smile | Statistical Machine Intelligence & Learning Engine | Machine Learning library

.container {
  display: flex;
}

.textarea {
   height: 100px;
}

.sidebar {
  display: flex;
  justify-content: space-between;
  flex-direction: column;
}

<div class="container">
  <textarea class="textarea"></textarea>
  <div class="sidebar">
    <div class="smile">😅</div>
    <div class="text">12</div>
  </div>
</div>

.container {
  display: flex;
}

.textarea {
   height: 100px;
}

.sidebar {
  display: flex;
  justify-content: space-between;
  flex-direction: column;
}

<div class="container">
  <textarea class="textarea"></textarea>
  <div class="sidebar">
    <div class="smile">😅</div>
    <div class="text">12</div>
  </div>
</div>

<div class="btnGp">
 <button #toggleEmojiTwitter class="emoji-button mt-2 mr-2" type="button" (click)="_isTwitterEmojiPickerVisible = !_isTwitterEmojiPickerVisible"><mdb-icon class="emoji-icon" far icon="smile"></mdb-icon></button>
        <span class="text-count" [hidden]="_isTwitterEmojiPickerVisible">
          <span>{{_twitterCharLimit - this._postsForm.get('twitterText').value.length}}</span>
        </span>
</div>

.btnGp{
display : flex;
justify-content : space-between;
flex-direction : column;
height : 100%;
}

.emoji-text-container{
 // a size of your choice
}

<div class="btnGp">
 <button #toggleEmojiTwitter class="emoji-button mt-2 mr-2" type="button" (click)="_isTwitterEmojiPickerVisible = !_isTwitterEmojiPickerVisible"><mdb-icon class="emoji-icon" far icon="smile"></mdb-icon></button>
        <span class="text-count" [hidden]="_isTwitterEmojiPickerVisible">
          <span>{{_twitterCharLimit - this._postsForm.get('twitterText').value.length}}</span>
        </span>
</div>

.btnGp{
display : flex;
justify-content : space-between;
flex-direction : column;
height : 100%;
}

.emoji-text-container{
 // a size of your choice
}

.w {
  border: 1px solid black;
  display: flex;
  min-height:200px;
}

.left {
  width:100%;
  border: 1px solid black;
}
.right {  
  width:70px;    
}
.icons {
  position:relative;
  height:100%;
  width:100%;  
}

.top, .bottom {  
  position:absolute;
}
.top {
  top:0;
}
.bottom {
   bottom:0;
}

<div class="w">
  <div class="left">1</div>
  <div class="right">    
    <div class="icons">
      <div class="top">top</div>    
      <div class="bottom">buttom</div>    
    </div>
  </div>
</div>

.w {
  border: 1px solid black;
  display: flex;
  min-height:200px;
}

.left {
  width:100%;
  border: 1px solid black;
}
.right {  
  width:70px;    
}
.icons {
  height:100%;
  display: flex;
  flex-direction: column;
  justify-content: space-between;
  background: gray;
  flex-wrap: nowrap;
}

<div class="w">
  <div class="left">1</div>
  <div class="right">    
    <div class="icons">
      <div class="top">top</div>    
      <div class="bottom">buttom</div>    
    </div>
  </div>
</div>

.w {
  border: 1px solid black;
  display: flex;
  min-height:200px;
}

.left {
  width:100%;
  border: 1px solid black;
}
.right {  
  width:70px;    
}
.icons {
  position:relative;
  height:100%;
  width:100%;  
}

.top, .bottom {  
  position:absolute;
}
.top {
  top:0;
}
.bottom {
   bottom:0;
}

<div class="w">
  <div class="left">1</div>
  <div class="right">    
    <div class="icons">
      <div class="top">top</div>    
      <div class="bottom">buttom</div>    
    </div>
  </div>
</div>

.w {
  border: 1px solid black;
  display: flex;
  min-height:200px;
}

.left {
  width:100%;
  border: 1px solid black;
}
.right {  
  width:70px;    
}
.icons {
  height:100%;
  display: flex;
  flex-direction: column;
  justify-content: space-between;
  background: gray;
  flex-wrap: nowrap;
}

<div class="w">
  <div class="left">1</div>
  <div class="right">    
    <div class="icons">
      <div class="top">top</div>    
      <div class="bottom">buttom</div>    
    </div>
  </div>
</div>

.w {
  border: 1px solid black;
  display: flex;
  min-height:200px;
}

.left {
  width:100%;
  border: 1px solid black;
}
.right {  
  width:70px;    
}
.icons {
  position:relative;
  height:100%;
  width:100%;  
}

.top, .bottom {  
  position:absolute;
}
.top {
  top:0;
}
.bottom {
   bottom:0;
}

<div class="w">
  <div class="left">1</div>
  <div class="right">    
    <div class="icons">
      <div class="top">top</div>    
      <div class="bottom">buttom</div>    
    </div>
  </div>
</div>

.w {
  border: 1px solid black;
  display: flex;
  min-height:200px;
}

.left {
  width:100%;
  border: 1px solid black;
}
.right {  
  width:70px;    
}
.icons {
  height:100%;
  display: flex;
  flex-direction: column;
  justify-content: space-between;
  background: gray;
  flex-wrap: nowrap;
}

<div class="w">
  <div class="left">1</div>
  <div class="right">    
    <div class="icons">
      <div class="top">top</div>    
      <div class="bottom">buttom</div>    
    </div>
  </div>
</div>

.w {
  border: 1px solid black;
  display: flex;
  min-height:200px;
}

.left {
  width:100%;
  border: 1px solid black;
}
.right {  
  width:70px;    
}
.icons {
  position:relative;
  height:100%;
  width:100%;  
}

.top, .bottom {  
  position:absolute;
}
.top {
  top:0;
}
.bottom {
   bottom:0;
}

<div class="w">
  <div class="left">1</div>
  <div class="right">    
    <div class="icons">
      <div class="top">top</div>    
      <div class="bottom">buttom</div>    
    </div>
  </div>
</div>

.w {
  border: 1px solid black;
  display: flex;
  min-height:200px;
}

.left {
  width:100%;
  border: 1px solid black;
}
.right {  
  width:70px;    
}
.icons {
  height:100%;
  display: flex;
  flex-direction: column;
  justify-content: space-between;
  background: gray;
  flex-wrap: nowrap;
}

<div class="w">
  <div class="left">1</div>
  <div class="right">    
    <div class="icons">
      <div class="top">top</div>    
      <div class="bottom">buttom</div>    
    </div>
  </div>
</div>

await mycursor.execute("SELECT * FROM guild_message_count WHERE guild_id = %s AND user_id = %s", (message.author.guild.id, message.author.id))
        in_database2 = await mycursor.fetchone()
        if in_database2:
            await mycursor.execute("UPDATE guild_message_count SET user_id = %s, message_count = message_count + %s WHERE guild_id = %s AND user_id = %s", (message.author.id, count, message.author.guild.id, message.author.id))
        else:
            await mycursor.execute("INSERT INTO guild_message_count (user_id, message_count, guild_id) VALUES (%s, %s, %s)", (message.author.id, count, message.author.guild.id))

INSERT INTO guild_message_count
        (user_id, message_count, guild_id)
        VALUES 
        (%s, %s, %s)
    ON DUPLICATE KEY UPDATE
        message_count = message_count + 1

await mycursor.execute("SELECT * FROM guild_message_count WHERE guild_id = %s AND user_id = %s", (message.author.guild.id, message.author.id))
        in_database2 = await mycursor.fetchone()
        if in_database2:
            await mycursor.execute("UPDATE guild_message_count SET user_id = %s, message_count = message_count + %s WHERE guild_id = %s AND user_id = %s", (message.author.id, count, message.author.guild.id, message.author.id))
        else:
            await mycursor.execute("INSERT INTO guild_message_count (user_id, message_count, guild_id) VALUES (%s, %s, %s)", (message.author.id, count, message.author.guild.id))

