Getting rid of extra periods - cleaning data using R

0 votes

I have the following data

1) 100      |  101.25  | 102.25. | .   | .. | 201.5. |
2) 200.05.  |  200.56. | 205     | ..  | .  | 3000   |
3) 300.98   |  300.26. | 2001.56.| ... | 0.2| 5.65.  |

Expected output

1) 100   | 101.25   | 102.25  |NA | NA |201.5
2) 200.05|200.26    | 205     |NA | NA |3000
3) 300.98|300.26    |2001.26  |NA |0.2 |5.65
Nov 13, 2018 in Data Analytics by Ali
• 10,420 points
15 views

1 answer to this question.

Your answer

Your name to display (optional):
Privacy: Your email address will only be used for sending these notifications.
0 votes

Just try removing the periods using sub function 

x <- c("101.25", "200.56.", "300.26")
x <- sub("\\.$", "", x)
answered Nov 13, 2018 by Maverick
• 10,040 points

Related Questions In Data Analytics

0 votes
1 answer

How to forecast season and trend of data using STL and ARIMA in R?

You can use the forecast.stl function for the ...READ MORE

answered May 18, 2018 in Data Analytics by DataKing99
• 8,100 points
421 views
0 votes
1 answer

what are the different ways of getting/reading data into for cleaning

there different functions that allow you to ...READ MORE

answered Nov 14, 2018 in Data Analytics by Maverick
• 10,040 points
11 views
0 votes
1 answer

How to change the value of a variable using R programming in a data frame?

Try this: df$symbol <- as.character(df$symbol) df$symbol[df$symb ...READ MORE

answered Jan 11 in Data Analytics by Tyrion anex
• 8,280 points
63 views
0 votes
1 answer
0 votes
1 answer

Replace comma with a period in data cleaning using R

You can use the scan function in ...READ MORE

answered Nov 13, 2018 in Data Analytics by Maverick
• 10,040 points
22 views
0 votes
1 answer

Cleaning a Data Frame Using Regexp in R

The simplest way: library(dplyr) library(stringi) df %>% mutate(NUMERO_APPEL.fix = ...READ MORE

answered Nov 13, 2018 in Data Analytics by Maverick
• 10,040 points
17 views
0 votes
1 answer

How do I remove unnecessary redundant data from a dataset?

You can use dimensionality reduction methods such as ...READ MORE

answered Nov 13, 2018 in Data Analytics by Maverick
• 10,040 points
30 views
0 votes
1 answer

Manipulate character string using gsub() and perform multivariate data cleaning efficiently in R

gsubfn is perfect for this task: library(gsubfn) as.vector(sapply(gsubfn("[A-Z]", list(B="* 1", ...READ MORE

answered Nov 13, 2018 in Data Analytics by Maverick
• 10,040 points
16 views
0 votes
1 answer

Cleaning data using R

Try something like this: text1='"id","gender","age","category1","category2","category3","category4","category5","category6","category7","category8","category9","category10" 1,"Male",22,"movies","music","travel","cloths","grocery",,,,, 2,"Male",28,"travel","books","movies",,,,,,, 3,"Female",27,"rent","fuel","grocery","cloths",,,,,, 4,"Female",22,"rent","grocery","travel","movies","cloths",,,,, 5,"Female",22,"rent","online-shopping","utiliy",,,,,,,' d1 <- read.table(text=text1, sep=",", ...READ MORE

answered Nov 13, 2018 in Data Analytics by Maverick
• 10,040 points
22 views
0 votes
1 answer

Clean a set of data using R

Try this: NCM <- c(5,1,3,2,4) Mbrand <- c(1,5,3,4,2) fac<-factor(Mbrand, levels ...READ MORE

answered Nov 13, 2018 in Data Analytics by Maverick
• 10,040 points
20 views

© 2018 Brain4ce Education Solutions Pvt. Ltd. All rights Reserved.
"PMP®","PMI®", "PMI-ACP®" and "PMBOK®" are registered marks of the Project Management Institute, Inc. MongoDB®, Mongo and the leaf logo are the registered trademarks of MongoDB, Inc.