I've got this dataframe and I want to add the last two columns to another dataframe by summing them and grouping them by "Full.Name"
# A tibble: 6 x 5
# Groups: authority_dic, Full.Name [6]
authority_dic Full.Name Entity `2019` `2020`
<chr> <chr> <chr> <int> <int>
1 accomplished Derek J. Leathers WERNER ENTERPRISES INC 1 0
2 accomplished Dirk Van de Put MONDELEZ INTERNATIONAL INC 0 1
3 accomplished Eileen P. Drake AEROJET ROCKETDYNE HOLDINGS 1 0
4 accomplished G. Michael Sievert T-MOBILE US INC 0 3
5 accomplished Gary C. Kelly SOUTHWEST AIRLINES 0 1
6 accomplished James C. Fish, Jr. WASTE MANAGEMENT INC 1 0
This is the dataframe I want to add the two columns to: Like you can see the "Full.Name" column acts as the grouping column.
# A tibble: 6 x 3
# Groups: Full.Name [6]
Full.Name `2019` `2020`
<chr> <int> <int>
1 A. Patrick Beharelle 5541 3269
2 Aaron P. Graft 165 200
3 Aaron P. Jagdfeld 4 5
4 Adam H. Schechter 147 421
5 Adam P. Symson 1031 752
6 Adena T. Friedman 1400 1655
I can add one column using the following piece of code, but if I want to do it with the second one, it overwrites my existing one and I am only left with one instead of two columns added.
narc_auth_total <- narc_auth %>% group_by(Full.Name) %>% summarise(`2019_words` = sum(`2019`)) %>% left_join(totaltweetsyear, ., by = "Full.Name")
The output for this command looks like this:
# A tibble: 6 x 4
# Groups: Full.Name [6]
Full.Name `2019` `2020` `2019_words`
<chr> <int> <int> <int>
1 A. Patrick Beharelle 5541 3269 88
2 Aaron P. Graft 165 200 2
3 Aaron P. Jagdfeld 4 5 0
4 Adam H. Schechter 147 421 2
5 Adam P. Symson 1031 752 15
6 Adena T. Friedman 1400 1655 21
I want to do the same thing and add the 2020_words column to the same dataframe. I just cannot do it, but it cannot be that hard to do so. It should be summarized as well, just like the 2019_words column. When I add "2020" to my command, it says object "2020" not found.
Thanks in advance.
If I have understood you well, this will solve your problem:
narc_auth_total <-
narc_auth %>%
group_by(Full.Name) %>%
summarise(
`2019_words` = sum(`2019`),
`2020_words` = sum(`2020`)
) %>%
left_join(totaltweetsyear, ., by = "Full.Name")
Related
I am trying to obtain the largest 10 investors in a country but obtain confusing result using arrange in dplyr versus order in base R.
head(fdi_partner)
give the following results
# A tibble: 6 x 3
`Main counterparts` `Number of projects` `Total registered capital (Mill. USD)(*)`
<chr> <chr> <chr>
1 TOTAL 1818 38854.3
2 Singapore 231 11358.66
3 Korea Rep.of 377 7679.9
4 Japan 204 4325.79
5 Netherlands 24 4209.64
6 China, PR 216 3001.79
and
fdi_partner %>%
rename("Registered capital" = "Total registered capital (Mill. USD)(*)") %>%
mutate_at(c("Number of projects", "Registered capital"), as.numeric) %>%
arrange("Number of projects") %>%
head()
give almost the same result
# A tibble: 6 x 3
`Main counterparts` `Number of projects` `Registered capital`
<chr> <dbl> <dbl>
1 TOTAL 1818 38854.
2 Singapore 231 11359.
3 Korea Rep.of 377 7680.
4 Japan 204 4326.
5 Netherlands 24 4210.
6 China, PR 216 3002.
while the following code is working fine with base R
head(fdi_partner)
fdi_numeric <- fdi_partner %>%
rename("Registered capital" = "Total registered capital (Mill. USD)(*)") %>%
mutate_at(c("Number of projects", "Registered capital"), as.numeric)
head(fdi_numeric[order(fdi_numeric$"Number of projects", decreasing = TRUE), ], n=11)