INSERT INTO guild_message_count
        (user_id, message_count, guild_id)
        VALUES 
        (%s, %s, %s)
    ON DUPLICATE KEY UPDATE
        message_count = message_count + 1

import asyncio


async def delay(n):
    print(f"sleeping for {n} second(s)")
    await asyncio.sleep(n)
    print(f"done sleeping for {n} second(s)")


loop = asyncio.get_event_loop()
t1 = loop.create_task(delay(1))
t2 = loop.create_task(delay(2))
loop.run_until_complete(t1)
loop.close()

sleeping for 1 second(s)
sleeping for 2 second(s)
done sleeping for 1 second(s)
Task was destroyed but it is pending!
task: <Task pending name='Task-1' coro=<delay() running at aio.py:10> wait_for=<Future pending cb=[<TaskWakeupMethWrapper object at 0x7fa794c5b970>()]>

import asyncio


async def delay(n):
    print(f"sleeping for {n} second(s)")
    await asyncio.sleep(n)
    print(f"done sleeping for {n} second(s)")


loop = asyncio.get_event_loop()
t1 = loop.create_task(delay(1))
t2 = loop.create_task(delay(2))
loop.run_until_complete(t1)

pending = asyncio.all_tasks(loop=loop)
group = asyncio.gather(*pending)
loop.run_until_complete(group)

loop.close()

import asyncio


async def delay(n):
    print(f"sleeping for {n} second(s)")
    await asyncio.sleep(n)
    print(f"done sleeping for {n} second(s)")


async def main():
    t1 = asyncio.create_task(delay(1))
    t2 = asyncio.create_task(delay(2))
    await t2


asyncio.run(main())

import asyncio

asyncio.run(client.start('token'))

import asyncio


async def delay(n):
    print(f"sleeping for {n} second(s)")
    await asyncio.sleep(n)
    print(f"done sleeping for {n} second(s)")


loop = asyncio.get_event_loop()
t1 = loop.create_task(delay(1))
t2 = loop.create_task(delay(2))
loop.run_until_complete(t1)
loop.close()

sleeping for 1 second(s)
sleeping for 2 second(s)
done sleeping for 1 second(s)
Task was destroyed but it is pending!
task: <Task pending name='Task-1' coro=<delay() running at aio.py:10> wait_for=<Future pending cb=[<TaskWakeupMethWrapper object at 0x7fa794c5b970>()]>

import asyncio


async def delay(n):
    print(f"sleeping for {n} second(s)")
    await asyncio.sleep(n)
    print(f"done sleeping for {n} second(s)")


loop = asyncio.get_event_loop()
t1 = loop.create_task(delay(1))
t2 = loop.create_task(delay(2))
loop.run_until_complete(t1)

pending = asyncio.all_tasks(loop=loop)
group = asyncio.gather(*pending)
loop.run_until_complete(group)

loop.close()

import asyncio


async def delay(n):
    print(f"sleeping for {n} second(s)")
    await asyncio.sleep(n)
    print(f"done sleeping for {n} second(s)")


async def main():
    t1 = asyncio.create_task(delay(1))
    t2 = asyncio.create_task(delay(2))
    await t2


asyncio.run(main())

import asyncio

asyncio.run(client.start('token'))

import asyncio


async def delay(n):
    print(f"sleeping for {n} second(s)")
    await asyncio.sleep(n)
    print(f"done sleeping for {n} second(s)")


loop = asyncio.get_event_loop()
t1 = loop.create_task(delay(1))
t2 = loop.create_task(delay(2))
loop.run_until_complete(t1)
loop.close()

sleeping for 1 second(s)
sleeping for 2 second(s)
done sleeping for 1 second(s)
Task was destroyed but it is pending!
task: <Task pending name='Task-1' coro=<delay() running at aio.py:10> wait_for=<Future pending cb=[<TaskWakeupMethWrapper object at 0x7fa794c5b970>()]>

import asyncio


async def delay(n):
    print(f"sleeping for {n} second(s)")
    await asyncio.sleep(n)
    print(f"done sleeping for {n} second(s)")


loop = asyncio.get_event_loop()
t1 = loop.create_task(delay(1))
t2 = loop.create_task(delay(2))
loop.run_until_complete(t1)

pending = asyncio.all_tasks(loop=loop)
group = asyncio.gather(*pending)
loop.run_until_complete(group)

loop.close()

import asyncio


async def delay(n):
    print(f"sleeping for {n} second(s)")
    await asyncio.sleep(n)
    print(f"done sleeping for {n} second(s)")


async def main():
    t1 = asyncio.create_task(delay(1))
    t2 = asyncio.create_task(delay(2))
    await t2


asyncio.run(main())

import asyncio

asyncio.run(client.start('token'))

import asyncio


async def delay(n):
    print(f"sleeping for {n} second(s)")
    await asyncio.sleep(n)
    print(f"done sleeping for {n} second(s)")


loop = asyncio.get_event_loop()
t1 = loop.create_task(delay(1))
t2 = loop.create_task(delay(2))
loop.run_until_complete(t1)
loop.close()

sleeping for 1 second(s)
sleeping for 2 second(s)
done sleeping for 1 second(s)
Task was destroyed but it is pending!
task: <Task pending name='Task-1' coro=<delay() running at aio.py:10> wait_for=<Future pending cb=[<TaskWakeupMethWrapper object at 0x7fa794c5b970>()]>

import asyncio


async def delay(n):
    print(f"sleeping for {n} second(s)")
    await asyncio.sleep(n)
    print(f"done sleeping for {n} second(s)")


loop = asyncio.get_event_loop()
t1 = loop.create_task(delay(1))
t2 = loop.create_task(delay(2))
loop.run_until_complete(t1)

pending = asyncio.all_tasks(loop=loop)
group = asyncio.gather(*pending)
loop.run_until_complete(group)

loop.close()

import asyncio


async def delay(n):
    print(f"sleeping for {n} second(s)")
    await asyncio.sleep(n)
    print(f"done sleeping for {n} second(s)")


async def main():
    t1 = asyncio.create_task(delay(1))
    t2 = asyncio.create_task(delay(2))
    await t2


asyncio.run(main())

import asyncio

asyncio.run(client.start('token'))

import asyncio


async def delay(n):
    print(f"sleeping for {n} second(s)")
    await asyncio.sleep(n)
    print(f"done sleeping for {n} second(s)")


loop = asyncio.get_event_loop()
t1 = loop.create_task(delay(1))
t2 = loop.create_task(delay(2))
loop.run_until_complete(t1)
loop.close()

sleeping for 1 second(s)
sleeping for 2 second(s)
done sleeping for 1 second(s)
Task was destroyed but it is pending!
task: <Task pending name='Task-1' coro=<delay() running at aio.py:10> wait_for=<Future pending cb=[<TaskWakeupMethWrapper object at 0x7fa794c5b970>()]>

import asyncio


async def delay(n):
    print(f"sleeping for {n} second(s)")
    await asyncio.sleep(n)
    print(f"done sleeping for {n} second(s)")


loop = asyncio.get_event_loop()
t1 = loop.create_task(delay(1))
t2 = loop.create_task(delay(2))
loop.run_until_complete(t1)

pending = asyncio.all_tasks(loop=loop)
group = asyncio.gather(*pending)
loop.run_until_complete(group)

loop.close()

import asyncio


async def delay(n):
    print(f"sleeping for {n} second(s)")
    await asyncio.sleep(n)
    print(f"done sleeping for {n} second(s)")


async def main():
    t1 = asyncio.create_task(delay(1))
    t2 = asyncio.create_task(delay(2))
    await t2


asyncio.run(main())

import asyncio

asyncio.run(client.start('token'))

async def _check_channel(self, message, pool):
    async with pool.acquire() as conn:
        async with conn.cursor() as cursor:
            await cursor.execute(
                "SELECT ignore_channel_id FROM guild_channel_settings WHERE guild_id = %s",
                (message.author.guild.id,),
            )
            in_database = await cursor.fetchone()

    if in_database and in_database[0] is not None:
        channel_list = in_database[0].split(" ")
        for channelid in channel_list:

            try:
                channel_id_int = int(channelid)
            except ValueError:
                continue

            if int(message.channel.id) == channel_id_int:
                return False


async def _get_role_count(self, message, pool):
    async with pool.acquire() as conn:
        async with conn.cursor() as cursor:
            await cursor.execute(
                "SELECT ignore_role_id, bonus_role_id FROM guild_role_settings WHERE guild_id = %s",
                (message.author.guild.id,),
            )
            in_database = await cursor.fetchone()
    if in_database:
        first_item, second_item, *_ = in_database
        if first_item is not None:
            role_list = first_item.split(" ")
            for roleid in role_list:
                try:
                    roleid_int = int(roleid)
                except ValueError:
                    continue

                role = message.author.guild.get_role(roleid_int)
                if role is None:
                    continue
                if role in message.author.roles:
                    return False

        if second_item is not None:
            role_list = second_item.split(" ")
            count = 0
            for roleid in role_list:
                try:
                    roleid_int = int(roleid)
                except ValueError:
                    continue

                role = message.author.guild.get_role(roleid_int)
                if role is None:
                    continue
                if role in message.author.roles:
                    count += 1
            return count


@commands.Cog.listener("on_message")
async def on_message(self, message):
    if message.author.bot:
        return
    if message.type != discord.MessageType.default:
        return
    if isinstance(message.channel, discord.channel.DMChannel):
        return

    # Cooldown

    self.member_cooldown_list = [
        i
        for i in self.member_cooldown_list
        if i[1] + self.cooldown_val > int(time.time())
    ]
    member_index = next(
        (
            i
            for i, v in enumerate(self.member_cooldown_list)
            if v[0] == message.author.id
        ),
        None,
    )
    if member_index is not None:
        if self.member_cooldown_list[member_index][1] + self.cooldown_val > int(
            time.time()
        ):
            return

    self.member_cooldown_list.append((message.author.id, int(time.time())))

    loop = asyncio.get_running_loop()
    db_pool = await aiomysql.create_pool(
        minsize=3,
        host="<host>",
        port=3306,
        user="<user>",
        password="<password>",
        db="<db_name>",
        autocommit=False,
        loop=loop,
    )
    count = 1

    check_channel_task = asyncio.create_task(
        self._check_channel(self, message, db_pool)
    )
    role_count_task = asyncio.create_task(self._get_role_count(self, message, db_pool))

