aes(x = "Difference",ymin = X2.5., ymax = X97.5.)) +
  geom_errorbar(width = .2) +
  geom_hline(yintercept = 0, colour = "darkorange") +
  labs(x="")

##                    2.5% 97.5%
## median: FALSE-TRUE 2063  2236
  • The C.I shows us that Python is more popular during the week than during the weekend (Interval is exclusively above 0).
  • The former was not unexpected to be honest, given that there’s a whole business segment around Python despite its carefree vibes.


Looking at the confidence interval (C.I.) of the unpaired difference between Python popularity during the week and Python popularity during weekends we can say at a 95% degree of confidence that Python is significantly more popular during the week than during the weekend.


Python/Java on the weekend


Overview

data %>%
  filter(isWeekend) %>%
  filter(file_extension %in% c("py","java")) %>%
  ggplot(aes(x=file_extension, 
             y=users,
             group=file_extension, 
             fill=file_extension)) + 
  geom_boxplot() +
  ggtitle("Python vs Java (Weekends)") +
    theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank()) +
  labs(y="Number of users editing files")

  • In terms of sample we see more clearly that Python is more popular on weekends than Java.


Inference on two samples

cat(paste("Java on weekend days:\n"))
java.weekend
cat(paste("\n\nPython on weekend days:\n"))
python.weekend
## Java on weekend days:
##               2.5% 97.5%
## median(users) 2058  2167
## 
## 
## Python on weekend days:
##               2.5% 97.5%
## median(users) 2523  2644



df = data.frame(rbind(java.weekend, 
                      python.weekend[rownames(python.week),]))
df$medida = c("JAVA (Weekend)", "PYTHON (Weekend)")

df %>% 
  ggplot(aes(x = medida, ymin = X2.5., ymax = X97.5.)) + 
  geom_errorbar(width = .2) +
  labs(y= "Popularity", x="Group")


Looking at the confidence intervals (C.I.) of Java and Python popularity during the weekend we can say at a 95% degree of confidence that there’s a statistically significant difference between Java and Python popularity during the weekend.


Inference on the unpaired difference of two samples


  • Let’s bootstrap the unpaired difference between Java and Python popularity during the weekend.


data %>%
  filter(isWeekend) %>%
  filter(file_extension %in% c("py","java")) -> weekend

b.diff.means <- bootstrap2(weekend$users, 
                          treatment = weekend$file_extension, 
                          median, R = 10000)

means.diff = CI.percentile(b.diff.means, probs = c(.025, .975))
means.diff

data.frame(means.diff) %>%
  ggplot(aes(x = "Difference",ymin = X2.5., ymax = X97.5.)) +
  geom_errorbar(width = .2) +
  geom_hline(yintercept = 0, colour = "darkorange") +
  labs(x="")

##                   2.5%  97.5%
## median: java-py -547.5 -381.5


  • The C.I shows us that Python is more popular than Java during the weekend (Interval is exclusively below 0).
  • Python has a much more carefree vibe to it than Java (which is heavily tied to a business environment). It comes as no surprise that people would rather use Python on the weekend.


Looking at the confidence intervals (C.I.) of the unpaired difference between Java and Python popularity during the weekend we can say at a 95% degree of confidence that Python is significantly more popular during the weekend than Java.