如何识别R中两个数据集之间的不匹配？

Question

我有两个数据集。数据集1和数据集2如下：

dataSet1的： -

family_id     house_id   number_family_member
1             1052        2
2             5042        3
3             1111        2

Dataset2： -

family_id     house_id   age   gender
1             1052       24    male
1             1052       25    female
2             5042       23    male
2             5042       20    female
3             1111       1     male
3             1111       20    female
3             1111       21    female

以下是在dataset1中输入的成员数与在dataset2中输入的个人详细信息之间的不匹配。与家庭ID 2一样，数据集1中的成员数量为3，但数据集2中只有2个成员的条目。 如何识别两个数据集之间的这些类型的不匹配？

Answer 1

我们可以使用count来计算家庭成员的数量并创建新的数据框df3，然后使用setequal来比较df1和{{1} }。

df3

数据

library(dplyr)

df3 <- df2 %>% 
  count(family_id, house_id) %>%
  rename(number_family_member = n)

setequal(df1, df3)
# FALSE: Rows in x but not y: 2, 3. Rows in y but not x: 2, 3. 

Answer 2

可以使用aggregate和merge完成此操作。

agg <- aggregate(family_id ~ factor(family_id), dataset2, length)
mrg <- merge(agg, dataset1[c(1, 3)], by.x = "factor(family_id)", by.y = "family_id")

result <- data.frame(family_id = dataset1$family_id)
result$Match <- ifelse(dataset1$number_family_member == mrg$family_id, "match", "mismatch")
result
#  family_id     Match
#1         1     match
#2         2  mismatch
#3         3  mismatch

rm(agg, mrg)    # final clean up

DATA。

dataset1 <- read.table(text = "
family_id     house_id   number_family_member
1             1052        2
2             5042        3
3             1111        2
", header = TRUE)

dataset2 <- read.table(text = "
family_id     house_id   age   gender
1             1052       24    male
1             1052       25    female
2             5042       23    male
2             5042       20    female
3             1111       1     male
3             1111       20    female
3             1111       21    female
", header = TRUE)

Answer 3

这两种观点可能对您有所帮助：

dataset2 %>%
  add_count(family_id) %>%
  inner_join(dataset1) %>%
  mutate(match= n ==number_family_member)

# # A tibble: 7 x 7
#   family_id house_id   age gender     n number_family_member match
#       <int>    <int> <int> <fctr> <int>                <int> <lgl>
# 1         1     1052    24   male     2                    2  TRUE
# 2         1     1052    25 female     2                    2  TRUE
# 3         2     5042    23   male     2                    3 FALSE
# 4         2     5042    20 female     2                    3 FALSE
# 5         3     1111     1   male     3                    2 FALSE
# 6         3     1111    20 female     3                    2 FALSE
# 7         3     1111    21 female     3                    2 FALSE

dataset2 %>%
  count(family_id) %>%
  inner_join(dataset1) %>%
  mutate(match= n ==number_family_member)

# # A tibble: 3 x 5
#   family_id     n house_id number_family_member match
#       <int> <int>    <int>                <int> <lgl>
# 1         1     2     1052                    2  TRUE
# 2         2     2     5042                    3 FALSE
# 3         3     3     1111                    2 FALSE