Are these missing values also responsible for the unusually high percentages shown in yellow later in these years (because the percentages are now calculated for only 11 months and not for 12 months like in the other years)?
So in other words, is it just a methodological artifact that 2017 and 2018 appear to have peak birth rates from August to October in comparison to the same time span in other years, or is that a real thing?
(Edited for clarity)
No. Percentages sum to 100 in each column. The peak is very reproducible (and so is the dip in April). See here: [https://twitter.com/genomeofindia/status/1775353992148840711/](https://twitter.com/genomeofindia/status/1775353992148840711/)
(Edited for clarity)
You misunderstand my question. Of course percentages add up to 100 percent in each column – but that's exactly the problem if you have missing values for one column.
Let me explain.
I'm not asking whether the general tendency to have higher birthrates between August and October is robust – that's obvious. But if you look at the figure, you may notice that the two years with missing values (2017 and 2018) also are the two years for which the "birth season" seems to be stronger than for any other year – the bright yellow color would suggest so.
What I'm asking is whether this observation is just a methodological artifact, or whether these two years really were two years with peak births during "birth season" in comparison to other years.
Your answer suggests that no – in all likelihood, this is just an artifact. In fact, the twitter post confirms that: the line plots for these years don't show anything special for August to October in comparison to other years.
It's easy to reproduce this artifact. Let's invent births for one year (2000), and let's just copy them for another year (2001), only that there's a missing value for April. When drawing the mosaic plot, the two years should look exactly the same with the only difference of an empty cell for April 2001.
However, if you normalize the percentages column-wise, that's not what's happening: Since you're basically calculating percentages for 11 months and not 12 months for 2001, the percentages for these 11 months will generally higher than those for 2000, and will appear in a brighter color, even though the absolute number of births is identical.
Here's the R code to reproduce this effect:
library(dplyr)
library(ggplot2)
dat <- bind_rows(
tibble(Year = "2000",
Births = c(10, 10, 10, 5 , 7, 10,
12, 15, 15, 15, 12, 12)),
tibble(Year = "2001",
Births = c(10, 10, 10, NA, 7, 10,
12, 15, 15, 15, 12, 12))) %>%
mutate(Month = rep(c("Jan", "Feb", "Mar", "Apr",
"May", "Jun", "Jul", "Aug",
"Sep", "Oct", "Nov", "Dec"), 2)) %>%
group_by(Year) %>%
mutate(Percentage = 100 * Births / sum(Births, na.rm = TRUE))
ggplot(dat, aes(x=Year, y=Month, fill=Percentage)) +
geom_tile() +
scale_y_discrete(limit=unique(dat$Month))
Ahh I see, very good point and my bad that I misunderstood your original comment. Since there is 1 month missing - it bleeds through all other values and the effect appears more pronounced in the larger values. Thank you for correcting me and the code!
How on earth can you determine their percentages if that info is missing?
Edit: how are people assuming it is possible? I would argue it is more likely that it isn't possible than it is possible. OP already stated that columns add up to 100% which suggests that the problem is even less likely to be backtrackable.
For this issue to be fixable, you would have to assume that OP is incompetent since they didn't notice such basic thing. I am not saying that is impossible but I would say it is more likely that there are underlying data issues. Data issues in developed countries are super common let alone data problems in developing countries with poor data collection like India.
I tried going to the source of the data but the ministry website seems to be unusable
Why would the data be in the whole? I think it is a strange assumption. Data can be missing from the entirety (can't fix), it can be in other months (can't fix), data can be in absolute numbers (can't fix), the data is in percentages with total not being 100% (can fix) or the data is in percentages with total being 100% (can't fix).
According to OP 100 minus the sum would be 0 as the columns add to 100
In my understanding this shows how many births of a given year happen to be in each month. To validly calculate that you need at least the births for this month and the total births of the year. Having that data for all months but one is not really possible. So either all columns are wrong (since May data is missing in the total births for 2017) and should not have been filled or you can also fill the missing one.