    # write to database

    mydb = await db_pool.acquire()
    mycursor = await mydb.cursor()
    await mycursor.execute(
        "SELECT * FROM guild_message_count WHERE guild_id = %s AND user_id = %s",
        (message.author.guild.id, message.author.id),
    )
    in_database = await mycursor.fetchone()

    role_count = await role_count_task
    if False in (role_count, await check_channel_task):
        await mycursor.close()
        db_pool.release(mydb)
        db_pool.close()
        await db_pool.wait_closed()
        return
    if role_count:
        count += role_count
    if in_database:
        await mycursor.execute(
            "INSERT INTO guild_message_count (user_id, message_count, guild_id) VALUES (%s, %s, %s) ON DUPLICATE KEY UPDATE message_count = message_count + 1",
            (message.author.id, count, message.author.guild.id),
        )

    await mydb.commit()
    await mycursor.close()
    db_pool.release(mydb)
    db_pool.close()
    await db_pool.wait_closed()

export default function GraphBackDrop() {
  return (
     <Container>
        <Grid
          container
          spacing={3}
          justifyContent="center"
          alignItems="stretch"
        >
          <Grid item xs={12}>
            <YourCardOne />
          </Grid>
          <Grid item xs={12} sm={6}>
            <YourCardTwo />
          </Grid>
          <Grid item xs={12} sm={6}>
            <YourCardThree />
          </Grid>
        </Grid>
     </Container>
  );
}

[data-aos] {
  pointer-events: none;
}
.aos-animate {
  pointer-events: auto;
}
.aos-init[data-aos][data-aos].aos-animate {
  transform: unset;
}

dictionary = smiles_df.set_index('ID')['SMILES'].to_dict()

dictionary = dict(zip(smiles_df['ID'], smiles_df['SMILES']))

dictionary = smiles_df.set_index('ID')['SMILES'].to_dict()

dictionary = dict(zip(smiles_df['ID'], smiles_df['SMILES']))

conditions = [df['Positive']>=0.80, df['Negative']>=0.80, ((df['Positive']>=0.50) & (df['Positive']<0.80)),
              ((df['Negative']>=0.50) & (df['Negative']<0.80)), df['Neutral']>=0.5]
values = ['Very Positive', 'Very Negative', 'Positive', 'Negative', 'Neutral']
df['Sentiment'] = np.select(conditions, values, default=np.nan)

                                               Text  Negative  Neutral  Positive      Sentiment
0                          I lost my phone. I am sad      0.80     0.15      0.05  Very Negative
1                                   How is your day?      0.10     0.80      0.10        Neutral
2                     Let's go out for dinner today.      0.06     0.55      0.39        Neutral
3  I am super pissed at my friend for cancelling ...      0.73     0.11      0.16       Negative
4                     I am so happy  I want to dance      0.00     0.10      0.90  Very Positive
5      I am not sure if I should laugh or just smile      0.08     0.24      0.68       Positive

conditions = [df['Positive']>=0.80, df['Negative']>=0.80, ((df['Positive']>=0.50) & (df['Positive']<0.80)),
              ((df['Negative']>=0.50) & (df['Negative']<0.80)), df['Neutral']>=0.5]
values = ['Very Positive', 'Very Negative', 'Positive', 'Negative', 'Neutral']
df['Sentiment'] = np.select(conditions, values, default=np.nan)

                                               Text  Negative  Neutral  Positive      Sentiment
0                          I lost my phone. I am sad      0.80     0.15      0.05  Very Negative
1                                   How is your day?      0.10     0.80      0.10        Neutral
2                     Let's go out for dinner today.      0.06     0.55      0.39        Neutral
3  I am super pissed at my friend for cancelling ...      0.73     0.11      0.16       Negative
4                     I am so happy  I want to dance      0.00     0.10      0.90  Very Positive
5      I am not sure if I should laugh or just smile      0.08     0.24      0.68       Positive

def your_fn(values):
  pos = values["Positive"]
  neu = values["Neutral"]
  neg = values["Negative"]

  # 1. If the value of negative or positive is most dominating and >= 0.8 (80%) then mark it as very negative or very positive.
  if (pos >= .8): 
    return "Very positive"
  if (neg >= .8):
    return "Very negative"
  
  # 2. If the value of negative or positive is most dominating but it is >= 0.5 but less than 0.8 then just negative or positive.
  if (pos >= .5): 
    return "Positive"
  if (neg >= .5):
    return "Negative"

  # 3. If the value of neutral is >= 0.5 then Neutral. There is no such thing as Very Neutral.
  if (neu >= .5):
    return "Neutral"
  
  return "-"

df['Sentiment'] = df.apply(your_fn, axis=1)

s = df.drop('Text', axis=1).idxmax(1)
m = df.drop('Text', axis=1).max(1)
df['Sentiment'] = np.where(m.ge(0.8)&s.ne('Neutral'), 'Very '+s, s)

                                                       Text  Negative  Neutral  Positive      Sentiment
0                                 I lost my phone. I am sad      0.80     0.15      0.05  Very Negative
1                                          How is your day?      0.10     0.80      0.10        Neutral
2                            Let's go out for dinner today.      0.06     0.55      0.39        Neutral
3  I am super pissed at my friend for cancelling the party.      0.73     0.11      0.16       Negative
4                            I am so happy  I want to dance      0.00     0.10      0.90  Very Positive
5             I am not sure if I should laugh or just smile      0.08     0.24      0.68       Positive

s = df.drop('Text', axis=1).idxmax(1)
m = df.drop('Text', axis=1).max(1)
df['Sentiment'] = np.where(m.ge(0.8)&s.ne('Neutral'), 'Very '+s, s)

                                                       Text  Negative  Neutral  Positive      Sentiment
0                                 I lost my phone. I am sad      0.80     0.15      0.05  Very Negative
1                                          How is your day?      0.10     0.80      0.10        Neutral
2                            Let's go out for dinner today.      0.06     0.55      0.39        Neutral
3  I am super pissed at my friend for cancelling the party.      0.73     0.11      0.16       Negative
4                            I am so happy  I want to dance      0.00     0.10      0.90  Very Positive
5             I am not sure if I should laugh or just smile      0.08     0.24      0.68       Positive

    @Override
    public void start(Stage primaryStage) throws Exception {

        TabPane pane = new TabPane();

        ImageView image = new ImageView(new Image(new FileInputStream("smile.jpg"),
                                      TAB_IMAGE_SIZE, TAB_IMAGE_SIZE, true, true));

//        image.setFitHeight(TAB_IMAGE_SIZE);
//        image.setFitWidth(TAB_IMAGE_SIZE);


        Tab imageTab = new Tab();
        imageTab.setGraphic(image);
        pane.getTabs().add(imageTab);

        for(int i = 0; i < 10; i++) {
            pane.getTabs().add(new Tab("Tab " + i));
        }


        primaryStage.setScene(new Scene(pane, 300, 300));
        primaryStage.show();
    }

import os
import csv
os.chdir('xxx')
from rdkit import Chem
from rdkit.Chem import rdChemReactions
from rdkit.Chem import AllChem
rxn = rdChemReactions.ReactionFromSmarts('xxx')
rct1 = Chem.SDMolSupplier('reactants_1.sdf')
rct2 = Chem.SDMolSupplier('reactants_2.sdf')
with open('output.csv', 'w', newline='') as f:
    for compound in rct1:
        compound = [compound] # put the mol into a list
        prods = AllChem.EnumerateLibraryFromReaction(rxn,[compound,rct2])
        prods2 = [Chem.MolToSmiles(x[0]) for x in list(prods)]
        writer = csv.writer(f)
        for item in prods2:
            writer.writerow([item])

kotlin {
    android()
    ios {
        binaries {
            framework {
                baseName = "shared"
            }
        }
        compilations.getByName("main") {
            val uikit by cinterops.creating {
            }

        }
    }

package = demo.cinterop
language = Objective-C
---

#import <Foundation/Foundation.h>
#import <UIKit/UIView.h>

@protocol UIViewWithOverrides
- (void) drawRect:(CGRect)aRect;
- (void) layoutSubviews;
@end

package demo

import demo.cinterop.UIViewWithOverridesProtocol
import kotlinx.cinterop.*
import platform.CoreGraphics.*
import platform.UIKit.*

