|
RAR-files |
| lcbdir-f7c6-xqzt.rar |
50,000,000 |
881E7390 |
| lcbdir-f7c6-xqzt.r00 |
50,000,000 |
78E84EE8 |
| lcbdir-f7c6-xqzt.r01 |
50,000,000 |
272009B0 |
| lcbdir-f7c6-xqzt.r02 |
50,000,000 |
ADA2213F |
| lcbdir-f7c6-xqzt.r03 |
50,000,000 |
791AFF9E |
| lcbdir-f7c6-xqzt.r04 |
28,546,314 |
0947CFE5 |
|
| Total size: |
278,546,314 |
|
|
| Archived
files |
|
Cleaning Bad Data in R\4.3. Formatting Data\11.Numbers stored as text.en.srt |
5,835 |
E8EB05AD |
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Cleaning Bad Data in R\4.3. Formatting Data\09.Converting dates.mp4
[6deccb5c1297ee9a]
|
10,655,115 |
34A8D54A |
|
Cleaning Bad Data in R\4.3. Formatting Data\13.Inconsistent spellings.mp4
[34961d0d1e6a1f55]
|
16,333,987 |
CECBE195 |
|
Cleaning Bad Data in R\4.3. Formatting Data\09.Converting dates.en.srt |
9,571 |
61776228 |
|
Cleaning Bad Data in R\4.3. Formatting Data\10.Unit conversions.en.srt |
6,607 |
E11E24B7 |
|
Cleaning Bad Data in R\4.3. Formatting Data\12.Text improperly converted to numbers.mp4
[3d89cf8d79f700b]
|
6,408,535 |
ECEC13CD |
|
Cleaning Bad Data in R\4.3. Formatting Data\12.Text improperly converted to numbers.en.srt |
5,359 |
7155782B |
|
Cleaning Bad Data in R\4.3. Formatting Data\13.Inconsistent spellings.en.srt |
12,666 |
8BD71A84 |
|
Cleaning Bad Data in R\4.3. Formatting Data\10.Unit conversions.mp4
[a9a7c6e14e90b88e]
|
7,857,295 |
F0B875DB |
|
Cleaning Bad Data in R\4.3. Formatting Data\11.Numbers stored as text.mp4
[4f98ab5387c0eb83]
|
8,703,636 |
8C744A0F |
|
Cleaning Bad Data in R\2.1. Missing Data\05.Missing rows.en.srt |
9,857 |
031B5BBB |
|
Cleaning Bad Data in R\2.1. Missing Data\03.Types of missing data.en.srt |
6,045 |
45832C41 |
|
Cleaning Bad Data in R\2.1. Missing Data\04.Missing values.mp4
[ed885ffa705e0aee]
|
22,597,226 |
9240120D |
|
Cleaning Bad Data in R\2.1. Missing Data\04.Missing values.en.srt |
20,514 |
DBA677A5 |
|
Cleaning Bad Data in R\2.1. Missing Data\06.Aggregations and missing values.en.srt |
8,056 |
D2A2178E |
|
Cleaning Bad Data in R\2.1. Missing Data\06.Aggregations and missing values.mp4
[3167ef4a5ea9442e]
|
9,337,540 |
398F0EE0 |
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Cleaning Bad Data in R\2.1. Missing Data\05.Missing rows.mp4
[b6268e26350ee04f]
|
15,235,079 |
23998884 |
|
Cleaning Bad Data in R\2.1. Missing Data\03.Types of missing data.mp4
[ba25d22f41788538]
|
5,743,281 |
274867D9 |
|
Cleaning Bad Data in R\Exercise Files\Ex_Files_Cleaning_Bad_Data_R.zip |
41,693,758 |
A95401BA |
|
Cleaning Bad Data in R\1.Introduction\01.Data is messy.en.srt |
1,898 |
DBBB4765 |
|
Cleaning Bad Data in R\1.Introduction\02.What you need to know.en.srt |
2,185 |
5C49C8A5 |
|
Cleaning Bad Data in R\1.Introduction\02.What you need to know.mp4
[e31ac18caaf98717]
|
1,709,063 |
D28B1BA4 |
|
Cleaning Bad Data in R\1.Introduction\01.Data is messy.mp4
[f514d2123f453469]
|
6,127,442 |
D68162EF |
|
Cleaning Bad Data in R\8.Conclusion\27.What's next.en.srt |
2,173 |
0BDF8093 |
|
Cleaning Bad Data in R\8.Conclusion\27.What's next.mp4
[a233242bec97d1db]
|
2,690,850 |
85C0F8BB |
|
Cleaning Bad Data in R\3.2. Duplicated Data\07.Duplicated rows and values.mp4
[83a54226bd761661]
|
8,739,467 |
EEBC067A |
|
Cleaning Bad Data in R\3.2. Duplicated Data\08.Aggregations in the data set.en.srt |
6,222 |
578CEE8D |
|
Cleaning Bad Data in R\3.2. Duplicated Data\07.Duplicated rows and values.en.srt |
8,359 |
56443112 |
|
Cleaning Bad Data in R\3.2. Duplicated Data\08.Aggregations in the data set.mp4
[d45e393db23c674d]
|
9,463,696 |
84DE2775 |
|
Cleaning Bad Data in R\6.5. Tidy Data\20.Variables, observations, and values.mp4
