r - 计算 dplyr 中选定列的摘要

Question

我有一个 df，其中包含国家名称、病例数（covid）和国家/地区的人口数量。所以我想总结一下案件的数量。问题是每一行的人口数量都是相同的，因此不应将它们相加。那么我怎样才能只总结案例列。

cases_names_pop <- cases_names_pop %>%  group_by(countriesAndTerritories) %>% summarise_all((sum))

这是我使用的数据：

structure(list(countriesAndTerritories = c("Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan"), cases = c(272L, 
0L, 228L, 214L, 0L, 200L, 185L, 246L, 252L, 154L, 232L, 282L, 
0L, 383L, 65L, 163L, 205L, 66L, 360L, 146L, 0L, 224L, 80L, 126L, 
58L, 40L, 121L, 86L, 95L, 132L, 76L, 157L, 123L, 0L, 113L, 199L, 
65L, 81L, 61L, 116L, 135L, 88L, 87L, 59L, 68L, 47L, 0L, 32L, 
66L, 129L, 96L, 0L, 10L, 77L, 68L, 62L, 145L, 44L, 7L, 5L, 17L, 
14L, 15L, 12L, 0L, 35L, 6L, 16L, 25L, 71L, 30L, 0L, 125L, 47L, 
0L, 17L, 40L, 99L, 75L, 35L, 34L, 28L, 24L, 26L, 96L, 74L, 20L, 
16L, 45L, 38L, 9L, 34L, 19L, 3L, 11L, 3L, 55L, 1L, 71L, 0L), 
    popData2019 = c(38041757L, 38041757L, 38041757L, 38041757L, 
    38041757L, 38041757L, 38041757L, 38041757L, 38041757L, 38041757L, 
    38041757L, 38041757L, 38041757L, 38041757L, 38041757L, 38041757L, 
    38041757L, 38041757L, 38041757L, 38041757L, 38041757L, 38041757L, 
    38041757L, 38041757L, 38041757L, 38041757L, 38041757L, 38041757L, 
    38041757L, 38041757L, 38041757L, 38041757L, 38041757L, 38041757L, 
    38041757L, 38041757L, 38041757L, 38041757L, 38041757L, 38041757L, 
    38041757L, 38041757L, 38041757L, 38041757L, 38041757L, 38041757L, 
    38041757L, 38041757L, 38041757L, 38041757L, 38041757L, 38041757L, 
    38041757L, 38041757L, 38041757L, 38041757L, 38041757L, 38041757L, 
    38041757L, 38041757L, 38041757L, 38041757L, 38041757L, 38041757L, 
    38041757L, 38041757L, 38041757L, 38041757L, 38041757L, 38041757L, 
    38041757L, 38041757L, 38041757L, 38041757L, 38041757L, 38041757L, 
    38041757L, 38041757L, 38041757L, 38041757L, 38041757L, 38041757L, 
    38041757L, 38041757L, 38041757L, 38041757L, 38041757L, 38041757L, 
    38041757L, 38041757L, 38041757L, 38041757L, 38041757L, 38041757L, 
    38041757L, 38041757L, 38041757L, 38041757L, 38041757L, 38041757L
    )), row.names = c(NA, 100L), class = "data.frame")

提前谢谢你的帮助！

标签： rdata-analysis