@ExportObjCClass
class MyView() : UIView(frame = CGRectMake(.0, .0, .0, .0)), UIViewWithOverridesProtocol {

    override fun layoutSubviews() {
        println("layoutSubviews")
        setNeedsDisplay()
    }

    override fun drawRect(aRect: CValue<CGRect>) {
        val rectAsString = aRect.useContents {
            "" + this.origin.x + ", " + this.origin.y + ", " + (this.origin.x +this.size.width) + ", " + (this.origin.y +this.size.height)
        }
        println("drawRect:: Rect[$rectAsString]")

        val context: CPointer<CGContext>? = UIGraphicsGetCurrentContext()
        CGContextSetLineWidth(context, 2.0)
        val components = cValuesOf(0.0, 0.0, 1.0, 1.0)
        CGContextSetFillColor(context, components)
        val square = CGRectMake(100.0, 100.0, 200.0, 200.0)
        CGContextFillRect(context, square)

    }

}

fun createMyView(): UIView = MyView()

struct ChartView: View {

    var body: some View {

        VStack {
            Text("Chart View")
            MyView()
                .frame(minWidth: 0, maxWidth: .infinity, minHeight: 0, maxHeight: .infinity)
        }
    }

}

struct ChartView_Previews: PreviewProvider {
    static var previews: some View {
        ChartView()
    }
}

struct MyView: UIViewRepresentable {

    func makeUIView(context: Context) -> UIView {
        UIChartViewKt.createMyView()
    }

    func updateUIView(_ uiView: UIView, context: Context) {
    }

}

kotlin {
    android()
    ios {
        binaries {
            framework {
                baseName = "shared"
            }
        }
        compilations.getByName("main") {
            val uikit by cinterops.creating {
            }

        }
    }

package = demo.cinterop
language = Objective-C
---

#import <Foundation/Foundation.h>
#import <UIKit/UIView.h>

@protocol UIViewWithOverrides
- (void) drawRect:(CGRect)aRect;
- (void) layoutSubviews;
@end

package demo

import demo.cinterop.UIViewWithOverridesProtocol
import kotlinx.cinterop.*
import platform.CoreGraphics.*
import platform.UIKit.*

@ExportObjCClass
class MyView() : UIView(frame = CGRectMake(.0, .0, .0, .0)), UIViewWithOverridesProtocol {

    override fun layoutSubviews() {
        println("layoutSubviews")
        setNeedsDisplay()
    }

    override fun drawRect(aRect: CValue<CGRect>) {
        val rectAsString = aRect.useContents {
            "" + this.origin.x + ", " + this.origin.y + ", " + (this.origin.x +this.size.width) + ", " + (this.origin.y +this.size.height)
        }
        println("drawRect:: Rect[$rectAsString]")

        val context: CPointer<CGContext>? = UIGraphicsGetCurrentContext()
        CGContextSetLineWidth(context, 2.0)
        val components = cValuesOf(0.0, 0.0, 1.0, 1.0)
        CGContextSetFillColor(context, components)
        val square = CGRectMake(100.0, 100.0, 200.0, 200.0)
        CGContextFillRect(context, square)

    }

}

fun createMyView(): UIView = MyView()

struct ChartView: View {

    var body: some View {

        VStack {
            Text("Chart View")
            MyView()
                .frame(minWidth: 0, maxWidth: .infinity, minHeight: 0, maxHeight: .infinity)
        }
    }

}

struct ChartView_Previews: PreviewProvider {
    static var previews: some View {
        ChartView()
    }
}

struct MyView: UIViewRepresentable {

    func makeUIView(context: Context) -> UIView {
        UIChartViewKt.createMyView()
    }

    func updateUIView(_ uiView: UIView, context: Context) {
    }

}

kotlin {
    android()
    ios {
        binaries {
            framework {
                baseName = "shared"
            }
        }
        compilations.getByName("main") {
            val uikit by cinterops.creating {
            }

        }
    }

package = demo.cinterop
language = Objective-C
---

#import <Foundation/Foundation.h>
#import <UIKit/UIView.h>

@protocol UIViewWithOverrides
- (void) drawRect:(CGRect)aRect;
- (void) layoutSubviews;
@end

package demo

import demo.cinterop.UIViewWithOverridesProtocol
import kotlinx.cinterop.*
import platform.CoreGraphics.*
import platform.UIKit.*

@ExportObjCClass
class MyView() : UIView(frame = CGRectMake(.0, .0, .0, .0)), UIViewWithOverridesProtocol {

    override fun layoutSubviews() {
        println("layoutSubviews")
        setNeedsDisplay()
    }

    override fun drawRect(aRect: CValue<CGRect>) {
        val rectAsString = aRect.useContents {
            "" + this.origin.x + ", " + this.origin.y + ", " + (this.origin.x +this.size.width) + ", " + (this.origin.y +this.size.height)
        }
        println("drawRect:: Rect[$rectAsString]")

        val context: CPointer<CGContext>? = UIGraphicsGetCurrentContext()
        CGContextSetLineWidth(context, 2.0)
        val components = cValuesOf(0.0, 0.0, 1.0, 1.0)
        CGContextSetFillColor(context, components)
        val square = CGRectMake(100.0, 100.0, 200.0, 200.0)
        CGContextFillRect(context, square)

    }

}

fun createMyView(): UIView = MyView()

struct ChartView: View {

    var body: some View {

        VStack {
            Text("Chart View")
            MyView()
                .frame(minWidth: 0, maxWidth: .infinity, minHeight: 0, maxHeight: .infinity)
        }
    }

}

struct ChartView_Previews: PreviewProvider {
    static var previews: some View {
        ChartView()
    }
}

struct MyView: UIViewRepresentable {

    func makeUIView(context: Context) -> UIView {
        UIChartViewKt.createMyView()
    }

    func updateUIView(_ uiView: UIView, context: Context) {
    }

}

kotlin {
    android()
    ios {
        binaries {
            framework {
                baseName = "shared"
            }
        }
        compilations.getByName("main") {
            val uikit by cinterops.creating {
            }

        }
    }

package = demo.cinterop
language = Objective-C
---

#import <Foundation/Foundation.h>
#import <UIKit/UIView.h>

@protocol UIViewWithOverrides
- (void) drawRect:(CGRect)aRect;
- (void) layoutSubviews;
@end

package demo

import demo.cinterop.UIViewWithOverridesProtocol
import kotlinx.cinterop.*
import platform.CoreGraphics.*
import platform.UIKit.*

@ExportObjCClass
class MyView() : UIView(frame = CGRectMake(.0, .0, .0, .0)), UIViewWithOverridesProtocol {

    override fun layoutSubviews() {
        println("layoutSubviews")
        setNeedsDisplay()
    }

    override fun drawRect(aRect: CValue<CGRect>) {
        val rectAsString = aRect.useContents {
            "" + this.origin.x + ", " + this.origin.y + ", " + (this.origin.x +this.size.width) + ", " + (this.origin.y +this.size.height)
        }
        println("drawRect:: Rect[$rectAsString]")

        val context: CPointer<CGContext>? = UIGraphicsGetCurrentContext()
        CGContextSetLineWidth(context, 2.0)
        val components = cValuesOf(0.0, 0.0, 1.0, 1.0)
        CGContextSetFillColor(context, components)
        val square = CGRectMake(100.0, 100.0, 200.0, 200.0)
        CGContextFillRect(context, square)

    }

}

fun createMyView(): UIView = MyView()

struct ChartView: View {

    var body: some View {

        VStack {
            Text("Chart View")
            MyView()
                .frame(minWidth: 0, maxWidth: .infinity, minHeight: 0, maxHeight: .infinity)
        }
    }

}

struct ChartView_Previews: PreviewProvider {
    static var previews: some View {
        ChartView()
    }
}

struct MyView: UIViewRepresentable {

    func makeUIView(context: Context) -> UIView {
        UIChartViewKt.createMyView()
    }

    func updateUIView(_ uiView: UIView, context: Context) {
    }

}

class CustomView : UIView {

    /* Data we need to use from the Kotlin Code */
    lazy var kotlinClass: KotlinClass? = nil

    ... init etc. ...

    override func updateConstraints() {
        ... my stuff ...
        super.updateConstraints()
    }

    override func draw(_ rect: CGRect) {
        ... call the kotlinClass' methods as you need ...
    }
}

func customViewFactory(kotlinClass: KotlinClass) -> UIView {
    return CustomView(kotlinClass: kotlinClass)
}

KotlinClass.Companion.shared.setCustomViewFactory(factory: customViewFactory(kotlinClass:))

class KotlinClass {

    companion object {
        /* To be used where I want to instantiate the custom UIView from the Kotlin code. */
        lateinit var customViewFactory: (kotlinClass: KotlinClass) -> UIView

        /* To be used early during the startup of the app from the Swift code. */
        fun setCustomViewFactory(factory: (kotlinClass: KotlinClass) -> UIView) {
            customViewFactory = factory
        }
    }

val customView = customViewFactory(this)

class CustomView : UIView {

    /* Data we need to use from the Kotlin Code */
    lazy var kotlinClass: KotlinClass? = nil

    ... init etc. ...

    override func updateConstraints() {
        ... my stuff ...
        super.updateConstraints()
    }

    override func draw(_ rect: CGRect) {
        ... call the kotlinClass' methods as you need ...
    }
}

func customViewFactory(kotlinClass: KotlinClass) -> UIView {
    return CustomView(kotlinClass: kotlinClass)
}

