Purrr在一个工作流中查看2个数据帧列表

在你们的大力帮助下，我能够构建以下工作流程：

results_2018 <- list_of_objects %>%
    map(~.x[, seq(from=1, to=length(unique(names(.x))))]) %>%
    map(~dplyr::arrange(.x, desc(Germany))) %>%
    map(~dplyr::top_n(.x, 10, Germany)) %>%
    map(~rename(.x, "Answers" = "Answer.Options"))

results_2019 <- list_of_objects_2 %>%
    map(~.x[, seq(from=1, to=length(unique(names(.x))))]) %>%
    map(~dplyr::arrange(.x, desc(Germany))) %>%
    map(~dplyr::top_n(.x, 10, Germany)) %>%
    map(~rename(.x, "Answers" = "Data.Points"))

合并所有数据集以接收新数据集。这是可行的，但将其放入单个

purr

和

dplyr

代码中会非常有帮助

purrr::map(list_obj, ~map(.x, function(x)
        x[, seq(from=1, to=length(unique(names(x))))] %>%
            dplyr::arrange(desc(Germany)) %>%
            dplyr::top_n(10, Germany) %>%
            dplyr::rename(Answers = 1) %>%
            map2(.x, .y, ~full_join(.x %>% select(Answers, Austria),
                                    .y %>% select(Answers, Austria),
                                     by = "Answers")

            %>%
            mutate(Difference = Austria.y - Austria.x) %>%
            rename_at(vars(contains(".x")),
                      ~str_replace(., ".x", "_2018")) %>%
            rename_at(vars(contains(".y")),
                      ~str_replace(., ".y", "_2019")) %>%
            set_names(c("Answers", "Austria_2018", "Austria_2019"
                        ,"Difference")))
    ))

这是我的尝试，但是

map

函数中的

map2

似乎不是这样工作的。是否有一种方法可以访问

结果\u 2018

和

结果\u 2019

，而无需将其存储在变量中

使用以下代码创建的示例数据：

results_2019 <- list_of_objects_2 %>%
    map(~.x[, seq(from=1, to=length(unique(names(.x))))]) %>%
    map(~dplyr::arrange(.x, desc(Germany))) %>%
    map(~dplyr::top_n(.x, 2, Germany)) %>%
    map(~dplyr::rename(.x, Answers = 1))