which gives
# A tibble: 11 x 3
`Main counterparts` `Number of projects` `Registered capital`
<chr> <dbl> <dbl>
1 TOTAL 1818 38854.
2 Korea Rep.of 377 7680.
3 Singapore 231 11359.
4 China, PR 216 3002.
5 Japan 204 4326.
6 Hong Kong SAR (China) 132 2365.
7 United States 83 783.
8 Taiwan 66 1464.
9 United Kingdom 50 331.
10 F.R Germany 37 131.
11 Thailand 36 370.
Can anybody help explain what's wrong with me?
dplyr (and more generally tidyverse packages) accept only unquoted variable names. If your variable name has a space in it, you must wrap it in backticks:
library(dplyr)
test <- data.frame(`My variable` = c(3, 1, 2), var2 = c(1, 1, 1), check.names = FALSE)
test
#> My variable var2
#> 1 3 1
#> 2 1 1
#> 3 2 1
# Your code (doesn't work)
test %>%
arrange("My variable")
#> My variable var2
#> 1 3 1
#> 2 1 1
#> 3 2 1
# Solution
test %>%
arrange(`My variable`)
#> My variable var2
#> 1 1 1
#> 2 2 1
#> 3 3 1
Created on 2023-01-05 with reprex v2.0.2
I am trying to run this line of code below to copy the city.output column to pm.city where it is not NA (in my sample dataframe, nothing is NA though) because city.output contains the correct city spellings.
resultdf <- dplyr::mutate(df, pm.city = ifelse(is.na(city.output) == FALSE, city.output, pm.city))
df:
pm.uid pm.address pm.state pm.zip pm.city city.output
<int> <chr> <chr> <chr> <chr> <fct>
1 1 1809 MAIN ST OH 63312 NORWOOD NORWOOD
2 2 123 ELM DR CA NA BRYAN BRYAN
3 3 8970 WOOD ST UNIT 4 LA 33333 BATEN ROUGE BATON ROUGE
4 4 4444 OAK AVE OH 87481 CINCINATTI CINCINNATI
5 5 3333 HELPME DR MT 87482 HELENA HELENA
6 6 2342 SOMEWHERE RD LA 45103 BATON ROUGE BATON ROUGE
resultdf (pm.city should be the same as city.output but it's an integer)
pm.uid pm.address pm.state pm.zip pm.city city.output
<int> <chr> <chr> <chr> <int> <fct>
1 1 1809 MAIN ST OH 63312 7 NORWOOD
2 2 123 ELM DR CA NA 2 BRYAN
3 3 8970 WOOD ST UNIT 4 LA 33333 1 BATON ROUGE
4 4 4444 OAK AVE OH 87481 3 CINCINNATI
5 5 4444 HELPME DR MT 87482 4 HELENA
6 6 2342 SOMEWHERE RD LA 45103 1 BATON ROUGE
An integer is instead assigned to pm.city. It appears the integer is the order number of the cities when they're in alphabetical order. Prior to this, I used the dplyr left_join method to attach city.output column from another dataframe but even there, there was no row number that I supplied explicitly.
This works on my computer in r studio but not when I run it from a server. Maybe it has something to do with my version of dplyr or the factor data type under city.output? I am pretty new to r.
The city.output is factor which gets coerced to integer storage values. Instead, convert to character with as.character
dplyr::mutate(df, pm.city = ifelse(!is.na(city.output), as.character(city.output), pm.city))
I have a table of information that looks like the following:
rusher_full_name receiver_full_name rushing_fpts receiving_fpts
<chr> <chr> <dbl> <dbl>
1 Aaron Jones NA 5 0
2 NA Aaron Jones 0 5
3 Mike Davis NA 0.5 0
4 NA Allen Robinson 0 3
5 Mike Davis NA 0.7 0
What I'm trying to do is get all of the values from the rushing_fpts and receiving_fpts to sum up depending on the rusher_full_name and receiver_full_name value. For example, for every instance of "Aaron Jones" (whether it's in rusher_full_name or receiver_full_name) sum up the values of rushing_fpts and receiving_fpts
In the end, this is what I'd like it to look like:
player_full_name total_fpts
<chr> <dbl>
1 Aaron Jones 10
2 Mike Davis 1.2
3 Allen Robinson 3
I'm pretty new to using R and have Googled a number of things but can't find any solution. Any suggestions on how to accomplish this?