KotlinClass.Companion.shared.setCustomViewFactory(factory: customViewFactory(kotlinClass:))

class KotlinClass {

    companion object {
        /* To be used where I want to instantiate the custom UIView from the Kotlin code. */
        lateinit var customViewFactory: (kotlinClass: KotlinClass) -> UIView

        /* To be used early during the startup of the app from the Swift code. */
        fun setCustomViewFactory(factory: (kotlinClass: KotlinClass) -> UIView) {
            customViewFactory = factory
        }
    }

val customView = customViewFactory(this)

class CustomView : UIView {

    /* Data we need to use from the Kotlin Code */
    lazy var kotlinClass: KotlinClass? = nil

    ... init etc. ...

    override func updateConstraints() {
        ... my stuff ...
        super.updateConstraints()
    }

    override func draw(_ rect: CGRect) {
        ... call the kotlinClass' methods as you need ...
    }
}

func customViewFactory(kotlinClass: KotlinClass) -> UIView {
    return CustomView(kotlinClass: kotlinClass)
}

KotlinClass.Companion.shared.setCustomViewFactory(factory: customViewFactory(kotlinClass:))

class KotlinClass {

    companion object {
        /* To be used where I want to instantiate the custom UIView from the Kotlin code. */
        lateinit var customViewFactory: (kotlinClass: KotlinClass) -> UIView

        /* To be used early during the startup of the app from the Swift code. */
        fun setCustomViewFactory(factory: (kotlinClass: KotlinClass) -> UIView) {
            customViewFactory = factory
        }
    }

val customView = customViewFactory(this)

class CustomView : UIView {

    /* Data we need to use from the Kotlin Code */
    lazy var kotlinClass: KotlinClass? = nil

    ... init etc. ...

    override func updateConstraints() {
        ... my stuff ...
        super.updateConstraints()
    }

    override func draw(_ rect: CGRect) {
        ... call the kotlinClass' methods as you need ...
    }
}

func customViewFactory(kotlinClass: KotlinClass) -> UIView {
    return CustomView(kotlinClass: kotlinClass)
}

KotlinClass.Companion.shared.setCustomViewFactory(factory: customViewFactory(kotlinClass:))

class KotlinClass {

    companion object {
        /* To be used where I want to instantiate the custom UIView from the Kotlin code. */
        lateinit var customViewFactory: (kotlinClass: KotlinClass) -> UIView

        /* To be used early during the startup of the app from the Swift code. */
        fun setCustomViewFactory(factory: (kotlinClass: KotlinClass) -> UIView) {
            customViewFactory = factory
        }
    }

val customView = customViewFactory(this)

class CustomView : UIView {

    /* Data we need to use from the Kotlin Code */
    lazy var kotlinClass: KotlinClass? = nil

    ... init etc. ...

    override func updateConstraints() {
        ... my stuff ...
        super.updateConstraints()
    }

    override func draw(_ rect: CGRect) {
        ... call the kotlinClass' methods as you need ...
    }
}

func customViewFactory(kotlinClass: KotlinClass) -> UIView {
    return CustomView(kotlinClass: kotlinClass)
}

KotlinClass.Companion.shared.setCustomViewFactory(factory: customViewFactory(kotlinClass:))

class KotlinClass {

    companion object {
        /* To be used where I want to instantiate the custom UIView from the Kotlin code. */
        lateinit var customViewFactory: (kotlinClass: KotlinClass) -> UIView

        /* To be used early during the startup of the app from the Swift code. */
        fun setCustomViewFactory(factory: (kotlinClass: KotlinClass) -> UIView) {
            customViewFactory = factory
        }
    }

val customView = customViewFactory(this)

names2use <- list.files(path2direcitonwherefilesare)
fileList <- lapply(names2use, function(x) { read.csv(file.path(path2direcitonwherefilesare, x), ...)

#my dummy data set
un01 <- data.frame(types=c("mean", "sd"), hill=c(0.1,0.2), boat=c(0.1,0.2))
un02 <- data.frame(types=c("mean", "sd"), trip=c(0.1,0.2), clip=c(0.1,0.2))
jp01 <- data.frame(types=c("mean", "sd"), hill=c(0.1,0.2), boat=c(0.1,0.2))
fileList <- list(un01, un02, jp01)
filenames <- c("un01", "un02", "jp01")

types <- unique(substr(filenames, start = 1, stop = 2))
infos <- as.character(un01[,1])
names2use <- c(paste(types, sort(rep(infos, length(types))), sep="_"))
rows <- unique(unlist(lapply(fileList, function(x) {names(x)})))[-1]

#creating empty an data.frame
data <- as.data.frame(matrix(NA, nrow=length(cols), ncol=length(names2use)+1))
data[,1] <- rows
names(data) <- c("word", sort(names2use))

i <- 1
for (file in fileList) {
  filename <- substr(filenames[[i]],1,2)
  for (n in 2:length(names(file))) {
    data[data$word==names(file)[n], substr(names(data),1,2) %in% filename] <- file[[names(file)[n]]]
  }
  i <- i + 1
}

> data
  word jp_mean jp_sd un_mean un_sd
1 hill     0.1   0.2     0.1   0.2
2 boat     0.1   0.2     0.1   0.2
3 trip      NA    NA     0.1   0.2
4 clip      NA    NA     0.1   0.2

names2use <- list.files(path2direcitonwherefilesare)
fileList <- lapply(names2use, function(x) { read.csv(file.path(path2direcitonwherefilesare, x), ...)

#my dummy data set
un01 <- data.frame(types=c("mean", "sd"), hill=c(0.1,0.2), boat=c(0.1,0.2))
un02 <- data.frame(types=c("mean", "sd"), trip=c(0.1,0.2), clip=c(0.1,0.2))
jp01 <- data.frame(types=c("mean", "sd"), hill=c(0.1,0.2), boat=c(0.1,0.2))
fileList <- list(un01, un02, jp01)
filenames <- c("un01", "un02", "jp01")

types <- unique(substr(filenames, start = 1, stop = 2))
infos <- as.character(un01[,1])
names2use <- c(paste(types, sort(rep(infos, length(types))), sep="_"))
rows <- unique(unlist(lapply(fileList, function(x) {names(x)})))[-1]

#creating empty an data.frame
data <- as.data.frame(matrix(NA, nrow=length(cols), ncol=length(names2use)+1))
data[,1] <- rows
names(data) <- c("word", sort(names2use))

i <- 1
for (file in fileList) {
  filename <- substr(filenames[[i]],1,2)
  for (n in 2:length(names(file))) {
    data[data$word==names(file)[n], substr(names(data),1,2) %in% filename] <- file[[names(file)[n]]]
  }
  i <- i + 1
}

> data
  word jp_mean jp_sd un_mean un_sd
1 hill     0.1   0.2     0.1   0.2
2 boat     0.1   0.2     0.1   0.2
3 trip      NA    NA     0.1   0.2
4 clip      NA    NA     0.1   0.2

# Use the tidyverse.
library(tidyverse)

# Function to transform CSVs into a standard form.
standardize_csv <- function(csv_data, csv_prefix){
  # Turn the word columns into a single column.
  csv_data %>% pivot_longer(
    # The word columns are every column except the 1st, which names the metric.
    cols = !1,
    names_to = "word"
  ) %>%
    # Include the CSV prefix for reference.
    mutate(
      csv_prefix = csv_prefix
    ) %>%
    # Rename and reformat the dataset.
    select(
      csv_prefix,
      word,
      metric = 1,
      value
    )
}

# Find all CSV variables named by your convention, within your workspace.
csv_list <- sapply(
  X = ls(pattern = "^[a-z]{2,2}0[1-5]$"),
  FUN = get,
  simplify = FALSE,
  USE.NAMES = TRUE
)

# Consolidate into the final form:
final_df <- csv_list %>%
  mapply(
    # Transform into standard form...
    FUN = standardize_csv,
    # ...the CSV datasets...
    csv_data = .,
    # ...with their prefixes.
    csv_prefix = gsub(
      x = names(.),
      pattern = "^([a-z]*)(\\d*)$",
      replacement = "\\1"
    ),
    
    SIMPLIFY = FALSE
  ) %>%
  # Stack those standardized datasets into a single table.
  do.call(
    what = bind_rows
  ) %>%
  # Pivot the table to have one column for each family and metric ('un_mean',
  # 'pm_SD', etc.).
  pivot_wider(
    names_from = c(csv_prefix, metric),
    names_glue = "{csv_prefix}_{metric}",
    values_from = value
  )