list(df2_A = structure(list(Answers = c("45 to 54", "35 to 44"
), Austria = c(23.4, 20.7), Belgium = c(21.6, 21.4), Denmark = c(22.6, 
20.3), France = c(20.9, 22.5), Germany = c(24.2, 21.9), Italy = c(19.1, 
24.2), Netherlands = c(22.3, 21), Poland = c(16.9, 22.2), Romania = c(18.7, 
24.1), Russia = c(20, 23.9), Spain = c(20.9, 26.9), Sweden = c(20.6, 
20), Switzerland = c(23.6, 20.8), UK = c(21.3, 22.2), USA = c(20.6, 
20.4)), row.names = c(NA, -2L), class = "data.frame"), df2_B = structure(list(
    Answers = c("PC / Laptop", "Smartphone"), Austria = c(88.8, 
    94.7), Belgium = c(87.9, 82.5), Denmark = c(76.8, 93.5), 
    France = c(88.9, 83.3), Germany = c(91.5, 86.7), Italy = c(82.2, 
    91), Netherlands = c(88.5, 85.7), Poland = c(89.8, 87.3), 
    Romania = c(88, 92.7), Russia = c(89.2, 85.8), Spain = c(88.4, 
    94), Sweden = c(83.5, 89.8), Switzerland = c(86.7, 94.2), 
    UK = c(86.6, 87.3), USA = c(84.8, 84.9)), row.names = c(NA, 
-2L), class = "data.frame"), df2_C = structure(list(Answers = c("Personal PC / Laptop", 
"Smartphone"), Austria = c(84.8, 88.1), Belgium = c(86.3, 72.5
), Denmark = c(78, 85.1), France = c(90.6, 61.4), Germany = c(91.8, 
64.4), Italy = c(87.3, 74.5), Netherlands = c(88.5, 65.6), Poland = c(91.9, 
68.8), Romania = c(86.5, 86.8), Russia = c(88.3, 68.3), Spain = c(89.7, 
78.5), Sweden = c(86.1, 77.6), Switzerland = c(83.8, 85.7), UK = c(88.8, 
70.8), USA = c(88.5, 67.6)), row.names = c(NA, -2L), class = "data.frame"), 
    df2_D = structure(list(Answers = c("Schooling until age 18", 
    "University degree"), Austria = c(15.1, 30.3), Belgium = c(31.6, 
    28.1), Denmark = c(22.1, 24), France = c(40.8, 25.3), Germany = c(41.5, 
    23.8), Italy = c(53.9, 19.8), Netherlands = c(16.1, 28.3), 
        Poland = c(31.1, 33.7), Romania = c(42.8, 16.6), Russia = c(9.8, 
        52.6), Spain = c(21.6, 32.6), Sweden = c(41, 31.4), Switzerland = c(10.1, 
        29.4), UK = c(24.1, 29.9), USA = c(25.2, 29.7)), row.names = c(NA, 
    -2L), class = "data.frame"), df2_E = structure(list(Answers = c("Male", 
    "Female"), Austria = c(50.6, 49.4), Belgium = c(50.2, 49.8
    ), Denmark = c(50.3, 49.7), France = c(49.5, 50.5), Germany = c(51.3, 
    48.7), Italy = c(52.7, 47.3), Netherlands = c(49.8, 50.2), 
        Poland = c(49.3, 50.7), Romania = c(50.9, 49.1), Russia = c(51.9, 
        48.1), Spain = c(50.5, 49.5), Sweden = c(49.2, 50.8), 
        Switzerland = c(50.4, 49.6), UK = c(49.4, 50.6), USA = c(48.8, 
        51.2)), row.names = c(NA, -2L), class = "data.frame"), 
    df2_F = structure(list(Answers = c("Android", "iOS (for iPhone)"
    ), Austria = c(67.7, 27.6), Belgium = c(51.3, 24.4), Denmark = c(47.3, 
    47.1), France = c(46.1, 17.7), Germany = c(51.9, 16.9), Italy = c(58.2, 
    16.9), Netherlands = c(47.2, 19.8), Poland = c(58.4, 6.9), 