library(tidyverse)
df %>%
mutate(player_full_name = coalesce(rusher_full_name, receiver_full_name)) %>%
group_by(player_full_name) %>%
summarise(total_fpts = sum(rushing_fpts+receiving_fpts))
Output
# A tibble: 3 x 2
player_full_name total_fpts
<chr> <dbl>
1 Aaron Jones 10
2 Allen Robinson 3
3 Mike Davis 1.2
Data
df <- data.frame(
rusher_full_name = c("Aaron Jones", NA, "Mike Davis", NA, "Mike Davis"),
receiver_full_name = c(NA, "Aaron Jones", NA, "Allen Robinson", NA),
rushing_fpts = c(5,0,0.5,0,.7),
receiving_fpts = c(0,5,0,3,0),
stringsAsFactors = FALSE
)
I have an large messy dataset but want to accomplish a straightforward thing. Essentially I want to fill a tibble based on every combination of two columns and sum a third column.
As a hypothetical example, say each observation has the company_name (Wendys, BK, McDonalds), the food_option (burgers, fries, frosty), and the total_spending (in $). I would like to make a 9x3 tibble with the company, food, and total as a sum of every observation. Here's my code so far:
df_table <- df %>%
group_by(company_name, food_option) %>%
summarize(total= sum(total_spending))
company_name food_option total
<chr> <chr> <dbl>
1 Wendys Burgers 757
2 Wendys Fries 140
3 Wendys Frosty 98
4 McDonalds Burgers 1044
5 McDonalds Fries 148
6 BK Burgers 669
7 BK Fries 38
The problem is that McDonalds has zero observations with "Frosty" as the food_option. Consequently, I get a partial table. I'd like to fill that with a row that shows:
8 McDonalds Frosty 0
9 BK Frosty 0
I know I can add the rows manually, but the actual dataset has over a hundred combinations so it will be tedious and complicated. Also, I'm constantly modifying the upstream data and I want the code to automatically fill correctly.
Thank you SO MUCH to anyone who can help. This forum has really been a godsend, really appreciate all of you.
Try:
library(dplyr)
df %>%
mutate(food_option = factor(food_option, levels = unique(food_option))) %>%
group_by(company_name, food_option, .drop = FALSE) %>%
summarise(total = sum(total_spending))
Newer versions of dplyr have a .drop argument to group_by where if you've got a factor with pre-defined levels they will not be dropped (and you'll get the zeros).
You can use tidyr::expand_grid():
tidyr::expand_grid(company_name = c("Wendys", "McDonalds", "BK"),
food_option = c("Burgers", "Fries", "Frosty"))
to create all possible variations
library(tidyverse)
# example data
df = read.table(text = "
company_name food_option total
1 Wendys Burgers 757
2 Wendys Fries 140
3 Wendys Frosty 98
4 McDonalds Burgers 1044
5 McDonalds Fries 148
6 BK Burgers 669
7 BK Fries 38
", header=T)
df %>% complete(company_name, food_option, fill=list(total = 0))
# # A tibble: 9 x 3
# company_name food_option total
# <fct> <fct> <dbl>
# 1 BK Burgers 669
# 2 BK Fries 38
# 3 BK Frosty 0
# 4 McDonalds Burgers 1044
# 5 McDonalds Fries 148
# 6 McDonalds Frosty 0
# 7 Wendys Burgers 757
# 8 Wendys Fries 140
# 9 Wendys Frosty 98
I am looking to join tables with customer id (easy enough) but then I want to multiply the columns to get updated values.
