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Input:
library("UsingR")
library("dplyr")
data("kid.weights")
attach(Kid.weights)
df <- data.frame(gender[1:6],
weight[1:6],
height[1:6],
Kg=weight[1:6] * 0.453592,
M=height[1:6] * 0.0254)
df
df %>%
group_by(df$gender) %>%
summarise(mean(df$weight))
Output:
> df %>%
+ group_by(df$gender) %>%
+ summarise(mean(df$weight))
# A tibble: 2 x 2
`df$gender` `mean(df$weight)`
<fct> <dbl>
1 F 58.3
2 M 58.3
I want to make data frame for mean(weight(kg)) or median(weight(kg)) to gender.
but it is not working. looks like.
how to it solve?
Once you use %>% you don't need to reference to df anymore:
df %>%
group_by(gender) %>%
summarise(mean(weight))
%>% is a pipeline which makes you accessible to the columns directly, on each group, df$gender and df$weight would give you the whole column.
Related
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I am trying to identify elements in a dataframe that contain non-ASCII characters. For example, in the dataframe below I would want all rows in the unicode_only column and the last two rows in the mixed column.
example_dataset <- tribble(
~ascii_only, ~unicode_only, ~mixed,
"a", "表", "c",
"b", "外", "表",
"c", "字", "外",
)
When I try filtering elements using the regular expression "[^[:ascii]]", however, some ASCII-only elements are included.
example_dataset %>%
mutate(row_number = row_number()) %>%
pivot_longer(c(everything(), -row_number),
names_to = "variable") %>%
select(variable, row_number, value) %>%
arrange(variable, row_number) %>%
filter(str_detect(value, "[^[:ascii]]"))
variable
row_number
value
ascii_only
2
b
mixed
2
表
mixed
3
外
unicode_only
1
表
unicode_only
2
外
unicode_only
3
字
Why would "[^[:ascii]]" match b?
The pattern is [:ascii:] and not [:ascii].
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I have a dataset with 3 factor columns and 4 numeric columns. I want to use group_by() to summarize it. But no matter how I try it doesn't work, there is no group.
freetick <- read.csv("FreeTickAll.csv", stringsAsFactors=FALSE)
library(dplyr)
group1 <- freetick %>% group_by(Habitat, Month) %>% summarize(
meanAd = mean(Adult),
meanNy = mean(Nymph),
meanLa = mean(Larva)
)
group1
The result:
> group1
meanAd meanNy meanLa
1 0.6129032 4.258065 20.1129
And my group1 data.frame also show:
mean Ad mean Ny mean La
1 0.6129032 4.258065 20.1129
If a function is common in multiple packages and those packages are loaded into the working env, then there is a possibility of masking the function from the last loaded package. In such cases, either restart the R session with only the package of interest loaded (dplyr in this case) or specify the function to be loaded explicitly from the package of interest (dplyr::summarise)
freetick %>%
dplyr::group_by(Habitat, Month) %>%
dplyr::summarise(meanAd = mean(Adult),
meanNy = mean(Nymph),
meanLa = mean(Larva))
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Use str_detect to find the ID of the novel Pride and Prejudice.
How many different ID numbers are returned?
library(tidyverse)
library(gutenbergr)
library(tidytext)
options(digits = 3)
An option is to filter the rows based on the substring "Pride and Prejudice" in the 'title' column and get the number of distinct 'gutenberg_id' s with n_distinct. If the ids are alreaydy unique, just do summarise(n = n())
library(gutenbergr)
library(stringr)
gutenberg_metadata %>%
filter(str_detect(title, "Pride and Prejudice")) %>%
summarise(n = n_distinct(gutenberg_id))
# A tibble: 1 x 1
# n
# <int>
#1 6
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Closed 4 years ago.
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Good Morning Everyone,
I have a small issue regarding a dataframe :
I have 165 differents countries, sometimes with more than 30 occurencies. What I would like to do is take only 30 occurencies for each country, and then apply the mean function on the related variables.
Do you have any idea how I can achieve this?
Here is the dataframe :
Thanks for your answer,
Rémi
Assuming you want to take out 30 rows for each group, we can do the following. Unfortunately, dplyr's sample_n cannot handle when the input data frame has less rows than you want to sample (unless you want to sample with replacement).
Where df is your data.frame:
Solution 1:
library(dplyr)
df %>% group_by(Nationality) %>%
sample_n(30, replace=TRUE) %>%
distinct() %>% # to remove repeated rows where nationalities have less than 30 rows
summarise_at(vars(Age, Overall, Passing), funs(mean))
Solution 2:
df %>% split(.$Nationality) %>%
lapply(function(x) {
if (nrow(x) < 30)
return(x)
x %>% sample_n(30, replace=FALSE)
}) %>%
do.call(what=bind_rows) %>%
group_by(Nationality) %>%
summarise_at(vars(Age, Overall, Passing), funs(mean))
Naturally without guarantee as you did not supply a working example.
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I have the following kind of data in my csv file
DriveNo Date and Time Longitude
156 2014-01-31 23:00:00 41.88367183
187 2014-01-31 23:00:01 41.92854
These data have a lot of noise. Sometimes, a driver(the DriveNo is unique) is present in two different locations at the same time , which is not possible and a noise. I tried to do it using distinct(select(five,DriveNo,Date and Time))
but i get the following error
Error: unexpected symbol in "distinct(select(five,DriveNo,Date and"
However, when i try
distinct(select(five,DriveNo,Longitude))
it works.But, i need it with DriveNo and Date and Time.
you can escape with backticks, like:
df %>%
select(DriveNo, `Date and Time`, Longitude) %>%
distinct()
or using group_by, like:
df %>%
group_by(DriveNo, `Date and Time`) %>%
select(Longitude) %>%
unique()