# View the result.
final_df

un01 <- structure(
  list(
    ...1 = c("mean", "n", "SD"),
    hill = c(2.26315789473684, 38, 2.0623289187203),
    user = c(1.13157894736842, 38, 1.57978877533674),
    name = c(2.42105263157895, 38, 1.96773403700762),
    paint = c(4.18421052631579, 38, 1.81369062527503),
    smile = c(6.05263157894737, 38, 1.98572430003074),
    back = c(4.10526315789474, 38, 2.0767629916461),
    mouse = c(7.35135135135135, 38, 1.75144727798686),
    potato = c(5.94736842105263, 38, 2.01276156159657),
    pain = c(1.34210526315789, 38, 1.69699759972873),
    life = c(4.05263157894737, 38, 1.99928863593695)
  ),
  row.names = c(NA, -3L),
  class = c("tbl_df", "tbl", "data.frame")
)

un05 <- structure(
  list(
    ...1 = c("mean", "n", "SD"),
    house = c(5.08823529411765, 34, 1.65817802638964),
    person = c(4.11764705882353, 34, 2.15694376772143),
    city = c(2.47058823529412, 34, 1.70978102651927),
    agressive = c(5, 34, 1.95401684183679)
  ),
  row.names = c(NA, -3L),
  class = c("tbl_df", "tbl", "data.frame")
)

pm01 <- structure(
  list(
    ...1 = c("mean", "n", "SD"),
    hill = c(5.3030303030303, 33, 1.64857606142484),
    user = c(4.24242424242424, 33, 1.96898297082375),
    name = c(7.18181818181818, 33, 1.18465568453838),
    paint = c(3.27272727272727, 33, 1.71887912738082),
    smile = c(1.15151515151515, 33, 4.8728523053289),
    back = c(4.3030303030303, 33, 2.02306772756204),
    mouse = c(7.06060606060606, 33, 1.57994054356869),
    potato = c(3.34666666666667, 33, 1.36289077492212),
    pain = c(4.17666666666667, 33, 1.25762044965971),
    life = c(4.22121212121212, 33, 1.40884650861919)),
  row.names = c(NA, -3L),
  class = c("tbl_df", "tbl", "data.frame")
)

# A tibble: 14 x 7
   word      pm_mean  pm_n pm_SD un_mean  un_n un_SD
   <chr>       <dbl> <dbl> <dbl>   <dbl> <dbl> <dbl>
 1 hill         5.30    33  1.65    2.26    38  2.06
 2 user         4.24    33  1.97    1.13    38  1.58
 3 name         7.18    33  1.18    2.42    38  1.97
 4 paint        3.27    33  1.72    4.18    38  1.81
 5 smile        1.15    33  4.87    6.05    38  1.99
 6 back         4.30    33  2.02    4.11    38  2.08
 7 mouse        7.06    33  1.58    7.35    38  1.75
 8 potato       3.35    33  1.36    5.95    38  2.01
 9 pain         4.18    33  1.26    1.34    38  1.70
10 life         4.22    33  1.41    4.05    38  2.00
11 house       NA       NA NA       5.09    34  1.66
12 person      NA       NA NA       4.12    34  2.16
13 city        NA       NA NA       2.47    34  1.71
14 agressive   NA       NA NA       5       34  1.95

# Use the tidyverse.
library(tidyverse)

# Function to transform CSVs into a standard form.
standardize_csv <- function(csv_data, csv_prefix){
  # Turn the word columns into a single column.
  csv_data %>% pivot_longer(
    # The word columns are every column except the 1st, which names the metric.
    cols = !1,
    names_to = "word"
  ) %>%
    # Include the CSV prefix for reference.
    mutate(
      csv_prefix = csv_prefix
    ) %>%
    # Rename and reformat the dataset.
    select(
      csv_prefix,
      word,
      metric = 1,
      value
    )
}

# Find all CSV variables named by your convention, within your workspace.
csv_list <- sapply(
  X = ls(pattern = "^[a-z]{2,2}0[1-5]$"),
  FUN = get,
  simplify = FALSE,
  USE.NAMES = TRUE
)

# Consolidate into the final form:
final_df <- csv_list %>%
  mapply(
    # Transform into standard form...
    FUN = standardize_csv,
    # ...the CSV datasets...
    csv_data = .,
    # ...with their prefixes.
    csv_prefix = gsub(
      x = names(.),
      pattern = "^([a-z]*)(\\d*)$",
      replacement = "\\1"
    ),
    
    SIMPLIFY = FALSE
  ) %>%
  # Stack those standardized datasets into a single table.
  do.call(
    what = bind_rows
  ) %>%
  # Pivot the table to have one column for each family and metric ('un_mean',
  # 'pm_SD', etc.).
  pivot_wider(
    names_from = c(csv_prefix, metric),
    names_glue = "{csv_prefix}_{metric}",
    values_from = value
  )

# View the result.
final_df

un01 <- structure(
  list(
    ...1 = c("mean", "n", "SD"),
    hill = c(2.26315789473684, 38, 2.0623289187203),
    user = c(1.13157894736842, 38, 1.57978877533674),
    name = c(2.42105263157895, 38, 1.96773403700762),
    paint = c(4.18421052631579, 38, 1.81369062527503),
    smile = c(6.05263157894737, 38, 1.98572430003074),
    back = c(4.10526315789474, 38, 2.0767629916461),
    mouse = c(7.35135135135135, 38, 1.75144727798686),
    potato = c(5.94736842105263, 38, 2.01276156159657),
    pain = c(1.34210526315789, 38, 1.69699759972873),
    life = c(4.05263157894737, 38, 1.99928863593695)
  ),
  row.names = c(NA, -3L),
  class = c("tbl_df", "tbl", "data.frame")
)

un05 <- structure(
  list(
    ...1 = c("mean", "n", "SD"),
    house = c(5.08823529411765, 34, 1.65817802638964),
    person = c(4.11764705882353, 34, 2.15694376772143),
    city = c(2.47058823529412, 34, 1.70978102651927),
    agressive = c(5, 34, 1.95401684183679)
  ),
  row.names = c(NA, -3L),
  class = c("tbl_df", "tbl", "data.frame")
)

pm01 <- structure(
  list(
    ...1 = c("mean", "n", "SD"),
    hill = c(5.3030303030303, 33, 1.64857606142484),
    user = c(4.24242424242424, 33, 1.96898297082375),
    name = c(7.18181818181818, 33, 1.18465568453838),
    paint = c(3.27272727272727, 33, 1.71887912738082),
    smile = c(1.15151515151515, 33, 4.8728523053289),
    back = c(4.3030303030303, 33, 2.02306772756204),
    mouse = c(7.06060606060606, 33, 1.57994054356869),
    potato = c(3.34666666666667, 33, 1.36289077492212),
    pain = c(4.17666666666667, 33, 1.25762044965971),
    life = c(4.22121212121212, 33, 1.40884650861919)),
  row.names = c(NA, -3L),
  class = c("tbl_df", "tbl", "data.frame")
)

# A tibble: 14 x 7
   word      pm_mean  pm_n pm_SD un_mean  un_n un_SD
   <chr>       <dbl> <dbl> <dbl>   <dbl> <dbl> <dbl>
 1 hill         5.30    33  1.65    2.26    38  2.06
 2 user         4.24    33  1.97    1.13    38  1.58
 3 name         7.18    33  1.18    2.42    38  1.97
 4 paint        3.27    33  1.72    4.18    38  1.81
 5 smile        1.15    33  4.87    6.05    38  1.99
 6 back         4.30    33  2.02    4.11    38  2.08
 7 mouse        7.06    33  1.58    7.35    38  1.75
 8 potato       3.35    33  1.36    5.95    38  2.01
 9 pain         4.18    33  1.26    1.34    38  1.70
10 life         4.22    33  1.41    4.05    38  2.00
11 house       NA       NA NA       5.09    34  1.66
12 person      NA       NA NA       4.12    34  2.16
13 city        NA       NA NA       2.47    34  1.71
14 agressive   NA       NA NA       5       34  1.95

# Use the tidyverse.
library(tidyverse)

# Function to transform CSVs into a standard form.
standardize_csv <- function(csv_data, csv_prefix){
  # Turn the word columns into a single column.
  csv_data %>% pivot_longer(
    # The word columns are every column except the 1st, which names the metric.
    cols = !1,
    names_to = "word"
  ) %>%
    # Include the CSV prefix for reference.
    mutate(
      csv_prefix = csv_prefix
    ) %>%
    # Rename and reformat the dataset.
    select(
      csv_prefix,
      word,
      metric = 1,
      value
    )
}

# Find all CSV variables named by your convention, within your workspace.
csv_list <- sapply(
  X = ls(pattern = "^[a-z]{2,2}0[1-5]$"),
  FUN = get,
  simplify = FALSE,
  USE.NAMES = TRUE
)

# Consolidate into the final form:
final_df <- csv_list %>%
  mapply(
    # Transform into standard form...
    FUN = standardize_csv,
    # ...the CSV datasets...
    csv_data = .,
    # ...with their prefixes.
    csv_prefix = gsub(
      x = names(.),
      pattern = "^([a-z]*)(\\d*)$",
      replacement = "\\1"
    ),
    