[87de8271929c3b95]
|
8,323,018 |
9EFA6630 |
|
Cleaning Bad Data in R\6.5. Tidy Data\19.What is tidy data.mp4
[fe33b3acb21424d]
|
11,025,325 |
AEE7C71B |
|
Cleaning Bad Data in R\6.5. Tidy Data\23.Making wide data sets long.en.srt |
8,470 |
D13F849C |
|
Cleaning Bad Data in R\6.5. Tidy Data\24.Making long data sets wide.en.srt |
6,801 |
91D5F101 |
|
Cleaning Bad Data in R\6.5. Tidy Data\21.Common data problems.mp4
[eaa54d37404a8742]
|
14,536,109 |
8C3CECD2 |
|
Cleaning Bad Data in R\6.5. Tidy Data\21.Common data problems.en.srt |
13,713 |
ACEF1F6B |
|
Cleaning Bad Data in R\6.5. Tidy Data\22.Wide vs. long data sets.en.srt |
6,069 |
E1947275 |
|
Cleaning Bad Data in R\6.5. Tidy Data\22.Wide vs. long data sets.mp4
[9da9270b8e8f8d5d]
|
5,779,620 |
920BC4F8 |
|
Cleaning Bad Data in R\6.5. Tidy Data\19.What is tidy data.en.srt |
6,567 |
BC4077B9 |
|
Cleaning Bad Data in R\6.5. Tidy Data\24.Making long data sets wide.mp4
[752b655e97d0b160]
|
7,956,174 |
CC84ED06 |
|
Cleaning Bad Data in R\6.5. Tidy Data\20.Variables, observations, and values.en.srt |
8,498 |
CE2DB0A5 |
|
Cleaning Bad Data in R\6.5. Tidy Data\23.Making wide data sets long.mp4
[27cd93a502c89b75]
|
11,132,654 |
E49C6F87 |
|
Cleaning Bad Data in R\5.4. Outliers\17.Outliers in subgroups.en.srt |
6,605 |
C8EFAE75 |
|
Cleaning Bad Data in R\5.4. Outliers\18.Detecting illogical values.mp4
[f57f84e553c95b5b]
|
6,306,108 |
3EAE8343 |
|
Cleaning Bad Data in R\5.4. Outliers\14.Screening for outliers.en.srt |
8,283 |
0CB55454 |
|
Cleaning Bad Data in R\5.4. Outliers\16.Outliers use case.mp4
[2364f742d186712c]
|
8,350,359 |
9E56AE3E |
|
Cleaning Bad Data in R\5.4. Outliers\15.Handling outliers.en.srt |
3,571 |
20A0715E |
|
Cleaning Bad Data in R\5.4. Outliers\16.Outliers use case.en.srt |
5,908 |
018AE16F |
|
Cleaning Bad Data in R\5.4. Outliers\14.Screening for outliers.mp4
[d8a8bdf59142e37c]
|
6,725,049 |
E3C052E6 |
|
Cleaning Bad Data in R\5.4. Outliers\18.Detecting illogical values.en.srt |
5,912 |
0FC1D135 |
|
Cleaning Bad Data in R\5.4. Outliers\15.Handling outliers.mp4
[25640cb3e0b20838]
|
2,965,854 |
AB0B50AE |
|
Cleaning Bad Data in R\5.4. Outliers\17.Outliers in subgroups.mp4
[a542a20b0d829bd0]
|
7,593,172 |
B715443E |
|
Cleaning Bad Data in R\7.6. Red Flags\26.Suspicious multiples.en.srt |
4,133 |
8D90336E |
|
Cleaning Bad Data in R\7.6. Red Flags\25.Suspicious values.mp4
[b77930ea7eb2d7de]
|
9,275,538 |
134CA733 |
|
Cleaning Bad Data in R\7.6. Red Flags\26.Suspicious multiples.mp4
[cb78366149ea144c]
|
5,076,028 |
306B7712 |
|
Cleaning Bad Data in R\7.6. Red Flags\25.Suspicious values.en.srt |
8,360 |
64043B47 |
|
Cleaning Bad Data in R\4.3. Formatting Data |
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Cleaning Bad Data in R\2.1. Missing Data |
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Cleaning Bad Data in R\Exercise Files |
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Cleaning Bad Data in R\1.Introduction |
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Cleaning Bad Data in R\8.Conclusion |
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Cleaning Bad Data in R\3.2. Duplicated Data |
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Cleaning Bad Data in R\6.5. Tidy Data |
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Cleaning Bad Data in R\5.4. Outliers |
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Cleaning Bad Data in R\7.6. Red Flags |
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Cleaning Bad Data in R |
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| Total size: |
278,539,215 |
|
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