Customer_Week_1<-data.frame(First_name=c("John","Mary","David","Paul"),
Last_name=c("Jackson","Smith","Williams", "Zimmerman"),
Factor_1=c(2,5,8,9),
Factor_2=c(.5,.5,.75,.75),
Factor_3=c(0,1,2,3))
Customer_Week_2<-data.frame(First_name=c("John","Mary","David","Paul"),
Last_name=c("Jackson","Smith","Williams", "Zimmerman"),
Factor_1=c(3,7,1,7),
Factor_2=c(.51,.65,.72,.4),
Factor_3=c(1,2,3,4))
Customer_week3<-Customer_Week_1%>%
left_join(Customer_Week_2, by = c("First_name","Last_name"))
The expected results can be found by in a vector by just
Customer_week3_expected<-Customer_Week_1[,3:5]*Customer_Week_2[,3:5]
And I know I can just manually type out every column. But I have dozens of columns and need to make this code as easy to follow as possible.
I also know that I can just bind the results vector to
Customer_week3<-Customer_Week_1%>%
left_join(Customer_Week_2, by = c("First_name","Last_name"))%>%
select(1:2)
But that does not look like best practice to me, and I would rather this be done with a join some way to ensure everything lines up when I am iterating over the customers(tables)
Assuming I understand the output you're trying to get, I can think of two methods. If you know that the names are in the first two columns and are the same in both data frames (this might not be the case in real life), you can use the same multiplication operation you tried above, bound to the first two columns of either of the data frames.
cbind(Customer_Week_1[1:2], Customer_Week_1[-1:-2] * Customer_Week_2[-1:-2])
#> First_name Last_name Factor_1 Factor_2 Factor_3
#> 1 John Jackson 6 0.255 0
#> 2 Mary Smith 35 0.325 2
#> 3 David Williams 8 0.540 6
#> 4 Paul Zimmerman 63 0.300 12
Or you can be more verbose but maybe more flexible, and eshape to a long data frame, then do a grouped operation to summarize products for each person and factor. Starting from the join you have above:
library(dplyr)
library(tidyr)
Customer_week3 <- Customer_Week_1 %>%
left_join(Customer_Week_2, by = c("First_name", "Last_name"))
Make long-shaped data, separate the Factor_1.x into Factor_1 and x, and make products as your summary calculation.
products <- Customer_week3 %>%
gather(key = factor, value = value, -First_name, -Last_name) %>%
separate(factor, into = c("factor", "week"), sep = "\\.") %>%
group_by(First_name, Last_name, factor) %>%
summarise(value = prod(value))
head(products)
#> # A tibble: 6 x 4
#> # Groups: First_name, Last_name [2]
#> First_name Last_name factor value
#> <fct> <fct> <chr> <dbl>
#> 1 David Williams Factor_1 8
#> 2 David Williams Factor_2 0.54
#> 3 David Williams Factor_3 6
#> 4 John Jackson Factor_1 6
#> 5 John Jackson Factor_2 0.255
#> 6 John Jackson Factor_3 0
If you need to get back to a wide format, spread back.
products %>%
spread(key = factor, value = value)
#> # A tibble: 4 x 5
#> # Groups: First_name, Last_name [16]
#> First_name Last_name Factor_1 Factor_2 Factor_3
#> <fct> <fct> <dbl> <dbl> <dbl>
#> 1 David Williams 8 0.54 6
#> 2 John Jackson 6 0.255 0
#> 3 Mary Smith 35 0.325 2
#> 4 Paul Zimmerman 63 0.3 12
Similar to #camille's reshaping, but in data.table (and disregarding Customer_week3):
library(data.table)
# long format
long = rbindlist(list(Customer_Week_1, Customer_Week_2), id=TRUE)
# aggregate
long[, lapply(.SD, prod), by=.(First_name, Last_name), .SDcols=patterns("^Factor")]
First_name Last_name Factor_1 Factor_2 Factor_3
1: John Jackson 6 0.255 0
2: Mary Smith 35 0.325 2
3: David Williams 8 0.540 6
4: Paul Zimmerman 63 0.300 12
Going longer (again as seen in #camille's answer) might also make sense, so as to avoid repeatedly fiddling with names of Factor_* columns:
longer = melt(long, meas=patterns("^Factor")) # analogous to gather
longer[, .(value = prod(value)), by=.(First_name, Last_name, variable)]