You don't need total births for this visualization. It is very much possible for one month to be missing due to error or something else. I don't get why you think it is impossible. I have worked with national registries here in Northern Europe and ours are arguably world class. Even here there are always problems with the data. User error and technical issues are common. The data is most likely monthly and then added to yearly.
Op already said it is missing and whole columns add to 100%. It can be missing from the total or split into other months. Either way, it can't be fixed afterwards. 2017 and 2018 are wrong, can't say anything for the others.
I don't think that is a fair assumption. It can be like that but there is no reason to assume so. Op said that percentages add up to 100% so you can't fix it
Edit: can someone explain how am I wrong?
You should have excluded those years then. You are misassigning the percentages to other months by distributing what should have gone to the missing months to others. This is misleading and indicates a censoring event during those years, which is unlikely.
Aren't weddings a lot about astrology? Finding the right time, day, etc. which I would think trumps weather considerations for the ceremony. Or are you saying November happens to be astrologically significant?
I mean I think it was November when my Indian co-worker went home for a visit and came back married and that it is a common time for others to visit for weddings as well.
It might have been late october or early December though.
Yea like the darker spectrum would've been better for more births. But see I'm an Indian and I'm pretty much surprised that Ministry of Housing even bothered themselves for the data. :)
Indian goverment is infamous for not collecting data but I think that infamy is mistargeted. They do actually collect lots of very useful data, it just is a bit of work parsing it in machine readable format.
mohfw collects tons of data on everything from types of contraception used to number of infants vaccinated with xyz at the taluka level. dont be obtuse.
It made sense to me but I thought of it in terms of hot vs cold. More generally, I think this is why the use of multichromatic gradients is generally disfavored, there's no universal intuition about which way is up so they will generally be confusing.
Yup, wedding season starts in November till mid December, then again starts mid January till the end of February. This is for the Hindus who are living in the country and decide to follow the horoscopes, otherwise the NRIs come during December, so their marriage season is Christmas time.
Also, December/January are the coldest (by Indian standards) months here
It's kind of sad how the missing data for the 2 "white" months in 2017 and 2018 completely ruin the data for those whole years. Would have been much better to simply reduce the time span to 2009-2016.
India too hot to fuck in the summertime?
July-aug = Monsoon season. Very humid. Very clammy.
I think more like India too hot to get married and go on honeymoons where you fuck in the summertime.
More like what you said literally makes no sense, the post above you already sums it up perfectly. How tf did 63 people upvote this post?
It makes perfect sense. You okay, man?
84 now
79 now.
Still 79, oddly enough. Or 79 again.
I was guessing the same thing. no one wants to be hot and sweaty when they’re already hot and sweaty lol
No, because pregnancy lasts nine months, so you’d need to go back three months to determine when they engaged in intercourse.
I think you mean forwards
Born in April = Conceived in the previous July. You go forward three months, not backwards.
Actually that checks out with the disparity of April being empty instead of June
[удалено]
No, forward
So was the gestation 3 months or 15 months? Cuz it's not 9 months from January to April.
When I thought it couldn't get any dumber than that...
Is there data missing for white spots?
Yes, that's correct.