        Romania = c(82.7, 13.7), Russia = c(55.9, 11.4), Spain = c(67.5, 
        13.5), Sweden = c(44.1, 33.8), Switzerland = c(52.9, 
        42.5), UK = c(40.6, 30), USA = c(39.4, 33.3)), row.names = c(NA, 
    -2L), class = "data.frame"), df2_G = structure(list(Answers = c("Clothing", 
    "Book(s)"), Austria = c(25.8, 21.9), Belgium = c(24, 13.2
    ), Denmark = c(20.5, 10.3), France = c(22.9, 13.8), Germany = c(27.2, 
    18.2), Italy = c(22.7, 19.5), Netherlands = c(24, 11), Poland = c(29.3, 
    20.5), Romania = c(19.9, 13.6), Russia = c(15.4, 8.1), Spain = c(24.3, 
    16.8), Sweden = c(24.7, 11.1), Switzerland = c(26.5, 16.6
    ), UK = c(22.9, 15.1), USA = c(20.6, 11.8)), row.names = c(NA, 
    -2L), class = "data.frame"), df2_H = structure(list(Answers = c("Free delivery", 
    "Easy returns policy"), Austria = c(72.5, 48.2), Belgium = c(71.4, 
    37.2), Denmark = c(67.4, 45.9), France = c(71.7, 29.7), Germany = c(68.9, 
    47.1), Italy = c(66, 31.6), Netherlands = c(69.7, 37.4), 
        Poland = c(62.5, 29.7), Romania = c(66.4, 39), Russia = c(67.6, 
        39.4), Spain = c(70.2, 39), Sweden = c(68.4, 32.3), Switzerland = c(70.2, 
        40.8), UK = c(71.8, 40.4), USA = c(69.6, 40.5)), row.names = c(NA, 
    -2L), class = "data.frame"), df2_I = structure(list(Answers = c("Clothing", 
    "Book(s)"), Austria = c(16.5, 15.2), Belgium = c(14.7, 8.7
    ), Denmark = c(14.1, 7.9), France = c(16.8, 9.6), Germany = c(17.1, 
    11.7), Italy = c(17.6, 13.5), Netherlands = c(16.6, 8.1), 
        Poland = c(15.8, 12.4), Romania = c(15.9, 10.8), Russia = c(12.2, 
        6.9), Spain = c(21, 14.6), Sweden = c(16.3, 8), Switzerland = c(17.1, 
        12.3), UK = c(10.8, 8.2), USA = c(10.8, 6.6)), row.names = c(NA, 
    -2L), class = "data.frame"), df2_J = structure(list(Answers = c("YouTube", 
    "WhatsApp"), Austria = c(88, 79.4), Belgium = c(80.4, 52.8
    ), Denmark = c(80.9, 20.6), France = c(79, 31.8), Germany = c(77.9, 
    74.7), Italy = c(88, 82), Netherlands = c(78.8, 80.9), Poland = c(90.3, 
    34.9), Romania = c(93.7, 70.7), Russia = c(88.1, 59.5), Spain = c(89.5, 
    85.4), Sweden = c(86.8, 29.1), Switzerland = c(86.3, 81.1
    ), UK = c(80.5, 58.1), USA = c(81.3, 17.2)), row.names = c(NA, 
    -2L), class = "data.frame"), df2_K = structure(list(Answers = c("Less than 30 minutes", 
    "30 minutes to 1 hour"), Austria = c(28.9, 24.6), Belgium = c(25.2, 
    21.6), Denmark = c(24.4, 23.6), France = c(27.6, 18.6), Germany = c(29.7, 
    21.2), Italy = c(22.1, 22.8), Netherlands = c(30.6, 23.2), 
        Poland = c(25.4, 23.9), Romania = c(15.4, 20.9), Russia = c(18.2, 
        22.6), Spain = c(25.3, 25.9), Sweden = c(25.2, 22.5), 
        Switzerland = c(30.9, 23.1), UK = c(23.9, 20.8), USA = c(20.9, 
        19.9)), row.names = c(NA, -2L), class = "data.frame"))