    SIMPLIFY = FALSE
  ) %>%
  # Stack those standardized datasets into a single table.
  do.call(
    what = bind_rows
  ) %>%
  # Pivot the table to have one column for each family and metric ('un_mean',
  # 'pm_SD', etc.).
  pivot_wider(
    names_from = c(csv_prefix, metric),
    names_glue = "{csv_prefix}_{metric}",
    values_from = value
  )

# View the result.
final_df

un01 <- structure(
  list(
    ...1 = c("mean", "n", "SD"),
    hill = c(2.26315789473684, 38, 2.0623289187203),
    user = c(1.13157894736842, 38, 1.57978877533674),
    name = c(2.42105263157895, 38, 1.96773403700762),
    paint = c(4.18421052631579, 38, 1.81369062527503),
    smile = c(6.05263157894737, 38, 1.98572430003074),
    back = c(4.10526315789474, 38, 2.0767629916461),
    mouse = c(7.35135135135135, 38, 1.75144727798686),
    potato = c(5.94736842105263, 38, 2.01276156159657),
    pain = c(1.34210526315789, 38, 1.69699759972873),
    life = c(4.05263157894737, 38, 1.99928863593695)
  ),
  row.names = c(NA, -3L),
  class = c("tbl_df", "tbl", "data.frame")
)

un05 <- structure(
  list(
    ...1 = c("mean", "n", "SD"),
    house = c(5.08823529411765, 34, 1.65817802638964),
    person = c(4.11764705882353, 34, 2.15694376772143),
    city = c(2.47058823529412, 34, 1.70978102651927),
    agressive = c(5, 34, 1.95401684183679)
  ),
  row.names = c(NA, -3L),
  class = c("tbl_df", "tbl", "data.frame")
)

pm01 <- structure(
  list(
    ...1 = c("mean", "n", "SD"),
    hill = c(5.3030303030303, 33, 1.64857606142484),
    user = c(4.24242424242424, 33, 1.96898297082375),
    name = c(7.18181818181818, 33, 1.18465568453838),
    paint = c(3.27272727272727, 33, 1.71887912738082),
    smile = c(1.15151515151515, 33, 4.8728523053289),
    back = c(4.3030303030303, 33, 2.02306772756204),
    mouse = c(7.06060606060606, 33, 1.57994054356869),
    potato = c(3.34666666666667, 33, 1.36289077492212),
    pain = c(4.17666666666667, 33, 1.25762044965971),
    life = c(4.22121212121212, 33, 1.40884650861919)),
  row.names = c(NA, -3L),
  class = c("tbl_df", "tbl", "data.frame")
)

# A tibble: 14 x 7
   word      pm_mean  pm_n pm_SD un_mean  un_n un_SD
   <chr>       <dbl> <dbl> <dbl>   <dbl> <dbl> <dbl>
 1 hill         5.30    33  1.65    2.26    38  2.06
 2 user         4.24    33  1.97    1.13    38  1.58
 3 name         7.18    33  1.18    2.42    38  1.97
 4 paint        3.27    33  1.72    4.18    38  1.81
 5 smile        1.15    33  4.87    6.05    38  1.99
 6 back         4.30    33  2.02    4.11    38  2.08
 7 mouse        7.06    33  1.58    7.35    38  1.75
 8 potato       3.35    33  1.36    5.95    38  2.01
 9 pain         4.18    33  1.26    1.34    38  1.70
10 life         4.22    33  1.41    4.05    38  2.00
11 house       NA       NA NA       5.09    34  1.66
12 person      NA       NA NA       4.12    34  2.16
13 city        NA       NA NA       2.47    34  1.71
14 agressive   NA       NA NA       5       34  1.95

# Use the tidyverse.
library(tidyverse)

# Function to transform CSVs into a standard form.
standardize_csv <- function(csv_data, csv_prefix){
  # Turn the word columns into a single column.
  csv_data %>% pivot_longer(
    # The word columns are every column except the 1st, which names the metric.
    cols = !1,
    names_to = "word"
  ) %>%
    # Include the CSV prefix for reference.
    mutate(
      csv_prefix = csv_prefix
    ) %>%
    # Rename and reformat the dataset.
    select(
      csv_prefix,
      word,
      metric = 1,
      value
    )
}

# Find all CSV variables named by your convention, within your workspace.
csv_list <- sapply(
  X = ls(pattern = "^[a-z]{2,2}0[1-5]$"),
  FUN = get,
  simplify = FALSE,
  USE.NAMES = TRUE
)

# Consolidate into the final form:
final_df <- csv_list %>%
  mapply(
    # Transform into standard form...
    FUN = standardize_csv,
    # ...the CSV datasets...
    csv_data = .,
    # ...with their prefixes.
    csv_prefix = gsub(
      x = names(.),
      pattern = "^([a-z]*)(\\d*)$",
      replacement = "\\1"
    ),
    
    SIMPLIFY = FALSE
  ) %>%
  # Stack those standardized datasets into a single table.
  do.call(
    what = bind_rows
  ) %>%
  # Pivot the table to have one column for each family and metric ('un_mean',
  # 'pm_SD', etc.).
  pivot_wider(
    names_from = c(csv_prefix, metric),
    names_glue = "{csv_prefix}_{metric}",
    values_from = value
  )

# View the result.
final_df

un01 <- structure(
  list(
    ...1 = c("mean", "n", "SD"),
    hill = c(2.26315789473684, 38, 2.0623289187203),
    user = c(1.13157894736842, 38, 1.57978877533674),
    name = c(2.42105263157895, 38, 1.96773403700762),
    paint = c(4.18421052631579, 38, 1.81369062527503),
    smile = c(6.05263157894737, 38, 1.98572430003074),
    back = c(4.10526315789474, 38, 2.0767629916461),
    mouse = c(7.35135135135135, 38, 1.75144727798686),
    potato = c(5.94736842105263, 38, 2.01276156159657),
    pain = c(1.34210526315789, 38, 1.69699759972873),
    life = c(4.05263157894737, 38, 1.99928863593695)
  ),
  row.names = c(NA, -3L),
  class = c("tbl_df", "tbl", "data.frame")
)

un05 <- structure(
  list(
    ...1 = c("mean", "n", "SD"),
    house = c(5.08823529411765, 34, 1.65817802638964),
    person = c(4.11764705882353, 34, 2.15694376772143),
    city = c(2.47058823529412, 34, 1.70978102651927),
    agressive = c(5, 34, 1.95401684183679)
  ),
  row.names = c(NA, -3L),
  class = c("tbl_df", "tbl", "data.frame")
)

pm01 <- structure(
  list(
    ...1 = c("mean", "n", "SD"),
    hill = c(5.3030303030303, 33, 1.64857606142484),
    user = c(4.24242424242424, 33, 1.96898297082375),
    name = c(7.18181818181818, 33, 1.18465568453838),
    paint = c(3.27272727272727, 33, 1.71887912738082),
    smile = c(1.15151515151515, 33, 4.8728523053289),
    back = c(4.3030303030303, 33, 2.02306772756204),
    mouse = c(7.06060606060606, 33, 1.57994054356869),
    potato = c(3.34666666666667, 33, 1.36289077492212),
    pain = c(4.17666666666667, 33, 1.25762044965971),
    life = c(4.22121212121212, 33, 1.40884650861919)),
  row.names = c(NA, -3L),
  class = c("tbl_df", "tbl", "data.frame")
)

# A tibble: 14 x 7
   word      pm_mean  pm_n pm_SD un_mean  un_n un_SD
   <chr>       <dbl> <dbl> <dbl>   <dbl> <dbl> <dbl>
 1 hill         5.30    33  1.65    2.26    38  2.06
 2 user         4.24    33  1.97    1.13    38  1.58
 3 name         7.18    33  1.18    2.42    38  1.97
 4 paint        3.27    33  1.72    4.18    38  1.81
 5 smile        1.15    33  4.87    6.05    38  1.99
 6 back         4.30    33  2.02    4.11    38  2.08
 7 mouse        7.06    33  1.58    7.35    38  1.75
 8 potato       3.35    33  1.36    5.95    38  2.01
 9 pain         4.18    33  1.26    1.34    38  1.70
10 life         4.22    33  1.41    4.05    38  2.00
11 house       NA       NA NA       5.09    34  1.66
12 person      NA       NA NA       4.12    34  2.16
13 city        NA       NA NA       2.47    34  1.71
14 agressive   NA       NA NA       5       34  1.95

# Use the tidyverse.
library(tidyverse)

# Function to transform CSVs into a standard form.
standardize_csv <- function(csv_data, csv_prefix){
  # Turn the word columns into a single column.
  csv_data %>% pivot_longer(
    # The word columns are every column except the 1st, which names the metric.
    cols = !1,
    names_to = "word"
  ) %>%
    # Include the CSV prefix for reference.
    mutate(
      csv_prefix = csv_prefix
    ) %>%
    # Rename and reformat the dataset.
    select(
      csv_prefix,
      word,
      metric = 1,
      value
    )
}

