R 如何策划海盗阴谋？
我最近提交了一份手稿，审稿人要求为我的数据提供一个盗版阴谋。我有两组“年轻人”和“老年人”，我测量了他们大脑反应的幅度。我花了3个小时学习如何将我的csv数据加载到R！但是现在，我真的不知道如何使用函数来制作海盗阴谋。第一列是年龄，第二列是年龄组（年轻人、老年人），第四列是大脑反应的幅度。有62行（个人）有谁能帮我做一个振幅的海盗阴谋来显示年轻人和老年人之间的差异R 如何策划海盗阴谋？,r,R,我最近提交了一份手稿，审稿人要求为我的数据提供一个盗版阴谋。我有两组“年轻人”和“老年人”，我测量了他们大脑反应的幅度。我花了3个小时学习如何将我的csv数据加载到R！但是现在，我真的不知道如何使用函数来制作海盗阴谋。第一列是年龄，第二列是年龄组（年轻人、老年人），第四列是大脑反应的幅度。有62行（个人）有谁能帮我做一个振幅的海盗阴谋来显示年轻人和老年人之间的差异 age ageGroup gender MMNamplitude ampLLRdevNEW 24 Young fe
age ageGroup gender MMNamplitude ampLLRdevNEW
24 Young female -2.6748016 -1.61673200
30 Young female -3.5406852 -4.43758060
24 Young female -4.7248983 -1.13789930
25 Young male -2.0330820 -1.20710680
24 Young male -3.1502962 -1.34792170
22 Young male -2.1730402 -2.18172410
22 Young male -1.0114062 -0.81161368
25 Young female -3.6766238 -2.80001140
23 Young female -2.7683165 -0.22415800
31 Young male -3.8966408 -2.70333150
25 Young male -3.1112120 -1.40961360
31 Young female -3.3528483 -3.71767590
26 Young female -0.4627196 0.64751029
24 Young female -2.1004868 0.25361124
21 Young female -3.2049625 -0.75276893
26 Young female -2.1916404 -0.53311187
24 Young female -5.1361308 -0.79336965
24 Young male -3.0489032 -2.04726050
19 Young male -3.5671501 -1.73471670
23 Young male -4.1360154 -4.67426160
22 Young male -2.2351420 1.53577610
19 Young male -4.4592948 -2.31338240
30 Young female -3.2692153 -2.00995450
24 Young male -1.4944552 -0.07673313
20 Young male -2.0538952 -1.10110350
20 Young male -2.6905904 -0.90252703
28 Young female -4.0401025 -2.06789450
26 Young female -1.6229513 -0.29561293
NA Young male -7.3316092 -4.88589050
30 Young female -3.5574262 -1.96863960
31 Young female -3.6331723 -5.46660800
NA Young female -0.4863969 1.42703760
28 Young female -1.1213547 -1.84548260
65 old male -4.2015524 -6.87044380
60 old female -1.2958775 -1.33513150
60 old female 0.3597395 -0.53502542
73 old female -1.6942397 -2.78447790
66 old male -0.7753630 -2.01488880
71 old male -1.4117705 -1.55971790
69 old female -2.6966374 -2.78534790
NA old female -2.7576666 -2.37085100
62 old male -1.3446590 -0.81808186
61 old female -1.1353959 -1.20741810
72 old male -3.3344913 -4.53399660
62 old male -2.9870763 -2.88575740
63 old female -1.7782377 -0.76250124
63 old female -2.6238792 -3.47005030
70 old female -2.8348722 -3.30441830
74 old male -1.2044405 -2.07105450
77 old male -2.4079964 -4.68829200
67 old female -2.9031038 -3.94690870
73 old male -3.7105155 -5.47033310
60 old male -4.1027393 -5.74142500
65 old male -3.3070505 -4.65865180
63 old female -2.5611329 -3.69858570
62 old female -2.6453319 -2.59949870
66 old female -1.7583463 -1.93183160
61 old male -2.3662858 -2.03517960
70 old male -3.7941179 -4.71021510
65 old female -2.4184918 -2.20737310
64 old male -1.9983604 -3.55956150
NA old male -2.4147241 -3.96302600
# first install needed packages
install.packages('tidyverse') # for data manipulation, visualization, etc.
install.packages('devtools') # needed to install ggpirate
devtools::install_github("mikabr/ggpirate") # pirate plot
library(tidyverse)
library(ggpirate)
dfdf %>%
ggplot(aes(ageGroup, MMNamplitude)) +
geom_pirate(aes(color=ageGroup), show.legend = TRUE) +
facet_wrap(~gender)
facet\u wrap# first install needed packages
install.packages('tidyverse') # for data manipulation, visualization, etc.
install.packages('devtools') # needed to install ggpirate
devtools::install_github("mikabr/ggpirate") # pirate plot
library(tidyverse)
library(ggpirate)
dfdf %>%
ggplot(aes(ageGroup, MMNamplitude)) +
geom_pirate(aes(color=ageGroup), show.legend = TRUE) +
facet_wrap(~gender)
facet\u wrap我使用了你数据的一个子集，因此你的输出可能会有所不同。你能给我们一个链接，解释什么是“海盗阴谋”吗？我可以用谷歌（，）搜索，但你应该让我们更容易。（至少在我的世界里，这还不是标准术语。）这里是海盗阴谋的链接：你能给我们一个解释什么是“海盗阴谋”的链接吗？我可以用谷歌（，）搜索，但你应该让我们更容易。（至少在我的世界里，这还不是标准术语。）以下是海盗阴谋的链接：对于一个新用户，我可以跳过不必要的管道
df%%>%ggplotggplot（df，…）tidyverseggplot2df%%>%ggplotggplot（df，…）tidyverseggplot2