英文:
How can I mutate data based on whether a string in R contains a specific value?
问题
For merging purposes, I summarized rows of an existing dataset (structured by country, year, and party) as follows, as I am not interested in the values of distinct parties, but whether a distinct kind of parties were part of the government in a year:
library(dplyr)vparty <- vparty %>%group_by(country_name, year) %>%summarise(across(everything(), ~toString(.)))
Now I have a data frame looking like this:
| country_name | year | apingov |
|---|---|---|
| Country1 | year1 | NA, NA, NA, 1, NA |
| Country1 | year2 | NA, NA, NA, 0, NA |
| Country2 | year1 | NA, 1, NA, NA, NA |
| Country2 | year2 | NA, NA, NA, NA, 0 |
What I want to do now is to mutate apingov depending on whether the string contains a "1" or a "0". For a string containing "1", the value of the variable shall be 1, for a string containing "0" = 0, when there is neither "1" nor "0" than the value shall be NA.
I tried different solutions I found here. However, none of them really worked for my specific case.
英文:
For merging purposes, I summarized rows of an existing dataset (structured by country, year and party) as follows, as I am not interested in the values of distinct parties, but whether a distinct kind of parties were part of the government in a year:
library(dplyr)vparty <- vparty%>%group_by(country_name, year) %>%summarise(across(everything(), ~toString(.)))
Now I have a data frame looking like this:
| country_name | year | apingov |
|---|---|---|
| Country1 | year1 | NA, NA, NA, 1, NA |
| Country1 | year2 | NA, NA, NA, 0, NA |
| Country2 | year1 | NA, 1, NA, NA, NA |
| Country2 | year2 | NA, NA, NA, NA, 0 |
What I want to do now is, to mutate apingov depending on whether the string contains a "1" or a "0". (It is not possible that there is 1 and 0 in the same string, and the NAs are not important when 1 or 0 is contained)- For a string containing 1, the value of the variable shall be 1, for a string containing 0 = 0, when there is neither 1 nor 0 than the value shall be NA.
I tried different solutions I found here. However, none of them really worked for my specific case.
答案1
得分: 2
你可以使用readr包中的parse_number来实现。我已经添加了一个额外的行，演示了当所有的apingov都是NA时的行为。
library(dplyr)vparty %>% mutate(apingov = parse_number(apingov))
如果除了"1"或"0"之外还有其他数字，上述的parse_number函数将不会报告NA。如果是这种情况，可以在case_when中使用grepl。
vparty %>%mutate(apingov = case_when(grepl("1", apingov) ~ "1",grepl("0", apingov) ~ "0",.default = NA))
输出
country_name year apingov1 Country1 year1 12 Country1 year2 03 Country2 year1 14 Country2 year2 05 Country2 year2 NA
数据
vparty <- structure(list(country_name = c("Country1", "Country1", "Country2","Country2", "Country2"), year = c("year1", "year2", "year1","year2", "year2"), apingov = c("NA, NA, NA, 1, NA", "NA, NA, NA, 0, NA","NA, 1, NA, NA, NA", "NA, NA, NA, NA, 0", "NA, NA, NA, NA, NA")), class = "data.frame", row.names = c(NA, -5L))
英文:
You can use parse_number from the readr package for that. I have added an extra row demonstrating the behaviour when all apingov is NA.
library(dplyr)vparty %>% mutate(apingov = parse_number(apingov))
If there are numbers other than "1" or "0", the above parse_number function would not report NA. Use grepl in case_when if that's the case.
vparty %>%mutate(apingov = case_when(grepl("1", apingov) ~ "1",grepl("0", apingov) ~ "0",.default = NA))
Output
country_name year apingov1 Country1 year1 12 Country1 year2 03 Country2 year1 14 Country2 year2 05 Country2 year2 NA
Data
vparty <- structure(list(country_name = c("Country1", "Country1", "Country2","Country2", "Country2"), year = c("year1", "year2", "year1","year2", "year2"), apingov = c("NA, NA, NA, 1, NA", "NA, NA, NA, 0, NA","NA, 1, NA, NA, NA", "NA, NA, NA, NA, 0", "NA, NA, NA, NA, NA")), class = "data.frame", row.names = c(NA, -5L))
答案2
得分: 1
我建议使用parse_number。以下是使用str_detect和case_when的版本：
library(dplyr)library(stringr)vparty %>%mutate(apingov = case_when(str_detect(apingov, "1") ~ 1,str_detect(apingov, "0") ~ 0,TRUE ~ NA_real_))country_name year apingov1 Country1 year1 12 Country1 year2 03 Country2 year1 14 Country2 year2 0
英文:
I would recommend parse_number. Here is a version with str_detect and case_when
library(dplyr)library(stringr)vparty %>%mutate(apingov = case_when(str_detect(apingov, "1") ~ 1,str_detect(apingov, "0") ~ 0,TRUE ~ NA_real_))country_name year apingov1 Country1 year1 12 Country1 year2 03 Country2 year1 14 Country2 year2 0
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