结果\u 2019%
map（~.x[，seq（from=1，to=length（unique（names（.x））））））]%>%
映射（~dplyr:：arrange（.x，desc（德国）））%>%
地图（~dplyr:：top_n（.x，2，德国））%>%
映射（~dplyr:：rename（.x，Answers=1））
列表（df2_A=结构（列表（答案=c）（“45到54”，“35到44”）
)，奥地利=c（23.4,20.7），比利时=c（21.6,21.4），丹麦=c（22.6，
法国=c（20.9,22.5），德国=c（24.2,21.9），意大利=c（19.1，
荷兰=c（22.3,21），波兰=c（16.9,22.2），罗马尼亚=c（18.7，
俄罗斯=c（20，23.9），西班牙=c（20.9，26.9），瑞典=c（20.6，
20） ，瑞士=c（23.6,20.8），英国=c（21.3,22.2），美国=c（20.6，
20.4），row.names=c（NA，-2L），class=“data.frame”），df2_B=structure（list(
答案=c（“个人电脑/笔记本电脑”、“智能手机”），奥地利=c（88.8，
比利时=c（87.9,82.5），丹麦=c（76.8,93.5），
法国=c（88.9,83.3），德国=c（91.5,86.7），意大利=c（82.2，
荷兰=c（88.5,85.7），波兰=c（89.8,87.3），
罗马尼亚=c（88，92.7），俄罗斯=c（89.2，85.8），西班牙=c（88.4，
瑞典=c（83.5,89.8），瑞士=c（86.7,94.2），
英国=c（86.6,87.3），美国=c（84.8,84.9）），row.names=c（NA，
-2L），class=“data.frame”），df2_C=结构（列表（Answers=C（“个人电脑/笔记本电脑”），
奥地利=c（84.8,88.1），比利时=c（86.3,72.5
)，丹麦=c（78,85.1），法国=c（90.6,61.4），德国=c（91.8，
意大利=c（87.3,74.5），荷兰=c（88.5,65.6），波兰=c（91.9，
罗马尼亚=c（86.5,86.8），俄罗斯=c（88.3,68.3），西班牙=c（89.7，
瑞典=c（86.1,77.6），瑞士=c（83.8,85.7），英国=c（88.8,，
70.8），USA=c（88.5，67.6）），row.names=c（NA，-2L），class=“data.frame”），
df2_D=结构（列表（答案=c）（“18岁之前的学校教育”），
“大学学位”），奥地利=c（15.1,30.3），比利时=c（31.6，
丹麦=c（22.1,24），法国=c（40.8,25.3），德国=c（41.5，
意大利=c（53.9,19.8），荷兰=c（16.1,28.3），
波兰=c（31.1,33.7），罗马尼亚=c（42.8,16.6），俄罗斯=c（9.8，
西班牙=c（21.6,32.6），瑞典=c（41,31.4），瑞士=c（10.1，
英国=c（24.1,29.9），美国=c（25.2,29.7）），row.names=c（NA，
-2L），class=“data.frame”），df2_E=structure（list（Answers=c（“男性”），
“女性”），奥地利=c（50.6,49.4），比利时=c（50.2,49.8
)，丹麦=c（50.3,49.7），法国=c（49.5,50.5），德国=c（51.3，
意大利=c（52.7,47.3），荷兰=c（49.8,50.2），
波兰=c（49.3,50.7），罗马尼亚=c（50.9,49.1），俄罗斯=c（51.9，
西班牙=c（50.5,49.5），瑞典=c（49.2,50.8），
瑞士=c（50.4,49.6），英国=c（49.4,50.6），美国=c（48.8，
51.2），row.names=c（NA，-2L），class=“data.frame”），
df2_F=结构（列表（答案=c（“安卓”、“iOS（用于iPhone）”
)，奥地利=c（67.7,27.6），比利时=c（51.3,24.4），丹麦=c（47.3，
法国=c（46.1,17.7），德国=c（51.9,16.9），意大利=c（58.2，
荷兰=c（47.2,19.8），波兰=c（58.4,6.9），
罗马尼亚=c（82.7,13.7），俄罗斯=c（55.9,11.4），西班牙=c（67.5，
瑞典=c（44.1,33.8），瑞士=c（52.9，
英国=c（40.6,30），美国=c（39.4,33.3）），row.names=c（NA，
-2L），class=“data.frame”），df2_G=结构（列表（答案=c（“衣服”），
奥地利=c（25.8,21.9），比利时=c（24,13.2
)，丹麦=c（20.5,10.3），法国=c（22.9,13.8），德国=c（27.2，
意大利=c（22.7,19.5），荷兰=c（24,11），波兰=c（29.3，
罗马尼亚=c（19.9,13.6），俄罗斯=c（15.4,8.1），西班牙=c（24.3，
瑞典=c（24.7,11.1），瑞士=c（26.5,16.6
)，英国=c（22.9,15.1），美国=c（20.6,11.8）），row.names=c（NA，
-2L），class=“data.frame”），df2_H=结构（列表（答案=c（“免费交付”），
“简易退货政策”），奥地利=c（72.5,48.2），比利时=c（71.4，
丹麦=c（67.4,45.9），法国=c（71.7,29.7），德国=c（68.9，
意大利=c（66,31.6），荷兰=c（69.7,37.4），
波兰=c（62.5,29.7），罗马尼亚=c（66.4,39），俄罗斯=c（67.6，
西班牙=c（70.2，39），瑞典=c（68.4，32.3），瑞士=c（70.2，
英国=c（71.8,40.4），美国=c（69.6,40.5）），row.names=c（NA，
-2L），class=“data.frame”），df2_I=结构（列表（答案=c（“衣服”），
奥地利=c（16.5,15.2），比利时=c（14.7,8.7
)，丹麦=c（14.1,7.9），法国=c（16.8,9.6），德国=c（17.1，
意大利=c（17.6,13.5），荷兰=c（16.6,8.1），
波兰=c（15.8,12.4），罗马尼亚=c（15.9,10.8），俄罗斯=c（12.2，
西班牙=c（21,14.6），瑞典=c（16.3,8），瑞士=c（17.1，
英国=c（10.8,8.2），美国=c（10.8,6.6）），row.names=c（NA，
-2L），class=“data.frame”），df2_J=structure（list（Answers=c（“YouTube”），
“WhatsApp”），奥地利=c（88,79.4），比利时=c（80.4,52.8
)，丹麦=c（80.9,20.6），法国=c（79,31.8），德国=c（77.9，
意大利=c（88，82），荷兰=c（78.8，80.9），波兰=c（90.3，
罗马尼亚=c（93.7,70.7），俄罗斯=c（88.1,59.5），西班牙=c（89.5，
瑞典=c（86.8,29.1），瑞士=c（86.3,81.1
)，英国=c（80.5,58.1），美国=c（81.3,17.2）），row.names=c（NA，
-2L），class=“data.frame”），df2_K=结构（列表（答案=c（“少于30分钟”），
"30
results_2019 <- list_of_objects_2 %>%
    map(~.x[, seq(from=1, to=length(unique(names(.x))))]) %>%
    map(~dplyr::arrange(.x, desc(Germany))) %>%
    map(~dplyr::top_n(.x, 2, Germany)) %>%
    map(~dplyr::rename(.x, Answers = 1))

