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Let's say my data set contains three columns: id (identification), case (character), and value(numeric). This is my dataset:
tdata <- data.frame(id=c(1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4), case=c("a","b","c","c","a","b","c","c","a","b","c","c","a","b","c","c"), value=c(1,34,56,23,546,34,67,23,65,23,65,23,87,34,321,56))
tdata
id case value
1 1 a 1
2 1 b 34
3 1 c 56
4 1 c 23
5 2 a 546
6 2 b 34
7 2 c 67
8 2 c 23
9 3 a 65
10 3 b 23
11 3 c 65
12 3 c 23
13 4 a 87
14 4 b 34
15 4 c 321
16 4 c 56
If you notice, for each ID, we have two c's. How can I rename them c1 and c2? (I need to distinguish between them for further analysis).
952
We can check if a variable is a factor or not using class() function. Similarly, levels of a factor can be checked using the levels() function.
Factors are the data objects which are used to categorize the data and store it as levels. They can store both strings and integers. They are useful in the columns which have a limited number of unique values. Like "Male, "Female" and True, False etc. They are useful in data analysis for statistical modeling.
Factors in R are stored as a vector of integer values with a corresponding set of character values to use when the factor is displayed. The factor function is used to create a factor. The only required argument to factor is a vector of values which will be returned as a vector of factor values.
In descriptive statistics for categorical variables in R, the value is limited and usually based on a particular finite group. For example, a categorical variable in R can be countries, year, gender, occupation. A continuous variable, however, can take any values, from integer to decimal.
How about:
within(tdata, case <- ave(as.character(case), id, FUN=make.unique))
How about this slightly modified approach:
library(dplyr)
tdata %>% group_by(id, case) %>% mutate(caseNo = paste0(case, row_number())) %>%
ungroup() %>% select(-case)
#Source: local data frame [16 x 3]
#
# id value caseNo
#1 1 1 a1
#2 1 34 b1
#3 1 56 c1
#4 1 23 c2
#5 2 546 a1
#6 2 34 b1
#7 2 67 c1
#8 2 23 c2
#9 3 65 a1
#10 3 23 b1
#11 3 65 c1
#12 3 23 c2
#13 4 87 a1
#14 4 34 b1
#15 4 321 c1
#16 4 56 c2
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