I have a df of lot #'s with all of the data associated with them. Some of that data is experimental. Those lot #'s start with X. For example, X42A7299, where any normal lot would be 42A7299. I want to exclude those rows. The DF is called all_cls4. Here is the code I have tried:
all_cls4new<- all_cls4 %>% filter(!str_detect(Lot_#, ^X))
this returns a +
I also get this result with filter and !grep. What am I missing?
library(dplyr)
library(stringr)
x <- tribble(
~lot, ~other_data,
"X42A7299", 45,
"42A7299", 100
)
x %>%
filter(!(str_detect(lot, '^X')))
#> # A tibble: 1 × 2
#> lot other_data
#> <chr> <dbl>
#> 1 42A7299 100
Also, be careful with a symbol in your column name (e.g. Lot_#). I would rename it to a "clean" name (e.g. snakecase). janitor::clean_names() is useful for this. If you use it as is, you will have to wrap in backticks:
x %>%
filter(!(str_detect(`Lot_#`, '^X')))
Related
So I am trying to write an automated report in R with Functions. One of the questions I am trying to answer is this " During the first week of the month, what were the 10 most viewed products? Show the results in a table with the product's identifier, category, and count of the number of views.". To to this I wrote the following function
most_viewed_products_per_week <- function (month,first_seven_days, views){
month <- views....February.2020.2
first_seven_days <- function( month, date_1, date_2){
date_1 <-2020-02-01
date_2 <- 2020-02-07
return (first_seven_days)}
views <-function(views, desc){
return (views.head(10))}
}
print(most_viewed_products_per_week)
However the output I get is this:
function (month,first_seven_days, views){
month <- views....February.2020.2
first_seven_days <- function( month, date_1, date_2){
date_1 <-2020-02-01
date_2 <- 2020-02-07
return (first_seven_days)}
views <-function(views, desc){
return (views.head(10))}
How do I fix that?
This report has more questions like this, so I am trying to get my function writing as correct as possible from the start.
Thanks in advance,
Edo
It is a good practice to code in functions. Still I recommend you get your code doing what you want and then think about what parts you want to wrap in a function (for future re-use). This is to get you going.
In general: to support your analysis, make sure that your data is in the right class. I.e. dates are formatted as dates, numbers as double or integers, etc. This will give you access to many helper functions and packages.
For the case at hand, read up on {tidyverse}, in particular {dplyr} which can help you with coding pipes.
simulate data
As mentioned - you will find many friends on Stackoverflow, if you provide a reproducible example.
Your questions suggests your data look a bit like the following simulated data.
Adapt as appropriate (or provide example)
library(tibble) # tibble are modern data frames
library(dplyr) # for crunching tibbles/data frames
library(lubridate) # tidyverse package for date (and time) handling
df <- tribble( # create row-tibble
~date, ~identifier, ~category, ~views
,"2020-02-01", 1, "TV", 27
,"2020-02-02", 2, "PC", 40
,"2020-02-03", 1, "TV", 12
,"2020-02-03", 2, "PC", 2
,"2020-02-08", 3, "UV", 200
) %>%
mutate(date = ymd(date)) # date is read in a character - lubridate::ymd() for date
This yields
> df
# A tibble: 5 x 4
date identifier category views
<date> <dbl> <chr> <dbl>
1 2020-02-01 1 TV 27
2 2020-02-02 2 PC 40
3 2020-02-03 1 TV 12
4 2020-02-03 2 PC 2
5 2020-02-08 3 UV 200
Notice: date-column is in date-format.
work your algorithm
From your attempt it follows you want to extract the first 7 days.
Since we have a "date"-column, we can use a date-function to help us here.
{lubridate}'s day() extracts the "day-number".
> df %>% filter(day(date) <= 7)
# A tibble: 4 x 4
date identifier category views
<date> <dbl> <chr> <dbl>
1 2020-02-01 1 TV 27
2 2020-02-02 2 PC 40
3 2020-02-03 1 TV 12
4 2020-02-03 2 PC 2
Anything outside the first 7 days is gone.