list(df2_A = structure(list(Answers = c("45 to 54", "35 to 44"
), Austria = c(23.4, 20.7), Belgium = c(21.6, 21.4), Denmark = c(22.6, 
20.3), France = c(20.9, 22.5), Germany = c(24.2, 21.9), Italy = c(19.1, 
24.2), Netherlands = c(22.3, 21), Poland = c(16.9, 22.2), Romania = c(18.7, 
24.1), Russia = c(20, 23.9), Spain = c(20.9, 26.9), Sweden = c(20.6, 
20), Switzerland = c(23.6, 20.8), UK = c(21.3, 22.2), USA = c(20.6, 
20.4)), row.names = c(NA, -2L), class = "data.frame"), df2_B = structure(list(
    Answers = c("PC / Laptop", "Smartphone"), Austria = c(88.8, 
    94.7), Belgium = c(87.9, 82.5), Denmark = c(76.8, 93.5), 
    France = c(88.9, 83.3), Germany = c(91.5, 86.7), Italy = c(82.2, 
    91), Netherlands = c(88.5, 85.7), Poland = c(89.8, 87.3), 
    Romania = c(88, 92.7), Russia = c(89.2, 85.8), Spain = c(88.4, 
    94), Sweden = c(83.5, 89.8), Switzerland = c(86.7, 94.2), 
    UK = c(86.6, 87.3), USA = c(84.8, 84.9)), row.names = c(NA, 
-2L), class = "data.frame"), df2_C = structure(list(Answers = c("Personal PC / Laptop", 
"Smartphone"), Austria = c(84.8, 88.1), Belgium = c(86.3, 72.5
), Denmark = c(78, 85.1), France = c(90.6, 61.4), Germany = c(91.8, 
64.4), Italy = c(87.3, 74.5), Netherlands = c(88.5, 65.6), Poland = c(91.9, 
68.8), Romania = c(86.5, 86.8), Russia = c(88.3, 68.3), Spain = c(89.7, 
78.5), Sweden = c(86.1, 77.6), Switzerland = c(83.8, 85.7), UK = c(88.8, 
70.8), USA = c(88.5, 67.6)), row.names = c(NA, -2L), class = "data.frame"), 
    df2_D = structure(list(Answers = c("Schooling until age 18", 
    "University degree"), Austria = c(15.1, 30.3), Belgium = c(31.6, 
    28.1), Denmark = c(22.1, 24), France = c(40.8, 25.3), Germany = c(41.5, 
    23.8), Italy = c(53.9, 19.8), Netherlands = c(16.1, 28.3), 
        Poland = c(31.1, 33.7), Romania = c(42.8, 16.6), Russia = c(9.8, 
        52.6), Spain = c(21.6, 32.6), Sweden = c(41, 31.4), Switzerland = c(10.1, 
        29.4), UK = c(24.1, 29.9), USA = c(25.2, 29.7)), row.names = c(NA, 
    -2L), class = "data.frame"), df2_E = structure(list(Answers = c("Male", 
    "Female"), Austria = c(50.6, 49.4), Belgium = c(50.2, 49.8
    ), Denmark = c(50.3, 49.7), France = c(49.5, 50.5), Germany = c(51.3, 
    48.7), Italy = c(52.7, 47.3), Netherlands = c(49.8, 50.2), 
        Poland = c(49.3, 50.7), Romania = c(50.9, 49.1), Russia = c(51.9, 
        48.1), Spain = c(50.5, 49.5), Sweden = c(49.2, 50.8), 
        Switzerland = c(50.4, 49.6), UK = c(49.4, 50.6), USA = c(48.8, 
        51.2)), row.names = c(NA, -2L), class = "data.frame"), 
    df2_F = structure(list(Answers = c("Android", "iOS (for iPhone)"
    ), Austria = c(67.7, 27.6), Belgium = c(51.3, 24.4), Denmark = c(47.3, 
    47.1), France = c(46.1, 17.7), Germany = c(51.9, 16.9), Italy = c(58.2, 
    16.9), Netherlands = c(47.2, 19.8), Poland = c(58.4, 6.9), 
        Romania = c(82.7, 13.7), Russia = c(55.9, 11.4), Spain = c(67.5, 
        13.5), Sweden = c(44.1, 33.8), Switzerland = c(52.9, 
        42.5), UK = c(40.6, 30), USA = c(39.4, 33.3)), row.names = c(NA, 
    -2L), class = "data.frame"), df2_G = structure(list(Answers = c("Clothing", 
    "Book(s)"), Austria = c(25.8, 21.9), Belgium = c(24, 13.2
    ), Denmark = c(20.5, 10.3), France = c(22.9, 13.8), Germany = c(27.2, 
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