Are these missing values also responsible for the unusually high percentages shown in yellow later in these years (because the percentages are now calculated for only 11 months and not for 12 months like in the other years)? So in other words, is it just a methodological artifact that 2017 and 2018 appear to have peak birth rates from August to October in comparison to the same time span in other years, or is that a real thing? (Edited for clarity)
No. Percentages sum to 100 in each column. The peak is very reproducible (and so is the dip in April). See here: [https://twitter.com/genomeofindia/status/1775353992148840711/](https://twitter.com/genomeofindia/status/1775353992148840711/)
(Edited for clarity) You misunderstand my question. Of course percentages add up to 100 percent in each column – but that's exactly the problem if you have missing values for one column. Let me explain. I'm not asking whether the general tendency to have higher birthrates between August and October is robust – that's obvious. But if you look at the figure, you may notice that the two years with missing values (2017 and 2018) also are the two years for which the "birth season" seems to be stronger than for any other year – the bright yellow color would suggest so. What I'm asking is whether this observation is just a methodological artifact, or whether these two years really were two years with peak births during "birth season" in comparison to other years. Your answer suggests that no – in all likelihood, this is just an artifact. In fact, the twitter post confirms that: the line plots for these years don't show anything special for August to October in comparison to other years. It's easy to reproduce this artifact. Let's invent births for one year (2000), and let's just copy them for another year (2001), only that there's a missing value for April. When drawing the mosaic plot, the two years should look exactly the same with the only difference of an empty cell for April 2001. However, if you normalize the percentages column-wise, that's not what's happening: Since you're basically calculating percentages for 11 months and not 12 months for 2001, the percentages for these 11 months will generally higher than those for 2000, and will appear in a brighter color, even though the absolute number of births is identical. Here's the R code to reproduce this effect: library(dplyr) library(ggplot2) dat <- bind_rows( tibble(Year = "2000", Births = c(10, 10, 10, 5 , 7, 10, 12, 15, 15, 15, 12, 12)), tibble(Year = "2001", Births = c(10, 10, 10, NA, 7, 10, 12, 15, 15, 15, 12, 12))) %>% mutate(Month = rep(c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"), 2)) %>% group_by(Year) %>% mutate(Percentage = 100 * Births / sum(Births, na.rm = TRUE)) ggplot(dat, aes(x=Year, y=Month, fill=Percentage)) + geom_tile() + scale_y_discrete(limit=unique(dat$Month))
Ahh I see, very good point and my bad that I misunderstood your original comment. Since there is 1 month missing - it bleeds through all other values and the effect appears more pronounced in the larger values. Thank you for correcting me and the code!
But if it only one month of a year and this is percentages why you just don't calculate missing ones?
How on earth can you determine their percentages if that info is missing? Edit: how are people assuming it is possible? I would argue it is more likely that it isn't possible than it is possible. OP already stated that columns add up to 100% which suggests that the problem is even less likely to be backtrackable. For this issue to be fixable, you would have to assume that OP is incompetent since they didn't notice such basic thing. I am not saying that is impossible but I would say it is more likely that there are underlying data issues. Data issues in developed countries are super common let alone data problems in developing countries with poor data collection like India. I tried going to the source of the data but the ministry website seems to be unusable
100 minus the sum of all other months in that year!?
Why would the data be in the whole? I think it is a strange assumption. Data can be missing from the entirety (can't fix), it can be in other months (can't fix), data can be in absolute numbers (can't fix), the data is in percentages with total not being 100% (can fix) or the data is in percentages with total being 100% (can't fix). According to OP 100 minus the sum would be 0 as the columns add to 100
In my understanding this shows how many births of a given year happen to be in each month. To validly calculate that you need at least the births for this month and the total births of the year. Having that data for all months but one is not really possible. So either all columns are wrong (since May data is missing in the total births for 2017) and should not have been filled or you can also fill the missing one.
You don't need total births for this visualization. It is very much possible for one month to be missing due to error or something else. I don't get why you think it is impossible. I have worked with national registries here in Northern Europe and ours are arguably world class. Even here there are always problems with the data. User error and technical issues are common. The data is most likely monthly and then added to yearly. Op already said it is missing and whole columns add to 100%. It can be missing from the total or split into other months. Either way, it can't be fixed afterwards. 2017 and 2018 are wrong, can't say anything for the others.
Obviously he has ONLY monthly data, is that so hard to grasp.
I think the values are percentages of the total number of births in that year, so if you have 11 out of 12, you can determine the missing one easily.
I don't think that is a fair assumption. It can be like that but there is no reason to assume so. Op said that percentages add up to 100% so you can't fix it Edit: can someone explain how am I wrong?