Next you want to summarise to get your product views total.
df %>%
## ---------- c.f. above ------------
filter(day(date) <= 7) %>%
## ---------- summarise in bins that you need := groups -------
group_by(identifier, category) %>%
summarise(total_views = sum(views)
, .groups = "drop" ) # if grouping is not needed "drop" it
This gives you:
# A tibble: 2 x 3
identifier category total_views
<dbl> <chr> <dbl>
1 1 TV 39
2 2 PC 42
Now pick the top-10 and sort the order:
df %>%
## ---------- c.f. above ------------
filter(day(date) <= 7) %>%
group_by(identifier, category) %>%
summarise(total_views = sum(views), .groups = "drop" ) %>%
## ---------- make use of another helper function of dplyr
top_n(n = 10, total_views) %>% # note top-10 makes here no "real" sense :), try top_n(1, total_views)
arrange(desc(total_views)) # arrange in descending order on total_views
wrap in function
Now that the workflow is in place, think about breaking your code into the blocks you think are useful.
I leave this to you. You can assign interim results to new data frames and wrap the preparation of the data into a function and then the top_n() %>% arrange() in another function, ...
This yields:
# A tibble: 2 x 3
identifier category total_views
<dbl> <chr> <dbl>
1 2 PC 42
2 1 TV 39
I'm trying to make objects directly from information listed in a tibble that can be called on by later functions/tibbles in my environment. I can make the objects manually but I'm working to do this iteratively.
library(tidyverse)
##determine mean from 2x OD Negatives in experimental plates, then save summary for use in appending table
ELISA_negatives = "my_file.csv"
neg_tibble <- as_tibble(read_csv(ELISA_negatives, col_names = TRUE)) %>%
group_by(Species_ab, Antibody, Protein) %>%
filter(str_detect(Animal_ID, "2x.*")) %>%
summarize(ave_neg_U_mL = mean(U_mL, na.rm = TRUE), n=sum(!is.na(U_mL)))
neg_tibble
# A tibble: 4 x 5
# Groups: Species_ab, Antibody [2]
Species_ab Antibody Protein ave_neg_U_mL n
<chr> <chr> <chr> <dbl> <int>
1 Mouse IgG GP 28.2 6
2 Mouse IgG NP 45.9 6
3 Rat IgG GP 5.24 4
4 Rat IgG NP 1.41 1
I can write the object manually based off the above tibble:
Mouse_IgG_GP_cutoff <- as.numeric(neg_tibble[1,4])
Mouse_IgG_GP_cutoff
[1] 28.20336
In my attempt to do this iteratively, I can make a new tibble neg_tibble_string with the information I need. All I would need to do now is make a global object from the Name in the first column Test_Name, and assign it to the numeric value in the second column ave_neg_U_mL (which is where I'm getting stuck).
neg_tibble_string <- neg_tibble %>%
select(Species_ab:Protein) %>%
unite(col='Test_Name', c('Species_ab', 'Antibody', 'Protein'), sep = "_") %>%
mutate(Test_Name = str_c(Test_Name, "_cutoff")) %>%
bind_cols(neg_tibble[4])
neg_tibble_string
# A tibble: 4 x 2
Test_Name ave_neg_U_mL
<chr> <dbl>
1 Mouse_IgG_GP_cutoff 28.2
2 Mouse_IgG_NP_cutoff 45.9
3 Rat_IgG_GP_cutoff 5.24
4 Rat_IgG_NP_cutoff 1.41
I feel like there has to be a way to do this to get this from the above tibble neg_tibble_string, and make this for all four of the rows. I've tried a variant of this and this, but can't get anywhere.
> list_df <- mget(ls(pattern = "neg_tibble_string"))
> list_output <- map(list_df, ~neg_tibble_string$ave_neg_U_mL)
Warning message:
Unknown or uninitialised column: `ave_neg_U_mL`.
> list_output
$neg_tibble_string
NULL
As always, any insight is appreciated! I'm making progress on my R journey but I know I am missing large gaps in knowledge.
As we already returned the object value in a list, we need only to specify the lambda function i.e. .x returns the value of the list element which is a tibble and extract the column
library(purrr)
list_output <- map(list_df, ~.x$ave_neg_U_ml)
If the intention is to create global objects, deframe, convert to a list and then use list2env
library(tibble)
list2env(as.list(deframe(neg_tibble_string)), .GlobalEnv)
I'm using the data here (specifically the cpi and grosses tibbles) and I would like to calculate each of the columns containing dollar amounts (denoted by having 'gross' or 'price' in their names) in terms of their 2020 value. I would then like to order these columns directly after their matches.
