Project Part 1

Preparing the Agricultural Value per Worker data for plotting.

1. I downloaded agriculture value added per worker data from Our World in Data. I selected this data because I am interested in the labor productivity of agricultural workers from 1991 to 2017.

2. This is the link to the data.

3. The following code chunk loads the package I will use to read in and prepare the data for analysis.

library(tidyverse)

4. Read the data in

agrictulture_value_added_per_worker_wdi <-
read_csv(here::here("_posts/2022-05-10-project-part-1/agriculture-value-added-per-worker-wdi.csv"))

5. Use glimpse to see the names and types of the columns.

glimpse(agrictulture_value_added_per_worker_wdi)

Rows: 5,233
Columns: 4
$ Entity                                                    <chr> "A…
$ Code                                                      <chr> "A…
$ Year                                                      <dbl> 20…
$ `Agriculture, value added per worker (constant 2010 US$)` <dbl> 87…

# View(agrictulture_value_added_per_worker_wdi)

6. Use output from glimpse (and View) to prepare the data for analysis.

• Create the object ‘regions’ that is a list of regions I want to extract from the dataset

• Change the name of the 1st column to Region and the 4th column to Value Added per Worker

• Use filter to extract the rows that I want to keep: Year >= 2000 and Region in regions

• Select the columns to keep: Region, Year, Valueaddedperworker

• Use mutate to convert Valueaddedperworker to dollars

• Assign the output to regional_value

• Display the first 10 rows of regional_value

regions <- c("China",
             "Indonesia",
             "India",
             "Liberia",
             "Cameroon",
             "world")

regional_value <- agrictulture_value_added_per_worker_wdi %>%
  rename(Region = 1, Valueaddedperworker = 4) %>%
  filter(Year >= 2000, Region %in% regions) %>%
  select(Region, Year, Valueaddedperworker) %>%
  mutate(Valueaddedperworker = Valueaddedperworker * 1e+0)

regional_value

# A tibble: 90 × 3
   Region    Year Valueaddedperworker
   <chr>    <dbl>               <dbl>
 1 Cameroon  2000                688.
 2 Cameroon  2001                720.
 3 Cameroon  2002                718.
 4 Cameroon  2003                681.
 5 Cameroon  2004                714.
 6 Cameroon  2005                696.
 7 Cameroon  2006                664.
 8 Cameroon  2007                666.
 9 Cameroon  2008                676.
10 Cameroon  2009                663.
# … with 80 more rows

Check that the total for 2017 equals the total in the graph

regional_value %>% filter(Year == 2017) %>%
  summarise(total_value = sum(Valueaddedperworker))

# A tibble: 1 × 1
  total_value
        <dbl>
1      12963.

Add a picture

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agricultural value per worker

Write the data to file in the project directory

write_csv(regional_value, file ="regional_value.csv")