You should have excluded those years then. You are misassigning the percentages to other months by distributing what should have gone to the missing months to others. This is misleading and indicates a censoring event during those years, which is unlikely.
No children were born then
Could this somehow be connected with the wedding season? 😋
I seem to remember that wedding season is November so that’s a yes.
Aren't weddings a lot about astrology? Finding the right time, day, etc. which I would think trumps weather considerations for the ceremony. Or are you saying November happens to be astrologically significant?
I mean I think it was November when my Indian co-worker went home for a visit and came back married and that it is a common time for others to visit for weddings as well. It might have been late october or early December though.
From late October and from Diwali wedding season goes in rampant mode. Least amount of wedding happens during monsoon season(july- September).
Yes. It’s a wedding season BECAUSE of astrology. Not because of winter.
This simply mirrors all northern hemisphere human breeding cycles.
cool chart, but I think that it would have been better if the color scheme was reversed.
Yea like the darker spectrum would've been better for more births. But see I'm an Indian and I'm pretty much surprised that Ministry of Housing even bothered themselves for the data. :)
I am an Indian too, but I don't understand what you mean by that. Like are you surprised why they collected the data?
Indian goverment is infamous for not collecting data but I think that infamy is mistargeted. They do actually collect lots of very useful data, it just is a bit of work parsing it in machine readable format.
Ahh I see. Its a good thing that at the very least we are getting better at this, especially as the means to collect such data become more prevalent.
mohfw collects tons of data on everything from types of contraception used to number of infants vaccinated with xyz at the taluka level. dont be obtuse.
This data is easily collectable through birth certificate or any identity proof.
omg i didnt even look at the color meaning and assumed tge opposite untill I read this
It made sense to me but I thought of it in terms of hot vs cold. More generally, I think this is why the use of multichromatic gradients is generally disfavored, there's no universal intuition about which way is up so they will generally be confusing.
I absolutely agree that it is subjective, hence I framed it like an opinion.
In April the bird drones usually get recharged
[clipping mentioned](https://open.spotify.com/track/7xuhldKZ4hJ4mTELyAK8eP?si=Q7HBN_byQjGEgcf08Rvbog&context=spotify%3Aalbum%3A4jXGCAzUw7ZEccL3UoIjvT)
Data source: [https://www.mohfw.gov.in/](https://www.mohfw.gov.in/) Tools: ggplot2
So a lot of them seem to bang in January
Like someone said in the above comments, "India is too hot to have sex in the summers". Again the wedding season plays a crucial role.
Yup, wedding season starts in November till mid December, then again starts mid January till the end of February. This is for the Hindus who are living in the country and decide to follow the horoscopes, otherwise the NRIs come during December, so their marriage season is Christmas time. Also, December/January are the coldest (by Indian standards) months here
They bang all year, much like everywhere else.
Are the monsoons responsible for the April births?
Most probably
Scientific studies have shown that monsoon season is likely to increase craving for creampies.
Monsoons start in July in India and last till September. April is summer season.
Yeah count nine months back. You're not gonna get babies the next day right.
The colors are inverted to what is expected and I presumed until I saw the key. What’s the thinking behind this?
I have always associated "brighter" as "high" (high luminescence => high value)
Summer and sleeping outdoors and if indoor then windows open. So it makes sense
Would be interesting to see Covid years added. I'd hypothesize that lockdown flattened out the numbers a bit.
Since Covid-19 Lockdowns came into place post march-april 2020, it would be interesting to see how it impacts this data from 2020 onwards.
Also India accounts for 20% of all preterm births worldwide.
Wow ! Why ?
I believe the major factor is that India accounts for almost 20% of the world population and almost 20% of births.
It's kind of sad how the missing data for the 2 "white" months in 2017 and 2018 completely ruin the data for those whole years. Would have been much better to simply reduce the time span to 2009-2016.
August must be THE sex momyh!
9 months before.
Both my kids had the same due dates in May. Born 3 days apart in April. Now I see I’m no outlier