The following method was inspired by the post here:
cpi_recent <- cpi %>% # pulls most recent cpi in the tibble
select(cpi) %>%
slice_tail() %>%
pull()
grosses_adj <- grosses %>%
mutate(year_month = floor_date(week_ending, 'month')) %>%
left_join(cpi, 'year_month') %>%
mutate(across(contains(c('gross', 'price')),
list(adj = ~ cpi_recent/cpi * .))) %>% # creates new col in 2020 dollars w/ _adj suffix
select(-year_month, -cpi)
However, this will place all new columns after the last column, by default.
Is there any way to dynamically order the new columns directly after the matching column, like: gross_1, gross_1_adj, price_2, price_2_adj, etc.? Of course this is trivial using select() manually, but I'm assuming there is some way to reference these columns dynamically using mutate() and the .after argument.
You can do this by following the approach from Stackoverflow: How to relocate several columns in one step using dplyr::relocate?
First of all, you'll need to store the variable names that you are you are using in across selection in a vector or list.
gross_price_vars <- names(grosses) %>% str_subset("gross|price")
gross_price_vars
# [1] "weekly_gross_overall" "weekly_gross" "potential_gross"
# "avg_ticket_price" "top_ticket_price"
With the help of reduce from the purrr package you can relocate the freshly generated variables after the ones you used for calculation and whose names you stored in gross_price_vars.
grosses_adj %>%
reduce(.x = gross_price_vars,
.f = ~ relocate(.x, str_c(.y, "_adj"), .after = .y),
.init = .)
# week_ending week_number weekly_gross_overall weekly_gross_overall_adj show theatre weekly_gross weekly_gross_adj
# <date> <dbl> <dbl> <dbl> <chr> <chr> <dbl> <dbl>
# 1 1985-06-09 1 3915937 9576099. 42nd Str~ St. Ja~ 282368 690508.
# 2 1985-06-09 1 3915937 9576099. A Chorus~ Sam S.~ 222584 544311.
# 3 1985-06-09 1 3915937 9576099. Aren't W~ Brooks~ 249272 609574.
# 4 1985-06-09 1 3915937 9576099. Arms and~ Circle~ 95688 233997.
# 5 1985-06-09 1 3915937 9576099. As Is Lyceum~ 61059 149315.
Keep in mind, that the .x inside relocate is another object than the .x outside from it. Inside relocate it referes to the .init argument, which is the data.frame, while .y refereres to the names the variable list (the .x argument from the reduce function).
The Variables generated from the mutate(across ...) function have the postfix "_adj" attached to it's original name. In that way we identify the new variables by str_c(old_varname, "_adj").
I would do something simpler, you can use peek_vars to put the columns in order if you're conscientious about how you're naming your new columns (I usually use prefixes or suffixes to select). It looks like it would work fine in this case.
The first columns (week_ending, week_number) will not be part of the sort, everything else after that will be alphabetical.
grosses_adj %>%
select(week_ending, week_number, sort(tidyselect::peek_vars()))
library(tidyverse)
I'm stuck on something that should be so simple! Using the code below, all I want to do is group and summarise the three "Var" columns. I want counts and sums (so that I can create three percentage columns, so bonus if you can include an easy way to accomplish this in your answer). However, I don't want to include the NA's. Removing the NA's from sum is easy enough by using "na.rm=TRUE", but I can't seem to figure out how to not include the NA's in the counts (using n() ) while using dplyr::summarise_at.
Am I missing something very simple?
Df%>%group_by(Group)%>%summarise_at(vars(Var1:Var3),funs(n(),sum((.),na.rm=TRUE)))
Group<-c("House","Condo","House","House","House","House","House","Condo")
Var1<-c(0,1,1,NA,1,1,1,0)
Var2<-c(1,1,1,1,0,1,1,1)
Var3<-c(1,1,1,NA,NA,1,1,0)
Df<-data.frame(Group,Var1,Var2,Var3)
I think your code was very close to getting the job done. I made some slight changes and have included an example of how you might include the percent calculation in the same step (although I am not sure of your expected output).
library(dplyr)
Df %>%
group_by(Group) %>%
summarise_all(funs(count = sum(!is.na(.)),
sum = sum(.,na.rm=TRUE),
pct = sum(.,na.rm=TRUE)/sum(!is.na(.))))