# Find all CSV variables named by your convention, within your workspace.
csv_list <- sapply(
  X = ls(pattern = "^[a-z]{2,2}0[1-5]$"),
  FUN = get,
  simplify = FALSE,
  USE.NAMES = TRUE
)

# Consolidate into the final form:
final_df <- csv_list %>%
  mapply(
    # Transform into standard form...
    FUN = standardize_csv,
    # ...the CSV datasets...
    csv_data = .,
    # ...with their prefixes.
    csv_prefix = gsub(
      x = names(.),
      pattern = "^([a-z]*)(\\d*)$",
      replacement = "\\1"
    ),
    
    SIMPLIFY = FALSE
  ) %>%
  # Stack those standardized datasets into a single table.
  do.call(
    what = bind_rows
  ) %>%
  # Pivot the table to have one column for each family and metric ('un_mean',
  # 'pm_SD', etc.).
  pivot_wider(
    names_from = c(csv_prefix, metric),
    names_glue = "{csv_prefix}_{metric}",
    values_from = value
  )

# View the result.
final_df

un01 <- structure(
  list(
    ...1 = c("mean", "n", "SD"),
    hill = c(2.26315789473684, 38, 2.0623289187203),
    user = c(1.13157894736842, 38, 1.57978877533674),
    name = c(2.42105263157895, 38, 1.96773403700762),
    paint = c(4.18421052631579, 38, 1.81369062527503),
    smile = c(6.05263157894737, 38, 1.98572430003074),
    back = c(4.10526315789474, 38, 2.0767629916461),
    mouse = c(7.35135135135135, 38, 1.75144727798686),
    potato = c(5.94736842105263, 38, 2.01276156159657),
    pain = c(1.34210526315789, 38, 1.69699759972873),
    life = c(4.05263157894737, 38, 1.99928863593695)
  ),
  row.names = c(NA, -3L),
  class = c("tbl_df", "tbl", "data.frame")
)

un05 <- structure(
  list(
    ...1 = c("mean", "n", "SD"),
    house = c(5.08823529411765, 34, 1.65817802638964),
    person = c(4.11764705882353, 34, 2.15694376772143),
    city = c(2.47058823529412, 34, 1.70978102651927),
    agressive = c(5, 34, 1.95401684183679)
  ),
  row.names = c(NA, -3L),
  class = c("tbl_df", "tbl", "data.frame")
)

pm01 <- structure(
  list(
    ...1 = c("mean", "n", "SD"),
    hill = c(5.3030303030303, 33, 1.64857606142484),
    user = c(4.24242424242424, 33, 1.96898297082375),
    name = c(7.18181818181818, 33, 1.18465568453838),
    paint = c(3.27272727272727, 33, 1.71887912738082),
    smile = c(1.15151515151515, 33, 4.8728523053289),
    back = c(4.3030303030303, 33, 2.02306772756204),
    mouse = c(7.06060606060606, 33, 1.57994054356869),
    potato = c(3.34666666666667, 33, 1.36289077492212),
    pain = c(4.17666666666667, 33, 1.25762044965971),
    life = c(4.22121212121212, 33, 1.40884650861919)),
  row.names = c(NA, -3L),
  class = c("tbl_df", "tbl", "data.frame")
)

# A tibble: 14 x 7
   word      pm_mean  pm_n pm_SD un_mean  un_n un_SD
   <chr>       <dbl> <dbl> <dbl>   <dbl> <dbl> <dbl>
 1 hill         5.30    33  1.65    2.26    38  2.06
 2 user         4.24    33  1.97    1.13    38  1.58
 3 name         7.18    33  1.18    2.42    38  1.97
 4 paint        3.27    33  1.72    4.18    38  1.81
 5 smile        1.15    33  4.87    6.05    38  1.99
 6 back         4.30    33  2.02    4.11    38  2.08
 7 mouse        7.06    33  1.58    7.35    38  1.75
 8 potato       3.35    33  1.36    5.95    38  2.01
 9 pain         4.18    33  1.26    1.34    38  1.70
10 life         4.22    33  1.41    4.05    38  2.00
11 house       NA       NA NA       5.09    34  1.66
12 person      NA       NA NA       4.12    34  2.16
13 city        NA       NA NA       2.47    34  1.71
14 agressive   NA       NA NA       5       34  1.95

# Use the tidyverse.
library(tidyverse)

# Function to transform CSVs into a standard form.
standardize_csv <- function(csv_data, csv_prefix){
  # Turn the word columns into a single column.
  csv_data %>% pivot_longer(
    # The word columns are every column except the 1st, which names the metric.
    cols = !1,
    names_to = "word"
  ) %>%
    # Include the CSV prefix for reference.
    mutate(
      csv_prefix = csv_prefix
    ) %>%
    # Rename and reformat the dataset.
    select(
      csv_prefix,
      word,
      metric = 1,
      value
    )
}

# Find all CSV variables named by your convention, within your workspace.
csv_list <- sapply(
  X = ls(pattern = "^[a-z]{2,2}0[1-5]$"),
  FUN = get,
  simplify = FALSE,
  USE.NAMES = TRUE
)

# Consolidate into the final form:
final_df <- csv_list %>%
  mapply(
    # Transform into standard form...
    FUN = standardize_csv,
    # ...the CSV datasets...
    csv_data = .,
    # ...with their prefixes.
    csv_prefix = gsub(
      x = names(.),
      pattern = "^([a-z]*)(\\d*)$",
      replacement = "\\1"
    ),
    
    SIMPLIFY = FALSE
  ) %>%
  # Stack those standardized datasets into a single table.
  do.call(
    what = bind_rows
  ) %>%
  # Pivot the table to have one column for each family and metric ('un_mean',
  # 'pm_SD', etc.).
  pivot_wider(
    names_from = c(csv_prefix, metric),
    names_glue = "{csv_prefix}_{metric}",
    values_from = value
  )

# View the result.
final_df

un01 <- structure(
  list(
    ...1 = c("mean", "n", "SD"),
    hill = c(2.26315789473684, 38, 2.0623289187203),
    user = c(1.13157894736842, 38, 1.57978877533674),
    name = c(2.42105263157895, 38, 1.96773403700762),
    paint = c(4.18421052631579, 38, 1.81369062527503),
    smile = c(6.05263157894737, 38, 1.98572430003074),
    back = c(4.10526315789474, 38, 2.0767629916461),
    mouse = c(7.35135135135135, 38, 1.75144727798686),
    potato = c(5.94736842105263, 38, 2.01276156159657),
    pain = c(1.34210526315789, 38, 1.69699759972873),
    life = c(4.05263157894737, 38, 1.99928863593695)
  ),
  row.names = c(NA, -3L),
  class = c("tbl_df", "tbl", "data.frame")
)

un05 <- structure(
  list(
    ...1 = c("mean", "n", "SD"),
    house = c(5.08823529411765, 34, 1.65817802638964),
    person = c(4.11764705882353, 34, 2.15694376772143),
    city = c(2.47058823529412, 34, 1.70978102651927),
    agressive = c(5, 34, 1.95401684183679)
  ),
  row.names = c(NA, -3L),
  class = c("tbl_df", "tbl", "data.frame")
)

pm01 <- structure(
  list(
    ...1 = c("mean", "n", "SD"),
    hill = c(5.3030303030303, 33, 1.64857606142484),
    user = c(4.24242424242424, 33, 1.96898297082375),
    name = c(7.18181818181818, 33, 1.18465568453838),
    paint = c(3.27272727272727, 33, 1.71887912738082),
    smile = c(1.15151515151515, 33, 4.8728523053289),
    back = c(4.3030303030303, 33, 2.02306772756204),
    mouse = c(7.06060606060606, 33, 1.57994054356869),
    potato = c(3.34666666666667, 33, 1.36289077492212),
    pain = c(4.17666666666667, 33, 1.25762044965971),
    life = c(4.22121212121212, 33, 1.40884650861919)),
  row.names = c(NA, -3L),
  class = c("tbl_df", "tbl", "data.frame")
)

# A tibble: 14 x 7
   word      pm_mean  pm_n pm_SD un_mean  un_n un_SD
   <chr>       <dbl> <dbl> <dbl>   <dbl> <dbl> <dbl>
 1 hill         5.30    33  1.65    2.26    38  2.06
 2 user         4.24    33  1.97    1.13    38  1.58
 3 name         7.18    33  1.18    2.42    38  1.97
 4 paint        3.27    33  1.72    4.18    38  1.81
 5 smile        1.15    33  4.87    6.05    38  1.99
 6 back         4.30    33  2.02    4.11    38  2.08
 7 mouse        7.06    33  1.58    7.35    38  1.75
 8 potato       3.35    33  1.36    5.95    38  2.01
 9 pain         4.18    33  1.26    1.34    38  1.70
10 life         4.22    33  1.41    4.05    38  2.00
11 house       NA       NA NA       5.09    34  1.66
12 person      NA       NA NA       4.12    34  2.16
13 city        NA       NA NA       2.47    34  1.71
14 agressive   NA       NA NA       5       34  1.95