You can install:
-
the latest development version from GitHub with
install.packages("devtools") devtools::install_github("ryanburge/blocks/building")
I love the functionality of tabyl, but it doesn’t take a weight
variable. Here’s the simple version ct()
library(tidyverse)
library(building)
cces <- read_csv("https://raw.githubusercontent.com/ryanburge/blocks/master/cces.csv")
cces %>%
ct(race)
#> # A tibble: 8 x 3
#> race n pct
#> <int> <int> <dbl>
#> 1 1 368 0.736
#> 2 2 54 0.108
#> 3 3 38 0.076
#> 4 4 13 0.026
#> 5 5 7 0.014
#> 6 6 9 0.018
#> 7 7 8 0.016
#> 8 8 3 0.006Note that you are presented with a count column and a pct column.
Let’s add weights
cces %>%
ct(race, commonweight_vv)
#> # A tibble: 8 x 3
#> race n pct
#> <int> <dbl> <dbl>
#> 1 1 348. 0.758
#> 2 2 43.9 0.096
#> 3 3 34.0 0.074
#> 4 4 7.09 0.015
#> 5 5 3.87 0.008
#> 6 6 15.2 0.033
#> 7 7 6.33 0.014
#> 8 8 0.704 0.002Notice that it’s pipeable. And if you don’t include the weight variable then it won’t be calculated with a weight.
Oftentimes in social science we like to see what our 95% confidence
intervals are, but that’s a lot of syntax. It’s easy with the mean_ci
function
cces %>%
mean_ci(gender)
#> # A tibble: 1 x 7
#> mean sd n level se lower upper
#> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 1.54 0.499 500 0.05 0.0223 1.49 1.58The default is a 95% confidence interval. However that can be changed easily.
cces %>%
mean_ci(gender, ci = .84)
#> # A tibble: 1 x 7
#> mean sd n level se lower upper
#> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 1.54 0.499 500 0.16 0.0223 1.50 1.57I wanted a simple function to calculate the mean and the median. It takes just one variable and computes both statistics.
money1 <- read_csv("https://raw.githubusercontent.com/ryanburge/pls2003_sp17/master/sal_work.csv")
money1
#> # A tibble: 1,025 x 3
#> X1 salary names
#> <int> <int> <chr>
#> 1 1 14736 Darin Casem
#> 2 2 21261 Jaelyn Groesbeck
#> 3 3 16831 Theodis Butler
#> 4 4 34400 Joewid Rettig
#> 5 5 31239 Breianna Gilbert
#> 6 6 51580 Marcus Gray II
#> 7 7 49699 Berenice Garcia
#> 8 8 66805 Elijah Garrett
#> 9 9 49321 Jeremiah Bishop Jr
#> 10 10 67126 Sultana al-Jabbour
#> # ... with 1,015 more rows
money1 %>%
mean_med(salary)
#> # A tibble: 1 x 2
#> mean median
#> <dbl> <int>
#> 1 1247953. 35853Here’s a simple function that generates a pearson correlation of two variables with a p-value.
x <- c(1, 2, 3, 7, 5, 777, 6, 411, 8)
y <- c(11, 23, 1, 4, 6, 22455, 34, 22, 22)
z <- c(34, 3, 21, 4555, 75, 2, 3334, 1122, 22312)
test <- data.frame(x,y,z) %>% as.tibble()
test %>%
filter(z > 10) %>%
corr(x,y)
#> # A tibble: 1 x 8
#> estimate statistic p.value n conf.low conf.high method alternative
#> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <chr> <chr>
#> 1 0.288 0.673 0.531 5 -0.594 0.856 Pearson's product-moment correlation two.sidedx <- c(1, 2, 3, NA, 5, NA, 6, NA, 8)
y <- c(11, 23, NA, 4, 6, NA, NA, NA, 22)
df <- data.frame(x,y) %>% as.tibble()
df
#> # A tibble: 9 x 2
#> x y
#> <dbl> <dbl>
#> 1 1 11
#> 2 2 23
#> 3 3 NA
#> 4 NA 4
#> 5 5 6
#> 6 NA NA
#> 7 6 NA
#> 8 NA NA
#> 9 8 22
na_zero(df, y)
#> # A tibble: 9 x 2
#> x y
#> <dbl> <dbl>
#> 1 1 11
#> 2 2 23
#> 3 3 0
#> 4 NA 4
#> 5 5 6
#> 6 NA 0
#> 7 6 0
#> 8 NA 0
#> 9 8 22Oftentimes I make many little dataframes that I need to bind_rows to put into one large dataframe. As long as those dataframes have the same naming convention that can be done.
dd1 <- data.frame(a = 1, b = 2)
dd2 <- data.frame(a = 3, b = 4)
dd3 <- data.frame(a = 5, b = 6)
bind_df("dd")
#> a b
#> 1 1 2
#> 2 3 4
#> 3 5 6I recode all the time, but unfortunately when you recode from numeric to character the factor levels are plotted in alphabetical order. There’s a way around that now.
I found this terrific function written by Dennis YL, where he had the same problem that I had.
cces <- read_csv("https://raw.githubusercontent.com/ryanburge/cces/master/CCES%20for%20Methods/small_cces.csv")
graph <- cces %>%
mutate(pid_new = frcode(pid7 == 1 ~ "Strong Democrat",
pid7 == 2 ~ "Not Strong Democrat",
pid7 == 3 ~ "Lean Democrat",
pid7 == 4 ~ "Independent",
pid7 == 5 ~ "Lean Republican",
pid7 == 6 ~ "Not Strong Republican",
pid7 == 7 ~ "Strong Republican",
TRUE ~ "REMOVE")) %>%
ct(pid_new)
graph %>%
filter(pid_new != "REMOVE") %>%
ggplot(., aes(x = pid_new, y = pct)) +
geom_col()
- let me know what you think on twitter @ryanburge
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