#> # A tibble: 2 x 10
#> Group Var1_count Var2_count Var3_count Var1_sum Var2_sum Var3_sum
#> <fctr> <int> <int> <int> <dbl> <dbl> <dbl>
#> 1 Condo 2 2 2 1 2 1
#> 2 House 5 6 4 4 5 4
#> # ... with 3 more variables: Var1_pct <dbl>, Var2_pct <dbl>,
#> # Var3_pct <dbl>
I've also used summarise_all instead of summarise_at as summarise_all works on all the variables which aren't group variables.
I think you just need to move your 'na.rm()' argument back in the parentheses. See below:
Group<-c("House","Condo","House","House","House","House","House","Condo")
Var1<-c(0,1,1,NA,1,1,1,0)
Var2<-c(1,1,1,1,0,1,1,1)
Var3<-c(1,1,1,NA,NA,1,1,0)
Df<-data.frame(Group,Var1,Var2,Var3)
out <- Df %>%
group_by(Group) %>%
mutate_at(vars(Var1:Var3), funs(total = sum(!(is.na(.))), sum = sum(., na.rm = T))) %>%
ungroup()
Intro
After recently taking Hadley Wickham's functional programming class I decided I'd try applying some of the lessons to my projects at work. Naturally, the first project I tried has proven to be more complicated than the examples worked demonstrated in the class. Does anyone have recommendations for a way to use the purrr package to make the task described below more efficient?
Project Background
I need to assign quintile groups to records in a spatial polygon dataframe. In addition to the record identifier there are several other variables and I need to calculate the quintile group for each.
Here's the crux of the problem: I have been asked to identify outliers in one particular variable and to omit those records from the entire analysis as long as it doesn't change the quintile composition of the first quintile group for any of the other variables.
Question
I have put together a dplyr pipeline (see the example below) that performs this checking process for a single variable, but how might I rewrite this process so that I can efficiently check each variable?
EDIT: While it is certainly possible to change the shape of the data from wide to long as an intermediary step, in the end it needs to return to its wide format so that it matches up with the #polygons slot of the spatial polygons dataframe.
Reproducible Example
You can find the complete script here: https://gist.github.com/tiernanmartin/6cd3e2946a77b7c9daecb51aa11e0c94
Libraries and Settings
library(grDevices) # boxplot.stats()
library(operator.tools) # %!in% logical operator
library(tmap) # 'metro' data set
library(magrittr) # piping
library(dplyr) # exploratory data analysis verbs
library(purrr) # recursive mapping of functions
library(tibble) # improved version of a data.frame
library(ggplot2) # dot plot
library(ggrepel) # avoid label overlap
options(scipen=999)
set.seed(888)
Load the example data and take a small sample of it
data("metro")
m_spdf <- metro
# Take a sample
m <-
metro#data %>%
as_tibble %>%
select(-name_long,-iso_a3) %>%
sample_n(50)
> m
# A tibble: 50 x 10
name pop1950 pop1960 pop1970 pop1980 pop1990
<chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Sydney 1689935 2134673 2892477 3252111 3631940
2 Havana 1141959 1435511 1779491 1913377 2108381
3 Campinas 151977 293174 540430 1108903 1693359
4 Kano 123073 229203 541992 1349646 2095384
5 Omsk 444326 608363 829860 1032150 1143813
6 Ouagadougou 33035 59126 115374 265200 537441
7 Marseille 755805 928768 1182048 1372495 1418279
8 Taiyuan 196510 349535 621625 1105695 1636599
9 La Paz 319247 437687 600016 809218 1061850
10 Baltimore 1167656 1422067 1554538 1748983 1848834
# ... with 40 more rows, and 4 more variables:
# pop2000 <dbl>, pop2010 <dbl>, pop2020 <dbl>,
# pop2030 <dbl>
Calculate quintile groups with and without outlier records
# Calculate the quintile groups for one variable (e.g., `pop1990`)
m_all <-
m %>%
mutate(qnt_1990_all = dplyr::ntile(pop1990,5))
# Find the outliers for a different variable (e.g., 'pop1950')
# and subset the df to exlcude these outlier records
m_out <- boxplot.stats(m$pop1950) %>% .[["out"]]
m_trim <-
m %>%
filter(pop1950 %!in% m_out) %>%
mutate(qnt_1990_trim = dplyr::ntile(pop1990,5))
# Assess whether the outlier trimming impacted the first quintile group
m_comp <-
m_trim %>%
select(name,dplyr::contains("qnt")) %>%
left_join(m_all,.,"name") %>%
select(name,dplyr::contains("qnt"),everything()) %>%
mutate(qnt_1990_chng_lgl = !is.na(qnt_1990_trim) & qnt_1990_trim != qnt_1990_all,
qnt_1990_chng_dir = if_else(qnt_1990_chng_lgl,
paste0(qnt_1990_all," to ",qnt_1990_trim),
"No change"))
With a little help from ggplot2, I can see that in this example six outliers were identified and that their omission did not affect the first quintile group for pop1990.
Importantly, this information is tracked in two new variables: qnt_1990_chng_lgl and qnt_1990_chng_dir.
> m_comp %>% select(name,qnt_1990_chng_lgl,qnt_1990_chng_dir,everything())
# A tibble: 50 x 14
name qnt_1990_chng_lgl qnt_1990_chng_dir qnt_1990_all qnt_1990_trim
<chr> <lgl> <chr> <dbl> <dbl>
1 Sydney FALSE No change 5 NA
2 Havana TRUE 4 to 5 4 5
3 Campinas TRUE 3 to 4 3 4
4 Kano FALSE No change 4 4
5 Omsk FALSE No change 3 3
6 Ouagadougou FALSE No change 1 1
7 Marseille FALSE No change 3 3
8 Taiyuan TRUE 3 to 4 3 4
9 La Paz FALSE No change 2 2
10 Baltimore FALSE No change 4 4
# ... with 40 more rows, and 9 more variables: pop1950 <dbl>, pop1960 <dbl>,
# pop1970 <dbl>, pop1980 <dbl>, pop1990 <dbl>, pop2000 <dbl>, pop2010 <dbl>,
# pop2020 <dbl>, pop2030 <dbl>
I now need to find a way to repeat this process for every variable in the dataframe (i.e., pop1960 - pop2030). Ideally, two new variables would be created for each existing pop* variable and their names would be preceded by qnt_ and followed by either _chng_dir or _chng_lgl.
Is purrr the right tool to use for this? dplyr::mutate_? data.table?
It turns out this problem is solvable using tidyr::gather + dplyr::group_by + tidyr::spread functions. While #shayaa and #Gregor didn't provide the solution I was looking for, their advice helped me course-correct away from the functional programming methods I was researching.
I ended up using #shayaa's gather and group_by combination, followed by mutate to create the variable names (qnt_*_chng_lgl and qnt_*_chng_dir) and then using spread to make it wide again. An anonymous function passed to summarize_all removed all the extra NA's that the wide-long-wide transformations created.
m_comp <-
m %>%
mutate(qnt = dplyr::ntile(pop1950,5)) %>%
filter(pop1950 %!in% m_out) %>%
gather(year,pop,-name,-qnt) %>%
group_by(year) %>%
mutate(qntTrim = dplyr::ntile(pop,5),
qnt_chng_lgl = !is.na(qnt) & qnt != qntTrim,
qnt_chng_dir = ifelse(qnt_chng_lgl,
paste0(qnt," to ",qntTrim),
"No change"),
year_lgl = paste0("qnt_chng_",year,"_lgl"),
year_dir = paste0("qnt_chng_",year,"_dir")) %>%
spread(year_lgl,qnt_chng_lgl) %>%
spread(year_dir,qnt_chng_dir) %>%
spread(year,pop) %>%
select(-qnt,-qntTrim) %>%
group_by(name) %>%
summarize_all(function(.){subset(.,!is.na(.)) %>% first})
Nothing wrong with your analysis it seems to me,
After this part
m <- metro#data %>%
as_tibble %>%
select(-name_long,-iso_a3) %>%
sample_n(50)
Just melt your data and continue your analysis but with group_by(year)
library(reshape2)
library(stringr)
mm <- melt(m)
mm[,2] <- as.factor(str_sub(mm[,2],-4))
names(mm)[2:3] <- c("year", "population")
e.g.,
mm %>% group_by(year) %>%
+ mutate(qnt_all = dplyr::ntile(population,5))